From ac185e23774d9482c949bb0c29e406f7068b47b8 Mon Sep 17 00:00:00 2001 From: chrisholder Date: Mon, 17 Feb 2025 23:55:00 +0000 Subject: [PATCH 001/216] added numba prange to all pairwise distances --- aeon/distances/_distance.py | 25 +++++++++++++++++--- aeon/distances/_mpdist.py | 14 ++++++++--- aeon/distances/_sbd.py | 29 +++++++++++++++++++---- aeon/distances/_shift_scale_invariant.py | 15 +++++++++--- aeon/distances/elastic/_adtw.py | 19 +++++++++++---- aeon/distances/elastic/_ddtw.py | 19 +++++++++++---- aeon/distances/elastic/_dtw.py | 19 +++++++++++---- aeon/distances/elastic/_edr.py | 19 +++++++++++---- aeon/distances/elastic/_erp.py | 19 +++++++++++---- aeon/distances/elastic/_lcss.py | 19 +++++++++++---- aeon/distances/elastic/_msm.py | 19 +++++++++++---- aeon/distances/elastic/_shape_dtw.py | 19 +++++++++++---- aeon/distances/elastic/_soft_dtw.py | 19 +++++++++++---- aeon/distances/elastic/_twe.py | 19 +++++++++++---- aeon/distances/elastic/_wddtw.py | 19 +++++++++++---- aeon/distances/elastic/_wdtw.py | 19 +++++++++++---- aeon/distances/mindist/_dft_sfa.py | 15 ++++++++---- aeon/distances/mindist/_paa_sax.py | 20 ++++++++++++---- aeon/distances/mindist/_sax.py | 20 ++++++++++++---- aeon/distances/mindist/_sfa.py | 11 +++++++-- aeon/distances/pointwise/_euclidean.py | 30 ++++++++++++++++++++---- aeon/distances/pointwise/_manhattan.py | 30 ++++++++++++++++++++---- aeon/distances/pointwise/_minkowski.py | 19 +++++++++++---- aeon/distances/pointwise/_squared.py | 29 +++++++++++++++++++---- 24 files changed, 377 insertions(+), 108 deletions(-) diff --git a/aeon/distances/_distance.py b/aeon/distances/_distance.py index 1263e11cb4..1cf5c35dc0 100644 --- a/aeon/distances/_distance.py +++ b/aeon/distances/_distance.py @@ -1,5 +1,6 @@ __maintainer__ = [] +import warnings from enum import Enum from typing import Any, Callable, Optional, TypedDict, Union @@ -173,6 +174,7 @@ def pairwise_distance( y: Optional[np.ndarray] = None, method: Union[str, DistanceFunction, None] = None, symmetric: bool = True, + n_jobs: int = 1, **kwargs: Unpack[DistanceKwargs], ) -> np.ndarray: """Compute the pairwise distance matrix between two time series. @@ -197,6 +199,10 @@ def pairwise_distance( function is provided as the "method" parameter, then it will compute an asymmetric distance matrix, and the entire matrix (including both upper and lower triangles) is returned. + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. kwargs : Any Extra arguments for distance. Refer to each distance documentation for a list of possible arguments. @@ -240,11 +246,13 @@ def pairwise_distance( [ 48.]]) """ if method in PAIRWISE_DISTANCE: - return DISTANCES_DICT[method]["pairwise_distance"](x, y, **kwargs) + return DISTANCES_DICT[method]["pairwise_distance"]( + x, y, n_jobs=n_jobs, **kwargs + ) elif isinstance(method, Callable): if y is None and not symmetric: - return _custom_func_pairwise(x, x, method, **kwargs) - return _custom_func_pairwise(x, y, method, **kwargs) + return _custom_func_pairwise(x, x, method, n_jobs=n_jobs, **kwargs) + return _custom_func_pairwise(x, y, method, n_jobs=n_jobs, **kwargs) else: raise ValueError("Method must be one of the supported strings or a callable") @@ -253,11 +261,22 @@ def _custom_func_pairwise( X: Optional[Union[np.ndarray, list[np.ndarray]]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, dist_func: Union[DistanceFunction, None] = None, + n_jobs: int = 1, **kwargs: Unpack[DistanceKwargs], ) -> np.ndarray: if dist_func is None: raise ValueError("dist_func must be a callable") + if n_jobs != 1: + warnings.warn( + "You are using a custom distance function with n_jobs > 1. " + "Aeon does not support parallelization for custom distance " + "functions. If it is an existing aeon distance try using the " + "string name instead.", + UserWarning, + stacklevel=2, + ) + multivariate_conversion = _is_numpy_list_multivariate(X, y) X, _ = _convert_collection_to_numba_list(X, "X", multivariate_conversion) if y is None: diff --git a/aeon/distances/_mpdist.py b/aeon/distances/_mpdist.py index c679daef5c..c9f195cba9 100644 --- a/aeon/distances/_mpdist.py +++ b/aeon/distances/_mpdist.py @@ -1,5 +1,6 @@ """Matrix Profile Distances.""" +import warnings from typing import Optional, Union import numpy as np @@ -287,6 +288,7 @@ def mp_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, m: int = 0, + **kwargs, ) -> np.ndarray: """Compute the mpdist pairwise distance between a set of time series. @@ -339,14 +341,20 @@ def mp_pairwise_distance( [2.82842712], [2.82842712]]) """ + if "n_jobs" in kwargs: + warnings.warn( + "n_jobs is not supported for the mpdist distance method and will be " + "ignored.", + UserWarning, + stacklevel=2, + ) + if m == 0: + m = int(X.shape[2] / 4) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion ) - if m == 0: - m = int(_X.shape[2] / 4) - if y is None: return _mpdist_pairwise_distance_single(_X, m) diff --git a/aeon/distances/_sbd.py b/aeon/distances/_sbd.py index 1097f27b5a..b07652ad13 100644 --- a/aeon/distances/_sbd.py +++ b/aeon/distances/_sbd.py @@ -2,14 +2,16 @@ __maintainer__ = ["SebastianSchmidl"] +import warnings from typing import Optional, Union import numpy as np -from numba import njit, objmode +from numba import njit, objmode, prange, set_num_threads from numba.typed import List as NumbaList from scipy.signal import correlate from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -117,6 +119,8 @@ def sbd_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, standardize: bool = True, + n_jobs: int = 1, + **kwargs, ) -> np.ndarray: """ Compute the shape-based distance (SBD) between all pairs of time series. @@ -138,6 +142,10 @@ def sbd_pairwise_distance( standardize : bool, default=True Apply z-score to both input time series for standardization before computing the distance. This makes SBD scaling invariant. Default is True. + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Returns ------- @@ -188,6 +196,17 @@ def sbd_pairwise_distance( [0.36754447, 0. , 0.29289322], [0.5527864 , 0.29289322, 0. ]]) """ + n_jobs = check_n_jobs(n_jobs) + set_num_threads(n_jobs) + if n_jobs > 1: + warnings.warn( + "You have set n_jobs > 1. For this distance function " + "unless your data is very large (> 10000 time series), it is " + "recommended to use n_jobs=1. If this function is slower than " + "expected try setting n_jobs=1.", + UserWarning, + stacklevel=2, + ) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, _ = _convert_collection_to_numba_list(X, "", multivariate_conversion) @@ -199,14 +218,14 @@ def sbd_pairwise_distance( return _sbd_pairwise_distance(_X, _y, standardize) -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _sbd_pairwise_distance_single( x: NumbaList[np.ndarray], standardize: bool ) -> np.ndarray: n_cases = len(x) distances = np.zeros((n_cases, n_cases)) - for i in range(n_cases): + for i in prange(n_cases): for j in range(i + 1, n_cases): distances[i, j] = sbd_distance(x[i], x[j], standardize) distances[j, i] = distances[i, j] @@ -214,7 +233,7 @@ def _sbd_pairwise_distance_single( return distances -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _sbd_pairwise_distance( x: NumbaList[np.ndarray], y: NumbaList[np.ndarray], standardize: bool ) -> np.ndarray: @@ -222,7 +241,7 @@ def _sbd_pairwise_distance( m_cases = len(y) distances = np.zeros((n_cases, m_cases)) - for i in range(n_cases): + for i in prange(n_cases): for j in range(m_cases): distances[i, j] = sbd_distance(x[i], y[j], standardize) return distances diff --git a/aeon/distances/_shift_scale_invariant.py b/aeon/distances/_shift_scale_invariant.py index 951b7ac560..66caa282fd 100644 --- a/aeon/distances/_shift_scale_invariant.py +++ b/aeon/distances/_shift_scale_invariant.py @@ -3,10 +3,11 @@ from typing import Optional, Union import numpy as np -from numba import njit +from numba import njit, prange, set_num_threads from numba.typed import List as NumbaList from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -160,6 +161,8 @@ def shift_scale_invariant_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, max_shift: Optional[int] = None, + n_jobs: int = 1, + **kwargs, ) -> np.ndarray: r"""Compute the shift-scale invariant pairwise distance between time series. @@ -193,6 +196,10 @@ def shift_scale_invariant_pairwise_distance( Maximum shift allowed in the alignment path. If None, then max_shift is set to min(X.shape[-1], y.shape[-1]) or if y is None, max_shift is set to X.shape[-1]. + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Returns ------- @@ -223,6 +230,8 @@ def shift_scale_invariant_pairwise_distance( >>> y_univariate = np.array([11., 12., 13.]) >>> single_pw =shift_scale_invariant_pairwise_distance(X, y_univariate) """ + n_jobs = check_n_jobs(n_jobs) + set_num_threads(n_jobs) if max_shift is None: if y is None: max_shift = X.shape[-1] @@ -308,7 +317,7 @@ def shift_scale_invariant_best_shift( raise ValueError("x and y must be 1D or 2D") -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _shift_invariant_pairwise_distance( x: NumbaList[np.ndarray], y: NumbaList[np.ndarray], max_shift: int ) -> np.ndarray: @@ -316,7 +325,7 @@ def _shift_invariant_pairwise_distance( m_cases = len(y) distances = np.zeros((n_cases, m_cases)) - for i in range(n_cases): + for i in prange(n_cases): for j in range(m_cases): distances[i, j] = shift_scale_invariant_distance(x[i], y[j], max_shift) return distances diff --git a/aeon/distances/elastic/_adtw.py b/aeon/distances/elastic/_adtw.py index feab2b4c18..7ab182f394 100644 --- a/aeon/distances/elastic/_adtw.py +++ b/aeon/distances/elastic/_adtw.py @@ -5,13 +5,14 @@ from typing import Optional, Union import numpy as np -from numba import njit +from numba import njit, prange, set_num_threads from numba.typed import List as NumbaList from aeon.distances.elastic._alignment_paths import compute_min_return_path from aeon.distances.elastic._bounding_matrix import create_bounding_matrix from aeon.distances.pointwise._squared import _univariate_squared_distance from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -203,6 +204,8 @@ def adtw_pairwise_distance( window: Optional[float] = None, itakura_max_slope: Optional[float] = None, warp_penalty: float = 1.0, + n_jobs: int = 1, + **kwargs, ) -> np.ndarray: r"""Compute the ADTW pairwise distance between a set of time series. @@ -226,6 +229,10 @@ def adtw_pairwise_distance( Penalty for warping. A high value will mean less warping. warp less and if value is low then will encourage algorithm to warp more. + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Returns ------- @@ -272,6 +279,8 @@ def adtw_pairwise_distance( [ 44., 0., 87.], [294., 87., 0.]]) """ + n_jobs = check_n_jobs(n_jobs) + set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion @@ -290,7 +299,7 @@ def adtw_pairwise_distance( ) -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _adtw_pairwise_distance( X: NumbaList[np.ndarray], window: Optional[float], @@ -306,7 +315,7 @@ def _adtw_pairwise_distance( bounding_matrix = create_bounding_matrix( n_timepoints, n_timepoints, window, itakura_max_slope ) - for i in range(n_cases): + for i in prange(n_cases): for j in range(i + 1, n_cases): x1, x2 = X[i], X[j] if unequal_length: @@ -319,7 +328,7 @@ def _adtw_pairwise_distance( return distances -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _adtw_from_multiple_to_multiple_distance( x: NumbaList[np.ndarray], y: NumbaList[np.ndarray], @@ -336,7 +345,7 @@ def _adtw_from_multiple_to_multiple_distance( bounding_matrix = create_bounding_matrix( x[0].shape[1], y[0].shape[1], window, itakura_max_slope ) - for i in range(n_cases): + for i in prange(n_cases): for j in range(m_cases): x1, y1 = x[i], y[j] if unequal_length: diff --git a/aeon/distances/elastic/_ddtw.py b/aeon/distances/elastic/_ddtw.py index 50007185fd..dffd3f71e2 100644 --- a/aeon/distances/elastic/_ddtw.py +++ b/aeon/distances/elastic/_ddtw.py @@ -5,7 +5,7 @@ from typing import Optional, Union import numpy as np -from numba import njit +from numba import njit, prange, set_num_threads from numba.typed import List as NumbaList from aeon.distances.elastic._alignment_paths import compute_min_return_path @@ -15,6 +15,7 @@ create_bounding_matrix, ) from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -172,6 +173,8 @@ def ddtw_pairwise_distance( y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, window: Optional[float] = None, itakura_max_slope: Optional[float] = None, + n_jobs: int = 1, + **kwargs, ) -> np.ndarray: """Compute the DDTW pairwise distance between a set of time series. @@ -191,6 +194,10 @@ def ddtw_pairwise_distance( itakura_max_slope : float, default=None Maximum slope as a proportion of the number of time points used to create Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Returns ------- @@ -237,6 +244,8 @@ def ddtw_pairwise_distance( [0., 0., 0.], [0., 0., 0.]]) """ + n_jobs = check_n_jobs(n_jobs) + set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion @@ -254,7 +263,7 @@ def ddtw_pairwise_distance( ) -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _ddtw_pairwise_distance( X: NumbaList[np.ndarray], window: Optional[float], @@ -274,7 +283,7 @@ def _ddtw_pairwise_distance( for i in range(n_cases): X_average_of_slope.append(average_of_slope(X[i])) - for i in range(n_cases): + for i in prange(n_cases): for j in range(i + 1, n_cases): x1, x2 = X_average_of_slope[i], X_average_of_slope[j] if unequal_length: @@ -287,7 +296,7 @@ def _ddtw_pairwise_distance( return distances -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _ddtw_from_multiple_to_multiple_distance( x: NumbaList[np.ndarray], y: NumbaList[np.ndarray], @@ -313,7 +322,7 @@ def _ddtw_from_multiple_to_multiple_distance( for i in range(m_cases): y_average_of_slope.append(average_of_slope(y[i])) - for i in range(n_cases): + for i in prange(n_cases): for j in range(m_cases): x1, y1 = x_average_of_slope[i], y_average_of_slope[j] if unequal_length: diff --git a/aeon/distances/elastic/_dtw.py b/aeon/distances/elastic/_dtw.py index 73cce697ab..0f15892477 100644 --- a/aeon/distances/elastic/_dtw.py +++ b/aeon/distances/elastic/_dtw.py @@ -5,13 +5,14 @@ from typing import Optional, Union import numpy as np -from numba import njit +from numba import njit, prange, set_num_threads from numba.typed import List as NumbaList from aeon.distances.elastic._alignment_paths import compute_min_return_path from aeon.distances.elastic._bounding_matrix import create_bounding_matrix from aeon.distances.pointwise._squared import _univariate_squared_distance from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -233,6 +234,8 @@ def dtw_pairwise_distance( y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, window: Optional[float] = None, itakura_max_slope: Optional[float] = None, + n_jobs: int = 1, + **kwargs, ) -> np.ndarray: r"""Compute the DTW pairwise distance between a set of time series. @@ -268,6 +271,10 @@ def dtw_pairwise_distance( itakura_max_slope : float, default=None Maximum slope as a proportion of the number of time points used to create Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Returns ------- @@ -314,6 +321,8 @@ def dtw_pairwise_distance( [ 42., 0., 83.], [292., 83., 0.]]) """ + n_jobs = check_n_jobs(n_jobs) + set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion @@ -330,7 +339,7 @@ def dtw_pairwise_distance( ) -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _dtw_pairwise_distance( X: NumbaList[np.ndarray], window: Optional[float], @@ -345,7 +354,7 @@ def _dtw_pairwise_distance( bounding_matrix = create_bounding_matrix( n_timepoints, n_timepoints, window, itakura_max_slope ) - for i in range(n_cases): + for i in prange(n_cases): for j in range(i + 1, n_cases): x1, x2 = X[i], X[j] if unequal_length: @@ -358,7 +367,7 @@ def _dtw_pairwise_distance( return distances -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _dtw_from_multiple_to_multiple_distance( x: NumbaList[np.ndarray], y: NumbaList[np.ndarray], @@ -374,7 +383,7 @@ def _dtw_from_multiple_to_multiple_distance( bounding_matrix = create_bounding_matrix( x[0].shape[1], y[0].shape[1], window, itakura_max_slope ) - for i in range(n_cases): + for i in prange(n_cases): for j in range(m_cases): x1, y1 = x[i], y[j] if unequal_length: diff --git a/aeon/distances/elastic/_edr.py b/aeon/distances/elastic/_edr.py index e14996ef7a..e3a2bf3bf1 100644 --- a/aeon/distances/elastic/_edr.py +++ b/aeon/distances/elastic/_edr.py @@ -5,13 +5,14 @@ from typing import Optional, Union import numpy as np -from numba import njit +from numba import njit, prange, set_num_threads from numba.typed import List as NumbaList from aeon.distances.elastic._alignment_paths import compute_min_return_path from aeon.distances.elastic._bounding_matrix import create_bounding_matrix from aeon.distances.pointwise._euclidean import _univariate_euclidean_distance from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -235,6 +236,8 @@ def edr_pairwise_distance( window: Optional[float] = None, epsilon: Optional[float] = None, itakura_max_slope: Optional[float] = None, + n_jobs: int = 1, + **kwargs, ) -> np.ndarray: """Compute the pairwise EDR distance between a set of time series. @@ -258,6 +261,10 @@ def edr_pairwise_distance( itakura_max_slope : float, default=None Maximum slope as a proportion of the number of time points used to create Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Returns ------- @@ -303,6 +310,8 @@ def edr_pairwise_distance( [0.75, 0. , 0.8 ], [0.6 , 0.8 , 0. ]]) """ + n_jobs = check_n_jobs(n_jobs) + set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion @@ -322,7 +331,7 @@ def edr_pairwise_distance( ) -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _edr_pairwise_distance( X: NumbaList[np.ndarray], window: Optional[float], @@ -338,7 +347,7 @@ def _edr_pairwise_distance( bounding_matrix = create_bounding_matrix( n_timepoints, n_timepoints, window, itakura_max_slope ) - for i in range(n_cases): + for i in prange(n_cases): for j in range(i + 1, n_cases): x1, x2 = X[i], X[j] if unequal_length: @@ -351,7 +360,7 @@ def _edr_pairwise_distance( return distances -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _edr_from_multiple_to_multiple_distance( x: NumbaList[np.ndarray], y: NumbaList[np.ndarray], @@ -368,7 +377,7 @@ def _edr_from_multiple_to_multiple_distance( bounding_matrix = create_bounding_matrix( x[0].shape[1], y[0].shape[1], window, itakura_max_slope ) - for i in range(n_cases): + for i in prange(n_cases): for j in range(m_cases): x1, y1 = x[i], y[j] if unequal_length: diff --git a/aeon/distances/elastic/_erp.py b/aeon/distances/elastic/_erp.py index 179b2f24f4..12ea5313ce 100644 --- a/aeon/distances/elastic/_erp.py +++ b/aeon/distances/elastic/_erp.py @@ -5,13 +5,14 @@ from typing import Optional, Union import numpy as np -from numba import njit +from numba import njit, prange, set_num_threads from numba.typed import List as NumbaList from aeon.distances.elastic._alignment_paths import compute_min_return_path from aeon.distances.elastic._bounding_matrix import create_bounding_matrix from aeon.distances.pointwise._euclidean import _univariate_euclidean_distance from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -255,6 +256,8 @@ def erp_pairwise_distance( g: float = 0.0, g_arr: Optional[np.ndarray] = None, itakura_max_slope: Optional[float] = None, + n_jobs: int = 1, + **kwargs, ) -> np.ndarray: """Compute the ERP pairwise distance between a set of time series. @@ -283,6 +286,10 @@ def erp_pairwise_distance( itakura_max_slope : float, default=None Maximum slope as a proportion of the number of time points used to create Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Returns ------- @@ -327,6 +334,8 @@ def erp_pairwise_distance( [16., 0., 28.], [44., 28., 0.]]) """ + n_jobs = check_n_jobs(n_jobs) + set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion @@ -343,7 +352,7 @@ def erp_pairwise_distance( ) -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _erp_pairwise_distance( X: NumbaList[np.ndarray], window: Optional[float], @@ -361,7 +370,7 @@ def _erp_pairwise_distance( n_timepoints, n_timepoints, window, itakura_max_slope ) - for i in range(n_cases): + for i in prange(n_cases): for j in range(i + 1, n_cases): x1, x2 = X[i], X[j] if unequal_length: @@ -374,7 +383,7 @@ def _erp_pairwise_distance( return distances -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _erp_from_multiple_to_multiple_distance( x: NumbaList[np.ndarray], y: NumbaList[np.ndarray], @@ -392,7 +401,7 @@ def _erp_from_multiple_to_multiple_distance( bounding_matrix = create_bounding_matrix( x[0].shape[1], y[0].shape[1], window, itakura_max_slope ) - for i in range(n_cases): + for i in prange(n_cases): for j in range(m_cases): x1, y1 = x[i], y[j] if unequal_length: diff --git a/aeon/distances/elastic/_lcss.py b/aeon/distances/elastic/_lcss.py index 23e1eb9fe2..6191335799 100644 --- a/aeon/distances/elastic/_lcss.py +++ b/aeon/distances/elastic/_lcss.py @@ -5,13 +5,14 @@ from typing import Optional, Union import numpy as np -from numba import njit +from numba import njit, prange, set_num_threads from numba.typed import List as NumbaList from aeon.distances.elastic._alignment_paths import compute_lcss_return_path from aeon.distances.elastic._bounding_matrix import create_bounding_matrix from aeon.distances.pointwise._euclidean import _univariate_euclidean_distance from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -228,6 +229,8 @@ def lcss_pairwise_distance( window: Optional[float] = None, epsilon: float = 1.0, itakura_max_slope: Optional[float] = None, + n_jobs: int = 1, + **kwargs, ) -> np.ndarray: """Compute the LCSS pairwise distance between a set of time series. @@ -250,6 +253,10 @@ def lcss_pairwise_distance( itakura_max_slope : float, default=None Maximum slope as a proportion of the number of time points used to create Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Returns ------- @@ -295,6 +302,8 @@ def lcss_pairwise_distance( [0.66666667, 0. , 0.75 ], [1. , 0.75 , 0. ]]) """ + n_jobs = check_n_jobs(n_jobs) + set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion @@ -312,7 +321,7 @@ def lcss_pairwise_distance( ) -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _lcss_pairwise_distance( X: NumbaList[np.ndarray], window: Optional[float], @@ -327,7 +336,7 @@ def _lcss_pairwise_distance( bounding_matrix = create_bounding_matrix( n_timepoints, n_timepoints, window, itakura_max_slope ) - for i in range(n_cases): + for i in prange(n_cases): for j in range(i + 1, n_cases): x1, x2 = X[i], X[j] if unequal_length: @@ -340,7 +349,7 @@ def _lcss_pairwise_distance( return distances -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _lcss_from_multiple_to_multiple_distance( x: NumbaList[np.ndarray], y: NumbaList[np.ndarray], @@ -357,7 +366,7 @@ def _lcss_from_multiple_to_multiple_distance( bounding_matrix = create_bounding_matrix( x[0].shape[1], y[0].shape[1], window, itakura_max_slope ) - for i in range(n_cases): + for i in prange(n_cases): for j in range(m_cases): x1, y1 = x[i], y[j] if unequal_length: diff --git a/aeon/distances/elastic/_msm.py b/aeon/distances/elastic/_msm.py index c51eca3ab6..24f87b66ab 100644 --- a/aeon/distances/elastic/_msm.py +++ b/aeon/distances/elastic/_msm.py @@ -5,13 +5,14 @@ from typing import Optional, Union import numpy as np -from numba import njit +from numba import njit, prange, set_num_threads from numba.typed import List as NumbaList from aeon.distances.elastic._alignment_paths import compute_min_return_path from aeon.distances.elastic._bounding_matrix import create_bounding_matrix from aeon.distances.pointwise._squared import _univariate_squared_distance from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -350,6 +351,8 @@ def msm_pairwise_distance( independent: bool = True, c: float = 1.0, itakura_max_slope: Optional[float] = None, + n_jobs: int = 1, + **kwargs, ) -> np.ndarray: """Compute the msm pairwise distance between a set of time series. @@ -374,6 +377,10 @@ def msm_pairwise_distance( itakura_max_slope : float, default=None Maximum slope as a proportion of the number of time points used to create Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Returns ------- @@ -419,6 +426,8 @@ def msm_pairwise_distance( [10., 0., 14.], [17., 14., 0.]]) """ + n_jobs = check_n_jobs(n_jobs) + set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion @@ -438,7 +447,7 @@ def msm_pairwise_distance( ) -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _msm_pairwise_distance( X: NumbaList[np.ndarray], window: Optional[float], @@ -455,7 +464,7 @@ def _msm_pairwise_distance( bounding_matrix = create_bounding_matrix( n_timepoints, n_timepoints, window, itakura_max_slope ) - for i in range(n_cases): + for i in prange(n_cases): for j in range(i + 1, n_cases): x1, x2 = X[i], X[j] if unequal_length: @@ -468,7 +477,7 @@ def _msm_pairwise_distance( return distances -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _msm_from_multiple_to_multiple_distance( x: NumbaList[np.ndarray], y: NumbaList[np.ndarray], @@ -486,7 +495,7 @@ def _msm_from_multiple_to_multiple_distance( bounding_matrix = create_bounding_matrix( x[0].shape[1], y[0].shape[1], window, itakura_max_slope ) - for i in range(n_cases): + for i in prange(n_cases): for j in range(m_cases): x1, y1 = x[i], y[j] if unequal_length: diff --git a/aeon/distances/elastic/_shape_dtw.py b/aeon/distances/elastic/_shape_dtw.py index 25a72cef10..c4933f0ff1 100644 --- a/aeon/distances/elastic/_shape_dtw.py +++ b/aeon/distances/elastic/_shape_dtw.py @@ -5,7 +5,7 @@ from typing import Optional, Union import numpy as np -from numba import njit +from numba import njit, prange, set_num_threads from numba.typed import List as NumbaList from aeon.distances.elastic._alignment_paths import compute_min_return_path @@ -13,6 +13,7 @@ from aeon.distances.elastic._dtw import _dtw_cost_matrix from aeon.distances.pointwise._squared import _univariate_squared_distance from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -525,6 +526,8 @@ def shape_dtw_pairwise_distance( transformation_precomputed: bool = False, transformed_x: Optional[np.ndarray] = None, transformed_y: Optional[np.ndarray] = None, + n_jobs: int = 1, + **kwargs, ) -> np.ndarray: """Compute the ShapeDTW pairwise distance among a set of series. @@ -563,6 +566,10 @@ def shape_dtw_pairwise_distance( The transformation of X, ignored if transformation_precomputed is False. transformed_y : np.ndarray, default = None The transformation of y, ignored if transformation_precomputed is False. + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Returns ------- @@ -609,6 +616,8 @@ def shape_dtw_pairwise_distance( [ 43., 0., 89.], [292., 89., 0.]]) """ + n_jobs = check_n_jobs(n_jobs) + set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion @@ -644,7 +653,7 @@ def shape_dtw_pairwise_distance( ) -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _shape_dtw_pairwise_distance( X: NumbaList[np.ndarray], window: Optional[float], @@ -663,7 +672,7 @@ def _shape_dtw_pairwise_distance( bounding_matrix = create_bounding_matrix( n_timepoints, n_timepoints, window, itakura_max_slope ) - for i in range(len(X)): + for i in prange(len(X)): for j in range(i + 1, n_cases): x1_, x2_ = X[i], X[j] x1 = _pad_ts_edges(x=x1_, reach=reach) @@ -695,7 +704,7 @@ def _shape_dtw_pairwise_distance( return distances -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _shape_dtw_from_multiple_to_multiple_distance( x: NumbaList[np.ndarray], y: NumbaList[np.ndarray], @@ -716,7 +725,7 @@ def _shape_dtw_from_multiple_to_multiple_distance( bounding_matrix = create_bounding_matrix( x[0].shape[1], y[0].shape[1], window, itakura_max_slope ) - for i in range(n_cases): + for i in prange(n_cases): for j in range(m_cases): x1_, y1_ = x[i], y[j] x1 = _pad_ts_edges(x=x1_, reach=reach) diff --git a/aeon/distances/elastic/_soft_dtw.py b/aeon/distances/elastic/_soft_dtw.py index 31b8743599..006498bbfd 100644 --- a/aeon/distances/elastic/_soft_dtw.py +++ b/aeon/distances/elastic/_soft_dtw.py @@ -5,7 +5,7 @@ from typing import Optional, Union import numpy as np -from numba import njit +from numba import njit, prange, set_num_threads from numba.typed import List as NumbaList from aeon.distances.elastic._alignment_paths import compute_min_return_path @@ -13,6 +13,7 @@ from aeon.distances.elastic._dtw import _dtw_cost_matrix from aeon.distances.pointwise._squared import _univariate_squared_distance from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -249,6 +250,8 @@ def soft_dtw_pairwise_distance( gamma: float = 1.0, window: Optional[float] = None, itakura_max_slope: Optional[float] = None, + n_jobs: int = 1, + **kwargs, ) -> np.ndarray: r"""Compute the soft-DTW pairwise distance between a set of time series. @@ -270,6 +273,10 @@ def soft_dtw_pairwise_distance( itakura_max_slope : float, default=None Maximum slope as a proportion of the number of time points used to create Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Returns ------- @@ -316,6 +323,8 @@ def soft_dtw_pairwise_distance( [ 41.44055555, 0. , 82.43894439], [291.99999969, 82.43894439, 0. ]]) """ + n_jobs = check_n_jobs(n_jobs) + set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion @@ -334,7 +343,7 @@ def soft_dtw_pairwise_distance( ) -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _soft_dtw_pairwise_distance( X: NumbaList[np.ndarray], window: Optional[float], @@ -350,7 +359,7 @@ def _soft_dtw_pairwise_distance( bounding_matrix = create_bounding_matrix( n_timepoints, n_timepoints, window, itakura_max_slope ) - for i in range(n_cases): + for i in prange(n_cases): for j in range(i + 1, n_cases): x1, x2 = X[i], X[j] if unequal_length: @@ -363,7 +372,7 @@ def _soft_dtw_pairwise_distance( return distances -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _soft_dtw_from_multiple_to_multiple_distance( x: NumbaList[np.ndarray], y: NumbaList[np.ndarray], @@ -380,7 +389,7 @@ def _soft_dtw_from_multiple_to_multiple_distance( bounding_matrix = create_bounding_matrix( x[0].shape[1], y[0].shape[1], window, itakura_max_slope ) - for i in range(n_cases): + for i in prange(n_cases): for j in range(m_cases): x1, y1 = x[i], y[j] if unequal_length: diff --git a/aeon/distances/elastic/_twe.py b/aeon/distances/elastic/_twe.py index fa4a3f4dea..69003dd54c 100644 --- a/aeon/distances/elastic/_twe.py +++ b/aeon/distances/elastic/_twe.py @@ -5,13 +5,14 @@ from typing import Optional, Union import numpy as np -from numba import njit +from numba import njit, prange, set_num_threads from numba.typed import List as NumbaList from aeon.distances.elastic._alignment_paths import compute_min_return_path from aeon.distances.elastic._bounding_matrix import create_bounding_matrix from aeon.distances.pointwise._euclidean import _univariate_euclidean_distance from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -250,6 +251,8 @@ def twe_pairwise_distance( nu: float = 0.001, lmbda: float = 1.0, itakura_max_slope: Optional[float] = None, + n_jobs: int = 1, + **kwargs, ) -> np.ndarray: """Compute the TWE pairwise distance between a set of time series. @@ -274,6 +277,10 @@ def twe_pairwise_distance( itakura_max_slope : float, default=None Maximum slope as a proportion of the number of time points used to create Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Returns ------- @@ -319,6 +326,8 @@ def twe_pairwise_distance( [13.005, 0. , 18.007], [19.006, 18.007, 0. ]]) """ + n_jobs = check_n_jobs(n_jobs) + set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion @@ -336,7 +345,7 @@ def twe_pairwise_distance( ) -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _twe_pairwise_distance( X: NumbaList[np.ndarray], window: Optional[float], @@ -359,7 +368,7 @@ def _twe_pairwise_distance( for i in range(n_cases): padded_X.append(_pad_arrs(X[i])) - for i in range(n_cases): + for i in prange(n_cases): for j in range(i + 1, n_cases): x1, x2 = padded_X[i], padded_X[j] if unequal_length: @@ -372,7 +381,7 @@ def _twe_pairwise_distance( return distances -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _twe_from_multiple_to_multiple_distance( x: NumbaList[np.ndarray], y: NumbaList[np.ndarray], @@ -399,7 +408,7 @@ def _twe_from_multiple_to_multiple_distance( for i in range(m_cases): padded_y.append(_pad_arrs(y[i])) - for i in range(n_cases): + for i in prange(n_cases): for j in range(m_cases): x1, y1 = padded_x[i], padded_y[j] if unequal_length: diff --git a/aeon/distances/elastic/_wddtw.py b/aeon/distances/elastic/_wddtw.py index 9a49728c30..0987cf7555 100644 --- a/aeon/distances/elastic/_wddtw.py +++ b/aeon/distances/elastic/_wddtw.py @@ -5,7 +5,7 @@ from typing import Optional, Union import numpy as np -from numba import njit +from numba import njit, prange, set_num_threads from numba.typed import List as NumbaList from aeon.distances.elastic._alignment_paths import compute_min_return_path @@ -13,6 +13,7 @@ from aeon.distances.elastic._ddtw import average_of_slope from aeon.distances.elastic._wdtw import _wdtw_cost_matrix, _wdtw_distance from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -177,6 +178,8 @@ def wddtw_pairwise_distance( window: Optional[float] = None, g: float = 0.05, itakura_max_slope: Optional[float] = None, + n_jobs: int = 1, + **kwargs, ) -> np.ndarray: """Compute the WDDTW pairwise distance between a set of time series. @@ -199,6 +202,10 @@ def wddtw_pairwise_distance( itakura_max_slope : float, default=None Maximum slope as a proportion of the number of time points used to create Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Raises ------ @@ -240,6 +247,8 @@ def wddtw_pairwise_distance( [0., 0., 0.], [0., 0., 0.]]) """ + n_jobs = check_n_jobs(n_jobs) + set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion @@ -258,7 +267,7 @@ def wddtw_pairwise_distance( ) -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _wddtw_pairwise_distance( X: NumbaList[np.ndarray], window: Optional[float], @@ -278,7 +287,7 @@ def _wddtw_pairwise_distance( for i in range(n_cases): X_average_of_slope.append(average_of_slope(X[i])) - for i in range(n_cases): + for i in prange(n_cases): for j in range(i + 1, n_cases): x1, x2 = X_average_of_slope[i], X_average_of_slope[j] if unequal_length: @@ -291,7 +300,7 @@ def _wddtw_pairwise_distance( return distances -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _wddtw_from_multiple_to_multiple_distance( x: NumbaList[np.ndarray], y: NumbaList[np.ndarray], @@ -318,7 +327,7 @@ def _wddtw_from_multiple_to_multiple_distance( for i in range(m_cases): y_average_of_slope.append(average_of_slope(y[i])) - for i in range(n_cases): + for i in prange(n_cases): for j in range(m_cases): x1, y1 = x_average_of_slope[i], y_average_of_slope[j] if unequal_length: diff --git a/aeon/distances/elastic/_wdtw.py b/aeon/distances/elastic/_wdtw.py index 3ad1767c9e..d3659bbd32 100644 --- a/aeon/distances/elastic/_wdtw.py +++ b/aeon/distances/elastic/_wdtw.py @@ -5,13 +5,14 @@ from typing import Optional, Union import numpy as np -from numba import njit +from numba import njit, prange, set_num_threads from numba.typed import List as NumbaList from aeon.distances.elastic._alignment_paths import compute_min_return_path from aeon.distances.elastic._bounding_matrix import create_bounding_matrix from aeon.distances.pointwise._squared import _univariate_squared_distance from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -241,6 +242,8 @@ def wdtw_pairwise_distance( window: Optional[float] = None, g: float = 0.05, itakura_max_slope: Optional[float] = None, + n_jobs: int = 1, + **kwargs, ) -> np.ndarray: """Compute the WDTW pairwise distance between a set of time series. @@ -263,6 +266,10 @@ def wdtw_pairwise_distance( itakura_max_slope : float, default=None Maximum slope as a proportion of the number of time points used to create Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Returns ------- @@ -308,6 +315,8 @@ def wdtw_pairwise_distance( [ 20.25043711, 0. , 39.64543037], [139.70656066, 39.64543037, 0. ]]) """ + n_jobs = check_n_jobs(n_jobs) + set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion @@ -324,7 +333,7 @@ def wdtw_pairwise_distance( ) -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _wdtw_pairwise_distance( X: NumbaList[np.ndarray], window: Optional[float], @@ -340,7 +349,7 @@ def _wdtw_pairwise_distance( bounding_matrix = create_bounding_matrix( n_timepoints, n_timepoints, window, itakura_max_slope ) - for i in range(n_cases): + for i in prange(n_cases): for j in range(i + 1, n_cases): x1, x2 = X[i], X[j] if unequal_length: @@ -353,7 +362,7 @@ def _wdtw_pairwise_distance( return distances -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _wdtw_from_multiple_to_multiple_distance( x: NumbaList[np.ndarray], y: NumbaList[np.ndarray], @@ -370,7 +379,7 @@ def _wdtw_from_multiple_to_multiple_distance( bounding_matrix = create_bounding_matrix( x[0].shape[1], y[0].shape[1], window, itakura_max_slope ) - for i in range(n_cases): + for i in prange(n_cases): for j in range(m_cases): x1, y1 = x[i], y[j] if unequal_length: diff --git a/aeon/distances/mindist/_dft_sfa.py b/aeon/distances/mindist/_dft_sfa.py index 5f6e856260..7d29cf8e9b 100644 --- a/aeon/distances/mindist/_dft_sfa.py +++ b/aeon/distances/mindist/_dft_sfa.py @@ -3,9 +3,10 @@ from typing import Union import numpy as np -from numba import njit, prange +from numba import njit, prange, set_num_threads from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -86,7 +87,7 @@ def _univariate_dft_sfa_distance( def mindist_dft_sfa_pairwise_distance( - X: np.ndarray, y: np.ndarray, breakpoints: np.ndarray + X: np.ndarray, y: np.ndarray, breakpoints: np.ndarray, n_jobs: int = 1, **kwargs ) -> np.ndarray: """Compute the DFT SFA pairwise distance between a set of SFA representations. @@ -98,6 +99,10 @@ def mindist_dft_sfa_pairwise_distance( A collection of SFA instances of shape ``(n_instances, n_timepoints)``. breakpoints: np.ndarray The breakpoints of the SAX transformation + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Returns ------- @@ -110,6 +115,8 @@ def mindist_dft_sfa_pairwise_distance( If X is not 2D array when only passing X. If X and y are not 1D, 2D arrays when passing both X and y. """ + n_jobs = check_n_jobs(n_jobs) + set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion @@ -132,7 +139,7 @@ def _dft_sfa_from_multiple_to_multiple_distance( distances = np.zeros((n_instances, n_instances)) for i in prange(n_instances): - for j in prange(i + 1, n_instances): + for j in range(i + 1, n_instances): distances[i, j] = _univariate_dft_sfa_distance(X[i], X[j], breakpoints) distances[j, i] = distances[i, j] else: @@ -141,7 +148,7 @@ def _dft_sfa_from_multiple_to_multiple_distance( distances = np.zeros((n_instances, m_instances)) for i in prange(n_instances): - for j in prange(m_instances): + for j in range(m_instances): distances[i, j] = _univariate_dft_sfa_distance(X[i], y[j], breakpoints) return distances diff --git a/aeon/distances/mindist/_paa_sax.py b/aeon/distances/mindist/_paa_sax.py index a53a8b35aa..e8950f94f7 100644 --- a/aeon/distances/mindist/_paa_sax.py +++ b/aeon/distances/mindist/_paa_sax.py @@ -1,9 +1,10 @@ __maintainer__ = [] import numpy as np -from numba import njit, prange +from numba import njit, prange, set_num_threads from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -91,7 +92,12 @@ def _univariate_paa_sax_distance( def mindist_paa_sax_pairwise_distance( - X: np.ndarray, y: np.ndarray, breakpoints: np.ndarray, n: int + X: np.ndarray, + y: np.ndarray, + breakpoints: np.ndarray, + n: int, + n_jobs: int = 1, + **kwargs, ) -> np.ndarray: """Compute the PAA SAX pairwise distance between a set of SAX representations. @@ -105,6 +111,10 @@ def mindist_paa_sax_pairwise_distance( The breakpoints of the SAX transformation n : int The original size of the time series + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Returns ------- @@ -118,6 +128,8 @@ def mindist_paa_sax_pairwise_distance( If X and y are not 1D, 2D arrays when passing both X and y. """ + n_jobs = check_n_jobs(n_jobs) + set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion @@ -139,7 +151,7 @@ def _paa_sax_from_multiple_to_multiple_distance( distances = np.zeros((n_instances, n_instances)) for i in prange(n_instances): - for j in prange(i + 1, n_instances): + for j in range(i + 1, n_instances): distances[i, j] = _univariate_paa_sax_distance( X[i], X[j], breakpoints, n ) @@ -150,7 +162,7 @@ def _paa_sax_from_multiple_to_multiple_distance( distances = np.zeros((n_instances, m_instances)) for i in prange(n_instances): - for j in prange(m_instances): + for j in range(m_instances): distances[i, j] = _univariate_paa_sax_distance( X[i], y[j], breakpoints, n ) diff --git a/aeon/distances/mindist/_sax.py b/aeon/distances/mindist/_sax.py index cdecfb2ebc..dec2582d86 100644 --- a/aeon/distances/mindist/_sax.py +++ b/aeon/distances/mindist/_sax.py @@ -3,9 +3,10 @@ from typing import Union import numpy as np -from numba import njit, prange +from numba import njit, prange, set_num_threads from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -85,7 +86,12 @@ def _univariate_sax_distance( def mindist_sax_pairwise_distance( - X: np.ndarray, y: np.ndarray, breakpoints: np.ndarray, n: int + X: np.ndarray, + y: np.ndarray, + breakpoints: np.ndarray, + n: int, + n_jobs: int = 1, + **kwargs, ) -> np.ndarray: """Compute the SAX pairwise distance between a set of SAX representations. @@ -99,6 +105,10 @@ def mindist_sax_pairwise_distance( The breakpoints of the SAX transformation n : int The original size of the time series + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Returns ------- @@ -112,6 +122,8 @@ def mindist_sax_pairwise_distance( If X and y are not 1D, 2D arrays when passing both X and y. """ + n_jobs = check_n_jobs(n_jobs) + set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion @@ -134,7 +146,7 @@ def _sax_from_multiple_to_multiple_distance( distances = np.zeros((n_instances, n_instances)) for i in prange(n_instances): - for j in prange(i + 1, n_instances): + for j in range(i + 1, n_instances): distances[i, j] = _univariate_sax_distance(X[i], X[j], breakpoints, n) distances[j, i] = distances[i, j] else: @@ -143,7 +155,7 @@ def _sax_from_multiple_to_multiple_distance( distances = np.zeros((n_instances, m_instances)) for i in prange(n_instances): - for j in prange(m_instances): + for j in range(m_instances): distances[i, j] = _univariate_sax_distance(X[i], y[j], breakpoints, n) return distances diff --git a/aeon/distances/mindist/_sfa.py b/aeon/distances/mindist/_sfa.py index e9c6cf8638..152c679471 100644 --- a/aeon/distances/mindist/_sfa.py +++ b/aeon/distances/mindist/_sfa.py @@ -3,9 +3,10 @@ from typing import Union import numpy as np -from numba import njit, prange +from numba import njit, prange, set_num_threads from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -78,7 +79,7 @@ def _univariate_sfa_distance( def mindist_sfa_pairwise_distance( - X: np.ndarray, y: np.ndarray, breakpoints: np.ndarray + X: np.ndarray, y: np.ndarray, breakpoints: np.ndarray, n_jobs: int = 1, **kwargs ) -> np.ndarray: """Compute the SFA mindist pairwise distance between a set of SFA representations. @@ -90,6 +91,10 @@ def mindist_sfa_pairwise_distance( A collection of SFA instances of shape ``(n_instances, n_timepoints)``. breakpoints: np.ndarray The breakpoints of the SAX transformation + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Returns ------- @@ -103,6 +108,8 @@ def mindist_sfa_pairwise_distance( If X and y are not 1D, 2D arrays when passing both X and y. """ + n_jobs = check_n_jobs(n_jobs) + set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion diff --git a/aeon/distances/pointwise/_euclidean.py b/aeon/distances/pointwise/_euclidean.py index f7f0a640d4..c1e9794d60 100644 --- a/aeon/distances/pointwise/_euclidean.py +++ b/aeon/distances/pointwise/_euclidean.py @@ -1,9 +1,10 @@ __maintainer__ = [] +import warnings from typing import Optional, Union import numpy as np -from numba import njit +from numba import njit, prange, set_num_threads from numba.typed import List as NumbaList from aeon.distances.pointwise._squared import ( @@ -11,6 +12,7 @@ squared_distance, ) from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -72,6 +74,8 @@ def _univariate_euclidean_distance(x: np.ndarray, y: np.ndarray) -> float: def euclidean_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, + n_jobs: int = 1, + **kwargs, ) -> np.ndarray: """Compute the Euclidean pairwise distance between a set of time series. @@ -85,6 +89,10 @@ def euclidean_pairwise_distance( ``(m_cases, m_timepoints)`` or ``(m_cases, m_channels, m_timepoints)``. If None, then the euclidean pairwise distance between the instances of X is calculated. + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Returns ------- @@ -128,6 +136,18 @@ def euclidean_pairwise_distance( [ 5.19615242, 0. , 8. ], [12.12435565, 8. , 0. ]]) """ + n_jobs = check_n_jobs(n_jobs) + set_num_threads(n_jobs) + if n_jobs > 1: + warnings.warn( + "You have set n_jobs > 1. For this distance function " + "unless your data is very large (> 10000 time series), it is " + "recommended to use n_jobs=1. If this function is slower than " + "expected try setting n_jobs=1.", + UserWarning, + stacklevel=2, + ) + multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, _ = _convert_collection_to_numba_list(X, "X", multivariate_conversion) if y is None: @@ -138,12 +158,12 @@ def euclidean_pairwise_distance( return _euclidean_from_multiple_to_multiple_distance(_X, _y) -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _euclidean_pairwise_distance(X: NumbaList[np.ndarray]) -> np.ndarray: n_cases = len(X) distances = np.zeros((n_cases, n_cases)) - for i in range(n_cases): + for i in prange(n_cases): for j in range(i + 1, n_cases): distances[i, j] = euclidean_distance(X[i], X[j]) distances[j, i] = distances[i, j] @@ -151,7 +171,7 @@ def _euclidean_pairwise_distance(X: NumbaList[np.ndarray]) -> np.ndarray: return distances -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _euclidean_from_multiple_to_multiple_distance( x: NumbaList[np.ndarray], y: NumbaList[np.ndarray] ) -> np.ndarray: @@ -159,7 +179,7 @@ def _euclidean_from_multiple_to_multiple_distance( m_cases = len(y) distances = np.zeros((n_cases, m_cases)) - for i in range(n_cases): + for i in prange(n_cases): for j in range(m_cases): distances[i, j] = euclidean_distance(x[i], y[j]) return distances diff --git a/aeon/distances/pointwise/_manhattan.py b/aeon/distances/pointwise/_manhattan.py index 5c4a80e7a2..1b034a360d 100644 --- a/aeon/distances/pointwise/_manhattan.py +++ b/aeon/distances/pointwise/_manhattan.py @@ -1,12 +1,14 @@ __maintainer__ = [] +import warnings from typing import Optional, Union import numpy as np -from numba import njit +from numba import njit, prange, set_num_threads from numba.typed import List as NumbaList from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -77,6 +79,8 @@ def _univariate_manhattan_distance(x: np.ndarray, y: np.ndarray) -> float: def manhattan_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, + n_jobs: int = 1, + **kwargs, ) -> np.ndarray: """Compute the manhattan pairwise distance between a set of time series. @@ -90,6 +94,10 @@ def manhattan_pairwise_distance( ``(m_cases, m_timepoints)`` or ``(m_cases, m_channels, m_timepoints)``. If None, then the manhattan pairwise distance between the instances of X is calculated. + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Returns ------- @@ -133,6 +141,18 @@ def manhattan_pairwise_distance( [ 9., 0., 16.], [21., 16., 0.]]) """ + n_jobs = check_n_jobs(n_jobs) + set_num_threads(n_jobs) + if n_jobs > 1: + warnings.warn( + "You have set n_jobs > 1. For this distance function " + "unless your data is very large (> 10000 time series), it is " + "recommended to use n_jobs=1. If this function is slower than " + "expected try setting n_jobs=1.", + UserWarning, + stacklevel=2, + ) + multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, _ = _convert_collection_to_numba_list(X, "X", multivariate_conversion) if y is None: @@ -142,12 +162,12 @@ def manhattan_pairwise_distance( return _manhattan_from_multiple_to_multiple_distance(_X, _y) -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _manhattan_pairwise_distance(X: NumbaList[np.ndarray]) -> np.ndarray: n_cases = len(X) distances = np.zeros((n_cases, n_cases)) - for i in range(n_cases): + for i in prange(n_cases): for j in range(i + 1, n_cases): distances[i, j] = manhattan_distance(X[i], X[j]) distances[j, i] = distances[i, j] @@ -155,7 +175,7 @@ def _manhattan_pairwise_distance(X: NumbaList[np.ndarray]) -> np.ndarray: return distances -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _manhattan_from_multiple_to_multiple_distance( x: NumbaList[np.ndarray], y: NumbaList[np.ndarray] ) -> np.ndarray: @@ -163,7 +183,7 @@ def _manhattan_from_multiple_to_multiple_distance( m_cases = len(y) distances = np.zeros((n_cases, m_cases)) - for i in range(n_cases): + for i in prange(n_cases): for j in range(m_cases): distances[i, j] = manhattan_distance(x[i], y[j]) return distances diff --git a/aeon/distances/pointwise/_minkowski.py b/aeon/distances/pointwise/_minkowski.py index d25b504403..f441727aa0 100644 --- a/aeon/distances/pointwise/_minkowski.py +++ b/aeon/distances/pointwise/_minkowski.py @@ -3,10 +3,11 @@ from typing import Optional, Union import numpy as np -from numba import njit +from numba import njit, prange, set_num_threads from numba.typed import List as NumbaList from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -131,6 +132,8 @@ def minkowski_pairwise_distance( y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, p: float = 2.0, w: Optional[np.ndarray] = None, + n_jobs: int = 1, + **kwargs, ) -> np.ndarray: """Compute the Minkowski pairwise distance between a set of time series. @@ -150,6 +153,10 @@ def minkowski_pairwise_distance( w : np.ndarray, default=None An array of weights, applied to each pairwise calculation. The weights should match the shape of the time series in X and y. + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Returns ------- @@ -202,6 +209,8 @@ def minkowski_pairwise_distance( [ 5.19615242, 0. , 8. ], [12.12435565, 8. , 0. ]]) """ + n_jobs = check_n_jobs(n_jobs) + set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, _ = _convert_collection_to_numba_list(X, "X", multivariate_conversion) if y is None: @@ -211,14 +220,14 @@ def minkowski_pairwise_distance( return _minkowski_from_multiple_to_multiple_distance(_X, _y, p, w) -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _minkowski_pairwise_distance( X: NumbaList[np.ndarray], p: float, w: Optional[np.ndarray] = None ) -> np.ndarray: n_cases = len(X) distances = np.zeros((n_cases, n_cases)) - for i in range(n_cases): + for i in prange(n_cases): for j in range(i + 1, n_cases): if w is None: distances[i, j] = minkowski_distance(X[i], X[j], p) @@ -232,7 +241,7 @@ def _minkowski_pairwise_distance( return distances -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _minkowski_from_multiple_to_multiple_distance( x: NumbaList[np.ndarray], y: NumbaList[np.ndarray], @@ -243,7 +252,7 @@ def _minkowski_from_multiple_to_multiple_distance( m_cases = len(y) distances = np.zeros((n_cases, m_cases)) - for i in range(n_cases): + for i in prange(n_cases): for j in range(m_cases): if w is None: distances[i, j] = minkowski_distance(x[i], y[j], p) diff --git a/aeon/distances/pointwise/_squared.py b/aeon/distances/pointwise/_squared.py index 045466ef51..a4de9cd131 100644 --- a/aeon/distances/pointwise/_squared.py +++ b/aeon/distances/pointwise/_squared.py @@ -1,12 +1,14 @@ __maintainer__ = [] +import warnings from typing import Optional, Union import numpy as np -from numba import njit +from numba import njit, prange, set_num_threads from numba.typed import List as NumbaList from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -76,6 +78,8 @@ def _univariate_squared_distance(x: np.ndarray, y: np.ndarray) -> float: def squared_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, + n_jobs: int = 1, + **kwargs, ) -> np.ndarray: """Compute the squared pairwise distance between a set of time series. @@ -89,6 +93,10 @@ def squared_pairwise_distance( ``(m_cases, m_timepoints)`` or ``(m_cases, m_channels, m_timepoints)``. If None, then the squared pairwise distance between the instances of X is calculated. + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Returns ------- @@ -132,6 +140,17 @@ def squared_pairwise_distance( [ 27., 0., 64.], [147., 64., 0.]]) """ + n_jobs = check_n_jobs(n_jobs) + if n_jobs > 1: + warnings.warn( + "You have set n_jobs > 1. For this distance function " + "unless your data is very large (> 10000 time series), it is " + "recommended to use n_jobs=1. If this function is slower than " + "expected try setting n_jobs=1.", + UserWarning, + stacklevel=2, + ) + set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, _ = _convert_collection_to_numba_list(X, "X", multivariate_conversion) @@ -143,12 +162,12 @@ def squared_pairwise_distance( return _squared_from_multiple_to_multiple_distance(_X, _y) -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _squared_pairwise_distance(X: NumbaList[np.ndarray]) -> np.ndarray: n_cases = len(X) distances = np.zeros((n_cases, n_cases)) - for i in range(n_cases): + for i in prange(n_cases): for j in range(i + 1, n_cases): distances[i, j] = squared_distance(X[i], X[j]) distances[j, i] = distances[i, j] @@ -156,7 +175,7 @@ def _squared_pairwise_distance(X: NumbaList[np.ndarray]) -> np.ndarray: return distances -@njit(cache=True, fastmath=True) +@njit(cache=True, fastmath=True, parallel=True) def _squared_from_multiple_to_multiple_distance( x: NumbaList[np.ndarray], y: NumbaList[np.ndarray] ) -> np.ndarray: @@ -164,7 +183,7 @@ def _squared_from_multiple_to_multiple_distance( m_cases = len(y) distances = np.zeros((n_cases, m_cases)) - for i in range(n_cases): + for i in prange(n_cases): for j in range(m_cases): distances[i, j] = squared_distance(x[i], y[j]) return distances From c4066233b51354768c87e68aa6ff84fd1b22c9f1 Mon Sep 17 00:00:00 2001 From: chrisholder Date: Tue, 18 Feb 2025 00:02:50 +0000 Subject: [PATCH 002/216] sfa update --- aeon/distances/mindist/_sfa.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/aeon/distances/mindist/_sfa.py b/aeon/distances/mindist/_sfa.py index 152c679471..82effbfc4d 100644 --- a/aeon/distances/mindist/_sfa.py +++ b/aeon/distances/mindist/_sfa.py @@ -132,7 +132,7 @@ def _sfa_from_multiple_to_multiple_distance( distances = np.zeros((n_instances, n_instances)) for i in prange(n_instances): - for j in prange(i + 1, n_instances): + for j in range(i + 1, n_instances): distances[i, j] = _univariate_sfa_distance(X[i], X[j], breakpoints) distances[j, i] = distances[i, j] else: @@ -141,7 +141,7 @@ def _sfa_from_multiple_to_multiple_distance( distances = np.zeros((n_instances, m_instances)) for i in prange(n_instances): - for j in prange(m_instances): + for j in range(m_instances): distances[i, j] = _univariate_sfa_distance(X[i], y[j], breakpoints) return distances From cbf5e191f23500b05aed0649de929dfe4e92e208 Mon Sep 17 00:00:00 2001 From: chrisholder Date: Tue, 18 Feb 2025 12:04:52 +0000 Subject: [PATCH 003/216] changed warning --- aeon/distances/_sbd.py | 2 +- aeon/distances/pointwise/_euclidean.py | 2 +- aeon/distances/pointwise/_manhattan.py | 2 +- aeon/distances/pointwise/_squared.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/aeon/distances/_sbd.py b/aeon/distances/_sbd.py index b07652ad13..f96515b8bc 100644 --- a/aeon/distances/_sbd.py +++ b/aeon/distances/_sbd.py @@ -201,7 +201,7 @@ def sbd_pairwise_distance( if n_jobs > 1: warnings.warn( "You have set n_jobs > 1. For this distance function " - "unless your data is very large (> 10000 time series), it is " + "unless your data has a large number of time points, it is " "recommended to use n_jobs=1. If this function is slower than " "expected try setting n_jobs=1.", UserWarning, diff --git a/aeon/distances/pointwise/_euclidean.py b/aeon/distances/pointwise/_euclidean.py index c1e9794d60..2b5f05b5fe 100644 --- a/aeon/distances/pointwise/_euclidean.py +++ b/aeon/distances/pointwise/_euclidean.py @@ -141,7 +141,7 @@ def euclidean_pairwise_distance( if n_jobs > 1: warnings.warn( "You have set n_jobs > 1. For this distance function " - "unless your data is very large (> 10000 time series), it is " + "unless your data has a large number of time points, it is " "recommended to use n_jobs=1. If this function is slower than " "expected try setting n_jobs=1.", UserWarning, diff --git a/aeon/distances/pointwise/_manhattan.py b/aeon/distances/pointwise/_manhattan.py index 1b034a360d..fe58f8e772 100644 --- a/aeon/distances/pointwise/_manhattan.py +++ b/aeon/distances/pointwise/_manhattan.py @@ -146,7 +146,7 @@ def manhattan_pairwise_distance( if n_jobs > 1: warnings.warn( "You have set n_jobs > 1. For this distance function " - "unless your data is very large (> 10000 time series), it is " + "unless your data has a large number of time points, it is " "recommended to use n_jobs=1. If this function is slower than " "expected try setting n_jobs=1.", UserWarning, diff --git a/aeon/distances/pointwise/_squared.py b/aeon/distances/pointwise/_squared.py index a4de9cd131..5c3405bc50 100644 --- a/aeon/distances/pointwise/_squared.py +++ b/aeon/distances/pointwise/_squared.py @@ -144,7 +144,7 @@ def squared_pairwise_distance( if n_jobs > 1: warnings.warn( "You have set n_jobs > 1. For this distance function " - "unless your data is very large (> 10000 time series), it is " + "unless your data has a large number of time points, it is " "recommended to use n_jobs=1. If this function is slower than " "expected try setting n_jobs=1.", UserWarning, From 5f74e4e67b322aa9a6c20283c2aa6aaa986d2df1 Mon Sep 17 00:00:00 2001 From: chrisholder Date: Fri, 28 Feb 2025 15:25:06 +0100 Subject: [PATCH 004/216] thread kneighbors --- .../distance_based/_time_series_neighbors.py | 109 +++++++++++------- .../tests/test_time_series_neighbors.py | 4 +- 2 files changed, 66 insertions(+), 47 deletions(-) diff --git a/aeon/classification/distance_based/_time_series_neighbors.py b/aeon/classification/distance_based/_time_series_neighbors.py index f89b1be636..4bde22da44 100644 --- a/aeon/classification/distance_based/_time_series_neighbors.py +++ b/aeon/classification/distance_based/_time_series_neighbors.py @@ -5,6 +5,7 @@ distances in aeon.distances. """ +import numbers from typing import Optional __maintainer__ = [] @@ -15,7 +16,8 @@ import numpy as np from aeon.classification.base import BaseClassifier -from aeon.distances import get_distance_function +from aeon.distances import pairwise_distance +from aeon.utils.validation import check_n_jobs WEIGHTS_SUPPORTED = ["uniform", "distance"] @@ -111,7 +113,6 @@ def _fit(self, X, y): y : array-like, shape = (n_cases) The class labels. """ - self.metric_ = get_distance_function(method=self.distance) self.X_ = X self.classes_, self.y_ = np.unique(y, return_inverse=True) return self @@ -163,61 +164,81 @@ def _predict(self, X): """ self._check_is_fitted() - preds = np.empty(len(X), dtype=self.classes_.dtype) - for i in range(len(X)): - scores = np.zeros(len(self.classes_)) - idx, weights = self._kneighbors(X[i]) - for id, w in zip(idx, weights): - predicted_class = self.y_[id] - scores[predicted_class] += w + indexes = self.kneighbors(X, return_distance=False)[:, 0] + return self.classes_[self.y_[indexes]] - preds[i] = self.classes_[np.argmax(scores)] + def kneighbors(self, X=None, n_neighbors=None, return_distance=True): + """Find the K-neighbors of a point. - return preds - - def _kneighbors(self, X): - """ - Find the K-neighbors of a point. - - Returns indices and weights of each point. + Returns indices of and distances to the neighbors of each point. Parameters ---------- - X : np.ndarray - A single time series instance if shape = (n_channels, n_timepoints) + X : 3D np.ndarray of shape = (n_cases, n_channels, n_timepoints) or list of + shape [n_cases] of 2D arrays shape (n_channels,n_timepoints_i) + The query point or points. + If not provided, neighbors of each indexed point are returned. + In this case, the query point is not considered its own neighbor. + n_neighbors : int, default=None + Number of neighbors required for each sample. The default is the value + passed to the constructor. + return_distance : bool, default=True + Whether or not to return the distances. Returns ------- - ind : array + neigh_dist : ndarray of shape (n_queries, n_neighbors) + Array representing the distances to points, only present if + return_distance=True. + neigh_ind : ndarray of shape (n_queries, n_neighbors) Indices of the nearest points in the population matrix. - ws : array - Array representing the weights of each neighbor. """ - distances = np.array( - [ - self.metric_(X, self.X_[j], **self._distance_params) - for j in range(len(self.X_)) - ] - ) + self._check_is_fitted() + n_jobs = check_n_jobs(self.n_jobs) + + if n_neighbors is None: + n_neighbors = self.n_neighbors + elif n_neighbors <= 0: + raise ValueError(f"Expected n_neighbors > 0. Got {n_neighbors}") + elif not isinstance(n_neighbors, numbers.Integral): + raise TypeError( + f"n_neighbors does not take {type(n_neighbors)} value, " + "enter integer value" + ) - # Find indices of k nearest neighbors using partitioning: - # [0..k-1], [k], [k+1..n-1] - # They might not be ordered within themselves, - # but it is not necessary and partitioning is - # O(n) while sorting is O(nlogn) - closest_idx = np.argpartition(distances, self.n_neighbors) - closest_idx = closest_idx[: self.n_neighbors] - - if self.weights == "distance": - ws = distances[closest_idx] - # Using epsilon ~= 0 to avoid division by zero - ws = 1 / (ws + np.finfo(float).eps) - elif self.weights == "uniform": - ws = np.repeat(1.0, self.n_neighbors) + query_is_train = X is None + if query_is_train: + X = self.X_ + n_neighbors += 1 else: - raise Exception(f"Invalid kNN weights: {self.weights}") + X = self._preprocess_collection(X, store_metadata=False) + self._check_shape(X) + + distances = pairwise_distance( + X, + self.X_ if not query_is_train else None, + method=self.distance, + n_jobs=n_jobs, + **self._distance_params, + ) + + sample_range = np.arange(distances.shape[0])[:, None] + neigh_ind = np.argpartition(distances, n_neighbors - 1, axis=1) + neigh_ind = neigh_ind[:, :n_neighbors] + neigh_ind = neigh_ind[ + sample_range, np.argsort(distances[sample_range, neigh_ind]) + ] + + if query_is_train: + neigh_ind = neigh_ind[:, 1:] + + if return_distance: + if query_is_train: + neigh_dist = distances[sample_range, neigh_ind] + return neigh_dist, neigh_ind + return distances[sample_range, neigh_ind], neigh_ind - return closest_idx, ws + return neigh_ind @classmethod def _get_test_params( diff --git a/aeon/classification/distance_based/tests/test_time_series_neighbors.py b/aeon/classification/distance_based/tests/test_time_series_neighbors.py index 7746439d94..d62ba3308a 100644 --- a/aeon/classification/distance_based/tests/test_time_series_neighbors.py +++ b/aeon/classification/distance_based/tests/test_time_series_neighbors.py @@ -45,9 +45,7 @@ def test_knn_on_unit_test(distance_key): # load arrowhead data for unit tests X_train, y_train = load_unit_test(split="train") X_test, y_test = load_unit_test(split="test") - knn = KNeighborsTimeSeriesClassifier( - distance=distance_key, - ) + knn = KNeighborsTimeSeriesClassifier(distance=distance_key, n_neighbors=3) knn.fit(X_train, y_train) pred = knn.predict(X_test) correct = 0 From 4c95abbd5fa1fc6a624b678c89255ff9a58da660 Mon Sep 17 00:00:00 2001 From: chrisholder Date: Fri, 28 Feb 2025 15:28:51 +0100 Subject: [PATCH 005/216] fix test --- .../distance_based/tests/test_time_series_neighbors.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/classification/distance_based/tests/test_time_series_neighbors.py b/aeon/classification/distance_based/tests/test_time_series_neighbors.py index d62ba3308a..917b2628d4 100644 --- a/aeon/classification/distance_based/tests/test_time_series_neighbors.py +++ b/aeon/classification/distance_based/tests/test_time_series_neighbors.py @@ -45,7 +45,7 @@ def test_knn_on_unit_test(distance_key): # load arrowhead data for unit tests X_train, y_train = load_unit_test(split="train") X_test, y_test = load_unit_test(split="test") - knn = KNeighborsTimeSeriesClassifier(distance=distance_key, n_neighbors=3) + knn = KNeighborsTimeSeriesClassifier(distance=distance_key) knn.fit(X_train, y_train) pred = knn.predict(X_test) correct = 0 From 3ff96b102437b0f84e5bda4a711c816e2dc364ad Mon Sep 17 00:00:00 2001 From: chrisholder Date: Mon, 3 Mar 2025 16:52:44 +0100 Subject: [PATCH 006/216] custom pairwise threaded --- aeon/distances/_distance.py | 66 +++++++++++++++++++++++++++---------- aeon/utils/numba/general.py | 26 +++++++++++++++ 2 files changed, 74 insertions(+), 18 deletions(-) diff --git a/aeon/distances/_distance.py b/aeon/distances/_distance.py index 1cf5c35dc0..25e9a13211 100644 --- a/aeon/distances/_distance.py +++ b/aeon/distances/_distance.py @@ -1,10 +1,11 @@ __maintainer__ = [] -import warnings from enum import Enum from typing import Any, Callable, Optional, TypedDict, Union import numpy as np +from joblib import Parallel, delayed +from numba import set_num_threads from typing_extensions import Unpack from aeon.distances._mpdist import mp_distance, mp_pairwise_distance @@ -84,6 +85,7 @@ squared_pairwise_distance, ) from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -264,40 +266,54 @@ def _custom_func_pairwise( n_jobs: int = 1, **kwargs: Unpack[DistanceKwargs], ) -> np.ndarray: + n_jobs = check_n_jobs(n_jobs) + set_num_threads(n_jobs) if dist_func is None: raise ValueError("dist_func must be a callable") - if n_jobs != 1: - warnings.warn( - "You are using a custom distance function with n_jobs > 1. " - "Aeon does not support parallelization for custom distance " - "functions. If it is an existing aeon distance try using the " - "string name instead.", - UserWarning, - stacklevel=2, - ) - multivariate_conversion = _is_numpy_list_multivariate(X, y) X, _ = _convert_collection_to_numba_list(X, "X", multivariate_conversion) + + if n_jobs > 1: + X = np.array(X) + if y is None: # To self - return _custom_pairwise_distance(X, dist_func, **kwargs) + return _custom_pairwise_distance(X, dist_func, n_jobs=n_jobs, **kwargs) y, _ = _convert_collection_to_numba_list(y, "y", multivariate_conversion) - return _custom_from_multiple_to_multiple_distance(X, y, dist_func, **kwargs) + if n_jobs > 1: + y = np.array(y) + return _custom_from_multiple_to_multiple_distance( + X, y, dist_func, n_jobs=n_jobs, **kwargs + ) def _custom_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], dist_func: DistanceFunction, + n_jobs: int = 1, **kwargs: Unpack[DistanceKwargs], ) -> np.ndarray: n_cases = len(X) distances = np.zeros((n_cases, n_cases)) - for i in range(n_cases): - for j in range(i + 1, n_cases): + def compute_single_distance(i, j): + return i, j, dist_func(X[i], X[j], **kwargs) + + indices = [(i, j) for i in range(n_cases) for j in range(i + 1, n_cases)] + + if n_jobs == 1: + for i, j in indices: distances[i, j] = dist_func(X[i], X[j], **kwargs) - distances[j, i] = distances[i, j] + distances[j, i] = distances[i, j] # Mirror for symmetry + else: + results = Parallel(n_jobs=n_jobs)( + delayed(compute_single_distance)(i, j) for i, j in indices + ) + + for i, j, dist in results: + distances[i, j] = dist + distances[j, i] = dist # Mirror for symmetry return distances @@ -306,15 +322,29 @@ def _custom_from_multiple_to_multiple_distance( x: Union[np.ndarray, list[np.ndarray]], y: Union[np.ndarray, list[np.ndarray]], dist_func: DistanceFunction, + n_jobs: int = 1, **kwargs: Unpack[DistanceKwargs], ) -> np.ndarray: n_cases = len(x) m_cases = len(y) distances = np.zeros((n_cases, m_cases)) - for i in range(n_cases): - for j in range(m_cases): + def compute_single_distance(i, j): + return i, j, dist_func(x[i], y[j], **kwargs) + + indices = [(i, j) for i in range(n_cases) for j in range(m_cases)] + + if n_jobs == 1: + for i, j in indices: distances[i, j] = dist_func(x[i], y[j], **kwargs) + else: + results = Parallel(n_jobs=n_jobs)( + delayed(compute_single_distance)(i, j) for i, j in indices + ) + + for i, j, dist in results: + distances[i, j] = dist + return distances diff --git a/aeon/utils/numba/general.py b/aeon/utils/numba/general.py index 10e96abde6..6fefa0e1d5 100644 --- a/aeon/utils/numba/general.py +++ b/aeon/utils/numba/general.py @@ -772,3 +772,29 @@ def get_all_subsequences(X: np.ndarray, length: int, dilation: int) -> np.ndarra out_shape = (n_timestamps - (length - 1) * dilation, n_features, np.int64(length)) strides = (s1, s0, s1 * dilation) return np.lib.stride_tricks.as_strided(X, shape=out_shape, strides=strides) + + +def is_numba_function(func) -> bool: + """Determine if a function is compiled with Numba. + + Parameters + ---------- + func : callable + The function to check. + + Returns + ------- + bool + True if the function is compiled with Numba. + """ + if hasattr(func, "nopython_signatures") or hasattr(func, "__numba__"): + return True + + if hasattr(func, "_numba_type_"): + return True + + module_name = getattr(func, "__module__", "") + if module_name and module_name.startswith("numba."): + return True + + return False From 2a74496c72d1e0c32b2d621345fe1e7d038320cc Mon Sep 17 00:00:00 2001 From: chrisholder Date: Mon, 3 Mar 2025 17:08:36 +0100 Subject: [PATCH 007/216] fixed --- aeon/utils/numba/general.py | 26 -------------------------- 1 file changed, 26 deletions(-) diff --git a/aeon/utils/numba/general.py b/aeon/utils/numba/general.py index 6fefa0e1d5..10e96abde6 100644 --- a/aeon/utils/numba/general.py +++ b/aeon/utils/numba/general.py @@ -772,29 +772,3 @@ def get_all_subsequences(X: np.ndarray, length: int, dilation: int) -> np.ndarra out_shape = (n_timestamps - (length - 1) * dilation, n_features, np.int64(length)) strides = (s1, s0, s1 * dilation) return np.lib.stride_tricks.as_strided(X, shape=out_shape, strides=strides) - - -def is_numba_function(func) -> bool: - """Determine if a function is compiled with Numba. - - Parameters - ---------- - func : callable - The function to check. - - Returns - ------- - bool - True if the function is compiled with Numba. - """ - if hasattr(func, "nopython_signatures") or hasattr(func, "__numba__"): - return True - - if hasattr(func, "_numba_type_"): - return True - - module_name = getattr(func, "__module__", "") - if module_name and module_name.startswith("numba."): - return True - - return False From 4afc8dfafdd6a6b870340d4efba5148ff78b5951 Mon Sep 17 00:00:00 2001 From: chrisholder Date: Tue, 4 Mar 2025 15:27:26 +0100 Subject: [PATCH 008/216] added threaded decorator --- aeon/distances/_distance.py | 6 +- aeon/distances/_mpdist.py | 53 +----- aeon/distances/_sbd.py | 7 +- aeon/distances/_shift_scale_invariant.py | 7 +- aeon/distances/elastic/_adtw.py | 7 +- aeon/distances/elastic/_ddtw.py | 7 +- aeon/distances/elastic/_dtw.py | 7 +- aeon/distances/elastic/_edr.py | 7 +- aeon/distances/elastic/_erp.py | 7 +- aeon/distances/elastic/_lcss.py | 7 +- aeon/distances/elastic/_msm.py | 7 +- aeon/distances/elastic/_shape_dtw.py | 7 +- aeon/distances/elastic/_soft_dtw.py | 7 +- aeon/distances/elastic/_twe.py | 7 +- aeon/distances/elastic/_wddtw.py | 7 +- aeon/distances/elastic/_wdtw.py | 7 +- aeon/distances/mindist/_dft_sfa.py | 7 +- aeon/distances/mindist/_paa_sax.py | 7 +- aeon/distances/mindist/_sax.py | 7 +- aeon/distances/mindist/_sfa.py | 7 +- aeon/distances/pointwise/_euclidean.py | 7 +- aeon/distances/pointwise/_manhattan.py | 7 +- aeon/distances/pointwise/_minkowski.py | 7 +- aeon/distances/pointwise/_squared.py | 7 +- aeon/utils/_threading.py | 52 +++++ aeon/utils/tests/test_threading_decorator.py | 189 +++++++++++++++++++ 26 files changed, 312 insertions(+), 142 deletions(-) create mode 100644 aeon/utils/_threading.py create mode 100644 aeon/utils/tests/test_threading_decorator.py diff --git a/aeon/distances/_distance.py b/aeon/distances/_distance.py index 25e9a13211..8a5a2fb369 100644 --- a/aeon/distances/_distance.py +++ b/aeon/distances/_distance.py @@ -5,7 +5,6 @@ import numpy as np from joblib import Parallel, delayed -from numba import set_num_threads from typing_extensions import Unpack from aeon.distances._mpdist import mp_distance, mp_pairwise_distance @@ -84,8 +83,8 @@ squared_distance, squared_pairwise_distance, ) +from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -259,6 +258,7 @@ def pairwise_distance( raise ValueError("Method must be one of the supported strings or a callable") +@threaded def _custom_func_pairwise( X: Optional[Union[np.ndarray, list[np.ndarray]]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, @@ -266,8 +266,6 @@ def _custom_func_pairwise( n_jobs: int = 1, **kwargs: Unpack[DistanceKwargs], ) -> np.ndarray: - n_jobs = check_n_jobs(n_jobs) - set_num_threads(n_jobs) if dist_func is None: raise ValueError("dist_func must be a callable") diff --git a/aeon/distances/_mpdist.py b/aeon/distances/_mpdist.py index c9f195cba9..7bfab4526c 100644 --- a/aeon/distances/_mpdist.py +++ b/aeon/distances/_mpdist.py @@ -1,14 +1,9 @@ """Matrix Profile Distances.""" -import warnings from typing import Optional, Union import numpy as np from numba import njit -from numba.typed import List as NumbaList - -from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation.collection import _is_numpy_list_multivariate def mp_distance(x: np.ndarray, y: np.ndarray, m: int = 0) -> float: @@ -288,6 +283,7 @@ def mp_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, m: int = 0, + n_jobs: int = 1, **kwargs, ) -> np.ndarray: """Compute the mpdist pairwise distance between a set of time series. @@ -341,51 +337,8 @@ def mp_pairwise_distance( [2.82842712], [2.82842712]]) """ - if "n_jobs" in kwargs: - warnings.warn( - "n_jobs is not supported for the mpdist distance method and will be " - "ignored.", - UserWarning, - stacklevel=2, - ) if m == 0: m = int(X.shape[2] / 4) - multivariate_conversion = _is_numpy_list_multivariate(X, y) - _X, unequal_length = _convert_collection_to_numba_list( - X, "X", multivariate_conversion - ) - - if y is None: - return _mpdist_pairwise_distance_single(_X, m) - - _y, unequal_length = _convert_collection_to_numba_list( - y, "y", multivariate_conversion - ) - - return _mpdist_pairwise_distance(_X, _y, m) - - -def _mpdist_pairwise_distance_single(x: NumbaList[np.ndarray], m: int) -> np.ndarray: - n_cases = len(x) - distances = np.zeros((n_cases, n_cases)) - - for i in range(n_cases): - for j in range(i + 1, n_cases): - distances[i, j] = mp_distance(x[i], x[j], m) - distances[j, i] = distances[i, j] - - return distances - - -def _mpdist_pairwise_distance( - x: NumbaList[np.ndarray], y: NumbaList[np.ndarray], m: int -) -> np.ndarray: - n_cases = len(x) - m_cases = len(y) - - distances = np.zeros((n_cases, m_cases)) + from aeon.distances._distance import pairwise_distance - for i in range(n_cases): - for j in range(m_cases): - distances[i, j] = mp_distance(x[i], y[j], m) - return distances + return pairwise_distance(X, y, method=mp_distance, m=m, n_jobs=n_jobs, **kwargs) diff --git a/aeon/distances/_sbd.py b/aeon/distances/_sbd.py index f96515b8bc..1e72d4eca6 100644 --- a/aeon/distances/_sbd.py +++ b/aeon/distances/_sbd.py @@ -6,12 +6,12 @@ from typing import Optional, Union import numpy as np -from numba import njit, objmode, prange, set_num_threads +from numba import njit, objmode, prange from numba.typed import List as NumbaList from scipy.signal import correlate +from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -115,6 +115,7 @@ def sbd_distance(x: np.ndarray, y: np.ndarray, standardize: bool = True) -> floa raise ValueError("x and y must be 1D or 2D") +@threaded def sbd_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, @@ -196,8 +197,6 @@ def sbd_pairwise_distance( [0.36754447, 0. , 0.29289322], [0.5527864 , 0.29289322, 0. ]]) """ - n_jobs = check_n_jobs(n_jobs) - set_num_threads(n_jobs) if n_jobs > 1: warnings.warn( "You have set n_jobs > 1. For this distance function " diff --git a/aeon/distances/_shift_scale_invariant.py b/aeon/distances/_shift_scale_invariant.py index 66caa282fd..e425ca702a 100644 --- a/aeon/distances/_shift_scale_invariant.py +++ b/aeon/distances/_shift_scale_invariant.py @@ -3,11 +3,11 @@ from typing import Optional, Union import numpy as np -from numba import njit, prange, set_num_threads +from numba import njit, prange from numba.typed import List as NumbaList +from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -157,6 +157,7 @@ def _univariate_shift_scale_invariant_distance( return min_dist, best_shifted_y +@threaded def shift_scale_invariant_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, @@ -230,8 +231,6 @@ def shift_scale_invariant_pairwise_distance( >>> y_univariate = np.array([11., 12., 13.]) >>> single_pw =shift_scale_invariant_pairwise_distance(X, y_univariate) """ - n_jobs = check_n_jobs(n_jobs) - set_num_threads(n_jobs) if max_shift is None: if y is None: max_shift = X.shape[-1] diff --git a/aeon/distances/elastic/_adtw.py b/aeon/distances/elastic/_adtw.py index 7ab182f394..f061f479ff 100644 --- a/aeon/distances/elastic/_adtw.py +++ b/aeon/distances/elastic/_adtw.py @@ -5,14 +5,14 @@ from typing import Optional, Union import numpy as np -from numba import njit, prange, set_num_threads +from numba import njit, prange from numba.typed import List as NumbaList from aeon.distances.elastic._alignment_paths import compute_min_return_path from aeon.distances.elastic._bounding_matrix import create_bounding_matrix from aeon.distances.pointwise._squared import _univariate_squared_distance +from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -198,6 +198,7 @@ def _adtw_cost_matrix( return cost_matrix[1:, 1:] +@threaded def adtw_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, @@ -279,8 +280,6 @@ def adtw_pairwise_distance( [ 44., 0., 87.], [294., 87., 0.]]) """ - n_jobs = check_n_jobs(n_jobs) - set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion diff --git a/aeon/distances/elastic/_ddtw.py b/aeon/distances/elastic/_ddtw.py index dffd3f71e2..a31745f2f0 100644 --- a/aeon/distances/elastic/_ddtw.py +++ b/aeon/distances/elastic/_ddtw.py @@ -5,7 +5,7 @@ from typing import Optional, Union import numpy as np -from numba import njit, prange, set_num_threads +from numba import njit, prange from numba.typed import List as NumbaList from aeon.distances.elastic._alignment_paths import compute_min_return_path @@ -14,8 +14,8 @@ _dtw_distance, create_bounding_matrix, ) +from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -168,6 +168,7 @@ def ddtw_cost_matrix( raise ValueError("x and y must be 1D or 2D") +@threaded def ddtw_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, @@ -244,8 +245,6 @@ def ddtw_pairwise_distance( [0., 0., 0.], [0., 0., 0.]]) """ - n_jobs = check_n_jobs(n_jobs) - set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion diff --git a/aeon/distances/elastic/_dtw.py b/aeon/distances/elastic/_dtw.py index 0f15892477..26add187dd 100644 --- a/aeon/distances/elastic/_dtw.py +++ b/aeon/distances/elastic/_dtw.py @@ -5,14 +5,14 @@ from typing import Optional, Union import numpy as np -from numba import njit, prange, set_num_threads +from numba import njit, prange from numba.typed import List as NumbaList from aeon.distances.elastic._alignment_paths import compute_min_return_path from aeon.distances.elastic._bounding_matrix import create_bounding_matrix from aeon.distances.pointwise._squared import _univariate_squared_distance +from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -229,6 +229,7 @@ def _dtw_cost_matrix( return cost_matrix[1:, 1:] +@threaded def dtw_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, @@ -321,8 +322,6 @@ def dtw_pairwise_distance( [ 42., 0., 83.], [292., 83., 0.]]) """ - n_jobs = check_n_jobs(n_jobs) - set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion diff --git a/aeon/distances/elastic/_edr.py b/aeon/distances/elastic/_edr.py index e3a2bf3bf1..19a4ec483b 100644 --- a/aeon/distances/elastic/_edr.py +++ b/aeon/distances/elastic/_edr.py @@ -5,14 +5,14 @@ from typing import Optional, Union import numpy as np -from numba import njit, prange, set_num_threads +from numba import njit, prange from numba.typed import List as NumbaList from aeon.distances.elastic._alignment_paths import compute_min_return_path from aeon.distances.elastic._bounding_matrix import create_bounding_matrix from aeon.distances.pointwise._euclidean import _univariate_euclidean_distance +from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -230,6 +230,7 @@ def _edr_cost_matrix( return cost_matrix[1:, 1:] +@threaded def edr_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, @@ -310,8 +311,6 @@ def edr_pairwise_distance( [0.75, 0. , 0.8 ], [0.6 , 0.8 , 0. ]]) """ - n_jobs = check_n_jobs(n_jobs) - set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion diff --git a/aeon/distances/elastic/_erp.py b/aeon/distances/elastic/_erp.py index 12ea5313ce..b1a7071a7b 100644 --- a/aeon/distances/elastic/_erp.py +++ b/aeon/distances/elastic/_erp.py @@ -5,14 +5,14 @@ from typing import Optional, Union import numpy as np -from numba import njit, prange, set_num_threads +from numba import njit, prange from numba.typed import List as NumbaList from aeon.distances.elastic._alignment_paths import compute_min_return_path from aeon.distances.elastic._bounding_matrix import create_bounding_matrix from aeon.distances.pointwise._euclidean import _univariate_euclidean_distance +from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -249,6 +249,7 @@ def _precompute_g( return gx_distance, x_sum +@threaded def erp_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, @@ -334,8 +335,6 @@ def erp_pairwise_distance( [16., 0., 28.], [44., 28., 0.]]) """ - n_jobs = check_n_jobs(n_jobs) - set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion diff --git a/aeon/distances/elastic/_lcss.py b/aeon/distances/elastic/_lcss.py index 6191335799..0cddbd9b0f 100644 --- a/aeon/distances/elastic/_lcss.py +++ b/aeon/distances/elastic/_lcss.py @@ -5,14 +5,14 @@ from typing import Optional, Union import numpy as np -from numba import njit, prange, set_num_threads +from numba import njit, prange from numba.typed import List as NumbaList from aeon.distances.elastic._alignment_paths import compute_lcss_return_path from aeon.distances.elastic._bounding_matrix import create_bounding_matrix from aeon.distances.pointwise._euclidean import _univariate_euclidean_distance +from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -223,6 +223,7 @@ def _lcss_cost_matrix( return cost_matrix +@threaded def lcss_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, @@ -302,8 +303,6 @@ def lcss_pairwise_distance( [0.66666667, 0. , 0.75 ], [1. , 0.75 , 0. ]]) """ - n_jobs = check_n_jobs(n_jobs) - set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion diff --git a/aeon/distances/elastic/_msm.py b/aeon/distances/elastic/_msm.py index 24f87b66ab..f75ab5daf8 100644 --- a/aeon/distances/elastic/_msm.py +++ b/aeon/distances/elastic/_msm.py @@ -5,14 +5,14 @@ from typing import Optional, Union import numpy as np -from numba import njit, prange, set_num_threads +from numba import njit, prange from numba.typed import List as NumbaList from aeon.distances.elastic._alignment_paths import compute_min_return_path from aeon.distances.elastic._bounding_matrix import create_bounding_matrix from aeon.distances.pointwise._squared import _univariate_squared_distance +from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -344,6 +344,7 @@ def _cost_independent(x: float, y: float, z: float, c: float) -> float: return c + min(abs(x - y), abs(x - z)) +@threaded def msm_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, @@ -426,8 +427,6 @@ def msm_pairwise_distance( [10., 0., 14.], [17., 14., 0.]]) """ - n_jobs = check_n_jobs(n_jobs) - set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion diff --git a/aeon/distances/elastic/_shape_dtw.py b/aeon/distances/elastic/_shape_dtw.py index c4933f0ff1..894db087c3 100644 --- a/aeon/distances/elastic/_shape_dtw.py +++ b/aeon/distances/elastic/_shape_dtw.py @@ -5,15 +5,15 @@ from typing import Optional, Union import numpy as np -from numba import njit, prange, set_num_threads +from numba import njit, prange from numba.typed import List as NumbaList from aeon.distances.elastic._alignment_paths import compute_min_return_path from aeon.distances.elastic._bounding_matrix import create_bounding_matrix from aeon.distances.elastic._dtw import _dtw_cost_matrix from aeon.distances.pointwise._squared import _univariate_squared_distance +from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -516,6 +516,7 @@ def shape_dtw_alignment_path( return (compute_min_return_path(cost_matrix), shapedtw_dist) +@threaded def shape_dtw_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, @@ -616,8 +617,6 @@ def shape_dtw_pairwise_distance( [ 43., 0., 89.], [292., 89., 0.]]) """ - n_jobs = check_n_jobs(n_jobs) - set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion diff --git a/aeon/distances/elastic/_soft_dtw.py b/aeon/distances/elastic/_soft_dtw.py index 006498bbfd..861b9cf428 100644 --- a/aeon/distances/elastic/_soft_dtw.py +++ b/aeon/distances/elastic/_soft_dtw.py @@ -5,15 +5,15 @@ from typing import Optional, Union import numpy as np -from numba import njit, prange, set_num_threads +from numba import njit, prange from numba.typed import List as NumbaList from aeon.distances.elastic._alignment_paths import compute_min_return_path from aeon.distances.elastic._bounding_matrix import create_bounding_matrix from aeon.distances.elastic._dtw import _dtw_cost_matrix from aeon.distances.pointwise._squared import _univariate_squared_distance +from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -244,6 +244,7 @@ def _soft_dtw_cost_matrix( return cost_matrix[1:, 1:] +@threaded def soft_dtw_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, @@ -323,8 +324,6 @@ def soft_dtw_pairwise_distance( [ 41.44055555, 0. , 82.43894439], [291.99999969, 82.43894439, 0. ]]) """ - n_jobs = check_n_jobs(n_jobs) - set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion diff --git a/aeon/distances/elastic/_twe.py b/aeon/distances/elastic/_twe.py index 69003dd54c..077172e0a2 100644 --- a/aeon/distances/elastic/_twe.py +++ b/aeon/distances/elastic/_twe.py @@ -5,14 +5,14 @@ from typing import Optional, Union import numpy as np -from numba import njit, prange, set_num_threads +from numba import njit, prange from numba.typed import List as NumbaList from aeon.distances.elastic._alignment_paths import compute_min_return_path from aeon.distances.elastic._bounding_matrix import create_bounding_matrix from aeon.distances.pointwise._euclidean import _univariate_euclidean_distance +from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -244,6 +244,7 @@ def _pad_arrs(x: np.ndarray) -> np.ndarray: return padded_x +@threaded def twe_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, @@ -326,8 +327,6 @@ def twe_pairwise_distance( [13.005, 0. , 18.007], [19.006, 18.007, 0. ]]) """ - n_jobs = check_n_jobs(n_jobs) - set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion diff --git a/aeon/distances/elastic/_wddtw.py b/aeon/distances/elastic/_wddtw.py index 0987cf7555..5e064a8162 100644 --- a/aeon/distances/elastic/_wddtw.py +++ b/aeon/distances/elastic/_wddtw.py @@ -5,15 +5,15 @@ from typing import Optional, Union import numpy as np -from numba import njit, prange, set_num_threads +from numba import njit, prange from numba.typed import List as NumbaList from aeon.distances.elastic._alignment_paths import compute_min_return_path from aeon.distances.elastic._bounding_matrix import create_bounding_matrix from aeon.distances.elastic._ddtw import average_of_slope from aeon.distances.elastic._wdtw import _wdtw_cost_matrix, _wdtw_distance +from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -172,6 +172,7 @@ def wddtw_cost_matrix( raise ValueError("x and y must be 1D or 2D") +@threaded def wddtw_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, @@ -247,8 +248,6 @@ def wddtw_pairwise_distance( [0., 0., 0.], [0., 0., 0.]]) """ - n_jobs = check_n_jobs(n_jobs) - set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion diff --git a/aeon/distances/elastic/_wdtw.py b/aeon/distances/elastic/_wdtw.py index d3659bbd32..3573cedfc3 100644 --- a/aeon/distances/elastic/_wdtw.py +++ b/aeon/distances/elastic/_wdtw.py @@ -5,14 +5,14 @@ from typing import Optional, Union import numpy as np -from numba import njit, prange, set_num_threads +from numba import njit, prange from numba.typed import List as NumbaList from aeon.distances.elastic._alignment_paths import compute_min_return_path from aeon.distances.elastic._bounding_matrix import create_bounding_matrix from aeon.distances.pointwise._squared import _univariate_squared_distance +from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -236,6 +236,7 @@ def _wdtw_cost_matrix( return cost_matrix[1:, 1:] +@threaded def wdtw_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, @@ -315,8 +316,6 @@ def wdtw_pairwise_distance( [ 20.25043711, 0. , 39.64543037], [139.70656066, 39.64543037, 0. ]]) """ - n_jobs = check_n_jobs(n_jobs) - set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion diff --git a/aeon/distances/mindist/_dft_sfa.py b/aeon/distances/mindist/_dft_sfa.py index 7d29cf8e9b..9d8bd80794 100644 --- a/aeon/distances/mindist/_dft_sfa.py +++ b/aeon/distances/mindist/_dft_sfa.py @@ -3,10 +3,10 @@ from typing import Union import numpy as np -from numba import njit, prange, set_num_threads +from numba import njit, prange +from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -86,6 +86,7 @@ def _univariate_dft_sfa_distance( return np.sqrt(2 * dist) +@threaded def mindist_dft_sfa_pairwise_distance( X: np.ndarray, y: np.ndarray, breakpoints: np.ndarray, n_jobs: int = 1, **kwargs ) -> np.ndarray: @@ -115,8 +116,6 @@ def mindist_dft_sfa_pairwise_distance( If X is not 2D array when only passing X. If X and y are not 1D, 2D arrays when passing both X and y. """ - n_jobs = check_n_jobs(n_jobs) - set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion diff --git a/aeon/distances/mindist/_paa_sax.py b/aeon/distances/mindist/_paa_sax.py index e8950f94f7..8d7fb42350 100644 --- a/aeon/distances/mindist/_paa_sax.py +++ b/aeon/distances/mindist/_paa_sax.py @@ -1,10 +1,10 @@ __maintainer__ = [] import numpy as np -from numba import njit, prange, set_num_threads +from numba import njit, prange +from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -91,6 +91,7 @@ def _univariate_paa_sax_distance( return np.sqrt(dist) +@threaded def mindist_paa_sax_pairwise_distance( X: np.ndarray, y: np.ndarray, @@ -128,8 +129,6 @@ def mindist_paa_sax_pairwise_distance( If X and y are not 1D, 2D arrays when passing both X and y. """ - n_jobs = check_n_jobs(n_jobs) - set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion diff --git a/aeon/distances/mindist/_sax.py b/aeon/distances/mindist/_sax.py index dec2582d86..8313de26bd 100644 --- a/aeon/distances/mindist/_sax.py +++ b/aeon/distances/mindist/_sax.py @@ -3,10 +3,10 @@ from typing import Union import numpy as np -from numba import njit, prange, set_num_threads +from numba import njit, prange +from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -85,6 +85,7 @@ def _univariate_sax_distance( return np.sqrt(dist) +@threaded def mindist_sax_pairwise_distance( X: np.ndarray, y: np.ndarray, @@ -122,8 +123,6 @@ def mindist_sax_pairwise_distance( If X and y are not 1D, 2D arrays when passing both X and y. """ - n_jobs = check_n_jobs(n_jobs) - set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion diff --git a/aeon/distances/mindist/_sfa.py b/aeon/distances/mindist/_sfa.py index 82effbfc4d..95dabd9cb6 100644 --- a/aeon/distances/mindist/_sfa.py +++ b/aeon/distances/mindist/_sfa.py @@ -3,10 +3,10 @@ from typing import Union import numpy as np -from numba import njit, prange, set_num_threads +from numba import njit, prange +from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -78,6 +78,7 @@ def _univariate_sfa_distance( return np.sqrt(2 * dist) +@threaded def mindist_sfa_pairwise_distance( X: np.ndarray, y: np.ndarray, breakpoints: np.ndarray, n_jobs: int = 1, **kwargs ) -> np.ndarray: @@ -108,8 +109,6 @@ def mindist_sfa_pairwise_distance( If X and y are not 1D, 2D arrays when passing both X and y. """ - n_jobs = check_n_jobs(n_jobs) - set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, unequal_length = _convert_collection_to_numba_list( X, "X", multivariate_conversion diff --git a/aeon/distances/pointwise/_euclidean.py b/aeon/distances/pointwise/_euclidean.py index 2b5f05b5fe..607032b887 100644 --- a/aeon/distances/pointwise/_euclidean.py +++ b/aeon/distances/pointwise/_euclidean.py @@ -4,15 +4,15 @@ from typing import Optional, Union import numpy as np -from numba import njit, prange, set_num_threads +from numba import njit, prange from numba.typed import List as NumbaList from aeon.distances.pointwise._squared import ( _univariate_squared_distance, squared_distance, ) +from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -71,6 +71,7 @@ def _univariate_euclidean_distance(x: np.ndarray, y: np.ndarray) -> float: return np.sqrt(_univariate_squared_distance(x, y)) +@threaded def euclidean_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, @@ -136,8 +137,6 @@ def euclidean_pairwise_distance( [ 5.19615242, 0. , 8. ], [12.12435565, 8. , 0. ]]) """ - n_jobs = check_n_jobs(n_jobs) - set_num_threads(n_jobs) if n_jobs > 1: warnings.warn( "You have set n_jobs > 1. For this distance function " diff --git a/aeon/distances/pointwise/_manhattan.py b/aeon/distances/pointwise/_manhattan.py index fe58f8e772..4d3892aed4 100644 --- a/aeon/distances/pointwise/_manhattan.py +++ b/aeon/distances/pointwise/_manhattan.py @@ -4,11 +4,11 @@ from typing import Optional, Union import numpy as np -from numba import njit, prange, set_num_threads +from numba import njit, prange from numba.typed import List as NumbaList +from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -76,6 +76,7 @@ def _univariate_manhattan_distance(x: np.ndarray, y: np.ndarray) -> float: return distance +@threaded def manhattan_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, @@ -141,8 +142,6 @@ def manhattan_pairwise_distance( [ 9., 0., 16.], [21., 16., 0.]]) """ - n_jobs = check_n_jobs(n_jobs) - set_num_threads(n_jobs) if n_jobs > 1: warnings.warn( "You have set n_jobs > 1. For this distance function " diff --git a/aeon/distances/pointwise/_minkowski.py b/aeon/distances/pointwise/_minkowski.py index f441727aa0..232e383eb1 100644 --- a/aeon/distances/pointwise/_minkowski.py +++ b/aeon/distances/pointwise/_minkowski.py @@ -3,11 +3,11 @@ from typing import Optional, Union import numpy as np -from numba import njit, prange, set_num_threads +from numba import njit, prange from numba.typed import List as NumbaList +from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -127,6 +127,7 @@ def _multivariate_minkowski_distance( return dist ** (1.0 / p) +@threaded def minkowski_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, @@ -209,8 +210,6 @@ def minkowski_pairwise_distance( [ 5.19615242, 0. , 8. ], [12.12435565, 8. , 0. ]]) """ - n_jobs = check_n_jobs(n_jobs) - set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, _ = _convert_collection_to_numba_list(X, "X", multivariate_conversion) if y is None: diff --git a/aeon/distances/pointwise/_squared.py b/aeon/distances/pointwise/_squared.py index 5c3405bc50..f7391c8565 100644 --- a/aeon/distances/pointwise/_squared.py +++ b/aeon/distances/pointwise/_squared.py @@ -4,11 +4,11 @@ from typing import Optional, Union import numpy as np -from numba import njit, prange, set_num_threads +from numba import njit, prange from numba.typed import List as NumbaList +from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -75,6 +75,7 @@ def _univariate_squared_distance(x: np.ndarray, y: np.ndarray) -> float: return distance +@threaded def squared_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, @@ -140,7 +141,6 @@ def squared_pairwise_distance( [ 27., 0., 64.], [147., 64., 0.]]) """ - n_jobs = check_n_jobs(n_jobs) if n_jobs > 1: warnings.warn( "You have set n_jobs > 1. For this distance function " @@ -150,7 +150,6 @@ def squared_pairwise_distance( UserWarning, stacklevel=2, ) - set_num_threads(n_jobs) multivariate_conversion = _is_numpy_list_multivariate(X, y) _X, _ = _convert_collection_to_numba_list(X, "X", multivariate_conversion) diff --git a/aeon/utils/_threading.py b/aeon/utils/_threading.py new file mode 100644 index 0000000000..9f33cd0d2a --- /dev/null +++ b/aeon/utils/_threading.py @@ -0,0 +1,52 @@ +import functools +import inspect +import os +import threading +from typing import Any, Callable + +from numba import set_num_threads + +from aeon.utils.validation import check_n_jobs + + +def threaded(func: Callable) -> Callable: + """Set thread count based on n_jobs parameter and restore it afterward. + + A decorator that sets the number of threads based on the n_jobs parameter + passed to the function, and restores the original thread count afterward. + + The decorated function is expected to have a 'n_jobs' parameter. + """ + + @functools.wraps(func) + def wrapper(*args: Any, **kwargs: Any) -> Any: + numba_env_threads = os.environ.get("NUMBA_NUM_THREADS") + + if numba_env_threads is not None and numba_env_threads.isdigit(): + original_thread_count = int(numba_env_threads) + else: + original_thread_count = threading.active_count() + + if "n_jobs" in kwargs: + n_jobs = kwargs["n_jobs"] + else: + sig = inspect.signature(func) + param_names = list(sig.parameters.keys()) + + n_jobs_index = param_names.index("n_jobs") + if n_jobs_index < len(args): + n_jobs = args[n_jobs_index] + else: + default = sig.parameters["n_jobs"].default + n_jobs = default if default is not inspect.Parameter.empty else None + + adjusted_n_jobs = check_n_jobs(n_jobs) + set_num_threads(adjusted_n_jobs) + + try: + result = func(*args, **kwargs) + return result + finally: + set_num_threads(original_thread_count) + + return wrapper diff --git a/aeon/utils/tests/test_threading_decorator.py b/aeon/utils/tests/test_threading_decorator.py new file mode 100644 index 0000000000..a147c646a7 --- /dev/null +++ b/aeon/utils/tests/test_threading_decorator.py @@ -0,0 +1,189 @@ +"""Test threading decorator.""" + +import os +from unittest.mock import MagicMock, patch + +import pytest + +from aeon.utils._threading import threaded + + +def check_n_jobs(n_jobs): + """Mock implementation of check_n_jobs.""" + return n_jobs if n_jobs is not None else 1 + + +def set_num_threads(n_threads): + """Mock implementation of set_num_threads.""" + pass + + +@pytest.fixture +def clean_env(): + """Save and restore environment variables between tests.""" + original_env = os.environ.copy() + yield + os.environ.clear() + os.environ.update(original_env) + + +def test_basic_functionality(): + """Test that the decorator correctly sets and restores thread count.""" + check_jobs_mock = MagicMock(side_effect=lambda x: x if x is not None else 1) + set_threads_mock = MagicMock() + + with patch("aeon.utils._threading.check_n_jobs", check_jobs_mock): + with patch("aeon.utils._threading.set_num_threads", set_threads_mock): + + @threaded + def sample_func(n_jobs=None): + return "executed" + + result = sample_func(n_jobs=4) + + assert result == "executed" + check_jobs_mock.assert_called_once_with(4) + assert set_threads_mock.call_count == 2 + + +def test_numba_env_variable(clean_env): + """Test that the decorator respects NUMBA_NUM_THREADS environment variable.""" + os.environ["NUMBA_NUM_THREADS"] = "8" + + check_jobs_mock = MagicMock(side_effect=lambda x: x if x is not None else 1) + set_threads_mock = MagicMock() + + with patch("aeon.utils._threading.check_n_jobs", check_jobs_mock): + with patch("aeon.utils._threading.set_num_threads", set_threads_mock): + + @threaded + def sample_func(n_jobs=None): + return "executed" + + sample_func(n_jobs=4) + + assert set_threads_mock.call_args_list[0][0][0] == 4 + assert set_threads_mock.call_args_list[1][0][0] == 8 + + +def test_fallback_to_threading_count(clean_env): + """ + Test the fallback mechanism to the system's active thread count. + + When the NUMBA_NUM_THREADS environment variable is not set or is invalid, + the decorator should use the system's active thread count as the baseline. + This ensures proper thread management even when no explicit configuration is + provided. + """ + check_jobs_mock = MagicMock(side_effect=lambda x: x if x is not None else 1) + set_threads_mock = MagicMock() + thread_count_mock = MagicMock(return_value=3) + + with patch("aeon.utils._threading.check_n_jobs", check_jobs_mock): + with patch("aeon.utils._threading.set_num_threads", set_threads_mock): + with patch("threading.active_count", thread_count_mock): + + @threaded + def sample_func(n_jobs=None): + return "executed" + + sample_func(n_jobs=4) + + assert set_threads_mock.call_args_list[1][0][0] == 3 + + +def test_positional_argument(): + """ + Test the extraction of n_jobs when passed as a positional argument. + + The threaded decorator needs to correctly identify the n_jobs parameter + regardless of how it's passed to the function. This test verifies that + when n_jobs is passed as a positional argument, the decorator correctly + extracts its value and uses it to configure the thread count. + """ + check_jobs_mock = MagicMock(side_effect=lambda x: x if x is not None else 1) + set_threads_mock = MagicMock() + + with patch("aeon.utils._threading.check_n_jobs", check_jobs_mock): + with patch("aeon.utils._threading.set_num_threads", set_threads_mock): + + @threaded + def sample_func(data, n_jobs=None): + return data + + sample_func("test_data", 4) + + check_jobs_mock.assert_called_once_with(4) + + +def test_keyword_argument(): + """ + Test the extraction of n_jobs when passed as a keyword argument. + + Functions decorated with the threaded decorator can receive the n_jobs + parameter as a keyword argument. This test ensures that the decorator + correctly identifies and extracts the n_jobs value when passed this way, + demonstrating the decorator's flexibility in handling different calling styles. + """ + check_jobs_mock = MagicMock(side_effect=lambda x: x if x is not None else 1) + set_threads_mock = MagicMock() + + with patch("aeon.utils._threading.check_n_jobs", check_jobs_mock): + with patch("aeon.utils._threading.set_num_threads", set_threads_mock): + + @threaded + def sample_func(data, n_jobs=None): + return data + + sample_func(data="test_data", n_jobs=4) + + check_jobs_mock.assert_called_once_with(4) + + +def test_default_value(): + """ + Test the use of default n_jobs value when not explicitly provided. + + When a function has a default value for the n_jobs parameter and is called + without specifying this parameter, the threaded decorator should use the + function's default value. This test verifies this behavior, ensuring that + default function parameters are properly respected by the decorator. + """ + check_jobs_mock = MagicMock(side_effect=lambda x: x if x is not None else 1) + set_threads_mock = MagicMock() + + with patch("aeon.utils._threading.check_n_jobs", check_jobs_mock): + with patch("aeon.utils._threading.set_num_threads", set_threads_mock): + + @threaded + def sample_func(data, n_jobs=2): + return data + + sample_func("test_data") + + check_jobs_mock.assert_called_once_with(2) + + +def test_exception_handling(): + """ + Test resource cleanup when exceptions occur in the decorated function. + + A robust decorator must ensure resources are properly managed even when + the decorated function raises an exception. This test verifies that the + threaded decorator correctly restores the original thread count even when + the function execution fails with an exception, preventing resource leaks. + """ + check_jobs_mock = MagicMock(side_effect=lambda x: x if x is not None else 1) + set_threads_mock = MagicMock() + + with patch("aeon.utils._threading.check_n_jobs", check_jobs_mock): + with patch("aeon.utils._threading.set_num_threads", set_threads_mock): + + @threaded + def sample_func(n_jobs=None): + raise ValueError("Test exception") + + with pytest.raises(ValueError, match="Test exception"): + sample_func(n_jobs=4) + + assert set_threads_mock.call_count == 2 From 3b6233ed8736df4dadb23da141c4174078455436 Mon Sep 17 00:00:00 2001 From: chrisholder Date: Tue, 4 Mar 2025 15:39:31 +0100 Subject: [PATCH 009/216] merge changes and fixed call --- aeon/classification/distance_based/_time_series_neighbors.py | 2 +- aeon/regression/distance_based/_time_series_neighbors.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/aeon/classification/distance_based/_time_series_neighbors.py b/aeon/classification/distance_based/_time_series_neighbors.py index 4bde22da44..b7f68e482c 100644 --- a/aeon/classification/distance_based/_time_series_neighbors.py +++ b/aeon/classification/distance_based/_time_series_neighbors.py @@ -137,7 +137,7 @@ def _predict_proba(self, X): """ preds = np.zeros((len(X), len(self.classes_))) for i in range(len(X)): - idx, weights = self._kneighbors(X[i]) + idx, weights = self.kneighbors(X[i]) for id, w in zip(idx, weights): predicted_class = self.y_[id] preds[i, predicted_class] += w diff --git a/aeon/regression/distance_based/_time_series_neighbors.py b/aeon/regression/distance_based/_time_series_neighbors.py index 9981e2dc12..1a2eff0163 100644 --- a/aeon/regression/distance_based/_time_series_neighbors.py +++ b/aeon/regression/distance_based/_time_series_neighbors.py @@ -134,7 +134,7 @@ def _predict(self, X): """ preds = np.empty(len(X)) for i in range(len(X)): - idx, weights = self._kneighbors(X[i]) + idx, weights = self.kneighbors(X[i]) preds[i] = np.average(self.y_[idx], weights=weights) return preds From b603ddb98b1dcf5bc1ef8d623f348fd450c5ca92 Mon Sep 17 00:00:00 2001 From: chrisholder Date: Tue, 4 Mar 2025 17:58:30 +0100 Subject: [PATCH 010/216] fix --- aeon/utils/_threading.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/aeon/utils/_threading.py b/aeon/utils/_threading.py index 9f33cd0d2a..0c1a798020 100644 --- a/aeon/utils/_threading.py +++ b/aeon/utils/_threading.py @@ -25,7 +25,11 @@ def wrapper(*args: Any, **kwargs: Any) -> Any: if numba_env_threads is not None and numba_env_threads.isdigit(): original_thread_count = int(numba_env_threads) else: - original_thread_count = threading.active_count() + active_count = threading.active_count() + if isinstance(active_count, int): + original_thread_count = threading.active_count() + else: + original_thread_count = 1 if "n_jobs" in kwargs: n_jobs = kwargs["n_jobs"] From 24b46b0116869bf49b66df2374f4c217f4c46436 Mon Sep 17 00:00:00 2001 From: chrisholder Date: Tue, 4 Mar 2025 19:25:38 +0100 Subject: [PATCH 011/216] fix --- aeon/utils/tests/test_threading_decorator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/utils/tests/test_threading_decorator.py b/aeon/utils/tests/test_threading_decorator.py index a147c646a7..0224985707 100644 --- a/aeon/utils/tests/test_threading_decorator.py +++ b/aeon/utils/tests/test_threading_decorator.py @@ -1,4 +1,4 @@ -"""Test threading decorator.""" +"""Test threading util decorator.""" import os from unittest.mock import MagicMock, patch From 9f2e0106c5ddb02c56e54e3446fe8257011dd7cf Mon Sep 17 00:00:00 2001 From: chrisholder Date: Fri, 7 Mar 2025 15:54:14 +0100 Subject: [PATCH 012/216] fixed --- .../distance_based/_time_series_neighbors.py | 22 ++++++++++--------- 1 file changed, 12 insertions(+), 10 deletions(-) diff --git a/aeon/classification/distance_based/_time_series_neighbors.py b/aeon/classification/distance_based/_time_series_neighbors.py index b7f68e482c..cf3f0faab2 100644 --- a/aeon/classification/distance_based/_time_series_neighbors.py +++ b/aeon/classification/distance_based/_time_series_neighbors.py @@ -17,7 +17,7 @@ from aeon.classification.base import BaseClassifier from aeon.distances import pairwise_distance -from aeon.utils.validation import check_n_jobs +from aeon.utils._threading import threaded WEIGHTS_SUPPORTED = ["uniform", "distance"] @@ -48,11 +48,10 @@ class KNeighborsTimeSeriesClassifier(BaseClassifier): n_timepoints)`` as input and returns a float. distance_params : dict, default = None Dictionary for metric parameters for the case that distance is a str. - n_jobs : int, default = None - The number of parallel jobs to run for neighbors search. - ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. - ``-1`` means using all processors. - for more details. Parameter for compatibility purposes, still unimplemented. + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Examples -------- @@ -164,10 +163,11 @@ def _predict(self, X): """ self._check_is_fitted() - indexes = self.kneighbors(X, return_distance=False)[:, 0] + indexes = self.kneighbors(X, return_distance=False, n_jobs=self.n_jobs)[:, 0] return self.classes_[self.y_[indexes]] - def kneighbors(self, X=None, n_neighbors=None, return_distance=True): + @threaded + def kneighbors(self, X=None, n_neighbors=None, return_distance=True, n_jobs=1): """Find the K-neighbors of a point. Returns indices of and distances to the neighbors of each point. @@ -184,6 +184,10 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True): passed to the constructor. return_distance : bool, default=True Whether or not to return the distances. + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Returns ------- @@ -194,8 +198,6 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True): Indices of the nearest points in the population matrix. """ self._check_is_fitted() - n_jobs = check_n_jobs(self.n_jobs) - if n_neighbors is None: n_neighbors = self.n_neighbors elif n_neighbors <= 0: From 541e7fd6bf5a3606af9708ab034f4ad4c868b3bc Mon Sep 17 00:00:00 2001 From: chrisholder Date: Fri, 7 Mar 2025 16:08:37 +0100 Subject: [PATCH 013/216] expanded threaded decorator to work with classes --- aeon/utils/_threading.py | 18 ++-- aeon/utils/tests/test_threading_decorator.py | 93 ++++++++++++++++++++ 2 files changed, 105 insertions(+), 6 deletions(-) diff --git a/aeon/utils/_threading.py b/aeon/utils/_threading.py index 0c1a798020..2a6efac7af 100644 --- a/aeon/utils/_threading.py +++ b/aeon/utils/_threading.py @@ -31,18 +31,24 @@ def wrapper(*args: Any, **kwargs: Any) -> Any: else: original_thread_count = 1 + n_jobs = None if "n_jobs" in kwargs: n_jobs = kwargs["n_jobs"] else: sig = inspect.signature(func) param_names = list(sig.parameters.keys()) - n_jobs_index = param_names.index("n_jobs") - if n_jobs_index < len(args): - n_jobs = args[n_jobs_index] - else: - default = sig.parameters["n_jobs"].default - n_jobs = default if default is not inspect.Parameter.empty else None + if "n_jobs" in param_names: + n_jobs_index = param_names.index("n_jobs") + if n_jobs_index < len(args): + n_jobs = args[n_jobs_index] + else: + default = sig.parameters["n_jobs"].default + n_jobs = default if default is not inspect.Parameter.empty else None + + if n_jobs is None and args and hasattr(args[0], "n_jobs"): + # This gets n_jobs if it belongs to a object (i.e. self.n_jobs) + n_jobs = args[0]["n_jobs"] adjusted_n_jobs = check_n_jobs(n_jobs) set_num_threads(adjusted_n_jobs) diff --git a/aeon/utils/tests/test_threading_decorator.py b/aeon/utils/tests/test_threading_decorator.py index 0224985707..76c0d80503 100644 --- a/aeon/utils/tests/test_threading_decorator.py +++ b/aeon/utils/tests/test_threading_decorator.py @@ -187,3 +187,96 @@ def sample_func(n_jobs=None): sample_func(n_jobs=4) assert set_threads_mock.call_count == 2 + + +def test_class_attribute(): + """ + Test the extraction of n_jobs from a class attribute. + + The threaded decorator should be able to extract the n_jobs value from + the first argument (typically 'self' in class methods) when it has an + n_jobs attribute. This test verifies that the decorator correctly identifies + and uses this attribute when the n_jobs parameter is not explicitly passed. + """ + check_jobs_mock = MagicMock(side_effect=lambda x: x if x is not None else 1) + set_threads_mock = MagicMock() + + with patch("aeon.utils._threading.check_n_jobs", check_jobs_mock): + with patch("aeon.utils._threading.set_num_threads", set_threads_mock): + + class TestClass: + def __init__(self, n_jobs): + self.n_jobs = n_jobs + + @threaded + def process_data(self, data): + return data + + test_instance = TestClass(n_jobs=5) + + test_instance.process_data("test_data") + + check_jobs_mock.assert_called_once_with(5) + assert set_threads_mock.call_count == 2 + + +def test_parameter_precedence_over_attribute(): + """ + Test that n_jobs parameter takes precedence over class attribute. + + When both a class attribute and a method parameter for n_jobs exist, + the parameter value should take precedence. This test verifies this + precedence rule, ensuring that explicit parameter values override + attribute values. + """ + check_jobs_mock = MagicMock(side_effect=lambda x: x if x is not None else 1) + set_threads_mock = MagicMock() + + with patch("aeon.utils._threading.check_n_jobs", check_jobs_mock): + with patch("aeon.utils._threading.set_num_threads", set_threads_mock): + + class TestClass: + def __init__(self, n_jobs): + self.n_jobs = n_jobs + + @threaded + def process_data(self, data, n_jobs=None): + return data + + test_instance = TestClass(n_jobs=5) + + test_instance.process_data("test_data", n_jobs=7) + + check_jobs_mock.assert_called_once_with(7) + assert set_threads_mock.call_count == 2 + + +def test_fallback_when_no_attribute(): + """ + Test fallback behavior when neither parameter nor attribute is available. + + When a class doesn't have an n_jobs attribute and the method doesn't + have an n_jobs parameter, the decorator should fall back to using None, + which will be converted to 1 by check_n_jobs. This test verifies this + fallback behavior. + """ + check_jobs_mock = MagicMock(side_effect=lambda x: x if x is not None else 1) + set_threads_mock = MagicMock() + + with patch("aeon.utils._threading.check_n_jobs", check_jobs_mock): + with patch("aeon.utils._threading.set_num_threads", set_threads_mock): + + class TestClass: + # No n_jobs attribute + pass + + @threaded + def process_data(self, data): + return data + + test_instance = TestClass() + + test_instance.process_data("test_data") + + check_jobs_mock.assert_called_once_with(None) + assert set_threads_mock.call_count == 2 From 32e93eb0a65d4d591c7e70f7ee6a9411bcf8ca5e Mon Sep 17 00:00:00 2001 From: chrisholder Date: Fri, 7 Mar 2025 16:12:33 +0100 Subject: [PATCH 014/216] fixed --- .../distance_based/_time_series_neighbors.py | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/aeon/classification/distance_based/_time_series_neighbors.py b/aeon/classification/distance_based/_time_series_neighbors.py index cf3f0faab2..6792ea0423 100644 --- a/aeon/classification/distance_based/_time_series_neighbors.py +++ b/aeon/classification/distance_based/_time_series_neighbors.py @@ -163,11 +163,11 @@ def _predict(self, X): """ self._check_is_fitted() - indexes = self.kneighbors(X, return_distance=False, n_jobs=self.n_jobs)[:, 0] + indexes = self.kneighbors(X, return_distance=False)[:, 0] return self.classes_[self.y_[indexes]] @threaded - def kneighbors(self, X=None, n_neighbors=None, return_distance=True, n_jobs=1): + def kneighbors(self, X=None, n_neighbors=None, return_distance=True): """Find the K-neighbors of a point. Returns indices of and distances to the neighbors of each point. @@ -184,10 +184,6 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True, n_jobs=1): passed to the constructor. return_distance : bool, default=True Whether or not to return the distances. - n_jobs : int, default=1 - The number of jobs to run in parallel. If -1, then the number of jobs is set - to the number of CPU cores. If 1, then the function is executed in a single - thread. If greater than 1, then the function is executed in parallel. Returns ------- @@ -220,7 +216,7 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True, n_jobs=1): X, self.X_ if not query_is_train else None, method=self.distance, - n_jobs=n_jobs, + n_jobs=self.n_jobs, **self._distance_params, ) From add714e3c9f05be35e4a73cf2c35607698310ff0 Mon Sep 17 00:00:00 2001 From: chrisholder Date: Fri, 7 Mar 2025 16:13:15 +0100 Subject: [PATCH 015/216] fix --- aeon/utils/_threading.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/utils/_threading.py b/aeon/utils/_threading.py index 2a6efac7af..adf32df0a6 100644 --- a/aeon/utils/_threading.py +++ b/aeon/utils/_threading.py @@ -48,7 +48,7 @@ def wrapper(*args: Any, **kwargs: Any) -> Any: if n_jobs is None and args and hasattr(args[0], "n_jobs"): # This gets n_jobs if it belongs to a object (i.e. self.n_jobs) - n_jobs = args[0]["n_jobs"] + n_jobs = args[0].n_jobs adjusted_n_jobs = check_n_jobs(n_jobs) set_num_threads(adjusted_n_jobs) From 7b026838412d90f8157969f176fcb2580bbcec02 Mon Sep 17 00:00:00 2001 From: chrisholder Date: Fri, 7 Mar 2025 16:14:48 +0100 Subject: [PATCH 016/216] merge --- .../distance_based/tests/test_time_series_neighbors.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/classification/distance_based/tests/test_time_series_neighbors.py b/aeon/classification/distance_based/tests/test_time_series_neighbors.py index 917b2628d4..0209d7de1c 100644 --- a/aeon/classification/distance_based/tests/test_time_series_neighbors.py +++ b/aeon/classification/distance_based/tests/test_time_series_neighbors.py @@ -45,7 +45,7 @@ def test_knn_on_unit_test(distance_key): # load arrowhead data for unit tests X_train, y_train = load_unit_test(split="train") X_test, y_test = load_unit_test(split="test") - knn = KNeighborsTimeSeriesClassifier(distance=distance_key) + knn = KNeighborsTimeSeriesClassifier(distance=distance_key, n_jobs=7) knn.fit(X_train, y_train) pred = knn.predict(X_test) correct = 0 From a94cc8def731bcf47881c68e20bcb8585596e9f5 Mon Sep 17 00:00:00 2001 From: chrisholder Date: Fri, 7 Mar 2025 16:58:09 +0100 Subject: [PATCH 017/216] added test for kneighbors --- .../tests/test_time_series_neighbors.py | 26 ++++++++++++++++++- 1 file changed, 25 insertions(+), 1 deletion(-) diff --git a/aeon/classification/distance_based/tests/test_time_series_neighbors.py b/aeon/classification/distance_based/tests/test_time_series_neighbors.py index 0209d7de1c..bc5aea2eed 100644 --- a/aeon/classification/distance_based/tests/test_time_series_neighbors.py +++ b/aeon/classification/distance_based/tests/test_time_series_neighbors.py @@ -1,5 +1,6 @@ """Tests for KNeighborsTimeSeriesClassifier.""" +import numpy as np import pytest from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier @@ -45,7 +46,7 @@ def test_knn_on_unit_test(distance_key): # load arrowhead data for unit tests X_train, y_train = load_unit_test(split="train") X_test, y_test = load_unit_test(split="test") - knn = KNeighborsTimeSeriesClassifier(distance=distance_key, n_jobs=7) + knn = KNeighborsTimeSeriesClassifier(distance=distance_key) knn.fit(X_train, y_train) pred = knn.predict(X_test) correct = 0 @@ -75,3 +76,26 @@ def test_knn_bounding_matrix(distance_key): if pred[j] == y_test[j]: correct = correct + 1 assert correct == expected_correct_window[distance_key] + + +@pytest.mark.parametrize("distance_key", distance_functions) +def test_knn_kneighbors(distance_key): + """Test knn kneighbors.""" + X_train, y_train = load_unit_test(split="train") + X_test, y_test = load_unit_test(split="test") + + knn = KNeighborsTimeSeriesClassifier(distance=distance_key) + knn.fit(X_train, y_train) + dists, ind = knn.kneighbors(X_test, n_neighbors=3) + assert isinstance(dists, np.ndarray) + assert isinstance(ind, np.ndarray) + assert dists.shape == (X_test.shape[0], 3) + assert ind.shape == (X_test.shape[0], 3) + indexes = ind[:, 0] + classes, y = np.unique(y_train, return_inverse=True) + pred = classes[y[indexes]] + correct = 0 + for j in range(0, len(pred)): + if pred[j] == y_test[j]: + correct = correct + 1 + assert correct == expected_correct_window[distance_key] From 447098955f44e9bbb9b4e0fc0447324b77d5266b Mon Sep 17 00:00:00 2001 From: chrisholder Date: Fri, 7 Mar 2025 16:58:13 +0100 Subject: [PATCH 018/216] added test for kneighbors --- .../distance_based/tests/test_time_series_neighbors.py | 1 - 1 file changed, 1 deletion(-) diff --git a/aeon/classification/distance_based/tests/test_time_series_neighbors.py b/aeon/classification/distance_based/tests/test_time_series_neighbors.py index bc5aea2eed..e68cad0019 100644 --- a/aeon/classification/distance_based/tests/test_time_series_neighbors.py +++ b/aeon/classification/distance_based/tests/test_time_series_neighbors.py @@ -43,7 +43,6 @@ @pytest.mark.parametrize("distance_key", distance_functions) def test_knn_on_unit_test(distance_key): """Test function for elastic knn, to be reinstated soon.""" - # load arrowhead data for unit tests X_train, y_train = load_unit_test(split="train") X_test, y_test = load_unit_test(split="test") knn = KNeighborsTimeSeriesClassifier(distance=distance_key) From d9d594ce1ee511a59220e95dbc20a5daf888f523 Mon Sep 17 00:00:00 2001 From: chrisholder Date: Wed, 2 Apr 2025 17:14:03 +0100 Subject: [PATCH 019/216] fix --- aeon/classification/distance_based/_time_series_neighbors.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/classification/distance_based/_time_series_neighbors.py b/aeon/classification/distance_based/_time_series_neighbors.py index 6792ea0423..36afc755ae 100644 --- a/aeon/classification/distance_based/_time_series_neighbors.py +++ b/aeon/classification/distance_based/_time_series_neighbors.py @@ -136,7 +136,7 @@ def _predict_proba(self, X): """ preds = np.zeros((len(X), len(self.classes_))) for i in range(len(X)): - idx, weights = self.kneighbors(X[i]) + weights, idx = self.kneighbors(X[i]) for id, w in zip(idx, weights): predicted_class = self.y_[id] preds[i, predicted_class] += w From 7658c1243e77ffd4b63d10b4e3aeef527472d15d Mon Sep 17 00:00:00 2001 From: "aeon-actions-bot[bot]" <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Date: Sat, 15 Mar 2025 18:30:41 +0000 Subject: [PATCH 020/216] Automated `CONTRIBUTORS.md` update (#2614) Co-authored-by: chrisholder <4674372+chrisholder@users.noreply.github.com> --- CONTRIBUTORS.md | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md index 1c9b7a4d12..225d9369cb 100644 --- a/CONTRIBUTORS.md +++ b/CONTRIBUTORS.md @@ -1,7 +1,7 @@ # Contributors -[![All Contributors](https://img.shields.io/badge/all_contributors-261-orange.svg)](#contributors) +[![All Contributors](https://img.shields.io/badge/all_contributors-262-orange.svg)](#contributors) This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! @@ -268,12 +268,13 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Sylvain Combettes
Sylvain Combettes
💻 🐛 TNTran92
TNTran92
💻 Taiwo Owoseni
Taiwo Owoseni
💻 + Tanish Yelgoe
Tanish Yelgoe
💻 Thach Le Nguyen
Thach Le Nguyen
💻 ⚠️ TheMathcompay Widget Factory Team
TheMathcompay Widget Factory Team
📖 Thomas Buckley-Houston
Thomas Buckley-Houston
🐛 - Tom Xu
Tom Xu
💻 📖 + Tom Xu
Tom Xu
💻 📖 Tomasz Chodakowski
Tomasz Chodakowski
💻 📖 🐛 Tony Bagnall
Tony Bagnall
💻 💼 📖 🎨 📋 🔍 🤔 📆 💬 👀 📢 🔣 Tvisha Vedant
Tvisha Vedant
💻 @@ -281,9 +282,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Utsav Kumar Tiwari
Utsav Kumar Tiwari
💻 📖 Vedant
Vedant
📖 Viktor Dremov
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💻 - ViktorKaz
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💻 📖 🎨 + ViktorKaz
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💻 📖 🎨 Vyomkesh Vyas
Vyomkesh Vyas
💻 📖 💡 ⚠️ Wayne Adams
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📖 William Templier
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📖 @@ -291,9 +292,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d William Zheng
William Zheng
💻 ⚠️ Yair Beer
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💻 Yash Lamba
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💻 - Yi-Xuan Xu
Yi-Xuan Xu
💻 ⚠️ 🚧 📖 + Yi-Xuan Xu
Yi-Xuan Xu
💻 ⚠️ 🚧 📖 Ziyao Wei
Ziyao Wei
💻 aa25desh
aa25desh
💻 🐛 abandus
abandus
🤔 💻 @@ -301,9 +302,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d alexbanwell1
alexbanwell1
💻 🎨 📖 bethrice44
bethrice44
🐛 💻 👀 ⚠️ big-o
big-o
💻 ⚠️ 🎨 🤔 👀 ✅ 🧑‍🏫 - bobbys
bobbys
💻 + bobbys
bobbys
💻 brett koonce
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📖 btrtts
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📖 chizzi25
chizzi25
📝 @@ -311,9 +312,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d danbartl
danbartl
🐛 💻 👀 📢 ⚠️ ✅ 📹 hamzahiqb
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🚇 hiqbal2
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📖 - jesellier
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💻 + jesellier
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💻 🐛 @@ -321,9 +322,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d neuron283
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nileenagp
💻 oleskiewicz
oleskiewicz
💻 📖 ⚠️ - pabworks
pabworks
💻 ⚠️ + pabworks
pabworks
💻 ⚠️ patiently pending world peace
patiently pending world peace
💻 raishubham1
raishubham1
📖 simone-pignotti
simone-pignotti
💻 🐛 @@ -331,9 +332,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d sri1419
sri1419
💻 tensorflow-as-tf
tensorflow-as-tf
💻 vNtzYy
vNtzYy
🐛 - ved pawar
ved pawar
📖 + ved pawar
ved pawar
📖 vedazeren
vedazeren
💻 ⚠️ vincent-nich12
vincent-nich12
💻 vollmersj
vollmersj
📖 From ebbe6149fe7d6167a470fd539cfdfc6f04e3e22c Mon Sep 17 00:00:00 2001 From: Kavya Rambhia <161142013+kavya-r30@users.noreply.github.com> Date: Sun, 16 Mar 2025 20:10:57 +0530 Subject: [PATCH 021/216] Updated Interval Based Notebook (#2620) --- examples/classification/interval_based.ipynb | 179 ++++++++----------- 1 file changed, 70 insertions(+), 109 deletions(-) diff --git a/examples/classification/interval_based.ipynb b/examples/classification/interval_based.ipynb index ca67f38465..45988a0f70 100644 --- a/examples/classification/interval_based.ipynb +++ b/examples/classification/interval_based.ipynb @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:05.163967Z", @@ -41,76 +41,32 @@ }, "outputs": [ { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mKeyboardInterrupt\u001B[0m Traceback (most recent call last)", - "Cell \u001B[1;32mIn[3], line 18\u001B[0m\n\u001B[0;32m 15\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01maeon\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mutils\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mdiscovery\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m all_estimators\n\u001B[0;32m 17\u001B[0m warnings\u001B[38;5;241m.\u001B[39mfilterwarnings(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mignore\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m---> 18\u001B[0m \u001B[43mall_estimators\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mclassifier\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtag_filter\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m{\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43malgorithm_type\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43minterval\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m}\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\aeon\\utils\\discovery.py:121\u001B[0m, in \u001B[0;36mall_estimators\u001B[1;34m(type_filter, exclude_types, tag_filter, exclude_tags, include_sklearn, return_names)\u001B[0m\n\u001B[0;32m 116\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m (\n\u001B[0;32m 117\u001B[0m \u001B[38;5;28many\u001B[39m(part \u001B[38;5;129;01min\u001B[39;00m modules_to_ignore \u001B[38;5;28;01mfor\u001B[39;00m part \u001B[38;5;129;01min\u001B[39;00m module_parts)\n\u001B[0;32m 118\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m._\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m module_name\n\u001B[0;32m 119\u001B[0m ):\n\u001B[0;32m 120\u001B[0m \u001B[38;5;28;01mcontinue\u001B[39;00m\n\u001B[1;32m--> 121\u001B[0m module \u001B[38;5;241m=\u001B[39m \u001B[43mimport_module\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodule_name\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 123\u001B[0m classes \u001B[38;5;241m=\u001B[39m inspect\u001B[38;5;241m.\u001B[39mgetmembers(module, inspect\u001B[38;5;241m.\u001B[39misclass)\n\u001B[0;32m 124\u001B[0m \u001B[38;5;66;03m# skip private estimators and those not implemented in aeon\u001B[39;00m\n", - "File \u001B[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\importlib\\__init__.py:127\u001B[0m, in \u001B[0;36mimport_module\u001B[1;34m(name, package)\u001B[0m\n\u001B[0;32m 125\u001B[0m \u001B[38;5;28;01mbreak\u001B[39;00m\n\u001B[0;32m 126\u001B[0m level \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;241m1\u001B[39m\n\u001B[1;32m--> 127\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m_bootstrap\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_gcd_import\u001B[49m\u001B[43m(\u001B[49m\u001B[43mname\u001B[49m\u001B[43m[\u001B[49m\u001B[43mlevel\u001B[49m\u001B[43m:\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpackage\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlevel\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m:1030\u001B[0m, in \u001B[0;36m_gcd_import\u001B[1;34m(name, package, level)\u001B[0m\n", - "File \u001B[1;32m:1007\u001B[0m, in \u001B[0;36m_find_and_load\u001B[1;34m(name, import_)\u001B[0m\n", - "File \u001B[1;32m:986\u001B[0m, in \u001B[0;36m_find_and_load_unlocked\u001B[1;34m(name, import_)\u001B[0m\n", - "File \u001B[1;32m:680\u001B[0m, in \u001B[0;36m_load_unlocked\u001B[1;34m(spec)\u001B[0m\n", - "File \u001B[1;32m:850\u001B[0m, in \u001B[0;36mexec_module\u001B[1;34m(self, module)\u001B[0m\n", - "File \u001B[1;32m:228\u001B[0m, in \u001B[0;36m_call_with_frames_removed\u001B[1;34m(f, *args, **kwds)\u001B[0m\n", - "File \u001B[1;32mC:\\Code\\aeon\\aeon\\classification\\convolution_based\\__init__.py:12\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;124;03m\"\"\"Convolution-based time series classifiers.\"\"\"\u001B[39;00m\n\u001B[0;32m 3\u001B[0m __all__ \u001B[38;5;241m=\u001B[39m [\n\u001B[0;32m 4\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mRocketClassifier\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m 5\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mMiniRocketClassifier\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 9\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mMultiRocketHydraClassifier\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m 10\u001B[0m ]\n\u001B[1;32m---> 12\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01maeon\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mclassification\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mconvolution_based\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01m_arsenal\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m Arsenal\n\u001B[0;32m 13\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01maeon\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mclassification\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mconvolution_based\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01m_hydra\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m HydraClassifier\n\u001B[0;32m 14\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01maeon\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mclassification\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mconvolution_based\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01m_minirocket\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m MiniRocketClassifier\n", - "File \u001B[1;32mC:\\Code\\aeon\\aeon\\classification\\convolution_based\\_arsenal.py:20\u001B[0m\n\u001B[0;32m 18\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01maeon\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mbase\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01m_base\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m _clone_estimator\n\u001B[0;32m 19\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01maeon\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mclassification\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mbase\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m BaseClassifier\n\u001B[1;32m---> 20\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01maeon\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mtransformations\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mcollection\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mconvolution_based\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m (\n\u001B[0;32m 21\u001B[0m MiniRocket,\n\u001B[0;32m 22\u001B[0m MultiRocket,\n\u001B[0;32m 23\u001B[0m Rocket,\n\u001B[0;32m 24\u001B[0m )\n\u001B[0;32m 27\u001B[0m \u001B[38;5;28;01mclass\u001B[39;00m \u001B[38;5;21;01mArsenal\u001B[39;00m(BaseClassifier):\n\u001B[0;32m 28\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 29\u001B[0m \u001B[38;5;124;03m Arsenal ensemble.\u001B[39;00m\n\u001B[0;32m 30\u001B[0m \n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 120\u001B[0m \u001B[38;5;124;03m >>> y_pred = clf.predict(X_test)\u001B[39;00m\n\u001B[0;32m 121\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n", - "File \u001B[1;32mC:\\Code\\aeon\\aeon\\transformations\\collection\\convolution_based\\__init__.py:13\u001B[0m\n\u001B[0;32m 11\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01m_hydra\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m HydraTransformer\n\u001B[0;32m 12\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01m_minirocket\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m MiniRocket\n\u001B[1;32m---> 13\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01m_minirocket_mv\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m MiniRocketMultivariateVariable\n\u001B[0;32m 14\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01m_multirocket\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m MultiRocket\n\u001B[0;32m 15\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01m_rocket\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m Rocket\n", - "File \u001B[1;32mC:\\Code\\aeon\\aeon\\transformations\\collection\\convolution_based\\_minirocket_mv.py:303\u001B[0m\n\u001B[0;32m 290\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m X_2d_t, lengths\n\u001B[0;32m 293\u001B[0m \u001B[38;5;66;03m# code below from the orignal authors: https://github.com/angus924/minirocket\u001B[39;00m\n\u001B[0;32m 296\u001B[0m \u001B[38;5;129;43m@njit\u001B[39;49m\u001B[43m(\u001B[49m\n\u001B[0;32m 297\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mfloat32[:](float32[:,:],int32[:],int32[:],int32[:],int32[:],int32[:],float32[:],\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\n\u001B[0;32m 298\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43moptional(int32))\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[0;32m 299\u001B[0m \u001B[43m \u001B[49m\u001B[43mfastmath\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[0;32m 300\u001B[0m \u001B[43m \u001B[49m\u001B[43mparallel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[0;32m 301\u001B[0m \u001B[43m \u001B[49m\u001B[43mcache\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[0;32m 302\u001B[0m \u001B[43m)\u001B[49m\n\u001B[1;32m--> 303\u001B[0m \u001B[38;5;28;43;01mdef\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;21;43m_fit_biases_multi_var\u001B[39;49m\u001B[43m(\u001B[49m\n\u001B[0;32m 304\u001B[0m \u001B[43m \u001B[49m\u001B[43mX\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 305\u001B[0m \u001B[43m \u001B[49m\u001B[43mL\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 306\u001B[0m \u001B[43m \u001B[49m\u001B[43mnum_channels_per_combination\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 307\u001B[0m \u001B[43m \u001B[49m\u001B[43mchannel_indices\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 308\u001B[0m \u001B[43m \u001B[49m\u001B[43mdilations\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 309\u001B[0m \u001B[43m \u001B[49m\u001B[43mnum_features_per_dilation\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 310\u001B[0m \u001B[43m \u001B[49m\u001B[43mquantiles\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 311\u001B[0m \u001B[43m \u001B[49m\u001B[43mseed\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 312\u001B[0m \u001B[43m)\u001B[49m\u001B[43m:\u001B[49m\n\u001B[0;32m 313\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43;01mif\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mseed\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mis\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mnot\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m:\u001B[49m\n\u001B[0;32m 314\u001B[0m \u001B[43m \u001B[49m\u001B[43mnp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrandom\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mseed\u001B[49m\u001B[43m(\u001B[49m\u001B[43mseed\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\decorators.py:232\u001B[0m, in \u001B[0;36m_jit..wrapper\u001B[1;34m(func)\u001B[0m\n\u001B[0;32m 230\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m typeinfer\u001B[38;5;241m.\u001B[39mregister_dispatcher(disp):\n\u001B[0;32m 231\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m sig \u001B[38;5;129;01min\u001B[39;00m sigs:\n\u001B[1;32m--> 232\u001B[0m \u001B[43mdisp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcompile\u001B[49m\u001B[43m(\u001B[49m\u001B[43msig\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 233\u001B[0m disp\u001B[38;5;241m.\u001B[39mdisable_compile()\n\u001B[0;32m 234\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m disp\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\dispatcher.py:905\u001B[0m, in \u001B[0;36mDispatcher.compile\u001B[1;34m(self, sig)\u001B[0m\n\u001B[0;32m 903\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m ev\u001B[38;5;241m.\u001B[39mtrigger_event(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnumba:compile\u001B[39m\u001B[38;5;124m\"\u001B[39m, data\u001B[38;5;241m=\u001B[39mev_details):\n\u001B[0;32m 904\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 905\u001B[0m cres \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_compiler\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcompile\u001B[49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreturn_type\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 906\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m errors\u001B[38;5;241m.\u001B[39mForceLiteralArg \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m 907\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mfolded\u001B[39m(args, kws):\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\dispatcher.py:80\u001B[0m, in \u001B[0;36m_FunctionCompiler.compile\u001B[1;34m(self, args, return_type)\u001B[0m\n\u001B[0;32m 79\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mcompile\u001B[39m(\u001B[38;5;28mself\u001B[39m, args, return_type):\n\u001B[1;32m---> 80\u001B[0m status, retval \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_compile_cached\u001B[49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreturn_type\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 81\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m status:\n\u001B[0;32m 82\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m retval\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\dispatcher.py:94\u001B[0m, in \u001B[0;36m_FunctionCompiler._compile_cached\u001B[1;34m(self, args, return_type)\u001B[0m\n\u001B[0;32m 91\u001B[0m \u001B[38;5;28;01mpass\u001B[39;00m\n\u001B[0;32m 93\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m---> 94\u001B[0m retval \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_compile_core\u001B[49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreturn_type\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 95\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m errors\u001B[38;5;241m.\u001B[39mTypingError \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m 96\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_failed_cache[key] \u001B[38;5;241m=\u001B[39m e\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\dispatcher.py:107\u001B[0m, in \u001B[0;36m_FunctionCompiler._compile_core\u001B[1;34m(self, args, return_type)\u001B[0m\n\u001B[0;32m 104\u001B[0m flags \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_customize_flags(flags)\n\u001B[0;32m 106\u001B[0m impl \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_get_implementation(args, {})\n\u001B[1;32m--> 107\u001B[0m cres \u001B[38;5;241m=\u001B[39m \u001B[43mcompiler\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcompile_extra\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtargetdescr\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtyping_context\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 108\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtargetdescr\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtarget_context\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 109\u001B[0m \u001B[43m \u001B[49m\u001B[43mimpl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 110\u001B[0m \u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreturn_type\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mreturn_type\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 111\u001B[0m \u001B[43m \u001B[49m\u001B[43mflags\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mflags\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mlocals\u001B[39;49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mlocals\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 112\u001B[0m \u001B[43m \u001B[49m\u001B[43mpipeline_class\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpipeline_class\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 113\u001B[0m \u001B[38;5;66;03m# Check typing error if object mode is used\u001B[39;00m\n\u001B[0;32m 114\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m cres\u001B[38;5;241m.\u001B[39mtyping_error \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m flags\u001B[38;5;241m.\u001B[39menable_pyobject:\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler.py:744\u001B[0m, in \u001B[0;36mcompile_extra\u001B[1;34m(typingctx, targetctx, func, args, return_type, flags, locals, library, pipeline_class)\u001B[0m\n\u001B[0;32m 720\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Compiler entry point\u001B[39;00m\n\u001B[0;32m 721\u001B[0m \n\u001B[0;32m 722\u001B[0m \u001B[38;5;124;03mParameter\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 740\u001B[0m \u001B[38;5;124;03m compiler pipeline\u001B[39;00m\n\u001B[0;32m 741\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 742\u001B[0m pipeline \u001B[38;5;241m=\u001B[39m pipeline_class(typingctx, targetctx, library,\n\u001B[0;32m 743\u001B[0m args, return_type, flags, \u001B[38;5;28mlocals\u001B[39m)\n\u001B[1;32m--> 744\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mpipeline\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcompile_extra\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfunc\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler.py:438\u001B[0m, in \u001B[0;36mCompilerBase.compile_extra\u001B[1;34m(self, func)\u001B[0m\n\u001B[0;32m 436\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstate\u001B[38;5;241m.\u001B[39mlifted \u001B[38;5;241m=\u001B[39m ()\n\u001B[0;32m 437\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstate\u001B[38;5;241m.\u001B[39mlifted_from \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m--> 438\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_compile_bytecode\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler.py:506\u001B[0m, in \u001B[0;36mCompilerBase._compile_bytecode\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 502\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 503\u001B[0m \u001B[38;5;124;03mPopulate and run pipeline for bytecode input\u001B[39;00m\n\u001B[0;32m 504\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 505\u001B[0m \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstate\u001B[38;5;241m.\u001B[39mfunc_ir \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m--> 506\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_compile_core\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler.py:472\u001B[0m, in \u001B[0;36mCompilerBase._compile_core\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 470\u001B[0m res \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m 471\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 472\u001B[0m \u001B[43mpm\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mstate\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 473\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstate\u001B[38;5;241m.\u001B[39mcr \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 474\u001B[0m \u001B[38;5;28;01mbreak\u001B[39;00m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler_machinery.py:356\u001B[0m, in \u001B[0;36mPassManager.run\u001B[1;34m(self, state)\u001B[0m\n\u001B[0;32m 354\u001B[0m pass_inst \u001B[38;5;241m=\u001B[39m _pass_registry\u001B[38;5;241m.\u001B[39mget(pss)\u001B[38;5;241m.\u001B[39mpass_inst\n\u001B[0;32m 355\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(pass_inst, CompilerPass):\n\u001B[1;32m--> 356\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_runPass\u001B[49m\u001B[43m(\u001B[49m\u001B[43midx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpass_inst\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstate\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 357\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 358\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mBaseException\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mLegacy pass in use\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler_lock.py:35\u001B[0m, in \u001B[0;36m_CompilerLock.__call__.._acquire_compile_lock\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m 32\u001B[0m \u001B[38;5;129m@functools\u001B[39m\u001B[38;5;241m.\u001B[39mwraps(func)\n\u001B[0;32m 33\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_acquire_compile_lock\u001B[39m(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 34\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mself\u001B[39m:\n\u001B[1;32m---> 35\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m func(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler_machinery.py:311\u001B[0m, in \u001B[0;36mPassManager._runPass\u001B[1;34m(self, index, pss, internal_state)\u001B[0m\n\u001B[0;32m 309\u001B[0m mutated \u001B[38;5;241m|\u001B[39m\u001B[38;5;241m=\u001B[39m check(pss\u001B[38;5;241m.\u001B[39mrun_initialization, internal_state)\n\u001B[0;32m 310\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m SimpleTimer() \u001B[38;5;28;01mas\u001B[39;00m pass_time:\n\u001B[1;32m--> 311\u001B[0m mutated \u001B[38;5;241m|\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[43mcheck\u001B[49m\u001B[43m(\u001B[49m\u001B[43mpss\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun_pass\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minternal_state\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 312\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m SimpleTimer() \u001B[38;5;28;01mas\u001B[39;00m finalize_time:\n\u001B[0;32m 313\u001B[0m mutated \u001B[38;5;241m|\u001B[39m\u001B[38;5;241m=\u001B[39m check(pss\u001B[38;5;241m.\u001B[39mrun_finalizer, internal_state)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler_machinery.py:273\u001B[0m, in \u001B[0;36mPassManager._runPass..check\u001B[1;34m(func, compiler_state)\u001B[0m\n\u001B[0;32m 272\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mcheck\u001B[39m(func, compiler_state):\n\u001B[1;32m--> 273\u001B[0m mangled \u001B[38;5;241m=\u001B[39m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcompiler_state\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 274\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m mangled \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m (\u001B[38;5;28;01mTrue\u001B[39;00m, \u001B[38;5;28;01mFalse\u001B[39;00m):\n\u001B[0;32m 275\u001B[0m msg \u001B[38;5;241m=\u001B[39m (\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCompilerPass implementations should return True/False. \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 276\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCompilerPass with name \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m did not.\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typed_passes.py:112\u001B[0m, in \u001B[0;36mBaseTypeInference.run_pass\u001B[1;34m(self, state)\u001B[0m\n\u001B[0;32m 106\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 107\u001B[0m \u001B[38;5;124;03mType inference and legalization\u001B[39;00m\n\u001B[0;32m 108\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 109\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m fallback_context(state, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mFunction \u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m failed type inference\u001B[39m\u001B[38;5;124m'\u001B[39m\n\u001B[0;32m 110\u001B[0m \u001B[38;5;241m%\u001B[39m (state\u001B[38;5;241m.\u001B[39mfunc_id\u001B[38;5;241m.\u001B[39mfunc_name,)):\n\u001B[0;32m 111\u001B[0m \u001B[38;5;66;03m# Type inference\u001B[39;00m\n\u001B[1;32m--> 112\u001B[0m typemap, return_type, calltypes, errs \u001B[38;5;241m=\u001B[39m \u001B[43mtype_inference_stage\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 113\u001B[0m \u001B[43m \u001B[49m\u001B[43mstate\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtypingctx\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 114\u001B[0m \u001B[43m \u001B[49m\u001B[43mstate\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtargetctx\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 115\u001B[0m \u001B[43m \u001B[49m\u001B[43mstate\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfunc_ir\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 116\u001B[0m \u001B[43m \u001B[49m\u001B[43mstate\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 117\u001B[0m \u001B[43m \u001B[49m\u001B[43mstate\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mreturn_type\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 118\u001B[0m \u001B[43m \u001B[49m\u001B[43mstate\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mlocals\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 119\u001B[0m \u001B[43m \u001B[49m\u001B[43mraise_errors\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_raise_errors\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 120\u001B[0m state\u001B[38;5;241m.\u001B[39mtypemap \u001B[38;5;241m=\u001B[39m typemap\n\u001B[0;32m 121\u001B[0m \u001B[38;5;66;03m# save errors in case of partial typing\u001B[39;00m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typed_passes.py:93\u001B[0m, in \u001B[0;36mtype_inference_stage\u001B[1;34m(typingctx, targetctx, interp, args, return_type, locals, raise_errors)\u001B[0m\n\u001B[0;32m 91\u001B[0m infer\u001B[38;5;241m.\u001B[39mbuild_constraint()\n\u001B[0;32m 92\u001B[0m \u001B[38;5;66;03m# return errors in case of partial typing\u001B[39;00m\n\u001B[1;32m---> 93\u001B[0m errs \u001B[38;5;241m=\u001B[39m \u001B[43minfer\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpropagate\u001B[49m\u001B[43m(\u001B[49m\u001B[43mraise_errors\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mraise_errors\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 94\u001B[0m typemap, restype, calltypes \u001B[38;5;241m=\u001B[39m infer\u001B[38;5;241m.\u001B[39munify(raise_errors\u001B[38;5;241m=\u001B[39mraise_errors)\n\u001B[0;32m 96\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m _TypingResults(typemap, restype, calltypes, errs)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typeinfer.py:1083\u001B[0m, in \u001B[0;36mTypeInferer.propagate\u001B[1;34m(self, raise_errors)\u001B[0m\n\u001B[0;32m 1080\u001B[0m oldtoken \u001B[38;5;241m=\u001B[39m newtoken\n\u001B[0;32m 1081\u001B[0m \u001B[38;5;66;03m# Errors can appear when the type set is incomplete; only\u001B[39;00m\n\u001B[0;32m 1082\u001B[0m \u001B[38;5;66;03m# raise them when there is no progress anymore.\u001B[39;00m\n\u001B[1;32m-> 1083\u001B[0m errors \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mconstraints\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpropagate\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1084\u001B[0m newtoken \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mget_state_token()\n\u001B[0;32m 1085\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdebug\u001B[38;5;241m.\u001B[39mpropagate_finished()\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typeinfer.py:160\u001B[0m, in \u001B[0;36mConstraintNetwork.propagate\u001B[1;34m(self, typeinfer)\u001B[0m\n\u001B[0;32m 157\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m typeinfer\u001B[38;5;241m.\u001B[39mwarnings\u001B[38;5;241m.\u001B[39mcatch_warnings(filename\u001B[38;5;241m=\u001B[39mloc\u001B[38;5;241m.\u001B[39mfilename,\n\u001B[0;32m 158\u001B[0m lineno\u001B[38;5;241m=\u001B[39mloc\u001B[38;5;241m.\u001B[39mline):\n\u001B[0;32m 159\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 160\u001B[0m \u001B[43mconstraint\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtypeinfer\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 161\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m ForceLiteralArg \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m 162\u001B[0m errors\u001B[38;5;241m.\u001B[39mappend(e)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typeinfer.py:583\u001B[0m, in \u001B[0;36mCallConstraint.__call__\u001B[1;34m(self, typeinfer)\u001B[0m\n\u001B[0;32m 581\u001B[0m fnty \u001B[38;5;241m=\u001B[39m typevars[\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfunc]\u001B[38;5;241m.\u001B[39mgetone()\n\u001B[0;32m 582\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m new_error_context(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mresolving callee type: \u001B[39m\u001B[38;5;132;01m{0}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m, fnty):\n\u001B[1;32m--> 583\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mresolve\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtypeinfer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtypevars\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfnty\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typeinfer.py:606\u001B[0m, in \u001B[0;36mCallConstraint.resolve\u001B[1;34m(self, typeinfer, typevars, fnty)\u001B[0m\n\u001B[0;32m 604\u001B[0m fnty \u001B[38;5;241m=\u001B[39m fnty\u001B[38;5;241m.\u001B[39minstance_type\n\u001B[0;32m 605\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 606\u001B[0m sig \u001B[38;5;241m=\u001B[39m \u001B[43mtypeinfer\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mresolve_call\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfnty\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpos_args\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkw_args\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 607\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m ForceLiteralArg \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m 608\u001B[0m \u001B[38;5;66;03m# Adjust for bound methods\u001B[39;00m\n\u001B[0;32m 609\u001B[0m folding_args \u001B[38;5;241m=\u001B[39m ((fnty\u001B[38;5;241m.\u001B[39mthis,) \u001B[38;5;241m+\u001B[39m \u001B[38;5;28mtuple\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39margs)\n\u001B[0;32m 610\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(fnty, types\u001B[38;5;241m.\u001B[39mBoundFunction)\n\u001B[0;32m 611\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39margs)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typeinfer.py:1577\u001B[0m, in \u001B[0;36mTypeInferer.resolve_call\u001B[1;34m(self, fnty, pos_args, kw_args)\u001B[0m\n\u001B[0;32m 1574\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m sig\n\u001B[0;32m 1575\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 1576\u001B[0m \u001B[38;5;66;03m# Normal non-recursive call\u001B[39;00m\n\u001B[1;32m-> 1577\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcontext\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mresolve_function_type\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfnty\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpos_args\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkw_args\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typing\\context.py:196\u001B[0m, in \u001B[0;36mBaseContext.resolve_function_type\u001B[1;34m(self, func, args, kws)\u001B[0m\n\u001B[0;32m 194\u001B[0m \u001B[38;5;66;03m# Prefer user definition first\u001B[39;00m\n\u001B[0;32m 195\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 196\u001B[0m res \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_resolve_user_function_type\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfunc\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkws\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 197\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m errors\u001B[38;5;241m.\u001B[39mTypingError \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m 198\u001B[0m \u001B[38;5;66;03m# Capture any typing error\u001B[39;00m\n\u001B[0;32m 199\u001B[0m last_exception \u001B[38;5;241m=\u001B[39m e\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typing\\context.py:248\u001B[0m, in \u001B[0;36mBaseContext._resolve_user_function_type\u001B[1;34m(self, func, args, kws, literals)\u001B[0m\n\u001B[0;32m 244\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mresolve_function_type(func_type, args, kws)\n\u001B[0;32m 246\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(func, types\u001B[38;5;241m.\u001B[39mCallable):\n\u001B[0;32m 247\u001B[0m \u001B[38;5;66;03m# XXX fold this into the __call__ attribute logic?\u001B[39;00m\n\u001B[1;32m--> 248\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_call_type\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkws\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\types\\functions.py:308\u001B[0m, in \u001B[0;36mBaseFunction.get_call_type\u001B[1;34m(self, context, args, kws)\u001B[0m\n\u001B[0;32m 305\u001B[0m nolitargs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mtuple\u001B[39m([_unlit_non_poison(a) \u001B[38;5;28;01mfor\u001B[39;00m a \u001B[38;5;129;01min\u001B[39;00m args])\n\u001B[0;32m 306\u001B[0m nolitkws \u001B[38;5;241m=\u001B[39m {k: _unlit_non_poison(v)\n\u001B[0;32m 307\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m k, v \u001B[38;5;129;01min\u001B[39;00m kws\u001B[38;5;241m.\u001B[39mitems()}\n\u001B[1;32m--> 308\u001B[0m sig \u001B[38;5;241m=\u001B[39m \u001B[43mtemp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapply\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnolitargs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnolitkws\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 309\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m 310\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m (utils\u001B[38;5;241m.\u001B[39muse_new_style_errors() \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m\n\u001B[0;32m 311\u001B[0m \u001B[38;5;28misinstance\u001B[39m(e, errors\u001B[38;5;241m.\u001B[39mNumbaError)):\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typing\\templates.py:350\u001B[0m, in \u001B[0;36mAbstractTemplate.apply\u001B[1;34m(self, args, kws)\u001B[0m\n\u001B[0;32m 348\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mapply\u001B[39m(\u001B[38;5;28mself\u001B[39m, args, kws):\n\u001B[0;32m 349\u001B[0m generic \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mgetattr\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mgeneric\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m--> 350\u001B[0m sig \u001B[38;5;241m=\u001B[39m \u001B[43mgeneric\u001B[49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkws\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 351\u001B[0m \u001B[38;5;66;03m# Enforce that *generic()* must return None or Signature\u001B[39;00m\n\u001B[0;32m 352\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m sig \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typing\\templates.py:613\u001B[0m, in \u001B[0;36m_OverloadFunctionTemplate.generic\u001B[1;34m(self, args, kws)\u001B[0m\n\u001B[0;32m 607\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 608\u001B[0m \u001B[38;5;124;03mType the overloaded function by compiling the appropriate\u001B[39;00m\n\u001B[0;32m 609\u001B[0m \u001B[38;5;124;03mimplementation for the given args.\u001B[39;00m\n\u001B[0;32m 610\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 611\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mnumba\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mcore\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mtyped_passes\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m PreLowerStripPhis\n\u001B[1;32m--> 613\u001B[0m disp, new_args \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_get_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkws\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 614\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m disp \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 615\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typing\\templates.py:712\u001B[0m, in \u001B[0;36m_OverloadFunctionTemplate._get_impl\u001B[1;34m(self, args, kws)\u001B[0m\n\u001B[0;32m 708\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m:\n\u001B[0;32m 709\u001B[0m \u001B[38;5;66;03m# pass and try outside the scope so as to not have KeyError with a\u001B[39;00m\n\u001B[0;32m 710\u001B[0m \u001B[38;5;66;03m# nested addition error in the case the _build_impl fails\u001B[39;00m\n\u001B[0;32m 711\u001B[0m \u001B[38;5;28;01mpass\u001B[39;00m\n\u001B[1;32m--> 712\u001B[0m impl, args \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_build_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcache_key\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkws\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 713\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m impl, args\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typing\\templates.py:816\u001B[0m, in \u001B[0;36m_OverloadFunctionTemplate._build_impl\u001B[1;34m(self, cache_key, args, kws)\u001B[0m\n\u001B[0;32m 814\u001B[0m \u001B[38;5;66;03m# Make sure that the implementation can be fully compiled\u001B[39;00m\n\u001B[0;32m 815\u001B[0m disp_type \u001B[38;5;241m=\u001B[39m types\u001B[38;5;241m.\u001B[39mDispatcher(disp)\n\u001B[1;32m--> 816\u001B[0m \u001B[43mdisp_type\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_call_type\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcontext\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkws\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 817\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m cache_key \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 818\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_impl_cache[cache_key] \u001B[38;5;241m=\u001B[39m disp, args\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\types\\functions.py:541\u001B[0m, in \u001B[0;36mDispatcher.get_call_type\u001B[1;34m(self, context, args, kws)\u001B[0m\n\u001B[0;32m 534\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mget_call_type\u001B[39m(\u001B[38;5;28mself\u001B[39m, context, args, kws):\n\u001B[0;32m 535\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 536\u001B[0m \u001B[38;5;124;03m Resolve a call to this dispatcher using the given argument types.\u001B[39;00m\n\u001B[0;32m 537\u001B[0m \u001B[38;5;124;03m A signature returned and it is ensured that a compiled specialization\u001B[39;00m\n\u001B[0;32m 538\u001B[0m \u001B[38;5;124;03m is available for it.\u001B[39;00m\n\u001B[0;32m 539\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[0;32m 540\u001B[0m template, pysig, args, kws \u001B[38;5;241m=\u001B[39m \\\n\u001B[1;32m--> 541\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdispatcher\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_call_template\u001B[49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkws\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 542\u001B[0m sig \u001B[38;5;241m=\u001B[39m template(context)\u001B[38;5;241m.\u001B[39mapply(args, kws)\n\u001B[0;32m 543\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m sig:\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\dispatcher.py:318\u001B[0m, in \u001B[0;36m_DispatcherBase.get_call_template\u001B[1;34m(self, args, kws)\u001B[0m\n\u001B[0;32m 316\u001B[0m \u001B[38;5;66;03m# Ensure an overload is available\u001B[39;00m\n\u001B[0;32m 317\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_can_compile:\n\u001B[1;32m--> 318\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcompile\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mtuple\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 320\u001B[0m \u001B[38;5;66;03m# Create function type for typing\u001B[39;00m\n\u001B[0;32m 321\u001B[0m func_name \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpy_func\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\dispatcher.py:905\u001B[0m, in \u001B[0;36mDispatcher.compile\u001B[1;34m(self, sig)\u001B[0m\n\u001B[0;32m 903\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m ev\u001B[38;5;241m.\u001B[39mtrigger_event(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnumba:compile\u001B[39m\u001B[38;5;124m\"\u001B[39m, data\u001B[38;5;241m=\u001B[39mev_details):\n\u001B[0;32m 904\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 905\u001B[0m cres \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_compiler\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcompile\u001B[49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreturn_type\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 906\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m errors\u001B[38;5;241m.\u001B[39mForceLiteralArg \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m 907\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mfolded\u001B[39m(args, kws):\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\dispatcher.py:80\u001B[0m, in \u001B[0;36m_FunctionCompiler.compile\u001B[1;34m(self, args, return_type)\u001B[0m\n\u001B[0;32m 79\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mcompile\u001B[39m(\u001B[38;5;28mself\u001B[39m, args, return_type):\n\u001B[1;32m---> 80\u001B[0m status, retval \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_compile_cached\u001B[49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreturn_type\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 81\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m status:\n\u001B[0;32m 82\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m retval\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\dispatcher.py:94\u001B[0m, in \u001B[0;36m_FunctionCompiler._compile_cached\u001B[1;34m(self, args, return_type)\u001B[0m\n\u001B[0;32m 91\u001B[0m \u001B[38;5;28;01mpass\u001B[39;00m\n\u001B[0;32m 93\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m---> 94\u001B[0m retval \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_compile_core\u001B[49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreturn_type\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 95\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m errors\u001B[38;5;241m.\u001B[39mTypingError \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m 96\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_failed_cache[key] \u001B[38;5;241m=\u001B[39m e\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\dispatcher.py:107\u001B[0m, in \u001B[0;36m_FunctionCompiler._compile_core\u001B[1;34m(self, args, return_type)\u001B[0m\n\u001B[0;32m 104\u001B[0m flags \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_customize_flags(flags)\n\u001B[0;32m 106\u001B[0m impl \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_get_implementation(args, {})\n\u001B[1;32m--> 107\u001B[0m cres \u001B[38;5;241m=\u001B[39m \u001B[43mcompiler\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcompile_extra\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtargetdescr\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtyping_context\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 108\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtargetdescr\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtarget_context\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 109\u001B[0m \u001B[43m \u001B[49m\u001B[43mimpl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 110\u001B[0m \u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreturn_type\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mreturn_type\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 111\u001B[0m \u001B[43m \u001B[49m\u001B[43mflags\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mflags\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mlocals\u001B[39;49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mlocals\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 112\u001B[0m \u001B[43m \u001B[49m\u001B[43mpipeline_class\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpipeline_class\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 113\u001B[0m \u001B[38;5;66;03m# Check typing error if object mode is used\u001B[39;00m\n\u001B[0;32m 114\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m cres\u001B[38;5;241m.\u001B[39mtyping_error \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m flags\u001B[38;5;241m.\u001B[39menable_pyobject:\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler.py:744\u001B[0m, in \u001B[0;36mcompile_extra\u001B[1;34m(typingctx, targetctx, func, args, return_type, flags, locals, library, pipeline_class)\u001B[0m\n\u001B[0;32m 720\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Compiler entry point\u001B[39;00m\n\u001B[0;32m 721\u001B[0m \n\u001B[0;32m 722\u001B[0m \u001B[38;5;124;03mParameter\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 740\u001B[0m \u001B[38;5;124;03m compiler pipeline\u001B[39;00m\n\u001B[0;32m 741\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 742\u001B[0m pipeline \u001B[38;5;241m=\u001B[39m pipeline_class(typingctx, targetctx, library,\n\u001B[0;32m 743\u001B[0m args, return_type, flags, \u001B[38;5;28mlocals\u001B[39m)\n\u001B[1;32m--> 744\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mpipeline\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcompile_extra\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfunc\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler.py:438\u001B[0m, in \u001B[0;36mCompilerBase.compile_extra\u001B[1;34m(self, func)\u001B[0m\n\u001B[0;32m 436\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstate\u001B[38;5;241m.\u001B[39mlifted \u001B[38;5;241m=\u001B[39m ()\n\u001B[0;32m 437\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstate\u001B[38;5;241m.\u001B[39mlifted_from \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m--> 438\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_compile_bytecode\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler.py:506\u001B[0m, in \u001B[0;36mCompilerBase._compile_bytecode\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 502\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 503\u001B[0m \u001B[38;5;124;03mPopulate and run pipeline for bytecode input\u001B[39;00m\n\u001B[0;32m 504\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 505\u001B[0m \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstate\u001B[38;5;241m.\u001B[39mfunc_ir \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m--> 506\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_compile_core\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler.py:472\u001B[0m, in \u001B[0;36mCompilerBase._compile_core\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 470\u001B[0m res \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m 471\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 472\u001B[0m \u001B[43mpm\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mstate\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 473\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstate\u001B[38;5;241m.\u001B[39mcr \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 474\u001B[0m \u001B[38;5;28;01mbreak\u001B[39;00m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler_machinery.py:356\u001B[0m, in \u001B[0;36mPassManager.run\u001B[1;34m(self, state)\u001B[0m\n\u001B[0;32m 354\u001B[0m pass_inst \u001B[38;5;241m=\u001B[39m _pass_registry\u001B[38;5;241m.\u001B[39mget(pss)\u001B[38;5;241m.\u001B[39mpass_inst\n\u001B[0;32m 355\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(pass_inst, CompilerPass):\n\u001B[1;32m--> 356\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_runPass\u001B[49m\u001B[43m(\u001B[49m\u001B[43midx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpass_inst\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstate\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 357\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 358\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mBaseException\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mLegacy pass in use\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler_lock.py:35\u001B[0m, in \u001B[0;36m_CompilerLock.__call__.._acquire_compile_lock\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m 32\u001B[0m \u001B[38;5;129m@functools\u001B[39m\u001B[38;5;241m.\u001B[39mwraps(func)\n\u001B[0;32m 33\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_acquire_compile_lock\u001B[39m(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 34\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mself\u001B[39m:\n\u001B[1;32m---> 35\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m func(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler_machinery.py:311\u001B[0m, in \u001B[0;36mPassManager._runPass\u001B[1;34m(self, index, pss, internal_state)\u001B[0m\n\u001B[0;32m 309\u001B[0m mutated \u001B[38;5;241m|\u001B[39m\u001B[38;5;241m=\u001B[39m check(pss\u001B[38;5;241m.\u001B[39mrun_initialization, internal_state)\n\u001B[0;32m 310\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m SimpleTimer() \u001B[38;5;28;01mas\u001B[39;00m pass_time:\n\u001B[1;32m--> 311\u001B[0m mutated \u001B[38;5;241m|\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[43mcheck\u001B[49m\u001B[43m(\u001B[49m\u001B[43mpss\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun_pass\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minternal_state\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 312\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m SimpleTimer() \u001B[38;5;28;01mas\u001B[39;00m finalize_time:\n\u001B[0;32m 313\u001B[0m mutated \u001B[38;5;241m|\u001B[39m\u001B[38;5;241m=\u001B[39m check(pss\u001B[38;5;241m.\u001B[39mrun_finalizer, internal_state)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler_machinery.py:273\u001B[0m, in \u001B[0;36mPassManager._runPass..check\u001B[1;34m(func, compiler_state)\u001B[0m\n\u001B[0;32m 272\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mcheck\u001B[39m(func, compiler_state):\n\u001B[1;32m--> 273\u001B[0m mangled \u001B[38;5;241m=\u001B[39m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcompiler_state\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 274\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m mangled \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m (\u001B[38;5;28;01mTrue\u001B[39;00m, \u001B[38;5;28;01mFalse\u001B[39;00m):\n\u001B[0;32m 275\u001B[0m msg \u001B[38;5;241m=\u001B[39m (\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCompilerPass implementations should return True/False. \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 276\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCompilerPass with name \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m did not.\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typed_passes.py:497\u001B[0m, in \u001B[0;36mBaseNativeLowering.run_pass\u001B[1;34m(self, state)\u001B[0m\n\u001B[0;32m 491\u001B[0m state[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mcr\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m _LowerResult(fndesc, call_helper,\n\u001B[0;32m 492\u001B[0m cfunc\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, env\u001B[38;5;241m=\u001B[39menv)\n\u001B[0;32m 493\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 494\u001B[0m \u001B[38;5;66;03m# Prepare for execution\u001B[39;00m\n\u001B[0;32m 495\u001B[0m \u001B[38;5;66;03m# Insert native function for use by other jitted-functions.\u001B[39;00m\n\u001B[0;32m 496\u001B[0m \u001B[38;5;66;03m# We also register its library to allow for inlining.\u001B[39;00m\n\u001B[1;32m--> 497\u001B[0m cfunc \u001B[38;5;241m=\u001B[39m \u001B[43mtargetctx\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_executable\u001B[49m\u001B[43m(\u001B[49m\u001B[43mlibrary\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfndesc\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43menv\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 498\u001B[0m targetctx\u001B[38;5;241m.\u001B[39minsert_user_function(cfunc, fndesc, [library])\n\u001B[0;32m 499\u001B[0m state[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mcr\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m _LowerResult(fndesc, call_helper,\n\u001B[0;32m 500\u001B[0m cfunc\u001B[38;5;241m=\u001B[39mcfunc, env\u001B[38;5;241m=\u001B[39menv)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\cpu.py:239\u001B[0m, in \u001B[0;36mCPUContext.get_executable\u001B[1;34m(self, library, fndesc, env)\u001B[0m\n\u001B[0;32m 226\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 227\u001B[0m \u001B[38;5;124;03mReturns\u001B[39;00m\n\u001B[0;32m 228\u001B[0m \u001B[38;5;124;03m-------\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 236\u001B[0m \u001B[38;5;124;03m an execution environment (from _dynfunc)\u001B[39;00m\n\u001B[0;32m 237\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 238\u001B[0m \u001B[38;5;66;03m# Code generation\u001B[39;00m\n\u001B[1;32m--> 239\u001B[0m fnptr \u001B[38;5;241m=\u001B[39m \u001B[43mlibrary\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_pointer_to_function\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 240\u001B[0m \u001B[43m \u001B[49m\u001B[43mfndesc\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mllvm_cpython_wrapper_name\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 242\u001B[0m \u001B[38;5;66;03m# Note: we avoid reusing the original docstring to avoid encoding\u001B[39;00m\n\u001B[0;32m 243\u001B[0m \u001B[38;5;66;03m# issues on Python 2, see issue #1908\u001B[39;00m\n\u001B[0;32m 244\u001B[0m doc \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcompiled wrapper for \u001B[39m\u001B[38;5;132;01m%r\u001B[39;00m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;241m%\u001B[39m (fndesc\u001B[38;5;241m.\u001B[39mqualname,)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\codegen.py:989\u001B[0m, in \u001B[0;36mJITCodeLibrary.get_pointer_to_function\u001B[1;34m(self, name)\u001B[0m\n\u001B[0;32m 975\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mget_pointer_to_function\u001B[39m(\u001B[38;5;28mself\u001B[39m, name):\n\u001B[0;32m 976\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 977\u001B[0m \u001B[38;5;124;03m Generate native code for function named *name* and return a pointer\u001B[39;00m\n\u001B[0;32m 978\u001B[0m \u001B[38;5;124;03m to the start of the function (as an integer).\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 987\u001B[0m \u001B[38;5;124;03m - non-zero if the symbol is defined.\u001B[39;00m\n\u001B[0;32m 988\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m--> 989\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_ensure_finalized\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 990\u001B[0m ee \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_codegen\u001B[38;5;241m.\u001B[39m_engine\n\u001B[0;32m 991\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m ee\u001B[38;5;241m.\u001B[39mis_symbol_defined(name):\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\codegen.py:567\u001B[0m, in \u001B[0;36mCodeLibrary._ensure_finalized\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 565\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_ensure_finalized\u001B[39m(\u001B[38;5;28mself\u001B[39m):\n\u001B[0;32m 566\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_finalized:\n\u001B[1;32m--> 567\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfinalize\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\codegen.py:762\u001B[0m, in \u001B[0;36mCPUCodeLibrary.finalize\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 756\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_final_module\u001B[38;5;241m.\u001B[39mlink_in(\n\u001B[0;32m 757\u001B[0m library\u001B[38;5;241m.\u001B[39m_get_module_for_linking(), preserve\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m,\n\u001B[0;32m 758\u001B[0m )\n\u001B[0;32m 760\u001B[0m \u001B[38;5;66;03m# Optimize the module after all dependences are linked in above,\u001B[39;00m\n\u001B[0;32m 761\u001B[0m \u001B[38;5;66;03m# to allow for inlining.\u001B[39;00m\n\u001B[1;32m--> 762\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_optimize_final_module\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 764\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_final_module\u001B[38;5;241m.\u001B[39mverify()\n\u001B[0;32m 765\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_finalize_final_module()\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\codegen.py:682\u001B[0m, in \u001B[0;36mCPUCodeLibrary._optimize_final_module\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 679\u001B[0m full_name \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mModule passes (full optimization)\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 680\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_recorded_timings\u001B[38;5;241m.\u001B[39mrecord(full_name):\n\u001B[0;32m 681\u001B[0m \u001B[38;5;66;03m# The full optimisation suite is then run on the refop pruned IR\u001B[39;00m\n\u001B[1;32m--> 682\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_codegen\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_mpm_full\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_final_module\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\llvmlite\\binding\\passmanagers.py:698\u001B[0m, in \u001B[0;36mModulePassManager.run\u001B[1;34m(self, module, remarks_file, remarks_format, remarks_filter)\u001B[0m\n\u001B[0;32m 683\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 684\u001B[0m \u001B[38;5;124;03mRun optimization passes on the given module.\u001B[39;00m\n\u001B[0;32m 685\u001B[0m \n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 695\u001B[0m \u001B[38;5;124;03m The filter that should be applied to the remarks output.\u001B[39;00m\n\u001B[0;32m 696\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 697\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m remarks_file \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m--> 698\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mffi\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mlib\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mLLVMPY_RunPassManager\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmodule\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 699\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 700\u001B[0m r \u001B[38;5;241m=\u001B[39m ffi\u001B[38;5;241m.\u001B[39mlib\u001B[38;5;241m.\u001B[39mLLVMPY_RunPassManagerWithRemarks(\n\u001B[0;32m 701\u001B[0m \u001B[38;5;28mself\u001B[39m, module, _encode_string(remarks_format),\n\u001B[0;32m 702\u001B[0m _encode_string(remarks_filter),\n\u001B[0;32m 703\u001B[0m _encode_string(remarks_file))\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\llvmlite\\binding\\ffi.py:192\u001B[0m, in \u001B[0;36m_lib_fn_wrapper.__call__\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m 190\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m__call__\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 191\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_lock:\n\u001B[1;32m--> 192\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_cfn(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n", - "\u001B[1;31mKeyboardInterrupt\u001B[0m: " - ] + "data": { + "text/plain": [ + "[('CanonicalIntervalForestClassifier',\n", + " aeon.classification.interval_based._cif.CanonicalIntervalForestClassifier),\n", + " ('DrCIFClassifier',\n", + " aeon.classification.interval_based._drcif.DrCIFClassifier),\n", + " ('IntervalForestClassifier',\n", + " aeon.classification.interval_based._interval_forest.IntervalForestClassifier),\n", + " ('QUANTClassifier',\n", + " aeon.classification.interval_based._quant.QUANTClassifier),\n", + " ('RSTSF', aeon.classification.interval_based._rstsf.RSTSF),\n", + " ('RandomIntervalClassifier',\n", + " aeon.classification.interval_based._interval_pipelines.RandomIntervalClassifier),\n", + " ('RandomIntervalSpectralEnsembleClassifier',\n", + " aeon.classification.interval_based._rise.RandomIntervalSpectralEnsembleClassifier),\n", + " ('SupervisedIntervalClassifier',\n", + " aeon.classification.interval_based._interval_pipelines.SupervisedIntervalClassifier),\n", + " ('SupervisedTimeSeriesForest',\n", + " aeon.classification.interval_based._stsf.SupervisedTimeSeriesForest),\n", + " ('TimeSeriesForestClassifier',\n", + " aeon.classification.interval_based._tsf.TimeSeriesForestClassifier)]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -157,8 +113,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "(67, 1) (67,) (50, 1) (50,)\n", - "(40, 6) (40,) (40, 6) (40,)\n" + "(67, 1, 24) (67,) (50, 1, 24) (50,)\n", + "(40, 6, 100) (40,) (40, 6, 100) (40,)\n" ] } ], @@ -239,7 +195,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "RISE Accuracy: 1.0\n" + "RISE Accuracy: 0.96\n" ] } ], @@ -263,18 +219,15 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 6, "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'SupervisedTimeSeriesForest' is not defined", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[1;32mIn[1], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m stsf \u001B[38;5;241m=\u001B[39m \u001B[43mSupervisedTimeSeriesForest\u001B[49m(n_estimators\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m50\u001B[39m, random_state\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m47\u001B[39m)\n\u001B[0;32m 2\u001B[0m stsf\u001B[38;5;241m.\u001B[39mfit(X_train, y_train)\n\u001B[0;32m 4\u001B[0m stsf_preds \u001B[38;5;241m=\u001B[39m stsf\u001B[38;5;241m.\u001B[39mpredict(X_test)\n", - "\u001B[1;31mNameError\u001B[0m: name 'SupervisedTimeSeriesForest' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "STSF Accuracy: 1.0\n", + "RSTSF Accuracy: 1.0\n" ] } ], @@ -307,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:06.471294Z", @@ -321,7 +274,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "CIF Accuracy: 0.98\n" + "CIF Accuracy: 1.0\n" ] } ], @@ -345,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -379,14 +332,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "DrCIF Accuracy: 0.98\n" + "DrCIF Accuracy: 0.94\n" ] } ], @@ -407,7 +360,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -428,27 +381,35 @@ }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "## 7. QUANT\n", "\n", "QUANT is a fast interval based classifier based on quantile features" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "QUANT accuracy = 0.88\n" + ] + } + ], "source": [ "quant = QUANTClassifier(interval_depth=1)\n", "quant.fit(X_train, y_train)\n", "print(\"QUANT accuracy =\", quant.score(X_test, y_test))" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", @@ -463,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -495,7 +456,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -506,7 +467,7 @@ "(112, 7)" ] }, - "execution_count": 2, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -527,7 +488,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -538,13 +499,13 @@ "(
, )" ] }, - "execution_count": 3, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" ] @@ -561,7 +522,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -572,13 +533,13 @@ "(
, )" ] }, - "execution_count": 4, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -639,7 +600,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.12.3" } }, "nbformat": 4, From ff4d50137113cc12491eea391225bd719a52a4aa Mon Sep 17 00:00:00 2001 From: Kavya Rambhia <161142013+kavya-r30@users.noreply.github.com> Date: Sun, 16 Mar 2025 21:22:30 +0530 Subject: [PATCH 022/216] [DOC] Added Docstring for regression forecasting (#2564) * Added Docstring for Regression * Added Docstring for Regression * exog fix --- aeon/forecasting/_regression.py | 40 ++++++++++++++++++++++++++++++--- 1 file changed, 37 insertions(+), 3 deletions(-) diff --git a/aeon/forecasting/_regression.py b/aeon/forecasting/_regression.py index 79393160b1..bf15e231dd 100644 --- a/aeon/forecasting/_regression.py +++ b/aeon/forecasting/_regression.py @@ -50,7 +50,11 @@ def _fit(self, y, exog=None): Parameters ---------- - X : Time series on which to learn a forecaster + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead. + exog : np.ndarray, default=None + Optional exogenous time series data. Included for interface + compatibility but ignored in this estimator. Returns ------- @@ -74,14 +78,44 @@ def _fit(self, y, exog=None): return self def _predict(self, y=None, exog=None): - """Predict values for time series X.""" + """ + Predict the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray, default = None + A time series to predict the next horizon value for. If None, + predict the next horizon value after series seen in fit. + exog : np.ndarray, default=None + Optional exogenous time series data. Included for interface + compatibility but ignored in this estimator. + + Returns + ------- + np.ndarray + single prediction self.horizon steps ahead of y. + """ if y is None: return self.regressor_.predict(self.last_) last = y[:, -self.window :] return self.regressor_.predict(last) def _forecast(self, y, exog=None): - """Forecast values for time series X. + """ + Forecast the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray + A time series to predict the next horizon value for. + exog : np.ndarray, default=None + Optional exogenous time series data. Included for interface + compatibility but ignored in this estimator. + + Returns + ------- + np.ndarray + single prediction self.horizon steps ahead of y. NOTE: deal with horizons """ From deefbaebeda940576434812aeb349ee17161f191 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Mon, 17 Mar 2025 12:01:15 +0000 Subject: [PATCH 023/216] GSoC announcement (#2629) --- docs/conf.py | 1 + docs/index.md | 9 +++++++++ 2 files changed, 10 insertions(+) diff --git a/docs/conf.py b/docs/conf.py index 65844dbb71..bea36e60dd 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -199,6 +199,7 @@ def find_source(): # documentation. html_theme_options = { + "announcement": "Announcement: aeon is taking part in the Google Summer of Code (GSoC) 2025! See the home page for more information.", # noqa: E501 "sidebar_hide_name": True, "top_of_page_button": "edit", "source_repository": "https://github.com/aeon-toolkit/aeon/", diff --git a/docs/index.md b/docs/index.md index 11b558839e..d1c45c1c6d 100644 --- a/docs/index.md +++ b/docs/index.md @@ -17,6 +17,15 @@ segmentation and similarity search. - We provide a range of tools for reproducing benchmarking results and evaluating time series algorithms implemented in `aeon` and other `scikit-learn` compatible packages. +```{admonition} GSoC 2025 +`aeon` is participating in Google Summer of Code 2025 under the NumFOCUS umbrella. If +you are interested in participating, please see our [GSoC 2025 project page](https://github.com/aeon-toolkit/aeon-admin/blob/main/gsoc/gsoc-2025-projects.md). +Feel free to ask questions on the dedicated [GitHub discussion](https://github.com/aeon-toolkit/aeon/discussions/2530) +or our [Slack channel](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-22vwvut29-HDpCu~7VBUozyfL_8j3dLA). +If you are interested in contributing, click on the "Contributing to aeon" link in the +sidebar for a contribution guide. Applications close April 8th. +``` + ## Community Channels **GitHub**: [github.com/aeon-toolkit/aeon](https://github.com/aeon-toolkit/aeon) From e922bcae411e24c5363aab41713a003f0412b43f Mon Sep 17 00:00:00 2001 From: "aeon-actions-bot[bot]" <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Date: Mon, 17 Mar 2025 13:33:03 +0000 Subject: [PATCH 024/216] Automated `pre-commit` hook update (#2632) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 9f455ec641..854da8dfd2 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -29,7 +29,7 @@ repos: args: [ "--create", "--python-folders", "aeon" ] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.9.10 + rev: v0.11.0 hooks: - id: ruff args: [ "--fix"] From c79fa9c349c51ccd366ba5539f13714c56b2879a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 17 Mar 2025 13:33:35 +0000 Subject: [PATCH 025/216] [MNT] Bump tj-actions/changed-files from 45 to 46 in the github-actions group (#2637) * [MNT] Bump tj-actions/changed-files in the github-actions group Bumps the github-actions group with 1 update: [tj-actions/changed-files](https://github.com/tj-actions/changed-files). Updates `tj-actions/changed-files` from 45 to 46 - [Release notes](https://github.com/tj-actions/changed-files/releases) - [Changelog](https://github.com/tj-actions/changed-files/blob/main/HISTORY.md) - [Commits](https://github.com/tj-actions/changed-files/compare/v45...v46) --- updated-dependencies: - dependency-name: tj-actions/changed-files dependency-type: direct:production update-type: version-update:semver-major dependency-group: github-actions ... Signed-off-by: dependabot[bot] * Update pr_precommit.yml --------- Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Matthew Middlehurst --- .github/workflows/pr_precommit.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pr_precommit.yml b/.github/workflows/pr_precommit.yml index 2f63ef2ba7..a74e8b12ea 100644 --- a/.github/workflows/pr_precommit.yml +++ b/.github/workflows/pr_precommit.yml @@ -37,7 +37,7 @@ jobs: python-version: "3.10" - name: Get changed files - uses: tj-actions/changed-files@v45 + uses: tj-actions/changed-files@v46.0.1 id: changed-files - name: List changed files From da13dee1dd0f0f65ec250d0aea4b2561156b0caa Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 17 Mar 2025 20:10:04 +0000 Subject: [PATCH 026/216] [MNT] Update numpy requirement in the python-packages group (#2643) Updates the requirements on [numpy](https://github.com/numpy/numpy) to permit the latest version. Updates `numpy` to 2.2.4 - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v1.21.0...v2.2.4) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production dependency-group: python-packages ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 44f3541692..1882fdf5c8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -47,7 +47,7 @@ requires-python = ">=3.9,<3.13" dependencies = [ "deprecated>=1.2.13", "numba>=0.55,<0.61.0", - "numpy>=1.21.0,<2.1.0", + "numpy>=1.21.0,<2.3.0", "packaging>=20.0", "pandas>=2.0.0,<2.3.0", "scikit-learn>=1.0.0,<1.7.0", From 26853e338ccf8bae65b726e413f16397a31d8100 Mon Sep 17 00:00:00 2001 From: Aryan Pola <98093778+aryanpola@users.noreply.github.com> Date: Tue, 18 Mar 2025 23:11:43 +0530 Subject: [PATCH 027/216] [MNT,DEP] _binary.py metrics deprecated (#2600) * functions deprecated * Empty-Commit * version changed --- .../metrics/anomaly_detection/_binary.py | 22 +++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/aeon/benchmarking/metrics/anomaly_detection/_binary.py b/aeon/benchmarking/metrics/anomaly_detection/_binary.py index 85d54a5cb6..085d7c04f9 100644 --- a/aeon/benchmarking/metrics/anomaly_detection/_binary.py +++ b/aeon/benchmarking/metrics/anomaly_detection/_binary.py @@ -6,11 +6,19 @@ import warnings import numpy as np +from deprecated.sphinx import deprecated from aeon.benchmarking.metrics.anomaly_detection._util import check_y from aeon.utils.validation._dependencies import _check_soft_dependencies +# TODO: Remove in v1.2.0 +@deprecated( + version="1.1.0", + reason="range_precision is deprecated and will be removed in v1.2.0. " + "Please use ts_precision from the range_metrics module instead.", + category=FutureWarning, +) def range_precision( y_true: np.ndarray, y_pred: np.ndarray, @@ -70,6 +78,13 @@ def range_precision( return ts_precision(y_true, y_pred, alpha=alpha, cardinality=cardinality, bias=bias) +# TODO: Remove in v1.2.0 +@deprecated( + version="1.1.0", + reason="range_recall is deprecated and will be removed in v1.2.0. " + "Please use ts_recall from the range_metrics module instead.", + category=FutureWarning, +) def range_recall( y_true: np.ndarray, y_pred: np.ndarray, @@ -131,6 +146,13 @@ def range_recall( return ts_recall(y_true, y_pred, alpha=alpha, cardinality=cardinality, bias=bias) +# TODO: Remove in v1.2.0 +@deprecated( + version="1.1.0", + reason="range_f_score is deprecated and will be removed in v1.2.0. " + "Please use ts_fscore from the range_metrics module instead.", + category=FutureWarning, +) def range_f_score( y_true: np.ndarray, y_pred: np.ndarray, From f1dceea1f5174cf3535299278f6baa6295696d02 Mon Sep 17 00:00:00 2001 From: Tanish Yelgoe <143334319+tanishy7777@users.noreply.github.com> Date: Wed, 19 Mar 2025 20:24:41 +0530 Subject: [PATCH 028/216] Support for unequal length timeseries in itakura parallelogram (#2647) --- aeon/distances/elastic/_bounding_matrix.py | 85 +++++++++++-------- aeon/distances/elastic/tests/test_bounding.py | 39 +++++++-- 2 files changed, 82 insertions(+), 42 deletions(-) diff --git a/aeon/distances/elastic/_bounding_matrix.py b/aeon/distances/elastic/_bounding_matrix.py index 2b710b9728..509dff6edf 100644 --- a/aeon/distances/elastic/_bounding_matrix.py +++ b/aeon/distances/elastic/_bounding_matrix.py @@ -63,44 +63,61 @@ def create_bounding_matrix( def _itakura_parallelogram(x_size: int, y_size: int, max_slope_percent: float): """Itakura parallelogram bounding matrix. - This code was adapted from tslearn. This link to the original code line 974: - https://github.com/tslearn-team/tslearn/blob/main/tslearn/metrics/dtw_variants.py + This code was adapted from pyts. This link to the original code: + https://pyts.readthedocs.io/en/latest/_modules/pyts/metrics/dtw.html#itakura_parallelogram """ - if x_size != y_size: - raise ValueError( - """Itakura parallelogram does not support unequal length time series. -Please consider using a full bounding matrix or a sakoe chiba bounding matrix -instead.""" - ) one_percent = min(x_size, y_size) / 100 max_slope = math.floor((max_slope_percent * one_percent) * 100) min_slope = 1 / float(max_slope) - max_slope *= float(x_size) / float(y_size) - min_slope *= float(x_size) / float(y_size) - - lower_bound = np.empty((2, y_size)) - lower_bound[0] = min_slope * np.arange(y_size) - lower_bound[1] = ( - (x_size - 1) - max_slope * (y_size - 1) + max_slope * np.arange(y_size) - ) - lower_bound_ = np.empty(y_size) - for i in range(y_size): - lower_bound_[i] = max(round(lower_bound[0, i], 2), round(lower_bound[1, i], 2)) - lower_bound_ = np.ceil(lower_bound_) - - upper_bound = np.empty((2, y_size)) - upper_bound[0] = max_slope * np.arange(y_size) - upper_bound[1] = ( - (x_size - 1) - min_slope * (y_size - 1) + min_slope * np.arange(y_size) - ) - upper_bound_ = np.empty(y_size) - for i in range(y_size): - upper_bound_[i] = min(round(upper_bound[0, i], 2), round(upper_bound[1, i], 2)) - upper_bound_ = np.floor(upper_bound_ + 1) - - bounding_matrix = np.full((x_size, y_size), False) - for i in range(y_size): - bounding_matrix[int(lower_bound_[i]) : int(upper_bound_[i]), i] = True + max_slope *= float(y_size - 1) / float(x_size - 2) + max_slope = max(max_slope, 1.0) + + min_slope *= float(y_size - 2) / float(x_size - 1) + min_slope = min(min_slope, 1.0) + + centered_scale = np.arange(x_size) - x_size + 1 + + lower_bound = np.empty(x_size, dtype=np.float64) + upper_bound = np.empty(x_size, dtype=np.float64) + + for i in range(x_size): + lb0 = min_slope * i + lb1 = max_slope * centered_scale[i] + y_size - 1 + lower_bound[i] = math.ceil(max(round(lb0, 2), round(lb1, 2))) + + ub0 = max_slope * i + 1 + ub1 = min_slope * centered_scale[i] + y_size + upper_bound[i] = math.floor(min(round(ub0, 2), round(ub1, 2))) + + if max_slope == 1.0: + if y_size > x_size: + for i in range(x_size - 1): + upper_bound[i] = lower_bound[i + 1] + else: + for i in range(x_size): + upper_bound[i] = lower_bound[i] + 1 + + for i in range(x_size): + if lower_bound[i] < 0: + lower_bound[i] = 0 + if lower_bound[i] > y_size: + lower_bound[i] = y_size + if upper_bound[i] < 0: + upper_bound[i] = 0 + if upper_bound[i] > y_size: + upper_bound[i] = y_size + + bounding_matrix = np.empty((x_size, y_size), dtype=np.bool_) + for i in range(x_size): + for j in range(y_size): + bounding_matrix[i, j] = False + + for i in range(x_size): + start = int(lower_bound[i]) + end = int(upper_bound[i]) + for j in range(start, end): + bounding_matrix[i, j] = True + return bounding_matrix diff --git a/aeon/distances/elastic/tests/test_bounding.py b/aeon/distances/elastic/tests/test_bounding.py index 32ac1987b0..f0c2d83737 100644 --- a/aeon/distances/elastic/tests/test_bounding.py +++ b/aeon/distances/elastic/tests/test_bounding.py @@ -1,7 +1,6 @@ """Test for bounding matrix.""" import numpy as np -import pytest from aeon.distances import create_bounding_matrix @@ -37,10 +36,34 @@ def test_itakura_parallelogram(): matrix = create_bounding_matrix(10, 10, itakura_max_slope=0.2) assert isinstance(matrix, np.ndarray) - with pytest.raises( - ValueError, - match="""Itakura parallelogram does not support unequal length time series. -Please consider using a full bounding matrix or a sakoe chiba bounding matrix -instead.""", - ): - create_bounding_matrix(5, 10, itakura_max_slope=0.2) + expected_result_5_7 = np.array( + [ + [True, False, False, False, False, False, False], + [False, True, True, True, True, False, False], + [False, False, True, True, True, False, False], + [False, False, True, True, True, True, False], + [False, False, False, False, False, False, True], + ] + ) + + expected_result_7_5 = np.array( + [ + [True, False, False, False, False], + [False, True, False, False, False], + [False, True, True, True, False], + [False, True, True, True, False], + [False, True, True, True, False], + [False, False, False, True, False], + [False, False, False, False, True], + ] + ) + + matrix = create_bounding_matrix(5, 7, itakura_max_slope=0.5) + assert isinstance(matrix, np.ndarray) + assert matrix.shape == (5, 7) + assert np.array_equal(matrix, expected_result_5_7) + + matrix = create_bounding_matrix(7, 5, itakura_max_slope=0.5) + assert isinstance(matrix, np.ndarray) + assert matrix.shape == (7, 5) + assert np.array_equal(matrix, expected_result_7_5) From db0ff4903225c06ab35f1d693d41f93f71737791 Mon Sep 17 00:00:00 2001 From: Tanish Yelgoe <143334319+tanishy7777@users.noreply.github.com> Date: Wed, 19 Mar 2025 21:32:42 +0530 Subject: [PATCH 029/216] [ENH] Implement DTW with Global alignment (#2565) * Implements Dynamic Time Warping with Global Invariances * Adds Numba JIT compilation support * Adds docs and numba support for dtw_gi and test_distance fixed * Fixes doctests * Automatic `pre-commit` fixes * Minor changes * Minor changes * Remove dtw_gi function and combine with private method _dtw_gi * Adds parameter tests * Fixes doctests * Minor changes --- aeon/distances/__init__.py | 8 + aeon/distances/_distance.py | 15 + aeon/distances/elastic/__init__.py | 10 + aeon/distances/elastic/_dtw_gi.py | 551 ++++++++++++++++++ .../tests/test_distance_correctness.py | 6 + aeon/distances/tests/test_distances.py | 1 + .../expected_distance_results.py | 7 + 7 files changed, 598 insertions(+) create mode 100644 aeon/distances/elastic/_dtw_gi.py diff --git a/aeon/distances/__init__.py b/aeon/distances/__init__.py index e1d3205ef2..d6ff3f776a 100644 --- a/aeon/distances/__init__.py +++ b/aeon/distances/__init__.py @@ -18,6 +18,10 @@ "dtw_pairwise_distance", "dtw_cost_matrix", "dtw_alignment_path", + "dtw_gi_distance", + "dtw_gi_pairwise_distance", + "dtw_gi_cost_matrix", + "dtw_gi_alignment_path", "ddtw_distance", "ddtw_pairwise_distance", "ddtw_alignment_path", @@ -111,6 +115,10 @@ dtw_alignment_path, dtw_cost_matrix, dtw_distance, + dtw_gi_alignment_path, + dtw_gi_cost_matrix, + dtw_gi_distance, + dtw_gi_pairwise_distance, dtw_pairwise_distance, edr_alignment_path, edr_cost_matrix, diff --git a/aeon/distances/_distance.py b/aeon/distances/_distance.py index 8a5a2fb369..726eb2e836 100644 --- a/aeon/distances/_distance.py +++ b/aeon/distances/_distance.py @@ -25,6 +25,10 @@ dtw_alignment_path, dtw_cost_matrix, dtw_distance, + dtw_gi_alignment_path, + dtw_gi_cost_matrix, + dtw_gi_distance, + dtw_gi_pairwise_distance, dtw_pairwise_distance, edr_alignment_path, edr_cost_matrix, @@ -494,6 +498,7 @@ def get_distance_function(method: Union[str, DistanceFunction]) -> DistanceFunct method Distance Function =============== ======================================== 'dtw' distances.dtw_distance + 'dtw_gi' distances.dtw_gi_distance 'shape_dtw' distances.shape_dtw_distance 'ddtw' distances.ddtw_distance 'wdtw' distances.wdtw_distance @@ -775,6 +780,16 @@ class DistanceType(Enum): "symmetric": True, "unequal_support": True, }, + { + "name": "dtw_gi", + "distance": dtw_gi_distance, + "pairwise_distance": dtw_gi_pairwise_distance, + "cost_matrix": dtw_gi_cost_matrix, + "alignment_path": dtw_gi_alignment_path, + "type": DistanceType.ELASTIC, + "symmetric": False, + "unequal_support": True, + }, { "name": "ddtw", "distance": ddtw_distance, diff --git a/aeon/distances/elastic/__init__.py b/aeon/distances/elastic/__init__.py index 0c386245ba..8e5d1aa9dd 100644 --- a/aeon/distances/elastic/__init__.py +++ b/aeon/distances/elastic/__init__.py @@ -10,6 +10,10 @@ "dtw_pairwise_distance", "dtw_cost_matrix", "dtw_alignment_path", + "dtw_gi_distance", + "dtw_gi_pairwise_distance", + "dtw_gi_cost_matrix", + "dtw_gi_alignment_path", "ddtw_distance", "ddtw_pairwise_distance", "ddtw_alignment_path", @@ -71,6 +75,12 @@ dtw_distance, dtw_pairwise_distance, ) +from aeon.distances.elastic._dtw_gi import ( + dtw_gi_alignment_path, + dtw_gi_cost_matrix, + dtw_gi_distance, + dtw_gi_pairwise_distance, +) from aeon.distances.elastic._edr import ( edr_alignment_path, edr_cost_matrix, diff --git a/aeon/distances/elastic/_dtw_gi.py b/aeon/distances/elastic/_dtw_gi.py new file mode 100644 index 0000000000..0026c37725 --- /dev/null +++ b/aeon/distances/elastic/_dtw_gi.py @@ -0,0 +1,551 @@ +r"""Dynamic time warping with Global Invariances (DTW-GI) between two time series.""" + +__maintainer__ = [] + +from typing import Optional, Union + +import numpy as np +from numba import njit +from numba.typed import List as NumbaList + +from aeon.distances.elastic._dtw import dtw_alignment_path, dtw_cost_matrix +from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation.collection import _is_numpy_list_multivariate + + +@njit(cache=True, fastmath=True) +def _path2mat( + path: list[tuple[int, int]], + x_timepoints: int, + y_timepoints: int, +): + r"""Convert a warping alignment path to a binary warping matrix.""" + w = np.zeros((x_timepoints, y_timepoints)) + for i, j in path: + w[i, j] = 1 + return w + + +@njit(cache=True, fastmath=True) +def _dtw_gi( + x: np.ndarray, + y: np.ndarray, + window: Optional[float] = None, + itakura_max_slope: Optional[float] = None, + init_p: np.ndarray = None, + max_iter: int = 20, + use_bias: bool = False, +): + r""" + Compute Dynamic Time Warping with Global Invariance between the two time series. + + Parameters + ---------- + x : np.ndarray + First time series, either univariate, shape ``(n_timepoints,)``, or + multivariate, shape ``(n_channels, n_timepoints)``. + y : np.ndarray + Second time series, either univariate, shape ``(n_timepoints,)``, or + multivariate, shape ``(n_channels, n_timepoints)``. + window : float or None, default=None + The window to use for the bounding matrix. If None, no bounding matrix + is used. window is a percentage deviation, so if ``window = 0.1`` then + 10% of the series length is the max warping allowed. + is used. + itakura_max_slope : float, default=None + Maximum slope as a proportion of the number of time points used to create + Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + init_p : array-like of shape (x_channels, y_channels), default=None + Initial linear transformation. If None, the identity matrix is used. + max_iter : int, default=20 + Maximum number of iterations for the iterative optimization. + use_bias : bool, default=False + If True, the feature space map is affine (with a bias term). + + Returns + ------- + - w_pi: binary warping matrix of shape (n0, n1) + - p: the final linear (Stiefel) matrix of shape (x_channels, y_channels) + - cost: final DTW cost considering global invariances + + If use_bias is True, also returns: + - bias + + """ + if x.ndim == 1 and y.ndim == 1: + x_ = x.reshape((1, x.shape[0])) + y_ = y.reshape((1, y.shape[0])) + elif x.ndim == 2 and y.ndim == 2: + x_ = x + y_ = y + else: + raise ValueError("x and y must be 1D or 2D") + + x_channels, x_timepoints = x_.shape + y_channels, y_timepoints = y_.shape + + x_m = np.sum(x_, axis=1) / x_.shape[1] + x_m = x_m.reshape((-1, 1)) + y_m = np.sum(y_, axis=1) / y_.shape[1] + y_m = y_m.reshape((-1, 1)) + + w_pi = np.zeros((x_timepoints, y_timepoints)) + if init_p is None: + p = np.eye(x_channels, y_channels, dtype=np.float64) + else: + p = init_p + + y_ = y_.astype(np.float64) + x_ = x_.astype(np.float64) + + bias = np.zeros((x_channels, 1)) + + for _ in range(max_iter): + w_pi_old = w_pi.copy() + y_transformed = p.dot(y_) + bias + + path, cost = dtw_alignment_path(x_, y_transformed, window, itakura_max_slope) + w_pi = _path2mat(path, x_timepoints, y_timepoints) + + if np.allclose(w_pi, w_pi_old): + break + + if use_bias: + m = (x_ - x_m).dot(w_pi).dot((y_ - y_m).T) + else: + m = x_.dot(w_pi).dot(y_.T) + + u, sigma, vt = np.linalg.svd(m, full_matrices=False) + p = u.dot(vt) + if use_bias: + bias = x_m - p.dot(y_m) + + y_trans = p.dot(y_) + bias + path, cost = dtw_alignment_path(x_, y_trans, window, itakura_max_slope) + + if use_bias: + return w_pi, p, bias, cost, x_, y_trans + else: + dummy_bias = np.zeros((x_channels, 1), dtype=np.float64) + return w_pi, p, dummy_bias, cost, x_, y_trans + + +@njit(cache=True, fastmath=True) +def dtw_gi_distance( + x: np.ndarray, + y: np.ndarray, + window: Optional[float] = None, + itakura_max_slope: Optional[float] = None, + init_p: np.ndarray = None, + max_iter: int = 20, + use_bias: bool = False, +) -> float: + r"""Compute the DTW_GI distance between two time series x and y. + + The DTW_gi distance between 2 timeseries x and y is the similarity + measure that estimates both temporal alignment and does feature space + transformation between time series simultaneously. This means that the + time series do not have to lie in the same ambient space. + A good background into DTW with global invariances can be found in [1]_. + This implementation is inspired by [2]_. + + For example, if we have two time series x and y of lengths n and m + respectively, and we assume that the time series do not lie in the + same ambient space. Lets assume that features of x lie in :math:`\mathbb{R}^p` + and features of y lie in :math:`\mathbb{R}^q`. To compare the two time series, + we need to find an optimum mapping from the feature space of y to the feature space + where features of x lie. So think of it as optimizing on a family of functions F + that map features from y onto the feature space in which features of x + lie. (This is just one way to do it, the mapping can be + in the opposite direction as well. But this function assumes the former way). + + More formally, we define Dynamic Time Warping with Global Invariances (DTW-GI) + as the solution of the following joint optimization problem: + + :math:`\text{DTW-GI}(\mathbf{x}, \mathbf{y}) = + \min_{f \in \mathcal{F}, \pi \in \mathcal{A}(\mathbf{x}, \mathbf{y})} + \sqrt{\sum_{(i,j) \in \pi} d(x_i, f(y_j))^2},` + + This similarity measure estimates temporal alignment + with feature space transformation between time series + allowing the alignment of time series that do not exist in the + same ambient space. + + Note: The optimal warping path :math:`P^*` for a given cost matrix can be found + exactly through a dynamic programming formulation. This can be a time consuming + operation, and it is common to put a restriction on the amount of warping allowed. + This is implemented through the bounding_matrix structure, that supplies a mask for + allowable warpings. The most common bounding strategies include the + Sakoe-Chiba band [3]_. The width of the allowed warping is controlled through the + ``window`` parameter which sets the maximum proportion of warping allowed. + + Parameters + ---------- + x : np.ndarray + First time series, either univariate, shape ``(n_timepoints,)``, or + multivariate, shape ``(n_channels, n_timepoints)``. + y : np.ndarray + Second time series, either univariate, shape ``(n_timepoints,)``, or + multivariate, shape ``(n_channels, n_timepoints)``. + window : float, default=None + The window to use for the bounding matrix. If None, no bounding matrix + is used. window is a percentage deviation, so if ``window = 0.1``, + 10% of the series length is the max warping allowed. + is used. + itakura_max_slope : float, default=None + Maximum slope as a proportion of the number of time points used to create + Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + init_p : array-like of shape (x_channels, y_channels), default=None + Initial linear transformation. If None, the identity matrix is used. + max_iter : int, default=20 + Maximum number of iterations for the iterative optimization. + use_bias : bool, default=False + If True, the feature space map is affine (with a bias term). + + Returns + ------- + float + DTW_GI distance between x and y, minimum value 0. + + Raises + ------ + ValueError + If x and y are not 1D or 2D arrays. + + References + ---------- + .. [1] T. Vayer, R. Tavenard, L. Chapel, N. Courty, R. Flamary, and Y. Soullard, + “Time Series Alignment with Global Invariances,” arXiv.org, 2020. + https://arxiv.org/abs/2002.03848 + + .. [2] Romain Tavenard, “DTW with Global Invariances,” Github.io, Dec. 17, 2020. + https://rtavenar.github.io/hdr/parts/01/dtw/dtw_gi.html + + .. [3] Sakoe H. and Chiba S.: Dynamic programming algorithm optimization for + spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal + Processing 26(1):43-49, 1978. + + Examples + -------- + >>> import numpy as np + >>> from aeon.distances import dtw_gi_distance + >>> x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) + >>> y = np.array([11, 12, 13, 14, 15, 16, 17, 18, 19, 20]) + >>> dtw_gi_distance(x, y) # 1D series + 768.0 + >>> x = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [0, 1, 0, 2, 0]]) + >>> y = np.array([[11, 12, 13, 14],[7, 8, 9, 20],[1, 3, 4, 5]] ) + >>> round(dtw_gi_distance(x, y), 1) # 2D series with 3 channels, unequal length + 359.2 + """ + if x.ndim == 1 and y.ndim == 1: + _x = x.reshape((1, x.shape[0])) + _y = y.reshape((1, y.shape[0])) + return _dtw_gi(_x, _y, window, itakura_max_slope, init_p, max_iter, use_bias)[3] + if x.ndim == 2 and y.ndim == 2: + return _dtw_gi(x, y, window, itakura_max_slope, init_p, max_iter, use_bias)[3] + raise ValueError("x and y must be 1D or 2D") + + +@njit(cache=True, fastmath=True) +def dtw_gi_cost_matrix( + x: np.ndarray, + y: np.ndarray, + window: Optional[float] = None, + itakura_max_slope: Optional[float] = None, + init_p: np.ndarray = None, + max_iter: int = 20, + use_bias: bool = False, +) -> np.ndarray: + r"""Compute the DTW_GI cost matrix between two time series. + + The cost matrix is the pairwise Euclidean distance between all points + :math:`M_{i,j}=(x_i-y_{\text{trans},j})^2`. Where `y_trans` is the time + series we get by finding the optimal mapping from feature space of y + to feature space where features of x lie. It is used in the DTW GI + path calculations. + + Parameters + ---------- + x : np.ndarray + First time series, either univariate, shape ``(n_timepoints,)``, or + multivariate, shape ``(n_channels, n_timepoints)``. + y : np.ndarray + Second time series, either univariate, shape ``(n_timepoints,)``, or + multivariate, shape ``(n_channels, n_timepoints)``. + window : float, default=None + The window to use for the bounding matrix. If None, no bounding matrix + is used. window is a percentage deviation, so if ``window = 0.1``, + 10% of the series length is the max warping allowed. + is used. + itakura_max_slope : float, default=None + Maximum slope as a proportion of the number of time points used to create + Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + init_p : array-like of shape (x_channels, y_channels), default=None + Initial linear transformation. If None, the identity matrix is used. + max_iter : int, default=20 + Maximum number of iterations for the iterative optimization. + use_bias : bool, default=False + If True, the feature space map is affine (with a bias term). + + Returns + ------- + np.ndarray (n_timepoints, m_timepoints) + dtw gi cost matrix between x and y. + + Raises + ------ + ValueError + If x and y are not 1D or 2D arrays. + + Examples + -------- + >>> import numpy as np + >>> from aeon.distances import dtw_gi_cost_matrix + >>> x = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]) + >>> y = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]) + >>> dtw_gi_cost_matrix(x, y) + array([[ 0., 1., 5., 14., 30., 55., 91., 140., 204., 285.], + [ 1., 0., 1., 5., 14., 30., 55., 91., 140., 204.], + [ 5., 1., 0., 1., 5., 14., 30., 55., 91., 140.], + [ 14., 5., 1., 0., 1., 5., 14., 30., 55., 91.], + [ 30., 14., 5., 1., 0., 1., 5., 14., 30., 55.], + [ 55., 30., 14., 5., 1., 0., 1., 5., 14., 30.], + [ 91., 55., 30., 14., 5., 1., 0., 1., 5., 14.], + [140., 91., 55., 30., 14., 5., 1., 0., 1., 5.], + [204., 140., 91., 55., 30., 14., 5., 1., 0., 1.], + [285., 204., 140., 91., 55., 30., 14., 5., 1., 0.]]) + """ + _, _, _, _, xnew, y_trans = _dtw_gi( + x, y, window, itakura_max_slope, init_p, max_iter, use_bias + ) + + return dtw_cost_matrix(xnew, y_trans, window, itakura_max_slope) + + +def dtw_gi_pairwise_distance( + X: Union[np.ndarray, list[np.ndarray]], + y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, + window: Optional[float] = None, + itakura_max_slope: Optional[float] = None, + unequal_length: bool = None, + init_p: np.ndarray = None, + max_iter: int = 20, + use_bias: bool = False, +) -> np.ndarray: + r"""Compute the DTW_GI pairwise distance between a set of time series. + + By default, this takes a collection of :math:`n` time series :math:`X` and returns a + matrix + :math:`D` where :math:`D_{i,j}` is the DTW_GI distance between the :math:`i^{th}` + and the :math:`j^{th}` series in :math:`X`. If :math:`X` is 2 dimensional, + it is assumed to be a collection of univariate series with shape ``(n_cases, + n_timepoints)``. If it is 3 dimensional, it is assumed to be shape ``(n_cases, + n_channels, n_timepoints)``. + + This function has an optional argument, :math:`y`, to allow calculation of the + distance matrix between :math:`X` and one or more series stored in :math:`y`. If + :math:`y` is 1 dimensional, we assume it is a single univariate series and the + distance matrix returned is shape ``(n_cases,1)``. If it is 2D, we assume it + is a collection of univariate series with shape ``(m_cases, m_timepoints)`` + and the distance ``(n_cases,m_cases)``. If it is 3 dimensional, + it is assumed to be shape ``(m_cases, m_channels, m_timepoints)``. + + Parameters + ---------- + X : np.ndarray or List of np.ndarray + A collection of time series instances of shape ``(n_cases, n_timepoints)`` + or ``(n_cases, n_channels, n_timepoints)``. + y : np.ndarray or List of np.ndarray or None, default=None + A single series or a collection of time series of shape ``(m_timepoints,)`` or + ``(m_cases, m_timepoints)`` or ``(m_cases, m_channels, m_timepoints)``. + If None, then the dtw gi pairwise distance between the instances of X is + calculated. + window : float or None, default=None + The window to use for the bounding matrix. If None, no bounding matrix + is used. + itakura_max_slope : float, default=None + Maximum slope as a proportion of the number of time points used to create + Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + init_p : array-like of shape (x_channels, y_channels), default=None + Initial linear transformation. If None, the identity matrix is used. + max_iter : int, default=20 + Maximum number of iterations for the iterative optimization. + use_bias : bool, default=False + If True, the feature space map is affine (with a bias term). + + Returns + ------- + np.ndarray + DTW_GI pairwise matrix between the instances of X of shape + ``(n_cases, n_cases)`` or between X and y of shape ``(n_cases, + n_cases)``. + + Raises + ------ + ValueError + If X is not 2D or 3D array and if y is not 1D, 2D or 3D arrays when passing y. + + Examples + -------- + >>> import numpy as np + >>> from aeon.distances import dtw_gi_pairwise_distance + >>> # Distance between each time series in a collection of time series + >>> X = np.array([[[1, 2, 3]],[[4, 5, 6]], [[7, 8, 9]]]) + >>> dtw_gi_pairwise_distance(X) + array([[ 0., 26., 108.], + [ 26., 0., 26.], + [108., 26., 0.]]) + + >>> # Distance between two collections of time series + >>> X = np.array([[[1, 2, 3]],[[4, 5, 6]], [[7, 8, 9]]]) + >>> y = np.array([[[11, 12, 13]],[[14, 15, 16]], [[17, 18, 19]]]) + >>> dtw_gi_pairwise_distance(X, y) + array([[300., 507., 768.], + [147., 300., 507.], + [ 48., 147., 300.]]) + + >>> X = np.array([[[1, 2, 3]],[[4, 5, 6]], [[7, 8, 9]]]) + >>> y_univariate = np.array([11, 12, 13]) + >>> dtw_gi_pairwise_distance(X, y_univariate) + array([[300.], + [147.], + [ 48.]]) + + >>> # Distance between each TS in a collection of unequal-length time series + >>> X = [np.array([1, 2, 3]), np.array([4, 5, 6, 7]), np.array([8, 9, 10, 11, 12])] + >>> dtw_gi_pairwise_distance(X) + array([[ 0., 42., 292.], + [ 42., 0., 83.], + [292., 83., 0.]]) + """ + multivariate_conversion = _is_numpy_list_multivariate(X, y) + _X, unequal_length = _convert_collection_to_numba_list( + X, "X", multivariate_conversion + ) + + if y is None: + # To self + return _dtw_gi_pairwise_distance( + _X, window, itakura_max_slope, unequal_length, init_p, max_iter, use_bias + ) + _y, unequal_length = _convert_collection_to_numba_list( + y, "y", multivariate_conversion + ) + return _dtw_gi_from_multiple_to_multiple_distance( + _X, _y, window, itakura_max_slope, unequal_length, init_p, max_iter, use_bias + ) + + +@njit(cache=True, fastmath=True) +def _dtw_gi_from_multiple_to_multiple_distance( + x: NumbaList[np.ndarray], + y: NumbaList[np.ndarray], + window: Optional[float] = None, + itakura_max_slope: Optional[float] = None, + unequal_length: bool = None, + init_p: np.ndarray = None, + max_iter: int = 20, + use_bias: bool = False, +) -> np.ndarray: + n_cases = len(x) + m_cases = len(y) + distances = np.zeros((n_cases, m_cases)) + + for i in range(n_cases): + for j in range(m_cases): + x1, y1 = x[i], y[j] + distances[i, j] = dtw_gi_distance( + x1, y1, window, itakura_max_slope, init_p, max_iter, use_bias + ) + return distances + + +@njit(cache=True, fastmath=True) +def _dtw_gi_pairwise_distance( + X: NumbaList[np.ndarray], + window: Optional[float] = None, + itakura_max_slope: Optional[float] = None, + unequal_length: bool = None, + init_p: np.ndarray = None, + max_iter: int = 20, + use_bias: bool = False, +) -> np.ndarray: + n_cases = len(X) + distances = np.zeros((n_cases, n_cases)) + + for i in range(n_cases): + for j in range(i + 1, n_cases): + x1, x2 = X[i], X[j] + distances[i, j] = dtw_gi_distance( + x1, x2, window, itakura_max_slope, init_p, max_iter, use_bias + ) + distances[j, i] = distances[i, j] + + return distances + + +@njit(cache=True, fastmath=True) +def dtw_gi_alignment_path( + x: np.ndarray, + y: np.ndarray, + window: Optional[float] = None, + itakura_max_slope: Optional[float] = None, + init_p: np.ndarray = None, + max_iter: int = 20, + use_bias: bool = False, +) -> tuple[list[tuple[int, int]], float]: + """Compute the DTW_GI alignment path between two time series. + + Parameters + ---------- + x : np.ndarray + First time series, shape ``(n_channels, n_timepoints)`` or ``(n_timepoints,)``. + y : np.ndarray + Second time series, shape ``(m_channels, m_timepoints)`` or ``(m_timepoints,)``. + window : float, default=None + The window to use for the bounding matrix. If None, no bounding matrix + is used. + itakura_max_slope : float, default=None + Maximum slope as a proportion of the number of time points used to create + Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + init_p : array-like of shape (x_channels, y_channels), default=None + Initial linear transformation. If None, the identity matrix is used. + max_iter : int, default=20 + Maximum number of iterations for the iterative optimization. + use_bias : bool, default=False + If True, the feature space map is affine (with a bias term). + + Returns + ------- + List[Tuple[int, int]] + The alignment path between the two time series where each element is a tuple + of the index in x and the index in y that have the best alignment according + to the cost matrix. + float + The DTW_GI distance betweeen the two time series. + + Raises + ------ + ValueError + If x and y are not 1D or 2D arrays. + + Examples + -------- + >>> import numpy as np + >>> from aeon.distances import dtw_gi_alignment_path + >>> x = np.array([[1, 2, 3, 6]]) + >>> y = np.array([[1, 2, 3, 4]]) + >>> dtw_gi_alignment_path(x, y) + ([(0, 0), (1, 1), (2, 2), (3, 3)], 4.0) + """ + w_pi, _, _, cost, _, _ = _dtw_gi( + x, y, window, itakura_max_slope, init_p, max_iter, use_bias + ) + min_alignment_path = [] + for i in range(len(w_pi)): + for j in range(len(w_pi[0])): + if w_pi[i, j] == 1: + min_alignment_path.append((i, j)) + + return min_alignment_path, cost diff --git a/aeon/distances/elastic/tests/test_distance_correctness.py b/aeon/distances/elastic/tests/test_distance_correctness.py index f33ca088c3..2a0c203b81 100644 --- a/aeon/distances/elastic/tests/test_distance_correctness.py +++ b/aeon/distances/elastic/tests/test_distance_correctness.py @@ -10,6 +10,7 @@ from aeon.distances import ( ddtw_distance, dtw_distance, + dtw_gi_distance, edr_distance, erp_distance, euclidean_distance, @@ -23,6 +24,7 @@ distances = [ "dtw", + "dtw_gi", "wdtw", "lcss", "msm", @@ -36,6 +38,7 @@ distance_parameters = { "dtw": [0.0, 0.1, 1.0], # window + "dtw_gi": [0.0, 0.1, 1.0], # window "wdtw": [0.0, 0.1, 1.0], # parameter g "wddtw": [0.0, 0.1, 1.0], # parameter g "erp": [0.0, 0.1, 1.0], # window @@ -63,6 +66,7 @@ "euclidean": 27.51835240, "squared": 757.25971908652, "dtw": [757.259719, 330.834497, 330.834497], + "dtw_gi": [259.5333502342899, 310.10738471013804, 310.10738471013804], "wdtw": [165.41724, 3.308425, 0], "msm": [70.014828, 89.814828, 268.014828], "erp": [169.3715, 102.0979, 102.097904], @@ -90,6 +94,8 @@ def test_multivariate_correctness(): for j in range(0, 3): d = dtw_distance(case1, case2, window=distance_parameters["dtw"][j]) assert_almost_equal(d, basic_motions_distances["dtw"][j], 4) + d = dtw_gi_distance(case1, case2, window=distance_parameters["dtw_gi"][j]) + assert_almost_equal(d, basic_motions_distances["dtw_gi"][j], 4) d = wdtw_distance(case1, case2, g=distance_parameters["wdtw"][j]) assert_almost_equal(d, basic_motions_distances["wdtw"][j], 4) d = lcss_distance(case1, case2, epsilon=distance_parameters["lcss"][j] / 50.0) diff --git a/aeon/distances/tests/test_distances.py b/aeon/distances/tests/test_distances.py index 3633c9ca30..4efe396ab2 100644 --- a/aeon/distances/tests/test_distances.py +++ b/aeon/distances/tests/test_distances.py @@ -57,6 +57,7 @@ def _validate_distance_result( assert isinstance(dist_result_to_self, float) # If unequal length swap where x and y are to ensure it works both ways around + if symmetric and original_x.shape[-1] != original_y.shape[-1] and check_xy_permuted: _validate_distance_result( original_y, diff --git a/aeon/testing/expected_results/expected_distance_results.py b/aeon/testing/expected_results/expected_distance_results.py index 8e7c6873c5..7126c5c624 100644 --- a/aeon/testing/expected_results/expected_distance_results.py +++ b/aeon/testing/expected_results/expected_distance_results.py @@ -41,6 +41,13 @@ 4.0997661869195205, 25.0, ], + "dtw_gi": [ + 0.344520787316184, + 0.344520787316184, + 0.2998607605839068, + 5.893210968537887, + 25.0, + ], "ddtw": [ 0.2963709096971962, 0.2963709096971962, From 218cf60e36dd9f476af491f2fb7fb53e9cae12f7 Mon Sep 17 00:00:00 2001 From: Tanish Yelgoe <143334319+tanishy7777@users.noreply.github.com> Date: Wed, 19 Mar 2025 22:11:16 +0530 Subject: [PATCH 030/216] [ENH] Adds kdtw kernel support for kernelkmeans (#2645) * Adds kdtw kernel support for kernelkmeans * Code refactor * Adds tests for kdtw clustering * minor changes * minor changes --- aeon/clustering/_kernel_k_means.py | 116 +++++++++++++++++++ aeon/clustering/tests/test_kernel_k_means.py | 24 ++++ 2 files changed, 140 insertions(+) diff --git a/aeon/clustering/_kernel_k_means.py b/aeon/clustering/_kernel_k_means.py index 6aab712def..98cac93127 100644 --- a/aeon/clustering/_kernel_k_means.py +++ b/aeon/clustering/_kernel_k_means.py @@ -3,9 +3,122 @@ from typing import Optional, Union import numpy as np +from numba import njit from numpy.random import RandomState from aeon.clustering.base import BaseClusterer +from aeon.distances.pointwise._squared import squared_pairwise_distance + + +@njit(cache=True, fastmath=True) +def _kdtw_lk(x, y, local_kernel): + channels = np.shape(x)[1] + padding_vector = np.zeros((1, channels)) + + x = np.concatenate((padding_vector, x), axis=0) + y = np.concatenate((padding_vector, y), axis=0) + + x_timepoints, _ = np.shape(x) + y_timepoints, _ = np.shape(y) + + cost_matrix = np.zeros((x_timepoints, y_timepoints)) + cumulative_dp_diag = np.zeros((x_timepoints, y_timepoints)) + diagonal_weights = np.zeros(max(x_timepoints, y_timepoints)) + + min_timepoints = min(x_timepoints, y_timepoints) + diagonal_weights[1] = 1.0 + for i in range(1, min_timepoints): + diagonal_weights[i] = local_kernel[i - 1, i - 1] + + cost_matrix[0, 0] = 1 + cumulative_dp_diag[0, 0] = 1 + + for i in range(1, x_timepoints): + cost_matrix[i, 1] = cost_matrix[i - 1, 1] * local_kernel[i - 1, 2] + cumulative_dp_diag[i, 1] = cumulative_dp_diag[i - 1, 1] * diagonal_weights[i] + + for j in range(1, y_timepoints): + cost_matrix[1, j] = cost_matrix[1, j - 1] * local_kernel[2, j - 1] + cumulative_dp_diag[1, j] = cumulative_dp_diag[1, j - 1] * diagonal_weights[j] + + for i in range(1, x_timepoints): + for j in range(1, y_timepoints): + local_cost = local_kernel[i - 1, j - 1] + cost_matrix[i, j] = ( + cost_matrix[i - 1, j] + + cost_matrix[i, j - 1] + + cost_matrix[i - 1, j - 1] + ) * local_cost + if i == j: + cumulative_dp_diag[i, j] = ( + cumulative_dp_diag[i - 1, j - 1] * local_cost + + cumulative_dp_diag[i - 1, j] * diagonal_weights[i] + + cumulative_dp_diag[i, j - 1] * diagonal_weights[j] + ) + else: + cumulative_dp_diag[i, j] = ( + cumulative_dp_diag[i - 1, j] * diagonal_weights[i] + + cumulative_dp_diag[i, j - 1] * diagonal_weights[j] + ) + cost_matrix = cost_matrix + cumulative_dp_diag + return cost_matrix[x_timepoints - 1, y_timepoints - 1] + + +def kdtw(x, y, sigma=1.0, epsilon=1e-3): + """ + Callable kernel function for KernelKMeans. + + Parameters + ---------- + X: np.ndarray, of shape (n_timepoints, n_channels) + First time series sample. + y: np.ndarray, of shape (n_timepoints, n_channels) + Second time series sample. + sigma : float, default=1.0 + Parameter controlling the width of the exponential local kernel. Smaller sigma + values lead to a sharper decay of similarity with increasing distance. + epsilon : float, default=1e-3 + A small constant added for numerical stability to avoid zero values in the + local kernel matrix. + + Returns + ------- + similarity : float + A scalar value representing the computed KDTW similarity between the two time + series. Higher values indicate greater similarity. + """ + distance = squared_pairwise_distance(x, y) + local_kernel = (np.exp(-distance / sigma) + epsilon) / (3 * (1 + epsilon)) + return _kdtw_lk(x, y, local_kernel) + + +def factory_kdtw_kernel(channels: int): + """ + Return a kdtw kernel callable function that flattened samples to (T, channels). + + Parameters + ---------- + channels: int + Number of channels per timepoint. + + Returns + ------- + kdtw_kernel : callable + A callable kernel function that computes the KDTW similarity between two + time series samples. The function signature is the same as the kdtw + function. + """ + + def kdtw_kernel(x, y, sigma=1.0, epsilon=1e-3): + if x.ndim == 1: + T = x.size // channels + x = x.reshape(T, channels) + if y.ndim == 1: + T = y.size // channels + y = y.reshape(T, channels) + return kdtw(x, y, sigma=sigma, epsilon=epsilon) + + return kdtw_kernel class TimeSeriesKernelKMeans(BaseClusterer): @@ -141,6 +254,9 @@ def _fit(self, X, y=None): if self.verbose is True: verbose = 1 + if self.kernel == "kdtw": + self.kernel = factory_kdtw_kernel(channels=X.shape[1]) + self._tslearn_kernel_k_means = TsLearnKernelKMeans( n_clusters=self.n_clusters, kernel=self.kernel, diff --git a/aeon/clustering/tests/test_kernel_k_means.py b/aeon/clustering/tests/test_kernel_k_means.py index f4af21f4f5..36a761a469 100644 --- a/aeon/clustering/tests/test_kernel_k_means.py +++ b/aeon/clustering/tests/test_kernel_k_means.py @@ -13,6 +13,12 @@ expected_results = [0, 0, 0, 0, 0] +expected_labels_kdtw = [0, 0, 0, 1, 2] + +expected_iters_kdtw = 2 + +expected_results_kdtw = [0, 2, 0, 0, 0] + @pytest.mark.skipif( not _check_estimator_deps(TimeSeriesKernelKMeans, severity="none"), @@ -37,3 +43,21 @@ def test_kernel_k_means(): for val in proba: assert np.count_nonzero(val == 1.0) == 1 + + kernel_kmeans_kdtw = TimeSeriesKernelKMeans( + kernel="kdtw", + random_state=1, + n_clusters=3, + kernel_params={"sigma": 2.0, "epsilon": 1e-4}, + ) + kernel_kmeans_kdtw.fit(X_train[0:max_train]) + kdtw_results = kernel_kmeans_kdtw.predict(X_test[0:max_train]) + kdtw_proba = kernel_kmeans_kdtw.predict_proba(X_test[0:max_train]) + + assert np.array_equal(kdtw_results, expected_results_kdtw) + assert kernel_kmeans_kdtw.n_iter_ == expected_iters_kdtw + assert np.array_equal(kernel_kmeans_kdtw.labels_, expected_labels_kdtw) + assert kdtw_proba.shape == (max_train, 3) + + for val in kdtw_proba: + assert np.count_nonzero(val == 1.0) == 1 From 3375173381df069090a5e3f00a0eeb6f7bf394a8 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Thu, 20 Mar 2025 10:27:34 +0000 Subject: [PATCH 031/216] [MNT] Skip some excected results tests when numba is disabled (#2639) * skip some numba tests * Empty commit for CI * Update testing_config.py --------- Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> --- aeon/testing/testing_config.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/aeon/testing/testing_config.py b/aeon/testing/testing_config.py index 3d05d6679d..18fc7043d9 100644 --- a/aeon/testing/testing_config.py +++ b/aeon/testing/testing_config.py @@ -52,7 +52,7 @@ "RSASTClassifier": ["check_fit_deterministic"], "SAST": ["check_fit_deterministic"], "RSAST": ["check_fit_deterministic"], - "MatrixProfile": ["check_persistence_via_pickle"], + "MatrixProfile": ["check_fit_deterministic", "check_persistence_via_pickle"], # missed in legacy testing, changes state in predict/transform "FLUSSSegmenter": ["check_non_state_changing_method"], "InformationGainSegmenter": ["check_non_state_changing_method"], @@ -73,6 +73,10 @@ EXCLUDED_TESTS_NO_NUMBA = { # See issue #622 "HIVECOTEV2": ["check_classifier_against_expected_results"], + # Other failures + "TemporalDictionaryEnsemble": ["check_classifier_against_expected_results"], + "OrdinalTDE": ["check_classifier_against_expected_results"], + "CanonicalIntervalForestRegressor": ["check_regressor_against_expected_results"], } From c4fa18543ef2717e612e422943062b89c2db12af Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Thu, 20 Mar 2025 12:23:00 +0000 Subject: [PATCH 032/216] [MNT] Remove REDCOMETs from testing exclusion list (#2630) * remove excluded estimators * redcomets fix --- aeon/classification/dictionary_based/_redcomets.py | 6 ++---- aeon/testing/testing_config.py | 1 - 2 files changed, 2 insertions(+), 5 deletions(-) diff --git a/aeon/classification/dictionary_based/_redcomets.py b/aeon/classification/dictionary_based/_redcomets.py index f286467416..601f573bdf 100644 --- a/aeon/classification/dictionary_based/_redcomets.py +++ b/aeon/classification/dictionary_based/_redcomets.py @@ -255,8 +255,7 @@ def _build_univariate_ensemble(self, X, y): sfa_clfs = [] for sfa in sfa_transforms: sfa.fit(X_smote, y_smote) - sfa_dics = sfa.transform_words(X_smote) - X_sfa = sfa_dics[:, 0, :] + X_sfa = sfa.transform_words(X_smote)[0] rf = RandomForestClassifier( n_estimators=self.n_trees, @@ -417,8 +416,7 @@ def _predict_proba_unvivariate(self, X) -> np.ndarray: pred_mat = np.zeros((X.shape[0], self.n_classes_)) for sfa, (rf, weight) in zip(self.sfa_transforms, self.sfa_clfs): - sfa_dics = sfa.transform_words(X) - X_sfa = sfa_dics[:, 0, :] + X_sfa = sfa.transform_words(X)[0] rf_pred_mat = rf.predict_proba(X_sfa) diff --git a/aeon/testing/testing_config.py b/aeon/testing/testing_config.py index 18fc7043d9..be7d04f0a7 100644 --- a/aeon/testing/testing_config.py +++ b/aeon/testing/testing_config.py @@ -24,7 +24,6 @@ # exclude estimators here for short term fixes EXCLUDE_ESTIMATORS = [ - "REDCOMETS", "HydraTransformer", # returns a pytorch Tensor ] From e6020acbc9ad9501c3d97cf58227973a39f90d7b Mon Sep 17 00:00:00 2001 From: Aryan Pola <98093778+aryanpola@users.noreply.github.com> Date: Sat, 22 Mar 2025 02:47:24 +0530 Subject: [PATCH 033/216] [ENH] Replace `prts` metrics (#2400) * Pre-commit fixes * Position parameter in calculate_bias * Added recall metric * merged into into one file * test added * Changes in test and range_metrics * list of list running but error! * flattening lists, all cases passed * Empty-Commit * changes * Protected functions * Changes in documentation * Changed test cases into seperate functions * test cases added and added range recall * udf_gamma removed from precision * changes * more changes * recommended changes * changes * Added Parameters * removed udf_gamma from precision * Added binary to range * error fixing * test comparing prts and range_metrics * Beta parameter added in fscore * Added udf_gamma function * f-score failing when comparing against prts * fixed f-score output * alpha usage * Empty-Commit * added test case to use range-based input for metrics * soft dependency added * doc update --------- Co-authored-by: Matthew Middlehurst Co-authored-by: Sebastian Schmidl <10573700+SebastianSchmidl@users.noreply.github.com> --- .../metrics/anomaly_detection/__init__.py | 8 + .../anomaly_detection/range_metrics.py | 521 +++++++++++++++++ .../anomaly_detection/tests/test_metrics.py | 537 ++++++++++++++++++ 3 files changed, 1066 insertions(+) create mode 100644 aeon/benchmarking/metrics/anomaly_detection/range_metrics.py create mode 100644 aeon/benchmarking/metrics/anomaly_detection/tests/test_metrics.py diff --git a/aeon/benchmarking/metrics/anomaly_detection/__init__.py b/aeon/benchmarking/metrics/anomaly_detection/__init__.py index fdbf13cec9..cf6ccac42c 100644 --- a/aeon/benchmarking/metrics/anomaly_detection/__init__.py +++ b/aeon/benchmarking/metrics/anomaly_detection/__init__.py @@ -14,6 +14,9 @@ "range_pr_auc_score", "range_pr_vus_score", "range_roc_vus_score", + "ts_precision", + "ts_recall", + "ts_fscore", ] from aeon.benchmarking.metrics.anomaly_detection._binary import ( @@ -35,3 +38,8 @@ range_roc_auc_score, range_roc_vus_score, ) +from aeon.benchmarking.metrics.anomaly_detection.range_metrics import ( + ts_fscore, + ts_precision, + ts_recall, +) diff --git a/aeon/benchmarking/metrics/anomaly_detection/range_metrics.py b/aeon/benchmarking/metrics/anomaly_detection/range_metrics.py new file mode 100644 index 0000000000..9084188f59 --- /dev/null +++ b/aeon/benchmarking/metrics/anomaly_detection/range_metrics.py @@ -0,0 +1,521 @@ +"""Calculate Precision, Recall, and F1-Score for time series anomaly detection.""" + +__maintainer__ = [] +__all__ = ["ts_precision", "ts_recall", "ts_fscore"] + +import numpy as np + + +def _flatten_ranges(ranges): + """ + If the input is a list of lists, it flattens it into a single list. + + Parameters + ---------- + ranges : list of tuples or list of lists of tuples + The ranges to flatten. each tuple shoulod be in the format of (start, end). + + Returns + ------- + list of tuples + A flattened list of ranges. + + Examples + -------- + >>> _flatten_ranges([[(1, 5), (10, 15)], [(20, 25)]]) + [(1, 5), (10, 15), (20, 25)] + """ + if not ranges: + return [] + if isinstance(ranges[0], list): + flat = [] + for sublist in ranges: + for pred in sublist: + flat.append(pred) + return flat + return ranges + + +def udf_gamma_def(overlap_count): + """User-defined gamma function. Should return a gamma value > 1. + + Parameters + ---------- + overlap_count : int + The number of overlapping ranges. + + Returns + ------- + float + The user-defined gamma value (>1). + """ + return_val = 1 + 0.1 * overlap_count # modify this function as needed + + return return_val + + +def _calculate_bias(position, length, bias_type="flat"): + """Calculate bias value based on position and length. + + Parameters + ---------- + position : int + Current position in the range + length : int + Total length of the range + bias_type : str, default="flat" + Type of bias to apply, Should be one of ["flat", "front", "middle", "back"]. + """ + if bias_type == "flat": + return 1.0 + elif bias_type == "front": + return 1.0 - (position - 1) / length + elif bias_type == "middle": + if length / 2 == 0: + return 1.0 + if position <= length / 2: + return position / (length / 2) + else: + return (length - position + 1) / (length / 2) + elif bias_type == "back": + return position / length + else: + raise ValueError(f"Invalid bias type: {bias_type}") + + +def _gamma_select(cardinality, gamma): + """Select a gamma value based on the cardinality type. + + Parameters + ---------- + cardinality : int + The number of overlapping ranges. + gamma : str + Gamma to use. Should be one of ["one", "reciprocal", "udf_gamma"]. + + Returns + ------- + float + The selected gamma value. + + Raises + ------ + ValueError + If an invalid `gamma` type is provided or if `udf_gamma` is required + but not provided. + """ + if gamma == "one": + return 1.0 + elif gamma == "reciprocal": + return 1 / cardinality if cardinality > 1 else 1.0 + elif gamma == "udf_gamma": + if udf_gamma_def(cardinality) is not None: + return 1.0 / udf_gamma_def(cardinality) + else: + raise ValueError("udf_gamma must be provided for 'udf_gamma' gamma type.") + else: + raise ValueError( + "Invalid gamma type. Choose from ['one', 'reciprocal', 'udf_gamma']." + ) + + +def _calculate_overlap_reward_precision(pred_range, overlap_set, bias_type): + """Overlap Reward for y_pred. + + Parameters + ---------- + pred_range : tuple + The predicted range. + overlap_set : set + The set of overlapping positions. + bias_type : str + Type of bias to apply, Should be one of ["flat", "front", "middle", "back"]. + + Returns + ------- + float + The weighted value for overlapping positions only. + """ + start, end = pred_range + length = end - start + 1 + + max_value = 0 # Total possible weighted value for all positions. + my_value = 0 # Weighted value for overlapping positions only. + + for i in range(1, length + 1): + global_position = start + i - 1 + bias_value = _calculate_bias(i, length, bias_type) + max_value += bias_value + + if global_position in overlap_set: + my_value += bias_value + + return my_value / max_value if max_value > 0 else 0.0 + + +def _calculate_overlap_reward_recall(real_range, overlap_set, bias_type): + """Overlap Reward for y_real. + + Parameters + ---------- + real_range : tuple + The real range. + overlap_set : set + The set of overlapping positions. + bias_type : str + Type of bias to apply, Should be one of ["flat", "front", "middle", "back"]. + + Returns + ------- + float + The weighted value for overlapping positions only. + """ + start, end = real_range + length = end - start + 1 + + max_value = 0.0 # Total possible weighted value for all positions. + my_value = 0.0 # Weighted value for overlapping positions only. + + for i in range(1, length + 1): + global_position = start + i - 1 + bias_value = _calculate_bias(i, length, bias_type) + max_value += bias_value + + if global_position in overlap_set: + my_value += bias_value + + return my_value / max_value if max_value > 0 else 0.0 + + +def _binary_to_ranges(binary_sequence): + """ + Convert a binary sequence to a list of anomaly ranges. + + Parameters + ---------- + binary_sequence : list + Binary sequence where 1 indicates anomaly and 0 indicates normal. + + Returns + ------- + list of tuples + List of anomaly ranges as (start, end) tuples. + + """ + ranges = [] + start = None + + for i, val in enumerate(binary_sequence): + if val and start is None: + start = i + elif not val and start is not None: + ranges.append((start, i - 1)) + start = None + + if start is not None: + ranges.append((start, len(binary_sequence) - 1)) + + return ranges + + +def ts_precision(y_pred, y_real, gamma="one", bias_type="flat"): + """ + Calculate Precision for time series anomaly detection. + + Precision measures the proportion of correctly predicted anomaly positions + out of all all the predicted anomaly positions, aggregated across the entire time + series. + + Parameters + ---------- + y_pred : list of tuples or binary sequence + The predicted anomaly ranges. + - For range-based input, each tuple represents a range (start, end) of the + anomaly where start is starting index (inclusive) and end is ending index + (inclusive). + - For binary inputs, the sequence should contain integers (0 or 1), where 1 + indicates an anomaly. In this case, y_pred and y_real must be of same length. + y_real : list of tuples, list of lists of tuples or binary sequence + The real/actual (ground truth) ranges. + - For range-based input, each tuple represents a range (start, end) of the + anomaly where start is starting index (inclusive) and end is ending index + (inclusive). + - If y_real is in the format of list of lists, they will be flattened into a + single list of tuples bringing it to the above format. + - For binary inputs, the sequence should contain integers (0 or 1), where 1 + indicates an anomaly. In this case, y_pred and y_real must be of same length. + bias_type : str, default="flat" + Type of bias to apply. Should be one of ["flat", "front", "middle", "back"]. + gamma : str, default="one" + Cardinality type. Should be one of ["reciprocal", "one"]. + + Returns + ------- + float + Precision + + Raises + ------ + ValueError + If an invalid `gamma` type is provided. + ValueError + If input sequence is binary and y_real and y_pred are of different lengths. + + References + ---------- + .. [1] Tatbul, Nesime, Tae Jun Lee, Stan Zdonik, Mejbah Alam,and Justin Gottschlich. + "Precision and Recall for Time Series." 32nd Conference on Neural Information + Processing Systems (NeurIPS 2018), Montréal, Canada. + http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series.pdf + """ + # Check if inputs are binary or range-based + is_binary = False + if isinstance(y_pred, (list, tuple, np.ndarray)) and isinstance( + y_pred[0], (int, np.integer) + ): + is_binary = True + elif isinstance(y_real, (list, tuple, np.ndarray)) and isinstance( + y_real[0], (int, np.integer) + ): + is_binary = True + + if is_binary: + if not isinstance(y_pred, (list, tuple, np.ndarray)) or not isinstance( + y_real, (list, tuple, np.ndarray) + ): + raise ValueError( + "For binary inputs, y_pred and y_real should be list or tuple, " + "or numpy array of integers." + ) + if len(y_pred) != len(y_real): + raise ValueError( + "For binary inputs, y_pred and y_real must be of the same length." + ) + + y_pred_ranges = _binary_to_ranges(y_pred) + y_real_ranges = _binary_to_ranges(y_real) + else: + y_pred_ranges = y_pred + y_real_ranges = y_real + + if gamma not in ["reciprocal", "one"]: + raise ValueError("Invalid gamma type for precision. Use 'reciprocal' or 'one'.") + + # Flattening y_pred and y_real to resolve nested lists + flat_y_pred = _flatten_ranges(y_pred_ranges) + flat_y_real = _flatten_ranges(y_real_ranges) + + total_overlap_reward = 0.0 + total_cardinality = 0 + + for pred_range in flat_y_pred: + overlap_set = set() + cardinality = 0 + + for real_start, real_end in flat_y_real: + overlap_start = max(pred_range[0], real_start) + overlap_end = min(pred_range[1], real_end) + + if overlap_start <= overlap_end: + overlap_set.update(range(overlap_start, overlap_end + 1)) + cardinality += 1 + + overlap_reward = _calculate_overlap_reward_precision( + pred_range, overlap_set, bias_type + ) + gamma_value = _gamma_select(cardinality, gamma) + total_overlap_reward += gamma_value * overlap_reward + total_cardinality += 1 + + precision = ( + total_overlap_reward / total_cardinality if total_cardinality > 0 else 0.0 + ) + return precision + + +def ts_recall(y_pred, y_real, gamma="one", bias_type="flat", alpha=0.0): + """ + Calculate Recall for time series anomaly detection. + + Recall measures the proportion of correctly predicted anomaly positions + out of all the real/actual (ground truth) anomaly positions, aggregated across the + entire time series. + + Parameters + ---------- + y_pred : list of tuples or binary sequence + The predicted anomaly ranges. + - For range-based input, each tuple represents a range (start, end) of the + anomaly where start is starting index (inclusive) and end is ending index + (inclusive). + - For binary inputs, the sequence should contain integers (0 or 1), where 1 + indicates an anomaly. In this case, y_pred and y_real must be of same length. + y_real : list of tuples, list of lists of tuples or binary sequence + The real/actual (ground truth) ranges. + - For range-based input, each tuple represents a range (start, end) of the + anomaly where start is starting index (inclusive) and end is ending index + (inclusive). + - If y_real is in the format of list of lists, they will be flattened into a + single list of tuples bringing it to the above format. + - For binary inputs, the sequence should contain integers (0 or 1), where 1 + indicates an anomaly. In this case, y_pred and y_real must be of same length. + gamma : str, default="one" + Cardinality type. Should be one of ["reciprocal", "one", "udf_gamma"]. + bias_type : str, default="flat" + Type of bias to apply. Should be one of ["flat", "front", "middle", "back"]. + alpha : float, default: 0.0 + Weight for existence reward in recall calculation. + + Returns + ------- + float + Recall + + Raises + ------ + ValueError + If input sequence is binary and y_real and y_pred are of different lengths. + + References + ---------- + .. [1] Tatbul, Nesime, Tae Jun Lee, Stan Zdonik, Mejbah Alam,and Justin Gottschlich. + "Precision and Recall for Time Series." 32nd Conference on Neural Information + Processing Systems (NeurIPS 2018), Montréal, Canada. + http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series.pdf + """ + is_binary = False + if isinstance(y_pred, (list, tuple, np.ndarray)) and isinstance( + y_pred[0], (int, np.integer) + ): + is_binary = True + elif isinstance(y_real, (list, tuple, np.ndarray)) and isinstance( + y_real[0], (int, np.integer) + ): + is_binary = True + + if is_binary: + if not isinstance(y_pred, (list, tuple, np.ndarray)) or not isinstance( + y_real, (list, tuple, np.ndarray) + ): + raise ValueError( + "For binary inputs, y_pred and y_real should be list or tuple, " + "or numpy array of integers." + ) + if len(y_pred) != len(y_real): + raise ValueError( + "For binary inputs, y_pred and y_real must be of the same length." + ) + + y_pred_ranges = _binary_to_ranges(y_pred) + y_real_ranges = _binary_to_ranges(y_real) + else: + y_pred_ranges = y_pred + y_real_ranges = y_real + + # Flattening y_pred and y_real to resolve nested lists + flat_y_pred = _flatten_ranges(y_pred_ranges) + flat_y_real = _flatten_ranges(y_real_ranges) + + total_overlap_reward = 0.0 + + for real_range in flat_y_real: + overlap_set = set() + cardinality = 0 + + for pred_range in flat_y_pred: + overlap_start = max(real_range[0], pred_range[0]) + overlap_end = min(real_range[1], pred_range[1]) + + if overlap_start <= overlap_end: + overlap_set.update(range(overlap_start, overlap_end + 1)) + cardinality += 1 + + existence_reward = 1.0 if overlap_set else 0.0 + + if overlap_set: + overlap_reward = _calculate_overlap_reward_recall( + real_range, overlap_set, bias_type + ) + gamma_value = _gamma_select(cardinality, gamma) + overlap_reward *= gamma_value + else: + overlap_reward = 0.0 + + recall_score = alpha * existence_reward + (1 - alpha) * overlap_reward + total_overlap_reward += recall_score + + recall = total_overlap_reward / len(flat_y_real) if flat_y_real else 0.0 + return recall + + +def ts_fscore( + y_pred, + y_real, + gamma="one", + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + beta=1.0, +): + """ + Calculate F1-Score for time series anomaly detection. + + F-1 Score is the harmonic mean of Precision and Recall, providing + a single metric to evaluate the performance of an anomaly detection model. + + Parameters + ---------- + y_pred : list of tuples or binary sequence + The predicted anomaly ranges. + - For range-based input, each tuple represents a range (start, end) of the + anomaly where start is starting index (inclusive) and end is ending index + (inclusive). + - For binary inputs, the sequence should contain integers (0 or 1), where 1 + indicates an anomaly. In this case, y_pred and y_real must be of same length. + y_real : list of tuples, list of lists of tuples or binary sequence + The real/actual (ground truth) ranges. + - For range-based input, each tuple represents a range (start, end) of the + anomaly where start is starting index (inclusive) and end is ending index + (inclusive). + - If y_real is in the format of list of lists, they will be flattened into a + single list of tuples bringing it to the above format. + - For binary inputs, the sequence should contain integers (0 or 1), where 1 + indicates an anomaly. In this case, y_pred and y_real must be of same length. + gamma : str, default="one" + Cardinality type. Should be one of ["reciprocal", "one", "udf_gamma"]. + p_bias : str, default="flat" + Type of bias to apply for precision. + Should be one of ["flat", "front", "middle", "back"]. + r_bias : str, default="flat" + Type of bias to apply for recall. + Should be one of ["flat", "front", "middle", "back"]. + p_alpha : float, default=0.0 + Weight for existence reward in Precision calculation. + r_alpha : float, default=0.0 + Weight for existence reward in Recall calculation. + beta : float, default=1.0 + F-score beta determines the weight of recall in the combined score. + beta < 1 lends more weight to precision, while beta > 1 favors recall. + + Returns + ------- + float + F1-Score + + References + ---------- + .. [1] Tatbul, Nesime, Tae Jun Lee, Stan Zdonik, Mejbah Alam,and Justin Gottschlich. + "Precision and Recall for Time Series." 32nd Conference on Neural Information + Processing Systems (NeurIPS 2018), Montréal, Canada. + http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series.pdf + """ + precision = ts_precision(y_pred, y_real, gamma, p_bias) + recall = ts_recall(y_pred, y_real, gamma, r_bias, r_alpha) + + if precision + recall > 0: + fscore = ((1 + beta**2) * (precision * recall)) / (beta**2 * precision + recall) + else: + fscore = 0.0 + + return fscore diff --git a/aeon/benchmarking/metrics/anomaly_detection/tests/test_metrics.py b/aeon/benchmarking/metrics/anomaly_detection/tests/test_metrics.py new file mode 100644 index 0000000000..61db5ded3a --- /dev/null +++ b/aeon/benchmarking/metrics/anomaly_detection/tests/test_metrics.py @@ -0,0 +1,537 @@ +"""Test cases for the range-based anomaly detection metrics.""" + +import numpy as np +import pytest + +from aeon.benchmarking.metrics.anomaly_detection import ( + range_f_score, + range_precision, + range_recall, +) +from aeon.benchmarking.metrics.anomaly_detection.range_metrics import ( + _binary_to_ranges, + ts_fscore, + ts_precision, + ts_recall, +) +from aeon.utils.validation._dependencies import _check_soft_dependencies + + +@pytest.mark.skipif( + not _check_soft_dependencies("prts", severity="none"), + reason="required soft dependency prts not available", +) +def test_single_overlapping_range(): + """Test for single overlapping range.""" + y_pred_bin = np.array([0, 1, 1, 1, 1, 0, 0]) + y_real_bin = np.array([0, 0, 1, 1, 1, 1, 1]) + + precision = ts_precision(y_pred_bin, y_real_bin, gamma="one", bias_type="flat") + recall = ts_recall(y_pred_bin, y_real_bin, gamma="one", bias_type="flat", alpha=0.0) + f1_score = ts_fscore( + y_pred_bin, + y_real_bin, + gamma="one", + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + rb_prec = range_precision(y_real_bin, y_pred_bin, cardinality="one", bias="flat") + rb_rec = range_recall(y_real_bin, y_pred_bin, cardinality="one", bias="flat") + rb_fsc = range_f_score( + y_real_bin, + y_pred_bin, + beta=1, + cardinality="one", + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision, + rb_prec, + decimal=6, + err_msg=f"Precision mismatch: ts_precision={precision} vs prts={rb_prec}", + ) + np.testing.assert_almost_equal( + recall, + rb_rec, + decimal=6, + err_msg=f"Recall mismatch: ts_recall={recall} vs prts={rb_rec}", + ) + np.testing.assert_almost_equal( + f1_score, + rb_fsc, + decimal=6, + err_msg=f"F1-Score mismatch: ts_fscore={f1_score} vs prts={rb_fsc}", + ) + + +@pytest.mark.skipif( + not _check_soft_dependencies("prts", severity="none"), + reason="required soft dependency prts not available", +) +def test_multiple_non_overlapping_ranges(): + """Test for multiple non-overlapping ranges.""" + y_pred_bin = np.array([0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0]) + y_real_bin = np.array([0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1]) + + precision = ts_precision(y_pred_bin, y_real_bin, gamma="one", bias_type="flat") + recall = ts_recall(y_pred_bin, y_real_bin, gamma="one", bias_type="flat", alpha=0.0) + f1_score = ts_fscore( + y_pred_bin, + y_real_bin, + gamma="one", + beta=1, + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + rb_prec = range_precision(y_real_bin, y_pred_bin, cardinality="one", bias="flat") + rb_rec = range_recall(y_real_bin, y_pred_bin, cardinality="one", bias="flat") + rb_fsc = range_f_score( + y_real_bin, + y_pred_bin, + beta=1, + cardinality="one", + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision, + rb_prec, + decimal=6, + err_msg=f"Precision mismatch: ts_precision={precision} vs prts={rb_prec}", + ) + np.testing.assert_almost_equal( + recall, + rb_rec, + decimal=6, + err_msg=f"Recall mismatch: ts_recall={recall} vs prts={rb_rec}", + ) + np.testing.assert_almost_equal( + f1_score, + rb_fsc, + decimal=6, + err_msg=f"F1-Score mismatch: ts_fscore={f1_score} vs prts={rb_fsc}", + ) + + +@pytest.mark.skipif( + not _check_soft_dependencies("prts", severity="none"), + reason="required soft dependency prts not available", +) +def test_multiple_overlapping_ranges(): + """Test for multiple overlapping ranges.""" + y_pred_bin = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]) + y_real_bin = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]) + + precision = ts_precision(y_pred_bin, y_real_bin, gamma="one", bias_type="flat") + recall = ts_recall(y_pred_bin, y_real_bin, gamma="one", bias_type="flat", alpha=0.0) + f1_score = ts_fscore( + y_pred_bin, + y_real_bin, + gamma="one", + beta=1, + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + rb_prec = range_precision(y_real_bin, y_pred_bin, cardinality="one", bias="flat") + rb_rec = range_recall(y_real_bin, y_pred_bin, cardinality="one", bias="flat") + rb_fsc = range_f_score( + y_real_bin, + y_pred_bin, + beta=1, + cardinality="one", + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision, + rb_prec, + decimal=6, + err_msg=f"Precision mismatch: ts_precision={precision} vs prts={rb_prec}", + ) + np.testing.assert_almost_equal( + recall, + rb_rec, + decimal=6, + err_msg=f"Recall mismatch: ts_recall={recall} vs prts={rb_rec}", + ) + np.testing.assert_almost_equal( + f1_score, + rb_fsc, + decimal=6, + err_msg=f"F1-Score mismatch: ts_fscore={f1_score} vs prts={rb_fsc}", + ) + + +@pytest.mark.skipif( + not _check_soft_dependencies("prts", severity="none"), + reason="required soft dependency prts not available", +) +def test_nested_lists_of_predictions(): + """Test for nested lists of predictions.""" + y_pred_bin = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1]) + y_real_bin = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0]) + + precision = ts_precision(y_pred_bin, y_real_bin, gamma="one", bias_type="flat") + recall = ts_recall(y_pred_bin, y_real_bin, gamma="one", bias_type="flat", alpha=0.0) + f1_score = ts_fscore( + y_pred_bin, + y_real_bin, + gamma="one", + beta=1, + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + rb_prec = range_precision(y_real_bin, y_pred_bin, cardinality="one", bias="flat") + rb_rec = range_recall(y_real_bin, y_pred_bin, cardinality="one", bias="flat") + rb_fsc = range_f_score( + y_real_bin, + y_pred_bin, + beta=1, + cardinality="one", + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision, + rb_prec, + decimal=6, + err_msg=f"Precision mismatch: ts_precision={precision} vs prts={rb_prec}", + ) + np.testing.assert_almost_equal( + recall, + rb_rec, + decimal=6, + err_msg=f"Recall mismatch: ts_recall={recall} vs prts={rb_rec}", + ) + np.testing.assert_almost_equal( + f1_score, + rb_fsc, + decimal=6, + err_msg=f"F1-Score mismatch: ts_fscore={f1_score} vs prts={rb_fsc}", + ) + + +@pytest.mark.skipif( + not _check_soft_dependencies("prts", severity="none"), + reason="required soft dependency prts not available", +) +def test_all_encompassing_range(): + """Test for all encompassing range.""" + y_pred_bin = np.array([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) + y_real_bin = np.array([0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0]) + + precision = ts_precision(y_pred_bin, y_real_bin, gamma="one", bias_type="flat") + recall = ts_recall(y_pred_bin, y_real_bin, gamma="one", bias_type="flat", alpha=0.0) + f1_score = ts_fscore( + y_pred_bin, + y_real_bin, + gamma="one", + beta=1, + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + rb_prec = range_precision(y_real_bin, y_pred_bin, cardinality="one", bias="flat") + rb_rec = range_recall(y_real_bin, y_pred_bin, cardinality="one", bias="flat") + rb_fsc = range_f_score( + y_real_bin, + y_pred_bin, + beta=1, + cardinality="one", + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision, + rb_prec, + decimal=6, + err_msg=f"Precision mismatch: ts_precision={precision} vs prts={rb_prec}", + ) + np.testing.assert_almost_equal( + recall, + rb_rec, + decimal=6, + err_msg=f"Recall mismatch: ts_recall={recall} vs prts={rb_rec}", + ) + np.testing.assert_almost_equal( + f1_score, + rb_fsc, + decimal=6, + err_msg=f"F1-Score mismatch: ts_fscore={f1_score} vs prts={rb_fsc}", + ) + + +@pytest.mark.skipif( + not _check_soft_dependencies("prts", severity="none"), + reason="required soft dependency prts not available", +) +def test_binary_input_example(): + """Comparing prts package(_binary.py) and range-based metrics(range_metrics.py).""" + y_pred_binary = np.array([0, 1, 1, 0]) + y_true_binary = np.array([0, 1, 0, 0]) + + precision = ts_precision( + y_pred_binary, y_true_binary, gamma="reciprocal", bias_type="flat" + ) + recall = ts_recall( + y_pred_binary, y_true_binary, gamma="reciprocal", bias_type="flat", alpha=0.0 + ) + f1_score = ts_fscore( + y_pred_binary, + y_true_binary, + beta=1, + gamma="reciprocal", + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + rb_prec = range_precision( + y_true_binary, y_pred_binary, cardinality="reciprocal", bias="flat" + ) + rb_rec = range_recall( + y_true_binary, y_pred_binary, cardinality="reciprocal", bias="flat" + ) + rb_fsc = range_f_score( + y_true_binary, + y_pred_binary, + beta=1, + cardinality="reciprocal", + p_bias="flat", + r_bias="flat", + ) + + np.testing.assert_almost_equal( + precision, + rb_prec, + decimal=6, + err_msg=f"Precision mismatch: ts_precision={precision} vs prts={rb_prec}", + ) + np.testing.assert_almost_equal( + recall, + rb_rec, + decimal=6, + err_msg=f"Recall mismatch: ts_recall={recall} vs prts={rb_rec}", + ) + np.testing.assert_almost_equal( + f1_score, + rb_fsc, + decimal=6, + err_msg=f"F1-Score mismatch: ts_fscore={f1_score} vs prts={rb_fsc}", + ) + + +@pytest.mark.skipif( + not _check_soft_dependencies("prts", severity="none"), + reason="required soft dependency prts not available", +) +def test_multiple_overlapping_ranges_with_gamma_reciprocal(): + """Test for multiple overlapping ranges with gamma=reciprocal.""" + y_pred_bin = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]) + y_real_bin = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]) + + precision = ts_precision( + y_pred_bin, y_real_bin, gamma="reciprocal", bias_type="flat" + ) + recall = ts_recall( + y_pred_bin, y_real_bin, gamma="reciprocal", bias_type="flat", alpha=0.0 + ) + f1_score = ts_fscore( + y_pred_bin, + y_real_bin, + gamma="reciprocal", + beta=1, + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + rb_prec = range_precision( + y_real_bin, y_pred_bin, cardinality="reciprocal", bias="flat" + ) + rb_rec = range_recall(y_real_bin, y_pred_bin, cardinality="reciprocal", bias="flat") + rb_fsc = range_f_score( + y_real_bin, + y_pred_bin, + beta=1, + cardinality="reciprocal", + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision, + rb_prec, + decimal=6, + err_msg=f"Precision mismatch: ts_precision={precision} vs prts={rb_prec}", + ) + np.testing.assert_almost_equal( + recall, + rb_rec, + decimal=6, + err_msg=f"Recall mismatch: ts_recall={recall} vs prts={rb_rec}", + ) + np.testing.assert_almost_equal( + f1_score, + rb_fsc, + decimal=6, + err_msg=f"F1-Score mismatch: ts_fscore={f1_score} vs prts={rb_fsc}", + ) + + +@pytest.mark.skipif( + not _check_soft_dependencies("prts", severity="none"), + reason="required soft dependency prts not available", +) +def test_multiple_overlapping_ranges_with_bias_middle(): + """Test for multiple overlapping ranges with bias_type=middle using range-binary conversion.""" # noqa E501 + y_pred_bin = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]) + y_real_bin = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]) + + # metric functions can also handle range-based input (internal binary conversion) + y_pred = _binary_to_ranges( + y_pred_bin + ) # Convert binary to range to show compatibility + y_real = _binary_to_ranges(y_real_bin) + + precision = ts_precision(y_pred, y_real, gamma="one", bias_type="middle") + recall = ts_recall(y_pred, y_real, gamma="one", bias_type="middle", alpha=0.0) + f1_score = ts_fscore( + y_pred, + y_real, + gamma="one", + beta=1, + p_bias="middle", + r_bias="middle", + p_alpha=0.0, + r_alpha=0.0, + ) + + rb_prec = range_precision(y_real_bin, y_pred_bin, cardinality="one", bias="middle") + rb_rec = range_recall(y_real_bin, y_pred_bin, cardinality="one", bias="middle") + rb_fsc = range_f_score( + y_real_bin, + y_pred_bin, + beta=1, + cardinality="one", + p_bias="middle", + r_bias="middle", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision, + rb_prec, + decimal=6, + err_msg=f"Precision mismatch: ts_precision={precision} vs prts={rb_prec}", + ) + np.testing.assert_almost_equal( + recall, + rb_rec, + decimal=6, + err_msg=f"Recall mismatch: ts_recall={recall} vs prts={rb_rec}", + ) + np.testing.assert_almost_equal( + f1_score, + rb_fsc, + decimal=6, + err_msg=f"F1-Score mismatch: ts_fscore={f1_score} vs prts={rb_fsc}", + ) + + +@pytest.mark.skipif( + not _check_soft_dependencies("prts", severity="none"), + reason="required soft dependency prts not available", +) +def test_multiple_overlapping_ranges_with_bias_middle_gamma_reciprocal(): + """Test for multiple overlapping ranges with bias_type=middle, gamma=reciprocal.""" + y_pred_bin = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]) + y_real_bin = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]) + + precision = ts_precision( + y_pred_bin, y_real_bin, gamma="reciprocal", bias_type="middle" + ) + recall = ts_recall( + y_pred_bin, y_real_bin, gamma="reciprocal", bias_type="middle", alpha=1.0 + ) + f1_score = ts_fscore( + y_pred_bin, + y_real_bin, + gamma="reciprocal", + beta=1, + p_bias="middle", + r_bias="middle", + p_alpha=0.0, + r_alpha=1.0, + ) + + rb_prec = range_precision( + y_real_bin, y_pred_bin, cardinality="reciprocal", bias="middle" + ) + rb_rec = range_recall( + y_real_bin, y_pred_bin, cardinality="reciprocal", bias="middle", alpha=1.0 + ) + rb_fsc = range_f_score( + y_real_bin, + y_pred_bin, + beta=1, + cardinality="reciprocal", + p_bias="middle", + r_bias="middle", + p_alpha=0.0, + r_alpha=1.0, + ) + + np.testing.assert_almost_equal( + precision, + rb_prec, + decimal=6, + err_msg=f"Precision mismatch: ts_precision={precision} vs prts={rb_prec}", + ) + np.testing.assert_almost_equal( + recall, + rb_rec, + decimal=6, + err_msg=f"Recall mismatch: ts_recall={recall} vs prts={rb_rec}", + ) + np.testing.assert_almost_equal( + f1_score, + rb_fsc, + decimal=6, + err_msg=f"F1-Score mismatch: ts_fscore={f1_score} vs prts={rb_fsc}", + ) From 2bd6e68ab4103dc7182be7b56ff8f16fa19874db Mon Sep 17 00:00:00 2001 From: Kaustubh <97254178+Kaustbh@users.noreply.github.com> Date: Sun, 23 Mar 2025 23:38:35 +0530 Subject: [PATCH 034/216] Clarify documentation regarding unequal length series limitation (#2589) Co-authored-by: Matthew Middlehurst --- aeon/transformations/collection/base.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/aeon/transformations/collection/base.py b/aeon/transformations/collection/base.py index 013001d80e..a3664eb4c4 100644 --- a/aeon/transformations/collection/base.py +++ b/aeon/transformations/collection/base.py @@ -64,8 +64,8 @@ def fit(self, X, y=None): X : np.ndarray or list Data to fit transform to, of valid collection type. Input data, any number of channels, equal length series of shape ``( - n_cases, n_channels, n_timepoints)`` or list of numpy arrays (any number - of channels, unequal length series) of shape ``[n_cases]``, 2D np.array + n_cases, n_channels, n_timepoints)`` or list of numpy arrays (number + of channels, series length) of shape ``[n_cases]``, 2D np.array ``(n_channels, n_timepoints_i)``, where ``n_timepoints_i`` is length of series ``i``. Other types are allowed and converted into one of the above. @@ -118,8 +118,8 @@ def transform(self, X, y=None): X : np.ndarray or list Data to fit transform to, of valid collection type. Input data, any number of channels, equal length series of shape ``( - n_cases, n_channels, n_timepoints)`` or list of numpy arrays (any number - of channels, unequal length series) of shape ``[n_cases]``, 2D np.array + n_cases, n_channels, n_timepoints)`` or list of numpy arrays (number + of channels, series length) of shape ``[n_cases]``, 2D np.array ``(n_channels, n_timepoints_i)``, where ``n_timepoints_i`` is length of series ``i``. Other types are allowed and converted into one of the above. @@ -171,10 +171,10 @@ def fit_transform(self, X, y=None): ---------- X : np.ndarray or list Data to fit transform to, of valid collection type. Input data, - any number of channels, equal length series of shape ``(n_cases, - n_channels, n_timepoints)`` or list of numpy arrays (any number of - channels, unequal length series) of shape ``[n_cases]``, 2D np.array ``( - n_channels, n_timepoints_i)``, where ``n_timepoints_i`` is length of + any number of channels, equal length series of shape ``( + n_cases, n_channels, n_timepoints)`` or list of numpy arrays (number + of channels, series length) of shape ``[n_cases]``, 2D np.array + ``(n_channels, n_timepoints_i)``, where ``n_timepoints_i`` is length of series ``i``. Other types are allowed and converted into one of the above. Different estimators have different capabilities to handle different @@ -222,8 +222,8 @@ def inverse_transform(self, X, y=None): X : np.ndarray or list Data to fit transform to, of valid collection type. Input data, any number of channels, equal length series of shape ``( - n_cases, n_channels, n_timepoints)`` or list of numpy arrays (any number - of channels, unequal length series) of shape ``[n_cases]``, 2D np.array + n_cases, n_channels, n_timepoints)`` or list of numpy arrays (number + of channels, series length) of shape ``[n_cases]``, 2D np.array ``(n_channels, n_timepoints_i)``, where ``n_timepoints_i`` is length of series ``i``. Other types are allowed and converted into one of the above. From 9816c794181c4543c4fb406f9457d58396625917 Mon Sep 17 00:00:00 2001 From: "aeon-actions-bot[bot]" <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Date: Mon, 24 Mar 2025 15:29:47 +0000 Subject: [PATCH 035/216] Automated `pre-commit` hook update (#2683) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 854da8dfd2..b09f5f22c8 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -29,7 +29,7 @@ repos: args: [ "--create", "--python-folders", "aeon" ] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.11.0 + rev: v0.11.2 hooks: - id: ruff args: [ "--fix"] From 74833987ea6c0bc9c6acdcd53f8f48071de05188 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 24 Mar 2025 15:30:14 +0000 Subject: [PATCH 036/216] [MNT] Bump tj-actions/changed-files in the github-actions group (#2686) Bumps the github-actions group with 1 update: [tj-actions/changed-files](https://github.com/tj-actions/changed-files). Updates `tj-actions/changed-files` from 46.0.1 to 46.0.3 - [Release notes](https://github.com/tj-actions/changed-files/releases) - [Changelog](https://github.com/tj-actions/changed-files/blob/main/HISTORY.md) - [Commits](https://github.com/tj-actions/changed-files/compare/v46.0.1...v46.0.3) --- updated-dependencies: - dependency-name: tj-actions/changed-files dependency-type: direct:production update-type: version-update:semver-patch dependency-group: github-actions ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/pr_precommit.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pr_precommit.yml b/.github/workflows/pr_precommit.yml index a74e8b12ea..809803fecc 100644 --- a/.github/workflows/pr_precommit.yml +++ b/.github/workflows/pr_precommit.yml @@ -37,7 +37,7 @@ jobs: python-version: "3.10" - name: Get changed files - uses: tj-actions/changed-files@v46.0.1 + uses: tj-actions/changed-files@v46.0.3 id: changed-files - name: List changed files From 9d85fa770c6ef725b10605562a233fce34e43428 Mon Sep 17 00:00:00 2001 From: Tanish Yelgoe <143334319+tanishy7777@users.noreply.github.com> Date: Thu, 27 Mar 2025 22:52:49 +0530 Subject: [PATCH 037/216] [ENH] Set `outlier_norm` default to True for Catch22 estimators (#2659) * sets outlier_norm=True by deafault * Minor changes * Docs improvement --- aeon/classification/feature_based/_catch22.py | 9 ++-- aeon/clustering/feature_based/_catch22.py | 11 ++-- aeon/regression/feature_based/_catch22.py | 9 ++-- .../collection/feature_based/_catch22.py | 11 ++-- .../feature_based/tests/test_catch22.py | 52 +++++++++---------- 5 files changed, 52 insertions(+), 40 deletions(-) diff --git a/aeon/classification/feature_based/_catch22.py b/aeon/classification/feature_based/_catch22.py index bec5deff88..26a56d0a91 100644 --- a/aeon/classification/feature_based/_catch22.py +++ b/aeon/classification/feature_based/_catch22.py @@ -43,8 +43,11 @@ class Catch22Classifier(BaseClassifier): true. If a List of specific features to extract is provided, "Mean" and/or "StandardDeviation" must be added to the List to extract these features. outlier_norm : bool, optional, default=False - Normalise each series during the two outlier Catch22 features, which can take a - while to process for large values. + If True, each time series is normalized during the computation of the two + outlier Catch22 features, which can take a while to process for large values + as it depends on the max value in the timseries. Note that this parameter + did not exist in the original publication/implementation as they used time + series that were already normalized. replace_nans : bool, default=True Replace NaN or inf values from the Catch22 transform with 0. use_pycatch22 : bool, default=False @@ -136,7 +139,7 @@ def __init__( self, features="all", catch24=True, - outlier_norm=False, + outlier_norm=True, replace_nans=True, use_pycatch22=False, estimator=None, diff --git a/aeon/clustering/feature_based/_catch22.py b/aeon/clustering/feature_based/_catch22.py index 33f0b79bc5..30fad7ff7e 100644 --- a/aeon/clustering/feature_based/_catch22.py +++ b/aeon/clustering/feature_based/_catch22.py @@ -42,9 +42,12 @@ class Catch22Clusterer(BaseClusterer): Extract the mean and standard deviation as well as the 22 Catch22 features if true. If a List of specific features to extract is provided, "Mean" and/or "StandardDeviation" must be added to the List to extract these features. - outlier_norm : bool, optional, default=False - Normalise each series during the two outlier Catch22 features, which can take a - while to process for large values. + outlier_norm : bool, optional, default=False + If True, each time series is normalized during the computation of the two + outlier Catch22 features, which can take a while to process for large values + as it depends on the max value in the timseries. Note that this parameter + did not exist in the original publication/implementation as they used + time series that were already normalized. replace_nans : bool, default=True Replace NaN or inf values from the Catch22 transform with 0. use_pycatch22 : bool, default=False @@ -103,7 +106,7 @@ def __init__( self, features="all", catch24=True, - outlier_norm=False, + outlier_norm=True, replace_nans=True, use_pycatch22=False, estimator=None, diff --git a/aeon/regression/feature_based/_catch22.py b/aeon/regression/feature_based/_catch22.py index f669cbe876..1ab04ee6e1 100644 --- a/aeon/regression/feature_based/_catch22.py +++ b/aeon/regression/feature_based/_catch22.py @@ -43,8 +43,11 @@ class Catch22Regressor(BaseRegressor): True. If a List of specific features to extract is provided, "Mean" and/or "StandardDeviation" must be added to the List to extract these features. outlier_norm : bool, optional, default=False - Normalise each series during the two outlier Catch22 features, which can take a - while to process for large values. + If True, each time series is normalized during the computation of the two + outlier Catch22 features, which can take a while to process for large values + as it depends on the max value in the timseries. Note that this parameter + did not exist in the original publication/implementation as they used time + series that were already normalized. replace_nans : bool, optional, default=True Replace NaN or inf values from the Catch22 transform with 0. use_pycatch22 : bool, optional, default=False @@ -110,7 +113,7 @@ def __init__( self, features="all", catch24=True, - outlier_norm=False, + outlier_norm=True, replace_nans=True, use_pycatch22=False, estimator=None, diff --git a/aeon/transformations/collection/feature_based/_catch22.py b/aeon/transformations/collection/feature_based/_catch22.py index daae46f583..0431da8df1 100644 --- a/aeon/transformations/collection/feature_based/_catch22.py +++ b/aeon/transformations/collection/feature_based/_catch22.py @@ -111,8 +111,11 @@ class Catch22(BaseCollectionTransformer): true. If a List of specific features to extract is provided, "Mean" and/or "StandardDeviation" must be added to the List to extract these features. outlier_norm : bool, optional, default=False - Normalise each series during the two outlier Catch22 features, which can take a - while to process for large values. + If True, each time series is normalized during the computation of the two + outlier Catch22 features, which can take a while to process for large values + as it depends on the max value in the timseries. Note that this parameter + did not exist in the original publication/implementation as they used time + series that were already normalized. replace_nans : bool, default=False Replace NaN or inf values from the Catch22 transform with 0. use_pycatch22 : bool, optional, default=False @@ -163,7 +166,7 @@ class Catch22(BaseCollectionTransformer): [1.15639531e+00 1.31700577e+00 5.66227710e-01 2.00000000e+00 3.89048349e-01 2.33853577e-01 1.00000000e+00 3.00000000e+00 8.23045267e-03 0.00000000e+00 1.70859420e-01 2.00000000e+00 - 1.00000000e+00 2.00000000e-01 0.00000000e+00 1.10933565e-32 + 1.00000000e+00 7.00000000e-01 2.00000000e-01 1.10933565e-32 4.00000000e+00 2.04319187e+00 0.00000000e+00 0.00000000e+00 1.96349541e+00 5.51667002e-01] """ @@ -181,7 +184,7 @@ def __init__( self, features="all", catch24=False, - outlier_norm=False, + outlier_norm=True, replace_nans=False, use_pycatch22=False, n_jobs=1, diff --git a/aeon/transformations/collection/feature_based/tests/test_catch22.py b/aeon/transformations/collection/feature_based/tests/test_catch22.py index 5b5bc28925..9ebc67866a 100644 --- a/aeon/transformations/collection/feature_based/tests/test_catch22.py +++ b/aeon/transformations/collection/feature_based/tests/test_catch22.py @@ -120,7 +120,7 @@ def test_catch22_wrapper_on_basic_motions(): 0.0616, 1.0, 0.5, - -0.2, + -0.2799, 0.04, 0.4158, 4.0, @@ -231,7 +231,7 @@ def test_catch22_wrapper_on_basic_motions(): 2.0, 1.0, -0.11, - -0.72, + -0.81, 1.7181, 8.0, 1.8142, @@ -255,7 +255,7 @@ def test_catch22_wrapper_on_basic_motions(): 4.0, 0.3333, -0.15, - 0.03, + 0.01, 32.285, 8.0, 1.9501, @@ -298,8 +298,8 @@ def test_catch22_wrapper_on_basic_motions(): 0.1303, 3.0, 0.3333, - -0.23, - -0.04, + -0.2299, + 0.06, 14.3938, 5.0, 2.0059, @@ -320,8 +320,8 @@ def test_catch22_wrapper_on_basic_motions(): 0.1047, 2.0, 0.3333, - 0.15, - -0.18, + 0.06, + -0.235, 7.1407, 6.0, 2.0097, @@ -342,7 +342,7 @@ def test_catch22_wrapper_on_basic_motions(): 0.064, 1.0, 0.3333, - 0.18, + 0.20, 0.3, 1.6007, 5.0, @@ -365,7 +365,7 @@ def test_catch22_wrapper_on_basic_motions(): 2.0, 0.3333, -0.14, - 0.1, + -0.0399, 7.3076, 5.0, 1.9736, @@ -389,7 +389,7 @@ def test_catch22_wrapper_on_basic_motions(): 2.0, 0.3333, -0.13, - 0.02, + -0.3399, 0.0081, 5.0, 2.133, @@ -410,8 +410,8 @@ def test_catch22_wrapper_on_basic_motions(): 0.5715, 2.0, 1.0, - -0.12, - -0.02, + -0.1399, + -0.0099, 0.1288, 7.0, 1.9505, @@ -476,8 +476,8 @@ def test_catch22_wrapper_on_basic_motions(): 6.8497, 2.0, 0.3333, - -0.06, - 0.05, + -0.0799, + 0.03, 0.0013, 7.0, 2.039, @@ -498,8 +498,8 @@ def test_catch22_wrapper_on_basic_motions(): 3.1416, 2.0, 1.0, - -0.155, - 0.125, + -0.1999, + 0.1200, 0.0212, 7.0, 1.8706, @@ -522,7 +522,7 @@ def test_catch22_wrapper_on_basic_motions(): 0.1723, 1.0, 1.0, - -0.01, + -0.0799, -0.17, 8.3186, 5.0, @@ -544,8 +544,8 @@ def test_catch22_wrapper_on_basic_motions(): 0.1222, 1.0, 1.0, - 0.09, - 0.01, + 0.08, + -0.0099, 5.3016, 4.0, 2.0075, @@ -566,8 +566,8 @@ def test_catch22_wrapper_on_basic_motions(): 0.0841, 2.0, 0.5, - 0.13, - -0.08, + -0.0199, + -0.1199, 1.7627, 5.0, 2.1476, @@ -611,7 +611,7 @@ def test_catch22_wrapper_on_basic_motions(): 1.0, 0.5, -0.05, - -0.11, + -0.0999, 0.2086, 6.0, 2.0597, @@ -656,8 +656,8 @@ def test_catch22_wrapper_on_basic_motions(): 0.0718, 1.0, 0.3333, - 0.03, - 0.13, + 0.025, + -0.1399, 0.501, 6.0, 2.0492, @@ -701,7 +701,7 @@ def test_catch22_wrapper_on_basic_motions(): 2.0, 0.5, -0.13, - 0.19, + 0.29, 0.3096, 6.0, 1.8881, @@ -745,7 +745,7 @@ def test_catch22_wrapper_on_basic_motions(): 3.0, 1.0, 0.11, - 0.35, + 0.285, 0.2719, 7.0, 1.7647, From 6ddfc954da61b44b7f8ce738cd45d6aa7cb96fc4 Mon Sep 17 00:00:00 2001 From: Akash Kawle <128881349+shinymack@users.noreply.github.com> Date: Thu, 27 Mar 2025 23:09:38 +0530 Subject: [PATCH 038/216] [MNT] Use MacOS for examples/ workflow (#2668) * update bash to 5.x for lastpipe support * added esig installation * install boost before esig * fixed examples path issue for excluded notebooks * switched to fixed version of macos * added signature_method.ipynb to excluded list * removed symlink for /bin/bash --- .github/utilities/run_examples.sh | 5 +++-- .github/workflows/periodic_tests.yml | 7 ++++++- .github/workflows/pr_examples.yml | 7 ++++++- 3 files changed, 15 insertions(+), 4 deletions(-) diff --git a/.github/utilities/run_examples.sh b/.github/utilities/run_examples.sh index fd7376c05b..fdbcdb9057 100755 --- a/.github/utilities/run_examples.sh +++ b/.github/utilities/run_examples.sh @@ -1,4 +1,4 @@ -#!/bin/bash +#!/opt/homebrew/bin/bash # Script to run all example notebooks. set -euxo pipefail @@ -23,6 +23,7 @@ if [ "$1" = true ]; then "examples/classification/shapelet_based.ipynb" "examples/classification/convolution_based.ipynb" "examples/similarity_search/code_speed.ipynb" + "examples/transformations/signature_method.ipynb" ) fi @@ -32,7 +33,7 @@ notebooks=() runtimes=() # Loop over all notebooks in the examples directory. -find "examples/" -name "*.ipynb" -print0 | +find "examples" -name "*.ipynb" -print0 | while IFS= read -r -d "" notebook; do # Skip notebooks in the excluded list. if printf "%s\0" "${excluded[@]}" | grep -Fxqz -- "$notebook"; then diff --git a/.github/workflows/periodic_tests.yml b/.github/workflows/periodic_tests.yml index 64e297d68f..1528959578 100644 --- a/.github/workflows/periodic_tests.yml +++ b/.github/workflows/periodic_tests.yml @@ -46,12 +46,17 @@ jobs: extra_args: --all-files run-notebook-examples: - runs-on: ubuntu-24.04 + runs-on: macos-14 steps: - name: Checkout uses: actions/checkout@v4 + - name: Install latest version of bash + run: | + brew install bash + /opt/homebrew/bin/bash --version + - name: Setup Python 3.10 uses: actions/setup-python@v5 with: diff --git a/.github/workflows/pr_examples.yml b/.github/workflows/pr_examples.yml index adc266319d..2d7ea6f129 100644 --- a/.github/workflows/pr_examples.yml +++ b/.github/workflows/pr_examples.yml @@ -19,12 +19,17 @@ concurrency: jobs: run-notebook-examples: - runs-on: ubuntu-24.04 + runs-on: macos-14 steps: - name: Checkout uses: actions/checkout@v4 + - name: Install latest version of bash + run: | + brew install bash + /opt/homebrew/bin/bash --version + - name: Setup Python 3.10 uses: actions/setup-python@v5 with: From b64ac00ab49fc22c40c50d90854110ff1c81080a Mon Sep 17 00:00:00 2001 From: TinaJin0228 <60577222+TinaJin0228@users.noreply.github.com> Date: Wed, 2 Apr 2025 22:50:45 +0800 Subject: [PATCH 039/216] Update dependencies.md (#2717) Correct typo in dependencies.md --- docs/developer_guide/dependencies.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/developer_guide/dependencies.md b/docs/developer_guide/dependencies.md index 53c0f326fd..9fb649c0bf 100644 --- a/docs/developer_guide/dependencies.md +++ b/docs/developer_guide/dependencies.md @@ -15,7 +15,7 @@ We are unlikely to add new core dependencies, without a strong reason. Soft depe should be the first choice for new dependencies, but ideally the code should be written in `aeon` itself if possible. -Al dependencies are managed in the [`pyproject.toml`](https://github.com/aeon-toolkit/aeon/blob/main/pyproject.toml) +All dependencies are managed in the [`pyproject.toml`](https://github.com/aeon-toolkit/aeon/blob/main/pyproject.toml) file following the [PEP 621](https://www.python.org/dev/peps/pep-0621/) convention. Core dependencies are listed in the `dependencies` dependency set and developer dependencies are listed in the `dev` and `docs` dependency sets. From e19ddb651933500bb3e2cf6c2628d4ad1dcefd56 Mon Sep 17 00:00:00 2001 From: "aeon-actions-bot[bot]" <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Date: Thu, 3 Apr 2025 10:10:23 +0100 Subject: [PATCH 040/216] Automated `pre-commit` hook update (#2708) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index b09f5f22c8..f707392b5d 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -48,7 +48,7 @@ repos: args: [ "--profile=black", "--multi-line=3" ] - repo: https://github.com/pycqa/flake8 - rev: 7.1.2 + rev: 7.2.0 hooks: - id: flake8 additional_dependencies: [ flake8-bugbear, flake8-print, Flake8-pyproject ] From df6df7fe122a357f4119d8032a127aa194d55d80 Mon Sep 17 00:00:00 2001 From: Kavya Rambhia <161142013+kavya-r30@users.noreply.github.com> Date: Thu, 3 Apr 2025 16:46:36 +0530 Subject: [PATCH 041/216] [ENH] Test Coverage for Pairwise Distance (#2590) * Pairwise distance matrix test * Empty commit for CI --------- Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> --- .../visualisation/distances/tests/__init__.py | 1 + .../tests/test_pairwise_distance_matrix.py | 34 +++++++++++++++++++ 2 files changed, 35 insertions(+) create mode 100644 aeon/visualisation/distances/tests/__init__.py create mode 100644 aeon/visualisation/distances/tests/test_pairwise_distance_matrix.py diff --git a/aeon/visualisation/distances/tests/__init__.py b/aeon/visualisation/distances/tests/__init__.py new file mode 100644 index 0000000000..e6ebea6816 --- /dev/null +++ b/aeon/visualisation/distances/tests/__init__.py @@ -0,0 +1 @@ +"""Testing for distances specific plotting.""" diff --git a/aeon/visualisation/distances/tests/test_pairwise_distance_matrix.py b/aeon/visualisation/distances/tests/test_pairwise_distance_matrix.py new file mode 100644 index 0000000000..23ab29179a --- /dev/null +++ b/aeon/visualisation/distances/tests/test_pairwise_distance_matrix.py @@ -0,0 +1,34 @@ +"""Test pairwise distance matrix plotting.""" + +import numpy as np +import pytest + +from aeon.utils.validation._dependencies import _check_soft_dependencies +from aeon.visualisation import plot_pairwise_distance_matrix + + +@pytest.mark.skipif( + not _check_soft_dependencies(["matplotlib", "seaborn"], severity="none"), + reason="skip test if required soft dependency not available", +) +def test_plot_pairwise_distance_matrix(): + """Test whether plot_pairwise_distance_matrix runs without error.""" + import matplotlib + import matplotlib.pyplot as plt + + matplotlib.use("Agg") + + distance_matrix = np.array([[0.0, 1.0], [1.0, 0.0]]) + a = np.array([1.0, 2.0]) + b = np.array([1.5, 2.5]) + path = [(0, 0), (1, 1)] + + ax = plot_pairwise_distance_matrix(distance_matrix, a, b, path) + fig = plt.gcf() + plt.gcf().canvas.draw_idle() + + assert isinstance(fig, plt.Figure) + assert isinstance(ax, plt.Axes) + assert len(fig.axes) > 0 + + plt.close() From 5c838a5620201237b9e4699bf70df934a0ecae8d Mon Sep 17 00:00:00 2001 From: Kaustubh <97254178+Kaustbh@users.noreply.github.com> Date: Thu, 3 Apr 2025 16:48:02 +0530 Subject: [PATCH 042/216] re-running notebook for fixing cell output error (#2597) --- .../classification/early_classification.ipynb | 691 ++++++++++++++---- 1 file changed, 549 insertions(+), 142 deletions(-) diff --git a/examples/classification/early_classification.ipynb b/examples/classification/early_classification.ipynb index 97cd1a23ac..b4649a46a6 100644 --- a/examples/classification/early_classification.ipynb +++ b/examples/classification/early_classification.ipynb @@ -2,6 +2,9 @@ "cells": [ { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "# Early time series classification with aeon\n", "\n", @@ -9,23 +12,28 @@ "\n", "This notebook gives a quick guide to get you started with running eTSC algorithms in aeon.\n", "\n" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "## Data sets and problem types\n", "The UCR/UEA [time series classification archive](https://timeseriesclassification.com/) contains a large number of example TSC problems that have been used thousands of times in the literature to assess TSC algorithms. Read the data loading documentation and notebooks for details on the aeon data formats and loading data for aeon." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "pycharm": { + "is_executing": true + } + }, + "outputs": [], "source": [ "# Imports used in this notebook\n", "import numpy as np\n", @@ -33,23 +41,20 @@ "from aeon.classification.early_classification._teaser import TEASER\n", "from aeon.classification.interval_based import TimeSeriesForestClassifier\n", "from aeon.datasets import load_arrow_head" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "is_executing": true - } - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", "execution_count": 2, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { - "text/plain": "(175, 1, 251)" + "text/plain": [ + "(175, 1, 251)" + ] }, "execution_count": 2, "metadata": {}, @@ -62,30 +67,444 @@ "arrow_test_X, arrow_test_y = load_arrow_head(split=\"test\")\n", "\n", "arrow_test_X.shape" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "## Building the TEASER classifier\n", "\n", "TEASER \\[1\\] is a two-tier model using a base classifier to make predictions and a decision making estimator to decide whether these predictions are safe. As a first tier, TEASER requires a TSC algorithm, such as WEASEL, which produces class probabilities as output. As a second tier an anomaly detector is required, such as a one-class SVM." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": 3, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { - "text/plain": "TEASER(classification_points=[25, 50, 75, 100, 125, 150, 175, 200, 251],\n estimator=TimeSeriesForestClassifier(n_estimators=10, random_state=0),\n random_state=0)", - "text/html": "
TEASER(classification_points=[25, 50, 75, 100, 125, 150, 175, 200, 251],\n       estimator=TimeSeriesForestClassifier(n_estimators=10, random_state=0),\n       random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" + "text/html": [ + "
TEASER(classification_points=[25, 50, 75, 100, 125, 150, 175, 200, 251],\n",
+       "       estimator=TimeSeriesForestClassifier(n_estimators=10, random_state=0),\n",
+       "       random_state=0)
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" + ], + "text/plain": [ + "TEASER(classification_points=[25, 50, 75, 100, 125, 150, 175, 200, 251],\n", + " estimator=TimeSeriesForestClassifier(n_estimators=10, random_state=0),\n", + " random_state=0)" + ] }, "execution_count": 3, "metadata": {}, @@ -99,42 +518,33 @@ " estimator=TimeSeriesForestClassifier(n_estimators=10, random_state=0),\n", ")\n", "teaser.fit(arrow_train_X, arrow_train_y)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "## Determine the accuracy and earliness on the test data\n", "\n", - "Commonly accuracy is used to determine the correctness of the predictions, while earliness is used to determine how much of the series is required on average to obtain said accuracy. I.e. for the below values, using just 43% of the full test data, we were able to get an accuracy of 69%." - ], - "metadata": { - "collapsed": false - } + "Commonly accuracy is used to determine the correctness of the predictions, while earliness is used to determine how much of the series is required on average to obtain said accuracy. I.e. for the below values, using just 34% of the full test data, we were able to get an accuracy of 65%." + ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, + "metadata": { + "collapsed": false + }, "outputs": [ { - "ename": "ValueError", - "evalue": "zero-size array to reduction operation minimum which has no identity", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[1;32mIn[10], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m hm, acc, earl \u001B[38;5;241m=\u001B[39m \u001B[43mteaser\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mscore\u001B[49m\u001B[43m(\u001B[49m\u001B[43marrow_test_X\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43marrow_test_y\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 2\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mEarliness on Test Data \u001B[39m\u001B[38;5;132;01m%2.2f\u001B[39;00m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;241m%\u001B[39m earl)\n\u001B[0;32m 3\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mAccuracy on Test Data \u001B[39m\u001B[38;5;132;01m%2.2f\u001B[39;00m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;241m%\u001B[39m acc)\n", - "File \u001B[1;32mC:\\Code\\aeon\\aeon\\classification\\early_classification\\base.py:314\u001B[0m, in \u001B[0;36mBaseEarlyClassifier.score\u001B[1;34m(self, X, y)\u001B[0m\n\u001B[0;32m 311\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcheck_is_fitted()\n\u001B[0;32m 313\u001B[0m \u001B[38;5;66;03m# boilerplate input checks for predict-like methods\u001B[39;00m\n\u001B[1;32m--> 314\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_check_X\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 315\u001B[0m X \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_convert_X(X)\n\u001B[0;32m 317\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_score(X, y)\n", - "File \u001B[1;32mC:\\Code\\aeon\\aeon\\classification\\early_classification\\base.py:631\u001B[0m, in \u001B[0;36mBaseEarlyClassifier._check_X\u001B[1;34m(self, X)\u001B[0m\n\u001B[0;32m 629\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_check_X\u001B[39m(\u001B[38;5;28mself\u001B[39m, X):\n\u001B[0;32m 630\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"To follow.\"\"\"\u001B[39;00m\n\u001B[1;32m--> 631\u001B[0m metadata \u001B[38;5;241m=\u001B[39m \u001B[43m_get_metadata\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 632\u001B[0m \u001B[38;5;66;03m# Check classifier capabilities for X\u001B[39;00m\n\u001B[0;32m 633\u001B[0m allow_multivariate \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mget_tag(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcapability:multivariate\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", - "File \u001B[1;32mC:\\Code\\aeon\\aeon\\classification\\early_classification\\base.py:707\u001B[0m, in \u001B[0;36m_get_metadata\u001B[1;34m(X)\u001B[0m\n\u001B[0;32m 705\u001B[0m metadata \u001B[38;5;241m=\u001B[39m {}\n\u001B[0;32m 706\u001B[0m metadata[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmultivariate\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m is_univariate(X)\n\u001B[1;32m--> 707\u001B[0m metadata[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmissing_values\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[43mhas_missing\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 708\u001B[0m metadata[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124munequal_length\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m is_equal_length(X)\n\u001B[0;32m 709\u001B[0m metadata[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mn_cases\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m get_n_cases(X)\n", - "File \u001B[1;32mC:\\Code\\aeon\\aeon\\utils\\validation\\collection.py:305\u001B[0m, in \u001B[0;36mhas_missing\u001B[1;34m(X)\u001B[0m\n\u001B[0;32m 303\u001B[0m \u001B[38;5;28mtype\u001B[39m \u001B[38;5;241m=\u001B[39m get_type(X)\n\u001B[0;32m 304\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mtype\u001B[39m \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnumpy3D\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mtype\u001B[39m \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnumpy2D\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[1;32m--> 305\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m np\u001B[38;5;241m.\u001B[39many(np\u001B[38;5;241m.\u001B[39misnan(\u001B[43mnp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmin\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m)\u001B[49m))\n\u001B[0;32m 306\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mtype\u001B[39m \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnp-list\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[0;32m 307\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m x \u001B[38;5;129;01min\u001B[39;00m X:\n", - "File \u001B[1;32m<__array_function__ internals>:180\u001B[0m, in \u001B[0;36mamin\u001B[1;34m(*args, **kwargs)\u001B[0m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2918\u001B[0m, in \u001B[0;36mamin\u001B[1;34m(a, axis, out, keepdims, initial, where)\u001B[0m\n\u001B[0;32m 2802\u001B[0m \u001B[38;5;129m@array_function_dispatch\u001B[39m(_amin_dispatcher)\n\u001B[0;32m 2803\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mamin\u001B[39m(a, axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, out\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, keepdims\u001B[38;5;241m=\u001B[39mnp\u001B[38;5;241m.\u001B[39m_NoValue, initial\u001B[38;5;241m=\u001B[39mnp\u001B[38;5;241m.\u001B[39m_NoValue,\n\u001B[0;32m 2804\u001B[0m where\u001B[38;5;241m=\u001B[39mnp\u001B[38;5;241m.\u001B[39m_NoValue):\n\u001B[0;32m 2805\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 2806\u001B[0m \u001B[38;5;124;03m Return the minimum of an array or minimum along an axis.\u001B[39;00m\n\u001B[0;32m 2807\u001B[0m \n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 2916\u001B[0m \u001B[38;5;124;03m 6\u001B[39;00m\n\u001B[0;32m 2917\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m-> 2918\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m_wrapreduction\u001B[49m\u001B[43m(\u001B[49m\u001B[43ma\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mminimum\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mmin\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 2919\u001B[0m \u001B[43m \u001B[49m\u001B[43mkeepdims\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mkeepdims\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minitial\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minitial\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mwhere\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mwhere\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numpy\\core\\fromnumeric.py:86\u001B[0m, in \u001B[0;36m_wrapreduction\u001B[1;34m(obj, ufunc, method, axis, dtype, out, **kwargs)\u001B[0m\n\u001B[0;32m 83\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 84\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m reduction(axis\u001B[38;5;241m=\u001B[39maxis, out\u001B[38;5;241m=\u001B[39mout, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mpasskwargs)\n\u001B[1;32m---> 86\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m ufunc\u001B[38;5;241m.\u001B[39mreduce(obj, axis, dtype, out, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mpasskwargs)\n", - "\u001B[1;31mValueError\u001B[0m: zero-size array to reduction operation minimum which has no identity" + "name": "stdout", + "output_type": "stream", + "text": [ + "Earliness on Test Data 0.34\n", + "Accuracy on Test Data 0.65\n", + "Harmonic Mean on Test Data 0.65\n" ] } ], @@ -143,55 +553,55 @@ "print(\"Earliness on Test Data %2.2f\" % earl)\n", "print(\"Accuracy on Test Data %2.2f\" % acc)\n", "print(\"Harmonic Mean on Test Data %2.2f\" % hm)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", - "source": [ - "### Determine the accuracy and earliness on the train data" - ], "metadata": { "collapsed": false - } + }, + "source": [ + "### Determine the accuracy and earliness on the train data" + ] }, { "cell_type": "code", "execution_count": 5, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Earliness on Train Data 0.31\n", - "Accuracy on Train Data 0.69\n" + "Earliness on Train Data 0.28\n", + "Accuracy on Train Data 0.72\n" ] } ], "source": [ "print(\"Earliness on Train Data %2.2f\" % teaser._train_earliness)\n", "print(\"Accuracy on Train Data %2.2f\" % teaser._train_accuracy)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "### Comparison to Classification on full Test Data\n", "\n", - "With the full test data, we would obtain 68% accuracy with the same classifier." - ], - "metadata": { - "collapsed": false - } + "With the full test data, we would obtain 67% accuracy with the same classifier." + ] }, { "cell_type": "code", "execution_count": 6, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", @@ -208,45 +618,45 @@ " .score(arrow_test_X, arrow_test_y)\n", ")\n", "print(\"Accuracy on the full Test Data %2.2f\" % accuracy)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "## Classifying with incomplete time series\n", "\n", "The main draw of eTSC is the capabilility to make classifications with incomplete time series. aeon eTSC algorithms accept inputs with less time points than the full series length, and output two items: The prediction made and whether the algorithm thinks the prediction is safe. Information about the decision such as the time stamp it was made at can be obtained from the state_info attribute.\n", "\n", "### First test with only 50 datapoints (out of 251)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": 7, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First 10 Finished prediction\n", - " [ 0 1 4 5 9 11 24 30 32 35]\n", + " [ 0 4 5 8 9 16 18 21 22 30]\n", "First 10 Probabilities of finished predictions\n", - " [[0.9 0. 0.1]\n", - " [0.3 0.1 0.6]\n", - " [0.8 0.1 0.1]\n", - " [0.7 0.3 0. ]\n", - " [0.5 0.2 0.3]\n", - " [0.6 0.2 0.2]\n", - " [0.1 0.2 0.7]\n", + " [[0.8 0. 0.2]\n", " [0.8 0. 0.2]\n", + " [0.6 0.1 0.3]\n", + " [0.2 0.2 0.6]\n", + " [0.6 0.3 0.1]\n", + " [0. 0.3 0.7]\n", " [0.3 0.1 0.6]\n", - " [0.9 0. 0.1]]\n" + " [0.1 0.3 0.6]\n", + " [0.8 0.1 0.1]\n", + " [0.8 0. 0.2]]\n" ] } ], @@ -256,96 +666,96 @@ "idx = (probas >= 0).all(axis=1)\n", "print(\"First 10 Finished prediction\\n\", np.argwhere(idx).flatten()[:10])\n", "print(\"First 10 Probabilities of finished predictions\\n\", probas[idx][:10])" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": 8, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy with 50 points on Test Data 0.57\n" + "Accuracy with 50 points on Test Data 0.65\n" ] } ], "source": [ "_, acc, _ = teaser.score(X, arrow_test_y)\n", "print(\"Accuracy with 50 points on Test Data %2.2f\" % acc)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "### We may also do predictions in a streaming scenario where more data becomes available from time to time\n", "\n", "The rationale is to keep the state info from the previous predictions in the TEASER object and use it whenever new data is available." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": 9, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Earliness on length 25 is 0.10\n", - "Accuracy on length 25 is 0.50\n", - "Harmonic Mean on length 25 is 0.64\n", + "Accuracy on length 25 is 0.43\n", + "Harmonic Mean on length 25 is 0.59\n", "...........\n", "Earliness on length 50 is 0.20\n", - "Accuracy on length 50 is 0.57\n", - "Harmonic Mean on length 50 is 0.67\n", + "Accuracy on length 50 is 0.67\n", + "Harmonic Mean on length 50 is 0.73\n", "...........\n", - "Earliness on length 75 is 0.27\n", - "Accuracy on length 75 is 0.72\n", - "Harmonic Mean on length 75 is 0.72\n", + "Earliness on length 75 is 0.26\n", + "Accuracy on length 75 is 0.62\n", + "Harmonic Mean on length 75 is 0.67\n", "...........\n", - "Earliness on length 100 is 0.32\n", - "Accuracy on length 100 is 0.62\n", - "Harmonic Mean on length 100 is 0.65\n", + "Earliness on length 100 is 0.29\n", + "Accuracy on length 100 is 0.64\n", + "Harmonic Mean on length 100 is 0.67\n", "...........\n", - "Earliness on length 125 is 0.35\n", - "Accuracy on length 125 is 0.69\n", + "Earliness on length 125 is 0.30\n", + "Accuracy on length 125 is 0.64\n", "Harmonic Mean on length 125 is 0.67\n", "...........\n", - "Earliness on length 150 is 0.37\n", - "Accuracy on length 150 is 0.69\n", + "Earliness on length 150 is 0.32\n", + "Accuracy on length 150 is 0.64\n", "Harmonic Mean on length 150 is 0.66\n", "...........\n", - "Earliness on length 175 is 0.39\n", - "Accuracy on length 175 is 0.68\n", - "Harmonic Mean on length 175 is 0.64\n", + "Earliness on length 175 is 0.33\n", + "Accuracy on length 175 is 0.63\n", + "Harmonic Mean on length 175 is 0.65\n", "...........\n", - "Earliness on length 200 is 0.40\n", - "Accuracy on length 200 is 0.69\n", + "Earliness on length 200 is 0.34\n", + "Accuracy on length 200 is 0.64\n", "Harmonic Mean on length 200 is 0.65\n", "...........\n", - "Earliness on length 251 is 0.40\n", - "Accuracy on length 251 is 0.67\n", - "Harmonic Mean on length 251 is 0.63\n", + "Earliness on length 251 is 0.34\n", + "Accuracy on length 251 is 0.66\n", + "Harmonic Mean on length 251 is 0.66\n", "...........\n", - "Time Stamp of final decisions [ 50 50 251 75 50 50 175 200 175 50 75 50 75 75 100 251 100 100\n", - " 125 75 100 100 75 100 50 125 75 100 75 75 50 75 50 125 175 50\n", - " 50 75 75 125 50 75 75 50 175 100 150 125 75 100 75 75 75 75\n", - " 50 100 50 175 75 50 200 50 50 50 75 200 75 125 75 125 150 175\n", - " 125 50 150 50 75 75 50 100 75 251 251 75 50 100 50 150 100 50\n", - " 75 100 251 50 50 50 200 100 75 50 200 100 50 50 50 50 251 100\n", - " 75 75 125 50 125 100 100 50 75 175 175 50 50 100 175 150 100 100\n", - " 50 100 100 100 175 50 50 100 100 175 251 125 125 100 100 125 100 125\n", - " 100 125 50 175 75 125 100 100 125 50 50 100 125 100 100 100 251 150\n", - " 50 75 175 125 50 50 125 75 50 100 175 50 100]\n" + "Time Stamp of final decisions [ 50 150 75 75 50 50 251 125 50 50 75 75 75 75 75 150 50 75\n", + " 50 75 125 50 50 251 75 75 75 75 75 75 50 175 251 50 175 50\n", + " 50 75 75 75 75 50 75 251 75 75 50 50 75 175 75 75 125 50\n", + " 75 50 50 100 175 75 150 50 75 50 75 75 75 75 50 75 50 75\n", + " 150 50 125 50 100 75 50 50 175 75 251 75 50 75 75 175 50 50\n", + " 75 50 50 50 75 50 75 100 75 100 50 50 50 50 50 50 50 150\n", + " 75 50 50 50 251 100 125 75 125 100 75 50 75 50 50 75 200 50\n", + " 50 100 50 50 75 75 50 150 50 75 50 75 200 50 75 75 200 200\n", + " 75 50 75 200 75 75 50 200 75 75 75 100 200 75 75 50 100 50\n", + " 50 75 50 75 75 75 75 50 75 75 50 100 75]\n" ] } ], @@ -378,22 +788,19 @@ " print(\"...........\")\n", "\n", "print(\"Time Stamp of final decisions\", final_decisions)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "\n", "#### References:\n", "\n", "\\[1\\] Schäfer, P., & Leser, U. (2020). TEASER: early and accurate time series classification. Data mining and knowledge discovery, 34(5), 1336-1362" - ], - "metadata": { - "collapsed": false - } + ] } ], "metadata": { From 0f3343d92e60f51decdd5d37f2c3b51e962a1266 Mon Sep 17 00:00:00 2001 From: Kavya Rambhia <161142013+kavya-r30@users.noreply.github.com> Date: Thu, 3 Apr 2025 19:45:49 +0530 Subject: [PATCH 043/216] Docstring (#2609) --- .../distances/_pairwise_distance_matrix.py | 19 ++++++++++++++++++- 1 file changed, 18 insertions(+), 1 deletion(-) diff --git a/aeon/visualisation/distances/_pairwise_distance_matrix.py b/aeon/visualisation/distances/_pairwise_distance_matrix.py index 1755149917..32cec2fe0c 100644 --- a/aeon/visualisation/distances/_pairwise_distance_matrix.py +++ b/aeon/visualisation/distances/_pairwise_distance_matrix.py @@ -15,7 +15,24 @@ def plot_pairwise_distance_matrix( b, path, ): - + """Plot a pairwise distance matrix between two time series. + + Parameters + ---------- + distance_matrix : np.ndarray + The pairwise distance matrix to plot. + a : np.ndarray + The first time series. + b : np.ndarray + The second time series. + path : list of tuple + The path of the minimum distances. + + Returns + ------- + ax : matplotlib.axes.Axes + The Axes object with the plot. + """ # Checks availability of plotting libraries _check_soft_dependencies("matplotlib", "seaborn") import matplotlib.pyplot as plt From f7b57dfe48538fd1dd201f19898e8e508ea51e37 Mon Sep 17 00:00:00 2001 From: Ayush Singh Date: Thu, 3 Apr 2025 19:50:02 +0530 Subject: [PATCH 044/216] [DOC] Add 'Raises' section to docstring #1766 (#2617) * [DOC] Add 'Raises' section to docstring #1766 * Automatic `pre-commit` fixes * Update _base.py * Automatic `pre-commit` fixes --------- Co-authored-by: ayushsingh9720 <199482418+ayushsingh9720@users.noreply.github.com> --- aeon/base/_base.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/aeon/base/_base.py b/aeon/base/_base.py index 84ab552db0..5a336c7397 100644 --- a/aeon/base/_base.py +++ b/aeon/base/_base.py @@ -86,6 +86,11 @@ class and object methods, class attributes ------- self : object Reference to self. + + Raises + ------ + TypeError + If 'keep' is not a string or a list of strings. """ # retrieve parameters to copy them later params = self.get_params(deep=False) From 0fd1b5105c5877b9623a70f5f3831489ca92b409 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Fri, 4 Apr 2025 20:07:02 +0100 Subject: [PATCH 045/216] [DOC] Contributor docs update (#2554) * contributing docs update * contributing docs update 2 * typos * Update contributing.md new section * Update testing.md testing update * Update contributing.md dont steal code * Automatic `pre-commit` fixes * Update contributing.md if --------- Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> Co-authored-by: Antoine Guillaume --- .github/PULL_REQUEST_TEMPLATE.md | 2 +- docs/contributing.md | 34 +++++++++++++++++++++++++++++--- docs/developer_guide/testing.md | 3 ++- 3 files changed, 34 insertions(+), 5 deletions(-) diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index c480942891..8953c4adb4 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -51,7 +51,7 @@ not applicable. To check a box, replace the space inside the square brackets wit --> ##### For all contributions -- [ ] I've added myself to the [list of contributors](https://github.com/aeon-toolkit/aeon/blob/main/.all-contributorsrc). Alternatively, you can use the [@all-contributors](https://allcontributors.org/docs/en/bot/usage) bot to do this for you after the PR has been merged. +- [ ] I've added myself to the [list of contributors](https://github.com/aeon-toolkit/aeon/blob/main/.all-contributorsrc). Alternatively, you can use the [@all-contributors](https://allcontributors.org/docs/en/bot/usage) bot to do this for you **after** the PR has been merged. - [ ] The PR title starts with either [ENH], [MNT], [DOC], [BUG], [REF], [DEP] or [GOV] indicating whether the PR topic is related to enhancement, maintenance, documentation, bugs, refactoring, deprecation or governance. ##### For new estimators and functions diff --git a/docs/contributing.md b/docs/contributing.md index a2acaaf67e..a090e1d30c 100644 --- a/docs/contributing.md +++ b/docs/contributing.md @@ -5,7 +5,7 @@ kinds of contributions, not just code. Improvements to docs, bug reports, and ta on communications or code of conduct responsibilities are all examples of valuable contributions beyond code which help make `aeon` a great package. -Please consider whether you will be able to tackle and issue or pull request before +Please consider whether you will be able to tackle and issue or pull request (PR) before assigning yourself to it. If the issue requires editing Python code, you should have some experience with Python and be able to run tests. If the issue tackles the specifics of a machine learning algorithm, some relevant knowledge of machine learning @@ -14,9 +14,22 @@ of knowledge is required to make a meaningful contribution to certain issues. ChatGPT is not a replacement for this knowledge. Pull requests from unknown contributors which do not attempt to resolve the issue being -addressed, completely disregard the pull request template, or consist of low quality AI +addressed, completely disregard the PR template, or consist of low quality AI generated output may be closed without review. +When implementing new algorithms, developers may require some benchmarking +against alternative implementations or published results. This is likely to +be the case for complex published algorithms which are not contributed by trusted +developers or the original authors. A developer may eventually do this themselves if the +contributor is unable to, but this is a time-consuming process and may delay the +merging of the PR significantly. Please be aware of this when assigning +yourself to an issue for such algorithms. + +When using code from another package or writing code inspired from another implementation, +please mention this in your PR. At the very least credit must be given where +applicable. If the package has a different license, using the code as is may not be +acceptable. Using others code without credit will like result in your PR being closed. + In the following we will give a brief overview of how to contribute to `aeon`. Making contributions to open-source projects takes a bit of proactivity and can be daunting at first, but members of the community are here to help and answer questions. If you get @@ -36,7 +49,8 @@ list may be a good place to start. it. **First ensure that the issue is not already being worked on. Look if there are any linked PRs and search the issue number in the pull requests list.** To assign yourself an **Issue/Pull Request**, please post a comment in the issue -including '@aeon-actions-bot', the username of people to assign and the word `assign`: +including '@aeon-actions-bot', the username of people to assign and the word `assign` +(Please note that anyone @'ed in the comment will be assigned to the issue): For example: ```python @@ -58,6 +72,9 @@ be patient, as Core Developers are volunteers and may be busy with other tasks o outside the package. It could take a while to get a review during slow periods, so please do not rush to @ developers or repeatedly ask for a review. Consider opening the PR as a draft until it is ready for review and passing tests. +7. Respond to reviews if applicable. If you disagree with a change, discuss with the reviewer +Push code as required. Please avoid force-pushing code unless necessary, as this can make +reviewing more difficult and interacts poorly with some CI elements. 8. Once your PR is approved, it will be merged into the `aeon` repository. Thanks for making a contribution! Make sure you are included in the [list of contributors](contributors.md). @@ -83,6 +100,17 @@ Alternatively, you can use the [@all-contributors](https://allcontributors.org/d bot to do this for you. If the contribution is contained in a PR, please only @ the bot when the PR has been merged. A list of relevant tags can be found [here](https://allcontributors.org/docs/en/emoji-key). +## Joining `aeon` as a Core Developer + +`aeon` Core Developers have write access to the repository and the ability to vote on +community decisions. For more details on this role, please refer to the +[about](about.md) and [governance](governance.md) pages. + +If you would like to become a Core Developer, the best way is to reach out and express +your interest. We are particularly open to dedicated contributors who have made +high-quality contributions to the project, as well as time series researchers and +industry professionals. + ## Further Reading For further information on contributing to `aeon`, please see the following pages. diff --git a/docs/developer_guide/testing.md b/docs/developer_guide/testing.md index 94ccf07f89..83fc37628a 100644 --- a/docs/developer_guide/testing.md +++ b/docs/developer_guide/testing.md @@ -166,7 +166,8 @@ The `aeon` PR testing workflow runs on every PR to the main branch. By default, will run a constrained set of tests excluding some tests such as those which are noticeably expensive or prone to failure (i.e. I/O from external sources). The estimators run will also be split into smaller subsets to spread them over -different Python version and operating system combinations. This is controlled by the +different Python version and operating system combinations. This can result in failures +in some runs (likely 3), while others pass without issue. This is controlled by the `PR_TESTING` flag in [`testing/testing_config.py`](https://github.com/aeon-toolkit/aeon/blob/main/aeon/testing/testing_config.py). A large portion of testing time is spent compiling `numba` functions. By default, From 8c3f7392a801304784f767c085e1d549072f06cd Mon Sep 17 00:00:00 2001 From: Akash Kawle <128881349+shinymack@users.noreply.github.com> Date: Sat, 5 Apr 2025 04:02:53 +0530 Subject: [PATCH 046/216] prevent assignment on PRs (#2703) --- .github/utilities/issue_assign.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/.github/utilities/issue_assign.py b/.github/utilities/issue_assign.py index 0acc002560..69622ea5e5 100755 --- a/.github/utilities/issue_assign.py +++ b/.github/utilities/issue_assign.py @@ -2,7 +2,7 @@ It checks if a comment on an issue or PR includes the trigger phrase (as defined) and a mentioned user. -If it does, it assigns the issue/PR to the mentioned user. +If it does, it assigns the issue to the mentioned user. """ import json @@ -19,10 +19,11 @@ issue_number = context_dict["event"]["issue"]["number"] issue = repo.get_issue(number=issue_number) comment_body = context_dict["event"]["comment"]["body"] +pr = context_dict["event"]["issue"].get("pull_request") # Assign tagged used to the issue if the comment includes the trigger phrase body = comment_body.lower() -if "@aeon-actions-bot" in body and "assign" in body: +if "@aeon-actions-bot" in body and "assign" in body and not pr: mentioned_users = re.findall(r"@[a-zA-Z0-9_-]+", comment_body) mentioned_users = [user[1:] for user in mentioned_users] mentioned_users.remove("aeon-actions-bot") From 530cc09b51cc706dcd57c839da4bcf317e188109 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Sat, 5 Apr 2025 14:09:07 +0100 Subject: [PATCH 047/216] Update run_examples.sh (#2701) --- .github/utilities/run_examples.sh | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/utilities/run_examples.sh b/.github/utilities/run_examples.sh index fdbcdb9057..4a8ce4d56b 100755 --- a/.github/utilities/run_examples.sh +++ b/.github/utilities/run_examples.sh @@ -5,7 +5,9 @@ set -euxo pipefail CMD="jupyter nbconvert --to notebook --inplace --execute --ExecutePreprocessor.timeout=600" -excluded=() +excluded=( + "examples/transformations/signature_method.ipynb" +) if [ "$1" = true ]; then excluded+=( "examples/datasets/load_data_from_web.ipynb" @@ -23,8 +25,6 @@ if [ "$1" = true ]; then "examples/classification/shapelet_based.ipynb" "examples/classification/convolution_based.ipynb" "examples/similarity_search/code_speed.ipynb" - "examples/transformations/signature_method.ipynb" - ) fi From c547ac671d2fb2b2f8d8ee3d8c97f89c772f1173 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Sat, 5 Apr 2025 14:09:49 +0100 Subject: [PATCH 048/216] [BUG] SevenNumberSummary bugfix and input rename (#2555) * summary bugfix * maintainer * test --- .../collection/feature_based/_summary.py | 12 +++---- .../feature_based/tests/test_summary.py | 33 ++++++++----------- 2 files changed, 19 insertions(+), 26 deletions(-) diff --git a/aeon/transformations/collection/feature_based/_summary.py b/aeon/transformations/collection/feature_based/_summary.py index 12dba4e756..ed9a90fbd9 100644 --- a/aeon/transformations/collection/feature_based/_summary.py +++ b/aeon/transformations/collection/feature_based/_summary.py @@ -1,6 +1,6 @@ """Summary feature transformer.""" -__maintainer__ = [] +__maintainer__ = ["MatthewMiddlehurst"] __all__ = ["SevenNumberSummary"] import numpy as np @@ -22,12 +22,12 @@ class SevenNumberSummary(BaseCollectionTransformer): Parameters ---------- - summary_stats : ["default", "percentiles", "bowley", "tukey"], default="default" + summary_stats : ["default", "quantiles", "bowley", "tukey"], default="default" The summary statistics to compute. The options are as follows, with float denoting the percentile value extracted from the series: - "default": mean, std, min, max, 0.25, 0.5, 0.75 - - "percentiles": 0.215, 0.887, 0.25, 0.5, 0.75, 0.9113, 0.9785 + - "quantiles": 0.0215, 0.0887, 0.25, 0.5, 0.75, 0.9113, 0.9785 - "bowley": min, max, 0.1, 0.25, 0.5, 0.75, 0.9 - "tukey": min, max, 0.125, 0.25, 0.5, 0.75, 0.875 @@ -89,10 +89,10 @@ def _get_functions(self): 0.5, 0.75, ] - elif self.summary_stats == "percentiles": + elif self.summary_stats == "quantiles": return [ - 0.215, - 0.887, + 0.0215, + 0.0887, 0.25, 0.5, 0.75, diff --git a/aeon/transformations/collection/feature_based/tests/test_summary.py b/aeon/transformations/collection/feature_based/tests/test_summary.py index d35e54f9ac..faf1315573 100644 --- a/aeon/transformations/collection/feature_based/tests/test_summary.py +++ b/aeon/transformations/collection/feature_based/tests/test_summary.py @@ -1,28 +1,21 @@ """Test summary features transformer.""" +import numpy as np import pytest from aeon.transformations.collection.feature_based import SevenNumberSummary -def test_summary_features(): - """Test get functions.""" - x = SevenNumberSummary() - f = x._get_functions() - assert len(f) == 7 - assert callable(f[0]) - x = SevenNumberSummary(summary_stats="percentiles") - f = x._get_functions() - assert len(f) == 7 - assert isinstance(f[0], float) - assert f[1] == 0.887 - x = SevenNumberSummary(summary_stats="bowley") - f = x._get_functions() - assert len(f) == 7 - assert callable(f[0]) - assert f[6] == 0.9 - x = SevenNumberSummary(summary_stats="tukey") - assert len(x._get_functions()) == 7 +@pytest.mark.parametrize("summary_stats", ["default", "quantiles", "bowley", "tukey"]) +def test_summary_features(summary_stats): + """Test different summary_stats options.""" + sns = SevenNumberSummary() + t = sns.fit_transform(np.ones((10, 2, 5))) + assert t.shape == (10, 14) + + +def test_summary_features_invalid(): + """Test invalid summary_stats option.""" with pytest.raises(ValueError, match="Summary function input invalid"): - x = SevenNumberSummary(summary_stats="invalid") - x._get_functions() + sns = SevenNumberSummary(summary_stats="invalid") + sns.fit_transform(np.ones((10, 2, 5))) From d727d95036535793450248d18a605565f4e16f26 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Sat, 5 Apr 2025 22:57:47 +0100 Subject: [PATCH 049/216] readme (#2556) --- README.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index e1475d6d85..cd50191e93 100644 --- a/README.md +++ b/README.md @@ -29,7 +29,7 @@ does not apply: | Overview | | |-----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| **CI/CD** | [![github-actions-release](https://img.shields.io/github/actions/workflow/status/aeon-toolkit/aeon/release.yml?logo=github&label=build%20%28release%29)](https://github.com/aeon-toolkit/aeon/actions/workflows/release.yml) [![github-actions-main](https://img.shields.io/github/actions/workflow/status/aeon-toolkit/aeon/pr_pytest.yml?logo=github&branch=main&label=build%20%28main%29)](https://github.com/aeon-toolkit/aeon/actions/workflows/pr_pytest.yml) [![github-actions-nightly](https://img.shields.io/github/actions/workflow/status/aeon-toolkit/aeon/periodic_tests.yml?logo=github&label=build%20%28nightly%29)](https://github.com/aeon-toolkit/aeon/actions/workflows/periodic_tests.yml) [![docs-main](https://img.shields.io/readthedocs/aeon-toolkit/stable?logo=readthedocs&label=docs%20%28stable%29)](https://www.aeon-toolkit.org/en/stable/) [![docs-main](https://img.shields.io/readthedocs/aeon-toolkit/latest?logo=readthedocs&label=docs%20%28latest%29)](https://www.aeon-toolkit.org/en/latest/) [![!codecov](https://img.shields.io/codecov/c/github/aeon-toolkit/aeon?label=codecov&logo=codecov)](https://codecov.io/gh/aeon-toolkit/aeon) [![openssf-scorecard](https://api.scorecard.dev/projects/github.com/aeon-toolkit/aeon/badge)](https://img.shields.io/ossf-scorecard/github.com/aeon-toolkit/aeon?label=openssf%20scorecard&style=flat) | +| **CI/CD** | [![github-actions-release](https://img.shields.io/github/actions/workflow/status/aeon-toolkit/aeon/release.yml?logo=github&label=build%20%28release%29)](https://github.com/aeon-toolkit/aeon/actions/workflows/release.yml) [![github-actions-main](https://img.shields.io/github/actions/workflow/status/aeon-toolkit/aeon/pr_pytest.yml?logo=github&branch=main&label=build%20%28main%29)](https://github.com/aeon-toolkit/aeon/actions/workflows/pr_pytest.yml) [![github-actions-nightly](https://img.shields.io/github/actions/workflow/status/aeon-toolkit/aeon/periodic_tests.yml?logo=github&label=build%20%28nightly%29)](https://github.com/aeon-toolkit/aeon/actions/workflows/periodic_tests.yml) [![docs-main](https://img.shields.io/readthedocs/aeon-toolkit/stable?logo=readthedocs&label=docs%20%28stable%29)](https://www.aeon-toolkit.org/en/stable/) [![docs-main](https://img.shields.io/readthedocs/aeon-toolkit/latest?logo=readthedocs&label=docs%20%28latest%29)](https://www.aeon-toolkit.org/en/latest/) [![!codecov](https://img.shields.io/codecov/c/github/aeon-toolkit/aeon?label=codecov&logo=codecov)](https://codecov.io/gh/aeon-toolkit/aeon) [![openssf-scorecard](https://api.scorecard.dev/projects/github.com/aeon-toolkit/aeon/badge)](https://scorecard.dev/viewer/?uri=github.com/aeon-toolkit/aeon) | | **Code** | [![!pypi](https://img.shields.io/pypi/v/aeon?logo=pypi&color=blue)](https://pypi.org/project/aeon/) [![!conda](https://img.shields.io/conda/vn/conda-forge/aeon?logo=anaconda&color=blue)](https://anaconda.org/conda-forge/aeon) [![!python-versions](https://img.shields.io/pypi/pyversions/aeon?logo=python)](https://www.python.org/) [![!black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![license](https://img.shields.io/badge/license-BSD%203--Clause-green?logo=style)](https://github.com/aeon-toolkit/aeon/blob/main/LICENSE) [![binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/aeon-toolkit/aeon/main?filepath=examples) | | **Community** | [![!slack](https://img.shields.io/static/v1?logo=slack&label=Slack&message=chat&color=lightgreen)](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-22vwvut29-HDpCu~7VBUozyfL_8j3dLA) [![!linkedin](https://img.shields.io/static/v1?logo=linkedin&label=LinkedIn&message=news&color=lightblue)](https://www.linkedin.com/company/aeon-toolkit/) [![!x-twitter](https://img.shields.io/static/v1?logo=x&label=X/Twitter&message=news&color=lightblue)](https://twitter.com/aeon_toolkit) | | **Affiliation** | [![numfocus](https://img.shields.io/badge/NumFOCUS-Affiliated%20Project-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org/sponsored-projects/affiliated-projects) | @@ -161,7 +161,8 @@ If you use `aeon` we would appreciate a citation of the following [paper](https: If you let us know about your paper using `aeon`, we will happily list it [here](https://www.aeon-toolkit.org/en/stable/papers_using_aeon.html). -## 💬 Further information +## 👥 Further information `aeon` was forked from `sktime` `v0.16.0` in 2022 by an initial group of eight core -developers. +developers. You can read more about the project's history and governance structure in +our [About Us page](https://www.aeon-toolkit.org/en/stable/about.html). From eb3a111256aa41c96f764c41e4e4a1555f006a76 Mon Sep 17 00:00:00 2001 From: Kaustubh <97254178+Kaustbh@users.noreply.github.com> Date: Sun, 6 Apr 2025 03:32:36 +0530 Subject: [PATCH 050/216] remove MutilROCKETRegressor from alias mapping (#2623) Co-authored-by: Matthew Middlehurst --- aeon/benchmarking/results_loaders.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/benchmarking/results_loaders.py b/aeon/benchmarking/results_loaders.py index fae88b6919..b3ed1deaec 100644 --- a/aeon/benchmarking/results_loaders.py +++ b/aeon/benchmarking/results_loaders.py @@ -29,7 +29,7 @@ "Arsenal": ["TheArsenal", "AFC", "ArsenalClassifier"], "ROCKET": ["ROCKETClassifier", "ROCKETRegressor"], "MiniROCKET": ["MiniROCKETClassifier"], - "MR": ["MultiROCKET", "MultiROCKETClassifier", "MultiROCKETRegressor"], + "MR": ["MultiROCKET", "MultiROCKETClassifier"], "Hydra": ["hydraclassifier"], "MR-Hydra": [ "Hydra-MultiROCKET", From ebcd632ac6d3fbb5a3237d8db5c75a427665dc4b Mon Sep 17 00:00:00 2001 From: "aeon-actions-bot[bot]" <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Date: Mon, 7 Apr 2025 16:39:56 +0100 Subject: [PATCH 051/216] Automated `pre-commit` hook update (#2731) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index f707392b5d..505c448aca 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -29,7 +29,7 @@ repos: args: [ "--create", "--python-folders", "aeon" ] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.11.2 + rev: v0.11.4 hooks: - id: ruff args: [ "--fix"] From 7d589b09db1332c74120fdae14787cb89b9b7d59 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 7 Apr 2025 16:40:10 +0100 Subject: [PATCH 052/216] [MNT] Bump the github-actions group with 2 updates (#2733) Bumps the github-actions group with 2 updates: [actions/create-github-app-token](https://github.com/actions/create-github-app-token) and [tj-actions/changed-files](https://github.com/tj-actions/changed-files). Updates `actions/create-github-app-token` from 1 to 2 - [Release notes](https://github.com/actions/create-github-app-token/releases) - [Commits](https://github.com/actions/create-github-app-token/compare/v1...v2) Updates `tj-actions/changed-files` from 46.0.3 to 46.0.4 - [Release notes](https://github.com/tj-actions/changed-files/releases) - [Changelog](https://github.com/tj-actions/changed-files/blob/main/HISTORY.md) - [Commits](https://github.com/tj-actions/changed-files/compare/v46.0.3...v46.0.4) --- updated-dependencies: - dependency-name: actions/create-github-app-token dependency-version: '2' dependency-type: direct:production update-type: version-update:semver-major dependency-group: github-actions - dependency-name: tj-actions/changed-files dependency-version: 46.0.4 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: github-actions ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/issue_assigned.yml | 2 +- .github/workflows/issue_comment_edited.yml | 2 +- .github/workflows/issue_comment_posted.yml | 2 +- .github/workflows/periodic_github_maintenace.yml | 2 +- .github/workflows/pr_opened.yml | 2 +- .github/workflows/pr_precommit.yml | 4 ++-- .github/workflows/precommit_autoupdate.yml | 2 +- .github/workflows/update_contributors.yml | 2 +- 8 files changed, 9 insertions(+), 9 deletions(-) diff --git a/.github/workflows/issue_assigned.yml b/.github/workflows/issue_assigned.yml index 589ea7ec98..7fa14b755a 100644 --- a/.github/workflows/issue_assigned.yml +++ b/.github/workflows/issue_assigned.yml @@ -14,7 +14,7 @@ jobs: steps: - name: Create app token - uses: actions/create-github-app-token@v1 + uses: actions/create-github-app-token@v2 id: app-token with: app-id: ${{ vars.PR_APP_ID }} diff --git a/.github/workflows/issue_comment_edited.yml b/.github/workflows/issue_comment_edited.yml index 1fe3283946..d9ec2b5651 100644 --- a/.github/workflows/issue_comment_edited.yml +++ b/.github/workflows/issue_comment_edited.yml @@ -15,7 +15,7 @@ jobs: steps: - name: Create app token - uses: actions/create-github-app-token@v1 + uses: actions/create-github-app-token@v2 id: app-token with: app-id: ${{ vars.PR_APP_ID }} diff --git a/.github/workflows/issue_comment_posted.yml b/.github/workflows/issue_comment_posted.yml index 752db0e385..a5a7d0f015 100644 --- a/.github/workflows/issue_comment_posted.yml +++ b/.github/workflows/issue_comment_posted.yml @@ -23,7 +23,7 @@ jobs: run: pip install -Uq PyGithub - name: Create app token - uses: actions/create-github-app-token@v1 + uses: actions/create-github-app-token@v2 id: app-token with: app-id: ${{ vars.PR_APP_ID }} diff --git a/.github/workflows/periodic_github_maintenace.yml b/.github/workflows/periodic_github_maintenace.yml index 99772f13d8..4340e40e88 100644 --- a/.github/workflows/periodic_github_maintenace.yml +++ b/.github/workflows/periodic_github_maintenace.yml @@ -16,7 +16,7 @@ jobs: steps: - name: Create app token - uses: actions/create-github-app-token@v1 + uses: actions/create-github-app-token@v2 id: app-token with: app-id: ${{ vars.PR_APP_ID }} diff --git a/.github/workflows/pr_opened.yml b/.github/workflows/pr_opened.yml index db957aa0e6..ccb2e9a515 100644 --- a/.github/workflows/pr_opened.yml +++ b/.github/workflows/pr_opened.yml @@ -29,7 +29,7 @@ jobs: run: pip install -Uq PyGithub - name: Create app token - uses: actions/create-github-app-token@v1 + uses: actions/create-github-app-token@v2 id: app-token with: app-id: ${{ vars.PR_APP_ID }} diff --git a/.github/workflows/pr_precommit.yml b/.github/workflows/pr_precommit.yml index 809803fecc..0a2ea89c94 100644 --- a/.github/workflows/pr_precommit.yml +++ b/.github/workflows/pr_precommit.yml @@ -18,7 +18,7 @@ jobs: steps: - name: Create app token - uses: actions/create-github-app-token@v1 + uses: actions/create-github-app-token@v2 id: app-token with: app-id: ${{ vars.PR_APP_ID }} @@ -37,7 +37,7 @@ jobs: python-version: "3.10" - name: Get changed files - uses: tj-actions/changed-files@v46.0.3 + uses: tj-actions/changed-files@v46.0.4 id: changed-files - name: List changed files diff --git a/.github/workflows/precommit_autoupdate.yml b/.github/workflows/precommit_autoupdate.yml index cc4e2896ab..985a4ec448 100644 --- a/.github/workflows/precommit_autoupdate.yml +++ b/.github/workflows/precommit_autoupdate.yml @@ -21,7 +21,7 @@ jobs: - if: always() name: Create app token - uses: actions/create-github-app-token@v1 + uses: actions/create-github-app-token@v2 id: app-token with: app-id: ${{ vars.PR_APP_ID }} diff --git a/.github/workflows/update_contributors.yml b/.github/workflows/update_contributors.yml index 2d80324ec7..5b69ccb12f 100644 --- a/.github/workflows/update_contributors.yml +++ b/.github/workflows/update_contributors.yml @@ -25,7 +25,7 @@ jobs: id: generate run: npx all-contributors generate - - uses: actions/create-github-app-token@v1 + - uses: actions/create-github-app-token@v2 id: app-token with: app-id: ${{ vars.PR_APP_ID }} From 4f4e1fef7be0af7d9993c6ecd0360e829facf9b4 Mon Sep 17 00:00:00 2001 From: HaroonAzamFiza Date: Fri, 11 Apr 2025 19:37:21 +0100 Subject: [PATCH 053/216] Fixed a few spelling/grammar mistakes on TSC docs examples (#2738) --- examples/classification/classification.ipynb | 386 +++++++++---------- 1 file changed, 193 insertions(+), 193 deletions(-) diff --git a/examples/classification/classification.ipynb b/examples/classification/classification.ipynb index 8ec2f4563b..6971838705 100644 --- a/examples/classification/classification.ipynb +++ b/examples/classification/classification.ipynb @@ -2,6 +2,10 @@ "cells": [ { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "_pBlXBeTh5IG" + }, "source": [ "# Time Series Classification\n", "\n", @@ -14,18 +18,18 @@ " be easy, because the basic usage is identical.\n", "\n", "\"time" - ], - "metadata": { - "collapsed": false, - "id": "_pBlXBeTh5IG" - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "weha73tPh5IH" + }, "source": [ "## Classification Notebooks\n", "\n", - "This note book gives an overview of TSC. More specific notebooks on TSC are base on\n", + "This notebook gives an overview of TSC. More specific notebooks on TSC are based on\n", "the type of representation or transformation they use:\n", "\n", "- [Convolution based](convolution_based.ipynb)\n", @@ -37,14 +41,14 @@ "- [Shapelet based](shapelet_based.ipynb)\n", "- [Hybrid](hybrid.ipynb)\n", "- [Early classification](early_classification.ipynb)\n" - ], - "metadata": { - "collapsed": false, - "id": "weha73tPh5IH" - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "EyjESzTQh5II" + }, "source": [ "## Data Storage and Problem Types\n", "\n", @@ -54,23 +58,26 @@ "multivariate, with at least three dimensions (x,y,z co-ordinates). The image above is\n", " a univariate problem: each series has its own label. The dimension of the time\n", " series instance is also often called the channel. We recommend storing time series\n", - " in 3D numpy array of shape `(n_cases, n_channels, n_timepoints)` and\n", - " where possible our single problem loaders will return a\n", + " in 3D numpy array of shape `(n_cases, n_channels, n_timepoints)` and,\n", + " where possible, our single problem loaders will return a\n", " 3D numpy. Unequal length classification problems are stored in a list of 2D numpy\n", " arrays. More details on data storage can be found in the [data storage](../datasets/datasets.ipynb) notebook." - ], - "metadata": { - "collapsed": false, - "id": "EyjESzTQh5II" - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bjW-qRxOh5II", + "outputId": "a17f6f06-04b2-4fed-877e-92ef9680cdef" + }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "ArrowHead series of type and shape (36, 1, 251)\n", "Motions type of shape (40,)\n" @@ -87,17 +94,14 @@ "motions, motions_labels = load_basic_motions(split=\"train\")\n", "print(f\"ArrowHead series of type {type(arrow)} and shape {arrow.shape}\")\n", "print(f\"Motions type {type(motions)} of shape {motions_labels.shape}\")" - ], - "metadata": { - "id": "bjW-qRxOh5II", - "outputId": "a17f6f06-04b2-4fed-877e-92ef9680cdef", - "colab": { - "base_uri": "https://localhost:8080/" - } - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "pPrsdjsOh5IJ" + }, "source": [ "We use 3D numpy even if the data is univariate: even though classifiers\n", "can work using a 2D array of shape `(n_cases, n_timepoints)`, this 2D shape can get\n", @@ -135,43 +139,39 @@ " involved a subject performing one of four tasks (walking, resting, running and\n", " badminton) for ten seconds. Time series in this data set have six dimensions or\n", " channels." - ], - "metadata": { - "collapsed": false, - "id": "pPrsdjsOh5IJ" - } + ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "9T5zoVT9h5IJ", - "outputId": "2aa3e84a-9fdd-4cd7-fcff-4f6f8172c5ce", "colab": { "base_uri": "https://localhost:8080/", "height": 469 - } + }, + "id": "9T5zoVT9h5IJ", + "outputId": "2aa3e84a-9fdd-4cd7-fcff-4f6f8172c5ce" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "[]" ] }, + "execution_count": 7, "metadata": {}, - "execution_count": 7 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": 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", 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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -187,33 +187,33 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "TtIuima2h5IK", - "outputId": "17310dc6-8ba5-45bb-8e2b-a07f80402bef", "colab": { "base_uri": "https://localhost:8080/", "height": 469 - } + }, + "id": "TtIuima2h5IK", + "outputId": "17310dc6-8ba5-45bb-8e2b-a07f80402bef" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "[]" ] }, + "execution_count": 8, "metadata": {}, - "execution_count": 8 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": 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", 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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -240,16 +240,23 @@ { "cell_type": "code", "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qG96PKaCh5IK", + "outputId": "07ae1abe-a9d2-4e19-f515-9ca20e017177" + }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "0.72" ] }, + "execution_count": 9, "metadata": {}, - "execution_count": 9 + "output_type": "execute_result" } ], "source": [ @@ -262,14 +269,7 @@ "rand_forest.fit(arrow2d, arrow_labels)\n", "y_pred = rand_forest.predict(arrow_test)\n", "accuracy_score(arrow_test_labels, y_pred)" - ], - "metadata": { - "id": "qG96PKaCh5IK", - "outputId": "07ae1abe-a9d2-4e19-f515-9ca20e017177", - "colab": { - "base_uri": "https://localhost:8080/" - } - } + ] }, { "cell_type": "markdown", @@ -290,44 +290,44 @@ "We show the simplest use cases for classifiers and demonstrate how to build bespoke\n", "pipelines for time series classification. An accurate and relatively\n", "fast classifier is the [ROCKET](https://link.springer.com/article/10.1007/s10618-020-00701-z) classifier. ROCKET is a convolution based algorithm\n", - "described in detail in the [convolution based](convolution_based.ipynb) note book." + "described in detail in the [convolution based](convolution_based.ipynb) notebook." ] }, { "cell_type": "code", + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-11-16T19:16:46.486243Z", "start_time": "2024-11-16T19:15:42.973051Z" }, - "id": "2xIrRErYh5IL", - "outputId": "372654b5-3fae-42e8-a315-da9e33ad8e38", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "2xIrRErYh5IL", + "outputId": "372654b5-3fae-42e8-a315-da9e33ad8e38" }, - "source": [ - "from aeon.classification.convolution_based import RocketClassifier\n", - "\n", - "rocket = RocketClassifier(n_kernels=2000)\n", - "rocket.fit(arrow, arrow_labels)\n", - "y_pred = rocket.predict(arrow_test)\n", - "\n", - "accuracy_score(arrow_test_labels, y_pred)" - ], "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "0.76" ] }, + "execution_count": 10, "metadata": {}, - "execution_count": 10 + "output_type": "execute_result" } ], - "execution_count": null + "source": [ + "from aeon.classification.convolution_based import RocketClassifier\n", + "\n", + "rocket = RocketClassifier(n_kernels=2000)\n", + "rocket.fit(arrow, arrow_labels)\n", + "y_pred = rocket.predict(arrow_test)\n", + "\n", + "accuracy_score(arrow_test_labels, y_pred)" + ] }, { "cell_type": "markdown", @@ -350,22 +350,22 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "u0rqqET8h5IL", - "outputId": "b1347f40-c82b-4ecf-ec72-500b1f7f8a12", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "u0rqqET8h5IL", + "outputId": "b1347f40-c82b-4ecf-ec72-500b1f7f8a12" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "0.8685714285714285" ] }, + "execution_count": 11, "metadata": {}, - "execution_count": 11 + "output_type": "execute_result" } ], "source": [ @@ -380,36 +380,27 @@ }, { "cell_type": "markdown", - "source": [ - "The LITETime Classifier is an efficient deep learning-based model for time series classification. It is designed to handle both univariate and multivariate time series data effectively, offering lightweight architecture and competitive performance. For simplicity, this notebook uses 10 epochs to demonstrate the classifier's functionality. To observe the full performance of deep learning models in aeon, it’s recommended to use the library's default epochs. The reduced epochs here simplify the demonstration and reduce runtime. Deep learning approaches for time series classification, are further described in the [deep learning notebook](./deep_learning.ipynb).\n" - ], "metadata": { "id": "gTQRU2rkuPvw" - } + }, + "source": [ + "The LITETime Classifier is an efficient deep learning-based model for time series classification. It is designed to handle both univariate and multivariate time series data effectively, offering lightweight architecture and competitive performance. For simplicity, this notebook uses 10 epochs to demonstrate the classifier's functionality. To observe the full performance of deep learning models in aeon, it’s recommended to use the library's default epochs. The reduced epochs here simplify the demonstration and reduce runtime. Deep learning approaches for time series classification, are further described in the [deep learning notebook](./deep_learning.ipynb).\n" + ] }, { "cell_type": "code", - "source": [ - "from aeon.classification.deep_learning import LITETimeClassifier\n", - "\n", - "lite_time = LITETimeClassifier(n_epochs=10, batch_size=32, random_state=42)\n", - "lite_time.fit(arrow, arrow_labels)\n", - "y_pred = lite_time.predict(arrow_test)\n", - "\n", - "accuracy_score(arrow_test_labels, y_pred)" - ], + "execution_count": null, "metadata": { - "id": "-nnwMXqtSzzc", - "outputId": "5ca88c72-3d6d-4d0b-90e7-b76da94aa62f", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "-nnwMXqtSzzc", + "outputId": "5ca88c72-3d6d-4d0b-90e7-b76da94aa62f" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 122ms/step\n", "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 126ms/step\n", @@ -419,51 +410,67 @@ ] }, { - "output_type": "execute_result", "data": { "text/plain": [ "0.3942857142857143" ] }, + "execution_count": 13, "metadata": {}, - "execution_count": 13 + "output_type": "execute_result" } + ], + "source": [ + "from aeon.classification.deep_learning import LITETimeClassifier\n", + "\n", + "lite_time = LITETimeClassifier(n_epochs=10, batch_size=32, random_state=42)\n", + "lite_time.fit(arrow, arrow_labels)\n", + "y_pred = lite_time.predict(arrow_test)\n", + "\n", + "accuracy_score(arrow_test_labels, y_pred)" ] }, { "cell_type": "markdown", - "source": [], "metadata": { "collapsed": false, "id": "3y4vwmA1h5IL" - } + }, + "source": [] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "OaBVEJmnh5IM" + }, "source": [ "## Multivariate Classification\n", "To use ``sklearn`` classifiers directly on multivariate data, one option is to flatten\n", "the data so that the 3D array `(n_cases, n_channels, n_timepoints)` becomes a 2D array\n", "of shape `(n_cases, n_channels*n_timepoints)`." - ], - "metadata": { - "collapsed": false, - "id": "OaBVEJmnh5IM" - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1mfxhLaZh5IM", + "outputId": "c0a7278f-7feb-45dc-a337-e0da2bcbbf60" + }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "0.925" ] }, + "execution_count": 14, "metadata": {}, - "execution_count": 14 + "output_type": "execute_result" } ], "source": [ @@ -475,71 +482,70 @@ "rand_forest.fit(motions2d, motions_labels)\n", "y_pred = rand_forest.predict(motions2d_test)\n", "accuracy_score(motions_test_labels, y_pred)" - ], - "metadata": { - "id": "1mfxhLaZh5IM", - "outputId": "c0a7278f-7feb-45dc-a337-e0da2bcbbf60", - "colab": { - "base_uri": "https://localhost:8080/" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "However, many ``aeon`` classifiers, including ROCKET and HC2, are configured to\n", - "work with multivariate input. This works exactly like univariate classification. For example:" - ], "metadata": { "collapsed": false, "id": "Hc2DrT2Fh5IM" - } + }, + "source": [ + "However, many ``aeon`` classifiers, including ROCKET and HC2, are configured to\n", + "work with multivariate input. This works exactly like univariate classification. For example:" + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yXZW8cAch5IM", + "outputId": "f3b7b3b7-8204-4e30-cca8-1f07b4d53d90" + }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "1.0" ] }, + "execution_count": 15, "metadata": {}, - "execution_count": 15 + "output_type": "execute_result" } ], "source": [ "rocket.fit(motions, motions_labels)\n", "y_pred = rocket.predict(motions_test)\n", "accuracy_score(motions_test_labels, y_pred)" - ], - "metadata": { - "id": "yXZW8cAch5IM", - "outputId": "f3b7b3b7-8204-4e30-cca8-1f07b4d53d90", - "colab": { - "base_uri": "https://localhost:8080/" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "A list of classifiers capable of handling multivariate classification can be obtained\n", - " with this code" - ], "metadata": { "collapsed": false, "id": "vW1usODIh5IM" - } + }, + "source": [ + "A list of classifiers capable of handling multivariate classification can be obtained\n", + " with this code" + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-efZQXWCh5IN", + "outputId": "778d4b99-7f28-4722-bbc1-c0937d8bdfb9" + }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "[('Arsenal', aeon.classification.convolution_based._arsenal.Arsenal),\n", @@ -625,8 +631,9 @@ " aeon.classification.interval_based._tsf.TimeSeriesForestClassifier)]" ] }, + "execution_count": 16, "metadata": {}, - "execution_count": 16 + "output_type": "execute_result" } ], "source": [ @@ -636,14 +643,7 @@ " tag_filter={\"capability:multivariate\": True},\n", " type_filter=\"classifier\",\n", ")" - ], - "metadata": { - "id": "-efZQXWCh5IN", - "outputId": "778d4b99-7f28-4722-bbc1-c0937d8bdfb9", - "colab": { - "base_uri": "https://localhost:8080/" - } - } + ] }, { "cell_type": "markdown", @@ -664,22 +664,22 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "xtlozU2Hh5IN", - "outputId": "9c5478f1-0184-4afa-87e3-1526988796fe", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "xtlozU2Hh5IN", + "outputId": "9c5478f1-0184-4afa-87e3-1526988796fe" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "0.9" ] }, + "execution_count": 17, "metadata": {}, - "execution_count": 17 + "output_type": "execute_result" } ], "source": [ @@ -702,38 +702,38 @@ }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "-7NDHcmzh5IN" + }, "source": [ "## sklearn Compatibility\n", "\n", "`aeon` classifiers are compatible with `sklearn` model selection and\n", "composition tools using `aeon` data formats. For example, cross-validation can\n", "be performed using the `sklearn` `cross_val_score` and `KFold` functionality:" - ], - "metadata": { - "collapsed": false, - "id": "-7NDHcmzh5IN" - } + ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "Pw_ZNfJvh5IN", - "outputId": "7963c9b6-673f-4d66-95df-e7aa418e39ce", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "Pw_ZNfJvh5IN", + "outputId": "7963c9b6-673f-4d66-95df-e7aa418e39ce" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "array([0.88888889, 0.66666667, 0.77777778, 0.77777778])" ] }, + "execution_count": 18, "metadata": {}, - "execution_count": 18 + "output_type": "execute_result" } ], "source": [ @@ -744,35 +744,35 @@ }, { "cell_type": "markdown", - "source": [ - "Parameter tuning can be done using `sklearn` `GridSearchCV`. For example, we can tune\n", - " the _k_ and distance measure for a K-NN classifier:" - ], "metadata": { "collapsed": false, "id": "aJNXKkYHh5IO" - } + }, + "source": [ + "Parameter tuning can be done using `sklearn` `GridSearchCV`. For example, we can tune\n", + " the _k_ and distance measure for a K-NN classifier:" + ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "K67ps0Bnh5IO", - "outputId": "460d0d39-ae25-4cfa-ea50-02dde57654da", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "K67ps0Bnh5IO", + "outputId": "460d0d39-ae25-4cfa-ea50-02dde57654da" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "0.8" ] }, + "execution_count": 19, "metadata": {}, - "execution_count": 19 + "output_type": "execute_result" } ], "source": [ @@ -792,34 +792,34 @@ }, { "cell_type": "markdown", - "source": [ - "Probability calibration is possible with the `sklearn` `CalibratedClassifierCV`:" - ], "metadata": { "collapsed": false, "id": "FtiuhfARh5IO" - } + }, + "source": [ + "Probability calibration is possible with the `sklearn` `CalibratedClassifierCV`:" + ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "oyywFEuhh5IO", - "outputId": "719c1f06-7eff-429b-dd26-e7da6be20972", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "oyywFEuhh5IO", + "outputId": "719c1f06-7eff-429b-dd26-e7da6be20972" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "0.7485714285714286" ] }, + "execution_count": 20, "metadata": {}, - "execution_count": 20 + "output_type": "execute_result" } ], "source": [ @@ -892,21 +892,26 @@ }, { "cell_type": "code", - "source": [], + "execution_count": null, "metadata": { "id": "ms0mSnWEU11v" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [] } ], "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, "interpreter": { "hash": "9d800c14abb2bd109b7479fe8830174a66f0a4a77373f77c2c7334932e1a4922" }, "kernelspec": { - "name": "python3", - "display_name": "Python 3" + "display_name": "Python 3", + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -919,12 +924,7 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" - }, - "colab": { - "provenance": [], - "gpuType": "T4" - }, - "accelerator": "GPU" + } }, "nbformat": 4, "nbformat_minor": 0 From ace55e7d88e5f2e24424c94ea3917b47c498b7f1 Mon Sep 17 00:00:00 2001 From: adityagh006 <142653450+adityagh006@users.noreply.github.com> Date: Sat, 12 Apr 2025 00:09:19 +0530 Subject: [PATCH 054/216] Fix docstring inconsistencies in benchmarking module (resolves #809) (#2735) * issue#809 Fix docstrings for benchmarking functions * Fixed docstrings in results_loaders.py * Fix docstring inconsistencies in benchmarking module - resolves #809 * Fix docstring inconsistencies in benchmarking module - resolves #809 --- .../metrics/anomaly_detection/thresholding.py | 31 +++++++++--------- aeon/benchmarking/metrics/segmentation.py | 4 +-- aeon/benchmarking/resampling.py | 32 +++++++++---------- 3 files changed, 34 insertions(+), 33 deletions(-) diff --git a/aeon/benchmarking/metrics/anomaly_detection/thresholding.py b/aeon/benchmarking/metrics/anomaly_detection/thresholding.py index 40dda9a9d3..f20b31f1ba 100644 --- a/aeon/benchmarking/metrics/anomaly_detection/thresholding.py +++ b/aeon/benchmarking/metrics/anomaly_detection/thresholding.py @@ -38,8 +38,8 @@ def percentile_threshold(y_score: np.ndarray, percentile: int) -> float: def sigma_threshold(y_score: np.ndarray, factor: float = 2) -> float: r"""Calculate a threshold based on the standard deviation of the anomaly scores. - Computes a threshold :math:`\theta` based on the anomaly scoring's mean - :math:`\mu_s` and the standard deviation :math:`\sigma_s`, ignoring NaNs: + Computes a threshold :math:``\theta`` based on the anomaly scoring's mean + :math:``\mu_s`` and the standard deviation :math:``\sigma_s``, ignoring NaNs: .. math:: \theta = \mu_{s} + x \cdot \sigma_{s} @@ -49,7 +49,7 @@ def sigma_threshold(y_score: np.ndarray, factor: float = 2) -> float: y_score : np.ndarray Anomaly scores for each point of the time series of shape (n_instances,). factor : float - Number of standard deviations to use as threshold (:math:`x`). + Number of standard deviations to use as threshold (:math:``x``). Returns ------- @@ -62,14 +62,15 @@ def sigma_threshold(y_score: np.ndarray, factor: float = 2) -> float: def top_k_points_threshold( y_true: np.ndarray, y_score: np.ndarray, k: int | None = None ) -> float: - """Calculate a threshold such that at least `k` anomalous points are found. + """Calculate a threshold such that at least ``k`` anomalous points are found. The anomalies are single-point anomalies. Computes a threshold based on the number of expected anomalies (number of anomalies). This method iterates over all possible thresholds from high to low to - find the first threshold that yields `k` or more anomalous points. If `k` is `None`, - the ground truth data is used to calculate the real number of anomalies. + find the first threshold that yields ``k`` or more anomalous points. If ``k`` + is ``None``,the ground truth data is used to calculate the real number of + anomalies. Parameters ---------- @@ -78,13 +79,13 @@ def top_k_points_threshold( y_score : np.ndarray Anomaly scores for each point of the time series of shape (n_instances,). k : optional int - Number of expected anomalies. If `k` is `None`, the ground truth data is used - to calculate the real number of anomalies. + Number of expected anomalies. If ``k`` is ``None``, the ground truth data + is used to calculate the real number of anomalies. Returns ------- float - Threshold such that there are at least `k` anomalous points. + Threshold such that there are at least ``k`` anomalous points. """ if k is None: return np.nanpercentile(y_score, (1 - y_true.sum() / y_true.shape[0]) * 100) @@ -95,15 +96,15 @@ def top_k_points_threshold( def top_k_ranges_threshold( y_true: np.ndarray, y_score: np.ndarray, k: int | None = None ) -> float: - """Calculate a threshold such that at least `k` anomalies are found. + """Calculate a threshold such that at least ``k`` anomalies are found. The anomalies are either single-points anomalies or continuous anomalous ranges. Computes a threshold based on the number of expected anomalous subsequences / ranges (number of anomalies). This method iterates over all possible thresholds from high to low to find the first threshold that yields `k` or more continuous - anomalous ranges. If `k` is `None`, the ground truth data is used to calculate the - real number of anomalies (anomalous ranges). + anomalous ranges. If ``k`` is ``None``, the ground truth data is used to + calculate the real number of anomalies (anomalous ranges). Parameters ---------- @@ -112,13 +113,13 @@ def top_k_ranges_threshold( y_score : np.ndarray Anomaly scores for each point of the time series of shape (n_instances,). k : optional int - Number of expected anomalies. If `k` is `None`, the ground truth data is used - to calculate the real number of anomalies. + Number of expected anomalies. If ``k`` is ``None``, the ground truth data + is used to calculate the real number of anomalies. Returns ------- float - Threshold such that there are at least `k` anomalous ranges. + Threshold such that there are at least ``k`` anomalous ranges. """ if k is None: k = _count_anomaly_ranges(y_true) diff --git a/aeon/benchmarking/metrics/segmentation.py b/aeon/benchmarking/metrics/segmentation.py index 4733134279..5dfac8891d 100644 --- a/aeon/benchmarking/metrics/segmentation.py +++ b/aeon/benchmarking/metrics/segmentation.py @@ -47,7 +47,7 @@ def hausdorff_error( .. seealso:: - This function wraps :py:func:`scipy.spatial.distance.directed_hausdorff` + This function wraps :py:func:``scipy.spatial.distance.directed_hausdorff`` Parameters ---------- @@ -56,7 +56,7 @@ def hausdorff_error( pred_change_points: array_like Integer indexes (positions) of predicted change points symmetric: bool - If `True` symmetric Hausdorff distance will be used + If ``True`` symmetric Hausdorff distance will be used seed: int, default=0 Local numpy.random.RandomState seed. Default is 0, a random shuffling of u and v that guarantees reproducibility. diff --git a/aeon/benchmarking/resampling.py b/aeon/benchmarking/resampling.py index 8ce9381203..b0f96a70c8 100644 --- a/aeon/benchmarking/resampling.py +++ b/aeon/benchmarking/resampling.py @@ -32,10 +32,10 @@ def resample_data(X_train, y_train, X_test, y_test, random_state=None): y_test : np.ndarray Test data labels. random_state : int, RandomState instance or None, default=None - If `int`, random_state is the seed used by the random number generator; - If `RandomState` instance, random_state is the random number generator; - If `None`, the random number generator is the `RandomState` instance used - by `np.random`. + If ``int``, random_state is the seed used by the random number generator; + If ``RandomState`` instance, random_state is the random number generator; + If ``None``, the random number generator is the ``RandomState`` instance + used by ``np.random``. Returns ------- @@ -93,10 +93,10 @@ def resample_data_indices(y_train, y_test, random_state=None): y_test : np.ndarray Test data labels. random_state : int, RandomState instance or None, default=None - If `int`, random_state is the seed used by the random number generator; - If `RandomState` instance, random_state is the random number generator; - If `None`, the random number generator is the `RandomState` instance used - by `np.random`. + If ``int``, random_state is the seed used by the random number generator; + If ``RandomState`` instance, random_state is the random number generator; + If ``None``, the random number generator is the ``RandomState`` instance + used by ``np.random``. Returns ------- @@ -136,10 +136,10 @@ def stratified_resample_data(X_train, y_train, X_test, y_test, random_state=None y_test : np.ndarray Test data labels. random_state : int, RandomState instance or None, default=None - If `int`, random_state is the seed used by the random number generator; - If `RandomState` instance, random_state is the random number generator; - If `None`, the random number generator is the `RandomState` instance used - by `np.random`. + If ``int``, random_state is the seed used by the random number generator; + If ``RandomState`` instance, random_state is the random number generator; + If ``None``, the random number generator is the ``RandomState`` instance + used by ``np.random``. Returns ------- @@ -200,10 +200,10 @@ def stratified_resample_data_indices(y_train, y_test, random_state=None): y_test : np.ndarray Test data labels. random_state : int, RandomState instance or None, default=None - If `int`, random_state is the seed used by the random number generator; - If `RandomState` instance, random_state is the random number generator; - If `None`, the random number generator is the `RandomState` instance used - by `np.random`. + If ``int``, random_state is the seed used by the random number generator; + If ``RandomState`` instance, random_state is the random number generator; + If ``None``, the random number generator is the ``RandomState`` instance + used by ``np.random``. Returns ------- From 4c15cc72f48432462d45778f47860fa6c96e6ce1 Mon Sep 17 00:00:00 2001 From: Aryan Pola <98093778+aryanpola@users.noreply.github.com> Date: Sat, 12 Apr 2025 00:47:19 +0530 Subject: [PATCH 055/216] [ENH] `best_on_top` addition in `plot_pairwise_scatter` (#2655) * Empty-Commit * best_on_top parameter added * changes --- aeon/visualisation/results/_scatter.py | 11 +- .../results/tests/test_scatter.py | 13 ++ examples/visualisation/plotting_results.ipynb | 119 +++++++++++++----- 3 files changed, 108 insertions(+), 35 deletions(-) diff --git a/aeon/visualisation/results/_scatter.py b/aeon/visualisation/results/_scatter.py index 65ecff69b5..8a3bf96f05 100644 --- a/aeon/visualisation/results/_scatter.py +++ b/aeon/visualisation/results/_scatter.py @@ -41,6 +41,7 @@ def plot_pairwise_scatter( title=None, figsize=(8, 8), color_palette="tab10", + best_on_top=True, ): """Plot a scatter that compares datasets' results achieved by two methods. @@ -66,6 +67,9 @@ def plot_pairwise_scatter( Size of the figure. color_palette : str, default = "tab10" Color palette to be used for the plot. + best_on_top : bool, default=True + If True, the estimator with better performance is placed on the y-axis (top). + If False, the ordering is reversed. Returns ------- @@ -129,7 +133,7 @@ def plot_pairwise_scatter( x, y = [min_value, max_value], [min_value, max_value] ax.plot(x, y, color="black", alpha=0.5, zorder=1) - # Choose the appropriate order for the methods. Best method is shown in the y-axis. + # better estimator on top (y-axis) if (results_a.mean() <= results_b.mean() and not lower_better) or ( results_a.mean() >= results_b.mean() and lower_better ): @@ -143,6 +147,11 @@ def plot_pairwise_scatter( second = results_b second_method = method_b + # if best_on_top is False, swap the ordering + if not best_on_top: + first, second = second, first + first_method, second_method = second_method, first_method + differences = [ 0 if i - j == 0 else (1 if i - j > 0 else -1) for i, j in zip(first, second) ] diff --git a/aeon/visualisation/results/tests/test_scatter.py b/aeon/visualisation/results/tests/test_scatter.py index 0c3f4d5bf8..1f8ca89c17 100644 --- a/aeon/visualisation/results/tests/test_scatter.py +++ b/aeon/visualisation/results/tests/test_scatter.py @@ -91,6 +91,19 @@ def test_plot_pairwise_scatter(): assert isinstance(fig, plt.Figure) and isinstance(ax, plt.Axes) + # best_on_top = False (reversed ordering) + fig_false, ax_false = plot_pairwise_scatter( + res[0], + res[1], + cls[0], + cls[1], + metric="accuracy", + title="Test Plot best_on_top False", + best_on_top=False, + ) + plt.gcf().canvas.draw_idle() + assert isinstance(fig_false, plt.Figure) and isinstance(ax_false, plt.Axes) + # Test error handling for metrics with pytest.raises(ValueError): plot_pairwise_scatter( diff --git a/examples/visualisation/plotting_results.ipynb b/examples/visualisation/plotting_results.ipynb index 535334c1d8..f8f8629922 100644 --- a/examples/visualisation/plotting_results.ipynb +++ b/examples/visualisation/plotting_results.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "id": "initial_id", "metadata": { "ExecuteTime": { @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "id": "bd9201e73b7ba7d7", "metadata": { "ExecuteTime": { @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "id": "da87284606d4cfd1", "metadata": { "ExecuteTime": { @@ -89,7 +89,7 @@ "outputs": [ { "data": { - "image/png": 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", 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8WI899pjc3OrfM3Xnzp2TJId/LHVFcnKyNm7cqNdee027du1ydXO0ZcsWHTt2TN26dbNPKy0tVXp6uhYsWKDff/9d7u7uDssEBATYryqUO3bsmDw8PNSsWbMr1pRfhWjevLnc3d2vWBMQECDpwhWMli1bVloDAHXF6dOnlZWVpezsbE2YMEGSVFZWJmOMPDw8tG7dOt155531+vxJhql7GWbatGkOP69cufK61+nUXi0oKLBfIp83b16lB83FYmJi1Lt372ta9/r16zVhwgSFh4eradOm9j9DjBs3zuHp0YuVlJRo/vz5io6OVsOGDeXt7a3AwED17t1b06dP12+//eZQf+jQIY0ZM0Zt27aVt7e3GjZsqLZt2+rBBx+s8GGmpaXJZrMpLi7OPm306NGy2WxKS0uTJPXv399+3+zo0aMlSQcPHpTNZrvsZ1NSUqLFixcrJiZGTZo0kY+Pjzp27Khp06bp1KlTFerfffdd+/rPnj2rF198Ue3bt5ePj49D267F8ePH9cILL6hz586aMmWKU8tWl7vuukvbt2/Xtm3b7K+oqCg9+uij2rZtW4XgK0m9evXS+vXrHaatW7dOUVFR8vT0vGJN+fHo5eWlbt26VahZv369vebWW29VQECAQ01RUZE2bNhwzcc1ANQWjRo1qnC+HTt2rDp06KBt27apR48ekurv+ZMMU7czzA3lzNNxy5cvN5LMzTffbIqLi51+uu5KT0q6u7sbm81mbrnlFhMREWFCQ0ONn5+fkWSaNWtmdu7cWWGZYcOG2Z9SbNeunenevbsJDg427u7uRpLJzs621+bm5prmzZsbSaZBgwamS5cuJiIiwjRt2tRIqvC0a2VPSs6ZM8f06dPHNGrUyEgyoaGhpk+fPqZPnz5mzpw59u3oMk9KFhQUmH79+hlJxs3NzYSEhJjQ0FDj5eVlJJlOnTqZo0ePVvqZJSYmmsjISGOz2UynTp1M165dzcCBA6/5szfGmEcffdRIMmlpacYYY0aNGmUkmVmzZjm1nut1tSebL+3tYerUqWbkyJH2nw8cOGAaNGhgpkyZYnbt2mWSk5ONp6enSUlJsddkZGQYd3d3k5SUZHbv3m2SkpKMh4eH+f777+01K1euNJ6eniY5Odns2rXLTJ482fj5+ZmDBw/aa5KSkkzjxo3NZ599ZrZv324eeeQR07JlS3Pq1Kkb+InUf/T2gLqqtvf2cPr0aZOdnW2ys7ONJDN37lyTnZ1tDh06ZIypeP68VGW9PdTX8ycZpm5nmHI3Irs4FX7Hjx9vJJkhQ4ZUaWNXOnCWLFli8vLyHKYVFhaaOXPmGEkmLi7OYV5WVpaRZIKDg82uXbsc5hUUFJilS5ean376yT5twoQJ9m2fPn3aoX737t1myZIlDtOu1E1IbGyskWRSU1MrzLvSgTNixAgjydx1111m//799un5+flm6NChRpIZPny4wzLln5m7u7tp3769w3s9d+5chW1czj//+U8jyTz22GP2aXUl/I4aNarCfkhLSzNdu3Y1Xl5epk2bNmbRokUV1vPpp5+aDh06GE9PT9OxY0ezatWqCjVvv/22CQkJMV5eXiYyMtJs2LDBYX5ZWZmZPn26CQgIMN7e3qZfv35m+/btzr9piyP8oq6q7eG3/HfVpa/y37OVnT8vVln4NaZ+nj/JMBfUxQxzsRoPv0OGDDGSzJQpU6q0sar2kRcTE2MkmV9++cU+bcWKFU61JSEhwUgyOTk511R/ow+cnJwc+/TKvvWePXvWBAcHG5vN5vDNufwzk2S2bNlyTW2/1Llz58xtt91mGjdubI4cOWKfXlvDL+of9jnqqtoefnHtyDAX1LUMc6kbkV2ceuCtfFQWPz8/Zxa7ZllZWUpJSdGuXbtUUFCg0tJSSRe6ZpGkH374wd79RnBwsCTp66+/Vn5+vpo2bXrFdZfXp6SkqEuXLjX+tGn50JCJiYlq2LBhhfkNGjTQgAEDtGzZMm3cuFEhISEO8++44w5FRkZWaduzZ8/Wvn37tGDBAh7UAgBYEhmm6lyZYaqDU+G3/A3f6I6fjTGaMGGCFi5ceMW6/Px8+//36tVLPXr00KZNmxQcHKz4+Hj169dPsbGxioyMrHBgjB8/Xu+9955mzZql999/X3fffbf69u2r/v37KzAw8Ia+n8ps375d0oUD6HIdfJf3fZiXl1dhXqdOnaq03fI+fSMjIzVu3LgqreNqjDEqKSm55vrygSgAAP8P58ZrVz7QgTPIMFXnqgxTXZwKv+XfWHJzc29oIz744AMtXLhQfn5+euONNxQfH6+goCB7NxyPPfaYli9f7nBicHNz05dffqmZM2fqww8/1N/+9jf97W9/kySFhIRoxowZ9qcXJSkiIkLp6emaPn26vvnmGy1ZskRLliyRzWZTfHy85s2bV607p6CgQJK0b98+7du374q15d2QXKyq31T/9Kc/qaSkRIsWLaq2LltKSkrk5eXl1DKNGjWql13IAICz3Nzc1KhRo2q7IlkfFRUV2Xv3uVZkmKpzVYapLk6F3969e+vtt9/Wd999p5KSksuODuOs5cuXS5LefPNNjRkzpsL8n3/+udLlbrrpJs2bN09vvfWWcnJylJ6erjVr1ig1NVVPPPGE/P39NXz4cHt9z5499dVXX+nMmTPKyMhQamqqPvroI61bt07x8fHasWOHmjRpckPe06X8/f0lSUuXLtWTTz5ZLduoTHZ2tmw2m+6///4K88oP5tdee00LFixQcHDwNY+ydjEPDw8VFRU5tYybm1ul3ZgBgNW4u7srPz9fZWVlrm5KnVGV/EGGqTpXZZjq4tSlt8GDB8vf31/Hjh1TSkrKDWvEwYMHJanS/vSKi4u1e/fuKy5vs9kUERGhiRMn6ptvvtHUqVMlXdhJlfH391dCQoKSkpK0Z88etWvXTnl5efryyy+v741cQefOnSVJO3bsqLZtXE5paamOHj1a4XX+/HlJ0pkzZ3T06FEdP368Suu32Wzy9PR06kXwBYD/x93d3enzqJVfVbnnlQxTda7MMNXBqfDbpEkTPf3005KkyZMn23f45WRkZFz23pCLlf9p4OjRoxXmLVu2zOlQ1rNnT0nS4cOHr1rboEED+9CF11JfVQ8++KAk6cMPP9TJkyerbTuX+u2332Qu9OpR4TVq1ChJ0qxZs2SMuer+BACgriLDVJ2rMkx1cfqmyxkzZqhXr146evSoevXqpQ8++MB+BbHc3r17NX78eMXFxenYsWNXXWdMTIykC0PYXXyQrF27Vs8995x8fHwqLLN8+XLNmjWrwsF78uRJ+wguFz9ZOG7cOH388ccqLCx0qE9PT9fXX39dof5Gi4qKUmJiok6ePKn4+HhlZ2c7zC8tLVVaWpoeffRR/f7779XWDgAArIoMUzX1LcM4fcOLl5eX1q1bp9GjR2vVqlV6/PHHNWbMGLVr106+vr46fPiw/Um/Vq1a6bbbbrvqOp9//nmtWLFCmzZtUkhIiDp06KDffvtNBw8etD/JWH5PTbnjx4/r5Zdf1ssvv6ygoCAFBgbq3Llz2rt3r4qKihQUFKRZs2bZ6zMzM7V48WJ5eHjo9ttvV8OGDXX06FH704mPPfaY+vfv7+zH4ZTk5GT93//9n9avX6/IyEi1bt1aLVu2VGFhofbt22e/STw5Obla2wEAgBWRYarOVRkmIyNDDzzwgP3nM2fOSJJeffVVzZs3zz49Ozvb3iXc1VTpcXt/f3+lpKQoPT1df/zjHxUcHKyDBw8qJydHxhjdc889Sk5O1t69exUaGnrV9bVu3VqZmZkaOnSovLy8tGfPHvn4+GjmzJlau3ZtpTelDxs2TK+99pri4+Pl7u6u7du369dff1VoaKhmz56tHTt2qHXr1vb6t956S5MmTVJYWJhOnDihbdu2SZISEhL0+eef6/3336/KR+EUf39/rV27VsuXL1dCQoIKCwu1detWnThxQmFhYXrhhRf0r3/9q9JviQAA4PqRYarGVRmmuLhYJ0+etL/KrywXFhY6TC/vV/la2Iwx5oa2EgAuUVxcLC8vryp1TwS4EscuUP/Q0SoAAAAsg/ALAAAAyyD8AgAAwDIIv3CpRYsWKSwsTI0aNVKjRo3Uq1evq3bUvWHDBnXr1k0+Pj5q27atFi9eXKFm1apV6ty5s7y9vdW5c2etXr26Qs3ChQt16623ysfHR926ddPGjRsd5htjNGPGDAUGBsrX11dxcXHauXPn9b1hALiB0tPTdd999ykwMFA2m01r1qy56jK///67XnrpJYWEhMjb21vt2rXT//zP/zjUcA5FfUb4hUu1atVKSUlJysrKUlZWlu6880498MADlz1B5ubmavDgwerbt6+ys7P14osvauLEiVq1apW9JjMzUw8//LBGjhypnJwcjRw5UomJidq0aZO95uOPP9bkyZP10ksvKTs7W3379tWgQYP0008/2Wtef/11zZ07VwsWLNDmzZsVEBCg+Ph4nT59uvo+EABwwtmzZxUeHq4FCxZc8zKJiYn6+uuvlZycrH//+99asWKFOnbsaJ/PORT1nqlBy5YtM5LMqFGjanKzVZKammokmdjY2MvOj4uLMw0bNjSSjCSTm5trcnNzjSQTEhJSo+2tT2666SbzzjvvVDrv+eefNx07dnSYNmbMGNOzZ0/7z4mJiebuu+92qElISDAjRoyw/xwdHW3Gjh3rUNOxY0czdepUY4wxZWVlJiAgwCQlJdnnnz9/3jRu3NgsXry4am/MwoqKiowkU1RU5OqmAE6pS8euJLN69eor1nz55ZemcePG5uTJk5et4RxaOTJM/XFdV34zMjL01FNPqWPHjmrcuLG8vb0VFBSke++9V++8847Onj17PauvtXbu3KmEhASlpaWpefPm6tOnj/r06VNr++dds2aNxowZo27duqlly5by8vJSkyZN1Lt3b82fP19FRUWubqKkCyPErFy5UmfPnlWvXr0qrcnMzNTAgQMdpiUkJCgrK0vFxcVXrCkfprKoqEhbtmypUDNw4EB7TW5uro4cOeJQ4+3trdjY2Gsa7hIAaqPPP/9cUVFRev311xUUFKT27dvr2WeftQ9QIFnnHEqGqf0Zxhijb7/9Vs8995x69uypJk2ayMvLS4GBgRo2bJhSU1OrtF6nR3iTLnQs/MQTT+iTTz6RJPn4+NhHR8nLy9MXX3yhL774Qi+//LK++uor+7jTdUmDBg3UoUMHh06myyUnJ6uoqEhPP/20fRjCcnl5eerQoYOCgoJqqqlX9d///d/KyMiQt7e3AgMDFR4erl9//VWZmZnKzMzUBx98oH/+859q0qSJS9q3fft29erVS+fPn5e/v79Wr16tzp07V1p75MgRtWjRwmFaixYtVFJSohMnTqhly5aXrTly5Igk6cSJEyotLb1iTfl/K6spH1EHAOqaAwcO6Ntvv5WPj49Wr16tEydO6E9/+pPy8/Pt9/3W93MoGabuZJhvvvlGAwYMkCS5ubnptttuk5+fn3788Ud99tln+uyzzzRt2jSH0fCuhdNXfouLizVw4EB98sknCggI0Hvvvaf8/Hzt2LFDmzdv1uHDh7Vz506NGTNGx48f1/79+53dRK0QHR2tPXv2VDpqyp49eyRJgwYNqjAvKChIe/bssY+1XRs8+eSTSk1N1enTp3XgwAFt3rxZv/zyizIzM9WqVStt2bJFL730ksva16FDB23btk3ff/+9xo0bp1GjRmnXrl2XrbfZbA4/m/9/nJaLp1dWc+m0G1UDAHVFWVmZbDabli9frujoaA0ePFhz587Vu+++63D1t76eQ8kwdSvDGGN02223aeHChTpx4oT+/e9/a+vWrTp58qT+/Oc/S5Jmz56t//3f/3VqvU6H35kzZyojI0MtWrRQZmamHn/8cfn6+jrUdO7cWYsXL1ZqaqpuueUWZzdR65WfIC5937XV6NGjFRcXV2F0op49e2ru3LmSdE1PCFcXLy8v3XbbbYqKitKrr76q8PBwzZ8/v9LagIAA+xWFcseOHZOHh4eaNWt2xZryKxDNmzeXu7v7FWsCAgIk6Yo1AFDXtGzZUkFBQWrcuLF9WqdOnWSM0S+//CKpfp9DyTB1K8NER0dr9+7dGjdunG666Sb7dC8vL73yyiv2AL906VKn1utU+C0oKLBfIp83b57atGlzxfqYmBj17t37mta9fv16TZgwQeHh4WratKn9zxDjxo1zeHr0YiUlJZo/f76io6PVsGFD+5/1e/furenTp+u3335zqD906JDGjBmjtm3bytvbWw0bNlTbtm314IMPauXKlQ61aWlpstlsiouLs08bPXq0bDab0tLSJEn9+/eXzWaTzWbT6NGjJUkHDx6UzWa77GdTUlKixYsXKyYmRk2aNJGPj486duyoadOm6dSpUxXq3333Xfv6z549qxdffFHt27eXj4+PQ9uqqvwJ38LCwute141ijLGP3X2pXr16af369Q7T1q1bp6ioKHu4v1xN+bHo5eWlbt26VahZv369vebWW29VQECAQ01RUZE2bNhwzcc0ANQ2ffr00eHDh3XmzBn7tL1798rNzU2tWrWSVH/PoWSYupdhGjVqJA+Py9+hGx8fL+nCMewUZ56OW758uZFkbr75ZlNcXOz003VXelLS3d3d2Gw2c8stt5iIiAgTGhpq/Pz8jCTTrFkzs3PnzgrLDBs2zP6UYrt27Uz37t1NcHCwcXd3N5JMdna2vTY3N9c0b97cSDINGjQwXbp0MREREaZp06ZGkgkPD3dYd2VPSs6ZM8f06dPHNGrUyEgyoaGhpk+fPqZPnz5mzpw59u3oMk9KFhQUmH79+hlJxs3NzYSEhJjQ0FDj5eVlJJlOnTqZo0ePVvqZJSYmmsjISGOz2UynTp1M165dzcCBA6/5s7+cJUuWGEnmzjvvvO51VcWf//xnk56ebnJzc80PP/xgXnzxRePm5mbWrVtnjDFm6tSpZuTIkfb6AwcOmAYNGpgpU6aYXbt2meTkZOPp6WlSUlLsNRkZGcbd3d0kJSWZ3bt3m6SkJOPh4WG+//57e83KlSuNp6enSU5ONrt27TKTJ082fn5+5uDBg/aapKQk07hxY/PZZ5+Z7du3m0ceecS0bNnSnDp1qgY+mfqlLj0xD1ysth+7p0+fNtnZ2SY7O9tIMnPnzjXZ2dnm0KFDxpiK59DTp0+bVq1ameHDh5udO3eaDRs2mNtvv908+eST9pr6eg4lw9S/DPPKK68YSaZr165OLedU+B0/fryRZIYMGeLURspd6cBZsmSJycvLc5hWWFho5syZYySZuLg4h3lZWVlGkgkODja7du1ymFdQUGCWLl1qfvrpJ/u0CRMm2Ld9+vRph/rdu3ebJUuWOEy7UjchsbGxRpJJTU2tMO9KB86IESOMJHPXXXeZ/fv326fn5+eboUOHGklm+PDhDsuUf2bu7u6mffv2Du/13LlzFbZxLUpKSszPP/9s3n77bdOwYUPj5+dnNm3aVKV1Xa//+I//MCEhIcbLy8vcfPPN5q677rIHX2OMGTVqVIV9kJaWZrp27Wq8vLxMmzZtzKJFiyqs99NPPzUdOnQwnp6epmPHjmbVqlUVat5++237tiMjI82GDRsc5peVlZnp06ebgIAA4+3tbfr162e2b99+Y964xdT2AAFcTm0/dst/V136Kv89W9k5dPfu3WbAgAHG19fXtGrVyjzzzDOmsLDQoaY+nkPJMBfU9QxTrqyszHTt2tVIMhMmTHBqWafC75AhQ4wkM2XKFKc2Uq6qfeTFxMQYSeaXX36xT1uxYoVTbUlISDCSTE5OzjXV3+gDJycnxz69sm+9Z8+eNcHBwcZmszl8cy7/zCSZLVu2XFPbL+ett96qcIIcMmQIgQ7VrrYHCOByOHbrDzLMBXU1w1yq/C/XXl5eZt++fU4t69Q9v+Wjsvj5+Tmz2DXLysrS1KlTdf/99ys2NlYxMTGKiYmx38vxww8/2GuDg4MlSV9//bXy8/Ovuu7y+pSUFHvvADWpfGjIxMRENWzYsML8Bg0aaMCAATLGVBgiUpLuuOMORUZGXlcbgoKC1KdPH0VHR9sfOEhNTdWKFStUWlp6XesGAKA2I8NUXW3IMBfbunWrJk2aJOlCbw/t2rVzanmn+vktf8M3uuNnY4wmTJighQsXXrHu4gOkV69e6tGjhzZt2qTg4GDFx8erX79+io2NVWRkZIWuVMaPH6/33ntPs2bN0vvvv6+7775bffv2Vf/+/RUYGHhD309ltm/fLunCAXS5Dr7L+z7My8urMK9Tp07X3YaHHnpIDz30kP3nTZs2acyYMXrllVeUn5+vRYsWVWm9xhiVlJRcd/tQf5UPQALUVRzDtYuHh4fTXaaRYaquNmSYcrm5ubr33nt1/vx5/eEPf9Czzz7r/EqcuUxcXffLvPfee0aS8fPzMwsXLjQ//vijw/1Hjz76qJFkli1b5rBcfn6+mTRpkmnWrJnDn/JDQkIq1BpjTGZmphk4cKDx8PCw19psNjNw4MAK99zc6D8ZDBgwoNL7sip7TZ8+/aqf2Y2Sl5dnvL29jZubm8OfKpxR/mdBXryu9GrUqJEpKSm5wUcwUL1KSkrsDwjxqj2vqtyGQoa5oC5nmF9//dW0a9fOSDL33HNPlW9HcurKb+/evfX222/ru+++U0lJyRW7n3DG8uXLJUlvvvmmxowZU2H+zz//XOlyN910k+bNm6e33npLOTk5Sk9P15o1a5SamqonnnhC/v7+Gj58uL2+Z8+e+uqrr3TmzBllZGQoNTVVH330kdatW6f4+Hjt2LGj2kY58/f3l3ShL7onn3yyWrZRFYGBgYqIiNCmTZuUk5OjkJAQp9fh4eFRa4ZIRu3l5uYmd3d3VzcDcIq7u7vy8/NVVlbm6qbgIlXJH2SYqqsNGSY/P1/x8fHav3+/YmNj9emnn1YYv+BaObXnBw8eLH9/fx07dkwpKSkaMWJElTZ6qYMHD0pSpf3pFRcXa/fu3Vdc3mazKSIiQhEREZo4caL+/Oc/KykpSUuXLnU4cMr5+/srISFBCQkJevnllxUWFqb9+/fryy+/1COPPHJD3tOlOnfurDVr1mjHjh3Vsv7rUX7LQlVvXbDZbFU+AAGgtnN3d+eLWz1Ahqk6V2eYM2fOaPDgwdqxY4e6d++uv//979c1SIdTD7w1adJETz/9tCRp8uTJ9h1+ORkZGZe9N+Ri5W/g6NGjFeYtW7ZMx48fd6aZ6tmzpyTp8OHDV61t0KCBfdzua6mvqgcffFCS9OGHH+rkyZPVth1nHTx4UDk5OZKk8PBwF7cGAIDqQYapOldmmN9//10PPPCANm3apDvuuENr166t9KE7Zzg9vPGMGTPUq1cvHT16VL169dIHH3yg8+fPO9Ts3btX48ePV1xcnI4dO3bVdcbExEiSpk2b5nCQrF27Vs8995x8fHwqLLN8+XLNmjWrwsF78uRJ+wguFz9ZOG7cOH388ccVRjJLT0+3j2F9I59EvFRUVJQSExN18uRJxcfHKzs722F+aWmp0tLS9Oijj152dLOq2LJli6ZPn64DBw5UmLd27VoNGjRIJSUlGjx4sNNPSwIAUJeQYarGVRmmtLRUI0aM0DfffKN27dpp/fr1atq06fWvuCo3Cp8+fdphZBJfX18TGhpqunfvboKCguzTW7Vq5dCH7OVufD506JB9lBJfX18TERFh2rRpYySZ/v37V3qz+MV91gYFBZnu3bs7jDQSFBRkH+HGGGPCw8ONJOPh4WE6depkoqOjTUhIiH0djz32mEObqqOD6NOnT5v4+Hj7Nlu3bm169OhhunTpYnx9fe3TL+74+XpvFr+4A/SAgAATFRVlwsLCTJMmTezTu3fvbo4fP16l9QMAUJeQYepOhvnoo4/s67399tvtI9Jd+rp0cI2rcfrKr3ThfpOUlBSlp6frj3/8o4KDg+1/PjfG6J577lFycrL27t2r0NDQq66vdevWyszM1NChQ+Xl5aU9e/bIx8dHM2fO1Nq1ayu9KX3YsGF67bXXFB8fL3d3d23fvl2//vqrQkNDNXv2bO3YsUOtW7e217/11luaNGmSwsLCdOLECW3btk2SlJCQoM8//1zvv/9+VT4Kp/j7+2vt2rVavny5EhISVFhYqK1bt+rEiRMKCwvTCy+8oH/961+VfkusqvDwcM2fP1/333+//Pz8tGfPHu3Zs0e+vr4aNGiQli1bpu+++07Nmze/YdsEAKC2IsNUjSsyzMVXkX/88UdlZGRU+tq8ebNT67UZ44LekgEAAAAXqNKVXwAAAKAuIvwCAADAMgi/AAAAsAzCLwAAACyD8AsAAADLIPwCAADAMgi/AAAAsAzCLwAAACyD8AsAAADLIPwCAADAMgi/AAAAsAzCLwAAACyD8AsAAADLIPwCAADAMgi/AAAAsAzCLwAAACyD8AsAAADLIPwCAADAMgi/AAAAsAzCLwAAACyD8AsAAADLIPwCAADAMgi/AAAAsAzCLwAAACyD8AsAAADLIPwCAADAMgi/AAAAsAzCLwAAACyD8AsAAADLIPwCAADAMgi/AAAAsAzCLwAAACyD8AsAAADLIPwCAADAMgi/AAAAsAzCLwAAACyD8AsAAADLIPwCAADAMgi/AAAAsAzCLwAAACyD8AsAAADLIPwCAADAMgi/AAAAsAzCLwAAACyD8AsAAADLIPwCAADAMgi/AAAAsAzCLwAAACzj/wO+EvGLlc3bKgAAAABJRU5ErkJggg==", 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" ] @@ -114,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "id": "9ba66c6d0ac437a5", "metadata": { "ExecuteTime": { @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 27, "id": "66493f5cdea9d644", "metadata": { "ExecuteTime": { @@ -144,9 +144,19 @@ "collapsed": false }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\aryan\\anaconda3\\envs\\aeon\\Lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", + " func(*args, **kwargs)\n", + "c:\\Users\\aryan\\anaconda3\\envs\\aeon\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", + " fig.canvas.print_figure(bytes_io, **kw)\n" + ] + }, { "data": { - "image/png": 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", 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", 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" ] @@ -157,13 +167,54 @@ ], "source": [ "_ = plot_pairwise_scatter(\n", - " classifier_accuracies[0], classifier_accuracies[1], classifiers[0], classifiers[1]\n", + " classifier_accuracies[0],\n", + " classifier_accuracies[1],\n", + " classifiers[0],\n", + " classifiers[1],\n", + " best_on_top=False,\n", ")" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 28, + "id": "a85b3be4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\aryan\\anaconda3\\envs\\aeon\\Lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", + " func(*args, **kwargs)\n", + "c:\\Users\\aryan\\anaconda3\\envs\\aeon\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", + " fig.canvas.print_figure(bytes_io, **kw)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = plot_pairwise_scatter(\n", + " classifier_accuracies[0],\n", + " classifier_accuracies[1],\n", + " classifiers[0],\n", + " classifiers[1],\n", + " best_on_top=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, "id": "a0ba27ecd0bf0a4b", "metadata": { "ExecuteTime": { @@ -175,7 +226,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -190,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "id": "a40d1305b6ba5e93", "metadata": { "ExecuteTime": { @@ -202,7 +253,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -233,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "id": "c2944d477b66ab8d", "metadata": { "ExecuteTime": { @@ -249,7 +300,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "id": "cf683faa01e14340", "metadata": { "ExecuteTime": { @@ -261,7 +312,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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" ] @@ -301,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 14, "id": "467f2ad0789368e8", "metadata": { "ExecuteTime": { @@ -386,7 +437,7 @@ "4 0.7 0.6 0.5 0.4" ] }, - "execution_count": 2, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -400,7 +451,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 15, "id": "ac865d14b86c42a1", "metadata": { "ExecuteTime": { @@ -415,13 +466,13 @@ "
" ] }, - "execution_count": 3, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -438,7 +489,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 16, "id": "3c26d721", "metadata": {}, "outputs": [ @@ -448,13 +499,13 @@ "
" ] }, - "execution_count": 4, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -473,7 +524,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 17, "id": "0be31bf2", "metadata": {}, "outputs": [ @@ -483,13 +534,13 @@ "
" ] }, - "execution_count": 5, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -511,7 +562,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 18, "id": "4f1e9e75", "metadata": {}, "outputs": [ @@ -521,13 +572,13 @@ "
" ] }, - "execution_count": 24, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -549,7 +600,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "id": "a9919cda13fadd3", "metadata": { "ExecuteTime": { @@ -564,13 +615,13 @@ "
" ] }, - "execution_count": 17, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -598,7 +649,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "aeon", "language": "python", "name": "python3" }, @@ -612,7 +663,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.11.10" } }, "nbformat": 4, From 792dbefbc5b0f916d3526c977ca7bce687dcec20 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Fri, 11 Apr 2025 20:19:19 +0100 Subject: [PATCH 056/216] [ENH] Add dummy clusterer tags (#2551) * dummy clusterer tags * len --- aeon/clustering/dummy.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/aeon/clustering/dummy.py b/aeon/clustering/dummy.py index 55dbbe92da..483d846a6f 100644 --- a/aeon/clustering/dummy.py +++ b/aeon/clustering/dummy.py @@ -54,6 +54,13 @@ class DummyClusterer(BaseClusterer): array([0, 1, 0]) """ + _tags = { + "X_inner_type": ["np-list", "numpy3D"], + "capability:missing_values": True, + "capability:multivariate": True, + "capability:unequal_length": True, + } + def __init__(self, strategy="uniform", n_clusters=3, random_state=None): self.strategy = strategy self.random_state = random_state @@ -78,8 +85,7 @@ def _fit(self, X, y=None): self : object Fitted estimator. """ - n_samples = X.shape[0] - + n_samples = len(X) if self.strategy == "random": rng = check_random_state(self.random_state) self.labels_ = rng.randint(self.n_clusters, size=n_samples) @@ -111,7 +117,7 @@ def _predict(self, X, y=None) -> np.ndarray: labels : ndarray of shape (n_samples,) Index of the cluster each sample belongs to. """ - n_samples = X.shape[0] + n_samples = len(X) if self.strategy == "random": rng = check_random_state(self.random_state) return rng.randint(self.n_clusters, size=n_samples) From 7240e98eac6d6bda79085cae35e0ed43f157f98d Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Fri, 11 Apr 2025 20:24:10 +0100 Subject: [PATCH 057/216] [ENH] Collection conversion cleanup and `df-list` fix (#2654) * collection conversion cleanup * notebook * fixes --------- Co-authored-by: Tony Bagnall --- .../tests/test_probability_threshold.py | 2 +- .../early_classification/tests/test_teaser.py | 2 +- aeon/testing/data_generation/_collection.py | 39 +- .../data_generation/tests/test_collection.py | 6 +- aeon/testing/tests/test_testing_data.py | 28 +- aeon/testing/utils/deep_equals.py | 13 +- aeon/utils/conversion/_convert_collection.py | 103 +++-- .../tests/test_convert_collection.py | 216 ++++++----- aeon/utils/data_types.py | 24 +- aeon/utils/validation/collection.py | 365 ++++++++---------- .../utils/validation/tests/test_collection.py | 56 ++- examples/datasets/datasets.ipynb | 239 +++++++++--- 12 files changed, 673 insertions(+), 420 deletions(-) diff --git a/aeon/classification/early_classification/tests/test_probability_threshold.py b/aeon/classification/early_classification/tests/test_probability_threshold.py index 559c0689d0..ad142d5449 100644 --- a/aeon/classification/early_classification/tests/test_probability_threshold.py +++ b/aeon/classification/early_classification/tests/test_probability_threshold.py @@ -32,7 +32,7 @@ def test_early_prob_threshold_near_classification_points(): X = X_test[:, :, :i] if i == 20: - with pytest.raises(ValueError): + with pytest.raises(IndexError): pt.update_predict_proba(X) else: _, decisions = pt.update_predict_proba(X) diff --git a/aeon/classification/early_classification/tests/test_teaser.py b/aeon/classification/early_classification/tests/test_teaser.py index e85ee8f1e1..2af3cf34bd 100644 --- a/aeon/classification/early_classification/tests/test_teaser.py +++ b/aeon/classification/early_classification/tests/test_teaser.py @@ -80,7 +80,7 @@ def test_teaser_near_classification_points(): X = X_test[:, :, :i] if i == 20: - with pytest.raises(ValueError): + with pytest.raises(IndexError): teaser.update_predict_proba(X) else: _, decisions = teaser.update_predict(X) diff --git a/aeon/testing/data_generation/_collection.py b/aeon/testing/data_generation/_collection.py index b2fbcf3ec4..b471acbc18 100644 --- a/aeon/testing/data_generation/_collection.py +++ b/aeon/testing/data_generation/_collection.py @@ -408,15 +408,11 @@ def make_example_dataframe_list( ... random_state=0, ... ) >>> print(data) - [ 0 1 - 0 0.000000 1.688531 - 1 1.715891 1.694503 - 2 1.247127 0.768763 - 3 0.595069 0.113426, 0 1 - 0 2.000000 3.166900 - 1 2.115580 2.272178 - 2 3.702387 0.284144 - 3 0.348517 0.080874] + [ 0 1 2 3 + 0 0.000000 1.688531 1.715891 1.694503 + 1 1.247127 0.768763 0.595069 0.113426, 0 1 2 3 + 0 2.000000 3.166900 2.115580 2.272178 + 1 3.702387 0.284144 0.348517 0.080874] >>> print(labels) [0 1] >>> get_type(data) @@ -428,14 +424,14 @@ def make_example_dataframe_list( for i in range(n_cases): n_timepoints = rng.randint(min_n_timepoints, max_n_timepoints + 1) - x = n_labels * rng.uniform(size=(n_timepoints, n_channels)) + x = n_labels * rng.uniform(size=(n_channels, n_timepoints)) label = x[0, 0].astype(int) if i < n_labels and n_cases > i: x[0, 0] = i label = i x = x * (label + 1) - X.append(pd.DataFrame(x, index=range(n_timepoints), columns=range(n_channels))) + X.append(pd.DataFrame(x, index=range(n_channels), columns=range(n_timepoints))) y[i] = label if regression_target: @@ -574,16 +570,16 @@ def make_example_multi_index_dataframe( ... random_state=0, ... ) >>> print(data) # doctest: +NORMALIZE_WHITESPACE - channel_0 channel_1 + channel 0 1 case timepoint - 0 0 0.000000 1.247127 - 1 1.688531 0.768763 - 2 1.715891 0.595069 - 3 1.694503 0.113426 - 1 0 2.000000 3.702387 - 1 3.166900 0.284144 - 2 2.115580 0.348517 - 3 2.272178 0.080874 + 0 0 0.000000 1.247127 + 1 1.688531 0.768763 + 2 1.715891 0.595069 + 3 1.694503 0.113426 + 1 0 2.000000 3.702387 + 1 3.166900 0.284144 + 2 2.115580 0.348517 + 3 2.272178 0.080874 >>> print(labels) [0 1] >>> get_type(data) @@ -616,8 +612,7 @@ def make_example_multi_index_dataframe( y[i] = label X = X.reset_index(drop=True) - X = X.set_index(["case", "timepoint"]).pivot(columns="channel") - X.columns = [f"channel_{i}" for i in range(n_channels)] + X = X.pivot(index=["case", "timepoint"], columns=["channel"], values="value") if regression_target: y = y.astype(np.float32) diff --git a/aeon/testing/data_generation/tests/test_collection.py b/aeon/testing/data_generation/tests/test_collection.py index 58a781656c..6fa3566983 100644 --- a/aeon/testing/data_generation/tests/test_collection.py +++ b/aeon/testing/data_generation/tests/test_collection.py @@ -178,13 +178,13 @@ def test_make_example_dataframe_list( assert all(isinstance(x, pd.DataFrame) for x in X) assert isinstance(y, np.ndarray) assert len(X) == n_cases - assert all([x.shape[1] == n_channels for x in X]) + assert all([x.shape[0] == n_channels for x in X]) if min_n_timepoints == max_n_timepoints: - assert all([x.shape[0] == min_n_timepoints for x in X]) + assert all([x.shape[1] == min_n_timepoints for x in X]) else: assert all( [ - x.shape[0] >= min_n_timepoints and x.shape[0] <= max_n_timepoints + x.shape[1] >= min_n_timepoints and x.shape[1] <= max_n_timepoints for x in X ] ) diff --git a/aeon/testing/tests/test_testing_data.py b/aeon/testing/tests/test_testing_data.py index f9afe264dd..ef69f55a90 100644 --- a/aeon/testing/tests/test_testing_data.py +++ b/aeon/testing/tests/test_testing_data.py @@ -122,10 +122,9 @@ def test_equal_length_univariate_collection(): assert not is_collection( EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0] ) - assert is_univariate( - EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0], - is_collection=False, - ) + # assert is_univariate( + # EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0], + # ) assert is_equal_length( EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0] ) @@ -199,10 +198,9 @@ def test_unequal_length_univariate_collection(): assert not is_collection( UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0] ) - assert is_univariate( - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0], - is_collection=False, - ) + # assert is_univariate( + # UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0], + # ) assert is_equal_length( UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0] ) @@ -276,10 +274,9 @@ def test_equal_length_multivariate_collection(): assert not is_collection( EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0] ) - assert not is_univariate( - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0], - is_collection=False, - ) + # assert not is_univariate( + # EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0], + # ) assert is_equal_length( EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0] ) @@ -365,10 +362,9 @@ def test_unequal_length_multivariate_collection(): assert not is_collection( UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0] ) - assert not is_univariate( - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0], - is_collection=False, - ) + # assert not is_univariate( + # UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0], + # ) assert is_equal_length( UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0] ) diff --git a/aeon/testing/utils/deep_equals.py b/aeon/testing/utils/deep_equals.py index 869bea1ae7..4a39014d7b 100644 --- a/aeon/testing/utils/deep_equals.py +++ b/aeon/testing/utils/deep_equals.py @@ -84,6 +84,8 @@ def _deep_equals(x, y, depth, ignore_index): def _series_equals(x, y, depth, ignore_index): if x.dtype != y.dtype: return False, f"x.dtype ({x.dtype}) != y.dtype ({y.dtype}), depth={depth}" + if x.shape != y.shape: + return False, f"x.shape ({x.shape}) != y.shape ({y.shape}), depth={depth}" # if columns are object, recurse over entries and index if x.dtype == "object": @@ -108,7 +110,12 @@ def _series_equals(x, y, depth, ignore_index): def _dataframe_equals(x, y, depth, ignore_index): if not x.columns.equals(y.columns): - return False, f"x.columns ({x.columns}) != y.columns ({y.columns})" + return ( + False, + f"x.columns ({x.columns}) != y.columns ({y.columns}), depth={depth}", + ) + if x.shape != y.shape: + return False, f"x.shape ({x.shape}) != y.shape ({y.shape}), depth={depth}" # if columns are equal and at least one is object, recurse over Series if sum(x.dtypes == "object") > 0: @@ -130,7 +137,9 @@ def _dataframe_equals(x, y, depth, ignore_index): def _numpy_equals(x, y, depth, ignore_index): if x.dtype != y.dtype: - return False, f"x.dtype ({x.dtype}) != y.dtype ({y.dtype})" + return False, f"x.dtype ({x.dtype}) != y.dtype ({y.dtype}), depth={depth}" + if x.shape != y.shape: + return False, f"x.shape ({x.shape}) != y.shape ({y.shape}), depth={depth}" if x.dtype == "object": for i in range(len(x)): diff --git a/aeon/utils/conversion/_convert_collection.py b/aeon/utils/conversion/_convert_collection.py index 0e3e28f1af..696b511a08 100644 --- a/aeon/utils/conversion/_convert_collection.py +++ b/aeon/utils/conversion/_convert_collection.py @@ -3,33 +3,31 @@ This contains all functions to convert supported collection data types. String identifier meanings (from aeon.utils.conversion import COLLECTIONS_DATA_TYPES) : + numpy3D : 3D numpy array of time series shape (n_cases, n_channels, n_timepoints) np-list : list of 2D numpy arrays shape (n_channels, n_timepoints_i) df-list : list of 2D pandas dataframes shape (n_channels, n_timepoints_i) numpy2D : 2D numpy array of univariate time series shape (n_cases, n_timepoints) pd-wide : pd.DataFrame of univariate time series shape (n_cases, n_timepoints) -pd-multiindex : pd.DataFrame with multi-index, +pd-multiindex : pd.DataFrame with MultiIndex, index [case, timepoint], columns [channel] -For the seven supported, this gives 42 different converters. +For the six supported, this gives 30 different converters. Rather than using them directly, we recommend using the conversion function convert_collection. """ +__maintainer__ = ["TonyBagnall", "MatthewMiddlehurst"] + from collections.abc import Sequence +from copy import deepcopy from typing import Union import numpy as np import pandas as pd from numba.typed import List as NumbaList -from aeon.utils.data_types import COLLECTIONS_DATA_TYPES -from aeon.utils.validation.collection import _equal_length, get_type - - -def convert_identity(X): - """Convert identity.""" - return X - +from aeon.utils.data_types import COLLECTIONS_DATA_TYPES, COLLECTIONS_UNEQUAL_DATA_TYPES +from aeon.utils.validation.collection import get_type, is_equal_length NUMPY3D_ERROR = ( "Input should be 3-dimensional NumPy array with shape (" @@ -61,8 +59,7 @@ def _from_numpy3d_to_np_list(X): """ if X.ndim != 3: raise TypeError(NUMPY3D_ERROR) - np_list = [x for x in X] - return np_list + return [x for x in X] def _from_numpy3d_to_df_list(X): @@ -86,7 +83,7 @@ def _from_numpy3d_to_df_list(X): """ if X.ndim != 3: raise TypeError(NUMPY3D_ERROR) - df_list = [pd.DataFrame(np.transpose(x)) for x in X] + df_list = [pd.DataFrame(x) for x in X] return df_list @@ -135,12 +132,12 @@ def _from_numpy3d_to_pd_multiindex(X): n_cases, n_channels, n_timepoints = X.shape multi_index = pd.MultiIndex.from_product( [range(n_cases), range(n_channels), range(n_timepoints)], - names=["instances", "columns", "timepoints"], + names=["case", "channel", "timepoint"], ) X_mi = pd.DataFrame({"X": X.flatten()}, index=multi_index) - X_mi = X_mi.unstack(level="columns") - X_mi.columns = [f"var_{i}" for i in range(n_channels)] + X_mi = X_mi.unstack(level=["channel"]) + X_mi.columns = X_mi.columns.droplevel() return X_mi @@ -161,7 +158,7 @@ def _from_np_list_to_df_list(X): n_cases = len(X) df_list = [] for i in range(n_cases): - df_list.append(pd.DataFrame(np.transpose(X[i]))) + df_list.append(pd.DataFrame(X[i])) return df_list @@ -185,9 +182,7 @@ def _from_np_list_to_pd_multiindex(X): def _from_df_list_to_np_list(X): - n_cases = len(X) - list = [np.transpose(np.array(X[i])) for i in range(n_cases)] - return list + return [x.to_numpy() for x in X] def _from_df_list_to_numpy3d(X): @@ -197,12 +192,12 @@ def _from_df_list_to_numpy3d(X): for i in range(len(X)): if not n == len(X[i]) or not set(X[i].columns) == cols: raise TypeError("Cannot convert unequal length series to numpy3D") - nump3D = np.array([x.to_numpy().transpose() for x in X]) + nump3D = np.array([x.to_numpy() for x in X]) return nump3D def _from_df_list_to_numpy2d(X): - if not _equal_length(X, "df-list"): + if not is_equal_length(X): raise TypeError( f"{type(X)} does not store equal length series." f"Cannot convert unequal length to numpy flat" @@ -212,7 +207,7 @@ def _from_df_list_to_numpy2d(X): def _from_df_list_to_pd_wide(X): - if not _equal_length(X, "df-list"): + if not is_equal_length(X): raise TypeError( f"{type(X)} does not store equal length series, " f"Cannot convert unequal length pd wide" @@ -222,9 +217,26 @@ def _from_df_list_to_pd_wide(X): def _from_df_list_to_pd_multiindex(X): - n = len(X) - mi = pd.concat(X, axis=0, keys=range(n), names=["instances", "timepoints"]) - return mi + df = pd.concat( + [x.melt(ignore_index=False).reset_index() for x in X], + axis=0, + keys=range(len(X)), + ).reset_index(level=0) + df.rename( + columns={ + df.columns[0]: "case", + df.columns[1]: "channel", + df.columns[2]: "timepoint", + }, + inplace=True, + ) + df = df.sort_values([df.columns[0], df.columns[1], df.columns[2]]) + df = df.pivot( + index=[df.columns[0], df.columns[2]], + columns=df.columns[1], + values=df.columns[3], + ) + return df def _from_numpy2d_to_numpy3d(X): @@ -246,7 +258,7 @@ def _from_numpy2d_to_df_list(X): if not isinstance(X, np.ndarray) or X.ndim != 2: raise TypeError(NUMPY2D_INPUT_ERROR) X_3d = X.reshape(X.shape[0], 1, X.shape[1]) - X_list = [pd.DataFrame(np.transpose(x)) for x in X_3d] + X_list = [pd.DataFrame(x) for x in X_3d] return X_list @@ -285,10 +297,9 @@ def _pd_wide_to_pd_multiindex(X): return _from_numpy3d_to_pd_multiindex(X_3d) -def _from_pd_multiindex_to_df_list(X): - instance_index = X.index.levels[0] - Xlist = [X.loc[i].rename_axis(None) for i in instance_index] - return Xlist +def _from_pd_multiindex_to_numpy3d(X): + df_list = _from_pd_multiindex_to_df_list(X) + return _from_df_list_to_numpy3d(df_list) def _from_pd_multiindex_to_np_list(X): @@ -296,9 +307,14 @@ def _from_pd_multiindex_to_np_list(X): return _from_df_list_to_np_list(df_list) -def _from_pd_multiindex_to_numpy3d(X): - df_list = _from_pd_multiindex_to_df_list(X) - return _from_df_list_to_numpy3d(df_list) +def _from_pd_multiindex_to_df_list(X): + df_list = [ + X.loc[i].melt(ignore_index=False).reset_index() for i in X.index.levels[0] + ] + return [ + x.pivot(index=x.columns[1], columns=x.columns[0], values=x.columns[2]) + for x in df_list + ] def _from_pd_multiindex_to_numpy2d(X): @@ -311,10 +327,15 @@ def _from_pd_multiindex_to_pd_wide(X): return _from_df_list_to_pd_wide(df_list) +def _copy_data(X): + return deepcopy(X) + + convert_dictionary = dict() -# assign identity function to type conversion to self +# assign copy function to type conversion to self for x in COLLECTIONS_DATA_TYPES: - convert_dictionary[(x, x)] = convert_identity + convert_dictionary[(x, x)] = _copy_data + # numpy3D -> * convert_dictionary[("numpy3D", "np-list")] = _from_numpy3d_to_np_list convert_dictionary[("numpy3D", "df-list")] = _from_numpy3d_to_df_list @@ -356,7 +377,7 @@ def _from_pd_multiindex_to_pd_wide(X): def convert_collection(X, output_type): """Convert from one of collections compatible data structure to another. - See :obj:`aeon.utils.conversion.COLLECTIONS_DATA_TYPE` for the list. + See :obj:`aeon.utils.data_types.COLLECTIONS_DATA_TYPE` for the list. Parameters ---------- @@ -392,7 +413,7 @@ def convert_collection(X, output_type): raise TypeError( f"Attempting to convert from {input_type} to {output_type} " f"but this is not a valid conversion. See " - f"aeon.utils.conversion.COLLECTIONS_DATA_TYPE " + f"aeon.utils.data_types.COLLECTIONS_DATA_TYPE " f"for the list of valid collections" ) return convert_dictionary[(input_type, output_type)](X) @@ -412,12 +433,12 @@ def resolve_equal_length_inner_type(inner_types: Sequence[str]) -> str: return "np-list" if "numpy2D" in inner_types: return "numpy2D" - if "pd-multiindex" in inner_types: - return "pd-multiindex" if "df-list" in inner_types: return "df-list" if "pd-wide" in inner_types: return "pd-wide" + if "pd-multiindex" in inner_types: + return "pd-multiindex" raise ValueError( f"Error, no valid inner types in {inner_types} must be one of " f"{COLLECTIONS_DATA_TYPES}" @@ -440,7 +461,7 @@ def resolve_unequal_length_inner_type(inner_types: Sequence[str]) -> str: return "pd-multiindex" raise ValueError( f"Error, no valid inner types for unequal series in {inner_types} " - f"must be np-list, df-list or pd-multiindex" + f"must be one of {COLLECTIONS_UNEQUAL_DATA_TYPES}" ) diff --git a/aeon/utils/conversion/tests/test_convert_collection.py b/aeon/utils/conversion/tests/test_convert_collection.py index 3776dc7f4f..5c89fc7f9a 100644 --- a/aeon/utils/conversion/tests/test_convert_collection.py +++ b/aeon/utils/conversion/tests/test_convert_collection.py @@ -1,5 +1,7 @@ """Unit tests for check/convert functions.""" +from copy import deepcopy + import numpy as np import pytest @@ -8,6 +10,7 @@ EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION, UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION, ) +from aeon.testing.utils.deep_equals import deep_equals from aeon.utils.conversion._convert_collection import ( _from_numpy2d_to_df_list, _from_numpy2d_to_np_list, @@ -23,15 +26,12 @@ resolve_equal_length_inner_type, resolve_unequal_length_inner_type, ) -from aeon.utils.data_types import COLLECTIONS_DATA_TYPES -from aeon.utils.validation.collection import ( - _equal_length, - get_n_cases, - get_type, - has_missing, - is_equal_length, - is_univariate, +from aeon.utils.data_types import ( + COLLECTIONS_DATA_TYPES, + COLLECTIONS_MULTIVARIATE_DATA_TYPES, + COLLECTIONS_UNEQUAL_DATA_TYPES, ) +from aeon.utils.validation import get_type @pytest.mark.parametrize("input_data", COLLECTIONS_DATA_TYPES) @@ -39,59 +39,138 @@ def test_convert_collection(input_data, output_data): """Test all valid and invalid conversions.""" # All should work with univariate equal length - X = convert_collection( - EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[input_data]["train"][0], output_data - ) - assert get_type(X) == output_data + X = EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[input_data]["train"][0] + Xc = convert_collection(X, output_data) + assert get_type(Xc) == output_data + assert _conversion_shape_3d(X, input_data) == _conversion_shape_3d(Xc, output_data) + # Test with multivariate if input_data in EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION: if output_data in EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION: - X = convert_collection( - EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION[input_data]["train"][0], - output_data, + X = EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION[input_data]["train"][0] + Xc = convert_collection(X, output_data) + assert get_type(Xc) == output_data + assert _conversion_shape_3d(X, input_data) == _conversion_shape_3d( + Xc, output_data ) - assert get_type(X) == output_data else: with pytest.raises(TypeError, match="Cannot convert multivariate"): - X = convert_collection( + convert_collection( EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION[input_data]["train"][0], output_data, ) + # Test with unequal length if input_data in UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION: - if ( - output_data in UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION - or output_data == "pd-multiindex" - ): - X = convert_collection( - UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[input_data]["train"][0], + if output_data in UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION: + X = UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[input_data]["train"][0] + Xc = convert_collection( + X, output_data, ) - assert get_type(X) == output_data + assert get_type(Xc) == output_data + assert _conversion_shape_3d(X, input_data) == _conversion_shape_3d( + Xc, output_data + ) else: with pytest.raises(TypeError, match="Cannot convert unequal"): - X = convert_collection( + convert_collection( UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[input_data]["train"][0], output_data, ) +def _conversion_shape_3d(X, input_data): + if input_data == "numpy3D": + return X.shape + elif input_data == "numpy2D" or input_data == "pd-wide": + return X.shape[0], 1, X.shape[1] + elif input_data == "pd-multiindex": + return ( + len(X.index.get_level_values(0).unique()), + X.columns.nunique(), + X.loc[X.index.get_level_values(0).unique()[-1]].index.nunique(), + ) + elif input_data == "df-list" or input_data == "np-list": + return len(X), X[-1].shape[0], X[-1].shape[1] + else: + raise TypeError(f"Unknown data type: {input_data}") + + @pytest.mark.parametrize("input_data", COLLECTIONS_DATA_TYPES) -def test_convert_df_list(input_data): - """Test that df list is correctly transposed.""" - X = convert_collection( - EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[input_data]["train"][0], "df-list" +def test_self_conversion(input_data): + """Test that data is correctly copied when converting to same data type.""" + X = deepcopy(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[input_data]["train"][0]) + Xc = convert_collection( + EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[input_data]["train"][0], input_data ) - assert X[0].shape == (20, 1) - if input_data in EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION: - X = convert_collection( - EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION[input_data]["train"][0], "df-list" - ) - assert X[0].shape == (20, 2) + assert X is not Xc + assert deep_equals(X, Xc) + + +@pytest.mark.parametrize("input_data", COLLECTIONS_DATA_TYPES) +def test_conversion_loop_returns_same_data(input_data): + """Test that chaining conversions ending at the start gives the same data.""" + dtypes = COLLECTIONS_DATA_TYPES.copy() + np.random.shuffle(dtypes) + Xc = deepcopy(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[input_data]["train"][0]) + for i in dtypes: + Xc = convert_collection(Xc, i) + Xc = convert_collection(Xc, input_data) + + eq, msg = deep_equals( + EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[input_data]["train"][0], + Xc, + ignore_index=True, + return_msg=True, + ) + assert eq, msg + + +@pytest.mark.parametrize("input_data", COLLECTIONS_MULTIVARIATE_DATA_TYPES) +def test_conversion_loop_returns_same_data_multivariate(input_data): + """Test that chaining conversions ending at the start gives the same data.""" + dtypes = COLLECTIONS_MULTIVARIATE_DATA_TYPES.copy() + np.random.shuffle(dtypes) + Xc = deepcopy(EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION[input_data]["train"][0]) + for i in dtypes: + Xc = convert_collection(Xc, i) + Xc = convert_collection(Xc, input_data) + + eq, msg = deep_equals( + EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION[input_data]["train"][0], + Xc, + ignore_index=True, + return_msg=True, + ) + assert eq, msg + + +@pytest.mark.parametrize("input_data", COLLECTIONS_UNEQUAL_DATA_TYPES) +def test_conversion_loop_returns_same_data_unequal(input_data): + """Test that chaining conversions ending at the start gives the same data.""" + dtypes = COLLECTIONS_UNEQUAL_DATA_TYPES.copy() + np.random.shuffle(dtypes) + Xc = deepcopy(UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[input_data]["train"][0]) + for i in dtypes: + Xc = convert_collection(Xc, i) + Xc = convert_collection(Xc, input_data) + + eq, msg = deep_equals( + UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[input_data]["train"][0], + Xc, + ignore_index=True, + return_msg=True, + ) + assert eq, msg def test_resolve_equal_length_inner_type(): """Test the resolution of inner type for equal length collections.""" + for input in COLLECTIONS_DATA_TYPES: + X = resolve_equal_length_inner_type([input]) + assert X == input + test = ["numpy3D"] X = resolve_equal_length_inner_type(test) assert X == "numpy3D" @@ -102,9 +181,16 @@ def test_resolve_equal_length_inner_type(): X = resolve_equal_length_inner_type(test) assert X == "np-list" + with pytest.raises(ValueError, match="no valid inner types"): + resolve_equal_length_inner_type(["invalid"]) + def test_resolve_unequal_length_inner_type(): """Test the resolution of inner type for unequal length collections.""" + for input in COLLECTIONS_UNEQUAL_DATA_TYPES: + X = resolve_unequal_length_inner_type([input]) + assert X == input + test = ["np-list"] X = resolve_unequal_length_inner_type(test) assert X == "np-list" @@ -112,64 +198,8 @@ def test_resolve_unequal_length_inner_type(): X = resolve_unequal_length_inner_type(test) assert X == "np-list" - -@pytest.mark.parametrize("data", COLLECTIONS_DATA_TYPES) -def test_get_n_cases(data): - """Test getting the number of cases.""" - assert get_n_cases(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0]) == 10 - - -@pytest.mark.parametrize("data", COLLECTIONS_DATA_TYPES) -def test_get_type(data): - """Test getting the type.""" - assert get_type(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0]) == data - - -@pytest.mark.parametrize("data", COLLECTIONS_DATA_TYPES) -def test_equal_length(data): - """Test if equal length series correctly identified.""" - assert _equal_length(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0], data) - - -@pytest.mark.parametrize("data", COLLECTIONS_DATA_TYPES) -def test_is_equal_length(data): - """Test if equal length series correctly identified.""" - assert is_equal_length(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0]) - - -@pytest.mark.parametrize("data", ["df-list", "np-list"]) -def test_unequal_length(data): - """Test if unequal length series correctly identified.""" - assert not _equal_length( - UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0], data - ) - - -@pytest.mark.parametrize("data", ["df-list", "np-list"]) -def test_is_unequal_length(data): - """Test if unequal length series correctly identified.""" - assert not is_equal_length( - UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0] - ) - - -@pytest.mark.parametrize("data", COLLECTIONS_DATA_TYPES) -def test_has_missing(data): - """Test if missing values are correctly identified.""" - assert not has_missing(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0]) - X = np.random.random(size=(10, 2, 20)) - X[5][1][12] = np.nan - assert has_missing(X) - - -@pytest.mark.parametrize("data", COLLECTIONS_DATA_TYPES) -def test_is_univariate(data): - """Test if univariate series are correctly identified.""" - assert is_univariate(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0]) - if data in EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION.keys(): - assert not is_univariate( - EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION[data]["train"][0] - ) + with pytest.raises(ValueError, match="no valid inner types"): + resolve_unequal_length_inner_type(["numpy3D"]) NUMPY3D = [ diff --git a/aeon/utils/data_types.py b/aeon/utils/data_types.py index 0202679fb3..aa6b14ba49 100644 --- a/aeon/utils/data_types.py +++ b/aeon/utils/data_types.py @@ -27,7 +27,29 @@ # of shape (n_channels, n_timepoints_i) "numpy2D", # 2D np.ndarray of shape (n_cases, n_timepoints) "pd-wide", # 2D pd.DataFrame of shape (n_cases, n_timepoints) - "pd-multiindex", # pd.DataFrame with multi-index, + "pd-multiindex", # pd.DataFrame with MultiIndex, index [case, timepoint], + # columns [channel] +] + +# subset of collections capable of handling multivariate time series +COLLECTIONS_MULTIVARIATE_DATA_TYPES = [ + "numpy3D", # 3D np.ndarray of format (n_cases, n_channels, n_timepoints) + "np-list", # python list of 2D np.ndarray of length [n_cases], + # each of shape (n_channels, n_timepoints_i) + "df-list", # python list of 2D pd.DataFrames of length [n_cases], each + # of shape (n_channels, n_timepoints_i) + "pd-multiindex", # pd.DataFrame with MultiIndex, index [case, timepoint], + # columns [channel] +] + +# subset of collections capable of handling unequal length time series +COLLECTIONS_UNEQUAL_DATA_TYPES = [ + "np-list", # python list of 2D np.ndarray of length [n_cases], + # each of shape (n_channels, n_timepoints_i) + "df-list", # python list of 2D pd.DataFrames of length [n_cases], each + # of shape (n_channels, n_timepoints_i) + "pd-multiindex", # pd.DataFrame with MultiIndex, index [case, timepoint], + # columns [channel] ] HIERARCHICAL_DATA_TYPES = ["pd_multiindex_hier"] # pd.DataFrame diff --git a/aeon/utils/validation/collection.py b/aeon/utils/validation/collection.py index 1bf02802a4..4c2fafbc65 100644 --- a/aeon/utils/validation/collection.py +++ b/aeon/utils/validation/collection.py @@ -6,7 +6,7 @@ import pandas as pd from numba.typed import List as NumbaList -__maintainer__ = ["TonyBagnall"] +__maintainer__ = ["TonyBagnall", "MatthewMiddlehurst"] def is_tabular(X): @@ -14,23 +14,19 @@ def is_tabular(X): Parameters ---------- - X : array-like + X : collection + See aeon.utils.data_types.COLLECTIONS_DATA_TYPES for details. Returns ------- bool True if input is 2D, False otherwise. """ - if isinstance(X, np.ndarray): - if X.ndim != 2: - return False - return True - if isinstance(X, pd.DataFrame): - return _is_pd_wide(X) + return get_type(X, raise_error=False) in ["numpy2D", "pd-wide"] def is_collection(X, include_2d=False): - """Check X is a valid collection data structure. + """Check X is a valid 3d collection data structure. Parameters ---------- @@ -44,40 +40,16 @@ def is_collection(X, include_2d=False): bool True if input is a collection, False otherwise. """ - if isinstance(X, np.ndarray): - if X.ndim == 3: - return True - if include_2d and X.ndim == 2: - return True - if isinstance(X, pd.DataFrame): - if X.index.nlevels == 2: - return True - if include_2d and _is_pd_wide(X): - return True - if isinstance(X, list): - if isinstance(X[0], np.ndarray): - if X[0].ndim == 2: - return True - if isinstance(X[0], pd.DataFrame): - return True - return False - - -def _is_pd_wide(X): - """Check whether the input DataFrame is "pd-wide" type.""" - # only test is if all values are float. - if isinstance(X, pd.DataFrame) and not isinstance(X.index, pd.MultiIndex): - for col in X: - if not np.issubdtype(X[col].dtype, np.floating): - return False - return True - return False + valid = ["numpy3D", "np-list", "df-list", "pd-multiindex"] + if include_2d: + valid += ["numpy2D", "pd-wide"] + return get_type(X, raise_error=False) in valid def get_n_cases(X): - """Return the number of cases in a collectiom. + """Return the number of cases in a collection. - Handle the single exception of multi index DataFrame. + Returns len(X) except for "pd-multiindex". Parameters ---------- @@ -89,7 +61,8 @@ def get_n_cases(X): int Number of cases. """ - if isinstance(X, pd.DataFrame) and isinstance(X.index, pd.MultiIndex): + t = get_type(X) + if t == "pd-multiindex": return len(X.index.get_level_values(0).unique()) return len(X) @@ -97,13 +70,13 @@ def get_n_cases(X): def get_n_timepoints(X): """Return the number of timepoints in the first element of a collection. - Handles the single exception of multi index DataFrames. If unequal length series, - returns the length of the first series. + If the collection contains unequal length series, returns the length of the first + series in the collection. Parameters ---------- X : collection - See aeon.utils.COLLECTIONS_DATA_TYPES for details. + See aeon.utils.data_types.COLLECTIONS_DATA_TYPES for details. Returns ------- @@ -111,25 +84,21 @@ def get_n_timepoints(X): Number of time points in the first case. """ t = get_type(X) - if t in ["numpy3D", "np-list"]: + if t in ["numpy3D", "np-list", "df-list"]: return X[0].shape[1] - if t in ["numpy2D", "df-list"]: - return X[0].shape[0] + if t in ["numpy2D", "pd-wide"]: + return X.shape[1] if t == "pd-multiindex": - return len(X.index.get_level_values(1).unique()) - if t == "pd-wide": - return len(X.iloc[0]) + return X.loc[X.index.get_level_values(0).unique()[0]].index.nunique() def get_n_channels(X): - """Return the number of channels in the first element of a collectiom. - - Handle the single exception of multi index DataFrame. + """Return the number of channels in the first element of a collection. Parameters ---------- X : collection - See aeon.utils.COLLECTIONS_DATA_TYPES for details. + See aeon.utils.data_types.COLLECTIONS_DATA_TYPES for details. Returns ------- @@ -139,13 +108,13 @@ def get_n_channels(X): Raises ------ ValueError - X is list of 2D numpy but number of channels is not consistent. - X is list of 2D pd.DataFrames but number of channels is not consistent. + X is list of 2D numpy arrays or pd.DataFrames but number of channels is not + consistent. """ t = get_type(X) if t == "numpy3D": - return X[0].shape[0] - if t == "np-list": + return X.shape[1] + if t in ["np-list", "df-list"]: if not all(arr.shape[0] == X[0].shape[0] for arr in X): raise ValueError( f"ERROR: number of channels is not consistent. " @@ -154,94 +123,20 @@ def get_n_channels(X): return X[0].shape[0] if t in ["numpy2D", "pd-wide"]: return 1 - if t == "df-list": - if not all(arr.shape[1] == X[0].shape[1] for arr in X): - raise ValueError( - f"ERROR: number of channels is not consistent. " - f"Found values: {np.unique([arr.shape[1] for arr in X])}." - ) - return X[0].shape[1] if t == "pd-multiindex": - return len(X.columns) - - -def get_type(X): - """Get the string identifier associated with different data structures. - - Parameters - ---------- - X : collection - See aeon.utils.COLLECTIONS_DATA_TYPES for details. - - Returns - ------- - input_type : string - One of COLLECTIONS_DATA_TYPES. - - Raises - ------ - ValueError - X pd.ndarray but wrong dimension - X is list but not of np.ndarray or p.DataFrame. - X is a pd.DataFrame of non float primitives. - - Examples - -------- - >>> from aeon.utils.validation import get_type - >>> get_type( np.zeros(shape=(10, 3, 20))) - 'numpy3D' - """ - if isinstance(X, np.ndarray): # "numpy3D" or numpy2D - if X.ndim == 3: - return "numpy3D" - elif X.ndim == 2: - return "numpy2D" - else: - raise ValueError( - f"ERROR np.ndarray must be 2D or 3D but found " f"{X.ndim}" - ) - elif isinstance(X, list): # np-list or df-list - if isinstance(X[0], np.ndarray): # if one a numpy they must all be 2D numpy - for a in X: - if not (isinstance(a, np.ndarray) and a.ndim == 2): - raise TypeError( - f"ERROR nnp-list must contain 2D np.ndarray but found {a.ndim}" - ) - return "np-list" - elif isinstance(X[0], pd.DataFrame): - for a in X: - if not isinstance(a, pd.DataFrame): - raise TypeError("ERROR df-list must only contain pd.DataFrame") - return "df-list" - else: - raise TypeError( - f"ERROR passed a list containing {type(X[0])}, " - f"lists should either 2D numpy arrays or pd.DataFrames." - ) - elif isinstance(X, pd.DataFrame): # Nested univariate, hierarchical or pd-wide - if isinstance(X.index, pd.MultiIndex): - return "pd-multiindex" - elif _is_pd_wide(X): - return "pd-wide" - raise TypeError( - "ERROR unknown pd.DataFrame, contains non float values, " - "not hierarchical nor is it nested pd.Series" - ) - raise TypeError( - f"ERROR passed input of type {type(X)}, must be of type " - f"np.ndarray, pd.DataFrame or list of np.ndarray/pd.DataFrame" - ) + return X.columns.nunique() def is_equal_length(X): """Test if X contains equal length time series. - Assumes input_type is a valid type (COLLECTIONS_DATA_TYPES). + Assumes input_type is a valid type + (See aeon.utils.data_types.COLLECTIONS_DATA_TYPES). Parameters ---------- X : collection - See aeon.utils.COLLECTIONS_DATA_TYPES for details. + See aeon.utils.data_types.COLLECTIONS_DATA_TYPES for details. Returns ------- @@ -259,7 +154,22 @@ def is_equal_length(X): >>> is_equal_length( np.zeros(shape=(10, 3, 20))) True """ - return _equal_length(X, get_type(X)) + input_type = get_type(X) + if input_type in ["numpy3D", "numpy2D", "pd-wide"]: + return True + + if input_type in ["np-list", "df-list"]: + for i in range(1, len(X)): + if X[i].shape[1] != X[0].shape[1]: + return False + return True + if input_type == "pd-multiindex": + cases = X.index.get_level_values(0).unique() + length = X.loc[cases[0]].index.nunique() + for case in cases: + if X.loc[case].index.nunique() != length: + return False + return True def has_missing(X): @@ -268,8 +178,7 @@ def has_missing(X): Parameters ---------- X : collection - input_type : string - One of COLLECTIONS_DATA_TYPES. + See aeon.utils.data_types.COLLECTIONS_DATA_TYPES for details. Returns ------- @@ -287,11 +196,11 @@ def has_missing(X): >>> m = has_missing( np.zeros(shape=(10, 3, 20))) """ type = get_type(X) - if type == "numpy3D" or type == "numpy2D": - return np.any(np.isnan(np.min(X))) + if type in ["numpy3D", "numpy2D"]: + return np.any(np.isnan(X)) if type == "np-list": for x in X: - if np.any(np.isnan(np.min(x))): + if np.any(np.isnan(x)): return True return False if type == "df-list": @@ -299,85 +208,151 @@ def has_missing(X): if x.isnull().any().any(): return True return False - if type == "pd-wide": + if type in ["pd-wide", "pd-multiindex"]: return X.isnull().any().any() - if type == "pd-multiindex": - if X.isna().values.any(): - return True - return False -def is_univariate(X, is_collection=True): - """Check if X is multivariate.""" - type = get_type(X) - if type == "numpy2D" and is_collection: - return True - if type == "numpy2D" and not is_collection: - return X.shape[0] == 1 - if type == "pd-wide": - return True - if type == "numpy3D": - return X.shape[1] == 1 - # df list (n_timepoints, n_channels) - if type == "df-list": - return X[0].shape[1] == 1 - # np list (n_channels, n_timepoints) - if type == "np-list": - return X[0].shape[0] == 1 - if type == "pd-multiindex": - return X.columns.shape[0] == 1 +def is_univariate(X): + """Check if X is multivariate. + Parameters + ---------- + X : collection + See aeon.utils.data_types.COLLECTIONS_DATA_TYPES for details. + + Returns + ------- + bool + True if series is univariate, else False. + + Raises + ------ + ValueError + X is list of 2D numpy arrays or pd.DataFrames but number of channels is not + consistent. + """ + return get_n_channels(X) == 1 -def _equal_length(X, input_type): - """Test if X contains equal length time series. - Assumes input_type is a valid type (COLLECTIONS_DATA_TYPES). +def get_type(X, raise_error=True): + """Get the string identifier associated with different data structures. Parameters ---------- X : collection - input_type : string - one of COLLECTIONS_DATA_TYPES + See aeon.utils.data_types.COLLECTIONS_DATA_TYPES for details. Returns ------- - boolean - True if all series in X are equal length, False otherwise + input_type : string + One of COLLECTIONS_DATA_TYPES. + raise_error : bool, default=True + If True, raise a ValueError if the input is not a valid type. + If False, returns None when an error would be raised. Raises ------ ValueError - input_type not in COLLECTIONS_DATA_TYPES. + X np.ndarray but does not have 2 or 3 dimensions. + X is a list but not of np.ndarray or pd.DataFrame or contained data has an + inconsistent number of channels. + X is a pd.DataFrame of non float primitives. + X is not a valid type. Examples -------- - >>> _equal_length( np.zeros(shape=(10, 3, 20)), "numpy3D") - True + >>> from aeon.utils.validation import get_type + >>> get_type( np.zeros(shape=(10, 3, 20))) + 'numpy3D' """ - always_equal = {"numpy3D", "numpy2D", "pd-wide"} - if input_type in always_equal: - return True - # np-list are shape (n_channels, n_timepoints) - if input_type == "np-list": - first = X[0].shape[1] - for i in range(1, len(X)): - if X[i].shape[1] != first: + msg = None + if isinstance(X, np.ndarray): # "numpy3D" or numpy2D + if X.ndim == 3: + return "numpy3D" + elif X.ndim == 2: + return "numpy2D" + else: + msg = f"ERROR np.ndarray must be 2D or 3D but found " f"{X.ndim}" + elif isinstance(X, list): # np-list or df-list + if isinstance(X[0], np.ndarray): + for a in X: + # if one a numpy they must all be 2D numpy + if not (isinstance(a, np.ndarray) and a.ndim == 2): + msg = f"ERROR np-list must contain 2D np.ndarray but found {a.ndim}" + break + if msg is None: + return "np-list" + elif isinstance(X[0], pd.DataFrame): + for a in X: + if not isinstance(a, pd.DataFrame): + msg = "ERROR df-list must only contain pd.DataFrame" + break + if not _is_pd_wide(a): + msg = ( + "ERROR df-list must contain non-multiindex pd.DataFrame with" + "numeric values" + ) + break + if msg is None: + return "df-list" + else: + msg = ( + f"ERROR passed a list containing {type(X[0])}, " + f"lists should either 2D numpy arrays or pd.DataFrames." + ) + elif isinstance(X, pd.DataFrame): # pd-multiindex or pd-wide + if _is_pd_multiindex(X): + return "pd-multiindex" + elif _is_pd_wide(X): + return "pd-wide" + else: + msg = ( + "ERROR unknown pd.DataFrame, DataFrames must contain numeric values " + "only and meet pd-multiindex or pd-wide specification." + ) + else: + msg = ( + f"ERROR passed input of type {type(X)}, must be of type " + f"np.ndarray, pd.DataFrame or list of np.ndarray/pd.DataFrame." + f"See aeon.utils.data_types.COLLECTIONS_DATA_TYPES" + ) + + if raise_error and msg is not None: + raise TypeError(msg) + return None + + +def _is_pd_multiindex(X): + """Check whether the input DataFrame is "pd-multiindex" type.""" + if ( + isinstance(X, pd.DataFrame) + and isinstance(X.index, pd.MultiIndex) + and not isinstance(X.columns, pd.MultiIndex) + and len(X.index.levels) == 2 + ): + for col in X: + if not np.issubdtype(X[col].dtype, np.floating) and not np.issubdtype( + X[col].dtype, np.integer + ): return False return True - # df-list are shape (n_timepoints, n_channels) - if input_type == "df-list": - first = X[0].shape[0] - for i in range(1, len(X)): - if X[i].shape[0] != first: + return False + + +def _is_pd_wide(X): + """Check whether the input DataFrame is "pd-wide" type.""" + if ( + isinstance(X, pd.DataFrame) + and not isinstance(X.index, pd.MultiIndex) + and not isinstance(X.columns, pd.MultiIndex) + ): + for col in X: + if not np.issubdtype(X[col].dtype, np.floating) and not np.issubdtype( + X[col].dtype, np.integer + ): return False return True - if input_type == "pd-multiindex": # multiindex dataframe - X = X.reset_index(-1).drop(X.columns, axis=1) - return ( - X.groupby(level=0, group_keys=True, as_index=True).count().nunique().iloc[0] - == 1 - ) - raise ValueError(f" unknown input type {input_type}") + return False def _is_numpy_list_multivariate( diff --git a/aeon/utils/validation/tests/test_collection.py b/aeon/utils/validation/tests/test_collection.py index 4c53572b32..b97a55bd58 100644 --- a/aeon/utils/validation/tests/test_collection.py +++ b/aeon/utils/validation/tests/test_collection.py @@ -12,14 +12,21 @@ make_example_3d_numpy, make_example_3d_numpy_list, ) -from aeon.testing.testing_data import EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION +from aeon.testing.testing_data import ( + EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION, + EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION, + UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION, +) from aeon.utils.data_types import COLLECTIONS_DATA_TYPES from aeon.utils.validation.collection import ( _is_numpy_list_multivariate, _is_pd_wide, + get_n_cases, get_type, has_missing, + is_equal_length, is_tabular, + is_univariate, ) @@ -56,7 +63,7 @@ def test_get_type(): "String_Column": ["Apple", "Banana", "Cherry", "Date", "Elderberry"], } df = pd.DataFrame(data) - with pytest.raises(TypeError, match="contains non float values"): + with pytest.raises(TypeError, match="contain numeric values only"): get_type(df) @@ -327,3 +334,48 @@ def test_is_numpy_list_multivariate_two_multi(): x_multi_numba_list, x_multi_2d_numba_list ) assert is_multivariate is True + + +@pytest.mark.parametrize("data", COLLECTIONS_DATA_TYPES) +def test_get_n_cases(data): + """Test getting the number of cases.""" + assert get_n_cases(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0]) == 10 + + +@pytest.mark.parametrize("data", COLLECTIONS_DATA_TYPES) +def test_get_type2(data): + """Test getting the type.""" + assert get_type(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0]) == data + + +@pytest.mark.parametrize("data", COLLECTIONS_DATA_TYPES) +def test_is_equal_length(data): + """Test if equal length series correctly identified.""" + assert is_equal_length(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0]) + + +@pytest.mark.parametrize("data", ["df-list", "np-list"]) +def test_is_unequal_length(data): + """Test if unequal length series correctly identified.""" + assert not is_equal_length( + UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0] + ) + + +@pytest.mark.parametrize("data", COLLECTIONS_DATA_TYPES) +def test_has_missing2(data): + """Test if missing values are correctly identified.""" + assert not has_missing(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0]) + X = np.random.random(size=(10, 2, 20)) + X[5][1][12] = np.nan + assert has_missing(X) + + +@pytest.mark.parametrize("data", COLLECTIONS_DATA_TYPES) +def test_is_univariate(data): + """Test if univariate series are correctly identified.""" + assert is_univariate(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0]) + if data in EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION.keys(): + assert not is_univariate( + EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION[data]["train"][0] + ) diff --git a/examples/datasets/datasets.ipynb b/examples/datasets/datasets.ipynb index c1cf05ff05..2f8bd9c2ba 100644 --- a/examples/datasets/datasets.ipynb +++ b/examples/datasets/datasets.ipynb @@ -7,15 +7,16 @@ "\n", "Getting data into the correct data structure is fundamental. This notebook describes\n", "the data structures used in `aeon` and links to more complex use cases. `aeon` models\n", - "abstract data types: single series and collections of series.\n", + "two abstract data types: **single series** and **collections of series**.\n", "\n", "A single time series can be univariate (each observation is a single value) or\n", "multivariate (each observation is a vector). We say that the length of the vector\n", "(its dimension) is the number of channels, which in code we denote `n_channels`.\n", "The length of the series is called the number of timepoints, or `n_timepoints` in\n", - "code. We generally store a single series\n", - "in a 2D numpy array with shape ``(n_channels, n_timepoints)``. Series estimators\n", - "should work with a univariate series stored as a 1D numpy array, but will internally convert to 2D." + "code. We generally store a single series in a 2D numpy array with shape\n", + "`(n_channels, n_timepoints)`, though data can be passed the other way around in some\n", + "cases using an `axis` parameter. Series estimators work with a univariate series stored\n", + "as a 1D numpy array, but will internally convert to 2D." ], "metadata": { "collapsed": false @@ -23,35 +24,190 @@ }, { "cell_type": "code", - "execution_count": 1, + "source": [ + "import numpy as np\n", + "\n", + "from aeon.visualisation import plot_series, plot_series_collection" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-03-20T00:21:22.315887Z", + "start_time": "2025-03-20T00:21:21.284145Z" + } + }, + "outputs": [], + "execution_count": 1 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-20T00:21:22.690311Z", + "start_time": "2025-03-20T00:21:22.323622Z" + } + }, + "cell_type": "code", + "source": [ + "# Univariate series length 100\n", + "X = np.random.random((1, 100))\n", + "print(X.shape)\n", + "plot_series(X)" + ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(1, 100)\n", - "(3, 200)\n", - "(10, 1, 50)\n", - "(5, 26, 100)\n" + "(1, 100)\n" ] + }, + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" } ], + "execution_count": 2 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-20T00:21:23.069673Z", + "start_time": "2025-03-20T00:21:22.984023Z" + } + }, + "cell_type": "code", "source": [ - "import numpy as np\n", - "\n", - "X = np.random.random((1, 100)) # Univariate series length 100\n", - "print(X.shape)\n", - "X = np.random.random((3, 200)) # three channel multivariate series length 200\n", + "# Three channel multivariate series length 200\n", + "X = np.array(\n", + " [\n", + " np.sin(np.arange(0, np.pi * 4, np.pi * 4 / 200)),\n", + " np.sin(np.arange(0, np.pi * 8, np.pi * 8 / 200)),\n", + " np.sin(np.arange(0, np.pi * 16, np.pi * 16 / 200)),\n", + " ]\n", + ")\n", "print(X.shape)\n", - "X = np.random.random((10, 1, 50)) # Collection of 10 univariate series of length 50\n", - "print(X.shape)\n", - "X = np.random.random((5, 26, 100)) # Collection of 5 multivariate time series with 26\n", - "# channels, length 100\n", - "print(X.shape)" + "plot_series(X)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3, 200)\n" + ] + }, + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } ], + "execution_count": 3 + }, + { "metadata": { - "collapsed": false - } + "ExecuteTime": { + "end_time": "2025-03-20T00:21:23.138096Z", + "start_time": "2025-03-20T00:21:23.076845Z" + } + }, + "cell_type": "code", + "source": [ + "# Collection of 5 univariate series of length 50\n", + "X = np.random.random((5, 1, 50))\n", + "plot_series_collection(X)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 4 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-20T00:21:23.209004Z", + "start_time": "2025-03-20T00:21:23.143127Z" + } + }, + "cell_type": "code", + "source": [ + "# Collection of 5 multivariate time series with 26 channels, length 100\n", + "X = np.random.random((5, 26, 100))\n", + "plot_series_collection(X)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 5 }, { "cell_type": "markdown", @@ -83,19 +239,6 @@ }, { "cell_type": "code", - "execution_count": 2, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['run' 'run' 'run' 'run' 'run']\n", - "[1 1 1 1 1]\n", - "[0.6 0.6 0.6 0.6 0.6]\n", - "[0.8899 0.8899 0.8899 0.8899 0.8899]\n" - ] - } - ], "source": [ "import numpy as np\n", "\n", @@ -118,8 +261,25 @@ "print(reg.predict(X))" ], "metadata": { - "collapsed": false - } + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-03-20T00:21:23.228783Z", + "start_time": "2025-03-20T00:21:23.218268Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['run' 'run' 'run' 'run' 'run']\n", + "[1 1 1 1 1]\n", + "[0.6 0.6 0.6 0.6 0.6]\n", + "[0.8899 0.8899 0.8899 0.8899 0.8899]\n" + ] + } + ], + "execution_count": 6 }, { "cell_type": "markdown", @@ -144,13 +304,6 @@ "metadata": { "collapsed": false } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } } ], "metadata": { From f1d4da281cc2ed57f8bdf0f895741a47b9ff68aa Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Fri, 11 Apr 2025 20:24:50 +0100 Subject: [PATCH 058/216] [MNT] Updated the release workflows (#2638) * edit release workflows to use trusted publishing * docs --- .github/workflows/fast_release.yml | 8 ++++++-- .github/workflows/release.yml | 8 ++++++-- docs/developer_guide/release.md | 10 +++++++--- 3 files changed, 19 insertions(+), 7 deletions(-) diff --git a/.github/workflows/fast_release.yml b/.github/workflows/fast_release.yml index 8127170713..78c7536bff 100644 --- a/.github/workflows/fast_release.yml +++ b/.github/workflows/fast_release.yml @@ -30,6 +30,12 @@ jobs: upload-wheels: runs-on: ubuntu-24.04 + environment: + name: release + url: https://pypi.org/p/aeon/ + permissions: + id-token: write + steps: - uses: actions/download-artifact@v4 with: @@ -38,5 +44,3 @@ jobs: - name: Publish package to PyPI uses: pypa/gh-action-pypi-publish@release/v1 - with: - password: ${{ secrets.PYPI_TOKEN }} diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 3b80dee509..5af7bfe5a9 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -101,6 +101,12 @@ jobs: needs: test-wheels runs-on: ubuntu-24.04 + environment: + name: release + url: https://pypi.org/p/aeon/ + permissions: + id-token: write + steps: - uses: actions/download-artifact@v4 with: @@ -109,5 +115,3 @@ jobs: - name: Publish package to PyPI uses: pypa/gh-action-pypi-publish@release/v1 - with: - password: ${{ secrets.PYPI_TOKEN }} diff --git a/docs/developer_guide/release.md b/docs/developer_guide/release.md index 13f0e41577..45c0d6dcf9 100644 --- a/docs/developer_guide/release.md +++ b/docs/developer_guide/release.md @@ -41,7 +41,11 @@ The release process is as follows, on high-level: Creation of the GitHub release trigger the `pypi` release workflow. -5. **Wait for the ``pypi`` release CI/CD to finish.** +5. **Approve the release workflow.** + The release workflow will be automatically created in the GitHub Actions tab. This + must be approved by a member of the release management workgroup before it will run. + +6. **Wait for the ``pypi`` release CI/CD to finish.** If tests fail due to sporadic unrelated failure, restart. If tests fail genuinely, something went wrong in the above steps, investigate, fix, and repeat. If the bug is known and sporadic (i.e. failure to read data from an external source), the release @@ -49,7 +53,7 @@ Creation of the GitHub release trigger the `pypi` release workflow. the workflow can be manually run from the GitHub Actions tab if more PRs are required. -6. **Release workflow completion tasks.** +7. **Release workflow completion tasks.** Once the release workflow has passed, check `aeon` version on `pypi`, this should be the new version. A validatory installation of `aeon` in a new Python environment should be carried out according to the installation instructions. If the installation @@ -58,7 +62,7 @@ Creation of the GitHub release trigger the `pypi` release workflow. ## `conda-forge` release and release validation -7. **Merge the ``conda-forge`` release PR.** +8. **Merge the ``conda-forge`` release PR.** After some time a PR will be automatically created in the [aeon conda-forge feedstock](https://github.com/conda-forge/aeon-feedstock). Follow the instructions in the PR to merge it, making sure to update any dependencies that have changed and dependency version bounds. From 5ab8b6b114bad1c536cce50ed0df18add06b17da Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Fri, 11 Apr 2025 20:25:12 +0100 Subject: [PATCH 059/216] [MNT,ENH] Update to allow Python 3.13 (#2608) * python 3.13 * tensorflow * esig * tensorflow * tensorflow * esig and matrix profile * signature notebook * remove prts * fix * remove annoying deps from all_extras * Update pyproject.toml --- .github/actions/cpu_all_extras/action.yml | 6 +++- .github/utilities/run_examples.sh | 3 +- .github/workflows/fast_release.yml | 5 ++-- .github/workflows/issue_assigned.yml | 4 +-- .github/workflows/issue_comment_edited.yml | 4 +-- .github/workflows/issue_comment_posted.yml | 4 +-- .github/workflows/periodic_tests.yml | 31 ++++++++++---------- .github/workflows/pr_core_dep_import.yml | 4 +-- .github/workflows/pr_examples.yml | 6 ++-- .github/workflows/pr_opened.yml | 4 +-- .github/workflows/pr_precommit.yml | 4 +-- .github/workflows/pr_pytest.yml | 15 ++++++---- .github/workflows/pr_typecheck.yml | 4 +-- .github/workflows/precommit_autoupdate.yml | 5 ++-- .github/workflows/release.yml | 12 ++++---- aeon/anomaly_detection/tests/test_cblof.py | 2 +- aeon/anomaly_detection/tests/test_copod.py | 2 +- aeon/testing/tests/test_all_estimators.py | 2 +- examples/transformations/preprocessing.ipynb | 2 +- pyproject.toml | 26 +++++++++------- 20 files changed, 81 insertions(+), 64 deletions(-) diff --git a/.github/actions/cpu_all_extras/action.yml b/.github/actions/cpu_all_extras/action.yml index ff75cd354f..da6a93c828 100644 --- a/.github/actions/cpu_all_extras/action.yml +++ b/.github/actions/cpu_all_extras/action.yml @@ -2,6 +2,10 @@ name: Pip install all_extras with CPU versions description: "For CI testing install the CPU version of dependencies with all extras if on ubuntu" inputs: + python_version: + description: "Python version used" + required: false + default: "3.11" additional_extras: description: "Comma-separated list of additional extras to install" required: false @@ -11,7 +15,7 @@ runs: using: "composite" steps: - name: Install CPU TensorFlow - if: runner.os == 'Linux' + if: ${{ runner.os == 'Linux' && inputs.python_version != '3.13' }} uses: nick-fields/retry@v3 with: timeout_minutes: 30 diff --git a/.github/utilities/run_examples.sh b/.github/utilities/run_examples.sh index 4a8ce4d56b..860a6ecb5b 100755 --- a/.github/utilities/run_examples.sh +++ b/.github/utilities/run_examples.sh @@ -6,7 +6,8 @@ set -euxo pipefail CMD="jupyter nbconvert --to notebook --inplace --execute --ExecutePreprocessor.timeout=600" excluded=( - "examples/transformations/signature_method.ipynb" + # try removing when 3.9 is dropped + "examples/transformations/signature_method.ipynb" ) if [ "$1" = true ]; then excluded+=( diff --git a/.github/workflows/fast_release.yml b/.github/workflows/fast_release.yml index 78c7536bff..695589ee74 100644 --- a/.github/workflows/fast_release.yml +++ b/.github/workflows/fast_release.yml @@ -11,9 +11,10 @@ jobs: steps: - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 + - name: Setup Python 3.11 + uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Build project run: | diff --git a/.github/workflows/issue_assigned.yml b/.github/workflows/issue_assigned.yml index 7fa14b755a..343f468781 100644 --- a/.github/workflows/issue_assigned.yml +++ b/.github/workflows/issue_assigned.yml @@ -25,10 +25,10 @@ jobs: with: sparse-checkout: .github/utilities - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Install PyGithub run: pip install -Uq PyGithub diff --git a/.github/workflows/issue_comment_edited.yml b/.github/workflows/issue_comment_edited.yml index d9ec2b5651..ddd9bf5520 100644 --- a/.github/workflows/issue_comment_edited.yml +++ b/.github/workflows/issue_comment_edited.yml @@ -26,10 +26,10 @@ jobs: with: sparse-checkout: .github/utilities - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Install PyGithub run: pip install -Uq PyGithub diff --git a/.github/workflows/issue_comment_posted.yml b/.github/workflows/issue_comment_posted.yml index a5a7d0f015..80dfa25aab 100644 --- a/.github/workflows/issue_comment_posted.yml +++ b/.github/workflows/issue_comment_posted.yml @@ -14,10 +14,10 @@ jobs: with: sparse-checkout: .github/utilities - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Install PyGithub run: pip install -Uq PyGithub diff --git a/.github/workflows/periodic_tests.yml b/.github/workflows/periodic_tests.yml index 1528959578..a25fd13c8e 100644 --- a/.github/workflows/periodic_tests.yml +++ b/.github/workflows/periodic_tests.yml @@ -18,10 +18,10 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Run check-manifest uses: pre-commit/action@v3.0.1 @@ -35,10 +35,10 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Run pre-commit uses: pre-commit/action@v3.0.1 @@ -57,17 +57,17 @@ jobs: brew install bash /opt/homebrew/bin/bash --version - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Use numba cache to set env variables but not restore cache uses: ./.github/actions/numba_cache with: cache_name: "run-notebook-examples" runner_os: ${{ runner.os }} - python_version: "3.10" + python_version: "3.11" restore_cache: "false" - uses: ./.github/actions/cpu_all_extras @@ -92,10 +92,10 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Install aeon and dependencies uses: nick-fields/retry@v3 @@ -117,17 +117,17 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Use numba cache to set env variables but not restore cache uses: ./.github/actions/numba_cache with: cache_name: "test-no-soft-deps" runner_os: ${{ runner.os }} - python_version: "3.10" + python_version: "3.11" restore_cache: "false" - name: Install aeon and dependencies @@ -157,7 +157,7 @@ jobs: fail-fast: false matrix: os: [ ubuntu-24.04, macOS-14, windows-2022 ] - python-version: [ "3.9", "3.10", "3.11", "3.12" ] + python-version: [ "3.9", "3.10", "3.11", "3.12", "3.13" ] steps: - name: Checkout @@ -184,6 +184,7 @@ jobs: - uses: ./.github/actions/cpu_all_extras with: + python_version: ${{ matrix.python-version }} additional_extras: "dev" - name: Show dependencies @@ -206,10 +207,10 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Disable Numba JIT run: echo "NUMBA_DISABLE_JIT=1" >> $GITHUB_ENV diff --git a/.github/workflows/pr_core_dep_import.yml b/.github/workflows/pr_core_dep_import.yml index 1042610d1a..dc1965deb6 100644 --- a/.github/workflows/pr_core_dep_import.yml +++ b/.github/workflows/pr_core_dep_import.yml @@ -24,10 +24,10 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Install aeon and dependencies uses: nick-fields/retry@v3 diff --git a/.github/workflows/pr_examples.yml b/.github/workflows/pr_examples.yml index 2d7ea6f129..99699f15ae 100644 --- a/.github/workflows/pr_examples.yml +++ b/.github/workflows/pr_examples.yml @@ -30,10 +30,10 @@ jobs: brew install bash /opt/homebrew/bin/bash --version - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - if: ${{ github.event_name != 'pull_request' || !contains(github.event.pull_request.labels.*.name, 'no numba cache') }} name: Restore numba cache @@ -41,7 +41,7 @@ jobs: with: cache_name: "run-notebook-examples" runner_os: ${{ runner.os }} - python_version: "3.10" + python_version: "3.11" - uses: ./.github/actions/cpu_all_extras with: diff --git a/.github/workflows/pr_opened.yml b/.github/workflows/pr_opened.yml index ccb2e9a515..f6f6e88bef 100644 --- a/.github/workflows/pr_opened.yml +++ b/.github/workflows/pr_opened.yml @@ -20,10 +20,10 @@ jobs: with: sparse-checkout: .github/utilities - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Install PyGithub run: pip install -Uq PyGithub diff --git a/.github/workflows/pr_precommit.yml b/.github/workflows/pr_precommit.yml index 0a2ea89c94..f4db6893d3 100644 --- a/.github/workflows/pr_precommit.yml +++ b/.github/workflows/pr_precommit.yml @@ -31,10 +31,10 @@ jobs: ref: ${{ github.head_ref }} token: ${{ steps.app-token.outputs.token }} - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Get changed files uses: tj-actions/changed-files@v46.0.4 diff --git a/.github/workflows/pr_pytest.yml b/.github/workflows/pr_pytest.yml index ae1c792243..4b5679f76d 100644 --- a/.github/workflows/pr_pytest.yml +++ b/.github/workflows/pr_pytest.yml @@ -24,10 +24,10 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - if: ${{ github.event_name != 'pull_request' || !contains(github.event.pull_request.labels.*.name, 'no numba cache') }} name: Restore numba cache @@ -35,7 +35,7 @@ jobs: with: cache_name: "test-no-soft-deps" runner_os: ${{ runner.os }} - python_version: "3.10" + python_version: "3.11" - name: Install aeon and dependencies uses: nick-fields/retry@v3 @@ -57,13 +57,15 @@ jobs: fail-fast: false matrix: os: [ ubuntu-24.04, macOS-14, windows-2022 ] - python-version: [ "3.9", "3.10", "3.11", "3.12" ] + python-version: [ "3.9", "3.10", "3.11", "3.12", "3.13" ] # skip python versions unless the PR has the 'full pytest actions' label pr-testing: - ${{ (github.event_name == 'pull_request' && !contains(github.event.pull_request.labels.*.name, 'full pytest actions')) }} exclude: - pr-testing: true python-version: "3.10" + - pr-testing: true + python-version: "3.12" steps: - name: Checkout @@ -90,6 +92,7 @@ jobs: - uses: ./.github/actions/cpu_all_extras with: + python_version: ${{ matrix.python-version }} additional_extras: "dev" - name: Show dependencies @@ -109,10 +112,10 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Disable Numba JIT run: echo "NUMBA_DISABLE_JIT=1" >> $GITHUB_ENV diff --git a/.github/workflows/pr_typecheck.yml b/.github/workflows/pr_typecheck.yml index 7f0f80a856..f6082ac585 100644 --- a/.github/workflows/pr_typecheck.yml +++ b/.github/workflows/pr_typecheck.yml @@ -24,10 +24,10 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Install aeon, dependencies and mypy uses: nick-fields/retry@v3 diff --git a/.github/workflows/precommit_autoupdate.yml b/.github/workflows/precommit_autoupdate.yml index 985a4ec448..a670feaf2f 100644 --- a/.github/workflows/precommit_autoupdate.yml +++ b/.github/workflows/precommit_autoupdate.yml @@ -13,9 +13,10 @@ jobs: steps: - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 + - name: Setup Python 3.11 + uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - uses: browniebroke/pre-commit-autoupdate-action@v1.0.0 diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 5af7bfe5a9..067e843002 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -13,9 +13,10 @@ jobs: steps: - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 + - name: Setup Python 3.11 + uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - uses: pre-commit/action@v3.0.1 with: @@ -28,9 +29,10 @@ jobs: steps: - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 + - name: Setup Python 3.11 + uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Build project run: | @@ -52,7 +54,7 @@ jobs: fail-fast: false matrix: os: [ ubuntu-24.04, macOS-14, windows-2022 ] - python-version: [ "3.9", "3.10", "3.11", "3.12" ] + python-version: [ "3.9", "3.10", "3.11", "3.12", "3.13" ] steps: - uses: actions/checkout@v4 diff --git a/aeon/anomaly_detection/tests/test_cblof.py b/aeon/anomaly_detection/tests/test_cblof.py index c8d9f5d9c8..090ef98064 100644 --- a/aeon/anomaly_detection/tests/test_cblof.py +++ b/aeon/anomaly_detection/tests/test_cblof.py @@ -21,7 +21,7 @@ def test_cblof_default(): pred = cblof.fit_predict(series, axis=0) assert pred.shape == (80,) - assert pred.dtype == np.float_ + assert np.issubdtype(pred.dtype, np.floating) assert 50 <= np.argmax(pred) <= 60 diff --git a/aeon/anomaly_detection/tests/test_copod.py b/aeon/anomaly_detection/tests/test_copod.py index b1cddaa4dc..abd2aa995f 100644 --- a/aeon/anomaly_detection/tests/test_copod.py +++ b/aeon/anomaly_detection/tests/test_copod.py @@ -21,7 +21,7 @@ def test_copod_default(): pred = copod.fit_predict(series, axis=0) assert pred.shape == (80,) - assert pred.dtype == np.float_ + assert np.issubdtype(pred.dtype, np.floating) assert 50 <= np.argmax(pred) <= 60 diff --git a/aeon/testing/tests/test_all_estimators.py b/aeon/testing/tests/test_all_estimators.py index 2716021bba..192d63b1d6 100644 --- a/aeon/testing/tests/test_all_estimators.py +++ b/aeon/testing/tests/test_all_estimators.py @@ -22,7 +22,7 @@ i = 0 elif i == 11: i = 1 - elif i == 12: + elif i == 13: i = 2 os_str = platform.system() diff --git a/examples/transformations/preprocessing.ipynb b/examples/transformations/preprocessing.ipynb index 99dcd8567f..cccc7ac42f 100644 --- a/examples/transformations/preprocessing.ipynb +++ b/examples/transformations/preprocessing.ipynb @@ -797,7 +797,7 @@ }, "cell_type": "code", "source": [ - "X[5][0][55] = np.NAN\n", + "X[5][0][55] = np.nan\n", "has_missing(X)" ], "outputs": [ diff --git a/pyproject.toml b/pyproject.toml index 1882fdf5c8..b5dc6c6dc9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,11 +42,12 @@ classifiers = [ "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", ] -requires-python = ">=3.9,<3.13" +requires-python = ">=3.9,<3.14" dependencies = [ "deprecated>=1.2.13", - "numba>=0.55,<0.61.0", + "numba>=0.55,<0.62.0", "numpy>=1.21.0,<2.3.0", "packaging>=20.0", "pandas>=2.0.0,<2.3.0", @@ -58,31 +59,33 @@ dependencies = [ # soft dependencies [project.optional-dependencies] all_extras = [ - # Upper bound set as <1.0.0 as 1.0 dropped support for python 3.9. We will remove - # the upper bound once we also drop support for python 3.9 later in 2025. - "esig>=0.9.7,<1.0.0; platform_system != 'Darwin' and python_version < '3.11'", "imbalanced-learn", "matplotlib>=3.3.2", "pycatch22>=0.4.5", "pyod>=1.1.3", - "prts>=1.0.0.0", "pydot>=2.0.0", "ruptures>=1.1.9", "seaborn>=0.11.0", + "sparse", "statsmodels>=0.12.1", "stumpy>=1.5.1", - "tensorflow>=2.14", + "tensorflow>=2.14; python_version < '3.13'", "torch>=1.13.1", "tsfresh>=0.20.0", "tslearn>=0.5.2", - "sparse" ] dl = [ - "tensorflow>=2.14", + "tensorflow>=2.14; python_version < '3.13'", ] unstable_extras = [ - "mrsqm>=0.0.7,<0.1.0; platform_system != 'Windows' and python_version < '3.12'", # requires gcc and fftw to be installed for Windows and some other OS (see http://www.fftw.org/index.html) - "mrseql>=0.0.4,<0.1.0; platform_system != 'Windows' and python_version < '3.12'", # requires gcc and fftw to be installed for Windows and some other OS (see http://www.fftw.org/index.html) + # requires gcc and fftw to be installed for Windows and some other OS (see http://www.fftw.org/index.html) + "mrsqm>=0.0.7,<0.1.0; platform_system != 'Windows' and python_version < '3.12'", + "mrseql>=0.0.4,<0.1.0; platform_system != 'Windows' and python_version < '3.12'", + # very outdated and used code is deprecated + "prts>=1.0.0.0", + # Upper bound set as <1.0.0 as 1.0 dropped support for python 3.9. We will remove + # the upper bound once we also drop support for python 3.9 later in 2025. + "esig>=0.9.7,<1.0.0; platform_system != 'Darwin' and python_version < '3.11'", ] # development dependencies @@ -177,6 +180,7 @@ addopts = ''' --reruns 2 --only-rerun "crashed while running" --only-rerun "zipfile.BadZipFile" + --only-rerun "accessible `.keras` zip file." ''' filterwarnings = ''' ignore::UserWarning From 4be76cbf1871de710bcd83340e0176d416814bdb Mon Sep 17 00:00:00 2001 From: Aryan Pola <98093778+aryanpola@users.noreply.github.com> Date: Sat, 12 Apr 2025 00:58:20 +0530 Subject: [PATCH 060/216] [ENH] Hard-Coded Tests for `test_metrics.py` (#2672) * Empty-Commit * hard-coded tests * changes --- .../anomaly_detection/tests/test_metrics.py | 605 +++++++++--------- 1 file changed, 320 insertions(+), 285 deletions(-) diff --git a/aeon/benchmarking/metrics/anomaly_detection/tests/test_metrics.py b/aeon/benchmarking/metrics/anomaly_detection/tests/test_metrics.py index 61db5ded3a..0fbbe16fa3 100644 --- a/aeon/benchmarking/metrics/anomaly_detection/tests/test_metrics.py +++ b/aeon/benchmarking/metrics/anomaly_detection/tests/test_metrics.py @@ -1,50 +1,34 @@ """Test cases for the range-based anomaly detection metrics.""" import numpy as np -import pytest -from aeon.benchmarking.metrics.anomaly_detection import ( - range_f_score, - range_precision, - range_recall, -) from aeon.benchmarking.metrics.anomaly_detection.range_metrics import ( - _binary_to_ranges, ts_fscore, ts_precision, ts_recall, ) -from aeon.utils.validation._dependencies import _check_soft_dependencies -@pytest.mark.skipif( - not _check_soft_dependencies("prts", severity="none"), - reason="required soft dependency prts not available", -) def test_single_overlapping_range(): """Test for single overlapping range.""" - y_pred_bin = np.array([0, 1, 1, 1, 1, 0, 0]) - y_real_bin = np.array([0, 0, 1, 1, 1, 1, 1]) + y_pred = np.array([0, 1, 1, 1, 1, 0, 0]) + y_real = np.array([0, 0, 1, 1, 1, 1, 1]) + expected_precision = 0.750000 + expected_recall = 0.600000 + expected_f1 = 0.666667 - precision = ts_precision(y_pred_bin, y_real_bin, gamma="one", bias_type="flat") - recall = ts_recall(y_pred_bin, y_real_bin, gamma="one", bias_type="flat", alpha=0.0) - f1_score = ts_fscore( - y_pred_bin, - y_real_bin, + precision = ts_precision(y_pred, y_real, gamma="one", bias_type="flat") + recall = ts_recall( + y_pred, + y_real, gamma="one", - p_bias="flat", - r_bias="flat", - p_alpha=0.0, - r_alpha=0.0, + bias_type="flat", + alpha=0.0, ) - - rb_prec = range_precision(y_real_bin, y_pred_bin, cardinality="one", bias="flat") - rb_rec = range_recall(y_real_bin, y_pred_bin, cardinality="one", bias="flat") - rb_fsc = range_f_score( - y_real_bin, - y_pred_bin, - beta=1, - cardinality="one", + f1_score = ts_fscore( + y_pred, + y_real, + gamma="one", p_bias="flat", r_bias="flat", p_alpha=0.0, @@ -53,53 +37,55 @@ def test_single_overlapping_range(): np.testing.assert_almost_equal( precision, - rb_prec, + expected_precision, decimal=6, - err_msg=f"Precision mismatch: ts_precision={precision} vs prts={rb_prec}", + err_msg=( + f"Precision failed for single overlapping range! " + f"Expected={expected_precision}, Got={precision}" + ), ) np.testing.assert_almost_equal( recall, - rb_rec, + expected_recall, decimal=6, - err_msg=f"Recall mismatch: ts_recall={recall} vs prts={rb_rec}", + err_msg=( + f"Recall failed for single overlapping range! " + f"Expected={expected_recall}, Got={recall}" + ), ) np.testing.assert_almost_equal( f1_score, - rb_fsc, + expected_f1, decimal=6, - err_msg=f"F1-Score mismatch: ts_fscore={f1_score} vs prts={rb_fsc}", + err_msg=( + f"F1-Score failed for single overlapping range! " + f"Expected={expected_f1}, Got={f1_score}" + ), ) -@pytest.mark.skipif( - not _check_soft_dependencies("prts", severity="none"), - reason="required soft dependency prts not available", -) def test_multiple_non_overlapping_ranges(): """Test for multiple non-overlapping ranges.""" - y_pred_bin = np.array([0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0]) - y_real_bin = np.array([0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1]) + y_pred = np.array([0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0]) + y_real = np.array([0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1]) - precision = ts_precision(y_pred_bin, y_real_bin, gamma="one", bias_type="flat") - recall = ts_recall(y_pred_bin, y_real_bin, gamma="one", bias_type="flat", alpha=0.0) - f1_score = ts_fscore( - y_pred_bin, - y_real_bin, + expected_precision = 0.000000 + expected_recall = 0.000000 + expected_f1 = 0.000000 + + precision = ts_precision(y_pred, y_real, gamma="one", bias_type="flat") + recall = ts_recall( + y_pred, + y_real, gamma="one", - beta=1, - p_bias="flat", - r_bias="flat", - p_alpha=0.0, - r_alpha=0.0, + bias_type="flat", + alpha=0.0, ) - - rb_prec = range_precision(y_real_bin, y_pred_bin, cardinality="one", bias="flat") - rb_rec = range_recall(y_real_bin, y_pred_bin, cardinality="one", bias="flat") - rb_fsc = range_f_score( - y_real_bin, - y_pred_bin, + f1_score = ts_fscore( + y_pred, + y_real, + gamma="one", beta=1, - cardinality="one", p_bias="flat", r_bias="flat", p_alpha=0.0, @@ -108,53 +94,55 @@ def test_multiple_non_overlapping_ranges(): np.testing.assert_almost_equal( precision, - rb_prec, + expected_precision, decimal=6, - err_msg=f"Precision mismatch: ts_precision={precision} vs prts={rb_prec}", + err_msg=( + f"Precision failed for multiple non-overlapping ranges! " + f"Expected={expected_precision}, Got={precision}" + ), ) np.testing.assert_almost_equal( recall, - rb_rec, + expected_recall, decimal=6, - err_msg=f"Recall mismatch: ts_recall={recall} vs prts={rb_rec}", + err_msg=( + f"Recall failed for multiple non-overlapping ranges! " + f"Expected={expected_recall}, Got={recall}" + ), ) np.testing.assert_almost_equal( f1_score, - rb_fsc, + expected_f1, decimal=6, - err_msg=f"F1-Score mismatch: ts_fscore={f1_score} vs prts={rb_fsc}", + err_msg=( + f"F1-Score failed for multiple non-overlapping ranges! " + f"Expected={expected_f1}, Got={f1_score}" + ), ) -@pytest.mark.skipif( - not _check_soft_dependencies("prts", severity="none"), - reason="required soft dependency prts not available", -) def test_multiple_overlapping_ranges(): """Test for multiple overlapping ranges.""" - y_pred_bin = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]) - y_real_bin = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]) + y_pred = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]) + y_real = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]) - precision = ts_precision(y_pred_bin, y_real_bin, gamma="one", bias_type="flat") - recall = ts_recall(y_pred_bin, y_real_bin, gamma="one", bias_type="flat", alpha=0.0) - f1_score = ts_fscore( - y_pred_bin, - y_real_bin, + expected_precision = 0.666667 + expected_recall = 0.400000 + expected_f1 = 0.500000 + + precision = ts_precision(y_pred, y_real, gamma="one", bias_type="flat") + recall = ts_recall( + y_pred, + y_real, gamma="one", - beta=1, - p_bias="flat", - r_bias="flat", - p_alpha=0.0, - r_alpha=0.0, + bias_type="flat", + alpha=0.0, ) - - rb_prec = range_precision(y_real_bin, y_pred_bin, cardinality="one", bias="flat") - rb_rec = range_recall(y_real_bin, y_pred_bin, cardinality="one", bias="flat") - rb_fsc = range_f_score( - y_real_bin, - y_pred_bin, + f1_score = ts_fscore( + y_pred, + y_real, + gamma="one", beta=1, - cardinality="one", p_bias="flat", r_bias="flat", p_alpha=0.0, @@ -163,53 +151,55 @@ def test_multiple_overlapping_ranges(): np.testing.assert_almost_equal( precision, - rb_prec, + expected_precision, decimal=6, - err_msg=f"Precision mismatch: ts_precision={precision} vs prts={rb_prec}", + err_msg=( + f"Precision failed for multiple overlapping ranges! " + f"Expected={expected_precision}, Got={precision}" + ), ) np.testing.assert_almost_equal( recall, - rb_rec, + expected_recall, decimal=6, - err_msg=f"Recall mismatch: ts_recall={recall} vs prts={rb_rec}", + err_msg=( + f"Recall failed for multiple overlapping ranges! " + f"Expected={expected_recall}, Got={recall}" + ), ) np.testing.assert_almost_equal( f1_score, - rb_fsc, + expected_f1, decimal=6, - err_msg=f"F1-Score mismatch: ts_fscore={f1_score} vs prts={rb_fsc}", + err_msg=( + f"F1-Score failed for multiple overlapping ranges! " + f"Expected={expected_f1}, Got={f1_score}" + ), ) -@pytest.mark.skipif( - not _check_soft_dependencies("prts", severity="none"), - reason="required soft dependency prts not available", -) def test_nested_lists_of_predictions(): """Test for nested lists of predictions.""" - y_pred_bin = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1]) - y_real_bin = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0]) + y_pred = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1]) + y_real = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0]) - precision = ts_precision(y_pred_bin, y_real_bin, gamma="one", bias_type="flat") - recall = ts_recall(y_pred_bin, y_real_bin, gamma="one", bias_type="flat", alpha=0.0) - f1_score = ts_fscore( - y_pred_bin, - y_real_bin, + expected_precision = 0.555556 + expected_recall = 0.566667 + expected_f1 = 0.561056 + + precision = ts_precision(y_pred, y_real, gamma="one", bias_type="flat") + recall = ts_recall( + y_pred, + y_real, gamma="one", - beta=1, - p_bias="flat", - r_bias="flat", - p_alpha=0.0, - r_alpha=0.0, + bias_type="flat", + alpha=0.0, ) - - rb_prec = range_precision(y_real_bin, y_pred_bin, cardinality="one", bias="flat") - rb_rec = range_recall(y_real_bin, y_pred_bin, cardinality="one", bias="flat") - rb_fsc = range_f_score( - y_real_bin, - y_pred_bin, + f1_score = ts_fscore( + y_pred, + y_real, + gamma="one", beta=1, - cardinality="one", p_bias="flat", r_bias="flat", p_alpha=0.0, @@ -218,53 +208,55 @@ def test_nested_lists_of_predictions(): np.testing.assert_almost_equal( precision, - rb_prec, + expected_precision, decimal=6, - err_msg=f"Precision mismatch: ts_precision={precision} vs prts={rb_prec}", + err_msg=( + f"Precision failed for nested lists of predictions! " + f"Expected={expected_precision}, Got={precision}" + ), ) np.testing.assert_almost_equal( recall, - rb_rec, + expected_recall, decimal=6, - err_msg=f"Recall mismatch: ts_recall={recall} vs prts={rb_rec}", + err_msg=( + f"Recall failed for nested lists of predictions! " + f"Expected={expected_recall}, Got={recall}" + ), ) np.testing.assert_almost_equal( f1_score, - rb_fsc, + expected_f1, decimal=6, - err_msg=f"F1-Score mismatch: ts_fscore={f1_score} vs prts={rb_fsc}", + err_msg=( + f"F1-Score failed for nested lists of predictions! " + f"Expected={expected_f1}, Got={f1_score}" + ), ) -@pytest.mark.skipif( - not _check_soft_dependencies("prts", severity="none"), - reason="required soft dependency prts not available", -) def test_all_encompassing_range(): """Test for all encompassing range.""" - y_pred_bin = np.array([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) - y_real_bin = np.array([0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0]) + y_pred = np.array([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) + y_real = np.array([0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0]) - precision = ts_precision(y_pred_bin, y_real_bin, gamma="one", bias_type="flat") - recall = ts_recall(y_pred_bin, y_real_bin, gamma="one", bias_type="flat", alpha=0.0) - f1_score = ts_fscore( - y_pred_bin, - y_real_bin, + expected_precision = 0.600000 + expected_recall = 1.000000 + expected_f1 = 0.750000 + + precision = ts_precision(y_pred, y_real, gamma="one", bias_type="flat") + recall = ts_recall( + y_pred, + y_real, gamma="one", - beta=1, - p_bias="flat", - r_bias="flat", - p_alpha=0.0, - r_alpha=0.0, + bias_type="flat", + alpha=0.0, ) - - rb_prec = range_precision(y_real_bin, y_pred_bin, cardinality="one", bias="flat") - rb_rec = range_recall(y_real_bin, y_pred_bin, cardinality="one", bias="flat") - rb_fsc = range_f_score( - y_real_bin, - y_pred_bin, + f1_score = ts_fscore( + y_pred, + y_real, + gamma="one", beta=1, - cardinality="one", p_bias="flat", r_bias="flat", p_alpha=0.0, @@ -273,43 +265,58 @@ def test_all_encompassing_range(): np.testing.assert_almost_equal( precision, - rb_prec, + expected_precision, decimal=6, - err_msg=f"Precision mismatch: ts_precision={precision} vs prts={rb_prec}", + err_msg=( + f"Precision failed for all encompassing range! " + f"Expected={expected_precision}, Got={precision}" + ), ) np.testing.assert_almost_equal( recall, - rb_rec, + expected_recall, decimal=6, - err_msg=f"Recall mismatch: ts_recall={recall} vs prts={rb_rec}", + err_msg=( + f"Recall failed for all encompassing range! " + f"Expected={expected_recall}, Got={recall}" + ), ) np.testing.assert_almost_equal( f1_score, - rb_fsc, + expected_f1, decimal=6, - err_msg=f"F1-Score mismatch: ts_fscore={f1_score} vs prts={rb_fsc}", + err_msg=( + f"F1-Score failed for all encompassing range! " + f"Expected={expected_f1}, Got={f1_score}" + ), ) -@pytest.mark.skipif( - not _check_soft_dependencies("prts", severity="none"), - reason="required soft dependency prts not available", -) -def test_binary_input_example(): - """Comparing prts package(_binary.py) and range-based metrics(range_metrics.py).""" +def test_range_based_input(): + """Test with input being range-based or bianry-based.""" + y_pred_range = [(1, 2)] + y_true_range = [(1, 1)] y_pred_binary = np.array([0, 1, 1, 0]) y_true_binary = np.array([0, 1, 0, 0]) - precision = ts_precision( - y_pred_binary, y_true_binary, gamma="reciprocal", bias_type="flat" + expected_precision = 0.5 + expected_recall = 1.000000 + expected_f1 = 0.666667 + + # for range-based input + precision_range = ts_precision( + y_pred_range, y_true_range, gamma="reciprocal", bias_type="flat" ) - recall = ts_recall( - y_pred_binary, y_true_binary, gamma="reciprocal", bias_type="flat", alpha=0.0 + recall_range = ts_recall( + y_pred_range, + y_true_range, + gamma="reciprocal", + bias_type="flat", + alpha=0.0, ) - f1_score = ts_fscore( - y_pred_binary, - y_true_binary, - beta=1, + f1_score_range = ts_fscore( + y_pred_range, + y_true_range, gamma="reciprocal", p_bias="flat", r_bias="flat", @@ -317,59 +324,105 @@ def test_binary_input_example(): r_alpha=0.0, ) - rb_prec = range_precision( - y_true_binary, y_pred_binary, cardinality="reciprocal", bias="flat" + np.testing.assert_almost_equal( + precision_range, + expected_precision, + decimal=6, + err_msg=( + f"Precision mismatch: " + f"ts_precision={precision_range} vs" + f"expected_precision_range={expected_precision}" + ), + ) + np.testing.assert_almost_equal( + recall_range, + expected_recall, + decimal=6, + err_msg=( + f"Recall mismatch: " + f"ts_recall={recall_range} vs expected_recall_range={expected_recall}" + ), ) - rb_rec = range_recall( - y_true_binary, y_pred_binary, cardinality="reciprocal", bias="flat" + np.testing.assert_almost_equal( + f1_score_range, + expected_f1, + decimal=6, + err_msg=( + f"F1-Score mismatch: " + f"ts_fscore={f1_score_range} vs expected_f_score_range={expected_f1}" + ), ) - rb_fsc = range_f_score( + + # for binary input + precision_binary = ts_precision( + y_pred_binary, y_true_binary, gamma="reciprocal", bias_type="flat" + ) + recall_binary = ts_recall( + y_pred_binary, y_true_binary, + gamma="reciprocal", + bias_type="flat", + alpha=0.0, + ) + f1_score_binary = ts_fscore( y_pred_binary, - beta=1, - cardinality="reciprocal", + y_true_binary, + gamma="reciprocal", p_bias="flat", r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, ) np.testing.assert_almost_equal( - precision, - rb_prec, + precision_binary, + expected_precision, decimal=6, - err_msg=f"Precision mismatch: ts_precision={precision} vs prts={rb_prec}", + err_msg=( + f"Precision mismatch: " + f"ts_precision={precision_range} vs " + f"expected_precision_binary={expected_precision}" + ), ) np.testing.assert_almost_equal( - recall, - rb_rec, + recall_binary, + expected_recall, decimal=6, - err_msg=f"Recall mismatch: ts_recall={recall} vs prts={rb_rec}", + err_msg=( + f"Recall mismatch: " + f"ts_recall={recall_range} vs expected_recall_binary={expected_recall}" + ), ) np.testing.assert_almost_equal( - f1_score, - rb_fsc, + f1_score_binary, + expected_f1, decimal=6, - err_msg=f"F1-Score mismatch: ts_fscore={f1_score} vs prts={rb_fsc}", + err_msg=( + f"F1-Score mismatch: " + f"ts_fscore={f1_score_range} vs expected_f_score_binary={expected_f1}" + ), ) -@pytest.mark.skipif( - not _check_soft_dependencies("prts", severity="none"), - reason="required soft dependency prts not available", -) def test_multiple_overlapping_ranges_with_gamma_reciprocal(): """Test for multiple overlapping ranges with gamma=reciprocal.""" - y_pred_bin = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]) - y_real_bin = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]) + y_pred = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]) + y_real = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]) + expected_precision = 0.666667 + expected_recall = 0.200000 + expected_f1 = 0.307692 - precision = ts_precision( - y_pred_bin, y_real_bin, gamma="reciprocal", bias_type="flat" - ) + precision = ts_precision(y_pred, y_real, gamma="reciprocal", bias_type="flat") recall = ts_recall( - y_pred_bin, y_real_bin, gamma="reciprocal", bias_type="flat", alpha=0.0 + y_pred, + y_real, + gamma="reciprocal", + bias_type="flat", + alpha=0.0, ) f1_score = ts_fscore( - y_pred_bin, - y_real_bin, + y_pred, + y_real, gamma="reciprocal", beta=1, p_bias="flat", @@ -378,76 +431,56 @@ def test_multiple_overlapping_ranges_with_gamma_reciprocal(): r_alpha=0.0, ) - rb_prec = range_precision( - y_real_bin, y_pred_bin, cardinality="reciprocal", bias="flat" - ) - rb_rec = range_recall(y_real_bin, y_pred_bin, cardinality="reciprocal", bias="flat") - rb_fsc = range_f_score( - y_real_bin, - y_pred_bin, - beta=1, - cardinality="reciprocal", - p_bias="flat", - r_bias="flat", - p_alpha=0.0, - r_alpha=0.0, - ) - np.testing.assert_almost_equal( precision, - rb_prec, + expected_precision, decimal=6, - err_msg=f"Precision mismatch: ts_precision={precision} vs prts={rb_prec}", + err_msg=( + f"Precision failed for multiple overlapping ranges! " + f"Expected={expected_precision}, Got={precision}" + ), ) np.testing.assert_almost_equal( recall, - rb_rec, + expected_recall, decimal=6, - err_msg=f"Recall mismatch: ts_recall={recall} vs prts={rb_rec}", + err_msg=( + f"Recall failed for multiple overlapping ranges! " + f"Expected={expected_recall}, Got={recall}" + ), ) np.testing.assert_almost_equal( f1_score, - rb_fsc, + expected_f1, decimal=6, - err_msg=f"F1-Score mismatch: ts_fscore={f1_score} vs prts={rb_fsc}", + err_msg=( + f"F1-Score failed for multiple overlapping ranges! " + f"Expected={expected_f1}, Got={f1_score}" + ), ) -@pytest.mark.skipif( - not _check_soft_dependencies("prts", severity="none"), - reason="required soft dependency prts not available", -) def test_multiple_overlapping_ranges_with_bias_middle(): - """Test for multiple overlapping ranges with bias_type=middle using range-binary conversion.""" # noqa E501 - y_pred_bin = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]) - y_real_bin = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]) - - # metric functions can also handle range-based input (internal binary conversion) - y_pred = _binary_to_ranges( - y_pred_bin - ) # Convert binary to range to show compatibility - y_real = _binary_to_ranges(y_real_bin) + """Test for multiple overlapping ranges with bias_type=middle.""" + y_pred = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]) + y_real = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]) + expected_precision = 0.750000 + expected_recall = 0.333333 + expected_f1 = 0.461538 precision = ts_precision(y_pred, y_real, gamma="one", bias_type="middle") - recall = ts_recall(y_pred, y_real, gamma="one", bias_type="middle", alpha=0.0) - f1_score = ts_fscore( + recall = ts_recall( y_pred, y_real, gamma="one", - beta=1, - p_bias="middle", - r_bias="middle", - p_alpha=0.0, - r_alpha=0.0, + bias_type="middle", + alpha=0.0, ) - - rb_prec = range_precision(y_real_bin, y_pred_bin, cardinality="one", bias="middle") - rb_rec = range_recall(y_real_bin, y_pred_bin, cardinality="one", bias="middle") - rb_fsc = range_f_score( - y_real_bin, - y_pred_bin, + f1_score = ts_fscore( + y_pred, + y_real, + gamma="one", beta=1, - cardinality="one", p_bias="middle", r_bias="middle", p_alpha=0.0, @@ -456,82 +489,84 @@ def test_multiple_overlapping_ranges_with_bias_middle(): np.testing.assert_almost_equal( precision, - rb_prec, + expected_precision, decimal=6, - err_msg=f"Precision mismatch: ts_precision={precision} vs prts={rb_prec}", + err_msg=( + f"Precision failed for multiple overlapping ranges! " + f"Expected={expected_precision}, Got={precision}" + ), ) np.testing.assert_almost_equal( recall, - rb_rec, + expected_recall, decimal=6, - err_msg=f"Recall mismatch: ts_recall={recall} vs prts={rb_rec}", + err_msg=( + f"Recall failed for multiple overlapping ranges! " + f"Expected={expected_recall}, Got={recall}" + ), ) np.testing.assert_almost_equal( f1_score, - rb_fsc, + expected_f1, decimal=6, - err_msg=f"F1-Score mismatch: ts_fscore={f1_score} vs prts={rb_fsc}", + err_msg=( + f"F1-Score failed for multiple overlapping ranges! " + f"Expected={expected_f1}, Got={f1_score}" + ), ) -@pytest.mark.skipif( - not _check_soft_dependencies("prts", severity="none"), - reason="required soft dependency prts not available", -) def test_multiple_overlapping_ranges_with_bias_middle_gamma_reciprocal(): """Test for multiple overlapping ranges with bias_type=middle, gamma=reciprocal.""" - y_pred_bin = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]) - y_real_bin = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]) + y_pred = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]) + y_real = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]) + expected_precision = 0.750000 + expected_recall = 0.166667 + expected_f1 = 0.272727 - precision = ts_precision( - y_pred_bin, y_real_bin, gamma="reciprocal", bias_type="middle" - ) + precision = ts_precision(y_pred, y_real, gamma="reciprocal", bias_type="middle") recall = ts_recall( - y_pred_bin, y_real_bin, gamma="reciprocal", bias_type="middle", alpha=1.0 + y_pred, + y_real, + gamma="reciprocal", + bias_type="middle", + alpha=0.0, ) f1_score = ts_fscore( - y_pred_bin, - y_real_bin, + y_pred, + y_real, gamma="reciprocal", beta=1, p_bias="middle", r_bias="middle", p_alpha=0.0, - r_alpha=1.0, - ) - - rb_prec = range_precision( - y_real_bin, y_pred_bin, cardinality="reciprocal", bias="middle" - ) - rb_rec = range_recall( - y_real_bin, y_pred_bin, cardinality="reciprocal", bias="middle", alpha=1.0 - ) - rb_fsc = range_f_score( - y_real_bin, - y_pred_bin, - beta=1, - cardinality="reciprocal", - p_bias="middle", - r_bias="middle", - p_alpha=0.0, - r_alpha=1.0, + r_alpha=0.0, ) np.testing.assert_almost_equal( precision, - rb_prec, + expected_precision, decimal=6, - err_msg=f"Precision mismatch: ts_precision={precision} vs prts={rb_prec}", + err_msg=( + f"Precision failed for multiple overlapping ranges! " + f"Expected={expected_precision}, Got={precision}" + ), ) np.testing.assert_almost_equal( recall, - rb_rec, + expected_recall, decimal=6, - err_msg=f"Recall mismatch: ts_recall={recall} vs prts={rb_rec}", + err_msg=( + f"Recall failed for multiple overlapping ranges! " + f"Expected={expected_recall}, Got={recall}" + ), ) np.testing.assert_almost_equal( f1_score, - rb_fsc, + expected_f1, decimal=6, - err_msg=f"F1-Score mismatch: ts_fscore={f1_score} vs prts={rb_fsc}", + err_msg=( + f"F1-Score failed for multiple overlapping ranges! " + f"Expected={expected_f1}, Got={f1_score}" + ), ) From 376705f8e1e21a3dc0edb549201c7635f6c866e1 Mon Sep 17 00:00:00 2001 From: "V_26@" Date: Sat, 12 Apr 2025 00:59:27 +0530 Subject: [PATCH 061/216] Changed single ticks to double (#2640) Co-authored-by: Matthew Middlehurst --- aeon/regression/_dummy.py | 8 ++++---- aeon/regression/base.py | 6 +++--- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/aeon/regression/_dummy.py b/aeon/regression/_dummy.py index b767de8d78..322dcbf01d 100644 --- a/aeon/regression/_dummy.py +++ b/aeon/regression/_dummy.py @@ -15,13 +15,13 @@ class DummyRegressor(BaseRegressor): This regressor is a wrapper for the scikit-learn DummyClassifier that serves as a simple baseline to compare against other more complex regressors. - The specific behaviour of the baseline is selected with the `strategy` parameter. + The specific behaviour of the baseline is selected with the ``strategy`` parameter. All strategies make predictions that ignore the input feature values passed - as the `X` argument to `fit` and `predict`. The predictions, however, - typically depend on values observed in the `y` parameter passed to `fit`. + as the ``X`` argument to ``fit`` and ``predict``. The predictions, however, + typically depend on values observed in the ``y`` parameter passed to ``fit``. - Function-identical to `sklearn.dummy.DummyRegressor`, which is called inside. + Function-identical to ``sklearn.dummy.DummyRegressor``, which is called inside. Parameters ---------- diff --git a/aeon/regression/base.py b/aeon/regression/base.py index f1acab5a00..dbe40732bb 100644 --- a/aeon/regression/base.py +++ b/aeon/regression/base.py @@ -167,7 +167,7 @@ def fit_predict(self, X, y) -> np.ndarray: allowed and converted into one of the above. Different estimators have different capabilities to handle different - types of input. If `self.get_tag("capability:multivariate")`` is False, + types of input. If ``self.get_tag("capability:multivariate")`` is False, they cannot handle multivariate series, so either ``n_channels == 1`` is true or X is 2D of shape ``(n_cases, n_timepoints)``. If ``self.get_tag( "capability:unequal_length")`` is False, they cannot handle unequal @@ -208,7 +208,7 @@ def score(self, X, y, metric="r2", metric_params=None) -> float: allowed and converted into one of the above. Different estimators have different capabilities to handle different - types of input. If `self.get_tag("capability:multivariate")`` is False, + types of input. If ``self.get_tag("capability:multivariate")`` is False, they cannot handle multivariate series, so either ``n_channels == 1`` is true or X is 2D of shape ``(n_cases, n_timepoints)``. If ``self.get_tag( "capability:unequal_length")`` is False, they cannot handle unequal @@ -220,7 +220,7 @@ def score(self, X, y, metric="r2", metric_params=None) -> float: (ground truth) for fitting indices corresponding to instance indices in X. metric : Union[str, callable], default="r2", Defines the scoring metric to test the fit of the model. For supported - strings arguments, check `sklearn.metrics.get_scorer_names`. + strings arguments, check ``sklearn.metrics.get_scorer_names``. metric_params : dict, default=None, Contains parameters to be passed to the scoring function. If None, no parameters are passed. From 5a0f229241862c1b3d43b8e373279f62e7a7d46e Mon Sep 17 00:00:00 2001 From: "allcontributors[bot]" <46447321+allcontributors[bot]@users.noreply.github.com> Date: Fri, 11 Apr 2025 21:47:23 +0100 Subject: [PATCH 062/216] =?UTF-8?q?=F0=9F=93=9D=20Add=20HaroonAzamFiza=20a?= =?UTF-8?q?s=20a=20contributor=20for=20doc=20(#2740)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 📝 Update CONTRIBUTORS.md [skip ci] * 📝 Update .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> --- .all-contributorsrc | 9 +++++++++ CONTRIBUTORS.md | 45 +++++++++++++++++++++++---------------------- 2 files changed, 32 insertions(+), 22 deletions(-) diff --git a/.all-contributorsrc b/.all-contributorsrc index cb0670d31a..0480bee415 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -2683,6 +2683,15 @@ "contributions": [ "code" ] + }, + { + "login": "HaroonAzamFiza", + "name": "HaroonAzamFiza", + "avatar_url": "https://avatars.githubusercontent.com/u/183639840?v=4", + "profile": "https://github.com/HaroonAzamFiza", + "contributions": [ + "doc" + ] } ], "commitType": "docs" diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md index 225d9369cb..5a79bfde55 100644 --- a/CONTRIBUTORS.md +++ b/CONTRIBUTORS.md @@ -1,7 +1,7 @@ # Contributors -[![All Contributors](https://img.shields.io/badge/all_contributors-262-orange.svg)](#contributors) +[![All Contributors](https://img.shields.io/badge/all_contributors-263-orange.svg)](#contributors) This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! @@ -128,12 +128,13 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Guilherme Arcencio
Guilherme Arcencio
💻 ⚠️ Guzal Bulatova
Guzal Bulatova
🐛 💻 📋 🧑‍🏫 📆 👀 ⚠️ HYang1996
HYang1996
💻 ⚠️ 📖 ✅ + HaroonAzamFiza
HaroonAzamFiza
📖 Harshitha Sudhakar
Harshitha Sudhakar
📖 💻 Hedeer El Showk
Hedeer El Showk
🐛 📖 💻 Huayi Wei
Huayi Wei
✅ - Ifeanyi30
Ifeanyi30
💻 + Ifeanyi30
Ifeanyi30
💻 Ilja Maurer
Ilja Maurer
💻 Ilyas Moutawwakil
Ilyas Moutawwakil
💻 📖 Ireoluwatomiwa
Ireoluwatomiwa
📖 @@ -141,9 +142,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Ivan Knyazev
Ivan Knyazev
📖 Jack Russon
Jack Russon
💻 James Large
James Large
💻 📖 ⚠️ 🚇 🚧 - James Morrill
James Morrill
💻 + James Morrill
James Morrill
💻 Jasmine Liaw
Jasmine Liaw
💻 Jason Lines
Jason Lines
💻 💼 📖 🎨 📋 🔍 🤔 📆 💬 👀 📢 💡 Jason Mok
Jason Mok
📖 @@ -151,9 +152,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Jaume Mateu
Jaume Mateu
💻 JonathanBechtel
JonathanBechtel
💻 🤔 ⚠️ Joren Hammudoglu
Joren Hammudoglu
🚇 - Juan Orduz
Juan Orduz
✅ 📖 + Juan Orduz
Juan Orduz
✅ 📖 Julian Cooper
Julian Cooper
💻 🤔 Juliana
Juliana
💻 Justin Shenk
Justin Shenk
📖 @@ -161,9 +162,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Kavin Anand
Kavin Anand
📖 Kavya Rambhia
Kavya Rambhia
💻 Kejsi Take
Kejsi Take
💻 - Kevin Lam
Kevin Lam
💻 💡 ⚠️ + Kevin Lam
Kevin Lam
💻 💡 ⚠️ Kevin Shah
Kevin Shah
📖 Kirstie Whitaker
Kirstie Whitaker
🤔 🔍 Kishan Manani
Kishan Manani
💻 📖 ⚠️ 🐛 🤔 @@ -171,9 +172,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Kutay Koralturk
Kutay Koralturk
💻 🐛 Leonidas Tsaprounis
Leonidas Tsaprounis
💻 🐛 🧑‍🏫 👀 Lielle Ravid
Lielle Ravid
💻 📖 - Logan Duffy
Logan Duffy
💻 📖 ⚠️ 🐛 🤔 + Logan Duffy
Logan Duffy
💻 📖 ⚠️ 🐛 🤔 Lorena Pantano
Lorena Pantano
🤔 Lorenzo Toniazzi
Lorenzo Toniazzi
💻 Lovkush
Lovkush
💻 ⚠️ 🤔 🧑‍🏫 📆 @@ -181,9 +182,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Luis Ventura
Luis Ventura
💻 Luis Zugasti
Luis Zugasti
📖 Lukasz Mentel
Lukasz Mentel
💻 📖 🚇 ⚠️ 🐛 🚧 🧑‍🏫 - Marcelo Trylesinski
Marcelo Trylesinski
📖 + Marcelo Trylesinski
Marcelo Trylesinski
📖 Marco Gorelli
Marco Gorelli
🚇 Margaret Gorlin
Margaret Gorlin
💻 💡 ⚠️ Mariam Jabara
Mariam Jabara
💻 @@ -191,9 +192,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Markus Löning
Markus Löning
💻 ⚠️ 🚧 📦 👀 🚇 💡 🐛 ✅ 💼 📖 🎨 📋 🔍 🤔 📆 💬 📢 🧑‍🏫 📹 Martin Walter
Martin Walter
💻 🐛 📆 🔍 🧑‍🏫 🤔 🎨 👀 📖 📢 Martina G. Vilas
Martina G. Vilas
👀 🤔 - Matthew Middlehurst
Matthew Middlehurst
🐛 💻 🔣 📖 🎨 💡 🤔 🚇 🚧 🧑‍🏫 📣 💬 🔬 👀 ⚠️ ✅ 📢 + Matthew Middlehurst
Matthew Middlehurst
🐛 💻 🔣 📖 🎨 💡 🤔 🚇 🚧 🧑‍🏫 📣 💬 🔬 👀 ⚠️ ✅ 📢 Max Patzelt
Max Patzelt
💻 Miao Cai
Miao Cai
🐛 💻 Michael F. Mbouopda
Michael F. Mbouopda
💻 🐛 📖 @@ -201,9 +202,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Michal Chromcak
Michal Chromcak
💻 📖 ⚠️ ✅ Mirae Parker
Mirae Parker
💻 ⚠️ Mohammed Saif Kazamel
Mohammed Saif Kazamel
🐛 - Morad :)
Morad :)
💻 ⚠️ 📖 + Morad :)
Morad :)
💻 ⚠️ 📖 Multivin12
Multivin12
💻 ⚠️ Márcio A. Freitas Jr
Márcio A. Freitas Jr
📖 Niek van der Laan
Niek van der Laan
💻 @@ -211,9 +212,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Nikola Shahpazov
Nikola Shahpazov
📖 Nilesh Kumar
Nilesh Kumar
💻 Nima Nooshiri
Nima Nooshiri
📖 - Ninnart Fuengfusin
Ninnart Fuengfusin
💻 + Ninnart Fuengfusin
Ninnart Fuengfusin
💻 Noa Ben Ami
Noa Ben Ami
💻 ⚠️ 📖 Oleksandr Shchur
Oleksandr Shchur
🐛 💻 Oleksii Kachaiev
Oleksii Kachaiev
💻 ⚠️ @@ -221,9 +222,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Patrick Müller
Patrick Müller
💻 Patrick Rockenschaub
Patrick Rockenschaub
💻 🎨 🤔 ⚠️ Patrick Schäfer
Patrick Schäfer
💻 ✅ - Paul
Paul
📖 + Paul
Paul
📖 Paul Rabich
Paul Rabich
💻 Paul Yim
Paul Yim
💻 💡 ⚠️ Philip
Philip
📖 @@ -231,9 +232,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Phillip Wenig
Phillip Wenig
💻 Piyush Gade
Piyush Gade
💻 👀 Pulkit Verma
Pulkit Verma
📖 - Quaterion
Quaterion
🐛 + Quaterion
Quaterion
🐛 Rafael Ayllón-Gavilán
Rafael Ayllón-Gavilán
💻 Rakshitha Godahewa
Rakshitha Godahewa
💻 📖 RavenRudi
RavenRudi
💻 @@ -241,9 +242,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Rick van Hattem
Rick van Hattem
🚇 Rishabh Bali
Rishabh Bali
💻 Rishav Kumar Sinha
Rishav Kumar Sinha
📖 - Rishi Kumar Ray
Rishi Kumar Ray
🚇 + Rishi Kumar Ray
Rishi Kumar Ray
🚇 Riya Elizabeth John
Riya Elizabeth John
💻 ⚠️ 📖 Ronnie Llamado
Ronnie Llamado
📖 Ryan Kuhns
Ryan Kuhns
💻 📖 ✅ 💡 🤔 👀 ⚠️ @@ -251,9 +252,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Sajaysurya Ganesh
Sajaysurya Ganesh
💻 📖 🎨 💡 🤔 ⚠️ ✅ Saransh Chopra
Saransh Chopra
📖 🚇 Satya Prakash Pattnaik
Satya Prakash Pattnaik
📖 - Saurabh Dasgupta
Saurabh Dasgupta
💻 + Saurabh Dasgupta
Saurabh Dasgupta
💻 Sebastiaan Koel
Sebastiaan Koel
💻 📖 Sebastian Hagn
Sebastian Hagn
📖 Sebastian Schmidl
Sebastian Schmidl
🐛 💻 📖 🔬 ⚠️ 👀 🔣 @@ -261,9 +262,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Shivansh Subramanian
Shivansh Subramanian
📖 💻 Solomon Botchway
Solomon Botchway
🚧 Stanislav Khrapov
Stanislav Khrapov
💻 - Stijn Rotman
Stijn Rotman
💻 + Stijn Rotman
Stijn Rotman
💻 Svea Marie Meyer
Svea Marie Meyer
📖 💻 Sylvain Combettes
Sylvain Combettes
💻 🐛 TNTran92
TNTran92
💻 @@ -271,9 +272,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Tanish Yelgoe
Tanish Yelgoe
💻 Thach Le Nguyen
Thach Le Nguyen
💻 ⚠️ TheMathcompay Widget Factory Team
TheMathcompay Widget Factory Team
📖 - Thomas Buckley-Houston
Thomas Buckley-Houston
🐛 + Thomas Buckley-Houston
Thomas Buckley-Houston
🐛 Tom Xu
Tom Xu
💻 📖 Tomasz Chodakowski
Tomasz Chodakowski
💻 📖 🐛 Tony Bagnall
Tony Bagnall
💻 💼 📖 🎨 📋 🔍 🤔 📆 💬 👀 📢 🔣 @@ -281,9 +282,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Utkarsh Kumar
Utkarsh Kumar
💻 📖 Utsav Kumar Tiwari
Utsav Kumar Tiwari
💻 📖 Vedant
Vedant
📖 - Viktor Dremov
Viktor Dremov
💻 + Viktor Dremov
Viktor Dremov
💻 ViktorKaz
ViktorKaz
💻 📖 🎨 Vyomkesh Vyas
Vyomkesh Vyas
💻 📖 💡 ⚠️ Wayne Adams
Wayne Adams
📖 @@ -291,9 +292,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d William Zeng
William Zeng
🐛 William Zheng
William Zheng
💻 ⚠️ Yair Beer
Yair Beer
💻 - Yash Lamba
Yash Lamba
💻 + Yash Lamba
Yash Lamba
💻 Yi-Xuan Xu
Yi-Xuan Xu
💻 ⚠️ 🚧 📖 Ziyao Wei
Ziyao Wei
💻 aa25desh
aa25desh
💻 🐛 @@ -301,9 +302,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d adoherty21
adoherty21
🐛 alexbanwell1
alexbanwell1
💻 🎨 📖 bethrice44
bethrice44
🐛 💻 👀 ⚠️ - big-o
big-o
💻 ⚠️ 🎨 🤔 👀 ✅ 🧑‍🏫 + big-o
big-o
💻 ⚠️ 🎨 🤔 👀 ✅ 🧑‍🏫 bobbys
bobbys
💻 brett koonce
brett koonce
📖 btrtts
btrtts
📖 @@ -311,9 +312,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d chrisholder
chrisholder
💻 ⚠️ 📖 🎨 💡 🐛 danbartl
danbartl
🐛 💻 👀 📢 ⚠️ ✅ 📹 hamzahiqb
hamzahiqb
🚇 - hiqbal2
hiqbal2
📖 + hiqbal2
hiqbal2
📖 jesellier
jesellier
💻 jschemm
jschemm
💻 julu98
julu98
🐛 @@ -321,9 +322,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d matteogales
matteogales
💻 🎨 🤔 neuron283
neuron283
💻 nileenagp
nileenagp
💻 - oleskiewicz
oleskiewicz
💻 📖 ⚠️ + oleskiewicz
oleskiewicz
💻 📖 ⚠️ pabworks
pabworks
💻 ⚠️ patiently pending world peace
patiently pending world peace
💻 raishubham1
raishubham1
📖 @@ -331,9 +332,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d sophijka
sophijka
📖 🚧 sri1419
sri1419
💻 tensorflow-as-tf
tensorflow-as-tf
💻 - vNtzYy
vNtzYy
🐛 + vNtzYy
vNtzYy
🐛 ved pawar
ved pawar
📖 vedazeren
vedazeren
💻 ⚠️ vincent-nich12
vincent-nich12
💻 From 42352883314679a7f19e07188ebd8260ee356806 Mon Sep 17 00:00:00 2001 From: Aryan Pola <98093778+aryanpola@users.noreply.github.com> Date: Sat, 12 Apr 2025 21:20:09 +0530 Subject: [PATCH 063/216] [ENH,MNT] Assign Bot (assigned issues>2) (#2702) * Empty-Commit * point 2 working * changes * changes in comment message --- .github/utilities/issue_assign.py | 39 ++++++++++++++++++++++++++++++- 1 file changed, 38 insertions(+), 1 deletion(-) diff --git a/.github/utilities/issue_assign.py b/.github/utilities/issue_assign.py index 69622ea5e5..a4ead41a10 100755 --- a/.github/utilities/issue_assign.py +++ b/.github/utilities/issue_assign.py @@ -3,6 +3,10 @@ It checks if a comment on an issue or PR includes the trigger phrase (as defined) and a mentioned user. If it does, it assigns the issue to the mentioned user. +Users without write access can only have up to 2 open issues assigned. +Users with write access (or admin) are exempt from this limit. +If a non-write user already has 2 or more open issues, the bot +comments on the issue with links to the currently assigned open issues. """ import json @@ -29,4 +33,37 @@ mentioned_users.remove("aeon-actions-bot") for user in mentioned_users: - issue.add_to_assignees(user) + user_obj = g.get_user(user) + permission = repo.get_collaborator_permission(user_obj) + + if permission in ["admin", "write"]: + issue.add_to_assignees(user) + else: + # First check if the user is already assigned to this issue + if user in [assignee.login for assignee in issue.assignees]: + continue + + # search for open issues only + query = f"repo:{repo.full_name} is:issue is:open assignee:{user}" + issues_assigned_to_user = g.search_issues(query) + assigned_count = issues_assigned_to_user.totalCount + + if assigned_count >= 2: + # link to issue + assigned_issues_list = [ + f"[#{assigned_issue.number}]({assigned_issue.html_url})" + for assigned_issue in issues_assigned_to_user + ] + + comment_message = ( + f"@{user}, you already have {assigned_count} open issues assigned. " + "Users without write access are limited to self-assigning two" + "issues.\n\n" + "Here are the open issues assigned to you:\n" + + "\n".join( + f"- {issue_link}" for issue_link in assigned_issues_list + ) + ) + issue.create_comment(comment_message) + else: + issue.add_to_assignees(user) From c322291dc51f4d90a33e43f016b762e8cb610d8e Mon Sep 17 00:00:00 2001 From: Ramana Raja <83065061+Ramana-Raja@users.noreply.github.com> Date: Sat, 12 Apr 2025 23:11:17 +0530 Subject: [PATCH 064/216] [MNT,ENH] Assign-bot (Allow users to type alternative phrases for assingment) (#2704) * added extra features * added comments * optimized code * optimized code * made changes requested by moderators * fixed conflicts * fixed conflicts * fixed conflicts --------- Co-authored-by: Ramana-Raja --- .github/utilities/issue_assign.py | 76 +++++++++++++++++-------------- 1 file changed, 41 insertions(+), 35 deletions(-) diff --git a/.github/utilities/issue_assign.py b/.github/utilities/issue_assign.py index a4ead41a10..1696fd33fc 100755 --- a/.github/utilities/issue_assign.py +++ b/.github/utilities/issue_assign.py @@ -24,46 +24,52 @@ issue = repo.get_issue(number=issue_number) comment_body = context_dict["event"]["comment"]["body"] pr = context_dict["event"]["issue"].get("pull_request") +commenter = context_dict["event"]["comment"]["user"]["login"] -# Assign tagged used to the issue if the comment includes the trigger phrase body = comment_body.lower() -if "@aeon-actions-bot" in body and "assign" in body and not pr: - mentioned_users = re.findall(r"@[a-zA-Z0-9_-]+", comment_body) - mentioned_users = [user[1:] for user in mentioned_users] - mentioned_users.remove("aeon-actions-bot") +if "@aeon-actions-bot" in body and not pr: + # Assign commenter if comment includes "assign me" + if "assign me" in body: + issue.add_to_assignees(commenter) + # Assign tagged used to the issue if the comment includes the trigger phrase + elif "assign" in body: + mentioned_users = re.findall(r"@[a-zA-Z0-9_-]+", comment_body) + mentioned_users = [user[1:] for user in mentioned_users] + mentioned_users.remove("aeon-actions-bot") - for user in mentioned_users: - user_obj = g.get_user(user) - permission = repo.get_collaborator_permission(user_obj) + for user in mentioned_users: + user_obj = g.get_user(user) + permission = repo.get_collaborator_permission(user_obj) - if permission in ["admin", "write"]: - issue.add_to_assignees(user) - else: - # First check if the user is already assigned to this issue - if user in [assignee.login for assignee in issue.assignees]: - continue + if permission in ["admin", "write"]: + issue.add_to_assignees(user) + else: + # First check if the user is already assigned to this issue + if user in [assignee.login for assignee in issue.assignees]: + continue - # search for open issues only - query = f"repo:{repo.full_name} is:issue is:open assignee:{user}" - issues_assigned_to_user = g.search_issues(query) - assigned_count = issues_assigned_to_user.totalCount + # search for open issues only + query = f"repo:{repo.full_name} is:issue is:open assignee:{user}" + issues_assigned_to_user = g.search_issues(query) + assigned_count = issues_assigned_to_user.totalCount - if assigned_count >= 2: - # link to issue - assigned_issues_list = [ - f"[#{assigned_issue.number}]({assigned_issue.html_url})" - for assigned_issue in issues_assigned_to_user - ] + if assigned_count >= 2: + # link to issue + assigned_issues_list = [ + f"[#{assigned_issue.number}]({assigned_issue.html_url})" + for assigned_issue in issues_assigned_to_user + ] - comment_message = ( - f"@{user}, you already have {assigned_count} open issues assigned. " - "Users without write access are limited to self-assigning two" - "issues.\n\n" - "Here are the open issues assigned to you:\n" - + "\n".join( - f"- {issue_link}" for issue_link in assigned_issues_list + comment_message = ( + f"@{user}, you already have {assigned_count} " + f"open issues assigned." + "Users without write access are limited to self-assigning two" + "issues.\n\n" + "Here are the open issues assigned to you:\n" + + "\n".join( + f"- {issue_link}" for issue_link in assigned_issues_list + ) ) - ) - issue.create_comment(comment_message) - else: - issue.add_to_assignees(user) + issue.create_comment(comment_message) + else: + issue.add_to_assignees(user) From 6169ed25b7fda188d107014b91f4238092649973 Mon Sep 17 00:00:00 2001 From: "allcontributors[bot]" <46447321+allcontributors[bot]@users.noreply.github.com> Date: Sat, 12 Apr 2025 19:00:45 +0100 Subject: [PATCH 065/216] =?UTF-8?q?=F0=9F=93=9D=20Add=20Ramana-Raja=20as?= =?UTF-8?q?=20a=20contributor=20for=20code=20(#2741)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 📝 Update CONTRIBUTORS.md [skip ci] * 📝 Update .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> --- .all-contributorsrc | 9 +++++++++ CONTRIBUTORS.md | 23 ++++++++++++----------- 2 files changed, 21 insertions(+), 11 deletions(-) diff --git a/.all-contributorsrc b/.all-contributorsrc index 0480bee415..95453ca9e6 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -2692,6 +2692,15 @@ "contributions": [ "doc" ] + }, + { + "login": "Ramana-Raja", + "name": "Ramana Raja", + "avatar_url": "https://avatars.githubusercontent.com/u/83065061?v=4", + "profile": "https://github.com/Ramana-Raja", + "contributions": [ + "code" + ] } ], "commitType": "docs" diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md index 5a79bfde55..18945d9902 100644 --- a/CONTRIBUTORS.md +++ b/CONTRIBUTORS.md @@ -1,7 +1,7 @@ # Contributors -[![All Contributors](https://img.shields.io/badge/all_contributors-263-orange.svg)](#contributors) +[![All Contributors](https://img.shields.io/badge/all_contributors-264-orange.svg)](#contributors) This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! @@ -237,13 +237,14 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Quaterion
Quaterion
🐛 Rafael Ayllón-Gavilán
Rafael Ayllón-Gavilán
💻 Rakshitha Godahewa
Rakshitha Godahewa
💻 📖 + Ramana Raja
Ramana Raja
💻 RavenRudi
RavenRudi
💻 Raya Chakravarty
Raya Chakravarty
📖 Rick van Hattem
Rick van Hattem
🚇 Rishabh Bali
Rishabh Bali
💻 - Rishav Kumar Sinha
Rishav Kumar Sinha
📖 + Rishav Kumar Sinha
Rishav Kumar Sinha
📖 Rishi Kumar Ray
Rishi Kumar Ray
🚇 Riya Elizabeth John
Riya Elizabeth John
💻 ⚠️ 📖 Ronnie Llamado
Ronnie Llamado
📖 @@ -251,9 +252,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Sagar Mishra
Sagar Mishra
⚠️ Sajaysurya Ganesh
Sajaysurya Ganesh
💻 📖 🎨 💡 🤔 ⚠️ ✅ Saransh Chopra
Saransh Chopra
📖 🚇 - Satya Prakash Pattnaik
Satya Prakash Pattnaik
📖 + Satya Prakash Pattnaik
Satya Prakash Pattnaik
📖 Saurabh Dasgupta
Saurabh Dasgupta
💻 Sebastiaan Koel
Sebastiaan Koel
💻 📖 Sebastian Hagn
Sebastian Hagn
📖 @@ -261,9 +262,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Sharathchenna
Sharathchenna
💻 Shivansh Subramanian
Shivansh Subramanian
📖 💻 Solomon Botchway
Solomon Botchway
🚧 - Stanislav Khrapov
Stanislav Khrapov
💻 + Stanislav Khrapov
Stanislav Khrapov
💻 Stijn Rotman
Stijn Rotman
💻 Svea Marie Meyer
Svea Marie Meyer
📖 💻 Sylvain Combettes
Sylvain Combettes
💻 🐛 @@ -271,9 +272,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Taiwo Owoseni
Taiwo Owoseni
💻 Tanish Yelgoe
Tanish Yelgoe
💻 Thach Le Nguyen
Thach Le Nguyen
💻 ⚠️ - TheMathcompay Widget Factory Team
TheMathcompay Widget Factory Team
📖 + TheMathcompay Widget Factory Team
TheMathcompay Widget Factory Team
📖 Thomas Buckley-Houston
Thomas Buckley-Houston
🐛 Tom Xu
Tom Xu
💻 📖 Tomasz Chodakowski
Tomasz Chodakowski
💻 📖 🐛 @@ -281,9 +282,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Tvisha Vedant
Tvisha Vedant
💻 Utkarsh Kumar
Utkarsh Kumar
💻 📖 Utsav Kumar Tiwari
Utsav Kumar Tiwari
💻 📖 - Vedant
Vedant
📖 + Vedant
Vedant
📖 Viktor Dremov
Viktor Dremov
💻 ViktorKaz
ViktorKaz
💻 📖 🎨 Vyomkesh Vyas
Vyomkesh Vyas
💻 📖 💡 ⚠️ @@ -291,9 +292,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d William Templier
William Templier
📖 William Zeng
William Zeng
🐛 William Zheng
William Zheng
💻 ⚠️ - Yair Beer
Yair Beer
💻 + Yair Beer
Yair Beer
💻 Yash Lamba
Yash Lamba
💻 Yi-Xuan Xu
Yi-Xuan Xu
💻 ⚠️ 🚧 📖 Ziyao Wei
Ziyao Wei
💻 @@ -301,9 +302,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d abandus
abandus
🤔 💻 adoherty21
adoherty21
🐛 alexbanwell1
alexbanwell1
💻 🎨 📖 - bethrice44
bethrice44
🐛 💻 👀 ⚠️ + bethrice44
bethrice44
🐛 💻 👀 ⚠️ big-o
big-o
💻 ⚠️ 🎨 🤔 👀 ✅ 🧑‍🏫 bobbys
bobbys
💻 brett koonce
brett koonce
📖 @@ -311,9 +312,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d chizzi25
chizzi25
📝 chrisholder
chrisholder
💻 ⚠️ 📖 🎨 💡 🐛 danbartl
danbartl
🐛 💻 👀 📢 ⚠️ ✅ 📹 - hamzahiqb
hamzahiqb
🚇 + hamzahiqb
hamzahiqb
🚇 hiqbal2
hiqbal2
📖 jesellier
jesellier
💻 jschemm
jschemm
💻 @@ -321,9 +322,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d kkoziara
kkoziara
💻 🐛 matteogales
matteogales
💻 🎨 🤔 neuron283
neuron283
💻 - nileenagp
nileenagp
💻 + nileenagp
nileenagp
💻 oleskiewicz
oleskiewicz
💻 📖 ⚠️ pabworks
pabworks
💻 ⚠️ patiently pending world peace
patiently pending world peace
💻 @@ -331,9 +332,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d simone-pignotti
simone-pignotti
💻 🐛 sophijka
sophijka
📖 🚧 sri1419
sri1419
💻 - tensorflow-as-tf
tensorflow-as-tf
💻 + tensorflow-as-tf
tensorflow-as-tf
💻 vNtzYy
vNtzYy
🐛 ved pawar
ved pawar
📖 vedazeren
vedazeren
💻 ⚠️ From 7c253283e1b64eaf25b3a7b96c68f7625841de72 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Sun, 13 Apr 2025 16:06:22 +0100 Subject: [PATCH 066/216] Release v1.1.0 (#2696) * v1.1.0 draft * finish --- README.md | 2 +- aeon/__init__.py | 2 +- docs/changelog.md | 1 + docs/changelogs/v1.0.md | 2 +- docs/changelogs/v1.1.md | 294 ++++++++++++++++++++++++++++++++++++++++ docs/conf.py | 1 - docs/index.md | 9 -- pyproject.toml | 2 +- 8 files changed, 299 insertions(+), 14 deletions(-) create mode 100644 docs/changelogs/v1.1.md diff --git a/README.md b/README.md index cd50191e93..3f6eb132f1 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ We strive to provide a broad library of time series algorithms including the latest advances, offer efficient implementations using numba, and interfaces with other time series packages to provide a single framework for algorithm comparison. -The latest `aeon` release is `v1.0.0`. You can view the full changelog +The latest `aeon` release is `v1.1.0`. You can view the full changelog [here](https://www.aeon-toolkit.org/en/stable/changelog.html). Our webpage and documentation is available at https://aeon-toolkit.org. diff --git a/aeon/__init__.py b/aeon/__init__.py index c77db1e335..f8c45d7805 100644 --- a/aeon/__init__.py +++ b/aeon/__init__.py @@ -1,3 +1,3 @@ """aeon toolkit.""" -__version__ = "1.0.0" +__version__ = "1.1.0" diff --git a/docs/changelog.md b/docs/changelog.md index 5ad9b146d4..f2c3d2b20b 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -9,6 +9,7 @@ To stay up to date with aeon releases, subscribe to aeon [here](https://libraries.io/pypi/aeon) or follow us on [Twitter](https://twitter.com/aeon_toolbox). +- [Version 1.1.0](changelogs/v1.1.md) - [Version 1.0.0](changelogs/v1.0.md) - [Version 0.11.1](changelogs/v0/v0.11.md) - [Version 0.11.0](changelogs/v0/v0.11.md) diff --git a/docs/changelogs/v1.0.md b/docs/changelogs/v1.0.md index 7a60562ea7..2a9eaa600d 100644 --- a/docs/changelogs/v1.0.md +++ b/docs/changelogs/v1.0.md @@ -9,7 +9,7 @@ has helped make this release possible. This major release includes breaking changes that could not happen in a regular minor release. This includes the removal of the old forecasting wrapper-based code, the datatypes module and the previous transformer module. Our focus now is on -writing more efficient code based on efficient array based bespoke implementations. +writing efficient code based on array-based bespoke implementations. ## Highlights diff --git a/docs/changelogs/v1.1.md b/docs/changelogs/v1.1.md new file mode 100644 index 0000000000..55047c1b12 --- /dev/null +++ b/docs/changelogs/v1.1.md @@ -0,0 +1,294 @@ +# v1.1.0 + +April 2025 + +## Highlights + +- Python 3.13 is now supported and dependency bounds have been raised +- `df-list` collections now require (`n_cases`, `n_channels`, `n_timepoints`) formatting. +Make sure each dataframe in the list has channels as the first dimension and timepoints are the second. +- The ROCKAD anomaly detector has been added ({user}`pattplatt`) +- THe KASBA clusterer has been added ({user}`chrisholder`) +- Lots of documentation improvements and bug fixes + +## Anomaly Detection + +### Documentation + +- [DOC] Anomaly Detection Overview Notebook ({pr}`2446`) {user}`itsdivya1309` + +### Enhancements + +- [ENH] Added ROCKAD anomaly detector to aeon ({pr}`2376`) {user}`pattplatt` +- [ENH] Replace `prts` metrics ({pr}`2400`) {user}`aryanpola` + +## Benchmarking + +### Deprecation + +- [MNT,DEP] _binary.py metrics deprecated ({pr}`2600`) {user}`aryanpola` + +### Documentation + +- Fix docstring inconsistencies in benchmarking module (resolves #809) ({pr}`2735`) {user}`adityagh006` + +### Enhancements + +- [ENH] Replace `prts` metrics ({pr}`2400`) {user}`aryanpola` +- [ENH] Remove MutilROCKETRegressor from alias mapping ({pr}`2623`) {user}`Kaustbh` +- [ENH] Hard-Coded Tests for `test_metrics.py` ({pr}`2672`) {user}`aryanpola` + +### Maintenance + +- [MNT,DEP] _binary.py metrics deprecated ({pr}`2600`) {user}`aryanpola` + +## Classification + +### Bug Fixes + +- [BUG] Passed stride parameter to LITETimeClassifier ({pr}`2502`) {user}`kavya-r30` +- [BUG] LITE Network : Fixed list arguments ({pr}`2510`) {user}`kavya-r30` + +### Documentation + +- [DOC] Add LITETimeClassifier Example to Classification Notebook ({pr}`2419`) {user}`sumana-2705` +- [DOC] Inserting the right paper reference ({pr}`2440`) {user}`adilsonmedronha` +- [DOC] LITE Time classifier metrics ({pr}`2464`) {user}`dschrempf` +- [DOC] Updated docstring to clarify class_weight parameter in MRHydraClassifier ({pr}`2505`) {user}`Akhil-Jasson` +- [DOC] added type hints to 'classification->convolution_based' module ({pr}`2494`) {user}`YashviMehta03` +- [DOC] Documentation improvement of BaseDeepClassifier and BaseCollectionEstimator ({pr}`2516`) {user}`kevinzb56` +- [DOC] Add 'Raises' section to docstring (#1766) ({pr}`2484`) {user}`Nikitas100` + +### Enhancements + +- [ENH] Added possibility for pooling strides in TimeCNN ({pr}`2485`) {user}`kavya-r30` +- [ENH] Replace SFA with SFAFast in REDCOMETS ({pr}`2418`) {user}`itsdivya1309` +- [ENH] Added class weights to feature based classifiers ({pr}`2512`) {user}`lucifer4073` +- [ENH] Set `outlier_norm` default to True for Catch22 estimators ({pr}`2659`) {user}`tanishy7777` + +### Maintenance + +- [MNT] Fixed wrong type annotations for aeon classes ({pr}`2488`) {user}`shinymack` +- [MNT] Raise version bound for `scikit-learn` 1.6 ({pr}`2486`) {user}`MatthewMiddlehurst` +- [MNT] Remove REDCOMETs from testing exclusion list ({pr}`2630`) {user}`MatthewMiddlehurst` + +## Clustering + +### Documentation + +- [DOC] Update Partitional clustering notebook ({pr}`2483`) {user}`Akhil-Jasson` +- [DOC] Notebook on Feature-based Clustering ({pr}`2579`) {user}`itsdivya1309` + +### Enhancements + +- [ENH] KASBA clusterer ({pr}`2428`) {user}`chrisholder` +- [ENH] Removed Reshape Layer from Deep Learning Clusterers ({pr}`2495`) {user}`kavya-r30` +- [ENH] Adds kdtw kernel support for kernelkmeans ({pr}`2645`) {user}`tanishy7777` +- [ENH] Add dummy clusterer tags ({pr}`2551`) {user}`MatthewMiddlehurst` + +### Maintenance + +- [MNT] Fix random state deep clustering checking test ({pr}`2528`) {user}`hadifawaz1999` +- [MNT] Raise version bound for `scikit-learn` 1.6 ({pr}`2486`) {user}`MatthewMiddlehurst` + +## Datasets + +### Enhancements + +- [ENH] Collection conversion cleanup and `df-list` fix ({pr}`2654`) {user}`MatthewMiddlehurst` + +## Distances + +### Bug Fixes + +- [BUG, ENH] SFA fix: Std-Normalization, as used in BOSS and WEASEL models, is potentially harmful for lower bounding ({pr}`2461`) {user}`patrickzib` + +### Documentation + +- [DOC] ddtw_distance Documentation Fix ({pr}`2443`) {user}`notaryanramani` +- [DOC] Distance function notebook #2395 ({pr}`2487`) {user}`kevinzb56` + +### Enhancements + +- [BUG, ENH] SFA fix: Std-Normalization, as used in BOSS and WEASEL models, is potentially harmful for lower bounding ({pr}`2461`) {user}`patrickzib` +- [ENH] Adds support for distances that are asymmetric but supports unequal length ({pr}`2613`) {user}`tanishy7777` +- [ENH] Support for unequal length in itakura parallelogram ({pr}`2647`) {user}`tanishy7777` +- [ENH] Implement DTW with Global alignment ({pr}`2565`) {user}`tanishy7777` + +## Forecasting + +### Documentation + +- [DOC] Added Docstring for regression forecasting ({pr}`2564`) {user}`kavya-r30` + +### Enhancements + +- [ENh] Forecasting tests ({pr}`2427`) {user}`TonyBagnall` + +## Networks + +### Bug Fixes + +- [BUG] LITE Network : Fixed list arguments ({pr}`2510`) {user}`kavya-r30` + +### Enhancements + +- [ENH] Added possibility for pooling strides in TimeCNN ({pr}`2485`) {user}`kavya-r30` +- [ENH] Add and Validate `n_layers`, `n_units`, `activation` & `dropout_rate` kwargs to MLPNetwork ({pr}`2338`) {user}`aadya940` +- [ENH] Test coverage for AEResNetNetwork Improved ({pr}`2518`) {user}`lucifer4073` +- [ENH] Test coverage for MLP Network improved ({pr}`2537`) {user}`shinymack` +- [ENH] Test coverage for FCNNetwork Improved ({pr}`2559`) {user}`lucifer4073` +- [ENH] Test coverage for AEFCNNetwork Improved ({pr}`2558`) {user}`lucifer4073` +- [ENH] Test coverage for TimeCNNNetwork Improved ({pr}`2534`) {user}`lucifer4073` +- [ENH] Test coverage for Resnet Network ({pr}`2553`) {user}`kavya-r30` + +## Regression + +### Bug Fixes + +- [BUG] LITE Network : Fixed list arguments ({pr}`2510`) {user}`kavya-r30` + +### Documentation + +- [DOC] Base collection class docstring formatting ({pr}`2452`) {user}`TonyBagnall` +- [DOC] Inconsistent double qoutes in regression module ({pr}`2640`) {user}`Val-2608` + +### Maintenance + +- [MNT] Fixed wrong type annotations for aeon classes ({pr}`2488`) {user}`shinymack` +- [MNT] Raise version bound for `scikit-learn` 1.6 ({pr}`2486`) {user}`MatthewMiddlehurst` + +## Segmentation + +### Enhancements + +- [ENH] Remove test exclusions ({pr}`2409`) {user}`TonyBagnall` + +## Transformations + +### Bug Fixes + +- [BUG] add ExpSmoothingSeriesTransformer and MovingAverageSeriesTransformer to __init__ ({pr}`2550`) {user}`Cyril-Meyer` +- [BUG] SevenNumberSummary bugfix and input rename ({pr}`2555`) {user}`MatthewMiddlehurst` + +### Documentation + +- [DOC] Base collection class docstring formatting ({pr}`2452`) {user}`TonyBagnall` +- [DOC] Create smoothing filters notebook ({pr}`2547`) {user}`Cyril-Meyer` +- [DOC] Clarify documentation regarding unequal length series limitation ({pr}`2589`) {user}`Kaustbh` + +### Enhancements + +- [ENH] Refactor BinSegSegmenter to BinSegmenter ({pr}`2408`) {user}`TonyBagnall` +- [ENH] Remove test exclusions ({pr}`2409`) {user}`TonyBagnall` + +## Unit Testing + +### Enhancements + +- [ENH] Remove test exclusions ({pr}`2409`) {user}`TonyBagnall` +- [ENh] Forecasting tests ({pr}`2427`) {user}`TonyBagnall` +- [ENH] adjust test for non numpy output ({pr}`2517`) {user}`TonyBagnall` +- [ENH] Collection conversion cleanup and `df-list` fix ({pr}`2654`) {user}`MatthewMiddlehurst` +- [MNT,ENH] Update to allow Python 3.13 ({pr}`2608`) {user}`MatthewMiddlehurst` + +### Maintenance + +- [MNT] Testing fixes ({pr}`2531`) {user}`MatthewMiddlehurst` +- [MNT] Fix random state deep clustering checking test ({pr}`2528`) {user}`hadifawaz1999` +- [MNT] Skip some excected results tests when numba is disabled ({pr}`2639`) {user}`MatthewMiddlehurst` +- [MNT] Remove REDCOMETs from testing exclusion list ({pr}`2630`) {user}`MatthewMiddlehurst` +- [MNT,ENH] Update to allow Python 3.13 ({pr}`2608`) {user}`MatthewMiddlehurst` + +## Visualisations + +### Documentation + +- [DOC] Added Missing Docstring for Plot Pairwise Distance Matrix ({pr}`2609`) {user}`kavya-r30` + +### Enhancements + +- [ENH] Test Coverage for Pairwise Distance ({pr}`2590`) {user}`kavya-r30` +- [ENH] `best_on_top` addition in `plot_pairwise_scatter` ({pr}`2655`) {user}`aryanpola` + +## Other + +### Documentation + +- [DOC] Contributing guide and template changes ({pr}`2423`) {user}`MatthewMiddlehurst` +- [DOC] Created a adding_typehints.md in developers_guide that Fixes issue #1857 ({pr}`2424`) {user}`vedpawar2254` +- [DOC] Add comment to readme.md ({pr}`2450`) {user}`TonyBagnall` +- [DOC] Contributing readme and other contributing updates ({pr}`2445`) {user}`MatthewMiddlehurst` +- [DOC] add note to install pandoc ({pr}`2489`) {user}`inclinedadarsh` +- [DOC] Added search functionality for estimator overview table ({pr}`2496`) {user}`kavya-r30` +- [DOC] Fixed tags appearance on the end on list in partition clustering notebook ({pr}`2504`) {user}`kavya-r30` +- [DOC] Update custom CSS for dataframe styling in documentation ({pr}`2508`) {user}`inclinedadarsh` +- [DOC] Improve type hint guide and add link to the page. ({pr}`2532`) {user}`MatthewMiddlehurst` +- [DOC] Fixed Output Error in Interval Based Notebook ({pr}`2620`) {user}`kavya-r30` +- [DOC] Add GSoC announcement to web page ({pr}`2629`) {user}`MatthewMiddlehurst` +- [DOC] Update dependencies.md ({pr}`2717`) {user}`TinaJin0228` +- [DOC] re-running notebook for fixing error in cell output ({pr}`2597`) {user}`Kaustbh` +- [DOC] Add 'Raises' section to docstring #1766 ({pr}`2617`) {user}`ayushsingh9720` +- [DOC] Contributor docs update ({pr}`2554`) {user}`MatthewMiddlehurst` +- [DOC] Add link to about us page and fix badge link in README ({pr}`2556`) {user}`MatthewMiddlehurst` +- [DOC] Fixed a few spelling/grammar mistakes on TSC docs examples ({pr}`2738`) {user}`HaroonAzamFiza` + +### Enhancements + +- [ENH] Add sphinx event to add capability table to estimators' docs individually ({pr}`2468`) {user}`inclinedadarsh` +- [DOC] Added search functionality for estimator overview table ({pr}`2496`) {user}`kavya-r30` +- [ENH,MNT] Assign Bot (assigned issues>2) ({pr}`2702`) {user}`aryanpola` +- [MNT,ENH] Assign-bot (Allow users to type alternative phrases for assingment) ({pr}`2704`) {user}`Ramana-Raja` + +### Maintenance + +- [MNT] Trying to diagnose ubuntu workflow failures ({pr}`2413`) {user}`MatthewMiddlehurst` +- [MNT] Set upper bound on esig version ({pr}`2463`) {user}`chrisholder` +- [MNT] Swapped tensorflow and pytorch to install only CPU version ({pr}`2416`) {user}`chrisholder` +- [MNT] Temporary exclusion of REDCOMETS from CI ({pr}`2522`) {user}`MatthewMiddlehurst` +- [MNT] Use MacOS for examples/ workflow ({pr}`2668`) {user}`shinymack` +- [MNT] issue-assign-bot (prevent assignment on PRs) ({pr}`2703`) {user}`shinymack` +- [MNT] Fix run_examples.sh exclusion ({pr}`2701`) {user}`MatthewMiddlehurst` +- [MNT] Updated the release workflows ({pr}`2638`) {user}`MatthewMiddlehurst` +- [ENH,MNT] Assign Bot (assigned issues>2) ({pr}`2702`) {user}`aryanpola` +- [MNT,ENH] Assign-bot (Allow users to type alternative phrases for assingment) ({pr}`2704`) {user}`Ramana-Raja` + +### Other + +- [GOV] Infrastructure workgroup lead and voting ambiguity fix ({pr}`2426`) {user}`MatthewMiddlehurst` + +## Contributors + +The following have contributed to this release through a collective 90 GitHub Pull Requests: + +{user}`aadya940`, +{user}`adilsonmedronha`, +{user}`adityagh006`, +{user}`Akhil-Jasson`, +{user}`aryanpola`, +{user}`ayushsingh9720`, +{user}`chrisholder`, +{user}`Cyril-Meyer`, +{user}`dschrempf`, +{user}`hadifawaz1999`, +{user}`HaroonAzamFiza`, +{user}`inclinedadarsh`, +{user}`itsdivya1309`, +{user}`Kaustbh`, +{user}`kavya-r30`, +{user}`kevinzb56`, +{user}`lucifer4073`, +{user}`MatthewMiddlehurst`, +{user}`Nikitas100`, +{user}`notaryanramani`, +{user}`patrickzib`, +{user}`pattplatt`, +{user}`Ramana-Raja`, +{user}`shinymack`, +{user}`sumana-2705`, +{user}`tanishy7777`, +{user}`TinaJin0228`, +{user}`TonyBagnall`, +{user}`Val-2608`, +{user}`vedpawar2254`, +{user}`YashviMehta03` diff --git a/docs/conf.py b/docs/conf.py index bea36e60dd..65844dbb71 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -199,7 +199,6 @@ def find_source(): # documentation. html_theme_options = { - "announcement": "Announcement: aeon is taking part in the Google Summer of Code (GSoC) 2025! See the home page for more information.", # noqa: E501 "sidebar_hide_name": True, "top_of_page_button": "edit", "source_repository": "https://github.com/aeon-toolkit/aeon/", diff --git a/docs/index.md b/docs/index.md index d1c45c1c6d..11b558839e 100644 --- a/docs/index.md +++ b/docs/index.md @@ -17,15 +17,6 @@ segmentation and similarity search. - We provide a range of tools for reproducing benchmarking results and evaluating time series algorithms implemented in `aeon` and other `scikit-learn` compatible packages. -```{admonition} GSoC 2025 -`aeon` is participating in Google Summer of Code 2025 under the NumFOCUS umbrella. If -you are interested in participating, please see our [GSoC 2025 project page](https://github.com/aeon-toolkit/aeon-admin/blob/main/gsoc/gsoc-2025-projects.md). -Feel free to ask questions on the dedicated [GitHub discussion](https://github.com/aeon-toolkit/aeon/discussions/2530) -or our [Slack channel](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-22vwvut29-HDpCu~7VBUozyfL_8j3dLA). -If you are interested in contributing, click on the "Contributing to aeon" link in the -sidebar for a contribution guide. Applications close April 8th. -``` - ## Community Channels **GitHub**: [github.com/aeon-toolkit/aeon](https://github.com/aeon-toolkit/aeon) diff --git a/pyproject.toml b/pyproject.toml index b5dc6c6dc9..06408e7b9d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "aeon" -version = "1.0.0" +version = "1.1.0" description = "A toolkit for machine learning from time series" authors = [ {name = "aeon developers", email = "contact@aeon-toolkit.org"}, From f9c0fef56096302aa3cbf963ff5a14c9a1746b40 Mon Sep 17 00:00:00 2001 From: "aeon-actions-bot[bot]" <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Date: Mon, 14 Apr 2025 11:37:07 +0100 Subject: [PATCH 067/216] Automated `pre-commit` hook update (#2743) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 505c448aca..b81fd89b64 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -29,7 +29,7 @@ repos: args: [ "--create", "--python-folders", "aeon" ] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.11.4 + rev: v0.11.5 hooks: - id: ruff args: [ "--fix"] From 8cf3277d33dc9e9c7f9a95810fb8ea8dcd777c78 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 14 Apr 2025 11:37:41 +0100 Subject: [PATCH 068/216] [MNT] Bump the github-actions group with 2 updates (#2744) Bumps the github-actions group with 2 updates: [crs-k/stale-branches](https://github.com/crs-k/stale-branches) and [tj-actions/changed-files](https://github.com/tj-actions/changed-files). Updates `crs-k/stale-branches` from 7.0.0 to 7.0.1 - [Release notes](https://github.com/crs-k/stale-branches/releases) - [Commits](https://github.com/crs-k/stale-branches/compare/v7.0.0...v7.0.1) Updates `tj-actions/changed-files` from 46.0.4 to 46.0.5 - [Release notes](https://github.com/tj-actions/changed-files/releases) - [Changelog](https://github.com/tj-actions/changed-files/blob/main/HISTORY.md) - [Commits](https://github.com/tj-actions/changed-files/compare/v46.0.4...v46.0.5) --- updated-dependencies: - dependency-name: crs-k/stale-branches dependency-version: 7.0.1 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: github-actions - dependency-name: tj-actions/changed-files dependency-version: 46.0.5 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: github-actions ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/periodic_github_maintenace.yml | 2 +- .github/workflows/pr_precommit.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/periodic_github_maintenace.yml b/.github/workflows/periodic_github_maintenace.yml index 4340e40e88..952150313b 100644 --- a/.github/workflows/periodic_github_maintenace.yml +++ b/.github/workflows/periodic_github_maintenace.yml @@ -23,7 +23,7 @@ jobs: private-key: ${{ secrets.PR_APP_KEY }} - name: Stale Branches - uses: crs-k/stale-branches@v7.0.0 + uses: crs-k/stale-branches@v7.0.1 with: repo-token: ${{ steps.app-token.outputs.token }} days-before-stale: 140 diff --git a/.github/workflows/pr_precommit.yml b/.github/workflows/pr_precommit.yml index f4db6893d3..547b4c6db6 100644 --- a/.github/workflows/pr_precommit.yml +++ b/.github/workflows/pr_precommit.yml @@ -37,7 +37,7 @@ jobs: python-version: "3.11" - name: Get changed files - uses: tj-actions/changed-files@v46.0.4 + uses: tj-actions/changed-files@v46.0.5 id: changed-files - name: List changed files From 78a4a75f4794eff285393a9d65b828a8f93ab963 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Sun, 20 Apr 2025 11:05:43 +0100 Subject: [PATCH 069/216] [DOC] Add implementation references (#2748) * implementation references * better attribution --- aeon/base/_compose.py | 4 +- .../dictionary_based/_redcomets.py | 3 +- aeon/clustering/_kernel_k_means.py | 50 +++++++------------ aeon/distances/elastic/_bounding_matrix.py | 8 ++- aeon/regression/deep_learning/_lite_time.py | 2 + aeon/segmentation/_ggs.py | 1 + .../convolution_based/_minirocket.py | 2 +- aeon/transformations/series/_bkfilter.py | 3 +- aeon/utils/show_versions.py | 11 ++-- .../results/_critical_difference.py | 4 +- docs/_sphinxext/sphinx_remove_toctrees.py | 1 + 11 files changed, 47 insertions(+), 42 deletions(-) diff --git a/aeon/base/_compose.py b/aeon/base/_compose.py index 0995e85de6..8661245806 100644 --- a/aeon/base/_compose.py +++ b/aeon/base/_compose.py @@ -12,8 +12,10 @@ class ComposableEstimatorMixin(ABC): """Handles parameter management for estimators composed of named estimators. - Parts (i.e. get_params and set_params) adapted or copied from the scikit-learn + Parts (i.e. get_params and set_params) adapted from the scikit-learn 1.5.0 ``_BaseComposition`` class in utils/metaestimators.py. + https://github.com/scikit-learn/scikit-learn/ + Copyright (c) 2007-2024 The scikit-learn developers, BSD-3 """ # Attribute name containing an iterable of processed (str, estimator) tuples diff --git a/aeon/classification/dictionary_based/_redcomets.py b/aeon/classification/dictionary_based/_redcomets.py index 601f573bdf..c593c8922f 100644 --- a/aeon/classification/dictionary_based/_redcomets.py +++ b/aeon/classification/dictionary_based/_redcomets.py @@ -66,7 +66,8 @@ class REDCOMETS(BaseClassifier): Notes ----- - Adapted from the implementation at https://github.com/zy18811/RED-CoMETS + Adapted from the implementation at https://github.com/zy18811/RED-CoMETS by + the code owner. References ---------- diff --git a/aeon/clustering/_kernel_k_means.py b/aeon/clustering/_kernel_k_means.py index 98cac93127..062b06ebc8 100644 --- a/aeon/clustering/_kernel_k_means.py +++ b/aeon/clustering/_kernel_k_means.py @@ -64,7 +64,7 @@ def _kdtw_lk(x, y, local_kernel): return cost_matrix[x_timepoints - 1, y_timepoints - 1] -def kdtw(x, y, sigma=1.0, epsilon=1e-3): +def _kdtw(x, y, sigma=1.0, epsilon=1e-3): """ Callable kernel function for KernelKMeans. @@ -81,6 +81,12 @@ def kdtw(x, y, sigma=1.0, epsilon=1e-3): A small constant added for numerical stability to avoid zero values in the local kernel matrix. + Notes + ----- + Inspired by the original implementation + https://github.com/pfmarteau/KDTW/tree/master + Copyright (c) 2020 Pierre-François Marteau, MIT License + Returns ------- similarity : float @@ -92,35 +98,6 @@ def kdtw(x, y, sigma=1.0, epsilon=1e-3): return _kdtw_lk(x, y, local_kernel) -def factory_kdtw_kernel(channels: int): - """ - Return a kdtw kernel callable function that flattened samples to (T, channels). - - Parameters - ---------- - channels: int - Number of channels per timepoint. - - Returns - ------- - kdtw_kernel : callable - A callable kernel function that computes the KDTW similarity between two - time series samples. The function signature is the same as the kdtw - function. - """ - - def kdtw_kernel(x, y, sigma=1.0, epsilon=1e-3): - if x.ndim == 1: - T = x.size // channels - x = x.reshape(T, channels) - if y.ndim == 1: - T = y.size // channels - y = y.reshape(T, channels) - return kdtw(x, y, sigma=sigma, epsilon=epsilon) - - return kdtw_kernel - - class TimeSeriesKernelKMeans(BaseClusterer): """Kernel K Means [1]_: wrapper of the ``tslearn`` implementation. @@ -255,7 +232,18 @@ def _fit(self, X, y=None): verbose = 1 if self.kernel == "kdtw": - self.kernel = factory_kdtw_kernel(channels=X.shape[1]) + n_channels = X.shape[1] + + def kdtw_kernel(x, y, sigma=1.0, epsilon=1e-3): + if x.ndim == 1: + T = x.size // n_channels + x = x.reshape(T, n_channels) + if y.ndim == 1: + T = y.size // n_channels + y = y.reshape(T, n_channels) + return _kdtw(x, y, sigma=sigma, epsilon=epsilon) + + self.kernel = kdtw_kernel self._tslearn_kernel_k_means = TsLearnKernelKMeans( n_clusters=self.n_clusters, diff --git a/aeon/distances/elastic/_bounding_matrix.py b/aeon/distances/elastic/_bounding_matrix.py index 509dff6edf..3b4d76b4a2 100644 --- a/aeon/distances/elastic/_bounding_matrix.py +++ b/aeon/distances/elastic/_bounding_matrix.py @@ -63,8 +63,14 @@ def create_bounding_matrix( def _itakura_parallelogram(x_size: int, y_size: int, max_slope_percent: float): """Itakura parallelogram bounding matrix. - This code was adapted from pyts. This link to the original code: + This code was adapted from the tslearn and pyts functions. + + pyts code: https://pyts.readthedocs.io/en/latest/_modules/pyts/metrics/dtw.html#itakura_parallelogram + Copyright (c) 2018, Johann Faouzi and pyts contributors, BSD-3 + tslearn code (line 974): + https://github.com/tslearn-team/tslearn/blob/main/tslearn/metrics/dtw_variants.py + Copyright (c) 2017, Romain Tavenard, BSD-2 """ one_percent = min(x_size, y_size) / 100 max_slope = math.floor((max_slope_percent * one_percent) * 100) diff --git a/aeon/regression/deep_learning/_lite_time.py b/aeon/regression/deep_learning/_lite_time.py index ffd050f176..9af8bbaf4e 100644 --- a/aeon/regression/deep_learning/_lite_time.py +++ b/aeon/regression/deep_learning/_lite_time.py @@ -105,6 +105,7 @@ class LITETimeRegressor(BaseRegressor): ----- Adapted from the implementation from Ismail-Fawaz et. al https://github.com/MSD-IRIMAS/LITE + by the code owner. References ---------- @@ -388,6 +389,7 @@ class IndividualLITERegressor(BaseDeepRegressor): ----- Adapted from the implementation from Ismail-Fawaz et. al https://github.com/MSD-IRIMAS/LITE + by the code owner. References ---------- diff --git a/aeon/segmentation/_ggs.py b/aeon/segmentation/_ggs.py index d8bdd21d71..0a1bb615af 100644 --- a/aeon/segmentation/_ggs.py +++ b/aeon/segmentation/_ggs.py @@ -23,6 +23,7 @@ Based on the work from [1]_. - source code adapted based on: https://github.com/cvxgrp/GGS + Copyright (c) 2018, Stanford University Convex Optimization Group, BSD-2 - paper available at: https://stanford.edu/~boyd/papers/pdf/ggs.pdf References diff --git a/aeon/transformations/collection/convolution_based/_minirocket.py b/aeon/transformations/collection/convolution_based/_minirocket.py index 603c381fb7..cdc62d42b0 100644 --- a/aeon/transformations/collection/convolution_based/_minirocket.py +++ b/aeon/transformations/collection/convolution_based/_minirocket.py @@ -55,7 +55,7 @@ class MiniRocket(BaseCollectionTransformer): Notes ----- Directly adapted from the original implementation - https://github.com/angus924/minirocket. + https://github.com/angus924/minirocket with owner permission. Examples -------- diff --git a/aeon/transformations/series/_bkfilter.py b/aeon/transformations/series/_bkfilter.py index 62440d1a2c..65f684b2bc 100644 --- a/aeon/transformations/series/_bkfilter.py +++ b/aeon/transformations/series/_bkfilter.py @@ -34,8 +34,9 @@ class BKFilter(BaseSeriesTransformer): Notes ----- - Adapted from statsmodels implementation + Adapted from statsmodels 0.14.4 implementation https://github.com/statsmodels/statsmodels/blob/main/statsmodels/tsa/filters/bk_filter.py + Copyright (c) 2009-2018 statsmodels Developers, BSD-3 References ---------- diff --git a/aeon/utils/show_versions.py b/aeon/utils/show_versions.py index 00cfe19a0e..1906415f2e 100644 --- a/aeon/utils/show_versions.py +++ b/aeon/utils/show_versions.py @@ -1,7 +1,4 @@ -"""Utility methods to print system info for debugging. - -Adapted from the sklearn show_versions function. -""" +"""Utility methods to print system info for debugging.""" __maintainer__ = ["MatthewMiddlehurst"] __all__ = ["show_versions"] @@ -37,6 +34,12 @@ def show_versions(as_str: bool = False) -> Union[str, None]: str or None The output string if `as_str` is True, otherwise None. + Notes + ----- + Adapted from the scikit-learn 1.5.0 show_versions function. + https://github.com/scikit-learn/scikit-learn/ + Copyright (c) 2007-2024 The scikit-learn developers, BSD-3 + Examples -------- >>> from aeon.utils import show_versions diff --git a/aeon/visualisation/results/_critical_difference.py b/aeon/visualisation/results/_critical_difference.py index df50cbdc45..7d7cb78aca 100644 --- a/aeon/visualisation/results/_critical_difference.py +++ b/aeon/visualisation/results/_critical_difference.py @@ -88,8 +88,8 @@ def plot_critical_difference( overall performance in general, and such comparisons should be seen as exploratory analysis rather than designed experiments to test an a priori hypothesis. - Parts of the code are adapted from here: - https://github.com/hfawaz/cd-diagram + Parts of the code are adapted from https://github.com/hfawaz/cd-diagram + with permission from the owner. Parameters ---------- diff --git a/docs/_sphinxext/sphinx_remove_toctrees.py b/docs/_sphinxext/sphinx_remove_toctrees.py index c27aee2d8e..25b00d251e 100644 --- a/docs/_sphinxext/sphinx_remove_toctrees.py +++ b/docs/_sphinxext/sphinx_remove_toctrees.py @@ -9,6 +9,7 @@ https://github.com/mne-tools/mne-lsl https://github.com/mne-tools/mne-lsl/blob/main/doc/_sphinxext/sphinx_remove_toctrees.py +Copyright © 2023-2024, authors of MNE-LSL, BSD-3 """ from pathlib import Path From c92958f3c8c78a3e09f8d1254d83862b5d714cc4 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Sun, 20 Apr 2025 11:08:12 +0100 Subject: [PATCH 070/216] use gpu installs for periodic tests (#2747) --- .github/workflows/periodic_tests.yml | 25 ++++++++++++++++++------- .github/workflows/pr_examples.yml | 3 +++ .github/workflows/release.yml | 9 +++++++-- 3 files changed, 28 insertions(+), 9 deletions(-) diff --git a/.github/workflows/periodic_tests.yml b/.github/workflows/periodic_tests.yml index a25fd13c8e..7a18e7e10f 100644 --- a/.github/workflows/periodic_tests.yml +++ b/.github/workflows/periodic_tests.yml @@ -70,9 +70,15 @@ jobs: python_version: "3.11" restore_cache: "false" - - uses: ./.github/actions/cpu_all_extras + - name: Install dependencies + uses: nick-fields/retry@v3 with: - additional_extras: "dev,binder" + timeout_minutes: 30 + max_attempts: 3 + command: python -m pip install .[all_extras,binder,dev] + + - name: Show dependencies + run: python -m pip list - name: Run example notebooks run: .github/utilities/run_examples.sh false @@ -182,10 +188,12 @@ jobs: python_version: ${{ matrix.python-version }} restore_cache: "false" - - uses: ./.github/actions/cpu_all_extras + - name: Install aeon and dependencies + uses: nick-fields/retry@v3 with: - python_version: ${{ matrix.python-version }} - additional_extras: "dev" + timeout_minutes: 30 + max_attempts: 3 + command: python -m pip install .[all_extras,dev] - name: Show dependencies run: python -m pip list @@ -215,9 +223,12 @@ jobs: - name: Disable Numba JIT run: echo "NUMBA_DISABLE_JIT=1" >> $GITHUB_ENV - - uses: ./.github/actions/cpu_all_extras + - name: Install aeon and dependencies + uses: nick-fields/retry@v3 with: - additional_extras: "unstable_extras,dev" + timeout_minutes: 30 + max_attempts: 3 + command: python -m pip install .[all_extras,unstable_extras,dev] - name: Show dependencies run: python -m pip list diff --git a/.github/workflows/pr_examples.yml b/.github/workflows/pr_examples.yml index 99699f15ae..cf32ccd3c1 100644 --- a/.github/workflows/pr_examples.yml +++ b/.github/workflows/pr_examples.yml @@ -47,6 +47,9 @@ jobs: with: additional_extras: "dev,binder" + - name: Show dependencies + run: python -m pip list + - name: Run example notebooks run: .github/utilities/run_examples.sh ${{ github.event_name == 'pull_request' && !contains(github.event.pull_request.labels.*.name, 'full examples run') }} shell: bash diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 067e843002..58d937e67e 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -57,9 +57,11 @@ jobs: python-version: [ "3.9", "3.10", "3.11", "3.12", "3.13" ] steps: - - uses: actions/checkout@v4 + - name: Checkout + uses: actions/checkout@v4 - - uses: actions/setup-python@v5 + - name: Setup Python + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} @@ -96,6 +98,9 @@ jobs: max_attempts: 3 command: python -m pip install "${{ env.WHEELNAME }}[all_extras,dev]" + - name: Show dependencies + run: python -m pip list + - name: Tests run: python -m pytest -n logical From f7618c605f4d7d3f6a2adff93e93a5ccabe2eb6c Mon Sep 17 00:00:00 2001 From: Akash Kawle <128881349+shinymack@users.noreply.github.com> Date: Sun, 20 Apr 2025 15:50:54 +0530 Subject: [PATCH 071/216] Use shape calculation in _fit to optimize QUANTTransformer (#2727) --- .../collection/interval_based/_quant.py | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/aeon/transformations/collection/interval_based/_quant.py b/aeon/transformations/collection/interval_based/_quant.py index 257bbd85d4..cdb31cb845 100644 --- a/aeon/transformations/collection/interval_based/_quant.py +++ b/aeon/transformations/collection/interval_based/_quant.py @@ -76,7 +76,6 @@ def __init__(self, interval_depth=6, quantile_divisor=4): def _fit(self, X, y=None): import torch - import torch.nn.functional as F X = torch.tensor(X).float() @@ -85,17 +84,19 @@ def _fit(self, X, y=None): if self.interval_depth < 1: raise ValueError("interval_depth must be >= 1") + in_length = X.shape[-1] + representation_functions = ( - lambda X: X, - lambda X: F.avg_pool1d(F.pad(X.diff(), (2, 2), "replicate"), 5, 1), - lambda X: X.diff(n=2), - lambda X: torch.fft.rfft(X).abs(), + in_length, # lambda X: X + in_length + - 1, # lambda X: F.avg_pool1d(F.pad(X.diff(), (2, 2), "replicate"), 5, 1) + in_length - 2, # lambda X: X.diff(n=2) + in_length // 2 + 1, # lambda X: torch.fft.rfft(X).abs() ) - self.intervals_ = [] - for function in representation_functions: - Z = function(X) - self.intervals_.append(self._make_intervals(input_length=Z.shape[-1])) + + for length in representation_functions: + self.intervals_.append(self._make_intervals(input_length=length)) return self From 917123bedb5b4e32febad75807aee10f9f0b983a Mon Sep 17 00:00:00 2001 From: Kaustubh <97254178+Kaustbh@users.noreply.github.com> Date: Sun, 20 Apr 2025 16:30:37 +0530 Subject: [PATCH 072/216] [REF] Refactor Anomaly Detection Module into Submodules by Algorithm Family (#2694) * Refactor Anomaly Detection Module into Submodules by Algorithm Family * updated documentation and references * implemented suggested changes * minor changes * added headers for remaining algorithm family * removing tree-based header --- aeon/anomaly_detection/__init__.py | 28 +------- .../distance_based/__init__.py | 19 ++++++ .../{ => distance_based}/_cblof.py | 2 +- .../{ => distance_based}/_kmeans.py | 2 +- .../{ => distance_based}/_left_stampi.py | 2 +- .../{ => distance_based}/_lof.py | 2 +- .../{ => distance_based}/_merlin.py | 2 +- .../{ => distance_based}/_one_class_svm.py | 0 .../{ => distance_based}/_stomp.py | 2 +- .../distance_based/tests/__init__.py | 1 + .../{ => distance_based}/tests/test_cblof.py | 2 +- .../{ => distance_based}/tests/test_kmeans.py | 2 +- .../tests/test_left_stampi.py | 2 +- .../{ => distance_based}/tests/test_lof.py | 2 +- .../{ => distance_based}/tests/test_merlin.py | 2 +- .../tests/test_one_class_svm.py | 2 +- .../{ => distance_based}/tests/test_stomp.py | 2 +- .../distribution_based/__init__.py | 9 +++ .../{ => distribution_based}/_copod.py | 2 +- .../{ => distribution_based}/_dwt_mlead.py | 2 +- .../distribution_based/tests/__init__.py | 1 + .../tests/test_copod.py | 2 +- .../tests/test_dwt_mlead.py | 2 +- .../outlier_detection/__init__.py | 11 ++++ .../{ => outlier_detection}/_iforest.py | 2 +- .../{ => outlier_detection}/_pyodadapter.py | 4 +- .../{ => outlier_detection}/_stray.py | 2 +- .../outlier_detection/tests/__init__.py | 1 + .../tests/test_iforest.py | 2 +- .../tests/test_pyod_adapter.py | 2 +- .../tests/test_stray.py | 2 +- .../whole_series/__init__.py | 7 ++ .../{ => whole_series}/_rockad.py | 0 .../whole_series/tests/__init__.py | 1 + .../{ => whole_series}/tests/test_rockad.py | 2 +- docs/api_reference/anomaly_detection.rst | 66 ++++++++++++++++--- docs/developer_guide/adding_typehints.md | 2 +- docs/getting_started.md | 2 +- .../anomaly_detection/anomaly_detection.ipynb | 4 +- 39 files changed, 140 insertions(+), 62 deletions(-) create mode 100644 aeon/anomaly_detection/distance_based/__init__.py rename aeon/anomaly_detection/{ => distance_based}/_cblof.py (98%) rename aeon/anomaly_detection/{ => distance_based}/_kmeans.py (99%) rename aeon/anomaly_detection/{ => distance_based}/_left_stampi.py (98%) rename aeon/anomaly_detection/{ => distance_based}/_lof.py (98%) rename aeon/anomaly_detection/{ => distance_based}/_merlin.py (99%) rename aeon/anomaly_detection/{ => distance_based}/_one_class_svm.py (100%) rename aeon/anomaly_detection/{ => distance_based}/_stomp.py (98%) create mode 100644 aeon/anomaly_detection/distance_based/tests/__init__.py rename aeon/anomaly_detection/{ => distance_based}/tests/test_cblof.py (97%) rename aeon/anomaly_detection/{ => distance_based}/tests/test_kmeans.py (96%) rename aeon/anomaly_detection/{ => distance_based}/tests/test_left_stampi.py (99%) rename aeon/anomaly_detection/{ => distance_based}/tests/test_lof.py (99%) rename aeon/anomaly_detection/{ => distance_based}/tests/test_merlin.py (96%) rename aeon/anomaly_detection/{ => distance_based}/tests/test_one_class_svm.py (95%) rename aeon/anomaly_detection/{ => distance_based}/tests/test_stomp.py (95%) create mode 100644 aeon/anomaly_detection/distribution_based/__init__.py rename aeon/anomaly_detection/{ => distribution_based}/_copod.py (97%) rename aeon/anomaly_detection/{ => distribution_based}/_dwt_mlead.py (99%) create mode 100644 aeon/anomaly_detection/distribution_based/tests/__init__.py rename aeon/anomaly_detection/{ => distribution_based}/tests/test_copod.py (96%) rename aeon/anomaly_detection/{ => distribution_based}/tests/test_dwt_mlead.py (96%) create mode 100644 aeon/anomaly_detection/outlier_detection/__init__.py rename aeon/anomaly_detection/{ => outlier_detection}/_iforest.py (98%) rename aeon/anomaly_detection/{ => outlier_detection}/_pyodadapter.py (98%) rename aeon/anomaly_detection/{ => outlier_detection}/_stray.py (98%) create mode 100644 aeon/anomaly_detection/outlier_detection/tests/__init__.py rename aeon/anomaly_detection/{ => outlier_detection}/tests/test_iforest.py (98%) rename aeon/anomaly_detection/{ => outlier_detection}/tests/test_pyod_adapter.py (98%) rename aeon/anomaly_detection/{ => outlier_detection}/tests/test_stray.py (98%) create mode 100644 aeon/anomaly_detection/whole_series/__init__.py rename aeon/anomaly_detection/{ => whole_series}/_rockad.py (100%) create mode 100644 aeon/anomaly_detection/whole_series/tests/__init__.py rename aeon/anomaly_detection/{ => whole_series}/tests/test_rockad.py (97%) diff --git a/aeon/anomaly_detection/__init__.py b/aeon/anomaly_detection/__init__.py index c1f87846e7..65343cd774 100644 --- a/aeon/anomaly_detection/__init__.py +++ b/aeon/anomaly_detection/__init__.py @@ -1,31 +1,7 @@ """Time Series Anomaly Detection.""" __all__ = [ - "CBLOF", - "COPOD", - "DWT_MLEAD", - "IsolationForest", - "KMeansAD", - "LeftSTAMPi", - "LOF", - "MERLIN", - "OneClassSVM", - "ROCKAD", - "PyODAdapter", - "STOMP", - "STRAY", + "BaseAnomalyDetector", ] -from aeon.anomaly_detection._cblof import CBLOF -from aeon.anomaly_detection._copod import COPOD -from aeon.anomaly_detection._dwt_mlead import DWT_MLEAD -from aeon.anomaly_detection._iforest import IsolationForest -from aeon.anomaly_detection._kmeans import KMeansAD -from aeon.anomaly_detection._left_stampi import LeftSTAMPi -from aeon.anomaly_detection._lof import LOF -from aeon.anomaly_detection._merlin import MERLIN -from aeon.anomaly_detection._one_class_svm import OneClassSVM -from aeon.anomaly_detection._pyodadapter import PyODAdapter -from aeon.anomaly_detection._rockad import ROCKAD -from aeon.anomaly_detection._stomp import STOMP -from aeon.anomaly_detection._stray import STRAY +from aeon.anomaly_detection.base import BaseAnomalyDetector diff --git a/aeon/anomaly_detection/distance_based/__init__.py b/aeon/anomaly_detection/distance_based/__init__.py new file mode 100644 index 0000000000..5eb342b780 --- /dev/null +++ b/aeon/anomaly_detection/distance_based/__init__.py @@ -0,0 +1,19 @@ +"""Distance basedTime Series Anomaly Detection.""" + +__all__ = [ + "CBLOF", + "KMeansAD", + "LeftSTAMPi", + "LOF", + "MERLIN", + "OneClassSVM", + "STOMP", +] + +from aeon.anomaly_detection.distance_based._cblof import CBLOF +from aeon.anomaly_detection.distance_based._kmeans import KMeansAD +from aeon.anomaly_detection.distance_based._left_stampi import LeftSTAMPi +from aeon.anomaly_detection.distance_based._lof import LOF +from aeon.anomaly_detection.distance_based._merlin import MERLIN +from aeon.anomaly_detection.distance_based._one_class_svm import OneClassSVM +from aeon.anomaly_detection.distance_based._stomp import STOMP diff --git a/aeon/anomaly_detection/_cblof.py b/aeon/anomaly_detection/distance_based/_cblof.py similarity index 98% rename from aeon/anomaly_detection/_cblof.py rename to aeon/anomaly_detection/distance_based/_cblof.py index 506974f6ca..18bb044c14 100644 --- a/aeon/anomaly_detection/_cblof.py +++ b/aeon/anomaly_detection/distance_based/_cblof.py @@ -7,7 +7,7 @@ import numpy as np -from aeon.anomaly_detection._pyodadapter import PyODAdapter +from aeon.anomaly_detection.outlier_detection._pyodadapter import PyODAdapter from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/_kmeans.py b/aeon/anomaly_detection/distance_based/_kmeans.py similarity index 99% rename from aeon/anomaly_detection/_kmeans.py rename to aeon/anomaly_detection/distance_based/_kmeans.py index c114911c3b..bb8f188a1d 100644 --- a/aeon/anomaly_detection/_kmeans.py +++ b/aeon/anomaly_detection/distance_based/_kmeans.py @@ -65,7 +65,7 @@ class KMeansAD(BaseAnomalyDetector): Examples -------- >>> import numpy as np - >>> from aeon.anomaly_detection import KMeansAD + >>> from aeon.anomaly_detection.distance_based import KMeansAD >>> X = np.array([1, 2, 3, 4, 1, 2, 3, 3, 2, 8, 9, 8, 1, 2, 3, 4], dtype=np.float64) >>> detector = KMeansAD(n_clusters=3, window_size=4, stride=1, random_state=0) >>> detector.fit_predict(X) diff --git a/aeon/anomaly_detection/_left_stampi.py b/aeon/anomaly_detection/distance_based/_left_stampi.py similarity index 98% rename from aeon/anomaly_detection/_left_stampi.py rename to aeon/anomaly_detection/distance_based/_left_stampi.py index d71cc5bd26..43078ce021 100644 --- a/aeon/anomaly_detection/_left_stampi.py +++ b/aeon/anomaly_detection/distance_based/_left_stampi.py @@ -44,7 +44,7 @@ class LeftSTAMPi(BaseAnomalyDetector): Internally,this is applying the incremental approach outlined below. >>> import numpy as np # doctest: +SKIP - >>> from aeon.anomaly_detection import LeftSTAMPi # doctest: +SKIP + >>> from aeon.anomaly_detection.distance_based import LeftSTAMPi # doctest: +SKIP >>> X = np.random.default_rng(42).random((10)) # doctest: +SKIP >>> detector = LeftSTAMPi(window_size=3, n_init_train=3) # doctest: +SKIP >>> detector.fit_predict(X) # doctest: +SKIP diff --git a/aeon/anomaly_detection/_lof.py b/aeon/anomaly_detection/distance_based/_lof.py similarity index 98% rename from aeon/anomaly_detection/_lof.py rename to aeon/anomaly_detection/distance_based/_lof.py index 99ac068584..2c3615d906 100644 --- a/aeon/anomaly_detection/_lof.py +++ b/aeon/anomaly_detection/distance_based/_lof.py @@ -7,7 +7,7 @@ import numpy as np -from aeon.anomaly_detection._pyodadapter import PyODAdapter +from aeon.anomaly_detection.outlier_detection._pyodadapter import PyODAdapter from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/_merlin.py b/aeon/anomaly_detection/distance_based/_merlin.py similarity index 99% rename from aeon/anomaly_detection/_merlin.py rename to aeon/anomaly_detection/distance_based/_merlin.py index 5928d156d6..b63224acd5 100644 --- a/aeon/anomaly_detection/_merlin.py +++ b/aeon/anomaly_detection/distance_based/_merlin.py @@ -43,7 +43,7 @@ class MERLIN(BaseAnomalyDetector): Examples -------- >>> import numpy as np - >>> from aeon.anomaly_detection import MERLIN + >>> from aeon.anomaly_detection.distance_based import MERLIN >>> X = np.array([1, 2, 3, 4, 1, 2, 3, 4, 2, 3, 4, 5, 1, 2, 3, 4]) >>> detector = MERLIN(min_length=4, max_length=5) >>> detector.fit_predict(X) diff --git a/aeon/anomaly_detection/_one_class_svm.py b/aeon/anomaly_detection/distance_based/_one_class_svm.py similarity index 100% rename from aeon/anomaly_detection/_one_class_svm.py rename to aeon/anomaly_detection/distance_based/_one_class_svm.py diff --git a/aeon/anomaly_detection/_stomp.py b/aeon/anomaly_detection/distance_based/_stomp.py similarity index 98% rename from aeon/anomaly_detection/_stomp.py rename to aeon/anomaly_detection/distance_based/_stomp.py index af39891149..3f8be36432 100644 --- a/aeon/anomaly_detection/_stomp.py +++ b/aeon/anomaly_detection/distance_based/_stomp.py @@ -38,7 +38,7 @@ class STOMP(BaseAnomalyDetector): Examples -------- >>> import numpy as np - >>> from aeon.anomaly_detection import STOMP # doctest: +SKIP + >>> from aeon.anomaly_detection.distance_based import STOMP # doctest: +SKIP >>> X = np.random.default_rng(42).random((10, 2), dtype=np.float64) >>> detector = STOMP(X, window_size=2) # doctest: +SKIP >>> detector.fit_predict(X, axis=0) # doctest: +SKIP diff --git a/aeon/anomaly_detection/distance_based/tests/__init__.py b/aeon/anomaly_detection/distance_based/tests/__init__.py new file mode 100644 index 0000000000..03b6c4a5e8 --- /dev/null +++ b/aeon/anomaly_detection/distance_based/tests/__init__.py @@ -0,0 +1 @@ +"""Distance based test code.""" diff --git a/aeon/anomaly_detection/tests/test_cblof.py b/aeon/anomaly_detection/distance_based/tests/test_cblof.py similarity index 97% rename from aeon/anomaly_detection/tests/test_cblof.py rename to aeon/anomaly_detection/distance_based/tests/test_cblof.py index 090ef98064..d1472af6a2 100644 --- a/aeon/anomaly_detection/tests/test_cblof.py +++ b/aeon/anomaly_detection/distance_based/tests/test_cblof.py @@ -3,7 +3,7 @@ import numpy as np import pytest -from aeon.anomaly_detection import CBLOF +from aeon.anomaly_detection.distance_based import CBLOF from aeon.testing.data_generation import make_example_1d_numpy from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/tests/test_kmeans.py b/aeon/anomaly_detection/distance_based/tests/test_kmeans.py similarity index 96% rename from aeon/anomaly_detection/tests/test_kmeans.py rename to aeon/anomaly_detection/distance_based/tests/test_kmeans.py index 9812d7696b..2647411b88 100644 --- a/aeon/anomaly_detection/tests/test_kmeans.py +++ b/aeon/anomaly_detection/distance_based/tests/test_kmeans.py @@ -6,7 +6,7 @@ import pytest from sklearn.utils import check_random_state -from aeon.anomaly_detection import KMeansAD +from aeon.anomaly_detection.distance_based import KMeansAD def test_kmeansad_univariate(): diff --git a/aeon/anomaly_detection/tests/test_left_stampi.py b/aeon/anomaly_detection/distance_based/tests/test_left_stampi.py similarity index 99% rename from aeon/anomaly_detection/tests/test_left_stampi.py rename to aeon/anomaly_detection/distance_based/tests/test_left_stampi.py index 589d163f7b..6444bccdfe 100644 --- a/aeon/anomaly_detection/tests/test_left_stampi.py +++ b/aeon/anomaly_detection/distance_based/tests/test_left_stampi.py @@ -8,7 +8,7 @@ import numpy as np import pytest -from aeon.anomaly_detection._left_stampi import LeftSTAMPi +from aeon.anomaly_detection.distance_based._left_stampi import LeftSTAMPi from aeon.testing.data_generation import make_example_1d_numpy from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/tests/test_lof.py b/aeon/anomaly_detection/distance_based/tests/test_lof.py similarity index 99% rename from aeon/anomaly_detection/tests/test_lof.py rename to aeon/anomaly_detection/distance_based/tests/test_lof.py index 846aa78a5a..033d11295b 100644 --- a/aeon/anomaly_detection/tests/test_lof.py +++ b/aeon/anomaly_detection/distance_based/tests/test_lof.py @@ -3,7 +3,7 @@ import numpy as np import pytest -from aeon.anomaly_detection import LOF +from aeon.anomaly_detection.distance_based import LOF from aeon.testing.data_generation import make_example_1d_numpy from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/tests/test_merlin.py b/aeon/anomaly_detection/distance_based/tests/test_merlin.py similarity index 96% rename from aeon/anomaly_detection/tests/test_merlin.py rename to aeon/anomaly_detection/distance_based/tests/test_merlin.py index 20fe7c697e..ccf7e3300d 100644 --- a/aeon/anomaly_detection/tests/test_merlin.py +++ b/aeon/anomaly_detection/distance_based/tests/test_merlin.py @@ -4,7 +4,7 @@ import numpy as np -from aeon.anomaly_detection import MERLIN +from aeon.anomaly_detection.distance_based import MERLIN TEST_DATA = np.array( [ diff --git a/aeon/anomaly_detection/tests/test_one_class_svm.py b/aeon/anomaly_detection/distance_based/tests/test_one_class_svm.py similarity index 95% rename from aeon/anomaly_detection/tests/test_one_class_svm.py rename to aeon/anomaly_detection/distance_based/tests/test_one_class_svm.py index c99f0ff755..7a3aca2042 100644 --- a/aeon/anomaly_detection/tests/test_one_class_svm.py +++ b/aeon/anomaly_detection/distance_based/tests/test_one_class_svm.py @@ -4,7 +4,7 @@ import pytest from sklearn.utils import check_random_state -from aeon.anomaly_detection import OneClassSVM +from aeon.anomaly_detection.distance_based import OneClassSVM def test_one_class_svm_univariate(): diff --git a/aeon/anomaly_detection/tests/test_stomp.py b/aeon/anomaly_detection/distance_based/tests/test_stomp.py similarity index 95% rename from aeon/anomaly_detection/tests/test_stomp.py rename to aeon/anomaly_detection/distance_based/tests/test_stomp.py index b1adfc1d12..b506c89ea0 100644 --- a/aeon/anomaly_detection/tests/test_stomp.py +++ b/aeon/anomaly_detection/distance_based/tests/test_stomp.py @@ -6,7 +6,7 @@ import pytest from sklearn.utils import check_random_state -from aeon.anomaly_detection import STOMP +from aeon.anomaly_detection.distance_based import STOMP from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/distribution_based/__init__.py b/aeon/anomaly_detection/distribution_based/__init__.py new file mode 100644 index 0000000000..e52a7512ba --- /dev/null +++ b/aeon/anomaly_detection/distribution_based/__init__.py @@ -0,0 +1,9 @@ +"""Distribution based Time Series Anomaly Detection.""" + +__all__ = [ + "COPOD", + "DWT_MLEAD", +] + +from aeon.anomaly_detection.distribution_based._copod import COPOD +from aeon.anomaly_detection.distribution_based._dwt_mlead import DWT_MLEAD diff --git a/aeon/anomaly_detection/_copod.py b/aeon/anomaly_detection/distribution_based/_copod.py similarity index 97% rename from aeon/anomaly_detection/_copod.py rename to aeon/anomaly_detection/distribution_based/_copod.py index ee448b96b8..bd2af0e084 100644 --- a/aeon/anomaly_detection/_copod.py +++ b/aeon/anomaly_detection/distribution_based/_copod.py @@ -7,7 +7,7 @@ import numpy as np -from aeon.anomaly_detection._pyodadapter import PyODAdapter +from aeon.anomaly_detection.outlier_detection._pyodadapter import PyODAdapter from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/_dwt_mlead.py b/aeon/anomaly_detection/distribution_based/_dwt_mlead.py similarity index 99% rename from aeon/anomaly_detection/_dwt_mlead.py rename to aeon/anomaly_detection/distribution_based/_dwt_mlead.py index e78bb1d7d9..cb0de0c015 100644 --- a/aeon/anomaly_detection/_dwt_mlead.py +++ b/aeon/anomaly_detection/distribution_based/_dwt_mlead.py @@ -78,7 +78,7 @@ class DWT_MLEAD(BaseAnomalyDetector): Examples -------- >>> import numpy as np - >>> from aeon.anomaly_detection import DWT_MLEAD + >>> from aeon.anomaly_detection.distribution_based import DWT_MLEAD >>> X = np.array([1, 2, 3, 4, 1, 2, 3, 3, 2, 8, 9, 8, 1, 2, 3, 4], dtype=np.float64) >>> detector = DWT_MLEAD( ... start_level=1, quantile_boundary_type='percentile', quantile_epsilon=0.01 diff --git a/aeon/anomaly_detection/distribution_based/tests/__init__.py b/aeon/anomaly_detection/distribution_based/tests/__init__.py new file mode 100644 index 0000000000..2f368970c0 --- /dev/null +++ b/aeon/anomaly_detection/distribution_based/tests/__init__.py @@ -0,0 +1 @@ +"""Distribution based test code.""" diff --git a/aeon/anomaly_detection/tests/test_copod.py b/aeon/anomaly_detection/distribution_based/tests/test_copod.py similarity index 96% rename from aeon/anomaly_detection/tests/test_copod.py rename to aeon/anomaly_detection/distribution_based/tests/test_copod.py index abd2aa995f..40969da0e7 100644 --- a/aeon/anomaly_detection/tests/test_copod.py +++ b/aeon/anomaly_detection/distribution_based/tests/test_copod.py @@ -3,7 +3,7 @@ import numpy as np import pytest -from aeon.anomaly_detection import COPOD +from aeon.anomaly_detection.distribution_based import COPOD from aeon.testing.data_generation import make_example_1d_numpy from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/tests/test_dwt_mlead.py b/aeon/anomaly_detection/distribution_based/tests/test_dwt_mlead.py similarity index 96% rename from aeon/anomaly_detection/tests/test_dwt_mlead.py rename to aeon/anomaly_detection/distribution_based/tests/test_dwt_mlead.py index c5d09bddc2..664d715122 100644 --- a/aeon/anomaly_detection/tests/test_dwt_mlead.py +++ b/aeon/anomaly_detection/distribution_based/tests/test_dwt_mlead.py @@ -6,7 +6,7 @@ import pytest from sklearn.utils import check_random_state -from aeon.anomaly_detection import DWT_MLEAD +from aeon.anomaly_detection.distribution_based import DWT_MLEAD def test_dwt_mlead_output(): diff --git a/aeon/anomaly_detection/outlier_detection/__init__.py b/aeon/anomaly_detection/outlier_detection/__init__.py new file mode 100644 index 0000000000..ad9b7868e5 --- /dev/null +++ b/aeon/anomaly_detection/outlier_detection/__init__.py @@ -0,0 +1,11 @@ +"""Time Series Outlier Detection.""" + +__all__ = [ + "IsolationForest", + "PyODAdapter", + "STRAY", +] + +from aeon.anomaly_detection.outlier_detection._iforest import IsolationForest +from aeon.anomaly_detection.outlier_detection._pyodadapter import PyODAdapter +from aeon.anomaly_detection.outlier_detection._stray import STRAY diff --git a/aeon/anomaly_detection/_iforest.py b/aeon/anomaly_detection/outlier_detection/_iforest.py similarity index 98% rename from aeon/anomaly_detection/_iforest.py rename to aeon/anomaly_detection/outlier_detection/_iforest.py index a410c3542d..f13152d0e7 100644 --- a/aeon/anomaly_detection/_iforest.py +++ b/aeon/anomaly_detection/outlier_detection/_iforest.py @@ -7,7 +7,7 @@ import numpy as np -from aeon.anomaly_detection._pyodadapter import PyODAdapter +from aeon.anomaly_detection.outlier_detection._pyodadapter import PyODAdapter from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/_pyodadapter.py b/aeon/anomaly_detection/outlier_detection/_pyodadapter.py similarity index 98% rename from aeon/anomaly_detection/_pyodadapter.py rename to aeon/anomaly_detection/outlier_detection/_pyodadapter.py index c520cc6f19..5a068857c6 100644 --- a/aeon/anomaly_detection/_pyodadapter.py +++ b/aeon/anomaly_detection/outlier_detection/_pyodadapter.py @@ -59,7 +59,9 @@ class PyODAdapter(BaseAnomalyDetector): -------- >>> import numpy as np >>> from pyod.models.lof import LOF # doctest: +SKIP - >>> from aeon.anomaly_detection import PyODAdapter # doctest: +SKIP + >>> from aeon.anomaly_detection.outlier_detection import ( + ... PyODAdapter + ... ) # doctest: +SKIP >>> X = np.random.default_rng(42).random((10, 2), dtype=np.float64) >>> detector = PyODAdapter(LOF(), window_size=2) # doctest: +SKIP >>> detector.fit_predict(X, axis=0) # doctest: +SKIP diff --git a/aeon/anomaly_detection/_stray.py b/aeon/anomaly_detection/outlier_detection/_stray.py similarity index 98% rename from aeon/anomaly_detection/_stray.py rename to aeon/anomaly_detection/outlier_detection/_stray.py index 2c5d669033..e7512e2d24 100644 --- a/aeon/anomaly_detection/_stray.py +++ b/aeon/anomaly_detection/outlier_detection/_stray.py @@ -54,7 +54,7 @@ class STRAY(BaseAnomalyDetector): Examples -------- - >>> from aeon.anomaly_detection import STRAY + >>> from aeon.anomaly_detection.outlier_detection import STRAY >>> from aeon.datasets import load_airline >>> import numpy as np >>> X = load_airline() diff --git a/aeon/anomaly_detection/outlier_detection/tests/__init__.py b/aeon/anomaly_detection/outlier_detection/tests/__init__.py new file mode 100644 index 0000000000..7ac2efbbaa --- /dev/null +++ b/aeon/anomaly_detection/outlier_detection/tests/__init__.py @@ -0,0 +1 @@ +"""Outlier based test code.""" diff --git a/aeon/anomaly_detection/tests/test_iforest.py b/aeon/anomaly_detection/outlier_detection/tests/test_iforest.py similarity index 98% rename from aeon/anomaly_detection/tests/test_iforest.py rename to aeon/anomaly_detection/outlier_detection/tests/test_iforest.py index 59c1121022..a66d1003fb 100644 --- a/aeon/anomaly_detection/tests/test_iforest.py +++ b/aeon/anomaly_detection/outlier_detection/tests/test_iforest.py @@ -4,7 +4,7 @@ import pytest from sklearn.utils import check_random_state -from aeon.anomaly_detection import IsolationForest +from aeon.anomaly_detection.outlier_detection import IsolationForest from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/tests/test_pyod_adapter.py b/aeon/anomaly_detection/outlier_detection/tests/test_pyod_adapter.py similarity index 98% rename from aeon/anomaly_detection/tests/test_pyod_adapter.py rename to aeon/anomaly_detection/outlier_detection/tests/test_pyod_adapter.py index eff4d5b325..ee75078133 100644 --- a/aeon/anomaly_detection/tests/test_pyod_adapter.py +++ b/aeon/anomaly_detection/outlier_detection/tests/test_pyod_adapter.py @@ -6,7 +6,7 @@ import pytest from sklearn.utils import check_random_state -from aeon.anomaly_detection import PyODAdapter +from aeon.anomaly_detection.outlier_detection import PyODAdapter from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/tests/test_stray.py b/aeon/anomaly_detection/outlier_detection/tests/test_stray.py similarity index 98% rename from aeon/anomaly_detection/tests/test_stray.py rename to aeon/anomaly_detection/outlier_detection/tests/test_stray.py index cbf6caabb3..8429a8a3c5 100644 --- a/aeon/anomaly_detection/tests/test_stray.py +++ b/aeon/anomaly_detection/outlier_detection/tests/test_stray.py @@ -5,7 +5,7 @@ import numpy as np from sklearn.preprocessing import MinMaxScaler -from aeon.anomaly_detection import STRAY +from aeon.anomaly_detection.outlier_detection import STRAY def test_default_1D(): diff --git a/aeon/anomaly_detection/whole_series/__init__.py b/aeon/anomaly_detection/whole_series/__init__.py new file mode 100644 index 0000000000..7098b8cd08 --- /dev/null +++ b/aeon/anomaly_detection/whole_series/__init__.py @@ -0,0 +1,7 @@ +"""Whole Time Series Anomaly Detection.""" + +__all__ = [ + "ROCKAD", +] + +from aeon.anomaly_detection.whole_series._rockad import ROCKAD diff --git a/aeon/anomaly_detection/_rockad.py b/aeon/anomaly_detection/whole_series/_rockad.py similarity index 100% rename from aeon/anomaly_detection/_rockad.py rename to aeon/anomaly_detection/whole_series/_rockad.py diff --git a/aeon/anomaly_detection/whole_series/tests/__init__.py b/aeon/anomaly_detection/whole_series/tests/__init__.py new file mode 100644 index 0000000000..9292e8d9bd --- /dev/null +++ b/aeon/anomaly_detection/whole_series/tests/__init__.py @@ -0,0 +1 @@ +"""Whole series anomaly detection tests.""" diff --git a/aeon/anomaly_detection/tests/test_rockad.py b/aeon/anomaly_detection/whole_series/tests/test_rockad.py similarity index 97% rename from aeon/anomaly_detection/tests/test_rockad.py rename to aeon/anomaly_detection/whole_series/tests/test_rockad.py index d9d133b9a8..7d3694b2c8 100644 --- a/aeon/anomaly_detection/tests/test_rockad.py +++ b/aeon/anomaly_detection/whole_series/tests/test_rockad.py @@ -4,7 +4,7 @@ import pytest from sklearn.utils import check_random_state -from aeon.anomaly_detection import ROCKAD +from aeon.anomaly_detection.whole_series import ROCKAD def test_rockad_univariate(): diff --git a/docs/api_reference/anomaly_detection.rst b/docs/api_reference/anomaly_detection.rst index 082c082fc4..3e22c445b7 100644 --- a/docs/api_reference/anomaly_detection.rst +++ b/docs/api_reference/anomaly_detection.rst @@ -13,29 +13,79 @@ Each detector in this module specifies its supported input data format, output d format, and learning type as an overview table in its documentation. Some detectors support multiple learning types. -Detectors ---------- +.. note:: -.. currentmodule:: aeon.anomaly_detection + Not all algorithm families are currently implemented. The documentation includes + placeholders for planned categories which will be supported in future. + +Distance-based +-------------- + +.. currentmodule:: aeon.anomaly_detection.distance_based .. autosummary:: :toctree: auto_generated/ :template: class.rst CBLOF - COPOD - DWT_MLEAD - IsolationForest KMeansAD LeftSTAMPi LOF MERLIN OneClassSVM - PyODAdapter - ROCKAD STOMP + +Distribution-based +----------------- + +.. currentmodule:: aeon.anomaly_detection.distribution_based + +.. autosummary:: + :toctree: auto_generated/ + :template: class.rst + + COPOD + DWT_MLEAD + +Encoding-based +-------------- + +The algorithms for this family are not implemented yet. + +Forecasting-based +----------------- + +The algorithms for this family are not implemented yet. + +Outlier-Detection +----------------- + +.. currentmodule:: aeon.anomaly_detection.outlier_detection + +.. autosummary:: + :toctree: auto_generated/ + :template: class.rst + + IsolationForest + PyODAdapter STRAY +Reconstruction-based +-------------------- + +The algorithms for this family are not implemented yet. + +Whole-Series +------------ + +.. currentmodule:: aeon.anomaly_detection.whole_series + +.. autosummary:: + :toctree: auto_generated/ + :template: class.rst + + ROCKAD + Base ---- diff --git a/docs/developer_guide/adding_typehints.md b/docs/developer_guide/adding_typehints.md index f3557ddbd9..5f77ce119b 100644 --- a/docs/developer_guide/adding_typehints.md +++ b/docs/developer_guide/adding_typehints.md @@ -25,7 +25,7 @@ When working with modules that use soft dependencies, additional considerations required to ensure that your code can still run even without these dependencies installed. -Here is an example snippet taken from [PyODAdapter](https://www.aeon-toolkit.org/en/stable/api_reference/auto_generated/aeon.anomaly_detection.PyODAdapter.html). +Here is an example snippet taken from [PyODAdapter](https://www.aeon-toolkit.org/en/stable/api_reference/auto_generated/aeon.anomaly_detection.outlier_detection.PyODAdapter.html). It uses the `pyod` library, which is a soft dependency. The `TYPE_CHECKING` constant is used to ensure that the `pyod` library is only imported at the top level while type checking is performed. `from __future__ import annotations` is used to allow forward diff --git a/docs/getting_started.md b/docs/getting_started.md index 36f18583cb..cf74ab9319 100644 --- a/docs/getting_started.md +++ b/docs/getting_started.md @@ -114,7 +114,7 @@ written the notebook. ```{code-block} python >>> from aeon.datasets import load_airline ->>> from aeon.anomaly_detection import STOMP +>>> from aeon.anomaly_detection.distance_based import STOMP >>> stomp = STOMP(window_size=200) >>> scores = est.fit_predict(X) # Get the anomaly scores ``` diff --git a/examples/anomaly_detection/anomaly_detection.ipynb b/examples/anomaly_detection/anomaly_detection.ipynb index 9f393437e9..7afd00aff8 100644 --- a/examples/anomaly_detection/anomaly_detection.ipynb +++ b/examples/anomaly_detection/anomaly_detection.ipynb @@ -185,7 +185,7 @@ "metadata": {}, "outputs": [], "source": [ - "from aeon.anomaly_detection import STOMP\n", + "from aeon.anomaly_detection.distance_based import STOMP\n", "from aeon.benchmarking.metrics.anomaly_detection import range_roc_auc_score\n", "\n", "detector = STOMP(window_size=200)\n", @@ -211,7 +211,7 @@ "source": [ "from pyod.models.ocsvm import OCSVM\n", "\n", - "from aeon.anomaly_detection import PyODAdapter\n", + "from aeon.anomaly_detection.outlier_detection import PyODAdapter\n", "from aeon.benchmarking.metrics.anomaly_detection import range_roc_auc_score\n", "\n", "detector = PyODAdapter(OCSVM(), window_size=3)\n", From 12690918ab288301f2938944a93a62fb44df7cfb Mon Sep 17 00:00:00 2001 From: "aeon-actions-bot[bot]" <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Date: Mon, 21 Apr 2025 11:08:52 +0100 Subject: [PATCH 073/216] Automated `pre-commit` hook update (#2756) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index b81fd89b64..7aefbe2a03 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -29,7 +29,7 @@ repos: args: [ "--create", "--python-folders", "aeon" ] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.11.5 + rev: v0.11.6 hooks: - id: ruff args: [ "--fix"] From ce29de4db0e9f16249f7805e7860620e8cc34879 Mon Sep 17 00:00:00 2001 From: Ahmed Zahran <136983104+Ahmed-Zahran02@users.noreply.github.com> Date: Fri, 25 Apr 2025 22:17:51 +0300 Subject: [PATCH 074/216] [ENH]Type hints/forecasting (#2737) * Type hints for primitive data types in base module * Type hints for primitive data types and strings in forecating module * type hints for primitives in foreacasting module * Revert "type hints for primitives in foreacasting module" This reverts commit 575122d14b28742140ef1e16a3a351dd5db5072b. * type hints for primitives in forecasting module --- aeon/base/_base_series.py | 2 +- aeon/forecasting/_ets.py | 83 ++++++++++++++++++--------------- aeon/forecasting/_regression.py | 4 +- aeon/forecasting/base.py | 2 +- 4 files changed, 49 insertions(+), 42 deletions(-) diff --git a/aeon/base/_base_series.py b/aeon/base/_base_series.py index 6c86940f5b..f46091142a 100644 --- a/aeon/base/_base_series.py +++ b/aeon/base/_base_series.py @@ -99,7 +99,7 @@ def _preprocess_series(self, X, axis, store_metadata): self.metadata_ = meta return self._convert_X(X, axis) - def _check_X(self, X, axis): + def _check_X(self, X, axis: int = 0): """Check input X is valid. Check if the input data is a compatible type, and that this estimator is diff --git a/aeon/forecasting/_ets.py b/aeon/forecasting/_ets.py index 2635b83457..efc99d6d47 100644 --- a/aeon/forecasting/_ets.py +++ b/aeon/forecasting/_ets.py @@ -83,15 +83,15 @@ class ETSForecaster(BaseForecaster): def __init__( self, - error_type=ADDITIVE, - trend_type=NONE, - seasonality_type=NONE, - seasonal_period=1, - alpha=0.1, - beta=0.01, - gamma=0.01, - phi=0.99, - horizon=1, + error_type: int = ADDITIVE, + trend_type: int = NONE, + seasonality_type: int = NONE, + seasonal_period: int = 1, + alpha: float = 0.1, + beta: float = 0.01, + gamma: float = 0.01, + phi: float = 0.99, + horizon: int = 1, ): self.error_type = error_type self.trend_type = trend_type @@ -190,14 +190,14 @@ def _predict(self, y=None, exog=None): @njit(nogil=NOGIL, cache=CACHE) def _fit_numba( data, - error_type, - trend_type, - seasonality_type, - seasonal_period, - alpha, - beta, - gamma, - phi, + error_type: int, + trend_type: int, + seasonality_type: int, + seasonal_period: int, + alpha: float, + beta: float, + gamma: float, + phi: float, ): n_timepoints = len(data) level, trend, seasonality = _initialise( @@ -236,15 +236,15 @@ def _fit_numba( def _predict_numba( - trend_type, - seasonality_type, - level, - trend, - seasonality, - phi, - horizon, - n_timepoints, - seasonal_period, + trend_type: int, + seasonality_type: int, + level: float, + trend: float, + seasonality: float, + phi: float, + horizon: int, + n_timepoints: int, + seasonal_period: int, ): # Generate forecasts based on the final values of level, trend, and seasonals if phi == 1: # No damping case @@ -264,7 +264,7 @@ def _predict_numba( @njit(nogil=NOGIL, cache=CACHE) -def _initialise(trend_type, seasonality_type, seasonal_period, data): +def _initialise(trend_type: int, seasonality_type: int, seasonal_period: int, data): """ Initialize level, trend, and seasonality values for the ETS model. @@ -307,17 +307,17 @@ def _initialise(trend_type, seasonality_type, seasonal_period, data): @njit(nogil=NOGIL, cache=CACHE) def _update_states( - error_type, - trend_type, - seasonality_type, - level, - trend, - seasonality, + error_type: int, + trend_type: int, + seasonality_type: int, + level: float, + trend: float, + seasonality: float, data_item: int, - alpha, - beta, - gamma, - phi, + alpha: float, + beta: float, + gamma: float, + phi: float, ): """ Update level, trend, and seasonality components. @@ -374,7 +374,14 @@ def _update_states( @njit(nogil=NOGIL, cache=CACHE) -def _predict_value(trend_type, seasonality_type, level, trend, seasonality, phi): +def _predict_value( + trend_type: int, + seasonality_type: int, + level: float, + trend: float, + seasonality: float, + phi: float, +): """ Generate various useful values, including the next fitted value. diff --git a/aeon/forecasting/_regression.py b/aeon/forecasting/_regression.py index bf15e231dd..2330073afc 100644 --- a/aeon/forecasting/_regression.py +++ b/aeon/forecasting/_regression.py @@ -37,7 +37,7 @@ class RegressionForecaster(BaseForecaster): with sklearn regressors. """ - def __init__(self, window, horizon=1, regressor=None): + def __init__(self, window: int, horizon: int = 1, regressor=None): self.window = window self.regressor = regressor super().__init__(horizon=horizon, axis=1) @@ -123,7 +123,7 @@ def _forecast(self, y, exog=None): return self.predict() @classmethod - def _get_test_params(cls, parameter_set="default"): + def _get_test_params(cls, parameter_set: str = "default"): """Return testing parameter settings for the estimator. Parameters diff --git a/aeon/forecasting/base.py b/aeon/forecasting/base.py index e67712c58a..cf2db8d80c 100644 --- a/aeon/forecasting/base.py +++ b/aeon/forecasting/base.py @@ -36,7 +36,7 @@ class BaseForecaster(BaseSeriesEstimator): "y_inner_type": "np.ndarray", } - def __init__(self, horizon, axis): + def __init__(self, horizon: int, axis: int): self.horizon = horizon self.meta_ = None # Meta data related to y on the last fit super().__init__(axis) From a87ed2de10a163a9b4d2693e8c5ea8cbf241f7a3 Mon Sep 17 00:00:00 2001 From: "aeon-actions-bot[bot]" <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Date: Wed, 30 Apr 2025 10:44:14 +0100 Subject: [PATCH 075/216] Automated `pre-commit` hook update (#2766) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 7aefbe2a03..fd55b5d285 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -29,7 +29,7 @@ repos: args: [ "--create", "--python-folders", "aeon" ] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.11.6 + rev: v0.11.7 hooks: - id: ruff args: [ "--fix"] From 0d4f5c770b0ccfdf2c5291cc92479654a7b17469 Mon Sep 17 00:00:00 2001 From: Adarsh Dubey Date: Fri, 2 May 2025 04:55:54 +0530 Subject: [PATCH 076/216] [ENH] Implement `load_model` function for ensemble classifiers (#2631) * feat: implement `load_model` function for LITETimeClassifier Implement separate `load_model` function for LITETimeClassifier, which takes in `model_path` as list of strings and `classes` and loads all the models separately and stores them in `self.classifiers_` * feat: implement `load_model` function for InceptionTimeClassifier Implement separate `load_model` function for InceptionTimeClassifier, which takes in `model_path` as list of strings and `classes` and loads all the models separately and stores them in `self.classifiers_` * fix: typo in load model function * feat: convert load_model functions to classmethods * test: implement test for save load for LITETIME and Inception classification models * Automatic `pre-commit` fixes * refactor: move loading tests to separate files --- .../deep_learning/_inception_time.py | 40 +++++++++++++++ .../deep_learning/_lite_time.py | 40 +++++++++++++++ .../tests/test_inception_time.py | 47 ++++++++++++++++++ .../deep_learning/tests/test_lite_time.py | 49 +++++++++++++++++++ 4 files changed, 176 insertions(+) create mode 100644 aeon/classification/deep_learning/tests/test_inception_time.py create mode 100644 aeon/classification/deep_learning/tests/test_lite_time.py diff --git a/aeon/classification/deep_learning/_inception_time.py b/aeon/classification/deep_learning/_inception_time.py index 00c4e479f9..eaacf44775 100644 --- a/aeon/classification/deep_learning/_inception_time.py +++ b/aeon/classification/deep_learning/_inception_time.py @@ -350,6 +350,46 @@ def _predict_proba(self, X) -> np.ndarray: return probs + @classmethod + def load_model(self, model_path, classes): + """Load pre-trained classifiers instead of fitting. + + When calling this function, all funcationalities can be used + such as predict, predict_proba, etc. with the loaded models. + + Parameters + ---------- + model_path : list of str (list of paths including the model names and extension) + The directory where the models will be saved including the model + names with a ".keras" extension. + classes : np.ndarray + The set of unique classes the pre-trained loaded model is trained + to predict during the classification task. + + Returns + ------- + None + """ + assert ( + type(model_path) is list + ), "model_path should be a list of paths to the models" + + classifier = self() + classifier.classifiers_ = [] + + for i in range(len(model_path)): + clf = IndividualInceptionClassifier() + clf.load_model(model_path[i], classes) + classifier.classifiers_.append(clf) + + classifier.n_classifiers = len(classifier.classifiers_) + + classifier.classes_ = classes + classifier.n_classes_ = len(classes) + classifier.is_fitted = True + + return classifier + @classmethod def _get_test_params(cls, parameter_set="default"): """Return testing parameter settings for the estimator. diff --git a/aeon/classification/deep_learning/_lite_time.py b/aeon/classification/deep_learning/_lite_time.py index f115d53122..bf3922f1d2 100644 --- a/aeon/classification/deep_learning/_lite_time.py +++ b/aeon/classification/deep_learning/_lite_time.py @@ -282,6 +282,46 @@ def _predict_proba(self, X) -> np.ndarray: return probs + @classmethod + def load_model(self, model_path, classes): + """Load pre-trained classifiers instead of fitting. + + When calling this function, all funcationalities can be used + such as predict, predict_proba, etc. with the loaded models. + + Parameters + ---------- + model_path : list of str (list of paths including the model names and extension) + The director where the models will be saved including the model + names with a ".keras" extension. + classes : np.ndarray + The set of unique classes the pre-trained loaded model is trained + to predict during the classification task. + + Returns + ------- + None + """ + assert ( + type(model_path) is list + ), "model_path should be a list of paths to the models" + + classifier = self() + classifier.classifiers_ = [] + + for i in range(len(model_path)): + clf = IndividualLITEClassifier() + clf.load_model(model_path=model_path[i], classes=classes) + classifier.classifiers_.append(clf) + + classifier.n_classifiers = len(classifier.classifiers_) + + classifier.classes_ = classes + classifier.n_classes_ = len(classes) + classifier.is_fitted = True + + return classifier + @classmethod def _get_test_params(cls, parameter_set="default"): """Return testing parameter settings for the estimator. diff --git a/aeon/classification/deep_learning/tests/test_inception_time.py b/aeon/classification/deep_learning/tests/test_inception_time.py new file mode 100644 index 0000000000..5c80fdbee3 --- /dev/null +++ b/aeon/classification/deep_learning/tests/test_inception_time.py @@ -0,0 +1,47 @@ +"""Tests for save/load functionality of InceptionTimeClassifier.""" + +import glob +import os +import tempfile + +import numpy as np +import pytest + +from aeon.classification.deep_learning import InceptionTimeClassifier +from aeon.testing.data_generation import make_example_3d_numpy +from aeon.utils.validation._dependencies import _check_soft_dependencies + + +@pytest.mark.skipif( + not _check_soft_dependencies("tensorflow", severity="none"), + reason="skip test if required soft dependency not available", +) +def test_save_load_inceptiontime(): + """Test saving and loading for InceptionTimeClassifier.""" + with tempfile.TemporaryDirectory() as temp: + temp_dir = os.path.join(temp, "") + + X, y = make_example_3d_numpy( + n_cases=10, n_channels=1, n_timepoints=12, return_y=True + ) + + model = InceptionTimeClassifier( + n_epochs=1, random_state=42, save_best_model=True, file_path=temp_dir + ) + model.fit(X, y) + + y_pred_orig = model.predict(X) + + model_file = glob.glob(os.path.join(temp_dir, f"{model.best_file_name}*.keras")) + + loaded_model = InceptionTimeClassifier.load_model( + model_path=model_file, classes=model.classes_ + ) + + assert isinstance(loaded_model, InceptionTimeClassifier) + + preds = loaded_model.predict(X) + assert isinstance(preds, np.ndarray) + + assert len(preds) == len(y) + np.testing.assert_array_equal(preds, y_pred_orig) diff --git a/aeon/classification/deep_learning/tests/test_lite_time.py b/aeon/classification/deep_learning/tests/test_lite_time.py new file mode 100644 index 0000000000..f6c3858eb7 --- /dev/null +++ b/aeon/classification/deep_learning/tests/test_lite_time.py @@ -0,0 +1,49 @@ +"""Tests for save/load functionality of LiteTimeClassifier.""" + +import glob +import os +import tempfile + +import numpy as np +import pytest + +from aeon.classification.deep_learning import LITETimeClassifier +from aeon.testing.data_generation import make_example_3d_numpy +from aeon.utils.validation._dependencies import _check_soft_dependencies + + +@pytest.mark.skipif( + not _check_soft_dependencies("tensorflow", severity="none"), + reason="skip test if required soft dependency not available", +) +def test_save_load_litetim(): + """Test saving and loading for LiteTimeClassifier.""" + with tempfile.TemporaryDirectory() as temp: + temp_dir = os.path.join(temp, "") + + X, y = make_example_3d_numpy( + n_cases=10, n_channels=1, n_timepoints=12, return_y=True + ) + + model = LITETimeClassifier( + n_epochs=1, random_state=42, save_best_model=True, file_path=temp_dir + ) + model.fit(X, y) + + y_pred_orig = model.predict(X) + + model_files = glob.glob( + os.path.join(temp_dir, f"{model.best_file_name}*.keras") + ) + + loaded_model = LITETimeClassifier.load_model( + model_path=model_files, classes=model.classes_ + ) + + assert isinstance(loaded_model, LITETimeClassifier) + + preds = loaded_model.predict(X) + assert isinstance(preds, np.ndarray) + + assert len(preds) == len(y) + np.testing.assert_array_equal(preds, y_pred_orig) From 9c42e5258acf9864bcec05f9d1a511bc15fb7fc2 Mon Sep 17 00:00:00 2001 From: Ali El Hadi ISMAIL FAWAZ <54309336+hadifawaz1999@users.noreply.github.com> Date: Fri, 2 May 2025 17:19:02 +0200 Subject: [PATCH 077/216] Update _ae_abgru.py (#2771) --- aeon/networks/_ae_abgru.py | 1 + 1 file changed, 1 insertion(+) diff --git a/aeon/networks/_ae_abgru.py b/aeon/networks/_ae_abgru.py index aac0e67d1b..ca9f0494ad 100644 --- a/aeon/networks/_ae_abgru.py +++ b/aeon/networks/_ae_abgru.py @@ -161,6 +161,7 @@ def build_network(self, input_shape, **kwargs): x = tf.keras.layers.Flatten()(x) x = tf.keras.layers.Dense(self.latent_space_dim)(x) elif self.temporal_latent_space: + shape_before_flatten = x.shape[1:] x = tf.keras.layers.Conv1D(filters=self.latent_space_dim, kernel_size=1)(x) encoder = tf.keras.models.Model(inputs=input_layer, outputs=x, name="encoder") From ab7864ea83d9735ad5cb7073c9869ca202e419e5 Mon Sep 17 00:00:00 2001 From: "aeon-actions-bot[bot]" <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Date: Mon, 5 May 2025 13:16:23 +0100 Subject: [PATCH 078/216] Automated `pre-commit` hook update (#2779) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index fd55b5d285..8ebd40a9b0 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -29,7 +29,7 @@ repos: args: [ "--create", "--python-folders", "aeon" ] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.11.7 + rev: v0.11.8 hooks: - id: ruff args: [ "--fix"] From 8a151e423a25c839badf8fd1843d9168ddd4d641 Mon Sep 17 00:00:00 2001 From: Kaustubh <97254178+Kaustbh@users.noreply.github.com> Date: Mon, 5 May 2025 19:41:28 +0530 Subject: [PATCH 079/216] [DOC] Fix Broken [Source] Link and Improve Documentation for suppress_output() (#2677) * Fix Broken [Source] Link and Improve Documentation for suppress_output() Function * modified docstring and added tests * modified docstring example * modifying docstring examples * modifying docstring examples * updating conf file * updated docstring --- aeon/testing/utils/output_suppression.py | 58 ++++++++++++++++++- .../utils/tests/test_output_supression.py | 51 +++++++++++++--- docs/conf.py | 2 + 3 files changed, 101 insertions(+), 10 deletions(-) diff --git a/aeon/testing/utils/output_suppression.py b/aeon/testing/utils/output_suppression.py index dd640e8137..80ae99eb81 100644 --- a/aeon/testing/utils/output_suppression.py +++ b/aeon/testing/utils/output_suppression.py @@ -11,7 +11,63 @@ @contextmanager def suppress_output(suppress_stdout=True, suppress_stderr=True): - """Redirects stdout and/or stderr to devnull.""" + """ + Context manager to suppress stdout and/or stderr output. + + This function redirects standard output (stdout) and standard error (stderr) + to `devnull`, effectively silencing any print statements or error messages + within its context. + + Parameters + ---------- + suppress_stdout : bool, optional, default=True + If True, redirects stdout to null, suppressing print statements. + suppress_stderr : bool, optional, default=True + If True, redirects stderr to null, suppressing error messages. + + Examples + -------- + Suppressing both stdout and stderr: + + >>> import sys + >>> with suppress_output(): + ... print("This will not be displayed") + ... print("Error messages will be hidden", file=sys.stderr) + + Suppressing only stdout: + + >>> sys.stderr = sys.stdout # Needed so doctest can capture stderr + >>> with suppress_output(suppress_stdout=True, suppress_stderr=False): + ... print("This will not be shown") + ... print("Error messages will still be visible", file=sys.stderr) + Error messages will still be visible + + Suppressing only stderr: + + >>> with suppress_output(suppress_stdout=False, suppress_stderr=True): + ... print("This will be shown") + ... print("Error messages will be hidden", file=sys.stderr) + This will be shown + + Using as a function wrapper: + + Suppressing both stdout and stderr: + + >>> @suppress_output() + ... def noisy_function(): + ... print("Noisy output") + ... print("Noisy error", file=sys.stderr) + >>> noisy_function() + + Suppressing only stdout: + + >>> @suppress_output(suppress_stderr=False) + ... def noisy_function(): + ... print("Noisy output") + ... print("Noisy error", file=sys.stderr) + >>> noisy_function() + Noisy error + """ with open(devnull, "w") as null: stdout = sys.stdout stderr = sys.stderr diff --git a/aeon/testing/utils/tests/test_output_supression.py b/aeon/testing/utils/tests/test_output_supression.py index 56f7b18ec5..e1d666fc3c 100644 --- a/aeon/testing/utils/tests/test_output_supression.py +++ b/aeon/testing/utils/tests/test_output_supression.py @@ -1,22 +1,55 @@ """Test output suppression decorator.""" +import io import sys from aeon.testing.utils.output_suppression import suppress_output -@suppress_output() def test_suppress_output(): """Test suppress_output method with True inputs.""" - print( # noqa: T201 - "Hello world! If this is visible suppress_output is not working!" - ) - print( # noqa: T201 - "Error! If this is visible suppress_output is not working!", file=sys.stderr - ) + + @suppress_output() + def inner_test(): + + print( # noqa: T201 + "Hello world! If this is visible suppress_output is not working!" + ) + print( # noqa: T201 + "Error! If this is visible suppress_output is not working!", file=sys.stderr + ) + + stdout_capture = io.StringIO() + stderr_capture = io.StringIO() + sys.stdout = stdout_capture + sys.stderr = stderr_capture + + inner_test() + + assert stdout_capture.getvalue() == "", "stdout was not suppressed!" + assert stderr_capture.getvalue() == "", "stderr was not suppressed!" -@suppress_output(suppress_stdout=False, suppress_stderr=False) def test_suppress_output_false(): """Test suppress_output method with False inputs.""" - pass + + @suppress_output(suppress_stdout=False, suppress_stderr=False) + def inner_test(): + print("This should be visible.") # noqa: T201 + print( # noqa: T201 + "This error message should also be visible.", file=sys.stderr + ) + + stdout_capture = io.StringIO() + stderr_capture = io.StringIO() + sys.stdout = stdout_capture + sys.stderr = stderr_capture + + inner_test() + + assert ( # noqa: T201 + "This should be visible." in stdout_capture.getvalue() + ), "stdout was incorrectly suppressed!" + assert ( # noqa: T201 + "This error message should also be visible." in stderr_capture.getvalue() + ), "stderr was incorrectly suppressed!" diff --git a/docs/conf.py b/docs/conf.py index 65844dbb71..76203f9890 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -172,6 +172,8 @@ def find_source(): import inspect import os + obj = inspect.unwrap(obj) + fn = inspect.getsourcefile(obj) fn = os.path.relpath(fn, start=os.path.dirname(aeon.__file__)) source, lineno = inspect.getsourcelines(obj) From ff0cb4fab25bffcdd2ef06c8b82a466bae66828b Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Thu, 8 May 2025 13:35:26 +0100 Subject: [PATCH 080/216] base transform tidy (#2773) --- aeon/transformations/base.py | 22 ++++++ aeon/transformations/collection/base.py | 100 +++++++++--------------- aeon/transformations/series/base.py | 49 +++++++----- 3 files changed, 89 insertions(+), 82 deletions(-) diff --git a/aeon/transformations/base.py b/aeon/transformations/base.py index 7e4998a910..f0ae37d008 100644 --- a/aeon/transformations/base.py +++ b/aeon/transformations/base.py @@ -5,6 +5,9 @@ from abc import abstractmethod +import numpy as np +import pandas as pd + from aeon.base import BaseAeonEstimator @@ -90,3 +93,22 @@ def fit_transform(self, X, y=None): Additional data, e.g., labels for transformation. """ ... + + def _check_y(self, y, n_cases=None): + # Check y valid input for supervised transform + if not isinstance(y, (pd.Series, np.ndarray)): + raise TypeError( + f"y must be a np.array or a pd.Series, but found type: {type(y)}" + ) + + if isinstance(y, np.ndarray) and y.ndim > 1: + raise TypeError(f"y must be 1-dimensional, found {y.ndim} dimensions") + + if n_cases is not None: + # Check matching number of labels + n_labels = y.shape[0] + if n_cases != n_labels: + raise ValueError( + f"Mismatch in number of cases. Number in X = {n_cases} nos in y = " + f"{n_labels}" + ) diff --git a/aeon/transformations/collection/base.py b/aeon/transformations/collection/base.py index a3664eb4c4..0972341771 100644 --- a/aeon/transformations/collection/base.py +++ b/aeon/transformations/collection/base.py @@ -19,7 +19,7 @@ class name: BaseCollectionTransformer fitted state inspection - check_is_fitted() """ -__maintainer__ = [] +__maintainer__ = ["MatthewMiddlehurst"] __all__ = [ "BaseCollectionTransformer", ] @@ -27,17 +27,15 @@ class name: BaseCollectionTransformer from abc import abstractmethod from typing import final -import numpy as np -import pandas as pd - from aeon.base import BaseCollectionEstimator from aeon.transformations.base import BaseTransformer +from aeon.utils.validation import get_n_cases class BaseCollectionTransformer(BaseCollectionEstimator, BaseTransformer): """Transformer base class for collections.""" - # tag values specific to CollectionTransformers + # default tag values for collection transformers _tags = { "input_data_type": "Collection", "output_data_type": "Collection", @@ -84,22 +82,25 @@ def fit(self, X, y=None): ------- self : a fitted instance of the estimator """ - if self.get_tag("requires_y"): - if y is None: - raise ValueError("Tag requires_y is true, but fit called with y=None") - # skip the rest if fit_is_empty is True if self.get_tag("fit_is_empty"): self.is_fitted = True return self + + if self.get_tag("requires_y"): + if y is None: + raise ValueError("Tag requires_y is true, but fit called with y=None") + + # reset estimator at the start of fit self.reset() # input checks and datatype conversion - X_inner = self._preprocess_collection(X) - y_inner = y - self._fit(X=X_inner, y=y_inner) + X = self._preprocess_collection(X, store_metadata=True) + if y is not None: + self._check_y(y, n_cases=self.metadata_["n_cases"]) - self.is_fitted = True + self._fit(X=X, y=y) + self.is_fitted = True return self @final @@ -139,18 +140,19 @@ def transform(self, X, y=None): ------- transformed version of X """ - # check whether is fitted - self._check_is_fitted() + fit_empty = self.get_tag("fit_is_empty") + if not fit_empty: + self._check_is_fitted() - # input check and conversion for X/y - X_inner = self._preprocess_collection(X, store_metadata=False) - y_inner = y + # input checks and datatype conversion + X = self._preprocess_collection(X, store_metadata=False) + if y is not None: + self._check_y(y, n_cases=get_n_cases(X)) - if not self.get_tag("fit_is_empty"): + if not fit_empty: self._check_shape(X) - Xt = self._transform(X=X_inner, y=y_inner) - + Xt = self._transform(X, y) return Xt @final @@ -192,14 +194,21 @@ def fit_transform(self, X, y=None): ------- transformed version of X """ - # input checks and datatype conversion + if self.get_tag("requires_y"): + if y is None: + raise ValueError("Tag requires_y is true, but fit called with y=None") + + # reset estimator at the start of fit self.reset() - X_inner = self._preprocess_collection(X) - y_inner = y - Xt = self._fit_transform(X=X_inner, y=y_inner) - self.is_fitted = True + # input checks and datatype conversion + X = self._preprocess_collection(X, store_metadata=True) + if y is not None: + self._check_y(y, n_cases=self.metadata_["n_cases"]) + Xt = self._fit_transform(X=X, y=y) + + self.is_fitted = True return Xt @final @@ -297,6 +306,7 @@ def _transform(self, X, y=None): ------- transformed version of X """ + ... def _fit_transform(self, X, y=None): """Fit to data, then transform it. @@ -341,41 +351,3 @@ def _inverse_transform(self, X, y=None): raise NotImplementedError( f"{self.__class__.__name__} does not support inverse_transform" ) - - def _update(self, X, y=None): - """Update transformer with X and y. - - private _update containing the core logic, called from update - - Parameters - ---------- - X : Input data - Data to fit transform to, of valid collection type. - y : Target variable, default=None - Additional data, e.g., labels for transformation - - Returns - ------- - self: a fitted instance of the estimator. - """ - # standard behaviour: no update takes place, new data is ignored - return self - - -def _check_y(self, y, n_cases): - if y is None: - return None - # Check y valid input for collection transformations - if not isinstance(y, (pd.Series, np.ndarray)): - raise TypeError( - f"y must be a np.array or a pd.Series, but found type: {type(y)}" - ) - if isinstance(y, np.ndarray) and y.ndim > 1: - raise TypeError(f"y must be 1-dimensional, found {y.ndim} dimensions") - # Check matching number of labels - n_labels = y.shape[0] - if n_cases != n_labels: - raise ValueError( - f"Mismatch in number of cases. Number in X = {n_cases} nos in y = " - f"{n_labels}" - ) diff --git a/aeon/transformations/series/base.py b/aeon/transformations/series/base.py index cdbd7e50c9..3afa1011bc 100644 --- a/aeon/transformations/series/base.py +++ b/aeon/transformations/series/base.py @@ -11,9 +11,6 @@ class name: BaseSeriesTransformer from abc import abstractmethod from typing import final -import numpy as np -import pandas as pd - from aeon.base import BaseSeriesEstimator from aeon.transformations.base import BaseTransformer @@ -21,7 +18,7 @@ class name: BaseSeriesTransformer class BaseSeriesTransformer(BaseSeriesEstimator, BaseTransformer): """Transformer base class for collections.""" - # tag values specific to SeriesTransformers + # default tag values for series transformers _tags = { "input_data_type": "Series", "output_data_type": "Series", @@ -58,19 +55,24 @@ def fit(self, X, y=None, axis=1): ------- self : a fitted instance of the estimator """ - # skip the rest if fit_is_empty is True if self.get_tag("fit_is_empty"): self.is_fitted = True return self + if self.get_tag("requires_y"): if y is None: raise ValueError("Tag requires_y is true, but fit called with y=None") + # reset estimator at the start of fit self.reset() + + # input checks and datatype conversion X = self._preprocess_series(X, axis=axis, store_metadata=True) if y is not None: self._check_y(y) + self._fit(X=X, y=y) + self.is_fitted = True return self @@ -101,9 +103,18 @@ def transform(self, X, y=None, axis=1): transformed version of X with the same axis as passed by the user, if axis not None. """ - # check whether is fitted - self._check_is_fitted() + fit_empty = self.get_tag("fit_is_empty") + if not fit_empty: + self._check_is_fitted() + X = self._preprocess_series(X, axis=axis, store_metadata=False) + if y is not None: + self._check_y(y) + + # #2768 + # if not fit_empty: + # self._check_shape(X) + Xt = self._transform(X, y) return self._postprocess_series(Xt, axis=axis) @@ -137,10 +148,20 @@ def fit_transform(self, X, y=None, axis=1): transformed version of X with the same axis as passed by the user, if axis not None. """ - # input checks and datatype conversion, to avoid doing in both fit and transform + if self.get_tag("requires_y"): + if y is None: + raise ValueError("Tag requires_y is true, but fit called with y=None") + + # reset estimator at the start of fit self.reset() + + # input checks and datatype conversion X = self._preprocess_series(X, axis=axis, store_metadata=True) + if y is not None: + self._check_y(y) + Xt = self._fit_transform(X=X, y=y) + self.is_fitted = True return self._postprocess_series(Xt, axis=axis) @@ -263,7 +284,8 @@ def _fit_transform(self, X, y=None): """ # Non-optimized default implementation; override when a better # method is possible for a given algorithm. - return self._fit(X, y)._transform(X, y) + self._fit(X, y) + return self._transform(X, y) def _inverse_transform(self, X, y=None): """Inverse transform X and return an inverse transformed version. @@ -325,12 +347,3 @@ def _postprocess_series(self, Xt, axis): return Xt else: return Xt.T - - def _check_y(self, y): - # Check y valid input for supervised transform - if not isinstance(y, (pd.Series, np.ndarray)): - raise TypeError( - f"y must be a np.array or a pd.Series, but found type: {type(y)}" - ) - if isinstance(y, np.ndarray) and y.ndim > 1: - raise TypeError(f"y must be 1-dimensional, found {y.ndim} dimensions") From 96ac60b98d7b5fb899e272499c7fb313afe438a6 Mon Sep 17 00:00:00 2001 From: Somto Onyekwelu <117727947+SomtoOnyekwelu@users.noreply.github.com> Date: Thu, 8 May 2025 13:35:45 +0100 Subject: [PATCH 081/216] DOC: Add Raises section for invalid weights in KNeighborsTimeSeriesClassifier (#1766) (#2764) Document the ValueError raised during initialization when an unsupported value is passed to the 'weights' parameter. Clarifies expected exceptions for users and improves API documentation consistency. Co-authored-by: Matthew Middlehurst --- .../classification/distance_based/_time_series_neighbors.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/aeon/classification/distance_based/_time_series_neighbors.py b/aeon/classification/distance_based/_time_series_neighbors.py index f89b1be636..ded113b69e 100644 --- a/aeon/classification/distance_based/_time_series_neighbors.py +++ b/aeon/classification/distance_based/_time_series_neighbors.py @@ -52,6 +52,12 @@ class KNeighborsTimeSeriesClassifier(BaseClassifier): ``-1`` means using all processors. for more details. Parameter for compatibility purposes, still unimplemented. + Raises + ------ + ValueError + If ``weights`` is not among the supported values. + See the ``weights`` parameter description for valid options. + Examples -------- >>> from aeon.datasets import load_unit_test From 35e1ade9243993f1d0a9ab12ac166e4deddff7bb Mon Sep 17 00:00:00 2001 From: Tanish Yelgoe <143334319+tanishy7777@users.noreply.github.com> Date: Thu, 8 May 2025 18:15:48 +0530 Subject: [PATCH 082/216] [ENH] Fixes Issue Improve `_check_params` method in `kmeans.py` and `kmedoids.py` (#2682) * Improves _check_params * removes function and adds a var * minor changes * minor changes * minor changes * line endings to LF * use variable instead of duplicating strings * weird file change * weird file change --------- Co-authored-by: Matthew Middlehurst --- aeon/clustering/_k_means.py | 16 +- aeon/clustering/_k_medoids.py | 24 +- aeon/distances/elastic/_dtw_gi.py | 1102 ++++++++++++++--------------- 3 files changed, 577 insertions(+), 565 deletions(-) diff --git a/aeon/clustering/_k_means.py b/aeon/clustering/_k_means.py index e4e459a5cf..8b682d3426 100644 --- a/aeon/clustering/_k_means.py +++ b/aeon/clustering/_k_means.py @@ -287,6 +287,13 @@ def _predict(self, X: np.ndarray, y=None) -> np.ndarray: def _check_params(self, X: np.ndarray) -> None: self._random_state = check_random_state(self.random_state) + _incorrect_init_str = ( + f"The value provided for init: {self.init} is " + f"invalid. The following are a list of valid init algorithms " + f"strings: random, kmeans++, first. You can also pass a " + f"np.ndarray of size (n_clusters, n_channels, n_timepoints)" + ) + if isinstance(self.init, str): if self.init == "random": self._init = self._random_center_initializer @@ -294,16 +301,13 @@ def _check_params(self, X: np.ndarray) -> None: self._init = self._kmeans_plus_plus_center_initializer elif self.init == "first": self._init = self._first_center_initializer + else: + raise ValueError(_incorrect_init_str) else: if isinstance(self.init, np.ndarray) and len(self.init) == self.n_clusters: self._init = self.init.copy() else: - raise ValueError( - f"The value provided for init: {self.init} is " - f"invalid. The following are a list of valid init algorithms " - f"strings: random, kmedoids++, first. You can also pass a" - f"np.ndarray of size (n_clusters, n_channels, n_timepoints)" - ) + raise ValueError(_incorrect_init_str) if self.distance_params is None: self._distance_params = {} diff --git a/aeon/clustering/_k_medoids.py b/aeon/clustering/_k_medoids.py index 12d0f2819d..b2abe27aef 100644 --- a/aeon/clustering/_k_medoids.py +++ b/aeon/clustering/_k_medoids.py @@ -46,13 +46,17 @@ class TimeSeriesKMedoids(BaseClusterer): The number of clusters to form as well as the number of centroids to generate. init : str or np.ndarray, default='random' Method for initialising cluster centers. Any of the following are valid: - ['kmedoids++', 'random', 'first']. + ['kmedoids++', 'random', 'first', 'build']. Random is the default as it is very fast and it was found in [2] to perform about as well as the other methods. Kmedoids++ is a variant of kmeans++ [4] and is slower but often more accurate than random. It works by choosing centroids that are distant from one another. First is the fastest method and simply chooses the - first k time series as centroids. + first k time series as centroids. Build [1] greedily selects the k medoids + by first selecting the medoid that minimizes the sum of distances + to all other points(this point is the most centrally located) and then + iteratively selects the next k-1 medoids that maximizes the decrease in sum + of distances of all other points to their respective medoids selected so far. If a np.ndarray provided it must be of shape (n_clusters,) and contain the indexes of the time series to use as centroids. distance : str or Callable, default='msm' @@ -428,6 +432,13 @@ def _assign_clusters( def _check_params(self, X: np.ndarray) -> None: self._random_state = check_random_state(self.random_state) + _incorrect_init_str = ( + f"The value provided for init: {self.init} is " + f"invalid. The following are a list of valid init algorithms " + f"strings: random, kmedoids++, first, build. You can also pass a " + f"np.ndarray of size (n_clusters, n_channels, n_timepoints)" + ) + if isinstance(self.init, str): if self.init == "random": self._init = self._random_center_initializer @@ -437,16 +448,13 @@ def _check_params(self, X: np.ndarray) -> None: self._init = self._first_center_initializer elif self.init == "build": self._init = self._pam_build_center_initializer + else: + raise ValueError(_incorrect_init_str) else: if isinstance(self.init, np.ndarray) and len(self.init) == self.n_clusters: self._init = self.init else: - raise ValueError( - f"The value provided for init: {self.init} is " - f"invalid. The following are a list of valid init algorithms " - f"strings: random, kmedoids++, first. You can also pass a" - f"np.ndarray of size (n_clusters, n_channels, n_timepoints)" - ) + raise ValueError(_incorrect_init_str) if self.distance_params is not None: self._distance_params = self.distance_params diff --git a/aeon/distances/elastic/_dtw_gi.py b/aeon/distances/elastic/_dtw_gi.py index 0026c37725..bff33e343e 100644 --- a/aeon/distances/elastic/_dtw_gi.py +++ b/aeon/distances/elastic/_dtw_gi.py @@ -1,551 +1,551 @@ -r"""Dynamic time warping with Global Invariances (DTW-GI) between two time series.""" - -__maintainer__ = [] - -from typing import Optional, Union - -import numpy as np -from numba import njit -from numba.typed import List as NumbaList - -from aeon.distances.elastic._dtw import dtw_alignment_path, dtw_cost_matrix -from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list -from aeon.utils.validation.collection import _is_numpy_list_multivariate - - -@njit(cache=True, fastmath=True) -def _path2mat( - path: list[tuple[int, int]], - x_timepoints: int, - y_timepoints: int, -): - r"""Convert a warping alignment path to a binary warping matrix.""" - w = np.zeros((x_timepoints, y_timepoints)) - for i, j in path: - w[i, j] = 1 - return w - - -@njit(cache=True, fastmath=True) -def _dtw_gi( - x: np.ndarray, - y: np.ndarray, - window: Optional[float] = None, - itakura_max_slope: Optional[float] = None, - init_p: np.ndarray = None, - max_iter: int = 20, - use_bias: bool = False, -): - r""" - Compute Dynamic Time Warping with Global Invariance between the two time series. - - Parameters - ---------- - x : np.ndarray - First time series, either univariate, shape ``(n_timepoints,)``, or - multivariate, shape ``(n_channels, n_timepoints)``. - y : np.ndarray - Second time series, either univariate, shape ``(n_timepoints,)``, or - multivariate, shape ``(n_channels, n_timepoints)``. - window : float or None, default=None - The window to use for the bounding matrix. If None, no bounding matrix - is used. window is a percentage deviation, so if ``window = 0.1`` then - 10% of the series length is the max warping allowed. - is used. - itakura_max_slope : float, default=None - Maximum slope as a proportion of the number of time points used to create - Itakura parallelogram on the bounding matrix. Must be between 0. and 1. - init_p : array-like of shape (x_channels, y_channels), default=None - Initial linear transformation. If None, the identity matrix is used. - max_iter : int, default=20 - Maximum number of iterations for the iterative optimization. - use_bias : bool, default=False - If True, the feature space map is affine (with a bias term). - - Returns - ------- - - w_pi: binary warping matrix of shape (n0, n1) - - p: the final linear (Stiefel) matrix of shape (x_channels, y_channels) - - cost: final DTW cost considering global invariances - - If use_bias is True, also returns: - - bias - - """ - if x.ndim == 1 and y.ndim == 1: - x_ = x.reshape((1, x.shape[0])) - y_ = y.reshape((1, y.shape[0])) - elif x.ndim == 2 and y.ndim == 2: - x_ = x - y_ = y - else: - raise ValueError("x and y must be 1D or 2D") - - x_channels, x_timepoints = x_.shape - y_channels, y_timepoints = y_.shape - - x_m = np.sum(x_, axis=1) / x_.shape[1] - x_m = x_m.reshape((-1, 1)) - y_m = np.sum(y_, axis=1) / y_.shape[1] - y_m = y_m.reshape((-1, 1)) - - w_pi = np.zeros((x_timepoints, y_timepoints)) - if init_p is None: - p = np.eye(x_channels, y_channels, dtype=np.float64) - else: - p = init_p - - y_ = y_.astype(np.float64) - x_ = x_.astype(np.float64) - - bias = np.zeros((x_channels, 1)) - - for _ in range(max_iter): - w_pi_old = w_pi.copy() - y_transformed = p.dot(y_) + bias - - path, cost = dtw_alignment_path(x_, y_transformed, window, itakura_max_slope) - w_pi = _path2mat(path, x_timepoints, y_timepoints) - - if np.allclose(w_pi, w_pi_old): - break - - if use_bias: - m = (x_ - x_m).dot(w_pi).dot((y_ - y_m).T) - else: - m = x_.dot(w_pi).dot(y_.T) - - u, sigma, vt = np.linalg.svd(m, full_matrices=False) - p = u.dot(vt) - if use_bias: - bias = x_m - p.dot(y_m) - - y_trans = p.dot(y_) + bias - path, cost = dtw_alignment_path(x_, y_trans, window, itakura_max_slope) - - if use_bias: - return w_pi, p, bias, cost, x_, y_trans - else: - dummy_bias = np.zeros((x_channels, 1), dtype=np.float64) - return w_pi, p, dummy_bias, cost, x_, y_trans - - -@njit(cache=True, fastmath=True) -def dtw_gi_distance( - x: np.ndarray, - y: np.ndarray, - window: Optional[float] = None, - itakura_max_slope: Optional[float] = None, - init_p: np.ndarray = None, - max_iter: int = 20, - use_bias: bool = False, -) -> float: - r"""Compute the DTW_GI distance between two time series x and y. - - The DTW_gi distance between 2 timeseries x and y is the similarity - measure that estimates both temporal alignment and does feature space - transformation between time series simultaneously. This means that the - time series do not have to lie in the same ambient space. - A good background into DTW with global invariances can be found in [1]_. - This implementation is inspired by [2]_. - - For example, if we have two time series x and y of lengths n and m - respectively, and we assume that the time series do not lie in the - same ambient space. Lets assume that features of x lie in :math:`\mathbb{R}^p` - and features of y lie in :math:`\mathbb{R}^q`. To compare the two time series, - we need to find an optimum mapping from the feature space of y to the feature space - where features of x lie. So think of it as optimizing on a family of functions F - that map features from y onto the feature space in which features of x - lie. (This is just one way to do it, the mapping can be - in the opposite direction as well. But this function assumes the former way). - - More formally, we define Dynamic Time Warping with Global Invariances (DTW-GI) - as the solution of the following joint optimization problem: - - :math:`\text{DTW-GI}(\mathbf{x}, \mathbf{y}) = - \min_{f \in \mathcal{F}, \pi \in \mathcal{A}(\mathbf{x}, \mathbf{y})} - \sqrt{\sum_{(i,j) \in \pi} d(x_i, f(y_j))^2},` - - This similarity measure estimates temporal alignment - with feature space transformation between time series - allowing the alignment of time series that do not exist in the - same ambient space. - - Note: The optimal warping path :math:`P^*` for a given cost matrix can be found - exactly through a dynamic programming formulation. This can be a time consuming - operation, and it is common to put a restriction on the amount of warping allowed. - This is implemented through the bounding_matrix structure, that supplies a mask for - allowable warpings. The most common bounding strategies include the - Sakoe-Chiba band [3]_. The width of the allowed warping is controlled through the - ``window`` parameter which sets the maximum proportion of warping allowed. - - Parameters - ---------- - x : np.ndarray - First time series, either univariate, shape ``(n_timepoints,)``, or - multivariate, shape ``(n_channels, n_timepoints)``. - y : np.ndarray - Second time series, either univariate, shape ``(n_timepoints,)``, or - multivariate, shape ``(n_channels, n_timepoints)``. - window : float, default=None - The window to use for the bounding matrix. If None, no bounding matrix - is used. window is a percentage deviation, so if ``window = 0.1``, - 10% of the series length is the max warping allowed. - is used. - itakura_max_slope : float, default=None - Maximum slope as a proportion of the number of time points used to create - Itakura parallelogram on the bounding matrix. Must be between 0. and 1. - init_p : array-like of shape (x_channels, y_channels), default=None - Initial linear transformation. If None, the identity matrix is used. - max_iter : int, default=20 - Maximum number of iterations for the iterative optimization. - use_bias : bool, default=False - If True, the feature space map is affine (with a bias term). - - Returns - ------- - float - DTW_GI distance between x and y, minimum value 0. - - Raises - ------ - ValueError - If x and y are not 1D or 2D arrays. - - References - ---------- - .. [1] T. Vayer, R. Tavenard, L. Chapel, N. Courty, R. Flamary, and Y. Soullard, - “Time Series Alignment with Global Invariances,” arXiv.org, 2020. - https://arxiv.org/abs/2002.03848 - - .. [2] Romain Tavenard, “DTW with Global Invariances,” Github.io, Dec. 17, 2020. - https://rtavenar.github.io/hdr/parts/01/dtw/dtw_gi.html - - .. [3] Sakoe H. and Chiba S.: Dynamic programming algorithm optimization for - spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal - Processing 26(1):43-49, 1978. - - Examples - -------- - >>> import numpy as np - >>> from aeon.distances import dtw_gi_distance - >>> x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) - >>> y = np.array([11, 12, 13, 14, 15, 16, 17, 18, 19, 20]) - >>> dtw_gi_distance(x, y) # 1D series - 768.0 - >>> x = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [0, 1, 0, 2, 0]]) - >>> y = np.array([[11, 12, 13, 14],[7, 8, 9, 20],[1, 3, 4, 5]] ) - >>> round(dtw_gi_distance(x, y), 1) # 2D series with 3 channels, unequal length - 359.2 - """ - if x.ndim == 1 and y.ndim == 1: - _x = x.reshape((1, x.shape[0])) - _y = y.reshape((1, y.shape[0])) - return _dtw_gi(_x, _y, window, itakura_max_slope, init_p, max_iter, use_bias)[3] - if x.ndim == 2 and y.ndim == 2: - return _dtw_gi(x, y, window, itakura_max_slope, init_p, max_iter, use_bias)[3] - raise ValueError("x and y must be 1D or 2D") - - -@njit(cache=True, fastmath=True) -def dtw_gi_cost_matrix( - x: np.ndarray, - y: np.ndarray, - window: Optional[float] = None, - itakura_max_slope: Optional[float] = None, - init_p: np.ndarray = None, - max_iter: int = 20, - use_bias: bool = False, -) -> np.ndarray: - r"""Compute the DTW_GI cost matrix between two time series. - - The cost matrix is the pairwise Euclidean distance between all points - :math:`M_{i,j}=(x_i-y_{\text{trans},j})^2`. Where `y_trans` is the time - series we get by finding the optimal mapping from feature space of y - to feature space where features of x lie. It is used in the DTW GI - path calculations. - - Parameters - ---------- - x : np.ndarray - First time series, either univariate, shape ``(n_timepoints,)``, or - multivariate, shape ``(n_channels, n_timepoints)``. - y : np.ndarray - Second time series, either univariate, shape ``(n_timepoints,)``, or - multivariate, shape ``(n_channels, n_timepoints)``. - window : float, default=None - The window to use for the bounding matrix. If None, no bounding matrix - is used. window is a percentage deviation, so if ``window = 0.1``, - 10% of the series length is the max warping allowed. - is used. - itakura_max_slope : float, default=None - Maximum slope as a proportion of the number of time points used to create - Itakura parallelogram on the bounding matrix. Must be between 0. and 1. - init_p : array-like of shape (x_channels, y_channels), default=None - Initial linear transformation. If None, the identity matrix is used. - max_iter : int, default=20 - Maximum number of iterations for the iterative optimization. - use_bias : bool, default=False - If True, the feature space map is affine (with a bias term). - - Returns - ------- - np.ndarray (n_timepoints, m_timepoints) - dtw gi cost matrix between x and y. - - Raises - ------ - ValueError - If x and y are not 1D or 2D arrays. - - Examples - -------- - >>> import numpy as np - >>> from aeon.distances import dtw_gi_cost_matrix - >>> x = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]) - >>> y = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]) - >>> dtw_gi_cost_matrix(x, y) - array([[ 0., 1., 5., 14., 30., 55., 91., 140., 204., 285.], - [ 1., 0., 1., 5., 14., 30., 55., 91., 140., 204.], - [ 5., 1., 0., 1., 5., 14., 30., 55., 91., 140.], - [ 14., 5., 1., 0., 1., 5., 14., 30., 55., 91.], - [ 30., 14., 5., 1., 0., 1., 5., 14., 30., 55.], - [ 55., 30., 14., 5., 1., 0., 1., 5., 14., 30.], - [ 91., 55., 30., 14., 5., 1., 0., 1., 5., 14.], - [140., 91., 55., 30., 14., 5., 1., 0., 1., 5.], - [204., 140., 91., 55., 30., 14., 5., 1., 0., 1.], - [285., 204., 140., 91., 55., 30., 14., 5., 1., 0.]]) - """ - _, _, _, _, xnew, y_trans = _dtw_gi( - x, y, window, itakura_max_slope, init_p, max_iter, use_bias - ) - - return dtw_cost_matrix(xnew, y_trans, window, itakura_max_slope) - - -def dtw_gi_pairwise_distance( - X: Union[np.ndarray, list[np.ndarray]], - y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, - window: Optional[float] = None, - itakura_max_slope: Optional[float] = None, - unequal_length: bool = None, - init_p: np.ndarray = None, - max_iter: int = 20, - use_bias: bool = False, -) -> np.ndarray: - r"""Compute the DTW_GI pairwise distance between a set of time series. - - By default, this takes a collection of :math:`n` time series :math:`X` and returns a - matrix - :math:`D` where :math:`D_{i,j}` is the DTW_GI distance between the :math:`i^{th}` - and the :math:`j^{th}` series in :math:`X`. If :math:`X` is 2 dimensional, - it is assumed to be a collection of univariate series with shape ``(n_cases, - n_timepoints)``. If it is 3 dimensional, it is assumed to be shape ``(n_cases, - n_channels, n_timepoints)``. - - This function has an optional argument, :math:`y`, to allow calculation of the - distance matrix between :math:`X` and one or more series stored in :math:`y`. If - :math:`y` is 1 dimensional, we assume it is a single univariate series and the - distance matrix returned is shape ``(n_cases,1)``. If it is 2D, we assume it - is a collection of univariate series with shape ``(m_cases, m_timepoints)`` - and the distance ``(n_cases,m_cases)``. If it is 3 dimensional, - it is assumed to be shape ``(m_cases, m_channels, m_timepoints)``. - - Parameters - ---------- - X : np.ndarray or List of np.ndarray - A collection of time series instances of shape ``(n_cases, n_timepoints)`` - or ``(n_cases, n_channels, n_timepoints)``. - y : np.ndarray or List of np.ndarray or None, default=None - A single series or a collection of time series of shape ``(m_timepoints,)`` or - ``(m_cases, m_timepoints)`` or ``(m_cases, m_channels, m_timepoints)``. - If None, then the dtw gi pairwise distance between the instances of X is - calculated. - window : float or None, default=None - The window to use for the bounding matrix. If None, no bounding matrix - is used. - itakura_max_slope : float, default=None - Maximum slope as a proportion of the number of time points used to create - Itakura parallelogram on the bounding matrix. Must be between 0. and 1. - init_p : array-like of shape (x_channels, y_channels), default=None - Initial linear transformation. If None, the identity matrix is used. - max_iter : int, default=20 - Maximum number of iterations for the iterative optimization. - use_bias : bool, default=False - If True, the feature space map is affine (with a bias term). - - Returns - ------- - np.ndarray - DTW_GI pairwise matrix between the instances of X of shape - ``(n_cases, n_cases)`` or between X and y of shape ``(n_cases, - n_cases)``. - - Raises - ------ - ValueError - If X is not 2D or 3D array and if y is not 1D, 2D or 3D arrays when passing y. - - Examples - -------- - >>> import numpy as np - >>> from aeon.distances import dtw_gi_pairwise_distance - >>> # Distance between each time series in a collection of time series - >>> X = np.array([[[1, 2, 3]],[[4, 5, 6]], [[7, 8, 9]]]) - >>> dtw_gi_pairwise_distance(X) - array([[ 0., 26., 108.], - [ 26., 0., 26.], - [108., 26., 0.]]) - - >>> # Distance between two collections of time series - >>> X = np.array([[[1, 2, 3]],[[4, 5, 6]], [[7, 8, 9]]]) - >>> y = np.array([[[11, 12, 13]],[[14, 15, 16]], [[17, 18, 19]]]) - >>> dtw_gi_pairwise_distance(X, y) - array([[300., 507., 768.], - [147., 300., 507.], - [ 48., 147., 300.]]) - - >>> X = np.array([[[1, 2, 3]],[[4, 5, 6]], [[7, 8, 9]]]) - >>> y_univariate = np.array([11, 12, 13]) - >>> dtw_gi_pairwise_distance(X, y_univariate) - array([[300.], - [147.], - [ 48.]]) - - >>> # Distance between each TS in a collection of unequal-length time series - >>> X = [np.array([1, 2, 3]), np.array([4, 5, 6, 7]), np.array([8, 9, 10, 11, 12])] - >>> dtw_gi_pairwise_distance(X) - array([[ 0., 42., 292.], - [ 42., 0., 83.], - [292., 83., 0.]]) - """ - multivariate_conversion = _is_numpy_list_multivariate(X, y) - _X, unequal_length = _convert_collection_to_numba_list( - X, "X", multivariate_conversion - ) - - if y is None: - # To self - return _dtw_gi_pairwise_distance( - _X, window, itakura_max_slope, unequal_length, init_p, max_iter, use_bias - ) - _y, unequal_length = _convert_collection_to_numba_list( - y, "y", multivariate_conversion - ) - return _dtw_gi_from_multiple_to_multiple_distance( - _X, _y, window, itakura_max_slope, unequal_length, init_p, max_iter, use_bias - ) - - -@njit(cache=True, fastmath=True) -def _dtw_gi_from_multiple_to_multiple_distance( - x: NumbaList[np.ndarray], - y: NumbaList[np.ndarray], - window: Optional[float] = None, - itakura_max_slope: Optional[float] = None, - unequal_length: bool = None, - init_p: np.ndarray = None, - max_iter: int = 20, - use_bias: bool = False, -) -> np.ndarray: - n_cases = len(x) - m_cases = len(y) - distances = np.zeros((n_cases, m_cases)) - - for i in range(n_cases): - for j in range(m_cases): - x1, y1 = x[i], y[j] - distances[i, j] = dtw_gi_distance( - x1, y1, window, itakura_max_slope, init_p, max_iter, use_bias - ) - return distances - - -@njit(cache=True, fastmath=True) -def _dtw_gi_pairwise_distance( - X: NumbaList[np.ndarray], - window: Optional[float] = None, - itakura_max_slope: Optional[float] = None, - unequal_length: bool = None, - init_p: np.ndarray = None, - max_iter: int = 20, - use_bias: bool = False, -) -> np.ndarray: - n_cases = len(X) - distances = np.zeros((n_cases, n_cases)) - - for i in range(n_cases): - for j in range(i + 1, n_cases): - x1, x2 = X[i], X[j] - distances[i, j] = dtw_gi_distance( - x1, x2, window, itakura_max_slope, init_p, max_iter, use_bias - ) - distances[j, i] = distances[i, j] - - return distances - - -@njit(cache=True, fastmath=True) -def dtw_gi_alignment_path( - x: np.ndarray, - y: np.ndarray, - window: Optional[float] = None, - itakura_max_slope: Optional[float] = None, - init_p: np.ndarray = None, - max_iter: int = 20, - use_bias: bool = False, -) -> tuple[list[tuple[int, int]], float]: - """Compute the DTW_GI alignment path between two time series. - - Parameters - ---------- - x : np.ndarray - First time series, shape ``(n_channels, n_timepoints)`` or ``(n_timepoints,)``. - y : np.ndarray - Second time series, shape ``(m_channels, m_timepoints)`` or ``(m_timepoints,)``. - window : float, default=None - The window to use for the bounding matrix. If None, no bounding matrix - is used. - itakura_max_slope : float, default=None - Maximum slope as a proportion of the number of time points used to create - Itakura parallelogram on the bounding matrix. Must be between 0. and 1. - init_p : array-like of shape (x_channels, y_channels), default=None - Initial linear transformation. If None, the identity matrix is used. - max_iter : int, default=20 - Maximum number of iterations for the iterative optimization. - use_bias : bool, default=False - If True, the feature space map is affine (with a bias term). - - Returns - ------- - List[Tuple[int, int]] - The alignment path between the two time series where each element is a tuple - of the index in x and the index in y that have the best alignment according - to the cost matrix. - float - The DTW_GI distance betweeen the two time series. - - Raises - ------ - ValueError - If x and y are not 1D or 2D arrays. - - Examples - -------- - >>> import numpy as np - >>> from aeon.distances import dtw_gi_alignment_path - >>> x = np.array([[1, 2, 3, 6]]) - >>> y = np.array([[1, 2, 3, 4]]) - >>> dtw_gi_alignment_path(x, y) - ([(0, 0), (1, 1), (2, 2), (3, 3)], 4.0) - """ - w_pi, _, _, cost, _, _ = _dtw_gi( - x, y, window, itakura_max_slope, init_p, max_iter, use_bias - ) - min_alignment_path = [] - for i in range(len(w_pi)): - for j in range(len(w_pi[0])): - if w_pi[i, j] == 1: - min_alignment_path.append((i, j)) - - return min_alignment_path, cost +r"""Dynamic time warping with Global Invariances (DTW-GI) between two time series.""" + +__maintainer__ = [] + +from typing import Optional, Union + +import numpy as np +from numba import njit +from numba.typed import List as NumbaList + +from aeon.distances.elastic._dtw import dtw_alignment_path, dtw_cost_matrix +from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation.collection import _is_numpy_list_multivariate + + +@njit(cache=True, fastmath=True) +def _path2mat( + path: list[tuple[int, int]], + x_timepoints: int, + y_timepoints: int, +): + r"""Convert a warping alignment path to a binary warping matrix.""" + w = np.zeros((x_timepoints, y_timepoints)) + for i, j in path: + w[i, j] = 1 + return w + + +@njit(cache=True, fastmath=True) +def _dtw_gi( + x: np.ndarray, + y: np.ndarray, + window: Optional[float] = None, + itakura_max_slope: Optional[float] = None, + init_p: np.ndarray = None, + max_iter: int = 20, + use_bias: bool = False, +): + r""" + Compute Dynamic Time Warping with Global Invariance between the two time series. + + Parameters + ---------- + x : np.ndarray + First time series, either univariate, shape ``(n_timepoints,)``, or + multivariate, shape ``(n_channels, n_timepoints)``. + y : np.ndarray + Second time series, either univariate, shape ``(n_timepoints,)``, or + multivariate, shape ``(n_channels, n_timepoints)``. + window : float or None, default=None + The window to use for the bounding matrix. If None, no bounding matrix + is used. window is a percentage deviation, so if ``window = 0.1`` then + 10% of the series length is the max warping allowed. + is used. + itakura_max_slope : float, default=None + Maximum slope as a proportion of the number of time points used to create + Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + init_p : array-like of shape (x_channels, y_channels), default=None + Initial linear transformation. If None, the identity matrix is used. + max_iter : int, default=20 + Maximum number of iterations for the iterative optimization. + use_bias : bool, default=False + If True, the feature space map is affine (with a bias term). + + Returns + ------- + - w_pi: binary warping matrix of shape (n0, n1) + - p: the final linear (Stiefel) matrix of shape (x_channels, y_channels) + - cost: final DTW cost considering global invariances + + If use_bias is True, also returns: + - bias + + """ + if x.ndim == 1 and y.ndim == 1: + x_ = x.reshape((1, x.shape[0])) + y_ = y.reshape((1, y.shape[0])) + elif x.ndim == 2 and y.ndim == 2: + x_ = x + y_ = y + else: + raise ValueError("x and y must be 1D or 2D") + + x_channels, x_timepoints = x_.shape + y_channels, y_timepoints = y_.shape + + x_m = np.sum(x_, axis=1) / x_.shape[1] + x_m = x_m.reshape((-1, 1)) + y_m = np.sum(y_, axis=1) / y_.shape[1] + y_m = y_m.reshape((-1, 1)) + + w_pi = np.zeros((x_timepoints, y_timepoints)) + if init_p is None: + p = np.eye(x_channels, y_channels, dtype=np.float64) + else: + p = init_p + + y_ = y_.astype(np.float64) + x_ = x_.astype(np.float64) + + bias = np.zeros((x_channels, 1)) + + for _ in range(max_iter): + w_pi_old = w_pi.copy() + y_transformed = p.dot(y_) + bias + + path, cost = dtw_alignment_path(x_, y_transformed, window, itakura_max_slope) + w_pi = _path2mat(path, x_timepoints, y_timepoints) + + if np.allclose(w_pi, w_pi_old): + break + + if use_bias: + m = (x_ - x_m).dot(w_pi).dot((y_ - y_m).T) + else: + m = x_.dot(w_pi).dot(y_.T) + + u, sigma, vt = np.linalg.svd(m, full_matrices=False) + p = u.dot(vt) + if use_bias: + bias = x_m - p.dot(y_m) + + y_trans = p.dot(y_) + bias + path, cost = dtw_alignment_path(x_, y_trans, window, itakura_max_slope) + + if use_bias: + return w_pi, p, bias, cost, x_, y_trans + else: + dummy_bias = np.zeros((x_channels, 1), dtype=np.float64) + return w_pi, p, dummy_bias, cost, x_, y_trans + + +@njit(cache=True, fastmath=True) +def dtw_gi_distance( + x: np.ndarray, + y: np.ndarray, + window: Optional[float] = None, + itakura_max_slope: Optional[float] = None, + init_p: np.ndarray = None, + max_iter: int = 20, + use_bias: bool = False, +) -> float: + r"""Compute the DTW_GI distance between two time series x and y. + + The DTW_gi distance between 2 timeseries x and y is the similarity + measure that estimates both temporal alignment and does feature space + transformation between time series simultaneously. This means that the + time series do not have to lie in the same ambient space. + A good background into DTW with global invariances can be found in [1]_. + This implementation is inspired by [2]_. + + For example, if we have two time series x and y of lengths n and m + respectively, and we assume that the time series do not lie in the + same ambient space. Lets assume that features of x lie in :math:`\mathbb{R}^p` + and features of y lie in :math:`\mathbb{R}^q`. To compare the two time series, + we need to find an optimum mapping from the feature space of y to the feature space + where features of x lie. So think of it as optimizing on a family of functions F + that map features from y onto the feature space in which features of x + lie. (This is just one way to do it, the mapping can be + in the opposite direction as well. But this function assumes the former way). + + More formally, we define Dynamic Time Warping with Global Invariances (DTW-GI) + as the solution of the following joint optimization problem: + + :math:`\text{DTW-GI}(\mathbf{x}, \mathbf{y}) = + \min_{f \in \mathcal{F}, \pi \in \mathcal{A}(\mathbf{x}, \mathbf{y})} + \sqrt{\sum_{(i,j) \in \pi} d(x_i, f(y_j))^2},` + + This similarity measure estimates temporal alignment + with feature space transformation between time series + allowing the alignment of time series that do not exist in the + same ambient space. + + Note: The optimal warping path :math:`P^*` for a given cost matrix can be found + exactly through a dynamic programming formulation. This can be a time consuming + operation, and it is common to put a restriction on the amount of warping allowed. + This is implemented through the bounding_matrix structure, that supplies a mask for + allowable warpings. The most common bounding strategies include the + Sakoe-Chiba band [3]_. The width of the allowed warping is controlled through the + ``window`` parameter which sets the maximum proportion of warping allowed. + + Parameters + ---------- + x : np.ndarray + First time series, either univariate, shape ``(n_timepoints,)``, or + multivariate, shape ``(n_channels, n_timepoints)``. + y : np.ndarray + Second time series, either univariate, shape ``(n_timepoints,)``, or + multivariate, shape ``(n_channels, n_timepoints)``. + window : float, default=None + The window to use for the bounding matrix. If None, no bounding matrix + is used. window is a percentage deviation, so if ``window = 0.1``, + 10% of the series length is the max warping allowed. + is used. + itakura_max_slope : float, default=None + Maximum slope as a proportion of the number of time points used to create + Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + init_p : array-like of shape (x_channels, y_channels), default=None + Initial linear transformation. If None, the identity matrix is used. + max_iter : int, default=20 + Maximum number of iterations for the iterative optimization. + use_bias : bool, default=False + If True, the feature space map is affine (with a bias term). + + Returns + ------- + float + DTW_GI distance between x and y, minimum value 0. + + Raises + ------ + ValueError + If x and y are not 1D or 2D arrays. + + References + ---------- + .. [1] T. Vayer, R. Tavenard, L. Chapel, N. Courty, R. Flamary, and Y. Soullard, + “Time Series Alignment with Global Invariances,” arXiv.org, 2020. + https://arxiv.org/abs/2002.03848 + + .. [2] Romain Tavenard, “DTW with Global Invariances,” Github.io, Dec. 17, 2020. + https://rtavenar.github.io/hdr/parts/01/dtw/dtw_gi.html + + .. [3] Sakoe H. and Chiba S.: Dynamic programming algorithm optimization for + spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal + Processing 26(1):43-49, 1978. + + Examples + -------- + >>> import numpy as np + >>> from aeon.distances import dtw_gi_distance + >>> x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) + >>> y = np.array([11, 12, 13, 14, 15, 16, 17, 18, 19, 20]) + >>> dtw_gi_distance(x, y) # 1D series + 768.0 + >>> x = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [0, 1, 0, 2, 0]]) + >>> y = np.array([[11, 12, 13, 14],[7, 8, 9, 20],[1, 3, 4, 5]] ) + >>> round(dtw_gi_distance(x, y), 1) # 2D series with 3 channels, unequal length + 359.2 + """ + if x.ndim == 1 and y.ndim == 1: + _x = x.reshape((1, x.shape[0])) + _y = y.reshape((1, y.shape[0])) + return _dtw_gi(_x, _y, window, itakura_max_slope, init_p, max_iter, use_bias)[3] + if x.ndim == 2 and y.ndim == 2: + return _dtw_gi(x, y, window, itakura_max_slope, init_p, max_iter, use_bias)[3] + raise ValueError("x and y must be 1D or 2D") + + +@njit(cache=True, fastmath=True) +def dtw_gi_cost_matrix( + x: np.ndarray, + y: np.ndarray, + window: Optional[float] = None, + itakura_max_slope: Optional[float] = None, + init_p: np.ndarray = None, + max_iter: int = 20, + use_bias: bool = False, +) -> np.ndarray: + r"""Compute the DTW_GI cost matrix between two time series. + + The cost matrix is the pairwise Euclidean distance between all points + :math:`M_{i,j}=(x_i-y_{\text{trans},j})^2`. Where `y_trans` is the time + series we get by finding the optimal mapping from feature space of y + to feature space where features of x lie. It is used in the DTW GI + path calculations. + + Parameters + ---------- + x : np.ndarray + First time series, either univariate, shape ``(n_timepoints,)``, or + multivariate, shape ``(n_channels, n_timepoints)``. + y : np.ndarray + Second time series, either univariate, shape ``(n_timepoints,)``, or + multivariate, shape ``(n_channels, n_timepoints)``. + window : float, default=None + The window to use for the bounding matrix. If None, no bounding matrix + is used. window is a percentage deviation, so if ``window = 0.1``, + 10% of the series length is the max warping allowed. + is used. + itakura_max_slope : float, default=None + Maximum slope as a proportion of the number of time points used to create + Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + init_p : array-like of shape (x_channels, y_channels), default=None + Initial linear transformation. If None, the identity matrix is used. + max_iter : int, default=20 + Maximum number of iterations for the iterative optimization. + use_bias : bool, default=False + If True, the feature space map is affine (with a bias term). + + Returns + ------- + np.ndarray (n_timepoints, m_timepoints) + dtw gi cost matrix between x and y. + + Raises + ------ + ValueError + If x and y are not 1D or 2D arrays. + + Examples + -------- + >>> import numpy as np + >>> from aeon.distances import dtw_gi_cost_matrix + >>> x = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]) + >>> y = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]) + >>> dtw_gi_cost_matrix(x, y) + array([[ 0., 1., 5., 14., 30., 55., 91., 140., 204., 285.], + [ 1., 0., 1., 5., 14., 30., 55., 91., 140., 204.], + [ 5., 1., 0., 1., 5., 14., 30., 55., 91., 140.], + [ 14., 5., 1., 0., 1., 5., 14., 30., 55., 91.], + [ 30., 14., 5., 1., 0., 1., 5., 14., 30., 55.], + [ 55., 30., 14., 5., 1., 0., 1., 5., 14., 30.], + [ 91., 55., 30., 14., 5., 1., 0., 1., 5., 14.], + [140., 91., 55., 30., 14., 5., 1., 0., 1., 5.], + [204., 140., 91., 55., 30., 14., 5., 1., 0., 1.], + [285., 204., 140., 91., 55., 30., 14., 5., 1., 0.]]) + """ + _, _, _, _, xnew, y_trans = _dtw_gi( + x, y, window, itakura_max_slope, init_p, max_iter, use_bias + ) + + return dtw_cost_matrix(xnew, y_trans, window, itakura_max_slope) + + +def dtw_gi_pairwise_distance( + X: Union[np.ndarray, list[np.ndarray]], + y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, + window: Optional[float] = None, + itakura_max_slope: Optional[float] = None, + unequal_length: bool = None, + init_p: np.ndarray = None, + max_iter: int = 20, + use_bias: bool = False, +) -> np.ndarray: + r"""Compute the DTW_GI pairwise distance between a set of time series. + + By default, this takes a collection of :math:`n` time series :math:`X` and returns a + matrix + :math:`D` where :math:`D_{i,j}` is the DTW_GI distance between the :math:`i^{th}` + and the :math:`j^{th}` series in :math:`X`. If :math:`X` is 2 dimensional, + it is assumed to be a collection of univariate series with shape ``(n_cases, + n_timepoints)``. If it is 3 dimensional, it is assumed to be shape ``(n_cases, + n_channels, n_timepoints)``. + + This function has an optional argument, :math:`y`, to allow calculation of the + distance matrix between :math:`X` and one or more series stored in :math:`y`. If + :math:`y` is 1 dimensional, we assume it is a single univariate series and the + distance matrix returned is shape ``(n_cases,1)``. If it is 2D, we assume it + is a collection of univariate series with shape ``(m_cases, m_timepoints)`` + and the distance ``(n_cases,m_cases)``. If it is 3 dimensional, + it is assumed to be shape ``(m_cases, m_channels, m_timepoints)``. + + Parameters + ---------- + X : np.ndarray or List of np.ndarray + A collection of time series instances of shape ``(n_cases, n_timepoints)`` + or ``(n_cases, n_channels, n_timepoints)``. + y : np.ndarray or List of np.ndarray or None, default=None + A single series or a collection of time series of shape ``(m_timepoints,)`` or + ``(m_cases, m_timepoints)`` or ``(m_cases, m_channels, m_timepoints)``. + If None, then the dtw gi pairwise distance between the instances of X is + calculated. + window : float or None, default=None + The window to use for the bounding matrix. If None, no bounding matrix + is used. + itakura_max_slope : float, default=None + Maximum slope as a proportion of the number of time points used to create + Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + init_p : array-like of shape (x_channels, y_channels), default=None + Initial linear transformation. If None, the identity matrix is used. + max_iter : int, default=20 + Maximum number of iterations for the iterative optimization. + use_bias : bool, default=False + If True, the feature space map is affine (with a bias term). + + Returns + ------- + np.ndarray + DTW_GI pairwise matrix between the instances of X of shape + ``(n_cases, n_cases)`` or between X and y of shape ``(n_cases, + n_cases)``. + + Raises + ------ + ValueError + If X is not 2D or 3D array and if y is not 1D, 2D or 3D arrays when passing y. + + Examples + -------- + >>> import numpy as np + >>> from aeon.distances import dtw_gi_pairwise_distance + >>> # Distance between each time series in a collection of time series + >>> X = np.array([[[1, 2, 3]],[[4, 5, 6]], [[7, 8, 9]]]) + >>> dtw_gi_pairwise_distance(X) + array([[ 0., 26., 108.], + [ 26., 0., 26.], + [108., 26., 0.]]) + + >>> # Distance between two collections of time series + >>> X = np.array([[[1, 2, 3]],[[4, 5, 6]], [[7, 8, 9]]]) + >>> y = np.array([[[11, 12, 13]],[[14, 15, 16]], [[17, 18, 19]]]) + >>> dtw_gi_pairwise_distance(X, y) + array([[300., 507., 768.], + [147., 300., 507.], + [ 48., 147., 300.]]) + + >>> X = np.array([[[1, 2, 3]],[[4, 5, 6]], [[7, 8, 9]]]) + >>> y_univariate = np.array([11, 12, 13]) + >>> dtw_gi_pairwise_distance(X, y_univariate) + array([[300.], + [147.], + [ 48.]]) + + >>> # Distance between each TS in a collection of unequal-length time series + >>> X = [np.array([1, 2, 3]), np.array([4, 5, 6, 7]), np.array([8, 9, 10, 11, 12])] + >>> dtw_gi_pairwise_distance(X) + array([[ 0., 42., 292.], + [ 42., 0., 83.], + [292., 83., 0.]]) + """ + multivariate_conversion = _is_numpy_list_multivariate(X, y) + _X, unequal_length = _convert_collection_to_numba_list( + X, "X", multivariate_conversion + ) + + if y is None: + # To self + return _dtw_gi_pairwise_distance( + _X, window, itakura_max_slope, unequal_length, init_p, max_iter, use_bias + ) + _y, unequal_length = _convert_collection_to_numba_list( + y, "y", multivariate_conversion + ) + return _dtw_gi_from_multiple_to_multiple_distance( + _X, _y, window, itakura_max_slope, unequal_length, init_p, max_iter, use_bias + ) + + +@njit(cache=True, fastmath=True) +def _dtw_gi_from_multiple_to_multiple_distance( + x: NumbaList[np.ndarray], + y: NumbaList[np.ndarray], + window: Optional[float] = None, + itakura_max_slope: Optional[float] = None, + unequal_length: bool = None, + init_p: np.ndarray = None, + max_iter: int = 20, + use_bias: bool = False, +) -> np.ndarray: + n_cases = len(x) + m_cases = len(y) + distances = np.zeros((n_cases, m_cases)) + + for i in range(n_cases): + for j in range(m_cases): + x1, y1 = x[i], y[j] + distances[i, j] = dtw_gi_distance( + x1, y1, window, itakura_max_slope, init_p, max_iter, use_bias + ) + return distances + + +@njit(cache=True, fastmath=True) +def _dtw_gi_pairwise_distance( + X: NumbaList[np.ndarray], + window: Optional[float] = None, + itakura_max_slope: Optional[float] = None, + unequal_length: bool = None, + init_p: np.ndarray = None, + max_iter: int = 20, + use_bias: bool = False, +) -> np.ndarray: + n_cases = len(X) + distances = np.zeros((n_cases, n_cases)) + + for i in range(n_cases): + for j in range(i + 1, n_cases): + x1, x2 = X[i], X[j] + distances[i, j] = dtw_gi_distance( + x1, x2, window, itakura_max_slope, init_p, max_iter, use_bias + ) + distances[j, i] = distances[i, j] + + return distances + + +@njit(cache=True, fastmath=True) +def dtw_gi_alignment_path( + x: np.ndarray, + y: np.ndarray, + window: Optional[float] = None, + itakura_max_slope: Optional[float] = None, + init_p: np.ndarray = None, + max_iter: int = 20, + use_bias: bool = False, +) -> tuple[list[tuple[int, int]], float]: + """Compute the DTW_GI alignment path between two time series. + + Parameters + ---------- + x : np.ndarray + First time series, shape ``(n_channels, n_timepoints)`` or ``(n_timepoints,)``. + y : np.ndarray + Second time series, shape ``(m_channels, m_timepoints)`` or ``(m_timepoints,)``. + window : float, default=None + The window to use for the bounding matrix. If None, no bounding matrix + is used. + itakura_max_slope : float, default=None + Maximum slope as a proportion of the number of time points used to create + Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + init_p : array-like of shape (x_channels, y_channels), default=None + Initial linear transformation. If None, the identity matrix is used. + max_iter : int, default=20 + Maximum number of iterations for the iterative optimization. + use_bias : bool, default=False + If True, the feature space map is affine (with a bias term). + + Returns + ------- + List[Tuple[int, int]] + The alignment path between the two time series where each element is a tuple + of the index in x and the index in y that have the best alignment according + to the cost matrix. + float + The DTW_GI distance betweeen the two time series. + + Raises + ------ + ValueError + If x and y are not 1D or 2D arrays. + + Examples + -------- + >>> import numpy as np + >>> from aeon.distances import dtw_gi_alignment_path + >>> x = np.array([[1, 2, 3, 6]]) + >>> y = np.array([[1, 2, 3, 4]]) + >>> dtw_gi_alignment_path(x, y) + ([(0, 0), (1, 1), (2, 2), (3, 3)], 4.0) + """ + w_pi, _, _, cost, _, _ = _dtw_gi( + x, y, window, itakura_max_slope, init_p, max_iter, use_bias + ) + min_alignment_path = [] + for i in range(len(w_pi)): + for j in range(len(w_pi[0])): + if w_pi[i, j] == 1: + min_alignment_path.append((i, j)) + + return min_alignment_path, cost From 16c7bb8f64e7e02a589f6979547e1f7467a88ffa Mon Sep 17 00:00:00 2001 From: Saad Al-Tohamy <92796871+saadaltohamy@users.noreply.github.com> Date: Thu, 8 May 2025 15:46:16 +0300 Subject: [PATCH 083/216] [ENH] Add type hints for deep learning regression classes (#2644) * type hints for cnn for regrssion * editing import modules Model & Optim * type hints for disjoint_cnn for regrssion * FIX type hints _get_test_params * ENH Change linie of importing typing * type hints for _encoder for regrssion * type hints for _fcn for regrssion * type hints for _inception_time for regrssion * type hints for _lite_time for regrssion * type hints for _mlp for regrssion * type hints for _resnet for regrssion * type hints for _base for regrssion * FIX: mypy errors in _disjoint_cnn.py file * FIX: mypy typing errors * Fix: Delete variable types, back old-verbose * FIX: add model._save in save_last_model_to_file function * FIX: Put TYPE_CHECKING downside * Fix: Put Any at the top --- aeon/regression/deep_learning/_cnn.py | 73 ++++---- .../regression/deep_learning/_disjoint_cnn.py | 81 +++++---- aeon/regression/deep_learning/_encoder.py | 70 ++++---- aeon/regression/deep_learning/_fcn.py | 71 ++++---- .../deep_learning/_inception_time.py | 158 ++++++++++-------- aeon/regression/deep_learning/_lite_time.py | 114 +++++++------ aeon/regression/deep_learning/_mlp.py | 63 ++++--- aeon/regression/deep_learning/_resnet.py | 73 ++++---- aeon/regression/deep_learning/base.py | 23 ++- 9 files changed, 411 insertions(+), 315 deletions(-) diff --git a/aeon/regression/deep_learning/_cnn.py b/aeon/regression/deep_learning/_cnn.py index 351e3964d3..e2f6635fa2 100644 --- a/aeon/regression/deep_learning/_cnn.py +++ b/aeon/regression/deep_learning/_cnn.py @@ -1,5 +1,7 @@ """Time Convolutional Neural Network (TimeCNN) regressor.""" +from __future__ import annotations + __maintainer__ = ["hadifawaz1999"] __all__ = ["TimeCNNRegressor"] @@ -7,12 +9,18 @@ import os import time from copy import deepcopy +from typing import TYPE_CHECKING, Any +import numpy as np from sklearn.utils import check_random_state from aeon.networks import TimeCNNNetwork from aeon.regression.deep_learning.base import BaseDeepRegressor +if TYPE_CHECKING: + import tensorflow as tf + from tensorflow.keras.callbacks import Callback + class TimeCNNRegressor(BaseDeepRegressor): """Time Series Convolutional Neural Network (CNN). @@ -120,39 +128,39 @@ class TimeCNNRegressor(BaseDeepRegressor): >>> X, y = make_example_3d_numpy(n_cases=10, n_channels=1, n_timepoints=12, ... return_y=True, regression_target=True, ... random_state=0) - >>> rgs = TimeCNNRegressor(n_epochs=20, bacth_size=4) # doctest: +SKIP + >>> rgs = TimeCNNRegressor(n_epochs=20, batch_size=4) # doctest: +SKIP >>> rgs.fit(X, y) # doctest: +SKIP TimeCNNRegressor(...) """ def __init__( self, - n_layers=2, - kernel_size=7, - n_filters=None, - avg_pool_size=3, - activation="sigmoid", - padding="valid", - strides=1, - dilation_rate=1, - n_epochs=2000, - batch_size=16, - callbacks=None, - file_path="./", - save_best_model=False, - save_last_model=False, - save_init_model=False, - best_file_name="best_model", - last_file_name="last_model", - init_file_name="init_model", - verbose=False, - loss="mean_squared_error", - output_activation="linear", - metrics="mean_squared_error", - random_state=None, - use_bias=True, - optimizer=None, - ): + n_layers: int = 2, + kernel_size: int | list[int] = 7, + n_filters: int | list[int] | None = None, + avg_pool_size: int | list[int] = 3, + activation: str | list[str] = "sigmoid", + padding: str | list[str] = "valid", + strides: int | list[int] = 1, + dilation_rate: int | list[int] = 1, + n_epochs: int = 2000, + batch_size: int = 16, + callbacks: Callback | list[Callback] | None = None, + file_path: str = "./", + save_best_model: bool = False, + save_last_model: bool = False, + save_init_model: bool = False, + best_file_name: str = "best_model", + last_file_name: str = "last_model", + init_file_name: str = "init_model", + verbose: bool = False, + loss: str = "mean_squared_error", + output_activation: str = "linear", + metrics: str | list[str] = "mean_squared_error", + random_state: int | np.random.RandomState | None = None, + use_bias: bool | list[bool] = True, + optimizer: tf.keras.optimizers.Optimizer | None = None, + ) -> None: self.n_layers = n_layers self.avg_pool_size = avg_pool_size self.padding = padding @@ -196,7 +204,9 @@ def __init__( use_bias=self.use_bias, ) - def build_model(self, input_shape, **kwargs): + def build_model( + self, input_shape: tuple[int, ...], **kwargs: Any + ) -> tf.keras.Model: """Construct a compiled, un-trained, keras model that is ready for training. In aeon, time series are stored in numpy arrays of shape (d,m), where d @@ -213,7 +223,6 @@ def build_model(self, input_shape, **kwargs): ------- output : a compiled Keras Model """ - import numpy as np import tensorflow as tf from tensorflow import keras @@ -239,7 +248,7 @@ def build_model(self, input_shape, **kwargs): ) return model - def _fit(self, X, y): + def _fit(self, X: np.ndarray, y: np.ndarray) -> TimeCNNRegressor: """Fit the regressor on the training set (X, y). Parameters @@ -316,7 +325,9 @@ def _fit(self, X, y): return self @classmethod - def _get_test_params(cls, parameter_set="default"): + def _get_test_params( + cls, parameter_set: str = "default" + ) -> dict[str, Any] | list[dict[str, Any]]: """Return testing parameter settings for the estimator. Parameters diff --git a/aeon/regression/deep_learning/_disjoint_cnn.py b/aeon/regression/deep_learning/_disjoint_cnn.py index cc2b0cb321..ac5e61d202 100644 --- a/aeon/regression/deep_learning/_disjoint_cnn.py +++ b/aeon/regression/deep_learning/_disjoint_cnn.py @@ -1,5 +1,7 @@ """DisjointCNN regressor.""" +from __future__ import annotations + __maintainer__ = ["hadifawaz1999"] __all__ = ["DisjointCNNRegressor"] @@ -7,12 +9,18 @@ import os import time from copy import deepcopy +from typing import TYPE_CHECKING, Any +import numpy as np from sklearn.utils import check_random_state from aeon.networks import DisjointCNNNetwork from aeon.regression.deep_learning.base import BaseDeepRegressor +if TYPE_CHECKING: + import tensorflow as tf + from tensorflow.keras.callbacks import Callback + class DisjointCNNRegressor(BaseDeepRegressor): """Disjoint Convolutional Neural Netowkr regressor. @@ -159,37 +167,37 @@ class DisjointCNNRegressor(BaseDeepRegressor): def __init__( self, - n_layers=4, - n_filters=64, - kernel_size=None, - dilation_rate=1, - strides=1, - padding="same", - activation="elu", - use_bias=True, - kernel_initializer="he_uniform", - pool_size=5, - pool_strides=None, - pool_padding="valid", - hidden_fc_units=128, - activation_fc="relu", - n_epochs=2000, - batch_size=16, - use_mini_batch_size=False, - random_state=None, - verbose=False, - output_activation="linear", - loss="mean_squared_error", - metrics="mean_squared_error", - optimizer=None, - file_path="./", - save_best_model=False, - save_last_model=False, - save_init_model=False, - best_file_name="best_model", - last_file_name="last_model", - init_file_name="init_model", - callbacks=None, + n_layers: int = 4, + n_filters: int | list[int] = 64, + kernel_size: int | list[int] | None = None, + dilation_rate: int | list[int] = 1, + strides: int | list[int] = 1, + padding: str | list[str] = "same", + activation: str | list[str] = "elu", + use_bias: bool | list[bool] = True, + kernel_initializer: str | list[str] = "he_uniform", + pool_size: int = 5, + pool_strides: int | None = None, + pool_padding: str = "valid", + hidden_fc_units: int = 128, + activation_fc: str = "relu", + n_epochs: int = 2000, + batch_size: int = 16, + use_mini_batch_size: bool = False, + random_state: int | np.random.RandomState | None = None, + verbose: bool = False, + output_activation: str = "linear", + loss: str = "mean_squared_error", + metrics: str | list[str] = "mean_squared_error", + optimizer: tf.keras.optimizers.Optimizer | None = None, + file_path: str = "./", + save_best_model: bool = False, + save_last_model: bool = False, + save_init_model: bool = False, + best_file_name: str = "best_model", + last_file_name: str = "last_model", + init_file_name: str = "init_model", + callbacks: Callback | list[Callback] | None = None, ): self.n_layers = n_layers self.n_filters = n_filters @@ -247,7 +255,9 @@ def __init__( activation_fc=self.activation_fc, ) - def build_model(self, input_shape, **kwargs): + def build_model( + self, input_shape: tuple[int, ...], **kwargs: Any + ) -> tf.keras.Model: """Construct a compiled, un-trained, keras model that is ready for training. In aeon, time series are stored in numpy arrays of shape (d,m), where d @@ -266,7 +276,6 @@ def build_model(self, input_shape, **kwargs): ------- output : a compiled Keras Model """ - import numpy as np import tensorflow as tf rng = check_random_state(self.random_state) @@ -291,7 +300,7 @@ def build_model(self, input_shape, **kwargs): return model - def _fit(self, X, y): + def _fit(self, X: np.ndarray, y: np.ndarray) -> DisjointCNNRegressor: """Fit the regressor on the training set (X, y). Parameters @@ -376,7 +385,9 @@ def _fit(self, X, y): return self @classmethod - def _get_test_params(cls, parameter_set="default"): + def _get_test_params( + cls, parameter_set: str = "default" + ) -> dict[str, Any] | list[dict[str, Any]]: """Return testing parameter settings for the estimator. Parameters diff --git a/aeon/regression/deep_learning/_encoder.py b/aeon/regression/deep_learning/_encoder.py index fd3bf855cb..7388ce0928 100644 --- a/aeon/regression/deep_learning/_encoder.py +++ b/aeon/regression/deep_learning/_encoder.py @@ -1,18 +1,27 @@ """Encoder Regressor.""" +from __future__ import annotations + __author__ = ["AnonymousCodes911", "hadifawaz1999"] __all__ = ["EncoderRegressor"] + import gc import os import time from copy import deepcopy +from typing import TYPE_CHECKING, Any +import numpy as np from sklearn.utils import check_random_state from aeon.networks import EncoderNetwork from aeon.regression.deep_learning.base import BaseDeepRegressor +if TYPE_CHECKING: + import tensorflow as tf + from tensorflow.keras.callbacks import Callback + class EncoderRegressor(BaseDeepRegressor): """ @@ -111,31 +120,31 @@ class EncoderRegressor(BaseDeepRegressor): def __init__( self, - n_epochs=100, - batch_size=12, - kernel_size=None, - n_filters=None, - dropout_proba=0.2, - activation="sigmoid", - output_activation="linear", - max_pool_size=2, - padding="same", - strides=1, - fc_units=256, - callbacks=None, - file_path="./", - save_best_model=False, - save_last_model=False, - save_init_model=False, - best_file_name="best_model", - last_file_name="last_model", - init_file_name="init_model", - verbose=False, - loss="mean_squared_error", - metrics="mean_squared_error", - use_bias=True, - optimizer=None, - random_state=None, + n_epochs: int = 100, + batch_size: int = 12, + kernel_size: list[int] | None = None, + n_filters: list[int] | None = None, + dropout_proba: float = 0.2, + activation: str = "sigmoid", + output_activation: str = "linear", + max_pool_size: int = 2, + padding: str = "same", + strides: int = 1, + fc_units: int = 256, + callbacks: Callback | list[Callback] | None = None, + file_path: str = "./", + save_best_model: bool = False, + save_last_model: bool = False, + save_init_model: bool = False, + best_file_name: str = "best_model", + last_file_name: str = "last_model", + init_file_name: str = "init_model", + verbose: bool = False, + loss: str = "mean_squared_error", + metrics: str | list[str] = "mean_squared_error", + use_bias: bool = True, + optimizer: tf.keras.optimizers.Optimizer | None = None, + random_state: int | np.random.RandomState | None = None, ): self.n_filters = n_filters self.max_pool_size = max_pool_size @@ -179,7 +188,9 @@ def __init__( activation=self.activation, ) - def build_model(self, input_shape, **kwargs): + def build_model( + self, input_shape: tuple[int, ...], **kwargs: Any + ) -> tf.keras.Model: """Construct a compiled, un-trained, keras model that is ready for training. In aeon, time series are stored in numpy arrays of shape (d, m), where d @@ -195,7 +206,6 @@ def build_model(self, input_shape, **kwargs): ------- output : a compiled Keras Model """ - import numpy as np import tensorflow as tf rng = check_random_state(self.random_state) @@ -222,7 +232,7 @@ def build_model(self, input_shape, **kwargs): return model - def _fit(self, X, y): + def _fit(self, X: np.ndarray, y: np.ndarray) -> EncoderRegressor: """Fit the classifier on the training set (X, y). Parameters @@ -299,7 +309,9 @@ def _fit(self, X, y): return self @classmethod - def _get_test_params(cls, parameter_set="default"): + def _get_test_params( + cls, parameter_set: str = "default" + ) -> dict[str, Any] | list[dict[str, Any]]: """Return testing parameter settings for the estimator. Parameters diff --git a/aeon/regression/deep_learning/_fcn.py b/aeon/regression/deep_learning/_fcn.py index a6905580ac..082b8a7038 100644 --- a/aeon/regression/deep_learning/_fcn.py +++ b/aeon/regression/deep_learning/_fcn.py @@ -1,5 +1,7 @@ """Fully Convolutional Network (FCN) regressor.""" +from __future__ import annotations + __maintainer__ = ["hadifawaz1999"] __all__ = ["FCNRegressor"] @@ -7,12 +9,18 @@ import os import time from copy import deepcopy +from typing import TYPE_CHECKING, Any +import numpy as np from sklearn.utils import check_random_state from aeon.networks import FCNNetwork from aeon.regression.deep_learning.base import BaseDeepRegressor +if TYPE_CHECKING: + import tensorflow as tf + from tensorflow.keras.callbacks import Callback + class FCNRegressor(BaseDeepRegressor): """Fully Convolutional Network (FCN). @@ -117,32 +125,32 @@ class FCNRegressor(BaseDeepRegressor): def __init__( self, - n_layers=3, - n_filters=None, - kernel_size=None, - dilation_rate=1, - strides=1, - padding="same", - activation="relu", - file_path="./", - save_best_model=False, - save_last_model=False, - save_init_model=False, - best_file_name="best_model", - last_file_name="last_model", - init_file_name="init_model", - n_epochs=2000, - batch_size=16, - use_mini_batch_size=False, - callbacks=None, - verbose=False, - output_activation="linear", - loss="mean_squared_error", - metrics="mean_squared_error", - random_state=None, - use_bias=True, - optimizer=None, - ): + n_layers: int = 3, + n_filters: int | list[int] | None = None, + kernel_size: int | list[int] | None = None, + dilation_rate: int | list[int] = 1, + strides: int | list[int] = 1, + padding: str | list[str] = "same", + activation: str | list[str] = "relu", + file_path: str = "./", + save_best_model: bool = False, + save_last_model: bool = False, + save_init_model: bool = False, + best_file_name: str = "best_model", + last_file_name: str = "last_model", + init_file_name: str = "init_model", + n_epochs: int = 2000, + batch_size: int = 16, + use_mini_batch_size: bool = False, + callbacks: Callback | list[Callback] | None = None, + verbose: bool = False, + output_activation: str = "linear", + loss: str = "mean_squared_error", + metrics: str | list[str] = "mean_squared_error", + random_state: int | np.random.RandomState | None = None, + use_bias: bool = True, + optimizer: tf.keras.optimizers.Optimizer | None = None, + ) -> None: self.n_layers = n_layers self.kernel_size = kernel_size self.n_filters = n_filters @@ -182,7 +190,9 @@ def __init__( use_bias=self.use_bias, ) - def build_model(self, input_shape, **kwargs): + def build_model( + self, input_shape: tuple[int, ...], **kwargs: Any + ) -> tf.keras.Model: """Construct a compiled, un-trained, keras model that is ready for training. In aeon, time series are stored in numpy arrays of shape (d,m), where d @@ -199,7 +209,6 @@ def build_model(self, input_shape, **kwargs): ------- output : a compiled Keras Model """ - import numpy as np import tensorflow as tf rng = check_random_state(self.random_state) @@ -225,7 +234,7 @@ def build_model(self, input_shape, **kwargs): return model - def _fit(self, X, y): + def _fit(self, X: np.ndarray, y: np.ndarray) -> FCNRegressor: """Fit the regressor on the training set (X, y). Parameters @@ -310,7 +319,9 @@ def _fit(self, X, y): return self @classmethod - def _get_test_params(cls, parameter_set="default"): + def _get_test_params( + cls, parameter_set: str = "default" + ) -> dict[str, Any] | list[dict[str, Any]]: """Return testing parameter settings for the estimator. Parameters diff --git a/aeon/regression/deep_learning/_inception_time.py b/aeon/regression/deep_learning/_inception_time.py index 96e8a38362..e0d46f8089 100644 --- a/aeon/regression/deep_learning/_inception_time.py +++ b/aeon/regression/deep_learning/_inception_time.py @@ -1,5 +1,7 @@ """InceptionTime and Inception regressors.""" +from __future__ import annotations + __maintainer__ = ["hadifawaz1999"] __all__ = ["InceptionTimeRegressor"] @@ -7,6 +9,7 @@ import os import time from copy import deepcopy +from typing import TYPE_CHECKING, Any import numpy as np from sklearn.utils import check_random_state @@ -15,6 +18,10 @@ from aeon.regression.base import BaseRegressor from aeon.regression.deep_learning.base import BaseDeepRegressor +if TYPE_CHECKING: + import tensorflow as tf + from tensorflow.keras.callbacks import Callback + class InceptionTimeRegressor(BaseRegressor): """InceptionTime ensemble regressor. @@ -179,39 +186,39 @@ class InceptionTimeRegressor(BaseRegressor): def __init__( self, - n_regressors=5, - n_filters=32, - n_conv_per_layer=3, - kernel_size=40, - use_max_pooling=True, - max_pool_size=3, - strides=1, - dilation_rate=1, - padding="same", - activation="relu", - use_bias=False, - use_residual=True, - use_bottleneck=True, - bottleneck_size=32, - depth=6, - use_custom_filters=False, - output_activation="linear", - file_path="./", - save_last_model=False, - save_best_model=False, - save_init_model=False, - best_file_name="best_model", - last_file_name="last_model", - init_file_name="init_model", - batch_size=64, - use_mini_batch_size=False, - n_epochs=1500, - callbacks=None, - random_state=None, - verbose=False, - loss="mean_squared_error", - metrics="mean_squared_error", - optimizer=None, + n_regressors: int = 5, + n_filters: int | list[int] = 32, + n_conv_per_layer: int | list[int] = 3, + kernel_size: int | list[int] = 40, + use_max_pooling: bool | list[bool] = True, + max_pool_size: int | list[int] = 3, + strides: int | list[int] = 1, + dilation_rate: int | list[int] = 1, + padding: str | list[str] = "same", + activation: str | list[str] = "relu", + use_bias: bool | list[bool] = False, + use_residual: bool = True, + use_bottleneck: bool = True, + bottleneck_size: int = 32, + depth: int = 6, + use_custom_filters: bool = False, + output_activation: str = "linear", + file_path: str = "./", + save_last_model: bool = False, + save_best_model: bool = False, + save_init_model: bool = False, + best_file_name: str = "best_model", + last_file_name: str = "last_model", + init_file_name: str = "init_model", + batch_size: int = 64, + use_mini_batch_size: bool = False, + n_epochs: int = 1500, + callbacks: Callback | list[Callback] | None = None, + random_state: int | np.random.RandomState | None = None, + verbose: bool = False, + loss: str = "mean_squared_error", + metrics: str | list[str] = "mean_squared_error", + optimizer: tf.keras.optimizers.Optimizer | None = None, ): self.n_regressors = n_regressors @@ -251,11 +258,11 @@ def __init__( self.metrics = metrics self.optimizer = optimizer - self.regressors_ = [] + self.regressors_: list[IndividualInceptionRegressor] = [] super().__init__() - def _fit(self, X, y): + def _fit(self, X: np.ndarray, y: np.ndarray) -> InceptionTimeRegressor: """Fit each of the Individual Inception models. Parameters @@ -313,7 +320,7 @@ def _fit(self, X, y): return self - def _predict(self, X) -> np.ndarray: + def _predict(self, X: np.ndarray) -> np.ndarray: """Predict the values of the test set using InceptionTime. Parameters @@ -337,7 +344,9 @@ def _predict(self, X) -> np.ndarray: return ypreds @classmethod - def _get_test_params(cls, parameter_set="default"): + def _get_test_params( + cls, parameter_set: str = "default" + ) -> dict[str, Any] | list[dict[str, Any]]: """Return testing parameter settings for the estimator. Parameters @@ -507,38 +516,38 @@ class IndividualInceptionRegressor(BaseDeepRegressor): def __init__( self, - n_filters=32, - n_conv_per_layer=3, - kernel_size=40, - use_max_pooling=True, - max_pool_size=3, - strides=1, - dilation_rate=1, - padding="same", - activation="relu", - use_bias=False, - use_residual=True, - use_bottleneck=True, - bottleneck_size=32, - depth=6, - use_custom_filters=False, - output_activation="linear", - file_path="./", - save_best_model=False, - save_last_model=False, - save_init_model=False, - best_file_name="best_model", - last_file_name="last_model", - init_file_name="init_model", - batch_size=64, - use_mini_batch_size=False, - n_epochs=1500, - callbacks=None, - random_state=None, - verbose=False, - loss="mean_squared_error", - metrics="mean_squared_error", - optimizer=None, + n_filters: int | list[int] = 32, + n_conv_per_layer: int | list[int] = 3, + kernel_size: int | list[int] = 40, + use_max_pooling: bool | list[bool] = True, + max_pool_size: int | list[int] = 3, + strides: int | list[int] = 1, + dilation_rate: int | list[int] = 1, + padding: str | list[str] = "same", + activation: str | list[str] = "relu", + use_bias: bool | list[bool] = False, + use_residual: bool = True, + use_bottleneck: bool = True, + bottleneck_size: int = 32, + depth: int = 6, + use_custom_filters: bool = False, + output_activation: str = "linear", + file_path: str = "./", + save_best_model: bool = False, + save_last_model: bool = False, + save_init_model: bool = False, + best_file_name: str = "best_model", + last_file_name: str = "last_model", + init_file_name: str = "init_model", + batch_size: int = 64, + use_mini_batch_size: bool = False, + n_epochs: int = 1500, + callbacks: Callback | list[Callback] | None = None, + random_state: int | np.random.RandomState | None = None, + verbose: bool = False, + loss: str = "mean_squared_error", + metrics: str | list[str] = "mean_squared_error", + optimizer: tf.keras.optimizers.Optimizer | None = None, ): # predefined self.n_filters = n_filters @@ -595,7 +604,9 @@ def __init__( use_custom_filters=self.use_custom_filters, ) - def build_model(self, input_shape, **kwargs): + def build_model( + self, input_shape: tuple[int, ...], **kwargs: Any + ) -> tf.keras.Model: """ Construct a compiled, un-trained, keras model that is ready for training. @@ -609,7 +620,6 @@ def build_model(self, input_shape, **kwargs): tf.keras.models.Model A compiled Keras Model """ - import numpy as np import tensorflow as tf rng = check_random_state(self.random_state) @@ -631,7 +641,7 @@ def build_model(self, input_shape, **kwargs): return model - def _fit(self, X, y): + def _fit(self, X: np.ndarray, y: np.ndarray) -> IndividualInceptionRegressor: """ Fit the regressor on the training set (X, y). @@ -721,7 +731,9 @@ def _fit(self, X, y): return self @classmethod - def _get_test_params(cls, parameter_set="default"): + def _get_test_params( + cls, parameter_set: str = "default" + ) -> dict[str, Any] | list[dict[str, Any]]: """Return testing parameter settings for the estimator. Parameters diff --git a/aeon/regression/deep_learning/_lite_time.py b/aeon/regression/deep_learning/_lite_time.py index 9af8bbaf4e..d21a0b391b 100644 --- a/aeon/regression/deep_learning/_lite_time.py +++ b/aeon/regression/deep_learning/_lite_time.py @@ -1,5 +1,7 @@ """LITETime and LITE regressors.""" +from __future__ import annotations + __author__ = ["aadya940", "hadifawaz1999"] __all__ = ["IndividualLITERegressor", "LITETimeRegressor"] @@ -7,6 +9,7 @@ import os import time from copy import deepcopy +from typing import TYPE_CHECKING, Any import numpy as np from sklearn.utils import check_random_state @@ -14,6 +17,10 @@ from aeon.networks import LITENetwork from aeon.regression.deep_learning.base import BaseDeepRegressor, BaseRegressor +if TYPE_CHECKING: + import tensorflow as tf + from tensorflow.keras.callbacks import Callback + class LITETimeRegressor(BaseRegressor): """LITETime or LITEMVTime ensemble Regressor. @@ -137,29 +144,29 @@ class LITETimeRegressor(BaseRegressor): def __init__( self, - n_regressors=5, - use_litemv=False, - n_filters=32, - kernel_size=40, - strides=1, - activation="relu", - output_activation="linear", - file_path="./", - save_last_model=False, - save_best_model=False, - save_init_model=False, - best_file_name="best_model", - last_file_name="last_model", - init_file_name="init_model", - batch_size=64, - use_mini_batch_size=False, - n_epochs=1500, - callbacks=None, - random_state=None, - verbose=False, - loss="mean_squared_error", - metrics="mean_squared_error", - optimizer=None, + n_regressors: int = 5, + use_litemv: bool = False, + n_filters: int = 32, + kernel_size: int = 40, + strides: int | list[int] = 1, + activation: str | list[str] = "relu", + output_activation: str = "linear", + file_path: str = "./", + save_last_model: bool = False, + save_best_model: bool = False, + save_init_model: bool = False, + best_file_name: str = "best_model", + last_file_name: str = "last_model", + init_file_name: str = "init_model", + batch_size: int = 64, + use_mini_batch_size: bool = False, + n_epochs: int = 1500, + callbacks: Callback | list[Callback] | None = None, + random_state: int | np.random.RandomState | None = None, + verbose: bool = False, + loss: str = "mean_squared_error", + metrics: str | list[str] = "mean_squared_error", + optimizer: tf.keras.optimizers.Optimizer | None = None, ): self.n_regressors = n_regressors @@ -191,11 +198,11 @@ def __init__( self.metrics = metrics self.optimizer = optimizer - self.regressors_ = [] + self.regressors_: list[IndividualLITERegressor] = [] super().__init__() - def _fit(self, X, y): + def _fit(self, X: np.ndarray, y: np.ndarray) -> LITETimeRegressor: """Fit the ensemble of IndividualLITERegressor models. Parameters @@ -240,7 +247,7 @@ def _fit(self, X, y): return self - def _predict(self, X) -> np.ndarray: + def _predict(self, X: np.ndarray) -> np.ndarray: """Predict the values of the test set using LITETime. Parameters @@ -263,7 +270,7 @@ def _predict(self, X) -> np.ndarray: return vals @classmethod - def _get_test_params(cls, parameter_set="default"): + def _get_test_params(cls, parameter_set: str = "default") -> dict | list[dict]: """Return testing parameter settings for the estimator. Parameters @@ -413,28 +420,28 @@ class IndividualLITERegressor(BaseDeepRegressor): def __init__( self, - use_litemv=False, - n_filters=32, - kernel_size=40, - strides=1, - activation="relu", - output_activation="linear", - file_path="./", - save_best_model=False, - save_last_model=False, - save_init_model=False, - best_file_name="best_model", - last_file_name="last_model", - init_file_name="init_model", - batch_size=64, - use_mini_batch_size=False, - n_epochs=1500, - callbacks=None, - random_state=None, - verbose=False, - loss="mean_squared_error", - metrics="mean_squared_error", - optimizer=None, + use_litemv: bool = False, + n_filters: int = 32, + kernel_size: int = 40, + strides: int | list[int] = 1, + activation: str | list[str] = "relu", + output_activation: str = "linear", + file_path: str = "./", + save_best_model: bool = False, + save_last_model: bool = False, + save_init_model: bool = False, + best_file_name: str = "best_model", + last_file_name: str = "last_model", + init_file_name: str = "init_model", + batch_size: int = 64, + use_mini_batch_size: bool = False, + n_epochs: int = 1500, + callbacks: Callback | list[Callback] | None = None, + random_state: int | np.random.RandomState | None = None, + verbose: bool = False, + loss: str = "mean_squared_error", + metrics: str | list[str] = "mean_squared_error", + optimizer: tf.keras.optimizers.Optimizer | None = None, ): self.use_litemv = use_litemv self.n_filters = n_filters @@ -474,7 +481,9 @@ def __init__( activation=self.activation, ) - def build_model(self, input_shape, **kwargs): + def build_model( + self, input_shape: tuple[int, ...], **kwargs: Any + ) -> tf.keras.Model: """ Construct a compiled, un-trained, keras model that is ready for training. @@ -487,7 +496,6 @@ def build_model(self, input_shape, **kwargs): ------- output : a compiled Keras Model """ - import numpy as np import tensorflow as tf rng = check_random_state(self.random_state) @@ -513,7 +521,7 @@ def build_model(self, input_shape, **kwargs): return model - def _fit(self, X, y): + def _fit(self, X: np.ndarray, y: np.ndarray) -> IndividualLITERegressor: """ Fit the Regressor on the training set (X, y). @@ -602,7 +610,7 @@ def _fit(self, X, y): return self @classmethod - def _get_test_params(cls, parameter_set="default"): + def _get_test_params(cls, parameter_set: str = "default") -> dict | list[dict]: """Return testing parameter settings for the estimator. Parameters diff --git a/aeon/regression/deep_learning/_mlp.py b/aeon/regression/deep_learning/_mlp.py index 7de083e72f..fe1b28754f 100644 --- a/aeon/regression/deep_learning/_mlp.py +++ b/aeon/regression/deep_learning/_mlp.py @@ -1,5 +1,7 @@ """Multi Layer Perceptron Network (MLP) regressor.""" +from __future__ import annotations + __author__ = ["Aadya-Chinubhai", "hadifawaz1999"] __all__ = ["MLPRegressor"] @@ -7,12 +9,18 @@ import os import time from copy import deepcopy +from typing import TYPE_CHECKING, Any +import numpy as np from sklearn.utils import check_random_state from aeon.networks import MLPNetwork from aeon.regression.deep_learning.base import BaseDeepRegressor +if TYPE_CHECKING: + import tensorflow as tf + from tensorflow.keras.callbacks import Callback + class MLPRegressor(BaseDeepRegressor): """Multi Layer Perceptron Network (MLP). @@ -108,28 +116,28 @@ class MLPRegressor(BaseDeepRegressor): def __init__( self, - n_layers=3, - n_units=500, - activation="relu", - dropout_rate=None, - dropout_last=None, - use_bias=True, - n_epochs=2000, - batch_size=16, - callbacks=None, - verbose=False, - loss="mean_squared_error", - metrics="mean_squared_error", - file_path="./", - save_best_model=False, - save_last_model=False, - save_init_model=False, - best_file_name="best_model", - last_file_name="last_model", - init_file_name="init_model", - random_state=None, - output_activation="linear", - optimizer=None, + n_layers: int = 3, + n_units: int | list[int] = 500, + activation: str | list[str] = "relu", + dropout_rate: float | list[float] | None = None, + dropout_last: float = 0.3, + use_bias: bool = True, + n_epochs: int = 2000, + batch_size: int = 16, + callbacks: Callback | list[Callback] | None = None, + verbose: bool = False, + loss: str = "mean_squared_error", + metrics: str | list[str] = "mean_squared_error", + file_path: str = "./", + save_best_model: bool = False, + save_last_model: bool = False, + save_init_model: bool = False, + best_file_name: str = "best_model", + last_file_name: str = "last_model", + init_file_name: str = "init_model", + random_state: int | np.random.RandomState | None = None, + output_activation: str = "linear", + optimizer: tf.keras.optimizers.Optimizer | None = None, ): self.n_layers = n_layers self.n_units = n_units @@ -168,7 +176,9 @@ def __init__( use_bias=self.use_bias, ) - def build_model(self, input_shape, **kwargs): + def build_model( + self, input_shape: tuple[int, ...], **kwargs: Any + ) -> tf.keras.Model: """Construct a compiled, un-trained, keras model that is ready for training. In aeon, time series are stored in numpy arrays of shape (d,m), where d @@ -185,7 +195,6 @@ def build_model(self, input_shape, **kwargs): ------- output : a compiled Keras Model """ - import numpy as np import tensorflow as tf from tensorflow import keras @@ -211,7 +220,7 @@ def build_model(self, input_shape, **kwargs): ) return model - def _fit(self, X, y): + def _fit(self, X: np.ndarray, y: np.ndarray) -> MLPRegressor: """Fit the Regressor on the training set (X, y). Parameters @@ -292,7 +301,9 @@ def _fit(self, X, y): return self @classmethod - def _get_test_params(cls, parameter_set="default"): + def _get_test_params( + cls, parameter_set: str = "default" + ) -> dict[str, Any] | list[dict[str, Any]]: """Return testing parameter settings for the estimator. Parameters diff --git a/aeon/regression/deep_learning/_resnet.py b/aeon/regression/deep_learning/_resnet.py index 7f89a18ade..e123427517 100644 --- a/aeon/regression/deep_learning/_resnet.py +++ b/aeon/regression/deep_learning/_resnet.py @@ -1,5 +1,7 @@ """Residual Network (ResNet) regressor.""" +from __future__ import annotations + __maintainer__ = ["hadifawaz1999"] __all__ = ["ResNetRegressor"] @@ -7,12 +9,18 @@ import os import time from copy import deepcopy +from typing import TYPE_CHECKING, Any +import numpy as np from sklearn.utils import check_random_state from aeon.networks import ResNetNetwork from aeon.regression.deep_learning.base import BaseDeepRegressor +if TYPE_CHECKING: + import tensorflow as tf + from tensorflow.keras.callbacks import Callback + class ResNetRegressor(BaseDeepRegressor): """ @@ -126,39 +134,39 @@ class ResNetRegressor(BaseDeepRegressor): >>> X, y = make_example_3d_numpy(n_cases=10, n_channels=1, n_timepoints=12, ... return_y=True, regression_target=True, ... random_state=0) - >>> rgs = ResNetRegressor(n_epochs=20, bacth_size=4) # doctest: +SKIP + >>> rgs = ResNetRegressor(n_epochs=20, batch_size=4) # doctest: +SKIP >>> rgs.fit(X, y) # doctest: +SKIP ResNetRegressor(...) """ def __init__( self, - n_residual_blocks=3, - n_conv_per_residual_block=3, - n_filters=None, - kernel_size=None, - strides=1, - dilation_rate=1, - padding="same", - activation="relu", - use_bias=True, - n_epochs=1500, - callbacks=None, - verbose=False, - loss="mean_squared_error", - output_activation="linear", - metrics="mean_squared_error", - batch_size=64, - use_mini_batch_size=False, - random_state=None, - file_path="./", - save_best_model=False, - save_last_model=False, - save_init_model=False, - best_file_name="best_model", - last_file_name="last_model", - init_file_name="init_model", - optimizer=None, + n_residual_blocks: int = 3, + n_conv_per_residual_block: int = 3, + n_filters: int | list[int] | None = None, + kernel_size: int | list[int] | None = None, + strides: int | list[int] = 1, + dilation_rate: int | list[int] = 1, + padding: str | list[str] = "same", + activation: str | list[str] = "relu", + use_bias: bool | list[bool] = True, + n_epochs: int = 1500, + callbacks: Callback | list[Callback] | None = None, + verbose: bool = False, + loss: str = "mean_squared_error", + output_activation: str = "linear", + metrics: str | list[str] = "mean_squared_error", + batch_size: int = 64, + use_mini_batch_size: bool = False, + random_state: int | np.random.RandomState | None = None, + file_path: str = "./", + save_best_model: bool = False, + save_last_model: bool = False, + save_init_model: bool = False, + best_file_name: str = "best_model", + last_file_name: str = "last_model", + init_file_name: str = "init_model", + optimizer: tf.keras.optimizers.Optimizer | None = None, ): self.n_residual_blocks = n_residual_blocks self.n_conv_per_residual_block = n_conv_per_residual_block @@ -201,7 +209,9 @@ def __init__( padding=self.padding, ) - def build_model(self, input_shape, **kwargs): + def build_model( + self, input_shape: tuple[int, ...], **kwargs: Any + ) -> tf.keras.Model: """Construct a compiled, un-trained, keras model that is ready for training. In aeon, time series are stored in numpy arrays of shape (d,m), where d @@ -218,7 +228,6 @@ def build_model(self, input_shape, **kwargs): ------- output : a compiled Keras Model """ - import numpy as np import tensorflow as tf self.optimizer_ = ( @@ -246,7 +255,7 @@ def build_model(self, input_shape, **kwargs): return model - def _fit(self, X, y): + def _fit(self, X: np.ndarray, y: np.ndarray) -> ResNetRegressor: """Fit the regressor on the training set (X, y). Parameters @@ -331,7 +340,9 @@ def _fit(self, X, y): return self @classmethod - def _get_test_params(cls, parameter_set="default"): + def _get_test_params( + cls, parameter_set: str = "default" + ) -> dict[str, Any] | list[dict[str, Any]]: """Return testing parameter settings for the estimator. Parameters diff --git a/aeon/regression/deep_learning/base.py b/aeon/regression/deep_learning/base.py index b48e3b2792..52b6f38c1d 100644 --- a/aeon/regression/deep_learning/base.py +++ b/aeon/regression/deep_learning/base.py @@ -5,15 +5,22 @@ because we can generalise tags and _predict """ +from __future__ import annotations + __maintainer__ = [] __all__ = ["BaseDeepRegressor"] from abc import abstractmethod +from typing import TYPE_CHECKING, Any import numpy as np from aeon.regression.base import BaseRegressor +if TYPE_CHECKING: + import tensorflow as tf + from tensorflow.keras.callbacks import Callback + class BaseDeepRegressor(BaseRegressor): """Abstract base class for deep learning time series regression. @@ -41,7 +48,7 @@ class BaseDeepRegressor(BaseRegressor): } @abstractmethod - def __init__(self, batch_size=40, last_file_name="last_model"): + def __init__(self, batch_size: int = 40, last_file_name: str = "last_model"): self.batch_size = batch_size self.last_file_name = last_file_name @@ -50,7 +57,7 @@ def __init__(self, batch_size=40, last_file_name="last_model"): super().__init__() @abstractmethod - def build_model(self, input_shape): + def build_model(self, input_shape: tuple[int, ...]) -> tf.keras.Model: """ Construct a compiled, un-trained, keras model that is ready for training. @@ -65,7 +72,7 @@ def build_model(self, input_shape): """ ... - def summary(self): + def summary(self) -> dict[str, Any] | None: """ Summary function to return the losses/metrics for model fit. @@ -77,7 +84,7 @@ def summary(self): """ return self.history.history if self.history is not None else None - def _predict(self, X): + def _predict(self, X: np.ndarray) -> np.ndarray: """ Find regression estimate for all cases in X. @@ -96,7 +103,7 @@ def _predict(self, X): y_pred = np.squeeze(y_pred, axis=-1) return y_pred - def save_last_model_to_file(self, file_path="./"): + def save_last_model_to_file(self, file_path: str = "./") -> None: """Save the last epoch of the trained deep learning model. Parameters @@ -110,7 +117,7 @@ def save_last_model_to_file(self, file_path="./"): """ self.model_.save(file_path + self.last_file_name + ".keras") - def load_model(self, model_path): + def load_model(self, model_path: str) -> None: """Load a pre-trained keras model instead of fitting. When calling this function, all functionalities can be used @@ -132,7 +139,9 @@ def load_model(self, model_path): self.model_ = tf.keras.models.load_model(model_path) self.is_fitted = True - def _get_model_checkpoint_callback(self, callbacks, file_path, file_name): + def _get_model_checkpoint_callback( + self, callbacks: Callback | list[Callback], file_path: str, file_name: str + ) -> list[Callback]: import tensorflow as tf model_checkpoint_ = tf.keras.callbacks.ModelCheckpoint( From af0a25c91ce3cf99f9bbbbbf4bb303d8e5519f80 Mon Sep 17 00:00:00 2001 From: Kaustubh <97254178+Kaustbh@users.noreply.github.com> Date: Thu, 8 May 2025 18:16:43 +0530 Subject: [PATCH 084/216] [DOC] Add RotationForest Classifier Notebook for Time Series Classification (#2592) * Add RotationForest Classifier Notebook for Time Series Classification * Added references and modified doc * minor modifications to notebook description * Update rotation_forest.ipynb --------- Co-authored-by: Matthew Middlehurst --- docs/examples.md | 13 ++ .../classification/img/rotation_forest.png | Bin 0 -> 182339 bytes examples/classification/rotation_forest.ipynb | 203 ++++++++++++++++++ 3 files changed, 216 insertions(+) create mode 100644 examples/classification/img/rotation_forest.png create mode 100644 examples/classification/rotation_forest.ipynb diff --git a/docs/examples.md b/docs/examples.md index 7817ea24be..025b43ff2e 100644 --- a/docs/examples.md +++ b/docs/examples.md @@ -118,6 +118,19 @@ Early TSC ::: +::: + +:::{grid-item-card} +:img-top: examples/classification/img/rotation_forest.png +:class-img-top: aeon-card-image-m +:link: /examples/classification/rotation_forest.ipynb +:link-type: ref +:text-align: center + +Rotation Forest Classifier + +::: + :::: ## Regression diff --git a/examples/classification/img/rotation_forest.png b/examples/classification/img/rotation_forest.png new file mode 100644 index 0000000000000000000000000000000000000000..c25b73ee51d0c4dc8295034dc9f4b01b7b28ad81 GIT binary 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z)upC${WcrNX0te)rDnWyHtI-N$HX(0K0B5X^3NN(;931)W7QXzo!a5*M9nc{z>_N0{^dn{OxHBeGTA$ zY!~$ZrxgAD^^Yw7-Ob-4@!zreH&Ff?H~)^r%a{M1Q~!*`e-p*OLGk}DQH-9P9+mBK UzW@_@Pd^w;S>qvA@#(Ao1-%*|Bme*a literal 0 HcmV?d00001 diff --git a/examples/classification/rotation_forest.ipynb b/examples/classification/rotation_forest.ipynb new file mode 100644 index 0000000000..6fc174249d --- /dev/null +++ b/examples/classification/rotation_forest.ipynb @@ -0,0 +1,203 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Rotation Forest Classifier\n", + "\n", + "RotationForest is an ensemble learning algorithm designed to improve the accuracy and diversity of decision tree-based classifiers. It was introduced as an extension of the popular RandomForest algorithm. The key idea behind RotationForest is to apply **Principal Component Analysis (PCA)** to rotate the feature space for each tree in the ensemble, creating diverse and accurate base classifiers.\n", + "\n", + "Unlike RandomForest, which selects a random subset of features at each node, RotationForest:\n", + "\n", + "- Divides features into random subsets and applies PCA transformation to each subset.\n", + "- Ensures all original features are used for each tree (instead of random feature selection).\n", + "- Uses a C4.5 decision tree (this implementation uses the scikit-learn CART).\n", + "\n", + "Rotation Forest is relevant for **Time Series Classification (TSC)** because it effectively captures complex feature interactions and correlations which are often critical in time series data using PCA-based rotations. It works well with feature extraction methods (e.g., **TSFresh**) and is used in TSC pipelines like **FreshPRINCE** and **STC**, making it robust for both **univariate** and **multivariate** time series data.\n", + "\n", + "In this notebook, we will see how to use the `RotationForestClassifier` algorithm for time series classification." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import necessary libraries\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import (\n", + " ConfusionMatrixDisplay,\n", + " accuracy_score,\n", + " classification_report,\n", + " confusion_matrix,\n", + ")\n", + "\n", + "from aeon.classification.sklearn import RotationForestClassifier\n", + "from aeon.datasets import load_italy_power_demand # univariate dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "italy, italy_labels = load_italy_power_demand(split=\"train\")\n", + "italy_test, italy_test_labels = load_italy_power_demand(split=\"test\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((67, 1, 24), (67,))" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "italy.shape, italy_labels.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "RotationForestClassifier is not a time series classifier. \n", + "A valid sklearn input such as a 2d numpy array is required." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert 3D array to 2D array\n", + "italy = italy.reshape(italy.shape[0], -1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(67, 24)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "italy.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.9708454810495627 \n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " 1 0.97 0.97 0.97 513\n", + " 2 0.97 0.97 0.97 516\n", + "\n", + " accuracy 0.97 1029\n", + " macro avg 0.97 0.97 0.97 1029\n", + "weighted avg 0.97 0.97 0.97 1029\n", + "\n" + ] + } + ], + "source": [ + "rotation = RotationForestClassifier()\n", + "rotation.fit(italy, italy_labels)\n", + "y_pred = rotation.predict(italy_test)\n", + "\n", + "accuracy = accuracy_score(italy_test_labels, y_pred)\n", + "print(\"Accuracy: \", accuracy, \"\\n\")\n", + "\n", + "report = classification_report(italy_test_labels, y_pred)\n", + "print(\"Classification Report:\\n\", report)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot confusion matrix\n", + "cm = confusion_matrix(italy_test_labels, y_pred)\n", + "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=rotation.classes_)\n", + "disp.plot(cmap=\"YlOrRd\")\n", + "plt.title(\"Confusion Matrix\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### References:\n", + "\n", + "\\[1\\] J. J. Rodriguez, L. I. Kuncheva and C. J. Alonso, \"Rotation Forest: A New Classifier Ensemble Method,\" in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 10, pp. 1619-1630, Oct. 2006, doi: 10.1109/TPAMI.2006.211.\n", + "\n", + "\\[2\\] Bagnall, A., Flynn, M., Large, J., Line, J., Bostrom, A., & Cawley, G. (2018). Is rotation forest the best classifier for problems with continuous features? ArXiv. https://arxiv.org/abs/1809.06705" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "myaeon", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From beadc751cbcb9e502fa74eab391108469d33a275 Mon Sep 17 00:00:00 2001 From: Sebastian Schmidl <10573700+SebastianSchmidl@users.noreply.github.com> Date: Fri, 9 May 2025 14:27:17 +0200 Subject: [PATCH 085/216] fix: Codeowners for benchmarking metrics AD (#2784) --- CODEOWNERS | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/CODEOWNERS b/CODEOWNERS index bc93e5d27a..a89833ba09 100644 --- a/CODEOWNERS +++ b/CODEOWNERS @@ -5,6 +5,7 @@ aeon/anomaly_detection/ @SebastianSchmidl @MatthewMiddlehurst aeon/benchmarking/ @TonyBagnall @MatthewMiddlehurst @hadifawaz1999 @dguijo +aeon/benchmarking/metrics/anomaly_detection/ @SebastianSchmidl @MatthewMiddlehurst aeon/classification/ @MatthewMiddlehurst @TonyBagnall aeon/classification/deep_learning/ @hadifawaz1999 @MatthewMiddlehurst @TonyBagnall @@ -17,8 +18,6 @@ aeon/distances/ @chrisholder @TonyBagnall aeon/networks/ @hadifawaz1999 -aeon/performance_metrics/anomaly_detection/ @SebastianSchmidl @MatthewMiddlehurst - aeon/regression/ @MatthewMiddlehurst @TonyBagnall @dguijo aeon/regression/deep_learning @hadifawaz1999 @MatthewMiddlehurst @TonyBagnall @dguijo From 21c2280147edb970c9c96bbf59a0a91a9980b792 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Fri, 9 May 2025 14:13:56 +0100 Subject: [PATCH 086/216] [GOV] Supporting Developer role (#2775) * supporting dev role * pr req * Update governance.md * typo * Automatic `pre-commit` fixes * aeon --------- Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> --- docs/governance.md | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/docs/governance.md b/docs/governance.md index c3f5440372..5f65142a3c 100644 --- a/docs/governance.md +++ b/docs/governance.md @@ -23,6 +23,15 @@ as detailed in the contributing guide. Contributors play a crucial role in shapi project through participating in discussions and influencing the decision-making process. +### Supporting Developers + +Supporting developers are contributors who have been nominated by a core developer +and granted write access to the `aeon` repository. No vote is required for this role, +but the nominator must notify the Core Developers and create a Pull Request. +Supporting developers can have their access revoked at any time by a core developer +if it is determined that they are abusing this access. Access will also be removed +after 6 months of inactivity. + ### Core Developers Core developers are community members that have made significant contributions and are From 0ede6e3d84a3f46c50ca125d2257d7a7e588098c Mon Sep 17 00:00:00 2001 From: Antoine Guillaume Date: Sat, 10 May 2025 09:05:45 +0200 Subject: [PATCH 087/216] [MNT, ENH, DOC] Rework similarity search (#2473) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * WIP remake module structure * Update _brute_force.py * Update test__commons.py * WIP mock and test * Add test for base subsequence * Fix subsequence_search tests * debug brute force mp * more debug of subsequence tests * more debug of subsequence tests * Add functional LSH neighbors * add notebook for sim search tasks * Updated series similarity search * Fix mistake addition in transformers and fix base classes * Fix registry and api reference * Update documentation and fix some leftover bugs * Update documentation and add default test params * Fix identifiers and test data shape for all_estimators tests * Fix missing params * Fix n_jobs params and tags, add some docs * Fix numba test bug and update testing data for sim search * Fix imports, testing data tests, and impose predict/_predict interface to all sim search estimators * Fix args * Fix extract test * update docs api and notebooks * remove notes * Patrick comments * Adress comments and clean index code * Fix Patrick comments * Fix variable suppression mistake * Divide base class into task specific * Fix typo in imports * Empty commit for CI * Fix typo again * Add check_inheritance exception for similarity search * Revert back to non per type base classes * Factor check index and typo in test --------- Co-authored-by: Patrick Schäfer Co-authored-by: Matthew Middlehurst Co-authored-by: baraline <10759117+baraline@users.noreply.github.com> --- aeon/similarity_search/__init__.py | 6 +- aeon/similarity_search/_base.py | 81 +++ aeon/similarity_search/_commons.py | 504 -------------- aeon/similarity_search/base.py | 232 ------- aeon/similarity_search/collection/__init__.py | 11 + aeon/similarity_search/collection/_base.py | 112 ++++ .../collection/motifs/__init__.py | 1 + .../collection/neighbors/__init__.py | 7 + .../collection/neighbors/_rp_cosine_lsh.py | 320 +++++++++ .../collection/neighbors/tests/__init__.py | 1 + .../neighbors/tests/test_rp_cosine_lsh.py | 1 + .../collection/tests/__init__.py | 1 + .../collection/tests/test_base.py | 19 + .../distance_profiles/__init__.py | 18 - .../euclidean_distance_profile.py | 102 --- .../squared_distance_profile.py | 319 --------- .../distance_profiles/tests/__init__.py | 1 - .../tests/test_euclidean_distance.py | 208 ------ .../tests/test_squared_distance.py | 200 ------ .../matrix_profiles/__init__.py | 14 - .../matrix_profiles/stomp.py | 633 ------------------ .../matrix_profiles/tests/__init__.py | 1 - .../matrix_profiles/tests/test_stomp.py | 205 ------ aeon/similarity_search/query_search.py | 428 ------------ aeon/similarity_search/series/__init__.py | 15 + aeon/similarity_search/series/_base.py | 119 ++++ aeon/similarity_search/series/_commons.py | 255 +++++++ .../series/motifs/__init__.py | 7 + .../similarity_search/series/motifs/_stomp.py | 528 +++++++++++++++ .../series/motifs/tests/__init__.py | 1 + .../series/motifs/tests/test_stomp.py | 149 +++++ .../series/neighbors/__init__.py | 9 + .../series/neighbors/_dummy.py | 207 ++++++ .../series/neighbors/_mass.py | 296 ++++++++ .../series/neighbors/tests/__init__.py | 1 + .../series/neighbors/tests/test_dummy.py | 31 + .../series/neighbors/tests/test_mass.py | 44 ++ .../series/tests/__init__.py | 1 + .../series/tests/test_base.py | 19 + .../series/tests/test_commons.py | 171 +++++ aeon/similarity_search/series_search.py | 436 ------------ aeon/similarity_search/tests/test__commons.py | 49 -- .../tests/test_query_search.py | 176 ----- .../tests/test_series_search.py | 74 -- .../_yield_estimator_checks.py | 8 +- aeon/testing/mock_estimators/__init__.py | 8 +- .../_mock_similarity_search.py | 21 - .../_mock_similarity_searchers.py | 38 ++ aeon/testing/testing_config.py | 4 - aeon/testing/testing_data.py | 176 +---- aeon/testing/tests/test_testing_data.py | 109 --- aeon/testing/utils/estimator_checks.py | 2 +- aeon/utils/base/_identifier.py | 2 + aeon/utils/base/_register.py | 16 +- aeon/utils/numba/general.py | 93 ++- aeon/utils/tags/_tags.py | 6 +- docs/api_reference/similarity_search.rst | 66 +- docs/api_reference/utils.rst | 8 +- docs/getting_started.md | 70 +- examples/similarity_search/code_speed.ipynb | 178 ++--- .../similarity_search/distance_profiles.ipynb | 6 +- .../similarity_search/similarity_search.ipynb | 571 ++++++++++------ 62 files changed, 3046 insertions(+), 4349 deletions(-) create mode 100644 aeon/similarity_search/_base.py delete mode 100644 aeon/similarity_search/_commons.py delete mode 100644 aeon/similarity_search/base.py create mode 100644 aeon/similarity_search/collection/__init__.py create mode 100644 aeon/similarity_search/collection/_base.py create mode 100644 aeon/similarity_search/collection/motifs/__init__.py create mode 100644 aeon/similarity_search/collection/neighbors/__init__.py create mode 100644 aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py create mode 100644 aeon/similarity_search/collection/neighbors/tests/__init__.py create mode 100644 aeon/similarity_search/collection/neighbors/tests/test_rp_cosine_lsh.py create mode 100644 aeon/similarity_search/collection/tests/__init__.py create mode 100644 aeon/similarity_search/collection/tests/test_base.py delete mode 100644 aeon/similarity_search/distance_profiles/__init__.py delete mode 100644 aeon/similarity_search/distance_profiles/euclidean_distance_profile.py delete mode 100644 aeon/similarity_search/distance_profiles/squared_distance_profile.py delete mode 100644 aeon/similarity_search/distance_profiles/tests/__init__.py delete mode 100644 aeon/similarity_search/distance_profiles/tests/test_euclidean_distance.py delete mode 100644 aeon/similarity_search/distance_profiles/tests/test_squared_distance.py delete mode 100644 aeon/similarity_search/matrix_profiles/__init__.py delete mode 100644 aeon/similarity_search/matrix_profiles/stomp.py delete mode 100644 aeon/similarity_search/matrix_profiles/tests/__init__.py delete mode 100644 aeon/similarity_search/matrix_profiles/tests/test_stomp.py delete mode 100644 aeon/similarity_search/query_search.py create mode 100644 aeon/similarity_search/series/__init__.py create mode 100644 aeon/similarity_search/series/_base.py create mode 100644 aeon/similarity_search/series/_commons.py create mode 100644 aeon/similarity_search/series/motifs/__init__.py create mode 100644 aeon/similarity_search/series/motifs/_stomp.py create mode 100644 aeon/similarity_search/series/motifs/tests/__init__.py create mode 100644 aeon/similarity_search/series/motifs/tests/test_stomp.py create mode 100644 aeon/similarity_search/series/neighbors/__init__.py create mode 100644 aeon/similarity_search/series/neighbors/_dummy.py create mode 100644 aeon/similarity_search/series/neighbors/_mass.py create mode 100644 aeon/similarity_search/series/neighbors/tests/__init__.py create mode 100644 aeon/similarity_search/series/neighbors/tests/test_dummy.py create mode 100644 aeon/similarity_search/series/neighbors/tests/test_mass.py create mode 100644 aeon/similarity_search/series/tests/__init__.py create mode 100644 aeon/similarity_search/series/tests/test_base.py create mode 100644 aeon/similarity_search/series/tests/test_commons.py delete mode 100644 aeon/similarity_search/series_search.py delete mode 100644 aeon/similarity_search/tests/test__commons.py delete mode 100644 aeon/similarity_search/tests/test_query_search.py delete mode 100644 aeon/similarity_search/tests/test_series_search.py delete mode 100644 aeon/testing/mock_estimators/_mock_similarity_search.py create mode 100644 aeon/testing/mock_estimators/_mock_similarity_searchers.py diff --git a/aeon/similarity_search/__init__.py b/aeon/similarity_search/__init__.py index f576c41f03..26b79c7da2 100644 --- a/aeon/similarity_search/__init__.py +++ b/aeon/similarity_search/__init__.py @@ -1,7 +1,5 @@ """Similarity search module.""" -__all__ = ["BaseSimilaritySearch", "QuerySearch", "SeriesSearch"] +__all__ = ["BaseSimilaritySearch"] -from aeon.similarity_search.base import BaseSimilaritySearch -from aeon.similarity_search.query_search import QuerySearch -from aeon.similarity_search.series_search import SeriesSearch +from aeon.similarity_search._base import BaseSimilaritySearch diff --git a/aeon/similarity_search/_base.py b/aeon/similarity_search/_base.py new file mode 100644 index 0000000000..a2345ee558 --- /dev/null +++ b/aeon/similarity_search/_base.py @@ -0,0 +1,81 @@ +"""Base class for similarity search.""" + +__maintainer__ = ["baraline"] +__all__ = [ + "BaseSimilaritySearch", +] + + +from abc import abstractmethod +from typing import Union + +import numpy as np +from numba.typed import List + +from aeon.base import BaseAeonEstimator + + +class BaseSimilaritySearch(BaseAeonEstimator): + """Base class for similarity search applications.""" + + _tags = { + "requires_y": False, + "fit_is_empty": False, + } + + @abstractmethod + def __init__(self): + super().__init__() + + @abstractmethod + def fit( + self, + X: Union[np.ndarray, List], + y=None, + ): + """ + Fit estimator to X. + + State change: + Changes state to "fitted". + + Writes to self: + _is_fitted : flag is set to True. + + Parameters + ---------- + X : Series or Collection, any supported type + Data to fit transform to, of python type as follows: + Series: 2D np.ndarray shape (n_channels, n_timepoints) + Collection: 3D np.ndarray shape (n_cases, n_channels, n_timepoints) + or list of 2D np.ndarray, case i has shape (n_channels, n_timepoints_i) + y: ignored, exists for API consistency reasons. + + Returns + ------- + self : a fitted instance of the estimator + """ + ... + + @abstractmethod + def predict( + self, + X: Union[np.ndarray, None] = None, + ): + """ + Predict method. + + Can either work with new series or with None (for case when predict can be made + using the data given in fit against itself) depending on the estimator. + + Parameters + ---------- + X : Series or Collection, any supported type + Data to fit transform to, of python type as follows: + Series: 2D np.ndarray shape (n_channels, n_timepoints) + Collection: 3D np.ndarray shape (n_cases, n_channels, n_timepoints) + or list of 2D np.ndarray, case i has shape (n_channels, n_timepoints_i + None : If None type is accepted, it means that the predict function will + work only with the data given in fit. (e.g. self matrix profile instead) + """ + ... diff --git a/aeon/similarity_search/_commons.py b/aeon/similarity_search/_commons.py deleted file mode 100644 index 1d20a6a5b0..0000000000 --- a/aeon/similarity_search/_commons.py +++ /dev/null @@ -1,504 +0,0 @@ -"""Helper and common function for similarity search estimators and functions.""" - -__maintainer__ = ["baraline"] - -import warnings - -import numpy as np -from numba import njit, prange -from numba.typed import List -from scipy.signal import convolve - -from aeon.utils.numba.general import ( - get_all_subsequences, - normalise_subsequences, - sliding_mean_std_one_series, - z_normalise_series_2d, -) - - -@njit(cache=True, fastmath=True) -def _compute_dist_profile(X_subs, q): - """ - Compute the distance profile between subsequences and a query. - - Parameters - ---------- - X_subs : array, shape=(n_samples, n_channels, query_length) - Input subsequences extracted from a time series. - q : array, shape=(n_channels, query_length) - Query used for the distance computation - - Returns - ------- - dist_profile : np.ndarray, 1D array of shape (n_samples) - The distance between the query all subsequences. - - """ - n_candidates, n_channels, q_length = X_subs.shape - dist_profile = np.zeros(n_candidates) - for i in range(n_candidates): - for j in range(n_channels): - for k in range(q_length): - dist_profile[i] += (X_subs[i, j, k] - q[j, k]) ** 2 - return dist_profile - - -@njit(cache=True, fastmath=True) -def naive_squared_distance_profile( - X, - q, - mask, - normalise=False, - X_means=None, - X_stds=None, -): - """ - Compute a squared euclidean distance profile. - - Parameters - ---------- - X : array, shape=(n_samples, n_channels, n_timepoints) - Input time series dataset to search in. - q : array, shape=(n_channels, query_length) - Query used during the search. - mask : array, shape=(n_samples, n_timepoints - query_length + 1) - Boolean mask indicating candidates for which the distance - profiles computed for each query should be set to infinity. - normalise : bool - Wheter to use a z-normalised distance. - X_means : array, shape=(n_samples, n_channels, n_timepoints - query_length + 1) - Mean of each candidate (subsequence) of length query_length in X. The - default is None, meaning that these values will be computed if normalise - is True. If provided, the computations will be skipped. - X_stds : array, shape=(n_samples, n_channels, n_timepoints - query_length + 1) - Standard deviation of each candidate (subsequence) of length query_length - in X. The default is None, meaning that these values will be computed if - normalise is True. If provided, the computations will be skipped. - - Returns - ------- - out : np.ndarray, 1D array of shape (n_samples, n_timepoints_t - query_length + 1) - The distance between the query and all candidates in X. - - """ - query_length = q.shape[1] - dist_profiles = List() - # Init distance profile array with unequal length support - for i in range(len(X)): - dist_profiles.append(np.zeros(X[i].shape[1] - query_length + 1)) - if normalise: - q = z_normalise_series_2d(q) - else: - q = q.astype(np.float64) - for i in range(len(X)): - # Numba don't support strides with integers ? - - X_subs = get_all_subsequences(X[i].astype(np.float64), query_length, 1) - if normalise: - if X_means is None and X_stds is None: - _X_means, _X_stds = sliding_mean_std_one_series(X[i], query_length, 1) - else: - _X_means, _X_stds = X_means[i], X_stds[i] - X_subs = normalise_subsequences(X_subs, _X_means, _X_stds) - dist_profile = _compute_dist_profile(X_subs, q) - dist_profile[~mask[i]] = np.inf - dist_profiles[i] = dist_profile - return dist_profiles - - -@njit(cache=True, fastmath=True) -def naive_squared_matrix_profile(X, T, query_length, mask, normalise=False): - """ - Compute a squared euclidean matrix profile. - - Parameters - ---------- - X : array, shape=(n_samples, n_channels, n_timepoints_x) - Input time series dataset to search in. - T : array, shape=(n_channels, n_timepoints_t) - Time series from which queries are extracted. - query_length : int - Length of the queries to extract from T. - mask : array, shape=(n_samples, n_timepoints_x - query_length + 1) - Boolean mask indicating candidates for which the distance - profiles computed for each query should be set to infinity. - normalise : bool - Wheter to use a z-normalised distance. - - Returns - ------- - out : np.ndarray, 1D array of shape (n_timepoints_t - query_length + 1) - The minimum distance between each query in T and all candidates in X. - """ - X_subs = List() - for i in range(len(X)): - i_subs = get_all_subsequences(X[i].astype(np.float64), query_length, 1) - if normalise: - X_means, X_stds = sliding_mean_std_one_series(X[i], query_length, 1) - i_subs = normalise_subsequences(i_subs, X_means, X_stds) - X_subs.append(i_subs) - - n_candidates = T.shape[1] - query_length + 1 - mp = np.full(n_candidates, np.inf) - - for i in range(n_candidates): - q = T[:, i : i + query_length] - if normalise: - q = z_normalise_series_2d(q) - for id_sample in range(len(X)): - dist_profile = _compute_dist_profile(X_subs[id_sample], q) - dist_profile[~mask[id_sample]] = np.inf - mp[i] = min(mp[i], dist_profile.min()) - return mp - - -def fft_sliding_dot_product(X, q): - """ - Use FFT convolution to calculate the sliding window dot product. - - This function applies the Fast Fourier Transform (FFT) to efficiently compute - the sliding dot product between the input time series `X` and the query `q`. - The dot product is computed for each channel individually. The sliding window - approach ensures that the dot product is calculated for every possible subsequence - of `X` that matches the length of `q` - - Parameters - ---------- - X : array, shape=(n_channels, n_timepoints) - Input time series - q : array, shape=(n_channels, query_length) - Input query - - Returns - ------- - out : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) - Sliding dot product between q and X. - """ - n_channels, n_timepoints = X.shape - query_length = q.shape[1] - out = np.zeros((n_channels, n_timepoints - query_length + 1)) - for i in range(n_channels): - out[i, :] = convolve(np.flipud(q[i, :]), X[i, :], mode="valid").real - return out - - -def get_ith_products(X, T, L, ith): - """ - Compute dot products between X and the i-th subsequence of size L in T. - - Parameters - ---------- - X : array, shape = (n_channels, n_timepoints_X) - Input data. - T : array, shape = (n_channels, n_timepoints_T) - Data containing the query. - L : int - Overall query length. - ith : int - Query starting index in T. - - Returns - ------- - np.ndarray, 2D array of shape (n_channels, n_timepoints_X - L + 1) - Sliding dot product between the i-th subsequence of size L in T and X. - - """ - return fft_sliding_dot_product(X, T[:, ith : ith + L]) - - -@njit(cache=True) -def numba_roll_1D_no_warparound(array, shift, warparound_value): - """ - Roll the rows of an array. - - Wheter to allow values at the end of the array to appear at the start after - being rolled out of the array length. - - Parameters - ---------- - array : np.ndarray of shape (n_columns) - Array to roll. - shift : int - The amount of indexes the values will be rolled on each row of the array. - Must be inferior or equal to n_columns. - warparound_value : any type - A value of the type of array to insert instead of the value that got rolled - over the array length - - Returns - ------- - rolled_array : np.ndarray of shape (n_rows, n_columns) - The rolled array. Can also be a TypedList in the case where n_columns changes - between rows. - - """ - length = array.shape[0] - _a1 = array[: length - shift] - array[shift:] = _a1 - array[:shift] = warparound_value - return array - - -@njit(cache=True) -def numba_roll_2D_no_warparound(array, shift, warparound_value): - """ - Roll the rows of an array. - - Wheter to allow values at the end of the array to appear at the start after - being rolled out of the array length. - - Parameters - ---------- - array : np.ndarray of shape (n_rows, n_columns) - Array to roll. Can also be a TypedList in the case where n_columns changes - between rows. - shift : int - The amount of indexes the values will be rolled on each row of the array. - Must be inferior or equal to n_columns. - warparound_value : any type - A value of the type of array to insert instead of the value that got rolled - over the array length - - Returns - ------- - rolled_array : np.ndarray of shape (n_rows, n_columns) - The rolled array. Can also be a TypedList in the case where n_columns changes - between rows. - - """ - for i in prange(len(array)): - length = len(array[i]) - _a1 = array[i][: length - shift] - array[i][shift:] = _a1 - array[i][:shift] = warparound_value - return array - - -@njit(cache=True) -def extract_top_k_and_threshold_from_distance_profiles_one_series( - distance_profiles, - id_x, - k=1, - threshold=np.inf, - exclusion_size=None, - inverse_distance=False, -): - """ - Extract the top-k smallest values from distance profiles and apply threshold. - - This function processes a distance profile and extracts the top-k smallest - distance values, optionally applying a threshold to exclude distances above - a given value. It also optionally handles exclusion zones to avoid selecting - neighboring timestamps. - - Parameters - ---------- - distance_profiles : np.ndarray, 2D array of shape (n_cases, n_candidates) - Precomputed distance profile. Can be a TypedList if n_candidates vary between - cases. - id_x : int - Identifier of the series or subsequence from which the distance profile - is computed. - k : int - Number of matches to returns - threshold : float - All matches below this threshold will be returned - exclusion_size : int or None, optional, default=None - Size of the exclusion zone around the current subsequence. This prevents - selecting neighboring subsequences within the specified range, useful for - avoiding trivial matches in time series data. If set to `None`, no - exclusion zone is applied. - inverse_distance : bool, optional - Wheter to return the worst matches instead of the bests. The default is False. - - Returns - ------- - top_k_dist : np.ndarray - Array of the top-k smallest distance values, potentially excluding values above - the threshold or those within the exclusion zone. - top_k : np.ndarray - Array of shape (k, 2) where each row contains the `id_x` identifier and the - index of the corresponding subsequence (or timestamp) with the top-k smallest - distances. - """ - if inverse_distance: - # To avoid div by 0 case - distance_profiles += 1e-8 - distance_profiles[distance_profiles != np.inf] = ( - 1 / distance_profiles[distance_profiles != np.inf] - ) - - if threshold != np.inf: - distance_profiles[distance_profiles > threshold] = np.inf - - _argsort = np.argsort(distance_profiles) - - if distance_profiles[distance_profiles <= threshold].shape[0] < k: - _k = distance_profiles[distance_profiles <= threshold].shape[0] - elif _argsort.shape[0] < k: - _k = _argsort.shape[0] - else: - _k = k - - if exclusion_size is None: - indexes = np.zeros((_k, 2), dtype=np.int_) - for i in range(_k): - indexes[i, 0] = id_x - indexes[i, 1] = _argsort[i] - return distance_profiles[_argsort[:_k]], indexes - else: - # Apply exclusion zone to avoid neighboring matches - top_k = np.zeros((_k, 2), dtype=np.int_) - exclusion_size - top_k_dist = np.zeros((_k), dtype=np.float64) - - top_k[0, 0] = id_x - top_k[0, 1] = _argsort[0] - - top_k_dist[0] = distance_profiles[_argsort[0]] - - n_inserted = 1 - i_current = 1 - - while n_inserted < _k and i_current < _argsort.shape[0]: - candidate_timestamp = _argsort[i_current] - - insert = True - LB = candidate_timestamp >= (top_k[:, 1] - exclusion_size) - UB = candidate_timestamp <= (top_k[:, 1] + exclusion_size) - if np.any(UB & LB): - insert = False - - if insert: - top_k[n_inserted, 0] = id_x - top_k[n_inserted, 1] = _argsort[i_current] - top_k_dist[n_inserted] = distance_profiles[_argsort[i_current]] - n_inserted += 1 - i_current += 1 - return top_k_dist[:n_inserted], top_k[:n_inserted] - - -def extract_top_k_and_threshold_from_distance_profiles( - distance_profiles, - k=1, - threshold=np.inf, - exclusion_size=None, - inverse_distance=False, -): - """ - Extract the best matches from a distance profile given k and threshold parameters. - - Parameters - ---------- - distance_profiles : np.ndarray, 2D array of shape (n_cases, n_candidates) - Precomputed distance profile. Can be a TypedList if n_candidates vary between - cases. - k : int - Number of matches to returns - threshold : float - All matches below this threshold will be returned - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestamp - exclusion_size` and - :math:`id_timestamp + exclusion_size` which cannot be returned - as best match if :math:`id_timestamp` was already selected. By default, - the value None means that this is not used. - inverse_distance : bool, optional - Wheter to return the worst matches instead of the bests. The default is False. - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(n_matches)``, contains the distance between - the query and its best matches in X_. The second array, of shape - ``(n_matches, 2)``, contains the indexes of these matches as - ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - # This whole function could be optimized and maybe made in numba to avoid stepping - # out of numba mode during distance computations - - n_cases_ = len(distance_profiles) - - id_timestamps = np.concatenate( - [np.arange(distance_profiles[i].shape[0]) for i in range(n_cases_)] - ) - id_samples = np.concatenate( - [[i] * distance_profiles[i].shape[0] for i in range(n_cases_)] - ) - - distance_profiles = np.concatenate(distance_profiles) - - if inverse_distance: - # To avoid div by 0 case - distance_profiles += 1e-8 - distance_profiles[distance_profiles != np.inf] = ( - 1 / distance_profiles[distance_profiles != np.inf] - ) - - if threshold != np.inf: - distance_profiles[distance_profiles > threshold] = np.inf - - _argsort_1d = np.argsort(distance_profiles) - _argsort = np.asarray( - [ - [id_samples[_argsort_1d[i]], id_timestamps[_argsort_1d[i]]] - for i in range(len(_argsort_1d)) - ], - dtype=int, - ) - - if distance_profiles[distance_profiles <= threshold].shape[0] < k: - _k = distance_profiles[distance_profiles <= threshold].shape[0] - warnings.warn( - f"Only {_k} matches are bellow the threshold of {threshold}, while" - f" k={k}. The number of returned match will be {_k}.", - stacklevel=2, - ) - elif _argsort.shape[0] < k: - _k = _argsort.shape[0] - warnings.warn( - f"The number of possible match is {_argsort.shape[0]}, but got" - f" k={k}. The number of returned match will be {_k}.", - stacklevel=2, - ) - else: - _k = k - - if exclusion_size is None: - return distance_profiles[_argsort_1d[:_k]], _argsort[:_k] - else: - # Apply exclusion zone to avoid neighboring matches - top_k = np.zeros((_k, 2), dtype=int) - top_k_dist = np.zeros((_k), dtype=float) - - top_k[0] = _argsort[0, :] - top_k_dist[0] = distance_profiles[_argsort_1d[0]] - - n_inserted = 1 - i_current = 1 - - while n_inserted < _k and i_current < _argsort.shape[0]: - candidate_sample, candidate_timestamp = _argsort[i_current] - - insert = True - is_from_same_sample = top_k[:, 0] == candidate_sample - if np.any(is_from_same_sample): - LB = candidate_timestamp >= ( - top_k[is_from_same_sample, 1] - exclusion_size - ) - UB = candidate_timestamp <= ( - top_k[is_from_same_sample, 1] + exclusion_size - ) - if np.any(UB & LB): - insert = False - - if insert: - top_k[n_inserted] = _argsort[i_current] - top_k_dist[n_inserted] = distance_profiles[_argsort_1d[i_current]] - n_inserted += 1 - i_current += 1 - return top_k_dist[:n_inserted], top_k[:n_inserted] diff --git a/aeon/similarity_search/base.py b/aeon/similarity_search/base.py deleted file mode 100644 index 5b0ce8c555..0000000000 --- a/aeon/similarity_search/base.py +++ /dev/null @@ -1,232 +0,0 @@ -"""Base class for similarity search.""" - -__maintainer__ = ["baraline"] - -from abc import abstractmethod -from collections.abc import Iterable -from typing import Optional, final - -import numpy as np -from numba import get_num_threads, set_num_threads -from numba.typed import List - -from aeon.base import BaseCollectionEstimator -from aeon.utils.numba.general import sliding_mean_std_one_series - - -class BaseSimilaritySearch(BaseCollectionEstimator): - """ - Base class for similarity search applications. - - Parameters - ---------- - distance : str, default="euclidean" - Name of the distance function to use. A list of valid strings can be found in - the documentation for :func:`aeon.distances.get_distance_function`. - If a callable is passed it must either be a python function or numba function - with nopython=True, that takes two 1d numpy arrays as input and returns a float. - distance_args : dict, default=None - Optional keyword arguments for the distance function. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - normalise : bool, default=False - Whether the distance function should be z-normalised. - speed_up : str, default='fastest' - Which speed up technique to use with for the selected distance - function. By default, the fastest algorithm is used. A list of available - algorithm for each distance can be obtained by calling the - `get_speedup_function_names` function of the child classes. - n_jobs : int, default=1 - Number of parallel jobs to use. - - Attributes - ---------- - X_ : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input time series stored during the fit method. - - Notes - ----- - For now, the multivariate case is only treated as independent. - Distances are computed for each channel independently and then - summed together. - """ - - _tags = { - "capability:multivariate": True, - "capability:unequal_length": True, - "capability:multithreading": True, - "fit_is_empty": False, - "X_inner_type": ["np-list", "numpy3D"], - } - - @abstractmethod - def __init__( - self, - distance: str = "euclidean", - distance_args: Optional[dict] = None, - inverse_distance: bool = False, - normalise: bool = False, - speed_up: str = "fastest", - n_jobs: int = 1, - ): - self.distance = distance - self.distance_args = distance_args - self.inverse_distance = inverse_distance - self.normalise = normalise - self.n_jobs = n_jobs - self.speed_up = speed_up - super().__init__() - - @final - def fit(self, X: np.ndarray, y=None): - """ - Fit method: data preprocessing and storage. - - Parameters - ---------- - X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - Input array to be used as database for the similarity search - y : optional - Not used. - - Raises - ------ - TypeError - If the input X array is not 3D raise an error. - - Returns - ------- - self - """ - prev_threads = get_num_threads() - X = self._preprocess_collection(X) - # Store minimum number of n_timepoints for unequal length collections - self.min_timepoints_ = min([X[i].shape[-1] for i in range(len(X))]) - self.n_channels_ = X[0].shape[0] - self.n_cases_ = len(X) - if self.metadata_["unequal_length"]: - X = List(X) - set_num_threads(self._n_jobs) - self._fit(X, y) - set_num_threads(prev_threads) - self.is_fitted = True - return self - - def _store_mean_std_from_inputs(self, query_length: int) -> None: - """ - Store the mean and std of each subsequence of size query_length in X_. - - Parameters - ---------- - query_length : int - Length of the query. - - Returns - ------- - None - - """ - means = [] - stds = [] - - for i in range(len(self.X_)): - _mean, _std = sliding_mean_std_one_series(self.X_[i], query_length, 1) - - stds.append(_std) - means.append(_mean) - - self.X_means_ = List(means) - self.X_stds_ = List(stds) - - def _init_X_index_mask( - self, - X_index: Optional[Iterable[int]], - query_length: int, - exclusion_factor: Optional[float] = 2.0, - ) -> np.ndarray: - """ - Initiliaze the mask indicating the candidates to be evaluated in the search. - - Parameters - ---------- - X_index : Iterable - Any Iterable (tuple, list, array) of length two used to specify the index of - the query X if it was extracted from the input data X given during the fit - method. Given the tuple (id_sample, id_timestamp), the similarity search - will define an exclusion zone around the X_index in order to avoid matching - X with itself. If None, it is considered that the query is not extracted - from X_ (the training data). - query_length : int - Length of the queries. - exclusion_factor : float, optional - The exclusion factor is used to prevent candidates close or equal to the - query sample point to be returned as best matches. It is used to define a - region between :math:`id_timestamp - query_length//exclusion_factor` and - :math:`id_timestamp + query_length//exclusion_factor` which cannot be used - in the search. The default is 2.0. - - Raises - ------ - ValueError - If the length of the q_index iterable is not two, will raise a ValueError. - TypeError - If q_index is not an iterable, will raise a TypeError. - - Returns - ------- - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean array which indicates the candidates that should be evaluated in the - similarity search. - - """ - if self.metadata_["unequal_length"]: - mask = List( - [ - np.ones(self.X_[i].shape[1] - query_length + 1, dtype=bool) - for i in range(self.n_cases_) - ] - ) - else: - mask = np.ones( - (self.n_cases_, self.min_timepoints_ - query_length + 1), - dtype=bool, - ) - if X_index is not None: - if isinstance(X_index, Iterable): - if len(X_index) != 2: - raise ValueError( - "The X_index should contain an interable of size 2 such as " - "(id_sample, id_timestamp), but got an iterable of " - "size {}".format(len(X_index)) - ) - else: - raise TypeError( - "If not None, the X_index parameter should be an iterable, here " - "X_index is of type {}".format(type(X_index)) - ) - - if exclusion_factor <= 0: - raise ValueError( - "The value of exclusion_factor should be superior to 0, but got " - "{}".format(len(exclusion_factor)) - ) - - i_instance, i_timestamp = X_index - profile_length = self.X_[i_instance].shape[1] - query_length + 1 - exclusion_LB = max(0, int(i_timestamp - query_length // exclusion_factor)) - exclusion_UB = min( - profile_length, - int(i_timestamp + query_length // exclusion_factor), - ) - mask[i_instance][exclusion_LB:exclusion_UB] = False - - return mask - - @abstractmethod - def _fit(self, X, y=None): ... - - @abstractmethod - def get_speedup_function_names(self): - """Return a dictionnary containing the name of the speedup functions.""" - ... diff --git a/aeon/similarity_search/collection/__init__.py b/aeon/similarity_search/collection/__init__.py new file mode 100644 index 0000000000..dea25853be --- /dev/null +++ b/aeon/similarity_search/collection/__init__.py @@ -0,0 +1,11 @@ +"""Similarity search for time series collection.""" + +__all__ = [ + "BaseCollectionSimilaritySearch", + "RandomProjectionIndexANN", +] + +from aeon.similarity_search.collection._base import BaseCollectionSimilaritySearch +from aeon.similarity_search.collection.neighbors._rp_cosine_lsh import ( + RandomProjectionIndexANN, +) diff --git a/aeon/similarity_search/collection/_base.py b/aeon/similarity_search/collection/_base.py new file mode 100644 index 0000000000..9bd6f7cb31 --- /dev/null +++ b/aeon/similarity_search/collection/_base.py @@ -0,0 +1,112 @@ +"""Base similiarity search for collections.""" + +__maintainer__ = ["baraline"] +__all__ = [ + "BaseCollectionSimilaritySearch", +] + +from abc import abstractmethod +from typing import final + +import numpy as np + +from aeon.base import BaseCollectionEstimator +from aeon.similarity_search._base import BaseSimilaritySearch + + +class BaseCollectionSimilaritySearch(BaseCollectionEstimator, BaseSimilaritySearch): + """ + Similarity search base class for collections. + + Such estimators include nearest neighbors on whole series or subsequences with + indexing or concenssus motifs search over a collection. + """ + + # tag values specific to CollectionTransformers + _tags = { + "input_data_type": "Collection", + "capability:multivariate": True, + "X_inner_type": ["numpy3D"], + } + + @final + def fit( + self, + X: np.ndarray, + y=None, + ): + """ + Fit method: data preprocessing and storage. + + Parameters + ---------- + X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) + Input array to be used as database for the similarity search. If it is an + unequal length collection, it should be a list of 2d numpy arrays. + y : optional + Not used. + + Raises + ------ + TypeError + If the input X array is not 3D raise an error. + + Returns + ------- + self + """ + self.reset() + X = self._preprocess_collection(X) + self.n_channels_ = self.metadata_["n_channels"] + self.n_cases_ = self.metadata_["n_cases"] + self._fit(X, y=y) + self.is_fitted = True + return self + + @abstractmethod + def _fit(self, X: np.ndarray, y=None): ... + + @final + def predict(self, X, **kwargs): + """ + Predict function. + + Parameters + ---------- + X : np.ndarray, 3D array of shape = (n_cases, n_channels, n_timepoints) + Collections of series to predict on. + kwargs : dict, optional + Additional keyword arguments to be passed to the _predict function of the + estimator. + + Returns + ------- + indexes : np.ndarray, shape = (n_cases, k) + Indexes of series in the that are similar to X. + distances : np.ndarray, shape = (n_cases, k) + Distance of the matches to each series + + """ + self._check_is_fitted() + X = self._preprocess_collection(X, store_metadata=False) + self._check_predict_series_format(X) + indexes, distances = self._predict(X, **kwargs) + return indexes, distances + + def _check_predict_series_format(self, X): + """ + Check whether a series X in predict is correctly formated. + + Parameters + ---------- + X : np.ndarray, shape = (n_channels, n_timepoints) + A series to be used in predict. + """ + if self.n_channels_ != X[0].shape[0]: + raise ValueError( + f"Expected X to have {self.n_channels_} channels but" + f" got {X[0].shape[0]} channels." + ) + + @abstractmethod + def _predict(self, X, **kwargs): ... diff --git a/aeon/similarity_search/collection/motifs/__init__.py b/aeon/similarity_search/collection/motifs/__init__.py new file mode 100644 index 0000000000..b7169f1ade --- /dev/null +++ b/aeon/similarity_search/collection/motifs/__init__.py @@ -0,0 +1 @@ +"""Motif discovery for time series collection.""" diff --git a/aeon/similarity_search/collection/neighbors/__init__.py b/aeon/similarity_search/collection/neighbors/__init__.py new file mode 100644 index 0000000000..f5cf0d925b --- /dev/null +++ b/aeon/similarity_search/collection/neighbors/__init__.py @@ -0,0 +1,7 @@ +"""Neighbors search for time series collection.""" + +__all__ = ["RandomProjectionIndexANN"] + +from aeon.similarity_search.collection.neighbors._rp_cosine_lsh import ( + RandomProjectionIndexANN, +) diff --git a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py new file mode 100644 index 0000000000..167ec538c6 --- /dev/null +++ b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py @@ -0,0 +1,320 @@ +"""Random projection LSH index.""" + +import numpy as np +from numba import get_num_threads, njit, prange, set_num_threads + +from aeon.similarity_search.collection._base import BaseCollectionSimilaritySearch +from aeon.utils.numba.general import AEON_NUMBA_STD_THRESHOLD, z_normalise_series_3d + + +@njit(cache=True) +def _bool_hamming_dist(X, Y): + """ + Compute a hamming distance on boolean arrays. + + Parameters + ---------- + X : np.ndarray of shape (n_timepoints) + A boolean array + + Y : np.ndarray of shape (n_timepoints) + A boolean array + + Returns + ------- + d : int + The hamming distance between X and Y. + + """ + d = np.uint64(0) + for i in range(X.shape[0]): + d += X[i] ^ Y[i] + return d + + +@njit(cache=True, parallel=True) +def _bool_hamming_dist_matrix(X_bool, collection_bool): + """ + Compute the distances between X_bool and each boolean array of collection_bool. + + Each array of collection_bool represent the hash value of a bucket in the index. + + Parameters + ---------- + X_bool : np.ndarray of shape (n_timepoints) + A 1D boolean array + collection_bool : np.ndarray of shape (n_cases, n_timepoints) + A 2D boolean array + + Returns + ------- + res : np.ndarray of shape (n_cases) + The distance of X_bool to all buckets in the index + + """ + n_buckets = collection_bool.shape[0] + res = np.zeros(n_buckets, dtype=np.uint64) + for i in prange(n_buckets): + res[i] = _bool_hamming_dist(collection_bool[i], X_bool) + return res + + +@njit(cache=True, fastmath=True) +def _nb_flat_dot(X, Y): + n_channels, n_timepoints = X.shape + out = 0 + for i in prange(n_channels): + for j in prange(n_timepoints): + out += X[i, j] * Y[i, j] + return out >= 0 + + +@njit(cache=True, parallel=True) +def _collection_to_bool(X, hash_funcs, start_points, length): + """ + Transform a collection of time series X to their boolean hash representation. + + Parameters + ---------- + X : np.ndarray of shape (n_cases, n_channels, n_timepoints) + Time series collection to transform. + hash_funcs : np.ndarray of shape (n_hash, n_channels, length) + The random projection vectors used to compute the boolean hash + start_points : np.ndarray of shape (n_hash) + The starting index where the random vector should be applied when computing + the distance to the input series. + length : int + Length of the random vectors. + + Returns + ------- + res : np.ndarray of shape (n_cases, n_hash) + The boolean representation of all series in X. + + """ + n_hash_funcs = hash_funcs.shape[0] + n_samples = X.shape[0] + res = np.empty((n_samples, n_hash_funcs), dtype=np.bool_) + for j in prange(n_hash_funcs): + for i in range(n_samples): + res[i, j] = _nb_flat_dot( + X[i, :, start_points[j] : start_points[j] + length], hash_funcs[j] + ) + return res + + +class RandomProjectionIndexANN(BaseCollectionSimilaritySearch): + """ + Random Projection Locality Sensitive Hashing index with cosine similarity. + + In this method based on SimHash, we define a hash function as a boolean operation + such as, given a random vector ``V`` of shape ``(n_channels, L)`` and a time series + ``X`` of shape ``(n_channels, n_timeponts)`` (with ``L<=n_timepoints``), we compute + ``X.V > 0`` to obtain the boolean result. + In the case where ``L k - current_k: + candidates = candidates[: k - current_k] + top_k[current_k : current_k + len(candidates)] = candidates + top_k_dist[current_k : current_k + len(candidates)] = dists[ + ids[_i_bucket] + ] + current_k += len(candidates) + _i_bucket += 1 + + return top_k[:current_k], top_k_dist[:current_k] + + def _collection_to_hashes(self, X): + return _collection_to_bool( + X, self.hash_funcs_, self.start_points_, self.window_length_ + ) diff --git a/aeon/similarity_search/collection/neighbors/tests/__init__.py b/aeon/similarity_search/collection/neighbors/tests/__init__.py new file mode 100644 index 0000000000..89bc3412fb --- /dev/null +++ b/aeon/similarity_search/collection/neighbors/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for similarity search for time series collection neighbors module.""" diff --git a/aeon/similarity_search/collection/neighbors/tests/test_rp_cosine_lsh.py b/aeon/similarity_search/collection/neighbors/tests/test_rp_cosine_lsh.py new file mode 100644 index 0000000000..82c1d102f3 --- /dev/null +++ b/aeon/similarity_search/collection/neighbors/tests/test_rp_cosine_lsh.py @@ -0,0 +1 @@ +"""Tests for RandomProjectionIndexANN.""" diff --git a/aeon/similarity_search/collection/tests/__init__.py b/aeon/similarity_search/collection/tests/__init__.py new file mode 100644 index 0000000000..d136a8571e --- /dev/null +++ b/aeon/similarity_search/collection/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for similarity search for time series collection base class and commons.""" diff --git a/aeon/similarity_search/collection/tests/test_base.py b/aeon/similarity_search/collection/tests/test_base.py new file mode 100644 index 0000000000..7f538cdd59 --- /dev/null +++ b/aeon/similarity_search/collection/tests/test_base.py @@ -0,0 +1,19 @@ +"""Test for collection similarity search base class.""" + +__maintainer__ = ["baraline"] + +from aeon.testing.mock_estimators._mock_similarity_searchers import ( + MockCollectionSimilaritySearch, +) +from aeon.testing.testing_data import FULL_TEST_DATA_DICT, _get_datatypes_for_estimator + + +def test_input_shape_fit_predict_collection(): + """Test input shapes.""" + estimator = MockCollectionSimilaritySearch() + datatypes = _get_datatypes_for_estimator(estimator) + # dummy data to pass to fit when testing predict/predict_proba + for datatype in datatypes: + X_train, y_train = FULL_TEST_DATA_DICT[datatype]["train"] + X_test, y_test = FULL_TEST_DATA_DICT[datatype]["test"] + estimator.fit(X_train, y_train).predict(X_test) diff --git a/aeon/similarity_search/distance_profiles/__init__.py b/aeon/similarity_search/distance_profiles/__init__.py deleted file mode 100644 index 4be73f9d8e..0000000000 --- a/aeon/similarity_search/distance_profiles/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -"""Distance profiles.""" - -__all__ = [ - "euclidean_distance_profile", - "normalised_euclidean_distance_profile", - "squared_distance_profile", - "normalised_squared_distance_profile", -] - - -from aeon.similarity_search.distance_profiles.euclidean_distance_profile import ( - euclidean_distance_profile, - normalised_euclidean_distance_profile, -) -from aeon.similarity_search.distance_profiles.squared_distance_profile import ( - normalised_squared_distance_profile, - squared_distance_profile, -) diff --git a/aeon/similarity_search/distance_profiles/euclidean_distance_profile.py b/aeon/similarity_search/distance_profiles/euclidean_distance_profile.py deleted file mode 100644 index 1dd781e467..0000000000 --- a/aeon/similarity_search/distance_profiles/euclidean_distance_profile.py +++ /dev/null @@ -1,102 +0,0 @@ -"""Optimized distance profile for euclidean distance.""" - -__maintainer__ = ["baraline"] - - -from typing import Union - -import numpy as np -from numba.typed import List - -from aeon.similarity_search.distance_profiles.squared_distance_profile import ( - normalised_squared_distance_profile, - squared_distance_profile, -) - - -def euclidean_distance_profile( - X: Union[np.ndarray, List], q: np.ndarray, mask: np.ndarray -) -> np.ndarray: - """ - Compute a distance profile using the squared Euclidean distance. - - It computes the distance profiles between the input time series and the query using - the squared Euclidean distance. The distance between the query and a candidate is - comptued using a dot product and a rolling sum to avoid recomputing parts of the - operation. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a numba TypedList - of 2D arrays of shape (n_channels, n_timepoints) - q : np.ndarray, 2D array of shape (n_channels, query_length) - The query used for similarity search. - mask : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - - Returns - ------- - distance_profiles : np.ndarray - 3D array of shape (n_cases, n_timepoints - query_length + 1) - The distance profile between q and the input time series X. - - """ - distance_profiles = squared_distance_profile(X, q, mask) - # Need loop as we can return a list of np array in the unequal length case - for i in range(len(distance_profiles)): - distance_profiles[i] = distance_profiles[i] ** 0.5 - return distance_profiles - - -def normalised_euclidean_distance_profile( - X: Union[np.ndarray, List], - q: np.ndarray, - mask: np.ndarray, - X_means: Union[np.ndarray, List], - X_stds: Union[np.ndarray, List], - q_means: np.ndarray, - q_stds: np.ndarray, -) -> np.ndarray: - """ - Compute a distance profile in a brute force way. - - It computes the distance profiles between the input time series and the query using - the specified distance. The search is made in a brute force way without any - optimizations and can thus be slow. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a numba TypedList - of 2D arrays of shape (n_channels, n_timepoints) - q : np.ndarray, 2D array of shape (n_channels, query_length) - The query used for similarity search. - mask : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Means of each subsequences of X of size query_length. Should be a numba - TypedList if X is unequal length. - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Stds of each subsequences of X of size query_length. Should be a numba - TypedList if X is unequal length. - q_means : np.ndarray, 1D array of shape (n_channels) - Means of the query q - q_stds : np.ndarray, 1D array of shape (n_channels) - - Returns - ------- - distance_profiles : np.ndarray - 3D array of shape (n_cases, n_timepoints - query_length + 1) - The distance profile between q and the input time series X. - - """ - distance_profiles = normalised_squared_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - # Need loop as we can return a list of np array in the unequal length case - for i in range(len(distance_profiles)): - distance_profiles[i] = distance_profiles[i] ** 0.5 - return distance_profiles diff --git a/aeon/similarity_search/distance_profiles/squared_distance_profile.py b/aeon/similarity_search/distance_profiles/squared_distance_profile.py deleted file mode 100644 index a42beeac2f..0000000000 --- a/aeon/similarity_search/distance_profiles/squared_distance_profile.py +++ /dev/null @@ -1,319 +0,0 @@ -"""Optimized distance profile for euclidean distance.""" - -__maintainer__ = ["baraline"] - - -from typing import Union - -import numpy as np -from numba import njit, prange -from numba.typed import List - -from aeon.similarity_search._commons import fft_sliding_dot_product -from aeon.utils.numba.general import AEON_NUMBA_STD_THRESHOLD - - -def squared_distance_profile( - X: Union[np.ndarray, List], q: np.ndarray, mask: np.ndarray -) -> np.ndarray: - """ - Compute a distance profile using the squared Euclidean distance. - - It computes the distance profiles between the input time series and the query using - the squared Euclidean distance. The distance between the query and a candidate is - comptued using a dot product and a rolling sum to avoid recomputing parts of the - operation. - - Parameters - ---------- - X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a numba TypedList - 2D array of shape (n_channels, n_timepoints) - q : np.ndarray, 2D array of shape (n_channels, query_length) - The query used for similarity search. - mask : np.ndarray, 3D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - - Returns - ------- - distance_profile : np.ndarray - 3D array of shape (n_cases, n_timepoints - query_length + 1) - The distance profile between q and the input time series X. - - """ - QX = [fft_sliding_dot_product(X[i], q) for i in range(len(X))] - if isinstance(X, np.ndarray): - QX = np.asarray(QX) - elif isinstance(X, List): - QX = List(QX) - distance_profiles = _squared_distance_profile(QX, X, q, mask) - if isinstance(X, np.ndarray): - distance_profiles = np.asarray(distance_profiles) - return distance_profiles - - -def normalised_squared_distance_profile( - X: Union[np.ndarray, List], - q: np.ndarray, - mask: np.ndarray, - X_means: np.ndarray, - X_stds: np.ndarray, - q_means: np.ndarray, - q_stds: np.ndarray, -) -> np.ndarray: - """ - Compute a distance profile in a brute force way. - - It computes the distance profiles between the input time series and the query using - the specified distance. The search is made in a brute force way without any - optimizations and can thus be slow. - - Parameters - ---------- - X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a numba TypedList - 2D array of shape (n_channels, n_timepoints) - q : np.ndarray, 2D array of shape (n_channels, query_length) - The query used for similarity search. - mask : np.ndarray, 3D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Means of each subsequences of X of size query_length - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Stds of each subsequences of X of size query_length - q_means : np.ndarray, 1D array of shape (n_channels) - Means of the query q - q_stds : np.ndarray, 1D array of shape (n_channels) - Stds of the query q - - Returns - ------- - distance_profiles : np.ndarray - 3D array of shape (n_cases, n_timepoints - query_length + 1) - The distance profile between q and the input time series X. - - """ - query_length = q.shape[1] - QX = [fft_sliding_dot_product(X[i], q) for i in range(len(X))] - if isinstance(X, np.ndarray): - QX = np.asarray(QX) - elif isinstance(X, List): - QX = List(QX) - - distance_profiles = _normalised_squared_distance_profile( - QX, mask, X_means, X_stds, q_means, q_stds, query_length - ) - if isinstance(X, np.ndarray): - distance_profiles = np.asarray(distance_profiles) - return distance_profiles - - -@njit(cache=True, fastmath=True, parallel=True) -def _squared_distance_profile(QX, X, q, mask): - """ - Compute squared distance profiles between query subsequence and time series. - - Parameters - ---------- - QX : List of np.ndarray - List of precomputed dot products between queries and time series, with each - element corresponding to a different time series. - Shape of each array is (n_channels, n_timepoints - query_length + 1). - X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a numba TypedList - 2D array of shape (n_channels, n_timepoints) - q : np.ndarray, 2D array of shape (n_channels, query_length) - The query used for similarity search. - mask : np.ndarray, 3D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - - Returns - ------- - distance_profiles : np.ndarray - 3D array of shape (n_cases, n_timepoints - query_length + 1) - The distance profile between q and the input time series X. - - """ - distance_profiles = List() - query_length = q.shape[1] - - # Init distance profile array with unequal length support - for i_instance in range(len(X)): - profile_length = X[i_instance].shape[1] - query_length + 1 - distance_profiles.append(np.full((profile_length), np.inf)) - - for _i_instance in prange(len(QX)): - # prange cast iterator to unit64 with parallel=True - i_instance = np.int_(_i_instance) - - distance_profiles[i_instance][mask[i_instance]] = ( - _squared_dist_profile_one_series(QX[i_instance], X[i_instance], q)[ - mask[i_instance] - ] - ) - return distance_profiles - - -@njit(cache=True, fastmath=True) -def _squared_dist_profile_one_series(QT, T, Q): - """ - Compute squared distance profile between query subsequence and a single time series. - - This function calculates the squared distance profile for a single time series by - leveraging the dot product of the query and time series as well as precomputed sums - of squares to efficiently compute the squared distances. - - Parameters - ---------- - QT : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) - The dot product between the query and the time series. - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - Q : np.ndarray - 2D array of shape (n_channels, query_length) representing query subsequence. - - Returns - ------- - distance_profile : np.ndarray - 2D array of shape (n_channels, n_timepoints - query_length + 1) - The squared distance profile between the query and the input time series. - """ - n_channels, profile_length = QT.shape - query_length = Q.shape[1] - _QT = -2 * QT - distance_profile = np.zeros(profile_length) - for k in prange(n_channels): - _sum = 0 - _qsum = 0 - for j in prange(query_length): - _sum += T[k, j] ** 2 - _qsum += Q[k, j] ** 2 - - distance_profile += _qsum + _QT[k] - distance_profile[0] += _sum - for i in prange(1, profile_length): - _sum += T[k, i + (query_length - 1)] ** 2 - T[k, i - 1] ** 2 - distance_profile[i] += _sum - return distance_profile - - -@njit(cache=True, fastmath=True, parallel=True) -def _normalised_squared_distance_profile( - QX, mask, X_means, X_stds, q_means, q_stds, query_length -): - """ - Compute the normalised squared distance profiles between query subsequence and input time series. - - Parameters - ---------- - QX : List of np.ndarray - List of precomputed dot products between queries and time series, with each element - corresponding to a different time series. - Shape of each array is (n_channels, n_timepoints - query_length + 1). - mask : np.ndarray, 3D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Means of each subsequences of X of size query_length - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Stds of each subsequences of X of size query_length - q_means : np.ndarray, 1D array of shape (n_channels) - Means of the query q - q_stds : np.ndarray, 1D array of shape (n_channels) - Stds of the query q - query_length : int - The length of the query subsequence used for the distance profile computation. - - Returns - ------- - List of np.ndarray - List of 2D arrays, each of shape (n_channels, n_timepoints - query_length + 1). - Each array contains the normalised squared distance profile between the query subsequence and the corresponding time series. - Entries in the array are set to infinity where the mask is False. - """ - distance_profiles = List() - Q_is_constant = q_stds <= AEON_NUMBA_STD_THRESHOLD - # Init distance profile array with unequal length support - for i_instance in range(len(QX)): - profile_length = QX[i_instance].shape[1] - distance_profiles.append(np.full((profile_length), np.inf)) - - for _i_instance in prange(len(QX)): - # prange cast iterator to unit64 with parallel=True - i_instance = np.int_(_i_instance) - - distance_profiles[i_instance][mask[i_instance]] = ( - _normalised_squared_dist_profile_one_series( - QX[i_instance], - X_means[i_instance], - X_stds[i_instance], - q_means, - q_stds, - query_length, - Q_is_constant, - )[mask[i_instance]] - ) - return distance_profiles - - -@njit(cache=True, fastmath=True) -def _normalised_squared_dist_profile_one_series( - QT, T_means, T_stds, Q_means, Q_stds, query_length, Q_is_constant -): - """ - Compute the z-normalised squared Euclidean distance profile for one time series. - - Parameters - ---------- - QT : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) - The dot product between the query and the time series. - T_means : np.ndarray, 1D array of length n_channels - The mean values of the time series for each channel. - - T_stds : np.ndarray, 2D array of shape (n_channels, profile_length) - The standard deviations of the time series for each channel and position. - Q_means : np.ndarray, 1D array of shape (n_channels) - Means of the query q - Q_stds : np.ndarray, 1D array of shape (n_channels) - Stds of the query q - query_length : int - The length of the query subsequence used for the distance profile computation. - Q_is_constant : np.ndarray - 1D array of shape (n_channels,) where each element is a Boolean indicating - whether the query standard deviation for that channel is less than or equal - to a specified threshold. - - Returns - ------- - np.ndarray - 2D array of shape (n_channels, n_timepoints - query_length + 1) containing the - z-normalised squared distance profile between the query subsequence and the time - series. Entries are computed based on the z-normalised values, with special - handling for constant values. - """ - n_channels, profile_length = QT.shape - distance_profile = np.zeros(profile_length) - - for i in prange(profile_length): - Sub_is_constant = T_stds[:, i] <= AEON_NUMBA_STD_THRESHOLD - for k in prange(n_channels): - # Two Constant case - if Q_is_constant[k] and Sub_is_constant[k]: - _val = 0 - # One Constant case - elif Q_is_constant[k] or Sub_is_constant[k]: - _val = query_length - else: - denom = query_length * Q_stds[k] * T_stds[k, i] - - p = (QT[k, i] - query_length * (Q_means[k] * T_means[k, i])) / denom - p = min(p, 1.0) - - _val = abs(2 * query_length * (1.0 - p)) - distance_profile[i] += _val - - return distance_profile diff --git a/aeon/similarity_search/distance_profiles/tests/__init__.py b/aeon/similarity_search/distance_profiles/tests/__init__.py deleted file mode 100644 index 566dda7367..0000000000 --- a/aeon/similarity_search/distance_profiles/tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Tests for distance profiles.""" diff --git a/aeon/similarity_search/distance_profiles/tests/test_euclidean_distance.py b/aeon/similarity_search/distance_profiles/tests/test_euclidean_distance.py deleted file mode 100644 index 2eafff78bb..0000000000 --- a/aeon/similarity_search/distance_profiles/tests/test_euclidean_distance.py +++ /dev/null @@ -1,208 +0,0 @@ -"""Tests for naive Euclidean distance profile.""" - -__maintainer__ = [] - - -import numpy as np -import pytest -from numba.typed import List -from numpy.testing import assert_array_almost_equal, assert_array_equal - -from aeon.similarity_search._commons import naive_squared_distance_profile -from aeon.similarity_search.distance_profiles.euclidean_distance_profile import ( - euclidean_distance_profile, - normalised_euclidean_distance_profile, -) -from aeon.utils.numba.general import sliding_mean_std_one_series - -DATATYPES = ["float64", "int64"] - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_euclidean_distance(dtype): - """Test Euclidean distance.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - expected = [T**0.5 for T in naive_squared_distance_profile(X, q, mask)] - dist_profile = euclidean_distance_profile(X, q, mask) - - assert_array_almost_equal(dist_profile, expected) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_euclidean_constant_case(dtype): - """Test Euclidean distance profile calculation.""" - X = np.ones((2, 1, 10), dtype=dtype) - q = np.zeros((1, 3), dtype=dtype) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - expected = [T**0.5 for T in naive_squared_distance_profile(X, q, mask)] - dist_profile = euclidean_distance_profile(X, q, mask) - - assert_array_almost_equal(dist_profile, expected) - - -def test_non_alteration_of_inputs_euclidean(): - """Test if input is altered during Euclidean distance profile.""" - X = np.asarray([[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]]) - X_copy = np.copy(X) - q = np.asarray([[3, 4, 5]]) - q_copy = np.copy(q) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - _ = euclidean_distance_profile(X, q, mask) - assert_array_equal(q, q_copy) - assert_array_equal(X, X_copy) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_normalised_euclidean_distance(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search_space_size = X.shape[-1] - q.shape[-1] + 1 - - X_means = np.zeros((X.shape[0], X.shape[1], search_space_size)) - X_stds = np.zeros((X.shape[0], X.shape[1], search_space_size)) - - for i in range(X.shape[0]): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds[i] = _std - X_means[i] = _mean - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - - dist_profile = normalised_euclidean_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - expected = [ - T**0.5 for T in naive_squared_distance_profile(X, q, mask, normalise=True) - ] - - assert_array_almost_equal(dist_profile, expected) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_normalised_euclidean_distance_unequal_length(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = List( - [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6]], dtype=dtype), - ] - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - X_means = List() - X_stds = List() - - for i in range(len(X)): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds.append(_std) - X_means.append(_mean) - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - mask = List( - [np.ones(X[i].shape[1] - q.shape[1] + 1, dtype=bool) for i in range(len(X))] - ) - - dist_profile = normalised_euclidean_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - expected = [ - T**0.5 - for T in naive_squared_distance_profile( - X, q, mask, normalise=True, X_means=X_means, X_stds=X_stds - ) - ] - for i in range(len(X)): - assert_array_almost_equal(dist_profile[i], expected[i]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_euclidean_distance_unequal_length(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = List( - [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6]], dtype=dtype), - ] - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - mask = List( - [np.ones(X[i].shape[1] - q.shape[1] + 1, dtype=bool) for i in range(len(X))] - ) - expected = [T**0.5 for T in naive_squared_distance_profile(X, q, mask)] - dist_profile = euclidean_distance_profile(X, q, mask) - for i in range(len(X)): - assert_array_almost_equal(dist_profile[i], expected[i]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_normalised_euclidean_constant_case(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = np.ones((2, 2, 10), dtype=dtype) - q = np.zeros((2, 3), dtype=dtype) - - search_space_size = X.shape[-1] - q.shape[-1] + 1 - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - - X_means = np.zeros((X.shape[0], X.shape[1], search_space_size)) - X_stds = np.zeros((X.shape[0], X.shape[1], search_space_size)) - for i in range(X.shape[0]): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds[i] = _std - X_means[i] = _mean - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - - dist_profile = normalised_euclidean_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - expected = [ - T**0.5 for T in naive_squared_distance_profile(X, q, mask, normalise=True) - ] - - assert_array_almost_equal(dist_profile, expected) - - -def test_non_alteration_of_inputs_normalised_euclidean(): - """Test if input is altered during normalised Euclidean distance profile.""" - X = np.asarray([[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]]) - X_copy = np.copy(X) - q = np.asarray([[3, 4, 5]]) - q_copy = np.copy(q) - - search_space_size = X.shape[-1] - q.shape[-1] + 1 - - X_means = np.zeros((X.shape[0], X.shape[1], search_space_size)) - X_stds = np.zeros((X.shape[0], X.shape[1], search_space_size)) - - for i in range(X.shape[0]): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds[i] = _std - X_means[i] = _mean - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - _ = normalised_euclidean_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - - assert_array_equal(q, q_copy) - assert_array_equal(X, X_copy) diff --git a/aeon/similarity_search/distance_profiles/tests/test_squared_distance.py b/aeon/similarity_search/distance_profiles/tests/test_squared_distance.py deleted file mode 100644 index cdb7b35cbc..0000000000 --- a/aeon/similarity_search/distance_profiles/tests/test_squared_distance.py +++ /dev/null @@ -1,200 +0,0 @@ -"""Tests for naive Euclidean distance profile.""" - -__maintainer__ = [] - - -import numpy as np -import pytest -from numba.typed import List -from numpy.testing import assert_array_almost_equal, assert_array_equal - -from aeon.similarity_search._commons import naive_squared_distance_profile -from aeon.similarity_search.distance_profiles.squared_distance_profile import ( - normalised_squared_distance_profile, - squared_distance_profile, -) -from aeon.utils.numba.general import sliding_mean_std_one_series - -DATATYPES = ["float64", "int64"] - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_euclidean_distance(dtype): - """Test Euclidean distance.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - expected = naive_squared_distance_profile(X, q, mask) - dist_profile = squared_distance_profile(X, q, mask) - - assert_array_almost_equal(dist_profile, expected) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_euclidean_constant_case(dtype): - """Test Euclidean distance profile calculation.""" - X = np.ones((2, 1, 10), dtype=dtype) - q = np.zeros((1, 3), dtype=dtype) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - expected = naive_squared_distance_profile(X, q, mask) - dist_profile = squared_distance_profile(X, q, mask) - - assert_array_almost_equal(dist_profile, expected) - - -def test_non_alteration_of_inputs_euclidean(): - """Test if input is altered during Euclidean distance profile.""" - X = np.asarray([[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]]) - X_copy = np.copy(X) - q = np.asarray([[3, 4, 5]]) - q_copy = np.copy(q) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - _ = squared_distance_profile(X, q, mask) - assert_array_equal(q, q_copy) - assert_array_equal(X, X_copy) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_normalised_euclidean_distance(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search_space_size = X.shape[-1] - q.shape[-1] + 1 - - X_means = np.zeros((X.shape[0], X.shape[1], search_space_size)) - X_stds = np.zeros((X.shape[0], X.shape[1], search_space_size)) - - for i in range(X.shape[0]): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds[i] = _std - X_means[i] = _mean - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - - dist_profile = normalised_squared_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - expected = naive_squared_distance_profile(X, q, mask, normalise=True) - - assert_array_almost_equal(dist_profile, expected) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_normalised_euclidean_distance_unequal_length(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = List( - [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6]], dtype=dtype), - ] - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - X_means = List() - X_stds = List() - - for i in range(len(X)): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds.append(_std) - X_means.append(_mean) - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - mask = List( - [np.ones(X[i].shape[1] - q.shape[1] + 1, dtype=bool) for i in range(len(X))] - ) - - dist_profile = normalised_squared_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - expected = naive_squared_distance_profile(X, q, mask, normalise=True) - for i in range(len(X)): - assert_array_almost_equal(dist_profile[i], expected[i]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_euclidean_distance_unequal_length(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = List( - [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6]], dtype=dtype), - ] - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - mask = List( - [np.ones(X[i].shape[1] - q.shape[1] + 1, dtype=bool) for i in range(len(X))] - ) - - expected = naive_squared_distance_profile(X, q, mask) - dist_profile = squared_distance_profile(X, q, mask) - for i in range(len(X)): - assert_array_almost_equal(dist_profile[i], expected[i]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_normalised_euclidean_constant_case(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = np.ones((2, 2, 10), dtype=dtype) - q = np.zeros((2, 3), dtype=dtype) - - search_space_size = X.shape[-1] - q.shape[-1] + 1 - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - - X_means = np.zeros((X.shape[0], X.shape[1], search_space_size)) - X_stds = np.zeros((X.shape[0], X.shape[1], search_space_size)) - for i in range(X.shape[0]): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds[i] = _std - X_means[i] = _mean - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - - dist_profile = normalised_squared_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - expected = naive_squared_distance_profile(X, q, mask, normalise=True) - - assert_array_almost_equal(dist_profile, expected) - - -def test_non_alteration_of_inputs_normalised_euclidean(): - """Test if input is altered during normalised Euclidean distance profile.""" - X = np.asarray([[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]]) - X_copy = np.copy(X) - q = np.asarray([[3, 4, 5]]) - q_copy = np.copy(q) - - search_space_size = X.shape[-1] - q.shape[-1] + 1 - - X_means = np.zeros((X.shape[0], X.shape[1], search_space_size)) - X_stds = np.zeros((X.shape[0], X.shape[1], search_space_size)) - - for i in range(X.shape[0]): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds[i] = _std - X_means[i] = _mean - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - _ = normalised_squared_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - - assert_array_equal(q, q_copy) - assert_array_equal(X, X_copy) diff --git a/aeon/similarity_search/matrix_profiles/__init__.py b/aeon/similarity_search/matrix_profiles/__init__.py deleted file mode 100644 index d04f1cbfd3..0000000000 --- a/aeon/similarity_search/matrix_profiles/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -"""Distance profiles.""" - -__all__ = [ - "stomp_normalised_euclidean_matrix_profile", - "stomp_euclidean_matrix_profile", - "stomp_normalised_squared_matrix_profile", - "stomp_squared_matrix_profile", -] -from aeon.similarity_search.matrix_profiles.stomp import ( - stomp_euclidean_matrix_profile, - stomp_normalised_euclidean_matrix_profile, - stomp_normalised_squared_matrix_profile, - stomp_squared_matrix_profile, -) diff --git a/aeon/similarity_search/matrix_profiles/stomp.py b/aeon/similarity_search/matrix_profiles/stomp.py deleted file mode 100644 index 509e68ad49..0000000000 --- a/aeon/similarity_search/matrix_profiles/stomp.py +++ /dev/null @@ -1,633 +0,0 @@ -"""Implementation of stomp for euclidean and squared euclidean distance profile.""" - -from typing import Optional - -__maintainer__ = ["baraline"] - - -from typing import Union - -import numpy as np -from numba import njit -from numba.typed import List - -from aeon.similarity_search._commons import ( - extract_top_k_and_threshold_from_distance_profiles_one_series, - get_ith_products, - numba_roll_1D_no_warparound, -) -from aeon.similarity_search.distance_profiles.squared_distance_profile import ( - _normalised_squared_dist_profile_one_series, - _squared_dist_profile_one_series, -) -from aeon.utils.numba.general import AEON_NUMBA_STD_THRESHOLD - - -def stomp_euclidean_matrix_profile( - X: Union[np.ndarray, List], - T: np.ndarray, - L: int, - mask: np.ndarray, - k: int = 1, - threshold: float = np.inf, - inverse_distance: bool = False, - exclusion_size: Optional[int] = None, -): - """ - Compute a euclidean euclidean matrix profile using STOMP [1]_. - - This improves on the naive matrix profile by updating the dot products for each - sucessive query in T instead of recomputing them. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a TypedList - of 2D arrays of shape (n_channels, n_timepoints) - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - The length of the subsequences considered during the search. This parameter - cannot be larger than n_timepoints and series_length. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - length + 1) - Boolean mask of the shape of the distance profiles indicating for which part - of it the distance should be computed. In this context, it is the mask for the - first query of size L in T. This mask will be updated during the algorithm. - k : int, default=1 - The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf - The number of best matches to return during predict for each subsequence. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, - the value None means that this is not used. - - References - ---------- - .. [1] Matrix Profile II: Exploiting a Novel Algorithm and GPUs to break the one - Hundred Million Barrier for Time Series Motifs and Joins. Yan Zhu, Zachary - Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth Funning, Abdullah - Mueen, Philip Berisk and Eamonn Keogh. IEEE ICDM 2016 - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(series_length - length + 1, n_matches)``, - contains the distance between all the queries of size length and their best - matches in X_. The second array, of shape - ``(series_length - L + 1, n_matches, 2)``, contains the indexes of these - matches as ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - MP, IP = stomp_squared_matrix_profile( - X, - T, - L, - mask, - k=k, - threshold=threshold, - exclusion_size=exclusion_size, - inverse_distance=inverse_distance, - ) - for i in range(len(MP)): - MP[i] = MP[i] ** 0.5 - return MP, IP - - -def stomp_squared_matrix_profile( - X: Union[np.ndarray, List], - T: np.ndarray, - L: int, - mask: np.ndarray, - k: int = 1, - threshold: float = np.inf, - inverse_distance: bool = False, - exclusion_size: Optional[int] = None, -): - """ - Compute a squared euclidean matrix profile using STOMP [1]_. - - This improves on the naive matrix profile by updating the dot products for each - sucessive query in T instead of recomputing them. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a TypedList - of 2D arrays of shape (n_channels, n_timepoints) - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - The length of the subsequences considered during the search. This parameter - cannot be larger than n_timepoints and series_length. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - length + 1) - Boolean mask of the shape of the distance profiles indicating for which part - of it the distance should be computed. In this context, it is the mask for the - first query of size L in T. This mask will be updated during the algorithm. - k : int, default=1 - The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf - The number of best matches to return during predict for each subsequence. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, - the value None means that this is not used. - - References - ---------- - .. [1] Matrix Profile II: Exploiting a Novel Algorithm and GPUs to break the one - Hundred Million Barrier for Time Series Motifs and Joins. Yan Zhu, Zachary - Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth Funning, Abdullah - Mueen, Philip Berisk and Eamonn Keogh. IEEE ICDM 2016 - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(series_length - length + 1, n_matches)``, - contains the distance between all the queries of size length and their best - matches in X_. The second array, of shape - ``(series_length - L + 1, n_matches, 2)``, contains the indexes of these - matches as ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - XdotT = [get_ith_products(X[i], T, L, 0) for i in range(len(X))] - if isinstance(X, np.ndarray): - XdotT = np.asarray(XdotT) - elif isinstance(X, List): - XdotT = List(XdotT) - - MP, IP = _stomp( - X, - T, - XdotT, - L, - mask, - k, - threshold, - exclusion_size, - inverse_distance, - ) - return MP, IP - - -def stomp_normalised_euclidean_matrix_profile( - X: Union[np.ndarray, List], - T: np.ndarray, - L: int, - X_means: Union[np.ndarray, List], - X_stds: Union[np.ndarray, List], - T_means: np.ndarray, - T_stds: np.ndarray, - mask: np.ndarray, - k: int = 1, - threshold: float = np.inf, - inverse_distance: bool = False, - exclusion_size: Optional[int] = None, -): - """ - Compute a euclidean matrix profile using STOMP [1]_. - - This improves on the naive matrix profile by updating the dot products for each - sucessive query in T instead of recomputing them. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a TypedList - of 2D arrays of shape (n_channels, n_timepoints) - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - The length of the subsequences considered during the search. This parameter - cannot be larger than n_timepoints and series_length. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Means of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Stds of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - T_means : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Means of each subsequences of T of size L. - T_stds : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Stds of each subsequences of T of size L. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - length + 1) - Boolean mask of the shape of the distance profiles indicating for which part - of it the distance should be computed. In this context, it is the mask for the - first query of size L in T. This mask will be updated during the algorithm. - k : int, default=1 - The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf - The number of best matches to return during predict for each subsequence. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, - the value None means that this is not used. - - References - ---------- - .. [1] Matrix Profile II: Exploiting a Novel Algorithm and GPUs to break the one - Hundred Million Barrier for Time Series Motifs and Joins. Yan Zhu, Zachary - Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth Funning, Abdullah - Mueen, Philip Berisk and Eamonn Keogh. IEEE ICDM 2016 - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(series_length - length + 1, n_matches)``, - contains the distance between all the queries of size length and their best - matches in X_. The second array, of shape - ``(series_length - L + 1, n_matches, 2)``, contains the indexes of these - matches as ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - MP, IP = stomp_normalised_squared_matrix_profile( - X, - T, - L, - X_means, - X_stds, - T_means, - T_stds, - mask, - k=k, - threshold=threshold, - exclusion_size=exclusion_size, - inverse_distance=inverse_distance, - ) - for i in range(len(MP)): - MP[i] = MP[i] ** 0.5 - return MP, IP - - -def stomp_normalised_squared_matrix_profile( - X: Union[np.ndarray, List], - T: np.ndarray, - L: int, - X_means: Union[np.ndarray, List], - X_stds: Union[np.ndarray, List], - T_means: np.ndarray, - T_stds: np.ndarray, - mask: np.ndarray, - k: int = 1, - threshold: float = np.inf, - inverse_distance: bool = False, - exclusion_size: Optional[int] = None, -): - """ - Compute a squared euclidean matrix profile using STOMP [1]_. - - This improves on the naive matrix profile by updating the dot products for each - sucessive query in T instead of recomputing them. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a TypedList - of 2D arrays of shape (n_channels, n_timepoints) - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - The length of the subsequences considered during the search. This parameter - cannot be larger than n_timepoints and series_length. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Means of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Stds of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - T_means : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Means of each subsequences of T of size L. - T_stds : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Stds of each subsequences of T of size L. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - length + 1) - Boolean mask of the shape of the distance profiles indicating for which part - of it the distance should be computed. In this context, it is the mask for the - first query of size L in T. This mask will be updated during the algorithm. - k : int, default=1 - The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf - The number of best matches to return during predict for each subsequence. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, - the value None means that this is not used. - - References - ---------- - .. [1] Matrix Profile II: Exploiting a Novel Algorithm and GPUs to break the one - Hundred Million Barrier for Time Series Motifs and Joins. Yan Zhu, Zachary - Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth Funning, Abdullah - Mueen, Philip Berisk and Eamonn Keogh. IEEE ICDM 2016 - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(series_length - length + 1, n_matches)``, - contains the distance between all the queries of size length and their best - matches in X_. The second array, of shape - ``(series_length - L + 1, n_matches, 2)``, contains the indexes of these - matches as ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - XdotT = [get_ith_products(X[i], T, L, 0) for i in range(len(X))] - if isinstance(X, np.ndarray): - XdotT = np.asarray(XdotT) - elif isinstance(X, List): - XdotT = List(XdotT) - - MP, IP = _stomp_normalised( - X, - T, - XdotT, - X_means, - X_stds, - T_means, - T_stds, - L, - mask, - k, - threshold, - exclusion_size, - inverse_distance, - ) - return MP, IP - - -def _stomp_normalised( - X, - T, - XdotT, - X_means, - X_stds, - T_means, - T_stds, - L, - mask, - k, - threshold, - exclusion_size, - inverse_distance, -): - """ - Compute the Matrix Profile using the STOMP algorithm with normalised distances. - - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a TypedList - of 2D arrays of shape (n_channels, n_timepoints) - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - Length of the subsequences used for the distance computation. - XdotT : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Precomputed dot products between each time series in X and the query series T. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Means of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Stds of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - T_means : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Means of each subsequences of T of size L. - T_stds : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Stds of each subsequences of T of size L. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - length + 1) - Boolean mask of the shape of the distance profiles indicating for which part - of it the distance should be computed. In this context, it is the mask for the - first query of size L in T. This mask will be updated during the algorithm. - k : int, default=1 - The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf - The number of best matches to return during predict for each subsequence. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, - the value None means that this is not used. - - Returns - ------- - tuple of np.ndarray - - MP : array of shape (n_queries,) - Matrix profile distances for each query subsequence. - - IP : array of shape (n_queries,) - Indexes of the top matches for each query subsequence. - """ - n_queries = T.shape[1] - L + 1 - MP = np.empty(n_queries, dtype=object) - IP = np.empty(n_queries, dtype=object) - for i_x in range(len(X)): - for i in range(n_queries): - dist_profiles = _normalised_squared_dist_profile_one_series( - XdotT[i_x], - X_means[i_x], - X_stds[i_x], - T_means[:, i], - T_stds[:, i], - L, - T_stds[:, i] <= AEON_NUMBA_STD_THRESHOLD, - ) - dist_profiles[~mask[i_x]] = np.inf - if i + 1 < n_queries: - XdotT[i_x] = _update_dot_products_one_series( - X[i_x], T, XdotT[i_x], L, i + 1 - ) - - mask[i_x] = numba_roll_1D_no_warparound(mask[i_x], 1, True) - ( - top_dists, - top_indexes, - ) = extract_top_k_and_threshold_from_distance_profiles_one_series( - dist_profiles, - i_x, - k=k, - threshold=threshold, - exclusion_size=exclusion_size, - inverse_distance=inverse_distance, - ) - if i_x > 0: - top_dists, top_indexes = _sort_out_tops( - top_dists, MP[i], top_indexes, IP[i], k - ) - MP[i] = top_dists - IP[i] = top_indexes - else: - MP[i] = top_dists - IP[i] = top_indexes - - return MP, IP - - -def _stomp( - X, - T, - XdotT, - L, - mask, - k, - threshold, - exclusion_size, - inverse_distance, -): - n_queries = T.shape[1] - L + 1 - MP = np.empty(n_queries, dtype=object) - IP = np.empty(n_queries, dtype=object) - for i_x in range(len(X)): - for i in range(n_queries): - Q = T[:, i : i + L] - dist_profiles = _squared_dist_profile_one_series(XdotT[i_x], X[i_x], Q) - dist_profiles[~mask[i_x]] = np.inf - if i + 1 < n_queries: - XdotT[i_x] = _update_dot_products_one_series( - X[i_x], T, XdotT[i_x], L, i + 1 - ) - - mask[i_x] = numba_roll_1D_no_warparound(mask[i_x], 1, True) - ( - top_dists, - top_indexes, - ) = extract_top_k_and_threshold_from_distance_profiles_one_series( - dist_profiles, - i_x, - k=k, - threshold=threshold, - exclusion_size=exclusion_size, - inverse_distance=inverse_distance, - ) - if i_x > 0: - top_dists, top_indexes = _sort_out_tops( - top_dists, MP[i], top_indexes, IP[i], k - ) - MP[i] = top_dists - IP[i] = top_indexes - else: - MP[i] = top_dists - IP[i] = top_indexes - - return MP, IP - - -def _sort_out_tops(top_dists, prev_top_dists, top_indexes, prev_to_indexes, k): - """ - Sort and combine top distance results from previous and current computations. - - Parameters - ---------- - top_dists : np.ndarray - Array of distances from the current computation. Shape should be (n,). - prev_top_dists : np.ndarray - Array of distances from previous computations. Shape should be (n,). - top_indexes : np.ndarray - Array of indexes corresponding to the top distances from current computation. - Shape should be (n,). - prev_to_indexes : np.ndarray - Array of indexes corresponding to the top distances from previous computations. - Shape should be (n,). - k : int, default=1 - The number of best matches to return during predict for each subsequence. - - Returns - ------- - tuple - A tuple containing two elements: - - A 1D numpy array of sorted distances, of length min(k, - total number of distances). - - A 1D numpy array of indexes corresponding to the sorted distances, - of length min(k, total number of distances). - """ - all_dists = np.concatenate((prev_top_dists, top_dists)) - all_indexes = np.concatenate((prev_to_indexes, top_indexes)) - if k == np.inf: - return all_dists, all_indexes - else: - idx = np.argsort(all_dists)[:k] - return all_dists[idx], all_indexes[idx] - - -@njit(cache=True, fastmath=True) -def _update_dot_products_one_series( - X, - T, - XT_products, - L, - i_query, -): - """ - Update dot products of the i-th query of size L in T from the dot products of i-1. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - Input time series on which the sliding dot product is computed. - T: np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - The length of the subsequences considered during the search. This parameter - cannot be larger than n_timepoints and series_length. - i_query : int - Query starting index in T. - - Returns - ------- - XT_products : np.ndarray of shape (n_cases, n_channels, n_timepoints - L + 1) - Sliding dot product between the i-th subsequence of size L in T and X. - - """ - n_channels = T.shape[0] - Q = T[:, i_query : i_query + L] - n_candidates = X.shape[1] - L + 1 - - for i_ft in range(n_channels): - # first element of all 0 to n-1 candidates * first element of previous query - _a1 = X[i_ft, : n_candidates - 1] * T[i_ft, i_query - 1] - # last element of all 1 to n candidates * last element of current query - _a2 = X[i_ft, L : L - 1 + n_candidates] * T[i_ft, i_query + L - 1] - - XT_products[i_ft, 1:] = XT_products[i_ft, :-1] - _a1 + _a2 - - # Compute first dot product - XT_products[i_ft, 0] = np.sum(Q[i_ft] * X[i_ft, :L]) - return XT_products diff --git a/aeon/similarity_search/matrix_profiles/tests/__init__.py b/aeon/similarity_search/matrix_profiles/tests/__init__.py deleted file mode 100644 index 3feb8d4ca5..0000000000 --- a/aeon/similarity_search/matrix_profiles/tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Tests for series methods.""" diff --git a/aeon/similarity_search/matrix_profiles/tests/test_stomp.py b/aeon/similarity_search/matrix_profiles/tests/test_stomp.py deleted file mode 100644 index ffcf7d0b6a..0000000000 --- a/aeon/similarity_search/matrix_profiles/tests/test_stomp.py +++ /dev/null @@ -1,205 +0,0 @@ -"""Tests for stomp algorithm.""" - -__maintainer__ = ["baraline"] - -import numpy as np -import pytest -from numba.typed import List -from numpy.testing import assert_almost_equal, assert_array_almost_equal, assert_equal - -from aeon.distances import get_distance_function -from aeon.similarity_search._commons import get_ith_products -from aeon.similarity_search.matrix_profiles.stomp import ( - _update_dot_products_one_series, - stomp_normalised_squared_matrix_profile, - stomp_squared_matrix_profile, -) -from aeon.utils.numba.general import sliding_mean_std_one_series - -DATATYPES = ["int64", "float64"] -K_VALUES = [1] - - -def test__update_dot_products_one_series(): - """Test the _update_dot_product function.""" - X = np.random.rand(1, 50) - T = np.random.rand(1, 25) - L = 10 - current_product = get_ith_products(X, T, L, 0) - for i_query in range(1, T.shape[1] - L + 1): - new_product = get_ith_products( - X, - T, - L, - i_query, - ) - current_product = _update_dot_products_one_series( - X, - T, - current_product, - L, - i_query, - ) - assert_array_almost_equal(new_product, current_product) - - -@pytest.mark.parametrize("dtype", DATATYPES) -@pytest.mark.parametrize("k", K_VALUES) -def test_stomp_squared_matrix_profile(dtype, k): - """Test stomp series search.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - - S = np.asarray([[3, 4, 5, 4, 3, 4, 5, 3, 2, 4, 5]], dtype=dtype) - L = 3 - mask = np.ones((X.shape[0], X.shape[2] - L + 1), dtype=bool) - distance = get_distance_function("squared") - mp, ip = stomp_squared_matrix_profile(X, S, L, mask, k=k) - for i in range(S.shape[-1] - L + 1): - q = S[:, i : i + L] - - expected = np.array( - [ - [distance(q, X[j, :, _i : _i + L]) for _i in range(X.shape[-1] - L + 1)] - for j in range(X.shape[0]) - ] - ) - id_bests = np.vstack( - np.unravel_index( - np.argsort(expected.ravel(), kind="stable"), expected.shape - ) - ).T - - for j in range(k): - assert_almost_equal(mp[i][j], expected[id_bests[j, 0], id_bests[j, 1]]) - assert_equal(ip[i][j], id_bests[j]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -@pytest.mark.parametrize("k", K_VALUES) -def test_stomp_normalised_squared_matrix_profile(dtype, k): - """Test stomp series search.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - - S = np.asarray([[3, 4, 5, 4, 3, 4, 5, 3, 2, 4, 5]], dtype=dtype) - L = 3 - mask = np.ones((X.shape[0], X.shape[2] - L + 1), dtype=bool) - distance = get_distance_function("squared") - X_means = [] - X_stds = [] - - for i in range(len(X)): - _mean, _std = sliding_mean_std_one_series(X[i], L, 1) - - X_stds.append(_std) - X_means.append(_mean) - X_means = np.asarray(X_means) - X_stds = np.asarray(X_stds) - - S_means, S_stds = sliding_mean_std_one_series(S, L, 1) - - mp, ip = stomp_normalised_squared_matrix_profile( - X, S, L, X_means, X_stds, S_means, S_stds, mask, k=k - ) - - for i in range(S.shape[-1] - L + 1): - q = (S[:, i : i + L] - S_means[:, i]) / S_stds[:, i] - - expected = np.array( - [ - [ - distance( - q, - (X[j, :, _i : _i + L] - X_means[j, :, _i]) / X_stds[j, :, _i], - ) - for _i in range(X.shape[-1] - L + 1) - ] - for j in range(X.shape[0]) - ] - ) - id_bests = np.vstack( - np.unravel_index(np.argsort(expected.ravel()), expected.shape) - ).T - - for j in range(k): - assert_almost_equal(mp[i][j], expected[id_bests[j, 0], id_bests[j, 1]]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_stomp_squared_matrix_profile_unequal_length(dtype): - """Test stomp with unequal length.""" - X = List( - [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6]], dtype=dtype), - ] - ) - L = 3 - mask = List( - [ - np.ones(X[0].shape[1] - L + 1, dtype=bool), - np.ones(X[1].shape[1] - L + 1, dtype=bool), - ] - ) - S = np.asarray([[3, 4, 5, 4, 3, 4, 5, 3, 2, 4, 5]], dtype=dtype) - - distance = get_distance_function("squared") - mp, ip = stomp_squared_matrix_profile(X, S, L, mask) - - for i in range(S.shape[-1] - L + 1): - q = S[:, i : i + L] - - expected = [ - [ - distance(q, X[j][:, _i : _i + q.shape[-1]]) - for _i in range(X[j].shape[-1] - q.shape[-1] + 1) - ] - for j in range(len(X)) - ] - assert_almost_equal(mp[i][0], np.concatenate(expected).min()) - - -@pytest.mark.parametrize("dtype", DATATYPES) -@pytest.mark.parametrize("k", K_VALUES) -def test_stomp_squared_matrix_profile_inverse(dtype, k): - """Test stomp series search for inverse distance.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - S = np.asarray([[3, 4, 5, 4, 3, 4, 5, 3, 2, 4, 5]], dtype=dtype) - L = 3 - mask = np.ones((X.shape[0], X.shape[2] - L + 1), dtype=bool) - distance = get_distance_function("squared") - mp, ip = stomp_squared_matrix_profile( - X, - S, - L, - mask, - k=k, - inverse_distance=True, - ) - - for i in range(S.shape[-1] - L + 1): - q = S[:, i : i + L] - - expected = np.array( - [ - [ - distance(q, X[j, :, _i : _i + q.shape[-1]]) - for _i in range(X.shape[-1] - q.shape[-1] + 1) - ] - for j in range(X.shape[0]) - ] - ) - expected += 1e-8 - expected = 1 / expected - id_bests = np.vstack( - np.unravel_index(np.argsort(expected.ravel()), expected.shape) - ).T - - for j in range(k): - assert_almost_equal(mp[i][j], expected[id_bests[j, 0], id_bests[j, 1]]) - assert_equal(ip[i][j], id_bests[j]) diff --git a/aeon/similarity_search/query_search.py b/aeon/similarity_search/query_search.py deleted file mode 100644 index 393439148d..0000000000 --- a/aeon/similarity_search/query_search.py +++ /dev/null @@ -1,428 +0,0 @@ -"""Base class for query search.""" - -__maintainer__ = ["baraline"] - -from typing import Optional, final - -import numpy as np -from numba import get_num_threads, set_num_threads - -from aeon.similarity_search._commons import ( - extract_top_k_and_threshold_from_distance_profiles, -) -from aeon.similarity_search.base import BaseSimilaritySearch -from aeon.similarity_search.distance_profiles.euclidean_distance_profile import ( - euclidean_distance_profile, - normalised_euclidean_distance_profile, -) -from aeon.similarity_search.distance_profiles.squared_distance_profile import ( - normalised_squared_distance_profile, - squared_distance_profile, -) - - -class QuerySearch(BaseSimilaritySearch): - """ - Query search estimator. - - The query search estimator will return a set of matches of a query in a search space - , which is defined by a time series dataset given during fit. Depending on the `k` - and/or `threshold` parameters, which condition what is considered a valid match - during the search, the number of matches will vary. If `k` is used, at most `k` - matches (the `k` best) will be returned, if `threshold` is used and `k` is set to - `np.inf`, all the candidates which distance to the query is inferior or equal to - `threshold` will be returned. If both are used, the `k` best matches to the query - with distance inferior to `threshold` will be returned. - - - Parameters - ---------- - k : int, default=1 - The number of best matches to return during predict for a given query. - threshold : float, default=np.inf - The number of best matches to return during predict for a given query. - distance : str, default="euclidean" - Name of the distance function to use. A list of valid strings can be found in - the documentation for :func:`aeon.distances.get_distance_function`. - If a callable is passed it must either be a python function or numba function - with nopython=True, that takes two 1d numpy arrays as input and returns a float. - distance_args : dict, default=None - Optional keyword arguments for the distance function. - normalise : bool, default=False - Whether the distance function should be z-normalised. - speed_up : str, default='fastest' - Which speed up technique to use with for the selected distance - function. By default, the fastest algorithm is used. A list of available - algorithm for each distance can be obtained by calling the - `get_speedup_function_names` function. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - n_jobs : int, default=1 - Number of parallel jobs to use. - store_distance_profiles : bool, default=False. - Whether to store the computed distance profiles in the attribute - "distance_profiles_" after calling the predict method. It will store the raw - distance profile, meaning without potential inversion or thresholding applied. - - Attributes - ---------- - X_ : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input time series stored during the fit method. This is the - database we search in when given a query. - distance_profile_function : function - The function used to compute the distance profile. This is determined - during the fit method based on the distance and normalise - parameters. - - Notes - ----- - For now, the multivariate case is only treated as independent. - Distances are computed for each channel independently and then - summed together. - """ - - def __init__( - self, - k: int = 1, - threshold: float = np.inf, - distance: str = "euclidean", - distance_args: Optional[dict] = None, - inverse_distance: bool = False, - normalise: bool = False, - speed_up: str = "fastest", - n_jobs: int = 1, - store_distance_profiles: bool = False, - ): - self.k = k - self.threshold = threshold - self.store_distance_profiles = store_distance_profiles - self._previous_query_length = -1 - self.axis = 1 - - super().__init__( - distance=distance, - distance_args=distance_args, - inverse_distance=inverse_distance, - normalise=normalise, - speed_up=speed_up, - n_jobs=n_jobs, - ) - - def _fit(self, X: np.ndarray, y=None): - """ - Check input format and store it to be used as search space during predict. - - Parameters - ---------- - X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - Input array to used as database for the similarity search - y : optional - Not used. - - Raises - ------ - TypeError - If the input X array is not 3D raise an error. - - Returns - ------- - self - - """ - self.X_ = X - self.distance_profile_function_ = self._get_distance_profile_function() - return self - - @final - def predict( - self, - X: np.ndarray, - axis=1, - X_index=None, - exclusion_factor=2.0, - apply_exclusion_to_result=False, - ) -> np.ndarray: - """ - Predict method : Check the shape of X and call _predict to perform the search. - - If the distance profile function is normalised, it stores the mean and stds - from X and X_, with X_ the training data. - - Parameters - ---------- - X : np.ndarray, 2D array of shape (n_channels, query_length) - Input query used for similarity search. - axis : int - The time point axis of the input series if it is 2D. If ``axis==0``, it is - assumed each column is a time series and each row is a time point. i.e. the - shape of the data is ``(n_timepoints,n_channels)``. ``axis==1`` indicates - the time series are in rows, i.e. the shape of the data is - ``(n_channels,n_timepoints)``. - X_index : Iterable - An Interable (tuple, list, array) of length two used to specify the index of - the query X if it was extracted from the input data X given during the fit - method. Given the tuple (id_sample, id_timestamp), the similarity search - will define an exclusion zone around the X_index in order to avoid matching - X with itself. If None, it is considered that the query is not extracted - from X_. - exclusion_factor : float, default=2. - The factor to apply to the query length to define the exclusion zone. The - exclusion zone is define from - :math:`id_timestamp - query_length//exclusion_factor` to - :math:`id_timestamp + query_length//exclusion_factor`. This also applies to - the matching conditions defined by child classes. For example, with - TopKSimilaritySearch, the k best matches are also subject to the exclusion - zone, but with :math:`id_timestamp` the index of one of the k matches. - apply_exclusion_to_result : bool, default=False - Wheter to apply the exclusion factor to the output of the similarity search. - This means that two matches of the query from the same sample must be at - least spaced by +/- :math:`query_length//exclusion_factor`. - This can avoid pathological matching where, for example if we extract the - best two matches, there is a high chance that if the best match is located - at :math:`id_timestamp`, the second best match will be located at - :math:`id_timestamp` +/- 1, as they both share all their values except one. - - Raises - ------ - TypeError - If the input X array is not 2D raise an error. - ValueError - If the length of the query is greater - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(n_matches)``, contains the distance between - the query and its best matches in X_. The second array, of shape - ``(n_matches, 2)``, contains the indexes of these matches as - ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - self._check_is_fitted() - prev_threads = get_num_threads() - set_num_threads(self._n_jobs) - - query_dim, query_length = self._check_query_format(X, axis) - - mask = self._init_X_index_mask( - X_index, - query_length, - exclusion_factor=exclusion_factor, - ) - - if self.normalise: - self.query_means_ = np.mean(X, axis=-1) - self.query_stds_ = np.std(X, axis=-1) - if self._previous_query_length != query_length: - self._store_mean_std_from_inputs(query_length) - - if apply_exclusion_to_result: - exclusion_size = query_length // exclusion_factor - else: - exclusion_size = None - - self._previous_query_length = query_length - - X_preds = self._predict( - self._call_distance_profile(X, mask), - exclusion_size=exclusion_size, - ) - set_num_threads(prev_threads) - return X_preds - - def _predict( - self, distance_profiles: np.ndarray, exclusion_size: Optional[int] = None - ) -> np.ndarray: - """ - Private predict method for QuerySearch. - - It takes the distance profiles and apply the `k` and `threshold` conditions to - return the set of best matches. - - Parameters - ---------- - distance_profiles : np.ndarray, 2D array of shape (n_cases, n_timepoints - query_length + 1) # noqa: E501 - Precomputed distance profile. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestamp - exclusion_size` and - :math:`id_timestamp + exclusion_size` which cannot be returned - as best match if :math:`id_timestamp` was already selected. By default, - the value None means that this is not used. - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(n_matches)``, contains the distance between - the query and its best matches in X_. The second array, of shape - ``(n_matches, 2)``, contains the indexes of these matches as - ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - - """ - if self.store_distance_profiles: - self.distance_profiles_ = distance_profiles - # Define id sample and timestamp to not "loose" them due to concatenation - return extract_top_k_and_threshold_from_distance_profiles( - distance_profiles, - k=self.k, - threshold=self.threshold, - exclusion_size=exclusion_size, - inverse_distance=self.inverse_distance, - ) - - def _check_query_format(self, X, axis): - if axis not in [0, 1]: - raise ValueError("The axis argument is expected to be either 1 or 0") - if self.axis != axis: - X = X.T - if not isinstance(X, np.ndarray) or X.ndim != 2: - raise TypeError( - "Error, only supports 2D numpy for now. If the query X is univariate " - "do X = X[np.newaxis, :]." - ) - - query_dim, query_length = X.shape - if query_length >= self.min_timepoints_: - raise ValueError( - "The length of the query should be inferior or equal to the length of " - "data (X_) provided during fit, but got {} for X and {} for X_".format( - query_length, self.min_timepoints_ - ) - ) - - if query_dim != self.n_channels_: - raise ValueError( - "The number of feature should be the same for the query X and the data " - "(X_) provided during fit, but got {} for X and {} for X_".format( - query_dim, self.n_channels_ - ) - ) - return query_dim, query_length - - def _get_distance_profile_function(self): - """ - Given distance and speed_up parameters, return the distance profile function. - - Raises - ------ - ValueError - If the distance parameter given at initialization is not a string nor a - numba function or a callable, or if the speedup parameter is unknow or - unsupported, raisea ValueError. - - Returns - ------- - function - The distance profile function matching the distance argument. - - """ - if isinstance(self.distance, str): - distance_dict = _QUERY_SEARCH_SPEED_UP_DICT.get(self.distance) - if distance_dict is None: - raise NotImplementedError( - f"No distance profile have been implemented for {self.distance}." - ) - else: - speed_up_profile = distance_dict.get(self.normalise).get(self.speed_up) - - if speed_up_profile is None: - raise ValueError( - f"Unknown or unsupported speed up {self.speed_up} for " - f"{self.distance} distance function with" - ) - self.speed_up_ = self.speed_up - return speed_up_profile - else: - raise ValueError( - f"Expected distance argument to be str but got {type(self.distance)}" - ) - - def _call_distance_profile(self, X: np.ndarray, mask: np.ndarray) -> np.ndarray: - """ - Obtain the distance profile function and call it with the query and the mask. - - Parameters - ---------- - X : np.ndarray, 2D array of shape (n_channels, query_length) - Input query used for similarity search. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean array which indicates the candidates that should be evaluated in - the similarity search. - - Returns - ------- - distance_profiles : np.ndarray, 2D array of shape (n_cases, n_timepoints - query_length + 1) # noqa: E501 - The distance profiles between the input time series and the query. - - """ - if self.normalise: - distance_profiles = self.distance_profile_function_( - self.X_, - X, - mask, - self.X_means_, - self.X_stds_, - self.query_means_, - self.query_stds_, - ) - else: - distance_profiles = self.distance_profile_function_(self.X_, X, mask) - - return distance_profiles - - @classmethod - def get_speedup_function_names(self) -> dict: - """ - Get available speedup for query search in aeon. - - The returned structure is a dictionnary that contains the names of all - avaialble speedups for normalised and non-normalised distance functions. - - Returns - ------- - dict - The available speedups name that can be used as parameters in - similarity search classes. - - """ - speedups = {} - for dist_name in _QUERY_SEARCH_SPEED_UP_DICT.keys(): - for normalise in _QUERY_SEARCH_SPEED_UP_DICT[dist_name].keys(): - speedups_names = list( - _QUERY_SEARCH_SPEED_UP_DICT[dist_name][normalise].keys() - ) - if normalise: - speedups.update({f"normalised {dist_name}": speedups_names}) - else: - speedups.update({f"{dist_name}": speedups_names}) - return speedups - - -_QUERY_SEARCH_SPEED_UP_DICT = { - "euclidean": { - True: { - "fastest": normalised_euclidean_distance_profile, - "Mueen": normalised_euclidean_distance_profile, - }, - False: { - "fastest": euclidean_distance_profile, - "Mueen": euclidean_distance_profile, - }, - }, - "squared": { - True: { - "fastest": normalised_squared_distance_profile, - "Mueen": normalised_squared_distance_profile, - }, - False: { - "fastest": squared_distance_profile, - "Mueen": squared_distance_profile, - }, - }, -} diff --git a/aeon/similarity_search/series/__init__.py b/aeon/similarity_search/series/__init__.py new file mode 100644 index 0000000000..1ecc20614a --- /dev/null +++ b/aeon/similarity_search/series/__init__.py @@ -0,0 +1,15 @@ +"""Similarity search for series.""" + +__all__ = [ + "BaseSeriesSimilaritySearch", + "MassSNN", + "StompMotif", + "DummySNN", +] + +from aeon.similarity_search.series._base import ( + BaseSeriesSimilaritySearch, +) +from aeon.similarity_search.series.motifs._stomp import StompMotif +from aeon.similarity_search.series.neighbors._dummy import DummySNN +from aeon.similarity_search.series.neighbors._mass import MassSNN diff --git a/aeon/similarity_search/series/_base.py b/aeon/similarity_search/series/_base.py new file mode 100644 index 0000000000..6139835e77 --- /dev/null +++ b/aeon/similarity_search/series/_base.py @@ -0,0 +1,119 @@ +"""Base similiarity search for series.""" + +__maintainer__ = ["baraline"] +__all__ = ["BaseSeriesSimilaritySearch"] + +from abc import abstractmethod +from typing import final + +import numpy as np + +from aeon.base import BaseSeriesEstimator +from aeon.similarity_search._base import BaseSimilaritySearch + + +class BaseSeriesSimilaritySearch(BaseSeriesEstimator, BaseSimilaritySearch): + """ + Base class for similarity search applications on single series. + + Such estimators include nearest neighbors on subsequences extracted from a series + or motif discovery on single series. + """ + + _tags = { + "input_data_type": "Series", + "capability:multivariate": True, + } + + @abstractmethod + def __init__(self, axis=1): + super().__init__(axis=axis) + + @final + def fit( + self, + X: np.ndarray, + y=None, + ): + """ + Fit method: data preprocessing and storage. + + Parameters + ---------- + X : np.ndarray, 2D array of shape (n_channels, n_timepoints) + Input series to be used for the similarity search operations. + y : optional + Not used. + + Raises + ------ + TypeError + If the input X array is not 2D raise an error. + + Returns + ------- + self + """ + self.reset() + X = self._preprocess_series(X, self.axis, True) + self.n_channels_ = self.metadata_["n_channels"] + timepoint_idx = 1 if self.axis == 1 else 0 + self.n_timepoints_ = X.shape[timepoint_idx] + self.X_ = X + self._fit(X, y=y) + self.is_fitted = True + return self + + @abstractmethod + def _fit( + self, + X: np.ndarray, + y=None, + ): ... + + @final + def predict(self, X, **kwargs): + """ + Predict function. + + Parameters + ---------- + X : np.ndarray, shape = (n_channels, n_tiempoints) + Series to predict on. + kwargs : dict, optional + Additional keyword argument as dict or individual keywords args + to pass to the estimator. + + Returns + ------- + indexes : np.ndarray, shape = (k) + Indexes of series in the that are similar to X. + distances : np.ndarray, shape = (k) + Distance of the matches to each series + + """ + self._check_is_fitted() + X = self._preprocess_series(X, self.axis, False) + self._check_predict_series_format(X) + indexes, distances = self._predict(X, **kwargs) + return indexes, distances + + @abstractmethod + def _predict(self, X, **kwargs): ... + + def _check_predict_series_format(self, X): + """ + Check wheter a series X is correctly formated regarding series given in fit. + + Parameters + ---------- + X : np.ndarray, shape = (n_channels, n_timepoints) + A series to be used in predict. + + """ + channel_idx = 0 if self.axis == 1 else 1 + if self.n_channels_ != X.shape[channel_idx]: + raise ValueError( + f"Expected X to have {self.n_channels_} channels but" + f" got {X.shape[channel_idx]} channels." + ) diff --git a/aeon/similarity_search/series/_commons.py b/aeon/similarity_search/series/_commons.py new file mode 100644 index 0000000000..646c38e5ff --- /dev/null +++ b/aeon/similarity_search/series/_commons.py @@ -0,0 +1,255 @@ +"""Helper and common function for similarity search series estimators.""" + +__maintainer__ = ["baraline"] + +import numpy as np +from numba import njit +from scipy.signal import convolve + +from aeon.utils.numba.general import AEON_NUMBA_STD_THRESHOLD + + +def _check_X_index(X_index: int, n_timepoints: int, length: int): + """ + Check wheter a X_index parameter is correctly formated and is admissible. + + Parameters + ---------- + X_index : int + Index of a timestamp in X_. + n_timepoints: int + Number of timepoints in the serie X_ + length: int + Length parameter of the estimator + + """ + if X_index is not None: + if not isinstance(X_index, int): + raise TypeError("Expected an integer for X_index but got {X_index}") + + max_timepoints = n_timepoints - length + if X_index >= max_timepoints or X_index < 0: + raise ValueError( + "The value of X_index cannot exced the number " + "of timepoint in series given during fit. Expected a value " + f"between [0, {max_timepoints - 1}] but got {X_index}" + ) + + +def fft_sliding_dot_product(X, q): + """ + Use FFT convolution to calculate the sliding window dot product. + + This function applies the Fast Fourier Transform (FFT) to efficiently compute + the sliding dot product between the input time series `X` and the query `q`. + The dot product is computed for each channel individually. The sliding window + approach ensures that the dot product is calculated for every possible subsequence + of `X` that matches the length of `q` + + Parameters + ---------- + X : array, shape=(n_channels, n_timepoints) + Input time series + q : array, shape=(n_channels, query_length) + Input query + + Returns + ------- + out : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) + Sliding dot product between q and X. + """ + n_channels, n_timepoints = X.shape + query_length = q.shape[1] + out = np.zeros((n_channels, n_timepoints - query_length + 1)) + for i in range(n_channels): + out[i, :] = convolve(np.flipud(q[i, :]), X[i, :], mode="valid").real + return out + + +def get_ith_products(X, T, L, ith): + """ + Compute dot products between X and the i-th subsequence of size L in T. + + Parameters + ---------- + X : array, shape = (n_channels, n_timepoints_X) + Input data. + T : array, shape = (n_channels, n_timepoints_T) + Data containing the query. + L : int + Overall query length. + ith : int + Query starting index in T. + + Returns + ------- + np.ndarray, 2D array of shape (n_channels, n_timepoints_X - L + 1) + Sliding dot product between the i-th subsequence of size L in T and X. + + """ + return fft_sliding_dot_product(X, T[:, ith : ith + L]) + + +@njit(cache=True, fastmath=True) +def _inverse_distance_profile(dist_profile): + return 1 / (dist_profile + AEON_NUMBA_STD_THRESHOLD) + + +@njit(cache=True) +def _extract_top_k_from_dist_profile( + dist_profile, + k, + threshold, + allow_trivial_matches, + exclusion_size, +): + """ + Given a distance profile, extract the top k lowest distances. + + Parameters + ---------- + dist_profile : np.ndarray, shape = (n_timepoints - length + 1) + A distance profile of length ``n_timepoints - length + 1``, with + ``length`` the size of the query used to compute the distance profiles. + k : int + Number of best matches to return + threshold : float + A threshold on the distances of the best matches. To be returned, a candidate + must have a distance below this threshold. This can reduce the number of + returned matches to be below ``k`` + allow_trivial_matches : bool + Whether to allow returning matches that are in the same neighborhood by + ignoring the exclusion zone defined by the ``exclusion_size`` parameter. + If False, the exclusion zone is applied. + exclusion_size : int + The size of the exlusion size to apply when ``allow_trivial_matches`` is + False. It is applied on both side of existing matches (+/- their indexes). + + Returns + ------- + top_k_indexes : np.ndarray, shape = (k) + The indexes of the best matches in ``distance_profile``. + top_k_distances : np.ndarray, shape = (k) + The distances of the best matches. + + """ + top_k_indexes = np.zeros(k, dtype=np.int64) - 1 + top_k_distances = np.full(k, np.inf, dtype=np.float64) + ub = np.full(k, np.inf) + lb = np.full(k, -1.0) + # Could be optimized by using argpartition + sorted_indexes = np.argsort(dist_profile) + _current_k = 0 + if not allow_trivial_matches: + _current_j = 0 + # Until we extract k value or explore all the array or until dist is > threshold + while _current_k < k and _current_j < len(sorted_indexes): + # if we didn't insert anything or there is a conflict in lb/ub + if _current_k > 0 and np.any( + (sorted_indexes[_current_j] >= lb[:_current_k]) + & (sorted_indexes[_current_j] <= ub[:_current_k]) + ): + pass + else: + _idx = sorted_indexes[_current_j] + if dist_profile[_idx] <= threshold: + top_k_indexes[_current_k] = _idx + top_k_distances[_current_k] = dist_profile[_idx] + ub[_current_k] = min( + top_k_indexes[_current_k] + exclusion_size, + len(dist_profile), + ) + lb[_current_k] = max(top_k_indexes[_current_k] - exclusion_size, 0) + _current_k += 1 + else: + break + _current_j += 1 + else: + _current_k += min(k, len(dist_profile)) + dist_profile = dist_profile[sorted_indexes[:_current_k]] + dist_profile = dist_profile[dist_profile <= threshold] + _current_k = len(dist_profile) + + top_k_indexes[:_current_k] = sorted_indexes[:_current_k] + top_k_distances[:_current_k] = dist_profile[:_current_k] + + return top_k_indexes[:_current_k], top_k_distances[:_current_k] + + +# Could add aggregation function as parameter instead of just max +def _extract_top_k_motifs(MP, IP, k, allow_trivial_matches, exclusion_size): + criterion = np.zeros(len(MP)) + + for i in range(len(MP)): + if len(MP[i]) > 0: + criterion[i] = max(MP[i]) + else: + criterion[i] = np.inf + idx, _ = _extract_top_k_from_dist_profile( + criterion, k, np.inf, allow_trivial_matches, exclusion_size + ) + return ( + [IP[i] for i in idx], + [MP[i] for i in idx], + ) + + +def _extract_top_r_motifs(MP, IP, k, allow_trivial_matches, exclusion_size): + criterion = np.zeros(len(MP)) + for i in range(len(MP)): + criterion[i] = len(MP[i]) + idx, _ = _extract_top_k_from_dist_profile( + _inverse_distance_profile(criterion), + k, + np.inf, + allow_trivial_matches, + exclusion_size, + ) + return [IP[i] for i in idx], [MP[i] for i in idx] + + +@njit(cache=True, fastmath=True) +def _update_dot_products( + X, + T, + XT_products, + L, + i_query, +): + """ + Update dot products of the i-th query of size L in T from the dot products of i-1. + + Parameters + ---------- + X: np.ndarray, 2D array of shape (n_channels, n_timepoints) + Input time series on which the sliding dot product is computed. + T: np.ndarray, 2D array of shape (n_channels, series_length) + The series used for similarity search. Note that series_length can be equal, + superior or inferior to n_timepoints, it doesn't matter. + L : int + The length of the subsequences considered during the search. This parameter + cannot be larger than n_timepoints and series_length. + i_query : int + Query starting index in T. + + Returns + ------- + XT_products : np.ndarray of shape (n_channels, n_timepoints - L + 1) + Sliding dot product between the i-th subsequence of size L in T and X. + + """ + n_channels = T.shape[0] + Q = T[:, i_query : i_query + L] + n_candidates = X.shape[1] - L + 1 + + for i_ft in range(n_channels): + # first element of all 0 to n-1 candidates * first element of previous query + _a1 = X[i_ft, : n_candidates - 1] * T[i_ft, i_query - 1] + # last element of all 1 to n candidates * last element of current query + _a2 = X[i_ft, L : L - 1 + n_candidates] * T[i_ft, i_query + L - 1] + + XT_products[i_ft, 1:] = XT_products[i_ft, :-1] - _a1 + _a2 + + # Compute first dot product + XT_products[i_ft, 0] = np.sum(Q[i_ft] * X[i_ft, :L]) + return XT_products diff --git a/aeon/similarity_search/series/motifs/__init__.py b/aeon/similarity_search/series/motifs/__init__.py new file mode 100644 index 0000000000..56e3bc276f --- /dev/null +++ b/aeon/similarity_search/series/motifs/__init__.py @@ -0,0 +1,7 @@ +"""Motif discovery for single series.""" + +__all__ = [ + "StompMotif", +] + +from aeon.similarity_search.series.motifs._stomp import StompMotif diff --git a/aeon/similarity_search/series/motifs/_stomp.py b/aeon/similarity_search/series/motifs/_stomp.py new file mode 100644 index 0000000000..0f43bbf487 --- /dev/null +++ b/aeon/similarity_search/series/motifs/_stomp.py @@ -0,0 +1,528 @@ +"""Implementation of STOMP with squared euclidean distance.""" + +__maintainer__ = ["baraline"] +__all__ = ["StompMotif"] + +from typing import Optional + +import numpy as np +from numba import njit +from numba.typed import List + +from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch +from aeon.similarity_search.series._commons import ( + _extract_top_k_from_dist_profile, + _extract_top_k_motifs, + _extract_top_r_motifs, + _inverse_distance_profile, + _update_dot_products, + get_ith_products, +) +from aeon.similarity_search.series.neighbors._mass import ( + _normalized_squared_distance_profile, + _squared_distance_profile, +) +from aeon.utils.numba.general import sliding_mean_std_one_series + + +class StompMotif(BaseSeriesSimilaritySearch): + """ + Estimator to extract top k motifs using STOMP, descibed in [1]_. + + This estimators allows to perform multiple type of motif search operations by using + different parameterization. We base oursleves on Figure 3 of [2]_ to establish the + following list, but modify the confusing naming for some of them. We do not yet + support "Learning" and "Valmod" motifs : + + - for "Pair Motifs" : This is the default configuration: { + "motif_size": 1, + } + + - for "k-motifs" : the extension of pair motifs: { + "motif_size": k, + } + + - for "r-motifs" (originaly named k-motifs, which was confusing as it is a range + based motif): { + "motif_size":np.inf, + "dist_threshold":r, + "motif_extraction_method":"r_motifs" + } + + Parameters + ---------- + length : int + The length of the motifs to extract. This is the length of the subsequence + that will be used in the computations. + normalize : bool + Wheter the computations between subsequences should use a z-normalied distance. + + Notes + ----- + This estimator only provides an exact computation method, faster approximate methods + also exist in the litterature. We use a squared euclidean distance instead of the + euclidean distance, if you want euclidean distance results, you should square root + the obtained results. + + References + ---------- + .. [1] Yan Zhu, Zachary Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael + Yeh, Gareth Funning, Abdullah Mueen, Philip Brisk, and Eamonn Keogh. 2016. + Matrix profile II: Exploiting a novel algorithm and GPUs to break the one hundred + million barrier for time series motifs and joins. In 2016 IEEE 16th international + conference on data mining (ICDM). IEEE, 739–748. + .. [2] Patrick Schäfer and Ulf Leser. 2022. Motiflets: Simple and Accurate Detection + of Motifs in Time Series. Proc. VLDB Endow. 16, 4 (December 2022), 725–737. + https://doi.org/10.14778/3574245.3574257 + """ + + def __init__( + self, + length: int, + normalize: Optional[bool] = False, + ): + self.normalize = normalize + self.length = length + super().__init__() + + def _fit( + self, + X: np.ndarray, + y=None, + ): + if self.normalize: + self.X_means_, self.X_stds_ = sliding_mean_std_one_series(X, self.length, 1) + return self + + def fit_predict(self, X, **kwargs): + """ + Fit and predict on a single series X in order to compute self-motifs. + + Parameters + ---------- + X : np.ndarray, shape = (n_channels, n_tiempoints) + Series to fit and predict on. + kwargs : dict, optional + Additional keyword argument as dict or individual keywords args + to pass to the estimator during predict. + + Returns + ------- + indexes : np.ndarray + Indexes of series in the that are similar to X. + distances : np.ndarray + Distance of the matches to each series + """ + self.fit(X) + return self.predict(X, is_self_computation=True, **kwargs) + + def _predict( + self, + X: np.ndarray, + k: Optional[int] = 1, + motif_size: Optional[int] = 1, + dist_threshold: Optional[float] = np.inf, + allow_trivial_matches: Optional[bool] = False, + exclusion_factor: Optional[float] = 0.5, + inverse_distance: Optional[bool] = False, + motif_extraction_method: Optional[str] = "k_motifs", + is_self_computation: Optional[bool] = False, + ): + """ + Exctract the motifs of X_ relative to a series X using STOMP matrix prfoile. + + To compute self-motifs, X is set to None. + + Parameters + ---------- + X : np.ndarray, shape=(n_channels, n_timepoint) + Series to use to compute the matrix profile against X_. Motifs will then be + extracted from the matrix profile. + k : int + The number of motifs to return. The default is 1, meaning we return only + the motif set with the minimal sum of distances to its query. + motif_size : int + The number of subsequences in a motif excluding the motif candidate. This + means that the number of subsequences in the returned motifs will be + ``motif_size + 1``. For example, with the default is 1, this means that we + extract motif pairs (the motif candidate from X and its best match in X_) + dist_threshold : float + The maximum allowed distance of a candidate subsequence of X to a query + subsequence from X_ for the candidate to be considered as a neighbor. + allow_trivial_matches: bool, optional + Whether a neighbor of a match to a query can also be considered as matches + (True), or if an exclusion zone is applied around each match to avoid + trivial matches with their direct neighbors (False). + exclusion_factor : float, default=0.5. + A factor of the query length used to define the exclusion zone when + ``allow_trivial_matches`` is set to False. For a given timestamp, + the exclusion zone starts from + :math:`id_timestamp - floor(length*exclusion_factor)` and end at + :math:`id_timestamp + floor(length*exclusion_factor)`. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, + the farther neighbors will be returned instead of the closest ones. + motif_extraction_method : str + A string indicating the methodology to use to extract the top k motifs from + the matrix profile. Available methods are "r_motifs" and "k_motifs": + - "r_motifs" means we rank motif set by their cardinality (number of matches + with a distance at most dist_threshold to the candidate motif), with higher + is better. + - "k_motifs" means rank motifs by their maximum distance to their matches. + For example, if a 3-motif has distances to its matches equal to + ``[0.1,0.2,0.5]`` will have a score of ``max([0.1,0.2,0.5])=0.5``. + is_self_computation : bool + Wheter X is equal to the series X_ given during fit. + + Returns + ------- + np.ndarray, shape = (k, motif_size) + The indexes of the best matches in ``distance_profile``. + np.ndarray, shape = (k, motif_size) + The distances of the best matches. + + """ + if motif_extraction_method not in ["k_motifs", "r_motifs"]: + raise ValueError( + "Expected motif_extraction_method to be either 'k_motifs' or 'r_motifs'" + f"but got {motif_extraction_method}" + ) + + MP, IP = self.compute_matrix_profile( + X, + motif_size=motif_size, + dist_threshold=dist_threshold, + allow_trivial_matches=allow_trivial_matches, + exclusion_factor=exclusion_factor, + inverse_distance=inverse_distance, + is_self_computation=is_self_computation, + ) + if motif_extraction_method == "k_motifs": + return _extract_top_k_motifs( + MP, IP, k, allow_trivial_matches, int(self.length * exclusion_factor) + ) + elif motif_extraction_method == "r_motifs": + return _extract_top_r_motifs( + MP, IP, k, allow_trivial_matches, int(self.length * exclusion_factor) + ) + + def compute_matrix_profile( + self, + X: np.ndarray, + motif_size: Optional[int] = 1, + dist_threshold: Optional[float] = np.inf, + allow_trivial_matches: Optional[bool] = False, + exclusion_factor: Optional[float] = 0.5, + inverse_distance: Optional[bool] = False, + is_self_computation: Optional[bool] = False, + ): + """ + Compute matrix profile. + + The matrix profile is computed on the series given in fit (X_). If X is + not given, computes the self matrix profile of X_. Otherwise, compute the matrix + profile of X_ relative to X. + + Parameters + ---------- + X : np.ndarray, shape = (n_channels, n_timepoints) + A 2D array time series against which the matrix profile of X_ will be + computed. + motif_size : int + The number of subsequences in a motif. Default is 1, meaning we extract + motif pairs (the query and its best match). + dist_threshold : float + The maximum allowed distance of a candidate subsequence of X to a query + subsequence from X_ for the candidate to be considered as a neighbor. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, + the worst matches to the query will be returned instead of the best ones. + exclusion_factor : float, default=0.5 + A factor of the query length used to define the exclusion zone when + ``allow_trivial_matches`` is set to False. For a given timestamp, + the exclusion zone starts from + :math:`id_timestamp - floor(length * exclusion_factor)` and end at + :math:`id_timestamp + floor(length * exclusion_factor)`. + is_self_computation : bool + Wheter X is equal to the series X_ given during fit. + + Returns + ------- + MP : TypedList of np.ndarray (n_timepoints - L + 1) + Matrix profile distances for each query subsequence. n_timepoints is the + number of timepoint of X_. Each element of the list contains array of + variable size. + IP : TypedList of np.ndarray (n_timepoints - L + 1) + Indexes of the top matches for each query subsequence. n_timepoints is the + number of timepoint of X_. Each element of the list contains array of + variable size. + """ + if is_self_computation and self.normalize: + X_means, X_stds = self.X_means_, self.X_stds_ + elif not is_self_computation and self.normalize: + X_means, X_stds = sliding_mean_std_one_series(X, self.length, 1) + + X_dotX = get_ith_products(X, self.X_, self.length, 0) + exclusion_size = int(self.length * exclusion_factor) + + if np.isinf(motif_size): + # convert infs here as numba seem to not be able to do == np.inf ? + motif_size = X.shape[1] - self.length + 1 + + if self.normalize: + MP, IP = _stomp_normalized( + self.X_, + X, + X_dotX, + self.X_means_, + self.X_stds_, + X_means, + X_stds, + self.length, + motif_size, + dist_threshold, + allow_trivial_matches, + exclusion_size, + inverse_distance, + is_self_computation, + ) + else: + MP, IP = _stomp( + self.X_, + X, + X_dotX, + self.length, + motif_size, + dist_threshold, + allow_trivial_matches, + exclusion_size, + inverse_distance, + is_self_computation, + ) + return MP, IP + + @classmethod + def _get_test_params(cls, parameter_set: str = "default"): + """Return testing parameter settings for the estimator. + + Parameters + ---------- + parameter_set : str, default="default" + Name of the set of test parameters to return, for use in tests. If no + special parameters are defined for a value, will return `"default"` set. + There are currently no reserved values for transformers. + + Returns + ------- + params : dict or list of dict, default = {} + Parameters to create testing instances of the class + Each dict are parameters to construct an "interesting" test instance, i.e., + `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + """ + if parameter_set == "default": + params = {"length": 3} + else: + raise NotImplementedError( + f"The parameter set {parameter_set} is not yet implemented" + ) + return params + + +@njit(cache=True, fastmath=True) +def _stomp_normalized( + X_A, + X_B, + AdotB, + X_A_means, + X_A_stds, + X_B_means, + X_B_stds, + L, + motif_size, + dist_threshold, + allow_trivial_matches, + exclusion_size, + inverse_distance, + is_self_mp, +): + """ + Compute the Matrix Profile using the STOMP algorithm with normalized distances. + + X_A : np.ndarray, 2D array of shape (n_channels, n_timepoints) + The series from which the queries will be extracted. + X_B : np.ndarray, 2D array of shape (n_channels, series_length) + The time series on which the distance profile of each query will be computed. + AdotB : np.ndarray, 2D array of shape (n_channels, series_length - L + 1) + Precomputed dot products between the first query of size L of X_A and X_B. + X_A_means : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) + Means of each subsequences of X_A of size L. + X_A_stds : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) + Stds of each subsequences of X of size L. + X_B_means : np.ndarray, 2D array of shape (n_channels, series_length - L + 1) + Means of each subsequences of X_B of size L. + X_B_stds : np.ndarray, 2D array of shape (n_channels, series_length - L + 1) + Stds of each subsequences of X_B of size L. + L : int + Length of the subsequences used for the distance computation. + motif_size : int + The number of subsequences to extract from each distance profile. + dist_threshold : float + The maximum allowed distance of a candidate subsequence of X to a query + subsequence from X_ for the candidate to be considered as a neighbor. + allow_trivial_matches : bool + Whether the top-k candidates can be neighboring subsequences. + exclusion_size : int + The size of the exclusion zone used to prevent returning as top k candidates + the ones that are close to each other (for example i and i+1). + It is used to define a region between + :math:`id_timestamp - exclusion_size` and + :math:`id_timestamp + exclusion_size` which cannot be returned + as best match if :math:`id_timestamp` was already selected. By default, + the value None means that this is not used. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, the + worst matches to the query will be returned instead of the best ones. + is_self_mp : bool + Whether X_A == X_B. + + Returns + ------- + MP : TypedList of np.ndarray (n_timepoints - L + 1) + Matrix profile distances for each query subsequence. n_timepoints is the + number of timepoint of X_. Each element of the list contains array of + variable size. + IP : TypedList of np.ndarray (n_timepoints - L + 1) + Indexes of the top matches for each query subsequence. n_timepoints is the + number of timepoint of X_. Each element of the list contains array of + variable size. + """ + n_queries = X_A.shape[1] - L + 1 + _max_timestamp = X_B.shape[1] - L + 1 + MP = List() + IP = List() + + for i_q in range(n_queries): + # size T.shape[1] - L + 1 + dist_profile = _normalized_squared_distance_profile( + AdotB, X_B_means, X_B_stds, X_A_means[:, i_q], X_A_stds[:, i_q], L + ) + + if i_q + 1 < n_queries: + AdotB = _update_dot_products(X_B, X_A, AdotB, L, i_q + 1) + + if inverse_distance: + dist_profile = _inverse_distance_profile(dist_profile) + + if is_self_mp: + ub = min(i_q + exclusion_size, _max_timestamp + 1) + lb = max(0, i_q - exclusion_size) + dist_profile[lb:ub] = np.inf + + _top_indexes, top_dists = _extract_top_k_from_dist_profile( + dist_profile, + motif_size, + dist_threshold, + allow_trivial_matches, + exclusion_size, + ) + top_indexes = np.zeros((len(_top_indexes), 2), dtype=np.int64) + for i_idx in range(len(_top_indexes)): + top_indexes[i_idx, 0] = i_q + top_indexes[i_idx, 1] = _top_indexes[i_idx] + MP.append(top_dists) + IP.append(top_indexes) + + return MP, IP + + +@njit(cache=True, fastmath=True) +def _stomp( + X_A, + X_B, + AdotB, + L, + motif_size, + dist_threshold, + allow_trivial_matches, + exclusion_size, + inverse_distance, + is_self_mp, +): + """ + Compute the Matrix Profile using the STOMP algorithm with non-normalized distances. + + X_A : np.ndarray, 2D array of shape (n_channels, n_timepoints) + The series from which the queries will be extracted. + X_B : np.ndarray, 2D array of shape (n_channels, series_length) + The time series on which the distance profile of each query will be computed. + AdotB : np.ndarray, 2D array of shape (n_channels, series_length - L + 1) + Precomputed dot products between the first query of size L of X_A and X_B. + L : int + Length of the subsequences used for the distance computation. + motif_size : int + The number of subsequences to extract from each distance profile. + dist_threshold : float + The maximum allowed distance of a candidate subsequence of X to a query + subsequence from X_ for the candidate to be considered as a neighbor. + allow_trivial_matches : bool + Wheter the top-k candidates can be neighboring subsequences. + exclusion_size : int + The size of the exclusion zone used to prevent returning as top k candidates + the ones that are close to each other (for example i and i+1). + It is used to define a region between + :math:`id_timestamp - exclusion_size` and + :math:`id_timestamp + exclusion_size` which cannot be returned + as best match if :math:`id_timestamp` was already selected. By default, + the value None means that this is not used. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, the + worst matches to the query will be returned instead of the best ones. + is_self_mp : bool + Wheter X_A == X_B. + + Returns + ------- + MP : TypedList of np.ndarray (n_timepoints - L + 1) + Matrix profile distances for each query subsequence. n_timepoints is the + number of timepoint of X_. Each element of the list contains array of + variable size. + IP : TypedList of np.ndarray (n_timepoints - L + 1) + Indexes of the top matches for each query subsequence. n_timepoints is the + number of timepoint of X_. Each element of the list contains array of + variable size. + """ + n_queries = X_A.shape[1] - L + 1 + _max_timestamp = X_B.shape[1] - L + 1 + MP = List() + IP = List() + + # For each query of size L in X_A + for i_q in range(n_queries): + Q = X_A[:, i_q : i_q + L] + dist_profile = _squared_distance_profile(AdotB, X_B, Q) + if i_q + 1 < n_queries: + AdotB = _update_dot_products(X_B, X_A, AdotB, L, i_q + 1) + + if inverse_distance: + dist_profile = _inverse_distance_profile(dist_profile) + + if is_self_mp: + ub = min(i_q + exclusion_size, _max_timestamp + 1) + lb = max(0, i_q - exclusion_size) + dist_profile[lb:ub] = np.inf + + _top_indexes, top_dists = _extract_top_k_from_dist_profile( + dist_profile, + motif_size, + dist_threshold, + allow_trivial_matches, + exclusion_size, + ) + top_indexes = np.zeros((len(_top_indexes), 2), dtype=np.int64) + for i_idx in range(len(_top_indexes)): + top_indexes[i_idx, 0] = i_q + top_indexes[i_idx, 1] = _top_indexes[i_idx] + MP.append(top_dists) + IP.append(top_indexes) + + return MP, IP diff --git a/aeon/similarity_search/series/motifs/tests/__init__.py b/aeon/similarity_search/series/motifs/tests/__init__.py new file mode 100644 index 0000000000..d0d8f2c42c --- /dev/null +++ b/aeon/similarity_search/series/motifs/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for series motif search methods.""" diff --git a/aeon/similarity_search/series/motifs/tests/test_stomp.py b/aeon/similarity_search/series/motifs/tests/test_stomp.py new file mode 100644 index 0000000000..67ff930de1 --- /dev/null +++ b/aeon/similarity_search/series/motifs/tests/test_stomp.py @@ -0,0 +1,149 @@ +""" +Tests for stomp algorithm. + +We do not test equality for returned indexes due to the unstable nature of argsort +and the fact that the "kind=stable" parameter is not yet supported in numba. We instead +test that the returned index match the expected distance value. +""" + +__maintainer__ = ["baraline"] + +import numpy as np +import pytest +from numpy.testing import assert_almost_equal, assert_array_almost_equal + +from aeon.similarity_search.series._commons import ( + _extract_top_k_from_dist_profile, + _inverse_distance_profile, + get_ith_products, +) +from aeon.similarity_search.series.motifs._stomp import _stomp, _stomp_normalized +from aeon.similarity_search.series.neighbors._dummy import ( + _naive_squared_distance_profile, +) +from aeon.testing.data_generation import make_example_2d_numpy_series +from aeon.utils.numba.general import ( + get_all_subsequences, + sliding_mean_std_one_series, + z_normalise_series_3d, +) + +MOTIFS_SIZE_VALUES = [1, 3] +THRESHOLD = [np.inf, 0.75] +THRESHOLD_NORM = [np.inf, 4.5] +NN_MATCHES = [True, False] +INVERSE = [True, False] + + +@pytest.mark.parametrize("motif_size", MOTIFS_SIZE_VALUES) +@pytest.mark.parametrize("threshold", THRESHOLD) +@pytest.mark.parametrize("allow_trivial_matches", NN_MATCHES) +@pytest.mark.parametrize("inverse_distance", INVERSE) +def test__stomp(motif_size, threshold, allow_trivial_matches, inverse_distance): + """Test STOMP method.""" + L = 3 + + X_A = make_example_2d_numpy_series( + n_channels=2, + n_timepoints=10, + ) + X_B = make_example_2d_numpy_series(n_channels=2, n_timepoints=10) + AdotB = get_ith_products(X_B, X_A, L, 0) + + exclusion_size = L + # MP : distances to best matches for each query + # IP : Indexes of best matches for each query + MP, IP = _stomp( + X_A, + X_B, + AdotB, + L, + motif_size, + threshold, + allow_trivial_matches, + exclusion_size, + inverse_distance, + False, + ) + # For each query of size L in T + X_B_subs = get_all_subsequences(X_B, L, 1) + X_A_subs = get_all_subsequences(X_A, L, 1) + for i in range(X_A.shape[1] - L + 1): + dist_profile = _naive_squared_distance_profile(X_B_subs, X_A_subs[i]) + # Check that the top matches extracted have the same value that the + # top matches in the distance profile + if inverse_distance: + dist_profile = _inverse_distance_profile(dist_profile) + + top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( + dist_profile, motif_size, threshold, allow_trivial_matches, exclusion_size + ) + # Check that the top matches extracted have the same value that the + # top matches in the distance profile + assert_array_almost_equal(MP[i], top_k_distances) + + # Check that the index in IP correspond to a distance profile point + # with value equal to the corresponding MP point. + for j, index in enumerate(top_k_indexes): + assert_almost_equal(MP[i][j], dist_profile[index]) + + +@pytest.mark.parametrize("motif_size", MOTIFS_SIZE_VALUES) +@pytest.mark.parametrize("threshold", THRESHOLD_NORM) +@pytest.mark.parametrize("allow_trivial_matches", NN_MATCHES) +@pytest.mark.parametrize("inverse_distance", INVERSE) +def test__stomp_normalised( + motif_size, threshold, allow_trivial_matches, inverse_distance +): + """Test STOMP normalised method.""" + L = 3 + + X_A = make_example_2d_numpy_series( + n_channels=2, + n_timepoints=10, + ) + X_B = make_example_2d_numpy_series(n_channels=2, n_timepoints=10) + X_A_means, X_A_stds = sliding_mean_std_one_series(X_A, L, 1) + X_B_means, X_B_stds = sliding_mean_std_one_series(X_B, L, 1) + AdotB = get_ith_products(X_B, X_A, L, 0) + + exclusion_size = L + # MP : distances to best matches for each query + # IP : Indexes of best matches for each query + MP, IP = _stomp_normalized( + X_A, + X_B, + AdotB, + X_A_means, + X_A_stds, + X_B_means, + X_B_stds, + L, + motif_size, + threshold, + allow_trivial_matches, + exclusion_size, + inverse_distance, + False, + ) + # For each query of size L in T + X_B_subs = z_normalise_series_3d(get_all_subsequences(X_B, L, 1)) + X_A_subs = z_normalise_series_3d(get_all_subsequences(X_A, L, 1)) + for i in range(X_A.shape[1] - L + 1): + dist_profile = _naive_squared_distance_profile(X_B_subs, X_A_subs[i]) + # Check that the top matches extracted have the same value that the + # top matches in the distance profile + if inverse_distance: + dist_profile = _inverse_distance_profile(dist_profile) + top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( + dist_profile, motif_size, threshold, allow_trivial_matches, exclusion_size + ) + + # Check that the top matches extracted have the same value that the + # top matches in the distance profile + assert_array_almost_equal(MP[i], top_k_distances) + + # Check that the index in IP correspond to a distance profile point + # with value equal to the corresponding MP point. + for j, index in enumerate(top_k_indexes): + assert_almost_equal(MP[i][j], dist_profile[index]) diff --git a/aeon/similarity_search/series/neighbors/__init__.py b/aeon/similarity_search/series/neighbors/__init__.py new file mode 100644 index 0000000000..047bfbe9c4 --- /dev/null +++ b/aeon/similarity_search/series/neighbors/__init__.py @@ -0,0 +1,9 @@ +"""Subsequence Neighbor search for series.""" + +__all__ = [ + "DummySNN", + "MassSNN", +] + +from aeon.similarity_search.series.neighbors._dummy import DummySNN +from aeon.similarity_search.series.neighbors._mass import MassSNN diff --git a/aeon/similarity_search/series/neighbors/_dummy.py b/aeon/similarity_search/series/neighbors/_dummy.py new file mode 100644 index 0000000000..399297b5c9 --- /dev/null +++ b/aeon/similarity_search/series/neighbors/_dummy.py @@ -0,0 +1,207 @@ +"""Implementation of NN with brute force.""" + +from typing import Optional + +__maintainer__ = ["baraline"] +__all__ = ["DummySNN"] + +import numpy as np +from numba import get_num_threads, njit, prange, set_num_threads + +from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch +from aeon.similarity_search.series._commons import ( + _check_X_index, + _extract_top_k_from_dist_profile, + _inverse_distance_profile, +) +from aeon.utils.numba.general import ( + get_all_subsequences, + z_normalise_series_2d, + z_normalise_series_3d, +) +from aeon.utils.validation import check_n_jobs + + +class DummySNN(BaseSeriesSimilaritySearch): + """Estimator to compute the on profile and distance profile using brute force.""" + + _tags = {"capability:multithreading": True} + + def __init__( + self, + length: int, + normalize: Optional[bool] = False, + n_jobs: Optional[int] = 1, + ): + self.normalize = normalize + self.n_jobs = n_jobs + self.length = length + super().__init__() + + def _fit( + self, + X: np.ndarray, + y=None, + ): + prev_threads = get_num_threads() + + set_num_threads(check_n_jobs(self.n_jobs)) + + self.X_subs = get_all_subsequences(self.X_, self.length, 1) + if self.normalize: + self.X_subs = z_normalise_series_3d(self.X_subs) + set_num_threads(prev_threads) + return self + + def _predict( + self, + X: np.ndarray, + k: Optional[int] = 1, + dist_threshold: Optional[float] = np.inf, + exclusion_factor: Optional[float] = 0.5, + inverse_distance: Optional[bool] = False, + allow_neighboring_matches: Optional[bool] = False, + X_index: Optional[int] = None, + ): + """ + Compute nearest neighbors to X in subsequences of X_. + + Parameters + ---------- + X : np.ndarray, shape=(n_channels, length) + Subsequence we want to find neighbors for. + k : int + The number of neighbors to return. + dist_threshold : float + The maximum distance of neighbors to X. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, + the farther neighbors will be returned instead of the closest ones. + exclusion_factor : float, default=0.5 + A factor of the query length used to define the exclusion zone when + ``allow_neighboring_matches`` is set to False. For a given timestamp, + the exclusion zone starts from + :math:`id_timestamp - floor(length * exclusion_factor)` and end at + :math:`id_timestamp + floor(length * exclusion_factor)`. + X_index : int, optional + If ``X`` is a subsequence of X_, specify its starting timestamp in ``X_``. + If specified, neighboring subsequences of X won't be able to match as + neighbors. + + Returns + ------- + np.ndarray, shape = (k) + The indexes of the best matches in ``distance_profile``. + np.ndarray, shape = (k) + The distances of the best matches. + + """ + if X.shape[1] != self.length: + raise ValueError( + f"Expected X to have {self.length} timepoints but" + f" got {X.shape[1]} timepoints." + ) + + X_index = _check_X_index(X_index, self.n_timepoints_, self.length) + dist_profile = self.compute_distance_profile(X) + if inverse_distance: + dist_profile = _inverse_distance_profile(dist_profile) + + exclusion_size = int(self.length * exclusion_factor) + if X_index is not None: + _max_timestamp = self.n_timepoints_ - self.length + ub = min(X_index + exclusion_size, _max_timestamp) + lb = max(0, X_index - exclusion_size) + dist_profile[lb:ub] = np.inf + + if k == np.inf: + k = len(dist_profile) + + return _extract_top_k_from_dist_profile( + dist_profile, + k, + dist_threshold, + allow_neighboring_matches, + exclusion_size, + ) + + def compute_distance_profile(self, X: np.ndarray): + """ + Compute the distance profile of X to all samples in X_. + + Parameters + ---------- + X : np.ndarray, 2D array of shape (n_channels, length) + The query to use to compute the distance profiles. + + Returns + ------- + distance_profile : np.ndarray, 1D array of shape (n_candidates) + The distance profile of X to X_. The ``n_candidates`` value + is equal to ``n_timepoins - length + 1``, with ``n_timepoints`` the + length of X_. + + """ + prev_threads = get_num_threads() + set_num_threads(check_n_jobs(self.n_jobs)) + if self.normalize: + X = z_normalise_series_2d(X) + distance_profile = _naive_squared_distance_profile(self.X_subs, X) + set_num_threads(prev_threads) + return distance_profile + + @classmethod + def _get_test_params(cls, parameter_set: str = "default"): + """Return testing parameter settings for the estimator. + + Parameters + ---------- + parameter_set : str, default="default" + Name of the set of test parameters to return, for use in tests. If no + special parameters are defined for a value, will return `"default"` set. + There are currently no reserved values for transformers. + + Returns + ------- + params : dict or list of dict, default = {} + Parameters to create testing instances of the class + Each dict are parameters to construct an "interesting" test instance, i.e., + `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + """ + if parameter_set == "default": + params = {"length": 20} + else: + raise NotImplementedError( + f"The parameter set {parameter_set} is not yet implemented" + ) + return params + + +@njit(cache=True, fastmath=True, parallel=True) +def _naive_squared_distance_profile( + X_subs, + Q, +): + """ + Compute a squared euclidean distance profile. + + Parameters + ---------- + X_subs : array, shape=(n_subsequences, n_channels, length) + Subsequences of size length of the input time series to search in. + Q : array, shape=(n_channels, query_length) + Query used during the search. + + Returns + ------- + out : np.ndarray, 1D array of shape (n_samples, n_timepoints_t - query_length + 1) + The distance between the query and all candidates in X. + + """ + n_subs, n_channels, length = X_subs.shape + dist_profile = np.zeros(n_subs) + for i in prange(n_subs): + for j in range(n_channels): + for k in range(length): + dist_profile[i] += (X_subs[i, j, k] - Q[j, k]) ** 2 + return dist_profile diff --git a/aeon/similarity_search/series/neighbors/_mass.py b/aeon/similarity_search/series/neighbors/_mass.py new file mode 100644 index 0000000000..695dce8844 --- /dev/null +++ b/aeon/similarity_search/series/neighbors/_mass.py @@ -0,0 +1,296 @@ +"""Implementation of NN with MASS.""" + +from typing import Optional + +__maintainer__ = ["baraline"] +__all__ = ["MassSNN"] + +import numpy as np +from numba import njit + +from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch +from aeon.similarity_search.series._commons import ( + _check_X_index, + _extract_top_k_from_dist_profile, + _inverse_distance_profile, + fft_sliding_dot_product, +) +from aeon.utils.numba.general import ( + AEON_NUMBA_STD_THRESHOLD, + sliding_mean_std_one_series, +) + + +class MassSNN(BaseSeriesSimilaritySearch): + """ + Estimator to compute the subsequences nearest neighbors using MASS _[1]. + + Parameters + ---------- + length : int + The length of the subsequences to use for the search. + normalize : bool + Whether the subsequences should be z-normalized. + + References + ---------- + .. [1] Abdullah Mueen, Yan Zhu, Michael Yeh, Kaveh Kamgar, Krishnamurthy + Viswanathan, Chetan Kumar Gupta and Eamonn Keogh (2015), The Fastest Similarity + Search Algorithm for Time Series Subsequences under Euclidean Distance. + """ + + def __init__( + self, + length: int, + normalize: Optional[bool] = False, + ): + self.normalize = normalize + self.length = length + super().__init__() + + def _fit( + self, + X: np.ndarray, + y=None, + ): + if self.normalize: + self.X_means_, self.X_stds_ = sliding_mean_std_one_series(X, self.length, 1) + return self + + def _predict( + self, + X: np.ndarray, + k: Optional[int] = 1, + dist_threshold: Optional[float] = np.inf, + allow_trivial_matches: Optional[bool] = False, + exclusion_factor: Optional[float] = 0.5, + inverse_distance: Optional[bool] = False, + X_index: Optional[int] = None, + ): + """ + Compute nearest neighbors to X in subsequences of X_. + + Parameters + ---------- + X : np.ndarray, shape=(n_channels, length) + Subsequence we want to find neighbors for. + k : int + The number of neighbors to return. + dist_threshold : float + The maximum allowed distance of a candidate subsequence of X_ to X + for the candidate to be considered as a neighbor. + allow_trivial_matches: bool, optional + Whether a neighbors of a match to a query can be also considered as matches + (True), or if an exclusion zone is applied around each match to avoid + trivial matches with their direct neighbors (False). + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, + the farther neighbors will be returned instead of the closest ones. + exclusion_factor : float, default=1. + A factor of the query length used to define the exclusion zone when + ``allow_trivial_matches`` is set to False. For a given timestamp, + the exclusion zone starts from + :math:`id_timestamp - floor(length * exclusion_factor)` and end at + :math:`id_timestamp + floor(length * exclusion_factor)`. + X_index : int, optional + If ``X`` is a subsequence of X_, specify its starting timestamp in ``X_``. + If specified, neighboring subsequences of X won't be able to match as + neighbors. + + Returns + ------- + np.ndarray, shape = (k) + The indexes of the best matches in ``distance_profile``. + np.ndarray, shape = (k) + The distances of the best matches. + + """ + if X.shape[1] != self.length: + raise ValueError( + f"Expected X to have {self.length} timepoints but" + f" got {X.shape[1]} timepoints." + ) + X_index = _check_X_index(X_index, self.n_timepoints_, self.length) + dist_profile = self.compute_distance_profile(X) + if inverse_distance: + dist_profile = _inverse_distance_profile(dist_profile) + + exclusion_size = int(self.length * exclusion_factor) + if X_index is not None: + _max_timestamp = self.n_timepoints_ - self.length + ub = min(X_index + exclusion_size, _max_timestamp) + lb = max(0, X_index - exclusion_size) + dist_profile[lb:ub] = np.inf + + if k == np.inf: + k = len(dist_profile) + + return _extract_top_k_from_dist_profile( + dist_profile, + k, + dist_threshold, + allow_trivial_matches, + exclusion_size, + ) + + def compute_distance_profile(self, X: np.ndarray): + """ + Compute the distance profile of X to all samples in X_. + + Parameters + ---------- + X : np.ndarray, 2D array of shape (n_channels, length) + The query to use to compute the distance profiles. + + Returns + ------- + distance_profiles : np.ndarray, 2D array of shape (n_cases, n_candidates) + The distance profile of X to all samples in X_. The ``n_candidates`` value + is equal to ``n_timepoins - length + 1``. If X_ is an unequal length + collection, returns a numba typed list instead of an ndarray. + + """ + QT = fft_sliding_dot_product(self.X_, X) + + if self.normalize: + distance_profile = _normalized_squared_distance_profile( + QT, + self.X_means_, + self.X_stds_, + X.mean(axis=1), + X.std(axis=1), + self.length, + ) + else: + distance_profile = _squared_distance_profile( + QT, + self.X_, # T + X, # Q + ) + + return distance_profile + + @classmethod + def _get_test_params(cls, parameter_set: str = "default"): + """Return testing parameter settings for the estimator. + + Parameters + ---------- + parameter_set : str, default="default" + Name of the set of test parameters to return, for use in tests. If no + special parameters are defined for a value, will return `"default"` set. + There are currently no reserved values for transformers. + + Returns + ------- + params : dict or list of dict, default = {} + Parameters to create testing instances of the class + Each dict are parameters to construct an "interesting" test instance, i.e., + `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + """ + if parameter_set == "default": + params = {"length": 20} + else: + raise NotImplementedError( + f"The parameter set {parameter_set} is not yet implemented" + ) + return params + + +@njit(cache=True, fastmath=True) +def _squared_distance_profile(QT, T, Q): + """ + Compute squared Euclidean distance profile between query and a time series. + + This function calculates the squared distance profile for a single time series by + leveraging the dot product of the query and time series as well as precomputed sums + of squares to efficiently compute the squared distances. + + Parameters + ---------- + QT : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) + The dot product between the query and the time series. + T : np.ndarray, 2D array of shape (n_channels, series_length) + The series used for similarity search. Note that series_length can be equal, + superior or inferior to n_timepoints, it doesn't matter. + Q : np.ndarray + 2D array of shape (n_channels, query_length) representing query subsequence. + + Returns + ------- + distance_profile : np.ndarray + 2D array of shape (n_channels, n_timepoints - query_length + 1) + The squared distance profile between the query and the input time series. + """ + n_channels, profile_length = QT.shape + query_length = Q.shape[1] + _QT = -2 * QT + distance_profile = np.zeros(profile_length) + for k in range(n_channels): + _sum = 0 + _qsum = 0 + for j in range(query_length): + _sum += T[k, j] ** 2 + _qsum += Q[k, j] ** 2 + + distance_profile += _qsum + _QT[k] + distance_profile[0] += _sum + for i in range(1, profile_length): + _sum += T[k, i + (query_length - 1)] ** 2 - T[k, i - 1] ** 2 + distance_profile[i] += _sum + return distance_profile + + +@njit(cache=True, fastmath=True) +def _normalized_squared_distance_profile( + QT, T_means, T_stds, Q_means, Q_stds, query_length +): + """ + Compute the z-normalized squared Euclidean distance profile for one time series. + + Parameters + ---------- + QT : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) + The dot product between the query and the time series. + T_means : np.ndarray, 1D array of length n_channels + The mean values of the time series for each channel. + T_stds : np.ndarray, 2D array of shape (n_channels, profile_length) + The standard deviations of the time series for each channel and position. + Q_means : np.ndarray, 1D array of shape (n_channels) + Means of the query q + Q_stds : np.ndarray, 1D array of shape (n_channels) + Stds of the query q + query_length : int + The length of the query subsequence used for the distance profile computation. + + + Returns + ------- + np.ndarray + 2D array of shape (n_channels, n_timepoints - query_length + 1) containing the + z-normalized squared distance profile between the query subsequence and the time + series. Entries are computed based on the z-normalized values, with special + handling for constant values. + """ + n_channels, profile_length = QT.shape + distance_profile = np.zeros(profile_length) + Q_is_constant = Q_stds <= AEON_NUMBA_STD_THRESHOLD + for i in range(profile_length): + Sub_is_constant = T_stds[:, i] <= AEON_NUMBA_STD_THRESHOLD + for k in range(n_channels): + # Two Constant case + if Q_is_constant[k] and Sub_is_constant[k]: + _val = 0 + # One Constant case + elif Q_is_constant[k] or Sub_is_constant[k]: + _val = query_length + else: + denom = query_length * Q_stds[k] * T_stds[k, i] + + p = (QT[k, i] - query_length * (Q_means[k] * T_means[k, i])) / denom + p = min(p, 1.0) + + _val = abs(2 * query_length * (1.0 - p)) + distance_profile[i] += _val + + return distance_profile diff --git a/aeon/similarity_search/series/neighbors/tests/__init__.py b/aeon/similarity_search/series/neighbors/tests/__init__.py new file mode 100644 index 0000000000..00ef2e73ec --- /dev/null +++ b/aeon/similarity_search/series/neighbors/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for series neighbors search methods.""" diff --git a/aeon/similarity_search/series/neighbors/tests/test_dummy.py b/aeon/similarity_search/series/neighbors/tests/test_dummy.py new file mode 100644 index 0000000000..e064b39fbf --- /dev/null +++ b/aeon/similarity_search/series/neighbors/tests/test_dummy.py @@ -0,0 +1,31 @@ +""" +Tests for stomp algorithm. + +We do not test equality for returned indexes due to the unstable nature of argsort +and the fact that the "kind=stable" parameter is not yet supported in numba. We instead +test that the returned index match the expected distance value. +""" + +__maintainer__ = ["baraline"] + +import numpy as np +from numpy.testing import assert_almost_equal + +from aeon.similarity_search.series.neighbors._dummy import ( + _naive_squared_distance_profile, +) +from aeon.testing.data_generation import make_example_2d_numpy_series +from aeon.utils.numba.general import get_all_subsequences + + +def test__naive_squared_distance_profile(): + """Test Euclidean distance with brute force.""" + L = 3 + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) + + dist_profile = _naive_squared_distance_profile(get_all_subsequences(X, L, 1), Q) + + for i_t in range(X.shape[1] - L + 1): + S = X[:, i_t : i_t + L] + assert_almost_equal(dist_profile[i_t], np.sum((S - Q) ** 2)) diff --git a/aeon/similarity_search/series/neighbors/tests/test_mass.py b/aeon/similarity_search/series/neighbors/tests/test_mass.py new file mode 100644 index 0000000000..b6bf1953ea --- /dev/null +++ b/aeon/similarity_search/series/neighbors/tests/test_mass.py @@ -0,0 +1,44 @@ +"""Tests for MASS algorithm.""" + +__maintainer__ = ["baraline"] + +import numpy as np +from numpy.testing import assert_almost_equal + +from aeon.similarity_search.series._commons import fft_sliding_dot_product +from aeon.similarity_search.series.neighbors._mass import ( + _normalized_squared_distance_profile, + _squared_distance_profile, +) +from aeon.testing.data_generation import make_example_2d_numpy_series +from aeon.utils.numba.general import sliding_mean_std_one_series, z_normalise_series_2d + + +def test__squared_distance_profile(): + """Test squared distance profile.""" + L = 3 + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) + QX = fft_sliding_dot_product(X, Q) + dist_profile = _squared_distance_profile(QX, X, Q) + for i_t in range(X.shape[1] - L + 1): + assert_almost_equal(dist_profile[i_t], np.sum((X[:, i_t : i_t + L] - Q) ** 2)) + + +def test__normalized_squared_distance_profile(): + """Test Euclidean distance.""" + L = 3 + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) + QX = fft_sliding_dot_product(X, Q) + X_mean, X_std = sliding_mean_std_one_series(X, L, 1) + Q_mean = Q.mean(axis=1) + Q_std = Q.std(axis=1) + + dist_profile = _normalized_squared_distance_profile( + QX, X_mean, X_std, Q_mean, Q_std, L + ) + Q = z_normalise_series_2d(Q) + for i_t in range(X.shape[1] - L + 1): + S = z_normalise_series_2d(X[:, i_t : i_t + L]) + assert_almost_equal(dist_profile[i_t], np.sum((S - Q) ** 2)) diff --git a/aeon/similarity_search/series/tests/__init__.py b/aeon/similarity_search/series/tests/__init__.py new file mode 100644 index 0000000000..4762fe16ce --- /dev/null +++ b/aeon/similarity_search/series/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for base class and commons functions.""" diff --git a/aeon/similarity_search/series/tests/test_base.py b/aeon/similarity_search/series/tests/test_base.py new file mode 100644 index 0000000000..33b78082c3 --- /dev/null +++ b/aeon/similarity_search/series/tests/test_base.py @@ -0,0 +1,19 @@ +"""Test for series similarity search base class.""" + +__maintainer__ = ["baraline"] + +from aeon.testing.mock_estimators._mock_similarity_searchers import ( + MockSeriesSimilaritySearch, +) +from aeon.testing.testing_data import FULL_TEST_DATA_DICT, _get_datatypes_for_estimator + + +def test_input_shape_fit_predict_collection_motifs(): + """Test input shapes.""" + estimator = MockSeriesSimilaritySearch() + datatypes = _get_datatypes_for_estimator(estimator) + # dummy data to pass to fit when testing predict/predict_proba + for datatype in datatypes: + X_train, y_train = FULL_TEST_DATA_DICT[datatype]["train"] + X_test, y_test = FULL_TEST_DATA_DICT[datatype]["test"] + estimator.fit(X_train, y_train).predict(X_test) diff --git a/aeon/similarity_search/series/tests/test_commons.py b/aeon/similarity_search/series/tests/test_commons.py new file mode 100644 index 0000000000..abed374318 --- /dev/null +++ b/aeon/similarity_search/series/tests/test_commons.py @@ -0,0 +1,171 @@ +"""Test _commons.py functions.""" + +__maintainer__ = ["baraline"] +import numpy as np +import pytest +from numba.typed import List +from numpy.testing import assert_, assert_array_almost_equal, assert_array_equal + +from aeon.similarity_search.series._commons import ( + _extract_top_k_from_dist_profile, + _extract_top_k_motifs, + _extract_top_r_motifs, + _inverse_distance_profile, + _update_dot_products, + fft_sliding_dot_product, + get_ith_products, +) +from aeon.testing.data_generation import ( + make_example_1d_numpy, + make_example_2d_numpy_series, +) + +K_VALUES = [1, 3, 5] +THRESHOLDS = [np.inf, 1.5] +NN_MATCHES = [False, True] +EXCLUSION_SIZE = [3, 5] + + +def test_fft_sliding_dot_product(): + """Test the fft_sliding_dot_product function.""" + L = 4 + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) + + values = fft_sliding_dot_product(X, Q) + # Compare values[0] only as input is univariate + assert_array_almost_equal( + values[0], + [np.dot(Q[0], X[0, i : i + L]) for i in range(X.shape[1] - L + 1)], + ) + + +def test__update_dot_products(): + """Test the _update_dot_product function.""" + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=20) + T = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + L = 7 + current_product = get_ith_products(X, T, L, 0) + for i_query in range(1, T.shape[1] - L + 1): + new_product = get_ith_products( + X, + T, + L, + i_query, + ) + current_product = _update_dot_products( + X, + T, + current_product, + L, + i_query, + ) + assert_array_almost_equal(new_product, current_product) + + +def test_get_ith_products(): + """Test i-th dot product of a subsequence of size L.""" + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + L = 5 + + values = get_ith_products(X, Q, L, 0) + # Compare values[0] only as input is univariate + assert_array_almost_equal( + values[0], + [np.dot(Q[0, 0:L], X[0, i : i + L]) for i in range(X.shape[1] - L + 1)], + ) + + values = get_ith_products(X, Q, L, 4) + # Compare values[0] only as input is univariate + assert_array_almost_equal( + values[0], + [np.dot(Q[0, 4 : 4 + L], X[0, i : i + L]) for i in range(X.shape[1] - L + 1)], + ) + + +def test__inverse_distance_profile(): + """Test method to inverse a TypedList of distance profiles.""" + X = make_example_1d_numpy() + X_inv = _inverse_distance_profile(X) + assert_array_almost_equal(1 / (X + 1e-8), X_inv) + + +def test__extract_top_k_motifs(): + """Test motif extraction based on max distance.""" + MP = np.array( + [ + [1.0, 2.0], + [1.0, 4.0], + [0.5, 0.9], + [0.6, 0.7], + ] + ) + + IP = np.array( + [ + [1, 2], + [1, 4], + [0, 3], + [0, 7], + ] + ) + IP_k, MP_k = _extract_top_k_motifs(MP, IP, 2, True, 0) + assert_(len(MP_k) == 2) + assert_array_equal(MP_k[0], [0.6, 0.7]) + assert_array_equal(IP_k[0], [0, 7]) + assert_array_equal(MP_k[1], [0.5, 0.9]) + assert_array_equal(IP_k[1], [0, 3]) + + +def test__extract_top_r_motifs(): + """Test motif extraction based on motif set cardinality.""" + MP = List() + MP.append(List([1.0, 1.5, 2.0, 1.5])) + MP.append(List([1.0, 4.0])) + MP.append(List([0.5, 0.9, 1.0])) + MP.append(List([0.6, 0.7])) + + IP = List() + IP.append(List([1, 2, 3, 4])) + IP.append(List([1, 4])) + IP.append(List([0, 3, 6])) + IP.append(List([0, 7])) + + IP_k, MP_k = _extract_top_r_motifs(MP, IP, 2, True, 0) + assert_(len(MP_k) == 2) + assert_array_equal(MP_k[0], [1.0, 1.5, 2.0, 1.5]) + assert_array_equal(IP_k[0], [1, 2, 3, 4]) + assert_array_equal(MP_k[1], [0.5, 0.9, 1.0]) + assert_array_equal(IP_k[1], [0, 3, 6]) + + +@pytest.mark.parametrize("k", K_VALUES) +@pytest.mark.parametrize("threshold", THRESHOLDS) +@pytest.mark.parametrize("allow_nn_matches", NN_MATCHES) +@pytest.mark.parametrize("exclusion_size", EXCLUSION_SIZE) +def test__extract_top_k_from_dist_profile( + k, threshold, allow_nn_matches, exclusion_size +): + """Test method to esxtract the top k candidates from a list of distance profiles.""" + X = make_example_1d_numpy(n_timepoints=30) + X_sort = np.argsort(X) + exclusion_size = 3 + top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( + X, k, threshold, allow_nn_matches, exclusion_size + ) + + if len(top_k_indexes) == 0 or len(top_k_distances) == 0: + raise AssertionError("_extract_top_k_from_dist_profile returned empty list") + for i, index in enumerate(top_k_indexes): + assert_(X[index] == top_k_distances[i]) + + assert_(np.all(top_k_distances <= threshold)) + + if allow_nn_matches: + assert_(np.all(top_k_distances <= X[X_sort[len(top_k_indexes) - 1]])) + + if not allow_nn_matches: + same_X = np.sort(top_k_indexes) + if len(same_X) > 1: + assert_(np.all(np.diff(same_X) >= exclusion_size)) diff --git a/aeon/similarity_search/series_search.py b/aeon/similarity_search/series_search.py deleted file mode 100644 index 3c36cf9c4a..0000000000 --- a/aeon/similarity_search/series_search.py +++ /dev/null @@ -1,436 +0,0 @@ -"""Base class for series search.""" - -__maintainer__ = ["baraline"] - -from typing import Union, final - -import numpy as np -from numba import get_num_threads, set_num_threads - -from aeon.similarity_search.base import BaseSimilaritySearch -from aeon.similarity_search.matrix_profiles.stomp import ( - stomp_euclidean_matrix_profile, - stomp_normalised_euclidean_matrix_profile, - stomp_normalised_squared_matrix_profile, - stomp_squared_matrix_profile, -) -from aeon.utils.numba.general import sliding_mean_std_one_series - - -class SeriesSearch(BaseSimilaritySearch): - """ - Series search estimator. - - The series search estimator will return a set of matches for each subsequence of - size L in a time series given during predict. The matching of each subsequence will - be made against all subsequence of size L inside the time series given during fit, - which will represent the search space. - - Depending on the `k` and/or `threshold` parameters, which condition what is - considered a valid match during the search, the number of matches will vary. If `k` - is used, at most `k` matches (the `k` best) will be returned, if `threshold` is used - and `k` is set to `np.inf`, all the candidates which distance to the query is - inferior or equal to `threshold` will be returned. If both are used, the `k` best - matches to the query with distance inferior to `threshold` will be returned. - - - Parameters - ---------- - k : int, default=1 - The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf - The number of best matches to return during predict for each subsequence. - distance : str, default="euclidean" - Name of the distance function to use. A list of valid strings can be found in - the documentation for :func:`aeon.distances.get_distance_function`. - If a callable is passed it must either be a python function or numba function - with nopython=True, that takes two 1d numpy arrays as input and returns a float. - distance_args : dict, default=None - Optional keyword arguments for the distance function. - normalise : bool, default=False - Whether the distance function should be z-normalised. - speed_up : str, default='fastest' - Which speed up technique to use with for the selected distance - function. By default, the fastest algorithm is used. A list of available - algorithm for each distance can be obtained by calling the - `get_speedup_function_names` function. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - n_jobs : int, default=1 - Number of parallel jobs to use. - - Attributes - ---------- - X_ : array, shape (n_cases, n_channels, n_timepoints) - The input time series stored during the fit method. This is the - database we search in when given a query. - distance_profile_function : function - The function used to compute the distance profile. This is determined - during the fit method based on the distance and normalise - parameters. - - Notes - ----- - For now, the multivariate case is only treated as independent. - Distances are computed for each channel independently and then - summed together. - """ - - def __init__( - self, - k: int = 1, - threshold: float = np.inf, - distance: str = "euclidean", - distance_args: Union[None, dict] = None, - inverse_distance: bool = False, - normalise: bool = False, - speed_up: str = "fastest", - n_jobs: int = 1, - ): - self.k = k - self.threshold = threshold - self._previous_query_length = -1 - self.axis = 1 - - super().__init__( - distance=distance, - distance_args=distance_args, - inverse_distance=inverse_distance, - normalise=normalise, - speed_up=speed_up, - n_jobs=n_jobs, - ) - - def _fit(self, X, y=None): - """ - Check input format and store it to be used as search space during predict. - - Parameters - ---------- - X : array, shape (n_cases, n_channels, n_timepoints) - Input array to used as database for the similarity search - y : optional - Not used. - - Raises - ------ - TypeError - If the input X array is not 3D raise an error. - - Returns - ------- - self - - """ - self.X_ = X - self.matrix_profile_function_ = self._get_series_method_function() - return self - - @final - def predict( - self, - X: np.ndarray, - length: int, - axis: int = 1, - X_index=None, - exclusion_factor=2.0, - apply_exclusion_to_result=False, - ): - """ - Predict method : Check the shape of X and call _predict to perform the search. - - If the distance profile function is normalised, it stores the mean and stds - from X and X_, with X_ the training data. - - Parameters - ---------- - X : np.ndarray, 2D array of shape (n_channels, series_length) - Input time series used for the search. - length : int - The length parameter that will be used to extract queries from X. - axis : int - The time point axis of the input series if it is 2D. If ``axis==0``, it is - assumed each column is a time series and each row is a time point. i.e. the - shape of the data is ``(n_timepoints,n_channels)``. ``axis==1`` indicates - the time series are in rows, i.e. the shape of the data is - ``(n_channels,n_timepoints)``. - X_index : int - An integer indicating if X was extracted is part of the dataset that was - given during the fit method. If so, this integer should be the sample id. - The search will define an exclusion zone for the queries extarcted from X - in order to avoid matching with themself. If None, it is considered that - the query is not extracted from X_. - exclusion_factor : float, default=2. - The factor to apply to the query length to define the exclusion zone. The - exclusion zone is define from - ``id_timestamp - query_length//exclusion_factor`` to - ``id_timestamp + query_length//exclusion_factor``. This also applies to - the matching conditions defined by child classes. For example, with - TopKSimilaritySearch, the k best matches are also subject to the exclusion - zone, but with :math:`id_timestamp` the index of one of the k matches. - apply_exclusion_to_result : bool, default=False - Wheter to apply the exclusion factor to the output of the similarity search. - This means that two matches of the query from the same sample must be at - least spaced by +/- ``query_length//exclusion_factor``. - This can avoid pathological matching where, for example if we extract the - best two matches, there is a high chance that if the best match is located - at ``id_timestamp``, the second best match will be located at - ``id_timestamp`` +/- 1, as they both share all their values except one. - - Raises - ------ - TypeError - If the input X array is not 2D raise an error. - ValueError - If the length of the query is greater - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(series_length - length + 1, n_matches)``, - contains the distance between all the queries of size length and their best - matches in X_. The second array, of shape - ``(series_length - L + 1, n_matches, 2)``, contains the indexes of these - matches as ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - self._check_is_fitted() - prev_threads = get_num_threads() - set_num_threads(self._n_jobs) - series_dim, series_length = self._check_series_format(X, length, axis) - - mask = self._init_X_index_mask( - None if X_index is None else [X_index, 0], - length, - exclusion_factor=exclusion_factor, - ) - - if self.normalise: - _mean, _std = sliding_mean_std_one_series(X, length, 1) - self.T_means_ = _mean - self.T_stds_ = _std - if self._previous_query_length != length: - self._store_mean_std_from_inputs(length) - - if apply_exclusion_to_result: - exclusion_size = length // exclusion_factor - else: - exclusion_size = None - - self._previous_query_length = length - - X_preds = self._predict( - X, - length, - mask, - exclusion_size, - X_index, - exclusion_factor, - apply_exclusion_to_result, - ) - set_num_threads(prev_threads) - return X_preds - - def _predict( - self, - X, - length, - mask, - exclusion_size, - X_index, - exclusion_factor, - apply_exclusion_to_result, - ): - """ - Private predict method for SeriesSearch. - - This method calculates the matrix profile for a given time series dataset by - comparing all possible subsequences of a specified length against a reference - time series. It handles exclusion zones to prevent nearby matches from being - selected and supports normalization. - - Parameters - ---------- - X : np.ndarray, 2D array of shape (n_channels, series_length) - Input time series used for the search. - length : int - The length parameter that will be used to extract queries from X. - axis : int - The time point axis of the input series if it is 2D. If ``axis==0``, it is - assumed each column is a time series and each row is a time point. i.e. the - shape of the data is ``(n_timepoints,n_channels)``. ``axis==1`` indicates - the time series are in rows, i.e. the shape of the data is - ``(n_channels,n_timepoints)``. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - length + 1) - Boolean mask of the shape of the distance profiles indicating for which part - of it the distance should be computed. In this context, it is the mask for - the first query of size L in T. This mask will be updated during the - algorithm. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestamp - exclusion_size` and - :math:`id_timestamp + exclusion_size` which cannot be returned - as best match if :math:`id_timestamp` was already selected. By default, - the value None means that this is not used. - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(series_length - length + 1, n_matches)``, - contains the distance between all the queries of size length and their best - matches in X_. The second array, of shape - ``(series_length - L + 1, n_matches, 2)``, contains the indexes of these - matches as ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - if self.normalise: - return self.matrix_profile_function_( - self.X_, - X, - length, - self.X_means_, - self.X_stds_, - self.T_means_, - self.T_stds_, - mask, - k=self.k, - threshold=self.threshold, - inverse_distance=self.inverse_distance, - exclusion_size=exclusion_size, - ) - else: - return self.matrix_profile_function_( - self.X_, - X, - length, - mask, - k=self.k, - threshold=self.threshold, - inverse_distance=self.inverse_distance, - exclusion_size=exclusion_size, - ) - - def _check_series_format(self, X, length, axis): - if axis not in [0, 1]: - raise ValueError("The axis argument is expected to be either 1 or 0") - if self.axis != axis: - X = X.T - if not isinstance(X, np.ndarray) or X.ndim != 2: - raise TypeError( - "Error, only supports 2D numpy for now. If the series X is univariate " - "do X = X[np.newaxis, :]." - ) - - series_dim, series_length = X.shape - if series_length < length: - raise ValueError( - "The length of the series should be superior or equal to the length " - "parameter given during predict, but got {} < {}".format( - series_length, length - ) - ) - - if series_dim != self.n_channels_: - raise ValueError( - "The number of feature should be the same for the series X and the data" - " (X_) provided during fit, but got {} for X and {} for X_".format( - series_dim, self.n_channels_ - ) - ) - return series_dim, series_length - - def _get_series_method_function(self): - """ - Given distance and speed_up parameters, return the series method function. - - Raises - ------ - ValueError - If the distance parameter given at initialization is not a string nor a - numba function or a callable, or if the speedup parameter is unknow or - unsupported, raisea ValueError. - - Returns - ------- - function - The series method function matching the distance argument. - - """ - if isinstance(self.distance, str): - distance_dict = _SERIES_SEARCH_SPEED_UP_DICT.get(self.distance) - if distance_dict is None: - raise NotImplementedError( - f"No distance profile have been implemented for {self.distance}." - ) - else: - speed_up_series_method = distance_dict.get(self.normalise).get( - self.speed_up - ) - - if speed_up_series_method is None: - raise ValueError( - f"Unknown or unsupported speed up {self.speed_up} for " - f"{self.distance} distance function with" - ) - self.speed_up_ = self.speed_up - return speed_up_series_method - else: - raise ValueError( - f"Expected distance argument to be str but got {type(self.distance)}" - ) - - @classmethod - def get_speedup_function_names(self): - """ - Get available speedup for series search in aeon. - - The returned structure is a dictionnary that contains the names of all - avaialble speedups for normalised and non-normalised distance functions. - - Returns - ------- - dict - The available speedups name that can be used as parameters in - similarity search classes. - - """ - speedups = {} - for dist_name in _SERIES_SEARCH_SPEED_UP_DICT.keys(): - for normalise in _SERIES_SEARCH_SPEED_UP_DICT[dist_name].keys(): - speedups_names = list( - _SERIES_SEARCH_SPEED_UP_DICT[dist_name][normalise].keys() - ) - if normalise: - speedups.update({f"normalised {dist_name}": speedups_names}) - else: - speedups.update({f"{dist_name}": speedups_names}) - return speedups - - -_SERIES_SEARCH_SPEED_UP_DICT = { - "euclidean": { - True: { - "fastest": stomp_normalised_euclidean_matrix_profile, - "STOMP": stomp_normalised_euclidean_matrix_profile, - }, - False: { - "fastest": stomp_euclidean_matrix_profile, - "STOMP": stomp_euclidean_matrix_profile, - }, - }, - "squared": { - True: { - "fastest": stomp_normalised_squared_matrix_profile, - "STOMP": stomp_normalised_squared_matrix_profile, - }, - False: { - "fastest": stomp_squared_matrix_profile, - "STOMP": stomp_squared_matrix_profile, - }, - }, -} diff --git a/aeon/similarity_search/tests/test__commons.py b/aeon/similarity_search/tests/test__commons.py deleted file mode 100644 index a97519ad31..0000000000 --- a/aeon/similarity_search/tests/test__commons.py +++ /dev/null @@ -1,49 +0,0 @@ -"""Test _commons.py functions.""" - -__maintainer__ = ["baraline"] - -import numpy as np -from numpy.testing import assert_array_almost_equal - -from aeon.similarity_search._commons import ( - fft_sliding_dot_product, - naive_squared_distance_profile, - naive_squared_matrix_profile, -) - - -def test_fft_sliding_dot_product(): - """Test the fft_sliding_dot_product function.""" - X = np.random.rand(1, 10) - q = np.random.rand(1, 5) - - values = fft_sliding_dot_product(X, q) - - assert_array_almost_equal( - values[0], - [np.dot(q[0], X[0, i : i + 5]) for i in range(X.shape[1] - 5 + 1)], - ) - - -def test_naive_squared_distance_profile(): - """Test naive squared distance profile computation is correct.""" - X = np.zeros((1, 1, 6)) - X[0, 0] = np.arange(6) - Q = np.array([[1, 2, 3]]) - query_length = Q.shape[1] - mask = np.ones((X.shape[0], X.shape[2] - query_length + 1), dtype=bool) - dist_profile = naive_squared_distance_profile(X, Q, mask) - assert_array_almost_equal(dist_profile[0], np.array([3.0, 0.0, 3.0, 12.0])) - - -def test_naive_squared_matrix_profile(): - """Test naive squared matrix profile computation is correct.""" - X = np.zeros((1, 1, 6)) - X[0, 0] = np.arange(6) - Q = np.zeros((1, 6)) - - Q[0] = np.arange(6, 12) - query_length = 3 - mask = np.ones((X.shape[0], X.shape[2] - query_length + 1), dtype=bool) - matrix_profile = naive_squared_matrix_profile(X, Q, query_length, mask) - assert_array_almost_equal(matrix_profile, np.array([27.0, 48.0, 75.0, 108.0])) diff --git a/aeon/similarity_search/tests/test_query_search.py b/aeon/similarity_search/tests/test_query_search.py deleted file mode 100644 index f97f6a50bf..0000000000 --- a/aeon/similarity_search/tests/test_query_search.py +++ /dev/null @@ -1,176 +0,0 @@ -"""Tests for QuerySearch.""" - -__maintainer__ = ["baraline"] - -import numpy as np -import pytest -from numpy.testing import assert_almost_equal, assert_array_equal - -from aeon.similarity_search.query_search import QuerySearch - -DATATYPES = ["int64", "float64"] - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_mean_std_equal_length(dtype): - """Test the mean and std computation of QuerySearch.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(normalise=True) - search.fit(X) - _ = search.predict(q, X_index=(1, 2)) - for i in range(len(X)): - for j in range(X[i].shape[1] - q.shape[1] + 1): - subsequence = X[i, :, j : j + q.shape[1]] - assert_almost_equal(search.X_means_[i][:, j], subsequence.mean(axis=-1)) - assert_almost_equal(search.X_stds_[i][:, j], subsequence.std(axis=-1)) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_mean_std_unequal_length(dtype): - """Test the mean and std computation of QuerySearch on unequal length data.""" - X = [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6, 5]], dtype=dtype), - ] - - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(normalise=True) - search.fit(X) - _ = search.predict(q, X_index=(1, 2)) - for i in range(len(X)): - for j in range(X[i].shape[1] - q.shape[1] + 1): - subsequence = X[i][:, j : j + q.shape[1]] - assert_almost_equal(search.X_means_[i][:, j], subsequence.mean(axis=-1)) - assert_almost_equal(search.X_stds_[i][:, j], subsequence.std(axis=-1)) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_threshold_and_k(dtype): - """Test the k and threshold combination of QuerySearch.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(k=3, threshold=1) - search.fit(X) - dist, idx = search.predict(q) - assert_array_equal(idx, [(0, 2), (1, 2)]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_inverse_distance(dtype): - """Test the inverse distance parameter of QuerySearch.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(k=1, inverse_distance=True) - search.fit(X) - _, idx = search.predict(q) - assert_array_equal(idx, [(0, 5)]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_euclidean(dtype): - """Test the functionality of QuerySearch with Euclidean distance.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(k=1) - search.fit(X) - _, idx = search.predict(q) - assert_array_equal(idx, [(0, 2)]) - - search = QuerySearch(k=3) - search.fit(X) - _, idx = search.predict(q) - assert_array_equal(idx, [(0, 2), (1, 2), (1, 1)]) - - _, idx = search.predict(q, apply_exclusion_to_result=True) - assert_array_equal(idx, [(0, 2), (1, 2), (1, 4)]) - - search = QuerySearch(k=1, normalise=True) - search.fit(X) - q = np.asarray([[8, 8, 10]], dtype=dtype) - _, idx = search.predict(q) - assert_array_equal(idx, [(1, 2)]) - - _, idx = search.predict(q, apply_exclusion_to_result=True) - assert_array_equal(idx, [(1, 2)]) - - search = QuerySearch(k=1, normalise=True) - search.fit(X) - _, idx = search.predict(q, X_index=(1, 2)) - assert_array_equal(idx, [(1, 0)]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_euclidean_unequal_length(dtype): - """Test the functionality of QuerySearch on unequal length data.""" - X = [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6, 5]], dtype=dtype), - ] - - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(k=1) - search.fit(X) - _, idx = search.predict(q) - assert_array_equal(idx, [(0, 2)]) - - search = QuerySearch(k=3) - search.fit(X) - _, idx = search.predict(q) - assert_array_equal(idx, [(0, 2), (1, 2), (1, 1)]) - - _, idx = search.predict(q, apply_exclusion_to_result=True) - assert_array_equal(idx, [(0, 2), (1, 2), (1, 4)]) - - search = QuerySearch(k=1, normalise=True) - search.fit(X) - q = np.asarray([[8, 8, 10]], dtype=dtype) - _, idx = search.predict(q) - assert_array_equal(idx, [(1, 2)]) - - _, idx = search.predict(q, apply_exclusion_to_result=True) - assert_array_equal(idx, [(1, 2)]) - - search = QuerySearch(k=1, normalise=True) - search.fit(X) - _, idx = search.predict(q, X_index=(1, 2)) - assert_array_equal(idx, [(1, 0)]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_speedup(dtype): - """Test the speedup functionality of QuerySearch.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(k=1, speed_up="fastest") - search.fit(X) - _, idx = search.predict(q) - assert_array_equal(idx, [(0, 2)]) - - search = QuerySearch( - k=1, - distance="euclidean", - speed_up="fastest", - normalise=True, - ) - search.fit(X) - q = np.asarray([[8, 8, 10]], dtype=dtype) - _, idx = search.predict(q) - assert_array_equal(idx, [(1, 2)]) diff --git a/aeon/similarity_search/tests/test_series_search.py b/aeon/similarity_search/tests/test_series_search.py deleted file mode 100644 index a10109359c..0000000000 --- a/aeon/similarity_search/tests/test_series_search.py +++ /dev/null @@ -1,74 +0,0 @@ -"""Tests for SeriesSearch similarity search algorithm.""" - -__maintainer__ = ["baraline"] - - -import numpy as np -import pytest - -from aeon.similarity_search.series_search import SeriesSearch - -DATATYPES = ["int64", "float64"] -K_VALUES = [1, 3] -normalise = [True, False] - -# See #2236 -# @pytest.mark.parametrize("k", K_VALUES) -# @pytest.mark.parametrize("normalise", normalise) -# def test_SeriesSearch_k(k, normalise): -# """Test the k and threshold combination of SeriesSearch.""" -# X = np.asarray([[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]]) -# S = np.asarray([[3, 4, 5, 4, 3, 4]]) -# L = 3 -# -# search = SeriesSearch(k=k, normalise=normalise) -# search.fit(X) -# mp, ip = search.predict(S, L) -# -# assert mp[0].shape[0] == ip[0].shape[0] == k -# assert len(mp) == len(ip) == S.shape[1] - L + 1 -# assert ip[0].shape[1] == 2 - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_SeriesSearch_error_predict(dtype): - """Test the functionality of SeriesSearch with Euclidean distance.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - S = np.asarray([[3, 4, 5, 4, 3, 4, 5]], dtype=dtype) - L = 100 - - search = SeriesSearch() - search.fit(X) - with pytest.raises(ValueError): - mp, ip = search.predict(S, L) - L = 3 - S = np.asarray( - [ - [3, 4, 5, 4, 3, 4], - [6, 5, 3, 2, 4, 5], - ], - dtype=dtype, - ) - with pytest.raises(ValueError): - mp, ip = search.predict(S, L) - - S = [6, 5, 3, 2, 4, 5] - with pytest.raises(TypeError): - mp, ip = search.predict(S, L) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_SeriesSearch_process_unequal_length(dtype): - """Test the functionality of SeriesSearch on unequal length data.""" - X = [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6, 5]], dtype=dtype), - ] - S = np.asarray([[3, 4, 5, 4, 3, 4]], dtype=dtype) - L = 3 - - search = SeriesSearch() - search.fit(X) - mp, ip = search.predict(S, L) diff --git a/aeon/testing/estimator_checking/_yield_estimator_checks.py b/aeon/testing/estimator_checking/_yield_estimator_checks.py index d583104e6b..6cf4ee7948 100644 --- a/aeon/testing/estimator_checking/_yield_estimator_checks.py +++ b/aeon/testing/estimator_checking/_yield_estimator_checks.py @@ -22,6 +22,7 @@ from aeon.regression import BaseRegressor from aeon.regression.deep_learning.base import BaseDeepRegressor from aeon.segmentation import BaseSegmenter +from aeon.similarity_search import BaseSimilaritySearch from aeon.testing.estimator_checking._yield_anomaly_detection_checks import ( _yield_anomaly_detection_checks, ) @@ -231,9 +232,10 @@ def check_inheritance(estimator_class): # Only transformers can inherit from multiple base types currently if n_base_types > 1: - assert issubclass( - estimator_class, BaseTransformer - ), "Only transformers can inherit from multiple base types." + assert issubclass(estimator_class, BaseTransformer) or issubclass( + estimator_class, BaseSimilaritySearch + ), "Only transformers or similarity search estimators can inherit from multiple" + "base types." def check_has_common_interface(estimator_class): diff --git a/aeon/testing/mock_estimators/__init__.py b/aeon/testing/mock_estimators/__init__.py index 219fc3e987..e9e83aa263 100644 --- a/aeon/testing/mock_estimators/__init__.py +++ b/aeon/testing/mock_estimators/__init__.py @@ -30,7 +30,8 @@ "MockMultivariateSeriesTransformer", "MockSeriesTransformerNoFit", # similarity search - "MockSimilaritySearch", + "MockSeriesSimilaritySearch", + "MockCollectionSimilaritySearch", ] from aeon.testing.mock_estimators._mock_anomaly_detectors import ( @@ -64,4 +65,7 @@ MockSeriesTransformerNoFit, MockUnivariateSeriesTransformer, ) -from aeon.testing.mock_estimators._mock_similarity_search import MockSimilaritySearch +from aeon.testing.mock_estimators._mock_similarity_searchers import ( + MockCollectionSimilaritySearch, + MockSeriesSimilaritySearch, +) diff --git a/aeon/testing/mock_estimators/_mock_similarity_search.py b/aeon/testing/mock_estimators/_mock_similarity_search.py deleted file mode 100644 index 55c9c435c7..0000000000 --- a/aeon/testing/mock_estimators/_mock_similarity_search.py +++ /dev/null @@ -1,21 +0,0 @@ -"""Mock similarity searchers useful for testing and debugging.""" - -__maintainer__ = ["baraline"] -__all__ = [ - "MockSimilaritySearch", -] - -from aeon.similarity_search.base import BaseSimilaritySearch - - -class MockSimilaritySearch(BaseSimilaritySearch): - """Mock similarity search for testing base class predict.""" - - def _fit(self, X, y=None): - """_fit dummy.""" - self.X_ = X - return self - - def predict(self, X): - """Predict dummy.""" - return [(0, 0)] diff --git a/aeon/testing/mock_estimators/_mock_similarity_searchers.py b/aeon/testing/mock_estimators/_mock_similarity_searchers.py new file mode 100644 index 0000000000..ddf001daf3 --- /dev/null +++ b/aeon/testing/mock_estimators/_mock_similarity_searchers.py @@ -0,0 +1,38 @@ +"""Mock series transformers useful for testing and debugging.""" + +__maintainer__ = ["baraline"] +__all__ = [ + "MockSeriesSimilaritySearch", + "MockCollectionSimilaritySearch", +] + +from aeon.similarity_search.collection._base import BaseCollectionSimilaritySearch +from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch + + +class MockSeriesSimilaritySearch(BaseSeriesSimilaritySearch): + """Mock estimator for BaseMatrixProfile.""" + + def __init__(self): + super().__init__() + + def _fit(self, X, y=None): + return self + + def _predict(self, X): + """top-1 motif start timestamp index in X, and distances to the match in X_.""" + return [0], [0.1] + + +class MockCollectionSimilaritySearch(BaseCollectionSimilaritySearch): + """Mock estimator for BaseMatrixProfile.""" + + def __init__(self): + super().__init__() + + def _fit(self, X, y=None): + return self + + def _predict(self, X): + """top-1 motif start timestamp index in X, and distances to the match in X_.""" + return [0, 0], [0.1] diff --git a/aeon/testing/testing_config.py b/aeon/testing/testing_config.py index be7d04f0a7..b17b9626d1 100644 --- a/aeon/testing/testing_config.py +++ b/aeon/testing/testing_config.py @@ -59,10 +59,6 @@ "ClaSPSegmenter": ["check_non_state_changing_method"], "HMMSegmenter": ["check_non_state_changing_method"], "RSTSF": ["check_non_state_changing_method"], - # Keeps length during predict to avoid recomputing means and std of data in fit - # if the next predict calls uses the same query length parameter. - "QuerySearch": ["check_non_state_changing_method"], - "SeriesSearch": ["check_non_state_changing_method"], # Unknown issue not producing the same results "RDSTRegressor": ["check_regressor_against_expected_results"], "RISTRegressor": ["check_regressor_against_expected_results"], diff --git a/aeon/testing/testing_data.py b/aeon/testing/testing_data.py index eb134cddda..3337f83b0c 100644 --- a/aeon/testing/testing_data.py +++ b/aeon/testing/testing_data.py @@ -10,7 +10,8 @@ from aeon.forecasting import BaseForecaster from aeon.regression import BaseRegressor from aeon.segmentation import BaseSegmenter -from aeon.similarity_search import BaseSimilaritySearch +from aeon.similarity_search.collection import BaseCollectionSimilaritySearch +from aeon.similarity_search.series import BaseSeriesSimilaritySearch from aeon.testing.data_generation import ( make_example_1d_numpy, make_example_2d_dataframe_collection, @@ -219,50 +220,6 @@ }, } -EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH = { - "numpy3D": { - "train": ( - make_example_3d_numpy( - n_cases=10, - n_channels=1, - n_timepoints=20, - random_state=data_rng.randint(np.iinfo(np.int32).max), - return_y=False, - ), - None, - ), - "test": ( - make_example_2d_numpy_series( - n_timepoints=10, - n_channels=1, - random_state=data_rng.randint(np.iinfo(np.int32).max), - ), - None, - ), - }, - "np-list": { - "train": ( - make_example_3d_numpy_list( - n_cases=10, - n_channels=1, - min_n_timepoints=20, - max_n_timepoints=20, - random_state=data_rng.randint(np.iinfo(np.int32).max), - return_y=False, - ), - None, - ), - "test": ( - make_example_2d_numpy_series( - n_timepoints=10, - n_channels=1, - random_state=data_rng.randint(np.iinfo(np.int32).max), - ), - None, - ), - }, -} - EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION = { "numpy3D": { "train": make_example_3d_numpy( @@ -401,50 +358,6 @@ }, } -EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH = { - "numpy3D": { - "train": ( - make_example_3d_numpy( - n_cases=10, - n_channels=2, - n_timepoints=20, - random_state=data_rng.randint(np.iinfo(np.int32).max), - return_y=False, - ), - None, - ), - "test": ( - make_example_2d_numpy_series( - n_timepoints=10, - n_channels=2, - random_state=data_rng.randint(np.iinfo(np.int32).max), - ), - None, - ), - }, - "np-list": { - "train": ( - make_example_3d_numpy_list( - n_cases=10, - n_channels=2, - min_n_timepoints=20, - max_n_timepoints=20, - random_state=data_rng.randint(np.iinfo(np.int32).max), - return_y=False, - ), - None, - ), - "test": ( - make_example_2d_numpy_series( - n_timepoints=10, - n_channels=2, - random_state=data_rng.randint(np.iinfo(np.int32).max), - ), - None, - ), - }, -} - UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION = { "np-list": { "train": make_example_3d_numpy_list( @@ -553,30 +466,6 @@ }, } -UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH = { - "np-list": { - "train": ( - make_example_3d_numpy_list( - n_cases=10, - n_channels=1, - min_n_timepoints=10, - max_n_timepoints=20, - random_state=data_rng.randint(np.iinfo(np.int32).max), - return_y=False, - ), - None, - ), - "test": ( - make_example_2d_numpy_series( - n_timepoints=10, - n_channels=1, - random_state=data_rng.randint(np.iinfo(np.int32).max), - ), - None, - ), - }, -} - UNEQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION = { "np-list": { "train": make_example_3d_numpy_list( @@ -685,30 +574,6 @@ }, } -UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH = { - "np-list": { - "train": ( - make_example_3d_numpy_list( - n_cases=10, - n_channels=2, - min_n_timepoints=10, - max_n_timepoints=20, - random_state=data_rng.randint(np.iinfo(np.int32).max), - return_y=False, - ), - None, - ), - "test": ( - make_example_2d_numpy_series( - n_timepoints=10, - n_channels=2, - random_state=data_rng.randint(np.iinfo(np.int32).max), - ), - None, - ), - }, -} - X_classification_missing_train, y_classification_missing_train = make_example_3d_numpy( n_cases=10, n_channels=1, @@ -825,12 +690,6 @@ for k, v in EQUAL_LENGTH_UNIVARIATE_REGRESSION.items() } ) -FULL_TEST_DATA_DICT.update( - { - f"EqualLengthUnivariate-SimilaritySearch-{k}": v - for k, v in EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH.items() - } -) FULL_TEST_DATA_DICT.update( { f"EqualLengthMultivariate-Classification-{k}": v @@ -843,12 +702,6 @@ for k, v in EQUAL_LENGTH_MULTIVARIATE_REGRESSION.items() } ) -FULL_TEST_DATA_DICT.update( - { - f"EqualLengthMultivariate-SimilaritySearch-{k}": v - for k, v in EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH.items() - } -) FULL_TEST_DATA_DICT.update( { f"UnequalLengthUnivariate-Classification-{k}": v @@ -861,12 +714,6 @@ for k, v in UNEQUAL_LENGTH_UNIVARIATE_REGRESSION.items() } ) -FULL_TEST_DATA_DICT.update( - { - f"UnequalLengthUnivariate-SimilaritySearch-{k}": v - for k, v in UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH.items() - } -) FULL_TEST_DATA_DICT.update( { f"UnequalLengthMultivariate-Classification-{k}": v @@ -879,12 +726,6 @@ for k, v in UNEQUAL_LENGTH_MULTIVARIATE_REGRESSION.items() } ) -FULL_TEST_DATA_DICT.update( - { - f"UnequalLengthMultivariate-SimilaritySearch-{k}": v - for k, v in UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH.items() - } -) FULL_TEST_DATA_DICT.update( { f"MissingValues-Classification-{k}": v @@ -916,9 +757,12 @@ def _get_datatypes_for_estimator(estimator): FULL_TEST_DATA_DICT. Each tuple is formatted (data_key, label_key). """ datatypes = [] - univariate, multivariate, unequal_length, missing_values = ( - _get_capabilities_for_estimator(estimator) - ) + ( + univariate, + multivariate, + unequal_length, + missing_values, + ) = _get_capabilities_for_estimator(estimator) task = _get_task_for_estimator(estimator) inner_types = estimator.get_tag("X_inner_type") @@ -1012,19 +856,19 @@ def _get_task_for_estimator(estimator): or isinstance(estimator, BaseEarlyClassifier) or isinstance(estimator, BaseClusterer) or isinstance(estimator, BaseCollectionTransformer) + or isinstance(estimator, BaseCollectionSimilaritySearch) ): data_label = "Classification" # collection data with continuous target labels elif isinstance(estimator, BaseRegressor): data_label = "Regression" - elif isinstance(estimator, BaseSimilaritySearch): - data_label = "SimilaritySearch" # series data with no secondary input elif ( isinstance(estimator, BaseAnomalyDetector) or isinstance(estimator, BaseSegmenter) or isinstance(estimator, BaseSeriesTransformer) or isinstance(estimator, BaseForecaster) + or isinstance(estimator, BaseSeriesSimilaritySearch) ): data_label = "None" else: diff --git a/aeon/testing/tests/test_testing_data.py b/aeon/testing/tests/test_testing_data.py index ef69f55a90..891bd5851a 100644 --- a/aeon/testing/tests/test_testing_data.py +++ b/aeon/testing/tests/test_testing_data.py @@ -6,19 +6,15 @@ from aeon.testing.testing_data import ( EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION, EQUAL_LENGTH_MULTIVARIATE_REGRESSION, - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH, EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION, EQUAL_LENGTH_UNIVARIATE_REGRESSION, - EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH, FULL_TEST_DATA_DICT, MISSING_VALUES_CLASSIFICATION, MISSING_VALUES_REGRESSION, UNEQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION, UNEQUAL_LENGTH_MULTIVARIATE_REGRESSION, - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH, UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION, UNEQUAL_LENGTH_UNIVARIATE_REGRESSION, - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH, ) from aeon.utils.data_types import COLLECTIONS_DATA_TYPES from aeon.utils.validation import ( @@ -108,30 +104,6 @@ def test_equal_length_univariate_collection(): EQUAL_LENGTH_UNIVARIATE_REGRESSION[key]["test"][1].dtype, np.floating ) - for key in EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH: - assert is_collection( - EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["train"][0], include_2d=True - ) - assert is_univariate(EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["train"][0]) - assert is_equal_length( - EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not has_missing( - EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not is_collection( - EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - # assert is_univariate( - # EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0], - # ) - assert is_equal_length( - EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - assert not has_missing( - EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - def test_unequal_length_univariate_collection(): """Test the contents of the unequal length univariate data dictionary.""" @@ -181,33 +153,6 @@ def test_unequal_length_univariate_collection(): UNEQUAL_LENGTH_UNIVARIATE_REGRESSION[key]["test"][1].dtype, np.floating ) - for key in UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH: - assert is_collection( - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["train"][0], - include_2d=True, - ) - assert is_univariate( - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not is_equal_length( - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not has_missing( - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not is_collection( - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - # assert is_univariate( - # UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0], - # ) - assert is_equal_length( - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - assert not has_missing( - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - def test_equal_length_multivariate_collection(): """Test the contents of the equal length multivariate data dictionary.""" @@ -257,33 +202,6 @@ def test_equal_length_multivariate_collection(): EQUAL_LENGTH_MULTIVARIATE_REGRESSION[key]["test"][1].dtype, np.floating ) - for key in EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH: - assert is_collection( - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["train"][0], - include_2d=True, - ) - assert not is_univariate( - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert is_equal_length( - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not has_missing( - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not is_collection( - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - # assert not is_univariate( - # EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0], - # ) - assert is_equal_length( - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - assert not has_missing( - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - def test_unequal_length_multivariate_collection(): """Test the contents of the unequal length multivariate data dictionary.""" @@ -345,33 +263,6 @@ def test_unequal_length_multivariate_collection(): UNEQUAL_LENGTH_MULTIVARIATE_REGRESSION[key]["test"][1].dtype, np.floating ) - for key in UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH: - assert is_collection( - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["train"][0], - include_2d=True, - ) - assert not is_univariate( - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not is_equal_length( - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not has_missing( - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not is_collection( - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - # assert not is_univariate( - # UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0], - # ) - assert is_equal_length( - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - assert not has_missing( - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - def test_missing_values_collection(): """Test the contents of the missing value data dictionary.""" diff --git a/aeon/testing/utils/estimator_checks.py b/aeon/testing/utils/estimator_checks.py index b2e0973dbf..d556ff0249 100644 --- a/aeon/testing/utils/estimator_checks.py +++ b/aeon/testing/utils/estimator_checks.py @@ -7,7 +7,7 @@ import numpy as np -from aeon.similarity_search.base import BaseSimilaritySearch +from aeon.similarity_search import BaseSimilaritySearch from aeon.testing.testing_data import FULL_TEST_DATA_DICT from aeon.utils.validation import get_n_cases diff --git a/aeon/utils/base/_identifier.py b/aeon/utils/base/_identifier.py index cf2722cfcb..03e8d8beaf 100644 --- a/aeon/utils/base/_identifier.py +++ b/aeon/utils/base/_identifier.py @@ -55,6 +55,8 @@ def get_identifier(estimator): identifiers.remove("collection-estimator") if len(identifiers) > 1 and "transformer" in identifiers: identifiers.remove("transformer") + if len(identifiers) > 1 and "similarity-search" in identifiers: + identifiers.remove("similarity-search") if len(identifiers) > 1: TypeError( diff --git a/aeon/utils/base/_register.py b/aeon/utils/base/_register.py index 1d81c2512c..5e81e29b33 100644 --- a/aeon/utils/base/_register.py +++ b/aeon/utils/base/_register.py @@ -24,7 +24,9 @@ from aeon.forecasting.base import BaseForecaster from aeon.regression.base import BaseRegressor from aeon.segmentation.base import BaseSegmenter -from aeon.similarity_search.base import BaseSimilaritySearch +from aeon.similarity_search._base import BaseSimilaritySearch +from aeon.similarity_search.collection import BaseCollectionSimilaritySearch +from aeon.similarity_search.series import BaseSeriesSimilaritySearch from aeon.transformations.base import BaseTransformer from aeon.transformations.collection import BaseCollectionTransformer from aeon.transformations.series import BaseSeriesTransformer @@ -36,6 +38,7 @@ "estimator": BaseAeonEstimator, "series-estimator": BaseSeriesEstimator, "transformer": BaseTransformer, + "similarity-search": BaseSimilaritySearch, # estimator types "anomaly-detector": BaseAnomalyDetector, "collection-transformer": BaseCollectionTransformer, @@ -44,14 +47,21 @@ "early_classifier": BaseEarlyClassifier, "regressor": BaseRegressor, "segmenter": BaseSegmenter, - "similarity_searcher": BaseSimilaritySearch, "series-transformer": BaseSeriesTransformer, "forecaster": BaseForecaster, + "series-similarity-search": BaseSeriesSimilaritySearch, + "collection-similarity-search": BaseCollectionSimilaritySearch, } # base classes which are valid for estimator to directly inherit from VALID_ESTIMATOR_BASES = { k: BASE_CLASS_REGISTER[k] for k in BASE_CLASS_REGISTER.keys() - - {"estimator", "collection-estimator", "series-estimator", "transformer"} + - { + "estimator", + "collection-estimator", + "series-estimator", + "transformer", + "similarity-search", + } } diff --git a/aeon/utils/numba/general.py b/aeon/utils/numba/general.py index 10e96abde6..58ab9d15e9 100644 --- a/aeon/utils/numba/general.py +++ b/aeon/utils/numba/general.py @@ -8,7 +8,9 @@ "first_order_differences_3d", "z_normalise_series_with_mean", "z_normalise_series", + "z_normalise_series_with_mean_std", "z_normalise_series_2d", + "z_normalise_series_2d_with_mean_std", "z_normalise_series_3d", "set_numba_random_seed", "choice_log", @@ -20,6 +22,8 @@ "slope_derivative_2d", "slope_derivative_3d", "generate_combinations", + "get_all_subsequences", + "compute_mean_stds_collection_parallel", ] @@ -273,7 +277,7 @@ def z_normalise_series_2d_with_mean_std( Parameters ---------- - X : array, shape = (n_channels, n_timestamps) + X : array, shape = (n_channels, n_timepoints) Input array to normalise. mean : array, shape = (n_channels) Mean of each channel of X. @@ -282,7 +286,7 @@ def z_normalise_series_2d_with_mean_std( Returns ------- - arr : array, shape = (n_channels, n_timestamps) + arr : array, shape = (n_channels, n_timepoints) The normalised array """ arr = np.zeros(X.shape) @@ -376,10 +380,10 @@ def get_subsequence( Parameters ---------- - X : array, shape (n_channels, n_timestamps) + X : array, shape (n_channels, n_timepoints) Input time series. i_start : int - A starting index between [0, n_timestamps - (length-1)*dilation] + A starting index between [0, n_timepoints - (length-1)*dilation] length : int Length parameter of the subsequence. dilation : int @@ -408,10 +412,10 @@ def get_subsequence_with_mean_std( Parameters ---------- - X : array, shape (n_channels, n_timestamps) + X : array, shape (n_channels, n_timepoints) Input time series. i_start : int - A starting index between [0, n_timestamps - (length-1)*dilation] + A starting index between [0, n_timepoints - (length-1)*dilation] length : int Length parameter of the subsequence. dilation : int @@ -451,15 +455,56 @@ def get_subsequence_with_mean_std( return values, means, stds +@njit(cache=True, fastmath=True, parallel=True) +def compute_mean_stds_collection_parallel(X): + """ + Return the mean and standard deviation for each channel of all series in X. + + Parameters + ---------- + X : array, shape (n_cases, n_channels, n_timepoints) + A time series collection + + Returns + ------- + means : array, shape (n_cases, n_channels) + The mean of each channel of each time series in X. + stds : array, shape (n_cases, n_channels) + The std of each channel of each time series in X. + + """ + n_channels = X[0].shape[0] + n_cases = len(X) + means = np.zeros((n_cases, n_channels)) + stds = np.zeros((n_cases, n_channels)) + for i_x in prange(n_cases): + n_timepoints = X[i_x].shape[1] + _s = np.zeros(n_channels) + _s2 = np.zeros(n_channels) + for i_t in range(n_timepoints): + for i_c in range(n_channels): + _s += X[i_x][i_c, i_t] + _s2 += X[i_x][i_c, i_t] ** 2 + + for i_c in range(n_channels): + means[i_x, i_c] = _s / n_timepoints + _std = _s2 / n_timepoints - means[i_x, i_c] ** 2 + if _s > AEON_NUMBA_STD_THRESHOLD: + stds[i_x, i_c] = _std**0.5 + + return means, stds + + @njit(fastmath=True, cache=True) def sliding_mean_std_one_series( X: np.ndarray, length: int, dilation: int ) -> tuple[np.ndarray, np.ndarray]: - """Return the mean and standard deviation for all subsequence (l,d) in X. + """ + Return the mean and standard deviation for all subsequence (l,d) in X. Parameters ---------- - X : array, shape (n_channels, n_timestamps) + X : array, shape (n_channels, n_timepoints) An input time series length : int Length of the subsequence @@ -468,14 +513,14 @@ def sliding_mean_std_one_series( Returns ------- - mean : array, shape (n_channels, n_timestamps - (length-1) * dilation) + mean : array, shape (n_channels, n_timepoints - (length-1) * dilation) The mean of each subsequence with parameter length and dilation in X. - std : array, shape (n_channels, n_timestamps - (length-1) * dilation) + std : array, shape (n_channels, n_timepoints - (length-1) * dilation) The standard deviation of each subsequence with parameter length and dilation in X. """ - n_channels, n_timestamps = X.shape - n_subs = n_timestamps - (length - 1) * dilation + n_channels, n_timepoints = X.shape + n_subs = n_timepoints - (length - 1) * dilation if n_subs <= 0: raise ValueError( "Invalid input parameter for sliding mean and std computations" @@ -493,7 +538,7 @@ def sliding_mean_std_one_series( _sum2 = np.zeros(n_channels) # Initialize first subsequence if it is valid - if np.all(_idx_sub < n_timestamps): + if np.all(_idx_sub < n_timepoints): for i_length in prange(length): _idx_sub[i_length] = (i_length * dilation) + i_mod_dil for i_channel in prange(n_channels): @@ -510,7 +555,7 @@ def sliding_mean_std_one_series( _idx_sub += dilation # As long as subsequences further subsequences are valid - while np.all(_idx_sub < n_timestamps): + while np.all(_idx_sub < n_timepoints): # Update sums and mean stds arrays for i_channel in prange(n_channels): _v_new = X[i_channel, _idx_sub[-1]] @@ -534,17 +579,17 @@ def normalise_subsequences(X_subs: np.ndarray, X_means: np.ndarray, X_stds: np.n Parameters ---------- - X_subs : array, shape (n_timestamps-(length-1)*dilation, n_channels, length) - The subsequences of an input time series of size n_timestamps given the + X_subs : array, shape (n_timepoints-(length-1)*dilation, n_channels, length) + The subsequences of an input time series of size n_timepoints given the length and dilation parameter. - X_means : array, shape (n_channels, n_timestamps-(length-1)*dilation) + X_means : array, shape (n_channels, n_timepoints-(length-1)*dilation) Mean of the subsequences to normalise. - X_stds : array, shape (n_channels, n_timestamps-(length-1)*dilation) + X_stds : array, shape (n_channels, n_timepoints-(length-1)*dilation) Stds of the subsequences to normalise. Returns ------- - array, shape = (n_timestamps-(length-1)*dilation, n_channels, length) + array, shape = (n_timepoints-(length-1)*dilation, n_channels, length) Z-normalised subsequences. """ n_subsequences, n_channels, length = X_subs.shape @@ -755,8 +800,8 @@ def get_all_subsequences(X: np.ndarray, length: int, dilation: int) -> np.ndarra Parameters ---------- - X : array, shape = (n_channels, n_timestamps) - An input time series as (n_channels, n_timestamps). + X : array, shape = (n_channels, n_timepoints) + An input time series as (n_channels, n_timepoints). length : int Length of the subsequences to generate. dilation : int @@ -764,11 +809,11 @@ def get_all_subsequences(X: np.ndarray, length: int, dilation: int) -> np.ndarra Returns ------- - array, shape = (n_timestamps-(length-1)*dilation, n_channels, length) + array, shape = (n_timepoints-(length-1)*dilation, n_channels, length) The view of the subsequences of the input time series. """ - n_features, n_timestamps = X.shape + n_features, n_timepoints = X.shape s0, s1 = X.strides - out_shape = (n_timestamps - (length - 1) * dilation, n_features, np.int64(length)) + out_shape = (n_timepoints - (length - 1) * dilation, n_features, np.int64(length)) strides = (s1, s0, s1 * dilation) return np.lib.stride_tricks.as_strided(X, shape=out_shape, strides=strides) diff --git a/aeon/utils/tags/_tags.py b/aeon/utils/tags/_tags.py index e1bacdd5ad..2c132902e4 100644 --- a/aeon/utils/tags/_tags.py +++ b/aeon/utils/tags/_tags.py @@ -138,7 +138,7 @@ class : identifier for the base class of objects this tag applies to "point belongs to.", }, "requires_y": { - "class": ["transformer", "anomaly-detector", "segmenter"], + "class": ["transformer", "anomaly-detector", "segmenter", "similarity-search"], "type": "bool", "description": "Does this estimator require y to be passed in its methods?", }, @@ -155,9 +155,9 @@ class : identifier for the base class of objects this tag applies to "values?", }, "input_data_type": { - "class": "transformer", + "class": ["transformer", "similarity-search"], "type": ("str", ["Series", "Collection"]), - "description": "The input abstract data type of the transformer, input X. " + "description": "The input abstract data type of the estimator, input X. " "Series indicates a single series input, Collection indicates a collection of " "time series.", }, diff --git a/docs/api_reference/similarity_search.rst b/docs/api_reference/similarity_search.rst index eb13cafd23..c62b0636f3 100644 --- a/docs/api_reference/similarity_search.rst +++ b/docs/api_reference/similarity_search.rst @@ -4,56 +4,70 @@ Similarity search ================= The :mod:`aeon.similarity_search` module contains algorithms and tools for similarity -search tasks. +search tasks. First, we distinguish between `series` estimator and `collection` +estimators, similarly to the `aeon.transformer` module. Secondly, we distinguish between +estimators used `neighbors` (with sufix SNN for subsequence nearest neighbors, or ANN +for approximate nearest neighbors) search and estimators used for `motifs` search. -Similarity search estimators ----------------------------- +Series Similarity search estimators +----------------------------------- -.. currentmodule:: aeon.similarity_search +.. currentmodule:: aeon.similarity_search.series.neighbors .. autosummary:: :toctree: auto_generated/ :template: class.rst - QuerySearch - SeriesSearch + DummySNN + MassSNN -Distance profile functions --------------------------- - -.. currentmodule:: aeon.similarity_search.distance_profiles +.. currentmodule:: aeon.similarity_search.series.motifs .. autosummary:: :toctree: auto_generated/ - :template: function.rst + :template: class.rst + + StompMotif - euclidean_distance_profile - normalised_euclidean_distance_profile - squared_distance_profile - normalised_squared_distance_profile -Matrix profile functions --------------------------- +Collection Similarity search estimators +----------------------------------- -.. currentmodule:: aeon.similarity_search.matrix_profiles +.. currentmodule:: aeon.similarity_search.collection.neighbors .. autosummary:: :toctree: auto_generated/ - :template: function.rst + :template: class.rst + + RandomProjectionIndexANN - stomp_normalised_euclidean_matrix_profile - stomp_euclidean_matrix_profile - stomp_normalised_squared_matrix_profile - stomp_squared_matrix_profile -Base ----- +Base Estimators +--------------- -.. currentmodule:: aeon.similarity_search.base +.. currentmodule:: aeon.similarity_search._base .. autosummary:: :toctree: auto_generated/ :template: class.rst BaseSimilaritySearch + + +.. currentmodule:: aeon.similarity_search.series._base + +.. autosummary:: + :toctree: auto_generated/ + :template: class.rst + + BaseSeriesSimilaritySearch + + +.. currentmodule:: aeon.similarity_search.collection._base + +.. autosummary:: + :toctree: auto_generated/ + :template: class.rst + + BaseCollectionSimilaritySearch diff --git a/docs/api_reference/utils.rst b/docs/api_reference/utils.rst index 40dea9f67c..8c4891dde0 100644 --- a/docs/api_reference/utils.rst +++ b/docs/api_reference/utils.rst @@ -87,7 +87,8 @@ Mock Estimators MockUnivariateSeriesTransformer MockMultivariateSeriesTransformer MockSeriesTransformerNoFit - MockSimilaritySearch + MockSeriesSimilaritySearch + MockCollectionSimilaritySearch Utilities ^^^^^^^^^ @@ -193,7 +194,9 @@ Numba first_order_differences_3d z_normalise_series_with_mean z_normalise_series + z_normalise_series_with_mean_std z_normalise_series_2d + z_normalise_series_2d_with_mean_std z_normalise_series_3d set_numba_random_seed choice_log @@ -205,6 +208,9 @@ Numba slope_derivative_2d slope_derivative_3d generate_combinations + get_all_subsequences + compute_mean_stds_collection_parallel + .. currentmodule:: aeon.utils.numba.stats diff --git a/docs/getting_started.md b/docs/getting_started.md index cf74ab9319..ccf29cee33 100644 --- a/docs/getting_started.md +++ b/docs/getting_started.md @@ -21,8 +21,9 @@ classical techniques for the following learning tasks: - [**Clustering**](api_reference/clustering), where a collection of time series without any labels are used to train a model to label cases ([more details](examples/clustering/clustering.ipynb)). -- [**Similarity search**](api_reference/similarity_search), where the goal is to evaluate - the similarity between a query time series and a collection of other longer time series +- [**Similarity search**](api_reference/similarity_search), where the goal is to find + time series motifs or nearest neighbors in an efficient way for either single series + or collections. ([more details](examples/similarity_search/similarity_search.ipynb)). - [**Anomaly detection**](api_reference/anomaly_detection), where the goal is to find values or areas of a single time series that are not representative of the whole series. @@ -309,45 +310,38 @@ new data. ### Similarity Search -The similarity search module in `aeon` offers a set of functions and estimators to solve -tasks related to time series similarity search. The estimators can be used standalone -or as parts of pipelines, while the functions give you the tools to build your own -estimators that would rely on similarity search at some point. - -The estimators are inheriting from the [BaseSimiliaritySearch](similarity_search.base.BaseSimiliaritySearch) -class accepts as inputs 3D time series (n_cases, n_channels, n_timepoints) for the -fit method. Univariate and single series can still be used, but will need to be reshaped -to this format. - -This collection, asked for the fit method, is stored as a database. It will be used in -the predict method, which expects a single 2D time series as input -(n_channels, query_length). This 2D time series will be used as a query to search for in -the 3D database. - -The result of the predict method will then depends on wheter you use the [QuerySearch](similarity_search.query_search.QuerySearch) -and the [SeriesSearch](similarity_search.series_search.SeriesSearch) estimator. In [QuerySearch](similarity_search.query_search.QuerySearch), the 2D series is a subsequence -for which we want to indentify the best (or worst !) matches in the 3D database. -For [SeriesSearch](similarity_search.series_search.SeriesSearch), we require a `length` parmater, and we will search for the best -matches of all subsequences of size `length` in the 2D series inside the 3D database. -By default, these estimators will use the Euclidean (or squared Euclidean) distance, -but more distance will be added in the future. - +The similarity search module in `aeon` offers a set of estimators to solve tasks +related to time series similarity search. The estimators can be used standalone for +data analysis purposes or as parts of pipelines, to perform other tasks such as +classification or clustering. + +Similarly to the transformation module, similarity search estimators are either defined +for single series or for collection of series. The estimators are inheriting from the +[BaseSimiliaritySearch](similarity_search._base.BaseSimiliaritySearch) class, which +both [BaseSeriesSimiliaritySearch](similarity_search.series._base.BaseSeriesSimiliaritySearch) +and [BaseCollectionSimiliaritySearch](similarity_search.collection._base.BaseCollectionSimiliaritySearch) +inherit from. + +All estimators use a `fit` `predict` interface, where `predict` outputs both the +indexes of the neighbors or motifs and a distance or similarity measure linked to them. +For example, using `StompMotif` to compute the matrix profile between two series : ```{code-block} python >>> import numpy as np ->>> from aeon.similarity_search import QuerySearch ->>> X = [[[1, 2, 3, 4, 5, 6, 7]], # 3D array example (univariate) -... [[4, 4, 4, 5, 6, 7, 3]]] # Two samples, one channel, seven series length ->>> X = np.array(X) # X is of shape (2, 1, 7) : (n_cases, n_channels, n_timepoints) ->>> top_k = QuerySearch(k=2) ->>> top_k.fit(X) # fit the estimator on train data -... ->>> q = np.array([[4, 5, 6]]) # q is of shape (1,3) : ->>> top_k.predict(q) # Identify the two (k=2) most similar subsequences of length 3 in X -[(0, 3), (1, 2)] +>>> from aeon.similarity_search.series import StompMotif +>>> X1 = np.array([1, 1, 2, 4, 6, 6, 7]) # single series (univariate) +>>> X2 = np.array([0, 1, 2, 2, 4, 5, 7, 9, 4, 6]) # single series (univariate) +>>> top_k = StompMotif(4).fit(X1) # 4 is length of the motif to search +>>> distances, indexes = top_k.predict(X2, k=1) ``` -The output of predict gives a list of size `k`, where each element is a set indicating -the location of the best matches in X as `(id_sample, id_timestamp)`. This is equivalent -to the subsequence `X[id_sample, :, id_timestamps:id_timestamp + q.shape[0]]`. +Some things to note on this example : + +- We defined `1D` series of shape `(n_timepoints)`, but internally, series estimator +will use a `2D` representation as `(n_channels, n_timepoints)`. +- The output of predict gives a two lists of size `k` (the number of motifs to extract) +which can be read as follows : `distances[i] = d(X1[:, indexes[i][0]],X2[:, indexes[i][1]])` + +For more examples and use cases you can check the example section of the module, +starting with the general [similarity search notebook](examples/similarity_search/similarity_search.ipynb) ## Transformers diff --git a/examples/similarity_search/code_speed.ipynb b/examples/similarity_search/code_speed.ipynb index f31155333d..0433b44962 100644 --- a/examples/similarity_search/code_speed.ipynb +++ b/examples/similarity_search/code_speed.ipynb @@ -27,15 +27,7 @@ "import pandas as pd\n", "from matplotlib import pyplot as plt\n", "\n", - "from aeon.similarity_search._commons import (\n", - " naive_squared_distance_profile,\n", - " naive_squared_matrix_profile,\n", - ")\n", - "from aeon.similarity_search.distance_profiles.squared_distance_profile import (\n", - " normalised_squared_distance_profile,\n", - " squared_distance_profile,\n", - ")\n", - "from aeon.similarity_search.matrix_profiles import stomp_squared_matrix_profile\n", + "from aeon.similarity_search.series import DummySNN, MassSNN\n", "from aeon.utils.numba.general import sliding_mean_std_one_series\n", "\n", "ggplot_styles = {\n", @@ -158,9 +150,9 @@ "for size in sizes:\n", " for query_length in query_lengths:\n", " X = rng.random((1, size))\n", - " _times = %timeit -r 7 -n 10 -q -o get_means_stds(X, query_length)\n", + " _times = %timeit -r 3 -n 3 -q -o get_means_stds(X, query_length)\n", " times.loc[(size, query_length), \"full computation\"] = _times.average\n", - " _times = %timeit -r 7 -n 10 -q -o sliding_mean_std_one_series(X, query_length, 1)\n", + " _times = %timeit -r 3 -n 3 -q -o sliding_mean_std_one_series(X, query_length, 1)\n", " times.loc[(size, query_length), \"sliding_computation\"] = _times.average" ] }, @@ -172,7 +164,7 @@ "outputs": [ { "data": { - "image/png": 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577qwefNmt3IOh8N47rnn3EbKr1q1ymOdDofDOPXUU53lWrVqZSxZsqRaueXLlxutW7c2LBaL237n7bP7cieNqly3aVRUlBEXF2e89dZbhsPhcCv3yy+/GB06dHCWveqqq3yqvzaudyuo6d+gQYOM999/v9b6nnjiCecyw4YN86kNn3zyiXOZ2NjYBn4iAAAAz8g7yDvIO5ou7/CmvjPNBCLv+P77793iWlRUVGudhYWFbsv88MMPPn8GAABgbuQn5CfkJ8GXn1TlOnOLJGPNmjVey7ru2zX9S0pKMu69916joKCgxnUfOHDAbbmqsfcmNTXVucyCBQvq9HkBAIC5kaOQo5CjcO0W51AAc2naIbAAYELbtm3T66+/7nz+6quvatq0adXKDR06VJ9//rliY2Nlt9sbs4lO9957rw4ePChJuvnmm/Xmm296HIndpk0bLVy4UKeffrqkirtQvfvuuzXWXV5ertjYWC1btkynnHJKtfcr7zoVHx+vyZMnS5IMw9Ds2bNrrNf1TgUTJ06sddS9P9x8880qLi6WJD3zzDN66qmnlJiYWK3cMcccoyVLlqhPnz6SpE8//VTff/99jXWXlJSoe/fu+uKLL9SrVy+39ywWi2655RZddNFFztfmzZvnsZ7PPvtMq1evliRZrVYtWrRIZ599drVyp59+uj755BNZrdZG2e/sdrveffddXX755bJYLG7v9e3b13k3C6nijhBlZWUBb1OltWvX6vzzz9f06dNVXl7utVx2drbzsS93n5Aq7mpQqaioSCUlJfVvKAAAgAfkHRXIO8g7pODOO3wViLzDtc6WLVsqJiam1jpjY2Pd9vecnByf2gIAAMyN/KQC+Qn5iRS8+YnD4dCtt97qfD5s2DANHjy4wfXm5ubq0Ucf1eDBg7Vt2zav5VzzE6l+eQ/5CQAA8BU5SgVyFHIUKXhzFKlpr93iHAoQehg0AwCN7LXXXpNhGJIqkqspU6Z4LdujRw+3A9SNKTMzU2+//bakik7iE088UWP5sLAwPfroo87n77zzTq3ruPHGG32aRnH69OnOx2+88YbXTnBpaaneeust5/PGmN5z/fr1+uKLLyRVTANfW7zi4uJ03333OZ/7sp3++c9/Kj4+3uv7ron7Dz/84LGMa0I6adIknXTSSV7rGzx4sK688spa2+UP48aN05gxY7y+P3bsWGeScvjwYY/TYdZHr1699Pe//13Lly/Xvn37ZLfbVVBQoI0bN+qpp55Sx44dnWX/7//+T7fccovXug4fPux87EuC5Kmcax0AAAD+QN5xFHkHeUdT5R3+FIi8oz51Vi1LLgMAAHxBfnIU+Qn5SbDmJ//4xz+0Zs0aSRUX8D399NO1LtOiRQtNnTpV//3vf7V161YdPnxYJSUl2rVrlxYuXKgzzzzTWXbr1q0aM2aMMjMzPdZVNbeoT95DfgIAAHxFjnIUOQo5CtduVa+jvnVWLUuOAgQXBs0AQCNbsWKF8/EVV1xRa/maErNAWrZsmXO0+gUXXKDo6Ohalxk2bJji4uIkSV999VWt5SdNmuRTW4YMGaIBAwZIkvbs2aPPPvvMY7kPP/zQebC9T58+OvHEE32qvyEWL17sfDx58uRqI+49qbyrg1T7doqOjta5555bY5mBAwc6H+/cudNjmVWrVjkfX3755bW20Zcy/nDxxRfX+L7FYlH//v2dz719vrq46aabtHnzZv3jH//Q6aefrrZt2yoiIkLx8fE67rjjdMcdd2jTpk0655xznMu88MILXmNls9mcjyMjI31qQ+XdOCpV3u0CAADAX8g7jiLvIO9oirzD3wKRd9Snzqr1kssAAABfkJ8cRX5CfhKM+clHH32khx56yPn8rrvu0sknn1zjMu3atdPevXv1+uuv65JLLlGPHj0UFxenyMhIdezYURdddJGWLl2qV155xbmP7NixQ/fcc4/H+lzzE6l+eQ/5CQAA8BU5ylHkKOQoXLtVgXMoQOgLb+oGAICZGIah9evXO5+PGDGi1mV69Oih5OTkRp+u79tvv3U+3rBhg2688cY6LZ+bm6vCwkJnIlZVRESEjj/+eJ/ru/baazVjxgxJFSPvx44dW62M64j8q6++uk7trS/X7bRixQqlp6fXukzl3SokadeuXTWW7dmzpyIiImos06pVK+fjQ4cOVXt/z549bnfuGjZsWK1tHDJkiCwWi1tbA8GXfaC2z1dXrvV507JlS7377rsaMGCAfvvtN0nSE0884fEkketBCV+nRa06pWdd7kgAAABQG/KOo8g7KpB3NH7e4W+ByDvqU2fVesllAABAbchPjiI/qUB+Elz5yZo1azR58mTn5z7zzDP18MMP17pcVFRUtYvMPLn22muVnp6uxx57TFLFXckfffRRtWnTxq1c1QtA7Xa7TxeFkp8AAIC6Ikc5ihylAjkK125JnEMBzIBBMwDQiPLz8906UWlpaT4tl5aW1uiJ1969e52Pv/rqK5/uPlBVbm6u18QrKSlJ4eG+/xm67LLLdOedd6qwsFAfffSRMjMzlZqa6nx/9+7dzrsYREZGNtoUla7b6dNPP63z8rm5uTW+n5CQUGsdrolZWVlZtfddk67Y2FglJyfXWmeLFi2UkJCgvLy8Wss2RF0/X2lpaSCb4yY2NlZ33XWXc6rYL774Qna7vdrdA1ynX/X1DgFVy9U0hSsAAEBdkXccRd5RgbwjePMOXwUi76hPnVXLkssAAIDakJ8cRX5SgfwkePKTX3/9Veecc44KCwslVVyU9/7779dpP/XFPffco2effVbFxcUqLy/X0qVLq901u2puUVxc7NOgGfITAABQV+QoR5GjVCBHCZ4cpaqmvHaLcyhA6LE2dQMAwEwOHz7s9jw2Ntan5bwlL4GUn5/f4Do8JQGV6jqSumXLlrrkkkskVXS+58yZ4/b+G2+8IYfDIUkaP368UlJS6tja+mnodiovL6/xfV+mDK2N637n6z4nNU7H3R+fL5DOPPNM5+OioiKPd6NwvfvBgQMHfKp3//79zsexsbE+3Y0NAADAV+QdR5F3VCDvCO68wxeByDuq3hnOZrPVWmdRUZEKCgqcz305sQcAAMyN/OQo8pMK5CfBkZ/s2LFDo0ePVnZ2tiSpb9++WrJkSUC2QXx8vNudtDdv3lytTNU7Tdcn7yE/AQAAviBHOYocpQI5SnDkKN401bVbnEMBQg+DZgCgEVXtyBYVFfm0XOUdnvypMknxxjXZ+9e//iXDMOr8r0uXLn5t8/Tp052PXafzNAxDr7/+uvN55ejyxuC6nd577716badAc93vfN3npMDsd81Nu3bt3J5nZWVVK9OzZ0/nY1+meJWkjIwM5+NevXrVs3UAAACekXc0DHlH/ZB3BFYg8g7XOn2t17VOT3UAAABURX7SMOQn9UN+UrM9e/bojDPOcN6Ru1u3blq6dGlAL+hyPd/i6VxL69atlZiY6HzuS35is9nc7tjN+RYAAOALcpSGIUepH3KU+muqa7c4hwKEHgbNAEAjSkhIcJsesGpHyZtdu3bVWqa2aR6rqm2UfZs2bZyPXUdWN6Xhw4erX79+kiruQvXtt99KklasWKHt27dLkjp37uw2wjzQgnE7VeV654aioqJapxWVKu5wEOjpPZuDqsmnpzuH9O7d2/l4//79Pu0Ha9eu9bg8AACAP5B3NAx5R/2QdwRWIPKOhIQEt5NN69atq1OdHTp0UMuWLWtdBgAAmBv5ScOQn9QP+Yl3Bw4c0BlnnKEdO3ZIkjp27Khly5ZVuxDN31zPt3i7S7tr3lLX/CQsLEw9evRoQAsBAIBZkKM0DDlK/ZCj1F9TXbvFORQg9DBoBgAakcViUf/+/Z3Pv/vuu1qX+e2335xTo9fEtZPlS/mNGzfW+L7rNOlff/11rfU1Fk93LHC9c8FVV10lq7Xx/rwF63Zy1bFjR7fk6/vvv691mR9//NGnOykE+xSdDVU14Wnfvn21Mt27d1fHjh2dz1euXFlrvatWrXI+Pv300+vfQAAAAA/IOxqOvKPuyDsCK1B5x2mnneb3OgEAAFyRnzQc+UndkZ94lp2drdGjR2vr1q2SKmZ3WbZsmd/vPu6J6/kWT+dapIblJyeeeKKioqLq30AAAGAa5CgNR45Sd+Qo9deU125xDgUILQyaAYBG5tqZevvtt2stP2fOHJ/qdT2g/fPPP9dY9scff3TeQcqbs88+W+Hh4ZKkb775RuvXr/epHYF2+eWXKyYmRpI0f/587d69W++9954kyWq1atq0aY3annHjxjkfv/feezpw4ECjrt9XI0eOdD5+5513ai3vy74pSdHR0c7HpaWldW9YkJs9e7bzcd++fd0S2EoWi0XnnXee8/kbb7xRY53ffvuttm3bJqnizmfnnnuufxoLAADggryjYcg76oe8I3AClXdMmDDB+Xj+/PkqLi72WmdxcbEWLFjgcVkAAICakJ80DPlJ/ZCfuDt06JDOPvts54WZSUlJWrp0qXr27BnwdS9btsztzuyjRo3yWM41x1i2bJl2795dY72ueRH5CQAAqAtylIYhR6kfcpT6acprtziHAoQWBs0AQCO7+uqrnY+/++67Gju4v//+u5599lmf6nUdNf/mm296LVdWVqZbbrml1vo6dOigyy+/XJJkGIauvPJKHTp0yKe2OBwOZWZm+lS2rhITEzVx4kRJFdNQXnTRRbLZbJKks846S506dQrIer0ZOnSo8+B+cXGxrrjiCtntdp+WtdvtPk236Q+uCencuXNrvFPG2rVra9yHXLVq1cr5eM+ePfVvYCM5fPiwz2XfffddzZ071/m88vvgyfXXX++8S8Znn32mpUuXeizncDj017/+1fn84osvVmpqqs9tAgAA8BV5R8OQd9QPeUdgBSLvOO+885x3X8vLy9Ojjz7qdf3/+Mc/lJeXJ0nq3Lmz24lIAACAmpCfNAz5Sf2QnxxVVFSkP/3pT/rpp58kSS1atNCSJUvUr1+/etVnt9t9jnl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"text/plain": [ "
" ] @@ -233,17 +225,20 @@ "for size in sizes:\n", " for _query_length in query_lengths:\n", " query_length = int(_query_length * size)\n", - " X = rng.random((1, 1, size))\n", + " X = rng.random((1, size))\n", " q = rng.random((1, query_length))\n", " mask = np.ones((1, size - query_length + 1), dtype=bool)\n", " # Used for numba compilation before timings\n", - " naive_squared_distance_profile(X, q, mask)\n", - " _times = %timeit -r 3 -n 7 -q -o naive_squared_distance_profile(X, q, mask)\n", + " mass = MassSNN(length=query_length).fit(X)\n", + " mass.compute_distance_profile(q)\n", + " dummy = DummySNN(length=query_length).fit(X)\n", + " dummy.compute_distance_profile(q)\n", + "\n", + " _times = %timeit -r 3 -n 3 -q -o dummy.compute_distance_profile(q)\n", " times.loc[(size, _query_length), \"Naive Euclidean distance\"] = _times.average\n", - " # Used for numba compilation before timings\n", - " squared_distance_profile(X, q, mask)\n", - " _times = %timeit -r 3 -n 7 -q -o squared_distance_profile(X, q, mask)\n", - " times.loc[(size, _query_length), \"Euclidean distance as dot product\"] = (\n", + "\n", + " _times = %timeit -r 3 -n 3 -q -o mass.compute_distance_profile(q)\n", + " times.loc[(size, _query_length), \"Euclidean distance with MASS\"] = (\n", " _times.average\n", " )" ] @@ -256,7 +251,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -281,7 +276,7 @@ "id": "f10127f4-6515-4ea5-a1d9-e14671eb70be", "metadata": {}, "source": [ - "The same reasoning holds for the normalised (squared) euclidean distance, we can use the `ConvolveDotProduct` value for the `speed_up` argument in `TopKSimilaritySearch` to use this optimization for both normalised and non normalised distances. In the normalised case, the formula used to computed the normalised (squared) euclidean distance is taken from the paper [Matrix Profile I: All Pairs Similarity Joins for Time Series](https://www.cs.ucr.edu/~eamonn/PID4481997_extend_Matrix%20Profile_I.pdf), see MASS algortihm." + "The same reasoning holds for the normalised (squared) euclidean distance, we can use the `normalize` parameter of the two estimators to set this option. In the normalised case, the formula used to computed the normalised (squared) euclidean distance is taken from the paper [Matrix Profile I: All Pairs Similarity Joins for Time Series](https://www.cs.ucr.edu/~eamonn/PID4481997_extend_Matrix%20Profile_I.pdf), see MASS algortihm." ] }, { @@ -300,34 +295,22 @@ "for size in sizes:\n", " for _query_length in query_lengths:\n", " query_length = int(_query_length * size)\n", - " X = rng.random((1, 1, size))\n", + " X = rng.random((1, size))\n", " q = rng.random((1, query_length))\n", - " n_cases, n_channels = X.shape[0], X.shape[1]\n", - " search_space_size = size - query_length + 1\n", - " X_means = np.zeros((n_cases, n_channels, search_space_size))\n", - " X_stds = np.zeros((n_cases, n_channels, search_space_size))\n", - " mask = np.ones((n_channels, search_space_size), dtype=bool)\n", - " for i in range(X.shape[0]):\n", - " _mean, _std = sliding_mean_std_one_series(X[i], query_length, 1)\n", - " X_stds[i] = _std\n", - " X_means[i] = _mean\n", - " q_means, q_stds = sliding_mean_std_one_series(q, query_length, 1)\n", - " q_means = q_means[:, 0]\n", - " q_stds = q_stds[:, 0]\n", + " mask = np.ones((1, size - query_length + 1), dtype=bool)\n", " # Used for numba compilation before timings\n", - " naive_squared_distance_profile(\n", - " X, q, mask, normalise=True, X_means=X_means, X_stds=X_stds\n", - " )\n", - " _times = %timeit -r 3 -n 7 -q -o naive_squared_distance_profile(X, q, mask, normalise=True, X_means=X_means, X_stds=X_stds)\n", + " mass = MassSNN(length=query_length, normalize=True).fit(X)\n", + " mass.compute_distance_profile(q)\n", + " dummy = DummySNN(length=query_length, normalize=True).fit(X)\n", + " dummy.compute_distance_profile(q)\n", + "\n", + " _times = %timeit -r 3 -n 3 -q -o dummy.compute_distance_profile(q)\n", " times.loc[(size, _query_length), \"Naive Normalised Euclidean distance\"] = (\n", " _times.average\n", " )\n", - " # Used for numba compilation before timings\n", - " normalised_squared_distance_profile(\n", - " X, q, mask, X_means, X_stds, q_means, q_stds\n", - " )\n", - " _times = %timeit -r 3 -n 7 -q -o normalised_squared_distance_profile(X, q, mask, X_means, X_stds, q_means, q_stds)\n", - " times.loc[(size, _query_length), \"Normalised Euclidean as dot product\"] = (\n", + "\n", + " _times = %timeit -r 3 -n 3 -q -o mass.compute_distance_profile(q)\n", + " times.loc[(size, _query_length), \"Normalised Euclidean distance with MASS\"] = (\n", " _times.average\n", " )" ] @@ -340,7 +323,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -359,20 +342,14 @@ "plt.show()" ] }, - { - "cell_type": "markdown", - "id": "df47932f-d736-4e23-b3ee-d79a94c6b46e", - "metadata": {}, - "source": [ - "# Series Search" - ] - }, { "cell_type": "markdown", "id": "a716ea8f-9b1d-428c-8b41-0ab17af814d1", "metadata": {}, "source": [ - "## Dot products" + "## Updating the dot products used in MASS when computing matrix profiles\n", + "\n", + "This is part of the STOMP algorithm, which update the dot products of the sliding query instead of recomputing it everytime. When you compute $MASS(X,q_i)$, and $q_i$ is taken from a series $Y$ such as $q_i = Y[i:i+L]$, you can compute the dot product of $q_0$, and then only update it for subsequent $q_1, ...$" ] }, { @@ -382,8 +359,10 @@ "metadata": {}, "outputs": [], "source": [ - "from aeon.similarity_search._commons import get_ith_products\n", - "from aeon.similarity_search.matrix_profiles.stomp import _update_dot_products_one_series\n", + "from aeon.similarity_search.series._commons import (\n", + " _update_dot_products,\n", + " get_ith_products,\n", + ")\n", "\n", "\n", "def compute_all_products(X, T, L):\n", @@ -409,7 +388,7 @@ " \"\"\"\n", " prods = get_ith_products(X, T, L, 0)\n", " for i in range(T.shape[1] - L + 1):\n", - " prods = _update_dot_products_one_series(X, T, prods, L, i)\n", + " prods = _update_dot_products(X, T, prods, L, i)\n", " return prods\n", "\n", "\n", @@ -428,11 +407,12 @@ " mask = np.ones((1, search_space_size), dtype=bool)\n", " # Used for numba compilation before timings\n", " compute_all_products(X, T, query_length)\n", - " _times = %timeit -r 3 -n 7 -q -o compute_all_products(X, T, query_length)\n", - " times.loc[(size, _query_length), \"compute_all_products\"] = _times.average\n", - " # Used for numba compilation before timings\n", " update_products(X, T, query_length)\n", - " _times = %timeit -r 3 -n 7 -q -o update_products(X, T, query_length)\n", + "\n", + " _times = %timeit -r 2 -n 2 -q -o compute_all_products(X, T, query_length)\n", + " times.loc[(size, _query_length), \"compute_all_products\"] = _times.average\n", + "\n", + " _times = %timeit -r 2 -n 2 -q -o update_products(X, T, query_length)\n", " times.loc[(size, _query_length), \"update_products\"] = _times.average" ] }, @@ -444,7 +424,7 @@ "outputs": [ { "data": { - "image/png": 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kv67seCV/zkoer+Ttkv2vPJUtr6nq/l6ojmeffVanT5/WokWLys1Fbr75Zk2bNq1auQgAAFVV5DB0JqvgQpFLhvP/hPPFLtn5Ff+NWRMmSa2bB7qKW6LOF7tEW4IUFR4kS7A/gynWAIUwaHDCQ/y15snB3m5GvQoPabg/ir74C9ZX2lwyoJk6daoefvhhj7YLDAysleO//fbbros9rr76ak2fPl39+/dX69atFRIS4gqcxo0bp02bNtXKqJu1rbbea0+rY2vjeL7SP2tDQECA3n//fT322GNasmSJ1q9fr127dqmgoEAHDhzQgQMH9OGHH+rjjz/WuHHjvN1cAABqTX6RXZsOp2rlL8n64Zdknc3K92i7QD+zhnaP1NhL2mpM79Zq3SK4jlsKoCEiF2lYfPEczlfaTC5Sc+QiDRu5CACgqTtyNlvf7kvSf/ee1v5TmZVvIKl5kL/GXNJGN17aVsNjWik4gNFNgabIEuKvH2YMrHzFRsJCLlKrfKXNJXORyZMn66GHHvJou9rKRd555x1XEczQoUP1wAMPqF+/fmVykfHjx2vz5s3kIrV0PF/pn7UhICBA7777rmbMmKGlS5fqxx9/1O7du0vlIrNmzdKsWbN04403eru5AIBGIKfA7prRJbFEkUtCep5OZRbI7qi7v2eC/E2KspwvcnH97yx4aWcJUpA/s9jWlYZ7NoUmy2wyKSI0oPIVUanIyEjX7eTkZMXExHi0XclRBM+ePVvhusnJyeVu502VTQVb8jld3Obik3mHw1HhPiqbbtcTJY9tGEaF063WNsMwNH/+fEnSkCFD9PXXX7udMt6bo6BUprL+WVRU5Gp/dftnye0q61sll5fcruQIImfOnFH37t3d7qOy52QymWQYRqV9tHi66otd/HuhPvTq1Usvv/yyXn75ZeXl5Wnz5s368ssvtXjxYmVnZ2v69On6+eef1bZt23ppDwAAdSEzr1BrD5zRyl+Ste7gWWXnezaSW/Mgf13bq7XG9mmjETGt1DyYcyGgqSMXqT3kIuUjFyEXqQpykZojFwEANCXxZ7K0Yo9z5pcDSVkebdMi2F9j+7TVuL5tdXX3lgryp/gFaOrMJpOsZCM1FhER4bqdnJysHj16eLRdVXKRkueBFc2qUZ8qa3Nt5CLuzjmr4uJcpHfv3jXep6cMw9Cnn34qSbrqqqu0bNkyt7lIRTP9eJsnuUhx+6ubi5Ts19XNCS/ORbp16+Z2H3Wdi1z8e6E+9OzZUy+99JJeeukl5eXlacuWLVqyZIk+//xznTt3Tg8//LC2bt1KLgIAqJRhGEo9V6iEksUuGReKXtJyqj7bXlVYQ/xLzOZSPLOL835kswCZm1DBa0NCIQyqrTpfAhcWFrr+GK/OFJ+omj59+rhu//TTTxoyZIhH20VHRys0NFQ5OTnatm1bhe/Vtm3bXLd79uxZat2SI1gUFRW53U/JE7SKjlXReiWPtWPHDt15552u+yaTqdQIDjt27HDd7tGjR6l9NWvWTJLzZL6itsTHx1fY5uJRHBwOh9v9mM1m9erVSwcOHNDmzZvr9WciLS3NdVI7fvx4ORyOck+Us7OzXc/VMIwybbz4/a7ofamoD1TX3r17lZeXJ3//8j/OikeTkCrun+W1vVjJC6W2bdtWqm9drOTPQ0xMjGufPXv2dD2+Y8cODR7sfnTnkv2zJLvdLsMwFBYWpqysLKWlpblts2EYOnLkiOt2yfWioqJksViUkZGhDRs2VOs98aR/u+Pv769hw4Zp2LBh6tWrl1555RXl5ubq22+/1ZQpUzzeT3GfLSwsbNAXJTVFZrNZFovFdT8jI6PSIA7wNvotqutMVr5i422KjUvTthMZKvJwBJFWYYEa2cOqa3tE6oqOLRTg5/yCqSg3W7Zcz4/vS323oVwcDtQmcpGGj1zEiVykLHIRchFyEdQVX/obHShGv0VNGIah+JQcrTqQqlUHU3Uk1bNgwxLsr2tjIjSmZ6QGd7K4spGcrEx5emmxL/VdchEA3tCvXz/X7U2bNmnYsGEebdepUydXLuLuHK3Yzz//7Lpdn4UcFdm5c6cmTpxY4fJivXr1KrUsLCxMkvMzpSKHDx+ucLkns34EBga6cpGtW7dWun5tstlsriKmW265xW0RTMlcpCHat2+fioqK3OYi+/fvd+UiF7/XnirZr3fs2FGtvlVyH7t27aowoyy5j/IU5yIVFSgZhqGjR4+Wu6xTp06uXGTTpk0VHsudmsxqExwcrBEjRmjEiBG65JJLXLnIDz/8UKVcBADQeBXaHTqVka+E9HwlZuQpocTMLonp+corqrvzfj+T1LaFs7glKjzYNaNLVHiQoixBCgui5KIh4l1BtZX8stRTDXGqzMbs0ksvVVRUlBITE/Xpp5/q0UcfdZ24V8Tf319DhgzR6tWrtW7dOp06dUrt27cvd93iUSL8/f01dOjQWm1/dX399df64x//qJCQEEml+925c+f01VdfSXJ+CX/xiAIdO3aU5AwusrOzy329UlNTtW7dugrbEBQUJEmuk2p3rr/+eh04cECHDh3SmjVrNGrUqEqeXe0o+SV9RaOVfPrppw364iybzabvv/9eN910U7nLFyxY4Lo9YsSIah2jbdu2iomJUVxcnL7++mu98sor5fYLu92uxYsXS3KO6FEyWOzfv7/Cw8OVnp6uzz//XI888ki54cDp06cVGxtbbjuK+3HHjh21f/9+7d69222bV61a5TacM5vNuu666/Tll19q48aN2rNnT6m2esLT/l2Za665xnU7NTW1WvswDKNan0eoPw6Hg/cIPod+C3cMw9DR1FzFxtu0Lt6m/Umej4bfNTJEI7pbNbK7Vb3bNisxGkjtfZbRd4H6RS7S8JGLkIu4Qy7iOXKRsshFUBX8jQ5fRL9FZQzD0MEzOVodl6bVcWk6YcvzaDtriLP4ZXRMhC6Pbi5/v+KLbmvn84y+CwClkYuElFl27tw5ff3115LIRYqRi1SsoeQixchFAACNQWZeUTkzujgLXpKzClSX36Y2CzQrKjxYUeeLXKLDgxRlcf7ftnlgiawCvoJ3DGjEzGazHnvsMUnSqVOn9Pjjj7s9EXE4HEpKSnLdf+CBByQ5T1yeeuopFRYWltlmwYIFrpOwm266qcFMU3nmzBn9z//8T7nLXn75ZddUovfff3+Z5cXhTEFBgf75z3+WWV5YWKhnnnlGubkVj2jVpk0bSdKxY8cqXO+hhx5yjbb65JNP6sCBAxWuv3LlSu3fv7/CdTzRsmVL1yhZy5YtU35+fpl1du7cqddee63Gx6prf/zjH0tNuVxsw4YNmj9/viRnsHDZZZdV+xjFPw8pKSn6wx/+UO46b775pg4ePChJmjJliuvkX3IGAXfffbck56gkM2fOLLN9UVGRnnnmmUrDguI+umPHDm3ZsqXM8uTkZLdtLDZjxgyZzWYZhqGHHnpIp06dcrtuecs86d/FoVNFF/qVDHE6depUYZsBAPAWh2Fod2KW3l13QnfM3qOJc/fqw58SKi2CMUnq1z5MTw7voCUP9NPn0/rpsWs6qE+7MKbEBYB6Qi5SFrmIE7lI1ZCLlEYuAgBoigzD0C9J2Xp//Qnd9sluTZ6/T3O2nKq0CCayWYB+M6C1PprYS98+OlB/uK6Lruxk4cISAKgH5CJlkYs4kYtUDblIaeQiAIDK2B2GkjLztf1EhpbvOaMPfjypl745pCnz92nUzO0aNXOHpn66Xy/9J14f/Jig5XvPavvJTCXVUhFMq7AAXRbdXOP7tNQjV0frLzd109x7++iHGQMV+8QVWji1r968NUZPjeioO/q30VWdLYoODyar8FHMCAM0cg8++KBWrlyp2NhYrVixQsOHD9e0adM0YMAAhYSE6MyZM9qxY4eWLl2qO+64Q88//7wkaezYsbrlllv09ddfKzY2VjfccIMeffRR9ejRQ+np6Vq2bJkWLlwoyTmd95///GdvPs1SBgwYoDlz5uj48eO6//771b59eyUnJ2v27NlavXq1JKlv377lBhvXXXedOnTooJMnT+q1115TWlqabrrpJgUHB+vAgQP65z//qb179+qKK67Q9u3b3bZh0KBB+umnn7Rz5069++67Gj16tEJDQyVJISEhateunSSpdevWmjlzph544AElJyfruuuu01133aXRo0erXbt2Kioq0qlTp7Rz50598803OnbsmBYsWKA+ffrU6DUym8264447NHv2bO3fv18333yzHn30UXXt2lWZmZlatWqV5syZo2bNmqlt27aVTu3rLX369FFcXJzGjBmjp556SgMHDlR+fr5WrVqljz/+2DUN7uuvv16j49x///1asmSJtm3bpkWLFikhIUHTpk1Tx44dlZycrIULF2rFihWSpM6dO+t3v/tdmX0899xz+uqrr3Tq1Cn96U9/0r59+zRx4kS1bNlSR44c0axZs7Rz504NGDBAu3btctuWqVOnas6cOSoqKtLkyZP17LPP6qqrrlJBQYG2bt2qWbNmqaioSF27dtWRI0fK3Uffvn31/PPP67XXXtPhw4c1fPhwPfDAAxo2bJisVqsyMzO1d+9erVixQn5+flq+fHmp7T3p31lZWZo8ebI6duyom266SQMHDlSHDh3k7++v5ORkff/9964Rgtq1a6frrruuGu8MAAB1I7/IoW0nMrUuPk3r49OVmlP2S77yBPiZdGUni0Z0t+qabuFq2SywjlsKAKgMuQi5SHnIRaqGXGR5qe3JRQAATYVhGNp3Ots188vpTM9G/W4dFqBR52d+6de+ufzMDAgCAN5CLkIuUh5ykaohF1leantyEQCAJOUV2pWQnq/EDOdMLs7bzv9PZ+ar0F5387oE+pnU3nJhJpfo8GBFhQcp2hKk9pZgBQdQ0NKUUAgDNHJms1nz5s3T448/rm+++UaHDx/W//t//8+jbT/44APZ7XatWLFCe/bs0aOPPlpmnbZt22rhwoWuE/WG4A9/+IM+/PBDrVmzRmvWrCmzvEePHlq4cKH8/cv+CgwMDNSHH36oSZMmKScnRx999JE++ugj13I/Pz/95S9/UXp6eoXBxrRp0zR37lzZbDb95S9/0V/+8hfXsqFDh+qrr75y3b/55ps1f/58PfHEE7LZbJo7d67mzp1b7n7NZrPrBLKmXn75ZW3dulX79u3Trl279PDDD5dabrVaNXv2bL3++usNNti49NJL9eCDD+r555/Xiy++WGZ5YGCgZs6cqcsvv7xGx/Hz89OCBQs0efJkbd26VT/++KN+/PHHMuvFxMRo8eLF5U6F26JFC3322We64447dObMGS1dulRLly4ttc5dd92loUOH6sknn3Tbll69eul//ud/9Morryg9PV2vvPJKqeVWq1Xz5s3Ta6+95jbYkKRnn31WZrNZb7zxhjIyMvTOO+/onXfeKbNeeVNYV6V/nzhxQrNmzXLbjjZt2mj+/PkeTcMNAEBdyswr0oYj6YqNt2nT0XTlFDo82i4syE/XdA3XiO5WDekSrmaBfnXcUgBAVZCLkIu4Qy7iOXKR0shFAACNmcMwtOeUs/hlTVyakrM8K35p0zxQo2MiNKZnhC5lNlwAaDDIRchF3CEX8Ry5SGnkIgDQNBiGobScIldxS0J6nhLT85WQ4fw/5Zxng4lWlyXEX1EWZ5FLdHiQ63aUJUitmweSO8CFQhigCQgNDdXs2bP1008/adGiRdqyZYvOnDkju92uVq1a6dJLL9XYsWN1++23l9ouODhYc+fO1ffff69FixZpx44dSktLU2hoqLp166Ybb7xRDz74YIM7IQkMDNTixYv173//W59//rkOHTqkwsJCde7cWbfddpsefvhhhYSEuN3+qquu0urVq/XOO+9o/fr1Sk1NVUREhAYNGqRHH31UgwcP1htvvFFhG9q1a6fvv/9e7777rjZu3KjTp08rL8/99PDXX3+9duzYofnz52vVqlU6cOCA0tPT5e/vr9atW6tnz5665pprNH78eEVFRVX7tSmpRYsWWrFihWbNmqWvv/5aR44ckZ+fn6KionTdddfpoYceUvv27WvlWHVpypQp6t27tz766CNt2bJFaWlpioyM1DXXXKMnn3xSPXv2rJXjWK1WffPNN/ryyy+1ZMkS7d27V+np6WrevLl69+6t8ePHa8qUKQoMdD/ye69evfTTTz/p/fff14oVK5SYmKiwsDD17t1bU6ZM0e23365FixZV2pZHHnlEMTEx+uijj7Rz507l5uaqbdu2Gj16tJ544glFR0d79JyeeeYZ3XLLLZozZ47Wr1+vhIQE5ebmKjw8XDExMRoxYoQmTZpUZjtP+neHDh20cuVKrVq1Slu3blVCQoLOnj2rc+fOyWKxKCYmRtdff72mTp2q5s2be9ReAABqW3JWvtbF2xQbb9OOk1myOzwblaR1WIBGdI/QyB5WXR7dnClyAaCBIxchFykPuUjVkItcQC4CAGhs7A5DuxKzXMUvnl7M0r5FkEb3dM780qdtM5m4CAUAGiRyEXKR8pCLVA25yAXkIgDQeBTZHTqdWeAscik1s0u+EtPzPB48tDrMJqlt8yBnkUt46dldosODFBZEeQM8YzIMo+7mH0KjlpKSUuVtkpOT5XA4ZDab1aZNmzpoFZqqDRs2aMKECZKk5cuX6+qrry61vORoHkVFRfXZNNSBgQMH6uTJk5o0aZJmzpzp7ebUGfqte3yeNFx+fn6yWq2u+zabTXa73YstAipHv206DMPQ4ZRcrTts07pDNv2SfM7jbbu1DNHI7laN7B6hXm1CG8TFHb7Ud1u2bOntJgC1jlwEDQm5SNNCLgI+TxouX/obHShGv22aihyGdiZkavXBNK09ZFNqjmfFLx3CgzQ6JkKjYyK9no/4Ut8lF0FjVdVshL9jUVfIRZoWchHwedJw+dLf6ECxxt5vs/OLXDO6lCxySUjPV1JWvjwcK7RaQgLMF83qcqHYpW2LQAUw4GiN+FLfrctchJIpAAAAAECjZXcY2nsqW7HxaYqNtykhPd+j7UyS+kc118juVo3oblUHa3DdNhQAAAAAAKAOFdkd2n4yU6vjnMUv6bmeXVDZKSJYY2KcM7/0aNUwBgcBAAAAAACSwzB0NrvAVezinNnlfLFLRr4yPDz3r67IZgHO4hZLsKLCSxe9RIT6kyGgzlEIAwAAAABoVPIKHdp2IkOx8Tatj7fJ5mG4E+hn0pWdLRrR3arhXa2KaBZQxy0FAAAAAACoO4V2h7Yedxa/rIu3KSPPs4yka2SIxvR0Fr90jQzhwhUA5fLz86vS+vwugTdc3O9MJpMMow6HPgdqAf3WMyaTqcqfRahbZrO5wvtAQ+QL/Tav0F5iJpc8nUzPU2J6vhJseUrMyFOBve4+I/zNJkWdL2zpYA1WdHiwosKD1eH8YyGB/B72Fl/ou/WBQhgAAAAAgM/LyC3ST0dsio23afOxDOUWOjzarkWwn4Z1tWpkd6uu6mxRKEENAAAAAADwYflFDm05nqHVB9O07rBN2fl2j7aLaRWqUednfukSGVLHrQTQGFit1iqtn5KSIsMwZDab5e/P5UqoPSUvhPfz86uwf3HRfOPRlH6X0G9LK77QNyAgoMqfRahfFovF200Aqswb/dYwDNlyCnU89ZxOpOXoRGqOjp///0RajpIy8+r0+C2C/dUpspk6RoaqY0SoOkWEum63s4TIz0xBuy9oqr9zm8ZfgwDQCB0+fFgFBQVV3q5ly5Zq1apVHbTIM2fPnlVKSkqVtwsMDFS3bt3qoEUAAMBXJWXmKzbepnXxNv18MlOeDnTSpnmgRna3amQPqy6Lai5/v6Y5MgYAAL6MXAQAAOCCvEKHNh9L1+q4NK0/bNO5As8GCOnVJlRjYiI1KiZCHa3BddxKAABQW+Lj46uVi7Rq1crrucjZs2ervF1gYKC6d+9eBy0CAKB+FNkdOpWep+NpJYpdzhe6nEjLUXa+ZzO4VofJJLW3hKhjhLO4pWNkqDq5il6ayRIaUGfHBuoahTAA4KN+85vf6OTJk1Xe7ve//72ef/75OmiRZ+bMmaM333yzytt16NBBP//8cx20CAAA+ArDMHQ4JVex8TbFxqfpQHKOx9v2aBWqkd2tGtHdqp6tQ8tMKw8AAHwLuQgAAGjqcgvs2nDUWfzy05F0j2fH7dO2mUbHRGhUTISiwyl+AQDAF91+++3VykVeeOEFvfjii3XQIs988sknev3116u8XYcOHbRnz546aBEAALUnO7/o/Cwu50oVuRxPzVFieq7sDg9H9qyG4ADz+UKXZq4il46RztldoqwhCvJndjE0ThTCAGgUrr766mqNGgHfxIUfAAA0HXaHod2JWa6ZXxIz8j3azmySBkQ114jzxS9c2AEAaMzIRZoWchEAAJqunAK7fjriLH7ZcCRdeUWeFb/0ax/mLH7pEaF2lqA6biWApsJms1Vp/cLCQjkczt9bRUV1N+I1mp6rrrqqVC5Ssn+ZTCb5+V248NNut8sw6u4i1IbO4XB49eev+HdAdRS3u2Qu0lh/l9BvK+ZwOORwOFRYWFjlzyLULbPZLIvF4rqfkZFRo597oD5Upd8ahqGz2YVKSM8r958tp24/lyJCAxQdHqzo8CDn/9bg8/eD1bJZgJvBQAuVk1Uoz4cYha/wpd+5Vqu1zvZtMvgrCdWUkpJS5W2Sk5PlcDhkNpvVpk2bOmgVUD5//wt1f431RBiND/3WPT5PGi4/P79Sf7zabDbZ7XYvtgioHP224ckrdGjL8QzFxtv042Gb0nM9+xwM8jfpyk4WjeweoWu6hcvayKfw9aW+27JlS283Aah15CLwJZxfwhfRb93j86Th8qW/0YFi9NuGLzu/SD8edha/bDqWrvyiyr/eN0kaEN1co2MidG0Pq9o0b3zFL77Ud8lF0FhVNRvh71h4C+eX8EX0W/f4PGm4fOlvdKDYxf02+WyqTqblKCEjXwnpeUpMz1dChvP/xIx85Xs4IEW12mI2qX2LQEWFByva4ix2iTpf9BJlCVJoILO64AJf+p1bl7kIM8IAAAAAALwuPbdQPx1O17rDNm06muHxiKaWYH9d0y1cI7tbdWUni0IIfwDUk5Ij8nmq/JGYgLp1cb8zmUyMIIkGj37rmYtHiIX3mc3mCu8DDRH9tmHKyitSbHyaVh1I1aZj6Sq0V/45aDZJl3dooTE9I3VtTKRahQXWQ0u9h74LAAAAAKgqwzCUmWd3zuKSka/TmQU6k3NCx1NzdDItR6cz8lSXSXSzQD/XjC5R54tdosODFGUJUpsWQfI3810iUBUUwgAAAAAAvOJURr7WxdsUG5+mXQlZ8uCaDklSuxaBGtk9QiO6WzUgujlhEACvqM4UzikpKTIMQ2azudSIfkB94qJ5+CL6bWnFF/oGBARU6/MI9cdisXi7CUCV0W+9x3auQD/8kqz/7jutDfEpHhW/+JlNGtI1Ujf2bauxl7RVq0Y484un6LsAAAAAAEmyOwwlZxUoMSNPCemlZ3ZJSM9Xdn7dzprRpnmgoixBzpldzhe5FBe8WIL9GTgPqEV84w4AAAAAqBeGYejQ2RzFxtsUG29T3Jkcj7eNaR2qEd2sGtnDqphWoYRDAAAAAADA56Vm52vlL8n6797T2ng4VXZH5cUv/maThnZvqZv6ttV1l7RVRLPGPfMLAAAAAAAXyy2wKzHDWeSSkJ5fqujldGaBijw4v66uQD9TieKWYEWFByn6/P12liAF+TNjKVBfKIQBAAAAANSZIoehXQlZWhdv07p4m05l5nu0ndkkXRbd3DXzS3tL0x3RFAAAAAAANB5nsvL0/f5kfbv3tDYfSZUn1+YE+Jl0TY9WuvHStrrukjYKD6X4BQAAAADQeBmGodScQmeRy/lil4QM58wuien5Ss0prNPjh4f4n5/NxTmTS3R4sKv4pWVYgMwM3Ak0CBTCAAAAAABqVV6hXZuPZSg23qYfD6crI6/Io+2C/M0a0tmikd2tGtY1XOGhAXXcUgCoPpvNVuVtCgsL5XA4JElFRZ79bgRqymQyyc/Pz3XfbrfLMOpuJDSgNtBvK+ZwOORwOFRYWFitzyPUHbPZLIvF4rqfkZHh+uwHGir6bf04k1Wg1XGpWnUwVTtPZsqTT7VAP5OGdrVqTM9IDe9mVfNg51f7Rv452fLP1W2DfYAv9V2r1ertJgAAAABAg1Nod+hURr5rZpfE9HwllLidV1R353h+JqltC2dhS7e2FnWMCFWniFBZA+xq29xfYUFcXg/4An5SAQAAAAA1lp5TqB+PpCs23qbNxzKU72EoZQnx1/BuVo3sbtWVnVooOMCv8o0AoAGw2+1V3oaLuOENF/c7+iF8Af3WM4ZhVOvzCPXH4XDwHsHn0G9rT1JmvtYcStPquDTtTsz2aJsgf5OGdgnX6JgIXdPNqmaBpQtD4R59FwAAAAAanqy8IudsLul5roKXhPR8JWbkKTmrwKNZUqsrNMDsnMklPEjRlvP/hztneGnbPFD+fmb5+fmVGrzAZrNxbgn4EAphAAAAAADVkpCep3XxNq2Lt2lXYpbHIVWUJUgjujuLX/pFNZe/mWmDAQAAAACA7zuVka/VcWlaHZeqfac9m7Ul2N+sa7o5i1+GdglXaCCDhAAAAAAAfIPdYehsdoGz2CXj/Kwurtld8pSZV7dFJa3CAhRlcRa3RIcHK8oS5LodHuIvk4lrEYDGjEIYAAAAAIBHDMPQwTM5WhdvU2y8TYfO5ni8bc/WoRrZ3aqRPSLUvWUIgRMAAAAAAGgUEtLztOpgmtbEpemXZM+KX0IDzLqmm/V88YuFGXIBAAAAAA1WXqH9/GwupWd2SczI16mMfBXa625alwA/k9pbnDO6RIcHuWZ1ibIEKcoSxPk00MRRCAMAAAAAcKvI7tDOxCzFHnLO/JKUVeDRdn4maWCHFhrR3aoR3axqZwmq45YCAAAAAADUj+NpuVodl6ZVcWmKO+PZQCHNAv00oruz+OWqzhYF+ZvruJUAAAAAAFTOMAzZcopKzeiSkJ7vKnhJOVdYp8e3BPsrKjzo/Gwu5wtezhe+tG4eKDODbAJwg0IYAAAAAEApuQV2bTqWodh4m346YvN4uuJgf7OGdLFoZHerhnW1yhLCKScAAAAAAGgcjqTkaHVcmlbHpSk+JdejbZoH+Wlkd6tG94zU4I4tFEjxCwAAAADAC4rsDiVlFVwocknPV0JG8e085RQ66uzYZpPUpnlgmSKX4pldmgdzXQGA6uG3BwAAAABAtpxCrT9sU2y8TVuPZyi/yLPpi60h/hre3aoR3a0a3NGi4AAu6AAAAAAAAL7PMAzFp+Rq9cE0rY5L1dG0PI+2s4T469rzM79c0bGFAvzISgAAAAAAdS87v0gJ52d0cc7m4ixySczIV1JmvuyeXQJQLcH+5lLFLdHhwYo6f79di0DOjQHUCQphAAAAAKCJSkjPU+whZ/HLnlNZcngYfEVZgnRtD6tGdo9Q3/Zh8jMzFTEAAAAAAPB9hmHo4JkLM7+csHlW/GIN8de1MREaHROhyzu0kD9ZCQAAAACgljkMQ2ezC5yzuZyf0SXxfOFLQka+MnKL6vT4kc0CXEUuzpldim8HKyLUXyYT58IA6heFMAAAAADQRBiGoQPJOYqNT1NsvE2HU3I93rZ3m2Ya0d2qkd2t6tYyhBALAAAAAAA0CoZh6Nfkc1p10Fn8kpiR79F2kc0CNKqHs/jlsujmDBQCAAAAAKix/CKHTmWUnNUlzzWzy6nMfOUX1d20Lv5mk9pbghRtCVJUeJCizhe8RFucs7yEBPrV2bEBoDoohAHQYJw4cUKXX365JOm9997T3Xff7eUWoS4sWrRITz75pCRpx44d6tixo5dbBABA41Zkd2hHQpbWxdu0Lt6m5KwCj7bzM5t0eYfmGtndquHdrGrbIqiOWwoAAMhGmgayEQAAvM9hGNp/Ots188vpTM/yktZhARoVE6ExMZHMkgsAQC0jF2kayEUANHWGYSgjt0gJFxW5FBe9nMkurNPjNw/yU3R48PnZXJwzukSdL3Zp3TyQ81wAPoVCGAAAAABoZHIK7Np4NF3r4m366Ui6svLtHm0XEmDW0C7hGtndqqu7hqtFMKeMAAAAAACgcXAYhvYkOotf1hxK83iwkLbNAzU6JkKje0bo0nZhMjNLLgAAAACgAkUOQ8mZ+a5il8T080UvGflKTM/XuQLPvr+vDpOk1s0DLxS5WJz/R4cHKcoSLEsI1wAAaDz4jQagSXnttdf0+uuvS5LOnj3r5dagoRo4cKBOnjypSZMmaebMmd5uDgAAHkk9V6gfD9sUG2/T1uMZKrB7NiVyRKi/hnezamR3qwZ1sijI31zHLQUAAN70xhtv6M0335RENgL3yEYAAI2F3WFoV2KWs/glLk0p5zwbWTfKEnR+5pcIXdK2mUwUvwAA0CiQi8AT5CIAPJFTYHcWuWTkK+F8oUvxrC6nMwtkd3j2fX11BPmbFWUJcs7kEh6saEuQos4Xu7RvEaRAvvMH0ERQCAMAAAAAPuqELU+xh9IUG2/T3lPZ8jRK6xAepJE9IjSyu1WXtgtjemMAAAAAANBoFDkM/XwyU6vj0rT2UJrScoo82q5DeJDG9IzU6JgI9WwdSvELAAAAADRhhmEo5VyhazaX4iKXhPR8JWbkeXyuWV0Rof6KslyYycU1w0t4kFo2C+CcFQBEIQwAAAAA+AyHYejXpHOKjbdpXbxNR1JzPd72krbNNLK7c+aXLpEhBGMAAAAAAKDRKLI7tN1V/GJTeq5nFyR1igjWmJgIjekZqe4tyUsAAAAAoCkpKHLoVKZzRpfE9DwlnC92SUzPV2JGvvKLHHV2bD+zSe1aBDqLWyznZ3YJDzo/00uwmgX61dmxAaCxoBAGAAAAABqwQrtDO05maV18mtbF23Qmu9Cj7fzMJg3q2EIju1s1vJtVrZsH1nFLAQAAAAAA6k+h3aEtx53FL+vi05SZZ/dou24tQzQ6JkKjYyLUlcFCAAAAAKBRy8gtUmKGcyaXi2d2OZNVIKMOj90s0Kyo8GBFWy7M5hJ9/n6bFkHyN3M+CgA1QSEM0Ig9/vjj+uyzz9ShQwf9/PPPbtdbtGiRnnzySUnSjh071LFjR9eygQMH6uTJk5o0aZJmzpypnTt3atasWdqyZYtSU1MVGRmp4cOH68knn1SPHj0qbI/dbte8efP02WefKS4uTiaTSZ07d9Ydd9yh6dOne/Sctm/frpUrV2rLli06dOiQ0tPTFRQUpPbt22vo0KGaPn26evbsWWa7hQsX6rHHHiv1WKtWrcqsd/HzL273F198oa+//lp79uyRzWZTs2bN1KNHD9188826//77FRIS4lH7PVUbr/uGDRs0YcIESdLy5cs1ZMgQLVq0SF988YXi4uKUkpKiiRMnaubMma5tHA6HlixZoiVLlmjPnj1KT09XixYt1KtXL91yyy2aPHmyAgMrvog2PT1d7733nr799lslJCQoLCxMl1xyiaZOnapbb7210ude/L78/ve/1/PPP+92vVtvvVUbN27U0KFD9dVXX7ld78SJE5o7d67WrVunkydPKisrSy1atFCPHj00fPhwTZw4UZ07dy61z2KfffaZPvvss1L7u/h4drtdn3/+uZYuXar9+/crPT1dwcHBatmypaKjo3XNNddo3Lhx5fZLAADcyc4v0qajGYqNt+mnI+k6V+DZhRyhAWZd3TVcI7pbdXWXcDUP5pQPANC0kY1cQDZCNkI2AgBoDPKLHNpyPEOrDqZp/WGbsvM9y0xiWoVqdM8Ije4Roc6Rtft3CwAADRW5yAXkIuQi5CJA42V3GDqTVaCE88UuieeLXIoLXrI8PG+srtZhAeeLXIpndrlQ7GIJ8WfwBQCoQ1wVBcBjCxYs0HPPPaeiogvTyZ86dUqLFy/WsmXL9MEHH7g9ac3Oztbdd9+tzZs3l3p879692rt3r5YuXap33nmnwuOXDF9KKiwsVFxcnOLi4jR//nz93//9nx544IFqPMOyEhISNHnyZO3fv7/U4wUFBdq6dau2bt2qOXPmaNGiRerWrVutHPNiNXndi+Xl5WnixIlat26d23VsNpsmT56srVu3lno8NTVVGzZs0IYNG/TJJ59o8eLF6tChQ7n7iIuL0x133KGkpKRSx16/fr3Wr1+v1atXa8iQIZ487VrxwQcf6NVXX1VhYemR89PS0rRlyxZt2bJFGzZsqDAUqYi7fl1YWKisrCwdPXpUP/74o/bs2aM5c+ZU+3kAAJqGlHMFWh+frtj4NG07kalCu2djz0SGBmh4d6tGdrdqUMcWCvQ313FLAQBoushGLiAbIRuRyEYAAPUjr9ChTcfStTouTT8etulcgcOj7Xq3aabRMREaFROhjtbgOm4lAACNH7nIBeQi5CISuQhQn3IL7M7ClhLFLonp+UrIyNepjHwVOepuXpdAP5OiLEHOmV3OF7lEnZ/hpb0lSEF8Pw8AXkMhDACP7Nu3T0uXLlXLli311FNPaeDAgcrLy9OqVav0j3/8Q/n5+Xr00UfVqVMnDRgwoMz2M2bMcJ34DRw4UI888oi6du2qM2fOaPHixfr666/13HPPVdiGoqIihYeH64YbbtCQIUPUtWtXhYaGKikpSXv37tU///lPpaam6sUXX1SPHj10zTXXuLa96aabNGDAAH3yySeaPXu2JGn9+vVljtGuXTvX7bS0NN18881KTExUUFCQJk+erKFDh6pjx446d+6c1q5dq3/+8586evSo7rrrLq1evVotWrSozsvrVk1f92J//vOftX//ft1www266667FB0drbNnzyorK0uSc4SKe++9V9u2bZPkHMHiwQcfVKdOnZSUlKSFCxfqv//9r+Li4nT77bdr7dq1CgsLK3WMrKwsTZw40RVoTJgwQZMmTVKrVq10+PBhzZo1S4sWLdKBAwdq9TVy5+2339Zrr70mSbJYLJo2bZqGDRsmq9WqzMxM7dmzR//5z39KVd2/9957ysnJcT2PG2+8US+99FKp/YaGhrpuv/nmm65+PXbsWN15552KiopSUFCQUlJStHfvXq1cuZLKfgCAW8fSchV7yKb1h23aeyrb42mXO1qDdW0PZ/FLn3ZhMvNZAwBAnWss2ci//vUv1xfvZCNkI2QjAICGKrfArg1HncUvPx1JV26hZ8Uvfdo205iekRrVw6qocIpfAACoLeQi5CLkIk7kIkDdMAxDaTlFSkjPU0J63vnZXPJdt1PPFVa+kxqwhPgr+nxxS1R4kKItzqKXqPAgtQoL5Pt4AGigKIRBw2M4ZMqzebsV9coItkqmhl0ZvH//fnXo0EHffvut2rRp43p86NChGjVqlH7zm9+osLBQzz//vFauXFlq25UrV+rbb7+VJI0ZM0bz58+Xv/+FXz/XXXed3nrrLb3++usVtmHMmDG64447Sp1USlK/fv00duxY/fa3v9Utt9yi/fv36/XXXy8ValgsFlksllJT2/bu3bvC4/3hD39QYmKiOnTooGXLlqlTp06lll999dW65ZZbNH78eB07dkzvv/++Xn755Qr3WVU1ed0v3s/vfve7MifoxebOnesKNIqnvi0+Ee/fv7+uv/56vfrqq/r73/+uY8eO6W9/+5v++Mc/ltrH22+/rcTEREnSyy+/rKefftq1rH///ho/frzuuecexcbGVuelqJI9e/bojTfekCR169ZNS5cuVfv27UutM2zYMM2YMcPVZkmu9zggIECS1KJFiwr7SfGoIOPHj3cVWJU0evRoPf3007LZmtbvNACAew7D0C9J5xR7KE2x8TYdS8vzeNtL2zXTyO4RGtndqs6RIXXYSgCAJ/z8/Kq8jUdfeJKLNFi+mo0U97vibKRly5au7chGLiAbcWoo2cjFvy9NJpMMo+5GdfRVJpOpWp9HqDtms7nC+0BD1JD67bl8u348bNOqgynacCRdeUWeFb/0j2ruLH6JiVB7C8UvTUVD6rsAalkTy0bIRer3mhFyEXKRYg05FwEaq0K7Q6czC5zFLSWKXIr/93QAhOrwM0ltWzgLW6IsF8/sEqSwIC6lBgBfxG9vNDimPJsiPxns7WbUq9QHt8oIifR2Myr1v//7v6VOrIsNGzZMU6ZM0Zw5c7Rz507t2rWr1EgTxaNpBAUF6Z133ikVaBT73e9+p6+//lq//vqr2+OXHHmjPC1atNALL7ygqVOnasuWLUpLS1NERISHz660EydOaPny5ZKk1157rUygUaxfv3568MEH9f7772vx4sW1HmpI1X/dS+rWrZuef/55t8coPiFv2bKlXn/99XIvznrhhRe0YsUKHTp0SPPnz9cLL7ygoKAgSc5pfxcsWCBJ6tOnj5566qky2wcEBOjdd9/VFVdcUWba2dr2wQcfyOFwyGQy6R//+EeZQKOkqKioah/nzJkzkqSrrrqqwvWsVmu1jwEA8H2Fdoe2n8hUbLxN6+JtSvFwtBp/s0mDO7bQ8O5WjehuVauwwDpuKQCgKqrzd35KSooMw5DZbC733FiSTDmpsjSxXCTjoZ9lhNZNLlLyYjh3r7lUurDJ39/f7bp/+ctfyj2PHDlypKZOnarZs2dr586d2rt3ry677DLX8rlz50pyZiPvvfeegoPLXqD5/PPPl8pG/Pz8yrSjQ4cObp+DJEVEROgPf/iD7r33Xm3ZskWZmZllshFPX5OS2cgbb7yhbt26lbvewIEDNX36dL333nv67LPP9D//8z8VtrE6qvu6l3xfu3fvrj/84Q9uiwaK86uWLVvqrbfecl3wUNLLL7/sGv10/vz5evnll91mI88++2yZfMXf318zZ87UZZdd5spGKupvFf2ukC4Ui5hMpjLrffjhh65sZPbs2erYsaPb/bjLvTxpQ3E2cvXVV1e4XsnBaaqDYo/Sin+OAwICyJ0aOIvF4u0mAFVW3/02M69Qa349o//uPa11cWeV70Hxi8kkDeocoXGXttUNl7ZTW4pfIH7nAo1JU7tmxFeuF5G4ZqQ8XDPCNSMS14wAJWXkFupkWo6Op+boQGKKEmy5rpldkrMK5KjDsW5CAsyKDj9f5GI5P7PL+YKXts0D5e/X8AtPAQBVw292AB4JDw/XjTfe6Hb5Pffc47q9bt0612273a6NGzdKcl6c0LZt23K3N5vNmjRpUpXadO7cOZ04cUIHDhzQr7/+ql9//bVUYLJ///4q7a+kH374QXa7XaGhoRo9enSF6xaf0CYlJSkhIaHaxyxPdV/3i02YMMHtBQNJSUmKi4uTJN1yyy1lpq8t5u/vr7vvvluSlJ6erj179riW7d69W+np6ZKkSZMmuR3luH379ho5cqTbdtYGh8Oh1atXS3JehNGvX786O1Zx2LR8+XLl5OTU2XEAAL4nO79I3x9I1UvfHNKYD37WE0sOasnuM5UWwTQLNGtsr0j9383dteqxgXrvzl66c0AbimAAAGgAwsPDNW7cOLfLJ0+e7LpdcmRLu92uDRs2SJKuvfZatxdtmM1m13m3p4qzkV9//VW//PKLfvnll1LZyL59+6q0v5K+//57VzZy3XXXVbju0KFDJUmnT5/WyZMnq33M8lT3db/Ybbfd5jYbOX36tA4ePCjJmaE0b9683PX8/f1dWUx6erp2797tWrZr1y5XNnL33Xe7zUaioqJ07bXXum1nbXA4HFq1apUk50Ux9ZGNLF26lGwEAOCxjJxCfbkjQQ/O3aYr/rxKT3+2Syt/Sa6wCMZskoZ0jdSfb+2jLS+N1ucPD9H9V3ehCAYAgHrCNSPucc0I14xwzQiaEodhKCkzX9tPZOqrvWf0wY8n9dI3hzT1030a+e5W9f/flbr5/Z/02MKf9f66E1q256y2ncjU6czaKYJp2SxAA6Ka66ZLWurhoVH687humn3PJVr56ECtf/IKLbqvr968NUZPjeyoOwe00VWdwxUdHkwRDAA0UswIA8Ajffv2rXBUx0svvVSBgYEqKCgoNULHsWPHXCd7JUfkLE9lyyUpNTVVs2bN0n/+8x8dOXJEhuH+L+TU1NRK9+fOrl27JEk5OTlug5jynDlzRtHR0dU+7sWq+7pf7JJLLnG7rOR2l19+eYXtGThwoOv2gQMHNGjQoDL7cDfCSMl9/PDDDxWuUxPHjx9XRkaGpMpH3aipSZMm6e2339a2bdt0xRVXaPz48Ro+fLiuvPLKUlMqAwCahrPZBVp3ftaXbScyVeRhkteyWYBGdLdqZHerLu/QQoH+hHAAADRE/fr1q/AcvW/fvq5z9F9++cX1+NGjR13ZSMnz6vJUtlxy5h0ffPCBvvnmGx0+fLhespGqnOOeOXOm0plrqqK6r/vF+vTp43ZZVbKRK664otR2gwc7RykueezKMq7LL79cK1eurHCdmiiZjQwZMqTOjiM5i37efPNNbd26VQMGDNCtt96qESNG6KqrriIbAQCUYjtXoB9+SdaKvae1IT7Fo9zEz2zSkK6RGte3ncb2aaOWYUH10FIAAFAerhnxDNeMcM0I14ygMcgrdCgxI0+JGc6ZXBLS85WYnq+EjDydyshXob3upnUJ8DOpvSVIUZYg1+wuUZbi/4MUHMDM0QCACyiEAeCRyk7Q/P39FR4erjNnzshms7keL3m7sn20bt26wuW7d+/WxIkTlZaW5kGLpby8PI/WK09KSkq1tsvNza32MctT3df9YuHh4W6XFY/K4cnxSk636+59btWqVYX7qGx5TZXsH+VND1ybnn32WZ0+fVqLFi3S2bNnNXv2bNeUwb169dLNN9+sadOmVdq3AQC+61hqrmLjbYqNT9O+0+c83q5LRLBG9ojQiO5WXdK2mcxuRsYCADRcFZ2DuVNYWCiHwznKdVFRUbnrmOzlP96YFdmLZLh5PWqq+PWW3L/mknN00pLrlbduZGRkhfuQ5DpHT0tLc61bMmOIiIiocB+RkZGl2nTxulXNRs6dOye73V5qxE9PX5MzZ854dIyLZWdnV/o6VUV1X3ep9PvavHlzt/speWFMVd6j1NRU17rV3Ye7/iY536uK9lN8sY9hGKXWK/netWrVqkbvR2VteOaZZ5SYmOjKRv71r3/pX//6l6SaZSMmk6lUv7Xb7RVe3NTUOBwOORwOFRYWVuvzCHXHbDbLYrG47mdkZJT6vQs0RHXZb9POFWjtoTStOpiqbccz5Mm1Uv5mkwZ3smhMz0iN7BEha2iAc0Fhjmw2RtnGBb70O9dqtXq7CQBQY1wz4hmuGeGaEa4ZgS8wDEPpuUUXilxKFrxk5OlsdmGdHr9FsJ+iLcGKCncWu0SFByn6fLFLq7BA+Zn57hwA4BkKYdDgGMFWpT641dvNqFdGcMMPP91NW1pf+ygoKND06dOVlpamgIAATZ8+XTfccIO6deum8PBwBQU5RwE7duyYa8SJmnw5XnyhRGRkpJYtW+bxdh07dqz2MctTG6+7JLdT3NbF8Wqrzb4gICBA7777rmbMmKGlS5fqxx9/1O7du1VQUKADBw7owIEDmjVrlmbNmlXhdMUAAN/hMAztO52t2EM2xcbbdMLm2ZcoJkl924dpRHerRnS3qnNESN02FABQ50peYO8pT85TyUUaLm+fM1c3G6luPkI2Un1kIzXPRi7utxTBlM8wjGp9HqH+OBwO3iP4nJr225RzBYo9ZNOqg2n6OSFTnkyYG+Bn0lWdLBodE6Hh3a1qEXzhK2x+huApfucCjUdTy0bIRTzDNSM1Qy5SN7hmBA1Zkd2hpKwCJaQ7i1yKZ3RJPF/scq6g7orIzSapnSVE7VsEuGZ2cf0fHlTqnA8AgJrgEwUNj8ksIySy8vVQKbPZLEmVjn5UPA1tRc6ePVvh8qKiItcoESVHNSo5qkRl+6hopNEff/xRx44dkyS9/vrrmjJlSrnrlRypoiYiIiIkOUcxjYmJ8TgUqG3Vfd2roirvUXJysuu2u/f5zJkz6tatm9t9VHYMk8kkwzCq3W+L37uL21uXevbsqZdeekkvvfSS8vLytGXLFi1ZskSff/65zp07p4cfflhbt26t0pTJAICGo6DIoW0nMhUbb9P6eJtSczwbASfAz6RBHVtoZPcIDe8WrpZhgXXcUgBAo0AuUqvIRqqPbKR8ZCPlIxsBgKbtTFaB1hxK05q4NO1MyJInl5wG+pk0pEu4s/ilW7jCgvjaGgBwHtlIrSEXqT5ykfKRi5SPXATekp1fdH42l/PFLhkXil6SMvM9mpWzuoL9za4ZXaItQYoKD1LHiFD16dRGUeEhCvQ3y2azUawOAKhTJIpAIxYWFibJORV4RQ4fPlzpvvbt26eioiL5+5f/a2P//v0qKCiQ5Jzes1jnzp0VEhKi3Nxc7dy5s8Jj7Nq1y+2ygwcPum7fdttt1dqH5PnIE3379tWSJUuUn5+vXbt26fLLL/dou9pW3de9Knr37u26vWPHDk2cONHtuiXfw5LHK7mPXbt2aciQIR7tozxhYWHKysqqMKAyDENHjx4td1mnTp1ksViUkZGhTZs2VXgsd2oyQklwcLBGjBihESNG6JJLLtErr7yi3Nxc/fDDD27DOABAw5OVV6QNR9O1Lt6mDUfSlVPo2Yg4YUF+GtY1XCO7WzWkS7iaBXrnixEAAOBENlIW2UhZZCNlkY0AACqTlJmv1XFpWh2Xpj2nsj3aJsjfrKu7WDS6Z6SGdSU3AQCgrpGLlEUuUha5SFnkImgoHIahlOzCUkUuCedndElIz1d6blGdHj8yNEBR4c4il2hLsKLDgxQV7vw/MjSgzM+Kn5+frNZmddomAABKMnu7AQDqTvGUq9nZ2YqPjy93nYKCAv3nP/+pdF82m03ff/+92+ULFixw3R4xYoTrtr+/v4YOHSpJio2NVVJSUrnbOxwOffbZZ273X1R04Q/3c+fOud3H/Pnz3e5Dkms6XEnKz893u97111/v+mP9448/rnCfdam6r3tVtG3bVjExMZKkr7/+WtnZ5X9hZbfbtXjxYknO0Tz69evnWta/f3/XCB+ff/652ymGT58+rdjY2ArbU9xvd+/e7XadVatWuQ3rzGazrrvuOknSxo0btWfPngqPV57iflIcGFXXNddc47qdmppao30BAOre6Yxczd90TI9+tl9jPvxZ/2/FYf1wMK3SIpjWYQH6zYDWmnlnL/0wY6D+clN3jekZycUcAAA0AGQjZZGNlEU2UhbZCACgPInpeZq/7bTuX7BPN/9jl96JPVFpEUywv1nX9YzQa+O764cZA/XGrTG6vhe5CQAA9YFcpCxykbLIRcoiF0F9yi9y6Fhqrn46bNPin5P09prjembpQf1m9m5d8+42jft4px767Ff973dH9MnmU/r+QKr2nT5XK0Uw/maTOlqDNaSzRb8Z0FpPj+iot27tocX39dX6J6/Q9zMGavY9ffTncd318NXRuqlPKw2Iaq6WzQJrVDAGAEBtoRCmkYiPj9df//pX3X///ZoyZYpefvllbdy40dvNgpcVhwmS9OGHH5a7ziuvvKLTp097tL8//vGP5U5Fu2HDBleY0L9/f1122WWllk+bNk2SM0R47rnnyp3y8O9//7t++eUXt8fu2rWr63bxifXF/vKXv1R6AltymtHiaXPL0717d91yyy2SpGXLlmnWrFkV7vf48eNaunRphetUV3Vf96p44IEHJEkpKSn6wx/+UO46b775pmuUlSlTppQKiIKCgnT33XdLco5IMnPmzDLbFxUV6Zlnnqk0KCjutzt27NCWLVvKLE9OTnbbxmIzZsyQ2WyWYRh66KGHdOrUKbfrlresTZs2kiruI8WBk7sAR1KpAKdTp04VthkAUP8Mw9CRlBx9silBt878SUP+ukavfLVfm49lyO6oeJ7orpEhmnZle827t4/+8/BlemFMF13V2aIAP06xAABoSMhGyio+55XIRkoiGymNbAQAUOykLU9zt5zSlPn7dOu/duvddSe073T5F58WaxZo1g29I/XmrT206rGB+uv4HhrTM1KhFL8AAFCvyEXKIhcpH7lIaeQiqE2GYSg9t1D7Tmfru19T9MnmRP3vd0f00OJfdNPHOzXs79t055w9enpZnN5ac1yLfk7Sj0fSdTQtT/lFFX9n7YmwID/1ahOqMTERum9wO708totmTeylr387QBueHqSlD/bX+3f20gtjumjyoHYa2SNC3VuFcv4GAPAJ5c+bCJ+yb98+vfrqqwoMDNTQoUMVEhKiLVu26O9//7tSU1M1fvx4bzcRXtKvXz8NGjRI27Zt0/z581VQUKC77rpLzZs315EjRzR//nz9+OOPrnUq0qdPH8XFxWnMmDF66qmnNHDgQOXn52vVqlX6+OOPXVOxvv7662W2vf7663X99dfr+++/1/fff6+bbrpJDz/8sLp27aqUlBQtXrxYy5cv14ABA9xOU3vttdeqVatWOnv2rP7617/q5MmTGjdunCIjI3XkyBF9+umnWr9+vQYPHqytW7e6fR6DBw923f5//+//6ZlnnlGbNm1cVeodO3Z0TSn75ptvavfu3Tp27Jj++Mc/6ttvv9XEiRPVq1cvBQYGymazaf/+/VqzZo1+/PFHjRs3Trfffntlb0uV1OR1r4r7779fS5Ys0bZt27Ro0SIlJCRo2rRp6tixo5KTk7Vw4UKtWLFCknPq4t/97ndl9vHcc8/pq6++0qlTp/SnP/1J+/bt08SJE9WyZUsdOXJEs2bN0s6dOyt8nyVp6tSpmjNnjoqKijR58mQ9++yzuuqqq1RQUKCtW7dq1qxZKioqUteuXXXkyJFy99G3b189//zzeu2113T48GENHz5cDzzwgIYNGyar1arMzEzt3btXK1askJ+fn5YvX15q+0GDBumnn37Szp079e6772r06NEKDQ2VJIWEhKhdu3bKysrS5MmT1bFjR910000aOHCgOnToIH9/fyUnJ+v777/Xp59+Kklq166da8QRAIB32R2G9p7O1rpDNsXGp+lkuvvRvkoySeoXFaaR3SM0ortVHa3BddtQAABQK8hGyho0aJDrNtnIBWQjy0ttTzYCAE3bsbRcrY5L0+q4NMWdyfFom2aBfhrR3arRMRG6qrNFQf4MFgIAgLeRi5RFLlI+cpHlpbYnF0FVFTkMJWfmKzEjXwnp+UpIzzt/O08J6fk6V1C2ALC2mCS1bh6oKEuQosODFR0epKjwYEWfv98i2I/ZWwAAjZbJqKgsGQ2e3W7X008/rbS0NL366qvq3LmzJCknJ0cvvfSSzp49q3fffVetWrWq9WOnpKRUeZvk5GQ5HA6ZzeZSoyyg7hw6dEi33nqrzp49W+7yxx57TD179tSTTz4pyTmiQvE0o5I0cOBAnTx5UpMmTdKVV16p559/vtSUs8UCAwM1c+ZM3XbbbeUeJzs7W5MmTXIbOPTt21fvvPOOxowZI0l67733XKNFFFuzZo3uu+8+5eXllbuPq6++Wq+99ppratGS+ygOKiTnCfxXX31V7j4ufv7JycmaPn26Nm/eXO76Jd1999167733Kl3PE7Xxum/YsEETJkyQJC1fvlxXX311hce02WyaPHlyhaFQTEyMFi9erA4dOpS7/MCBA7rjjjvKHY1Eku666y4NHTrUbX8r9tFHH+mVV14pdx9Wq1Xz5s3Ta6+9po0bN2ro0KFu38933nlHb7zxRrmvXbHytj99+rRGjBghm83mdv0TJ07o8ssvd7vfYm3atNGCBQvUv3//Ste9WMl+W9FzaIr4PGm4/Pz8ZLVaXfdtNlu5ozoB9Sm/yKFtxzMUG2/T+sM2peV49js10M+kwZ0sGtndqmu6WRXZLKCOWwpUjS/9zm3ZsqW3mwDUOnIR30E2cneZ88vp06eTjZSDbOSChpCNkIu4x+dJw+VLf6MDxYr77aHkLK3Ye1r/2ZWo+BTPil9aBPtpRPcIjY6J0OCOLRRI8QvqkS/9ziUXQWNV1WyEv2O9g1yEXEQiFyEXaVy89XmSU2B3Fbckni92ScjIV2J6nk5lFsjuqLvLcIP8TYqyBCsqPEjRxf+fL3ZpZwlqMAMR+NLf6EAx+i18lS/13brMRZgRxsft27dPycnJGjlypKsIRpJCQ0N122236cMPP9S6det05513eq+R8KoePXpo9erVeuedd7Rq1SolJyerRYsW6tevn6ZPn67rrrtOixYt8mhfU6ZMUe/evfXRRx9py5YtSktLU2RkpK655ho9+eST6tmzp9ttw8LC9NVXX2nu3Ln6/PPPFRcXJ5PJpM6dO2vChAl6+OGH3Z4IFxs1apR++OEHvffee/rxxx+Vmpoqi8WimJgY3Xnnnbr33nuVkJBQ6fOYNWuWBgwYoG+++Ubx8fHKzs6Ww+Eod902bdrom2++0cqVK7Vs2TJt375dZ86cUWFhoSwWi7p06aJBgwbp+uuvLzWtcG2qyeteFVarVd98842+/PJLLVmyRHv37lV6erqaN2+u3r17a/z48ZoyZYoCAwPd7qNXr1766aef9P7772vFihVKTExUWFiYevfurSlTpuj222/3qL898sgjiomJ0UcffaSdO3cqNzdXbdu21ejRo/XEE08oOjrao+f0zDPP6JZbbtGcOXO0fv16JSQkKDc3V+Hh4YqJidGIESM0adKkMtu1a9dO33//vd59911t3LhRp0+fLhOmdejQQStXrtSqVau0detWJSQk6OzZszp37pyrX15//fWaOnWqmjdv7lF7AQC1JzOvSD8dSde6eJs2Hk1XbmH5n/UXaxHsr2u6WTW8W7iGdLYw5TMAAI0A2UhZZCPlIxu5gGwEABo/wzAUn5KrNYdsWhu/R/Fnsj3azhLir2vPz/wyqGML+fs1jAuuAABA+chFyiIXKR+5yAXkIk2TYRhKPVfonNEl40KxS3Hxi6eDLVaXNcRf0eEXil2cM7s4C14imwXIzKwuAACUwYwwNZCRkaH4+HjFx8fr8OHDOnz4sLKysiRJI0aM0GOPPebxvs6ePatvv/1WP//8s1JTU+Xv76+2bdtqyJAhuv766xUUFFTudgsXLtTy5cv11FNPlancT09P10MPPaRLL71Uf/zjH6v/RN1g5NOmoeQoEzNnzvR2c6rN10ZKaCyvO2rG1/ptfeLzpOHypWpzND5JmflaF2/TunibdiRkeTzqTpvmgbrh0nYa26etBneJUHZmBv0WPsGXfucy8ikaI3KRpqMxnKP74vllY3jdUTO+2G/rC58nDZcv/Y2OpskwDB08k6PVcWlaHZemE7byR1O/WESov67t4Zz5ZWCHFvI3cwEWvM+XfueSi6CxYkaYpqExnJ/74vllY3jdUTO+2G/rS00/T4ochrafyNTxtFxXkUtCer4SM/KVX+TZ4IrV4WeS2rYIUvT54pYoy/lZXcKD1N4SpLAg3x/T3pf+RgeK0W/hq3yp7zIjTAP129/+tlb2s337dr3//vvKzc11PZafn+8qrlm9erVeeukltW3btsy2SUlJkpyV6BcLDw9XcHCwTp8+XSvtBAAAABoSwzB0OCVXseeLX35NPufxtt1ahmhkd6tGdo9Qn/bNFRERUYctBQAAAAAA8A7DMPRL0jlX8UtiRr5H20U2C9CoHhEa0zNCA6Kay4/iFwAAAAA+7uCZc3r+q0MenxdVVbNAs6LCgxVtCXL+Hx6kqPOzu7RtEcSgAgAA1DIKYWpJy5YtFRUVpd27d1dpu6NHj+rvf/+7CgoKFBwcrAkTJujSSy9VQUGBNmzYoNWrV+v06dP661//qtdee00hISGlts/JyZEkhYaGlrv/kJAQ1zoAAACAr7M7DO05laXYQzatO2xTQrpnIaVJ0oDo5hrR3aqR3a2KDg++sIxppAEAAAAAQCPiMAztO52t1QedxS9JWQUebde2RbBG9bBqVA+r+kWFyUxmAgAAAKCR2H4iU88uj9O5gpqNlt86LKBMsUv0+fuWEH++ewYAoB5RCFMDd955p7p166Zu3brp/7N37/FRlnf+/99zzDmTSQhJSDjliBw8iwgqQTwrKpZtf92ubbe73W6rbbWtdnW1tlVbW7Xa6va47drdfuuutYqHigcQUEFFRVE55MQxIQkhmUzOmdP9+yMSGQdhAkzuuZPX8/Hoo5nrc1/jB/w4mbnn+lxXTk6O9u3bp+uuu25Ez/Hwww8rEAjI4XDo1ltvVWVl5XBs9uzZKioq0p/+9Cc1Nzfr6aef1qc//enj/ccAAAAAktpAMKINu/1aU+fTKw0++frjO/7b7bDpzGkeVZd7dU6pV7kZrgRnCgAAAAAAYI6IYei9ph6tqu3QS3Udao27+cWty08s1qUnFunkkhz5/Z0Kh49tYRgAAAAAJJOXajt069/qFQgbR7zW7bBpkmeouaX4w/8fOtklRZM8qUp12UchYwAAEA8aYY7BsTal1NfXa+vWrZKkRYsWRTXBHHD55Zdr9erVampq0ooVK3T11VfL6fzoX9uBk2A+6dSX/v5+ZWRkHFOeAEamra1N+/fvH/E8t9utsrKyBGQEAID1+PtDemW7T2vrfXpth18DoUhc87JTHTqn1KuF5V7Nm+ZRutuR4EwBAADwcdwbAQBgdIQjht5p7Naq2g6truvQ/t5gXPOKPSlaXJmrxZW5mlOcrdzc3ARnCgAAMH5wXwRILo9v2qe7V+5Q5GM9MFO8qZpRkDHc5HKg4SU/083pmAAAWASNMCbasGHD8M+LFi065DV2u10LFy7Un//8Z/X29mrz5s066aSThuOFhYWSpObmZpWWlkbN7ezs1MDAgMrLyxOQPYBP8l//9V+65557Rjxv8uTJ2rhxYwIyAgDAGpr9g1rb4NOaOp/eaexSHBvySJIKs9yqrvCqutyrk4uz5HSwCw8AAICZuDcCAEDihCKGNu7pGm5+6eiL7+TcyTkpOr8qT4src1U1MV22Dxd22VjgBQAAcFxxXwRIDoZh6Pev79Wv1zXGxBZVeHXnZeVKcfK9MgAAVkYjjIlqamokSSkpKTFNLAebOXNm1JyDG2Fmzpyp5cuXa9OmTVqwYEHUvHfffTdmPjBSfMg2B3/vAIDxwDAM1e/v15q6Dq2p96lm36FPOTyUivx0VZcPnfxy8OINAACA443P6Obg7x0AgI+EwhG9ufvD5pd6n/z98TW/TMtN1fmVeVpclavyCWncPwGAMaq+vl5/+ctfVFNTo3A4rClTpuiyyy7T/PnzzU4NYwCfz83B3ztwbMIRQ/e+tEt/ebc1Jrb0xIn6t/OnyWHn8xEAAFZHI4yJGhuHuo0LCwvlcDg+8bpJkybFzDlgzpw5Kigo0Lp163TppZdq2rRpkqS+vj498cQTcjqdOvfcc49/8gA+0U033aSbbrrJ7DQAAEhKoYihTU3dWlPv09o6n/Z2DcY1z26TTi7OUnWFVwvLvCrOSU1wpgAAADha3BsBAODYBcMRvbFrqPllbX2HugbCcc0rm5Cm8ytztbgyV6UT0hOcJQDAbB988IHuuusuud1uzZ8/X2lpaXrjjTf0wAMPqL29XUuWLDE7RWDc4b4IYK5AKKLbVzToxZqOmNg/zyvWVxYUs0kAAABjBI0wJgkEAuru7pYk5eXlHfbazMxMpaSkaHBwUO3t7VExh8Ohr3zlK7rrrrt0++23R93YaGtr0zXXXKOJEyeOKLeP/zM+yeGadz4JbyJhho/Xnc1mk2EYJmUDxIe6jY/NZjuq30dIHLvdftjHGJ/6g2G9vtOvNbXternBp844dy1Ncdp11vQcLarI1TllXnnTXQnJj7qFVVG7AAAAADC2DIYien2nX6tqO/Ryg089g/E1v1ROTNfiylwtrsjVtLy0BGcJAEgW4XBYv/nNb2S32/WDH/xgeOPUZcuW6eabb9YjjzyiefPmKT8/39xEAQAYJb2BsG5cXqsNu7uixm2SvnPeVH3m1EJzEgMAAAlBI4xJBgYGhn9OTT3ybtapqakaHByMmnfA7Nmzdccdd+jRRx/V+vXrh4+6/dznPndUR91+9atfjeu6Rx99dMTPvX//fhmGIbvdLqeT8oM5WDQPK6Juox1Y6OtyueT1ek3OBofj8XjMTgEm8fUGtGrbPr2wuUUv17VpIBiJa5433aXzZhTowlkFOqdigtLdo/+ekbqFVVG7AAAAAGA9A8GIXtvZqZU1HXqlwae+OO+hnFCQMdT8UpmryV5OzgWA8eiDDz5Qa2urqqurh5tgJCk9PV1Lly7VL3/5S61du1bLli0zL0kAAEZJR29Q33h8m7a19kWNO+02/fDSMl044/CblQMAAOuhE8EkgUBg+Od4GkIOXHPwvIOVl5frlltuOT7JAQAAAEdhT0efXtjSqhc2t+jNnR2KxHmQVYk3TRfOLNSFswp0+lSvnA5OtQAAAAAAAGNXfyCsdTs6taq2Q69u71R/nM0vs4sytLgyT+dVeFWcQ/MLAJjJ7/ervr5e9fX1amhoUENDg7q7uyVJCxcu1LXXXhv3c7W1tWnFihXauHGj2tvb5XQ6VVhYqLPOOksXXXSRUlJSDjlv8+bNkqSTTjopJnbyySdLkrZs2TLCPxkAANbT1Dmg6x7bpj2dg1Hj6S677rmqUmdOZTM5AADGIhphTOJ2u4d/DoVCR7z+wDUHz0uUX/3qVwn/ZwAAAMD6DMPQluYuvbC5VS9sadXW5q4jT/rQzKJsXTirQBfOLNQJRVmy2WwJzBQAAAAAAMBcvYGwXt3eqVU17Vq3w6/BUHzNLycVZ2pxZa7Oq8hVYfahF0IDAEbfl7/85ePyPG+99ZYefPBB9ff3D48NDg4ON9esWrVKN998swoLC2PmtrS0SJKKiopiYjk5OUpNTVVzc/NxyRMAgGRVu69XX/9rjdp7g1Hj3jSnfv6pKs0szDQpMwAAkGg0wpgkNfWjXZoGBgaOeP2Baw6elyh5efEdA+jz+Ub83MFgUJHI0I39eBqAgOPBZrPJ4XAMPw6HwzKMOLeoB0xC3R5eJBJRJBJRMBg8qt9HSBy73S6P56PdVPx+//DvfowNoYihd/Z0aU1dh1bXdai5a/DIkyQ5bNKpk7NVXZGn6gqvJnkOvK8Nq7OzM2H5xoO6hVVZqXa9Xq/ZKQBJgcZPAACA8aVnMKSXG4ZOfnltR6cC4SPf47VJOqUkS+d92PwyMSvxm+QBAI7NhAkTVFxcrE2bNo1o3o4dO/TAAw8oEAgoNTVVV111lWbPnq1AIKB169Zp1apVam5u1o9//GPdfffdSktLi5rf19cnSUpPTz/k86elpQ1fkwy4LwIAON427unSDU/UqjcQjhqflJ2ih/5uhqZ4OUkTAICxjEYYk7jdbmVlZam7u1vt7e2Hvbanp0eDg0MLDONtUhkN4XD4yBd9zIEbGwcWMNvt9uOdFhDj480DNBPACqjbT3bgd4g09HvlaH4fYfREIhH+HY0B/YGwXt/l15o6n17d3in/QHwNzSlOu+ZP92hhuVdnl+YoJ801HEvmuqBuYVXULpD8uC8CADhWhmEM3xfh9wiQnLoGQnq53qeVtR16Y5dfwTiaX+w26dSSbJ1flavqCq8mZND8AgDJbtmyZSorK1NZWZlycnK0b98+XXfddSN6jocffliBQEAOh0O33nqrKisrh2OzZ89WUVGR/vSnP6m5uVlPP/20Pv3pTx/vP8ao4r4IAOBYHXxfZH9vSNc9uS1mw4HyCWl6cNkM5WfyuQoAgLGORhgTlZSUaOvWrWppaVE4HI7a+f9ge/fujZpjZW63e/gkmL6+PmVmcvQgAGBkDt65yu3mxgWQKL6+oF5p6NSa+qFFG4Oh+BryctKcOqfMq+pyr86cmq1U16Hf4wIAMB5xXwQAcKwOPmHe5XId5koAo6mzL6g1DT69VNOhN3Z3KRw58n0Uh006fUq2Flfmqbrcq9wM/psGACs51qaU+vp6bd26VZK0aNGiqCaYAy6//HKtXr1aTU1NWrFiha6++mo5nR8t8zlwEswnnfrS39+vjIyMY8rzeOK+CADgWB24L+LvD+mZbf6YJphTSrL0s6sqlZXKslgAAMYDfuObqKqqSlu3btXg4KC2b9+uioqKQ163ZcuWqDlWlp6ePnwTpru7W5FIRGlpaXI6nRyDCwD4RIZhKBQKqb+/X729vcPjn3TUO4Cj09g5oDX1Pr1c79O7Td2KY82GJKnYk6Lqcq+qK7w6cVKWHHbe1wEAcCjcFwEAHC3DMDQwMCC/3z88lpaWZmJGADp6g1pT79PK2na9vbtLcRz8IofdpjOnZmtxZa4WlnujTs8FAIwvGzZsGP550aJFh7zGbrdr4cKF+vOf/6ze3l5t3rxZJ5100nC8sLBQktTc3KzS0tKouZ2dnRoYGFB5eXkCsj863BcBABytg++L9AcjavIPalNLIOqa6nKv7rysXKkuThwDAGC8oBHGRHPnztXy5cslSatXrz5kI0wkEtHatWslSRkZGZo1a9ZopnjcuVwupaWlqb+/X5LU29ur3t5e2Ww2bmwgoQ4+VvnAEZlAsqNuP2IYhgwj+pvktLQ0dj4FjpFhGKrZ16c1dT6tqe9Q/f7+uOfOKEhXdXmuqsu9KpuQxns5AADiwH0RmIXPl7Ai6jbax/8O3G43J+UCJtjfE9DqOp9W1XZoY2NXXJuIuBw2zZvq0eLKXJ1b7lU2OxMDACTV1NRIklJSUmKaWA42c+bMqDkHN8LMnDlTy5cv16ZNm7RgwYKoee+++27MfLNxXwRm4fMlrIi6jXbg76AvENZu34B2dgZV7wsOx6+ck6+bL5guJxs2AgAwrnCn1UTl5eU64YQTtHXrVq1evVrV1dUxx90+88wzampqkiRdcsklUcfcms3hcBzVvNzcXHV3d6urq2t47FALnIFE4QMirIi6jZadna2srCxuiiehg2/IHeoxzBcMR/ROY7dW17VrTW2HWroDR54kyWGTTpvi0aKKXC2syFVRdkqCMx091C2sitoFrMnj8cjhcKinp2d4jPsiGE18voQVUbfR3G63vF4v90WAUbKvO6CX6jq0qrZD7zZ2K553bW6HTfOn52hxZa7OKctRZkryfL8HAEgOjY2NkoZOdTnc2otJkybFzDlgzpw5Kigo0Lp163TppZdq2rRpkqS+vj498cQTcjqdOvfcc49/8seA+yIwG58vYUXU7dDvitbugDr6QtrZGdTv3/1oY4IvzZukry4o4T4JAADjEHddj8G2bdvU0tIy/Pjgxo6WlhatWbMm6vrq6uqY5/jiF7+o2267TYFAQHfeeaeWLl2qWbNmKRAIaP369Vq5cqUkqaioSEuWLEnIn+Noeb3eo56bm5urQCCgnp4e9fb2KhAI8KYdAPCJ7Ha73G63MjIylJmZyY6nFuLxeMxOAZJ6B0N6ubZNL2xp1aqtreoaCMU1L83lUHVVvi6cVaBFVROVkz4+/tujbmFV1C5gDTabTVlZWUpLS9Pg4KAGBwcVDoe5L4KEsdlsUadpBoNBFhgh6VG3sex2+/AO2m63m8UdQIK1dA1qVe1Q88t7e3uOPEFSitOuBdM9WlyVp7NLc5ThProN5QAAY18gEFB3d7ckKS8v77DXZmZmKiUlRYODg2pvb4+KORwOfeUrX9Fdd92l22+/XfPnz1daWpreeOMNtbW16ZprrtHEiRNHnN/H/zmf5Gg3T83JyVFmZqYGBgY0MDDAfREklM1mi9p0OBQKjfvPl0h+1G20cMTQ+809eqe5X++1BlTvCw43wdx0/nR99rQicxPEMDbxgxVRt7AqancIjTDHYNWqVVq7du0hYzU1NcNH2R5wqEaY6dOn6/rrr9eDDz6o/v5+PfLIIzHXFBUV6eabb1ZaWtpxyTtZuN1u5ebmKjc31+xUAAAAxpz9PYNatbVVL2xu1Sv1+xUIxfclUl6GW+efUKALZxVoQfkEpbpYtAEAwKEc7WKPg+enpIydE9aQvOx2e1SzpN/vZ4ERkh51C6viy0dra+oc0Mqadq2sadcHzfE1v6S57DqnzKvzq/J0dqlXaRZsfqFuYVXULqxsYGBg+OfU1NQjXp+amqrBwcGoeQfMnj1bd9xxhx599FGtX79e4XBYU6ZM0ec+9znNnz//qPL76le/Gtd1jz766FE9PwAA8WrvGdQ/Pvym3mv0R427HDbd9+mTdcVJkz5hJpIBm/jBiqhbWNV4rV0aYZLA6aefrnvvvVfPPvusNm7cqI6ODjmdThUWFmrevHm6+OKLWRgBAACAI9q5v1cvbGnRi1ta9dYun+LdGGhqXroumlWoC2cW6JQpXjns7CwMAMCRHMtJuYCZxuuNcFgbdQuronaT3879vXr2g2ateL9F7zf5jzxBUmaKU4tPmKhLZhepuip/zG0iQt3CqqhdWEkgEBj++eAd/z/JgWsOnnew8vJy3XLLLccnOQAAksSejj59/g8btGN/b9R4utuh31xzms6pyDcpMwAAkCxohDkG1157ra699trj8lz5+fn6whe+oC984QvH5flGg8/nMzsFIC7sIAkrom5hVdTu6DIMQ1taerWmrkOr69rVsL8/7rkzCzO0qCJPiypyVTohTTbbUPNLl78zQdkmL+oWVmWl2qVhAAAAAECyaGjr0bPvNevZD1q0tbkrrjlZqU5dMLNAl84u0tkVnKALADg2brd7+OdQKHTE6w9cc/C8RPrVr341Kv8cAAA+ydbmLn3hDxu0r3swajw3w63/+uIZOmlyjjmJAQCApEIjDI5aOBw2OwXgqEQiEeoXlkPdwqqo3eMvFI7o7cZuranz6eUGn1q7D70D3Mc57DadNjlL1eVenVvmVWH2RycOJuvCebNQt7AqahcAAAAAYhmGobp9PXr2/aGTX2pau+Oa50lz6cKZBbp0TpEWlE+Q22lPcKYAgPEiNTV1+OeBgYEjXn/gmoPnJVJeXl5c17F5KqzAShtKAQeM97rduKdL3/zrVvUMRn/nVZSdol9+eqamZBr8DkpS4712YU3ULazKSrWbyI1TaYQBAAAAklxvIKzXdnRqTb1Pr27vjLnp90nSXXbNn56j6gqvFkzPUVYqb/8BADhe+KINVmGlG+HAAdQtrIraTR6GYai2rU8rt7VrVU27dnTEd4puTppT51Xm6fyqPJ0+JVsux1DzS2+3X72JTNhE1C2sykq1y0m5+Di3262srCx1d3ervb39sNf29PRocHBoN/x4G1RGC5vywIrYUApWNJ7qdk19h/79mXoNhoyo8bIJaXpo2QzlZ7rHzd/FWDCeahdjB3ULqxqvtctKOAAAACAJ7e8N6JWGTq2p82nDbr+CYePIkyTlpju1sNyrheW5OmNKtlLYrRQAgIQYjzcSMTaM1xvhsDbqFlZF7Y4uwzBUs69Pq2o7tKq2Q7t9R95hXxq6l7KoIleLK3N16uRsOe22A884Lv/9UbewKmoXVlNSUqKtW7eqpaVF4XBYDofjkNft3bs3ag4AAGPV8vf36Ucv7FDkY1+Ln1ycpZ8trVQ2mz4CAICP4d0BAAAAkCR2dfRrTb1Pa+t9en9vj+JrfZGmeFO1sNyr6nKvZhdlyjG8YAMAAAAAAGDsMgxDm1t6taq2Qy/VdqjJPxjXvAkZLp1XOdT8cnJxFvdSAACjrqqqSlu3btXg4KC2b9+uioqKQ163ZcuWqDkAAIw1hmHo4Q179R+vNMbEzi3L0Y8ur1Cqi80fAQBALBphAAAAAJNEDENbWnq1tt6nNXUd2tER306lkjSrMEPVFV5Vl+dqWm6qbDYWbAAAAAAAgLEvYhj6oLlHq2qGTn5p6Q7ENa8gy63zKnK1uCpXJ07KlJ17KQAAE82dO1fLly+XJK1evfqQjTCRSERr166VJGVkZGjWrFmjmSIAAAkXMQzdv3q3HtnYEhO7Yna+brlw+kGndgIAAESjEQZH7ZOO5gWSjd1uP+xjIBlRt7AqavfIguGI3trdpdV17VpT51NbT3yLNZx2m86Y4lF1Za6qy72amJWS4EzHD+oWVkXtAgAAABgvwhFD7+3tHj75ZV9PMK55RdluLf7w5JdZRTS/AACSR3l5uU444QRt3bpVq1evVnV1tSorK6OueeaZZ9TU1CRJuuSSS+R0JtcSH9aMwAq4jw4rGi91GwxH9L1nG/Tc1v0xsS/NK9Z1505hM0iLGS+1i7GFuoVVUbtDkutTMizF6/WanQJwVDwej9kpACNG3cKqqN0h3QNBralp0wtbWrVm2z51D4bimpeZ4lR1Vb4unFWo6qp8Zae6EpwpJOoW1kXtAgAAABhLwhFD7zR+2PxS16H23viaX4o9KVpcmavzq3J1QkEGC6cAAAmxbds2tbR8tHt9V1fX8M8tLS1as2ZN1PXV1dUxz/HFL35Rt912mwKBgO68804tXbpUs2bNUiAQ0Pr167Vy5UpJUlFRkZYsWZKQP8exYM0IrIj76LCisVi3vYMhfeNPb+uVutgmmO9dPlNfOnu6CVnheBuLtYuxj7qFVY3X2qURBgAAAEiAfV0DenFrq17Y3Kr1DfsVDBtxzcvPStEFMwt04cwCnVWWpxQnO6oBAAAAAIDxIRQx9PaeLq2q7dCaug519MW3mcgUb+rwyS9VE9NpfgEAJNyqVau0du3aQ8ZqampUU1MTNXaoRpjp06fr+uuv14MPPqj+/n498sgjMdcUFRXp5ptvVlpa2nHJGwAAs7X3DOpLD7+pTY3+qHGn3ab7Pn2Srjy52KTMAACA1dAIAwAAABwn9ft69OKWVr2wpUXv7O6Me15pfoYunFmoC2cV6OSSHNntLNYAAAAAAADjQygc0Zu7h5pfVtf75O+Pr/llem6qzq/K0+LKXJVNSKP5BQBgSaeffrruvfdePfvss9q4caM6OjrkdDpVWFioefPm6eKLL1ZKSorZaQIAcFw0+vr0+d9v0Pb9vVHj6W6HfvUPp2lhZb5JmQEAACuyGYYR39bUwMf4fD6zUwDiYrfbo4798vv9ikQiJmYEHBl1C6sab7UbMQx9sLdHq+uGdind2dEf99w5kzK1qCJX1RW5mp6XnsAscSTjrW4xdlipdr1er9kpAMfd/v37zU4BiIvD4Yh6Hfb5fAqHwyZmBBwZdQuronbjFwhFtGGXX6tqO7S2waeugfj+nsonpA2f/FI6gfspxwN1C6uyUu1OmDDB7BSAhGDNCKzASvfRgQPGat3Wt/Xqa49uVVtPIGo8J82pB5edoNmTskzKDMfLWK1djG3ULazKSrWbyPUinAiDo5asNxKBI4lEItQvLIe6hVWNxdoNhCJ6a0+X1tT7tLbep/beYFzznHab5k7J1sIKrxaWeTUh0z0cG2t/R1Y3FusW4wO1CwAAACCZDYYien2nXytr2/Vyfad6A/F9fqmcmK7zK3N1XmWupuWmJThLAAAQL+5Fwoq4jw4rGgt1+25jt254okbdg9F/jsIstx5aNkPT8tIs/2dErLFQuxh/qFtY1XitXRphAAAAgCPoGQzp1e2dWlvv0/odneoNxNdBn+F26OzSHC0s92r+dI8yU3j7DQAAAAAAxo+BYFjrdwyd/PJKg099wfjuqcwsyNB5H578MtmbmuAsAQAAAACJ8nKDTzc/XafBkBE1XpqXpoeWzdDELPcnzAQAADg8VuIBAAAAh7CvO6CXG3xaU+/TW7u7FIoYR54kKT/TpYVlXlVXeHXa5Gy5HPYEZwoAAAAAAJA8+gNhrdvRqVW1HXp1e6f642x+mVOUqcUfnvwyyZOS4CwBAAAAAIn21PttuuuF7Qp/7Kv2Eydl6v6lVfKksXwVAAAcPd5JAAAAAJIMw9DOjgGtqe/QmjqfNrf0xj23NC9NC8u9Wlju1czCDNlttgRmCgAAAAAAkFx6A2G90uDTqtoOrd/h12AovuaXk4o/bH6pyFVhNs0vAAAAADAWGIahP25o1kOv7ImJnVOaox8vKVeqy2FCZgAAYCyhEQYAAADjVsQw9P7eHq2p92ltvU+7fQNxzbNJmjMpU9UfNr9MzU1LbKIAAAAAAABJpmcwpJcbOrWypl2v7/Qr8PEtfg/BJumUkiwtrszVoopcTcxyJz5RAAAAAMCoiRiGHlizW39+uyUmtmTWBP37RaVy2tlYEgAAHDsaYQAAADCuDIYienN3l9bWd+jl+k619wXjmudy2DR3ikfVFV6dU5ajCRks1AAAAAAAAOOLvz+klz88+eX1nX6FIkdufrHbpNMmZ2txZa6qK7zcUwEAYIxwONjJH8nPbrcf9jGQjKxct8FwRN9f0aBnt+yPiX3xzGJ9Y+EU2Ww0wYxVVq5djF/ULayK2h1CIwwAAADGvO6BkNbt6NSaOp/W7+hUXzAS17zMFIfOLs1RdblXZ03PUYabL3UAAAAAAMD40tkX1JoGn1bVdGjD7i6F42h+cdik06dk6/yqPFWXe+VNd41CpgAAYDR5vV6zUwBGzOPxmJ0CMGJWqdu+QEjf/NNGvVwb2wRz62Un6J/PKTUhK5jJKrULHIy6hVWN19qlEQZHjd09YBV0PsKKqFtYVTLVbmvXoNbUd2h1bYfe3tMV1w6lkjQx061FlbmqrsjVaZOz5XLw399Yl0x1C4wEtQsAAAAgUTp6g1pd36FVtR16e3eXwnHcVnHabZo7dejkl4XlXuWk0fwCAAAAAONBR29A//jwm9q0pzNq3Gm36Z6/O1FLTykxJzEAADCm0QiDo8buHrCq8dr5CGujbmFVo1m7hmGobl+PXtjcohe2tOq9Rn/ccysLMnXhzEJdOKtAc4o9HMc8zvGaC6uidgEAAAAci/09Aa2u82llbbveaexWPHuKuBw2zZvm0eLKXJ1b5lV2Kl89AgAAAMB40tTZr2t+/4a2t/VGjae5HPrlP5yqRVUTTcoMAACMddyNBgAAgGWFI4Y27vbphc0tenFLq3a298U1z2aTTp/q1YUzC3XBzAJNm5CR4EwBAAAAAACST2v3oFbX+bSqtkPvNnYrnvN03Q6b5k/P0eLKXJ1TlqPMFL5uBABgPPL5fGanAByR3W6P2kTK7/crEomYmBFwZFaq24a2Pn3t0S3a1xOIGvekOvXg352gORNd/L4YR6xUu8AB1C2sykq1m8iDN7gzDQAAAEsZCIa1rn6/XtjcqpVbW9XeGzjyJElup13nlE/QhbMKdN6MAuVnpSQ4UwAAAAAAgOTT0jWolbUdeqm2Q+/t7YlrTorTrrNLh5pfFpTmKMPtSHCWAAAg2YXDYbNTAEYsEolQu7CcZK3bTU3duuGJGnUNROdWkOXWQ8tmaHpeWlLmjdGTrLULHA51C6sar7VLIwyOGt3asAordT4CB1C3sKpE1W7XQEivNPi0urZd63d0qj8Y33NmpTh0bnmuqityNX96jtIPLNII9cnni+/0GIx9vObCqqxUu4nc4QMAAADAkTV2Duil2g6tqu3Q5pbeuOakuQ5qfpmeozSaXwAAAAAAkl5t8Om7T9drMBT9vVRpXpoeXFalAjalBAAAo4BGGBy18dg5hrFhvHY+wtqoW1jVsdRuS9eg1tT7tLbep417uhQ24ptXkOVWdblX1RVenVKcJafDPhzjvyPEg9dcWBW1CwAAAOBgu30DWlXbrlW1HdrWGt+GIBluu84t82pxZa7mTctRqst+5EkAAAAAgHHjmQ/adMfz22O+vz9xUqbuX1olTxpLUgEAwOjgXQcAAACSgmEYatjfrzX1Pq2pj3+BhiSVT0hTdXmuqiu8qpqYLpvNlsBMAQAAAAAAktPO9n6trO3QS7Udqm2L795KZopDC8u8Or8qV3OnepTipPkFAAAAABDrvzfs1S9e3hMzfnZpju5eUq5UFyeJAgCA0UMjDAAAAEwTjhja1NQ9fPJLk38wrnl2m3RycZYWlnu1sNyrkpzUBGcKAAAAAACQfA5sLLKqtkOraju0vb0/rnmeVKcWlg+d/DJ3arZcDppfAAAAAACHFjEM/WLtbv3prZaY2OWzJujWC6fLyedKAAAwymiEAQAAwKgaCEb0xi6/1tT79EqDT539objmpThtOnOqR9XluTqnLEfedFeCMwUAAAAAAEg+hmGorq1vuPllZ8dAXPNy0pxaVDHU/HL65GwWKQEAAAAAjigUjuiHz+/Qs1v2x8SuOaNI3zh3smw2mwmZAQCA8Y5GGAAAACRcZ39Qr27v1Np6n17b4ddAKBLXvOxUh84p9aq6wqt5Uz1Kc3OUMgAAAAAAGH8Mw9C21j6tqm3XqtoO7emM71TdvHTXUPNLVa5OKcmW087iJAAAcHw4HHxng+Rnt9sP+xhIRslUt/2BsG58sk7rtnfGxG6onqrPn1k8+kkhaSVT7QLxom5hVdTuEBphAAAAkBB7Ovr01Nt7tbq2Q+80dilsxDevKNut6vJcLSz36uSSLBZoAAAAAACAcckwDG1u6dWq2g69VNuhJn98zS8TMlw6rzJX51fm6qTiLDm4twIAABLA6/WanQIwYh6Px+wUgBEzq259vQFd+8ibemd3Z9S4w27TTz91oj51WokpecE6eM2FFVG3sKrxWrs0wgAAAOC4MAxDNft69frbbXphc6u2NHfFPbcyP10Ly4dOfqnMT+foZAAAAAAAMC5FDEPv7+0Zbn5p6Q7ENa8gy63zKnO1uDJXJ07KlJ17KwAAAACAo7S3s1+f/8MG1e/riRpPddn1q8+dpkUzJpqUGQAAwEdohAEAAMBRC0UMvdvYrbUNPq2t82lvV3w7k9pt0snFWaqu8GphmVfFOakJzhQAAAAAACA5hSOG3tvbPdz8sq8nGNe8omy3Flfm6vyqPM0szKD5BQAAAABwzOpau/X5P2xQs38gatyT5tIfvniGTpvKiWAAACA50AiDo+ZwOMxOAYiL3W4/7GMgGVG3SGb9wbBe39Gp1XUderneJ/9AKK55qU67zpqeo+qKXJ1T5pU33ZXgTIH48JoLq6J2AQAAAOsa2lyka6j5pc6n9t74ml+KPSk6v2ro5JcTCjI4VRcAAJjG5/OZnQJwRHa7XR6PZ/ix3+9XJBIxMSPgyMys201N3frmY1tj1gAUZLn1H5+eqdJsXv/xyXjNhRVRt7AqK9Wu15u4JloaYXDUElmYQCId/OIPWAV1C7N19Aa0amurXtjSqlfq2jQQjO+NszfdpcUnFOjCmQU6pyJfaW4aaZH8eM2FVVG7AAAAQHILRQy9vWeo+WV1bYd8/fFtLjLFm/rhyS+5qsxPp/kFAAAkhXA4bHYKwIhFIhFqF5YzWnX76vZOffepOg2GotcCTM9N1YPLZqgwO4X/fjAivObCiqhbWNV4rV0aYQAAAHBIezr69PzmFr2wpVVv7exQxIhvXok3TRfOLNSFswp0+lSvnA5OKAAAAAAAAONTKBzRht1DzS9r6n3yx9n8UpqXpsWVQye/lE1Io/kFAAAAAJAwf9vcph8+t13hj60JmFOUqfuvrlROmsucxAAAAA6DRhgAAABIkgzD0Oa9XXphS6te2NyibS3dcc+dNSl7uPllRmEWizMAAAAAAMC4NRgKa139fi1/a5fW1HeoayC+nfjKJ6Tp/Ko8nVfhVemE9ARnCQAAAACA9Kc3m/XA2t0x4/One/STJRVKcztMyAoAAODIaITBUfP5fGanAMTFbrfL4/EMP/b7/YpEIoeZAZiPusVoCUUMvbOnS6vrOrS6rl0tXYG45jls0qmTs1VdkafqCq8meVIlSXa7EdUEQ+3CCnjNhVVZqXa9Xq/ZKQAAAAAJNRiK6I3tfr2yY5dWbmlV92B8J79UTUzX4spcnVeZq2m5aQnOEgAAAACAIYZh6Bcv79H/vNkcE7t05gR976LpcjrsJmQGAAAQHxphcNTC4fh2MAOSTSQSoX5hOdQtjqf+QFiv7fRrTb1Pr273xb0raarTrrOme1Rd7tXZpV550j56K/lJ9UntwoqoW1gVtQsAAACMroFgWOt3+LWytkOvNvjUF4yvMX1mQYYWV+VqcWWuSnJSE5wlAAAAAADRQuGI7nxhh57ZvD8m9g+nF+obC6fIftAGmAAAAMmIRhgAAIBxwNcX1MsNPq2p92nDLr8GQ0Zc83LSnDq3zKvqCq/mTvEo1cWOLwAAAAAAYPzqC4S1bnunVtV26NXtnRoIxdf8Mqcoc/jkl0melARnCQAAAADAoQ0Ew/q3p+v16vbOmNg3zp2sz8+dNPpJAQAAHAUaYQAAAMaoxs4Bran3aW29T5uauhWJr/dFxZ4UVZcPNb+cOClLDjs7vQAAAAAAgPGrZzCkVz9sflm/w6/BOJpfbDbp5OIsnVeRq0UVXhVm0/wCAAAAADCXvz+kG56o0Xt7e6LGHTbptotKdfnsfJMyAwAAGDkaYQAAAMYIwzC0rbVPa+o7tKbep4b9/XHPPaEgQwvLvaou96psQppsHHMMAAAAAADGse6BkF5u8GlVbYde3+lXIHzkHUZskuZOz9Wlc4p08exCucP9CofDiU8WAAAAAIAjaO0e1Ncfq9H29uh1BClOu+5eUq5zyrwmZQYAAHB0aIQBAACwsFA4oo2N3cMnv7R2B+Ka57BJp03O1sJyrxaWsyspAAAAAACAvz+ktfVDzS9v7PIrFMfxunabdPrkbJ1XmavFVRNUMblgOObzxb9JCQAAAAAAibKjvV/XPbYtZj1BdqpD9y+t0knFWSZlBgAAcPRohAEAALCYvkBY63d0am29T69u71T3YHw7i6a57Jo/PUcLy706uzRH2am8FQQAAGNDfX29/vKXv6impkbhcFhTpkzRZZddpvnz55udGgAASHKdfUGt+bD5ZcPuLoXjaH5x2KQzpnq0uDJX1eVeedNdQ+MOR6LTBQAASBq894EV2O32wz4GktHxrtv393br63/ZKv9AKGp8YqZbv/z0TJXlpx/T8wMH8JoLK6JuYVXU7hBWPwIAAFhAe29QrzT4tKbepw27/AqEj7woQ5Jy0506t8yr6nKvzpjqUYpzfL7pBQAAY9cHH3ygu+66S263W/Pnz1daWpreeOMNPfDAA2pvb9eSJUvMThEAACSZ9t6g1tR3aFVNh97e06V4brM47Tad+WHzy7nlOcpJcyU+UQAAgCTm9XrNTgEYMY/HY3YKwIgdS92urtmnr/3vFvUHozfXLM3P0P/805kqzkk71vSAT8RrLqyIuoVVjdfapREGAAAgSe32DWhtvU9r6jv0XlOP4mt9kSbnpKi6PFcLK7yaU5Qph92W0DwBAADMEg6H9Zvf/EZ2u10/+MEPNG3aNEnSsmXLdPPNN+uRRx7RvHnzlJ+fb26iAADAdPt7AnqprkOrajv0TmO34jj4RS6HTfOmDTW/LCzzKovTdQEAAAAAFrH8nSZ95y+bFPrYB+CTJ+foD188Q7kZbpMyAwAAOD64Yw8AAJAkIoahrS29WlPv09p6n7a398c9d2ZhhqrLh05+mZ6XJpuN5hcAADD2ffDBB2ptbVV1dfVwE4wkpaena+nSpfrlL3+ptWvXatmyZeYlCQAATNPaPaiXan1aVduhTU3dcW0ykuK0af70HC2uzNXZpTnKTOGrNAAAAACAtfznK9t159+2xoyfW5mvX//DqUp381kXAABYH+9oAAAATBQMR/T2nm6tre/Q2nqf9vUE45rnsNt0+uQsVZfn6tzyHBVkpSQ4UwAAgGh+v1/19fWqr69XQ0ODGhoa1N3dLUlauHChrr322rifq62tTStWrNDGjRvV3t4up9OpwsJCnXXWWbrooouUknLo9zqbN2+WJJ100kkxsZNPPlmStGXLlhH+yQAAgJU1+we1qq5DL9V26L29PXHNSXHadXbpUPPLgtIcZbgdCc4SAADA+nw+n9kpAEdkt9vl8XiGH/v9fkUiERMzAo7sWOrWMAz9Yu0uPfzG3pjYpTMn6PuXlmmwt1uDvcctXWAYr7mwIuoWVmWl2vV6vQl7bhphAAAARlnPYEiv7fBrTb1P63Z0qmcwHNe8dJdd86fnqLrCqwXTc5SVyls5AABgni9/+cvH5XneeustPfjgg+rv/+g0vMHBweHmmlWrVunmm29WYWFhzNyWlhZJUlFRUUwsJydHqampam5uPi55AgCA5NXYOaBVtR1aVduhLS3xreZJc9l1TplXiytzNX+aR2k0vwAAAIxIOBzf91tAMolEItQuLCfeug1FDN31wg49/UFbTOzvTyvU9dVTZJfBfwMYNbzmwoqoW1jVeK1dVk/iqDkcfCkEa7Db7Yd9DCQj6nbs2d8T0Nr6Dq2u69CGXX4Fw0Zc8/IyXKouz1V1Ra7mTvXI7UzuWqB2YUXULayK2kUymTBhgoqLi7Vp06YRzduxY4ceeOABBQIBpaam6qqrrtLs2bMVCAS0bt06rVq1Ss3Nzfrxj3+su+++W2lpaVHz+/r6JEnp6emHfP60tLThawAAwNiy2zegVbXtWlXboW2t8f2+z3Dbde6HzS/zpuUo1cV7aAAAAACA9Q0Ew7r56Xq9sr0zJnbdOZP1hblFstlso58YAABAAtEIg6OWyKOKgEQ6+DgwwCqoW2va3tajF7a06oXNLXpnT6eM+HpfVDohQxfMKtCFMwt1yuQc2e3WvSFF7cKKqFtYFbWL0bZs2TKVlZWprKxMOTk52rdvn6677roRPcfDDz+sQCAgh8OhW2+9VZWVlcOx2bNnq6ioSH/605/U3Nysp59+Wp/+9KeP9x8DAABYyM72fq388OSXurb4ml+yUhxaWD7U/HKmBTYZAQAAAABgJLoGQrrhiRptauqJGnfYpH+/sFRXzMk3KTMAAIDEohEGAADgOIlEDG1q7NQLW1r14pZW1e/rOfKkD508OUcXftj8Uj4xM4FZAgAAHB/H2pRSX1+vrVu3SpIWLVoU1QRzwOWXX67Vq1erqalJK1as0NVXXy2n86PbWQdOgvmkU1/6+/uVkZFxTHkCAADzGIahhv39WvVh88v29v645nlSncPNL3OnZsvloPkFAAAAADD27OsO6LrHtsV8Xk5x2vTjJRU6t4yNrgEAwNhFIwwAAMAxCIQiem17u17Y3KIXt7RqX/dgXPNcDpvOKpugC2cW6IKZBSrITk1wpgAAAMllw4YNwz8vWrTokNfY7XYtXLhQf/7zn9Xb26vNmzfrpJNOGo4XFhZKkpqbm1VaWho1t7OzUwMDAyovL09A9gAAIFEMw1BtW99w88uujoG45uWkObWoIleLK3N1+uQsOWl+AQAAAACMYTvb+3XdY9vU0h2IGs9Kcej+pVU6uSTLpMwAAABGB40wOGo+n8/sFIC42O12eTye4cd+v1+RSMTEjIAjo26TW/dgSOsaOrWmvkPrGnzqCYTjmpfhdmhBaY4WVeRpQVmOslI+fCsW7pfPF9+OpsmO2oUVUbewKivVrtfLjmOIVVNTI0lKSUmJaWI52MyZM6PmHNwIM3PmTC1fvlybNm3SggULoua9++67MfMBAEByMgxD21r7tKq2XatqO7SnM76NRvLSXVpU4dX5Vbk6uSRbTrstwZkCAAAAAGC+D5p79M3Ha+TvD0WN52e69OCnZqg8P92kzAAAAEYPjTA4auFwfIt+gWQTiUSoX1gOdWu+tp6A1tb7tLbepzd3dykUMeKaNyHDpYXlXlWXe3Xa5Gy5nR/tRjoe/p1Su7Ai6hZWRe3CahobGyUNnericDg+8bpJkybFzDlgzpw5Kigo0Lp163TppZdq2rRpkqS+vj498cQTcjqdOvfcc0eUV3t7e1zXHS5nIJnY7fbDPgaSEXU7PhiGoQ+ae7Sypl0ra9q11x9f80t+pluLq3J1flWeTi7OliOJml+oXVgRdQuronYBAMB49dqOTt30VJ36g9Gbo03NTdVDn5qhIk+KSZkBAACMLhphAAAAPsHO9n6tqfdpTX2HPmjujXvetNxUVZd7VV2Rq5mFGbLbkmdBBgAAQDIIBALq7u6WJOXl5R322szMTKWkpGhwcDCmScXhcOgrX/mK7rrrLt1+++2aP3++0tLS9MYbb6itrU3XXHONJk6cOKLcvvrVr8Z13aOPPjqi5wWSxcGniQFWQd2OHZGIoXf2+PTs+y1a8X6z9voH4ppX5EnVJbOLdNmJhTplslf2JGp+ORxqF1ZE3cKqqF0AADAePLd1v25fsV3hj23cObMwQ7+4uko56S6TMgMAABh9NMIAAAB8KPLhTqRr6nxaU+/Tbl98izEk6cRJmVpY7tXCcq+m5aYlMEsAAADrGxj46H1WamrqEa9PTU3V4OBg1LwDZs+erTvuuEOPPvqo1q9fr3A4rClTpuhzn/uc5s+ff1zzBgAAIxeOGHprZ4dWfNCiFR80q7UrvpNfSrxpunROkS6ZXaiTSnIs0/wCAAAAAEAiPPJ2i+5bvStmfN40j356RYXS3ZxiDgAAxhcaYQAAwLgWCEX05u4uran36eUGn9p7g3HNczlsOmNKtqrLc3VuWY4mZLoTnCkAAMDYEQgEhn92Oo98e+rANQfPO1h5ebluueWW45Lbr371q+PyPAAAjGehcEQbdnZoxfstem5zi9q642t+mZqXPnTyy5wizS7Olo1TdgEAAAAA45xhGHro5d16eENzTOyiGXn6/iWlcjnsJmQGAABgLhphAADAuNMzGNKr2zu1tt6ndds71ReMxDUvw+3Q2aU5qi736qzpHmWm8FYKAADgaLjdHzURh0KhI15/4JqD5yVKXl5eXNf5fL4EZwIcH3a7XR6PZ/ix3+9XJBLfZyDALNStNQXDEb29u0sra9r1Ul27fH1H/h0vSVNzU3VB1QSdX5WnyonpHza/RNTZ2ZnQfBOB2oUVUbewKivVrtfrNTsFICEcDk4eQPKz2+2HfQwko4PrNBSO6IfPbdfy91pjrvv704r07cXTZGcTCSQJXnNhRdQtrIraHcLqTQAAMK683ODTD1Zsl38gvsUY+ZkuVZd7tbA8V6dNzmInFQAAgOMgNTV1+OeBgYEjXn/gmoPnmS0cDpudAnBUIpEI9QvLoW6TVzA8dNLuqtoOran3yd8f3/2W0rw0La7M1eLKXJVNSBs++SVZFzAfLWoXVkTdwqqoXWD00eQFKzq4iRJIdgPBsK778ztauTW2CebGi6r0teoyTlJFUuM1F1ZE3cKqxmvt0ggDAADGBcMw9P/ebtHP1+yWcYRrS/PStLDcq+pyr04ozGAHFQAAgOPM7XYrKytL3d3dam9vP+y1PT09GhwclBT/aS0AACBxAqGI3tjl16raDq2t96l7ML5FxxX56VpcmavzKrwqnZCe4CwBAAAAALAuf19Q//zfb+rNndEnk9tt0o+vnqPPnDHFpMwAAACSB40wAABgzAuFI/rJql164r19h4zbJM2ZlPnhyS9eTc1NG90EAQAAxqGSkhJt3bpVLS0tCofDcjgch7xu7969UXMAAMDoGwhG9PquTq2q7dDL9Z3qDcTX/FI1MV3nV+XqvIpc7rcAAAAAABCH1q4Bff73G1TT2h01nuK068HPnqILZxWalBkAAEByoREGAACMad0DIX336Tpt2NUVEztjSrYumpGnc8q8ystwmZAdAADA+FVVVaWtW7dqcHBQ27dvV0VFxSGv27JlS9QcAAAwOgaCYa3bMXTyy6sNPvUFI3HNm1mYocWVuVpcmauSnNQEZwkAAACz+Hy+I18EmMxut8vj8Qw/9vv9ikTi+2wDmGFXR7++9ugW7fUPRo1npjj080+doFMnpfD6i6TFay6siLqFVVmpdr1eb8Kem0YYAAAwZjV2Duj6x2u0s2MgJva1s0v0j2dOks1mMyEzAAAAzJ07V8uXL5ckrV69+pCNMJFIRGvXrpUkZWRkaNasWaOZ4mF90gk2QLKx2+2HfQwkI+rWPH2BsF5t8GllTbte2e7TQJzNLydOytL5VXlaXJWrSZ7x2/xC7cKKqFtYFbULmC8cju+UQCCZRCIRahdJa3Nzj775eI06+0NR4xMyXXroUzNUnp9O/cJSeM2FFVG3sKrxWrs0wgAAgDHp3cZuffvJWvk/dpMoxWnTDy4p0/lVeSZlBgAAAEkqLy/XCSecoK1bt2r16tWqrq5WZWVl1DXPPPOMmpqaJEmXXHKJnM7kuZWVyJ1rgEQ6eHcowCqo28TqHgjqpW379Oz7zVpT06bB0JGbX2w26fSpXl0yu0gXzy7UpJy0UcjUeqhdWBF1C6uidgEAgJW9vtOvG5+sVf/HNqSYPiFDDy2rUkGmy6TMAAAAklfyrB4AAAA4Tp7dsl93PL9dwbARNZ6X7tJ9Sys1uyjTpMwAAADGjm3btqmlpWX4cVdX1/DPLS0tWrNmTdT11dXVMc/xxS9+UbfddpsCgYDuvPNOLV26VLNmzVIgEND69eu1cuVKSVJRUZGWLFmSkD8HAADjkb8/qFVbW/Xs+816uXa/AuEjN7/YbdLc6bm6dE6RLppVqILs8XvyCwAAAAAAx8tzW/fr+yu2KxSJXt9wYolH//XFM2QP9o3LHd4BAACOhEYYAAAwZkQMQ79Z16jfv743JlY+IU0PXF2lwuwUEzIDAAAYe1atWqW1a9ceMlZTU6OampqosUM1wkyfPl3XX3+9HnzwQfX39+uRRx6JuaaoqEg333yz0tLYaR4AgGPl6w3oh89s0TPv7Y3ZQORQ7DbprLI8XTJ7qPklP4v7KgAAAAAAHC//u7FF9760K2b87PIJ+vU1pykzxSmfr8+EzAAAAJIfjTAAAGBMGAhG9IPnGvRiTUdM7OzSHN11ebky3A4TMgMAAMDhnH766br33nv17LPPauPGjero6JDT6VRhYaHmzZuniy++WCkpybfo1ufzmZ0CEBe73S6PxzP82O/3KxI58skPgJmo28TY2tKj7yyv0V7/4GGvc9ptmjvVo8VVeaquyFVuumsoEOpj8c0RULuwIuoWVmWl2vV6vWanAAAAkoxhGPrVukb94RCbfF50wgQ9+Lkz5HbaTcgMAADAOmiEAQAAltfeG9S3l9fog+bemNhnTy3U9dVT5LDbTMgMAABg7Lr22mt17bXXHpfnys/P1xe+8AV94QtfOC7PNxrC4bDZKQBHJRKJUL+wHOr22D31fpvuXrlDgU84BcZpt+nMqR4trszVwnKvPGkffX3E3/3Ro3ZhRdQtrIraBQAAVhGKGLr7xR1a/n5bTOwzpxTopgtKaYIBAACIA40wOGoOB7vqwxrsdvthHwPJiLqNX11br77x2Fa1dAWixh026aYLSvXpUwpNymx8onZhRdQtrIraBQAAwJEEQhHdu3qXHt+0Lybmcth01rSh5pdzy7zKSuUrIwAAAAAAEmkgGNGtf6vXmvrYE8e/enaJvnTmJNltbPIJAAAQD77VwFHjCGdY1cHHpANWQd0e2uqaffr6nzerZzAUNZ6V4tRDnztVCyvzTcoMB1C7sCLqFlZF7QIAAOBgLV2D+u5TddrcEnuC7knFmbp7SYXyM90mZAYAAAAAwPjTPRDSt5bX6p3G7qhxu026+YLpWnriRJMyAwAAsCYaYQAAgCU9vG6HfvjMFkWM6PESb5r+8MUzVFmQZU5iAAAAAAAAJntzt1+3PF0vX38oJvb/nVqgby6cIpeDEwUBAAAAABgN+3sC+vpfa1TX1hc17nbY9KPLy1VdkWtSZgAAANZFIwwAALCUUDiiHz6zRf/92q6Y2KlTcvTbz5+uCZkpJmQGAACA8cThcJidAhAXu91+2MdAMqJuj55hGPrvDXv1i7W7YjYPSXXaddvFZbp0FifoJgq1CyuibmFV1C4AALCK3b4BXfeXbdrbNRg1nuF26GdLK3Xa5GyTMgMAALA2GmFw1Hw+n9kpAHGx2+3yeDzDj/1+vyKRiIkZAUdG3R5az2BI332yVut3dMbELj5hgr5/abkcwT75fH2xkzEqqF1YEXULq7JS7Xq9XrNTAI476hpWdfDvDsAqqNv49AyGdONfNmnFBy0xsal56fr1P5ymE4pYXDOaqF1YEXULq6J2AQBAMtra0qtv/HVbzImteRkuPfipKlVOzDApMwAAAOujEQZHLRwOm50CcFQikQj1C8uhbqW9/kFd/3iNtrf3x8T+ZX6xvnxWsWw2Y9z/PSUbahdWRN3CqqhdAACA8at+X4++8j9vqaGtNya2eMZE/ewzJ8uT5jIhMwAAAAAAxqc3dvl14/Ja9QWjNzGbnJOih5bNUHFOqkmZAQAAjA00wgAAgKT3/t5ufXt5rTr6ondJcTts+t7Fpbr4hAkmZQYAAAAAAGCuFe836zt/2aTeQHRTtM0m3XB+pa5bVC673WZSdgAAAAAAjD8vbmvXbc82KBQxosZPKMjQz6+uUm4Gm1UAAAAcKxphAABAUnt+W7t+sKJBgXD0DSJvmlP3XlWpk4qzTMoMAAAA45nP5zM7BSAudrtdHo9n+LHf71ckEjnMDMB81G18QhFD//HyLj38xt6YWHaqU3ddXqGzy7zy+ztHP7lxitqFFVG3sCor1a7X6zU7BQAAMIr+b2OL7n1pl4yPjc+dkq17rqpUhtthSl4AAABjDY0wAAAgKRmGof98rUm/Wd8UEyvNS9P9Sys5KhgAAACmCYfDR74ISEKRSIT6heVQt7F8fUHd8ky93tzdFROrnJiun15RoZKcVP7eTEbtwoqoW1gVtQuMPoeDhdxIfna7/bCPgePJMAz98pU9+s/XGmNiF87I0x2XVcjtPHINUrewKmoXVkTdwqqo3SE0wgAAgKQzGIrozue3a8XW9pjYvGke3b2kXJkpvI0BAAAAAADjzwfNPfruU3Vq7Q7ExC6fNUH/dv50pbrG55deAAAAGD2cdgQrOvg0MeB4CkcM3br8Az2yIbYJ5vNnTdXtS2bJYbcd1XNTt7AqahdWRN3CqsZr7bKCFAAAJBVfX1DfebJWm5p6YmJ/d/JEffu8aXIe5Q0iAAAAAAAAqzIMQ0+816Z7XtqpYNiIijntNn3nvKn61EkTZbNx3wQAAAAAgNEyEAzrm//7jp7f3BoT+9YFlfr6eeV8VgcAAEgAGmEAAEDS2L6/Tzc8Uasm/2DUuN0mfWvRVH3mlAJuEAEAAAAAgHFnIBjRT1ft1FMftMXE8jNd+skVFTpxUpYJmQEAAAAAMH51DQT15T++pTd2dESN223SnVfN0d+fOcWkzAAAAMY+GmEAAEBSeH2nX//2dJ16BsNR4+kuu360pEJnl+aYkxgAAAAAAICJ9voHddNTtdrW2hcTO7UkSz9eUqG8DJcJmQEAAGA88/l8ZqcAHJHdbpfH4xl+7Pf7FYlETMwIY0lbT0DX/WWLavdFf153O2z60ZJKLa7MOqrXSuoWVkXtwoqoW1iVlWrX6/Um7LlphAEAAKZ77N1W3bNqp8JG9HhhllsPXF2l8vx0cxIDAAAAPoHD4TA7BSAudrv9sI+BZETdfuS1HZ26+ala+QdCMbFrzpikb1RPldPO6bnJgtqFFVG3sCpqFzBfOBw+8kVAkolEItQujos9vgFd99g2NfkHo8Yz3A7dd1WlTp+SfdxqjbqFVVG7sCLqFlY1XmuXRhgAAGCacMTQA2t365G3W2JiswozdN/SSk3IcJuQGQAAAHB4idy5Bkikg3eHAqxiPNZtJGLol2vqdd+LtTI+tnFIutuhny47UZefOMmc5BC38Vi7sD7qFlZF7QIAgNGyrbVXX39sm3z90ZtW5KW79ItlVaqamGFSZgAAAOMLjTAAAMAUvYGwbn2mXq9s74yJXVCVq9svLlOqix3cAAAAAADA+NI1ENS3H92kF7e0xsRKJ2ToN9ecpoqCLBMyAwAAAABgfHtzt1/fWV6r3kAkarwkJ0UPLZuhkpxUkzIDAAAYf2iEAQAAo66la1A3PFGrura+mNg/zZukrywokd1mMyEzAAAAAAAA89S0dOtf//S2duzvjYldNKtA9/7dScpKdZmQGQAAAAAA49vKmnbd9myDguHoo1urJqbrF5+aobwMPq8DAACMJhphAADAqNrc3KNvLa9Ve28watzlsOnWC6frsln5JmUGAAAAxM/n85mdAhAXu90uj8cz/Njv9ysSiRxmBmC+8Vq3z23Zrx88V6+BYPSf1W6Trjt3ir54ZrFC/T3y9ZuUII5ovNYurI26hVVZqXa9Xq/ZKQAAgGP0l3da9dNVO2V8bPyMKdm658oKZaawDBMAAGC08Q4MAACMmpU17bp9xXYNhqK/jPKkOXXvlRU6pSTbpMwAAACAkQmHw2anAByVSCRC/cJyxnrdhsIR/fzlPXrk7ZaYmCfNqR9dXq4zp3qSdnEvPtlYr12MTdQtrIraBQAAiWAYhn67vkm/e60pJra4Mld3XFomt9NuQmYAAACgEQYAACScYRh6eMNe/ccrjTGxabmpeuDqKpXkpJqQGQAAAAAAgHn29wZ089P1eqexOyY2syBDP72yQoXZKSZkBgAAAADA+BaOGPrpqp3666Z9MbFlJ03UjYunyWG3mZAZAAAAJBphAABAggXDEd31wg49s3l/TGzulGzdfUWFslN5SwIAAAAAAMaXTU3d+u5TddrfG4yJXTUnXzcunqYUdpUFAAAAAGDUDYYiuu1v9XqpzhcT+5f5xfryWcWy2WiCAQAAMBOrTgEAQMJ09gd105N12niIXU2XnjhR3108VU4HCzoAAAAAAMD4YRiGHn2nVT9bs1vhiBEVcztsumnxNF114kSTsgMAAAAAYHzrGQzp28tr9fae6HUONknfPX+alp1cYE5iAAAAiEIjDAAASIidHf264fEa7ekcjBq3Sfpm9RR97rRCdkgBAAAAAADjykAwrLte3KEVW9pjYoVZbv30ygrNLMw0ITMAAAAAALC/N6Bv/LVGtfv6osZdDpvuuLRM51flmZQZAAAAPo5GGAAAcNy9tduvG5+sU/dgOGo8zWXXnZeVa2G516TMAAAAAAAAzNHYOaAbn6xTXVtfTGzu1Gz96LJy5aS7TMgMAAAAAAA0dg7o2r9sU5M/erPPDLdd911VqdOneEzKDAAAAIdCIwwAADiulr+/Tz9+cafCESNqfGKmSz9bWqUZBRkmZQYAAAAAAGCOVxt8uu3ZhphNQyTpH8+cpH9dUCKHnZNzAQAAAAAww7bWXn3zrzVq7wtGjeelu/TzT7HOAQAAIBnRCIOj5nA4zE4BiIvdbj/sYyAZWbFuI4ahX6zZpT9u2BsTO6EwQw9cfYImZrlNyAyjyYq1C1C3sCpqFwAAIPlFDEO/W9+k373WFBPLcNv1/UvKtKgi14TMAAAAAACAJL21269vL69VbyASNV7sSdF//N0MleSkmpQZAAAADodGGBw1r9drdgrAUfF4OKoU1pPsddsXCOn6/31XL2xpjYldNKtA93/mZKW7edsxHiV77QKHQt3CqqhdYHSxQQisgsZJWNFYqVt/f1C3PlOnV7d3xsRK89J039IZmpaXNvqJIWHGSu1ifKFuYVXULgAAOB5W1Xbo1r/VKxg2osYrJ6brF5+q0oQMNvsEAABIVqxIBQAAx6TFP6B//u839UFTV0zsXxeW6aaLqmS320zIDAAAAEgcNgiBVdE4CSuyYt1u3uvXv/7pA+3p6I+JXTanSD9ddqIyUviKZqyzYu0C1C2sitoFAAAj9di7rfrJyp0yPjZ+2uQs3XdVpTL53A4AAJDUeLcGAACO2gdNfv3TH99Ua9dg1LjTbtOPls7Rp8+YbFJmAAAAAAAA5nh8Y6Nufvx9DYYiUeMOu003XzJD/3T2dNlsbBoCAAAAAIAZDMPQ715r0m/XN8XEFlV4dedl5UpxctocAABAsqMRBkfN5/OZnQIQF7vdHrULlN/vVyQSOcwMwHxWqNvVdR265elaDQSj88pOdeq+pVU6fUomvyvGISvULvBx1C2sykq1y8kZAABgPAiEIrrjmS36n9d3xcQmZLr14GdP1VlleSZkBgAAAAAAJCkcMXTvSzv1l3f3xcSuPmmivrt4mhx2Nq8AAACwAhphcNTC4bDZKQBHJRKJUL+wnGSqW8Mw9Ke3WvSLtbtjjgienJOiB66u0tTctKTJF+ZKptoF4kXdwqqoXWB00fQNq7BS4yRwgBXrdl/3oG5cXqv39nbHxOZMytQ9V1apINvO748xzoq1C1C3sCor1S4bhAAAkBwCoYhue7ZBq2o7YmJfPqtY/zK/mBNcAQAALIRGGAAAELdQOKK7V+7U8vfbYmKnlmTpp1dWKCfNZUJmAAAAwOii8QxWReMkrCjZ6/btPV26+ek6dfSFYmJ/d/JEfWvRVLkc9qT+MyAxkr12gUOhbmFV1C4AADicnsGQbnyyTm/u7ooat0m6afE0/d0pBeYkBgAAgKNGIwwAAIhL10BI330q9saQJC2ZNUG3XDhdLofdhMwAAAAAAABGn2EY+vPbQ6fmhj92bG6K06abL5iuy2flm5McAAAAkGAOh8PsFIAjstvth32M8aG9N6Cv/2Wbtrb2Ro27HDbddXmFLpgxwaTMDo26hVVRu7Ai6hZWRe0OoREGAAAc0R7fgK5/oka7OgZiYtedM1lfmFvEEcEAAAAAAGDc6AuEdcfz2/ViTUdMrNiTop9eWaGqiRkmZAYAAACMDq/Xa3YKwIh5PB6zU8Ao293ep3965F3tau+LGs9wO/S7z5+u+eXJ1QRzKNQtrIrahRVRt7Cq8Vq7NMIAAIDD2rinSzc+WSf/QChqPMVp1w8vLdPiylyTMgMAAAAAABh9uzr6deOTddre3h8Tmz/dozsuLZcnja9fAAAAAAAw0+a9fn3xv95UW/dg1HhehlsP/+NczSkZnwtGAQAAxgq+iQEAAJ/omc1tuvP5HQpFjKjxvAyX7l9aqZmFmSZlBgAAAAAAMPrW1HXo9hUN6g1EYmJfPqtYX55fLDun5gIAAAAAYKrXGtr1L//9lroHozf8nJybpv/+0pmaPoFTXAEAAKyORhgAABAjYhj61auN+q839sbEKvPT9bOllSrMTjEhMwAAAAAAgNEXjhj69bpD3yvJSnHoh5eW6ZwyrwmZAQAAAObw+XxmpwAckd1ul8fz0akffr9fkUjsxgYYW1bVtOuWp2sVCEdv+Fk5MV0P/d1M5TgC8vkCJmV3ZNQtrIrahRVRt7AqK9Wu15u4705ohAEAAFEGgmHdvmK7VtV2xMTOKc3RXZeXK93tMCEzAAAAAACA0dfZF9S//61eb+zqiolV5KfrnisrVJKTakJmAAAAgHnC4bDZKQAjFolEqN0x7vFN+3T3yh2KRPfA6NSSLP1saaUyUxyWqwHqFlZF7cKKqFtY1XitXRphAADAsP29AX3riVptaemNif39aYX65sIpcthtJmQGAAAAAAAw+ra29OrGJ2vV0h27U+wlJ+Tp3y+crlQXG4YAAAAAAGAmwzD0+9f36tfrGmNiiyq8uvOycqU47SZkBgAAgEShEQYAAEiS6tr6dP3jNWr92MIOh0266fxp+tRJBSZlBgAAAAAAMPqWv79PP125U4Fw9DayDrtN36qeok+fUiCbjQ1DAAAAAAAwU8QwdM+qXfrLu60xsaUn5uvfzp/Ohp8AAABjEI0wAABArzb4dMsz9eoLRqLGM1McuntJheZN85iUGQAAAJCcHA52/4c12O32wz4GkpHZdRsIRfSTlTv0+KbYBTQTMl2658oqnVySPao5wRrMrl3gaFC3sCpqFwAASEOf4W9f0aAXazpiYv88r1hfWVDMJhYAAABjFI0wAACMY4Zh6H83tur+NbsUid7cVMWeFD1wdZWm56WZkxwAAACQxLxer9kpAEfF42GjA1jPaNZtU2e/vvZ/b2tToz8mNndarh763CmamJU6avnA2njNhRVRt7AqahcAgPGnNxDWjctrtWF3V9S4TdJ3zpuqz5xaaE5iAAAAGBU0wgAAME6FIobuXbVTj23aFxM7qThT915ZKW+6y4TMAAAAAAAARt+6+v36+iPvqKM3EBP70oLpuvnSGXI52G0eAAAAAACzdfQG9c3Ha7S1tTdq3Gm36YeXlunCGXkmZQYAAIDRQiMMAADjUM9gSP/2dL1e3xm7u+klM/N024WlcjtZ2AEAAAAAAMY+wzD067Xbdc/z22JOzE1zOXT3p+boypOLzUkOAAAAAABEaeoc0HWPbdOezsGo8XSXXfdcVakzp3JSHAAAwHhAIwwAAONMU+eAbniiVtvb+2Ni/7qgRP80b5JsNpsJmQEAAADW4fP5zE4BiIvdbpfH89GX/36/X5FIxMSMgCMbzbrtGQzp9mfr9VJtR0xssjdV9y2tUkV+Oq/7iAuvubAi6hZWZaXa9Xq9ZqcAAMCYUdfWp+se26b23mDUuDfNqZ9/qkozCzNNygwAAACjjUYYAADGkU1N3frO8lr5+kNR426HTd+/hOOBAQAAgHiFw2GzUwCOSiQSoX5hOYmq2+37+3TjU3Xa1TEQEzunLEc/vKRMWalO/pvBUeM1F1ZE3cKqqF0AAMa+jXu69K3lteoZjP6dPyk7RQ8uq9LU3DSTMgMAAIAZaIQBAGCcWLFlv374/HYFw0bUeG66U/ddVak5k7JMygwAAAAAAGB0raxp1w+e267+YPTO8TZJ/3p2if7xzEmyc2IuAAAAAABJYU1dh255pl6Bj613KJ+QpgeXzVB+ptukzAAAAGAWGmEAABjjDMPQb9c36XevNcXEyiak6YGlVSrypJiQGQAAAAAAwOgKRQz9x8t79D9vNcfEPKlO3XlZmc6anjP6iQEAAAAAgENa/t4+/ejFHYpE98DolJIs/eyqSmWlsgQSAABgPOJdIAAAY9hgKKIfPLddL2xrj4nNn+7Rjy4vV2YKbwcAAAAAAMDY19Eb1M3P1OntPd0xsaqJ6brnykpNYrMQAAAAAACSgmEY+sMbe/WrVxtjYtXlXt15WblSXXYTMgMAAEAyYOUrAABjVHtvUN9ZXqv3m3tiYp85pUA3LJoqp91mQmYAAAAAAACj6/293fruU3Xa1xOMiS2Zna/vLp7G4hkAAAAAAJJExDB030u79H/vtMbErpyTr5svmM56BwAAgHGORhgAAMag+rY+3fBEjZq7AlHjdpv0nfOm6tOnFJqUGQAAAAAAwOgxDEN/3bRP9760S6GIERVz2m26cfFUXX3iRNlsLJ4BAAAAACAZBMMR3b5iu17Y1h4T+9K8SfrqghI+xwMAAIBGGAAAxpr1Ozp189N16g1EosYz3Hb9eEmF5k/PMScxAAAAAACAUTQQjOjulTv0zOb9MbGCLLd+ckWFZhdlmpAZAAAAAAA4lN5AWDc9Was3dnXFxL5z3lT9f6ey6ScAAACG0AgDAMAY8ug7Lbr3pV362AanKsp26/6lVSrPTzcnMQAAAAAAgFHU1Dmgm56qU82+vpjY6ZOz9aPLy5Wb4TIhMwAAAAAAcCi+vqC++dcabWntjRp32m36/iWluviECSZlBgAAgGREIwwAAGNAKGLo/tW79H/vtMbE5hRl6t6rKpXH4g4AAAAAADAOvLajU//+t3p1DYRjYp8/o0hfO2eynHabCZkBAAAAAIBD2esf1HWPbdNu30DUeJrLrnuurNC8aTnmJAYAAICkRSMMAAAW1zMY0i3P1Gv9Dn9M7MIZefreRaVKddlNyAwAAAAAAGD0RAxDf3h9r36zrlEfOyxX6S67br+kTIsrc03JDQAAAAAAHFp9W5+ue2yb9vcGo8Zz0pz6+dVVmlWUaVJmAAAASGY0wgAAYGHN/kFd/0SNGvb3x8S+fFax/mV+sWw2djgFAAAAAABjW/dASN9b0aBXGjpjYtNyU3XPlZWanpc2+okBAAAAAIBP9E5jl254olY9g9GnuhZlu/XgshmalstneQAAABwajTAAAFjUB809+vYTtWrvi94VxeWw6XsXleqSmRNMygwAAAAAAGD01Lf16cYna7WnczAmdl6FV7dfUqYMt8OEzAAAAAAAwCdZW+/TLc/UaTAUfa5r2YQ0PbRshvIz3SZlBgAAACugEQYAAAt6cVu7vv9cQ8wNoZw0p+69qlInF2eZlBkAAAAwPjgcLKiGNdjt9sM+BpLRSOp2xZY2/XBFgwZCkeg5NukbC6fq83MncVouRg2vubAi6hZWRe0CAGBtT76/T3e9sEOR6CUPOrk4Sz9bWqnsVJY1AgAA4PB4xwgAgIUYhqHfv75Xv17XGBObnpuq+6+uUklOqgmZAQAAAOOL1+s1OwXgqHg8HrNTAEbsUHUbDEd019+26uH1O2NiuRluPfTZUzS/nNNyYS5ec2FF1C2sitoFAMAaDMPQwxv26j9eiV3zcG5Zjn50eYVSXTS4AgAA4MhohAEAwCICoYjufGGHnt2yPyZ25tRs3b2kQlnsigIAAAAAAMa4fV0DuvbPG/XmTl9M7KTJOfrV507VpJw0EzIDAAAAAACfJGIYun/1bj2ysSUmdsXsfN1y4XQ57ZzqCgAAgPiwWhYAAAvw9QX1rce36d2m7pjYp06aqBvPmyqng11RAAAAAADA2Pbmzg597f9tVFv3YEzs78+cotuXzFSK02FCZgAAAAAA4JMEwxH94Lntem5re0zsH8+cpK+dXSKbjSYYAAAAxI9GGBw1h4MvE2ENdrv9sI+BZHRwndbv69EX/+d9NXYORF1jk/Tt86bp708v4oYQkgavubAi6hZWRe0C5vL5Yk8hAJKR3W6Xx+MZfuz3+xWJREzMCDiyQ9VtOBzWI2+36P7VOxWKGFHXux023XJhqa48sUB93V3qG+2EgQ/xmgsrom5hVVaqXa/Xa3YKAACYqi8Q1k1P1en1nf6Y2LcXTdVnTys0ISsAAABYHY0wOGrcsINVHXxTHEh2r9bt11f/39vqHghFjae7HXrws6do8QkFJmUGxIfXXFgRdQuronaB0RUOh81OATgqkUiE+oXl9A4E9cPn6g+5a2xRtls/vaJSJxRmUNtIOrzmwoqoW1gVtQsAQHLq7Avqm4/XaHNLb9S4w27TDy4p1cUnTDApMwAAAFgdjTAAACSpP7+xW7c9+YHCH9vltMiTqt9/4QzNnJRtUmYAAAAAAACjY+f+Xn35T++rri32nJczp2brrsvLlZPmMiEzAAAAAABwOM3+QV33123a1TEQNZ7msuunV1TorOk55iQGAACAMYFGGAAAkkw4YujHz27Vf766IyZ2YolH//n50zUxO9WEzAAAAAAAAEbPyi2tuuHRd2NOypWkL82bpK/ML5HDbjMhMwAAAAAAcDj1bX36+l+3qa0nGDXuSXPq51dXaXZRpkmZAQAAYKygEQZHzefzmZ0CEBe73S6PxzP82O/3KxKJmJgR8Mn6AmHd8nSt1tbHvsYurszVHZdXyBXul8/Xb0J2wJHxmgsrom5hVVaqXa/Xa3YKAADAQsIRQ/e9UKMHX6qPiWW4HfrBpaWqLs81ITMAAAAAAHAk7zZ164bHa9Q9GI4aL8xy66FlMzQtL82kzAAAADCW0AiDoxYOh498EZCEIpEI9Yuk1No9qBueqFXtvr6Y2JfmFetfFxTLbuP1F9bCay6siLqFVVG7AABgLPD3h3Tbsw1av6MzJlaal6Z7rqzQ1FwWzAAAAAAAkIxebvDp5qfrNBgyosZL89L04LIqFWSlmJQZAAAAxhoaYQAASAJbW3p1wxM12t8bfSywy2HTj5bO0fllmSxsBQAAAAAAY9q21l7d9GSd9nYNxsQunJGn2y6crjS3w4TMAAAAAHycw8F7cyQ/u91+2Mc4vp58r1V3PNegcHQPjE4qztLPPzVDnjSXOYlZDHULq6J2YUXULayK2h1CIwwAACZ7qbZDtz3boMFQJGo8J92lX//DaZpXmiefz2dSdgAAAAAAAIn3zAdt+vHKHTE7xjrsNt1QPVWfOWWibDabSdkBAAAA+Div12t2CsCIeTwes1MYkwzD0K/XbtdPnmuIiS2eMVEP/f2pbGxxDKhbWBW1CyuibmFV47V2aYQBAMAkhmHojxua9dAre2JiU3NT9fCX5mn6hAwTMgMAAAAAABgdgVBE963epb9u2hcTm5CZov/4+1NU6bVzUi4AAAAAAEkoEjF017Nb9ftXd8TElp1WoruvniOnY3zuUA4AAIDEohEGAAATBMMR/fjFnXrqg7aY2OmTs3Xv0ipNowkGAAAAAACMYa3dg/ruU3X6oLk3JnbaVK9++blTVZCdykm5AAAAAAAkoWA4opsee09PvNMUE/vKwlL928UzON0VAAAACUMjDAAAo8zfH9JNT9Xq7T3dMbEr5+Tr386fplS3y4TMAAAAAAAARsdbu7t0yzN16ugLxcQ+c2qh7rj6FLmd7BgLAAAAJCsa1mEFdrtdHo9n+LHf71ckEjExo7GjPxDWd5bXaP2OzpjYtxZN0zVzC9XZGRvDkVG3sCpqF1ZE3cKqrFS7Xq83Yc9NIwwAAKNot29A1z9eo92+gahxm6SvnztZ15xRxI4oAAAAAABgzDIMQ396q0UPvbxbYSM6luK0698vnK4lcwpoggEAAACSXDgcNjsFYMQikQi1exx09gd1/eM1MSe8Ouw23X5xqS6dOYG/5+OIuoVVUbuwIuoWVjVea5dGGAAARslbu7t001O16hqIfsOR6rTrzsvKVF2Ra1JmAAAAAAAAidcbCOuO57ZrZW1HTKzYk6J7rqxQ5cQMEzIDAAAAAADxaOka1HWPbdPOjujNP1Oddv3kigotKM0xJzEAAACMOzTCAAAwCp56v00/enGHQpHorU7zM126f2mVZhSwyAMAAAAAAIxdOzv6dePyWu342EIZSTq7NEc/vLRM2al8ZQEAAAAAQLJq2N+nrz+2Tft6glHjnlSnfv6pKs0uyjQpMwAAAIxHfKsEAEACRQxD//HKHv1xQ3NMrGpiuu5fWqWJWW4TMgMAAAAAABgdL9V26AfPNag3EIkat0n6l/nF+qezimW32cxJDgAAAAAAHNGmpm7d8ESNugbCUeMFWW49tGyGpuelmZQZAAAAxisaYQAASJD+QFjfW9Gg1XW+mNjCcq/uuLRM6W6HCZkBAAAAAAAkXihi6FevHnqDkKwUh+64rFxnl+aMfmIAAAAAACBurzb49N2n6zUYit7gojQvTb/4VJUKs1NMygwAAADjGY0wAAAkQFtPQN96olZbW3tjYtecXqTrzp0sh52dTgEAAAAAwNjk6wvq35+p14bdXTGxyvx0/fTKCpXkpJqQGQAAAAAAiNczH7Tpjue3K2xEj584KVP3L62SJ43lhwAAADAH70QBADjOtrX26ltP1GhfTzBq3GG36ebzp+mqEyealBkAAAAAAEDibW7u0U1P1am1OxATu3TmBN1ywTSlujglFwAAAACAZPbfG/bqFy/viRlfMD1HP7minM/2AAAAMBWNMAAAHEdr63269W/16g9GHwmcleLQT66o0NypHpMyAwAAAAAASLwn3tunn67aqeDHtop12m369qKpWnbyRNlsnJILAAAAAECyihiGfrF2t/70VktM7LKZE3TbRdPldNhNyAwAAAD4CI0wAAAcB4Zh6P+91aKfr92tj50IrJKcFD2wtErT8tJMyQ0AAIw9tkC3XM1vSfvekravka7+rZQ6xey0gHHF4WC3Q1iD3W4/7GPgeBkMRXT3i9u1/L19MbH8TLfuuapKJxVnxfVc1C2sitqFFVG3sCpqFwCAxAiFI/rh8zv07Jb9MbFrzijSN86dzAYXAAAASAo0wgAAcIxC4Yh+smqXnjjEQo9TSrJ0zxUVykl3mZAZAAAYM8IBOVs3yb1nnVx71snZukk2I/xRfPsaaebnTUsPGI+8Xq/ZKQBHxePhpFIcf42+Pn31fzfq/SZ/TGzu9Fw99PenaGJW6lE/P3ULq6J2YUXULayK2gUA4Nj1B8L6t6frtW5HZ0zsmwun6JozikY/KQAAAOAT0AgDAMAx6BoI6d+eqtOG3V0xsctnTdAtF0yX28kuZAAAYIQMQ472Wrka1w01v+zdIFuw75OvpxEGAACY5JW6Nn3jkXfk6wvGxP757On67iUz5HJwbwQAAAAAgGTW2R/UDY/X6v3mnqhxh0267eJSXT4r36TMAAAAgEOjEQYAgKPU2Dmg6x+v0c6OgZjYteeU6ItzJ3EkMAAAiJu9e69ce9YPNb80rpe9b3/8k3etl0IDko1T6AAAwOiIRAz9am2D7nuhRhEjOpbudugnnzpRS06aZE5yAAAAAAAgbi1dg/r6Y9u042NrH1Kcdv3kigqdXZpjTmIAAADAYdAIAwDAUXinsUvfebJO/v5Q1HiK06YfXFKm86vyTMoMAABYhW3AL1fT63I1rpdrzzo5O3eMaL7hcMs2+UyptFoqXSTZXZJxxGkAjhOfz2d2CkBc7Ha7PB7P8GO/369IJGJiRhgLugdD+t7f6rWmriMmNsWbqp8tnaGy/LSjfq2kbmFV1C6siLqFVVmpdr1er9kpAADwiXa09+u6x7aptTsQNZ6d6tADV1fpxElZJmUGAAAAHB6NMAAAjNDfNrfpzhd2KBiOXmmal+HSz66q1KyiTJMyAwAASS00KFfLRrn2rJOrcb2c+96XzRjZAo1Q/iwFSuYrOHmBIiVnyjvxoF3WfT4pHD7OSQP4JGH+e4NFRSIR6hfHpGF/n258sk67fbEn5FaXe/X9S0qVmeI8rnVG3cKqqF1YEXULq6J2AQAYuff2duuGx2vlH4jeALQgy60HP1Wl0gnpJmUGAAAAHBmNMAAAxCliGPr1ukb94fW9MbGK/HTdv7RShdkpJmQGAACSkhGRY/9Wufesk2vPerma35QtFLtg9HDCWSUKTl6gwOQFCpacJSMtdzjmcDiOd8YAAACH9cK2dt3x/Hb1B6Obee026atnl+gLcyfJbrOZlB0AAAAAAIjXq9s79d2n6jQYiv6MPz03VQ8um8HaBwAAACQ9GmEAAIjDQDCiHzzXoBdrOmJiZ5fm6K7Ly5XhZjEqAADjnb1rj1x71g01vzS+JvuAb0TzIyk5Ck6e/9GpL54pCcoUAAAgfqFwRL94eY/+/HZLTMyT5tRdl5Vr3jSPCZkBAAAAAICR+tvmNv3wue0KG9Hjc4oydf/VlcpJc5mTGAAAADACNMIAAHAE+3sD+vYTtdrc0hsT++xphbp+4RQ57Ox2CgDAeGTr75Cr6fUPm1/Wy9G1e0TzDUeKgpPO+LD5ZYHC+TMlmz1B2QIAAIxce29QNz9dp42N3TGxEwoy9NMrKlTkYZdYAAAAAACs4E9vNuuBtbHfZcyf7tFPllQojQ1AAQAAYBE0wgAAcBj1bX26/vEatXQHosYdNunGxdO07OQCkzIDAACmCA3ItfctuRqHTn1xtG2RTcaR533IkE2hiXMUnLxAwcnzFSw8TXKycBQAACSn9/Z267tP1amtJxgTu3JOvm5aPE0pTpp4AQAAAABIdoZh6Bcv79H/vNkcE7tkZp5uv6hUTgef8QEAAGAdNMIAAPAJXt3eqVuerlNfMBI1nuF26CdXlGvetBxzEgMAAKMnEpazbbNce9bJ1bhOrua3ZQsHjjzvIGHPNAUmLxhqfimeJyPVk6BkAQAAjg/DMPTYu/t03+pdCkWim35dDptuWjxNS0+caFJ2AAAAAABgJELhiO58YYee2bw/JvYPpxfqGwunyG6zmZAZAAAAcPRohAEA4BD+d2OLfrZ6lz621kPFnhTdv7RSpRPSzUkMAAAklmHI7t8p9571Q80vTa/JPtg1oqeIpOUqUPJh40vJfEWyixOULAAAwPE3EAzrRy/u1LNbYhfHFGS59dMrKjSrKNOEzAAAAAAAwEgNBMP6t6fr9er2zpjYN86drM/PnTT6SQEAAADHwag1wvT19UmS0tMPvXD4wQcf1KOPPqr9+/dr+vTp+upXv6olS5aMVnoAAEiSQhFD9720S395tzUmduKkTN13VaW86S4TMgMAAIli69svV+P6oeaXxnVydO8d0XzDmaZg8dzh5pdwXqVksycoWwAAgMRp7BzQTU/WqbatLyY2d0q27rq8nPsiAAAAAABYhL8/pBueqNF7e3uixh026baLSnX57HyTMgMAAACO3ag0wjz99NO66qqrlJmZqcbGRmVlZUXFv/SlL+mPf/yjJMkwDNXW1ur555/XnXfeqZtvvnk0UgQAQD2DId3ydL3W7/THxC4+IU+3XVSqFCeLWgEAsLxAr1zNbw6f+uJs3zai6YbNoVDBiQpOXqBAyQKFCk+WHO7E5AoAADBKXt3eqdv+Vq/uwXBM7Itzi/SvZ0+W024zITMAAAAAADBSrd2D+vpjNdre3h81nuK06+4l5TqnzGtSZgAAAMDxMSqNMM8//7wMw9AVV1wR0wTz6quv6uGHH5bNZlN6eroqKyu1bds29ff363vf+56WLFmi2bNnj0aaAIBxbK9/UNc/HnsTSJK+Mr9Y/3xWsWw2FnsAAGBJkZCcre/J1bhO7j3r5Wx5R7ZIcERPEfKWKzh5voKTFyg46UwZKVlHngQAAGABEcPQf77WpN+tb5LxsViG267vX1KmRRW5puQGAAAAAABGbkd7v657bJtauwNR49mpDt2/tEonFfMdBwAAAKxvVBphXn/9ddlsNi1atCgm9tvf/laSNGnSJL322msqKSnRnj17dPbZZ6uxsVG/+c1v9OCDD45GmgCAceq9vd36zvJadfSFosbdDptuv6RMF83IMykzAABwVAxDDl+DXHvWydW4Xq7G12UP9ozoKcLpEz9qfCmZr0hmYYKSBQAAME/XQEjfe7ZBr27vjIlNz03VPVdWalpe2ugnBgAAAAAAjsoHzT365l9r5B+IXv8wMdOlB5fNUNmEdJMyAwAAAI6vUWmE2bdvnySpqqoqJvbcc8/JZrPp61//ukpKSiRJkydP1te//nXddNNNWrt27WikCAAYp57f1q4frGhQIBy956k3zan7llbqxEnshAIAgBXYe1qHml4+bH5x9LaOaH7Elalg8ZnDzS9hb7nEaXAAAGAMq93XqxufrFOTfzAmdn5lrm67uFQZbocJmQEAAAAAgKOxfkenbnqyTgOhSNT41NxU/ceyGSrMTjEpMwAAABx3kYjU+r6UkiXZvGZnY4pRaYRpa2uTJGVlRS8m3rx5s/bv3y+bzaYrr7wyKnb66adLknbt2jUaKQIAxhnDMPSfrzXpN+ubYmKleWl64OoqTfJwEwgAgGRlC3TL1fTGh40vr8nZUTei+YbdqVDhKQqULFBw8nyFJp4oOVwJyhYAACC5PLtlv+56YYcGP7YwxmGTvn7uFH3u9ELZaAoGAAAAAMAyVmzZr+8/t13hSPQmoLMKM/Tzq6uUk853IAAAAJZmGHL4GuRqfE3uptelvW9I/T7prOukud8xOztTjEojjMMxtGtcR0dH1Pirr74qScrPz485LcbrHepMGhgYGIUMAQDjyWAoojuf364VW9tjYmdN8+jHS8qVmTIqvyIBAEC8wgE5WzfJvWedXHtelbP1PdmM8IieIpRXpWDJAgUmz1dw0lzJnZGgZAEAAJJTMBzR/Wt269F3Yk/P86Y59eMl5Tp9iseEzAAAAAAAwNH6f2816/41u2PGz5rm0U+vqFAaJ74CAABYkr1rj1yNrw01vzS+JntfW+xFO9bSCJNIxcXFqq+v17vvvqvq6urh8b/97W+y2Ww655xzYub4/X5J0oQJE0YjRQDAOOHrC+rby2v13t6emNjfnVygb583VU47O54CAGA6w5CjvVauxnVDzS97N8gW7BvRU4QzixScvGCo+aXkLBkZ+QlKFgAAIPm19QT03afqDnlPZHZRhn5yRYUKsjgdFwAAAAAAqzAMQw+9skd/3NAcE7vkhDx97+JSuRx2EzIDAADA0bD3tMrV9Ppw84uju/HIk1rel63fJ7mzE59gkhmVRphzzjlHdXV1euihh/QP//APmjBhgt58800999xzkqSLLrooZs7WrVslSYWFhaORIgBgHNi+v083PFGrJv9g1LjdJn1r0VT9f6fyOwcAADPZu/fKtWf9cPOLvT/29LbDiaRkK1h8loKTh059iXimSTYaXAEAADbu6dLNT9ervS8YE1t20kR9a9FUuZ0sjAEAAAAAwCpCEUN3vbBDT38Quyv4359WqOurp8jOdyQAAABJzdbfIVfTG8ONL87O7SN/ktxS2bubpDwaYRLia1/7mh5++GHt2LFDpaWlqqys1JYtWxQKhZSbm6vPfOYzMXNeeukl2Ww2zZw5czRSBACMca/v9Ou7T9WpNxCOGk932fWjJRU6uzTHnMQAABjHbAP+D3eyWC/XnnVydu4Y0XzD7law6NShU18mn61Q/izJ7khQtgAAANZjGIYe2diin6/ZrbARHUtx2nTz+dN1+WxOzQMAAAAAwEoGgmHd/Ey9XmnojIldd85kfWFukWw0wQAAACQdW6BbzqY35W58Ta6m1+Tcv3XEzxHOLFRo8nylVF0gTTtHypmssM8nhcNHnjzGjEojzKmnnqp77rlHN954o3p6erRx40ZJksvl0u9+9ztlZWVFXe/3+/W3v/1NklRdXT0aKQIAxrDH3m3VPat2xiz4KMxy64Grq1Sen25OYgAAjDehQbla3h469WXPOjnbPpDNiIzsKfJnKVAyf6j5peh0yZWWoGQBAACsrT8Q1h0v7NAL22JP2ZuUnaKfXlmhGQUZJmQGAAAAAACOVtdASDc8UaNNTT1R4w6b9O8XluqKOWx4AQAAkDSC/XI1v/3hJrGvybnvfdmMkTWsRNJyFSw5S8HieQqUnKWIZ5ocTqdSvN4EJW0do9III0k33HCDzj//fD322GNqaWlRUVGRPvvZz6qqqirm2jVr1uiMM86QJF1++eWjlSIAYIwJRww9sHa3Hnm7JSY2qzBD9y2t1IQMtwmZAQAwThgROdq2yN24bqj5Ze+bsoUHR/QU4ezJCpbMV2DyAgVLzpKRlpugZAEAAMaO3b4B3fhkrRr298fE5k/z6I7LyuVJG7WvBwAAAAAAwHGwrzug6x7bpu3t0Z/3U5w2/XhJhc4tYzEkAACAqcIBOVs3ydX4mtyNr8nZ8q5skcCIniLizlKw+Myh5peSsxTOrZQ47e+QRvWbrjlz5mjOnDlHvO7KK6/UlVdeOQoZAQDGqt5AWLc+U69XtnfGxC6oytXtF5cp1WUf/cQAABjj7F175Nr9qtyN6+VqfE32Ad+I5kdSvQqWnPVh48t8RTxTEpQpAADA2LS23qfvPdug3kDsjmL/PK9YX55fLIedL0wAAAAAALCSnR39uu4v29TSHb2QMivFofuXVunkkiyTMgMAABjHIiE527bI1fja0P+a35ItFLtJ2eEYzjQFJ53+4akvZymUP0uyOxKU8NjClm8AgDGnpWtQNzxRq7q2vpjYP82bpK8sKJGdDlkAAI4LW3/H0BGue9bJvWe9HF27RzTfcKQoOGmugpOHTn0JTzhBstGsCgAAMFLhiKHfrG/UH17fGxPLTHHoh5eWsTMsAAAAAAAW9EFzj775eI38/aGo8fxMlx781AyV56eblBkAAMA4Y0TkaK8danppel2upjdkD3SP7CnsbgWLTvmo8aXgRMnhTlDCYxuNMACAMeWD5h59e3mt2nuDUeMuh023Xjhdl83KNykzAADGiGC/XM1vDTW+NK6Xo22LbDLinm7IptDEOQpOXjD0v8JTJWdKAhMGAAAY+zr7g7r1bw16fac/JlY+IU33XFmpyd5UEzIDAAAAAADH4vWdnbrxyTr1ByNR41O8qfqPZTNU5OE7FgAAgIQxDNk7d8rd9OGJL42vyz7QMbKnsDkUmniigiXzhppfik6TnHxnczwc10aYl19++Xg+3bBzzz03Ic8LABhbVta06/YVDRoMRS/G9aQ5de+VFTqlJNukzAAAsLBIWM62D+Tas27of80bZYsERvQUYc80BQ40vhTPk5HqSVCyAAAA48/Wll7d9FStmrti36NdfEKe/v2C6UpzO0zIDAAAAAAAHIvntu7X7Su2KxyJXgMxszBDP7+6St50l0mZAQAAjF327r1yNa4fbnxx9LaM+DlCE2YqWHKWAiVnKTTpdBnurARkiuPaCFNdXS2bzXY8n1I2m02hUOjIFwIAxi3DMPRfb+zVL19tjIlNy03VA1dXqSSHDloAAOJyYDeLxg8bX5pel32wa0RPEUnLG2p8KZmvYMl8RbKLE5QsAADA+Pbk+/v0k5U7FQhHL4hx2G26oXqKPnNKwXG/Zw8AAAAAABLvkbdbdN/qXTHj86Z59NMrKpTOphcAAADHha1vv1yNr8nd+JpcTa/J4d894ucIecuGTnspPkvB4rky0nITkCk+7rg2wkhDi5EBABgtgVBEP3pxh57ZvD8mNndKtu6+okLZqcf91x0AAGOKrW+/3HvWy/Vh84ujp3lE8w1XuoKT5n7Y/LJA4bxKiQWXAAAACRMIRXTPS7v0xHv7YmJ5GS79ZEmFTi5hdzEAAAAAAKzGMAz98tVG/dcbe2NiF83I0/cvKZXLYTchMwAAgLHBNtApV9MbQye+NL0uZ0fdiJ8jnD35oMaXMxXJLEhApjiS47oyePXq1Z8YCwQCuvXWW/Xmm28qPz9fn/70pzV37lwVFAz9i29tbdWbb76pRx99VPv27dMZZ5yhu+66Sy4XRzgeycsvv6xt27Zp+/bt2r17t0KhkL72ta+purra7NQAIKE6+4K68ak6vdPYHRNbeuJEfXfxVDm5AQQAQKxAr1x7Nww3vzjba0Y03bA5FCo4ScHJCxSYvEChgpMkhztByQIAAOBgLV2D+u5Tddrc0hsTO7k4S3cvKdeETN6bAQAAAABgNaGIoR+/uENPvt8WE/v/Ti3QtxZNlZ2NyAAAAEbEFuiRs/mt4VNfHG1bZNPIDv4Ip08canwpOUvBknmKZE9OULYYiePaCLNw4cJDjhuGoUsvvVRvvfWW/umf/kkPPPCAMjIyYq675pprdPfdd+v666/Xf/7nf+pnP/uZnn322eOZ4pj0f//3f2pra1NWVpa8Xq/a2mI/DAHAWLOzo1/XP16jxs7BqHGbpG9WT9HnTiuUjRtAAAAMCQfl3PeeXHvWy924Ts6Wd2SLhEb0FCFvuYKTFwz9r3iuDDc7jAMAAIy2N3f7dfPT9ersj30v99lTC/XNhZPZFAQAAAAAAAsaCEZ0yzN1ermhMyZ27Tkl+uLcSayBAAAAiEdoUK6WjUMnvjS+Jue+90a8RiaSkqNgybzh5pdwTqnEe7Gkc1wbYT7J73//ez3//PO64IIL9Lvf/e6w16anp+u3v/2tdu3apeeff16//e1v9S//8i+jkaZlfeUrX1FRUZHy8/O1fPly/fnPfzY7JQBIqDd3+3XTk3XqHgxHjae57LrzsnItLPealBkAAEnCMOTw1cu1Z51ce9bL1fSG7MGeET1FOH2igpPnDzW+lMxXJLMwQckCgDU5HA6zUwDiYrfbD/sY1mAYhv64Ya8eXLtLkY9tUpbqtOt7l5Tpkpn55iSXANQtrIrahRVRt7AqahcAMJZ0DYT0rSdq9W5Td9S43SbdcuF0XTVnokmZAQAAWMCBzWE/bHxxtWyULRwY0VNEXJkKFc9V4EDjS16VZONeQ7IblUaYhx9+WDabTV/72tfinnPttdfqxRdf1B//+EcaYY7gxBNPNDsFABg1y9/bpx+v3Knwx1Z9TMx06WdLqzSjIPbEMQAAxgN7T4tcjeuHm18cfftGND/iyvxwN4uh5pewt4zdLADgMLxeGvBhTR6Px+wUMELdA0Hd+Jf39NzmlpjY1Lx0/fofTtMJRdkmZDZ6qFtYFbULK6JuYVXULgDAqtp6ArrusW1q2N8fNZ7itOmuy8tVXZ5rUmYAAABJKhL+/9m78/CoyrN/4N+zTXayrzMJJJCwCrIlkKCA+4pi1f7at2q1i7W4VatWq7ZaYeMZbgABAABJREFU96Xa4luXt7VWrb5vq3UXNwSUJCQsKmsCIYFkJgkJ2deZc+ac3x8DgWGAZCCzJd/PdeWSOc85T271ZpYzz/3ckPbvgMlaCsVWCqV+PQS116spDCkMasYcqGZX4YuWMg0Q/VJWQcPIL//HKioqAABZWVlDviYzM9PtWl/o6OhAVVUVqqqqsHv3buzevRtdXa7K+oULF2LZsmVDnqu5uRkrVqzApk2b0NLSAlmWkZaWhvnz5+Pcc89FWFiYr/41iIhGBadu4Lmv6vDahgaPscmpUfjj0jwkR5sCEBkREVFgCPYuKPVlA4UvcluVV9cbogItbSYcmYVQLUXQUk4BJMVH0RIRERHRiahq6sL1r23E7uYej7EzJ6Xgj98/FbERfA9HRERERDSaffXVV6ioqEB1dTVqa2uhaRp++ctfYtGiRYEOjYgGsbe1Dze+VYGGTvcdy6PDJDyzNA8zLSN74wsiIiKiITEMSK27oNjWuTq+2Mog2ju8m0JUoKWdCtU8Dw7LfGhppwIS1/aHOr8UwvT39wMA6urqMHPmzCFdU1dXBwCw2+0+i+tnP/vZsMyzYcMGLF++HH19hyrz7Xb7QHHNypUrcffddyMtLW1Yfh8R0WjT53Di3o93Y01Vm8fY4tx4PHj+eESYpABERkRE5EdOB+TGb2GyFkOpK4a8bzMEw+nVFFriJKiZRa7il/S5gImd1IiIiIiC1cdbGnDHv79Dj8P9PZ8gALedlYdliydAFNnBj4iIiIhotPu///s/NDc3IyYmBvHx8Whubg50SEQ0BNsaunHLfyrR3qe5HU+KUrD88knITY4MUGREREREAWYYEDtroVhLD3R9WQexd793UwgitORpUC2uji9q+mxA4furkcYvhTATJkzAli1b8MILL2DJkiVDuuaFF14AAIwfP96XoQ1ISkqC2WzGd99959V1NTU1ePbZZ+FwOBAeHo5LL70U06ZNg8PhQHFxMVauXImGhgY8+uijeOyxxxAREeGjfwMiopGpqcuBX71Ticomz9Z11+SnY9lpmRAFLvogIqIRyDAgtVRCqSt2Fb/YyiFofYNfdxhndDrUzAUHur4UwohM8lGwRESjT1ubZ6E+UTASRRGxsbEDjzs6OqDregAjosFouoHn1uzFP8rrPcbGhMt45OJcFOXEo6Oj3f/B+QnzlkIVc5dCEfOWQlUo5W58fHygQ6AR7vrrr0d6ejqSk5Px7rvv4o033gh0SEQ0iHV7OnDHezvRp7q/dmXFh+O5yychI5a7kxMREdHoInY3QLEe7PhSCqnL8zuSwWiJEw8VvmTkwwhjd72Rzi+FMFdeeSU2b96MTz/9FL/85S/xxz/+EeHh4Uc912634/bbb8cnn3wCQRDw//7f//NZXJdffjnGjx+P8ePHIy4uDk1NTbjxxhu9muOVV16Bw+GAJEm49957kZeXNzA2bdo0pKen4/XXX0dDQwM++OADXHnllR5zvPrqq1BVdci/84ILLkB6erpXcRIRhaKKfT341TuVaO52f46URAH3nD0Ol5ySEqDIiIiIfEPsqodSV3yg+KUEYl+LV9frYWOgmucPdH3RY8e5tgwnIqJh53R615WLKFjous78DWKtPSru+bAKG+o6PcYmpkTiiSW5MMeFj7r/h8xbClXMXQpFzFsKVcxdGs2mT58e6BCIyAufVrTgdx/vhqYbbsenpEbhT9+biPhIJUCREREREfmP0NcCxVbmKnyxlkJur/F6DmfsODgOFr6YC7g57Cjkl0KY2267Da+//joqKirw4osv4t1338WVV16JuXPnIiUlBYIgYN++fVi/fj3+/e9/o7GxEQAwceJE3HbbbT6L62hFKd6oqqrCjh07AACLFy92K4I56KKLLsKqVatgs9mwYsUKXHbZZZBl9//sn3/+Oex2+5B/77x581gIQ0Qj3updrbj3o93o19x3QBkTLuGJJXmYk8VqXSIiCn1CfwcU2zpX4UtdMaSOPV5db4gmqBmzoVqKoGYWQUueCoiSb4IlIiIiIp/a2tCNu97fhX1dDo+xi6cm4a6zshGuiAGIjIiIiIhoZOro6EBVVRWqqqqwe/du7N69G11dXQCAhQsXYtmyZUOeq7m5GStWrMCmTZvQ0tICWZaRlpaG+fPn49xzz0VYGLs7EBHwv5sa8fSXe2EccTx/7Bg8eUkeokz8joeIiIhGJsHeCaW+/FDhS0ul13M4o9OhWgoPdH2ZBz2aa+lHO78UwoSHh2PVqlW48MILsWnTJjQ2NmL58uVHPdcwXG/1Z86ciQ8//DCobwaUl5cP/Hnx4sVHPUcURSxcuBBvvPEGenp6sG3bNsyYMcPtnNdee82ncRIRhRLDMPDa+gYs/6rO4+ZPVnw4nlmah7EJEQGJjYiI6KRpdiiNG6HUlUCpK4bcvBWCoQ9+3QEGBDiTp8CRWeQqfkmfDSh8XSQiIiIKZYZh4D+bm/DUl3uhOt3vhsiigF+fMRbfm+HaUIqIiIiIiIbPz372s2GZZ8OGDVi+fDn6+voGjtnt9oHimpUrV+Luu+9GWlrasPw+Igo9hmHg+WIrXl5X7zF29sQEPHjBeCgSN78gIiKiEUTthdKw8VDhi5frYwBAj0iEapk/0PVFH5MF8LsSOoxfCmEAIDU1FWVlZXjhhRfw/PPPY/v27Uc9b/Lkybjhhhtwww03QJKCu8q9stJVjRYWFoacnJxjnjdlyhS3a44shCEiIhfVqeOxL/bgvS3NHmOzLDF48pI8xEb47aWLiIjo5Bk6pObtMFmLXcUv9eshOIfeDRIAnGOy4MgshJpZBNU8D0ZEgo+CJSIiIiJ/61d1PL5yDz7Y6nkvJCVaweNLcnFKRkwAIiMiIiIiGl2SkpJgNpvx3XffeXVdTU0Nnn32WTgcDoSHh+PSSy/FtGnT4HA4UFxcjJUrV6KhoQGPPvooHnvsMUREcGMjotFG0w089nkN3j3KOojvz0zF7WeMhcgFnURERBTqnHbIjd/BZC2FYiuF3PgtBF31ago9bAxUcwFUs6vwxZmQy8IXOi6/riaWJAnLli3DsmXL0NjYiC1btqC1tRUAEB8fj1NOOQXp6aHTpshqtQIA0tLSjlu0k5GR4XENERG56+zXcOd7u7ChrtNj7OJpybjn7HHcAYWIiEKC2FELpa4YJmsJFGspxP42r67Xw+NdO1pkFkG1FEKPzfJRpEREREQUSPUddtz5/k5U7Ov1GJudGYNHL8pFQpQSgMiIiIiIiEaHyy+/HOPHj8f48eMRFxeHpqYm3HjjjV7N8corr8DhcECSJNx7773Iy8sbGJs2bRrS09Px+uuvo6GhAR988AGuvPJKjzleffVVqOrQF4hdcMEFIbW2hmg061d13PtRFVZXeX5XdMMCC64ryGAHWCIiIgpNuga5aaur44ttHZSGDRC0fq+mMJRIqBlzoZrnw2GZB2fSFEAM7iYaFFwCtq1+WlpaSLd9dTgc6OrqAgAkJiYe99zo6GiEhYXBbrejpaVl2GNZuXIlKioqAAC1tbUDx7Zt2wYAmDRpEs4888whzzfUGIO9Yw/RQaIoHvcxBV5tWx9ufmsH9rZ6vhG6eWEWflxgHnU3f5i3FKqYuxSKTjZvhb5WyHUlUOqKIdeuhdRZ59X1hhQGzZwPNWsBtMwiOJOnAIIrBgEA33XTsfA5l4iIKHSV1rTj3o92o6Nf8xi7ak46lp2eCVkcXfdCiIiIiIj87WhFKd6oqqrCjh07AACLFy92K4I56KKLLsKqVatgs9mwYsUKXHbZZZBl96U6n3/+Oez2oXcSnzdvHgthiEJAV7+G29/diU3WLrfjogDcfXY2lk5PCVBkRERERCfA0CG1VEKxlsJkLYVsK4eodns3hWSCmjYLqsXV8UVLmQ5I3BCMTlzACmFCXX//ocXa4eHhg54fHh4Ou93udt1wqaiowJo1a9yOVVZWorKycuCxN4UwN9xww5DO+9e//jXkOYmCSWxsbKBDoMOUVbfg+te3or3XfZejcEXEs98/FedN401cgHlLoYu5S6Fo0Lx19AK1pUD1atdP42bvfoEgAhkzgZxFQM4iCJZ8KEo4+NGeThafc4mIiIKfbhj4e1k9XlhrhXHEWKQi4v7zcnDWxONvvERERERERMGhvLx84M+LFy8+6jmiKGLhwoV444030NPTg23btmHGjBlu57z22ms+jZOI/G9/twM3vV2JXc3uXWBNkoCHL5qAxbkJAYqMiIiIaIgMA1J7javjy4GuL2K/Z5e7404hSNBSpw8UvqhpswB58DX3REPFQpgT5HA4Bv585G4dR3PwnMOvGy7Lli3DsmXLhn1eIiJfe2ujFXf/ZzNUp/vSj5SYMPz1mjmYbokLTGBERESH051A/bdA9SpX4UtdGeD08n194oSBwheMWwBExA9/nEREREQU1Lr6NfxuxW58tbvdY2xsQjieuiQP2YkR/g+MiIiIiIhOyMHNScPCwpCTk3PM86ZMmeJ2zZGFMEQ0stS29ePGf1egvtO901OUScIfl+ZhduaYAEVGREREdHxip9Wt8EXq2efV9QYEOJOnwHGw40v6HBimaB9FS+TnQhhN0/DRRx/h66+/RnV1Nbq6uuB0Oo97jSAIWLlypZ8iHDqTyTTwZ03TBj3/4DmHXxesnn/++UCHQEQjnK4beOqzSvxl9W6PsSnpY/C3H89BeiwXfhARUYAYBtCy+1Dhy56vgf4O7+aISj5U+JK9EIjL9EGgRERERBQqqpp7ccd7O1HXbvcYW5wbj9+dl4PoMO5bRUREREQUSqxWKwAgLS0NkiQd87yMjAyPa4hoZNrR2IOb365AW5/7WrLEKAXLvzcReSlRAYqMiIiIyJPQ0wSTdR0Um6v4Reqs83oOLSEXqnmeq+OLuQBGeNzwB0p0DH77Zm3t2rW46qqrUFtbO3DMMIxjni8IAgzDgCAI/gjPa+Hhh1oz9ff3D3r+wXMOvy5YJSYmDum8tjbvWlwRBYooioiNjR143NHRAV3XAxjR6NanOnH/R1X4orLFY+z0CfF49OI8hOv9aGsb/Ll1JGPeUqhi7lIoEkURsZIdqF4DVK+GvvtLiF31Xs1hKJHQzAVQM4ugZi2AnjgROPhZxgDA987kA6H0nBsfzy5IREQ0en2yYz8e+rQG/Zr767QoAL9ckIlr8tOD9j44EREREREdncPhQFdXF4DB11hER0cjLCwMdrsdLS2e35GerJUrV6KiogIABtbkrFy5Etu2bQMATJo0CWeeeeaQ5xtqjMcr/iEKFqIoHvfxcFq3px23v1OBXof75//M+HA8f+UUmOOCf80YBQd/5i3RcGLuUigabXkr9LVBtpVBriuBYi2B1Frl9RzO2CxolkKomfOhWebDiEoZGBvZ//WCy2jL3WPxSyFMRUUFzjvvPPT19cEwDJhMJuTm5iIhISFk/8ObTCbExMSgq6tr0JsA3d3dsNtdO/0NtcgkFAzWzYcoWOm6zvwNkP3dDtz27k5sb+zxGPuv2Wm4eWEWJJHPL0fDvKVQxdyloOXogVJfDtOBD/doqRgYGsonFEOQoKXOgJpZBEdmEbTUGYB0WPfHIC1GoJGNz7lERETBRXPq+NOaOry5qdFjLC5CxiMXTUD+2NijXElERERERMHu8A1Th7Ihanh4OOx2+5A2WvVWRUUF1qxZ43assrISlZWVA4+9KYS54YYbhnTev/71ryHPSRQsDt9cajh9uLkev3prB1Sn+6bQ08xj8Mq1+UiKDvPJ76XRwVd5S+RrzF0KRSMub/s7gdpSoOYroGYN0LgVrt1cvRCTDmQvBLJPA8adBil+LCQAfHcTXEZc7g6RXwphHnnkEfT29kKSJDzwwAO4+eabER0d7Y9f7VMWiwU7duxAY2MjnE7nMXe7qK+vd7uGiGg02tnUg1+9sxP7uhxuxyUBuOusbFw2I+UYVxIREQ0Dpwq5aTOUuhKYrMWQG7+BoGuDX3cYLSEXqqXQ1fXFnA/DFOOjYImIiIgo1O3vceDuD6rwjbXLY2xKWhSeWJKLtDH8moiIiIiIKFQ5HIe+85TlwZfeHDzn8OuGy7Jly7Bs2bJhn5eIhubV0j343fvbYByxprRoQiJe+NFsxIQrgQmMiIiIRh+1D6grO1D48hVg2wQYXm6mGZkIjDsNyD7dVQCTOB5gV3sKUn4phPnyyy8hCAJuueUW3HPPPf74lX4xceJE7NixA3a7HdXV1cjNzT3qedu3b3e7hohotPl6dxt++2EVelX33fGjwyQ8viQXBdz9lIiIhpthQGqrglJXDKWuBIqtDKLa7dUUzqjUQ4UvlkLo0ak+CpaIiIiIRpJvbV34zfu7sL9H9RhbOj0Zvz5jHMLk0OyUTkRERERELibToQ7hmjb4pksHzzn8umD1/PPPBzoEopBgGAae+Xwn/vxllcfYhdPT8ccrZyBMPvqmykRERETDQnMA9ZsOFb7UlQFOL4vvw8YAY4sOFL6cDqRMAUR+h0GhwS+FMPv37wcALF261B+/zm/y8/Px7rvvAgBWrVp11EIYXdcHWtBGRUVh6tSp/gyRiCigDMPAm5sa8ezqWuhH7H5ijg3Ds5dNRHZiRGCCIyKiEUfsboRiLRkofpF6m7ybwBQDZJ+G3rR82M3z4YznrhZERERENHSGYeD/vtmHZ1bXwnnEjRCTJODOs8bh0lPYEZeIiIiIaCQIDw8f+HN/f/+g5x885/DrglViYuKQzmtra/NxJEQnTxRFxMYe2pizo6MDuq4f54qhceoGHv2sGm9/t89j7Puz0nDHmePQ29WJ3pP+TTQa+SpviXyNuUuhKOTyVndCat4Gua4EirUUcv16CKp37zgMORxaxlyolvnQMgvhTJkGiIeVE3R0DHPQ5AuhlLvx8fE+m9svhTDJycmor69HRMTIWuw8YcIETJ48GTt27MCqVauwaNEi5OXluZ3z4YcfwmazAQDOP//8IbXEDRWSxF0LKDSIR1SnHvmYfEN16nhi5R689a3njZ+Zlhg8vXQS4iPZAvhYmLcUqpi75Ff2Tii2Msi1xVDq1kJq9dxx63gMUYGWPhPOrNMQPuU8IGMWIMlQOzoAXQff7VKw43MuERFR8OhXnXj4sxqs2NHiMZYWY8ITl+RiSlp0ACIjIiIiIiJfMJlMiImJQVdXF1paPD8HHK67uxt2ux3A0ItMQoHT6Qx0CERe03X9pHPXrum496MqrNrlWQz2iyILfjIvAzB08K8IDZfhyFuiQGDuUigKurw1DEitO6FYS10/tjKIji7vphAVaGkz4bDMdxW/pE4HpLDDTgD4xiX0BV3u+olfqjIWLFiAf/3rX9i6dStmzZrlj185JBUVFWhsbBx43NnZOfDnxsZGrF692u38RYsWeczx4x//GPfddx8cDgceeughLF26FFOnToXD4UBJSQm++OILAEB6ejouvvhin/x7BIovK7SIfOnwKkjyjY4+Fbe/sQlf79rvMbZ0phmPfe8UtgD2EvOWQhVzl4aV5gCs64Hq1a4f20bA8PJDXOo0IGcRkLMIQtZ8KGHROLIsk3lLoYq5S0REFBh1bf248/1d2NXsuetawdgxePjCCYjjZiBEREREvqE5vL9HSDRMLBYLduzYgcbGRjidzmNuJlpfX+92DRGFrm67htve2YlNVvcFqKIA3HXWOHxvRmqAIiMiIqIRwTAgduyFYi2FyVYKxboOYt/xC+89phBEaCmnQDW7Cl/U9NmAMrIaWRAd5JdCmNtuuw1vv/02/vSnP+GHP/xh0HRFWblyJdasWXPUscrKSlRWVrodO1ohTHZ2Nm699VYsX74cfX19ePPNNz3OSU9Px9133z3iOuIQER1NbUsvrvvHelQ1dXuM3X52Hm48YwIEQQhAZEREFHJ0HWjafqjwZW8x4GVLV4yxAOMXATmLgezTgegUHwRKRERERKPV17vbcN/Hu9Ft91x8eW1BBn5RZIEk8j4IERER0bDR7JD3fYewhvXAvo1AXTlw/uNAzpJAR0aj0MSJE7Fjxw7Y7XZUV1cjNzf3qOdt377d7RoiCk37ux24+e1K7DxiIwxFEvDwhRNwRl5CgCIjIiKiUCZ21R/o9rIOirUUUneD13NoSZMPFL7Mg5qRDyMsxgeREgUfv1SkzJ07F88++yxuvvlmXHbZZXj55ZeRlJTkj1/tF3PmzMFTTz2Fjz/+GJs2bUJraytkWUZaWhrmzZuH8847D2FhYYNPREQU4jbsacXPX9uI1h6H2/EwWcTTV87ARdMzAhQZERGFjPa6Q4UvNWuAnmbvrg+PdRW85CxyFb8k5AAswCQiIiKiYaYbBv6nxIb/KbV5jEWZJDxwfg4W5XIBDBEREdFJU/ug7PsGiq0ciq0c8r5vIDjdv4fC3mIWwlBA5Ofn49133wUArFq16qiFMLquD2zQGhUVhalTp/ozRCIaJnVt/bjxrQrYOuxux6NMEp6+NA9zssYEKDIiIiIKNULv/oGiF5O1FFLHXq/n0OJyXN1eLPOhmvNhRCT6IFKi4OeXQpgHH3wQgOsmwIcffoixY8fi7LPPxqRJkxAZGTno9ffff79P4lq2bBmWLVs2LHMlJyfjmmuuwTXXXDMs84WCtra2QIdANCSiKCI2NnbgcUdHB3RdD2BEI9PH25rx+xVVUJ2G2/GESAXPfm8STsmI4POGF5i3FKqYu+Qtob8DsrUUcu1aKHXFkNprvLrekEzQ0udAyyqCmrkAzpRpgCgdOqG9fdA5mLcUqkIpd+Pj4wMdAhER0bDp6NNw38dVKKnp8BjLSYzAk5fkYmwCu4MTERERnRBHD5TGTVBsZVDqyyHv2wxBV49/zZ5iwDCOfw6RD0yYMAGTJ0/Gjh07sGrVKixatAh5eXlu53z44Yew2VwF9Oeffz5k2S/LdPxCkqTBTyIKMFEUj/t4KHY0duPGf+9Aa6/761FilIL/vmIKJqZGnVSMREcajrwlCgTmLoUif+StYO+AbC2DbC2BUlcKqaXS6zmcMWZomYXQMguhWubDiE4bGOPftNGJz7kugmH4/o6QKIoQDtuF2TAMt8eDcTqdvgiLTtL+/fsDHQLRkEiS5Lbwrq2tjc8rw8gwDLx0jB1QxydF4NmlE5Eey65Y3mLeUqhi7tKgNDuUxo1Q6kqg1BVDbt4KwRj6wn0DApzJU+DILIJqKYKaPhtQTm6RIfOWQlUo5e5I6gpLdBDvi1CoCKXXi1BQ2dSDO9/b5bEDLACcPTEB952bg0gTF4OdLOYthSrmLoUi5i0FmuDogly/AUr9gY4vzVsh6JrX87RfVwotMsUHEZ483hcJXhUVFWhsbBx43NnZiddffx0AMHHiRJx55plu5y9atMhjjpqaGtx3331wOBwIDw/H0qVLMXXqVDgcDpSUlOCLL74AAKSnp+Oxxx5DRASL5olCSUnVfvz8tY3otru/No1NjMRr1xUgK3HwDaCJiIholLF3A7XrgJo1wJ6vgYbvAC/WxQAAolOBcacB2ae7fuLHAV6suycaLfy21cSR9TZ+qL8hIiIf61d1PPDJbnxe2eoxVpgdi0cumoDosJGzqxEREZ0AQ4fUvB0ma7Gr+KV+PQSn56LB43GOyYIjsxBqZhFU8zwYEQk+CpaIiIiI6Ng+3NaMRz+vgV1zv7ctCcDNC7Pww9lpXm0ARURERDQaCf0dUBrWu4pebGWQ92/3aqMc4OBmOZMh5ywExhUBWYUwHCLAIi7y0sqVK7FmzZqjjlVWVqKy0n2n5qMVwmRnZ+PWW2/F8uXL0dfXhzfffNPjnPT0dNx9990sgiEKMR9vacCt//stHE7316mpGWPwyrX5SI7hhqBEREQEQO0HrOuBmq9cP7YNgLcbPITHAdmnAdkLXYUvSXksfCEaAr+sTtZ1LyvZiIgo6LX0qPj1uzuxpaHbY+z7M1Pxq8VjIYt8M0ZENBqJHbVQ6ophspZAsZZC7G/z6no9PB6qZf6Bri+F0GOzfBQpEREREdHgVKeOP67ai39/2+QxlhAp49GLczE7c0wAIiMiIiIKfkJfK5T69VBsZVDqyyHtr4AA7zbNNAQRWvJUqBn50MwFUNPnQIxKcOtmBId39yCJhtOcOXPw1FNP4eOPP8amTZvQ2toKWZaRlpaGefPm4bzzzkNYGBfME4WS19btxf3vbcWR+zwXjk/Ei1fNRky4EpjAiIiIKPCcGlD/javjS81XQF0ZoPV7N4cpGhhbeKjjS+opgCj6Jl6iEYzb9BMRkdeqmnvxq3cq0dDpcDsuCsCvzxiLK2emBSgyIiIKBKGvFYq1FKa6YijWEkiddV5db0hhUDPyoWYWwpFZBGfSZEDgB3wiIiIiCrymLgfuen/XUTcCmZ4RjccuzkVKjCkAkREREREFJ6GnGUp9uevHVga5dZfXcxiiDC3lFKgZBVDNc6Glz4ZhivFBtDTaLVu2DMuWLRuWuZKTk3HNNdfgmmuuGZb5QkFbGwvQKPiJoojY2NiBxx0dHcfd0NkwDLxYXIcXi60eY2dNTMTDF+VC6+tGW59PwiUC4H3eEgUL5i6FoiHlraFDat4O2VoKua4USn05BIfndwbHY0gmaOlzoGUWQs2cD2fKdEA6rLC2o+Nk/jVoFAql51y3jUyGGQthiIjIKyU17bj7g13ocbi/aEaZRDx6cS4Ks+MCExgREfmP2gelYcNA1xe5eZtXlxuC6Poi21IINbMIatosQOZueEREREQUXDbWdeLuD3ahtVfzGLvi1FTctjgLisQCbiIiIhrdxO4GKLb1kOvLoNjKIbdXez2HIZqgpc1wbZZjLoCaNhNQIn0QLRENJ6fTGegQiLym6/oxc9epG3hi5R68/Z1nR9jLZ6TgjjPHQRIM5j753fHyliiYMXcpFOm6DqemQWrbDcVa6vqxlUG0t3s1jyHK0FJnQLXMh2qe7/qce+S6GP79oGE0Wp9zWQhDJ0ySpECHQDQk4hEt4458TEP3vxsb8OTKGuhHtP9NHxOGP18+CROSowIT2AjEvKVQxdwdoXQnpKYtUGqLIdethdywEYLTMfh1h3HG5UDNKoKWWQTNMh9G+KFdCQL9rpJ5S6GKuUtEROQbhmHgnxsbsXxNLZxH3AMJkwXcc3Y2LpyaHJjgiIiIiAJM7LRCsZUd6PhSDqmz1us5DCkMatpMqOYCaBn5UNNOBeTw4Q+WiIhoiOyajvs+qsKXuzw7Hf280IyfzTdDEIQAREZERER+0bYHqPkKkZVfQKkrgdjb7NXlBgRoyVNdhS+W+VDT5wAmrqUk8jW/F8K0trbi73//O7744gts3boVra2tAICEhARMmzYNZ511Fq699lokJCT4OzTyki9bFRH50uHtwGhoNKeOP3y4Hf8o3esxNjMrDi9dNQfJMdzJ35eYtxSqmLshyjCAlt1A9SqgejWw52ug38s2rFHJQM4i10/2QkhxmQEveBkq5i2FKuYuERHRyet1OPGHT6vxeWWrx5g5NgxPXJKLiSn88oqIiIhGCcOA2LH3QNGLq/hF6qr3fho5Amr6bKjmfKgZBdBSTwEkfq9ERETBoduu4fZ3d2JjXZfbcQHAXWeNw+WnpgYmMCIiIvIZsXsfFNs6mGzrgPp1QLtrkwdvPqlqCXlQLfNchS8ZBW4bwhKRf/i1EObFF1/Er3/9a/T29gJw7ax3kM1mQ319PT777DP8/ve/x9NPP42f//zn/gyPiIiOoqtfxU1vfoPVlZ5VzhfPyMCTl09HuBIqS5uJiOiYupuA6jWuwpfq1UCn1bvrlShgXNGh4peUKQB3xiIiIiKiELKntQ93vrcL1S19HmNF2XH4w4XjMSacTdaJiIhoBDMMSO3VUGxlkG3lUOrXQ+pp9HoaXYmGljEbakYBVHM+tORpgKT4IGAiIqKT09Kj4ua3K1DZ1Ot2XJEE/OGC8ThrYmKAIiMiIqLhJPS1ujZ4sJZCsZZCbq/2eg5n7Fg4DnZ8Mc+DEZnkg0iJyBt++9busccew29/+9uB4pfY2FjMnDkTaWlpAIDGxkZ888036OjoQE9PD2644Qa0t7fjzjvv9FeIRER0BGtbL37yygZU7uvyGLv5zFz86qxctv8lIgpV9m5gb8mhwpembd5dL0iAZc6hwhfzHEA2DX+cRERERER+sGpXK36/Yjd6HLrbcQHAzwrN+Ol8M0TeAyEiIqKRxtAhte6CYit3dX2pL4fYu9/rafSwMVDT50Iz50PNyIeWPAUQWUBMRETBzdrejxvfqoC13e52PMok4ulL8zAni7u6ExERhSrB0QXZth4maykUWynk/Tu8nsMZleYqejnwo8dk+CBSIjoZfrn7tHXrVtx3330wDAPp6el48sknccUVV0BR3Hd90TQN//73v3HHHXegvr4e9957Ly688EJMnTrVH2GSl9ra2gIdAtGQiKKI2NhDNyg6Ojqg6/pxriAA2Gzrwq/+U4HWXtXtuCIJ+P35E3DB1GS0t7cHJrhRgHlLoYq5G8ScKqR9m6HUrYVcWwy5cRMEXfNuioRcqFlF0DIXQDUXAGExhwa7egD0DG/MfsK8pVAVSrkbHx8f6BCIiIiOyqkbeH5tHV4pb/AYiwmT8IcLJ2BBTpz/AyMiIiLyBUOHtL8CSn3ZgeKXDRD7W72eRg+Ph5oxF6o5H2pGAZyJEwFR8kHARBTMJIl/7yn4iaJ41McV+7px4793oKXHfT1EQqSC/75yMialRvstRqIjHStviYIdc5cCSu2DXL8BsrUUirUE0r4tEAynd3NEJsFhmQfVMh+apRB63DjgwAZZAgC++6VgwudcF78Uwjz33HNwOp1ITk5GaWkpsrKyjh6MLOMHP/gBFixYgLlz56K5uRnPPfccnn/+eX+ESV5yOr18kSAKErquM38H8VlFC36/YjccTsPteFyEjKcuzcOp5hj+N/Qz5i2FKuZuABkGpLYqKHXFUOpKoNjKIKrdXk3hjEqFaimEmlkE1VIIPTr1iBNG5v9b5i2FKuYuERGRd9p7VdzzURXK93Z6jOUmR+LJS3JhiQsPQGREREREw0TXIDdvh1JfDtlWDqVhPUS753ufQaeJSIRqLoCakQ/VnA9nQi4gjM4FFkR0CDe/oVAUGxuLkt378fM3t6Pb7r5hXFZCJF69Lh/jkqICFB3R0R2+KRpRKGHukk9pDsC2Aaj5yvVjXQ84Hd7NERYLjFsAZJ/u+kmZDJMgwOSbiIl8arQ+5/qlEObLL7+EIAi4++67j1kEc7jMzEzcdddduP3227Fy5Uo/REhERABgGAb+tq4eLxRbPcayE8LxzGUTuQCEiCiIid2NUKwlA8UvUm+TV9frSjRUy7yB4hdn/PiB3S2IiIiIiEaSbQ3duOv9XWjs8vxi7Pwpifjt2dkIV7i/GxEREYUYpwq5eeuBbi9lkOs3er05DnBgg5yMfGjmfKjmAjjjcnifkIiIRoQVWxpwy/9+C4fTvbv6lPQxeOW6uUiJ4XoIIiKioOTUgMbvDhW+1K4D1F7v5lAigaz5hwpf0mewuylRiPNLIYzNZgMAFBYWDvmaoqIiAEB9fb1PYiIiIncOTccfPqvGiu0tHmMFY8fgsYtzERPul5cNIiIaIsHeBaW+bKDwRW6r8up6Q1Sgpc2EI7MQqqUIWup0QORzPRERERGNbO9ubsLjK/dAPaITriQKuG1RFq6cmQqBCz2JiIgoFDjtkPdtcd0jtJVDadwEwduFQACcMRkHur24ur7osWNZ+EJERCPOP8v24t53t8Jwvx2AeTkJeOnqORgTrgQmMCIiIvKk60DTdlfRy56vgT3FgL3DuzkkE2DJP1T4Yp4NyOz3QjSS+GWVmyS5KuY0TRvkzEOcTicAQBTZUpmIyNfaelX8+r2d+M7muSvY92ak4I4zxkKW+HxMRBRwTgfkxm9hshZDqSuGvG8zBMPp1RRa4iSomUWu4pf0uYCJ7d2JiIiIaHSwazqeXLkH725p9hhLilLw+JJczDDHBCAyIiIioiHS7FAav4FcXw7FVgal8RsITrvX0zjHZEE15w8Uv+hjLD4IlohGura2tkCHQDQoURQxZswY/HllFZ75YqfH+Bl5CXjk4jw4+7rR1heAAImOQhRFxMbGDjzu6OiAruvHuYIoODB36aQYBsT2Gsh1JVCspZCtpRD7Wr2bQpDgTJ0ONbMQmmU+tPTZgBJx6ISuHgA9btcwbylUhVLuxsfH+2xuvxTCZGVlYceOHVi5cuWQu8KsXLly4FoiIvKdmpY+3PqfStg63L8oEQD8anEWfjArjbugEhEFiqFDatkJpa7YVfxiK4egeXcX3hmdDjVzwYGuL4UwIpN8FCwRERERUfBq7LTjzvd2Yfu+Ho+xmZYYPHrxBCRFcSc4IiIiCjJqL5TGb1xFL/XlkBu/g6A7vJ5Gi8uGdrDjizkfenS6D4IlotHm4Aa3RMHMqRu4/71teG3dXo+xy2ak4K4zx0ESDOYzBTVd15mjFJKYuzQYsaseitVV+KJY10HqafR6Di1pClTLfDgs86FlzIFhOmKzKy9zkHlLoWq05q5fCmHOPvtsbN++HU899RQuvfRSnHLKKcc9f+vWrXjyySchCALOOeccf4RIRDQqle3twF3v70K33f0FMEIR8chFE3DaeN9VYhIR0dGJnTbXB/26YpisJRD7Wry6Xg8bA9U8f6Drix47DmBBIxERERGNYmV7O3DPh1Xo6PPsWP7D2Wm4+fRMdsIlIiKioCA4uiE3bIRiK4dSXwa5aQsE3fM9zGC0hNwD3V5cXV+MqBQfREtERBTcHJqO362owueVnt+1/Wy+GT8vNHNTUCIiIj8Sepqh2Ephsq6DYiuF1FHr9Rxa/HiolvmudTHmfBgRCT6IlIhChV8KYW699Va88MIL6O7uxoIFC3Dffffh2muvRWJiott5LS0t+Pvf/46HH34YXV1dCA8Px6233uqPEImIRp23v9uHJ77YA6fhfjw1xoRnluYhLyUqMIEREY0yQn8HFNs6V+FL3VpIHZ47Uh2PIZqgZsyGaimCmlkELXkqIEo+ipaIiIiIKHQYhoF/lDfgL2vroB9x/yNcFnHfeTk4d1Li0S8mIiIi8gPB3gm5fgOU+jIotnLIzdsgGN7t3mlAgDNx4oGilwKo5rkwIvgeh4iIRrduu4Y73tuF9bWdbscFAHeeOQ5XzEwNTGBERESjiNDf7upwai2FYlsHuXWX13M4x2QeVvhSAD2ar+FEdIhfCmHGjh2LF198Eddeey26u7tx11134Te/+Q2ys7ORkpICQRCwb98+1NTUwDAMGIYBQRDw4osvIisryx8h0gmQJC6wpNAgiuJxH482Tt3AM6v24J8bGjzGpqZH45nLJiE52hSAyOhwzFsKVczdIdD6ITdsgly7FkpdMaR9myHAGPy6AwwIcKZMhZZZBDVzAbSMOYASMTDOd2jeY95SqGLuEhERHVu3XcMDn1Rj1a42j7Gs+HA8sSQXE5IjAxAZERERjWZCXxuUhvUHOr6UQ2re7tW9QQAwBBHOpMkHil7yoWbMhREe55uAiYiIQlBLj4pb/lOBin29bscVScBDF+bizLz4AEVGREQ0srm6nG6AYi2FyVp6Qp95nZEprsIXy3yolnnQx2T6KFoiGgn8UggDAFdffTUSExNx/fXXo76+HoZhYPfu3aiurgbg2p3voIyMDLz00ku44IIL/BUenYD4eH4wpNAUGxsb6BACpseu4Zb//QZf7GjyGLvglDQ8fcWpiDBxCXUwGs15S6GNuQtA14HGzUD1atdPbSmg9Xs3R/w4IGcRkLMIwrjTIUclQgYQPuzBEsC8pdDF3KXR6quvvkJFRQWqq6tRW1sLTdPwy1/+EosWLQp0aEQUINX7e/Hr93ahts3zfffCCfF44PwcRIf57dY4ERERjWJC734o9etdO+DWl0NuqfR6DkOQoKVMg5qRD9VcAC19DoywGB9ES0REFPqs7f246a0K1LXb3Y5HmSS8dPUcTEmU4HR6132NiIiIjkGzQ2nc5Or4Yi2F3LQZgq55NYUeFgfVMm+g+MUZlwMIgo8CJqKRxq/f9l144YXYs2cP3nnnHXzxxRfYunUrWltbAQAJCQmYNm0azjrrLFx66aVQFMWfoRERjXj17X34yT82YEdDp8fYssXjcfvZEyGKfBNJRDQsWmsOFb7UrAH6PHehPq6IBCBnoav4JXshkJDtgyCJiIhGhv/7v/9Dc3MzYmJiEB8fj+bm5kCHREQB9EVlCx74pBp9qu52XABwwwILflyQAZFfohEREZGPCD1NA0Uviq0ccluV13MYogIt5RSo5gKoGfnQ0mfBMEX7IFoiIqKRZWdTD256uxItParb8cQoE165Nh+nWGLR1ubld3ZERER0iFOF3LR5oPBFadwEwenwagpdiYZmzofjYOFL4kRAEH0UMBGNdH7f9k6WZVxxxRW44oor/P2riYhGrc3Wdvz0HxvQ1OW+64kiCXj0sum4fLYlQJEREY0QPftdBS8Hi1/aa727Xg4HxhYOdH1B6imAyA/6REREQ3H99dcjPT0dycnJePfdd/HGG28EOiQiCgBNN/DcV7V4fUOjx1hsuIyHLxqPeePi/B8YERERjWhiV/2BopcyKLZySB17vJ7DkEzQUk+Fas53dX1JmwUoEcMfLBHRMJMkKdAhEA3YUNuBX71dgW6He7cXc1wYXv/pfGQnRQEARH7/RiHgyDxl3lKoYO6OQLoT0v4dkOtKoNSVQK4vh6D2ejWFIYVBy5gLLXM+1MxCOFNOAcRDS9cD/Y6SeUuhirnr4vdCGBo5uEsChQpRFBEbGzvwuKOjA7quH+eKkWVlZQvu/XAX+jX3f+fYcBlPXzYRszOj+Pc5CI32vKXQNWpyV+2DXF8OpbYYct1ayM3bvbrcEEQ4U06BmrUAWmYRtPRZrmKYgzo6hjlgOp5Rk7c04oRS7sbHxwc6BBrBpk+fHugQiCjAWnpU3P3BLmyydnmMTU6NwuNLcpERGxaAyIiIiGhEMQyIXdaBohelvhxSZ53308jhUNNmQs0ogGrOh5Z6KiDzvQoRhR7e86Ng8cnWRtz87x1wHLEmYnL6GPzj2rlIGXPoO7jD76kThQrmLYUq5m4IMgyguQKo+dq1GeyetUB/u3dziDJgmQtknw5knw7BMheKHAYFQChs+cC8pVA1WnOXhTB0wpxO5+AnEQUhXddHRf4ahoF/lDfgua89v4TJig/Hny6biMz48FHx32IkGC15SyPPiMld3Qm5eSuUurVQ6oqhNHwDQfeuvasWlw01swiqpRCqeR6M8CM+gIyE/04jxIjJWxp1mLvkrY6ODlRVVaGqqgq7d+/G7t270dXlWkS+cOFCLFu2bMhzNTc3Y8WKFdi0aRNaWlogyzLS0tIwf/58nHvuuQgL46IuIvKNLfVduOv9XWjqVj3GlkxLxl1njUOYPDp3wSIiIqKTZBgQO/YMFL0otjJI3Q1eT6MrUdDSZx0ofJkLLWU6IJl8EDAREdHo82Z5LX77zhbohvvx/OwE/PWaORgTrgQmMCIiolBgGEBbDVDz1YGfr4GeJu/mEEQg/VQg+zRX8UvWfMAU5ZNwiYiO5JdCmC1btuCSSy6BJElYvXo1zGbzcc+32WxYuHAhDMPAihUrkJeX548wiYhGDNWp45HP9+CDrc0eY3OzxuDxJbkYE85aSCKiYzIMiO17YLIWuwpfrKUQHZ67Sx+PHpEIR2YRVEsR1MxC6DEZPgqWiIjoxPzsZz8blnk2bNiA5cuXo6+vb+CY3W4fKK5ZuXIl7r77bqSlpQ3L7yMiAlwbgLz9XROe+nIvtCNWuyiSgDvPHIdLT0mGIAgBipCIiIhCjmFAats9UPQi28oh9Xq5AAiAboqGlj4XqjkfqrkAWvJU1464RERENGwMw8BzX1bh6c93eoydOzUVf/p/MxGuSAGIjIiIKMh12IA9Xx8qfunwvtMpUqYOdHzB2EIgIm7YwyQiGgq/3HF7/fXXsWfPHpx77rmDFsEAgNlsRl5eHj799FO8/vrrePDBB/0QJRHRyNDep+LO93Zhk9VzwfalpyTjN2eNgyxxJ1QioiMJPc0wWUsOFL6UeL27o6FEQs3IHyh+cSbmAVx0R0REISIpKQlmsxnfffedV9fV1NTg2WefhcPhQHh4OC699FJMmzYNDocDxcXFWLlyJRoaGvDoo4/iscceQ0REKDQ9J6Jg16/qeOyLGny4bb/HWGqMCY8vycW09OgAREZEREQhxdAhteyCUl820PVF7Gvxeho9LBZqxlyo5gKoGflwJk0GRC68JaKRr62tLdAh0CilGwae+KIG/7ep0WPsshmpuOecHPR1d6IPgCiKiI2NHRjv6OiArut+jJbIe8xbClXM3eAk9LZAtq2DUlcCua4UUnu113M447KhWeZDzSyEZpkHIzLp0GC/AfSH7vtC5i2FqlDK3fj4eJ/N7ZdCmDVr1kAQBCxZsmTI11xyySX45JNPsHLlShbCEBEN0d7WPvzqnZ2obet3Oy4AuHlhFn40J407oRIRHSA4uiHXr4epztX1RW713DHqeAxBgpY6A2pmERyZRdBSZwCSyUfREhERDb/LL78c48ePx/jx4xEXF4empibceOONXs3xyiuvwOFwQJIk3HvvvW5dfadNm4b09HS8/vrraGhowAcffIArr7zSY45XX30VqqoO+XdecMEFSE9P9ypOIho5bO39uOP9XdjZ1OsxNjdrDB65aALiI5UAREZERERBT3dCaqlwFb3Y1kGp3wDR3u79NOEJrm4vGa6OL64NcbgBGRGNPk6nM9Ah0Cjk0HT8bsVufF7Z6jH203lmXF9kBgwdx0pPXdeZuxRymLcUqpi7gSHYO12dTq2lUKylkFsqvZ7DGZ0O1VII1TIPqnke9JiMI04Yuf9fmbcUqkZr7vqlEGbnTteiwunTpw/5mmnTpgEAKiu9fxImIhqNNtR24s73d6Kz3/3FLFwW8dCF47EoNyFAkRERBQmnCrnpOyh1JTDVFUPe9y0EXfNqCi0h1/VhP7MIqjkfhinGR8ESERH53tGKUrxRVVWFHTt2AAAWL17sVgRz0EUXXYRVq1bBZrNhxYoVuOyyyyDL7rejPv/8c9jt9iH/3nnz5rEQhmiUKq5ux30fV3nc+wCAq+em45enZUIWuQEIERERHaBrkJu3QbG5Or7IDRsgOrq8nyYy+UDhSwFUcz6c8RPYCZqIiCgAehxO3PHeTpTv7XQ7LgD49Rlj8f1ZaYEJjIiIKJDUXigNGw8VvjRvhWB41xVCj0iEapkPh2U+VMt86GOy+LmXiEKCXwphuru7AQDR0dFDvubguZ2dnYOcSURE721pwiOf74FTN9yOJ0creGbpRExKjQpQZEREAWQYkFp3QbEWw1RXAtlWBlHt8WoKZ1TqocIXSyH06FQfBUtERBR6ysvLB/68ePHio54jiiIWLlyIN954Az09Pdi2bRtmzJjhds5rr73m0ziJKPTphoG/ldrwUokNxhFjkYqI350/HmfmcQMQIiKiUc/pgNy0xdXxpb4McsMmr+8HAoAzOm2g6EXNKIAeN44LgIiIiAKstUfFLf+pxI597q/tsijgwQvG45xJiQGKjIiIyM+cdsiN38FkLYViK4Xc+C0EXfVqCj1sDFRzAVSzq/DFmZDLz71EFJL8UggTHx+P/fv3o7Gx0WOxw7E0NjYCAGJiuMs2EdGx6IaB576qw6vrGzzGJqZE4pmlE5ESYwpAZEREgSF2N0KpK4ZidXV9EXubvbpeV6JdrV0PFL8448fzwz4REdExHOziGxYWhpycnGOeN2XKFLdrhnpviIgIALr6Ndz/8W58Xd3uMTYuIRxPXZKHcYkR/g+MiIiIAu/A4h+l3tXxRWn8BoLW5/00MRa3ji/6mEzeEyQiIgoitvZ+3PR2JWrb+t2ORyoinrw0DwVjYwMUGRERkR/oGuSmra6OL7ZSKA0bIWj9g193GEOJhJoxF6p5PhyWeXAmTQFEyUcBExH5j18KYXJzc7F//3588sknOPfcc4d0zYoVKwAA48eP92VoREQhq8/hxP0rdmPVrjaPsUUT4vGHC8YjwsQ3rEQ0sgn2Lii2dQPFL3Lbbq+uN0QFWtpMODILoVqKoKVOB0S/vEUmIiIKeVarFQCQlpYGSTr2Z4+MjAyPa4iIhmJXcy/ueG8nrO12j7EzcuPxu/PHI4r3PoiIiEYPrR9K4yZX0YutHPK+byA4HV5P44wd69r5NiMfqrkAekzG4BcRERFRQOxq7sWNb1Wgpcd9p/v4CBl/+t5ETEmLDlBkREREPmLokFoqoVhLYbKWQraVQ1S7vZtCMkFNmwXV4ur4oqVMByTFRwETEQWOX1b5nXvuuSgpKcFLL72En//855g8efJxz9+2bRv+53/+B4Ig4LzzzvNHiEREIaW524Hb3tnp0fYXAK6am46bTs+EyN3KiGgkctohN34LU10xlLoSyE2bIRhOr6bQEidBzSxyFb+kzwVMUT4KloiIaORyOBzo6uoCACQmJh733OjoaISFhcFut6OlpWXYY1m5ciUqKioAALW1tQPHtm3bBgCYNGkSzjzzzCHPN9QYj1f8QxRMRFE87uNg9fG2Zvzhk93o13S346IA3LJwLK7Kz4DAex8jVqjmLRFzl0JRUOetowdyw0bItjIo1jJI+76DoKuDX3cEZ/x4aOYCqJYCaOYCGNFpA2MCAL6zD01BnbtERDQsNtV14rZ3d6Lb7v5dYMaYMCy/fCLGJrBDLBERjQCGAam9xtXxxVoKxbYOYr/nxtjHnUKQoKVOHyh8UdNmAXK4jwImIgoefimEueGGG/DEE0+gt7cXZ5xxBv7nf/4HF1100VHPff/993H99dejr68PkZGRWLZsmT9CpBPABR8UKkbajfCKfd245a0KNHW773ImiwLuPicHl81IDVBkNJxGWt7S6DHsuWvokPZXQK4rhlK7FrKtHILW59UUenQG1KwFULOKoFkKYUQlD4zx3QwBfM6l0MXcpUDq7z/Ucj08fPAb6eHh4bDb7W7XDZeKigqsWbPG7VhlZSUqKysHHntTCHPDDTcM6bx//etfQ56TKJjExsYGOoTjcmg6Hvl4B14p2eMxlhhlwvIfzkTh+CT/B0YBFex5S3QszF0KRQHN2/5OoHYdsHctsKcYaPgW0DXv50mZAowtAsYVAWOLIEWnQAIQNtzxUlDhcy4R0ciyelcr7vmwCg6n4XZ8QlIEll8+CcnRpgBFRkREdPLETqtb4YvUs8+r6w0IcCZPgcM8z9XxJWMuDBO7pBHR6OOXQpikpCS88MILuOqqq9DU1IRLLrkEOTk5WLBgAdLT0wEADQ0N+Prrr1FTUwPDMCAIAp5//nmkpnJBd7CKj48PdAhEJySUb4R/vn0fbn5zG/pU9x1PxoTLeOFHs1E4gQtBRqpQzlsa3U4od9trgerVB37WAL37vbs+PBbIPh3IWQTkLIaYkIMwQeAX3TRkfM6lUMXcJX9yOA4V5svy4LeXDp5z+HXDZdmyZdxIhWiEaOrsxy//uQkb9nrudjcjMw7P/9csZMRxt1ciIqIRo68N2FsK7C0G9qwFGjcDhj74dW4EIG0aMHaBq/AlqxCIOn7XSiIiIgpu725uwiOf10B3r4HBTEsM/nhpHmLC/bLcjYiIaNgIPU0wWddBsbmKX6TOOq/n0BJyoR4ofFHNBTDC44Y/UCKiEOO3Twb/9V//BV3XccMNN6C3txe7d+9GdXW12zmG4foEExUVheeffx4/+tGP/BUeEVFQMwwDf/26Bo+s2AHjiJs9YxMj8fKP52J8Mqu6iShE9bYCe74+VPzSWj3YFe4kE5A170DhyyIg/VRAZK8XIiIiXzKZDu24qGmD79B88JzDrwtWzz//fKBDIBqVymtaseyNTWjusnuM/bAgC7+7eArCZL7PJyIiCmk9La6il73Fro4v+7YCMAa9zI0gAukzDnR8WeC6LxjBzfuIiAJBkvgZjYaXYRh4eZ0Nz31V6zG2KDcBj16ci3DFu7xjZ3UKRcxbClXM3UOEvjbItjLIdSVQrCWQWqu8nsMZmwXNUgg1cz40y3wYUSkDY6P3v+zwY95SqGLuuvi1RP6qq67C2WefjT//+c/46KOPsHXr1oHiF1EUccopp+Diiy/GjTfeyE4wREQHqE4d97+3FW+We1aC52cn4MUfzUZ8VPAvJiMiGqD2A3XrDhW+1H8L777wFoD06YcKXzLnAaZIHwRKRERExxIeHj7w5/7+/kHPP3jO4dcFq8TEoe0e3dbm2bGCKBiJoujWNayjowO67u1O675jGAbe3NiAZ1bthXbEVq8mScA95+Tgkump6O3qRG+AYiT/C/a8JToW5i6FIl/mrdDT7Fr4YyuDYiuD1LLT6zkMUYYz5RRo5gKolgJo6XOAsJhDJ/QD6Od789EolJ5z4+NZrEUjE3ObhpOuG3jww+14pcSzCOb7czLx8NJpkKWTX9zHzuoUipi3FKpGVe72dwK1pUDNV0DNGqDxBDZ+iEkHsk93/Yw7DVL8WEgAwnwRLx3TqMpbGlFGa+76vVdkWloaHnnkETzyyCPQNA2tra0AgISEBMgyW1eGEi74oFARSjfCj9TZr+GOdytRvrfDY+ziacm499zxgKMHbY6eAERHvhTKeUuj21FzV1MhNW+HXLcWSm0x5Pr1EJyeuzwfjzM2C1pmEdSsBa6dLiISDg322F0/RCeIz7kUqkIpd/ml+MhjMpkQExODrq4utLS0HPfc7u5u2O2u1+qhFpmEAqfTGegQiE6IrutBk799Dice/rwGn+zwfB5JH2PCk5fkYVJqVNDES4ETTHlL5A3mLoWik8lbsbsRiq0ccn2Z65/tXnZ+BmCIJmip06Ga86FmFEBNnwUoR2yCw79XdBR8ziUiCl0OTcft//4OH3xX7zG2bPF4/PqciRAEIQCRERERHYPaB9SVHSh8+QqwbQIMLz+PRCYC4047UPyyEEgcD/D1jojIKwGtPJFlGSkpKYOfSEGJNxIpVIXKjXBrez9u+U8l9rZ67q687DQLfpyfAUEwQuLfhU5eqOQtEQDAMIC2GqB6NSIqPodcVwLR3u7VFHp4PFTLfDgyi6BaCqHHZrmfwL8P5EN8zqVQxdwlf7NYLNixYwcaGxvhdDohSdJRz6uvr3e7hogIAGrb+nHHezuxe3+fx9j8cbH4w4XjERehBCAyIiIiGiqx0wblQNGLUl8GqcNzB/fBGFIY1LRToWUUuIpf0mYCcvB3kiQiIqLh0W3XcMPrG/H1rv0eY7+7eAquLcoOQFRERERH0BxA/aZDhS91ZYDT4d0cYWOAsUWHur6kTAHEk+92RkQ0mrEFCxFREPrG2olfv7cLHX2a2/EwWcAD54/HWRNHzi7KRDQyCH0tUKylCLOWArYSoN31pbdpiNcbUhjUjHyomYVwZBbBmTQZEPiBn4iIKJhNnDgRO3bsgN1uR3V1NXJzc4963vbt292uISL6ancb7v94N7rtngWcP5mXgZ8XWiCJ3PmOiIgoqBgGxM5aV9GLrQxKfTmkLpv308gRUNNnue4FmgugpU4HpDAfBExERL7W1tYW6BAoxLX2qrjp39uxvbHH7bgsCnjwwgk4f0rcSedZKHVWJzqIeUuhakTlru6E1LwNcl0JFGsp5Pr1ENRer6Yw5HBoGXOhWuZDyyyEM2UaIB62ZLujY5iDphMxovKWRpVQyt34+Hifze33Qphdu3bh1VdfRWlpKRobG9HX14dPP/0UEyZMGDhn69atqK2tRVRUFBYuXOjvEImIAurDbc146NMaaLrhdjwxSsEfL83D1PToAEVGRHQYtQ9K/Xoo1mKY6kog798++DWHMQQRWsopUC2FUDOLoKbNAmR+4U1ERBRK8vPz8e677wIAVq1addRCGF3XsWbNGgBAVFQUpk6d6s8QfepYHXCIgo14xI5yRz72J6du4IW1dfhrqdVjLNok4Q8X5WJRbkIAIqNgE0x5S+QN5i6FomPmrWFAbK+GbC2DbCuDYiuD2N3o9fyGKRpa+hyolgJo5nw4U6YD0qGub3xXTSeKz7lEgcfu1HQy6jvsuPGtCtS29bsdj1BEPHlJLuaNi/NJjrGzOoUi5i2FqpDKXcOA1LoTirXU9WMrg+jo8m4KUYGWNhMOy3xX8cuRGz8YAELlv8coFlJ5S3SY0Zq7fiuE0XUdd955J/70pz9B13UYhmuBtyAIcDjcW4TV1tbioosugizLqKmpgdls9leYREQBoxsGXii24uV19R5jucmReGZpHtLGcJE4EQWIrkFu2grFWgylrhhKwzcQdO/avGpx2a6iF0shVPM8GOGxg19EREREQWvChAmYPHkyduzYgVWrVmHRokXIy8tzO+fDDz+EzebaJfr888+HLI+c5sS+3LmGyJcO3x3Kn9p6HLjt/77FVzubPcYmpsbghatmIzspKgCRUSgIVN4SnSzmLoUUwwCaKxC7Zy2wtxjYWwJ07/N+nvBYIKsQGFcEjC2CkDYdiiRDGfxKopPC51wiotBR1dyLm96uQHO36nY8LkLGny6byM1BiYjI9wwDYsdeKNZSmGylUKzrIPa1eDfFwQ1gza7CFzV9NqBE+ChgIiI6Gr+tPrj++uvx8ssvwzAMmM1mzJ8/H2+99dZRz73ggguQnZ2NPXv24K233sItt9zirzCJiAKiX9Xx+xW78cXOVo+xBTlxePiiCYgycV80IvIjw4DUXuMqerEWuz70e7nbBaKSYbcUwmEuhJpZCD0mwzexEhER0QmpqKhAY+OhHZ07OzsH/tzY2IjVq1e7nb9o0SKPOX784x/jvvvug8PhwEMPPYSlS5di6tSpcDgcKCkpwRdffAEASE9Px8UXX+yTfw8iCn5bbR34xesbYW3r8xhbMiMDj33vFESaRk6hHBERUUjQdaBpG7CnGNi71lX40uvdoh8AQEQCMLYQGLcAGFsEpE4FRH6fQUREREf3rbULv3qnEl12992q08eYsPzySRiXwAXERETkG2JX/YFuL+ugWEshdTd4PYeWOMlV9GKZDzUjH0ZYjA8iJSKiofLLt4srV67E3/72NwiCgHvuuQcPPPAAJEk6bnviK664Ak888QS+/PJLFsIQ0Yi2v8eB29/ZiW2NPR5jP5idhlsXZkEShQBERkSjjdDTDJO1ZKD4RepuHPyiwxhKJDRzAZSJZwM5i4CUKehtbx+VbReJiIhCwcqVK7FmzZqjjlVWVqKystLt2NEKYbKzs3Hrrbdi+fLl6Ovrw5tvvulxTnp6Ou6++25ERPBLbKLR6N8b6nDvu1th13S347Io4J4LJuPaonEQBN73ICIi8jndCTRuPlD4cqDjS3+79/NEJbsKXg4WviRPAo7znS8RERHRQWuq2nDPh7tg1wy34+OTIrD8e5OQEmMKUGRERDQSCb37B4peTNZSSB17vZ5Di8s5VPhizocRkeiDSImI6ET5pRDmpZdeAuDq9PLQQw8N6Zr8/HwAwLZt23wWFxFRoO1q7sWv/lOJxi6H23FJAO44cxwuPzU1QJER0WggOLoh16+Hqa4YSl0x5NadXl1vCBK01BlQM4vgyCyCljoDkikC8fHxPoqYiIiIgtGcOXPw1FNP4eOPP8amTZvQ2toKWZaRlpaGefPm4bzzzkNYWFigwxx2bW1tgQ6BaEhEUURsbOzA446ODui6fpwrhodD0/HEyhq8/e0+j7GkKAWPXzIRszLHoL293eexUOgJVN4SnSzmLgUVpwqpeStkaxkUWxnk+g0QvO34DECPSoVmLoBqKYBmLoAePx44vIi1o2MYgyYaulB6zuU9cyIi4L0tTXj4sxro7jUwONUcgz8uzcOYcHaKJSKikyPYO6HYylxdX6ylXq+BAQBnTAZUS+GBwpd50KPTfBApERENF798iigtLYUgCPjJT34y5GssFgsAoLHRu53IiYhCxdrqdtzzwS70qu435aNMEh5fMgHzxsUFJjAiGrmcKuSm76DUlcBUVwx537cQdM2rKbSEXNeH/swi124XJrZ5JSIiClXLli3DsmXLhmWu5ORkXHPNNbjmmmuGZb5QwK53FKp0Xfd5/u7rsuOu93dha4Nn99sZ5mg8fnEukqJN/HtEQ+aPvCXyBeYu+ZXTAblpMxRbueuncSMEtdf7aWLMUDPmQjUXQM3Ihx471r3wJUgLDYj4nEtEFJwMw8A/yhvw3Nd1HmOnj4/DIxflIlxhdzkiIjoBjh4oDRtchS+2dZCbt0EwvPvMqkcmwWGeP9D1RR+T6f4ZmIiIgppfCmGampoAAOPGjRvyNYqiAAA0zbvFmUREwc4wDPzfN/vwx1V7PXY7MceG4ZmlechJigxMcEQ0shgGpNZdUKzFMNWVQLaVQVQ9F6IdjzMq9VDhi6UQejQ7VRERERER0bFtqO3A3R9Uoa3P877u92em4tZFWVAkLnAhIiI6aZod8r5vXUUv9WVQGr+BoPV7PY0zNgtS9unAuCJgbBE6MYbFBERERDQsdMPAM6tr8eZGz02Ql0xLxj3nZEMWudiYiIiGSLNDafwGiu1Ax5d933m9+aseFgvVPO9A4cs8OOMnsPCFiCiE+aUQJioqCu3t7Whubh7yNVarFQCQkJDgq7CIiPxO0w08/eUe/PvbJo+x6RnRePrSPMRHKgGIjIhGCrG7AUpdCRSrq+uL2Dv0918AoCvRUC3zBopfnPHj+aGfiIiIiIgGZRgGXt/QiOVf1Xps/BEmi/jtOdm4YEpSYIIjIiIaCdQ+KI2boNS7Or7I+76F4HR4PY0Wlw0tI9/V8cWcDyHWgvj4+EMntLUNY9BEREQ0WqlOHQ98Uo1PdrR4jP04Px3LTsuEwO8giYjoeHQNctMWV8cXaymUho0QnHbvplCioGXMhWqZD4dlPpyJkwBR8lHARETkb34phMnJycGmTZuwfft2nH322UO6ZsWKFQCAqVOn+jI0IiK/6bZruPuDKpTu6fAYO29yIu47NwdhMndEJSLvCPYuKLZ1UOqKoVhLILft9up6Q1Sgpc2EI7MQqqUIWup0QPTLW0QiIiIiIhohehxOPPhJNVbubPUYs8SF4clL8pCbzO63RERE3hAc3ZAbNh7o+FIOuWkLBF31eh4tIRdqRj5Ucz7UjHwYUSlu41z+Q0RERMOt1+HEXe/vOuraiNsWZ+GHs9MDEBUREQU9Q4e0fwdMBwpf5PoNENVu76aQTFDTZ7s6vpjnQ0s5BZC4KTUR0Ujll1WO55xzDjZu3Ij//u//xk033QRRPP5C7+3bt+OVV16BIAi44IIL/BEinQBJ4q1xCg1HPucM9hzkC7b2ftz81g5Ut/R5jP1iQSZ+XmjhbifkJhjyloKUZofc+A3kumIotWsh7fsOgqF7N0XSJGiZC6BmFUHLyAdMUQNjJ/vqztylUMS8pVDF3CUKLN4XoVDh69eLmpZe3P5OJWqOcs/j9PHx+MNFuRgTzmJ78g7f51CoYu7SyRDsHZBtGyDbyiDbyiA1bYVgOL2eR0uaBM08D5q5AJo5H0Zk4sDY0TKSeUuhirlLRBSc2ntV3PKfSmxr7HE7LokCHjg/B+dNZrdYIiI6wDAgte0+1PHFVgbR3u7dFKIMLXXGQOGLmjYTkMN8Ey8REQUdwTAMw9e/ZN++fZgwYQJ6e3vxk5/8BH/5y18gyzJEUYQgCNiyZQumTJkCAPj8889x7bXXor6+HomJiaipqUF0dLSvQyQi8pmNe9vw81c3oKXH4XbcJIt46ooZWDIjI0CREVFI0HWgaRtQvdr1s7cEUHu9m2OMBRi/CMhZDGSfDkSnDHoJERERERHRYD7Z2oBf/3szuu2a23FBAG49Mw83nTEBosiNP4iIiI6qt9V1r29vMbBnLbBvK+DlhjcQRCDtFGDsAmBcEZA1H4hM8E28RERER2hrawt0CBRk6jv6sexfO7Cn1X2zjHBFxFOXTkRRTrzfYxJFEbGxsQOPOzo6oOtevuci8jPmLYWqoeSu2FEHua4EsrUESl0JxN5mr36HAQHOlGnQMguhWuZDy5jrtvkrkbf4nEuhKpRyNz7ed58D/FIIAwD//Oc/cfXVVwMALBYLLrzwQrzwwgsQBAE//elPYRgGiouLUVFRAcMwIIoi3nvvPVx44YX+CI+IyCfe+9aGO97aDIfm/gKTGGXCS1fPweyx/r/RQ0QhoL32UOFL9Rqgd79314fHugpecha5il8Sclwr0YiIiIiIiIaB5tTx5GeVeHFNtcdYbISCZ//fqVg8kQX4REREbrqbXUUve4uBPcWuzW+8JUhAxqnA2CJg3AIga57rXiARERFRgFU2duGal8vR2Nnvdjw+UsHLP56LmVlcG0FENCp1NgB7vgZq1gA1X7nWw3grebJrDUz26a5NICL4mkJERC5+K4QBgH/961+4/vrr0dHRAeEoizEPhhIdHY1//OMfWLp0qb9CIyIaVoZh4E8rd+HZL3Z5jOWlRuNv18xFZkJkACIjoqDU2+r64H+w+KXVczHZcUkm15feOYtcP+mnAqI07GESERERERG1dNtx05vfoGR3i8fYlPQxeOFHs5GVyHseRERE6Gp0dXo5WPiyv9L7OUQFMM86UPhSBGQWAGExwx8rERER0UnYsKcV172yHp397h1jzXER+Md1+ZiQEh2gyIiIyO96Wg4Uvnzl+mnxXDs3qIScA0Uvp7n+Gc1Nl4iI6Oj8WggDAC0tLfjLX/6CDz74AN9++y007dCHoKlTp2LJkiW45ZZbkJLCF69gxza3FCr83QLMrul4YEUVVmz37OBQmB2Hxy7JQ0yY7LPfTyNDKLWuoxOg9UOu3wi5bi2U2mJITVsgYOhvyVytXqdCyyyCmrkAWsYcQInwYcBDx9ylUMS8pVAVSrnry1a3RIHC+yIUKobz9WJrfRd+/W4l9nU5PMYumpaMe87JQYTConw6eaH0PofocMzd0U3oqodiK4NsLYNsK4PUXuP1HIZkgpZ2KjTzPGjmAmjps3x+3495S6EqlHKX90WIaCRbuWMffvnPTbBr7s/BeanR+Md1+UiPDY7vMImIyEf6O4G9JYcKX/Zt8X6OmAwgZ+Gh4pe4zOGPk4iIRiS/r8ROTEzEfffdh/vuuw+6rqO1tRVOpxMJCQlQFMXf4dBJcDqdgQ6B6ITouu6z/G3tUfHr93Zic323x9iVM1Nx2+KxkEWBf3/Ia77MW/ID3Qlp/3aY6kqgWIuh1G+A4LR7NYVzTBYcmYVQM4ugmufBiEg44oTgzA/mLoUi5i2FKuYukX/x7xuFqhN5vTAMA+9sbsaTX+6B6nQv4pdFAb8+Yyy+NyMFgsC/G+QbfJ9DoYq5O4IZBsQuKxRbGRRbOZT6ckiddd5PI4dDTZsJNaMAqjkfWuqpgBzmfpKfc4h5S6GKuUvkf9wkhN7f0oQHV1ThiFsFmGGOwZ++Nwnhej/a2voDE9wBoVQ4SXQQ85aCmtoHuX4DZGspFGsJpH1bIBjevQ/XIxKhWeZBzSyEZimEHjcOEATXoAGA7zHIj/icS6EqlHLXlxuEBLQlgSiKSEpKCmQIRETDpnp/L279z07Ud7ovbhcF4LbFY/H/ZqUFKDIi8jvDgNhZC1NdMZS6EijWUoj2dq+m0MPjoVrmw5FZBNVSCD02yzexEhERERERHYdd0/H4F3vw/tZmj7HkaAWPL8nF9IyYAERGRETkR4YBsWPPQNGLYiuD1N3g/TRKJNS02VDN+a7Cl5TpgGTyQcBERES+x+Kz0cswDLy6vgHLv/IsBD4tJw6PXjwB4YoYlDnCwkkKRcxbCiinA/K+76BYS2GylkJu/BaC7tkx/Hh0UwxUcwFUyzyo5kI4E3MBQTzshOBcuE2jE59zKVSN1twNaCEMEdFIsW5PO+56vwo9DvcXkiiTiIcvysWCnLjABEZEfiP0tbg++B8ofpG6rF5db0hhUDPyoWYWwpFZBGfSZPcP/kRERERERH5W32HHXe/vwo59PR5jsywxePTiXCRGscs3ERGNQIYBqW33QNGLbCuH1Nvk9TS6KRpa+hxX4UtGAbTkqYDE104iIiIKXbph4E+ra/HPjY0eYxdPTcJvz8mGLPE7TiKikKVrkJu3Q7GWun4aNkDQ+rybQ4kEsuajN20uHAc/C4uSb+IlIqJRzS+FMKqqYteuXQCA8ePHIyzMvaV3f38/fvvb3+Jf//oX9u/fj+zsbNxwww246aab/BEeEdFJeevbfXhy5R6Pdr9pMSY8e9lETEiODExgRORbah+U+vVQrMUw1ZVA3r/dq8sNQYSWcgpUSyHUzCKoabMAOWzwC4mIiIiIiPxg3Z52/PbD3ejo1zzGfjQnDTeengVZFAIQGRERkQ8YOqSWXVDqywa6voh9LV5Po4fFQs2Y69rwxpwPZ9IULvYhIiKiEUNz6njg02qs2O75Punquem46fRMCALvFRARhRRDh9Sy01X0YlsHxVYG0dHl3RSiCVraTGhZhYiYfC5gng3IJtjb2kZldwIiIvIfvxTCvPPOO/jBD36AhIQEWK2eu6MvXboUn332GQzDtYq8oqICt956KyorK/Hcc8/5I0QiIq85dQPPrq7Fm5s8dzqZlh6Fpy7NQ1KUKQCREZFP6Brkpq1QrMVQ6oqhNHzjdbtXLS7bVfRiKYRqngcjPNZHwRIREREREZ0Y3TDwSlk9nl9rxRF7fiBCEXH/uTk4e1JiQGIjIiIaNroTUkvFgaKXMij1GyD2t3k/TXiCq/DFnA/VXABn4kR2eSYiIqIRqc/hxF3v70LJng6PsVsXZeFHc9IDEBUREXnNMCC274HJdqDji3UdxP5W76YQJGgp06Fa5kG1zHdt/KpEQJIkRMTH+yhwIiIiT34phPn0009hGAYuvfRSj24wH330ET799FMIggCLxYK5c+eivLwcNpsNzz//PH74wx+isLDQH2ESEQ1Zj8OJ335YhbXV7R5jZ09MwO/OG49whV92EYU0w4DUXuMqerEWuz78e7nrhR6RCEdmEVRLEdTMQugxGT4KloiIiIiI6OR12zX8bkU11lR5LgTOig/HU5fkIieJnW+JiCgE6Rrk5u1QbGVQ6ssh16/3+l4fAOiRya6il4wCqBlz4UzIBbjrOREREY1w7X0qbv1PJbY29Lgdl0QBvzsvBxdMSQpQZERENBRiVz0Ua8lA4YvU47np82C0pClQLfPhsMyHljEHhinGB5ESERF5xy+FMJs2bYIgCFi4cKHH2MsvvwwAyMvLQ3l5OWJiYtDR0YHCwkJUVFTgr3/9KwthiCioNHba8at3dmJXc6/H2E/mZeD6IgtEfvFFFJKEnmaYrCUDxS9St3cf/g0lEmpG/kDxizMxj1+EExEREY1QkiQFOgSiIRFF8biPD6pq7sHt71Sitq3fY+yMvAQ8cMEERIf55XYy0ZDzlijYMHeDiFOF1LQZsrUMiq0McsNGCI5ur6fRo9OhmgugWQqgmQugx2W73e8bCe8ImbcUqpi7RET+0dhpx41vVWBPq/v9gnBZxONLclGUExeYwIiI6JiEnmYotlKYrOug2EohddR6PYcWP97V7cU8H6o5H0ZEgg8iJSIiOjl++eayqakJADBhwgS347quY+XKlRAEATfddBNiYlxVorGxsbjxxhuxbNkylJaW+iNEIqIh2drQjdvf2YmWXtXtuCIJuPecbFw4NTlAkRHRiRAc3ZDr18NUVwylrhhy606vrjcECVrqDKiZRXBkFkFLnQFIJh9FS0RERETBJD4+PtAhEJ2Q2NhYj2MffFePO9/aij7V6XZcFIA7zp2EXyzMgcAifwqgo+UtUShg7vqRZgdsG4E9xcDetUBdOaB6bmY1qLgsYOwCYFwRMLYIYvw4hAkCwoY/4qDFvKVQxdwlIhp+1ft7cdPbldjX5XA7Hhsu49nL8nBKBrsBEBEFA6G/3dUB1VoKxbYOcusur+dwjsmEap53oPhlHvToVB9ESkRENLz8Ugizf/9+AEBERITb8W+//RadnZ0QBAEXXnih29i0adMAAHV1df4IkYhoUF9UtuB3K3bDrhlux2MjZDx9SR5OtfAmD1FIcKoI3/oGwqo+hrzvWwi65tXlWkIuVEsh1Mwi164XbPdKREREREQhSnXqeGxFBf62tsZjLD5SwfIfzMKC3KQAREZERDQItQ+wrj9Q+FLs+rPm2dVsUAk5wNgiYNwC1z/jMoc/ViIiIqIQtLm+C7f+pxKd/e6bZqTGmPDc5ZOQnRhxjCuJiMjXBEc35IYNUKylMFlLITVvhwBj8AsP44xMcRW9WOZDtcyDPoafh4mIKPT4pRAmLCwMmqYNFMQc9NVXXwEALBYLxo4d6zZ2sDuM0+n+gYqIyN8Mw8DLZfV4fq3VY2xcQjievWwiLHHhAYiMiLwldtYh5tNboez7dsjXOKNSDxW+WAq56wUREREREY0ITV39uPGNb1Be0+oxNt0Si+d/NBvmOC5qISKiIOHoAerKDhW+2DYCTsfg1x0pKe+wwpdCYEzG8MdKREREFOLW7m7DXR9Uwa7pbsezE8Kx/PJJSBszmvrlEREFAc0OpXGTq+OLtRRy02avN33Vw+KgWuYNFL8443IAdgEnIqIQ55dCmLFjx2L79u0oKyvDmWeeOXD8gw8+gCAIOP300z2uaW11fQGbnJzsjxCJiI7Koel4+LMafLR9v8dYftYYPL4kFzHhfnkqJaKTZKr6GNFf3g3R0X3c83Ql+sCHf1fxizN+PD/8ExEREZGHtra2QIdANCSiKCI2NnbgcUdHBzbVtuPO93aiudtzAfHS6Sm46+wchBn9aGs7gZ31iYbB0fJW1/XjXEEUHJi7w8jedWB32zLItjJITVu8XuQDAM7EiVDNBdAsBdAy8mFEHfa9oxMA39MxbylkhVLuxsfHBzoEIqIh+3BbM/7wSTWcRzQWmJ4RjWeWTkRsBNdHEBH5nFOF3LR5oPBFadwEwcvNIHQlGlrGXDgOFr4kTQIE0UcBExERBYZfPp0sXrwY27Ztw/Lly7F06VJMnjwZ77//PlavXg0AuOCCCzyu2bp1KwAgPT3dHyESEXlo71Vxx/u78I21y2Ns6fQU3HXmWMgSPyAQBT2tH1FfP4SIbW8eddgQFWhpM+HILIRqKYKWOh0QeQOXiIiIiI6PXYwpFBmGgTc32PDUyj3QdPcVLYok4K4zx+HS6SkADOY4BRVd15mTFJKYu0Mn2Duh1K+HbCuDUl8OuXkbBMO7Be0GBDiTJkM150PNyIeaMRdGRIL7Sfz/MSjmLYUq5i4R0cl7tbwef/6qzuN4UXYcHl8yAeGKFICoiIhGAd0Jaf8OmKylUGylUOrXQ1B7vZrCkMKgZsyBanYVvmgp07j2hYiIRjy/vNLddNNNeOmll9DU1IRp06YhPj4ebW1tMAwDFosF3/ve9zyu+eyzzyAIAqZPn+6PEImI3Oxp6cOt71TC2m53Oy4AuGVRFv5rdhoEdoggCnpS6y7EfHIz5NadHmPO2LHoWfBbOMzzAFNUAKIjIiIiIiLynz6HE/e8swXvfGPzGEuLMeHxJbmYmh4dgMiIiGg0EvpaodSvh1JfDsVWDmn/DggwBr/wMIYgQkueCjUjH5q5AGr6HBjhsYNfSERERERudMPA8jV1eG1Dg8fYhVOScN+52dwklIhoOBkGpNZdUGzrXB1fbGUQ7R3eTSHK0FJPhWqZD4dlPrS0UwEpzDfxEhERBSm/FMLk5ubitddew3XXXYeenh60trYCAOLi4vDmm2/CZDK5nd/Y2IjPP/8cAHDGGWf4I0QiogHleztw1/u70GV33zUqQhHx0IUTsHAC25cTBT3DQNiOfyP6qwcgaP0ew/15l6Bn0YMwTFzkRUREREREI9/elh784vVN2NHQ6TGWP3YMHrlwAuIilQBERkREo4XQu3+g6EWxlR1145rBGKIMLfkUV8cXcz609NkwTDE+iJaIiIho9NCcOv7waQ0+2r7fY+yquem4+fRMbhJKRHSyDANiZy0Ua+mBri/rIPZ6Pu8edwpBhJY8DaplnqvrS8YcQIn0UcBEREShwW+9z6644gosXLgQH330ERobG5Geno4lS5YgISHB49zNmzfjhz/8IQDgggsu8FeI5CVJYstTCg2iKB738eH+8+0+PPp5NTTdfee5lGgT/nz5ZExMZdcI8g9v8paOYO9C1Jf3wLTzA48hQ45A7+IH4Zh8OUTesPUJ5i6FIuYthSrmLhERDcXXu9tw74e70NmveYxdW5CBXxRZIIn8fERERMNL7G6EYiuHXF8Opb4ccttur+cwRBO01OmuwpeMAqjps7jIh4iIiGgY9Tmc+M0HVSiuafcYu2VhFq6am+7/oIiIRgixuwGK9WDHl1JIXfVez6ElToRqme/6yciHETbGB5ESERGFLr8VwgBASkoKrr322kHPO+ecc3DOOef4ISI6GfHx7IpBoSk2NtbjmFM38NiKHfifr2s8xk4xx+Kv18xB6phwf4RHdFRHy1s6Ctsm4K1rgbY9nmOp0yBc/ndEJeeBJW3+w9ylUMS8pVDF3CUiosPphoH/KbHhr6U2GEeMRZlE/P788Vic67lJERER0YkQO21Q6stcHV/qyyB11Ho9hyGFQU07FVpGgav4JW0mIPO+PBERUbDi5qmhrb1Pxc1vVWBLfbfbcUkAfnfBBFw8LSVAkQ0vbihFoYh5G5qE3hbItnVQ6kog15VCaq/2eg5nXDY0y3yomYXQLPNgRCYNjIVCFjB3KRQxbylUMXdd/FoIQ0QUjHrsGm7532/xxY59HmPnTU3DH78/A5EmPl0SBTVdB9b9Bfji94Cueo7P/SlwzkOAEuH30IiIiIiIiPyts1/DfR/tPuqOrjmJEXjiklyMS+DnIyIiOkGGAbGz1lX0YiuDUl8Oqcvm/TRyBNT0WVAz8qGaC6ClTgekMB8ETERERL7AzVNDV317H372v5tR1eReBBOuiHj+v2Zj8aSRUQRzNNxQikIR8zZI9bUDe0uAmq9cP03bvJ9jjAXIPv3Az2mQYi2QAIyUT8bMXQpFzFsKVaM1d7mym4hGtYaOPvzklQ3Y3tDpMfaLheNx57kTIYpCACIjoiHr2Q+8ewOw6zPPsfBYYMlzwJQl/o+LiIiIiIgoACqbenDne7tg67B7jF14SjruOSsLYdy0l4iIvGEYkNprIB8oelFs5ZB6Gr2eRleioKXPhmougJqRDy1lGiCZfBAwERERER1LVVMXrvpbORo6+t2Ox0YoePnHczF7LAuciIiOytED1K47VPjS8C1g6N7NEZV8qPBl3GlAQg4gcF0aERHRiWIhDJ2wtra2QIdANCSiKLpVO3Z0dEDXdWxv7Matb1egudvhdr4sCrj33BxcMj0VHR3tfo6WyOVYeUvuZGspoj65FWKPZ0cnLX0Wes77M/QxFoCvWX7D3KVQxLylUBVKucvdIWkkkiRWElDw+XBrEx76tBp2zf31QBIF3H3+JPxkQTY6OzuD9vWC6CBRFI/7mChYjZjcNQyIrbugWMsg29ZBtpVD7G32ehrdFAPNnA/NXADNXABnylRAPPTVJN9NBYcRk7c06jB3iYi8t6m2Dde9sh7tvarb8fTYcLx6XT5yU2MCFBkRURDS7IB1w6HCF+t6QFcHv+5w4bGugpeDxS/Jk1j4QkRENIxYCEMnzOl0BjoEohOi6zq+qGjGfR/tRv8RC0PGhEt4Ykke5mSNYY5TUNF1nTl5OF1D5PrnELH+OQgw3IYMCOib/Qv05t8CSArA/24BxdylUMS8pVDF3CXyLxZ4UTBxaDoe+mg7Xi3d6zGWFG3C8h/MwvzxiQBGb2t0Cm3MWwpVIZO7ug40bQP2FAN71wJ7S4DeFu/niYgHxha5fsYVQUydBpMogT1fQkvI5C3REZi7RP7HzVNDy9rdbbjj3UqPNRLZCRH4y/enIMmkjcj/p6G0oRTRQczbANE1SE1boNSVQK4rhdywAYLWP/h1hzGUSGgZc6FmFkKzFMKZPAUQD9sGor19eGMOMsxdCkXMWwpVoZS7vvxenYUwRDSqGIaBV8ps+PPqvUcsnQey4sPxzNI8jE2ICEhsRDQ0YncDYj67DUp9uceYHpmErrOehpq1IACRERERERER+V9jRz9++c+N2FTb7jE2MysOf/mvWUiP5b0OIiI6jO4EGjcfKHwpdhW+9Ld7P09UMjC2EBi7ABhXBCRPBtiRgYiIaNTgpjyh4+Pt+/HAJ9Vw6u6rJKalR+HZyyYiLkIeNf8/uaEUhSLmrY8YOqSWSijWUpispa5uqGq3d1NIJqhps6Ba5kO1zIOWMh2QDtsOwsCo3ryVuUuhiHlLoWq05i4LYYho1HBoOu59dwv+tcHqMTbLEoMnL8lDbASfFomCmalmJaK/uBOivd1jzJF5GrrOfgpGZJL/AyMiIiIiIgqAddUtuPGNb7C/2+4xdtW8sbjvoikwyVyQTEQ06jlVoOE7YM9aV+FL7TrA3un9PDHpBzq+FALjFgBJeYAgDH+8RERERDRsXt/QgGdX13ocL8yOxeMX5yLCJB3lKiKikUvZswrhO96GYlsHsd+7TliGIEFLnX6g8GU+1LRZgBzuo0iJiIhoMFzxTUSjQnuvAze8vgml1S0eYxdPS8Y9Z4+DInFhCFHQctoRVfIEIr57xWPIECT0zrsdfbN+Bgj8e0xERERE/tHW5t0XZETDyTAM/HNDA55dtQfOI1rehskifntuDi6eloKerg70hVBrdKKDROYthaigyV2nA9K+zVCsZZBt6yA3bISg9no/TYwZmrnA9WMpgB471r3wpb19+GKmgAmavCXyUijlbnx8fKBDIKJRyDAM/PmrOry2vsFj7PwpifjduTmQuUaCiEYRob8dUWt+j/BdHwz5GgMCnMlT4DDPg2qZDy1jLgxTtA+jJCIiIm+wEIaIRry9rX247Z3vUL2/x2PsxtMycU1+OgTuWkcUtMT2Goz59BbIzds8xpwxZnSd8yy09FkBiIyIiIiIRrPR2FqagkOvw4k/fFqNzytbPcbMsWF44pJcTEyJOmaOjtbW6BTamLcUqvyWu5od8r5vodjKodSXQWn8BoLW7/U0zjGZUDPyoZrzoZrnQR9jcT8hSBeY0/Dicy6FKuYuEdEhmlPHQ5/V4MNt+z3GfjQnDTcvzILINRJENIooe1cj+st7IPXsG/RcLSEX6oHCF9VcACM8zvcBEhER0QlhIQwRjWgb6zpx53u70NGvuR0Pk0X84YLxOCMvIUCREdFQhFW+h6jV90FUPQvZ7OPPQ/cZj8IIGxOAyIiIiIiIiPxvb2sf7nhvF6pb+jzGCrNj8YcLJiA2grd8iYhGPLUPSuMmKPXlUGzlkPd9C8Hp8HoaLS4bWkY+VHMB1Iy50GMyfBAsEREREflTv+rEbz6owtrqdo+xm0/PxNX5fM9HRKOH4OhGZPGjiNj2v8c8xzkmC6plHhyW+VDN82BEpfgxQiIiIjoZ/FaUiEasD7Y24+HPaqDphtvxpGgFf7w0D1PS2KqSKGipvYhe83uEV7ztMWRIJvScdh/6p/4A4E5FREREREQ0SqyuasXvPq5Gj8Nzl+ufzTfjZ4Vm7uZKRDRSOXqgNGw8UPhSBrlpCwRd9XoaLSH3UMeXjHwu7iEiIiIaYTr6NPzqnUpsru92Oy4JwL3n5uDiackBioyIyP9kWzliVt4JqbPOY0wPG4Pegl/BMe4Mz26oREREFDJYCENEI45uGPjL13V4pbzBY2xK+hj88dJcJEXx6Y8oWEn7dyDmk5sht1d7jGnx49F17p/hTJoUgMiIiIiIiIj8z6kbeKHYir+X1XuMRYdJ+MMF43Ha+PgAREZERL4i2LsgN2yAYiuDUl8OuWkrBMOzEHIwWuKkA0UvBVAz5sCITPJBtEREREQUDPZ12XHTW5UeXWTDZBGPXTyB9w6IaPTQ7Iha9zTCv30ZAgyPYUfWaeg+41Ho0ekBCI6IiIiGk99Xgn/33Xf4+uuvUV1dja6uLjidx79xLwgC/va3v/kpOiIKdf2qE/d/vBtf7mrzGDtrcgr+9P9mwtE7+HMPEQWAYSB86z8RtfZhCE6Hx3D/5CvQffr9gBIZgOCIiIiIiIj8r71XxW8/qkLZ3k6PsdzkSDyxJBeZ8eEBiIyIiIaT0N8OpX49FFs5lPoySPt3QDB0r+YwBBHOpMmuohdzPtSMuTDC43wTMBEREREFlT0tfVj2VgX2dbl/xxoTJuHZyyZihjkmQJEREfmXvG8zor+4A3JblceYoUSip+hu9E/9AcDO2kRERCOC3wphKisrcd1112HdunVDvsYwDBbCENGQ7e924LZ3dmL7vh6PsZ8uyMbdF0yGJApw9AYgOCI6LqG/A9Ff3o2w6k89xnQlGj2L/gD7xCUBiIyIiIiIiCgwdjT24M73d6Kh03OjgPMnJ+K352QjXJECEBkREZ0soa8Fim09lPoyKLZySC2VR92l9ngMQYKWMg1qRj5UcwG09DkwwrjAkYiIiGi02drQjVv+U4mOPs3teEq0guWXT8L4JG4ySESjgFNF5Ia/IGLDfx+1o6qaPgddZz0BPXZsAIIjIiIiX/FLIYzNZsPpp5+O/fv3wzBcN/Kjo6MRHx8PURT9EQIRjXA7m3rwq3d2euxwIgnA3efk4KeLJwcoMiIajNywETGf3Qqpq95jTE2ehq5z/wQ9bpz/AyMiIiIiIgqQ97Y04fEv9sDhdF8ULYkCfrUoC9+fmQqBuxYSEYUMoad5oOhFqS+H3LrL6zkMUYGWcgpUcwHUjHxo6bNgmKJ9EC0RERERhYqSmnbc+d4u9Gvu3QTHJoTjvy+fhLQxYQGKjIjIf6TWXYj+/NdQmrd6jBmiCb3zbkPfqdcBIjcVIiIiGmn8Ugjz8MMPo7m5GYIg4Kc//Sl+/etfIy8vzx+/mohGga92t+G3H1ahT3W/uRMdJuHxJbkozEkIUGREdFyGjoiNLyKy7Jmj7sjRN+M69BTeAUimAARHRERERETkfw5Nx5Nf7sU7m5s8xpKiFDy2JBenmrnbPxFR0OuwAXuLEbnzS0jWdZDba7yewpBM0FJPhWrOd3V9SZsFKBE+CJaIiIiIQtGK7fvx+0+q4dTdN9GYmhaFP102EXGRSoAiIyLyE92JiO/+jsh1T0NwenbV1pKnouusp+BM5DpVIiKikcovhTCffPIJBEHA1VdfjZdeeskfv5KIRgHDMPDGxkY8u7oWxhFj5tgwPHvZRGQn8otBomAk9DQj5ovbYaor9hjTw+PRdeYTULPPCEBkREREREREgdHYaced7+/C9sYej7GZlhg8etEEJEVzowAiomAkdlqh2MpgalgPNKwH2vYAALzZf9uQw6GmzYSaUQDVnA8t9VRA5g7eREREROTpjY0N+OOqWo/j88fF4okluYgwsesBEY1sYkctYlbeCaV+vceYIUjom/NL9M5ZBkgsCiQiIhrJ/FIIU19fDwC4+uqr/fHriGgU0Jw6nvhyL/7znecOqaeaY/DUJbnc4YQoSCm1XyPm89sh9rV4jKkZBeg654/Qo9MCEBkRERER0dBJEhcU0PAp29OO37y/E+19msfYD2en49bFY6FI4gnNLYricR8TBSPmLQU1w4DYsReytQyyzfUjddm8n0aJhJY+G5p5HlRLAZyp0906I/OdBvkLn3MpVDF3iWg0MgwDz31dh3+UN3iMnTc5Eb87L+eE7x8QEYUEw0D4tjcRVfwoBLXXY1iLH4/us56Cljo9AMERERGRv/mlECY+Ph5NTU2Ii4vzx68johGuq1/Dbz7YhbK9nR5j509JxH3n5MAk8+YOUdBxqogsexaRm17wGDIEEb1zb0LfnGWAyK/5iYiIiCj4xcfHBzoEGgEMw8ALa6rx5KcV0I9odxuhSHjse6fgklPNw/o7Y2Njh3U+In9g3lJAGQawfxewdy2wpxjYWwx0eS48HFTYGCBrHjC2CBi3AEL6DCiSAgUA+5pTMOFzLoUq5i4RjXSabuCRz2rw/tZmj7EfzE7DrxZlQRSEAERGROQfYncjor+8G6barzzGDAjon3EteubfDsjhAYiOiIiIAsEvhTBz5szBxx9/jJ07d2LmzJn++JVENEJZ2/vxq/9Uoqa132PsF0UW/GReBgTe3CEKOmKnFTGf3gpl3zceY86oVHSd8ww0c0EAIiMiIiIiIgqMrn4Vd/x7Mz7Z1ugxNi4xEi9cNRuT0sYEIDIiolFO14HmHcDeEmDPWtc/ezw7kw8qPA4YW3ig8KUISJvODWCIiIiI6IT0q07c/WEVvt7d7jF242mZuCY/neskiGjkMgyE7fwAUV/9HqK9w2PYGWNB11lPcM0JERHRKOSXQpibb74ZH330EV566SV8//vf98evJKIR6FtbF3797k6092lux8NkAb8/bzzOnpQYoMiI6HhMVZ8g+svfQHR0eYw5xi5G11lPwIhICEBkREREREREgVHV1IWfv7YR1c09HmNnTU7B01eeitgIJQCRERGNQroT2LftQNFLsavwpa/V+3kiEw8UvixwFb6kTAVEdi4nIiIiopPT2a/hV+9U4jtbt9txSQB+e04OlpySHKDIiIh8T+hrRfTq+xG2e8VRx/unfB89C+6BYYr2c2REREQUDPxSCHP22WfjrrvuwuOPP44bbrgBf/7zn6Eo/CKXiIZuxfb9ePDTaqhOw+14YqSCp5fmYVo6P9AQBR2tH1FrH0bE1jc8hgxRQU/hXeif8WOAuxMRERERUQhqa2sLdAgUoj6v2I/fr6hCr0N3Oy4A+OVpWbhuvhl6fzfaPJvhnhBRFBEbGzvwuKOjA7quH+cKosBj3pJP6Rqkpm2QbWWun/r1EO2d3k8TmQTNXADNPA+qpQB6Qi5ESWLuUsjhcy6FqlDK3fj4+ECHQEQhqqnLgZversDu/X1ux8NkAY9enIvTx/P5hYhGLlPNSkSvugdi736PMWdkCrrPeATquMUBiIyIiIiChV8KYV599VVMnjwZhYWFeOmll/DBBx/g8ssvx6RJkxAZGTno9VdffbUfoiSiYGQYBl4stuGv62weYxOSIvDM0olIjw0LQGREdDxSaxViPr0Zckulx5gzNgtd5/wJWur0AERGRERERDQ8nE5noEOgEKPpBv77qzq8tqHBYyw2XMZDF47H/Ow4GLoOX2aXruvMXwo5zFs6KU4VctMWKPXlUGxlkBs2QVS7B7/uyGmi0qCa86Fm5EMzF8AZl+2+wYuue2z4wtylUMS8pVDF3CWikWZPax9u/HcFGrscbsdjwiQ8s3QiTrXEBCgyIiLfEhxdiPr6IYTveOuo4/bci9B9+u9hRLAYkIiIaLTzSyHMj3/8YwiH3fxvaGjA8uXLh3StIAgshCEapfpVHQ98shufV7Z6jBVlx+Hhi8YjOswvT2NENFSGgbAdbyH6qwcgaH0ew/25F6Nn8R9gmHhjloiIiIiIRo/WHhV3f7gLG+u6PMYmpkTiyUvykMGNPoiIhofTDnnfZii2MlfxS8Omo96nGnSaGDPUjHyo5gKo5nzoY7LY2ZiIiIiI/GJbQzdu+U8l2vs0t+PJ0QqWf28SJiQPvukwEVEoUqwliF55F6Sueo8xPSwO3YsehCP3wgBERkRERMHIbyvIDcPw168iohGgpUfF7e9WYmtDj8fY/5uVilsXjYUs8ktHomAiOLoQtfp+hO9832PMkMPRffrvYZ98ORcMEBERERHRqLKlvgt3vb8LTd2qx9jF05Jx15njEK6IAYiMiGiE0PqhNH4DxVYOub4MSuO3EJx2r6dxxmZBzSg40PWlAPoYsw+CJSIiIiI6vnV72nHHe7vQp+pux7Piw/Hfl09COjfSIKKRSO1DVOkTiNj86lGHHWMXo+uMR2BEpfg5MCIiIgpmfimEqamp8cevIT+TJCnQIdAItau5Bze/tQONne4tfiUBuPOsbFw5K92r+URRPO5jomAUankr7duMqBU3QerY6zGmJU5CzwXPQU+YAL5yjHyhlrtEAPOWQhdzl4gouBmGgbe/a8JTX+6FprtvEiSLAu44cywum57i1kmbiIiGQO2F0rAJSn2Zq/hl32YIumPw646gxY8/0PElH1pGPvToNB8ES0RERDRycc3I8FuxvRn3f1TlcR9hano0/nz5ZCREKgGKLHTxPjqFotGWt1LjN4j69HZI7dUeY4YShd6Fv4NjyhUQeR816I223KWRgXlLoYq56+KXQpixY8f649eQn8XHxwc6BBqBVlU24aY3tqHb7t7iNyZMxnP/NQsL85JP+nfExsae9BxE/ha0eWsYwLrngc/vB3TP3Y0x5yeQz30YsUqE/2OjoBC0uUt0HMxbClXMXSKi4NGv6njsixp8uG2/x1hqjAmPL8nFtPToAERGRBR6BEcX5IaNUGzlrsKX5i0QdG3wC4+gJeRBNR/s+JIPIzLJB9ESERERjR5cMzK8Xl5bgwc/3OVx/LTcJLzwo9mICvPLEq8Rj/fRKRSN2LzVHMCax4C1zwCG7jk+7jQIl/w3ouLHIsr/0dEwGLG5SyMa85ZC1WjNXX5KIqKg8UpxDR78cDuO2NwElvgIvPzjuchLjQlMYER0dD0twHu/BHZ+4jkWFgtcshyYcon/4yIiIiIiIgogW3s/7nx/Fyqbej3G5mSOwSMXTUBCFHdwJSI6FsHeCaV+PWRbGZT6csjN2yAcbUHMcRgQ4EyaPFD0ombMhRGR4KOIiYiIiIhOzlOfVuK5VVUex5fMyMBTV8yASR6duzsT0QjWuBV45xfAvi2eY3I4cNbvgfzrgVG6uz0RERENDQthiCjgNKeOBz/cjldL93qMzcqKw0tXz0FSdFgAIiOiY9qzFnj7p0BXg+eYZS7wvb8B8ewIR0REREREo0tpTTvu/Wg3Ovo9OxVcNTcdy07LhCwKAYiMiCh4CX1tUOrLXT+2ckj7d0CAMfiFhzEEEVryVKgZ+dDMBVDT58AIH5074BERERFRaPl0W+NRi2B+XDgO9180BSLvIxDRSKI7geI/AaseAXTVczxjFrD0RSA5z/+xERERUchhIQydsLa2tkCHQCNAt13DXe/tRElNu8fYeZOT8PsLJkBSe9HW5rmL6lCJoujW9qujowO67t0OgkT+FrR5qzsRXr4c4eV/PupOnH1zbkD/vNsAKABfJ0aloM1douNg3lKoCqXcjY+PD3QIREQ+pRsGXl5XjxeLrR5LtyMVEfefl4OzJiYGJDYiomAj9O4fKHpRbGWQW3d6PYchytCST3F1fDHnQ0ufDcPEjuJERERE/sQ1IyfPMAz8+YtKj+M3np6F6+alo6Oj3f9BjTChdB+d6KCRmrdiWw2iPr8dcsMmjzFDlNFfcAv659wAiDLXm4SokZq7NLIxbylUhVLu+nK9yLAWwpxxxhkAAEEQsHLlSo/jJ+LIuSh4OJ3OQIdAIa6+w45b/1OJ6pY+j7GfzTfj54VmCIIx7Lmm6zrzl0JOMOSt2N2ImM9ug1Jf5jGmRySi6+ynoWad5jrAv2N0QDDkLpG3mLcUqpi7RESB0dWv4f4Vu/H17naPsbEJ4XjqkjxkJ0b4PzAioiAhdu+DXF/mKnypL4fcttvrOQxRgZY6w1X4klEANW0mYIryQbRERERENFS8F3nyvrV1YVtDt9ux2xZn4Yez04N2EVuo4310CkUhn7eGjvAt/0RUyeMQNM81YlpCHrrOfgrO5KmAAa43GUFCPndpVGLeUqgarbk7rIUwq1evBuAqXjnyuCAIMIyht7I/eP6RcxHRyLClvgu3v7sTrb2a23FFEvC783Jw3uSkAEVGREej1HyJmJV3Quz33HXDkVmErrOehhGVHIDIiIiIiIiIAqequRd3vLcTde12j7EzcuNx/3k5iA5jU24iGl3ErnootrIDXV/KIHXs9XoOQwqDmnYqtIwCV/FL2kxADvdBtEREREREgfPGhga3x+ljTLhyZlqAoiEiGn5iVz2iv/wNTHXFHmOGIKJv5s/QW3ALIIUFIDoiIiIKdcP6Lezpp59+1MKVYx0notHp04oWPLBiNxxO9+K4+AgZT12ahxnmmABFRkQenHZElTyJiO/+7jFkCBJ6592Gvlk/BwQxAMEREREREREFzic79uOhT2vQr7nv0CoKwLLTMnH13HTeEyWikc8wIHbWHej24ur6InVZvZ9GjoCaPgtqRj5UcwG01OlcBENEREREI5qtvR+rq9w3Ifz+rDTIIu8lENEIYBgIq/gPor5+EKKj22PYGZuFrrOehJY+JwDBERER0Ujhk44wQz1ORKOLYRj4a6kNL5bYPMZyEiPwzNI8mOO4qx9RsBDb9yDm01v+P3v3Hd5Wfbd//D5Hki1bchxn7z1ICASySEJ2AmGW+dBFS2lLSxtW2QlQVggQVoBS2tI+pYu2PJRdaCA7hBkCCYSQvfdwHEu2tc75/cGvAXFEyLB1dKT367p6Nfp+JPl2+EiOjr9DgZ0fO2qpsjaqPnmakq37u5AMAAAAANyTTFmaNneD/rFou6PWuMSvu8/spoEdyl1IBgBZYNsy967bv+glsOUd+SLbDvlprEBIydb9lWh7ghJtBinZoo/kK2qAwAAAAEBu+ucH22V9Ye/Q0oCps49p7l4gAKgnRs0uhefcrOI1r2es1x5zoaJDrpeKQllOBgAA8k29LoQBgK8SS1qaPH2NXl2221Eb3Klc95zZTeFi3pKAXFG8/EWF5twsMxF11GJdTlZkzD2yg0zsAgAAAFBYdkXiuvGlVfpwc7WjdnSrkO79Rne1asQJBgDyiG3LV7kq7cQXs2bnIT+NVVSmZJuBn5/40ry3ZHI9GAAAAIUpEkvqhY92pI2ddWwL5kwA8Lyi1dMVnn2zzLo9jloq1EqRsfco0WG4C8kAAEA+4hMUgAZXWZPQtS+s0OLNzqMuz+/bQteO7cTxvkCuSNQoPO8OBZf9n6Nk+4oUHXaT6vp8VzJ4zQIAAKCw+Xw+tyMgyz7ctE/XPb9cu6IJR+2841rq+rGdVeQ3XUh2YKZpHvA2kIvoWxfZlny7lsu/+Z3//793ZdY6Nzf6OlawsZJtBynZ9gQl256gVLNekvn5z858/SlK78KL6Ft4Fb0LwMte/HinonFr/23TkL51fEsXEwHAkTFi+xSad7uCy5/PWK/rebaiI26VXdwou8EAAEBeYyEMgAa1ZleNfvHcCm2uiqWNm4b0i1Ed9a1+LWUwoR7ICb5dn6ps+hXyV6521JKNu6j6lEc+m7QAAAAAQBUVFW5HQJbYtq0n31ynu/69TEnLTqsV+U1NPruPLhjQ3qV0h668nNM94T30bQOyUtK2j6T1C6R1C6QNb0q1lYf+PKXNpE4nSh2HSZ1OlNm8l4pMU0X1n9hT6F14EX0Lr6J3AXhFyrL1j/e3p42N7Fahto2DLiUCgCMT2DBf4Zk3yhfd5qhZJU0UGTVZ8a7jXUgGAADyHQthADSYt9dV6caXVioSS6WNlwZMTTmjm4Z1ZdIQkBNsW8Glf1do/p0yUnFHua7X+YqMuFUKlLoQDgAAAADcUxNPauKzH+mFD7c4am0bl+g3F/bXMe2YcAfAY/askZa99P8XvrwtxaoO/TnCrf7/wpcTpU7DpGY9OEEYAAAAOAhzVlVqy770jUS/27+1S2kA4AjEowq9ea9KPv5bxnKsy0mKjJosu7RZloMBAIBCwUIYAA3imQ+3676Z65RK3yhVLcuKNO3cnurenAn1QC4wYvsUnjVRxav/46hZgZCio+5UrOdZLiQDAAAAAHet2xXVpX99X59uq3bUhndvpke+dbwqQoV+1gEAT0nUSXPvlRY8LNmpr7//FzVql77wpUkXFr4AAAAAh+Gp97em3e7dMqS+bcMupQGAw+PfslBlM6+Tr2qDo2YVlSk64lbFep7NtQMAANCgWAgDoF6lLFvT5m7Q3993HnfZu1VID57dQ83CTBIBcoF/2wcqm36lfNWbHbVk86O1b/zDshp3diEZAAAAkPsqKyvdjoAGNG/VHt30svOUW0n68ZB2unRYeykeVWU86kK6Q2OapsrLPz+1pqqqSpZluZgI+Hr0bf3zb35HpTMmyrd3zUHdP9WovZJtT1Cy3QlKth0sq1G79Mkre/c2TFCPo3fhRfQtvMpLvVtRUeF2BAA5YunWiBZvjqSNfWdAKxlMFAfgFcmYSt95SCUf/F6GbEc53v5ERcbcI6usjQvhAABAoWEhDIB6E42ndPPLqzR/zV5HbWyPJrr91C4KBnzZDwYgnW2pZNETKn37ARkZdv+s7XuxokOvk3zFLoQDAAAAvCGVOsSd9OEJKcvWE29u1u/fdm4YECry6fbTumhUtyaSbcmrLWBZFv0Lz6FvD58Rq1bpW1NV8vFTB7xfqryTEm0HKdH2BCXaDHJOWMnRicW5jt6FF9G38Cp6F4AXPPWlDUVbhAMa16OJS2kA4ND4di5V2evXyL9npaNm+0sUPfFG1fX5jmSYLqQDAACFiIUwAOrFtn0xXf3cCq3YWeOoXXxCG/1sWDuZ7GICuM6o2aWy169V0cb5jppV3FiRcVMV7zzWhWQAAAAA4K6q2qRu+fcqvbmuylHr0rRE953VXR2blLiQDAAOT9HaGQrNuVW+qPP0btsMKHbUOYq3G6pE2xNkh1q4kBAAAAAoHNurY5qxYk/a2AXHt5Lfx4RxADnOSqrk/cdV+t6vZFhJRznRqp+qx02V1bizC+EAAEAhYyEMgCO2dGtEVz+/QrujibRxv2no5pM764w+zV1KBuCLAhsXqOz1q2XW7HLUEm0Gqvrkh2SFW7uQDAAAAADc9en2qG54caU2V8UctZOPaqpbTu6skiJOuQXgDUbNLoXn3a7iVa9krCdaHqfImClKNe2Z5WQAAABA4Xr6g+1KWfb+20G/qXOOZUE6gNzm27NK4RnXKbBjiaNmm0WqOeEq1R7/Y8nk2ikAAMg+FsIAOCIzV+zRL19ZrVjSShsvD/p131nd1a99I5eSAdgvlVDpuw+r5P3fyJCdVrJlqHbg5aoZOEEy+WcBAAAAgMLz8sc7dfeMtYol0z8v+QzpylEd9O1+rWRwyi0AL7BtFS9/TqH5d8mM7XWW/SWKDr5Gdcd+nwkqAAAAQBbVxlN6bsmOtLEz+zRTeQm/nwWQo2xLwcV/Uuit+2SknJsHJZv1UvW4B5RqxiYbAADAPXyiAnBYbNvWk+9u0WPzNzlqHSqCevjcnmpfEXQhGYAvMvdtVtlrVyqw7QNHLRVqqchJDyrRbrALyQAAAADAXYmUpQdmrdczi3c4ak1LA7r7zG5s8AHAM8x9mxSefbOKNs7PWI+3H6bI6MmyGrXPcjIAAAAALy/dpX11qbSxb/Vr5VIaADgwc98mlc24XoEt7zhqtmGqtv+lqhl4ueQrciEdAADA51xbCLNp0yZt27ZNNTU1GjhwoEpKStyKAuAQJVKWpry2Vi8t3eWoDezQSPd+o7saBVlnB7itaPV0hWfdKDO2z1GLdxyl6nFTZZc0dSEZAAAAALhre3VMN764Sh9tjThqx7YJ695vdFfzML/IBeABVkrBJX9W6O0HZCRrneXickWH3aTYUedKnG4FAAAAZJ1l2/r7om1pY8O7NFbHJsyTApBjbFvFnzyt0Bt3yUxEHeVk4y6KjLtPyVbHZT8bAABABlmdqV5dXa2pU6fqySef1JYtW/aPf/TRR+rdu/f+2//4xz/07LPPqry8XE888UQ2IwL4GntrE7r+hZVatKnaUTv7mOa6cVwn+X2mC8kA7JeMKbRgiko++qujZJsBRYdcp7rjLpYMXqsAAAAACs/CDfs06eWV2lOTdNQuOL6lfjGqgwJc2wDgAb7dyxWeNUmB7R9mrMe6n67I8F/KLm2W3WAAAAAA9ntjzV5tqKxLG/vOAE6DAZBbjOgOlc2apKL1szPWa/v+QNHB10oBFvEBAIDckbWFMCtXrtRpp52mNWvWyLbt/eNGhh3IBg8erAsvvFC2beuiiy7SsGHDshUTwAGs31Orq55dro17Y2njhqQrRnbQhQNaZXxNA8geX+Vqlf3nCvl3f+qopRp1UPX4h5VseawLyQAAAADAXbZt628Lt+nReRuUstNrxX5TN53cWaf1ZrI4AA9IxVS68HGVvP8bGVbCWQ61UnTU7Yp3HudCOAAAAABf9NTC9NNgujcv1YD2jVxKAwBORStfVnjOrTJjex21VFkbRcZOVaLdkOwHAwAA+BpZWQhTV1en008/XatXr1YoFNKECRM0YsQInXHGGRnv36lTJ40ePVqzZs3Siy++yEIYIAcs3FCl619cqX11qbTxoN/U5NO7alT3Ji4lAyDpsyNqP/2XwnNvk5GsdZRj3c9QZPRk2UVlLoQDAAAAAHdF4ynd+Z81mrFij6PWtrxY953VXT1ahFxIBgCHxr91kcKzJspfuSpjvbbPd1Qz5HrZxVwDAgAAANy2fEdUCzfuSxv7Tn82GAWQG4zaSoXn3arilf/OWK/r9T+KDr+JeSYAACBnZWUhzOOPP65Vq1YpFApp/vz5Ou644772Maeeeqpmzpypt956q+EDAjigFz7aoSmvr1PKSt8utXk4oIfO6amjWjJRBHCTEY8oNOeXCq54wVGz/UFFRtymWK/zJS6oAgAAAChA6/bU6rrnV2jtnjpH7cTOjXXn6V3VKJi1g7MB4PDEowq9fb+CS/4iQ7ajnGzcWZHRU5RsO8iFcAAAAAAy+fv76afBNC0NaPxRTV1KAwCfC6ybrbJZE2XW7HTUrNJmioyeonjnsS4kAwAAOHhZ+Q3vs88+K8MwdOWVVx7UIhhJ6tu3ryRp5cqVDZgMwIFYtq1fzduoP7+31VHr2aJUD53TUy3KilxIBuC/fDs+VqPpV8hXtd5RSzbpoepTHlGqSXcXkgEAAACA+2av3KPbXl2taNxKGzckXTK0rX48pK1MNg0AkOMC6+cqPOdm+aq3OGq24VNtv5+oZuDlkr/YhXQAAAAAMtkVjWv6p7vTxs4/roWK/KZLiQBAMuLVCr0xRcFPns5Yj3U7TZGRt8suaZLlZAAAAIcuKwthli1bJkk6+eSTD/oxTZt+tgPC3r17GyISgK9RG0/pl6+u1uyVlY7aqG4VuvO0riop8rmQDIAkybYVXPKkQgvulWElHOXaPt9RdNhNkj/oQjgAAAAAcFfSsvWbNzbqyXedm3uUFft05+ndNKxL4+wHA4BDYNTuUeiNuxRc/nzGerL50aoec49SzXtnNxgAAACAr/XMBzuUSH1+mmORz9B5fVu6mAhAoQtselvhmTfIV73JUbOKyxUZeZvi3c+U2DgIAAB4RFYWwkQiEUlSOBw+6MfEYjFJUiAQaJBMAL7azkhcv3huuT7dXuOofW9ga10+oj27pQIuMmorFZ55g4rXzXTUrKIyRcZMUbzbaS4kAwAAAAD3VdYkdNPLq/Tuhn2OWo/mpZp6Vne1a8ymAQBymG2raOVLCs+7U2bdHmfZV6yaE65S7XE/lMys/JoHAAAAwCGoS1h6ZvH2tLHTejdTkxBzoAC4IFmn0Fv3q2TxHzOW4x1HKjL6bllhFusBAABvycpvSJo2bapt27Zp3bp16tev30E9ZunSpZKkVq1aNWQ0AF/y6faorn5uuXZE0k+Y8JmGJo7rpLOPbeFSMgCS5N/8rspe+4V80W2OWqLlcaoeP01Wo/YuJAMAAAAA9y3dGtH1L67U9uq4o3Za72aadFInBQOccAsgd5nVWxSe80sVrZ+dsR5vO1iR0XfJatwpu8EAAAAAHLT/LNulvbXJtLFv92f+E4Ds829fovCMa+WvXO2oWYGQosMmKdb7m5wCAwAAPCkrC2H69eunV155RfPmzdO55557UI/585//LMMwNGTIkAZOB+C/5qzao5tfXq26pJU2Xlbs09Szumtgh3KXkgGQlVLJu4+q9L1HZNiWo1zT76eqOeEXko9dhAAAAAAUpueW7NDUmeuUSNlp437T0DWjO+r841rI4Be6AHKVbSn48VMqffM+mYmIo2wVlSl64kTFel/A5BQAAAAgh9m2rafeT9/UcHCncnVtVupSIgAFKRVX6cLHVLLwcRl2ylFOtBmk6nFT2WgVAAB4WlYWwpx//vn697//rd/97ne6+uqr1aFDhwPef9q0aZo3b54Mw9C3v/3tbEQECppt2/rrwm16ZO4G2V+qtW9crIfO7alOTUpcyQZA0r6tCj/3AwU2ve0oWSVNVD3uASU6jnAhGAAAAAC4L5a0NHXmOr3w0U5HrXk4oHu/0V3HtilzIRkAHBxf5WqFZ01UYOv7GeuxLicrOuI2WeGWWU4GAAAA4FC9va5Ka3bXpo19h9NgAGSRb/dylc24Tv6dSx0121ek6OBrVXfcxZJhupAOAACg/mRlIcz3vvc9PfTQQ1qyZIlGjRqlxx57TKeccsr+umEYsm1bCxcu1LRp0/SPf/xDhmFo+PDhOvXUU7MREShYyZSle2as0/MZJov0a1emqWd1V+MSTpgAXLNiuvT8zxSo2e0oxdsNVfVJD8gOtXAhGAAAAAC4b2tVTDe8uFKfbI86av3alWnKmd3ULFTkQjIAOAiphEo++J1K3/2VDCvuKFulzRUZcZvi3U7J8GAAAAAAuejLp8F0bhLUkE7lLqUBUFCslEo+/L1K356W8TpDonkfRU66X6km3V0IBwAAUP+yshDGNE29+OKLGjZsmNatW6czzjhDpaWlMgxDkjRq1ChVV1crFotJ+ux0iq5du+rpp5/ORjygYO2rS+qGF1fqvQ37HLUzj26mSSd3VsDH6n/AFam4NP0m6a1fOUq24VPNCVeptt9PJdPnQjgAAAAAPh//Fnfb22v3auJLK7S3NumoXTiwta4Y2ZHrGvrs2uyBbgO5qBD61rd9iUpnXC//rk8z1mO9L1Dt8JtkB8vFTxzvKITeRf6hb+FV9C6AXLRmV43eWleVNvbt/q33z48CgIZiVq1X2YzrMp42a5t+1QyYoNr+P5N8bIYMAADyR1YWwkhShw4d9OGHH+ryyy/X008/rWj0810ad+78/CQKwzB0wQUX6PHHH1dFRUW24gEFZ2Nlna56brnW76lz1C4b3l4XDeJiDOAWs2q9yqZfJe1Y4qilwq1VPX6akq0HZD8YAAAAgP24buUe27b16zmr9cBry2XZ6bXSIp/uPe9Yndm3jTvhPKC8nF1o4T151bfxqDR7ivT2ryXbctYrOklnPqziLqNUnPVwqG951bsoGPQtvIreBZAL/r4o/TSY8hK/TuvdzKU0AAqCbSv48VMKLbhbRrLWUU426a7qcfcp1eIYF8IBAAA0rKwthJGkJk2a6G9/+5umTJmif//731q4cKF27NihVCqlpk2b6vjjj9eZZ56pHj16ZDMWUHAWbdyn615Yqaq69B1Ti/2m7jitq8b2aOJSMgBFK15SePbNMhMRRy3W5SRFxtwjO9g4+8EAAAAAIAdU1yV0zdOL9don2x21zs1C+u33+qtHyzIXkgHAQVgzR3rxCmnvemfNMKXBP5dG3yQVlWY9GgAAAHCoOC03XWVNQq98sjtt7H+Oa6VQkNMX3MQJYvCig+1bo3qrQjOuV2DDfEfNlqFYv0tUO+RqyR/ktFlkBe+58CL6Fl5F734mqwth/qtjx476+c9/7saXRj3iooY3vfTxDt3x6molv7RlarNQQNPOO0pHt86/ySK84cMTEjUqnXu7ipf+01nzFat2xM2qO+ZCmZzUhBzHey68iL6FV9G7AArNiu3VuvQv72vNrqijdnLvlrr/gr5qxOQSALmotlKafrP04V8z11v2kb7xiNS2f3ZzAQAAAEeA03LT/XXRSsWSn5/6GPAZ+snonqpoFHQxFb6ME8TgRY6+tW1pydPSK9dJsSrnAyo6yTj7cQU7DhXvQHAT77nwIvoWXlWovWvYtm1//d0AeJ1l2Xrg9eV6bPZqR61X60b6w0UD1KZxiQvJAGj7Uun/LpZ2LXfWmnaXzv9fqfWx2c8FAAAA4CtVVla6HaGgvLZsl257dZVqE1bauGlIPx/eQRcPbsvGAV/BNM20i99VVVWyLOsAjwDclzd9a9sKrHpVpXN+KbNml7PsK1LdoCtV1/8nko+FfPkgb3oXBYW+hVd5qXdZLADkv1gypWH3ztbO6tj+sXP7tdWDFxznXigA+Sm6S3r5KmnZS5nr/S+WTp4sFYezGgsAAMANrpwIAyC7auMpXfN/H+qVj7Y5amOPaqGHv328wsW8HQBZZ9vS+3+U/jNRStY568d9Vzp1KhcoAAAAgByUSqXcjlAQkpatR+du0N/ed17TKC/x667Tu2lwp3LZliX+ixwcy7LoX3iOF/vWjGxXaN6tKl7zesZ6ovUARcZMUaqi62cDHvv+cHC82LsAfQuvoncBuOnlxVvTFsFI0o+GdXYpDYC89em/pZeulKI7nbWy1tI3fiV1H5f9XAAAAC7J+sx3y7L0ySefaM2aNaqurj6oi1Hf//73s5AMyE87qut0yZ8WavEm51GYPxrWWZNO6yWfyY6pQNbV7v3sAsUnzztrgZB0xkNS329mOxUAAAAA5Izd0YQmvrRSizZVO2q9WoY09Rvd1bq82IVkAHAAtq3iT/6p0IJ7ZMad719WIKSaoderrs93JMN0ISAAAABQPzgt9zO2bet3c1eljQ3o0EhtSiz+jnKAl04QA/7L0bfbNyg451YVL3s24/1jPc9W7ajbZQfLJd534CLec+FF9C28yku925An5WZtIUxNTY0mT56s3//+99q9e/dBP84wDBbC5Cg+sOe+FTuiuvKZZdpWHU8b9xnSDSd10f8c30r7qva6Ey6LvPSGj8Lg2/aBQq9eLt++TY5asvnRip76qNS0m8q/ME7fwit4z4UX0bfwKi/1bkNe2ACQn5ZsqdYNL67UzkjCUTvrmOa6fmwnFfuZQA4gt5h71yk8e5KKNr+TsR7vOFqRUXfIKmuT5WQAAABA/eMUps8s3FCl5TuiaWPf7teKv58cxQli8JzVs1X23M9kRrY6SlawQpFRdyre7dTPBuht5Bjec+FF9C28qlB7NysLYSKRiEaPHq1FixbJtu1sfElkQSG+YLzkjdWVmvTyKtUk0ifChYt9uufM7hrcqbxg/xsW6hs+coBtqeSDJ1T69oMyrKSjXHvsRYqeeIPkK5bvS5NY6Vt4Fb0LL6Jv4VX0LoB8YNu2nvlwhx6YvV5JK/06YsBn6PqxnXTOsS1cSgcAX8FKquTDP6j0nYdlpGLOckkTRYb/UvHuZ0gGp3MDAAAA+eRv729Lu92+cbGGd23sThgA+SMelV6/VXrvCWXaDijWeZwio++SXdos69EAAAByRVYWwkyePFnvv/++JGnw4MH6yU9+or59+6px48YyTXZuBOqTbdv6x6LtemjOen1pvojalhfroXN6qEuzUnfCAQXMqNmlshnXqmjDfEfNKm6syLh7Fe88zoVkAAAAAJAb6hIp3f36Ov37k12OWsuyIt37je7q0zrsQjIA+Gq+nZ+obNaN8u9cmrFe1/NsRYfdJLukSZaTAQAAAGhoGyrr9MbqvWlj3+rXSiYL4AEcAd/W96UZ10l71jhqVlFY0eG3KHbUeWy2AQAACl5WFsI888wzMgxDp512ml544QUWvwANJGnZun/mOj2zeIej1rdtWPef1UMVpQEXkgGFLbBxgcpev0ZmzU5HLdF6gKpPfkhWWRsXkgEAAABAbti0t07Xv7BSK3bWOGqDOjTSXWd045oGgNySrFPpu4+q5IMnZNjOU/lSZW0UGTVZiY4jXQgHAAAAIBv+/v42fXF/0rJin87s09y1PAA8LhVT6TuPqOSD30m25SjH2w1RZOxU5pcAAAD8f1lZCLN582ZJ0hVXXMEiGKCBRGJJ3fjSKr29rspRO7VXU908vouK/bz+gKyykip9Z5pK3v+NDKUf0WTLUO3ACaoZeLlkZuXHMQAAAADkpHW7a/Wjv3+iqrqko3bRoNb62bD28pvsbgggd/g3v6uyWRPlq1rnqNkyVHfs9xUdfI1UFMp+OAAAAABZsa8uqZc+Tt8I8exjW6i0yOdSIgBe5tu1TGWvXyv/7k8dNdsfVHTI9ao79nuSwdwvAACA/8rKzNsWLVpo06ZNatasWTa+HFBwNu+t0y+eW6E1u2sdtUtPbKcfDW4jg+Mwgawyq7eobPqVCmxb5KilSlsocvKDSrQb4kIyAAAAAMgdtm3rrtfXOhbBhIpM3XpKV43p0cSlZADgZMSqVfrmvSpZ+veM9WST7oqMnqJk635ZTgYAAAAg255bskN1yc9PbPAZ0jePb+liIgCeZCVVsuh3Kn33ERlWwllvO0D7xk5VolHH7GcDAADIcVlZCDNo0CBt2rRJy5cv1/HHH5+NLwkUjMWbq3Xt8ytUWZs+YaTIZ+jWU7tq/FFNXUoGFK6i1dMVnnWjzNg+Ry3ecaSqx06VXcriUAAAAACYuWKPPthUnTbWuUlQ953VQ52alriUCgCcitbOUGjOL+WLbnfUbDOg2v4/U82ASyVfsQvpAAAAAGRTMmXp6Q/SPxuM7dlUrRrxeQDAwfNVrlF4xnUKbP/QWTQD0uiJ0tArZe2rllKprOcDAADIdVlZCPOLX/xCzz77rH71q1/pm9/8JidTAPXkP8t26Y7/rFE8ZaeNNyn164Gze+iYNmUuJQMKVDKm0IK7VfLRXxwl2/SrZsh1qj3uhxxVCwAAAACSYklLj8zdmDbWulGRnrywj0JFPpdSAUA6o2aXwvNuV/GqVzLWEy2PU2TMFKWa9sxyMgAAAABumbmyUtur42lj3+nfyqU0ADzHthRc8meF3rpPRrLOUU42O0r+838vtTrGhXAAAADekZWFMEOHDtW9996r66+/Xt/61rf029/+Vo0bN87Glwbykm3b+t2bm/XEW5sdtS5NSzTt3J5qU85OI0A2+SrXqGz6FfLvWuaopRp1UPX4aUq27OtCMgAAAADITU+9v1Vb9sXSxq4c2YFFMAByg22r+NNnFXrjLpmxKmfZX6LokGtUd8z3JZP3LQAAAKBQ2LatpxZuTRs7tk1YfVqHXUoEwEvMfZsVnnWDija95ajZhqnafj9VbPBVqmjO4joAAICvk5WFMJJ07bXXqmvXrrrkkkvUvn17nXTSSerRo4dKS0u/9rG//OUvs5AQ8IZY0tId/1mj6Z/udtSGdirXlDO7KVyctZc2AEnFy/6l8LzbZCRqHLVY99MVGXWX7GJOaAIAAACA/9oViet/396SNnZ8uzKN7dHEpUQA8Dlz30aFZ9+soo1vZKzH2w9XZPRkWY3aZTkZAAAAALct2RLR0m3RtDFOgwHwtWxbxcv+pdD8O2UmIo5yqryTqsfdp2TrfvL52HADAADgYGRttvyOHTv03HPPqaqqSpZl6YUXXjjox7IQBvjMnmhC176wQku2OD8QXXB8S109uqP8puFCMqAwGfGIQnNvVXD5846a7Q8qMvyXivW+QDJ4XQIAAADAFz32xibVJqz9tw1J14zuKIPPTwDcZKUUXPInhd5+UEay1lkubqzo8JsU63kO13sAAACAAvW3L50G07pRkUZ1Z2MPAF/NiO5UePZNKl43M2O99pjvKTr0einw9RuKAwAA4HNZWQize/dujRgxQitXrpRt29n4kkDeWb2rRr94doW27IuljZuGdPXojvpWP3YYAbLJt3OpGv3nCvmq1jlqySbdVT3+EaWa9sh+MAAAAADIccu2RfXyxzvTxs7s01xHtQy5lAgAJN/u5QrPmqTA9g8z1mPdT1dk+C9llzbLbjAAAAAAOWPz3jrNWVWZNvbNfq3YsBTAVypa9arCc26RWVfpqKXCrRUZe68S7U90IRkAAID3ZWUhzJQpU7RixQpJ0vnnn6+f//zn6tu3rxo3bswuj8BBeGvtXt340ipF46m08VCRqbvO6K5hXRq7EwwoRLb92c6gC+6VYcUd5dqjv6XosJulQIkL4QAAAAAgt9m2rftnr9MXt8opDZj6+fB2rmUCUOBSMZUufFwl7/9GhpVwlkOtFB11h+Kdx7oQDgAAAEAu+ecH22V94aJGacDU2cc0dy8QgJxl1O1VaN7tCq54MWO97qjzFB1+i+zisiwnAwAAyB9ZWQjz4osvyjAMXXjhhfrTn/6UjS8J5I3/+2C77p+1TqkvHabUqqxI087tqW7NORYTyBajtlLhWTeqeO0MR80qCisyeori3U93IRkAAAAAeMOM5Xu0eHMkbeyHg9uqWajIpUQACpl/6/sKz5okf+WqjPXaPt9RzZDrmZQCAAAAQJFYUi98tCNt7KxjWihcnJWpVwA8JLB+jsKzJskX3e6oWSVNFRl9l+JdTnIhGQAAQH7JyqexzZs3S5J++MMfZuPLAXkhZdl6aM56/WOR80NRn9Yh3X92DyaJAFnk3/Keyl67Sr7INkct0bKvqsc/LKtRexeSAQAAAIA31CUsPTx3Q9pY2/Jifbt/K5cSAShURjyi0rfuV/Cjv8qQ7agnG3dWZPQUJdsOciEdAAAAgFz04sc7FY1b+28bkr7Zr6V7gQDknnhUoQVTVLL0HxnLsa6nKDLqDtklTbMcDAAAID9lZSFMs2bNtHnzZpWVsWsacDCi8ZQmvbRKC9buddRO6tlEt57SVcGAmf1gQCGyUip5/3GVvvuwDNtylGuOv0Q1g6+WfCxMAwAAAIAD+dv7W7WtOp42dsXIDir2c40DQPYE1s9ReM4t8lVvcdRsw6fafj9RzcDLJX+xC+kAAAAA5KKUZeufX9rEdFT3CrVrHHQpEYBc49/ynspmXCffvo2OmlXcSNERtynW4xuSYbiQDgAAID9lZSHM8OHD9Y9//EMff/yx+vXrl40vCXjWtn0xXfXscq3aVeuo/WhwG/30xHYy+VAEZIUZ2a7w61eraPPbjppV0kTV4+5XouNIF5IBAAAAgLfsjMT15Dvpk877tSvTmO4VLiUCUGiM2j0KzZ+s4IoXMtaTzY9W9Zh7lGreO8vJAAAAAOS6uasqtbkqljb23f6tXUoDIKckYyp9+wGVfPi/GU+djXcYrsiYu2WFec8AAACob1lZCHPNNdfoX//6l+6//35dcMEFCgbZEQHI5OOtEV3z3ArtrkmkjQd8hm4+ubNOP7q5S8mAwhNYP0dlr18ns26PoxZvN0SRcQ/ICnPUNQAAAAAcjMfmb1Rt4vNTNg1JV4/uKIPNPgA0NNtW8YqXFJp/Z8brPLY/qJpBV6n2uIslMyu/MgEAAADgMU+9vy3tdu+WIfVtG3YpDYBc4d++ROEZ18lfucpRswOlip44UXVHf5tTYAAAABpIVn6r069fP/3+97/Xj3/8Y5188sn6/e9/rx49emTjSwOeMWP5bt366mrFkum7A5SX+PXAWT10XLsyl5IBBSYVV+lb96v0wz84SrZhquaEq1Tb71LJ9LkQDgAAAAC8Z+nWiF5euitt7KxjmuuoliGXEgEoFGb1FoXn3KKi9XMy1uNtBysyZoqs8o7ZDQYAAADAM5ZujejDzdVpY98Z0IrNPYBClkqodOGvVbLwMRl2ylFOtB6g6nFTud4AAADQwLKyEOaHP/yhJKl3795644031Lt3bx177LHq0aOHSktLD/hYwzD0hz84JyMD+eS1T3dr0svO3QE6NQlq2rk91a4xpygB2WBWbVDZ9CsV2LHEUUuFW6n65IeVbDPAhWQAAAAA4E22beuB2evTxkJFpn42rL1LiQAUBNtS8KO/qfSt+2Qmoo6yVVSm6LBJivX6H3ZlBQAAAHBAXz4NpkU4oHE9mriUBoDbfHtWKvz6tQrs/NhRs80i1Qy+WrXH/ZDNVQEAALIgKwthnnzyyf07IRiGIcuytHjxYi1evPiAj7Ntm4UwyHu7owndM2OtY3xQh0a69xvdVRbMyssUKHhFK19WePZNMuMRRy3WeZwiY++VHWyc/WAAAAAA4GGvLd+jJVvSP2f9cHBbNQ0FXEoEIN/5KlcrPGuiAlvfz1iPdRmvyMjbZIdaZDkZAAAAAK/ZXh3TjBV70sYuOL6V/D7TpUQAXGOlVLL4jyp9+wEZqbijnGx+tKrH3a9U0x4uhAMAAChMWZlh36FDB44EBb7CfTPXaV9d+jGZ5xzbQjeM7cjFEyAbErUKz79TwU/+6SjZZpGiwyaq7pjvsTsoAAAAAByiukRKj8zdkDbWtrxY3+7XyqVEAPJaKq6SRb9T6XuPybCcE1Ks0uaKjLxd8a7jXQgHAAAAwIue/mC7Upa9/3bQb+qcY1lUDxQas2qDymZer8CW9xw12/CpdsDPVTNgguRj8x8AAIBsyspCmHXr1mXjywCeM2flHsfuISf1bKJJJ3Vi8RiQBb7dy1U2/Ur596x01JKNO6t6/CNKNe/tQjIAAAAA8L6/Ltym7dXpk9GvGtVBRX42/gBQv/zblyg860b5dy/PWK/rfYGiQ2+UHSzPcjIAAAAAXlUbT+m5JTvSxs44upnKS7Iy1QpALrBtFS/9h8ILpshI1DjKyYquioy7X8mWx7oQDgAAAHw6A1xSXZfUPTPWpY2Vl/h13RgWwQANzrZV/Mk/FZ53h4xUzFGu63mOIiNvl4pCLoQDAAAAAO/bUR3Xk+9sSRsb0L6RRnWrcCkRgLyUqFHo7YcUXPKkDNtylFONOigy5i4l2g11IRwAAAAAL3t56S7tq0uljX2rP6fcAoXCjGxXePZEFa2f66jZMlTX92JFh1wj+YMupAMAAIDEQhjANQ/P3aBd0UTa2DWjO6pJiGMygYZkxKoVnj1JxatecdTsQKkiI29X7KhzXUgGAAAAAPnjV/M3qi75+aR005CuHt2BzT8A1JvAxgUKz75Jvn0bHTXbMFV73A9VM+gqKVCS/XAAAAAAPM2ybf190ba0seFdGqtTEz5fAHnPtlW84iWF5t0mM1blKKfK2ql63FQl256Q/WwAAABIw0IYwAXvbajS8x/tTBsb2rlcp/Zq6lIioDD4ty9W2fQrM06QSDbrrX3jH5FV0dmFZAAAAAC8xufzuR0hZ320pVqvfLIrbeycY1uqV+tGLiUqbKZpHvA2kIsO1LdGXZVK5k9W8Sf/l/GxyWZHqWbcVKVaHiveqZFtvOfCi+hbeBW9C6AhLVizVxsq69LGvjOA02CAfGfU7lF4zi9VvPrVjPW63t9UdNgk2UXhLCcDAABAJiyEAbKsLpHS5Olr08ZKA6YmndSZXVGBhmJbKvngDyp9+34ZVtJRrj32+4oOvVHyF7sQDgAAAIAXVVRUuB0hJ9m2rYf+/knaWFmxXxPPPEYVYT5z5YLy8nK3IwCHrLy8XLJt6ZMXpFeuk6I7nHfyFUsjr5f/xCvVyMep28gNvOfCi+hbeBW9C6A+PfV++mkw3ZuXakB7NvgA8lnR2pkKz54ks2aXo5YqbaHImClKdBrtQjIAAAB8lXpdCPPfnTANw1AymXSMH44vPxfgdb9ZsEmbq2JpY5eNaK9WjZgMAjQEo2aXymZcp6IN8xw1q7hckbH3Kt7lJBeSAQAAAED+eXHxFn2wYW/a2OVju6kZi2AAHIl9W6V/XyMt/3fmeoch0jcelZp1z24uAAAAAHlnxY6o3tuwL23sO/1bsbEpkKeMeLVC8ycruOyZjPVY9zMUGXGb7BI2RgIAAMg19boQxrbtQxoHCs3SrRHHziF924Z1/nEtXUoE5LfApjcVfu0a+Wqcu4QmWvdX9cnTZJW1cSEZAAAAAOSfmnhS97z6adpYp6al+sHQzi4lAuB5liUt+pP0+i+l2D5nvahMOuk2qf8PJdPMejwAAAAA+efvX5rT0bQ0oPFHNXUpDYCGFNj0lsIzr5eveoujZhU3VmTUHYp3P92FZAAAADgY9boQ5tZbbz2kcaCQJFKW7py+RtYX1oUFfIZuObmLTHYOAeqXlVTpu4+oZOGvZSh9MaYtQ7UDfq6aQVdIZr3+GAQAAABQQCorK92OkHN++8ZGba2qSxu7YmQHRaurFHUpEyTTNFVeXr7/dlVVlSzLcjER8PVM01R5cpf00pXSuvkZ7xPvPFY1oyfLLmstVVVlOSGQGe+58CL6Fl7lpd6tqGAHecArdkXj+s+nu9PGzj+uhYr8LLwH8kqiVqG37lPJkj9lLMc7jlb1mCmyQy2yHAwAAACHgoUwQJb86d2tWrWrNm3skiFt1alpiUuJgPxkVm9R2WtXKbD1fUfNKm2u6pMeUKL9iS4kAwAAAJBPUqmU2xFyyrZ9Mf3xnc1pY4M6NNLwzo34u8oxlmXx3wS5zUqqePH/Su88LCXrnOWSJooM/6Xi3c+QDEOin5HDeM+FF9G38Cp6F0B9eOaDHUqkPt9oschn6Ly+LV1MBKC++bd9qPCMa+Xfu9ZRswIhRYffoliv8z+75gAAAICcxlb4QBas2VWjP7ydPhmkR/NSfX9ga5cSAfmpaM1rCs+8UWbMuQtovMMIVY+7T3ZpMxeSAQAAAEB+e2z+RsWSn+++bBrS1aM7yuAXxgAOgW/nUpXNmij/zqUZ63U9z1F02E2yS9hVHQAAAED9qktYembx9rSxU3s3U5NQwKVEAOpVKq7Sdx9RyaLfyrCdp8jF256gyNipshq1cyEcAAAADkdWFsLMmzdPkjRw4ECVlBzc6Rd1dXV69913JUkjRoxosGxAQ0tZtia/tjZt1xDTkG4Z30V+H8fnAvUiGVPozXtUsuTPjpJt+lUz+FrVHv8jyeA1BwAAAAD17aMt1Xp12e60sXOObaFuzUtdSgTAc5J1n01G+eD3MmznTu6psraKjJqsREd+VwAAAACgYfxn2S7trU2mjX27XyuX0gCoT75dn6psxrXy71rmqNm+YkWHXqe6Yy9iTgkAAIDHZGUhzKhRo2SappYsWaLevXsf1GM2b968/3HJZPLrHwDkqP/7cLuWbImkjV04oLV6tQq5lAjIL2blWjWafoX8uz5x1FKN2qt6/MNKtuzrQjIAAAAAyH+WbeuB2evTxsLFPl16IjsnAjg4/s3vqGzWJPmq1mWoGtIJl2pfv8uU8gWzHQ0AAABAgbBtW0+9vy1tbHCncjb5ALzOSqnkgydU+s40GVbCUU60OFaRk+5XqqKrC+EAAABwpLKyEEb67ENjNh8H5IItVTE9Nn9j2lj7xsX6yVAmgwD1ofjT5xSe+0sZiRpHLdbtNEVGT5FdXOZCMgAAAAAoDP9Ztlsfb42mjf14SFtVlAZcSgTAK4xYtUrfvEclS/+R+Q7Nj5K+8ajUfpBUWSmlnCfFAAAAAEB9eGd9ldbsrk0b+05/ToMBvMzcu1ZlM65TYNsHjppt+lUz8HLV9r9UMrM2fRIAAAD1LGf/JWdZliTJ5/O5nAQ4PLZta8rra1WbsNLGbzq5i4IBjtIEjkg8qvDcWxVc/pyjZPuKFRnxS8V6f1MyDBfCAQAAAEBhqI2n9Kt5G9LGOlQE9c3jW7qUCIBXFK15XaG5t8oX3e6o2WZAdQMnqOSkSZK/2IV0AAAAAArNUwvTT4Pp3CSoIZ3KXUoD4IjYloIf/U2hN++Vkax1lJNNeqj6pPuVan60C+EAAABQn3J2Icz69eslSeXlfLCEN/37k116e11V2ti5fVtoQIdGLiUC8oNv5ycqm36F/HvXOmrJJt1VPf5hpZr2dCEZAAAAABSWP7+3VTsiibSxq0Z1UMDHBiAAMjNqdik873YVr3olYz3R8nhFxkyRWvRSCYtgAAAAAGTB2t21evNLczu+3b+1DDZdBDzHrN6i8KwbVbRxgaNmG6Zqj79ENSdcKfm45gAAAJAPGmQhzIYNGzKOb926VeFw+ICPjcViWr16tW655RYZhqGjj2b1NbxndzShB2evTxtrHg7oihHtXUoE5AHbVvCjvyj0xt0yrLijXNf7m4oMv0UKlLgQDgAAAAAKy7Z9Mf35vS1pYyd0bKThXRq7EwhAbrNtFX/6L4XemCIzVuUsB0oVHXyN6o75nmT6xDnxAAAAALLlqffTT4MpL/HrtN7NXEoD4LDYtoqXP6fQvDtkxqsd5VR5B1WPu0/J1gNcCAcAAICG0iALYTp37uwYs21bJ5988iE/1/e///36iARk1f2z1mlfXSptbOJJnRUuztlDmICcZtTtVXjWjSpe87qjZhWFFRl9l+Ldz3AhGQAAAAAUpkfnbVQsae+/bRrSL0Z1ZLdUAA7mvo0Kz75ZRRvfyFiPdxiuyKjJshq1y3IyAAAAAIVub01Cr3yyM23svL4tFAxw2i3gFUbNLoXn3JxxPokk1R5zoaJDrpeKQllOBgAAgIbWILPybds+pPFMgsGgrrjiCv3whz+sr1hAVsxZuUevL9+TNnZSzyYa0bXCpUSAt/m3LlTZ9Kvki2x11BItjlX1+IdllXdwIRkAAAAAFKbFm6s1/dPdaWPn9W2hbs1LXUoEICdZKQWX/Emhtx+Ukax1losbKzr8ZsV6ni2xiA4AAACAC55ZvCNtow+/aeiC41q6mAjAoShaPV3h2TfLrNvjqKVCrRQZe48SHYa7kAwAAADZ0CALYf74xz+m3b744otlGIbuvPNOtW3b9isfZxiGgsGgWrdureOPP17hcLgh4gENprouqXtmrEsbKw/6dd2YTq7kATzNSqlk0W9U+s7DMuyUo1xz/I9VM/gayVfkQjgAAAAAKEyWbeuB2evTxsqKffrpUE5yAPA5367lCs+eqMD2xRnrse5nKDL8FtmlzbKcDAAAAAA+E09a+r8Pt6eNnXxUUzUL8/tnINcZsX0KzbtdweXPZ6zX9Txb0RG3yi5ulN1gAAAAyKoGWQhz0UUXpd2++OKLJUlnn322evfu3RBfEsgJj8zboF3RRNrYNWM6qkko4FIiwJuM6A6VvX61ija95ahZwSaqHjdViU6jXUgGAAAAAIXtlU926ZNt0bSxS4a2VeNSrn0AkJSKqfS9x1Sy6LcyrKSzHGqlyKg7leg8xoVwAAAAAPC515bv1u4vze/4Tv9WLqUBcLACG+YrPPNG+aLbHDWrpIkioyYr3nW8C8kAAACQbQ2yEObLZs+eLUnq3LlzNr4c4IqFG6r03JKdaWNDO5fr1F5NXUoEeFNg/VyVzbhWZq3z6Np428GKnPSgrDDHUQMAAABAttXEU3ps/sa0sQ4VQf3PcXxGAyD5t76v8KxJ8leuyliv7fNd1Qy9TnZRWZaTAQAAAPlrz549euutt/TBBx9o8+bN2rt3r8LhsHr27KmzzjpL3bt3dztiTrJtW0+9nz6Jvl+7Mh3VMuRSIgBfK1Gj0IJ7VPLx3zKWY11OUmTUZE6fBQAAKCBZWQgzcuTIbHwZwDV1iZQmv7Y2baw0YGriuM4yDMOlVIDHpOIqfftBlX7whKNkG6ZqBl2p2v4/k0yfC+EAAAAAAH96d4t2RtJ3Sr16VAcFfKZLiQDkAiMeUelb9yv40V9lyHbUk407KzLmbiXbDHQhHQAAAJDfXn31Vb3wwgtq2bKl+vbtq0aNGmnr1q1677339N577+nKK6/U0KFD3Y6Zc97fWK0VO2rSxr47oLVLaQB8Hf/WhSqbcZ18VRscNauoTNERtyrW82yJOVoAAAAFJSsLYYB899sFm7VpbyxtbMLw9mpdXuxSIsBbzKoNKnvtKgW2L3bUUuFWqj55GpMlAAAAAMBFW6ti+uvCrWljQzqV68Qujd0JBCAnBNbNVnjOLfJFtjpqtulX7fGXqGbg5ZKf66QAAABAQ+jWrZtuu+029e7dO2182bJluuOOO/TEE09o4MCBCgQCLiXMTU+9n/4Zpn3jYg3v2tidMAC+WjKm0nenqWTRExk334i3P1GRMffIKmvjQjgAAAC4jYUweYCjbt21dGtEf/vSRZK+bcP6n+NbupQI8JaiVa8oPGuizHjEUYt1HqfImHtkl1S4kAwAAAAA8F+PzNugWPLzXzb7DOkXozpwEi5QoIza3QrNn6zgihcz1hPN+ygy9h6lmvXKcjIAAACgsJxwwgkZx3v16qU+ffpo8eLF2rBhg7p27ZrlZLlrY2Wd5q/emzb2rX6tZHKNA8gpvp1LVfb6NfLvWemo2f4SRU+8UXV9viMZnFYNAABQqFgIkwc46tY9iZSlO6evkfWFTQcCPkO3nNyFiyTA10nUKvTGZJUs/YejZJtFn120OPb7HF0LAAAAAC77cFO1Xl++J23svONaqkuzUpcSAXCNbat4xYsKzZ8ss26Ps+wPqmbQVao97mLJ5NcPAAAAyG1VVVVatWqVVq1apdWrV2v16tWqrq6WJI0cOVITJkw46OfauXOnXn31VS1atEi7d++W3+9Xq1atNGTIEI0fP17Fxdk/JdHn86X9Pz7z90Xb0s6VKCv26cw+zV3LA+BLrKRK3n9cpe/9SoaVdJQTrfqpetxUWY07uxAOAAAAuYTfROUBjrp1z5/e3apVu2rTxn48pK06NS1xKRHgDb7dK1Q2/YqMO3ekyjtp3/iHlWrRx4VkAAAAAIAvsmxbD8xenzbWKOjTT4a2dSkRALeY1VsUnnOLitbPyViPtxuiyOi7ZJV3zG4wAAAA4DBdcskl9fI8Cxcu1KOPPqra2s/nDsRisf2La2bOnKmJEyeqVatW9fL1DsauXbv00UcfqaKiQh06dMja1811++qSeunjnWljZx/bQqVFLBYCcoGvcrXCr1+rwI4ljpptFqnmhKtUe/yPJZPXLAAAAFgIkxc46tYda3fX6g9vb04b6968VBcNbO1SIsADbFvFnzyt8Pw7ZCTrHOW6nmcrOvJ22UVhF8IBAAAAAL7s30t3adn2aNrYT4a2U+MSNlwBCoZtKfjRX1X61v0yE1FH2SoqU3TYJMV6/Q8n+wIAAMCzmjVrprZt22rx4sWH9Li1a9dq2rRpisfjCgaDOvvss9WnTx/F43EtWLBAM2fO1NatW3X33XfrnnvuUUlJw2+qmUwm9eijjyqRSOi73/2uTNNs8K/pFc8t2aHahLX/ts+Qvnl8SxcTAZD02bWHxX9S6K37ZKRijnKyWS9Vj3tAqWY9XQgHAACAXFXwC2E46haHI2XZunP6GiVSnx+YaxrSLeM7y+/jIhKQiRGrVnjOTSpe+W9HzfaXKDLydsV6nedCMgAAAABAJjXxlH41f2PaWKcmQZ3ft4VLiQBkm2/PKoVnTVRg26KM9VjXUxQZcavsEO8LAAAA8J7zzz9fXbt2VdeuXdW4cWPt2LFDl1122SE9x5NPPql4PC6fz6ebb75ZPXr02F/r06ePWrdurb/+9a/aunWrXnrpJV1wwQWO5/jzn/+sRCJx0F/ztNNOU+vWmTfotCxLv/71r7Vs2TKNHTtWI0aMOKTvJ58lU5ae/mB72tiYHk3UqlH25/IA+Jy5b5PKZlyvwJZ3HDXbMFXb/1LVDLxc8hW5kA4AAAC5rOAXwnDULQ7H/324XUu2RNLGvjugtXq34hQLIBP/9iUqm36lfPs2OGrJZr1UPf4RpSq6uJAMAAAAAPBV/vjOFu2Opk/E+cWojmwCAhSCVFwli36r0vd+LcOKO8pWaXNFRt6ueNfxLoQDAAAA6kemRSmHYtWqVVq2bJkkafTo0WmLYP7rjDPO0OzZs7V582a9+uqrOvfcc+X3p0/Vef311xWLOU9A+CqDBw/OuBDGsiw9/vjjeuONNzR8+PB6mw+TL2aurNT26vTPN98dkHlBEYAssG0VL/s/heZPzngCbbJxF0XG3adkq+Oynw0AAACeUPALYb6Io25xMLZWxfTYl3ZDbde4WD8d2talREAOsy2VfPi/Kn3rPhlW0lGuPeZ7ip44UfKzyw4AAAAA5JLNe+v0t4Vb08aGdi7XiV0auxMIQNb4ty9WeOaN8u9ZkbFe1/sCRU+cKLu4UZaTAQAAALnl3Xff3f/n0aNHZ7yPaZoaOXKknnrqKUWjUS1dulR9+/ZNu89f/vKXI87y35Ng5s2bpxNPPFETJkxgnsgX2Latp750nePYNmH1ac1mp4AbjOgOlc2apKL1szPWa/v+QNHB10qBhp9jBwAAAO8q+IUwHHWLQ2Hbtqa8vla1CStt/OaTOysY8LmUCshNRu1ulc24XkXr5zhqVnEjRcbcw66hAAAAAJCjHp23UfGUvf+2z/jsNBgAeSxRo9DbDym45EkZtuUopxp1UGTMXUq0G+pCOAAAACD3LF++XJJUXFysLl26fOX9evfunfaYLy+EOVJfXAQzdOhQXX755SyC+ZIlWyJaui39xInv9G/lUhqgsBWtfFnhObfKjO111FJlbRQZO1WJdkOyHwwAAACeU/ALYTjqFofilU926a11VWlj5xzbQgM6lLuUCMhNgU1vKfza1fLV7HDUEq36qfrkabIacYoSAAAAAOSiRRv3acaKPWlj/3N8S3Vuyg6MQL4KbFyg8Oyb5Nu30VGzDVO1x/1INYOuZCdWAAAA4As2bdokSWrVqpV8vq/eOLNNmzaOx9SX/84RmTdvngYPHswimK/w1Pvb0m63blSkUd2buJQGKExGbaXC825V8cp/Z6zX9fofRYffJLuoLMvJAAAA4FUFvxDmSHHUbeHYHU3owdkb0saahwO6cmR7lxIBOchKqvS9R1Xy3mMyZKeVbBmq7X+pak64SjL58QMAAAAAuShl2Xpg9vq0sfKgX5cMaedSIgANyajbq9AbUxT89F8Z68lmvVQ95m6lWhyT5WQAAABAbovH46qurpYkNW3a9ID3DYfDKi4uViwW0+7du+s1xzPPPKO5c+cqGAyqTZs2+te/nP+2HzRokDp16nTQz3mwGQ+0+CeXbKmq0+yV6Rt+fLt/GxUH+J11IfjyvCnmUbnDv3a2QjOul1mz01GzSpupZuw9SnQZJ/7rfIa+hVfRu/Ai+hZeRe9+hk91R4ijbgvH/bPWqaoumTZ247jOChfzMgIkyazeorLXr1Zgy3uOmlXaXNUnPaBE+xNdSAYAAAAAOFgvL92p5Ttq0sZ+MrStyku4/gHkFdtW0epXFZ57m8xa5yQ321ekmoFXqPb4H0u+gAsBAQAAgNxWV1e3/8/BYPBr7x8MBhWLxdIeVx927ty5P8+zzz6b8T4tWrQ4pIUwP/vZzw7qfk8//fRBP6ebHlvwiawv7OEYKvLpByN7qFGQzzqFqLy83O0IhSVWLU2fJC36c+Z677Nlnv6gwqEDLygsdPQtvIrehRfRt/CqQu1dfoN9hDjqtjDMWbVHry9P3yHkpJ5NNLJbhUuJgNxStHaGwjNukBnb66jFOwxX9bj7ZZc2y34wAAAAAMBBi8ZT+vX89OtWnZsEdV7fFi4lAtAQzMg2hebequK1MzLWE20GKjJ6ilIVX73xEwAAAFDo4vH4/j/7/V8/9ea/9/ni4+rDhAkTNGHChHp9znxSXZfQP9/bmDZ2wcD2LIIBsmHtfOmFn0t7NzhrwcbS6Q9Ifc6TDCPr0QAAAJAfWAhzBPL1qNt8O+b2SFXHkrp3xvq0sfKgXzec1KVg/g68jiPAGlAyppIFdyv44ZOOkm36VTv0OsX6XSLT4O/8UNG38Cp6F15E38Kr6F0A9e2Pb2/W7ppE2tjVozvK7+P9BcgLtqXipf9U6M17ZMYjjrIVCKvmxBtUd/S3JK7lAAAAAAdUVFS0/8/JZPJr7//f+3zxcbnq8ccfdztCvXl64SZVxz7/72MY0sVDO7uYCCgAiVpp5h3S27/OXO92kvSNR6VGrbObCwAAAHmHhTBHIF+Pus23Y26P1NRnP9LOSPquLLd+42h1a9fSpUQ4UoV6BFi9271a+tcPpG1LnLXGHWSc978qbT9QpVkPlp/oW3gVvQsvom/hVfQugCOxeW+d/vb+trSxEzs31pDOjd0JBKBemZVrVTb7JgW2vJOxHus0VtFRt8sKMwkFAAAAOBhfnCNyMHNA/nufg5lb4rav2wj2vyorKxs4yZFJWbb+d/7qtLHR3ZuozIypsjLmUipkm2maadfOq6qqZFmWi4nym2/bYoVeu1q+ytWOmh0IqWbEzYof/S0pZUg5/h7iJvoWXkXvwovoW3iVl3q3oqKiwZ6bhTBHgKNu89+bq3fp7++mH9E5okdznXN8W5cSATli8T+lf18tZdg9VL3Pks58RCppnPVYAAAAAIDD8/DcDUqk7P23faahX4zu4GIiAPUilVDJh39Q6bsPy0g5r0tbJU0UGXGr4t1O/2xrZAAAAAAHpaioSGVlZaqurtbu3bsPeN9IJKJY7LOFFwe7yMQLUqmU2xEOaNaKPdpclb7g5dv9WuZ8bjQsy7LogYaQiqt04WMqWfi4DNv595toM0jV46bKatReytEJmrmMvoVX0bvwIvoWXlWovctCmCOQr0fd5tMxt0eiNp7SxGc/ShsrLfJpyjl9ZPBLYRSqWER65Tpp8VPOmj8onXK31P9iJk4AAAAAgIe8v3GfZq1M34HxguNbqlOTEpcSAagPvh0fq2z2JPl3Ls1Yr+t5jqLDbpJd0nA7cQEAAAD5rF27dlq2bJm2bdumVColn8+X8X5btmxJewyy46kvnXzbq2VIx7UtcykNkL98u5erbMZ1Ga8/2L4iRQdfq7rjLpYM04V0AAAAyGcshDkC+XrUbb4cc3ukps1ep/W7a9LGLhvRQSFxTK7XeOkIsFzm2/mJQq9eJl/lGkct1aSbIqf+Slazo6S9e7MfLg/Rt/AqehdeRN/Cq7zUuw151C2AI5OybD0we33aWHmJX5cM4TRcwLOSdSp992GVfPCHjLuwpsraKjJqshIdR7gQDgAAAMgfPXv21LJlyxSLxbRmzRp179494/0++eSTtMeg4X2yLaIPN1enjX13QCs2PQXqk5X67BTatx+SYTlPoU0076PISfcr1STzeyMAAABwpFgIcwQK/ajbfD5C6ZNtEf3lvS1pY8e2Ceu8Y5vn9fddKAr1CLDDZtsKfvRXhRZMkZFyXryo632BIsNvkQKlEn+vDYa+hVfRu/Ai+hZeRe8COBwvfbxTK3akbwRy6dB2ahTksiHgRYFNbys8e5J8VesdNVuG6vpepOgJV0tFIRfSAQAAAPll0KBBev755yVJs2fPzrgQxrIszZ07V5IUCoV09NFHZzNiwfryaTAtwgGN69HEpTRA/jGr1qtsxvUKbF3oqNmmXzUDLlNt/0slX8CFdAAAACgU/Eb7CHHUbf5JpizdOX2tLPvzsYDP0C3ju8hnsjsICotRV6XwrBtVvOY1R80KhBUZfafiPb7hQjIAAAAAwJGKxJL69Rsb08a6NC3ROX1buJQIwOEyYvsUWnCPgp/8M2M92aS7ImPuVrLV8VlOBgAAAOSvbt26qVevXlq2bJlmz56tUaNGqUePHmn3efnll7V582ZJ0qmnniq/P3+m6XzV/Bi37aiOacbyPWlj3+zfWsVFTMgvRKZpHvA2DpFtq+ijv6p0/hQZyVpHOdWku6InP6hUy2OUm+8Q3kDfwqvoXXgRfQuvonc/kz+fsF3CUbf550/vbdXKnek7of5ocFt1blriUiLAHf6t76vstavkq97iqCVaHKPq8Q/LKu/oQjIAAAAAQH3437e3aE9NMm3s6tEd5WcjEMBTita8ptCcW+Wr2eGo2WZANQMmqLb/TyVfkQvpAAAAgNz16aefatu2z08O2bdv3/4/b9u2TXPmzEm7/6hRoxzP8YMf/EC33HKL4vG4Jk+erHPOOUdHH3204vG43nzzTc2YMUOS1Lp1a5155pkN8n24paKiwu0IGf3unU+V/MLOpyUBn340sqcal/KZCFJ5ebnbEbyrarP04mXS6lkZioY09DL5Rt+sRoFg1qPlO/oWXkXvwovoW3hVofYuC2GOEEfd5pe1u2v1+7c2p411b16qiwa1dikR4ALbUsn7v1XpOw/JsFOOcu1xP1J0yLVMngAAAAAAD9u0t05/X7QtbWx4l8Ya3KkwL5ICXmREdyo873YVr341Yz3R8nhFxkxRqmmPjHUAAACg0M2cOXP/XI4vW758uZYvX542lmkhTOfOnXXVVVfp0UcfVW1trf7+97877tO6dWtNnDhRJSVsvpkNo3u20Ipt1Zr56WebBZzXvy2LYIAjYdvSkqelV6+T6qqc9YpO0tmPSx2HZj0aAAAAChsLYY5QIR91m6vH3B4uy7Y1+bW1SqQ+3xnENKRbT+2mIEfkehpHgB08I7pDoelXK7DxDUfNClYoevIDSnYewxG2WUDfwqvoXXgRfQuvoncBHImH525IuwbiNw1dNaqDi4kAHDTbVvGyfym0YIrMmHMCih0oVXTwNao75nuSyVUcAAAAoKENGDBA999/v1555RUtWrRIe/bskd/vV6tWrTR48GCdcsopKi4udjtmwRjUuYkGdW6i1Tsj+uOCtbr4xM5uRwK8K7pLevkqadlLmev9L5ZOniwVh7MaCwAAAJAkw7Zt++vvlr8yHXX717/+VZLUs2dPjR07Nu3+mXb4WLt27f6jboPB4AGPur3nnnvY5SNH/enNdbr1xaVpYz8Z0UWTTuvlUiIgy1bNkJ67VIrudNY6DZfO/Z3UqE32cwEAAABADtq1a5fbEQ7bwg1VuvTpT9PGvtu/lX4xuqNLidCQfD6fKioq9t+urKxUKuU8ARbeYFZtUHjOzSrauCBjPd5hhCKjJstq1DbLyeoXfQuvonfhRfQtvMpLvdusWTO3IwANorKy0u0IwNcyTVPl5Z+fgFxVVSXLslxM5B2B1a+pdOYkmbXO66BWqKWi4+5VstOo7AcrAPQtvIrehRfRt/AqL/XuF6/f1Lf8OJrkCHDULSRpU2WNpv4nfQJIx6al+sW4Hl/xCCCPpBLSrDulBQ87a4YpjZooDb+GHUQBAAAA5KQ9e/borbfe0gcffKDNmzdr7969CofD6tmzp8466yx1797d7Yg5JWXZemD2hrSxxiV+/XiItyfNA3nPSim4+EmF3nlIRrLWWQ5WKDr8ZsV6nCUZhgsBAQAAABSaXF18BhyIZVn07tcwYtUKzb9DwU+fzViv63GWoiNulR0sl/i7zAr6Fl5F78KL6Ft4VaH2bsEvhKkvHHXrXbZt66bnPlY0nv4GcPe5x6ikiIn/yHOV66RnfiRtXuislbWRzvu91OnErMcCAAAAgIP16quv6oUXXlDLli3Vt29fNWrUSFu3btV7772n9957T1deeaWGDh3qdsyc8cJHO7VyZ03a2KUntlNZkMuEQK7y7fpU4VkTFdixJGO9rvuZig6/WXYpO40DAAAAAIDDF9i4QOGZN8gX2eqoWcEKRUbdqXi3U11IBgAAADgV/G+4J0yYoAkTJtTLczVv3lwXXXSRLrroonp5vlyXL8fcvvzxDs1dsTNt7Ny+LdWriS9vvsdC56UjwLIpsPIVlc64QWa82lGLdx6nmpPuk11SIfE6cAV9C6+id+FF9C28yku925BH3QLdunXTbbfdpt69e6eNL1u2THfccYeeeOIJDRw4UIFAwKWEuSMSS+rxNzamjXVtVqKzj23hUiIAB5SKqfS9x1Sy6LcyrKSzHG6lyMg7leg8xoVwAAAAAAAgbyRqFHpzqko++kvGcqzzOEVG38UmHAAAAMgpBb8QBocvH45Q2hNN6P6Z69LGmoUCunx4u7z4/pBZoR4Btl+yTqE37lLJx085SrYZUPTEG1R37A8kw+AY2xxS8H0Lz6J34UX0LbyK3kWhOuGEEzKO9+rVS3369NHixYu1YcMGde3aNcvJcs8f3tqiytr0yfTXjO4ov2m4lAjAV/FvXajwrEnyV67OWK895kLVDLlWdlFZlpMBAAAAAIB84t+6SGUzrpWvar2jZhWFFR1+i2JHnffZHBIAAAAgh7AQBgXtvlnrVFWXPgHkxpM6qSzISwP5ybdnpcr+c4X8e1Y4aqnyjto3/mGlWhzjQjIAAAAAbqiqqtKqVau0atUqrV69WqtXr1Z19WenRo4cOfKQTtHduXOnXn31VS1atEi7d++W3+9Xq1atNGTIEI0fP17FxcUN9W18JZ/Pl/b/hWxjZZ3+vmhb2tiIro01qGP5VzwCgBuMeLVK37pfJR/9NWM92biLImPuVrLNgCwnAwAAAAAAeSUVU+k7j6jkg9/JsJ2nzcfbDVFk7FRZZW1cCAcAAAB8PWb7o2DNXVWp15fvSRs7qWcTjerWxKVEQAOybRUve0bhebfJSNY5ynU9zlJ01B2yi8LZzwYAAADANZdcckm9PM/ChQv16KOPqra2dv9YLBbbv7hm5syZmjhxolq1alUvX+9g7Nq1Sx999JEqKirUoUOHrH3dXDVt7gYlLXv/bb9p6KpRHV1MBODLAutmKzznZvki2xw12/Srtt9PVDPgMsmf/YWFAAAAAPBlbDwCLzBN84C3C5Vv5ycqfe1q+Xd96qjZ/qBqT7xRsb7fl2GY4pWeffQtvIrehRfRt/AqevczLIRBQYrEkrpnxtq0sUZBn64d08mdQEADMuLVCs2+RcGVLzlqtr9EkZG3cYwtAAAAADVr1kxt27bV4sWLD+lxa9eu1bRp0xSPxxUMBnX22WerT58+isfjWrBggWbOnKmtW7fq7rvv1j333KOSkpIG+g4+l0wm9eijjyqRSOi73/1uwV74+69311dp7qrKtLFv9WupDhVBlxIB+CKjdrdC8ycruOLFjPVEi2MUGXO3Us16ZTkZAAAAAHy1iooKtyMAh6y8vMBPR04lpQXTpDn3SFbCWW87QMY5v1Vps24qzXo4fJWC71t4Fr0LL6Jv4VWF2rsshEFBenjuRu2MpH+gu3p0RzUNBVxKBDQM//YlKnvtSvmqNjhqyaZHqfqUR5Sq6OpCMgAAAAC54Pzzz1fXrl3VtWtXNW7cWDt27NBll112SM/x5JNPKh6Py+fz6eabb1aPHj321/r06aPWrVvrr3/9q7Zu3aqXXnpJF1xwgeM5/vznPyuRyPCL169w2mmnqXXr1hlrlmXp17/+tZYtW6axY8dqxIgRh/T95JukZevB2evTxipK/PrxkLYuJQKwn22reMULCs2fLLOu0ln2B1Vzwi9U2/cHksmlfAAAAAAAcAR2rZKev1Ta9J6zZgak0ROloVdKPq5BAAAAwBv4lysKzsIN+/Tckh1pY4M7lev03s1cSgQ0ANtWcPEfFXpzqowMu3jUHnOhoidOlPzs/gsAAAAUskyLUg7FqlWrtGzZMknS6NGj0xbB/NcZZ5yh2bNna/PmzXr11Vd17rnnyu9PvyT1+uuvKxaLHfTXHTx4cMaFMJZl6fHHH9cbb7yh4cOH65JLLjnE7yj/PL9kh1btqk0b+9mw9goXc1kQcJO5b7PCc29R0fq5GevxdkMUGX2XrPKOWU4GAAAAAADyimVJ7z0hvX6rlKx11lv2kc75jdTqmOxnAwAAAI4Av/HGYfP5fG5HOGR1iZTuen1t2lhJwNQtp3R1TMJB/jBN84C3841Ru0elr12ronWzHDWruJFqxk1Votsp8t4ruLAUWt8if9C78CL6Fl5F7yIXvPvuu/v/PHr06Iz3MU1TI0eO1FNPPaVoNKqlS5eqb9++aff5y1/+csRZ/nsSzLx583TiiSdqwoQJBf+6qK5L6jcLNqWNdW9eqrOOae5SIgCyLQWX/EWht++XkahxlK3iRoqeOEmxXudLhuFCQAAAAAA4OJWVzpMtgVxjmqbKy8v3366qqpJlWS4myi5z3yaVvn69ApvedNRsw1Rd/0tVd8KVkr9Y4jWdMwq9b+Fd9C68iL6FV3mpdysqKhrsuZn5j8PWkI3ZUO5+dZk2VtaljV1/ylE6upNzF1nkry+++eedtfOlZy+Rqrc6a+0GyTzv9wpXsJOoF+V13yKv0bvwIvoWXkXvwg3Lly+XJBUXF6tLly5feb/evXunPebLC2GO1BcXwQwdOlSXX355wS+CkaTfv7VZe2uTaWPXjO4gn8nkesANvj0rFZ41UYFtH2Ssx7qeqsiIW2WHWKwGAAAAIPelUim3IwCHzLKswuhd21bxsn8pNP9OmYmIo5wq76Tqcfcp2brf/x8ogL8TDyuYvkXeoXfhRfQtvKpQe5eFMCgYSzbt1RPz1qSN9evQWN8f0smdQEB9SiWleVOluVMl2V8qGtKwX0ijJ0m+gBvpAAAAAOSpTZs+O22kVatWBzw5tk2bNo7H1BfLsvT4449r3rx5Gjx48BEvgtm9e/dB3S/XT8pdv6dW//xge9rY6O5NdELnJi4lgls4QSwHpOIKLnxcwfcek5GKO8pWqIVqRt2hRLdTxH+dz9C38Cp6F15E38Kr6F0AADIzojsVnnOzitfOyFivPeZ7ig69XgqUZjkZAAAAUL9YCIOCkEhZuv6ZJbK+sD6gyGfq3vOOZRdUeF/V5s9OgVm/wFkLtZDO/a3UdUz2cwEAAADIa/F4XNXV1ZKkpk2bHvC+4XBYxcXFisViB73Q5GA988wzmjt3roLBoNq0aaN//etfjvsMGjRInTp1Oqjn+9nPfnZQ93v66acPJWbWXffiKiW/cCEk4DN029nHqqIi5GIq5AJOEMuyTQulFy+XdnySud7v+zJPulPhksZZjeU19C28it6FF9G38Cp6FwAAqWjVqwrPuUVmXaWjlgq3VmTsvUq0P9GFZAAAAED9YyEMDltlpfNDU676w1ub9Om26rSxHw1pq2ZFSU99Hzg8pmmmXfyuqqqSZVkuJqo/gTUzVPr6tTLr9jpqiQ7DFT35Qdmh5hJ97jn53LfIb/QuvIi+hVd5qXcrKircjoAGUFdXt//PwWDwa+8fDAYVi8XSHlcfdu7cuT/Ps88+m/E+LVq0OOiFMPlg/sqdmrFsR9rYD0/srI5NWQQDZE08Ks2aLL39uJyn90qq6Cx94xGp84isRwMAAAAAAPnFqNur0LzbFVzxYsZ63VHnKTr8FtnFZVlOBgAAADQcFsLgsKVSKbcjHJR1u2v12wUb08a6NSvR9we28sz3gPplWZb3/9unYgq9eZ9KFv/RUbINn2oGX6PafpdIhil5/XuFpDzpWxQkehdeRN/Cq+hdZFs8Ht//Z7//6y8x/fc+X3xcfZgwYYImTJhQb8/3+OOP19tzucG2bU39z/K0sWbhIl02pptLiYACtGqm9PJV0t4Nzprhk4ZeJo2aKAVKsh4NAAAAAADkl8D6uQrPmihfdLujZpU0VWT0XYp3OcmFZAAAAEDDYiEM8ppl27rztTVKpD7fddE0pFvGd1HAZ7qYDDh85t61ajT9Svl3LnXUUmVtVX3yNCVb93MhGQAAAIBCUlRUtP/PyWTya+//3/t88XG5qGnTpgd1v1w+Yfau07tq2px1mrVijyTpZ8PaK1kbUWWty8HgCi+dIOZ1Rt1elcy7U8XL/pWxnmzeWzXjpirVoo8UqZNUvydk5RP6Fl5F78KL6Ft4lZd6l5NyAQANIh5VaMHdKln694zlWNdTFBl1h+ySg7veCQAAAHgNC2GQ1575cLsWb46kjX2nfysd3TrsUiLgyBQvf1GhOTfLTEQdtVjXUxQZc7fs4kYuJAMAAABQaILB4P4/19V9/WTu/97ni4/zslw+galNo4CmfqO7Fm6o0gsf7dQZvZvmdF5kFyeINQDbVtGqVxSed7vM2t3Osq9INYOuVO1xP5J8AU7vPQz0LbyK3oUX0bfwKnoXyD6fz+d2BOBrmaZ5wNte5d/8rkpfu0a+fRsdNau4kWpH3aF4z7NkGoYL6XCk8rVvkf/oXXgRfQuvonc/w0IY5K1t+2L61bz0D3xty4t16YntXEoEHIFEjcLzbldw2TOOku0rUnT4Lao7+tsSFzEAAAAAZElRUZHKyspUXV2t3budE7+/KBKJKBaLSTr4E1dw5AZ0KNeADuVff0cAh82MbFNo7q0qXjsjYz3RZpCqR0+RVdE5y8kAAAAAoGFx2hG86IuniXlSok6adaf01mOSbGe961iZ33hUofK2CmU9HBqK5/sWBYvehRfRt/CqQu1dFsIgL9m2rSmvr1VNIv3465vHd1YwwK4k8Bbfrk9VNv0K+StXO2rJiq6qHv+IUs2OciEZAAAAgELXrl07LVu2TNu2bVMqlfrKnUC3bNmS9hgA8DzbUnDpP1T65r0y4xFH2SoKq2boDao7+luSUZi7cAEAAAAAgHq05QPpuUulnZ86a4GQdPKd0oAfsoEqAAAACgYLYZCXXl22W2+urUobO/uY5hrILqjwEttW8OOnFHpjsoxU3FGu6/U/ioz4pRQodSEcAAAAAEg9e/bUsmXLFIvFtGbNGnXv3j3j/T755JO0xwCAl5mVa1U2e5ICW97NWI91HqfoyNtlhVtlORkAAAAAAMg7qYQ0/wFp3n2SlXTWOwyRzv611KRL9rMBAAAALmIhDA7bV+3y6ra6RErT5mxIG2sWDujqMZ1zNjMalmmaB7ydi4y6KpXOvFFFq1511OxASNExU5Q46izR0fnLi30LSPQuvIm+hVfRu8gFgwYN0vPPPy9Jmj17dsaFMJZlae7cuZKkUCiko48+OpsRGwzXGOAV/LyoR6mEihc9oZJ3pmXctMQqaaaaUbcp0f10GYbBdZsjQN/Cq+hdeBF9C6+idwH3VVZWuh0B+Fqmaaq8/PNNc6uqqmRZlouJDo25e6VCr10t/46PHDXbV6TaIdcqdvyPJMMn8ZrMG17vWxQuehdeRN/Cq7zUuxUVFQ323CyEwWFryMY8Uv978SDd8MwSLd9eLUm665xj1aF1c5dTIVd88c0/J218V3rmR1LVBmetdV8Z5/9R4aZds58Lrsr5vgW+Ar0LL6Jv4VX0LtzQrVs39erVS8uWLdPs2bM1atQo9ejRI+0+L7/8sjZv3ixJOvXUU+X358flqFy+LgIcCD8vDtOWD6UXL5O2OSeeSJL6fkfm+LsULm2S1ViFgr6FV9G78CL6Fl5F7wLZl0ql3I4AHDLLsrzRu1ZKJYv/qNK3H8i4GUey+dGqHne/Uk17SLYkL3xPOGye6VvgS+hdeBF9C68q1N7Nj5kHwJcc176xXrp8mH4zd7XW7Ixo/NGt3I4EfD3LkhZMk2ZNluwMP5AGT5DG3Sr5i7MeDQAAAEB++vTTT7Vt27b9t/ft27f/z9u2bdOcOXPS7j9q1CjHc/zgBz/QLbfcong8rsmTJ+ucc87R0UcfrXg8rjfffFMzZsyQJLVu3Vpnnnlmg3wfANBg4jXSnLultx7LfL2mcQfpjGlSt7FZjwYAAAAAAPKPWbVBZTNvUGDLu46abfhUO+DnqhkwQfIFXEgHAAAA5A7Dtm3b7RBAQ7JtW4ZhuB0DOLDq7dJzP5HWzHHWSppIZz8u9Twl67EAAAAA5LfHHntMc+fOPej7P/300xnHFy5cqEcffVS1tbUZ661bt9bEiRPVqhUbVQDwkLXzpBevkCrXZiga0uCfSaNvkorDWY8GAAAAAG7btWuX2xGAr+Xz+dJOdq6srMzdnbJtW8VL/6HwgikyEjWOcrKiqyLj7ley5bEuhEM2eapvgS+gd+FF9C28yku926xZswZ7bk6EwWGrrKx0OwJwUEzTTDsOvaqqSpZluZgonX/9PIWmXy2z1nmhMNH2BEVPeVh2uJXEa66g5HrfAl+F3oUX0bfwKi/17hcvwCA/DRgwQPfff79eeeUVLVq0SHv27JHf71erVq00ePBgnXLKKSouzq/TLbkuAq/w0s+LXGHEqlQy/24VL/1HxnqqaU9Fx92jVKvjpZqEVMP7QX2jb+FV9C68iL6FV3mpd7kuAgD4OmZku8KzJ6povXPTIluG6vperOiQayR/0IV0AAAAQG5iIQwOW66uHAO+jmVZudG/qYRK35mm0kW/cZRsw1TNwMtVO2CCZPqkXMgLV+VM3wKHiN6FF9G38Cp6F4djwoQJmjBhQr08V/PmzXXRRRfpoosuqpfny3W83uBV/Lw4sKLV0xWae5t8NTscNdsMqGbABNX2/6nkK+J6TRbRt/AqehdeRN/Cq+hdAIAn2baKVr6k8NzbZMaqHOVUWTtVj5uqZNsTXAgHAAAA5DYWwgCAC8x9m1Q2/SoFtn/gqKVCLVV98kNcyAAAAAAAAMgSI7pT4Xm3qXj1fzLWE62OV2TM3Uo16Z7dYAAAAAAAIC8ZtXsUnnurile9krFe1/ubig6bJLsonOVkAAAAgDewEAYAsqxo1X8Unj1RZmyfoxbvOFrV46bKLmniQjIAAAAAAIACY9sqXvaMQgumZLxWYwdKFR18reqOufCzU3sBAAAAAACOUNHamQrPniSzZpejliptociYKUp0Gu1CMgAAAMA7WAgDANmSrFPojSkq+fhvjpJtBhQdeoPq+v5AMozsZwMAAAAAACgwZtUGhWffpKJNb2asxzuOVGTUZFllbbKcDAAAAAAA5CMjXq3Q/MkKLnsmYz3W/QxFRtwmu6Qiy8kAAAAA72EhDABkgW/PKpVNv1L+3Z86aqnyDqo++WElWx7rQjIAAAAAwOHw+TgZAt5gmuYBbxckK6niD/+okrcekJGsc5aDFaod+UvFe54twzDEqz376Ft4Fb0LL6Jv4VX0LuA+ro3AC3Lp54V/45sqff06+ao3O2pWsLFqRk9WoscZ4icacqlvgUNB78KL6Ft4Fb37GRbCAEBDsm0VL/uXwvNuk5GsdZTrup+p6Og7ZReVZT8bAAAAAOCwVVSwKyO8qby83O0I7tr2sfTiZdKWDzLX+5wv89R7FQo1Uyi7yXAABd+38Cx6F15E38Kr6F0g+7g2Ai9y5edFvEaaebv0zm8y17uPl/mNRxQua5XdXPAM/p0Dr6J34UX0LbyqUHuXhTAA0ECMeEShObcouOJFR832BxUZcZtivc6XDMOFdAAAAAAAAAUkUSfNu09aME2yks56o3bSGQ9KPcZnPRoAAAAAAMhTmxZKz/1U2r3KWSsKS6fcIx1/IfNGAAAAgMPAQhgAaAC+HR+p0fQr5ata76glm/ZU9fhHlGrSzYVkAAAAAAAABWb9W9KLl0u7V2auD7xEGnerVMyJvQAAAAAAoB4k49Lce6U3HpRsy1nvNFw66zGpomP2swEAAAB5goUwAFCfbFvBxU8q9Oa9MqyEo1zb57uKDpsk+YMuhAMAAAAA1JfKykq3IwAHxTTNtOPQq6qqZFkZJmDko1i1St6cquCSv2Qspyq6KjruXqXaDJBqklINr+tcUdB9C0+jd+FF9C28yku9W1FR4XYEoEFwbQRe4MbPC3PXpwq9drX8Oz9x1GxfsWpPvEGx434gyZR4HSEDL/07B/giehdeRN/Cq7zUuw15XYSFMDhsPp/P7QjAQTFN84C364tRu0elr1+norUzHTWrqEw146Yq0f1U8crBwchW3wL1jd6FF9G38Cp6F3BXKpVyOwJwWCzLKoj+DaybrfCcm+WLbHPUbNOv2n4/Vc2ACZK/WCqAvw+vK5S+Rf6hd+FF9C28it4Fso/XHLyoQX9eWCmVfPCESt+ZlnHj1ETLvoqMu0+piq6SZUviNYSDw79z4FX0LryIvoVXFWrvshAGh42da+BVX1wFWW/WvSH96xKpeouz1m6gzPP+oDBH2uIINEjfAllA78KL6Ft4Fb0LAIBk1O5WaN6dCq58KWM90eJYRcbcrVSzo7KcDAAAAAAA5Ctz71qVzbhOgW0fOGq26VfNwMtV2/9SyWSqHgAAAFBf+Nc1ABwJKyXNu0+ae69kZzhWbNgvpNE3Sb5A9rMBAAAAAAAUCttW8fLnFXrjLpl1lc6yP6joCVerru8PJJPzegEAAAAAQD2wLQU/+ptCb94rI1nrKCeb9FD1Sfcr1fxoF8IBAAAA+Y2FMABwuPZt+ewUmPVvOGuh5tI5v5W6jc1+LgAAAAAAgAJi7tus8JybVbRhXsZ6vN1QRUbfJau8Q5aTAQAAAACAfGVWb1F41o0q2rjAUbMNU7XHX6KaE66UfMUupAMAAADyHwthcNgqK507KwK5yDRNlZeX779dVVUly8pwessh8K+dpdBr12TcYTTRfpii4x+UHWoh8TrBYWqIvgWygd6FF9G38Cov9W5FRYXbEQAA+chKKfjRXxR6+wEZiRpnubiRoifepFiv8yTDcCEgAAAAAADIO7at4uXPKTTvDpnxakc5Vd5B1ePuU7L1ABfCAQAAAIWDhTA4bKlUyu0IwGGxLOvw+zcVV+jN+1Sy+H8dJdvwqWbw1art9xPJMCVeI6hHR9S3gIvoXXgRfQuvoneB7PL5fG5HAA6KaZoHvO1V5u6VCs24Xv5tH2Ssx7udpppRt8kOtRCvVu/J175F/qN34UX0LbyK3gUAuMGo2aXwnJtVvOb1jPXaYy5UdMj1UlEoy8kAAACAwsNCGAA4SObedSqbfqUCOz921FJlbVR98jQlW/d3IRkAAAAAINs46Qhe9cXTxDwpGZfeeFCad79kJZz1cCvp9AdU1OsMFWU/HRqI5/sWBYvehRfRt/AqehcA0NCKVk9XeM7NMmv3OGqpUCtFxt6jRIfhLiQDAAAAChMLYQDgIBSteFHh2bfITEQctViXkxUZc4/sIBfYAQAAAAAAGsymhdILl0k7l2Wu97tIOukOqaRxVmMBAAAAAID8ZcT2KTTvdgWXP5+xXtfzbEVH3Cq7uFF2gwEAAAAFjoUwAHAgiRqF592h4LL/c5RsX5Giw25SXZ/vSobhQjgAAAAAAIACEItIsyZL7/xGku2sN+kinfmI1JldVwEAAAAAQP0JbJiv8Mwb5Ytuc9SskiaKjJqseNfxLiQDAAAAwEIYAPgKvl3LVTb9CvkrVzlqycZdVH3KI0o16+VCMgAAAACA2yorK92OABwU0zRVXv75KbZVVVWyLMvFRIfGv36eSmdOlK96s6NmGz7F+v1YtYN/IfmDEq/LvOH1vkXhonfhRfQtvMpLvVtRUeF2BADAoUrUKLTgHpV8/LeM5ViXkxQZNVl2abMsBwMAAADwXyyEAYAvs20Fl/5dofmTZaRijnLdUecpMvI2KVCa/WwAAAAAgJyQSqXcjgAcFsuyPNG/Rm2lQm/cpeDy5zLWk816q3rM3Uq16PPZgAe+Jxw+r/Qt8GX0LryIvoVX0bsAgPri37pQZTOuk69qg6NmFZUpOuJWxXqeLRlG9sMBAAAA2I+FMADwBUZsn8KzJql49auOmhUIKTrqjs8uaAAAAAAAAKD+2baKVv1b4Xl3yKzd7Sz7ilUz6ErVHvdDyRdwISAAAAAAQJJ8Pp/bEYCvZZrmAW+nScZU8vaDKn7/dzJkO8qJ9sMUPWmq7LI2ovvRkA6pb4EcQu/Ci+hbeBW9+xkWwgDA/+ff9oHKpl8pX/VmRy3Z/GjtG/+wrMadXUgGAAAAAACQ/8zIVoXm3KridTMz1hNtTlD1mLu4PgMAAAAAOaCiosLtCMAhKy8vz1zYulh67lJpxyfOWqBUOukOBQb8SI0LdIIh3PWVfQvkOHoXXkTfwqsKtXdZCAMAtqWSRU+o9J0HZVhJR7m278WKDr1O8hW7EA4AAAAAACDP2ZaCS/+h0gX3ykxEHGWrKKzo0BsVO/qbksGEEwAAAAAAUE9SSemNh6S590gZ5ouo/QnS2Y9LTbtmPxsAAACAA2IhDICCZtTsUtnr16po43xHzSpurMi4qYp3HutCMgAAAAAAgPxnVq5V2exJCmx5N2M91nmcoiNvlxVuleVkAAAAAAAgr+1cIT33U2nLImfNVySNniQNvUIyfdnPBgAAAOBrsRAGQMEKbFygstevllmzy1FLtBmo6pMfkhVu7UIyAAAAAACAPJdKqOSD36v0vUdkpOKOslXSVJGRtyne9VTJMFwICAAAAAA4kMrKSrcjAF/LNE2Vl5fvv11VVSUrlVTxh39UyYKpMlIxx2OSzXopOv4hWc2Okqr2ZTMuIOkr+tayXEwEHBx6F15E38KrvNS7FRUVDfbcLITBYfP52PEA3mCaZvptO6Xg2w8ouPBxGbLTarYM1Z1wheoGXS7D9Isuh1scfful20CuonfhRfQtvIreBQB4lW/HRyqbNVH+Xcsy1uuOOk/RYZNkBxtnNxgAAAAA4KClUim3IwCHbu96hV67VkWb33GUbMNUbf9LVTPw8s9OhKHHkSMsy+I9F55E78KL6Ft4VaH2LgthcNgacoUW0GD2blD5cz+WNjovaqistYxzn1BJ5+EqyX4y4IC+uHoX8BJ6F15E38Kr6F0gu9ggBF6RUwsnE7UqeWeaihc9IcN27kqVatRONWPuVrLjcLG8s7DlVN8Ch4DehRfRt/AqehcAcEhsW/rgL2r0n4ky4hFHOdm4iyLj7lOy1XHZzwYAAADgsLAQBkDh+ORF6cXLpLoqZ637ydLZj0uhZtnPBQAAAADwHDYIgVe5tnBy7TzpxSukyrXOmmFKJ/xMvjE3qawolP1syHks+IVX0bvwIvoWXkXvAgC+UvW2z65JrJwuI0O5tu8PFB18rRRgy1QAAADAS1gIAyD/Jeqk126S3vu9s2YGpHG3SYN/LrFTFAAAAAAAQP2q3Su9fou06M+Z6y16S9/4ldSuf1ZjAQAAAACA/BdY8bI05xapttJRS5W1UWTsVCXaDXEhGQAAAIAjxUIYHLbKSueHRCDXmHtWKfzq5fLtWuaopco7KHrKo0q16itVZTglBnCRaZppu5dVVVXJsiwXEwEHh96FF9G38Cov9S4nZwBAgfrkRemVa6XIdmfNVySNuE468SrJX5T1aAAAAAAAIH8ZtZUKz7tNxStfzliv6/U/ig6/SXZRWXaDAQAAAKg3LITBYUulUm5HAL6abav4038pPPc2GclaRznW/QxFRk/+7KIGvQwPsCyL9114Er0LL6Jv4VX0LpBdbBACr3Bj4aQR3aHSObeqaNWrGevJ1v0VHXuPrKbdpeqopGiD5oH3eGnBL/BF9C68iL6FV3mpd9kgBACyK7ButspmTZRZs9NRs0qbKTJ6iuKdx7qQDAAAAEB9YiEMgLxjxCMKzb1VweXPO4v+EkVH3qbao86TDCPr2QAAAAAA+YGFZ/CqBl04adsqXvZ/Ci24W2Zsn/NrB0KqGXKt6o65UDJMNifBQWPBL7yK3oUX0bfwKnoXAGDEIwq9cZeCnzyd+Q69z9a+Yb9Usqg8cx0AAACAp7AQBkBe8e34WI2mXyFf1XpnsUVv6fw/Kh5oyUQLAAAAAACAemRWrVd49k0q2vRWxnq840hFRk2WVdYmy8kAAAAAAEC+829+R2UzrpevepOzGGwsnf6A1Oc82Xv3Ml8EAAAAyBMshAGQH2xbwSV/UmjBvTKsuLM+4IfS+ClSoESqrMx+PgAAAAAAgHxkJVXy4R9V+u40Gck6ZzlYoejwWxTr8Q1O5wUAAAAAAPUrWafQWw8ouPiPMmQ7yomOoxQ47zdSo9YuhAMAAADQkFgIA8DzjNpKhWfeoOJ1Mx01q6hMNePuUXjQhS4kAwAAAAAAyF++XcsUnjVRgR0fZazX9ThL0eE3yS5pmuVkAAAAAAAg3/m3L1F4xrXyV6521KxASNFhk5Q85juqaNTEhXQAAAAAGhoLYQB4mn/zuyp77RfyRbc5aomWx6l6/DQZFZ2yHwwAAAAAACBfJWMqfe9RlXzwhAwr6Sinwq0VGXWnEp1GuxAOAAAAAADktVRCpQt/pZKFj8uwU45yos0gVY+bKqtRe/k4nRYAAADIWyyEAeBNVkolC3+t0vcekWFbjnJNv5+q5oRfSL6AfC7EAwAAAAAAyEf+Le8pPGuS/HvXOGq2DNUdc6FqhlwruyjsQjoAAAAAAJDPfLuXq2zGdfLvXOqo2b4iRQdfq7rjLpYM04V0AAAAALKJhTAAPMeMbFf49V+oaPM7jppV0kTV4x5QouMIF5IBAAAAAADkJyNerdI371PJx3/LWE9WdFVkzBQlWw/IcjIAAAAAAJD3rJRKPvyDSt9+SIYVd5QTzfsoctL9SjXp7kI4AAAAAG5gIQwATwmsm62yGdfLrNvjqMXbDVX1SQ/IDrVwIRkAAAAAAEB+CqydpfDcW+SLbHPUbNOv2v6XqmbAzyVfsQvpAAAAAABAPjOr1qtsxvUKbF3oqNmmXzUDLlNt/0slX8CFdAAAAADcwkIYAN6Qiqv0rftV+uEfHCXb8KnmhKtU2++nkulzIRwAAAAAAED+MWp2KTz/ThWvfDljPdHiWEXG3KNUs55ZTgYAAAAAcJvPx+/m0cBsW0Uf/U2lb0yRkahxlFNNuit68oNKtTxGX9WNpmke8DaQi+hbeBW9Cy+ib+FV9O5nWAgDIOeZVetVNv1KBXZ85Kilwq1VPX6akq0HuJAMAAAAAAAgD9m2ipc/r9D8yTJje51lf1DRwdeo7tiL2JQEAAAAAApURUWF2xGQz/ZtkV64TFo9M0PRkIZeJt/om9UoEDykpy0vL6+ffEAW0bfwKnoXXkTfwqsKtXdZCAMgpxWtfFnhWTfJTEQctViXkxQZc4/sYOPsBwMAAAAAFDR2PYVXHOqOUOa+TSqdNUmB9fMy1hPth6lm7BRZ5R2+crdV4Eixkxm8it6FF9G38Cp6FwDylG1LS56WXr1Oqqty1is6SWc/LnUcmvVoAAAAAHILC2EA5KZErcLz71Dwk6cdJdssUnTYJNUdc6FkGC6EAwAAAAAUOnY9hVd95Y5QVkp69wlp5h1SIuqsBxtL46cocNx3VM71GGRZoe5kBu+jd+FF9C28it4FgDwQ3SW9fJW07KXM9f4XSydPlorDWY0FAAAAIDexEAZAzvHtXq6y6VfKv2elo5Zs3FnV4x9RqnlvF5IBAAAAAADkoR3LpBcvlza9l7ne+2zp1KlSWcusxgIAAAAA5K7Kykq3IyCPBFa/ptKZk2TW7nLUrFBLRcfdq2SnUVJNQqo5+N4zTTNtsWRVVZUsy6qPyECDoW/hVfQuvIi+hVd5qXcbcoNJFsIAyB22reKl/1B4/p0yUjFHue6ocxUZcZtUFMp+NgAAAAAAgHyTjEtvPCjNu1+yEs56WWvp9Aeko07PfjYAAAAAQE5LpVJuR0AeMGLVCs2/Q8FPn81Yr+txlqIjbpUdLJfqoecsy6J34Tn0LbyK3oUX0bfwqkLtXRbC4LD5fD63IyCPGLEqlc6cpKKV/3bU7ECpakZPVrzXuTqcrjNN84C3gVxE38Kr6F14EX0Lr6J3AXex6ym84qt2hPJtXaTQzBvl270i4+Nifb6t2mE3yi4ul+h3ZJmXdjIDvojehRfRt/AqL/VuQ+58CgBeFti4QOGZN8gX2eqoWcEKRUbdqXi3U11IBgAAAMALWAiDw8YFO9SbTQulZy6W9m5w1lodK+P8PyrUrJvq6xyYL14UB7yCvoVX0bvwIvoWXkXvAtlViDvqID9YddUKLpiq4JI/y5DtqKfKO6p6zN1Ktj3h/w/Q63Bfoe5kBu+jd+FF9C28it4FAA9J1Cr05r0q+egvGcuxzuMUGX2X7NJmWQ4GAAAAwEtYCAPAPZYlvfWoNPMOyUo66ydcKp10h+Qvzn42AAAAAACAfLNqhhq9cIV81ZsdJdvwqfb4H6tm0BWSP+hCOAAAAAAAkO/8WxepbMZ18lWtc9SsorCiw29R7KjzJMPIfjgAAAAAnsJCGADuiOyUnvuptHqms1ZSIZ31a+mo07KfCwAAAAAAIN/U7JH+M1Fa8g/5MpSTzY9W9egpSrXok/VoAAAAAACgAKRiKn3nEZV88DsZtuUox9sNUWTsVFllbVwIBwAAAMCLWAiDw1ZZWel2BHiUf8MChaZfJbNmp6OWaDNQ0VMell3WRqqnHjNNU+Xl5ftvV1VVybKcF1aAXELfwqvoXXgRfQuv8lLvVlRUuB0BAAqTbSuw/EVp3h1SzS5n2VesmkFXqvb4H0kml4oBAAAAAED98+1aprLXr5V/96eOmu0PKjr0BtUdc6FkmC6kAwAAAOBV/HYThy2VSrkdAV5jJVX67sMqWfi4DNlpJVuGagdOUM3Ayz+beNGA/WVZFv0Lz6Fv4VX0LryIvoVX0bsAgC8yI1sVnvNLFa2blbGeaHOCqsfcJatx5ywnAwAAAAAABcFKqmTR71T67iMyrISjnGh5vKrH3SergmsTAAAAAA4dC2EAZIVZvUVlr12lwNb3HbVUaQtFTn5QiXZDXEgGAAAAAACQR2xLwY//rtI3p8pMRJzlojJFht6g2NHfZKdVAAAAAADQIMzKtSqbcZ0C2z9w1Gwz8NkJtf0u4YRaAAAAAIeNTxMAGlzRmtcUnnmjzFiVoxbvOFLVY6fKLm3mQjIAAAAAAID84atco/DsSQpseS/zHXqerqphtyhZ0jy7wQAAAAAAQGGwLQWX/EWht6bKSNY5ysmmR6n6pPuVatbLhXAAAAAA8gkLYQA0nGRMoQV3q+SjvzhKtulXzZDrVHvcD9l9FAAAAAAA4EikEir54AmVvveojFTcWQ81l067X+p9luy9e6VUKusRAQAAAABAfjOrtyg883oVbXrLUbMNU7X9fqqaQZdLvmIX0gEAAADINyyEAdAgfJVrVDb9Svl3feKopRq1V/X4h5Vs2deFZAAAAAAAAPnDv32JwrMnyb9rWcZ6rNf5Kj7zPqm0SZaTAQAAAACAgmDbKv70XwrNv1NmPOIop8o7qXrcfUq27udCOAAAAAD5ioUwAOpd8afPKjz3VhmJGkct1u00RUZPkV1c5kIyAAAAAACAPJGoVem701Ty4f/KsC1HOdWovSKj75LVaYSKSytcCAgAAAAAAPKdEd2p8JybVbx2RsZ67THfU3To9VKgNMvJAAAA/l97dx4mV1nmjf+u6r3TSaezQEJAloQ1LAZCBNmSIIpsosOLy7jhDAqDo76jjqIyqKDA4IaOg9tPUREQFZGwuAAhQAATCPsWEvYsLEmn03unu+r3B2/aFN1JOqHTp0/V53NdXlPnec45dXfynaRyU895gGJnIQwweLpao27ef0X1k9f2mcqXV0fLEf8VnfucGpHJDH1tAAAAAEWi4sW7om7ul6Os6fk+c/lMNjoOOC1a3/KZiIraKBv68gAAAIASULnkpqi77ZzIdjT2meupmxgtR18U63Y6LIHKAACAUmAhDDAoyl55NEb9+VNR1vRsn7nuMbtH8zu+Hz1j9xj6wgAAAACKRKajKUbcdWFUP3Z1v/PdY/eMltkXRPf2BwxxZQAAAECpyHQ0xYjbvxrVi6/rd75jr3+K1iPOiXzVyKEtDAAAKCkWwgBvTD4f1Q/9KkbMvzAyua4+0+1T3xeth38loqImgeIAAABg2ygrs88GQ6tiyZ+jdu45kW17pc9cvqwyOmb8e3Qc9ImIssqCXWCy2WzBua8/huFIbkkr2SWN5Ja0kl2AZFQ8Ny/qbj07ylpf6jOXqxkbLbO+EV27HZNAZQAAQKmxEAbYapn2xqi79YtR9czNfeZylXXRMuub0bX78QlUBgAAANtWQ0ND0iVQKppXRtz4uYjH5/Q/v9MhkTnp+1Ezfs8YyGNI6uvrB7U8GApyS1rJLmkkt6SV7AJsY12tMWL+BVHz6JX9TndOPjZaZn498jVjh7gwAACgVFkIA2yV8uULY+RfPxNlLSv7zK3b/oBofsclkRu1UwKVAQAAABSBfD5i0a8i/npORGdT3/nKuoi3fTVi+r9EePo1AAAAsI2UL18YI2/+fJStfaHPXK5qVLQe+dXo3OOkiEwmgeoAAIBSZSEMsGVyPVFz36VRu+CSyORzfabbpp0ebYf8R0RZZQLFAQAAABSBVUsj5nw64tk7+p/f/e0Rx38nYrSHkDoybS0AAF+HSURBVAAAAADbSHdn1P79O1Fz//8Xmcj3me560xHRMvuCyNVNTKA4AACg1FkIAwxYtuWlqPvbf0Tlsnv6zOVqxkTz274V63Y+KoHKAAAAYGg1NjYmXQLFKNcdVff/PGru+U5kujv6TteMibYjz411e54Ukc9EDCCH2Ww26uvre4+bmpoil+v7cBMYTuSWtJJd0khuSas0ZbehoSHpEgC2WNnLD8fImz8f5auf6jOXr6iN1sPOjo6p77cLDAAAkBgLYYABqXjuthh58+cj2766z1zXjodGy9u+Hbm67ROoDAAAAIZeT09P0iVQZMpeeSzqbj07Kl55pN/5jj3eFa1HfCXyNWMi3sAX/HK5nPySOnJLWskuaSS3pJXswtArKytLugS2hZ51Ub3wh1G98H8ik+vuM71uh4Oj7ZhvRW70zpGGBGSz2U0ew3Akt6SV7JJGcktaye5rLIQBNq2nK2rv+XbU3v+zPlP5TDba3vKZaD/wjIhsGlocAAAAAMNMd2fULvxB1Cz6SWTyfb+411M3MVpmnR/rdp459LUBAADARtjtqAi9/ETEtWdELL+/71xZZcTsc6Li0LOiPsXfD9lwNzFIC7klrWSXNJJb0qpUs2shDLBR2abnY+RfPxMVLz3YZ66nbkI0v/2S6N5hegKVAQAAAKRf+fKFUXfr2VG+5pk+c/nIRMd+H4y2Qz8X+cq6BKoDAAAASkIuF3HP/0bc8vWIns6+8xMPiHj3jyO223voawMAANgIC2GAflU+dUPUzf1SZLta+sx17vq2aDn6oshXjx76wgAAAABSLtPVHLV3/XfUPHJFv/PdDVOiZfY3o3viQUNcGQAAAFBSVj8T8aezIp6b33cuUxZx5Ocijvx8RFnF0NcGAACwCRbCAIXWtUfdHedF9WO/7TOVz1ZG62FfjI79PxyRySRQHAAAAEC6VT5zS4y47b+irHVln7l8tjzaDzoj2qb/W0RZVQLVAQAAwMA0NjYmXQJvRD4flY9cGbV3nB+ZdW19pnsaJkfr278TPRMOiFjb9wGqaZHNZqO+vr73uKmpKXK5XIIVwebJLWklu6SR3JJWacpuQ0PDNru3hTBAr7JVi2PkXz4V5auf6jPXPXrXaH7HJdEzfmoClQEAAACkW6bt1ai74+tR9dQN/c6v2/6AaJl1QfSM23OIKwMAAIAt19PTk3QJbKVsy0tRN/fsqHxuXp+5fGSi44DTovXQz0aUV0cU2e9zLpeTXVJHbkkr2SWN5Ja0KtXsWggDROTzUfXYb6PujvMi093RZ7pjz3dHy1Ffi6gckUBxAAAAACmWz0fVk3+MEXd8I7Kda/pOl9dE6yGffW0H3mzZ0NcHAAAAlIZ8PiqfmhN1874a2c6mPtM9I3eM5rf9d3RPeksCxQEAAGwZC2HYamVl/sN8UehcGyNu/VJULr6+z1S+ojbaZp0XXXv/U6T5dzubzW7yGIYjuSWtZJc0klvSSnYBhr/s2hej7ravROXzd/Q737XT4dEy6/zIjdppiCsDAAAASkmmfXXUzTs3qpbc2O98xz7vjdbDvxT5yrohrgwAAGDrWAjDVmtoaEi6BN6oF++L+P1pEWue6zs3Yb/InHJZjBg3JYptH5j6+vqkS4AtJrekleySRnJLWskuwDCS64nqh38VI+75TmTWtfWdrqqP1sO/HJ17vScik0mgQAAAAKBUVDxza4yce3Zk217tM9dTu120zP5mrNtlVgKVAQAAbD0LYaAU5XIRd/9PxC1fi8h1952f8YmIY74eUVE99LUBAAAApFjZqsVRd+vZUfHSA/3Od045LlqOPDfyteOGtjAAAACgpGS6mmPEHd+I6sd/1+985+4nRMuRX418jQfhAgAA6WMhDJSallcirj0jYsnNfeeqR0ec/L8Rex0/5GUBAAAApFpPZ9Te+6Ooue/SyOTW9Z0esX20HvW16NrtmASKAwAAAEpJxYt3R90t/xllzcv7zOWqRkfLzK9H1+6+GwIAAKSXhTBstcbGxqRLYAuVv3BXjPjLZyLb+nKfue6J06Pl2EsiP2pSRJH93maz2aivr+89bmpqilwul2BFsHlyS1rJLmkkt6RVmrLb0OCJgkBxK1+xKOrmfinKVz/V73z71PdH21u/EPmqkUNcGQAAAFBS1rXHiLsvjpqHftnvdNfOs6J59jcjP2K7IS4MAABgcFkIw1br6elJugQGKtcdtQu+HzX3/m9kIl8wlY9MtE//t2ib8amIbHlECfy+5nI5+SV15Ja0kl3SSG5JK9kFSEBXa4y459tR/dCv+vRcIiJ66neJ5tnfjO5Jb0mgOAAAAKCUlK98IOpu/nyUr3m6z1yuYkS0HnFOdO59SkQmk0B1AAAAg8tCGChy2eblMfKv/zcqVtzbZy5XOz6aj/l2rNvpsAQqAwAAAEiviuduj7rbvhJlzcv6zOUzZdE+7fRom/HvEeXVCVQHAAAAlIyerqhd+IOoue9Hkcn33TW8a9JbouXo/47cqB0TKA4AAGDbsBAGiljl03+Lulu+ENnOpj5zXW86Iprf9q3I145LoDIAAACAdMq0r44Rd34jqp+8tt/57vFTo3n2BdEzfurQFgYAAACUnLJXn4yRN38uyl99rM9cvqwqWt/6+ejY/yMRmWwC1QEAAGw7FsJAMerpjBHzL4yah37VZyqfLY+2Qz4b7dP+VaMDAAAAYKDy+ah8ak7U3XFeZNtX950uq4q2t3w62t/8LxFZbVcAAABgG8r1RM39P4vav38vMrmuPtPrtj8gWt52cfQ0TE6gOAAAgG3Pf5GFIpNtfCZG/fXTUf7Ko33mekbuGM3vuCS6J7x56AsDAAAASKls8/Kom3duVD57a7/zXZPeEi2zvhG50bsOcWUAAABAqcmueTZG3vz5qFi5qM9cPlsebQf/e7QfdIYHdQAAAEXNv3igiFQ98ceom/dfkVnX1meuc8px0TLrG5GvGpVAZQAAAAAplM9F9SNXRO1dF0d2XUuf6VzlyGg97IvRuc+pdt4FAAAAtq18Lqof/k2MuOuiyHS395nuHrNHNB/zregZPzWB4gAAAIaWhTBQDLpao+72r0b1E9f0mcqXVUXLkf8Vnfu8NyKTGfraAAAAoAiVlZUlXQLbWHb1kqi95eyoWL6w3/muyW+PtpnnRb5u+xjOachms5s8huFIbkkr2SWN5Ja0kl2g1GSbl0fdrV+Myhfm95nLZ7LRPu30aHvLpyPKqhKoDgAAYOhZCAMpV/bKYzHyL5+K8jXP9JnrHrN7NL/jkugZu2cClQEAAEDxamhoSLoEtpWedRHzvxcx778jerr6zo/YLuK4i6Nyn3dFZQofOlJfX590CbDF5Ja0kl3SSG5JK9kFilY+H1VP/jFG3P71yHY195nuqX9TNL/t4uieOD2B4gAAAJJjIQykVT4f1Q9fHiPmfzMy/Xwpo2Of90bLEedEVNQkUBwAAABACi1bFHHdv0e89Ej/89M+GHHMeRG1Y4a2LgAAAKDkZNpejbrbvhJVT/+t3/n2/T4YrYf+Z0TliCGuDAAAIHkWwkAKZTrWRN2tX+y32ZGrrIuWWd+Irt1PSKAyAAAAgBTqaouY+42Ie/43Ip/rO9+wS8SJl0TsNnOoKwMAAABKUOXSv0TdbV+JbPvqPnM9IyZEy9EXxro3HZFAZQAAAMODhTCQMuUr7o2Rf/lMlLWs6DO3brv9o/kdl0Su/k0JVAYAAAClo7GxMekSGCTlz8+P2lvPjrKm5/vM5TPZ6Hzzx6L90P+IqKiNSOHvezabjfr6+t7jpqamyOX6WewDw4jcklaySxrJLWmVpuw2NDQkXQKQIpnOtTHi9q9F9ZPX9jvfsefJ0XrkuZGvGjW0hQEAAAwzFsJAWuR6ombRj6L275dEJt/TZ7pt2r9G2yGfjSirTKA4AAAAKC09PX3/bU66ZDqaYsT8C6L68d/1O989ds9omX1hdG+//2sDRfJ7nsvl5JfUkVvSSnZJI7klrWQXKAYVz98Zdbd+IcpaVvaZy9WMiZaZ50fX5HckUBkAAMDwYyEMpECm9eUY+bfPRuWLd/WZy1WPiea3/Xes22VWApUBAAAApE/lkj9H3e1fjWzbK33m8tnKaJvxyWifdroHjgAAAADb3rq2GDH/wqh55Df9Tnfudky0zDw/8rXjhrgwAACA4ctCGBjmKp6bFyNv/lxk21f3meuadEi0HPOdyNVtn0BlAAAAAOmSbXkpRtz+1ah6+q/9zq+beFC0zL4gehomD21hAAAAQEnKdDZH/TXvjfJVT/aZy1WOjNYjz43OPU+OyGSGvjgAAIBhzEIYGK561kXt378TtYt+0mcqn8lG24xPRftB/xaRLUugOAAAAIAUyeej6rGrY8T8CyLb1dxnOlcxItre+p/Rse8HIjLZBAoEAAAASlHtwh/0uwima6fDo2X2BZEbuUMCVQEAAAx/FsLAMJRd+0KM/MtnouKlB/rM9dRNiOa3fy+6dzh46AsDAAAASJnsmmejbu6Xo3LZPf3Od+08M1pmnueLJQAAAMCQyrQ3RvWjVxaM5ctrovWwL0bHvv9sFxgAAIBNsBAGhpnKJTdG3a1nR7arpc9c565vi5bZF0a+piGBygAAAABSJNcdNQ/8PGr//r3I9HT2na4eEy1HnhNdu5/oiyUAAADAkKt++NeRWdfWe5zPlMWaU/4QPeP2TLAqAACAdLAQBoaL7o4Yccf5UfO6p31EROSzldF62BeiY/+P+GIGAAAAwGaUvfJYjLz1i1H+yqP9znfseXK0Hv7lyNeMGeLKAAAAACKiqzVqHvxlwVDn7idYBAMAADBAFsLAMJBpezXq//ThKF/1ZJ+5nvpdYu07Lome7fZNoDIAAACAFOnujNqFP4iaRT+JTL6nz3TPyB2iZeZ5sW7nmUNfGwAAAMD/U/3YbyPbuaZgrP2gTyRTDAAAQApZCAPDQN3cL/e7CKZjz5Oj9aivRb6yLoGqAAAAANKjfNmCqJv7pShf80yfuXxkomP/D0XbIZ/VZwEAAACS1dMZNff/fwVDnbscHT1j7QYDAAAwUBbCQMLKXn4kqp65uWAsX14TLUd9LTr3/qeEqgIAAABIh0xnc9TedVHUPHplv/PdDVOiZfYF0T3xwCGuDAAAAKCvqieujbLWlQVj7QedkVA1AAAA6WQhDCSsduEPCo5zVaOj6ZTfRU/DbglVBAAAAJAOlc/cHCNuO7fPl0ciIvLZimg/6Ixom35mRFlVAtUBAAAAvE6uJ2oX/aRgaN0Ob/EADwAAgC1kIQwkqOzlh/vsBtN+4OkWwQAAAABsQqbt1ai7/WtRteTGfufXbX9AtMy+IHrG7jnElQEAAABsXOXSP0dZ07MFY212gwEAANhiFsJAgmoXvG43mOqGaN/vQwlVAwAAADDM5fNR9cQ1MeLOb0a2c03f6fKaaD3ks9Gx/4cjsmVDXx8AAACUiLIy/+7eYvl81C76UcFQ9/ipkdt1ZpRlMsnUVOSy2ewmj2E4klvSSnZJI7klrWT3NRbCQELKX3ooqp69pWCsfdrpEZUjEqoIAAAAYPjKrn0x6uZ+JSpfuKPf+a6dDo+WWedHbtROQ1wZAAAAlJ6GhoakS0ifp26OeOWxgqHymZ+PhjFjEiqo9NTX1yddAmwxuSWtZJc0klvSqlSzayEMJKR24et3gxkT7ft9MKFqAAAAAIapXE9UP/SrGHHPtyPT3d53uqo+Wg//cnTu9Z4IT08FAAAAhqs7v1N4PHZKxN4nJVMLAABAylkIAwkof+mhqHz21oKx9gP/1W4wAAAAABsoW/Vk1N36pah46YF+5zt3Pz5ajvivyNeOG9rCAAAAALbE83+PeG5+4dhhn4nIliVSDgAAQNpZCAMJqF3w/YLjXPWYaN/XbjAAAAAAERHR0xm1914aNff9KDK5dX2nR0yI1plfi65d35ZAcQAAAEBjY2PSJaTKiLkXReUGx7m6idH0prdH+HXcprLZbNTX1/ceNzU1RS6XS7Ai2Dy5Ja1klzSSW9IqTdltaGjYZve2EAaGWPlLD0Xlc3MLxuwGAwAAAPCa8hWLou7Ws6O8cUm/8+37fiDaDv3PyFeNHOLKAAAAgPV6enqSLiE1yl59MiqfuaVgrO3N/xI9URbh13FI5XI52SV15Ja0kl3SSG5Jq1LNroUwMMT63Q1mvw8lVA0AAADAMNHVGiPu+VZUP/TryES+z3RP/S7RPPub0T3pLQkUBwAAALB1ahb9qOA4V90QHVPfm1A1AAAAxcFCGLZaWVlZ0iWkTtnKB/rsBtMx/RNRVu0JpttSNpvd5DEMR3JLWskuaSS3pJXsAsWk4vk7om7ul6KseXmfuXymLNoP/Hi0HfzJiPLqBKoDAAAA2DrZpuej6qnrC8ba9/9IREVtQhUBAAAUBwth2GoNDQ1Jl5A+N/5v4XHtuKg98t+jtnJEMvWUqPr6+qRLgC0mt6SV7JJGcktayS6QVhXPzo1RN3w8Mvlcn7nu8VOjefaF0TN+nwQqAwAAAHhjahb9pKDnkasYER37fyjBigAAAIqDhTAwVF68L+KpvxaOHfbpCItgAAAAgFK1rj3q5v1Xn0Uw+bKqaHvLZ6L9zR+LyGphAgAAAOmTaX05qh//Q8FYx9T3Rb56dDIFAQAAFBH/FRmGym0XFB7Xjos4+F+SqQUAAABgGKi5/6dR1ry8YKxr0iHRMusbkRu9SzJFAQAAAAyCmgd+HplcV+9xPlsZHW/2PREAAIDBYCEMW62xsTHpElKjbOX9MWrJ3wrG2g78eHS2dkW0dm3kKgZLNpuN+vr63uOmpqbI5XKbuAKSJ7ekleySRnJLWqUpuw0NDUmXAAxD2bXLova+HxWMdU16S6w9+fKITCahqgAAAADeuExHU1Q/ckXBWOde745c3fYJVQQAAFBcLIRhq/X09CRdQmqMuOd7Bce5mrHRNvX9EX4NE5HL5eSX1JFb0kp2SSO5Ja1kF0ibEfMviExPZ+9xPpON1iPOtQgGAAAASL3qh38d2XWtvcf5TDbaDvx4ghUBAAAUl2zSBUCxK195f1Q+N69grP3Aj0dU1CZUEQAAAECyKl68O6qW3lQw1rHvP0fPuD0TqggAAABgkKxrj5oHf1kw1DXlnZEbvUsy9QAAABQhO8LANla74PsFx7masdG+7z8nVA0AAAAwGMrKypIuIb1y3VF3x3mFQ9Wjo/Otn/Xrug1ks9lNHsNwJLekleySRnJLWskuMJxVP/bbyHasLhhrO/CMhKoBAAAoThbCwDZUvvL+qHz+9oKxtgM/EVFRk1BFAAAAwGBoaGhIuoT0+vtPIlY9WTCUPfq/YvTEXRMqqLTU19cnXQJsMbklrWSXNJJb0kp2gWGjpytq7v9ZwVDXzjOjZ/w+CRUEAABQnDwWBbah2gWXFBznasdFx74fSKgaAAAAgIS1roqYe37h2Pb7RRz00UTKAQAAABhMVYuvi7KWFQVjbQedmVA1AAAAxctCGNhGylcsisrn7ygYsxsMAAAAUNJuPS+io6lw7Lj/jsiWJVMPAAAAwGDJ56Jm0U8KhtZNnB7dO0xPqCAAAIDiVZ50AVCsahd+v+A4VzsuOqa+P6FqAAAAgMHU2NiYdAmpU/byIzHyvssis8FY1x4nReuovSP8em4z2Ww26uvre4+bmpoil8slWBFsntySVrJLGsktaZWm7DY0NCRdAjBEKp/+W5Q3Li0YazvojISqAQAAKG4WwsA2YDcYAAAAKG49PT1Jl5Au+XzU3XZuZCL/j6Hymmh5639Gzq/lkMrlcvJL6sgtaSW7pJHcklayCyQun4+a+y4tGOoet3es23lmMvUAAAAUuWzSBUAxql3w+t1gxkfHvh9IqBoAAACAZFU+NScqVtxXMNY2/czI1U1MqCIAAACAwVPxwvyoePnhgrG2Az8Rkcls5AoAAADeCAthYJCVr7gvKl/oZzeY8uqEKgIAAABIUFdrjJh/UcFQz6idov3N/5pQQQAAAACD6/W7wfTUvym6prwzoWoAAACKn4UwMMj63w3m/QlVAwAAAJCs2vsujbLWlQVjrYd/OaK8KqGKAAAAAAZP+coHonLZPQVjbdM+HpEtT6giAACA4mchDAyi8hX3RuULdxaMtR1kNxgAAACgNGWbnoua+/+/grGunY6Irl3fllBFAAAAAIOrz24wtdtF597vSagaAACA0mAhDAyifneDmWo3GAAAAKA0jbjzm5HJdfUe57Pl0XrEVyIymQSrAgAAABgcZasWR9UzNxeMdbz5YxFldsIFAADYliyEgUFSvvzeqHxhfsGY3WAAAACAUlXx3O19vwiy34ejZ8yUhCoCAAAAGFw1i35ccJyrqo+OfT0wFQAAYFuzEAYGSe3Cwt1gemq3sxsMAAAAUJp6umLEHecVDOVqxkbbjE8lVBAAAADA4MqufTGqFs8pGOvY/8ORr6xLqCIAAIDSYSEMDIL+doNptxsMAAAAUKKqH/p1lK95umCs9dDPR75qZEIVAQAAAAyumvt/Fpl8T+9xvrwm2vf/SIIVAQAAlA4LYWAQ1C64pODYbjAAAABAqcq0vhK1Cwp3zl233X7Rufc/JVQRAAAAwODKtL0a1Y9dXTDWMfV9ka9pSKgiAACA0mIhDLxB5csXRuWLdxWMtR90RkR5VUIVAQAAACRnxD3fiuy6loKx1iPPjchoRQIAAADFoebByyLT09l7nM9WRPu0f0mwIgAAgNLivz7DG9RnN5gR20fH1PclVA0AAABAcspfejCqH/99wVjHXu+J7gnTEqoIAAAAYHBlOpuj+uFfF4x17nly5OomJlQRAABA6bEQBt6A8mULovLFuwvG7AYDAAAAlKR8Lkbc/rWCoVzFiGg99PMJFQQAAAAw+KofvjyyXf/YDTcfmWg/8OMJVgQAAFB6LISBN6B24fcLjntGbB8d+7w3oWoAAAAAklP1xB+j4qUHC8baD/5k5Edsl1BFAAAAAIOsuyNqHvxFwVDX5GOjp2G3hAoCAAAoTRbCwFayGwwAAADAazJdzTHi7v8uGOsevWu0H/DRZAoCAAAA2AaqH/tdZNtXFYy1HXRGQtUAAACULgthYCvVLrik4LhnxAS7wQAAAAAlqWbh/0S27dWCsdYjvhJRVplQRQAAAACDrGdd1Nz/04Khrp2OiJ7t9k2oIAAAgNJlIQxshfJlf4/KZfcUjNkNBgAAAChFZY1Lo+bBywrGunaZHet2nplIPQAAAADbQtVT10dZ87KCsXa7wQAAACTCQhjYCv3vBnNqQtUAAAAAJCSfjxF3nBeZXPc/hrKV0XL4lxMsCgAAAGCQ5XNRs+jHBUPrtp8W6ya9JaGCAAAASpuFMLCFKl68JyqX/b1grH36mXaDAQAAAEpO5bO3RuXzdxSMtb/5tMiN3iWZggAAAAC2gcpnbony1U8VjLVPPyMik0moIgAAgNJmIQxsoZqF3y84fm03mP+TUDUAAAAACenpjBF3nl84VLtdtE//t4QKAgAAANgG8vmoue9HBUPdY/aIrl1mJ1QQAAAAFsLAFtjobjBldoMBAAAASkvNA7+IsqbnC8baDvtC5CvrEqoIAAAAYPBVLLsnKl56oGCs/aAzIjK+dgUAAJAU/yKDLVC74HW7wdTZDQYAAAAoPdmWlVF77w8LxtZNmBade7wroYoAAAAAto3X7wbTM2qn6Nz9+ISqAQAAIMJCGBiwihfviYrlr9sN5qB/sxsMAAAAUHJq7/rvyKxr6z3ORyZajjw3IpNJsCoAAACAwVX+0kNR+cKdBWPt006PyJYnVBEAAAARFsLAwOTzUbvgkoKhnrqJ0bHPKQkVBAAAAJCM8hX3RvXiPxWMde5zavRst19CFQEAAABsG6/fDSZXOy469vZdEQAAgKRZCAMDULHsnqhYvqBgrH263WAAAACAEpPribrbv144VDkyWg/9bEIFAQAAAGwbZY1Lo/LpvxaMtR/wsYhy3xUBAABImoUwsDkb2w1m739KqCAAAACAZFQ9/rsof+XRgrG2t3w68jVjE6oIAAAAYNuoue/HkYl873GucmR07PeBBCsCAABgPQthYDMqlt0dFcsXFozZDQYAAAAoNZmOphhx97cLxrrH7B4d+34woYoAAAAAto1s8/KoWvyngrGO/T4Y+cqRCVUEAADAhiyEgU3J56N2wfcLhl7bDeaUhAoCAAAASEbtgksi27G6YKz1iHMiyioSqggAAABg26i5/2eRyXX3HufLqqL9gI8mVxAAAAAFypMuAIazihfv2shuMJUJVQQAAAAw9MpWPRnVD19eMNa52zti3U6HJVQRAAAAlLaurq648sor4+mnn46VK1dGS0tL1NbWxoQJE2L27NlxxBFHRHm5rwVtjUz7qqh+7LcFYx37nBr52nEJVQQAAMDr2REGNqa/3WBG7mA3GAAAAKC05PMx4o7zIpPv+cdQWWW0Hn52gkUBAABAaevo6Ii//vWvERExbdq0OP7442PGjBmxevXquPTSS+Oiiy6KXC6XcJXpVPPgLyPT3dF7nM+WR/u00xOsCAAAgNfz6AfYiIoX74qKFfcWjLUdZDcYAAAAoLRULv1LVL54d8FY+4Efj9yonRKqCAAAAKirq4tf/vKXfXZ96enpifPPPz8efPDBeOCBB+LAAw9MqMJ0ynQ1R/XDvy4Y69zjpMiNmpRQRQAAAPTHjjDQn3w+ahdcUjDUM3JSdO79TwkVBAAAAJCA7o4YMf+bBUM9dROj7cAzEioIAAAAiIjIZrN9FsFERJSVlcXBBx8cERErV64c6rJSr/qRKyPbubb3OB+ZaD/wEwlWBAAAQH/sCAP9eG03mPsKxtqm2w0GAAAAKC01i34aZc3LCsZaDzs7oqImoYoAAADgjWtqaoolS5bEkiVLYunSpbF06dJobm6OiIijjjoqzjrrrAHf65VXXombbropFi1aFKtWrYry8vKYMGFCHHroofGOd7wjqqqqttWP0a9cLhcPPvhgRETstJPdXLdId2fUPPDzgqGu3Y6JnjFTEioIAACAjbEQBl5vY7vB7PWehAoCAAAAGHrZtcui9r5LC8bW7fCW6JpyXEIVAQAAwOA4/fTTB+U+9957b/zgBz+I9vb23rHOzs7exTW33HJLnH322TFhwoRBeb/+dHd3xzXXXBMREc3NzfHII4/EsmXLYubMmbHffvtts/ctRtVP/CGyba8UjLUfdGZC1QAAALApFsLA61S8MN9uMAAAAEDJG3HXhZHp6ew9zmey0XLkf0VkMglWBQAAAINr3LhxMWnSpN5dVAbqmWeeie9973vR1dUV1dXVcfLJJ8e+++4bXV1dMX/+/LjllltixYoVccEFF8SFF14YNTXbZnfV7u7u+P3vf997nMlk4sQTT4wPfOAD2+T9ilauO2oW/bRgqGvHt0b39vsnVBAAAACbYiEMbKjf3WB2tBsMAAAAUFIqXrw7qpbcWDDWse8HomfcXglVBAAAAIPnlFNOicmTJ8fkyZNj9OjR8fLLL8cnP/nJLbrHZZddFl1dXVFWVhZf+cpXYo899uid23fffWPixIlx+eWXx4oVK2LOnDlx6qmn9rnHr371q1i3bt2A3/O4446LiRMnFoxVV1fH1VdfHblcLhobG+O+++6LK6+8MhYvXhxnn3121NbWbtHPVaqqnroxytY+XzDWftAZCVUDAADA5lgIAxuoeGF+VKxcVDBmNxgAAACgpOS6Y8TtXy8cqhodbTM+k0w9AAAAMMj6W5SyJZYsWRKPP/54RETMmjWrYBHMeieccELMnTs3li1bFjfddFO85z3vifLywq/p/O1vf4vOzs4+127MIYcc0mchzHrZbDbGjh0bb3/722PkyJHx3e9+N6655pr44Ac/uAU/WYnK56Nm0Y8KhtZtt3+s2/GtCRUEAADA5lgIA+vl81G74HsFQ3aDAQAAAEpN9SNXRPnqxQVjbYf8R+RrGhKqCAAAAIaXBQsW9L6eNWtWv+dks9k46qij4oorrojW1tZ49NFH44ADDig459e//vU2qW/9+zz22GPb5P7FpuLZuVG+6smCsfaDzojIZBKqCAAAgM2xECblurq64sorr4ynn346Vq5cGS0tLVFbWxsTJkyI2bNnxxFHHNHniSL0r+KFO6Ni5f0FY20HnxVRVpFQRQAAAMCm6IsMvkz76qj9+/cKxrrH7R0dU9+XTEEAAAAwDD355GuLJqqqqmK33Xbb6Hn77LNPwTWvXwizraxevToiIsrKyobk/VItn4/a+y4tGOpumBJdux2TUEEAAAAMRDbpAnhjOjo64q9//WtEREybNi2OP/74mDFjRqxevTouvfTSuOiiiyKXyyVcZQrk81G74JKCoZ5RO0Xnnu9OqCAAAABgc/RFBl/t378b2c6mgrGWI/4rIuuLMwAAALDeiy++GBEREyZM2ORikx122KHPNYNZQ2dnZ5/xzs7O+NWvfhURr/VL2LTy5QujYuWigrH2Az8ekfGVKgAAgOHMIzFTrq6uLn75y1/2ebppT09PnH/++fHggw/GAw88EAceeGBCFaZDxfN39N0NZrrdYAAAAGA40xcZXGWvPBrVj1xZMNa5+wnRPWlGQhUBAADA8NPV1RXNzc0RETF27NhNnltXVxdVVVXR2dkZq1atGtQ67rrrrrjhhhtir732ivHjx0dNTU2sXr06HnjggWhubo699947TjjhhC2+70DrLJbdZkYs+lHBcc/ISdG997uL5ucrddlsdpPHMBzJLWklu6SR3JJWsvsaC2FSLpvN9hvesrKyOPjgg+PRRx+NlStXJlBZiuTzUbvg+wVDr+0Gc3Iy9QAAAAADoi8yiPL5qLv965GJ/D+Gyquj9a1fSLAoAAAAGH46Ojp6X1dXV2/2/Orq6ujs7Cy4bjAcdNBB0djYGIsXL47FixdHR0dH1NbWxpve9KY47LDDYtasWVu1mOPMM88c0HlXX331Ft972FnxYMRz8wqGyg7/TDSM2y6hgtjW6uvrky4BtpjcklaySxrJLWlVqtkt6YUwTU1NsWTJkliyZEksXbo0li5d2vvUjqOOOirOOuusAd/rlVdeiZtuuikWLVoUq1ativLy8pgwYUIceuih8Y53vCOqqqq21Y/Rr1wuFw8++GBEROy0005D+t5pU/H8HVHxkt1gAAAAKC36Imyo8qk5UbHi3oKxtoPOjNzIHRKqCAAAAIanrq6u3tev36W2P+vP2fC6wTB58uSYPHnyoN6z5Nz53cLj2nER0z6YTC0AAABskZJeCHP66acPyn3uvffe+MEPfhDt7e29Y52dnb1fIrnlllvi7LPPjgkTJgzK+/Wnu7s7rrnmmoiIaG5ujkceeSSWLVsWM2fOjP3222+bvW/q5fNRu+CSgqGeUW+yGwwAAABFT1+EXuvaYsT8iwqGekbtFO3TBicjAAAAUEwqKyt7X3d3d2/2/PXnbHjdcHbppZcmXcLQWLU04rE/FY4dcmZEZW0y9QAAALBFSnohzIbGjRsXkyZN6n1a6EA988wz8b3vfS+6urqiuro6Tj755Nh3332jq6sr5s+fH7fcckusWLEiLrjggrjwwgujpqZmm9Tf3d0dv//973uPM5lMnHjiifGBD3xgm7xfsah4/vaoeOmBgjG7wQAAAFBq9EVKW+19l0ZZ68qCsdbDvxRRPrQ7+QAAAEAaVFdX977u6OjY7Pnrz9nwuuFs7NixAzqvsbFxG1eybdXe+t9Rlc/1Hucr66Jpj1Min/Kfi0LZbDbq6+t7j5uamiKXy23iCkie3JJWsksayS1plabsNjQ0bLN7l/RCmFNOOaV3q9jRo0fHyy+/HJ/85Ce36B6XXXZZdHV1RVlZWXzlK1+JPfbYo3du3333jYkTJ8bll18eK1asiDlz5sSpp57a5x6/+tWvYt26dQN+z+OOOy4mTpxYMFZdXR1XX3115HK5aGxsjPvuuy+uvPLKWLx4cZx99tlRW+uJFX1sdDeYdyVUEAAAAAwdfREiIrJNz0XNop8VjHXtdHh07XpMQhUBAADA8FZZWRkjR46M5ubmWLVq1SbPbWlpic7OzogY+AKTtOjp6Um6hK2WbVkZlY//oWCsfd9/ju7yuogU/1xsXi6XS3V2KU1yS1rJLmkkt6RVqWa3pBfC9Pfliy2xZMmSePzxxyMiYtasWQVf9ljvhBNOiLlz58ayZcvipptuive85z1RXl74y/63v/2tt/ExEIccckifL3ysl81mY+zYsfH2t789Ro4cGd/97nfjmmuuiQ9+8INb8JOVhorn50XFS4VPum072G4wAAAAlAZ9ESIiRtz5zcjkunqP89nyaD3inIhMJsGqAAAAYHjbcccd4/HHH4+VK1dGT09PlJWV9Xve8uXLC65heKi5//+LTO4fD2bJl1VG+wGnJVgRAAAAW6qkF8K8UQsWLOh9PWvWrH7PyWazcdRRR8UVV1wRra2t8eijj8YBBxxQcM6vf/3rbVLf+vd57LHHtsn9Uy2fj9oF3y8Y6ql/U3TueXIy9QAAAEDK6IukX8Vzt0fVMzcXjHXs96HoGTMloYoAAAAgHfbcc894/PHHo7OzM55++unYfffd+z1vw77EnnvuOVTlsQmZ9saofuyqgrGOvf9P5EeMT6giAAAAtkY26QLS7Mknn4yIiKqqqthtt902et4+++zT55qhsHr16oiIjT55pJRVPHdb391gpp8VkbU2DAAAAAZCXyTlerpixB3nFQzlasZE24xPJVQQAAAApMeMGTN6X8+dO7ffc3K5XMybNy8iIkaMGBFTp04dktrYtJqHfhWZdW29x/lMWbQfeHqCFQEAALA1fOv/DXjxxRcjImLChAmb/FLFDjvs0Oeawaxh/PjxUVVVVTDe2dkZv/rVryIiYtq0aVt0z1WrVg3ovNR+kSSfjxELf1Aw1FO/c3Tv809Rlk3pz8QmZbPZTR7DcCS3pJXskkZyS1rJLkkr1r5Iqah+6NdRvubpgrHWQz8f+apRCVUEAAAA6TFlypTYe++94/HHH4+5c+fGzJkzY4899ig45/rrr49ly5ZFRMQ73/nOKC8vrq/opPI7I12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" ] @@ -463,76 +443,10 @@ "plt.show()" ] }, - { - "cell_type": "markdown", - "id": "d11a14ed-65f0-41d5-ad00-1e3877733d2e", - "metadata": {}, - "source": [ - "## Stomp vs naive" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "63d8fe31-86b8-408b-b5c6-6c18987fdc08", - "metadata": {}, - "outputs": [], - "source": [ - "# Sizes are limited to not time-out the CI, you can test with more sizes locally !\n", - "sizes = [500, 1000, 2500, 5000]\n", - "query_lengths = [0.05, 0.1]\n", - "times = pd.DataFrame(\n", - " index=pd.MultiIndex(levels=[[], []], codes=[[], []], names=[\"size\", \"query_length\"])\n", - ")\n", - "\n", - "for size in sizes:\n", - " for _query_length in query_lengths:\n", - " query_length = int(_query_length * size)\n", - " X = rng.random((1, 1, size))\n", - " T = rng.random((1, size))\n", - " search_space_size = size - query_length + 1\n", - " mask = np.ones((1, search_space_size), dtype=bool)\n", - " # Used for numba compilation before timings\n", - " naive_squared_matrix_profile(X, T, query_length, mask)\n", - " _times = %timeit -r 1 -n 3 -q -o naive_squared_matrix_profile(X, T, query_length, mask)\n", - " times.loc[(size, _query_length), \"Naive\"] = _times.average\n", - " # Used for numba compilation before timings\n", - " stomp_squared_matrix_profile(X, T, query_length, mask)\n", - " _times = %timeit -r 1 -n 3 -q -o stomp_squared_matrix_profile(X, T, query_length, mask)\n", - " times.loc[(size, _query_length), \"Stomp\"] = _times.average" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "cc4801ae-bb48-46d1-8e71-21c045c69773", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(ncols=len(query_lengths), figsize=(20, 5), dpi=200)\n", - "for j, (i, grp) in enumerate(times.groupby(\"query_length\")):\n", - " grp.droplevel(1).plot(label=i, ax=ax[j])\n", - " ax[j].set_title(f\"query length {i}\")\n", - " ax[j].set_yscale(\"log\")\n", - "ax[0].set_ylabel(\"time in seconds\")\n", - "plt.show()" - ] - }, { "cell_type": "code", "execution_count": null, - "id": "391737ea-a185-4ac9-906d-90724a279017", + "id": "61dac86c-a1f3-4899-bcd5-33c8468e4c07", "metadata": {}, "outputs": [], "source": [] @@ -540,8 +454,8 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (Spyder)", - "language": "python3", + "display_name": "Python 3 (ipykernel)", + "language": "python", "name": "python3" }, "language_info": { @@ -554,7 +468,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/examples/similarity_search/distance_profiles.ipynb b/examples/similarity_search/distance_profiles.ipynb index ec56fcc6bf..d2bf3fd87f 100644 --- a/examples/similarity_search/distance_profiles.ipynb +++ b/examples/similarity_search/distance_profiles.ipynb @@ -37,11 +37,11 @@ "We can then find the \"best match\" between $Q$ and $X$ by looking at the distance profile minimum value and extract the subsequence $W_{\\text{argmin} P(X,Q)}$ as the best match.\n", "\n", "### Trivial matches\n", - "One should be careful of what is called \"trivial matches\" in this situation. If $Q$ is extracted from $X$, it is extremely likely that it will match with itself, as $dist(Q,Q)=0$. To avoid this, it is common to set the parts of the distance profile that are neighbors to $Q$ to $\\infty$. This is the role of the `q_index` parameter in the similarity search `predict` methods. The `exclusion_factor` parameter is used to define the neighbors of $Q$ that will also get $\\infty$ value.\n", + "One should be careful of what is called \"trivial matches\" in this situation. If $Q$ is extracted from $X$, it is extremely likely that it will match with itself, as $dist(Q,Q)=0$. To avoid this, it is common to set the parts of the distance profile that are neighbors to $Q$ to $\\infty$. This is the role of the `q_index` parameter in the similarity search `predict` methods. The `exclusion_factor` parameter is used to define the neighbors of $Q$ that will also get $\\infty$ value.\n", "\n", - "For example, if $Q$ was extracted at index $i$ in $X$ (i.e. $Q = \\{x_i, \\ldots, x_{i+(l-1)}\\}$), then all points in the interval `[i - l//exclusion_factor, i + l//exclusion_factor]` will the set to $\\infty$ in the distance profile to avoid a trivial match.\n", + "For example, if $Q$ was extracted at index $i$ in $X$ (i.e. $Q = \\{x_i, \\ldots, x_{i+(l-1)}\\}$), then all points in the interval `[i - floor(l*exclusion_factor), i + floor(l*exclusion_factor)]` will the set to $\\infty$ in the distance profile to avoid a trivial match.\n", "\n", - "The same reasoning can also be applied for the best matches of $Q$. It is highly likely that the two best matches will be neighbours, as if $W_i$ and $W_{i+/-1}$ share $l-1$ values. The `apply_exclusion_to_result` boolean parameter in `predict` allows you to apply the exclusion zone defined by `[i - l//exclusion_factor, i + l//exclusion_factor]` to the output of the algorithm.\n" + "The same reasoning can also be applied for the best matches of $Q$. It is highly likely that the two best matches will be neighbours, as if $W_i$ and $W_{i+/-1}$ share $l-1$ values. The `apply_exclusion_to_result` boolean parameter in `predict` allows you to apply the exclusion zone defined by `[i - floor(l*exclusion_factor), i + floor(l*exclusion_factor)]` to the output of the algorithm.\n" ] }, { diff --git a/examples/similarity_search/similarity_search.ipynb b/examples/similarity_search/similarity_search.ipynb index cdbaa86948..6bb339f13f 100644 --- a/examples/similarity_search/similarity_search.ipynb +++ b/examples/similarity_search/similarity_search.ipynb @@ -7,12 +7,27 @@ "source": [ "# Time Series Similarity Search with aeon\n", "\n", - "The goal of Time Series Similarity Search is to asses the similarities between a time\n", - " series, denoted as a query `q` of length `l`, and a collection of time series,\n", - " denoted as `X`, with lengths greater than or equal to `l`. In this\n", - " context, the notion of similiarity between `q` and the other series in `X` is quantified by similarity functions. Those functions are most of the time defined as distance function, such as the Euclidean distance. Knowing the similarity between `q` and other admissible candidates, we can then perform many other tasks for \"free\", such as anomaly or motif detection.\n", + "\"time\n", "\n", - "\"time" + "The objectives of the similarity search module in aeon is to provide estimators with a `fit`/`predict` interface to solve the following use cases :\n", + "\n", + "- Nearest neighbors search on time series subesequences or whole series\n", + "- Motifs search on time series subsequences\n", + "\n", + "Similarly to the `transformer` module, the `similarity_search` module split estimators between `series` estimators and `collection` estimators, such as :\n", + "\n", + "- `series` estimators take as input a single time series of shape `(n_channels, n_timepoints)` during fit and predict.\n", + "- `collection` estimators take as input a time series collection of shape `(n_cases, n_channels, n_timepoints)` during fit, and a single series of shape `(n_channels, n_timepoints)` during predict.\n", + "\n", + "Note that the above is a general guideline, and that some estimators can also take `None` as input during predict, or series of length different to `n_timepoints`. We'll explore the different estimators in the next sections.\n", + "\n", + "### Other similarity search notebooks\n", + "\n", + "This notebook gives an overview of similarity search module and the available estimators. The following notebooks are also avaiable to go more in depth with specific subject of similarity search in aeon:\n", + "\n", + "- [The theory and math behind the similarity search estimators in aeon](distance_profiles.ipynb)\n", + "- [Analysis of the performance of the estimators provided by similarity search module](code_speed.ipynb)\n", + "\n" ] }, { @@ -22,25 +37,34 @@ "metadata": {}, "outputs": [], "source": [ - "def plot_best_matches(top_k_search, best_matches):\n", + "# Define some plotting functions we'll use later !\n", + "def plot_best_matches(\n", + " X_fit, X_predict, idx_predict, idx_matches, length, normalize=False\n", + "):\n", " \"\"\"Plot the top best matches of a query in a dataset.\"\"\"\n", - " fig, ax = plt.subplots(figsize=(20, 5), ncols=3)\n", - " for i_k, (id_sample, id_timestamp) in enumerate(best_matches):\n", + " fig, ax = plt.subplots(figsize=(20, 5), ncols=len(idx_matches))\n", + " if len(idx_matches) == 1:\n", + " ax = [ax]\n", + " for i_k, id_timestamp in enumerate(idx_matches):\n", " # plot the sample of the best match\n", - " ax[i_k].plot(top_k_search.X_[id_sample, 0], linewidth=2)\n", + " ax[i_k].plot(X_fit[0], linewidth=2)\n", " # plot the location of the best match on it\n", + " match = X_fit[0, id_timestamp : id_timestamp + length]\n", " ax[i_k].plot(\n", - " range(id_timestamp, id_timestamp + q.shape[1]),\n", - " top_k_search.X_[id_sample, 0, id_timestamp : id_timestamp + q.shape[1]],\n", + " range(id_timestamp, id_timestamp + length),\n", + " match,\n", " linewidth=7,\n", " alpha=0.5,\n", " color=\"green\",\n", " label=\"best match location\",\n", " )\n", " # plot the query on the location of the best match\n", + " Q = X_predict[0, idx_predict : idx_predict + length]\n", + " if normalize:\n", + " Q = Q * np.std(match) + np.mean(match)\n", " ax[i_k].plot(\n", - " range(id_timestamp, id_timestamp + q.shape[1]),\n", - " q[0],\n", + " range(id_timestamp, id_timestamp + length),\n", + " Q,\n", " linewidth=5,\n", " alpha=0.5,\n", " color=\"red\",\n", @@ -66,73 +90,30 @@ " plt.show()" ] }, - { - "cell_type": "markdown", - "id": "7e06b213-6038-4901-b98e-2433625115c4", - "metadata": {}, - "source": [ - "## Similarity search Notebooks\n", - "\n", - "This notebook gives an overview of similarity search module and the available estimators. The following notebooks are avaiable to go more in depth with specific subject of similarity search in aeon:\n", - "\n", - "- [Deep dive in the distance profiles](distance_profiles.ipynb)\n", - "- [Analysis of the speedups provided by similarity search module](code_speed.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "ca967c08-9a05-411a-a09a-ad8a13c0adb9", - "metadata": {}, - "source": [ - "## Expected inputs and format\n", - "For both `QuerySearch` and `SeriesSearch`, the `fit` method expects a time series dataset of shape `(n_cases, n_channels, n_timepoints)`. This can be 3D numpy array or a list of 2D numpy arrays if `n_timepoints` varies between cases (i.e. unequal length dataset).\n", - "\n", - "The `predict` method expects a 2D numpy array of shape `(n_channels, query_length)` for `QuerySearch`. In `SeriesSearch`, the predict methods also expects a 2D numpy array, but of shape `(n_channels, n_timepoints)` (`n_timepoints` doesn't have to be the same as in fit) and a `query_length` parameter." - ] - }, { "cell_type": "markdown", "id": "d1fd75ae-84c2-40be-95f6-bd7de409317d", "metadata": {}, "source": [ - "## Available estimators\n", - "\n", - "All estimators of the similarity search module in aeon inherit from the `BaseSimilaritySearch` class, which requires the following arguments:\n", - "- `distance` : a string indicating which distance function to use as similarity function. By default this is `\"euclidean\"`, which means that the Euclidean distance is used.\n", - "- `normalise` : a boolean indicating whether this similarity function should be z-normalised. This means that the scale of the two series being compared will be ignored, and that, loosely speaking, we will only focus on their shape during the comparison. By default, this parameter is set `False`.\n", + "### A word on base clases\n", "\n", - "Another parameter, which has no effect on the output of the estimators, is a boolean named `store_distance_profile`, set to `False` by default. If set to `True`, the estimators will expose an attribute named `_distance_profile` after the `predict` function is called. This attribute will contain the computed distance profile for query given as input to the `predict` function.\n", + "All estimators of the similarity search module in aeon inherit from the `BaseSimilaritySearch` class, which define the some abstract methods that estimator must implement, such as `fit` and `predict` and some private function used to validate the format of the time series you will provide. Then, the two submodules `series` and `collection` also define a base class (`BaseSeriesSimilaritySearch` and `BaseCollectionSeriesSearch`) that their respective estimator will inherit from. If you ever want to extend the module or create your own estimators, these are the classes you'll want to use to define the base structure of your estimator.\n", "\n", - "To illustrate how to work with similarity search estimators in aeon, we will now present some example use cases." - ] - }, - { - "cell_type": "markdown", - "id": "01fa67c2-0126-4152-98a9-fa0df84c4629", - "metadata": {}, - "source": [ - "### Query search" - ] - }, - { - "cell_type": "markdown", - "id": "8e99b251-d156-4989-b5a0-3a2c79cb75d4", - "metadata": {}, - "source": [ - "We will use the GunPoint dataset for this example, which can be loaded using the `load_classification` function." + "### Load a dataset\n", + "In the following, we'll use an easy dataset (`ArrowHead`) to help build intuition. Don't hesitate to swap it with other datasets to explore ! We load it using the `load_classification` function." ] }, { "cell_type": "code", "execution_count": 2, - "id": "f8a6bb7e-b219-41f1-b508-b849c45672eb", + "id": "20d3b591-f275-4548-a7d2-75b16380b055", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -146,7 +127,7 @@ "from aeon.datasets import load_classification\n", "\n", "# Load GunPoint dataset\n", - "X, y = load_classification(\"GunPoint\")\n", + "X, y = load_classification(\"ArrowHead\")\n", "\n", "classes = np.unique(y)\n", "\n", @@ -162,12 +143,43 @@ }, { "cell_type": "markdown", - "id": "5392f7f4-1825-4b15-9248-27eeecb1af3c", + "id": "01fa67c2-0126-4152-98a9-fa0df84c4629", "metadata": {}, "source": [ - "The GunPoint dataset is composed of two classes which are discriminated by the \"bumps\" located before and after the central peak. These bumps correspond to an actor drawing a fake gun from a holster before pointing it (hence the name \"GunPoint\" !). In the second class, the actor simply points his fingers without making the motion of taking the gun out of the holster.\n", + "## 1. Series estimators\n", "\n", - "Suppose that we define our input query for the similarity search task as one of these bumps:" + "First, we'll explore estimators of the `series` module, where you must provide single series of shape `(n_channels, n_timepoints)` during fit." + ] + }, + { + "cell_type": "markdown", + "id": "78f17f93-28b3-49c0-be5f-1d430a273b0c", + "metadata": {}, + "source": [ + "### 1.1 Subsequence nearest neighbors with MASS\n", + "\n", + "To perform nearest neighbors search on subsequences on a series, we can use the `MassSNN` estimator.\n", + "\n", + "It takes as parameter during initialisation :\n", + "- `length` : an integer giving the length of the subsequences to extract from the series. It is also the expected length of the series given in `predict`\n", + "- `normalize`: a boolean indicating wheter the subsequences should be independently z-normalized (`(X-mean(X))/std(X)`) before the distance computations. This results in a scale-independent matching.\n", + " \n", + "To parameterize the search, additional parameters are available when calling the `predict` method:\n", + "\n", + "- `k` (int) : the number of nearest neighbors to return.\n", + "- `dist_threshold` (float) : the maximum allowed distance for a candidate subsequence to be considered as a neighbor.\n", + "- `allow_trivial_matches` (bool) : wheter a neighbors of a match to a query can be also considered as matches (True), or if an exclusion zone is applied around each match to avoid trivial matches with their direct neighbors (False).\n", + "- `inverse_distance` (bool) : if True, the matching will be made on the inverse of the distance, and thus, the farther neighbors will be returned instead of the closest ones.\n", + "- `exclusion_factor` (float): A factor of the `length` used to define the exclusion zone when `allow_trivial_matches` is set to False. For a given timestamp, the exclusion zone starts from `id_timestamp - floor(length*exclusion_factor)` and end at `id_timestamp + floor(length*exclusion_factor)`.\n", + "- `X_index` (int): If series given during predict is a subsequence of series given during fit, specify its starting timestamp. If specified, neighboring subsequences of X won't be able to match as neighbors." + ] + }, + { + "cell_type": "markdown", + "id": "33105406-fc83-4143-9345-af589a06a00a", + "metadata": {}, + "source": [ + "First, we'll select a series from the dataset to use during fit. This is the series we want our neighbors to come from." ] }, { @@ -178,83 +190,108 @@ "outputs": [ { "data": { - "image/png": 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ma7sw8InIpeRdNKKqyYzHXWiytgsDn4hcykcnOiZrF7vQZG2XIQn83NxchIWFQaPRID09vdf7bW1tWLt2LTQaDWJiYlBSUjIU3RIR3ZVr1R2TtWtdbLK2i92Bb7VasWnTJuzbtw+FhYXYtWsXCgsLe7TZsWMHJk2ahCtXruD555/Hyy+/bG+3RER3rXuy1kW+WXs7uwM/Pz8fGo0GISEhcHNzQ3JyMnJycnq0ycnJQUpKCgBg9erV+Pzzz+GgOysSkZMyW2z4WF+GpbOU8PdyrcnaLnYHfnl5OQIDv/u0DAgIQHl5eb9tFAoFvLy8UF1d3etYGRkZ0Ol00Ol0MJlM9pZGRNSta7L2iQWuN1nbxaEmbVNTU6HX66HX6+Hr63oTKkQ0fLq+Wbt4putmi92Br1arUVZW1v3cYDBArVb328ZisaC+vh5Tpkyxt2siojtSWn0TR4pcd7K2i92BHx0djaKiIhQXF8NsNiMrKwuJiYk92iQmJiIzMxMA8Mknn2Dp0qUQBNc96UQ0srJOlkImwGUna7vYvVuvQqHA9u3bkZCQAKvVivXr10Or1WLLli3Q6XRITEzEhg0bsG7dOmg0GkyePBlZWVlDUTsR0aDarTZk6w1YOsvPZSdruwiig94uo9PpoNfrpS6DiEa5fV9X4McfnsYfntZh6Sw/qcsZdgNlp0NN2hIRDbWP8ksx1csD35+plLoUyTHwichpldV0TdYGufRkbRcGPhE5rV35nZO10QFSl+IQGPhE5JRunaxVeY2VuhyHwMAnIqd0oNCIqqY2POmCyyD3h4FPRE7pwxPXXP6btbdj4BOR0ympasbRK9Uut2ftYBj4ROR0duWXQi4TsNbF9qwdDAOfiJxKm8WKj08ZEB/uB6WL7Vk7GAY+ETmV3POVqGk24wlO1vbCwCcip/LRiVIETR6Hf9H4SF2Kw2HgE5HTuHKjCSeKa/D4giDIOFnbCwOfiJzGRydKMUYuYI2O36ztCwOfiJxCa7sVfzltwHKtP3w83aUuxyEx8InIKez9ugL1Le140oX3rB2MXYFfU1OD+Ph4hIaGIj4+HrW1tb3anDlzBosWLYJWq0VkZCT+/Oc/29MlEVGfPjpRihCf8Vg0g9un9seuwE9PT0dcXByKiooQFxeH9PT0Xm3GjRuHP/3pT7hw4QJyc3Pxs5/9DHV1dfZ0S0TUwzeVjdBfq8XjC4K4feoA7Ar8nJwcpKSkAABSUlKwe/fuXm1mzpyJ0NBQAMDUqVOhVCphMpns6ZaIqIePTlyDm1yGx+ZzsnYgdu1pazQaoVKpAAD+/v4wGo0Dts/Pz4fZbMaMGTP6fD8jIwMZGRkAwA8FIrojLWYr/lpQjgfn+GPyeDepy3Fogwb+smXLUFlZ2ev1rVu39nguCMKAv0pVVFRg3bp1yMzMhEzW9y8WqampSE1NBdCxLyMR0WD+du46GlsteDJmmtSlOLxBAz8vL6/f9/z8/FBRUQGVSoWKigoolX3vGdnQ0ICHHnoIW7duxcKFC++9WiKi23x4ohQapSeigydJXYrDs2sMPzExEZmZmQCAzMxMrFy5slcbs9mMRx55BE899RRWr15tT3dERD1cuF6Ps2V1eIKTtXfErsBPS0vDgQMHEBoairy8PKSlpQEA9Ho9Nm7cCADIzs7G4cOHsXPnTkRFRSEqKgpnzpyxu3Aioo9OlMJdIcNj93Gy9k4IoiiKUhfRF51OB71eL3UZROSgmtosiNmahxWzVXgraa7U5TiMgbKT37QlolFp+xdX0Gy2chnku8DAJ6JR57f/vILfH7qKJF0A7gvylrqcUYOBT0SjyvtHvsUb//gGq6KmYtujkZysvQsMfCIaNTK/KsHrn13EQ3NUeHPNXG5QfpcY+EQ0Knx0ohSv7bmA+Ag//Do5Cgo54+tu8YwRkcP75JQBr+z+GrFhvtj+xDyMYdjfE541InJoOWfKsfmTs7h/hg9+98P5cFfIpS5p1GLgE5HD2vd1BV7IPovo4Ml47ykdPMYw7O3BwCcih3Sg0Ih/21WAqEBv/OHpaIx1Y9jbi4FPRA7n8GUTNn14GtqpE/HHH0VjvLtdK7lTJwY+ETmUU9dq8ez/nkKI73j8aX0MJnqMkbokp8HAJyKH8U1lI9bvPAnlRHf8acMCeI1j2A8lBj4ROYTS6ptYt+MEPMbI8MGGGCgneEhdktPhwBgRSe5GYyvW/eEEzFYbsp9dhMDJ46QuySnxCp+IJFXf0o6nduTD1NiGPz4djZl+E6QuyWkx8IlIMi1mKzbsPImrpib8z7r5mBfEbQqHk12BX1NTg/j4eISGhiI+Ph61tbX9tm1oaEBAQAB+8pOf2NMlETkJs8WGH394CqdLa/FO8jw8EOordUlOz67AT09PR1xcHIqKihAXF4f09PR+27766qtYvHixPd0RkZOw2UT8x8dncfAbE7Y+Mgc/mKOSuiSXYFfg5+TkICUlBQCQkpKC3bt399nu1KlTMBqNWL58uT3dEZETEEURr+25gD1nr+PlFbPw+ALuWDVS7Ap8o9EIlarjk9nf3x9Go7FXG5vNhhdffBFvvvnmoMfLyMiATqeDTqeDyWSypzQickA2m4hf/K0Q/3v8Gp5dHIIfL5khdUkuZdDbMpctW4bKysper2/durXHc0EQ+tx55t1338UPfvADBAQMvqt8amoqUlNTAXRsxEtEzqPdasPmT87h04JybPiX6Uh7cJbUJbmcQQM/Ly+v3/f8/PxQUVEBlUqFiooKKJXKXm2OHTuGI0eO4N1330VTUxPMZjM8PT0HHO8nIufS2m7FTz46jbyLN/BSQhieWzKDWxNKwK4vXiUmJiIzMxNpaWnIzMzEypUre7X58MMPux/v3LkTer2eYU/kQhpa27ExU4+TJTX4z1WzsW7hNKlLcll2jeGnpaXhwIEDCA0NRV5eHtLS0gAAer0eGzduHJICiWj0qmpqw+MZx3H6Wsetlwx7aQmiKIpSF9EXnU4HvV4vdRlEdI8MtTfx1I58XK9vwe9+OB+xYb2HfGnoDZSdXEuHiIbclRuNWLcjH01tFnywIQa64MlSl0Rg4BPREDtbVoen/5gPuUyGP6cuQsTUiVKXRJ0Y+EQ0JCxWG3bllyJ93yVMGu+GDzbEINhnvNRl0S0Y+ERkF1EU8c9vbuC/9l7ClRtNWBQyBb9aGwV/L65n72gY+ER0zy5WNGDrZxfx5ZUqTPcZj/ee0mFZuJL32DsoBj4R3bUbja14e/9l/FlfBq+xY/DawxF4MmYa3BRccd2RMfCJ6I61mK14/8i3+N2hq2i32rDh/un4t6Wh3Ht2lGDgE9GAbDYRBWV12F9YiZyC66hsaMUKrT/SHpzFSdlRhoFPRL20Waz46mo19l8w4kChEVVNbVDIBCyaMQXvJEchJmSK1CXSPWDgExGAjjVv/nnpBvYXGnHw0g00m60Y7ybHkjAllmv9sCRMCa+xHLoZzRj4RC6qzWLF6Wt1OHa1CkevVuNsWR0sNhE+nm5IjJqK5RH+WDRjCjzGyKUulYYIA5/IRVhtIr4ur8dXV6vw1ZVqnCypQZvFBpkAzAnwRuriEMSFKxEVOAlyGW+rdEYMfKJbtLZbIRMEp7i9sM1ixfnyeuhLanGypBYniqvR2GoBAIT5TcATMUH43gwfxIRMxkQPDtW4AgY+OT1RFFHTbEZFfStuNLaiqtEMU1MbTI23/Ol83tTWEYg+nm7wm+jR/cd/ogf8JrrDz6vjsf9ED3iPG+NQXzCqbTbj1LVanLxWg1MltThXXg+zxQYACJ4yDv8aqcKiGT5YFDIFvhPcJa6WpMDAJ6dQWn0TFysbUFnfior6VlTWt+B6fSsq61tR2dDaHXy3muihgM8Ed/h6ukM7dSJ8J7jDx9Md7VYbjA2tMDa0obK+FWfL6lDdbO718+4KGVReHvD38oDKa2zn3x0fBiqvsVBOdMeU8W5QyIf2t4U2ixVlNTdx1dSM4qpmXLnRhILSWlw1NQMAxsgFaKd6IWXRNMyfNhnzp01iwBMAOwO/pqYGa9euRUlJCYKDg5GdnY1Jkyb1aldaWoqNGzeirKwMgiBg7969CA4OtqdrcnGiKOLC9Qbsv1CJf1ww4htjY/d7bnIZ/DuDeF6Qd0cQT/SAf2cIKzuD/W4mI9ssVpga22BsaEVlfRsqGzo+VCo6P1Tyi2tgbGiFxdZzewlBAKaMd4OPpzt8J7hDOcEDvhPcu/+4dw4dCejcF7rzZ7p+VhSB63Ut+LaqGd92Bryh9iZu7cbH0w2RAd549L4A6KZNwtxAb060Up/s2gBl8+bNmDx5MtLS0pCeno7a2lr88pe/7NVuyZIleOWVVxAfH4+mpibIZDKMGzduwGNzAxS6ncVqw8mSWuwvrMT+C0aU17VAJgC64MlI0PpjQfBkqLw9MHmcG2QSTDrabCKqm82orG/F9foWmBrbcOP2YaOGVpia2tBuvbt/duPc5JjuMx7TfcYjxGc8Qnw9Md1nPIJ9xvNWSeph2DZAycnJwcGDBwEAKSkpWLJkSa/ALywshMViQXx8PADA09PTni7JBZ26Votd+aX4/KIRtTfb4aaQYXGoD/49LhRx4UpM8XSM4QqZTOi+cp8T4NVvO1EUUd/SDlNjG8xWG7ouuUQRECHe8riDyssDygnuDjVfQKOTXYFvNBqhUqkAAP7+/jAajb3aXL58Gd7e3nj00UdRXFyMZcuWIT09HXJ57185MzIykJGRAQAwmUz2lEZOoMVsxX//4xJ2flUCT3cF4mYpkaD1x+KZvhjvPnqnnwRBgPc4N3iPc5O6FHIxg/6rWbZsGSorK3u9vnXr1h7PBUHo8wrEYrHgyJEjKCgoQFBQENauXYudO3diw4YNvdqmpqYiNTUVQMevJeS69CU1+I+Pz6Kk+iaeWjQNL6+YNapDnsgRDPovKC8vr9/3/Pz8UFFRAZVKhYqKCiiVvTcpDggIQFRUFEJCQgAAq1atwvHjx/sMfKLWdive/Mc32HG0GGrvsfjomRh8b4aP1GUROQW77hdLTExEZmYmACAzMxMrV67s1SY6Ohp1dXXdQzRffPEFIiIi7OmWnNSpa7X4wTtH8P6XxXhiQRByf7aYYU80hOwK/LS0NBw4cAChoaHIy8tDWloaAECv12Pjxo0AALlcjjfffBNxcXGYM2cORFHEM888Y3/l5DRa263Ytvci1vz+K7RZbPhgQwy2PjIHnhzCIRpSdt2WOZx4W6ZrOFNWhxezz+CqqRmPLwjC//nBLEzg1/yJ7tmw3ZZJdK/MFht+80UR3j14FcoJ7vjT+gVYPNNX6rKInBoDn0bcN5WNeCH7DC5cb8Bj9wXgtcQILt5FNAIY+DRirDYR7x35Fm/vv4yJYxXIWDcfy7X+UpdF5DIY+DQirlU348Xss9Bfq8UKrT+2PjLbYb4hS+QqGPg0rERRxIcnSvFfey9CLhPwq7VzsSpKzWUCiCTAwKdhU1p9E/835zwOXzbhgVAf/PfqSKi8xkpdFpHLYuDTkGq32vD5RSM+yi/DkSITPBRy/Oeq2fhhTBCv6okkxsCnIVFWcxNZJ0uRrTfA1NgGlZcH/j0uFMnRQfD38pC6PCICA5/s0HE1fwMf5ZfiSJEJAoCls5R4fEEQloQpuRE2kYNh4NOAujb1qKhvwfW6Vlyva+l43LnL061X80m6QEz15hg9kaNi4A8hURRhtYlot4pot9nQbrF1PO7c5MImirCJHVtciKIIm4ger9tsgMVmg00UYbV13LdutYmwiiJsnY9FdLQXRbHzZzs2zeg4VsdxzBYbzBYb2jr7N1tsMFut3a+320RYrR3HvbWPW1+7abagor4VFXWtMFt77gfrrpBhqvdY3BfkjTXzA7EkzHfI920loqHndIFf02xG3FsHIQgCZAK6/5YJAmSdk4YyWcfzrgEH4ZbHENDjdVtn2NrEjgAWxa5QRPdji1WE2WrrDnZHJJcJcJPL4KaQYYxcgFwmQCGTQSYD5ELHc7ms4xwp5AI8FHJEBnhjhdYDU73HQuXV8fdU77GYNG4MJ2CJRiGnC3w3hQwPz53aeRWMHle+NhG3vN57KzlRFLsfo/PKueuDQi4TIAgd4SgTBMhkAuSdHxxj5DKMkcvgJheg6Hw8Ri50hqusI0wFATIZIKDjODLhu787hroFKLpCV9bxuCt8u/qXd/7M7T/fvQF253M3haw73Lv+5ng6ETld4Hu6K/D/Vs6WugwiIofDgVciIhdhV+DX1NQgPj4eoaGhiI+PR21tbZ/tNm/eDK1Wi/DwcPz0pz+Fgy7BT0Tk1OwK/PT0dMTFxaGoqAhxcXFIT0/v1earr77C0aNHce7cOZw/fx4nT57EoUOH7OmWiIjugV2Bn5OTg5SUFABASkoKdu/e3auNIAhobW2F2WxGW1sb2tvb4efnZ0+3RER0D+wKfKPRCJVKBQDw9/eH0Wjs1WbRokWIjY2FSqWCSqVCQkICwsPD7emWiIjuwaB36SxbtgyVlZW9Xt+6dWuP5x23Bfa+9e/KlSu4ePEiDAYDACA+Ph5HjhzBAw880KttRkYGMjIyAAAmk+nO/guIiOiODBr4eXl5/b7n5+eHiooKqFQqVFRUQKlU9mrz6aefYuHChfD09AQAPPjggzh27FifgZ+amorU1FQAHRvxEhHR0LFrSCcxMRGZmZkAgMzMTKxcubJXm6CgIBw6dAgWiwXt7e04dOgQh3SIiCQgiHbcI1ldXY2kpCSUlpZi2rRpyM7OxuTJk6HX6/H73/8e77//PqxWK5577jkcPnwYgiBgxYoVePvttwc9to+PD4KDg++1NJhMJvj6+t7zz4+00VYvwJpHymirebTVCzhXzSUlJaiqqurzZ+wKfEem0+mg1+ulLuOOjbZ6AdY8UkZbzaOtXsB1auY3bYmIXAQDn4jIRTht4Hfd7TNajLZ6AdY8UkZbzaOtXsB1anbaMXwiIurJaa/wiYioJwY+EZGLcLrAz83NRVhYGDQaTZ+rdzqi4OBgzJkzB1FRUQ77DeP169dDqVRi9uzvNpe50+WxpdJXzT//+c+hVqsRFRWFqKgo7N27V8IKeyorK0NsbCwiIiKg1WrxzjvvAHDs89xfzY58nltbW7FgwQLMnTsXWq0Wr732GgCguLgYMTEx0Gg0WLt2Lcxms8SVduiv3qeffhrTp0/vPsdnzpwZ/GCiE7FYLGJISIh49epVsa2tTYyMjBQvXLggdVmDmjZtmmgymaQuY0CHDh0ST506JWq12u7XXnrpJXHbtm2iKIritm3bxM2bN0tVXp/6qvm1114T33jjDQmr6t/169fFU6dOiaIoig0NDWJoaKh44cIFhz7P/dXsyOfZZrOJjY2NoiiKotlsFhcsWCAeO3ZMXLNmjbhr1y5RFEXx2WefFd99910py+zWX70pKSnixx9/fFfHcqor/Pz8fGg0GoSEhMDNzQ3JycnIycmRuiynsHjxYkyePLnHa3eyPLaU+qrZkalUKtx3330AgAkTJiA8PBzl5eUOfZ77q9mRCYLQvbZXe3s72tvbIQgCvvjiC6xevRqAY53n/uq9F04V+OXl5QgMDOx+HhAQ4PD/5wM6/gddvnw55s+f371a6GhwJ8tjO6Lt27cjMjIS69evd6jhkVuVlJSgoKAAMTExo+Y831oz4Njn2Wq1IioqCkqlEvHx8ZgxYwa8vb2hUHSsJ+lo2XF7vV3n+JVXXkFkZCSef/55tLW1DXocpwr80erLL7/E6dOnsW/fPvz2t7/F4cOHpS7prvW3PLaj+fGPf4yrV6/izJkzUKlUePHFF6UuqZempiY89thj+PWvf42JEyf2eM9Rz/PtNTv6eZbL5Thz5gwMBgPy8/Nx6dIlqUsa0O31nj9/Htu2bcOlS5dw8uRJ1NTU4Je//OWgx3GqwFer1SgrK+t+bjAYoFarJazoznTVqFQq8cgjjyA/P1/iiu5M1/LYAPpdHtvR+Pn5QS6XQyaT4ZlnnnG4c93e3o7HHnsMTz75JB599FEAjn+e+6vZkc9zF29vb8TGxuLYsWOoq6uDxWIB4LjZ0VVvbm4uVCoVBEGAu7s7fvSjH93ROXaqwI+OjkZRURGKi4thNpuRlZWFxMREqcsaUHNzMxobG7sf79+/v8ddJY7sTpbHdjRdwQl07NXgSOdaFEVs2LAB4eHheOGFF7pfd+Tz3F/NjnyeTSYT6urqAAAtLS04cOAAwsPDERsbi08++QSAY53nvuqdNWtW9zkWRRG7d+++s3M85FPKEvvss8/E0NBQMSQkRHz99delLmdQV69eFSMjI8XIyEgxIiLCYWtOTk4W/f39RYVCIarVavH9998Xq6qqxKVLl4oajUaMi4sTq6urpS6zh75q/uEPfyjOnj1bnDNnjvjwww+L169fl7rMbkeOHBEBiHPmzBHnzp0rzp07V/zss88c+jz3V7Mjn+ezZ8+KUVFR4pw5c0StViv+4he/EEWx499idHS0OGPGDHH16tVia2urxJV26K/e2NhYcfbs2aJWqxWffPLJ7jt5BsKlFYiIXIRTDekQEVH/GPhERC6CgU9E5CIY+ERELoKBT0TkIhj4REQugoFPROQi/j/99XdQ6wfDdwAAAABJRU5ErkJggg==", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 251)\n" + ] } ], "source": [ - "# We will use the fourth sample an testing data\n", - "X_test = X[3]\n", - "mask = np.ones(X.shape[0], dtype=bool)\n", - "mask[3] = False\n", - "# Use this mask to exluce the sample from which we will extract the query\n", - "X_train = X[mask]\n", - "\n", - "q = X_test[:, 20:55]\n", - "plt.plot(q[0])\n", - "plt.show()" + "from aeon.similarity_search.series import MassSNN\n", + "\n", + "series_fit = X[2]\n", + "series_predict = X[3]\n", + "\n", + "length = 50\n", + "snn = MassSNN(length=length, normalize=False).fit(series_fit)\n", + "\n", + "plt.plot(series_fit[0], label=\"series fit\")\n", + "plt.plot(series_predict[0], label=\"series predict\")\n", + "plt.legend()\n", + "plt.show()\n", + "print(series_fit.shape)" ] }, { "cell_type": "markdown", - "id": "fcf10a34-930a-4fce-86f8-4dfa207cad11", + "id": "320ef728-ca92-4fd5-9686-2b9739fcab83", "metadata": {}, "source": [ - "Then, we can use the `QuerySearch` class to search for the top `k` matches of this query in a collection of series. The training data for `QuerySearch` can be seen as the database in which want to search for the query on." + "Then we'll take a subsequence of size `length` in another series of the same class to use in `predict` :" ] }, { "cell_type": "code", "execution_count": 4, - "id": "80eaab8f-204f-439f-84c8-ad3462f1575e", + "id": "98560db4-4289-4072-8662-2cde2ad5c44a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "match 0 : [195 26] with distance 0.1973741999473598 to q\n", - "match 1 : [92 23] with distance 0.20753669049486048 to q\n", - "match 2 : [154 22] with distance 0.21538593730366784 to q\n" + "match 0 : 177 with distance 2.550008590853018\n", + "match 1 : 176 with distance 2.6262080735121316\n", + "match 2 : 31 with distance 2.7331649479116393\n" ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "from aeon.similarity_search import QuerySearch\n", - "\n", - "# Here, the distance function (distance and normalise arguments)\n", - "top_k_search = QuerySearch(k=3, distance=\"euclidean\")\n", - "# Call fit to store X_train as the database to search in\n", - "top_k_search.fit(X_train)\n", - "distances_to_matches, best_matches = top_k_search.predict(q)\n", - "for i in range(len(best_matches)):\n", - " print(f\"match {i} : {best_matches[i]} with distance {distances_to_matches[i]} to q\")" + "starting_timestep_predict = 30\n", + "\n", + "indexes, distances = snn.predict(\n", + " series_predict[:, starting_timestep_predict : starting_timestep_predict + length],\n", + " k=3,\n", + " allow_trivial_matches=True,\n", + ")\n", + "for i in range(len(indexes)):\n", + " print(f\"match {i} : {indexes[i]} with distance {distances[i]}\")\n", + "plot_best_matches(\n", + " series_fit, series_predict, starting_timestep_predict, indexes, length\n", + ")" ] }, { "cell_type": "markdown", - "id": "3dc402cf-80b7-4d0c-b07c-2f8e7822ac97", + "id": "fcf10a34-930a-4fce-86f8-4dfa207cad11", "metadata": {}, "source": [ - "The similarity search estimators return a list of size `k`, which contains a tuple containing the location of the best matches as `(id_sample, id_timestamp)`. We can then plot the results as:" + "The `predict` method returns two lists, containing the starting timesteps of the matches in `series_fit` and the squared euclidean distance of these matches to the subsequence we gave in `predict`. Now, you can then play with the different parameters of `predict` to customize your search results to your needs!\n", + "\n", + "It is also possible to get the distance profile which is used to extract the best matches :" ] }, { "cell_type": "code", "execution_count": 5, - "id": "23efe48e-8257-4ecc-93a2-d72f19024ab5", + "id": "7d2bd3f7-7eb9-4406-be1c-b6fcd9c76730", "metadata": {}, "outputs": [ { "data": { - "image/png": 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ZAKWlpemvXXLJJZSVlQEwb9489u7de8SLo3Xr1rFixQpqamoAuP7663nhhRewWq0sX748PXK1srISAK/Xy+rVq9mxYweGYRCNRo/5nl955RV++9vfAsmLvS996Uvpr61atQqLxcK8efPo6Og45nMdiS8U5Scv7eE361vpD0QIROOY5rH/XGOVh8WTymkcygiKJ5LP5Q1GKXPbqS524nFYsVmTpwwG0NYfYm+Pn729AVp6A8QTJoFInK0HBrj76a0AuOwW3j2nlmvOmsS7ZtXqFEJEJAe09gX4y/Yuntl0gFd29RBLHHkjsVoMaoqdTChzMaHUxYQyF3WlLsrcdtr6g7R5g9gtFmIJkx2dA2xp97F2Wxdrt3Vx/vQqvvOhxUwsd4/juzs2XQfl7nWQiIzmD8f47K9e54XtXThtFt4zr45zp1URjMTo9Ufp80fwhaKYJkTiCXqHfg3J4JI3EMUXivFWq5e3Wr2HPL/dajC5wkOxK1mBMGdCCTNrS7BZDdx2K2c1VVBb4hrvty1yTAoSnaCDJ0gYhoFpmsyfP59XXnnlkMc/+eSTvPDCCzz++ON8/etf5+233z7mazidTgAsFgt2uz39mhaLhVgsRigU4nOf+xzr169n8uTJ3HXXXSc8+jT1GgBWq5VYLHZCf/5o/vVf/5WLL76Y3/3udzQ3N7NixYpTer6RazWPJ6pzENM0eeC1Fv79ma3JU94hhpEMAM2sLabUbcdlt+KyWXHZLbjsVjwOK+dNr2JefemYTA6JxBKs3dbJ05sOsLGlnz3dfp56+wBPvX2AxioPn14+navOaMDtsJ7ya4mIyIkzTZMef4T9fUHa+oPs7w/S2hfkgDdEfzDC/v4gLb3B9OOtFoMLZlRx6dw6plR5KHM7sFoMrIZBbamT6mLnCR0A9PojPLKhlR/+ZRd/3dXD5d99gZ/ddDZnTKk4HW/3pOg66Niy7TpIRA7vq49v5oXtXVQWOfjpjctYPLn8hJ/DF4ryzn4fu7sHGQjF6BoIs6trkB0dg+zvD7K7259+7Is7ug/58/PqS7nj/XO5YEb1qbwVkTGlINEJ2rdvH6+88grnnXce999/PxdeeCGzZ8+mq6sr/fvRaJTt27czd+5cWlpauPjii7nwwgt58MEHGRwcpKSkBJ/Pd9JrSF0IVVdXMzg4yMMPP5xOPS4pKWFgYACA2bNn097ezrp161i2bBkDAwPpNOsTcfbZZ3PbbbfR3d1NRUUFDzzwALfeeivnnnsun/vc59izZ086zbqyshKv10tDQwMwOpV65NoOdv755/Pggw9yww038Ktf/WpUf4JT0TMY5rYH3+DlnT0AnDO1kn94zywWTirDZbOMawqow2bhvfMn8N75EwBo9wb53Rv7eeC1feztCfCV373NN57awvsX1nPbe2bSkGWnxyIi+aatP8gb+/p5Y18fb7T0s7nNRzAaP+qfKXHZOHdaFZfOq+PSuXVUFI1dr7nKIgefWj6NVWc08KWH3+T5bV186eG3eOoLF2VNebKug3LrOkhEDi+RMPnD5gMA/Pyms1nQUHZSz1PqsnPe9CrOm151yNcCkRitfUECkThdA2E2t/nY2+PHBLoHw6xv7mNzu4/rf/w3PnL2ZM6ZWkVdqYv6smTmqcuug2PJDAWJTtDs2bP5/ve/z0033cS8efP47Gc/i8Ph4OGHH+a2227D6/USi8X4h3/4B2bNmsXHPvYxvF4vpmly2223UV5ezpVXXsk111zDY489dkjDxuNRXl7Opz71KRYsWMCECRPSadQAN954I5/5zGfSDRsfeughbr31VoLBIG63m2efffaE33N9fT133303F198cbph4wc/+EEA1qxZw9VXX00ikaC2tpY//elPfOlLX2L16tV87Wtf4/3vf3/6eS6++GLuvvtulixZwj//8z+Peo3//u//5uMf/zjf/va30w0bx8Idv9vEyzt7qCxy8G8r5/OBRfVjkhU0FurL3HxuxQxuvmgaT206wL0v7eHNln4eWt/Cy7u6eeSz51NXqhRUEZGx1OuP8IPnd/LnrZ2jTnhTSl02Gio8NJS7aSh30VDhpr7MTVWRg6piJzNqi097eXBNiZP/+dhZXPbdF9jROchPX97Dzcunn9bXPF66Dsqt6yARObzN7T76A1Eayt3Mn1h67D9wEjwOG7PqStK/vnRe3aivh2NxfvSX3XzvuR088FoLD7w23BfJajE4q7GCS4ZaU1QVOxEZL4aZpXmrS5cuZf369aN+b8uWLTzU8dC4reFwUz0kO23ZsoW5c+eO+r3ntnTwiZ+tp8hh5Q//uJxJFZ4Mre747ewc4H/9+k3ebPUyq66YX3/6PE1Ek8M63GdkodH3QE6UaZp8eM2r/G1PssFwidPGGY0VnDG5nDOmlLN4UvmYZgadqrXbOrnxp+vwOKw897/eRX9bs66D5LAOdx2kz0h9D+TIfviXXdz99FauXTqJb12zOKNr2dLu43dv7KetP0iHL0S7N/kjPtTzzmmzcPHsWtq9QfoCUS6bX8d1y6YwvaYoaw6/Jfcc7fMx5zKJdMEixyMQiXHnY+8A8I+XzsqJABHAjNoS7vv42XzoR6+wvWOQj9+3jl998hw8jpz7pyoiknUe3tDK3/b0Ulnk4AfXn8nSxoqsnjyzYnYtl8+fwDPvHGDNC7u5doZF10EiImPg5Z3J/kDZ0Atobn0pc+tHZzN5g1Fe3tnNwxta+fPWTp5550D6a//vxT38vxf3UFfq5LxpVfzzFXNVfSBjKnuvjEROwa/XtbC/P8i8+lJuPL8p08s5IRVFDn7xibNpKHfzxr5+Pv2LDURiiUwvS0Qkp/X6I3zjqS0A/Mv753LutKqsDhCl3PLuGQD87o39alosIjIGQtE4rw1llJ4/PfNBosMpc9u5YmE9P7lxGc/+03LuvnohD958Lg9/5jyuXTqJUpeNDl+YRze2cd2PXqGtP3jsJxU5Ttl/dSRyEtbt7QNg9fmNOXETcLD6Mje/+MTZVBU5eHFHN197cnOmlyQiktO+9sRm+gJRzp9exVVnNGR6OcdtQUMZCxpK6Q9ECUZ1YCAicqpe39dHOJZgzoQSakqyv9fPjNoSPnz2FM6dVsXSpkq+dc1iNt75Xv74j8uZP7GU5p4A1615hXavAkUyNnLv7lnkOGzc1w/AksnZMzb4RE2rKea+j5+NzWLwi1f3smFvb6aXJCKSk57d3MFv39iP02bh61ctzLkeDtctmwJAIDx2Y9pFRApVqtTsopnZmUV0PCwWg1l1Jdz/yXNZPKmMlt4gH//pOnyhaKaXJnlAQSLJO10DYfb3BylyWJlRW5zp5ZyShZPK+PS7pmGa8M+/fVtlZyIiJ8gbiPKV370NwP++bDZTq4syvKITt3LxRFx2C6FYgnAsnunliIjktDeGDpPPmXro2PpcU+ax87ObzmZaTRFbDwzwuV++rvsFOWUKEkne2djSD8CiSeWnfUzxeLj13TNpqvKwvWOQe1/ak+nliIjklB+9sIvOgTBLGyv4+AVTM72ck1LmtnPFgnoA+gM6JRYRORV7ewIAOX+YnFLucfCzj59NdbGDl3Z285OXdb8gp0ZBIsk7G1uS/YiWTCnP7ELGiMtu5d8+uACANS/swq9yAxGR47Z+qEfd5y+ekdMHB5ctmADAYEh7gIjIyQrH4rR5g1gtBg0V7kwvZ8xMrvTwH9cuAeD7z++kzx/J7IIkpylIlAViMV3wjaVUJtGSyeUZXcdYWj6zmjOmlNMXiPLAa/syvRwRkZyQSJhsbvMBML+h9BiPzm7nTqvCAAKROPFE/kw50zWQiIynlt4gpgkTy13Yc3C4zdG8a1YNF82sZiAU457nd2Z6OZLD8utfxjj5+te/zqxZs7jwwgv5yEc+wne+8x1WrFjB+vXrAeju7qapqQmAeDzO//7f/5tly5axaNEifvSjHwGwdu1aLrroIlauXMm8efO48847+e53v5t+jTvuuIP/+q//Gu+3lvMSCZO3WrxAfgWJDMPg1qExyD96YTehqHpSiIgcy77eAIPhGLUlTmpLXJlezikpc9ux2yyYmBnNKNU1kIjksn29fgCaqnKvP93x+PLlczAM+PkrzbT0BjK9HMlRtkwv4KTcdVfGXmPDhg08+OCDbNy4kVgsxplnnslZZ511xKe59957KSsrY926dYTDYS644ALe+973AvD666+zadMmpk6dSnNzM1dffTX/8A//QCKR4MEHH+S11147He8sr+3qGmQgHKO+zEVdaW7fEBzs4tm1zKsvZXO7j99saOWGcxszvSQRkay2qS15aDB/Ym5nEaW4bMmzPePf7gKX/fS+2GGug3QNJCK5rrk7GTiZUunJ8EpOjwUNZaxa0sDv3tjPj17YxddWLcz0kiQHKZPoBL344otcddVVeDweSktLWbly5VEf/8c//pGf//znLFmyhHPOOYeenh527NgBwNlnn83Uqckmmk1NTVRVVfHGG2/wxz/+kTPOOIOqqtzvuD/e8rHULMUwDD61PPn35ZlN7RlejYhI9ntnqNRsQUNZhlcyNpz25GVbpibX6BpIRHLdvqHsmnzNJAL43IrpAPxmfSvdg+EMr0ZyUW5mEmUhm81GIpG8aAuFQunfN02T//7v/+ayyy4b9fi1a9dSVDT6w+mTn/wk9913HwcOHOCmm246/YvOQzs6B4H8OTU+2EUzawDYsLePcCyO02bN8IpERLLXpv35lUnksFpIGAaxhEk8YWZNI25dA4lIrmjuSZabTanKz0wigJl1Jbxnbi3Pbunk539t5p/eOzvTS5Ico0yiE7R8+XIeffRRgsEgAwMDPP7440DyFGzDhg0APPzww+nHX3bZZfzP//wP0WhyZO327dvx+/2Hfe6rrrqKZ555hnXr1h1yQSXHZ0/3UJ1xdX6eDlQXO5lZW0womuCtVm+mlyMikrVMc0TT6on5kUlkGAZFzuT5XiayiXQNJCK5bl9PMpOoMY+DRACfflcym+jnr+4lENGAADkxyiQ6QWeeeSbXXXcdixcvpra2lmXLlgHwxS9+kWuvvZY1a9bw/ve/P/34T37ykzQ3N3PmmWdimiY1NTU8+uijh31uh8PBxRdfTHl5OVarMkROxt6e/G5GB8kJNzs6B3l1Vw/LmiozvRwRkax0wBeixx+h1GVjUh6NOS52WkkA0XgCN+N7raBrIBHJZfGESUtffvckSlnaWMGZU8p5fV8/T7zZzrXLJmd6SZJDcjNINB6Nq4/ijjvu4I477hhaSnItc+bM4a233ko/5mtf+xoAFouFb3zjG3zjG98Y9RwrVqxgxYoVo34vkUjw6quv8pvf/Ob0LT6PJRIme4dOB/I1kwiSQaJfvLqXv+3p5dZML0ZEJEu9s384i8gwsqMsayx4HDZ2/dPtuO1WSutKxv31dQ0kp8NNN93EE088QW1tLZs2bTrk62vXruWDH/xguo/V1VdfzZ133jney5Qc19YfJBo3qS1x4nHk5m3w8TIMg787axKv7+vn2S0dChLJCVG5WZbYvHkzM2bM4JJLLmHmzJmZXk5OOuALEY4lqC52UuzM3w/+c6Yls4fW7+3NWPNSEZFsN9y0Oj/6EaW47VYMDELROPGEmenljAldA8mNN97IM888c9THXHTRRWzcuJGNGzcqQCQnJdW0Ot9LzVLePacWgBd3dBOKxjO8Gskl+XsnPU7uGqOspnnz5rF79+4xea5C1ZzqR5TnH/ypvkQ7Ogd5q7WfpSo5ExE5xI7OAQBmT8ivIJHFYuCyWwhG4wSj8YweiugaSMbK8uXLaW5uzvQyJM+lmlY35nFbipHqy9zMn1jKO20+Xtndw8WzazO9JMkRY5JJdNNNN1FbW8uCBQsO+/W1a9dSVlbGkiVLWLJkCV/96lfH4mVFRmkugFKzlHOnJUcDv7KrJ8MrERHJTsNjjvPv4MDjSPbsCaoZqRSQV155hcWLF/O+972Pd95554iPW7NmDUuXLmXp0qV0dXWN4wol26WbVud5P6KRLhnKJvrzls4Mr0RyyZgEicYzRdQ08yO1WsZO6u9E6nRgagEEiZZNTWYPbWzpz+xCRESyVCq7NN/GHJumiXuol0YgovIBKYxr4zPPPJO9e/fy5ptvcuutt7Jq1aojPvbmm29m/fr1rF+/npqamvFbpGS9VO/SfNsXjuaSuXUAPLeloyA+K2RsjEmQaPny5VRWnv6SF5fLRU9Pj/6CS5ppmvT09OByudI3BIVQZ7ywITnOeVObN8MrERHJPv2BCL5QDI/DSk2xM9PLGTOp6yC3PXn5piCRjLwOymelpaUUFxcDcMUVVxCNRunu7s7wqiTXtHmDAHk18fJYFjaUUVPipM0bYkv7QKaXIzli3ArZUymiEydO5Dvf+Q7z588/5DFr1qxhzZo1AIdND500aRKtra1KHZVRXC4XkyZNorlnHwBNBVBn3Fjpodhpo8MXpmsgTE1J/twEiYicqvRpcaUnryabjbwO6uoPkjAh3uvCasmf9ygnLnUdlM8OHDhAXV0dhmHw2muvkUgkqKqqyvSyJMcc8IYAqCvN76DqSBaLwYpZNfxmQysv7uhi3sT86tMnp8e4BIlSKaLFxcU89dRTrFq1ih07dhzyuJtvvpmbb74ZgKVLlx7ydbvdnh59KTJSImGmbwoKoSeRxWIwr76U15p7eafNywo1ohMRSdubpxNsRl4HfePev/Hijm5++LEzuXxufYZXJnJqPvKRj7B27Vq6u7uZNGkS//Zv/0Y0GgXgM5/5DA8//DD/8z//g81mw+128+CDD+ZVAFhOv1g8QfdgGIDaksIJEkGyl+lvNrSyrrmXT79reqaXIzlgXIJEpaXDEcsrrriCz33uc3R3d1NdXT0eLy8F4IAvRDiWoLrYmdFJL+Np3sRUkMinIJFktZtuuoknnniC2tpaNm3adMjX165dywc/+MH0ze/VV1+t8cZySvZ25/8EmyWTy3lxRzdvtnq5fIGCRJLbHnjggaN+/ZZbbuGWW24Zp9VIPuoaDJMwobrYgcM2Jh1XcsbZQ71M1zX3kUiYWJR9KscwLv9CDhw4kO4jpBRROR1STavzcYrNkSwY6kv0jvoSSZYbz+EGIpC/mUQjzZmQPIDbdkA9JkREjqUQS81SJlW4qS9z4Q1G2d6pPUOObUxSLpQiKpnW3F04pWYp84dqijft92V4JSJHt3z5cpqbmzO9DCkgw2OO83dPmFNfAihIJCJyPDp8ySDRhAIMEhmGwdlTK3lsYxvr9vSmDxlEjmRMgkRKEZVMa+kbblJaKGbUFuOwWdjXG8AbjFLmtmd6SSIn7XiGG4gcr1R2aT5nEjVVFeG0WdjfH8QXilLq0h4gInIk6UyissILEgEsa0oGif62p5cbzmvK9HIkyxVWQabkrf19yZGWDeWFM9LSbrUwZ0LyJHlzm7KJJHelhhu8+eab3HrrraxateqIj12zZg1Lly5l6dKlmnQphxWMxOkcCGO3GtTn8c2A1WIwsy45ElzZRCIiR3fAl2xaXYiZRADnpPsS9abbwIgciYJEkhf29w8FiSoKJ0gEMH+i+hJJ7istLaW4OHmze8UVVxCNRunu7j7sY2+++WbWr1/P+vXrqampGc9lSo7YN9SPaFKFB5s1vy9zUiUDWxUkEhE5qkIuN4NkBUKFx06HL5zeJ0WOJL+vnqRgFGImEQz3JdrcrkwiyV0abiBjKVVqVgjlx6ls0m0HtAeIiBxNoZebGYbB0qZkNtH65r4Mr0ayXWHMCpe8Fokl6BgIYTFgQoF98M+sTWZf7OoczPBKRI5Mww1kPKWaVhfCtMvZQ0Gire3KJBIROZpCzyQCWDK5nD9t7mBTm5e/O2tSppcjWUxBIsl5B7whTBPqy1zY87y04GAzUkGiLj+maerGWrKShhvIeEql0U8uiEyiZDbpto4B7QEiIkdgmiYHFCRi3lAFwjuajCzHUFh31JKXCrUfEUBlkYNyj53BcIzOgXCmlyMiknHtQyUFhVB+XFPipKrIwUAoRtvQ+xYRkdEGwjECkTguu4VSd+HmSIxsU5FIqHm1HJmCRJLzUkGiiQVwQ3AwwzCYXpPMJtqpkjMRkXRJQaH0nRguOdPJsIjI4XR4h7OICjnjsrbERW2Jk8FwTM2r5agUJJKcV6hNq1Nm1KRKzhQkEhEptJICTTgTETm61L5QVyD7wtGksoneadPBghyZgkSS8/b3JyPhhVhuBjC9tghQ82oRkWg8QfdgGMNIlmIVglRvut1d/gyvREQkO6UmmxXagJvDmT+xDIBNbd4Mr0SymYJEkvPSPYkKNZNo6AZhpzKJRKTAdQ2EMU2oLnYWzCCDaTVDBwXaA0REDkuTzYYtaFAmkRxbYVxBSV5LlZtNKtRMolS5WadOkUWksBVaqRkMB4l2dw1immpEKiJyMJWbDUtlEm1u82rPkCNSkEhyWiJh0taf/OAvxMbVAJMqPDhsFg74QgyEoplejohIxqSakxbSjUBNsZMSpw1fKEaPP5Lp5YiIZJ0OX3ICsMrNkofqpS4b3YMRTUaWI1KQSHJa92CYSDxBZZEDj6MwR1paLQbTqlMnycomEpHClc4kKiuMfkSQnHI5nE2kPUBE5GCdyiRKMwyDeUPNqzftV18iOTwFiSSnpfoRTSwv7A/96ZpwJiJSkOVmMLwH7NYeICJyiFQmUV1p4RwgHM28+mTJmaZiypEoSCQ5rdCbVqdMTzWv1oQzESlghVhuBmpeLSJyJPGESddgMkhUKFMvj2VWXfK+YUeHgkRyeAoSSU5LNa1uKPdkeCWZNX3oBmFPt0oNRKRwDZebFVqQKJVJpD1ARGSknsEw8YRJZZEDp82a6eVkhZl1JQBs79DBghyegkSS09q9qabVhXVDcLAplckg2b7eQIZXIiKSOenmpAWaSbRbBwUiIqOk9oVaZRGlzawbblMRT2jCmRxKQSLJae3eZCZRfVlhl5ulg0Q9AY2zFJGCZJomB1LlZgWWSdRUVYRhJA8KIrFEppcjIpI1OtS0+hClLjv1ZS7CsYQOmOWwFCSSnJbKJKov8EyiyiIHRQ4rA+EY3mA008sRERl3vlCMYDSOx2GlxFlY0y5ddisN5W7iCZN9vcomEhFJ6RhIBYmUSTTScMmZ+hLJoRQkkpzW1j8UJCqwU+ODGYbBlKpkucHeHp0IiEjh6Rgx2cwwjAyvZvwNT7lUkEhEJGV4sllh3yscbFatmlfLkSlIJDkrEkvQPRjGajGoLdEH/5TKZMmd0kZFpBAdKNDJZinpvkQKEomIpHUOHSDUFujecCSz1LxajkJBIslZ6RrjEidWS+GdGh9MzatFpJAV6mSzlGnVySBRs5pXi4ikjbxfkGGp5tUqN5PDUZBIclZbf7JpdaHeEBwsFSRqUZBIRApQZ4E3J20cKjne06MgkYhISnrqpe4XRkn1JNrd5ScW18ADGU1BIslZqVPj+vLCnmyWop5EIlLI0plEBdqcdKoyiUREDtE5UNgHCEdS7LTRUO4mEk+wVwfMchAFiSRnpZpWT9TJAKByMxEpbIXenHRiuRuH1ULnQJhAJJbp5YiIZFw0nqB7MILFgKoiR6aXk3VSJWdqXi0HU5BIcla7N1VupkwigIZyN4aR/L5EYkobFZHC0jmQDBLVFmgmkdViMHlogEFztw4LRES6hvaF6mInNqtuew82s1ZTMeXw9K9Fcla7V5lEIzlsFiaWuUmYw/2aREQKRXqCTQFPu2waKjtuVl8iEZHhptUFmmF6LNNqUkEiTTiT0RQkkpyVyiRST6JhqZIz1RaLSCFJJMz0iXFNAU+waRrqS7RHfYlEREaUIRfuvnA0U7VnyBEoSCQ568BQJlG9MonS1JdIRApRXyBCLGFS5rbjslszvZyMSQWJ9iqTSEQk3bS6VplEhzWtJrln7O7yY5pmhlcj2URBIslJ4Vic7sEINotBdbFOB1KmVCWDRC0KEolIAdFpcdLUVLmZehKJiAyXmxVwGfLR1BQ7KXHa8Aaj9PojmV6OZBEFiSQnpbKI6kpdWC1GhleTPdLlZjpFFpECkj4tLvAbgabq5B6wR3uAiIgOEI7BMIzhbCKVnMkIChJJTmpXqdlhDZebqXG1iBSOzqEbgdoC7kcEUF/mxmG10DUQZjAcy/RyREQySo2rjy3VvHq3mlfLCAoSSU5S0+rDSwWJWnoDqi0WkYKhvhNJVouRLjtu1qmwiBS49AGCMomOaFr1cF8ikRQFiSQntfUrk+hwyj12Spw2BsMx+gLRTC9HRGRcdA4okyilqSrVvFp9iUSksHUMKJPoWFKZRLsUJJIRFCSSnKTJZodnGMOnyOpLJCKFIlVSoNNimDrUl6hZe4DkkJtuuona2loWLFhw2K+bpsltt93GjBkzWLRoEa+//vo4r1ByTSgapz8QxWYxqPQ4Mr2crDU1lUnUrXIzGaYgkeSkdLlZmcrNDjbcl0inyCJSGFKZRDothqahC/49KjeTHHLjjTfyzDPPHPHrTz/9NDt27GDHjh2sWbOGz372s+O4OslFXSMyTC0acnNEqSDRvp4AsXgiw6uRbKEgkeQklZsd2ci+RCIihUCNq4elys3Uk0hyyfLly6msrDzi1x977DH+/u//HsMwOPfcc+nv76e9vX0cVyi5ZjjDVPcKR+N2WGkodxNLmLT0afCNJClIJDnpwNAHf325PvgPlio3UyaRiBQC0zRHnBhrT0hlEjWrJ5Hkkf379zN58uT0rydNmsT+/fszuCLJdh2+VIapDg+OZVpNqnm1Ss4kaUyCRKojlvEUisbp9UewWw2qi/TBf7BUJpGalopIIegPRInEE5S4bLgd1kwvJ+PqS104bRa6B8MMhDTAQArPmjVrWLp0KUuXLqWrqyvTy5EMSWUSqQz52DThTA42JkEi1RHLeEo1ra4rdanG+DBUbiYihSQ1vUalZkkWi0FjlQ4LJL80NDTQ0tKS/nVraysNDQ2HfezNN9/M+vXrWb9+PTU1NeO1RMkymmx2/FITztS8WlLGJEikOmIZT21DTasnqmn1YU0sd2O1GLT7QoRj8UwvR0TktOr0qWn1wRqr1Lxa8svKlSv5+c9/jmmavPrqq5SVlVFfX5/pZUkWU6+645cqN9ulTCIZYhuPFzlSHbE+3OVktA81rZ6gptWHZbdamFjuoqU3SGtfkOlDpwMiIvko3ZxUNwJpqWk1e3t0wS+54SMf+Qhr166lu7ubSZMm8W//9m9Eo8lyyc985jNcccUVPPXUU8yYMQOPx8NPf/rTDK9Ysp3KzY7fVJWbyUHGJUh0vNasWcOaNWsAVEMsR6Sm1cc2pdJDS2+Qfb0BBYlEJK91pppW60YgrSmdSaRyM8kNDzzwwFG/bhgG3//+98dpNZIPUkEiHSof28QyNy57spedLxSl1GXP9JIkw8Zlutnx1hGrhliOR1u/ys2ORX2JJNtowIGcLp3KJDpEU3VyD2hWJpGIFKh0KbKmXh6TxWIMHy4om0gYpyCR6ohlLLV7dTJwLJOHgkT71LRUsoQGHMjpoj3hUKnSgWb1JBKRAuQPxxgIx3DaLJS6s6pwJmtNV/NqGWFM/tWojljGU+qGQJlER9ZYOdSPQplEkiWWL19Oc3PzEb9+pAEHOlCQY0mXIGtPSKsrceG0WejxR1Q6ICIFJ1WGXFfqwjA0Cfl4pJpXqy+RwBgFiVRHLOOpfWi6mXoSHZnKzSTXaMCBnKy2/lSQSHtCSqp0YFvHAHu7AyycVJbpJYmIjJvhptUqQz5eChLJSMq/k5zQ4m0hGAtiN4roDQzgtLqp9DgyvayslQoS7esNYJqmTlEkb2jAgYwUjsXpHgxjMdST6GBN1R62dQywp8evIJGIFIQ/7vwDjb94nL6glUt3BJmZaML/Ygj3+cuxWHXbezTTqpPlZru6VG4mChJJjnil9RU2d22mzx/BZ2um3O3innU7KXWWcnHTxTSWN2Z6iVmlzGOnzG3HG4zS449QXaybJ8luJzLg4OabbwZg6dKl47Y+yU7pxqSlLmzWcWmzmDOa1JdIRAqIaZq8ufsVHHs30dcfZHpvgNroG7za+xwvx1/kf53/RYocRZleZtaaOpRJ1NzjJ5EwsVh0wFzIdEUlOcEX9gEwEI4BUOQ06A320tzfTMJMZHJpWSuVTbRXzaslB2jAgZyM1LRLNa0+1NQqBYlEpHCE42GMgQEAovHkvYHDZiFU5MQwLHjsnkwuL+uVuuxUFzsJRRO0D5XrSeFSJpHkBF/YB6bJYCgZJCpxDf/VLXWWZmpZWW1KpYe393tp6Q1wVmNFppcjBU4DDuR00CCDI2usGj4VFhHJd76wD5c/mV0aiQ0FiawWwkVOSpwlar1wHKbVFNE9GGZ31yAN5dpXC5mCRJL1EmaCgfAAi//4Fg27u5gwGKFmUgVTA35a5zZQ4izJ9BKz0uQRfYlEMk0DDuR0SAWJ1LT6UFNT5WbKJhWRAjAQHsCZChINZRLZbRYCHiclDt0rHI/pNUW8tqeX3V1+LppZk+nlSAap3Eyy3mBkEBMTpz+EdTDExIEgMzu9TNrcise04bCqgfXhNFYpSCQi+S017VLlZoeqK3Xitlvp9UfwBqOZXo6IyGnlC/tw+pMHB6MyiYpdqjo4TqnDhT0qUy54ChJJ1kuVmjkD4eEPfVvyr66zQlHuI0lPONMpsojkqbb+oXIzpcUfwjCM9GGB+hKJSL5LBonCmKaZziRy2CyEPU4FiY5TU5WCRJKkIJFkPV/Yhz0cwxpLpD/0nTYLcZuFotKqDK8ue01RuZmI5LkDvmQmkcrNDq9JfYlEpEAMRAZwBsLEEiamCVaLgdViEBrqSSTHNq1Ge4YkKUgkWc8X9uEMJGuMw7GDTgZcZZlcWlarL3Nhsxgc8IUIReOZXo6IyJhr70/1JFIm0eE0pfoSdeuwQETyW6px9chSM4BwkTKJjtfkSg8WA1p6A+nvoxQmNa6WrJdKH40nTOIJE8MAm8Vg0OOkQh/6R2SzWmiocLO3J0BrX4AZtTpFEZE8ce+9RCNRzlu/h4CriJq3ymDZUnApo2ikqdVD5WY6FRaRPOcLeZkQCOONj25NoXKz4+e0WZlY7qa1L0hLX4DpNcWZXpJkiDKJJOulMolSEW2n1YJhGGpEdxxUciYiecc0ob0d/559zOhp4YKeXVifezb5+zKK+kuISKEI9XdjSZij+pfGHDbiDpumm52AdPPqLu0bhUxBIsl6qUyicCxZMqWTgeM3Wc2rRSTPhAf6MaNRBkIxAIqdNnA4lEV0GKmL/b3KJBKRPBZLxIj39wEHTTbzOAHUk+gEpPYNZaAWNpWbSdbzhX1M8IdGTCqwAqoxPh6NQ0GivcokEpE88btX76N+7wv0B0wiRoioUcLWiBtf2zrm1cyj2KH0+JSaEiceh5W+QBRvIEqZx57pJYmIjLmB8EC6f+nIyWahIidF9iJsFt3yHq90JpEyUAuaMokkq5mmmc4kGpk+CsokOh6p8cd7lUkkInki3NeFiclgOEjC8BM3+tkS3s9TO57CH9FF7UiGYdCYKjnTqbCI5KmByAAu/1CQaOSQG002O2FNChIJChJJlvNH/STMxKieRKkgUaKkGKfVmcnlZb2p1ckTdX3Qi0g+ME2TaH8vAJH4cAlyuCi5F+jg4FDp5tXaB0QkT6UOlOHQcjPtCydmWnoqpvaMQqYgkWQ1X9gHpokzECY8onE1gLOyBsMwMrm8rNdY5cEwko2ro3GNshSR3OaP+rEPJjMj08MMhkoKHFYHLpv6Eh1MzatFJN8lg0QhYHS5mYbcnLiGcjc2i0GbN0QwEs/0ciRDFCSSrOYL+7BFYlhjiVGZRHGbBU9pVYZXl/1cdisN5W7iCVMTzkQk53lD3nRJQTh9WmxNnxbr4OBQTWpeLSJ5biA8gNMfJp4wiSdMDANsFkOZRCfBZrUwJdWuolf7RqFSkEiymi/sG64xHnky4HFS6irL5NJyhkZZiki+8IV9w81JD+o7oRuBw0tnEqk3nYjkqdTeMLLUzDAMQkVOShzqSXSiplbp3qHQKUgkWS1VY5w6GbDoZOCETVMDOhHJE96wF6c/TCJhEhs6LbZbk3tCmVMHB4fTpJ5EIpLnfMF+nIHIqANl0CTkk5U+YFYGasFSkEiyWupkIBxLNSi1YhiGPvRPQOqDfrduEEQkx/lC3uSeEB99Wqy+E0dWU+ykyGHFG4zS549kejkiImMu7O3BMM1RmURRp42Ezaq94SQ0qQqh4ClIJFktlUk08kMfUCbRCZhWk5pwNpjhlYiInJrBvk4s8YN71FmJ2a2UqQT5sAzDSF/wN+tUWETyjGmahHu7gINaUxQlBxmUOFVudqKmac8oeAoSSVY7pMY4lT6qU+Pjls4k0mmAiOS4UG8ncNCI4yInGIb2hKNI9SXSBb+I5Bt/1I8jNdksNrp/qcPqwGl1ZnJ5OalJrSoKnoJEkrVM00xnEoVHjDoGZRKdiInlbhw2C50DYQbDsUwvR0TkpEX6upP/TZcgJ28EAPUkOopUX6I93WpeLSL55bADDawWwh6Hpl6epAmlLlx2C92DEXyhaKaXIxmgIJFkrUA0QCwRw+kPHdKILlbswW1zZ3J5OcNqMWiqUuNSEcltCTNBzNsLMOrgIFSUDBLp4ODI0plE2gNEJM/4wj4cgcNPQtZks5NjsRjaNwqcgkSStXxhH5jmYcvNXJW1Ohk4AWpeLSK5bjAyiGMwCIy8EbASLnLisrlw2lRScCSpPWCvys1EJM+kqg7g0FJk9ao7eVNVclbQFCSSrOUL+7BF41hjiVEf+gmrBU9pVYZXl1tSzat3d6l5tYjkJm/Ie+iNwNBpsbKIjm5kfwnTNDO8GhGRseMdmnppmibRoQMEu81CRHvDKVFfosKmIJFkLW94xA1BfLi0IOx2UKqTgROi0wARyXUj+06EDzot1o3A0VUVOSh22vCFYvQF1F9CRPJHcm+IEI0nA+B2q4HFMAh7nOpVdwpS9w4qNytMChJJ1krVGMcSCeIJE4thYLUYSh89CdMUJBKRHJc6OIgnTOIJE8NI3gyEi1y6ETgGwzBGNK/WPiAi+cMb6h/VmsJuHRpyowOEU6ID5sKmIJFkrdSp8ciyAsMwlD56EtIf9F0qNRCR3OQbKikYWX5sGIZuBI6TmpCKSD4K+HqwxBOjmlbHbRZidqsOlU/BVJUpFzQFiSRrJWuMI+kbAudQ02r1nzhxlUUOytx2BsIxugcjmV6OiMgJ8/d3JW8EYiNvBKy6EThO6dIBNa8WkTyRMBOEe7uAg5pWe5xgGLpfOAVVRQ5KhsqUe/26dyg0ChJJ1kplEo3sPQEQ9jhUWnCCDMMYnnCm5tUikoOCvR0AhONxYKhHXZFuBI7XtJrUHqAgkYjkB3/Ejz0QAhiVSRT2OHFak5Mv5eQky5R1uFCoFCSSrGSaZron0chTY1Am0clSXyIRyWWRvp7kf9N7gpWw2wGgg4PjMH1oyuUuHRSISJ4YNeRmxKGyWlOMjeEDZt07FBoFiSQr+aN+4mYcp//QIFGiuEgnAydBDehEJFfFE3Fi3l7goJKCIieAbgaOw8g9IJ5QfwkRyX0jp15GhzKJ7ENZpipDPnXKJCpcChJJVvKFfQA4g5FR6aMArooaDMPI2Npy1bShU+TdChKJSI4ZiAzgCBx0WmxLnha7bW7sVnsml5cTSlx26kqdhGMJ2vqDmV6OiMgpS/UvhUN7Eunw4NSpCqFwKUgkWckb8mLEE9hDkXRPIueIIJGcOPUkEpFc5Q0NlxSM3BPCHp0Wn4hp1cnDgp3aByQLPfPMM8yePZsZM2Zw9913H/L1++67j5qaGpYsWcKSJUv48Y9/nIFVSjYZmUk0uieRQ0GiMdCUDhIFMrwSGW8KEklW8oV9OIIRSJijTo2jTjulRZUZXl1uaqr2ALCvN0BsaCMVEckFvrAPZ3DotPigGwH1Izp+02vVX0KyUzwe5/Of/zxPP/00mzdv5oEHHmDz5s2HPO66665j48aNbNy4kU9+8pMZWKlkE1/Yh9MfJp4wiSdMDANsFiN5gKC94ZRNrRoqN+v2Y5oqUy4kChJJVkrdEMQTJgnTxGIYWA2DcJHSR0+Wx2GjvsxFNG6yX6UGIpJDUs1JD3cjoD3h+Kl5tWSr1157jRkzZjBt2jQcDgcf/vCHeeyxxzK9LMly3rAXRzA8fHhgtWDofmHMlHnsVBY5CEbjdPjCmV6OjCMFiSQrpW4IUh/6TtvQh75b6aOnIl1yptpiEckh3tDQjUAsDoBz6EYgonKzE5LuTacgkWSZ/fv3M3ny5PSvJ02axP79+w953COPPMKiRYu45ppraGlpGc8lShYa9PdhD8dG9SMyDYOoy6G9YYwM3zto3ygkYxIkUg2xjDVf2IcjEE73nkg1rda0glMzrUalBiKSewYGe7GHY6P2BNOAiA4OTsj0oT1gl/YAyUFXXnklzc3NvPXWW1x66aWsXr36sI9bs2YNS5cuZenSpXR1dY3zKmW8JMwEkd5uAKKx4clmEbcD02JobxgjTemSM/UlKiSnHCRSDbGcDslpBeFR/YgAIiotOCVTq3WKLCK5J9jXCYycbGZN3wio78Txm1jmxmW30DUQxheKZno5ImkNDQ2jMoNaW1tpaGgY9ZiqqiqcTicAn/zkJ9mwYcNhn+vmm29m/fr1rF+/npoaDTvJVwPhARzBwzetdtvcOKyOTC4vb6QOmPcok6ignHKQSDXEMtYSZoKByADOQGRU+iig/hOnSJlEIpKLwn3JbIDRNwLJm0Vllx4/i8UYcVigfUCyx7Jly9ixYwd79uwhEonw4IMPsnLlylGPaW9vT//897//PXPnzh3vZUoWSQ+5gVH3CzpQHlupTCJNOCsspxwkUg2xjDV/xE/CTOAcUW7mHMokMouLcdlcmVxeTptRo/HHIpJbovEoCZ8XYFS5WdjjxMCgxFGSyeXlnNRhwa5O7QOSPWw2G/fccw+XXXYZc+fO5dprr2X+/Pnceeed/P73vwfge9/7HvPnz2fx4sV873vf47777svsoiWjBiIDw0GiEQcIKkMeW6meRM09OlgoJLbxeJErr7ySj3zkIzidTn70ox+xevVq/vznPx/yuDVr1rBmzRoA1RAXMG84eTPgCIRHfegDOCqqM7aufNBQ7sZtt9I1EMYbjFLmtmd6SSIiR5UaZADDp8VOq4WAx0mxoxirxZrJ5eWc1IQzNSGVbHPFFVdwxRVXjPq9r371q+mff/Ob3+Sb3/zmeC9LstRgZPCwmUSDbgcVjuJMLi2vNFV7ANjXEyCeMLFajAyvSMbDKWcSqYZYxpov7AMYXW42FCRyVejvxamwWIz0KfJOnSLLONKAAzlZvrAP58E3AjaLRhyfpHTz6k6dCotI7kr2JDp8JlGJUxmmY8XjsDGh1EUknqCtP5jp5cg4OeUgkWqIZaz5wj6s0TiWaCw97thhs5CwGBSXK0h0qlKnyCo1kPGiAQdyKryhEZlEI3sSuTXi+GSk9wCVHYtIDkuVm5mmSTQ+oieR20GxMonGVCqbaHe3DhcKxSkHiVRDLGPNG/LiCEaIJ0wSJlgtBjbLUI2xbghO2Yxa9SWS8aUBB3IqfGEfjkCYWCJBPGFiMcBmMQgXOTXZ7CSk+kvs7QkQG7qxEhHJNalys1jCxBy6X7BYjGQmkXrVjanUwINmBYkKxpj0JFINsYyl1LSC8EGTzSJuB7UqLThlqSCRMolkvBxuwMHf/va3Qx73yCOP8MILLzBr1iz+8z//c9SfkcLlDXtxBsIjSs2sGIahCTYnqchpo77MRbs3RGtfkKahoJGISC4ZCA9QGzzMJGSVm425qUOZRHsUJCoYp5xJJDLWUkGig/sRRdwOnRqPAWUSSTa68soraW5u5q233uLSSy9l9erVh33cmjVrWLp0KUuXLtWAgwLhC3lH96hL3Qh4nCo3O0lqXi0iuW4w5MMeihwy5CaqcrMxl8okUpCocChIJFknHSQa+tB3pj70XRppORaaqoqwWgxaegOEovFML0cKgAYcyKkI9HVhmOaog4OY3UrcbtWecJKmqXm1iOSweCJOdNCLYY6ebBZ12khYLQoSjTFlEhUeBYkkqyTMRLoRXfgwmUS6ITh1DpuFxkoPCVMf9jI+NOBATpZpmgT7kxljqT3BabMQ8SQDisouPTlqXi0iuSzVjwhGT72MuBy4bW5sljHpqCJDJld6sBjQ2hdIf78lvylIJFllMDJIwkyMLjcbKi2guBinzZnB1eWP6amSM/UlknGgAQdyssLxMIY/GcwedSPgdmA1rDotPknpcrMuHRSISO4ZGSRKTTazD+0N6kc09pw2Kw0VbhIm7OsNZHo5Mg4UZpWs4gv7AHAEI/QdlEnkKKvM2LryzYzaYv60uUNBIhk3GnAgJ8Mb8mJPnRbHRweJSpwlGIaRyeXlrHS5mTKJRCQHpaoOYHS5WUT9iE6bpqoiWnqDNHf70/1NJX8pk0iyijfkBUg2oosl++WkehI5y6sytq58M0OlBiKSA7xhb/pGYFS5mQYZnJIJpS48Dis9/gj9gUimlyMickJGlZsdfIDgUCbR6TBtaBKmWlUUBgWJJKukM4kCEcLx4XHHAJ7y2oytK9+kRh7v7VHKqIhkr9QgA3Nk4+qh02JNNjt5FovB1OpUNpEu+EUktwyEj5xJpHKz0yN177CnR3tGIVCQSLKKL+wD08TiD2OaYLUYWC3JcgJPhYJEY6WpKjmloLnbj2maGV6NiMjheUPJTKJ4wiRhmliM5J6gTKJTp+bVIpKrUplECdMklkhex9qthsrNTqPUwcIeHSwUBAWJJKt4w15skRjRSAwYblodt1kpKVG52VipLHJQ4rIxEI7R41epgYhkp1S52chyAsMwNO1yDKT6Eql5tYjkmoHIAI5AeFQWUWpvULnZ6ZEKEjUrk6ggKEgkWSVVWhAZ0XsC0A3BGDOM4VKDvfqwF5EslcokCh9mT1C52alJ7QH7erUHiEhuSZWbReOjh9wok+j0aSh3Y7catHtDBCPxTC9HTjMFiSSrpIJE4YMmm0Vcdt0QjLHGqlQDOvUlEpHs5A17cYQio06LAZWbjYEplamyY+0BIpJbUuVmqb3BPmJvUE+i08NmtTA5tW/ogDnvKUgkWSNhJtInA5HDnAwok2hsTR3Rl0hEJNskzAQD/j7s4dhwkMhmwTQg6tTBwalqqhrOJlVvOhHJFQkzQSDgxRaNH7Q3GERddmUSnUaacFY4FCSSrDEQHsDExBGKjvrQB6C4BIfVkcHV5Z8m1RaLSBbzR/zYgmGAUdmlUacdh92F0+rM5PJyXrnHTqnLhj8SV286EckZgWgA+9DeMPJQOeK247S5dL9wGjVWaTpyoVCQSLKGN+wFGCo3S9a6Oq1WAOxl5ZlaVt5KfdArSCQi2SjVtBqGbwScNks6s9QwjEwuL+cZhpE+LFBvOhHJFQPhAZz+oSDRiFLkiEulZqdb41AVwr5eBYnynYJEkjW8oeEg0cGZRM7y6oytK1+lpxR0B1RqICJZJ9W0Ghi1J6hp9dhRXyIRyTUDkQE8viAwOpMoVOxSqdlplupJpIEH+U9BIskavrAPAHsgfEhPIldZVcbWla8qhkoNBsMxugdVaiAi2SWVSWSaJpGh7NJ0kEhNq8fEyL5EIiK5YCA8gNuXDGyPzCQKlHnUv/Q0m1KpTKJCoSCRZI1UuZkxGMI0wWoxsFqS5QSeitpMLi0vqdRARLJZKpMonjBJDO0JNosyicZSqnRgry74RSRHDEQG8HiTWfDREYfKwTIPJQ6Vm51OkyrcGAa09YfS33vJTwoSSdZIZRIxEAKSvSdSFCQ6PVKnyJpSICLZxhf24QhFh5tWjxhxrEyisTE8wEBBIhHJDQPhZLlZ6gDBYiQPlQNlHvUkOs2cNiv1pS7iCZO2/mCmlyOnkYJEkjW8IS+WeAIGk83oHCOCRCWVEzK1rLymCWcikq1S5WYH96hLNa6WU9c4VDqgbFIRyRWDQS9uX/CQ1hSBUrcyicbBZJWcFQQFiSRr+MK+5If+QafGYY+TsiL1JDodplYnP+iVSSQi2SZVbnbwjYDKzcZOTYkTt91KfyCKNxDN9HJERI4p0t2BYZoHTTazE3PalUk0DtSXqDAoSCRZIRqP4o/6cfsC6Q9958iTAX3onxYzapLf152dgxleiYjIsFgihj/qxxGMpMvNnMokGnOGYYzoS6TDAhHJftGuA8DoyWaBsuTnmDKJTr/UnqEgUX5TkEiyQqofkccXJBxPTbGxAmBWVWKz2DK2tnw2vXa4J1FMDehEJEt4Q8lBBqPKzYayS+2lFdoTxlCqN536EolItosn4hjdPcDoyWbB0qEgkQ6VT7t0uZn2jLymIJFkhVSQyO0NHNJ/wlKjptWni8dho6HcTTRuarqNiGQNX9iHNRrHGouP2hMSFoOiUpUfj6XGobLjvSo7FpEsNxgZxONLNkwe3huSTas9do8OEMaBys0Kg4JEkhW84eSpsecwQSJ7jZpWn04z64oB2NGhkjMRyQ6+sA/XYHLSZSSWzC512ixD/YjKM7iy/NNYmcwk0kGBiGS7gcgAHm/ysyoaNwGwWy1qWj2OGoeyT/f1BDBNM8OrkdNFQSLJCulMIl9guMZ4qLTAVdeQsXUVgpm1ySDRzs6BDK9ERCTJG/bi8SYvQMPpvhNWgiVu9SMaY01VmnAmIrlhIDwcJBp5qBwo86jUbJxUeOwUO20MhGP0a+BB3lKQSLKCN+TFHoqCP4Jpgs1iYLUYJCwG7pr6TC8vr82sTW6qO9S8WkSyRHLaZYBYwsQ0wTq0JwTKPAoSjbHGavUkEpHcMOjtwh5OBiZSh8o2h41QiTKJxothGMN9iZSBmrcUJJKs4A17R2cRDZWaBUvclLkrMrm0vDc9nUmkIJGIZAdf2IfHFzykaXWwzEOZqyyTS8s79aUuHDYLXQNh/OFYppcjInJE4Y42AEzTJDp0zxAv92BaDGUSjaNGBYnynoJEkhUOuSGwDd8Q6NT49JoxIkgUT6i2WEQyzxtKlpuFD9oTlEk09iwWg8kVbkAX/CKS3UI9HcBwFpHdaiGcmmymTKJxM6VKQaJ8pyCRZAVvyDs02Wy4QSlAoNStU+PTrMxtp67USTiWYH9fMNPLERHBFxrKLh0KEo3cExQkGntNQ41I1ZdIRLJZtK8bGNGPyGoQKnICaG8YR+lyM5Up5y0FiSTjwrEw4XgYjy84fGpstQIQKi+m2FGcyeUVhFQ20Q41rxaRDIvGo8T8A9jDsVElyAmrhXCRS6fFp0FqWo36EolINov19QCkS80cNgvhYheAys3G0RSVm+U9BYkk41KTzTzewCHlZpaqGiyG/pqebqnm1epLJCKZlppsBqSzSx02C8ESN8WuUqwWayaXl5eaqjXhTESyX6K/D4BILNkewW61pDOJdIAwftSTKP/p7lsyzhv2gmniHgge0rjaXjshk0srGMOZRAoSiUhmpSabAensUqfVqlKz0yh1KrxXmUQikqUi8QgW38DQz0dkEhW5MDAochRlcnkFZWK5G4sB7d7hfrKSXxQkkozzhX3YQ1Es8cTwDYHNQtxmpai8JsOrKwzTapIb655unSKLSGb5wr4RmUTDNwKabHb6DPckUpBIRLLTYGQQlz8MMGryZajISbGjWJUH48hhs1Bf5iZhwv5+9TPNR/rXJBnnC/tw+cPJcZYHfeiX6oZgXEyrTmYSKUgkIpnmDXnx+IKYpjnqtFiTzU6fhgo3VotBmzdIKBrP9HKkQD3zzDPMnj2bGTNmcPfddx/y9XA4zHXXXceMGTM455xzaG5uHv9FSsZ4B7qxh6PAcJDIbrcS8TjVjygDGjXhLK8pSCQZ5w15cfpDROMmJmCzGFgsBuFil24IxkldqRO33UqvP4I3EM30ckSkgPnCPtzeALGEiWmC1WJgtRgqNzuN7FYLkyrcmCa09umCX8ZfPB7n85//PE8//TSbN2/mgQceYPPmzaMec++991JRUcHOnTv5x3/8R7785S9naLWSCQOdremfh4f61ZmlbkyLQZlTh8rjTc2r85uCRJJxqUyig5tWh4qc+tAfJ4Zh0FQ9VHKmxqUikkG+YD/ugeCo8mOAQJlHe8JppL5EkkmvvfYaM2bMYNq0aTgcDj784Q/z2GOPjXrMY489xurVqwG45ppreO655zBNMxPLlQzwd7cBJLNMU31wypKfWxXuikwtq2BNTgWJdN+QlxQkkozzhr04/WEi8eSpgNOWnFwT9jh1ajyOpqWCRN1qXi0imRPs7cCSMEf1nIg67cScdu0Jp1GqL5HKjiUT9u/fz+TJk9O/njRpEvv37z/iY2w2G2VlZfT09IzrOiVzgj0HgGTTapNkBmS0xAVAuas8cwsrUCo3y28KEklGmaaJL+zD6Q+lT41TmUQqNxtfU9NBIn3Yi0jmRHq7kv8dsSeEipM3Ampcffqks0kVJJIct2bNGpYuXcrSpUvp6urK9HJkjIR6OgEIR4ezTMPFChJlynC5mRpX5yMFiSSjwvEwkXjksOVmkWI3xY7iTC6voOgGQUQyLRKPYHh9wHDPCactOcjAwNCecBrNqE1+b3d2KptUxl9DQwMtLS3pX7e2ttLQ0HDEx8RiMbxeL1VVVYc8180338z69etZv349NTWakpsvokMHCCNLkUNFTkBBokxIBYlaegMq+8xDYxIk0jQCOVnekBcApz+UDhI5rcm/lvaKagzDyNjaCs1UlZuJSIalBhnAyEwiK+FiFyXOEo04Po1SQaJdXdoDZPwtW7aMHTt2sGfPHiKRCA8++CArV64c9ZiVK1fys5/9DICHH36Yd7/73bpOLBCxRAyzvx8gPYHRabcSLlImUaaUexyUumwMhmP0+iOZXo6MsVO+2tI0AjkVvrAPI57AEYwcUm7mqqzN5NIKTqonUXO3TgREJDO8YS8ufxhg1J6gQQanX32pC7fdSvdghP6ALvhlfNlsNu655x4uu+wy5s6dy7XXXsv8+fO58847+f3vfw/AJz7xCXp6epgxYwb/9//+38MeTEt+GnmAcHAmUZG9CIfVkcnlFawp6kuUt2yn+gQjpxEA6WkE8+bNSz/mscce46677gKS0whuueUWTNNU9F+S/YiCEQxzdP+JiMtOaVFlhldXWCqKHJS57XiDUboGw9QONQMUERkv/aF+nIOjM4mcNgvhIhcTdFJ8WlksBtNri9i038eurkHOatQeLOPriiuu4Iorrhj1e1/96lfTP3e5XPzmN78Z72VJFugP9uEMpA4QkplErqEs0zrtDRkzpdLDpv0+9vUGOGOKJszlk1POJBrLaQRqNFd4vGEvzsFQcpxlfDhIFC5S0+pMSJecdakvkYiMP2/IizMQJnHInuBU0+pxMKNGfYlEJPv4etuxDO0JqUwiq8dOzG5VqVkGpaZi7tJ9Q97JquJ+NZorPL6wD5c/TDSeLG+yWw0shkGoyKkgUQZMU/NqOU3Uu06OR6rcLJVFZLda0nuCys1OPzWvFpFs5O9qA0geIAztD2aZBwyDCrcyWDIl3ctOe0beOeUg0VhOI5DC4wv7cAbC6dRRh9UKoFPjDElnEvUoSCRjR73r5HgN+LqxRWKjSs0SFoOI26E9YRwoSCQi2cjfnQwSpVtTWC1Ei9W0OtO0Z+SvUw4SaRqBnApvKFluFjmoabXKzTJj2lCpwY4OfdjL2BnZu87hcKR714302GOPsXr1aiDZu+65555TA/UCFOw+AIxuTBr2OMEwdCMwDtIX/JpwJiJZJNzbmfxvam+wWwgpSJRx04fuG/Z0+4kNlQNKfjjlIJGmEcjJMk0zXW52cJBI5WaZMbe+BIAt7b4Mr0TyyVj2rpP8lTATxPqT/88jqexSm4Xw0I2Ays1Ov8aqImwWg9a+YHrMtIhIpkV6k71qw0OfS06blVCRE1CQKJOKnDYmlrmIxBO09AUzvRwZQ6c83Qw0jUBOTiAaIJqI4vSHGBhxagwQK/ZQZC/K5PIKUmNVER6HlXZviF5/hMoijRSV7LJmzRrWrFkDoAEHeWYgPIBjMHmROZxJZCXsceKyuXDanJlcXkGwWy00VnnY1eVnV9cg8ycqMCcimRWNRzH7+4HRWaaDRcokygbTa4tp84bY2TmYblshuS+rGldLYekP9QMM9SQaHSRyVtWqJDEDrBaDOROS2USb25RNJGNjLHvXacBB/vKGvTj9qRHHw3uCmlaPr+macCYiWaQ/1I8rvTcMZRLZk3tDiaMEm2VMch7kJKkvUX5SkEgypj/UjzUSwx6OjfjQt2IaBkUVdRleXeFKnRxvbvdmeCWSL9S7To5Hf6gfpz8EMKoEOVzsUtPqcaRpNSKSTfpCfem9IRQdkWVa7FIWURZQkCg/KfQqGdMf6scZCGOa5ugmpUVOyj2VGV5d4Zo3MdkLSplEMlZG9q6Lx+PcdNNN6d51S5cuZeXKlXziE5/ghhtuYMaMGVRWVvLggw9metkyzrwhLy7/QXvC0GnxJN0IjBs1rxaRbNLn68QRigKke6W5HFbCbgeVbt0vZNqMGu0Z+UhBIsmYvlAfLn+YeMIknjCxGAY2i4G/yEm1uyLTyytY8+qHgkRqXi1jSL3r5Fi84eS0y1jCJGGaWC0GNouFcJFL5WbjaDiTyJ/hlYiIgK+zBTsQSySIJUwMA8wSF6bVoiBRFhiZfWqaprLA84TKzSRj+kP9OAdDo7KIDMMgXKT00UyaPaEEq8VgV5df021EZNx4g8ns0shBPerCRU6Vm40jjTQWkWzi724DIDxUauayWQkXuwEUJMoCVcVOKjx2BsMxOnzhTC9HxoiCRJIxqUZ0qX5ELnvyr2OoyKkgUQa57Fam1xQRT5hsOzCQ6eWISIHw93ViSQyXmjlsFmJ2KzGHTZlE40gjjUUkmwS62wEIpkrN7FZCRclplwoSZQf1Jco/ChJJRpimmW5SGh7RhA5QI7osoJIzERlPpmkS6u0ARkyvsSVLzUAjjsfbdF3wi0gWiCfiRHu7AQhHhw+Vw8XJvUFBouwwHCTS4XK+UJBIMsIf9RNLxHD6w6PKzQBixR6K7EWZXF7BS004e6dNE85E5PQLxUJYfcmAxPBks+RpsdWwUuwozuTyCo5OhUUkG4ycepmabJbKJHLb3Ljt7kwuT4ZMV/PqvKMgkWREf6gfAJc/TCh1ajxUbuasrFXTswybPaEEgO0d+rAXkdOvN9h7mBHHydPiUmep9oRxpiCRiGSD3mAvzsGhvSE2XG4WLnIpiyiLaM/IPwoSSUb0BfvANA9bbuaunpDJpQkws254UoGIyOmWmnYJjOhTlzwt1o3A+NOpsIhkg95gb3pvSGcS2SzaG7LMcJBIUzHzhYJEkhH9oX4coShGPDGq3Cxmt1JWVpfh1cmEUhfFThs9/gg9g5pUICKnV+q02DTNUTcC4SIXFe6KDK+u8Bw80lhEJBN6Az04/SHiCZNoPIFhJIcahItdVHmqMr08GTKxzI3bbqV7MIw3EM30cmQMKEgkGdEf6sc5GCKWMEmYJlaLgdViEC5S0+psYBiGGpeKyLjpCyYziaLx5J5gsxjYrDotzpSqIgflGmksIhnm62nDkjAJpZpW26zEnXZiDpv2hixisRhMr032k93ZpebV+UBBIsmI/lA/roOaVhuGQajIqSBRlpihcgMRGSepnkTDPeqGp13qRmD8GYaR3gN2aQ8QkQwJHWhN/nfEZLNQkRPQZLNsk75v0OFyXlCQSDIiNa0gNc4y1Y8oXKxMomyR6ku0Q82rReQ06x/owhGKpnvUuWwWTAPCbgcVLpWbZYIakYpIJiXMBJbdewAIpQ6V7VYCZR5AQaJsoz0jvyhIJOMuYSYOm0kEKJMoi+gUWUTGQzQeJdLXDYw8LbYScTsxrRb1JMqQ1AX/tg6VDojI+OsP9VPRetDeYLPSN7ESl82F2+bO5PLkINOVSZRXFCSScdcf6iduxnH6wwRH3BAAxEtL8Ng9mVyeDFEmkYiMh5GTzYbLzZIlBSWOEhxWRyaXV7Dm1pcCsO2AgkQiMv4623dS3JeclhWMDJeb9TZUUumuxDCMTC5PDpLOJNLhcl5QkEjGXXcgeSrg9IcIDX3oux3JIFFRdb0+9LPEpAoPDpuFA74QAyFNKhCR0yPVjwhGjji2Ei7WZLNMmj2hBEgGiRIJTTgTkfHV9/Y6ABKmiT8SA8CcUE64yEltUW0mlyaH0VhVhNVi0NoXTGd+Se5SkEjGXZe/CwDnYCidSeQeyiQqrZmcsXXJaFaLodRRETntUpPNgHSfOpfdqslmGVZd7KS62MlgOMb+/mCmlyMiBSa0dRMAgUgc00zuCwON1QBMKp2UyaXJYThsFhqrPJimWlXkAwWJZNx1B7pxDQSx+MPEEiZWi4HdamAaUFajD/1soiZ0InK69QZ7cQ6GiMUTxBImFiO5J4SLNNks0+bWJ7OJtqrkTETGUTwagV27ABgMJbOIip02ehuSe4KCRNlJE87yh4JEMu66A91UtvWNqC+2YhgGA1UlVJfUZXh1MtJMBYlE5DTrDfbi8oeHp9fYLBiGQajIqclmGTZnqORsa7svwysRkULSveNNjEgEgMFwMkjkLHbirSnFbrGr3CxLpXrZbdrvzfBK5FQpSCTjyjTNZJBof+8hpWa9DZVUe6ozuTw5yKy65A3CpjZ92IvI6dEX6sPpD40qNQMIFyuTKNPmTEhe8CuTSETGU9+m9emfp4JE4cZqTKuFiSUTsRi6hc1GZ0wpB+CNff0ZXYecOv0Lk3Hlj/oJhf1UtPUNB4mGmlb3T6rWDUGWObOxHEh+2MfiicwuRkTyTjwRp3+oJ1G6abU9eWminkSZl2peveWAMolEZPwEtr0NQDSeIBSNYxgQnZbMHlKpWfY6Y3Iy+/et/V4iMd035DIFiWRcdQe6KevyYY3F0+VmbruVmMOGbXIjVos1wyuUkWpLXDRWeQhE4jpJFpEx1x/qx+4PYYknCMWSe4LTbiVus2LzFOO2uzO8wsI2o7YYq8WguduvaTUiMj6CQaItzcBwFlGxw4Z3kvoRZbsyj53pNUVEYgm2qEw5pylIJOMqVWoGpC843XYrvRMrqC5WfXE2OqsxeSqwrrk3wysRkXzTHeimor0PYNTBQbDUTZXKjzPOZbcyrbqIhAk7OtSbTkROv+COLQQjAWC4aXWiqphQSfLQoKG0IWNrk2M7Y0ryvuGNfX0ZXomcCgWJZFylgkTxhEk4lsAAnHaL+hFlsWVNyZOb9Xv1YS8iY6sr0EXl/l5M0xzVp653YgU1RTUZXp0AzBlqRKqSMxEZDy1vrE3/3BeKAhCakrxHKHWWUuoszcSy5Dil+xK19Gd0HXJqFCSScdXf3Upx72A6i8hpt2IxDPomKkiUrZYOZRKtb07eyImIjJWugQ4q2nqJxk3iCROrxcBuNXRwkEVSE842tylIJCKnl2madL71CpCsOBgIxbAYYJk9AYDJpZMzuTw5Dqm+RGpendsUJJJxFdm5DWDUibG/vIhwkZMaj06Ns9H0mmLKPXY6fGFa+4KZXo6I5JFg807s4dioPSFht+GrLdOekCWWTC4HdCosIqdf+953iPV0AdA5EAagothJcHIVAAtqF2RsbXJ8ZtUV43FY2dcboHswnOnlyElSkEjGjT/ix97cAkAgMjzZrG9iMuJc5anK2NrkyCwWg7OG6ovX71VfIhEZG6ZpYu7cAYzoR+Sw0ldfTsJqUblZllg0qQzDgM1tXjWvFpHTavtffw9AwjTpGgoSORqriTlsFDuKmVU1K5PLk+Ngs1pYPKkcgNfVqiJnKUgk46bFu4/KtuSHRaoRXbHTRl99BeWuclw2VyaXJ0exdKgv0Wt79GEvImNjIDJASUsHMDq7tLehEpvFRrmrPIOrk5QSl52ZtcVE4yabNa1GRE6T7T3b8W78GwD9gSjReAKX3Up8VrLUbMmEJZqCnCPObCwHYIOaV+csBYlk3BxofgdnIIxpmsMjLd12+ieUq8Y4y10wI5nl9fzWThIJ9SUSkVPX3bWPkp4BYHQmUW9DJVXuKiyGLlGyRarkbKN6TIjIGDNNkw1tG3jktZ9R2uUlnjDZ35ecblZb4qS7KZlVemb9mZlcppyAZenDZVUg5Cpdgcm48W3dCCRLzRKmictmIVBfTtxuZXKZgkTZbGFDGRNKXRzwhXh7vzfTyxGRPDCw7S2MoZhzKpPIHBpzrKbV2WVJqhGp+hKJyBjyhrz8dONPeXz741Tu64KEyY7OAfyROA6bheKGCgJlHqaWT6XSXZnp5cpxOquxAosBb7d604dAklsUJJJxEU/Eie3cDsBAqtTMZaevPnnhqUyi7GYYBu+dXwfAHzcfyPBqRCQfRLZvASCWSBCNJ7AYEBgac6x+RNklNdJ4Y4tKB0RkbOzp28OPNvyIfd59ABTv6mB7xyD9gSg2i8HcCaX0T6sFw2B54/IMr1ZORInLztz6UmIJkze0b+QkBYlkXLR7Wylt6wFgIBQFoMRlo29iBQ6rg7riukwuT47De+cla8L/+E5HhlciIjnPNEns2gkMl5q57Fb6G5Inxcokyi6z6krwOKy09AY1rUbGXG9vL5deeikzZ87k0ksvpa/v8DeVVquVJUuWsGTJElauXDnOq5Sx9E7nO/zirV8QiAYwTZMtO7vYt66ZvkAEi2Ewe0IJboeV7inVXDD5AqZWTM30kuUEnT1VJWe5TEEiGRedm17DNlROMDDUj8hd4mKgupSGkgb1nsgB50yrpNRlY0fnILu7BjO9HBHJZX19xHq7gRFBIqeN/gnlANR4lEmUTawWg4UNZYD6EsnYu/vuu7nkkkvYsWMHl1xyCXffffdhH+d2u9m4cSMbN27k97///TivUo4lEo8QTxy7tKh9oJ3fbf0dCTOZRfrHdzoIPb8FI56gssjB4slllLjshD0OmuZfwHumvWccVi9j7eyhvkTrmhUkykW2TC9ACoP/7Q0AhGNxIrEEVotBYGoNpsVQP6IcYbdauGRuHb97Yz9/eKeDz64ozvSSRCRH+be8RTSRzCpN9SPy15YRc9gwMKjyVGVyeXIYZ0yp4G97elm3t5f3zFP2r4ydxx57jLVr1wKwevVqVqxYwb//+79ndlFyTKZpste7lw1tG9jdtxt/1A+Ay+ZiavlUzpl0Dk3lTaP+TCAa4MFNDxJLxEgkTH6/sY3eDi8faO9lRm0x1cXO9GNdi87iPXNWYRjGeL4tGSOpyciv7+0nGk9gtyohIJfo/5acdrF4lOiWTQAMpvoROW30NiZPitWPKHdcNtSX6LGN+zFNTTkTkZPTs2ld+uepfSE0JRkYqnRXYrPoDCvbpKZc/mVbV4ZXIvmmo6OD+vp6ACZMmEBHx+HL2kOhEEuXLuXcc8/l0UcfHccVysF8YR+/eOsX3LfxPt7ufDsdIAIIxUJs6d7CfRvv4/6376c3mMwkicajPLjpQbzh5ACUF3d209IXYEV7L2dMGA4Q2S12ZtXNZ/lHbtfI+xxWU+JkWnURwWicTRp6k3N0FSan3c5NL2AZSJYn9QYiABR5HOwd6j0xqXRSxtYmJ+biObVUeOxsPTDAO20+FgyVH4iIHLdEgsHtbwMQT5gMDpUgW2Ylg9DKLs1OZ0+tpMhhZeuBAfb3B2kod2d6SZJD3vOe93DgwKGDL77+9a+P+rVhGEfMHNm7dy8NDQ3s3r2bd7/73SxcuJDp06cf8rg1a9awZs0aALq6FNQca/2hfu7beB/9oX4AYvEEO7sG6fSFsVoMil025tWXYrda2N6znT19e7io8SJ29OygxdcCwJZ2H2/s62N+l5frYmE8zuQtaYWrgnk187Bf9C4o0zVmrjtnWiW7u/38ZXsXZ0ypyPRy5AQoSCSn3d5X/4ATiMYT9PqTQSLrjDriDhsTSybitutCM1c4bVZWndHAT19u5qF1LQoSiciJa2nB5+0EkoMMTMDhcTAwIXkBObVcDUqzkdNm5aKZNTzzzgH+vLWTG85tzPSSJIc8++yzR/xaXV0d7e3t1NfX097eTm1t7WEf19DQAMC0adNYsWIFb7zxxmGDRDfffDM333wzAEuXLh2D1UuKN+QdFSB6s6WfV3b3EIqO7kX02u5ezp1exYKJpUQTUf6858/pr+3t8fOnLR1M7/FxS3cvpSXJDKK6ojrmVM/BcDrhwgvH7T3J6XP5gnoeeK2F329s4wuXzFTpYA45pXIzTSOQY+ka6CD+zttDPw9jmlDuseOfkTwxPrP+zEwuT07Ch85KnvI/tnH/IRcFIiLHMvjWekKxEADeYLIvUWByNeZQv4KDe1hI9nj3nOTN+5+3aMqljJ2VK1fys5/9DICf/exnfPCDHzzkMX19fYTDycl63d3dvPzyy8ybN29c11noTNPkt1t+mw4QrW/u5fltnYSicWpKnJw3rYrzplVRV+rCH4nx3JYOHt7QijcQTT9HW3+QJ95qZ35bL7fs72RCsQOAEkcJs6tnJ4MIF1wAHk8m3qKMsQumV1Fd7GB3t5+3VXKWU04pSKRpBHI0pmny6h/uxeMLYpomHb7kTUFdiYvuyVU4rU4W1i7M8CrlRM2bWMrChjJ8oRh/eOfQ1HERkSMyTfo3/i39S99QP6LI7AlAsh9RmUsZitlqxZxkL8G/7upJT6UTOVW33347f/rTn5g5cybPPvsst99+OwDr16/nk5/8JABbtmxh6dKlLF68mIsvvpjbb79dQaJxtqN3B3u9ewHYuK+Pl3Z2YzUTfKzCwV1mhI/vO8DV4RA3LKjjfQvq8Tis7O8P8vNXm3nizTaefrudX6/bxzm721nd1klThRvDMHBYHSyoXZCcdDxrlrKI8ojNauEDiyYC8OgbbRlejZyIUyo30zQCOZKEmeCpbU9gfeFFAPqDUcKxBA6rBXN6LRGPk2V1i3DanMd4JslG1y6bzNv7vfzk5WZWLp6o9FEROT6dnQwcSN5kxOIJ/OEYGAaWucmLSJWaZbfaEheLJ5XxZquXv+7q5pK5mnImp66qqornnnvukN9funQpP/7xjwE4//zzefvtt8d7aTLCy/teBpKTindvbOGy/T2sJE6De/h2sra5ixmv7WRuQyVLG2t4xB9nc4+fnV2DuKMxrt2+nwtiESbXFGMYBlbDyqLU/UBTE3zoQ2BVs+p8suqMBu77azOPv9XGV66Yg01TznLCKQWJTnQagc1m4/bbb2fVqlWn8rKSZeKJODt7d9Lc30ynv5P+UD89wR4m7Ghnji9IMBJnV2eycXVdqYt9S5oAOGviWRlctZyKvzuzgf96djtvtvTz562dulEQkeOS2LI5XaqQyiLyTSjHUZQ8MJhaoSBRtnv3nDrebPXy29f367NfpEC0+lqTWUSmif3pt/jo+t2Uuuw0TCwFkmPvF9YuxGqxsqt3F0ZrF1WtPSy2WzlQVUJHX5DKTi/1HjsuexEAFsPCwrqFFDuKkwGij3wE7PYMvks5HRZPKqOpykNzT4A/be7gfQvrM70kOQ7HDBJpGoEczWBkkPvfvp+2gdEphCXdA8z8205C0ThbDviIJUzK3XZcc+vpn1BOY1kjE4onZGjVcqo8DhufXTGD//PEZv7jj9u5eHYtFouyieRQvb29XHfddTQ3N9PU1MSvf/1rKioOnXBhtVpZuDBZfjplyhSVJuepA+ueJxxP9hXpH5p26Z9eh2Po6+pHlP2uXTaJe57fwdOb2tnb46exqijTSxKR0+yvLX8F02Tq33bQ/XozUWBiuQsAA4PFdYvTg2jm186ny9/F9p7tEI0y6UA/kwDKXOnn89g9zK2eS4mzBBYsgFWrwKZ5SvnIMAw+dm4jX3tyC//74bdorCpi3lBwcSwkEibeYJRQLM6EUpeqG8bIMf81ahqBHEkoFuKXb/2SA4PJIGIsnqA3ECF+wMvM5zexfyBMuy+IaUKx08bMuhLePHMqNouNK2ZekeHVy6m6/pwprHlhF5vbfTy1qT1dcywyUqp33e23387dd9/N3Xfffdiy5FTvOslfZnMz+7dvAJLTLrsHk8Ei+8JJANQW1SZPlCWr1Ze5+eCSBh7e0MqPX9zD/1m1INNLEpHTqD/Uz5auLUx5ex/u13YTjSfwOKyUuZNZP4ebVFxTVEOlu5IOfwed/k68IS8mJh67h0mlk6gvrk/ezC9fDhdfDLqxz2s3XTCVN1r6efKtdj5+32t85Yq5vGduHUXO0aEI0zTxR+Ls6wmwt8dPc0+AA94gALFEsr9t10CYWMIcmpodpS8QIZ4wAZhaXcT7Fkzgo+dMYVKFmp+filMK2aamEdx+++1HnUbg8XhwOp3paQRf+tKXTuVl5TQJxULs7d/LYGQQu9VOhauCCcUTsFsPTf2MJWI8tOmhdIDonf1e/rytkwn9g1yzaS8tseGGltXFTpqqPPQ21TBQW86H532IumKlqOc6l93KLe+eyb8+uokvP/wW9WUuzmqszPSyJMuod50AYJoc+P39+KN+ANq9IRImxCaUUVyfbFS9qG5RJlcoJ+Dm5dN4eEMrv17fwj+8ZyZVxeovKJKvNh7YiMvrp+mNPWwaGkJTX5ZsOm1gMKlmGkyfmQz0HDgAPh8AVouViSUTmVgykYSZIJaI4bAO5Y06nXDVVTBnTqbelowji8XgPz60mK6BMK/t6eULD27EabPw7jm1nNVYwQs7unltTw+haOKknr/ElQxp7On284O1u1jzwm5WndHAZ1dMZ3qNDp9OxikFiW6//XauvfZa7r33XhobG/n1r38NJKcR/PCHP+THP/4xW7Zs4dOf/jQWi4VEIqFpBFlon3cff2n+C3v695Awh/5xmial3QPU7etliX0yc+e/C8uixVCTnGzy9I6n2dO/B4CdnYM8u7WDmV1ert3VTonDisVpxWIY1JY6KXHZ8ZcXse2C2Vw5+0pmV8/O1FuVMfbRs6ewbk8vv3+zjdU/Wce3r1nEe+dPwKrSMxmi3nUCEN+1k9a3k01PY/FEetqlef5MINnPYulEZRDnill1Jbx7Ti1/3trJf/95J3etnH9cf65nMMyuLj8tvQESponDZuG8aVXUlrqO/YdFZNwlzARvtL/BpC37CQRjBCJxbBaDqqHR9bZl5+D+1L+CYyj4k0hAczO8+SZs2wah5Ge9xbAkA0RWKyxdmpxgVlKSoXclmeCyW/nZx8/mwXX7ePKtdtbv7ePpTQd4etPotjZOm4XJlR6aqjw0VhUxsdyN1UiWrdWVOqkpceG0WbBZDSo8Dio8Dhw2C7F4gteae3loXQuPv9nGwxtaeeT1Vq5YWM/XVy2g3OM4wsrkcE4pSKRpBLlvZ+9O7n/7/nRwyDkYon5HOxN2deAaTH6wd9GMuXULc/8yF+ukyWyf7OEtYwu47LT1B/nDW/u5aE8H1w4OMqnh0BrTYLGLXe8/nw+dcY0CRHnGajH4v9cuJmGaPPFWO5/91es0Vnm48fwmPrR0MsVO1ZcXAvWuk6MKhdh5/z2jsojiCZN4dQmJRVMAOLvhbFw2BQpyyT9dOou/bO/ivr82c+m8Oi6YUX3Ex76xr4/vP7+LZ7ccGiS2GPCuWTX83VmTeM/cOlz2o082Ckbi7OsN0FTtwWnTFCSR02l33278vm4m7Gxn70DyvqCmxInFMGid28Dy6z87HCACsFhg2rTkj3gc9u2D3l4wTXC5kg2qi5XZUajcDisfv2AqH79gKu3eIE+9fYBN+72cPbWSS+fVUelxnHSPU5vVwvnTqzl/ejX/dOksfviXXTy8oZUn32rHbbfynQ8tHuN3k990B1fAAtEAv9vyu2QKaDzBwCu7mPHXbXQFInitFtx2K5Mq3XgcNroD3bzZ8SZNYS8HXtnE+Zh0VJXQ1uLl0z0+pjmtNFQf2rwyWlaC+8ab+PTCKzTuPk/ZrBa+e90SzpxSwU9e3sPengD/9vhm/u8ft3PThVP5zLum43boQj6fqXedHIkZibDr+/+H9t1vAuAPx2jrT/YXCK+Yi8Vi4LA6OHfSuZlcppyEBQ1l3Pbumfzns9v54m/e5KnbLqKiaPRJ7d4eP19/cgt/3JwMDjlsFubWl9JY6cFutdDjD/PSjm6e39bF89u6KHXZ+MDiifzdmZNYMrkcq8XANE329QZ4bGMbz2w6wLaOAeIJkxKnjUvm1vKF98xi6mGuP0Tk1L3e/jr1Ow5AJE6PPzlsoKbESdxmZeD8pUwum3LkP2y1wtSpyR8iB6kvc/OJC0/P343GqiK+efUiPnHhVC7/7ov89vVWPr18GjPrlL12vBQkKmBP73g6fbK7/fltnPHCZvxDXwsm4gSjcbyhKLPqSihz2/GFfbzV8VbyAaZJz1stTAvFKHHZmFpdlM4QSE0rMCfU41p9E9bSsgy8OxlPNquFmy6cyurzm/jT5g5++vIe/ranl/96bge/Xt/CnR+Yp5GXBUq96wrX/q7d7PrhN0js2glAwjTZ2TmICdgnVWIZali9bOIyPHY1mMxFn794Omu3d/LGvn4u/o+1fOqiaUyqcNMzGOGFHV28vLObaNzE47Cy+vwmbrpgKjUlow+MegbD/H6oNOCdNh/3/20f9/9tH267lanVRRzwhegdujmFZAZrQ7mb/f1BHt3Yxl+2d3Hvjcs4c8qhUxNF5OTt9+1na+dmzt7SSo8/TDxhUuy04XHYaJ05gcVN5xzXJKloNEprayuhodIzEQCXy8WkSZOw2w/tfTtWZtSW8OGzJ/PLV/fxH3/czg9vOOu0vVa+UZCoQG3r3sbbnckywL7NbSx6aQtWw2BqdRGlbhuxuMn+/iC9/ghbD/hoqiqitsSZ3gz29QYYCMWwWy3MrC3BMAwshoUlE5ZQ6ixNNqK76qpkYzopGFaLweULJnD5ggmsa+7lrt+/wzttPj77q9e5bH4dX7hkFnPrSzSesoCod11uiifiNPc30xXoIhKPYDEsNETdNBkVGPX14HYf8c8mzAR/2vhbIr+4j5LeQQAisQS7uwcJRuNYnTaiHzwTDIMKVwXLG5eP19uSMWazWrjno2fyDw++wbrmPr79h22jvm4Y8HdnTuJLl8+m7gh9h6qKnenyg60HfDyyoZWn3j7A/v4gm9uTDXBLXDbePaeWVWc0cO7UKtwOK83dfr76xGb+vLWTj/6/V/nJ6mWcf5SSNxE5fpF4hEe2PELV3i4cgyHa+5MBntoSJ6YBbfMmc1Xd8ZXvtLa2UlJSQlNTk67/BEhOMevp6aG1tZWppznT7LZ3z+ThDa08884BNrb0s2Ry+Wl9vXxhmKZpZnoRh7N06VLWr1+f6WXkpWg8yj2v3YM37CUcjmH7rz9QNBiiscpDfdnwhb8JvIaV7d4gpaEo8w2TKZUeBsMxtncMADCvvpTSoRGYs6pmMbG0AVasSI601EZQ8OIJk/v/tpe7n96KP5KceFdX6mTFrFpWzK7h/BnV6RGqcmL0GanvwenSE+jh1+/8mg5/B66BIBN2dVDT3EVRv59KdyVz6xZgnzMPFi+GWbOSJQVDTNPkmbU/xvKb3+AMREiYJh3eEK39weSIWquFwevOwTZnIgYGq5espqm8KXNvVsaEaZq8uKObhze0kjBNihw2ljZVsGJ27SGZQ8erzx9hd/cg9WVu6stch725jMUT3PG7TTy0voW6Uid//Md3aU8ZQZ+R+h4cL9M0CcVCBKIB9g/s5/k9z9MX6mPJ02/g29xOmzeI02Zh0aRy+hqriV93LdctuO64nnvLli3MmTNHASIZxTRNtm7dyty5c0/7a/37M1v5n7W7WDy5nN999vyT7nuUb472+ahMogL00r6X8Ia9AOx9eSeLBkOUuGxMGHHK56sp5Z13zSNc7MLf7uOxrZ1UeP0s6vIy1+enGJhS6UkHiOqK6qg/YzlccgnUq6xIkqwWgxvOa+KSuXX895938NyWTjp8YR5a38JD61uwWgwWTyrjQ0sn83dnTsJhs2R6ySIFrX2gnV++9Uvivd0senUHlft7SZ8lGQa9wV427t/AolgU59at4PHA/PnJYJHLxc6XHsP5p0ewJEx8oSi7OgcJx5KDEcqKnAysOhPb7IkAnDvpXAWI8oRhGCyfVcPyWTVj9pwVRQ7OKqo86mNsVgvfuHohOzoHeH1fP197YjPfVnNSkWNKmAk6BjvY693L7r7d7O3fSzgeHvWY4t5BLM3dtHmTfeRm1BZjtRjsnzuJv5tywQm9ngJEcrDx/Dvx+Ytn8NvXW3mzpZ8H17Xw0XOO0ktLAAWJCk5fsI+XW5JjiDt8Ico2NmMA06qL0yVjZ511JYMfvYad235DOOxjTn0ptaVOXtrZw59L3DzXVMd5DgsfnODBn4DyomrOv/TzGPWTMvvmJGtNLHfzzasXYZomm9t9rN3WxdptyT4Wrw/9+O/ndnDtssm8Z24dcyaUYLMmA0bhWByH1aILDJHTrGOwg/s23ofR388ZT75Od7uX9UOTyKyWZDlyVbETf9TPhvYNzK+ZTxnAunWwbh2DkUHa21/HYppEYgm2HxggljBx26001JbQfsUSApOrAFhYu5D3THtPRt+v5AerxeBb1yzmiu+9yG82tHLFonounn34BvkihcI0TcLxMAPhAfxRP4ORQXoCPXQFuugOdNMT6CGaiB79z7+wja0HkiWfE8vdlLjs+Ms8zF52OZNKc+eav7m5mQ984ANs2rTplJ5n7dq1OBwOzj///DFa2bD77ruP9773vUycOPGoj1m/fj333HPPUZ9rxYoVfOc73xmz4R4bN26kra2NK664AoDf//73bN68mdtvv31Mnn88FDtt/OsH5nHL/W/w789s5bL5dVQVqyXK0ShIVEBM0+TJHU8SS8QwTZO31+/lA/1+JpS50tOnppZPpeh9KymqnMynz/o0j2x5hN19u6kscrJy8UQGQzHavEGmVRdxwGrBZXPxd2fdjMN99NM+EUieGsyfWMb8iWV8/uIZDISiPLelkx+s3cn2jkG+++wOvvvsDqwWg9oSJ4PhGAOhGC67hYZyN9NriplTX8oHl0xkeo1GqIqMlYSZ4JEtj2AODrD4D2/S1tpHh2/4VDmWMNnROUg0nmBCmZtIPMLGAxuZXjmdSaWTSJgJtnRtIWEmME2THZ3JAFGZ2860pireec9CfDWluG1uLptxGYvrFivwK2NmRm0x/+vSWXzz6a185bdv84d/XE6pS2Vnkt+6A910+jvpDfbSF+yjL9RHX7CPQDRAJB7B5Pg6igyEouzrDdA9ECEYjTMQihLrGuCGjXtJmFBd7GRSRbIdhf/MBVx1EgH+u9bedcJ/5mTdteL0vNbatWspLi4+bUGiBQsWHDVIlCkbN25k/fr16SDRypUrWblyZYZXdeLev7Ceh2a28OKObn74l13c8X71vjwa1XYUkNfbX2dnb3LKzOZ2H5O2tmG3WmgY+uD32D00TFsM06YBUOQo4mOLPsal0y7FaU1GW4tdNmbVJbM8bBYbfzf376hUgEhOUonLzqozGnjmC8v56ceX8dFzptBQ7iaeMGn3hhgIxbBaDELRBLu6/Pxxcwffe24H7/vui/znn7YTjsUz/RZE8sKWri10DnYwb+07dO/rpcMXxjBgVl0xZ0+tZEplcvpYc0+A3V2DJBImJiY7e3fS6mulub85PS2ztS+YHmxQPbee11cuxVdTyln1Z3HL2bewZMISBYhkzH3yomksmVxOuzfEN57ckunliJx2a5vX8ut3fs2zu59lQ/sGdvftpi/URzgePmyAyDRN+vwRtncM8Ned3Ty2cT8/fnE39760hz9t7uCNlj62HvDR3uvn8reb8VgMZtWVMKO2GIthkHA6uOgDn8duzb0AbCwW4/rrr2fu3Llcc801BAIBADZs2MC73vUuzjrrLC677DLa29sB+N73vse8efNYtGgRH/7wh2lubuaHP/wh//mf/8mSJUt48cUXRz3/XXfdxerVq7noootobGzkt7/9LV/60pdYuHAhl19+OdFoMmvrq1/9KsuWLWPBggXcfPPNmKbJww8/zPr167n++utZsmQJwWCQdevWcf7557N48WLOPvtsBgaSvWDb2tq4/PLLmTlz5nFNgX3ggQdYuHAhCxYs4Mtf/nL695955hnOPPNMFi9ezCWXXALAa6+9xnnnnccZZ5zB+eefz7Zt24hEItx555089NBDLFmyhIceeoj77ruPW265BUhmab373e9m0aJFXHLJJezbtw+AG2+8kdtuu43zzz+fadOm8fDDD5/K/74xYRgG//uy2QA8+FoLA6EjZ9KJMokKRnegmz/s+gOQPDH42zvtfKqznylVHmyWZKxwRuUMLOeeN6rhtMWwcMGUC1jWsIx3Ot9hS/cWegI91BbVcuGUC2kobcjI+5H8YrEYXDy7Nl0iEI7F6fSFKXbaKPfYGQzHaOkNsqNzgL9s6+K3b+znv57bwSu7e7jv48vwOPRRJnKyTNPkxX0vUn6gn6K2PrYP9Z+YU1dKmcdOwmIQevc8tng8PLm3j0m9A1zoHeQDhonLIH34ADAYjrG/P0jMYmBfPpsty+eQsFp4/8z3s6xhWabeohQAq8XgOx9axBXfe4kH17Xw/kX1XDRz7HokiWSbClcF8YRJKBonFjcJRuN4g1H84RjReIJwLEEgEsMfiRMIx/CFkr9vmCaOeAJ7PIHFNJlowDSXjUkuGxWJOAv2dFJT6cJudacD+g6rg+mX/z11VY0ZftcnZ9u2bdx7771ccMEF3HTTTfzgBz/gC1/4ArfeeiuPPfYYNTU1PPTQQ9xxxx385Cc/4e6772bPnj04nU76+/spLy/nM5/5DMXFxXzxi1887Gvs2rWL559/ns2bN3PeeefxyCOP8K1vfYurrrqKJ598klWrVnHLLbdw5513AnDDDTfwxBNPcM0113DPPfekS8QikQjXXXcdDz30EMuWLcPn8+Eemii6ceNG3njjDZxOJ7Nnz+bWW29l8uTJh11PW1sbX/7yl9mwYQMVFRW8973v5dFHH+WCCy7gU5/6FC+88AJTp06lt7cXgDlz5vDiiy9is9l49tln+cpXvsIjjzzCV7/61VFlbvfdd1/6NW699VZWr17N6tWr+clPfsJtt93Go48+CkB7ezsvvfQSW7duZeXKlVxzzTVj8b/ylCyaVM45Uyv5255eHlrXwicvmpbpJWUt3VnlOdM02dy1mSe2P5FMPTVN/ri5gzn7e6h12agudgBQ7ammsmJiclrNYTisDs6oP4Mz6s8Yz+VLgXLarEweylyAZMbRvIl25k0s5YNLGrhu2WRue/ANXtvTy6d+vp57Vy/DZbce5RlF5Eh29u7kwOABFmxu5YA3RMKEco+dMo+dqNPOG+9bQqC8iKnAtbUlPPl2O7+sKOF5l5Wb6zxMbO3F4w2QiCd4tb+fzZOqCZ8zg7MXJw8RzphwhgJEMi5m1JbwhUtm8u0/bOPrT27hqduqNcVGclo4Fqd7MELXQJh9vQE2t/nY3TXIAV+Inf3bMTo3csW2/RgWA6dhUG0xqDAMTAMME6wJE2c8jjsaxxmP47JYKLZb8DhtFDmseBw2XPbD9H0cMUiktqiWGdOX4bjiQ+P87sfO5MmTueCCZLPtj33sY3zve9/j8ssvZ9OmTVx66aUAxONx6oeG7yxatIjrr7+eVatWsWrVquN6jfe9733Y7XYWLlxIPB7n8ssvB2DhwoU0NzcD8Pzzz/Otb32LQCBAb28v8+fP58orrxz1PNu2baO+vp5ly5L7Zmlpafprl1xyCWVlZQDMmzePvXv3HjFItG7dOlasWEFNTTJYfv311/PCCy9gtVpZvnx5evR8ZWWyIsTr9bJ69Wp27NiBYRjp7KejeeWVV/jtb38LJINeI7ObVq1ahcViYd68eXR0dBzzucbLpy6axt/29PLTl5u58fymdA/UbBKNJ2jvD9HaF2B/fzI7ezAcS7fhGAzHCIRjxE2ThAkfWFjPtcsO//fgZClIlGf8ET9tA220D7bTNtBGq6+VwcggkAwY/XVXD609flZ19DOtpgjDMLBZbMysnAlnnAEOR4bfgcixnTOtivs/dS7X/ehVXt7Zw//6zZvc85EzVMIicoISZoK1zWtx+4KU7+tmjy8EQEN58tRyzwXz+PilX+Lx7Y+zz7uP2lIXH142hYfWt7A/EOHegTjvOW8WBvDclk6ayyqo8Di4fkHyQrvSXcn7Zr4vU29PCtAnL5rKr17dy9YDAzz5djtXLs6+Hh9SeCKxBN2DYToHwnT6Qsn/DoTpGgjhDUaJxk2CkTjdg2EGQjESpkkgkswMOpKo4aAunmBCJILFMLBaDFx2Kw6rJflrq4HDYcFutWK3WnDYLNiPckNsYOCwOrBb7XjsHoodxVR7qvE4i+G6j4Izdxv9Hnx9aBgGpmkyf/58XnnllUMe/+STT/LCCy/w+OOP8/Wvf5233377mK/hHPr+WCwW7HZ7+jUtFguxWIxQKMTnPvc51q9fz+TJk7nrrrsIhUIn9D6cI/4fWK1WYrHYCf35o/nXf/1XLr74Yn73u9/R3NzMihUrTun5Rq41PSU1C7x7Ti3TaorY3eXnqU0HWDkOe4Q/HGP93j46vCG6BsN0DYTpGgzTMximPxBN//sNx+IMhmIc8CUP7I7XvPrSYz/oBClIlMMSZoJgNEhXoIu9/XvZ0buDVl/rER5r8pdtXbzZ2s+MvgHOLLanN4qZlTNx2pywTCe9kjum1xRz/6fO4arvv8yTb7Xznrm1XHVG9k3b2N4xQEtvgL5AlP5ABG8wSnWxk9kTSpgzoYRyjwKzkjmvtLzC/oH9TN+6n47+IPGESanLRonLTqjIyYwLrqSmqIbVi1fz3J7n+GvLX3E7rFy1pIGH1u9jX2+An7y0B6vFIJ4wcdgsXDZ/AjarBQODVXNW4bDq77iMH6fNyq2XzOSff/s2//nsdt63YEJWnhRLdvvFq3sJR+OUuu04bRZC0TjBSJxQLJH8eTROOJr8eSSWIGGaxM3k9XYiYaaDPJ2+MJ0DIfoCJ9f/xGoxqC52UFvior7MxZz6UmbWFjOx3E2RK8Qfnnmdxf6Bk3pui2Gh2FFMmbOMCncFZc4yrJbDZGVfeik05HZ7iX379vHKK69w3nnncf/993PhhRcye/Zsurq60r8fjUbZvn07c+fOpaWlhYsvvpgLL7yQBx98kMHBQUpKSvD5fCe9hlRAqLq6msHBQR5++OF0CVZJSUm679Ds2bNpb29n3bp1LFu2jIGBgXS52Yk4++yzue222+ju7qaiooIHHniAW2+9lXPPPZfPfe5z7NmzJ11uVllZidfrpWHo//PIkrKRazvY+eefz4MPPsgNN9zAr371Ky666KITXud4s1gMPnHhVO743SZ+/OJurlxUf9oOmXd3DfLNp7fyl+1dRGKJ4/5zhgH1ZS4mV3hoqHBT5rZT7LRR7LJR7LRR4rLhcdiwWQwMg3TfyLGkIFEWisajtPpaiZtx4ok4cTNOLBEjGo/ij/rp8nexf2A/fcG+I04uME2T/mCUTl+Y/f0Bdnb6CURiWA34FDEqipIX7TWeGmqLamHmTKhUA2rJLbPqSvj/rpzPlx55izsffYdlTZVMqhj7D8oTZZomL+7o5vvP7+Rve3qP+LjpNUU8979WjN/CREZo7m/mz3v+jDUap/ydVjb3J3sRNQz9G+qY18g1k88DwGqx8t7p78Xx/7d35+FR1mejx7/P7JklCSSZBJNAyAokJBAC1FZRsIIeFCqgorgigktV6oVWL4++nh4RjstrPbzWHlypVemrRVFBUFDUqqAkUJQ1QiJZICQhezKTWX7nj4EIskjWmYT7c11zzfo8c89vZp57cue36E1sKN5AhNXI9NwENu6rpri6GY/PT2qMnQsznNgtgZ8W5yaey8CIgcF5ceKsNmNUAs9v2Mu+yibe3VrOjFGh9w8EEdqWfr6XksMtXbY/nRZYJcwZbibGbsbpsOAMN+N0mImwmjDpA72Aou1mIsKM6HQaFoOOflbTKYdM+pWfT0+zBtHRnkFmgxmjzohJb8JqtGIz2bAarZj15tP/cRwRARddBNnZnX35QZeRkcFzzz3H7NmzGTZsGLfffjsmk4m3336bu+++m7q6OrxeL/Pnzyc9PZ3rrruOuro6lFLcfffdREZGcvnllzNjxgxWrlzJkiVL2l0QiYyM5NZbbyUrK4u4uLi24WQQmOj5tttuIywsjK+//pp//OMf3HXXXbS0tBAWFsa6deva/ZoHDBjA4sWLGT9+PEopJk+ezNSpUwFYunQp06ZNw+/343Q6+fjjj7n//vu58cYbeeyxx5g8eXLbfsaPH8/ixYsZMWIEDz744HHPsWTJEm6++WaefPJJYmJieOWVV9odZzBMz03g6Y/2sK20jm+KDjM2OarLn6OwooFrXthEVWNgIZCRAyMZHG0jxhE4BsQ4zETZzERaAxPB+/wKi1GP1aTHGW7GbAjuNBqaCqX+X8fIy8tj8+bNwQ4jKGpaanh207Pt2kYpRXVTK/sqm9h/uJlDDa4TKpaRYUbm6H2ctyvQ28husjMibgQGnQFmzQoUioToZZRSzHstn492VDB2cH/evPVXQZ2DYltpLY+v3snGfYHikMNiYNSgwBCcSKuRcIuRg3UudlU0kOa089SVJ58H7JeczcfIo6QNOsbldbHlwBbWF63H6/cSt7MU74p8Gt1eou1mUp12fAYdvvn3MDFr6nHbKqV4f8/7FBwoaLvN6/Pj8vjbikMA2bHZ/G7I79Bp0oNDBMeKglLu/e9/k9g/jPX3XojJcPZ9FuUY2fE2+Otne6mod1Hf4qXV58di0BFm0mMx6rEYdJiNesKMgesmgw69DnSadswJLEY9MY5AYSjKZkbfDb9N1r/7DAnrviHMGIbFYCHMEDjX6/SnPv5qWmB6CYPhp5PNBlZrYEiZ1RpY6TglBXSd/97s3LmToUOHdno/ou8J5mfjmY/38Oz6Qn47NJYXb8zr0n3/cKiBmUs3UtXYynmp0Tx9VQ6x4ZYufY6ucLrjo/QkCkEn7epJ4Me5TyncHj8V9S4qG9y0eHzUu7wcqG2hxXP8cuA2k4FYu4nEMAPpDjNDqutI2RIoEEVaIslyZgUKRP37Q2pqt78uIbqDpmksmjacgv21bCo6zIv/2sfccSk9HodSir9+to8n1u5CKYgIMzLvgmSu/9UgHJbet1ys6B2UUrh9blp9rfiVH7/y4/Mf6X3q99Dqa8XtdVPvrqeyuZKDjQepaKzApwL5wu/z4/p8Dy63F5NeR1JUoBdRfXoSl2RMPOH5NE3jsvTLiDBH8FXJV7h9bgx6HfYjw3msRiuTUiaRHZstc4SJoJo6Ip7nPv2BvZVNvJVfwqyxvXNVJhEct13Q878jOuKiS++A864Hrxd8vp9ORxkMYDQGCj8WS+C6ph23krEQZ6Przx3E85/tZf2uCvZWNpISY++S/R5qcHHjy99S1djK+WnRvHBDXq9cXKfPFYn+mV/Kv0trcTrMxIZbSI6xMyjKitmgw6DTYdBrR8bvBQ6OPr/C7T0yrvhn562+QE8cv1/R6PZS2+yhvLaF6qZWNA0MOg2PT+Hx+fEeOW89ctnr9+P1K3z+wO2B88B1r1/hPXqb34/fD2ajDpNeh8+vaPE28aNrLxN2l+JBwwN40fBqoI4c0/V+hc3nJ8brw+LxoleKMJ2OOIuB2DADkWYDYSiMbg/az/qKneM4h5R+KT8Voy65RJKF6NWi7GaemDGc2a9u5qm1ezg/LYah3TCJ26m4vT4e+Od3vLOlDIA55w3mrglpRFilOCQ6p6SuhJW7V7YVfrx+b9tQZIXCr858jPvP1Ta3suNfPzChrAaA5Bhb29wt2ZfPIcx48jkQdJqOC5Iu4DcDf0NRTRH7avbR2NpIfHg82bHZWI3BH/IphF6nce/FGdz5RgFL1v/A9NyEXvlDXYjTMpt79YTSQgRLtN3M9Nx43vymhOc++YH/vHpEp/fZ3OplzrLNlNW2MHJgZK8tEEEfLBJ9XljJyq3lv/g4nRboFuptz9ThPUTRSrPOw9CSqhPu047EbTPpsZkNgZUKjDrs5sDEepqmgfKDq/W47WxGGzG2wPxDx/2AHzMG0tO7+yUJ0e0mDInl2rEDeWPTfn7/RgHv/f48bObuP8Q1ub3Mey2ff/1QhdWk55mrRzApM67bn1ecHXzKR1XzibmgvZRSNLX6qGpwU9Xo5sfDzZQcbmbazjKMeh2pTjsRYYGiZnhGNilDf/2L+zToDKRFpZEWJUOVRWi6NCuOoQPC2Xmgnr9v/JE55ycHOyQhhBAh4o4LU3k7v5R3tpZx67jkTv2DWSnFgrf+zbbSOgb2t/bqAhH0wSLRzNEDyUmI5FCDm/LaFvZWNlJW24LHG+jZc7R3j//ICgQAZoMuMMbYqMNs+On86Ph1DbBbDIRbjAyItOB0WAJDv/wKg16HSa9h1Osw6HUYj17WaUd6LR29rEOv0zDqA0vcHdurSacFlrxze/0Y9To0zc/SjRvJ3V+OTgt079e0QBxn0n3fqDPSL6wfDpMDu8mOw+wIDCv7OaczsGKBEH3E/5w8lG+LDlN4qJEHVnzH/505oluHvNQ2t3LTK9+ytaSWaLuZZbNHk3lORLc9n+gaNU2tOCyGXrHikd+v0eT20nJkZZ2j526vHwjksqO9U4+et/Vw9f/U07XF48P1syHJ6TUN/NrtZmB8RFu+CzeHk3XZLUF4pUJ0PZ1O475J6cx+dTPPritk6oh4YhzS60IIIQQk9rcya+wgXv2qmCfX7ublmzq+0vdfNuxl9XcHcZgNvHzTaKLtvTvX9Lki0bkpUZybcvoZypUKFIv8SmHS60Ju3gSlFJEmwxlNsqjX9Bj1RmxGG+HmcCItkYSbw3/5NSUlwfTpgXHKQvQRVpOB56/LZcp/fcn7/y4nOz6CW8d1z3+OK+pdXP/SJvZUNJLQL4y/3zKWpGhbtzyX6FoPvfsdn+w6ROY5EQyJc+CwGLGbA70zbebA8qJWkx77MdcD9+m7NWfUNXvYebCe70rr2FZWx3eltfxQXUqjYV+X7N9s1BNtMxFtN5OmvMwoP4DF+dMY/Dh7HGlJo9BnDu+S5xMiFIzPcDI+I4ZPd1ey+MNdPH1VxxYLEEII0ff8fkIqb20u4ZNdh9i0r7pDK519squCpz7ajabBn2eOINXZNfMbBVOfKxKdCU0L9OgJVZqmkeRIpJ+lHzpNh04L/FGi03QYdUaMeiNWo5VwczgmvemXd2ix/DRmOTERRo6E+HiZh0j0SalOB/9nejZ3vbmFhat3AnR5oejH6iaue2kTJYdbSHPaee2WscRFhN6qBeLkDje14vL4yf+xhvwfa9q1rcWoY1B/G0nRVpKibSRFBU6Doqw4HebT9k7y+vw0uLw0uLwcanCxp6KRPRUNFB5qoLCikUMN7hO20Wt6wvUaSc0ujGYDRrMBk8mA0WhA6QIr6JiVwqIUNo8Pi9+PCYVRgUUpTH4/Ri1w2eH1YKpuxFjuwX64se05THoTKf1SiLXHwgUXdslqNkKECk3T+I/LM/nyh8/5Z0Ep14xJJC+pf7DDEkIIEQKi7WbmjkvhmXV7+N+rdvDenee1a5XkvZWN3PPmVpSCBRPTuWhobDdG23POyiJRbzBr9GywjA6sUHDsigVKBU7HrlYQFha4rtcHbjOZApf1+sD9Bnmbxdnl8pxzqG3x8PC737Nw9U5cHh93XdQ186bsPtjAdS9torLBTU5CBK/ePIZ+tjMo1oqQsXzuudQ0tbKtrI6iykaaWn00ub00ub00uo9cbvXSeOS2JrePptbAZZfHz+6KBnZXNJywX50G/W0mTHodRoMO45GCUYPLQ4PLS3Or74RtjhVm1JMeaycrPoLshAiy4iOIDvfw6vqtjH3nmy5tA72mJ9ISSZw9jihrVGCp5LQ0yOvaZWCFCAVJ0TbmXZDMkk9+YMFb/+aDu8/H3gNz1gkhhAh9t44bzPJv9/N9WT1v55dy1ejEM9quweVh7t820+D2cmlWHHeO7zurhUuGDFUmE4waFewohOi1rv/VICwGHX/85zae/ngPLq+PBRMzOjVUqGB/DTe/8i11LR7OTY7ihRvz5A+NXqqfzcQF6TFckB7Tru3qWjzsr26mqLqJ4qomio+c7z/cQnWTm6rG1lNuq9PAYTHisBjobzORGmMnLdZBeqyd9FgH8ZFhJ/z3qt5dj+4XFljQa3r0Oj0aWluv06Ono/cZdAbCDGFYjVZsJhthhrDjvwt2O/zud9LDVPRZd45P5eMdFew62MAjK7/nP68aEeyQhBC9lNfrxSD/hO8zrCYDD1w6hHuWb+WJtbu5dHgcDsvpp2Spa/Fw8yvfsLeyiYxYB09dmRNyU9h0hny6hRB91pV5iZiNev7wj6089+leaps9/MflmWc039fP/auwirmvbaa51cfFw2JZcs3IXr1qgeiYiDAjwxMiGJ5w4gTlrV4/tS2tgYmjvX48Pj8KcFgMOCxGbCZ9u39A2Iw2bs6+Advm44ce67SjCytonf9RYrXCVVeBTebUEn2Xxajnv64dyWVL/sWKgjJGDerHrLGDuvx5XB4f28vrKaxooKiqicNNrTS4vOh1GhajnrgIM4n9rPS3mYgIMxIeFigcu71Hh6MGeh7WNnuoaW4lJcbOJVmyYqboAx59NGjPsXDhQpYtW4bT6SQxMZFRo0bxwQcf8NRTT5GXl0dVVRV5eXkUFxfj8/l44IEH2LBhA263mzvvvJN58+axYcMGHn74Yfr168euXbuYOXMm/fv3Z/78+QA89NBDOJ1O7rnnnu5/naLLTck5h2VfFVOwv5aH3/2eZ64+9eI3h5taueHlTXxfVk98ZBgv3pjXIysq96S+9WqEEOJnpuScg9mg4643tvD6pv3sPtjAkmtHMiAi7Iy2r6h38dynP/DGpv14/YppI+N5YkZ2r1gZS/Qsk0GH09G1c1PpdXqizf3AeGaf13ZxOGDECBg9GsI7vuyrEL1FqtPB/5qSyR//+R0PvfM9Xp/ixl8ndWqfSim2l9fzya5DfL6nkm2ldbT6/F0TMIEcJkUiITouPz+f5cuXs3XrVrxeL7m5uYw6zWiNl156iYiICL799lvcbje/+c1vmDhxIgAFBQV8//33DB48mOLiYqZNm8b8+fPx+/0sX76cb77p2qHhoudomsaiadlc8ZcveXdrOelxDu648MThY4caXFz3YmDhmqQoK6/f+iviI7vhN1qQSZFICNHnTcqM479vO5fbXstn8481XPjkBm78dRLX/2oQif2tJ92mutHNXz/by9++/hG314+mwa3nD+bBS4e2a0I7ITrNaAysSHmyOergxDnqjs5JZzIFbtfrA4+x2QInkynwuKgomaRanHWuHj2QBpeXx1bt5D/e2843xYe5+ddJ5A7sd8bHdr9fsfNgPZ/sPMQ7W8vYV9nUdp+mQUasg2HnhJMSYyPGYcZuNuJXipZWH2W1LZTWtFDb3Eq9y0NdS6DnkMWoP9Lr0IDDbCTSaiTCaiQ7PrKbWkKIs8MXX3zBFVdcgdUa+L03ZcqU0z7+o48+Ytu2bbz99tsA1NXVUVhYiMlkYsyYMQwePBiApKQkoqKi2LJlCxUVFYwcOZKoqPavjCVCR0acgz9fPYJ5f8/nybW70Wsac85PRn8kN+w8UM8drxdQVNVEmtPO63PG4gzvmwvXSJFICHFWGJEYyft3ncfD737Pmu0HWfr5PpZ+vo+B/a2kx9rbDvKNLi+FhxoprGjAe2QumEsy4/jDxelkxDmC+RLE2So2Fm66KdhRCNFnzDk/GZvZwMPvfs+qbQdYte0ANpOetFgHJoMOn19h1GuEGfVYjjnpdVBc1cz28jpqmj1t+4u2m5mYGcv4DCdjBvcnIuz0c1kIIYLPYDDg9wd6/blcrrbblVIsWbKESZMmHff4DRs2YPvZsOw5c+bw6quvcvDgQWbPnt39QYtuNzEzjj9eMoTFH+5i0Ye7+GDbAXISIzhQ62L9rkMADBsQzmu3jCHKbg5ytN1HikRCiLNGjMPMX68fxbbSWv7fZ/v4orCS/Yeb2X+4+YTHahqMz4jh3oszTjr/jBBCiN7rmjEDuSA9htc3/ciKgjIO1LnYWlJ7xtsPiLBwXmo0/2P4AM5Pi5YhyEKEqHHjxnHTTTfx4IMP4vV6ef/995k3bx5JSUnk5+czZsyYtl5DAJMmTeL5559nwoQJGI1G9uzZQ3x8/En3fcUVV/DII4/g8Xh44403euoliW522wUpZMQ6eHDFd3xXVsd3ZXUAmA06rhkzkD9cnN7n/xkgRSIhxFknOyGS52bl4vMrdh6op7SmmYp6NzqdhtWoZ1CUlSEDwmXlMiGE6MPOiQzjvklDuG/SEKob3fxwqBG/AoNew+P10+Lx4fL4cXl8tHh8eHx+EvtZyYhzkNAvrE+tZCNEt+uJiatPIjc3l6uvvpqcnBycTiejR48GYMGCBVx11VUsXbqUyZMntz1+zpw5FBcXk5ubi1KKmJgY3n333ZPu22QyMX78eCIjI9HrZTGTvmT8ECcf3TuOdTsqaHR70TSNScNi++zwsp/TlFKnX1s3SPLy8ti8eXOwwxBCiJAkx0hpAyGEOB05RkobhIKdO3cydOjQYIfR5tFHH8Vut7NgwYJO78vv95Obm8tbb71FWlpaF0R3dgm1z8bZ5nTHR+kbK4QQQgghhBBCnKEdO3aQmprKRRddJAUi0efIWAohhBBCCCGEEH3eo1007G3YsGHs27evS/YlRKiRnkRCCCGEEEIIIYQQQopEQgghhBBCiOB46623yMzMRKfTnXb+oDVr1pCRkUFqaiqLFy/uwQhFZ4XoFLgiiOQzEdqkSCSEEEIIIYQIiqysLFasWMG4ceNO+Rifz8edd97Jhx9+yI4dO3jzzTfZsWNHD0YpOspisVBdXS1FAdFGKUV1dTUWy9mxUlhvJHMSCSGEEEIIIYLiTFY3+uabb0hNTSU5ORmAmTNnsnLlSoYNG9bd4YlOSkhIoLS0lMrKymCHIkKIxWIhISEh2GGIU5AikRBCCCGEECJklZWVkZiY2HY9ISGBTZs2BTEicaaMRiODBw8OdhhCiHaQIpEQQgghhBCi2/z2t7/l4MGDJ9y+cOFCpk6d2qXPtXTpUpYuXQogvVeEEKIDpEgkhBBCCCGE6Dbr1q3r1Pbx8fGUlJS0XS8tLSU+Pv6kj507dy5z584FIC8vr1PPK4QQZyOZuFoIIYQQQggRskaPHk1hYSFFRUW0trayfPlypkyZEuywhBCiT9JUiE41Hx0dTVJSUoe2raysJCYmpmsD6kISX+eFeowSX+dIfL+suLiYqqqqoMYQbJIngkfi65xQjw9CP0aJ75f1ljzxzjvvcNddd1FZWUlkZCQjRoxg7dq1lJeXM2fOHFavXg3A6tWrmT9/Pj6fj9mzZ/PQQw/94r4lTwSPxNc5El/nhHp8EPwYT5cjQrZI1Bl5eXls3rw52GGcksTXeaEeo8TXORKf6G6h/h5KfJ0j8XVeqMco8YnuFurvocTXORJf50h8nRfKMcpwMyGEEEIIIYQQQgghRSIhhBBCCCGEEEII0UeLREdXNAhVEl/nhXqMEl/nSHyiu4X6eyjxdY7E13mhHqPEJ7pbqL+HEl/nSHydI/F1XijH2CfnJBJCCCGEEEIIIYQQ7dMnexIJIYQQQgghhBBCiPbpc0WiNWvWkJGRQWpqKosXLw52OJSUlDB+/HiGDRtGZmYmzz77LACHDx/m4osvJi0tjYsvvpiampqgxunz+Rg5ciSXXXYZAEVFRYwdO5bU1FSuvvpqWltbgxZbbW0tM2bMYMiQIQwdOpSvv/46pNrvmWeeITMzk6ysLK655hpcLldQ22/27Nk4nU6ysrLabjtVeymluPvuu0lNTSU7O5uCgoKgxHffffcxZMgQsrOzueKKK6itrW27b9GiRaSmppKRkcHatWuDEt9RTz/9NJqmtS0XGYz2E50neaJjJE90nOSJzscneUL0JMkT7RfKOQIkT7SX5Imuj++oXpEnVB/i9XpVcnKy2rt3r3K73So7O1tt3749qDGVl5er/Px8pZRS9fX1Ki0tTW3fvl3dd999atGiRUoppRYtWqTuv//+YIapnn76aXXNNdeoyZMnK6WUuvLKK9Wbb76plFJq3rx56i9/+UvQYrvhhhvUCy+8oJRSyu12q5qampBpv9LSUpWUlKSam5uVUoF2e+WVV4Lafp999pnKz89XmZmZbbedqr1WrVqlLrnkEuX3+9XXX3+txowZE5T41q5dqzwej1JKqfvvv78tvu3bt6vs7GzlcrnUvn37VHJysvJ6vT0en1JK7d+/X02cOFENHDhQVVZWKqWC036icyRPdJzkiY6RPNE18UmeED1F8kTHhHKOUEryRHtJnuj6+JTqPXmiTxWJvvrqKzVx4sS2648//rh6/PHHgxjRiaZMmaI++ugjlZ6ersrLy5VSgQN/enp60GIqKSlREyZMUOvXr1eTJ09Wfr9fRUVFtX3Jft6uPam2tlYlJSUpv99/3O2h0n6lpaUqISFBVVdXK4/HoyZPnqzWrFkT9PYrKio67qB0qvaaO3eueuONN076uJ6M71grVqxQ1157rVLqxO/wxIkT1VdffRWU+KZPn662bt2qBg0a1HZQD1b7iY6TPNExkic6TvJE18R3LMkTojtJnmi/UM4RSkme6CjJE10fX2/JE31quFlZWRmJiYlt1xMSEigrKwtiRMcrLi5my5YtjB07loqKCgYMGABAXFwcFRUVQYtr/vz5PPHEE+h0gY9DdXU1kZGRGAwGILjtWFRURExMDDfffDMjR45kzpw5NDU1hUz7xcfHs2DBAgYOHMiAAQOIiIhg1KhRIdN+R52qvULxO/Pyyy9z6aWXAqET38qVK4mPjycnJ+e420MlPnHmQv09kzzRfpInuobkic6RPNF3hPp7Fop5IpRzBEie6CqSJzqnN+WJPlUkCmWNjY1Mnz6dP//5z4SHhx93n6ZpaJoWlLg++OADnE4no0aNCsrz/xKv10tBQQG33347W7ZswWaznTA2PJjtV1NTw8qVKykqKqK8vJympibWrFkTlFjOVDDb65csXLgQg8HArFmzgh1Km+bmZh5//HH+9Kc/BTsU0cdJnugYyRNdT/JE+0ieED0lFPNEqOcIkDzRHSRPtE9vyxN9qkgUHx9PSUlJ2/XS0lLi4+ODGFGAx+Nh+vTpzJo1i2nTpgEQGxvLgQMHADhw4ABOpzMosX355Ze89957JCUlMXPmTD755BPuueceamtr8Xq9QHDbMSEhgYSEBMaOHQvAjBkzKCgoCJn2W7duHYMHDyYmJgaj0ci0adP48ssvQ6b9jjpVe4XSd+bVV1/lgw8+4PXXX29LOqEQ3969eykqKiInJ4ekpCRKS0vJzc3l4MGDIRGfaJ9Qfc8kT3Sc5ImuIXmi4yRP9C2h+p6Fap4I9RwBkie6iuSJjutteaJPFYlGjx5NYWEhRUVFtLa2snz5cqZMmRLUmJRS3HLLLQwdOpR777237fYpU6awbNkyAJYtW8bUqVODEt+iRYsoLS2luLiY5cuXM2HCBF5//XXGjx/P22+/HfT44uLiSExMZPfu3QCsX7+eYcOGhUz7DRw4kI0bN9Lc3IxSqi2+UGm/o07VXlOmTOFvf/sbSik2btxIREREWzfSnrRmzRqeeOIJ3nvvPaxW63FxL1++HLfbTVFREYWFhYwZM6ZHYxs+fDiHDh2iuLiY4uJiEhISKCgoIC4uLmTaT5w5yRPtJ3micyRPdA3JE6KnSJ5on1DPESB5oqtInui4XpcngjMVUvdZtWqVSktLU8nJyeqxxx4Ldjjqiy++UIAaPny4ysnJUTk5OWrVqlWqqqpKTZgwQaWmpqqLLrpIVVdXBztU9emnn7atSLB37141evRolZKSombMmKFcLlfQ4tqyZYsaNWqUGj58uJo6dao6fPhwSLXfI488ojIyMlRmZqa67rrrlMvlCmr7zZw5U8XFxSmDwaDi4+PViy++eMr28vv96o477lDJyckqKytLffvtt0GJLyUlRSUkJLR9R+bNm9f2+Mcee0wlJyer9PR0tXr16qDEd6xjJ5oLRvuJzpM80XGSJzpG8kTn45M8IXqS5ImOCdUcoZTkifaSPNH18R0r1POEppRSwS1TCSGEEEIIIYQQQohg61PDzYQQQgghhBBCCCFEx0iRSAghhBBCCCGEEEJIkUgIIYQQQgghhBBCSJFICCGEEEIIIYQQQiBFIiGEEEIIIYQQQgiBFImEEEIIIYQQQgghBFIkEkIIIYQQQgghhBBIkUgIIYQQQgghhBBCAP8fB/n2YoDJK9oAAAAASUVORK5CYII=", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -262,205 +299,310 @@ } ], "source": [ - "plot_best_matches(top_k_search, best_matches)" + "distance_profile = snn.compute_distance_profile(\n", + " series_predict[:, starting_timestep_predict : starting_timestep_predict + length],\n", + ")\n", + "plt.figure(figsize=(7, 2))\n", + "plt.plot(distance_profile)\n", + "plt.show()" ] }, { "cell_type": "markdown", - "id": "877b1b32-d978-4c54-a4e7-b475496f710a", + "id": "b5240535-5123-4ac5-a5e0-e0502ef80b3e", "metadata": {}, "source": [ - "You may also want to search not for the top-k matches, but for all matches below a threshold on the distance from the query to a candidate. To do so, you can use the `threshold` parameter of `QuerySearch` :" + "### 1.2 Motif search with StompMotif estimator" + ] + }, + { + "attachments": { + "f492cb89-5bf3-4641-8be2-a77805f20b88.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "6aecb58e-9de9-4264-959e-4180ab3fa27a", + "metadata": {}, + "source": [ + "When doing motif search, it's important to define the type of motif you want to extract from a series. We'll use the figure and definitions given by [1] and make some adjustement to clear out some confusion due to the naming of each method:\n", + "\n", + "![image.png](attachment:f492cb89-5bf3-4641-8be2-a77805f20b88.png)\n", + "\n", + "For now, the `StompMotif` estimators supports only the following configuration, which you will have to specify using the parameters of the `predict` method :\n", + "\n", + "- for **\"Pair Motifs\"** : This is the default configuration with ```{\"motif_size\": 1}```, meaning we extract the closest match to each candidate, so we end up with the pair ```(candidate, closest match)```\n", + "\n", + "- for **\"k-motif\"**, which we define as the extension of **Pair motifs** to : ```{\"motif_size\": k}```. For ```k=2```, we would extract ```(candidate, closest match 1, closest match 2)```\n", + "\n", + "- for **\"r-motifs\"**, which we renamed from **k-motif** in the figure, because it is a range-based method : ```{\"motif_size\": np.inf, \"dist_threshold\": r, \"motif_extraction_method\": \"r_motifs\"}```\n", + "\n", + "These configuration will extract the best motif only, if you want to extract more than one motifs, you can use the `k` parameter to extract the `top-k` motifs. \n", + "\n", + "**The term `k` of `top-k` motifs, while also used in `k-motifs`, is not the same. To avoid confusion of both terms, we use `motif_size` instead of `k` to specify the size of the motifs to extract. This avoids the phrasing \"extracting the `top-k` `k-motif`\", which would be confusing and ill defined. Rather, we extract the `top-k` `motif_size-motifs`**.\n", + "\n", + "The `top-k` using `motif_extraction_method=\"r_motifs\"` will be the motifs with the highest cardinality (i.e. the more matches in range `r`), while for `motif_extraction_method=\"k_motifs\"`,which is the default value, the best motifs will be those who minimize the maximum pairwise distance." ] }, { "cell_type": "code", "execution_count": 6, - "id": "23ad7adb-2b01-4425-a2e8-c393f3721a0f", + "id": "ff23faf5-2941-441a-8c4c-0cf66eaca121", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "match 0 : [195 26] with distance 0.1973741999473598 to q\n", - "match 1 : [92 23] with distance 0.20753669049486048 to q\n", - "match 2 : [154 22] with distance 0.21538593730366784 to q\n", - "match 3 : [176 25] with distance 0.21889484294879047 to q\n", - "match 4 : [23 20] with distance 0.22668346183441293 to q\n", - "match 5 : [167 23] with distance 0.24774491003815066 to q\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\antoine\\Documents\\aeon\\aeon\\similarity_search\\query_search.py:270: UserWarning: Only 6 matches are bellow the threshold of 0.25, while k=inf. The number of returned match will be 6.\n", - " return extract_top_k_and_threshold_from_distance_profiles(\n" - ] + "data": { + "text/plain": [ + "([array([[ 40, 192]]), array([[192, 40]]), array([[158, 8]])],\n", + " [array([0.21749257]), array([0.21749257]), array([0.23961497])])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "# Here, the distance function (distance and normalise arguments)\n", - "top_k_search = QuerySearch(k=np.inf, threshold=0.25, distance=\"euclidean\")\n", - "top_k_search.fit(X_train)\n", - "distances_to_matches, best_matches = top_k_search.predict(q)\n", - "for i in range(len(best_matches)):\n", - " print(f\"match {i} : {best_matches[i]} with distance {distances_to_matches[i]} to q\")" + "from aeon.similarity_search.series import StompMotif\n", + "\n", + "motif = StompMotif(length=length, normalize=True)\n", + "motif.fit_predict(series_fit, k=3, motif_size=1)" ] }, { "cell_type": "markdown", - "id": "0efd83a5-b36f-4809-be96-94de734d931c", + "id": "ace51787-71c2-4f0e-bf37-b46b51ace354", "metadata": {}, "source": [ - "You may also combine the `k` and `threshold` parameter :" + "The above use of `fit_predict` is equivalent to the following calls, with `is_self_computation=True` indicating that the series in fit is the same that the series in predict, so it shouldn't match the same subsequences as motifs : " ] }, { "cell_type": "code", "execution_count": 7, - "id": "65db1593-3873-4a47-9e2a-d8dfcf42dd1a", + "id": "c5dde2db-178b-444c-99ab-f659137638b8", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "match 0 : [195 26] with distance 0.1973741999473598 to q\n", - "match 1 : [92 23] with distance 0.20753669049486048 to q\n", - "match 2 : [154 22] with distance 0.21538593730366784 to q\n" - ] + "data": { + "text/plain": [ + "([array([[ 40, 192]]), array([[192, 40]]), array([[158, 8]])],\n", + " [array([0.21749257]), array([0.21749257]), array([0.23961497])])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "# Here, the distance function (distance and normalise arguments)\n", - "top_k_search = QuerySearch(k=3, threshold=0.25, distance=\"euclidean\")\n", - "top_k_search.fit(X_train)\n", - "distances_to_matches, best_matches = top_k_search.predict(q)\n", - "for i in range(len(best_matches)):\n", - " print(f\"match {i} : {best_matches[i]} with distance {distances_to_matches[i]} to q\")" + "motif = StompMotif(length=length, normalize=True)\n", + "motif.fit(series_fit)\n", + "motif.predict(series_fit, k=3, motif_size=1, is_self_computation=True)" ] }, { "cell_type": "markdown", - "id": "ff62a385-d58e-4fb1-95dd-eb0474711531", + "id": "d16036a3-f5b9-41d2-ae23-a1bcf0737c93", "metadata": {}, "source": [ - "It is also possible to return the **worst** matches (not that the title of the plots are not accurate here) to the query, by using the `inverse_distance` parameter :" + "While the above example only use `series_fit` to search motifs in the same series, we also support giving another series in `predict`, which will use this series to search for the motifs matching subsequences in the series given during `fit`. For those familiar with the matrix profile notations, this is the case of using `MP(A,B)`, while not using a series in `predict` is doing a self matrix profile `MP(A,A)`." ] }, { "cell_type": "code", "execution_count": 8, - "id": "6d6078ab-9104-462e-9856-1d0fc9594b24", + "id": "59117ea7-2cbf-49d6-829a-792805b4aaf7", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "([array([[149, 4]]), array([[ 62, 201]]), array([[3, 5]])],\n", + " [array([0.15686187]), array([0.27831027]), array([0.29831867])])" ] }, + "execution_count": 8, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "# Here, the distance function (distance and normalise arguments)\n", - "top_k_search = QuerySearch(k=3, inverse_distance=True, distance=\"euclidean\")\n", - "top_k_search.fit(X_train)\n", - "distances_to_matches, best_matches = top_k_search.predict(q)\n", - "plot_best_matches(top_k_search, best_matches)" - ] - }, - { - "cell_type": "markdown", - "id": "b5240535-5123-4ac5-a5e0-e0502ef80b3e", - "metadata": {}, - "source": [ - "## Using the speed_up option for similarity search" + "from aeon.similarity_search.series import StompMotif\n", + "\n", + "motif.predict(\n", + " series_predict,\n", + " k=3,\n", + " motif_size=1,\n", + ")" ] }, { "cell_type": "markdown", - "id": "b5e13c31-2aa3-4987-8d44-8a296c81a318", + "id": "9190fdf4-db3d-4d51-b2c8-41b88a9f6f74", "metadata": {}, "source": [ - "In the similarity search module, we implement different kind of optimization to decrease the time necessary to extract the best matches to a query. You can find more information about these optimization in the other notebooks of the similarity search module. An utility function is available to list the optimizations currently implemented in aeon :" + "You can also return the matrix profile with the same parameterization as `predict` (minus `motif_extraction_method` parameter) using :" ] }, { "cell_type": "code", "execution_count": 9, - "id": "d22e2d74-f44d-4c81-ba1b-72d618bd5862", + "id": "4c36738a-e6a0-4452-aee2-ccbad99d6d8b", "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "{'normalised euclidean': ['fastest', 'Mueen'],\n", - " 'euclidean': ['fastest', 'Mueen'],\n", - " 'normalised squared': ['fastest', 'Mueen'],\n", - " 'squared': ['fastest', 'Mueen']}" + "
" ] }, - "execution_count": 9, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "QuerySearch.get_speedup_function_names()" + "MP, IP = motif.compute_matrix_profile(series_predict)\n", + "\n", + "plt.figure(figsize=(7, 2))\n", + "plt.plot([MP[i][0] for i in range(len(MP))])\n", + "plt.show()" ] }, { "cell_type": "markdown", - "id": "bf12616c-6ace-478b-806f-5419c2c19f2b", + "id": "7d2522e0-e6f4-412e-b0cb-2945016d188a", "metadata": {}, "source": [ - "By default, the `fastest` option is used, which use the best optimisation available. You can change this behavior by using the values of t with the corresponding distance function and normalization options in the estimators, for example with a `QuerySearch` using the `normalised euclidean` distance:" + "# 2. Collection estimators\n", + "\n", + "Now, we'll explore estimators of the `collection` module, where you must provide single series of shape `(n_cases, n_channels, n_timepoints)` during fit and predict." ] }, { - "cell_type": "code", - "execution_count": 10, - "id": "6313f26a-5788-42dc-881a-40746458414c", + "cell_type": "markdown", + "id": "5aea3e4f-e613-4646-b012-e64c5ec9586f", "metadata": {}, - "outputs": [], "source": [ - "top_k_search = QuerySearch(distance=\"euclidean\", normalise=True, speed_up=\"Mueen\")" + "## 2.1 Approximate nearest neighbors with RandomProjectionIndexANN\n", + "\n", + "This method uses a random projection locality sensitive hashing index based on cosine similarity. W we define a hash function as a boolean operatio such as, given a random vector ``V`` of shape ``(n_channels, L)`` and a time ser ``X`` of shape ``(n_channels, n_timeponts)`` (with ``L<=n_timepoints``), we com \n", + " ``X.V > 0`` to obtainhash of ``X``e \r\n", + " In the case where ``L 0``` instead.\n", + "\n", + "The ```RandomProjectionIndexANN``` estimators use the parameter ```n_hash_funcs``` to create that much random hash function as defined above. Each series `X` of the collection given in fit is then represented as an array of ```n_hash_funcs``` boolean, which is then hashed to a dictionnary as ``h(bool_arry): case_id_array}```.\n", + "\n", + "To compute the nearest neighbors of a series ``X`` given in predict, we first transform this series to a boolean array using our previously defined hash functions, and theusedthe resulting o `h(bool_aryy)``` to look at the bucket in which ``X`` falls, and consider the ```case_id_array``` as the indexes of its neighbors. If this bucket doesn't exists, we compute a distance matrix between the boolean array of ``X`` and every boolean array making the keys of the dictionnary to get similar buckets.\n", + "\n", + "This method will not provide exact results, but will perform approximate searchs. This also ignore any temporal correlation and consider series as high dimensional points due to the cosine similarity distance.y distance.\r\n" ] }, { - "cell_type": "markdown", - "id": "6ab51d84-7220-4333-b50e-2db695eaf45d", + "cell_type": "code", + "execution_count": 10, + "id": "cc719800-0119-42f9-9018-32288c2db69b", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "match 0 : 32 with distance 1.0\n" + ] + }, + { + "data": { + "image/png": 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oAkwD0dTUhNtvv10Fa6Rd1htvvIHPPvvMLijy3HPPqZ+dBFPk5/buu++qII6Fo6Oj+nlkZGSo/0Z79+7FP//zP6v7li9frgIjEgSR9yUX2dfuJDgkAZPq6mocOHAAn376qQoG3X///XaPy87OVj8/CVrJRR4rP5+RxPZcRERERERERERkVVMDZGTImTygogKQzGkJjFiyu7tfHwp5zn37gIsXgS1bgJiY4dt/Ipo0JLjws5/9TFUrSJWHnMCX7S996UuqSuGFF15QXyMiItTj5eS8BDXk9v/6r/9S991zzz2qKkJMmzZNf+6AgAD1NSQk5IYzRSQAIoGEhIQEtS2VJhIokaoICcKkpqaqyo99+/bpJ/2ffPJJ/fvldSXIsGjRIlWRId/T2+tLBYUET375y1/isce0eS/ymrfccovd/sj73Lx5s7r+wx/+UFWEZGVlYcaMGTf8mUoQprW1FX/+85/1+SLyelKRI0Gl0NBQ/Od//if+8R//Ed/4xjf075N9t5B5JRZSASKP/5u/+Rv8+te/hqurq6oIkf9mUmHTlz179qj/nrm5ueq/s5B9kvciwRzL60lwRap9pGJHPProo+p7u1ffDCcGTYiIiIiIiIiIpjoZ1iuBErkUF4/e60pQ5vnngYULgfXrATe30XttIhr3li5dqk6+Wyxbtgw/+clPVBssOeEuX6XSwpZUlkjLKSEts77yla9g9+7dWL9+vQqgzJ49e9D7IW2wLAETIYEFCRZI8MP2Nkv7LXH69GnVXkoqTWTYu5z8FxLIkSBLby5fvqz2f926df3uj+17kNZZQl57IEETeQ1p6WU7kH3FihVq/6TqxcHBAcXFxf3ug1Sm/OhHP8KVK1dQX1+vqlwkECMVOfKzGgjZDwmWWAImQn4uEkCS+yxBE/k5WwImlvdr+3MeCQyaEBERERERERFNVSUl2vyR3Nyx3Y9Tp7TKlk2bADnpx0HxRHQDUrHh5OSkghPy1ZYlmPHUU0+pVlQffPCBCpzIiX4Junzta18b1Gu5uLjYbUtgobfbLIERSwssubzyyitqALoES2S7ra2tz9eRGSOD3R9LUMny2jfL/Qb7IG3G7rzzThWMkmoPqZg5fPgwvvjFL6r3NtCgyUD193MeKQyaEBERERERERFNNdL7XoYlHzkiZ9pG9rXkhN5A2njJYN/XXgOSkoDgYPv75ISotN9JTpZm+iO2q0Q0vhw/ftxu+9ixY0hKSlJBknnz5qlKE6k6WLlyZZ/PIZUM0jpKLjIv5Pe//70KmkgbKWE7vH24SAVGVVWVmr1hqaQ4JcFhG729vrw3CVpI+ykJ+IyElJQU1e5KAjuWapPPP/9czSmRFmje3t6qukP2wTKs3pYEqSRoIcEn+R7x+uuv93hvN/q5yn4UFBSoi+VnJDNcamtr+6zEGS38K0NERERERERENJXk52uD2A8fHtmAiQRLVqyQ5vvSY2fg1SMywFmCObaXQ4e0gMrvfgcUFo7cPhPRuCLVGc8884xqG/XXv/4Vv/jFL/Q5G9KW6+GHH8YXvvAF7Ny5U83GkIHxUk0ilSWW2RuffPKJui89PV3NHJGT9SI2NlZVLchw8YqKClW5MlxiYmJU4ED2V4abyyB1GQpvq7fXd3Nzw7e+9S01VF3me8gQdAkU/fGPfxy2fZOfmbyOzEy5ePGi+plIEElmhVgGzv/bv/2bCorIHJbMzEz1s5P3ImQgvMx4sbw3me3ym9/8xu41JOgi70cCL5WVlaptV3fSLk1mzcj+yPPLfzv5b7lq1SoslJaNY4iVJkREREREREREU4HRKJN3gZMnhz7AfaDkxNu2bVp1iNi4EZBBzO++C5SVDf15S0sBOXkoJ9Sk3z5noBANiK/Bt0ebo/GwTzciJ9FbWlqwePFiVV0iAZOnn35av18GvluGlhcVFSEoKEjNQZH2UUKqHb761a+isLAQPj4+2LhxoxokLyIjI9UQ9W9/+9t44okn1GtJBcZwkHZc8lzf/e53VeBh/vz5+N///V9s3bpVf0xfr/+9730Pzs7O+P73v69mi8gMD6mSGS7SPksCSfKzlLkhsi2zXn7605/qj3nsscfUjBL5WcnQefm57tixQ90n81DksTI0Xip3br31VhWokv23WL58udrn+++/X1Xc/OAHP1CBGFsSMNq1a5cK2MhzSNWK/PexBGfGkkNn50j/lRxdMnjG19cXdXV16h8CEREREREREdGUDpRI5calS9rX9vbBP4dUiNheut9muy3DeufOBZYs0VpqdSftWo4e1VqDSYuwmyEzCyQYIy27uu+vM/OEiYSc+JYqi/j4eFVdQDRVf9/rBxE34F8QIiKiybo4zsoCioq0hbEsJBMSOFCTiIiIaCpobQWuXdMCJXJMOJTghL8/MHMmIH3lw8KG7zhSAim33KI97/vvAzk5Q38uaaXz5pu93xcUBCxbBixYMPTnJyKiKYlBEyIioslCeoRevQpcvqwtPm0Xx9KCITYW2LJFW0ASERER0eTS0qIdC0qgJDtbq+gYLBnou3ix9F4Z3kBJbwICgEcfBc6fBz75RDuWHU6VlcB772nD5VevHt7nJiKiSY1BEyIioomgrU1b/F65AhQXa9sWlsVsXV3/vanz8rSBnytXatl9bFlARERENLHJsZ8cH54+rSXN3MxQd5k9Ir32JVgyWuQ4VgI0iYnA7t3AuXPD/xrSBkze04wZw//cREQ0KfFsCRER0Xgl2XbSVkEqRyRgcrM9n4VkHMrCMSNDqzqJiRmOPSUiIiKi0VZfD+zapR0n3gxJpFmzRmtlJZUmY8HTE9i+Hbj1Vmt7WVs1NcCxY/0eD3eYO1DaWKourR2tcHF0gZerF4I8ghDw1htw+fJXWHFNREQDwqAJERHReCLVIpItKBepDLmZbMH+VFQAzz8PTJ+uZd3JzBNZrBIRERHR+K8uuXAB+PBDbXbJzZAEmm3bgMBAjAuyH33ty7x5wAcf9DoDpbK5EplVmTCajHZBlJaOFlQ0V8Ch8grcfpIJx6e/DH/fUHV/Z2cn5H+9XRcODg7wd/NHsGcwHB1uPpgkz1veVI6SxhKYO80I9ghGpE/ksDw3ERENLwZNiIiIxpoEMCRIIhUl0nprNEkli1ykNUJ0tBY8kSDKeFk4ExEREZFVU5M2PF2OG4fK1VVLnJk9G0hKGtm5JcNJjk9lBsrFi8DHH6ufhbHDiMzqTBU06Y8ERFpK8lH+4rP4YFXqoN6zu7M7kgKTkByYjMSARBicDQP+XgmOFNQV4HLlZVypvILa1toezz09cDqSg5KR4J8wqOcmIqKR49BpCaFPEvX19fD19UVdXR18fHzGeneIiGgUNRgbcK3qGuqMdWqB0p2bsxtCPEMQ5xcHVydXjBn50yttBywVJTKkcrwJDrYGUCIjJ85imoiIiGiykkQXacclgZPBMhi0Y7vUVCAhAXBxwUTR2NaI4oZiFXCQY3w5jeXQ2grD0ZOoSD8MGG2qbTo7YW5oRVWjEW4uTvD1cIFzt5Zj2QsTUDAzekj74uTghHj/ePgYfOwqVCyn1izX5X8mswn5dfloam8a1HPPDp2NtOA0ODk6DWkfibprbW1Fbm4u4uPj4ebmNta7QzRmv++DiRuw0oSIiCY8yTDbm7sXp4pPwdRpuuHjpb+xZInNCJqhMrvcXdxHZT9RVgacOgVcvar1oB5pcoAgAQ9pYTDYHAmpfpHL4cOAl5c1gBIfzwHyRERERKPt5EmtNdVgjwXl+E0CJdOmTahjOAmQXK64rCo0pFLD0jrLjozmi5mvb5rMnUjPr4Hfx+cwq6ZF3SZ5Pz5uLgjwdIW/hytcnR0x7XQ2GgK9UBvuP+j9krVGVnXWzb25Gzy3XA7mHcTmpM0qiEJERKOPlSZERDRqqpqrcLXqKrKrs1VvYcmmCvcOV6XuQwleyJ8wKXP/KOsj1BuHFoSQHsJRPlGq8sRB/ufgoH+V+7rf5uniiVCvUCQFJMHTdYAzQORP7WefAUeODD54MVjyt0/aLaSkAHFxgJOTVtXy7rta0OZmSTuHxERtAS7tHNxHKeBERERENFWdPg28997AHivHZnIcKIESSXaRY8FxpqW9RbXUkoHtTW09qzAscz8Go6SuBXsul6Oy0QgnsxkPn8tBRIMWOLHl7uKkAimtLs7YOT8Bld4e6vDcOs9Ee5x8cXQAwnzcMC3YC7GBHnBxGvrsEXNnJ0rqWpFd3ojcyiZ0mDsRE+CBacGe6mtfzz0ndA5uS7ht4OsOol6w0oSmkuGqNGHQhIiIRoylh6+0zJJgSX+9hiVAIW2zUoJS1FcpR5fbLBcJWthuS5n+x1kfq+ftrt1kVguT7lwcHeEoq59hIPsT6xer9jclOEUv0e9BBrlLwOLs2Zt/UWmrYBncLgGRbq0G1LY8pjcmE3D0KLB/P9DRcfP7Ynk9WYyvXw+Ehw/PcxIRERGRlRxDSkuugZy6kRkld9wxLpNapI2uJDtJ5cj12uu9ttIdCmOHCUeyqnCuqFaLdHRVlywLcMeWQ5fQUtOMNlPvr9Xq7IQ3ZsaiyOfGAQknRwcV3EgI9kKkvzscHRy62nBpERa9OVfXdUsgpsloQnZFI3IqGtHcZur3uSU4kxjsBXdXpx5zTzYkbMC8sHkqiYtosBg0oamklUGT3jFoQkQ09uRPy5nSM6qsvPuww5FU29yG/VcrcL2q977Bzk6OiAvwQGKIF+KCPFWf4+Ei1SoSQJGWX4EegdZAxVtvAZcuDf2JpTWWBEksrbFuNluwuho4dEgbHtpq0//5ZsjibdMmYNGi4Xk+IiIiIgIuXAB27rxxwMTDA7jzTq26ZBzpMHfgfNl5pJeko7C+cEjPUd/SjqyKRlWhUdFoVIlRKiDRFZTo/qMJ9jJgfWooQn3c4FdSgzmfnEOzsQPVTW2oaW5DiwQubOIO7U5O2DkzDgX+XmrbEpOQL1INIi2/hpsEXCT3qMPU87mlfdgtiUGYFenbI0AS7BGskrWkQj/SO5IBFBowBk1oKmll0KR3DJoQEY2tssYyvH/tfRTUF/T5mLqWdhRWN8PF2VGVuhucby4QIIuZ03k1OJ5bNeCFjSxWovzdVQBF+htbbrOsPSzXZdPB5rqnwfmGwZZQz1Ck+iVh7oGr8CkoH/yCJiBAC5JIa4WoqJEZwi4Bnbw86zD64ZixIoGTxYuHY++IiIiIpraMDC35RqqW+yNz57Zs0RJtxpG82jy8d+29fivNpUpEKjF6qxrPq2pCVnkTyhsGluQjyVHLpgViXrSfXWV59MUCJJzK7vd7zU6OyFidhqrorsSnLh0mMwpqWroqRZrQ3Db0am1nJwfEBniqtUd8kCecHR1QUNOM7Iom5FY0oanbc4f5umHdjFAEe/deRS4tgyV4IpeEgATVapioLwyaDF5bWxtcpTU1TTgMmvSBQRMiorHRZmrD/uv7cazwWI9ye/lTU1rfqhYb0sNXeg1byKIm2l9K3T1VubsEJQajuFb6F5ehqqlNv02eI9Cz5wFORYMRLe03HhTfH4lfRPpp1SpSPu/l1nN/ndo6MGvPBfiV1aly+iCPIAR7BsPb1bvvAEpYmBYkkWBJSMjIBEr6IocCpaXWAMrNzD5h4ISIiIjo5khF8Btv3DhgcvvtwNKlo3vceAOtHa34NPtTnC453ev9TcYOvXKksKal15a6ffF2c4HB2bErmcma1CRD3pdMC4Svu0vPb+rsROqBSwi5XtHvc3c6OODyrSkojw/p/f7OTpTVG1WbrdqW9q7EKm0/uv5PsSRbqetdrbckUSs20LPPuSXy3OUNRpwtqMXlEmsikzzX/Bg/LJ0W2O88FZkTKQPjLUEUPze/ft8rTT09TiL/139pSXRjTboofPe7N3xYU1MTvvKVr2Dnzp3w9vbGN7/5Tbz33nuYO3cunn32WfVv5e2338Zdd92lf4+fn5+67/HHH1fbBQUF+Md//Efs3r0bjo6OWLlyJX7+858jTtpeA+pxtbW1WLRoEX71q1/BYDDgiSeewOuvv46LFy/a7Y+87pYtW/Af//Efw/4jofETNBncmSkiIqJeFkZXK69ib+5e1Bnr7O5r6zDjRG41LpXU95mZZTZ3qkwyuey9Wq6GLXq5OncNYke/X2XRda2sQX8uWaDMjfZXWWZS2t7jtTo7VZAlq7xRZXU1tLYP+v3Kuq6wplld9sv++rqp4ElisCeijO0Iyq9EaHYZ3LsGT7Z0tKiqG7lIBpiU1UsQxdfdH46xsdbWW/7+GDPyg5OZJHJZswaoqQGuXtUuUo1yowW7rQ8/1L4ycEJEREQ0ODJ37rPPgGPHbvxYCZgsW4bxQk78y7ySjzI/QkOb9fhcyDpAggFyDF5S36rPHhmIEG+DSlaS5CoJjgy0gjstOA0LIxYipyYH124PguOH+xFUUNXn4x06O5Fy8BKc2k0omd5zVp+8rhz3y2W4yXNLO7Hb08KQGu6DvVfKVSsx+ZlKNf210gbMjPRVFSpSedL9Z2DqNCGrOktdPsz8UFW9Sxuv2aGzEeAeMOz7S5OABEzGQ9BkgP7pn/4JBw4cwK5duxASEoLvfve7SE9PV8GLgWhvb8ftt9+OZcuW4dChQ3B2dsZ//ud/YuPGjTh//rxeUbJnzx51Iv3TTz9V23Jy/Yc//CFOnjypginizJkz6nskgEOTG4MmREQEk9mEemM9vFy94OLUS4ZWN1XNVfpw9/y6/F4rSyQoIUGFRmPPYIksCqYFeaqqD7vghRQ81A1tzkaItxvWp4QgxMftBi25PNRl1XQtoyuvqlm1ALDti2x7XfVN7gqWWIIu0l6s643CqaAKhvQcuFfVw9VshrOnKzrl4uLUY0Ej1ThFDUXIay1F1sbFCE8LQEpwIKb5eo+vP8gSwJGsRbm0tADXrmkBlKwsqVMeWOBEfmBLlozG3hIRERFNfOXlWjuugVT8btgwbgImLe0tKlgis0tkwHv3NUFGcT0OZVbA2NEzCcfHzQURfjJUvefzBnkbVKCk1+qRfkiVxaakTariQkgFxrpp61A96wE0vfYyYJMxbnusripGAEy/YkZdWAyaFsxCcUOxWvM0tfc+L3EonB2dkeCfAHcXd2RWZfZ47ugADzy8NAanr9fgxPVq1Xq4wdiBozlV6uJlcFbBExkaH+3vrtqSdVfWVKYu0gVA5i5K8GRmyEx4uHgM2/sgGi2NjY344x//iJdffhnr1q1Tt7344ouIkjbWA/Taa6/BbDbjD3/4g/7v/oUXXlDVKPv378dtt92mbvP09FSPsW3LJcEWeawlaCLXV61ahWnTpg3zO6XxZlydoyEiotElQ9oP5R3CpYpLqiJC+Lv5q8wkGWge7ROtDiokqCLBEVk0yKWqparfYY37rparNlwWUpYuPXynBXuqg3zbFlyW4IWU6EupvgxpHAwpVV+eEIg50r94EK0JLBldchkMWfxJK7DSnErEHb6KoNIa/b5mlUnXoloNuDk7wt/TVWXEyeLGcnDWbnDG+Q1z0BBoQGnpGZwpPQODkwFJgUlqkHxyULJaTI0b7u7AnDnaRbIfc3K0wfZnz/b/fR99BBQXAwsWANHR46ptBBEREdGY0zN1OoHTp4Hdu7VjrRtZuxZYsQJjSRKBpNL8YvlFVd0glQ7d1TS14bPLZSiq1dYYFtJCV1WOhHipoe3DNczczdkNiyIWYWXsyl7newR4BSPgiW8A778PpKf3+1yR6fmAbwJw61aY0Ymi+iJcqbyi1kEyo0VLqRo4adEb5xen1liJAYn6/knimTy3BJ1OFJ1Ah1n77+/s6KjajSWHeWPf1QpVkW8hCWkXiurURQImMf4eva6xLArrC9Xl46yPkRSQhGXRy9S+EE0U2dnZar7IEpuEvICAACTLPKcBOnfuHLKyslRrr+5tnOT5LWbNmtVjjsmXvvQlPPnkk/jpT3+q2nr95S9/wc9+9rObek80MYyjszJERDRa2k3tOJx/GJ8XfK4fnFvUtNbgSMERdZHKk3CvcBUwMZqsc0h6I624zhfW4lhutRqaaCH9e9ckB8Ova9h6f8GL5YlBaGkzqYwqVeGhV3rIdtd1m69yCfRy7TGYXRZN6+LXqcwqCwkKyeJOFiVSVTNUjuZOzM8tQ9y563D0cERrtJ8K9MjFtqqmtcOMkrpWdXF10gIonoGeyL1tLloC7Qd1ys9WFp1ykUXV2vi1mBs2d9gWkcPG2RmYPl27REYCH3zQ/+PPndMuQUHAvHla4GWcDSklIiIiGhUyN+7oUaCoaEDBEUnUaW5vtgtIGFcsRdvcBDg3lqm2SwOpEB8usj9SMZ1ekq6OWSVw0hs5jj+dV43juVqVhEVKmA8WxweoY+KhCPMKUwlG4d7hcHRw7Jppov6/qtqQ9rc3TDxydAS2bAEMBu2/RX/27QOMRjhu2IBo32h12ZCwQa2jLNUhtvtgOW7vfpvMG+nrv5O8D8tzS8BHWmtlVmfq98v6afu8SFWVn1OpDYyX4fGWn6usuXIqG9VFyHpKgidJIV4I9LIfIC8BGukSIJdZIbNUNY783IgmA/m31n1kt7Tksq1WWbBgAV555ZUe3xscHKxfl0qT7mR2icw3kZkpElCR592xY8ewvwcafxg0ISKaQuRAQqpKdmfv7jF/pDeNbY12B+7dSZsqqSiRi8z4sF0Yebo6Y1VysDpoH8zJf3dX+wDIYMki4PbE21XAp7tp/tOwMXEjShpLcLniMrJrslW2WF+Lvu58Kuox/chVeNVYs70kYCNtBeRi7DChpqldBVDqbealtJnMyDSa8JegQLReLENCcKPKsJPye8kksyU9oHdd3aWy2bYmb4Wna88Dt3Ghqzz5hoETUVkJSF9YucgiVapXPDysl7AwLagitxMRERFNNpJE8s47WsbPDUhCU3Z1tmqvZNsCN39WDHI8M4D0S2pbTspLgpClQnykZldI+y1pvSXBEtmnPh/XZkJmeQPOFdSqqmwLH3cXrJsRohKpBkMCIBHeESpQIu/P332Y5v/JukRa8cgx6f79/T/2yBGtNe3mzXrVtARA/JyGf9C6vL+HZj2k1mpSFWI7F8bbzQVzovzURdoK51c1a0GUyia7uZFl9a3qciynSs0+kUCVVKt0r0C5UH5BtVLbNmObqnwhGs8SEhLg4uKC48ePIyYmRt1WU1ODa9euqTZZlsBHSUmJ/j2ZmZlobpY+EJr58+erFl0yD+VGw7+7k/knjz32mGrLJUGTBx54AO5ct04JDJoQEU0RZY1l+Cjrox69hkVRTQvOFdbC281Z9Q4O93XrNdAhFR4yc8SS6VTV1LP6RL5NDuiXJQTC4GwfAJFMKwlcSI9huUiWlgQvpPpDghc3Q9qKbZ6++YYH/vK+ZAEml3XQeqJaAkraHBNLZYvMOelEp0laUuXCISMDbWdPoaozGBUu6LW3sbzfMF+5uKkFjbQlqG5uQ46DE15Ni0W9m6uU+ajeznKRYfVxgZ4qgCJfbYfXSxbYc6eew7bkbap114QPnFgYjdqlttZ62/nzwOefA/fdB8TGDv9+EhEREY2VqiqtJdQAAiYSoDhXdg6tHdYZf2YnR2QtSkBxcoRdu1M5Xi2oL1AXSYgK8QxBcmByj4QbCa7InI9In8hek4r6IvM8pGWUVJV0r0y3kISh7PImXC1rQH51s12mt+zq/Bh/LJ0WqNrpdhfsEawHfHwMPU9ierp4jlzVtTzv6tWAtOGRtmj9OXVKm/EngRNJ9hlB8n7TQtLUemZv7l6cKj7Vo/WZb0ML1pZU4a6Kejgb21USW2WjERUNRlX1Lv8F6txckefnifTKRhzKckG0vwdSwn0wPdRbtU0WEpR5+fzLWBixELcl3NZrSzOaxJxuLlFxNPfDy8sLX/ziF9Uw+MDAQBX4+Jd/+RfVKsti7dq1+OUvf6kGvZtMJnzrW99SgRaLhx9+GD/+8Y+xbds2/Pu//7uah5KXl6eGuf/zP//zDeejPPXUU0hJSVHXP5d1K00JDJoQEU1yUta/L3efOuju3n9XSr0PZVbiWpk1k+l0Xo2qEkkI8VQBFBmwLmXgEiTJrWpCa3vPnsXCW4YSBntiZoRvj2HsslhbErUEq+NWq9ZZFr5uvlrwIvIWVJ8/gaqMk2iur0KbhwGV82egw9NdBS9sL5aAhiWoIYs/6csrww2dHId+8Gcpo1czIFtbJT1FG34uX+UkvwRFXDzh7eepXk9+rhLoqWiqsMsEs5DFoVd0ABoSw+AyPRwralqRVd6I61VNKqBiaWkmP3u5yAJGgk0yn8Uy0FEqfV658Ioq15fFzGi2YBhU4EQWnnIy4GY0NclEP+COO4CFCzkDhYiIiCY+sxl4+23pEzOggMnZ0rN2LXEbA7xw6dYUNPvduEqjvKlcXfoix7nSBkqCFH1VpsgcQ6l0OF50XM3B6I0cf0uA5GJxPXIqGu0qzS2kTZRUl3RfE0j1yPLo5WowubTTGnPLl2sVJzcKamVkaHP91q+XlPURP041OBtwR9IduCXmFmSUX0TB5eNovXAGgfkV8KizZs8LXwAq997bBUZ3R1XxXlnXiLRyLUGpzNMNOQHeuBTgjfOR/tgwKxwBntbWXbJGlMomWWtIUtvNrKdoAvnudzGRSMBDWmxJqyyZS/KP//iPqKuzds74yU9+gieeeAIrV65EREQEfv7zn+O0zIrq4uHhgYMHD6pgyt13342GhgZERkaqwfIDqTxJSkrC8uXLUV1dbTdbhSY3h87uTd8muPr6evj6+qp/PIMtuSIimkwkqHC6+LTKUrIMebeQ/rfp+TU4cb3Gbv7IoDgAYT5umBYkgwe9EOTl2ms2WJxjIO4IXYFQn3Atk0QyQuSr9HKWoWvXrgF5eYCpWzBGMkPuvVebnzEa6uu1XtMSKLl+vef+9EOyASWAIpd8bzMqY4LUpdm3Zzaa/LzzqptVAEUqdozdglCBngZsnBmmSuptycLynpR7VB/ncUkOSqXiRE4O3CwZHi/BE5mhQkRERDRRHTwI7N07pIBJxrRQ/MXXF/UdPY+tnJ0cVPWADFP3dR9aUo1Upki1h4UkJskcQ0na6Y1UMVwqrlPBkvqWnkEgaSE1PdRLtYPqbcC7JB1tmb4FgR6BGHcuXNCCWwM5jpW5flJ1EhExtNeSNUZxMSAnfCUxS5K1LJfetqXKpa1NVftUt1SjqrkKVS1VfVb/2LZLkwoUuRi7focaDC44HB8G3xWJmBfj3+O/kSSjyVzF+eHzR6zdG40NGXiem5uL+Ph4uLnZBzMnqtWrV2Pu3Ll49tlnR/y15NS5BE7+9m//Fs8888yIvx6N3O/7YOIGDJoQEU0y8rEuLbikF273vsNyX3ZFEw5mVtgtdmQuh7TTcnZ0QHZ5ozqp31vWmFRPxAZ6qAGDcvFw7XlS28Fkhl9ZHUJL6rGsORAxHTdRWi8BlnvuAdLSBvb4xkYtC6ymRssWk++X15ZLX9elwkECJbJ4uRlSHbFqFZoMjqq1lrQdy6nJ6VFSb0t+xtIaTfo/Xyqp13/mUnWyIiEI82L87H520t5MssAWRy4e0yHxEhyShX2DsUGV8kuVT4xvDBzKy4EDB7Sf5yCCTr2Kjgbuv5+D44mIiGhikv76v//9DU/Edw+YGD1c8fncePyupLnPCm9bQV4G1epVKsT7SmIaKlk75FU143xhnao47376yN3FSbV8kkBJX+193Z3d1fGrnIwfy+PXG5LkqTfeGNgxrLyPgADtsZIIJl/l4u2ttZqVpK9p07T2X0J+bvn5WkvaS5e0QMhNkP8O9cZ6dUwuARSpgO/3sa0dav6J5fcpW6pOFiVg+fyYPoNu8X7xqnWXtFCTgfU0sTFoMnQVFRV49dVX8Z3vfAcFBQXw9x+m+Uo0Yhg06QODJkQ0VTW1NakhjWdKz/Raml/VaMSBaxWqnN5CFi5zonxVr2EJnFhI2yhpIyUBFOmTKzM6JEgS6e/eY3C5eh6TGcF5FQjOq0RoWRMiXQNV+f+w9MaVRcnWrdqg8O5kESrBDmmhJZebDXwMRUgIsGWLdpK/lwqUzKpMNbNFvrab+27NIFlgH18sVV8tJIPwtrRQlblnS1oqyKwTdxf3Uf8d+yT7E/V71p0ETWSfVPagBKJkUXjmDCCBlKGSv+OSyScLz/G8yCYiIiKyJSfSf/tbOdvW92NcXNBkMiK9NB2tJiPaDc6ojgrEyfhQvHapfEABk+4k8ab7IZO05QrxNqigykArU2RWyaXiepwrrENtc1v3J0RsgLTk9cG0YC99RkZ38rqSWHN74u2DmqUypqQK/tVXB9RO7YakYjouDggKAi5f1ipLRogE3iR4Im2D64y9v47ZrLVVK63X5uW0Ojvh4PQI+C5PQmqkb69zZ4RUI0mL5dTg1PEd9KJ+MWgydPJ7HxQUpFp+PfTQQyP6WjQ8GDTpA4MmRDSVSPm89BuWk9hXK6/2WtUgC67jOdU4W1hrlx0mJ+RXJQer7LSbEVBYhfnnKhDZZlAnzL1dvUfmgFraNS1erFWR5OZqF6kqae47s2pEyULo1luBFSsGNMCu3dSO7JpsVYEilSi2Az4tOsxmHM2uwul8qZTRbjO4OGFDSggSQ7ztHutr8MU9qfeoYMVIk98bGUr6SdYnPVq92XJxdMH6aeutlTDy+1ZUBJw9q7U8a2jQ58MMigzmW7sWiI9n8ISIiIjGv08+AY4e7fv+wEAUPnQnXr/2tqoYsCivb8VbZ4r09q1SvbFlTkSPE9oylzCrvAnZFY0o6zoJPlBy7C8BFKkelypzWx3mTlwt1SqgLTP4LLwMzkiL8EFqhG+/gRd/N3/MCp01fuaWDJZUhPzlL1p7rAlI1hhljWWq40BvFSiSECe/N5IkJ7ICvLFvRhQi4wJ7nU1pEeoZqoInkrzF4MnEMxmDJkR9YdCkDwyaENFkJPNJpIdtaWOp3aWvnsPa93SqDLHPsyrRYpOp5uPmglunB6nF0lAPeGWI4wyHYMw/X4HIkiY1rHBUSMm7nHgfS/IzS04GNmxQC96hkCGb0kJNKlBk2Gb3BU1BdTM+yShVvaMtZkf54takYH1IvNoVOGBFzAo1JNLNeWQOfqVv8vvX3kduba7d7XL4IIMmZdFsu08i1jcWd824C/7uvZQuS+sCaUkgwS7Jutu/v//Bm7YkW0+CJzEjHygiIiIiGhJJFHnxxT6Pb0zoxKH103GgI0vNEek7YOKO7fMi4ercf2skCaDkVDSpWXlNNseOFjLPQh4zVFH+Hpgb7afmGDr2UVUiSVNSVSKXCO+IiX9SvbYW+PBDbfbiBCXH6rJWlGN4WUd2T9SStmsVDVoyk/wWFvt4IDPQB7UxQYiYHoIZ4b69/u6FeYVhw7QNSAhIGLX3QjePQROaSloZNOkdgyZENNHJx7K01yqoL9CDI5It1F9rJ1v1re24XFyPjBL7IY0yMHJRbAAWxPr3OMktvYbXxK/BrJBZqhriSuUVXKu6hjaTtRzfx+CD6YHTkewVh/jzBXA+eerm51ZMFDKUPiEBmDFDaxXl0XPA+1BJwOTdq++qn3n3CqE9V8qRWdZglxm4aVYYAjztg1QSMFkevRxLIpcMSwBLqmJkHovs04XyCz0GTcoC67PLZSqz0dvgjPWpoYgN9LR7jLRmkwWV9ELud+Esi9G33hpcBYr0iF6yBEhK0ubTEBEREY0H0pb05ZflxESvd9e21mJvjAnnU+0Tb7oHTCJ83XHXAAImA1XdZBxUZYqsFVLCvDEn2q/PqnSZtSdVBzI0PN4/fvLNvZBTZXKc+tFHWhBlPAkLA1JSgMREbZ1iS373srK0S2WlWluWNJYguzq7R1cCCaaV1xtR1dSmEu4sat1ccT3UD1iWgITksF5bd8naQ471J3yAbIpg0ISmklYGTXrHoAkRTWTSaktOoPc2k6Q/ki0kGWYZxfXIq27SWztZyIDGW5KCVJWJLalUWBCxAGvj18LDxT4QICfKpS9uU3sTAoxO8C+shIMcfEtbrLZuvY2Hg6entgCQXsLjgQRGpKJEAiVykr77gmQYyZ9iGQD6UdZHdoEquV3+m+6/Vo4OU6e+iF2THIzUcJ8eixT5byhVJ4siFsHFyWXQvZAlUCaBkqzqrF6DdNKmQVq9Sfuw7ocPsyJ9sTIpuMfiXoImm5I29b+QrqwE/vpXoKpqUPsMGcInLdtk3g0P/omIiGgsyXy9N99EfX0FihuK1Sw4S+KJVJTIsVOlnyvSN81DZ9dJaLntckmDOtaztEuK8HPHXXPtAyZxfnF6S1apWJbqAXmNobBUpsiJ8h6LBjm88nBFSriP3bxDW4HugWr9MCd0Djxd7ZNmJiWZb3LoEPD552OXMGYwaOskWZfIZaCDqKWtsfxeHjyIlpoKdZzf28wTWUtWNbapAEpTmzVZyuTggMvRwWi7dTqmJ9hXvIu5YXOxNXnr5AuYTeKTyLGxsfAYxgRAovGopaUF169fH99Bk4MHD+LHP/4xTp8+jZKSErz99tu46667+v2e/fv345lnnkFGRgaio6Pxr//6r3j88ccH/JoMmhDRRCUzSV7PeL3XuSS2TGatLZJk+6tLYyvKG4z6Qqv7kMZFcf6qrL47aaF0R9IdCOv0BI4cAQoLtcHqMqvD9iIntMvKbu7NhYZqJ7Xl+WWxIRe5LoESabUk1RuRkVrrK1mQfPopxkRAgHUxInM0RrmKQUrnd17eqYJntqoajfjwQknX4laTFOKFFYlB8PNw7bVFggzdTAtO6zf7S3oey+9dRkVGr9lntvKqmlTli231kizmbX/vJCi3ITUU0QH2v2+rYlepSqZ+Sd9oqTiRhd1guboCc+Zo82X8/Ab//URERERDJadUjh9Xc0zyaq73aGlqYXZyxKktC9Ds56kHL/ZcLsf1qib9Mb0FTKTS+760+1R7XFt1rXXqJLhc8uryVDvfkSInxVOCUrAocpFaQ0zJ6gJJ7pE1ytWrA28t2xdZY0hSlgyJlzWSBEXkq+Viuy3XBzA/sV/SGvejj9B57pxaZ8jvaF+/L9LirbiuRQVRLNodHXEhLgSdK5ORMi0Qjjb//WW9cXfK3XByvMl9pBFlNpuRmZkJJycnBAcHw9XVdWr+O6ZJr7OzExUVFWhubkZSUpL6nR+XQZOPPvoIn3/+ORYsWIC77777hkETiXrOnDkTf/M3f4OnnnoKe/bswd///d/jgw8+wO233z6g12TQhIgmorzaPLx0/qUebZCkRVNlY1dwpOtS1dwGs7nvj24fdxekhcuQRh94d6ssUfcbfHBbwm1IC0qFw/nzwMcfD/+gQzmJLQsBCYZICyWZRTIYJ08CH3wwtNeWOSNSqu7rqy1oJDgjX/u7Lo+V9luycBnjg0dZwBzMO4gD1w/Y9bmWKo8D1ypwsciaHSYHuvLfenF8gPrv3p20TNictBneBm+7QElmVSYull9UFSX9BUo6TGYU1DTjUnEDMsutbcKcHB2wKC4AC2P9VSXMoaxK9VgL6Xt9S2KQno0mFU0Pz34YiQGJN3jzZu2//YED2syTwZIg39KlwC23sPKEiIiIRp4kAsnsi9OnVeWHVO32JWtxIgpTo9QJnYtFcvxUYZd8khzmjXUzQu0CJsmBybg37d4eAZPujB1GVDRXqMphW3IsKa1+JbAylMoUTxdPVVUilcOyhiD5YRu1IISciJOLHH/KV6lIkap8aeklSUDymO6io4FZs4C0NC15bLRduQK8/z5aaytV8ER+N7qvPy2a2zpQWNOikvUsWpydUDAnFlGbZsPRphKpr8AejS9tbW0qoV1OJhNNZg4ODoiKioKXl1eP+8ZN0MTuhRwcbhg0+da3vqUCJBcvXtRve+CBB1BbW4uP5aTeADBoQkQTjRysvnDmBRhN1pkOV0sbcCS7EnU2Wf398TI4I8rfHWkRvuprb1kjchArvWelfZNrsxF4773hH24oJ6rXrAEWLNAWEDfj7Flg164bZ3LJIkUGhEtwRi5DHM4+3sig+LcuvYWGNvvB99fKGrD3SrkKqFnIUM5ZEb5YFB+gfhe6zztZP229ui4L5tya3H4DJfK8kvGYXd6kvkqwxlaknzvWpYQiwNNa4VLb3IZPL5WhqNa6OEwK8VbzVyy/i9I67MsLvgxfN9+BLUaPHdMqoAYz68RCFqGrV2u/h5x5QkRERCNBToq//rpqXVtvrMeZkjN6woucZqltadeOozqB0hA/7FuRrFJJssobVVKKhaerM9amhCAh2P7kzkADJgNlW5kiARTbtUf3WYcyzH1W6Cw12J0nwodAEoGkil/aDsuMEUnMkhkkUtU+1uSEucxpuXBBJWtJO2aZeSLzdvoMnlS3oLrZGjwxhvvB4dEVMHtZk5Ti/eLx4KwH1VxDGr/ks6mjowOmqTKblKYkFxeXHhUmEz5ocuutt2L+/Pl49tln9dteeOEFVW0ib2YgGDQhoolE2jE9f+Z5NLY12p0U//BiSW8thlURhPQZDvE2IFguXm4I8naFh2vfixkZ0pgWkoY1cWvg7+YHSHWJHCgPZ3WJ7JicoF67dliHpOPyZS24Y5sNIyfBIyKA+HjtIhlbIzhvZCzJkPh3rrzTI2vR2GHCmfxapOfX2GUoWipA5CLXB0oCJTIY9FqZtojvrZJJ+lqvTAxSFUy9BeXUXJaCWhzOqlQt5MSq6cGYF2PtuRztE43H5z4+8PJ9ORkhgRMJoEj23mAFBwO33aYF04iIiIiGi8yKeOUV1ca23dSOU8Wn7IIQ0ta0pE471s7z88TO1FgYnXse/0jC08qkoB7zQ6Ra+N7Ue0es5ZEct8kcPduqZguDk4Fte6aCoiLg3Dmt3VhdnapSkmCaXHpLsJIkKVmnWpYJjn4eMD28HMZQX7tjfakul6QtIqLxaMIGTaZPn44nnngC3/nOd/TbPvzwQ2zevFmVj7m7u/f4HqPRqC62b15moTBoQkTjXYOxQQVMalpr7BZYu84V6yetQ7zdEObr1hUgMSDIy7XHAL6+hHiGYH74fMwKmQVPKauWMvGMDCAvb3jfSGwscMcd2nDCkSDBnUuXtPYH8rkurzeFWi/Jn+njRcfxafanPRYwEuw4nVejghW2FSGBngY1W0R+d/oigRcZAiqLn7zqvgMl04I8VeZjTKAHXAbwu5dd3oj3zhfrFTD3LohCuK/17/eyqGVq3sqgNDYCR4+q1hdDCvYtWqT9jrLqhIiIiIbjZPNf/gI0NanjtPNl5+2O52UWXWa5lhB1NiwAuxPDYe52DCItdNenhCA20LNHstOquFWqMpzDtWlUyClBmV8pwZNr19CWn4uCugIUNRT1mHsisw2vljXoCVIGD1d03rMI9UnWdWCCfwIemf0IA29ENOGDJhO+zvJHP/oRfvjDH471bhARDYrMlXj5/Mt2C6ySuha8f75EP3ktmWeymBrMAadkhkkp/byweYioM8PhylVg15+AiorhfQMykFBmlsydq80tGcmDYgmQzJ+PqUr++y+NWooY3xhVdVLeVG4X1JBh8PNi/HDyuhY8kcV7VZMRr53Kx7xofyxLCNSDHTLYMadSWm9pFSWWBY8tb4MzEkK8VKBEWnFJ4KM3C8IX4NbYW9X8ldMlp/Xb5XsXxPqrYI78Ln94vgQPLYmFu6uWKXm08KjKapR2XTLrRDIoA9wDVEm/wdnQ+w9BepFu2ACsWqVaCahhq+XWn8MNyZwUCbzcc8/Nt40jIiKiqUtOLL/5pl4BKwO1bY/npZVRdkWTqt/YkxAO89JErPUywKHrmE4OmV2dHHtNRon0jsS2GdtU4hPRqJFfSkl+k8uqVXAtK0PC3r2IvnQB+XX5qvLEEjyRGYopYT64XFqv1hHG5jZ4vHYM4bfNRMmiBPVc2TXZOFZ4DMuil431OyMiuikTvj0XK02IaKKRUngJmMhBqEV1kxGvnyrU51TICevNs8L7PGFtyUQL9QpFmFeYfpHFllNTszYLRAYRDqeQEOvcEGmJ1UePSBo5smC5UHYB+6/vt1ugW5Q3tKrZIhUN1r+LvrK4CfdBflUziutbem37JnNQZAZJUqgXwn3d+g3UyUJehsvH+sXqt8lg+dcyXtMHScoi6q30QhR3zTiJC/TEtrkR/T6vlPHflnCbqo66ITl0uX5dC57IQMuBkiqlBx+cUpVKRERENAzk2EOSMKTFbdcplMrmSlwst85j7TCb1YD3OnMn3p0RDdeZUbgtNfSGCVAyM2Rt/FqVJMPqEho3CgqAzz5Dw7WLqpqq3WxtlSuJWFdK69Fu0v4tuLs4IWRZArJWpcLk4qR+p2WWYbBn8Bi+ASIiTK5B8NKO64JkkXZ56KGHUF1dzUHwRDQpSM/jVy68ooZ8W9S3tuP1kwVoNGonnGWQ+11zI+3acMlARtvgiFyCPIJ69jmWE8mS/SZZ9UMZni0tjGSQekeH9SLtBGR4oe8ABnjTqDCZTThbehYH8g6owaP293WqWSfHcqp6rSSxDZRIcG56mDcibhAokYCGDCNNDU7F9MDpvT42vSQd7159V99ubO3AK8fz0NIVCFyeEITF8TcefikBmUWRizBgxcXA7t3a7/5Ag3+PPKK1eiMiIiLqTgZ3S0VJZSVQW6vNL5GvbdZB2E1tTThTekZPGJHTKplljcg1d+KNtDgg1Bf3L4q+YWtTqSTelrwNgR6BI/62iAZNThdmZaFh1xu4cHm/Sv6zaGkz4XJJPdq6WgTLzM2glHBcWDcL7e6uiPCOwBfnfXHE5vIQEU3ooEljYyOyujKd582bh5/+9KdYs2YNAgICEBMTo2aXFBUV4c9//rN6TG5uLmbOnImvfvWrePLJJ7F37158/etfxwcffIDbbx9Y/3MGTYhoPAdM/nrxr8ipybEr4X/jVCFqmrUDUJldsmNBFAw2gyIXRy7GHYl39J+lJh/lMvPhs88As33v2QGZORPYtGl4h7jTiJOF+uni06rypKVDq+qwqG5qw2eXylBcZ709wNNVBUrkEurT/5BPH4OPGkIql1jf2BsueORwYtfVXSqYYyHVLTvPFqrqFnkpCQZG+Lmr647SoqIrqcKWZFjKsHg5iTBg8vsvJzc+/RSoqrrx4yUA+OijWjCQiIiISDQ3A3v2AOnpejVJb8c7pY2lyKrOsps1J9W1x9o78VZaLDo8DHhwcQz8PFz1+/3c/FT2vRz9yFdpTSotdSUphbMfaNxrbUXjX/6EC5+/DaPJWtEuyVEXCutg7vr3khzmDUOYL85vmI1Wb3esjlutLkRE48W4CZrs379fBUm6e+yxx/CnP/0Jjz/+OK5fv64eZ/s9//AP/4BLly4hKioK3/ve99TjBopBEyIarye3X734qlpg2WbnSAujykbtwFMWVvctjIKHq3Xmggxxvzvl7v4XU9KiUNpxybD0oVSXbN4MpKYO/ntp3JBsxw8zP0RGRYbd7fInXoY1yu+atMjy97Qu3nvja/BFWkiaqiiRVm+DXcRLYPAP6X9AWVOZfptUvMilL06ODlgQ44/lidYAhrerN7688MvwcvUa1OvDZAJOndJOeNhkg/ZKAoRPPsnACRER0VQnCUcSKJHjhxb7JJTux/PXqq7ZzZcTdS3teLcd2JUcjQ4nR2ydE4FpwdZjGAmMPDDzAQZHaGIzm9H04S5cfOd3dslaZfWtyK1sUtelsmp2lC86vdxwfv0sNAf54qn5T6mqEyKi8WDcBE3GAoMmRDQeWynJvAdZZFnI7JKd6YUo75o94Wlwxn0Lo9X8CYvEgEQ8OPPB/jP8S0qAt97S2gcMhpwwnj0bWLlSC5zQpHCl8grev/Y+GtsG3p5NAhQSKEkLTkOUT9RNL+ilv/fvTv9OL9+Xw4y3zxQhv7q53++7PS1MzV6xiPOLwxfmfGFovb2lZdcrrwBN2gKuTzLw8umntRZ0RERENPUUFgIffqgdO/RzLF/bWovM6ky0drTa3Vfb3IbXXd3wUWwoOh0csCQ+AMsSrAkZUlHy9IKnVatTosmg+eQRnP39f6KtrdmapFXagNqWdr2yfXqoN0zOTri4Jg3OScnq34CLk3WdS0Q0Vhg0YdCEiMYJWWS9eelNXK68rN9m7JCASZHKyhFSWXLvgii7KoBon2g8OudRuDr1URkgM0skG+7s2T7bB/QQGgpMn65dIiN5oniSamlvwe7s3arPdl8C3QOREpyiWm8NpaLkRjLKM/DGpTfsgoSHMivR0NoOGbMihx7yVWauyPB64ezkgPsXxqgWdRYroldgQ8KGoe2EtOl6+WWtD3l/duzQ2tMRERHR1Kou+eQT4PjxHndJ8klNSw0a2hrU9eb2nokf0o4ov6YFLwf64VSkFiSJCfDAXfMiVQtS4eLoorLsQ71CR+ENEY2ewotHcelXP4Bri5Yk1dZhxrnCWn2eYmKIF4K8DCqQeOWWGfBauBxzwuYgwT8Bnq5M2COiscOgCYMmRDQOSKsiCZhcrbqq3yYHlG+fKURJnXai2N3FCfcujEKAp/VEcYhnCJ6Y+wTcXdx7PqkMZj92DDh0SGvLNRApKYDMhfLzG4Z3RRNFYX2hmnciWZGyaJcZJVK9JMGSII+Rb0n1UeZHOF7U80REdzJ35WJxnboulVbSA9zNxVpddX/a/Wqfh0SCixI4KS3tfzD8V76iDV0hIiKiyU9OgbzzDnDunN3Nxg4jLlVcQp1ROy7pi8xxuFLVjL/GheFakK+6LTbQE3fMDLM7hpEWu7NDZ4/QmyAaW3vS34LxxT/Co04LKkrL6azyRr397pwoP7g6a0l615ZNR3FyhJrpE+4djqSAJMwMmYlgz+AxfQ9ENPXUM2jCoAkRjS1ZdMnQ9+u11/Xb2k1mvHOmCEW1Wg9YWVTJ0HfJwrGtAJAh2N4Gb/snlI/qK1eA3btvnDlvISeB168Hli/nCWEakyor+TdgO8enNx0mM14/Ja3qtEDitCAvbJkTrle/GJwM+NKCLw090CPBxVdfBXJz+37M/fdrwUUiIiKa/C5c0Nrbdjt2Ty9Jtxty3V2bwRm5JuBAkwlHIgJQ626Ao6MDbkkIwrwYP7vK3cWRi7EpadOIvg2isSQzfv54+BcIe28ffCvq1W2ZZQ2oamrTk6FmhHnr/y4yFyeiKDXK7jnCvMJUYFECKJLgRUQ00hg0YdCEiMZ4KPcrF15BcUOxXYXJu+eKUVijZeIYXJxwz/xIhHhb+xv7u/njiXlP9DxglCz5jz8GrlsDMDckc0qk7VB8/DC8I6KhL6YO5h3E8cLj/Z6EqG9px19O5Ks2XmJ5QhAWxwfo9/u5+eHe1HsR6RM5tB1pbwf+7/+Ahoa+Z5t8+csMLhIREU129fXAr38NtLbaJXpIW1PbmXBymqQozB/nw/yR1+mI6+ZOFLV0oNHYoT/G38NVVZeE+NjPK5keOF1VyvY7l5BoEpD17vMnfosZ+y8iqKBKJQmeL6xTX0WQl6vqqCABFKk+yV4wDQWzYno8j1SgxPrFYn74fBVAGdJMQyKiAWDQhEETIhojda11eOn8S2oYtkVLmwnvnLXOMJEy5XvmRyHUZoElJ4WlwkS+6mSI9d69QHr6wOeWiKgo4L77AH4G0jhh7jSrAaoyPFWuy6HHkYIjdrN+rlc1qX8n6NRiF9vnRane4BayeFo/bT2WRS0b2gwWaWsnwce+PPggkJw8+OclIiKiiUGOp196CcjJsbmpExkVGXbH7hUuznjO2xvpHr20yu2SGu6D1ckhevshi6VRS7Fh2gYGTGjK2H99Pw7k7EPSsWuIuFaCmuY2NRjelhy5e7k5w8/DFeVLElG7NLHP43lpVb19xnbVxouIaLgxaMKgCRGNMhkQebnissqqt+2DLNlob6cX6mXKBmdHdTI4zNcaMJHKEplh4u/ur91gMmlDKQ8cGPjcEuHkpLXiWrUKcHYexndHNPykDcbv039vd5LiWE6Vuljm/dy3MBr+nq523yc9kFfGrlQD7yUjtKm9SWWn+br5qvt6nQVkqTZ59lktGNmbyEjgqadYbUJERDRZyfH1Rx/Z3ZRdnY2C+gJ13ezkiCvJkfjv5k7Ud2iZ8rYkQBLibVCzGpJC7Vvperp44q4ZdyEpMGmE3wTR+CKVWn8880cU1xch7lwe4s5eR3FtCwpqmvvM+7swPQI1y5OQHO6rqlC6k2SpW2NvxcqYlQxAEtGwYtCEQRMiGgVy0lYy5TPKM5Bbm6sy6G3VNrdhZ3oR6lvb1baHqzPunh9pN8PE29VbteQKcO9qRdTSAvz1r0B+/uB2RuYx3HYb4N8VeCGaACqaKlTgpM2kBRXlkGTX2WJVdSJcnByxLiUEM8IG9vfc3dldVaNIaX+v2WtHjmhzgfryyCNAYuIQ3w0RERGNW5WVwG9+A3RY22uVNJTgatVVffvsrSn4dUUryhuMevuthBAvFSiRi5zc7e34IsE/AdtTtsPL1WuU3gzR+Dum/+3p36rWvJGXCpF0Igsmc6dqwVvb0o665ja0dgtE1rq5IiPED5WJoQhLCEFymLea+Wkr3Ctc/duS6hMiouHAoAmDJkQ0QuQjM78uHyeLT6rKElOnNoOhu4oGI94+U4TmNm1h5uPugrvnRaqSZAtZWElLLn3AtdkMvPyyXcuAGwoNBTZu5OwSmrAk6PjGpTf0bZlr8vqpAlR3VWeJtAhfrE4OVkGUgZDhqxsTN/bsh9zWplWbNGuzhXqIjgaefJLVJkRERJOJVHE//zxQVKTfJG1Dz5WeQ6f0BZURgvEh+F8/P2RVaHNNfNxc8MDiaJX01BcnByesm7Zu6K1DiSaRs6Vn8c6Vd9T1iKvFmH70mt39coxf29yOqiYjGlqtwUtR7O2OzIhABK1ORkKkX49/Z2vj12JZ9DLOOiGim8agCYMmRDQCrYTOl51XwZLypvI+HydBksyyRhzJroSxK5sm0NMV2+dHwctgXXT5GnzxhTlfQKBHoPWb9+wBDh0a+KD3tWuBefMARx480sT2cdbHOFZ4TN9u6zBj39VyXC6p128L8HTF5lnhCLSp1BLmzk5V+i/DJW2lBKXg7pS74eLUreRf/o3Jv7W+fOELwLRpN/2eiIiIaJzYv1+72LTVTS9JV1nxwuhhwLOz4nG0qF5vw3X/wugexxwWcuI2LTgNt8TcglCv0FF6E0TjnwQiP835VLXQDcssQfKRq3Do5Yyjsd2EyqY2VDYY0dJuTUJsdHXGybWzMHdhLNxd7atOZgTNwI7UHXB2ZBtqIho6Bk0YNCGiYdLU1qTmlJwpPaO3EOpOBr1nlTfiWnkDCrv1bpVh73fNjbQ76JPKkkdnP6pmMOiuXAFefXVgc0uWLAFuvRVws85FIZrovZBfOv8Srtdet7v9UnG9Cp60m7QApLOTA6YFealMteY2uXSohZajgwNmRfri1qRgONoET2J8Y/DgzAft55zInCCpNpFWeL0JCNDa3QUHA0FB2ldD7ydNiIiIaJy7dk07xpaKbjlfYKzHhbILaDdr7XPFG3On4ZXKVnVdCka2zYlEXJCnXXW4HFNIqyAZTh3hHQEPF48xeDNE41+7qV21vZN5QbUnDiJm3+leAycWcjxfWNOiV5lL265Xl87ArbPCMS3YvuWdBCslcMLKLiIaKgZNGDQhomEgFSV/PvdnlSnTndncidzKJlwoqkNetQRKen6UxgV6YtOscJWtZiGLrIdnPQxPV0/7Hsu///2Nh74nJ2tzSwJtqlOIJgkJSr6e8TqyqrPsbpcF1IcXSlDZeIN/HwDig7R/c7ZtvCRI+cjsR+DnZlPqf+AAsG/fwHZMKrmSkoDNmwEeVxAREU0cGRnAW2/pAZPK5kpcqrhkN4fwTHQw/sPBVR3bi9XJIZgb7WdXHf6lBV/ivBKiIZA1ctWpg2h+9SVUN1Wqtnh9Pq6pTa2vZRbKhVB/fJAchdRwH6xKDobB2ZqAuDx6OW5LuG0U3wURTSYMmjBoQkQ3qa61Dn9I/wMa2hrsbm8ydiCjuA4XCuvQYLTvxSpkQOT0UG8khXoh2MtglwUT5xenst4Nzgb7GQsSMKmo6Htn5LNs2zYgIWGY3h3R+CSHJJ8XfI69uXvtTmh0mMw4mFmB84V1+m1SUeLh4qR6jVc2GfWTHWE+btg6N8KuB7m3qzcenfOodYhkayvws5/dOFBpy9cXeOwxrRKFiIiIxuXxu7TdKqgvQMDVAiQdyoCXi6cKeEgSVGZ1pt3ji11c8E9hwWjqOiMyJ8oPa2ZYB067Ornii/O+yBZcRDcrMxPYtQvG2iqVmCiX7utsS4venMpGNfvk7ZQYXA32RbivO+6ZHwlnm6QomV24NGrpKL8JIpoMGDRh0ISIbkJrRyueP/O83ewSyXI/kVut2nDJDAVb3m4SKPFSwZIQb/tAiUVyYLIqJbabryDP8+abWhZcX5ydtcHUERHD9O6Ixr/8uny8dekt1BmtQRJL0FJmBXm4OsHg7Kj/WyuobsZ754vVQkv4ebhi+9xI+HpY/735GHzw9IKnrZmie/cCBw8ObsfkuEICJ6z2IiIiGlcyyjPw7tV3YTQZex1C3V1lcxt+EBmKIk+thWdMgIdqqWtp8+kABzw460FMD5w+KvtPNOlJ0tLZs8D580BxsZotVNJQgsL6QnTCur6WU5QVDUZcrTfid/MS0WBwQWKwFzbNDlcteS3/Pu9Nuxepwalj+IaIaCJi0IRBEyK6idkKr1x4BTk1Ofpt5fWteON0oT5XQXEA4gM9MTvKD3GBHn32VZVBdTIkcmXMSjg52g+zw5EjwO7d/e+QVJjIsHeiKaalvQW7ru7ClcorA3q8LK7eOVukAitCAivb5kaquUIWsrC6L+0+baO5WZttItVeg+HtrQVOZN4JERERjblTxafwwbUP1InXqIwCJJ7M7vfxpXUteNHHG0ejQ/SAyZ2zI+xa6jKTnWgESZeFCxdUEKWhogiXKy+rIIotOab/sMWMV9Ji1bAhaZu3anqwvu6WdbbMCY31ix2jN0FEExGDJgyaENEQyMfhO1fewbmyc/pt9S3tePVkgRpQJ9xdnJAW6auGTksrrr4EugciLSQN88Pn289SsCgoAF54Qe+x3KsFC4AtW27yXRFN7H+TJ4pOYE/uHjXz5EbqW9vxzpkifZCkzDa5d0EUQmwCJ/en3Y+U4BRt49w54O23B79jXl5a4ESGxBMREdGYHScczj+sjhN8KuoRe/Y6Aouq1XF7h6lTJVDYtvSRx+dXN+NdL0/smRauTsSmhPtgfUoonLoqTMTCiIXYnLSZw6aJRlptLfDcczC3tiC3Jle11rO7u7kNv/L0xslIrcp7ZVIwFsT66/e7O7vjyXlPItiTx+RENDAMmjBoQkRDsC93Hw7kHdC3W9tNeP1UgX4CNtzXDXfPj7IbMm3L381fBUpmhsxEqGdo3wstKU3+zW+0g8S+REYCTzyhtecimuIk8+xMyRnUtNao4IlnV39yN2c3HMw7aNfGS/7dvnuuGMW1LWo7yMuABxfH6CdDZL7JVxd/VX2vIu3xjh4Fioq0lnkD5empBU5CrL3PiYiIaHTIaYzPcj7DxZMfIPZ8HgKKa9Tt8vdfAiMWUj3i6arNQGtpN+H9QF8cjA1VAZNFcQFYnhBod8w+I2gG7k29t2eFOBGNDGnXtXOnPpcooyLDLlmqpKkN348MRYWX1krvjpnhSA7z1u+XNcEjsx9BmFfYGOw8EU00DJowaEJEgyQnZKUVkEWH2awy1gtrtBOvfu4uuH9RDNxd7RdQUhacFpymMtKifKJunJEmH7lvvAFcutT/ydinn9YGTxNRv+qN9Xjl/CsoayqzGxwvFWIyi0gsTwjC4njrAPcF4QuwJblbFVdHB1BVpbULqKwErl4FSkr6f3EPD+ALXwDCuEgjIiIaLeZOMz499CJaPtwFv1ItCUlOaxTUtOhJE705EBeKozEhEi/B6uQQNfjdllSI3zn9Tjg69J4gRUQjQNbHb70FXLyoNhvbGtXa3NRp0h9ysQP4QUwYTI6OKhFK5g9FB3jo90sy1IMzH2SrLiIa1rgBjwaIaMorqCvAe9fes89cu1SmB0ykJddd8yLtAiZSVbJh2gY8s+wZbE/Zjmjf6IGV8Ken9x8wkefYsYMBE6IBkgHvT8x7AnF+cfpt0opjQ6pUe2nbx3Or9IoxcbrkNK7XXrd/IqnqCg0FZs4EVq/WKr3irM/ZK5mL8uKLNw6uEBER0bDNH/xs50/g+sKf7QImuZVNdgGTAE9XeLs565WmnyWEq4CJbG+eFdEjYLI6bjW2TN/CgAnRaJMD9s2b9fWvVI5I5wYZ9m6R5tSJL1fWqACLydyJ984Xo7SuVb+/taMVL51/Cdeqro3JWyCiyYlHBEQ0pXWYO9QcE8lYsziaXYUrpQ3quiysts6NgJ+Hq35/jG+Mau+zImYFPFysGS43VF4OfPRR/49ZuxaIjx/COyGauiS7TMrykwKS9NtkAPz8GK3nsSyu9lwuUydVLN69+i7aTe19P6mrK/Dww8C0af2/eEuLFjgpLh6Gd0JERER9kb/jB9/9BVze/0BaZqjbzJ2dyCpvRHmDVl0q4gI9MT3UG2kRvlgYFwCXexYh7I7ZuCUxSLXsTAzx0h8rQZKtyVtV0IQzTIjGiLs7sH27FkCRBEV3fyQHJet3y7/NDcZW3FXfqLbbOsx4K70Q+VXNduv6Vy++ivNl58fgDRDRZMSgCRFNaTIPoaqlSt++XFKPE9ertQ0HrWdquK/WP1UEeQSp0l9pyzUo7e3Am29qLYD6Iidnb7ll8G+CiNS/yW0ztllnlQBYOi0Qvu4u6npRbQsuFFlnn1S3VNvNMOqViwvw4INAQkL/j5M5RX/+szYXhYiIiEbEpT2vArvegUNXDoQkRVwrbUCVTTVpYrAXwny1Y4EOFydcXpWKhjlaoEQCKDLrzMLF0QUPzHxAteUiojEmFd7Ll+ubMqMk3i/eLnDySH09VrZrAdJ2kxnvnCtCZpmW7CgkEXLn5Z04WnDULlmKiGgoGDQhoimrrLEMh/MP69uNxg7su1qub69KCrbLRJPh0w/PehjuLtYgyoDt3q1VmvQ3x8Qmu4aIBk/K+W9LuE3fdnFyxLqUUH37cFYlGlutgcsjBUdw4PoBtLS33DhwkmStYuk3cFJQcJPvgoiIiLorO/IpKl/5vR4wkQqTq6UNqG3RqkblEDo51BtB3gaYnRxRmBqFk3ctRnl8SJ/HDI/PfRzTA6eP5tsgov6sWWM3K1A6PER4R+jbzg4O+HplDRYbtFOZZnMnPrhYgguF1sQo8Un2J/jrxb+iwWgNqBARDRaDJkQ0JUkWirTnsW3Ltf9KuSr1FSlhPpjX1drHkon20KyHVKnwoEiGy9GjwMmT/T/urrsAb+9Bvgsi6m5e2Dy7rLSYAA+khmsD3uTf994r5Xrmmfz733d9H3527Gf4JOsTNVS+VzLv5P77gek3OLFiNAIvvQTk5w/jOyIiIpramtOPI+f5n6DT5rg9v7oZ9a3tejtdOXb38XVH/sxoHNuxFLlLkrFmzjasi1+HtOA0BLoHqra68nVZ1DJ8ddFXEekTOYbvioh6Pea+916tXVdXdYm035VuDxYuJjP+oaQCCwK6Ehk7gT1XynAit9quukTmm/z65K+RUZ4x+u+DiCYFh85JVrNWX18PX19f1NXVwcdHO0lCRNTdscJj+DjrY31beiG/f75YH/z+hWVx+uB3GUInpfu2fVUHpKkJ2LULuHaDgXTLlgG33z6Ed0FEvZHWW7JIkt7GorXdhD8fzUNzm7a9aVa46nXenZODE+aGzcXa+LXwdPXs+cQmE/DGG8CVK/3vgMxDeeQRICZmmN4RERHR1GS+cB7nf/0D1LbU6LdVNhrVsbulwkSSI+rmxyNvdiza3Vzg6uSq2unG+3NOINGElJurJSKZzXqi05XKKyhvsnZuqAv2wS8TInHSpspkdpQfbk0KgrOTfX74rJBZ2JS0aWgdI4gIUzVuwEoTIppyaltrsTd3r74tJ1T3XbFpy5UcrAdMxJr4NYMPmOTkAL/5zY0DJuHhwLp1g3tuIupXgHsA1sSt0bfdXJywOjlY3/4ko1QNhq9ttvZAF6ZOE06XnMYvT/wSZ0vP9uyF7OSkZb+lpva/A21twMsvs1UXERHRzbh4Edf/+BO7gIkkQORUNNkNfa9YkYysxYkqYOLu7I7H5jzGgAnRRBYfD2zapG86OjgiJSgFkd7W6jDfinp8saIatyQE6redL6zFX07ko6y+1e7pLpRfUAlVhfWFo/QGiGgyYNCEiKYUOQn6/rX30Wayniz9PKsSTV0Z6LLwkn7ItgPoVkSvGPgLSCb6p59qmTENDTfORt+xQytDJqJhtSx6GcK9wvXtpBAvJAR76YNjZSj8i0ev48MLJSjvtrBq6WjBO1fewUvnX1JVKz0CJ/fcA6SlDSxwUsjFGRER0aBlZKDi5d8ivzZPv6nDbMa1skY1z0TIUPfGVTOQP0ur7JT2W0/Me4Jtt4gmg4ULgSVL9E1p1ZUYkIg4vzj9trDsMmxtM2JDSqhq0yeqm9rw2skCHMupUsf8Fg1tDXjx7IuoaKoY5TdCRBMVgyZENKVcLL+IrOosfbuwplmdPLUMjV6bEqIOyCxtubYmb4WTo7Xq5IbzDGQQ9Oefa7NMbmTzZiDQmhlDRMNHMtLk3698FfLveuPMMCyI9Vf/1oX8M71W1qAy0t4+U4TyBvvgSU5NDp47+ZwaGG87/0gPnMyaNbAZJ0VFI/AOiYiIJifThfPI/eP/4lLpRbvEp5zyJlUhLjxcndC5IQ0Fc+L04/YdqTsQ4tn74HcimoCkhXVCgr4px/MSNJE5JxaJxzOx1OCIhxbHINjboG6TwKoETV4/VaCCKBbt5na8d+29ntXkRES9YNCEiKaMssYyfJD5gb7dYTJjz2VrW67lCYHwcXOxy1SP8I4Y2JNLv9WdO4E8azZcnyQos349MGfOIN8BEQ1GuHe4XaWYBEtWJgXji7fEY3lCkJpfZJFX1YS/nijA4axK9dlgu7janb0bf0z/I+parT2T4egIbN9+43/HlsBJsTYziYiIiPpWceoA0n/5L8irzkWnTHjuUlLXiuqutpqSUe6wYSaK5llbcK2btg7T/KeNyT4T0QiR421pjRtkHQQvpJpM2nVJsNTR3Im0/RkIdXHEA4tisCQ+QE+ClDZdrxzPU8f5Fvl1+Wo+ChHRjTBoQkRTQmVzJf587s9o7bBmkp+4Xo2arsVXmI8b5kT76ff5uflhddzqgb/Anj3A1as3fpy3N/Doo8AttwzyHRDRUMhMohlBM+xukxkni+MDVPBkTXKIHiyVrLNT16vx8rE8FFQ3231PUUMRnj/zvH27LlnIbdt248BJa6tWhZaerrXwIyIiIjsd5g4c//RPyPj1D9FktG9xW9VoRL7N3+X2NSmoWpKob8vf+UG10yWiicPNDXjoIcDdfoh7qFcoZofOhquTK9waW5Fy6AqcHIBlCUG4f2E0/D1c1eOkRddnl8vtkqI+zfkUJjOPyYmofw6dk6wurb6+Hr6+vqirq4OPj89Y7w4RjQM1LTXqZKf0MbWQE6LSjkdKdx0dHPDQkhjVF9ni0dmPIiHAWgrcr7NngXfeufHjkpO1E6weHkN6H0Q0NHKoIwPeD+YdRL2xvsf9spg6eb1aXWx7H6dF+KjKFAmyWPgYfNSA2UCPQPtKM/kMOH/+xjvj7w+sWgXMnq0FXYiIiKbw3+fihmKcKzuHolP7kLj7lJyg0O9vN5lxvbIJVTbtdSRY4rnZmqwQ6B6ILy34Etyc3UZ9/4loFGVlAa+80qMNdrupHZnVmShvKkfO/Hjkz47tut2MXWeLUFjTorZvSQzCwrgA/fvuSLwDS6KsM1OIaGqoH0TcgEETIprUpJ3OC2dfQG1rrX5bcW2LCpjIgZSQjHNp1WMxJ3QOtqdsH9gL5OcDL77Yf/a4DHq/7TZg0SKtNRcRjQnJKLtQfgGH8w+r6rPuqpuM+OxSOYrrtMWV8HB1xvZ5kXqPZOHt6o3H5j6GII+gni36Llr7r/dL5hmtXg3MnMnPBSIimlIa2xpxtvQszpWeQ0VzBQxNRix65wScu+aVWKpLciub0GGTzJA/Jw5Bd89XCU/CxdEFT81/SmWcE9EUsG8fcOBAr3dJ0ORadSZOrU9Fbbi/uq2iwYhXTuRBOv25OjviieXxcHfVkqHcnd3xjaXfYMCVaIqpZ9CEQRMi0hZkL5x5AVUtVfpt5fWteDO9EG0dWsAkPsgTd86OUL2RhaeLJ766+KvwcBlANUhtLfD73wNN1h6pPXh5ae24QrmYIxov5NDnatVVFTwprC/scd+Fojo128TyOSGVJnfPi0SIj3VR5eXqhcfnPt4zcPLWW0BGxsB3JikJ2LEDMFiDMkRERJPVscJj2Ju7F20ma/VI6oFLCMnV5gxKUpMES2yHNzs7OqDllunoWJsKB5sqzXtS7sGs0Fmj/A6IaMzIsfbLLwM5Ob3ebeww4kJTDj5bF49WL+24/dNLpcgo1irN50b7YXVyiP54aeu3IWHDKO08EU20uAH7QhDRpCQteGSGiW3ApLLRiJ1nivQToTEBHtg8K1wPmIiNiRsHFjCR4c5/+Uv/AROpMHnwQQZMiMYZGQ4p/c+/OO+LuDf1XhUAsb1vdpQfvrA0DuG+2mKrtd2Et84UqWGStkHZP539EyqaKqxPLCdy7rkHSE0d+M5kZgJvvKEtAomIiCaxE0Un8HHWx3YBE7+SGgTnlKG+pR3ZFY04W1BrFzCRuQRed8yGaV2aXcBETnYyYEI0xViOtfs40WlwNmC2VwIWfXge3pVaa26ZceLspH12nC+sQ43N54sEcaWVNxFRbxg0IaJJRzLHf3f6d6pE10IGvu9ML1InP0WEnzu2zInQD6DEwoiFA1t8SYGetOEptz5/r+66C4iMvIl3QkQjSQIkaSFp+Oqir2J++Hy7+7zcpC1XFCJ8taGTxnYTdqYXorSuZ+DE9rNGX8zNmjW4Hs379w/DOyIiIhqfqpqr8EnWJ3a31TW0wvn9sypQcqmkXrXSscwWk+qSxBAvGDakonRxgt7K0gEOWBu/FuunrR+T90FEY8zTU6vS7mM2oAyGT3QNxdyPzyAorwJeBmcsiNHadck8U6kmtzB1mrAnd8+o7ToRTSwMmhDRpHK+7Lw6iSknMy3qWtqx83Qhmts61Haojxu2zY2Ai03AZFbILGxK2jSwFzl8GLh6tf/HyKBnmVVAROOeu4s7tiZvVQPeA9ytAyKl9/Fd8yIR6dcVOOkwY+eZQpTYzDxpam9SnzlljWXWJ3RyAu6+W7sEWJ+vXwcPAleuDOO7IiIiGh+k9eVHWR+pE5QWhzMrcenN02gsqFZ/Xy2kAjzE26CqPs2zonF9/jQ9YBLsEaxmmNwae6tKfCCiKSomBtjQd1utKJ8oeHS6IG1/BqIvFmBBjJ+aUyikoq2oazi8uFh+sUe7XiIiwaAJEU0K5k4zPsv5DDsv70SHWQuOCGmn88apAjQYtduCvAxqqLPBWRsAJ1KCUtTgd0eHAXwk5uYCe/f2/xhpzSMDnoloQon3j8dXFn4F0wOn2wVOts21Bk6kvd/bZ4pQXGtdbDW3N+PFcy+itLHU+mRyMmf2bOCrXwW2bgX8/G68A2+/DVT2HFBPREQ0kV2pvIKs6ix9O7+qGVevlWFFvrVS09fdRVWWzI/xx7RgLzi5uSBrkVZhIsfoK2NW4ssLv4xIH1ZxExGApUuBlJRe73JydMI0/2lw6AQSTmVj5sks3BKvVZuIg5kVKphrsTt7t902EZFg0ISIJryW9ha8evFVNdTZ1rWyBrxxugCNXQET6Yl89/xINdTZIjEgEfek3jOwgElDA/Dmm1p7rr5ERADbt+sZcUQ0sbg4ueC+tPuQHJjco+Ikyt9DD5y8c7aXwMnZboETS9XJ/PnA174G3Hmn1lKgv1lJr76qfSUiIpoEZH6JzDGx6DCbse9qOdbmlMDVZFbzwyRQkhLuo5KbLLMGc+fFoc3DgBDPEFVdsm7aOjg7apniRERqvd1PO2z57LDMLYy4VoId1woR6OGqJ1ZeLdNmnoj8unwcLTw6SjtORBMFgyZENGFJNoi04/rliV/iWtU1u9uPZlfiwwsl6DBpAQ5ZkO1YEKWX5Yp4v3jcn3b/wBZgJpM2rLm/we/e3sADDwAuLjf5zohoLMlnggROZFi8hbTzk7Z+MQEedhUnRTaBk5aOFhU4KWko6fmkEjxZuBB46CHtel+k0uSdd/oPzhIREU0QB/MOos5Yp2+n59XCp6gaKRV1ataA/F2V5ARbTX6eqJmVhHXx6/D0gqcR4R0xBntOROOewQA89hiQbE12spAWfpIgaRFSUIXH0a5vf55VhQ6TtTWgdK3Iq80bhZ0moomCQRMimpAqmyvx53N/Vu24ZKaARbvJjA/Ol+B4brV+W2q4D+5ZEAVPgzU4Eu0TjQdnPaiyygdkzx4gP7/v+2UQ3b33Aj4+Q3xHRDSeSFn/van3qvZ9toGTrXOsgRP5vHnnTBEKa5rtAyfnXkRxQ3HvTyzZcJs39//ily9rs5OIiIgm+PH60QJr9nZ9azvOZpXjtiztb2R8kKfdbBIZ8h7kEYQZX3gGz9zyT1gZu5LVJUTUP1dX4P77gWXLetzl5+aHQPdAfXvZ9TIkexvU9YbWduy7WmHX7vvNS2/azUYloqmNQRMimlBkXsm+3H147uRzyK3NtbuvuqkNr50sQFaFdqAja7CVScHYkBoKZwlq2AyGe3j2w3B10spzb0hOYB450v9jZBCdDKQjokkVONmRugOpwan6bc5dgZPYQE9r4ORsMQqqrYGT1o5WFdTtM3Ai7bqk6qQ/MjspJ2eY3gkREdHoksrvDzM/1Ie/O3aYUPPxRTx17AoCW4wI83GzS2iSIe/Lo5dj5vqHkDB/nfobTEQ0ILLWv/12LTGpW5vshIAEFZAVzm0deLy1WR3Pi4ziOnWxaGhrwFuX3lIBFCIiBk2IaMKoa63D70//HgfyDugLMCFltUeyK/Hy8TxUNmqzAKTMf+ucSCyI9bfLYJsVMguPzXkMbs5uA3vR6mqtVU5/ZACdDKIjoklHTtrck3IP0oLT9NtkobVldjjiugIn8hm061xRj8DJS+de6jnjxGLjRiAqqu8XlvZcMkOpvn4Y3w0REdHoyKjIQE5NDhxMZkRcKULSS4eQnJ4Dj3aTqtyMCnDXHyvVJEmBSXDx8NISkYiIhmLRIq0VrlSfdPFw8UC4d7i+PeN6Oe6K9Na3914pR0WDdZ6gJGbuv75/FHeaiMYrBk2IaEIwmU3468W/oqypzO7261VNeOlYHk7kVsNs1mYA+Hm44oFF0ark35b0Rb475e6Bt+RqbARefrn/ocwBAcC2bRz8TjTZAyep96igq23g5M454frnjMxPkuHw+dX2rbqk4qSs0f5zS3sCZ+C++/ofDN/crM1SkplKREREE0BzezN2Z+/GO1fegVdVAxa/fQKJR66htKBGf4y0ubStApc5g6oCfM0abUYgEdFQJSVpx9g25DPG4KS15XLo7MTGwgrMivRV2yZzJz44Xwxjh8luFpPtzFQimpoYNCGiCeFU8Sm7jO1GY4ca9C7zBOpatIFujo4OWBQXgIeXxCDAUzsoErIIe2DmA6ovsm3VSb8kUPLKK1qlSV8sJz3dBli1QkQTlqODI7anbMfs0Nn6bXLCZ/PscEwL8tIXXe92qziRk0cSOKlosvZM1skMJPkMsTlx1ENBAfDZZ8P8boiIiIaXVFhKC91njz2LIwVH4FZRg3kfnYF7YyuK61pg7NDa3Xi7OSPIy5oF7uXqpQ16Dw0FFi8ew3dARJNGYqJ26SJJk9Ju19KmKyi/Ett8XBDira3ja1va8emlMtVW0OLty28jqzprDHaeiMYLBk2IaNxrN7XjcL51KHJzWwf+ejwf18oa9Nsi/dzx8OIYrEgMUiX/tsPfvjjvi5gRNGPgL9jRAbz6KlBS0v/j7rwTCAsb5LshookcOLlrxl2YEzqnR+AkIdhLrzjZda4YRTUt+mOa2pvUcHgZiNtDbCxw2239v/DRo8ClS8P4ToiIiIaHnGQ8XXwaPz/2c9VCt83UBmdjO9L2ZcCpw4zG1g4U1Vr/JnYf/j49cDocXFyArVv7TyIgIhoMOb62+azxdfNV800skk/nYPOsMBictc+drPJGnCmotasYf/n8y/jg2gfqc42Iph4elRDRuJdekq6Gslmcul6DprYOdd3NxQm3pYZhx4IoBHpZq0tEgn8CvjT/Swj1Ch34i5nNwM6dQK79kPleBznPnTvId0JEkyFwsm3GNswNs/77d3J0wKZZUnFinXEirbqKbU4SNbY14sWzL6KkoZdg7JIlQKp12Hyvdu0CqqqG8Z0QERHdPOn9/96199QJRqWzEymHrgDVjSrB6WJxnRrTJcJ93eDhah3+Hu4VDp+EVOCLXwQiI8foHRDRpBQSAixYYHdTpHckgj2C1XXv6kZML67G7WnWJMjDmZV2QV5xsvgkfnPqNyioKxilHSei8YJBEyKaUFUmTcYOnC+q1U9USiuu1Agfu4w1Gfa2fcZ2PDL7EXi69jMvoDtZ0X300Y0zuqOjgTvuGMK7IaJJEzhJ3oZ5YfPsAyc2w+HbuwInJXXWhZcEf/+Q/gfVtsS2/F9lwclspMDA/lsGvv460K61IyQiIhpr12uvq+oSW4Enc1CTnodzhXWobrJmZ3u4OiHK30PfNgb6I+Yr39YCJuHWIc1ERMNm9Wq7ofByziA5KBnuzu5qe1p6LhL93bEwLkBtmzs7sTO9EGfya+yO1atbqvH8meexJ2cPzJ1aq0EimvwYNCGice10yWm7KpPTeTWq/Y2YHekHbzf7oe4Lwhfga4u/hjlhcwY+v8TiwAHg5Mn+HxMcDDz0ECBtBIhoypLPl63JW+0qTqRV152zw9WAW9HWYcbbZ4pQWteqP8bUaVIDcl86/xIajNbPNhgM2nyT/j5bysqAjz8eoXdEREQ0uMSm966+p2/LCcasz7PQ9ME5VDYa9dulbW5coAdmRviqBINmH3dcWpWKoGf+Be5pc+za5xARDSsvL2DlSrubnB2dkRaSppKgDM1GRF8swPJpgYjuCurKjMID1yqwM70I9a3WZKVOdOJQ/iG8nvE6AydEU8SoBE1+9atfIS4uDm5ubliyZAlOnDjR52P/9Kc/qRMRthf5PiKaem5UZbIwzl+/z9PFE0/OexJbkrfA3UXLHBmUq1eB/fv7f4yvL/Doo4D7EJ6fiCZt4MRuxomTI7bOidAXXhI42Xmm0G4Gk8ipycFzp57Dlcor1htlCK7MSurP6dNAaekwvxMiIqLBOZh3EFUt1raR2ZnlmLb3ola5rU5MOqgkgrnRfgjzdYejowNqwvxwctsieM9fhvkR9m1ziIhGxNKl2jrehperl5qnJGIv5MOnphHb5kaozyuLgppmvHwsD5dL6u2qTuTY/VTxqVF8A0Q0aYMmr732Gp555hn84Ac/QHp6OubMmYPbb78d5eXlfX6Pj48PSkpK9EteXt5I7yYRjdMqE5kD0GuVSZQfPA3Wnsi3xNyCGN+Yob1Qayvw/vv9P8bDQwuY+PgM7TWIaFLPOJkVMss+cDI3ApF+7nrg5MMLJdidUaquWzS3N+PVi6/itYuvoaKpQrtxzpwe/Zd72Lt3hN4NERHRjZU1luHzgs/17ebmNoS8dwYe7Sa1LX//5sb4IcLPXSU6CaOHq6owmR6Sgh2pOwZfEU5ENBRSxb1uXY+bw7zC1FwlR5MZafsy4NZhwurkENw9PwpeXecZ5Lj9k4xSfHC+RLXetQ0ad5i1GatENHmNeNDkpz/9Kb70pS/hiSeeQGpqKn7zm9/Aw8MDzz//fJ/fIwdQYWFh+iVUMi+JaEq5YZVJrL9dpsjCiIVDf7FPPwUa7LPAexxoSUuuoKChvwYRTerAyfaU7UgLTrNrR7JtbiSmh3rrt10qqcdfjufZtesSlysv49cnf423L7+NmpYabWZSf/3dr10DCgtH5s0QERH1Q9rSvHv1Xbv2NMZ30hFS16SuB3sZEB3goVpWWnQ6OKBs0yrsWPQYHpj5wNCqwomIhmrWLCAyssfNUm0S6hkK98ZWpB68DAdzp6qQe2RpLFLCrcmSWRWN+DyrUt+WxM5zpedGbfeJaBIGTdra2nD69GmsX7/e+oKOjmr76NGjfX5fY2MjYmNjER0djW3btiEjI6PPxxqNRtTX19tdiGjqVZm4OA1xxkhurtbupi+y4Lv/fiAqamjPT0RTJnByd8rdSAlK0W9zdXbEHTPDcFtqmAqiiNqWdrx+qgAnr1erYZO2fZLPlZ3DL078Au/nfIyGrRsBJ6e+X3DPnpF9Q0RERL04WXQSRQ1F+nbrqesIv1JkbckVaB32LjxcPBBz35fw6ObvqgHMrDAholEnnzu3397LzQ6YETRDBU+CimsRe+66ut3NxQm3p4WpWYXOTtpn1sXierS0adV04kjBEc42IZrkRjRoUllZCZPJ1KNSRLZL++jHnZycrKpQdu3ahZdffhlmsxnLly9HYR8ZlT/60Y/g6+urXyTQQkSTt8pEDlq6V5nI8PehvVA78J51gGWvNm8GEhOH9vxENKU4OTqpliO2gRNZjKVG+ODhJTEI9dFmtEmwRLLVXj2Rj+uVTXZ9kmXxJX2Sf5X9V+QnBvcf8JULERHRKKlrrcOeXGvQ3qm6AX4fW7Ot44I89SQBIa1zF97+BBI2PshgCRGNrZgYYPnyHjfLZ1OEdwQWRSzC7Cs1CMy3VpQkhnhjZoQ2D6XDZMa5Qu2chJCZTlcrr47SzhPRpB0EPxjLli3DF77wBcydOxerVq3Czp07ERwcjN/+9re9Pv473/kO6urq9EtBQcGo7zMRDS85YWhbZXLKtsokchirTPbtA6qr+75/2jRg/vyhPTcRTdnAyb1p92J13Go4wHqCyM/DFfctjMbiuABYbi5vMOKds0V443Qhimpa7J6ntaMVL/vlo6TVunDrtdrEJuBCREQ0UiTA/0HmB2gztaltmQPg/uZJafqvtv3cXRDo6WqX2BSXtAiOW7dpWd5ERGNNuuDEx/d6l7QNnBc2D1sudcCr3qjfPi/GXw/6niuotZttIometslPRDS5jGjQJCgoCE5OTigrK7O7XbZlVslAuLi4YN68ecjKyur1foPBoAbH216IaOJqMDaowWp2VSaF1iqTBcNVZVJUBPTTJlDNMdmyhYs8IhpSqy4Jmjw570n4ufnpt8s8puWJQdgxP0r1fLcorm3BG6cL8PaZIpTVW+edtLm74rOgehQ3FPf+QlKFm5k5sm+GiIgIQEZFBq5VXdO3/fdmoKOwRl13dHBAfJCnXTXJ9PCZcHzgQcDVGkghIhpT0np7xw6gj/OG8hkW5xaGu84b4dSuteLydXfB9FAvdb2l3YRLxdaRANKqMK8ub5R2nogmVdDE1dUVCxYswB6bvtvSbku2paJkIKS914ULFxDe30BUIpoUJEvjvWvvoaXDmnEtff9N5mGuMjGZgHff7T9De906wN8aoCEiGqxo32j8zcK/wezQ2Xa3R/l74KElMdg0Kxz+HtaTSXlVTfjriXyk52knoUT+zGhcqs9GUb21f7ydvXtZbUJERCOqpb0FH2V+pG8HZpXAfCxb344OcIfBxTqHK8onCj73PiJZlKO+r0RE/fL01GaW9jM7MK7NA7MPXoVDV1XJwtgAu1mr5q7zE+Lz/M9HeIeJaNK253rmmWfw+9//Hi+++CIuX76Mr3zlK2hqasITTzyh7pdWXNJiy+Lf//3fsXv3buTk5CA9PR2PPPII8vLy8NRTT430rhLRGJMhyLYZbJJxfa6wbvirTA4flpK3vu+Xoe+LFw/tuYmIbLg5u6kB8XIxOBnsMtmmh3rj0aWx2JAaCm83awD4YGaFChiLdjcXFKZFI7M6E4X1vcx3kxlxly6NzpshIqIp6dOcT9HU3qSuu9c1w+uj8yrjWkhCU1jXzC7L373YdfcAM2eO2f4SEfUrMhLYtKnPu50dnTGzzoAZh6+o5KRgbwNiAz3VffWt7bhW3qA/Vo7Ryxr7ObdARBPWiAdN7r//fvzv//4vvv/976s5JWfPnsXHH3+sD4fPz89HSUmJ/viamhp86UtfQkpKCjZt2oT6+nocOXIEqampI72rRDSG6o31+DjrY327w2zGJxmleo9Qye4YliqT2lrgoLX9Vw+ScbJ1q1a6S0Q0TKTa5CuLvmI3JF44OjogLcIXjy2PxSKZd9JFBsUfz6lS1wtSo9BucEFWdRby6/J7n8/U3j7yb4KIiKac67XXkV6Srq47dpgQv/scyiutswendWvLNS1lOVw2bxmTfSUiGjCZXTpvXp93S8VcWG4Fko5nqcDJQpsEztPXa+xmmXxewGoTosnIoXOSTS2SIIuvr68aCs/5JkQTg3wM/eXCX1SWhsXhrEqc6sq0lsyOBxbFqHkAwtvVG19f8vWhBU3eeQc4e7bv+9esAVatGsK7ICIaGGm1tTd3L7JrrK1NLE7kVuNItnX4++L4ACybFoiYiwVIOJ2jbksKSEKkT6T9N8oxj3x+zZnDoC8REQ2LdlM7fnPqN6hq0YL4005koeGzDDS0asPfI3zdERPooT8+yD8SM//1/4DAwDHbZyKiAevoAJ5/HijufX7glcorKG0sRe7cOFyfE4tXTxbo8we3z4vUq09knqGcn7CdZUhEEz9uwFU1EY25s6Vn7QImJXUtOJ1XrWdh354WpgdMxMbEjUMLmFRUAOfO9X1/SAhwyy2Df14iokGQgMejcx7F43MfR7RPtN19EiRZmRRsF0T5PKsKhTMi1GB4IRUnjW3WLF+lvh7YtQt47jngitZKgIiI6GYczDuoB0w8qxvhcjxbD5gYnB0R5e9u185m2he+wYAJEU0czs7afBNv717vthynx5+9jqgrxXbVJqdsZhCaO804VnhsFHaYiEYTgyZENKbqWuvs23KZzNidUaaf71saH4ggL+scgJkhM5EWkja0F7vRwORt2/odCEdENJzi/OLw5Lwncef0O+EAa2BY5jetnm4NnJzKq8aB3BrkzY5V253oVPOfei0WluDwq68CL7wANFj7LRMREQ2G9OjXW850diLm8BUUVmlzTcS0YC+V3GQRsXYbPOZxJiARTTC+vsAjjwBu1tlMFp6ungh01wLBiScysayxCX7uWvJmQXWzXnUiThefVuc2iGjyYNCEiMaMnPB79+q7MJqM+m1Hc6pQ09ymrof6uNllc3i6eGJTUt8D2/pVVARcvtz3/bNmaQPhiIhGkfSBXxixENtmbLMLnMyN8cfaGSH6dnp+DQ76e6PVy02fA1XSaJ0J10N+PvCHPwCN3SpSiIiIbkCypuUYXb6KsOwy1F0qgcmsBeuDvQzw7TpxKAzR8Yi//2/GbH+JiG6KzFx++GHApWc3ixjfGPXVoRNIO3wVm7XCb+XUdWu1Sbu5HW9eehMms2l09pmIRhyDJkQ0Zs6VnbPr6V9c24LT+dqBh7Tjui011C6DTbKxPVysfZMHZc+evu+T/v8yC4CIaIzMDZuL7Snb7QIns6P8sMYmcLIvsxIXFyeis2vgbk5NDowd1qBzD3V1wM6dgFk76UVERDQQJ4tOoqihSF13NrbDd/9lVHclNTk7OtjNMXFwdMKMp74Nh15ONhIRTRjR0cB99/WYDejr5gtfg6+67tDZie2X8jGjsUVtZ1U0qHMYFgX1Bdh/ff8o7zgRjRQGTYhoTLS0t2B39m59u13acl0qk74zigw+DrRpyzUrZBZSglOG9mI5OdqlL/PnAwEBQ3tuIqJhMjt0Nnak7lDDJPXbIn31IZONxg6832LG2Y1z0RjghQ5zR6/D5O3IZ9/hwyO960RENElIe5k9udZko6hT2SguqtW344I84eJk/TsVuO5O+Mcmj/p+EhENu6QkYPv2HjdH+1pnEDp3duJvC0sRWd+kOn9/dLEUre3W6pJD+YfU/EEimvgYNCGiMSGLseb2Zn37eG41arsy2MJ83TDfpi2Xl6sX7ki6Y2gvJEcy/VWZyPC3W28d2nMTEQ0zmdl0b+q9esWJtO+SNl3OTtr2ucJaXHM34NSWBbh0awryHOpR1awN6e3Tvn3A9eujsftERDTByazBNpN2TO5dUQ/z0WyV3CSkl3+gp7U3jat/EFLuYVsuIppEpG33HfbnHmSuiW3Hi2hPV3wxqwihDS1oaG3Hp5fK7GYNvn35bTQYOVuQaKJj0ISIRl1RfZEalGZR3WREep5tW64wOHa1nxFbpm8ZeluuK1e0eSZ9WbIE8PEZ2nMTEY0AqapbErVE35a+8UviA61x4MtlkLby5dNCcWL7Ynya7AyTe8/hlTr5pjff5HwTIiLqV2ZVJi5XajMAHcydCNmXgfKuQcdybB4f5KmC+RaJD/0dnNzcx2x/iYhGhJwjWL1a35TPvWn+0+y20wLc8cilPAQ2tSK7ohHnCqwVeU3tTdh5eac+F4qIJiYGTYhoVMmBwweZH6Czqw+XZGTsu1IBc1dmhgx+D7DJYJsTOgfJQUMs+Zc+/nv39n2/wQCsWDG05yYiGkFr4tbAx2AN6M6P8Uegp9aysLzBiLOF2sKs08kRVxJ8ceDOmYCfX99PKAGTt9/WAihERETdtJva8WHmh/p2+KVC1Fwr07ejA9xhcHHStwNmLUbIwlWjvp9ERKNi1Spg2TJ9M8gjCGFeYfq2wdkJM/3d8OCFXPi2tOFQViXKuoLMIrc2F4fyDo36bhPR8GHQhIhGVXpJOoobivXta2WNKKjR2nT5uLlgYVyAXVuujYkbh/5i588DFRV93y8BE48hVrAQEY0gg7MBm5I26dtShbcuxToU/mh2lWoHYHG4Mh0Vd6zqMbzSTnY2cIiLNyIi6unzgs9R01oDr6oGzN59Dt57LqK5TevT7+HqhDAfa0Wjq8EDM77wjKRbj+EeExGNIPl8u+02YMEC/aakgCS7Dhj+Hq5IcnfGXZfzYTaZ8eGFEhg7rPNNZCh8Xm3eqO86EQ0PBk2IaNQ0tTVhT451vogcUBy8Zg1qrE4OthsseVvCbXB3GWLJf10d8Mknfd/v6QksXTq05yYiGgUzgmaoi0WEnztmRfqq69Jfft/VCrsqvneb0tG5fn3/T8r5JkRE1E11SzVOXdyN1AOXsPC90/DKr0JhTYt+f/e2XDF3PAjXYGvGNRHRpCSfe5s3a3NOVBKTE9KC0+DoYD1nER3ggcT2dqSV16KupR17Lpfr802ku8b71963m3dCRBMHgyZENGo+y/kMLR3WBdixnGo0tXWo69OCPDEt2Eu/L84vDrNCtIOTIbXl2rkTaLG+Vg8y/N3V2gaMiGg8uiPxDrg6WT+rViQGwcPVWV3PqWhEVrl1TklBfQHOxbsDyf20NJRF2zvvAO3WKhUiIpq6OtvbcebF/8GCt44iJLdc3ZZX1QSTDM8CEOJtgLebi/5437B4RG1+YMz2l4hoVEkV9113ATO0RCZPV09VcaLf7eCApBAvrC6ogJPZjGtlDcgortfvr2iuwJXKK2Oy60R0cxg0IaJRUVBXgDOlZ/TtykYjznYNS5O2M6uSrW1nJHND2tLYZrQNysGDQF4/ZbDS99+mzJaIaLzydfPF2vi1+rabixNWTQ/Wtw9cLVdVJxaf5nyG1js3Ar5aRUqvamuBI0dGbqeJiGjCKHzt93A6dgwOXZnQkild1dSmrjs7OqgsagsnByckPfp1wMUaRCEimvScnIAdO4CEBLUps01CPEPsjs/neblgbkm12v48q9Lu+FzaH7LahGjiYdCEiEacscOI9669p2/LAcPeK9ay1cVxAfB1ty6+lkYttTsIGRQJlhw40P9jpH2Ns5apTUQ03i2OXIxwr3B9e3qoF2IDPdX1BmMHTuRqCzTR1N6EfaXHgHvv7X++yeHDWvCEiIimrLbr2Sjct0vfNnd2IreySd+OCfCwa50bvGIDvNLmjfp+EhGNOTl/cP/9KjFJkjunB06Hu7O1lXiglwHbaurh2mFCS7sJl2yqTQrrC5FXx9kmRBMNgyZENKKkz/7OyztR3qSV+4vLJQ0ortVaZ/l5uGJBnL9+n4/BB6vjVg/txaQdl7Tl6i+LY/ZsIC1taM9PRDQGpPpuS/IWOECrvpOF2urpwXB01LZP59egpisrWJwoOoEyPxctQNwXac/16acjv/NERDQ+dXYi65Vfwmgy6jeV1LWitV0bYuxlcEawt0Fd73BxQvWK+Uh6/B/HbHeJiMactPdes0ZddXZ0Rmpwqn58LhLcnbGoqEpdP51XA3NXm0Pxef7nY7DDRHQzGDQhohElg9+vVl3Vtxta23Eo0zq8eE1yMJxtsqFvT7jdrn//gEmg5N13tQHwfQkI0Aa5DbXtFxHRGInwjlAVJxb+nq5YEKMFnGVBtv9ahd3QyQ8yP0DnkiVARETfT5qRAeTmjvzOExHRuJO57y2UZ1pb5xrbTSiqabYb/t7p5IiC1Cgcv2cpFtz7DTg5sy0XEU1xkoQZrLXK9TZ4I9InUr/L0+CM26rr4NHWgfrWdlwrb9Dvy6zORFlj2ZjsMhENDYMmRDRizpaeVf07LaSv57tni1W5qpCBaZYWMyLBP0FlawzJqVPA5ct93y+BmXvuAQxaxhwR0USzJn4NPF2sn5mL4wPgbXDWh/ZmV1hbquTX5eNCZQZwxx39P+lHH0nUZeR2moiIxp3r5ddw/c0/6NsSdL9e1QxLUnSorxvq06Jw/O4lyF6ciNTYhYj1ix27HSYiGi/kvMK6dfpmtE+0XbVJnJcrlhZoSaKnrtfYzTKxPTdCROMfgyZENCLkhN17V+3nmHx8sRQVjVoLAJlhsnZGqN1gySEPf6+qAj75pP/HyIFNpDULhIhoonFzdsOGhA36tvSZv9V2KPy1Cruhk7uzd8MYHgLMmdP3k5aXAydPjtxOExHRuCItc4+8+mO4NLfqt0nb3JrmNv1vS/vqFFy9ZQaMXm49/vYQEU15yclAVJS6anA2INTLel7Dx80Zq6rq4NPahspGI/KqrRV8F8svoraVMwWJJgoGTYho2NW01ODVi6/C1KlVlIij2VXIrmhU112dHbF1TgTcXZ30+1fErECgR+DQXnD3bqCjo+/7ExKA5cuH9txEROPInNA5KqPNIjHESw3qtbQ/PHndOhS+sa0R+6/v12abSA/mvuzbBzRZq1SIiGhyajA24M3P/4Cwc9n6bRUNRhTUaLMGRWiMP0rnWKtK1savhZer16jvKxHRuCWJnjazA22PzSUJNMrHgFvyyvVqE9t5r0cLjo7yzhLRUDFoQkTDythhxF8v/hXN7daMiisl9TjRdSJPji82zQxHoJe1TVaMbwxWxa4a2gtKP/6r1pkpPXh6Atu3c44JEU0KshBTVXm2Q+GTQ6xD4fNqUNuVLSyOFx1HqUMTsKqfz9jWVmDv3pHfeSIiGtNj9FcuvIKgo2fh2FWVWNfSjpyupCYRHeCB+lUpMDtriU3hXuFYGLFwzPaZiGjciosDEhPVVU9XTwS6WxNAAzxdsai6HoFNrSisaUZpnbWyL70k3e5cCRGNXwyaENGw2pO7R5X925b7f3rZOvDs1qRgxAVZe/L7ufnh/rT74eRorToZMOkPKlUm/ZGAiRez44ho8gj3DseiyEV2C7P50dpQeJMMhb9qHQovGW07L+9E+6IFQGA/1Xzp6UBBwcjvPBERjTqT2YTXM15Hc85VhORqx+nNbR24VtYAS7f9UB8DvBJDUB4foradHZ2xNXkrHB14yoCIqFc2s00kEdRCkpoifN2wIl/7vD2dZ60Ebze340TRiVHeUSIaCh4BEdGwqW6pxqniU/p2fUs73j9frE7iiVmRvpgb7aff7+rkiodmPaQyM4bk/HmgpKTv+xcu1LM/iIgmkzVxPYfCe3UNhb9e1YSscmvmsASyP8vb1/9QeAmyvP46UF8/sjtORESjTlo1ZldnIfFElto2dphwpaRBP0b393BBXKAnspYk6dXZ22dsV0F6IiLqQ3g4MHOmuurr5gtfg69+V7CXAbNqGtVsk8yKRn1ulDheeBxtJus2EY1PDJoQ0bA5lHdIZTVbSIVJc5s21yTa30O1kLEMepfWMjtSdyDEU8tmG7T2dmDPnr7vNxiANWuG9txEROOcu4s71k+z9lKWWVG2Q+H3XClHo7HDrk1XVkDX4Mq+NDQAr76qfb4SEdGkUNJQgs8LPkdodhl8KhvQYTbjamkD2rpadHkanJEY4o2yxDA0BPuo225LuA1pIWljvOdERBPA2rWAo2OPahNpnRvhY8C8kmpISZ+00LVo6WhRbbqIaHxj0ISIhm34+7myc/p2UU0LCqq1Xp3ebi7YPDscTl099y2LsemB04f+gseO9Z8Rfcst2jwTIqJJam7YXET5ROnbSSFeSAzW2hG2tpuwO6NUb9Ml3rnyDprXrgSc+mmHWFwM7NqlVZ4QEdGEb8u16+ouFQyflp6r/iZklzfpSU0GZ0fMCPOW8m/kzp+mblsSuQTLopaN8Z4TEU0QAQHAnDnaVfcAu0rwUG8DFpbVwNlkxuWSejTZJDR9nv85OszWbSIafxg0IaJhcSjfvsrkWG6Vfn3ZtEC4uVhP0s0Pn4+lUUuH/mJNTcDhw33f7+MDLL2J5ycimqBD4delhKqsYZFf3YyzBbX64xvbGvFuxWF0rlzZ/xNfvAgcOjSyO09ERCPuaOFRlDaWIupSIQzNRlQ3tektYpwdHTAjzAcuTo7InxkDo6cBKUEpuD3xdr0ynIiIBqDr3IN8dkb7Rus3Ozs5IsbdGWnltaodYnq+tdqkoa2B1SZE4xyDJkQ0LFUmZ0vP6ttFtdYqE193Fy2DrUukdyQ2J22+ucXY/v2A0dj/QDYXl6E/PxHRBBHhHYGVsdYgiLurE25LDdW3D2dVorLR+nl5pfIKzkz37r9Nl9i7F7hyZWR2moiIRlxlc6WaZeLa0obY8/loN5nVzCuL+CBP9Tej1dOAgpnRqnLx7pS7OfidiGiwQkOB+Hh1VdqPG5wM+l3hvu5YUlylqrjPFdahuc1aXXI4/zCrTYjGMR4REdGwV5kcz6myG04s/TwtpAe/k2M/rWFupKICOH26/2Fss2cP/fmJiCaYVbGrVEDaIjbQE/Oi/dV1yWr7+GKp6mFv8XH2J6jauAoIucFMqZ07gbKykdtxIiIaEdKG692r76qTcbFnr8Opw4S8qma0myyD310R4OmqrktbLj/vYDw480G4ODHpiIjoZqpNJPBsW20icwdTnYC42kZ0mMx2s03qjfU4U3JmTHaXiG6MQRMiuim1rbV2VSbFtS2qJYylyiQlTBsoKWJ9YxHnFzf0FzOZgA8/BGxO/vVw++1SFzv01yAimmAkEC3ZwS6O1pNdKxIDEeSlZblJpcmRLGswu83UhrdzP4T5gfsBD4++n7itDXjtNQ6GJyKaYE4Vn0J+XT48apsQca0Etc1tetWhzBiUKhOp+m4I9EZFQhjuTb0Xnq6cBUhENGRJSYC/lrQU7hVud1we4eeOxcXV6nr3ahNJQGW1CdH4xKAJEd2UQ3ndZpn0U2WyOm710NtySaDk7beB3Ny+HyPtZuJuIihDRDRBBXoEYmPiRrseyhtnhqmTY0J6KOdXaQFtUVhfiBPNmcB99wGO/RwOVlcDR4+O7M4TEdGwqWutw6c5n6rr007nwGwyI6fS2pYrNsBDZT6L7EUJWBFzC8K9w8dsf4mIJgU5nl6yRE9oivGN0e+Sz9xl7W3wbzaqapP0POvMQVabEI1fDJoQ0U1VmZwpPdNrlYmPmmVirTKRg4YhV5l0dgIffKANJ+7vIGXDhqE9PxHRJDA/fD5mBM3Qt6XSZEVCkL79yaVStLab9O09OXtQE+oLbNrU/xMfPgw0No7MThMR0bAxmU2qLZdUFPqV1CCooErNGWzr0BKcfNxcEOzdVYUYHQin+ASsils1xntNRDRJzJsHGAz63EHbapNIqTYp0RJMzxXWoqXNekzOahOi8YlBEyIaktaOVuzO3m0/yyRXKzkVS+IC9Aznm6oykYDJp5/2P8dELFgABFlPDhIRTTXyGbtl+hZ4uXrpt82L8UNMgNaCq8nYoQbDW7Sb2/H+tffRKZ+fixf336Zr376R3XkiIrop7aZ2vHrxVWTXZKvj54ST2WhobUdpfau6Xw7LpwVrbbk6HRyQszABW5O3wtnReax3nYhocpCAydy5fVabrG9ugaHDhHaZbZJvP9vEtuU5EY0PDJoQ0aC0tLdg//X9ePbYs7hUcUm/vaSuBXlVTdYqk3D7KpN4v/ihveChQ8CRI/0/xtsbWLNmaM9PRDSJSE/6u2bcpW/LybH1qaFwcdIO+S4W1aGwxtqmS06unSs7p82Diojo+4nT0zkUnohoHCczvXT+JWRWZ6rt0JxyeFY2ILvC2pYr2t8Dbi5O6nrx9HCkpaxCrF/smO0zEdGkJC26upJFu1ebxHi7Ym65Fiw5V9Ct2iTvkKoWJKLxg0ETIhpw9tq+3H0qWCJBE1mc2TqWY60yWTxcVSYnTgB79/b/GHd34JFH+h9mTEQ0hSQGJGJJpNZT2dKOZUWitRLvs8vlqp+yxSdZn6DR1KIFTvqr+tu9e+R2moiIhqSxrRF/OvsnNfhdOHaYEH0qG1dKG/SWjJ4GZ4T5uqnrHS5OqF4yG+unrR/T/SYimpQCAoDp0/Vqk2jfaP0ug7MT7mxogkNnp6o2kZmDFnXGOrvW50Q09hg0IaIBDZT8Q/ofcCDvAIwmo/19Le347FKZtcrEzQUpNlUm0T7RQ6syuXIF+PDD/h/j6go8/DAQGjr45ycimsTWTVsHfzd/fXtOlC/Cfd3V9drmNhyzaafY0tGCj7M+BmJjgZSUvp80OxvIyhrZHSciokHNF3z+zPMobSzVbws7nYPczArUt7arbUcHByR0teUS+bNisHHOPTA4a333iYhomC1dql+N9I60qzaZbnBEWmWdun6W1SZE4xqDJkTUr87OTrxz5R2UNdm3ZZGTbp9eKsWLR67jYrH2R18sjh+GKpO6OmDXrv4f4+QEPPAAEBU1uOcmIpoCXJ1csSV5i74tn8MbUkP0z+fTeTUo7+pzLy6WX8TVyqvA+vXa52tfPvkEMFurVIiIaGxID3wJmFS3WIPg5tJaNH56CU1t2kBhZ0cHpIR7w8NVm1ti9DDAf/UdSApMGrP9JiKa9OLigJCQPqtNHqiqhYvJ1Gu1yYXyC2Oyy0TUE4MmRNSva1XXkFubq2/LQMlPMkrx4tE8ZBTXwywtW7oGmy1PCEJahH2VyTT/aYN7QTkZt3Mn0NLS92McHYF77wWmDfK5iYimEPn8nRc2T98O8DSo9omWgPhnl8tgNmuf4UKGwpcZOoBFi/p+0ooKbb4JERGNGXOnGW9eelMFTixqGo1ofP0k2roCJjLLKjXCB95u1gzn8qWzcHvKnWOyz0REU4YkjfZTbZLk5oSlRVV6tYmllaI4UnBEHacT0dhj0ISI+nW86Lh+XXrgv3G6EJdL6vU/5AZnRyydFognV8SrKhNLVYkDHFSv5EFXmcjg97y8/h9z113AjBlDeDdERFPLbQm3wcvVS99eGBeAQE+tJUt5g9Euu62hrQG/O/07HJ3mCrOb1vu+V/v2AUb7Vo1ERDR6Msoz9BkmQioHL35wASGV9frxuSQyWSpMhDEsGLff/U/wcOEcQCKiETd7NuDj02e1yZbaevi0tqlqkwtF1s4d5U3lyKpmO1yi8YBBEyLqk/zBzqnJ0bdloGR9i9Yf2eDihGUSLLklXgVN3Fzs27lIW65Yv9jBvWB+PnDgQP+PueMO7QCEiIhuyN3FHXck3qFvS3uu9akhEtlWjuZUoaa5Tb/f1GnCJ8UH8WF4I1ra+6j4a2rSWih2aNnMREQ0ulUm+6/v17fbOsz4+GQ+ll8rUtserk5Ii/C1OzY3uLhh8dM/RIgX5wASEY0KZ2et7W2XCO8Iu2qTaG8DVl/XWqCfya9Fh037W6k2IaKxx6AJEfXpeKG1ykQqS6R01OKuuRFYMi1QZUnY8nPzw/1p92NV3KrBvVhrK/DWW/33yp81C1i8eHDPS0Q0xaUGp2JGkLU6TwbCz4vShsSbzJ14/WQBsssb7b7ndJQjDjVm2A0XtnPpEvDKK9pnNxERjZoLZRdQ1aK1dRHnCmux6FoRPNs74GVwRmq4j2qbayGVJbO3Pg3/aaljtMdERFOUnL/omsHq7OiMSJ9I/S53Fyfc0tiMyPomNLd14GpJg36ftEcvbigek10mIisGTYioV5JhfL7svL5dVNuCykatHUuYj5s66WbL380f25K34WuLv4aU4JTBvZi0+nrvPW0AfF/8/YE779T6gxIR0YBJm8RNSZtgcNLaconliYHw83BV11vaTXjvfDF2Z5TC2KH1VO50csS1+bG4UnnFrgWMndxc4IUXgAbrIo+IiEa2yuRAnrUqWz6zi88XYl6JNgw+IdgLzk7WJb63qzfmJq2E522bx2R/iYimNDl3sXGj3WwTRwfrZ3S4nxvWZZeo8yGn82vsZpmw2oRo7DFoQkS9Si9JR7tZa8VlKRm1mBvjp1+X2SUqWLLka5gXPk/16xy0M2eAjIz+B7/fcw9gsJ7wIyKigfMx+Kj5JhYyIPi+hVHqBJvFpZJ6vHIsH4U1zWq7MiYItaG+qk1jZXNl709cVgb88Y9AZR/3ExHRsJGEpuqWaut2Xg1WXy5Q14O8DHB3dbJLaJobNheum7fyGJqIaKxIpYlUnKjjbxeEe4Xrd3m7uSC5vR1p5bWobmpDbmWT3eyqmhbr7EEiGn0MmhBRr1lsJ4pO6Nt1Le3IqdRat3i6OiMpxFu/LykwSQVLbDMmBkWqSz76qP/HrF2rl7USEdHQzA+fj6SAJH1bBgTfOTscG1JD9VYu9a3teDO9EIcyKyC5bteWJ6PDxQlXK6+izWSdfWKntlYLnBRoJ+6IiGj4mcwmHLhuX2Xi+HkmQpu0NomR/tYqcCcHJ9Wa0Wl6MpDKtlxERGNKZpu4aPNMonzsz2tIBw+ZbeJiMuF0njVI0olOHCs8Nuq7SkRWDJoQUQ9ycqzOaG2Vdb6wVnXQErOjfNUgYYulUUtv7sWOHAHarRUtPUybBqxYcXOvQUREqk3XvWn3Is4vzu42GRj88JJYRPp1nXDrhFq0ncqrQbOvB85vmI1ml05cq7pm1zbATksL8PLLQEXFKL0bIqKpV2VS02o9oVZ+IhfLs7Se98FSZWIz+F1OyrkY3IFNm9jalohorPn66uc03F3cEewRrN/l7+GCYLMZq3PLUFTTjNK6VrvuH83tWgU4EY0+Bk2IqIfjRdYB8O0mMy4WaQEUCZbMjPTV75M/9vF+8UN/ITnJJq25+uLhAWzfzsUeEdEwcXVyxaOzH8XKmJWqvaKFr7sLdiyIwsqkYP0j92hOFSoajKgP8cWZTfNR6NCA8qbyvp/caARefx1o66MihYiI/n/2/gO6rSy7E37/RCbAnHMmRYpBOatUClVS5ZxTd3W7k8OMPR7Ps+eb5TC2X7/xRPe47f46VnV35ZyTVKqSqpSzKFHMOUcQBEFEvnXuJe4lRBKiVMz8/7ywiIMLAgduFXHv2WfvfeNZJuN6mei6rMg5UK78Fb86y0TayXzTTUBMzDzMloiIJhBBk4gI6W56ZHrABqbkSBPWtfXiltp2nG5QSzCKcumn2k7Ny3SJiEETIrpKx1AHGgYalHFF+yCcHp90vyAxHBajTjm2KW2T9CV/w06fDr64dt99QLhaCoyIiL450XtqT84ePLvmWanmvZ/4e74uMxrrMuVFNp9vFJ9c6oDH55MyTs7cuRbnRtsx4lF3wE0gMk3ef19qaElERDPjXMc5DIzI/QUNDhfi3jgFrdsrjePDjTCNyzIRi3H6hCRmahMRLSSiPJco0zXWazDKpPaJFdmCot/g+rZe5B+5gsEhdY3keMtxeHyeeZky0XLHoAkRBRBfyn6iDMu55nEN4NPVL3aTzoSyxLIbfyOvFziuvtcEBQXyjYiIZkVGZAZ+tOFHWJe8LuDxzTkxUkNhoWfIieN18o43l9mIk/tKcNrQO3WZLuHCBeDMmdmdPBHRMsoyOdR4SLqv8Xix4tPzsHXKWeBi65JSWlFkoGh0SBU7mO++G9CpG52IiGgBEA3hx3q1pkeo2SYaTQiSIuRz77L2fpjfO4MQn3yubXfb8euzv0a7rX2eJk20fDFoQkQKu8uOi10XlXFznwN9dnmXQ0pkKBIjTAENhUWZlxtWXg7YbFMf37r1xl+biIimRfwdv3vF3bi74G7lMZ1Gg33FSdIFnHCqsQ9tAw7pvsegw4FtyahJUb8PJvXRR0A7L+6IiL4JEaA+UH9A7jU4Ooqiw1cwXNsN79hi2tVZJlIvk9vuALLU3lVERLRAiCodd94JaLWICY2BWW9WDom1Fs1YFY+IK63IEyUYvXLFjzZbG35++uf4uOZjOD3OeZs+0XIzJ0GTn/70p8jKyoLJZMKmTZtw4sSJoM9/7bXXUFhYKD2/tLQUH3744VxMk2jZE43Gxqd+nmtWm02uzlCzTEQd/I2pG2/8jcQO5aNHpz6enAxkZt746xMR0XURgfDCuEJlLBbiNmfHKn+yP73UIfW4EnxaDV4r8GAoXu1xNYHHI07ogJEgpbyIiChowGR/zacoP/0RMs81YO0HZxBV14mOQfnvqlhbuzrLJGXXPcDmzfM4ayIiCkqsdTz0EEJ0uoBsE51Wg4RwOdtExMWHzzSg4MBFpeTtKEZxrOUYfnryp6jqrZq36RMtJ7MeNHnllVfwH/7Df8Df/M3f4MyZM1i1ahX27duHrq7JG4keOXIEjz/+OL773e/i7NmzuO+++6RbudiVTkSzxu11BzSAHxh2oa7XLt0PM+qQGx+mHFsRtyKgBud1a2gAOjqmPr5lC5u/ExHNIdHPRGSbWPQW5bH1mdFIipQzSgYcbnxV3aMcc4V48XapHqOmIBknfX3Au++yvwkR0XSJgHNzM0aPHMGFn/w/CPnv/x1rPzyL7HMNCO8eRGPvsJplEmaEcVyWSVzpJhjuupfn0EREC11REfDYY0iISg2o3uE/7xZ67S60Ha2D5+vqgLK4g85BvHjxRSmAQkSLPGjyv/7X/8L3vvc9PPvss1i5ciV+9rOfwWw249e//vWkz//nf/5n3HbbbfiLv/gLFBUV4e///u+xdu1a/Mu//MtsT5VoWTvdfhpDriFlLPUyGftuLkuLgnasTIuwOe0b7mA7cmTqYxERQHHxN3t9IiK6bhaDBXcV3KWMRXkuUaZLp5X//p9vGUDjWDBdqPH1oG7nquAvevly8P5VRETLmcsFVFQAn3wC/PKXwI9/jNFf/hK1L/8r+s8egc4lZ4CLBbO6brvUZ0rJMolWs0xcMVHI/t5/kkq+EBHRIpCfD+1TTyMlRi2nKMotis2q/jJdUpb3FxV473QLrMPugF//pOYTdA51zvm0iZaTWQ2auFwunD59Grfccov6hhqNND46RWke8fj45wsiM2Wq5zudTgwODgbciOj6s0y+avpKGdudHlxslRtMisWy0lS1BEuiJRGZkd+gdFZ3N1BdPfXxTZt4wUdENE+K4ouwKlENhESbDbgpL14Z77/cCc9YmS7hI00tfNuu0YPq00+BxsbZmTAR0WIzNAScPg28+CLwT/8kSjPIZWtbWjDq8aCmrwYtgy3K0/0Bk+6xgImQFx8Go04+X3aZ9Aj/9vdhDAtSMpGIiBae7Gwk/eg/QmMKDSiRuyo9EtFmvTQOc3kQXtGK3x5rwOlGtXy6KNf1ZeOX8zJtouViVoMmPT098Hq9SExMDHhcjDumKM0jHr+e5//4xz9GZGSkcktPV2sCEtGNZZmcauhXUv/LUqMQalCDGJvSNkllXG7YsSBppAYDsG7djb82ERF9Y7fn344IY4QyLkuLRHqM3KjS5vTgfIscVBd6hntwfmVs8D5UPh/w6qsAN7YQ0XIuu3XyJPCrXwH/838C770HVFXJj49TP1CPVltrQMCkdlzARJyB5yeEITZsrO69VoOm27di7co9c/yBiIhoJphyCpDyx38Fj1EOkggiKF6QGC7dDFoNNraItVUfDld3o6ZLXbep7KnEsHt4nmZOtPTNSSP42fRXf/VXsFqtyq25uXm+p0S06LNMLrQOKFkm6zKjlWNhhjCUJpTe+JvZ7cD581MfX7sWCFYfn4iIZp1JZ8J9hfcpYxEov7kgXl6tA3CyoQ9Oj1c5frDpS3geuA+wqP1QJv37L3ZTX7VASES05LndwO9/D3zwgdSvZKo+TyII3WRtuipgMqSW5BIBk8RwNWCiCUHVzSW4bdf3YNTJjxER0eKTV7IDRd/+C+kcfPz5d4zFgFXpUVipD0Fen0163F8RRPCOelHexf7PRIsyaBIXFwetVovOzsA6e2KclJQ06e+Ix6/n+UajEREREQE3Ipq5LBOLUacc256xHXqtugPiuokddlMtmInsFVGai4iI5l1OdA7Wp6xXxnFhRhQlyudYI24vTjf0BzSkPDFYATz4YPAGxK2twIcfsjE8ES0f4u+dCJY0NAR9msvrknYMq782ihopYOKSxuJPqwiYiAU0p9mI9vxkXLh7E/be/adIi0ib9Y9BRESzK3XTLVhffCvSI9IR4t+pBEi9ZbNizdjZ0SeNm/rsGBpR11TOdZybl/kSLQezGjQxGAxYt24dDhw4oDzm8/mk8ZYtWyb9HfH4+OcLn3322ZTPJ6K5yzJZl/wNSmeJXcbBmgGvXAlEq+9HRETza1fWLhi0BmW8OTdWag4vnGkekL4z/A43HsZIRgqwe3fwFz1zRq7lT0S0HJw6BZwLvqAlAiRVvVVw+9Qmv+3WEfSOBUzcOi3CV2egd9dKnLhvA44+vBl1N5Xg3p0/kALcRES0BGg00G27CbkxuViXsi6gVK7IOlnl9SDNapdi8RUdasnbNlsbG8ITLdbyXP/hP/wH/OIXv8Dzzz+PiooK/OhHP4Ldbsezzz4rHX/mmWekElt+//7f/3t8/PHH+J//83/iypUr+Nu//VucOnUKf/zHfzzbUyVaduY0y+STTwCHY+rjDIwSES0oFoMFW9PVJu+RoXqUpcqNhkUz+BP18o43weFx4EjzEWD7djkIHsxHH8klaoiIlrKmJvnv3TV02jul0lx+fRoNDmj1+DwnCb9bnYPqH+xG1/0b0LoyDcNRFui1BjxZ9iSyo7Nn+QMQEdGcWrMGMJulDatrktbArJd7CvobxIveJsLltkEp4O7HbBOiRRo0efTRR/E//sf/wF//9V9j9erVOHfunBQU8Td7b2pqQnt7u/L8rVu34sUXX8TPf/5zrFq1Cq+//jrefvttlJSUzPZUiZaVOc0yqakBLlyY+nh6OpDG0gJERAvNlrQtARdsG7NjoNdqlJrKA8PyTmjhaPNRDLntwL33AvHxU7+o1ys3hrfJtZmJiJYc8fdN/J3z+YI+bcQzggpbAzpzEnF5RxG+emAj/lN+Bt4oysCJtHgklaYhZ6w0oiCy/54qewpZUVlz8CGIiGhO6fXAxo1KdklSmNqmwKTXYq3dgZhhJ/qHXegYHFGOXei8AK9P7TdIRIuoEbzIEmlsbITT6cTx48exaVzfgi+++ALPPfdcwPMffvhhVFZWSs8vLy/HHXfcMRfTJFpW5izLxOUC3n8/+HNuuunGXpuIiGaVaC68I3OHMjYbdFibIQfVfaOjOFrXqxwTpWUONR4SDeeAxx6TfwZbUHz3XfY3IaKlxx8YHlLPsycwGjG6YQPe3xyDQ49sRMWOInTlJOKLzmH02F1KL6ktubEBAZMnS59EZlTmXHwKIiKaDyJoIoInABIt8mZzv4RwAza1dCvZJn52tx01fTVzPFGipW9OgiZEtIyzTA4eBAbk155UYSFQUHDjr09ERLNKNISPNMpluYS1mVEI1Wul+5UdNnTZ1J1up9pOoc/RB8TGyo3hg6muDp6FSES0GH38cfAShKKr+4MP4lhZDMrDhjE6lr3XOuDAqSa57KHoH7WvOAk6jXq5fnve7QyYEBEtdWazXKZrbPNSTGiMcijWYkRZtxVhTjcqO21SuVw/lugimnkMmhAtQ+c7z89NlklbG3Ds2NTHxS5kZpIRES1oOo0Ou7J3KWOjTosN2eoF3JEaNdvEN+rDq5dehcvrkgPiO3dee3Ex2G5sIqLFwp9Bd/Jk8Oft3Imu1CgcqD+gPOTy+PDJpQ5gLPluS06sVL/erzCuEKuTVs/a1ImIaAER/V5FgB0IKNGl1YQgPlSPdW290vdGbbddOVbZWwm7Sx0T0TfHoAnRMiRqXs56lokoTXCt0iu33AJEqHWaiYhoYSpLLEO8We1TIhrCh5vkgHpDrx3NfcPKsY6hDrx++XUpgIKbbwZWrJj6hR0O4IMPZnfyRESzSZSi/eIL4Cc/Ac6cCf7cFSvg2LIBr5S/Ao/Pozx8qKobgw63dD8lMjTgfNyit+Dugrul+vZERLQMREcDxcXS3ThznLSByU8E1Ne098Hg8eJyu1qiS5x3l3eVz8t0iZYqBk2IlhmRYdJsVUsGiLROf5ZJ6UxmmYgMk46O4M3f16+/sdcmIqI5pQnRYE/OHmWs02qkndB+H1/qwNCIugBY1VuFT2o+kXfJ3XcfEBY29YtXVACXL8/e5ImIZoNo8n76tBwsEUETtxz0mFJsLLz33oPXKl5Hr0PN0KvrHkJ5m1W6r9dqsLc4EZpxAZJ7VtwDi8Eye5+DiIgWnq1blXPwBEuC8nCESYdIjGJVRz8a++ywjajfPSzRRTSzGDQhWmau9FzBqD/3H0Btl1oWpTQ14ptnmYjMEnEBKXqZTEWrBe65R0k5JSKihW9F7AqkR6Qr48LkcCRHhipZi++cb5VKBfgdbz2OE60ngNBQ4M47g7+4yDYZVrNViIgWfHbJb38LvPfe9EoMGgzAY4/h45YvUNdfpzw8MOzCp5c7lfGOgnhEmQ3KeE3SGqyIC5KtR0RES1NKCpCdPaFEl8g6FNkmG1t7oPH6UNFuU461D7VLGd9ENDMYNCFaZiq6K5T7YpGr1eqQ7kebDYixGAMa/153lolo+P7738sXkB51x/EE27cD8WqZFyIiWvjERdotObcoY7ET+u5VyYgIlb8rum1OfFzeDt+4sowfVX8kZZ2gqEgpMzApu13ub0JEtBjs3w80NEz/+ffdhxOuepxsU/udOD1evHu+DSNurzTOibOgJEXdwBRlisJtebfN7LyJiGjx2LZN+hFuCJdKNfrFhRkR7nRjZbcVFe2DGB137s1sE6KZw6AJ0TLicDtQP1CvjOtE47Cx79e8hMDSKSvjV15/dsm//RtQWxv8uXFxwE03Xd/EiYhoQciMypR2PvuZDTrctzoFBp18SlnXY5dq8/uJzEbR30Ta9Xb77YDZPPWLX7gAvPoq8NlnwIkTwJUrQF/f7H4gIqLrZbXK573TIbKr770XtUlGKYjs5/ON4sOLHeizu5TNS3uLk5S+JSEIwf2F98OoUzc0ERHRMpObCyQmSt8N47NNTHqtVKZrU3MP+u1OtFtHAvrXen1yMJ6IvhkGTYiWEbHbV2rMO6amWy0nMD5oEhsaG9DwN6iRETW7xOm89vNFWS6d2jeFiIgWlzvy70BaRJoyFlmKd5WlKDX4zzUP4HzzgHLc5XXhxYsvwqYflQMnwYjeJl9/DXz4IfDyy3KvgN/8Rl6kJCJaCI4cAbzTWJASGXZ/+IfoLkjDq5deDSiPe6i6G429dmXx697VKdJPv63pW6UgNRERLWPi3Hos20T0NREBdb/4cBPih0eQ0z8U0BB+2D2MS92X5mW6REsNgyZEy0hFj1qaS5QCaO6T68eHm/RICFd3shXFFyk73YISJbheeuna2SV+mzcDGRk3MHMiIlooROnGx0seR7QpWnksI8aM3YVqk8ovqrpQ3yMvCAqDzkEpcOIqKgBWXGd9/sZGOXAiSkASEc0n0b/kWlkmaWnAd74DPPooWvQO/Pb8b+H0qhuLzrcMSMFlQaMJwV1lyQF9THKic7A7e/fsfQYiIlo8RHnbyEgp8zAmNEZ5OMZikDYsbW7uRlWnTSr56PdV01cBJbuI6MYwaEK0TIidvjV9NcpYLGb5687nxYcFBEmK4oqm96KnTsmLWdOxYQOwb991zpqIiBYii8GCJ0qfgElnUh4rSY3E+kz5Yk58vXx4sV3qczK+OeUbFW/Cd8ftgPE6S86IgIkInPT3z9yHICK6XkePBu/b9+CDwHe/i9H0dJxqO4XfnP0NbC61SW9T7zC+qFRLGO5ekYC0aHNAtvfDKx+GVqNmnRAR0TImyjxu2SLdHV+iS6sJQVyYARlWO2L7hnCxRc3K7rJ3obqvel6mS7SUMGhCtEyIgInHp17k1XRNXporwhiBlPCUa7+gywUcPnzt50VEAE8+Cdx5p5xeSkRES0K8JR6PFD8CTYh6OrktLxb5Y98pbq8P75xrxZBT/e6p7K3Ep93HbiyILkp0Pfcc+5wQ0fwYHgZOqo3cJxBZdKWlcPs8eLfyXbxf9T68o+rOXxFE/uBim7L7d21GtBRs9gvVhUrB6FB96Ox+DiIiWlzWrgVCQxFrjoVeo1ceTo6Uvy82tfTgbNMAPD61FPvXTV/Py1SJlhIGTYiWiYputTSXWMhq7JPLppgNWiRHmQKyTKZVmuvYMcCull6Z1Jo1Ui1n5Od/g5kTEdFCJcrI3FVwlzIW3x/7ipOQGCF/r4iAybvnWqXvHb9jLcdwMsEj1/u/0cBJb+/MfAAioukS575i09BUduzAwMgAfn321zjbcTbg0JWOQbxyqglOj/y3MDvOgu35ccpxEXwWQWixIEZERBTAYJAqd4jvivEbXEMNWsSYDVjRY4XeaseVdjWzsdHaiGZr8zxNmGhpYNCEaBkQGSaiCbyfaDzp8cq73HLiw5TmvcLK+JXXfkGHQ26COZWwMDm75N57AZMakCEioqVnbfJabM/Yrox1Wg3uWZUi9csSumxOfFTeoZSEFD6s+QjVu1bJ5QbMammaaRkclAMnPT0z9yGIiIIZGQGOH5/ysC8nG8dHW/CzUz+TShH6eX2j+LKyGx+Xdyjn3iKofHtJcsD59x35dyA7OnuWPwQRES1aGzcCOh3SItICsrxTokKl9vAbW3pwqrE/4Hz762ZmmxB9EwyaEC0D9f31AQ0opyrNZdFbkB6Zfu0XFAETcfE4lbvuYnYJEdEysid7T0DQ3WLU4b7VKTDo5FPNuu4hfFWtBjlGMYrXKt9E57bVwH/8j8Cf/ZnUBwAPPQTs3QukpgZ/Q5tNDpx0dc3ehyIi8jtxAnCq59LjieySF6Jb8FHNRxjxqOfHdqcHb55pwdlmtRfTyuQIPLwuTfnbKGxO24z1Ketn+QMQEdGiJjamrloFvVaP5LBk9WGTDhEmPco6++EaGEbtuLWeKz1X0G1X+2gR0fVh0IRoGajoqQjY8VbXI5fVEhds6eOaTxbGFQbsWpjU0FDQnXbSQpeo6UxERMuGKMt1f+H9SA1Xgx2xYUbcWZqslHw809SPCy0DynGX14UXL74Im9sOREYC6elASQmwdSvwzDNARsa1v49E4KStbfY+GBGRKMklSnNdxelxSuVvv0ADasPdAcfaBhx48UQTWgcc0lijCcHuwgTcujJRysbzy4/Jx97cvXPwIYiIaNET58ghIdJG1xApv0SWEmWCzjeKdW29UraJv3eWwGwTohvHoAnREucb9Uk7DPya+4bhGqunnBMXBq1G/bItip9Gffmvvgpez3n3bjZ8JyJahsTOt8dLH0eUKUp5LDPWgt0rEpTxwcpuNPSq/bCsTiteKn9JCqAEMBqBp54CMjOv3Zj5+eeBpqYZ/CREROOcOiX/rRkjFqNaB1txovUEOu2daCxT/06NuL04eKULr51uljJNhDCjTsouKUuLCugbKLLzHi159NobloiIiITYWKCwECadCfGWeOXhyFC91Kt2bVsv+vrtaOmXA/bCxc6LGHQOztOEiRY3nqERLXFN1iYMu9ULvZruyUtzGbVGZEdlX7sB78mTUx/PygJycr7hjImIaLEKM4ThidInpO8Uv9K0SKzLjFYWGz+82I6eIbXMTZutDW9WvCkF+Sc0vRT9scR3SzCiZM7vfgfU1s7wpyGiZc/tDujjJwK8F7suorqvGt5RLwbjI9CfHCX9bStvteL5Iw043zIA/ybf1KhQPL4xA8mRocpriN3Bt+bciodXPgydRjcfn4qIiBarbdukHxmRaka2CMiL3iahHi/KOvpxsqFPOSa+q442H52XqRItdgyaEC1xomyAn2gK5q9xqdOGIDNWLc21Im4FtBpt8Bc7dAjweqc+ziwTIqJlL8GSgEeKHwnYPb09Lw658XKgXmQ7vnOuTdmFLYiMyP11+ye+mD9wkp197YXNF18ErqiZlURE39jFi3IpQAA9wz042XoSfQ51MUpkmXQMOvHyyWbsr+iEwy2fJ+u1GmzLi8MDa9OkHk9+Zr0ZT696GtsytgVknRAREU1LWpqUiS02KsWExigPx1oMMOo02Njag+ZeO7oG1R5bp9tPw+FWs0+IaHoYNCFawsSut/H9TER9Zf/FXFaMRbqg8yuKu0Zprr4+4OzZqY+Lxu/Xqj9PRETLQm5MLu7Mv1MZi8XB20qSkBhhksa2ETfePd8Gt1fNLjnSfARfN30tNVUOyDrR64EnngDy8oK/qQjqv/oqUF4+C5+IiJals2fh9XlR2VOJ8q5yuH1q7xJrlAVv2D14+VQTOsctThUkhuOZLZnYkBUTUAY3JTwF31/3feREMyubiIhmPttEZDVGjrhR1G2VepuMz5I82RakYggRTYr5wERLWPNgc0D9ypqxLJOrS3PpNXppgSuogwcB31WlU67OMiEiIhqzLmUdeh29UjBEEIH6e1al4OUTTbA5PdIi4yflHbizTG0W/1ndZ9JNZKlEGCMQZ46TGiWvSV4Dw2OPAW+8AVSomwEmEN9Tb78NJCYC8WqtZyKi69bXh+G6SqkevMMTuENXZMr9FHqca7Eqj8VYDNi1IgHpMWomt9/a5LW4I/8OluMiIqJvTmxYjY9HZNeodL7sX/NJCDeipX8YG1t68HxCJAZyYxFlNkjHRKbk9ozt7KNFdB34XwvREia+GMeX5vIHTTSaEGTFWZRjeTF5MGjlL9NJtbfL5QmmsnIlkJw8Q7MmIqKlQtTtH5/JKMrU3LsmFQadRumz9VVNz4TfE5kmIuOkpq8GH9V8hJ+d+hm6nf3Aww8DZWXB39TjAT74QKRbzvwHIqJlw3HqGM62nw0ImEhN4AccONo7jJMWuU+JyCa5KT8eT27KnBAwiTJF4bGSx3DPinsYMCEiopkhNhtt3SptOkqPSFceFus8yZEmJA05kNFvx7nmAeWYzWVDfX/9PE2YaHFi0IRoiRpyDeFy92VlXN9jx9BY/fjMGDNMerV/SVF8kNJcYtHps8+Cf2Hv2jVDsyYioqVEXMw9UPSAVJbGLy7MiDtK1OyS0439uDhut/ZkRA+BVy+9CveoF7j/fmD9+uBv3NAAXLgwMx+CiJYf0QfwizcDynE53V5cbh9Ec98wLsZHYTQkBPHhRjy5KQPrMqMDSnGJAMnNmTfjjzb8EQrjCufpQxAR0ZJVWgqEh0tZ2aJflp8ohStOsTe3dONKhw1en7qJ6EInz42JrgeDJkRL1Jn2M/CKxaUxF8YtSJWlRSn3tSFaFMQWTP1CtbVAXd3Ux1etYgkUIiKakl6rx+MljyPSGKk8JrIdd65QvzsOVHbi11/X4/XTLfj0UgeO1fXictsgBkfUBcvu4W6caD0hB+vvvFPaYRfUp58CDja9JKLrZ628gN622oByXBdarbCNyBuQyhOjsD4rBo9tyECMxRjwuytiV+APN/whdmXvkv7+ERERzTidDti8eUK2iU6rQYzZgOz+IUT2DaGx164cE/1uRX8TIpoeBk2IliBR1uR022llPDDsUr4sI0L1yIxVdyKUJJTApDNN8UK+4FkmWi2wc+cMzpyIiJaicGM4nih9Akaturi4Ki0KazKi5cEoMOhwS3WYxU5uETT59HIHfn+sEUNji5TC181fw+lxyoGTW28FNmyY+k3tduDAgVn9XES0NF3Z/zJGxR+mMU19w8pu3b6YMOy6KQ/b8+ICsktEXXnxd+7x0scRExozL/MmIqJlZN06wGhEgiVB2gzrFxcun29vau1BRbtNeVwETK70XJmXqRItRgyaEC1BVb1VsDqtk2eZpEZCM1YSRdiQGmTBSZQ26eyc+vimTUCUmrVCREQ0lcSwRDxc/HBAA8qb8uOwNTdOKnFjHFc20s/l8eFkQ58yHnYPy9kmgvguu+UWqTTBlE6fBlpbZ/iTENFS1m/thP282hdQZLxZHXLWm1GnQeYtRUiLDuxdUhxfjB+t/1Hw7G0iIqKZZDJJJWu1Gq1UpssvMlQPnSYEhd1W9LT0YcStViA533F+niZLtPgwaEK0xBvAu70+XGoflO6L3XDFKWp5lOSwZKSGp07+Im438PnnU79JaChw000zOGsiIlrq8mLycHve7cpYBPE3ZsdIDZR/dHMufrQzV7p/V1kK9Fr5NLW8zRpQputI8xGMeEbkgdEI3HZb8L5c778vZ04SEU3DuS9ehsatZri19qtl/pJjLRjIS1bGInvu/sL78dDKhxCqlxvDExERzRmxkVWrRVJYUsD5teghqBkdxbqmbtR0DSnH6vrrYHOq2SdENDUGTYiWmN7hXtT2qzWYqzptUuNKoSAxHKEGbUCWib8R7wQnTgCDcrBlUiJgIgInRERE10F89+zI3DHpMaNOK2Wd5CWEYXW6nMkoSuKcrFezTRweB463HFd/aeVKIDd36jdsbwdOqpsJiIim0ufog/X4l1NmmWgKU+A2yX1KRDmUH67/IVYlrZr6fJqIiGg2RUQAZWWIMkXBoDUoD4ugibCqox99Fe3K46L0ZHlX+bxMlWixYdCEaIk51XZKuT86OorzzdaA+vF+oo9JaULp5C8yPAwcPjz1m4iSXBs3ztCMiYhoudmdvRsPFD0QtO7/2oxoGHTyqeqltkFl4VI42nIUDvfY7m9/Y3jREHMqInPSxl11RBTc0fKPEdmuBmlbxmWZpEaHojNf3ckrsuaiQ8f6MhEREc2XrVul4H2iJVF5yGLUwqTXQu/z4dZDlxB/tBohXjnz+nwnS3QRTQeDJkRLiNvrxtmOs8q4c9CJLptcwiQh3ITECLUB7+qk1dBr5Z1yE4iAychY6ZPJ7N4dfHGKiIjoGsoSy/AnG/8Ef7zxj/F4yePSAqToC+AnMiPXpMsLkr7RUZwYl20iynMdazmmvlhMDLB9+9Rv5nQCb70FeNSSO0REV2eZdB87gJCx/u+DDrd082eZRMWGoS9VDvRmRmYiKyprPqdLREQki48HCgqk/oF+IogSHyZnnohcyKhjNVj7wRlY+u3oGOpAl71rHidMtDgwaEK0hFzsuqjWeRc7CFoGlPur0iIDSgdsSJmiAXx/v1yaayrJyUDpFBkqRERE10F8L4nGlSviVmBT2iYp+0SUF/BbkxGlZJtcbh/EwLBLOSaCJqIxvEIETUTwZCp1dcDrrwNetRkmEZHf4YZDSKhRS5i0DKhZJqLxe1duIkbHei3tzNrJklxERLRwbNuGMEOYdPOLHSvRJXQPORHWa8O6904hvbwZ59vPzdNEiRYPBk2IlghRimt8A3iHyyv1MxGMei0KksKVY7nRuYg1x06dZRJsQenWW+VSKERERDNMq9EG9DsRZQXWZUQr33PHx2WbOL1OHG0+qv6yyIAUZbqCuXIFePttNoYnoglZJnXlh2G2Dk+aZRIXZkBHnlyai1kmRES04GRkSD3+xpfoEufR4Sa5QsiI2wu7ywuNbxS5p2ox+N7r8I3yfJgoGAZNiJaIVlsr2ofU3XGX2qxS81yhODkC+rGdcf4mvJMaGgIuXJj6TfLygJycGZw1ERFRoFWJqxBtUvsErM6IkoL/wpWOQfTZ1WyT463HYXfZ1V8WDeGL1RJfk7p4EXj/fRGFmYXZE9FidKjxqiyT/sAsk+HoMAzFyLt3mWVCREQLjvheeuABxK9YG/Bw/Lhskx6bU7kfe74KzZXqplsimohBE6IlYnyWiaj9fqFVbQBflhap3I80RqIgtmCKFzk5db138SUsskyIiIhmOdtELEr6GXXjs02A4/W9yjGX14Wvm78OfIHbbwfC1ezKSZ05A3zyCQMnRCRlmVxsO4eEhi41y2REzjIxjc8yCQlhlgkRES1cFgtM3/8RnDdvx+hYcD/GYlAKhfQMOaW1Ir+OT9+cr5kSLQoMmhAtAU6PE5e7Lyvjxt5hpaRAZqwFUWa5AZiwLmUdNCGT/KfvdstBk6msXAkkqqmeREREs6U0sRSxoWoZydXpUQgdyzap7LShd8gZ0NskoJllWBjwzDOA2Rz8TY4dAw4enIXZE9FicrD+IKJae6F3eiZkmaRGmwFNCDpzEqQxs0yIiGhB02iQdPvDOH3XWtijLNBpNYgeWw/y+EZhHVsnEpznT8Pd2z2PkyVa2Bg0IVoCRMDE7VO//MqnyDLRhmixNjkwXVNx7hwwPK6h7tW2bp2h2RIREQUngvvjs01EM/h1mWMlu0aBI7Vqtomox/xu5buBdZnj44GnnwZMpuBvdOgQUFk58x+AiBaFzqFOlHeVI34sy8Q6SZZJX0oMXGYjs0yIiGhRKIovgjM+BqfvXoemknTEhU9eosvn9aDlk9fmaZZECx+DJkRLwPnO8wEN4Ot75fruZoMO2bEW5djK+JUIM8j1mAOIFE2x43YqmZlAauoMz5qIiGhqxQnFiDfHK+NV6VHS95pQ2z0k3fxaBltwqu1U4AskJwNPPgkY1GzLSX36KRvDEy1TBxsOIsTrRVxTD0ZHR9HcNxyQZSKySrpy5EzrXdm7mGVCREQLnkFrkAInPq0GdetzMVKSDp1G/v7qG3bBM+68t+/r/cE3zxItYwyaEC1yAyMDaBhoUMZVnTb4xhrAFyWHQzP25egvzTUpscu2V921O8GWLTM4YyIiouvPNtFrNdiRH6eMD17pgtPjVcb76/bDOqJmWkrS04HHHwd0crBlUuL7TzSHJ6JlpXWwFVd6riCmtQ86txcDDjeGxkp0iXKAIstELDj1pMcyy4SIiBaVVYmrlPstZRmIHWsIL/bL9g65lGM2ex/6D306L3MkWugYNCFa5C50XggYX24fVO4XJUco96NMUdIF36SOHJn6DWJjgRUrZmCmRERE10dkSCaHJSvjFUnhUq8uQSxuHqkJbAr/QfUH0m7xANnZwKOPAlq5J8qkvvgC8KoBGCJa+j6v/1z6GV/fNSHLJD0mVMoq6UuNgdegw+7s3fM4UyIiouuTHZ2NcEO4dH84yoLRQvV8usM6EnC+3HHgHbnHLREFYNCEaBETX3TnO9TSXH12FzoHR6T78WFGxI3tJvDvNJi0pEBrK9DUFDzLhKUIiIhoHojvrbsK7kIIQpTx7sIEqamlcL51AG0DatPmqt4qqc/XBPn5wN69U79Rfz9wXv0+JaKlTWRp1/bXQuPxIq65VzqHHnbJgVOLQac0ze3KikdeTB4yo6bYeERERLRAM7ZXJanZJtYNuQgzypnXDrcXthE5s1Lo6WmC6+TxeZkn0ULGoAnRItZqa0WvQ91lWzE+yyRFzTIRyhLLrj/LxGwGVqlftERERHMtNSIVm9I2KePIUD225sTKg1Fgf0VnQG3mD6s/hMOtBlIU69cDUVFTv9GXXwIe9QKSiJbupqMDdQek+6I0l8btQXO/Y0KWiSjN1ZsexywTIiJalNYlr1M2HlkTI6HPVnsF+jfbCt5RL9o/e5M9/oiuwqAJ0SJ2ruNcwAXglbGgibjQW5Eop2IK6RHpiDWPLTBdvbP28iQ7cv02bAD0+hmeNRER0fURi5aizKTf6owoJEaYpPtih/iphn7lmN1tx6e1k9RmFuW5duyY+k2sVuDs2RmeOREtNDV9NWgebJbuJzR0o3vIiRG3nGUSbtJJgVmhNy0WBcklSAlPmdf5EhER3Yjo0Gjkx+YrY8fWPLUhvN0Fl0cNknS2XMHopUvzMk+ihYpBE6JFyuPzoLyrXBm39DtgG2temRVrhmUs9VIYn5YZ4PhxuRPYZETTXBE0ISIimmcGrQF35t+pjDUhIbilKFEpO3myoQ99dqdy/GzHWdT21U58IZE9GRMz9RsdOsRsE6IlTGwy8vcyEaW5opu6pXNov/Ros/J3pTsrgVkmRES0qG1IUdd0BjLjYUqRNyGJVaAum5ptMuweRu/+96ZeHyJahhg0IVqkRN32Ec/INRvAa0O0KI4vnvgCLS3A6dNTv0FZGRAWNoMzJiIiunFip1xpQqkyjg83Yl1mtHTf6xvF/styM2e/t6+8LV0ATsg2ufnmqd/EZgNOnZqF2RPRQnCp+xLah9ql+7Etfejtcyg7bUWGScRYlolXp0HK2psRb1FLmRARES02oi9XtEk+Xxa9ap1b1cyTrkFnwLlzZ/VZoL5+PqZJtCAxaEK0SI1vAO/2+lDTNSTdN+g0yImzKMdWxK1AqD408Je7uoAXXgDc7uAN4ImIiBaQ2/JuQ6hO/U7bnB2DqLFFzjarA+eaB5RjNpcN71W+F3AxKCktBeLipn6Tr74K/v1IRItS51Cn9DfBL6a2A60D43uZmJX7fenxuLng1jmfIxER0UwS2ZMbUtVsE3tRKgzR8nqRy+tD/7B6ztsz3APHgU+ZbUI0hkETokXI7rKjuq9aGYuAiQicCAWJ4dBp1f+0VyetntjH5He/AxyTNMn1y88H4rmzjoiIFhaLwYJ9efuUsfi+21OUqIy/qulB75BapquipyKg/5dEowF27pz6TYaGgBMnZnjmRDSfbE4bXrj4Apxe+e+D1u2F61Kbcv4cYzYgbFxp2/gNN0u14ImIiBY7sSak08jfcT6tBsMbc5RjHeMawo9iFJ2XjgF1dfMyT6KFhkETokVI9DLxjapNuyrGleZaOa40l0VvQW50buBCkAiYiPIjwWzfPsMzJiIimhmrElchJzonYHf46vQopUzXx+Ud8PjU78iPaj5Cn6Mv8EWKi4GEhKnf5OuvAacafCGixcvldeHFiy9i0KmeL49cbEZ7j5ylLaTFqBlso3o91u54dM7nSURENBvMejNKEkqU8ej6bGhMcqb2oMMNh8urHGuztcH32WfMNiGa7aBJX18fnnzySURERCAqKgrf/e53MSQWbYPYuXOnlD42/vbDH/5wNqdJtOic71RLc9lG3GjqH1ZqMSdHmpRjpYml0Gq08mBkBPj978V/mMFffPNmIDNzlmZORET0zYhzw7sL7oZeI1/sCdvz4hBrMUj3u4ecOFrbG7Bg+lbFWwGbDURN56DZJsPDwIULs/QJiGiuiP/u37j8htLHROi2OdF1pEYZi3Nns0HNMoletRkRYbFzPlciIqK5aAjvNeoxuCZLGXeOyzYR5829tReBioo5nyPRsgqaiIDJpUuX8Nlnn+H999/HoUOH8P3vf/+av/e9730P7e3tyu2f/umfZnOaRItKs7VZiv77XemwiTxKpQG8WEwavxtXImqzv/QS0NER/MVLSoC9e2dn4kRERDNElM25ukzXbSXJ0Grk78DTTf1o7lObwDcPNuNw4+HAFykqApKSpn4TUaKLu+yIFrVPaj5BZW+lMh5yevDRqSZk9gwqZbkyxvUy0YZoUbDj/nmZKxER0WxJjUhFSniK+sBNBXDptcqGI5Gt7ddqawU+/xwYl7lNtBzNWtCkoqICH3/8MX75y19i06ZN2L59O/7v//2/ePnll9HWpi74TsZsNiMpKUm5iUwVIgJGPCN4o+INZSya244vzVWUpP63kmBJQFLY2GLQgQNAY2PwF8/NBe6/X671TkREtMCtS16HgtgCZRwfbsTW3LEG76PAJ5c6MOJWyw182fglWgZb1BcQmwx27Zr6Dbq7gYaG2Zk8Ec264y3Hcbz1uDJ2eXx451wrktr7ofONwmLUITchLGDDUW7SSphXls3TjImIiGbPxtSNyn1dmAntq+QKIyJg0jOuJ+DAyADsbY3Auav6AhItM7O2Onr06FGpJNf69euVx2655RZoNBocP66evE7mhRdeQFxcHEpKSvBXf/VXGBYlEqbgdDoxODgYcCNaikSA5J0r70hfYH5VnUPos7uk+6lRoYg06wOyTKSLwJ6eaze0TUsDHn0U0I6V8iIiIlrgxHfcvSvulfp3+a3NiEJ6tFnZUf75lS7p+9NfpkeU6fL4POqLFBQAsUHK8LAhPNGi1G5rxye1nyhj3+goPipvR/+AA9sau2DQabAiMVzJThPSI9KRsmEXoFNLdRERES0VxfHFCNWpPbw02wswNFaeUpTo8p8zC1J1ky++ADzjzpuJlplZC5p0dHQg4aoGmzqdDjExMdKxqTzxxBP4/e9/j4MHD0oBk9/97nd46qmnpnz+j3/8Y0RGRiq39PT0Gf0cRAuF2ClX0aPWley3u7C/olMZrxprgiuEIARliWVqlkmwtErx3+mTTwIGuRY8ERHRYmExWHBv4b0BgZS9xYkw6uRT3KpOm1zGckyvoxcnWscFQsTmgg1qjecJrlwBrNZZmj0RzQax6PNRzUcBfYwOVXWjvseOXfUdSHC6UJgULgVO/OLMcciJzgGKi+dp1kRERLNLr9VjTfIaZRwfY8GVojTp/rDLK2048usY6oB3oB84eXJe5kq0KIMmf/mXfzmhUfvVtyviAvMGiZ4n+/btQ2lpqdQT5be//S3eeust1NbWTvp8EVixWq3Krbm5+Ybfm2ihah1sxWe1nyljj9eHDy62w+31KWW58hPClOP5sfkIN4YDTU3BG3hFRQEiKBmq7jYgIiJaTESJrvHNLcNNeuwpSlTGByu7YB93Efhlw5ewu+zqC6xeDejVTM0AYsfdqVOzNHMimg2Xuy+jydqkjEXw9FzzALL7bFjf1ouCxPCAxu/hhnAUxRUhRJwPi3K1RERES9T6lPXSJls/7aZcDJjkDbQd4xrCe0e96LR3AocPixI/8zJXokUXNPnzP/9zqV9JsFtOTo7Ui6Srqyvgdz0eD/r6+qRj0yX6oQg1NTWTHjcajVLPk/E3oqXE4XbgtcuvSV9afl9WdSs1J6PNBuwqTFDqMYsvwN3Zu+WFns/UQMsE4vlPPAHwvxkiIlrk9ubulXaK+4lF0cKxPl+ij8HRul7lmNPrxMGGg+ovm0zAqlVTv/jp0yxNQLRIiPJ7n9UFbjQ6XN2DULcHd1a1IDvOgshQNUhq0plQmlgKrUYLbNvG0lxERLSkxYTGIC8mTxnnJ0fgeG6ydF+UfvdvzPVv3h2120X/hXmZK9GiC5rEx8ejsLAw6M1gMGDLli0YGBjAaXGhOebzzz+Hz+dTAiHTcW6s8VBysvwfMdGy62NSGdjH5ErHIC62yqVCdNoQ3FmWHFBeQARMpAbwIuMrWObV2rVyaS4iIqIlUG7ggaIHoAlRvw93FMQp34+X2qzotqm75E63nUbnkFriMmiJLtFb79KlWZo5Ec2ko81HA86bzzQNwOZwYV91G1K1IYgPNyrHdBodShNKYdAagNRUYOvWeZo1ERHR3NmQqp736rQaaNZkoMtikvbddg2q58t2tx2DzkHgyBFABE+IlplZ62lSVFSE2267Dd/73vdw4sQJfP311/jjP/5jPPbYY0hJSZGe09raKgVZxHFBlOD6+7//eynQ0tDQgHfffRfPPPMMduzYgbKysf4MRMvI6fbTuNJzJaCPyYEKNYNrV0EC4sLUiz+xY2B7xnbA6wX275/6hUUZkp07Z2/iREREcywlPAU7s9TvNlF+Z2NWjHRfXASKLE1/g8tRjEpNopWGl4mJQFbW1C/OhvBEC57NacPhpsPKWJTlO9nQh5KuART2WJEZaw7IzBYNcUVfJOm8+IEHAK12HmdPREQ0N8S6UZRJ7Ylblh6NQ9mJkzaEb7W1Ai4Xs01oWZq1oInwwgsvSEGRPXv24I477sD27dvx85//XDnudrtRWVmJYbGDD6IPtQH79+/H3r17pd8TpcAefPBBvPfee7M5TaIFyeV14fP6z6fuY5IcgZUpEQH1mO8vvF++GDxzBuhVS5FMIHbShYfP7gcgIiKaY1vTtyLaFK2MV2dEKaV4WvqHUdet7pKr669DVW+V+ssbN079wq2t8o2IFixx3izOn/2+rumB2T6CW2vakBhhCuhjIoKs0aFjfyv27gViY+djykRERHNOZGaL3iZ+4lzZm5+E1ggzXF4f+ofdyrFue7f83Soawjsc8zRjoiUYNImJicGLL74Im80mNWn/9a9/jbAwtVl1VlaWFMHcObbjPT09HV9++SV6e3sxMjKC6upq/NM//RP7lNCyVN5VjmG3HFAUvqrpUfqYxFgM2H1VH5OHVj4k75YTTbq++GLqF7ZYWH6AiIiWJFFuR/Q3Ucca3JSv9jo5XN0Nr0/dPSeyTby+sZ5hK1YE7/PFbBOiBavd1o5zHXJZZ6FrcASX2624s7IF5tFRpEWHBvydyIoayyzLzwfWqwtHREREy8GapDXQhmgDsk2+zFKzTfxEdrb4jpXWmXguTMvMrAZNiOjGiGDiydaTynjY5QnoY3JHaTL02sA+JplRmfLgWvUmRZDSqJb0IiIiWkoK4wrVBVEAufFhSI2SF0wHHG6cb1b7HfQ5+nCidewCUJTmWbdu6hcWfU1Yz5loQZ43f1zzsbSw4x+Lcnx5vTZkWO1IiwoNOG8Wfx9EHySYzcA99wBjm5CIiIiWC7HhtiShRBlnxZoxkByNxigLrA43HK6xTUUA2mxtcsmuY8fkUl1EywSDJkQLkPhSah9qV8aX2waVnbFlqVGT9zERbDY5aDIVUXpANIAnIiJaokQW5m15t0lZmP7xzQXxIi1Tcqy+V9qM4Pdl45ewu8aCISJoMlVfA48HOHt29j8AEU3biGdEyhhrtDYqj9V0DaF1wIENrT0w6bVIjDQpx0J1oVJpLsndd7NcLRERLVvjG8KL8+Wy1EgcyUiQxp02NdvE6XWi19Erl+c6dWpe5ko0Hxg0IVqATrapWSYiou/PMhFK0yKV+2GGMLWPiSDKcrnV+pMT3HILm1wSEdGSlxSWhDXJa5RxQoQJxcny96fL48Oxut6ARdfP6j6TB6KM7MqVU7+wqOfsk3uLEdH8EWX1RJbYT47/BMdajgX0ADxc04M4+wgyB+zIiDFDMy6TRGw2ErXcsXo1UFQ0T7MnIiKaf6nhqUgOS1bGxSmRaIkOQ2u4Gd02Z0BJ29bBsd5+YpOu2EhEtAwwaEK0wDjcDqmfiV9j77CUHimIC79os0E5tjF1o9zHROjulhvATyU9HSgsnMWZExERLRyidKVBq35nbs2NVUr0iM0I/j5hguiFcKXnyrUbwlutQE3NLM6aiK6lqrcK/3bq3/Bh9YcB/f+EM00DGHS4sb61FxGhekSb9cqxaFM0YkJjgNBQ4Pbb52HmREREC4fYfDs+2yTUoEVBUgSOZMRLAZPecefK/SP98nfu0BAzr2nZYNCEaIERCzcenxq5v9CiZpmUpUUp98UuubXJ40pt7d8v0lKmfuG9e1mzmYiIlg2Rjbkjc4cythh12JAVI90XX5efV3TBN+57893KdzHkGgLS0oBkddfdpNkmRDRvvUtevPgieoZ7Ao71D7vw3vk2HKntQajbg5LuAWTGmNVs7LEsE2ksStWyvx8RERFKE0ph0qllLFelRaE2JhydFhM6BkfkXiZjKnsq4Rv1AV9/DXjVnidESxWDJkQLiPhCOtWm1oi0jbhR3zsk3Q8z6pATN5ZVAqAorkhaEJI0NACVlVO/sCg/IDJNiIiIlpHNaZsRZVI3HKzNiEJkqLzzvM3qwOnGfuWY2D0nAifSpeEGddfdBCLTpF/9PSKaG6fbTweU4hJG3F58UdmF3x1tRG23fM5c1tGPtDCDFCj1E31MpOxsETQJlk1GRES0jOi1eqxJUkvaJkWakBgZiqMZCRh2eTHkVDf0Wp1WKXAyKs6Dy9XqKERLFYMmRAtI/UC93GBrTHmrVUkeKUmNhEaj7pZT0ijFEz4bq8U+GY1G7mVCRES0zOg0OuzN3auOtRrsLU5SEi+P1vWia3AkoOzPmfYzQEkJYFJ33QUQ37vBymES0YwTWWCf1arnuyJL7ExjP35zpAHnmgeUrDGLTovHvS4py8RPG6JFVlSWupEoUu0PSEREtNytT1kfMC5Li0RlXAT6Qo2o7bbD7VX7+XXaO9FobQQOHw5e6YRoCWDQhGgBGZ9lImpIlrcOSvdFKYGSFPUCL84ch8zITHlw+TLQOtaUazLr1gGxsbM4ayIiooVLZGZmR2Ur49SoUKzPlMt0+Xyj+PhSh9Q82k+U/+nzDgGrVk39oiJowrIERHPmQN0BOL1qbfVDVd04VN0Np1v+71CnDcGm7Bj8x/QwZOlCAspy5cbkqv2NNm2a+8kTEREtYLHmWORG5yrjFYnhiAkz4Vh6nJTRWdVpCyhp2zDQgM6GS0BFxTzNmGhuMGhCtEDYnDa1CS2Auu4h2F1yKqQoyxVm0gXsBJAuBsWCjehlMhWDAbj55tmdOBER0QImvi/vLbwXRq3aw2BzTiziw+Rxn92Fr2vULE+3z403K96Eb924vmFXs9t5oUg0R1oHW3G2Q206K7LDzrUMyIMQoCg5At/akoUtuXHIrm4P+N2MyAwkh431KEpKAjIy5nTuREREi8GW9C0Bmdn3rk5BXVocBo162EY80vrU+P4mYu2q9+O3mG1CSxqDJkQLhCgHIjXVmrQBvJplotfosTpptTw4dSp4XfVt24Cwsb4nREREy5Toa3JH/h3KWKsJwW0lSdJP4WxzP5r6hpXjLYMteL37SwynxE/9ouI7mIhmlVig+ajmo4DxF1XdkJsPAdty47CvOAnhJj3C+oYQ1SEHU0TfP3G+nBOdo2adiCyTcRkoREREJMuLyZOys/0iQvW4a00aTmbK58I9Qy609DuU46MYRUX5QfQf/3Je5ks0Fxg0IVoARLBENLf0E7tem/vlxZuoUD0yxtVlLkkogUlnAkZGgC+DfEGJYMkWdbcAERHRclaWWIbi+GJlHBtmxPa8OGX86aUOqQSB3+Xuy/id4Qrq+uvg8alNMBUNDUB39+xPnGgZO995Xgpi+lV22tA2IC/aRJkNWJMRpRxLvdwiZZQVxhViXfI6KViqMJuB0tK5nTwREdEiIjKzEywJAU3hU3avxJBBrnrSOuBAt00tlSnOjytf+VeMOtXHiJYSBk2IFgDReHbQKfcvES6OyzIpTYsKqMusNID/+mtgWN0VO8GuXXJ5LiIiIpK+S+8suBPhhnDlsdXpUUgf25gw5PTg8ytdAaUHOtOjUeNsx/GW49LC7fhjEmabEM2aEc8I9tepZWhdHh8OV/co45sL4qHTyJez+hE3NvWbsTF1I5LCkgLOnZUefzq11C0REREFEptznyh9QsrW9MtOiYRrc54yrusZwqDDrYxH+rtQ98Hv53yuRHOBQROiBeBk60nlvtvrw+V2OWgiyoasTI5QjqWEp0g32GzAsWNTv2B8PLBmzexOmoiIaJEx683SLjo/sbC6d2UijDr5lFg0unz1VLOyk92n1aAjP1nqc1LTV4PyrvLArJPz5wGXa+4/CNEycKjxEIZcQ8r4ZEMf7E75v7/sOIt081vfHoLssHRoNdqJLyQCKxvGNh0RERHRlESW5uMlj0tl4f0sN69AWIK86UjsHxLnyx6vWlp+4MAHgFXd+Eu0VDBoQjTPeoZ7UNtfq4zFF5DTI38BFSSGI9SgXvxtSBm74BNludxqdH+CW26RLxCJiIhoQs1msRvdT/RC2FOUqIzbrSNS4OSDC20YGHahrSAZo2Ob1nsdvbjYeVHtQSZKZZaXz/lnIFoO58fHWtQNQuK/xTNN/cqmIpFl4qfzjmJbh35idonfypVAhLoJiYiIiKaWGpGKB4oeQAjk79VRvQ64rQyRoXIgxeMblXqc+NnsfbB9+Pa8zZdotnBVlWgBZZmIsh/nm+UGllc3gBepksUJxUBvL3DmzNQvmJkJFBTM3oSJiIgWuVtzbkWcWe1nIjYp3Lc6FTEWtaxlddcQfnusER+32dCZqPZGsDqtqO1TNzuwRBfRLDR/r/5IDU6KrJOqbnh9cnm8NRnRUj8Tv1t7oxDmvKp03niiATwRERFNW1F8Efbm7lXGPbmJiChIUsZdtpGA5/cc+xxoUXuQES0FDJoQzSOnx4lzHeeUccfgCLrGGmslhJuQFGFSjq1KXAWD1gAcPAj41IvICW69VdQbmd2JExERLWJ6rV7aQacNUbM5s+IseGpTJvYUJsI81vDS5xvFueYB/MIZElCGoNXWinZbuzxoawPq6+f+QxAtUV83fx2QhV3fY0ddj126bzHqsDErRjkWMWrE2lr52KRSUoC0tNmdMBER0RK0OW0z1iSNlX0PCUHnjkLpe1gYdnmVkplCx1AHfB99KNfvIloiGDQhmkcXOi/A6ZWDJML4LJNV6ZETG8C3twcvA1JUxAtDIiKiaRA9wkTgRKdRm0NrNCEoTYvEt7dmYVN2LHRa+Xv4jNmEszZXQCP4qt4qDDoH5cGrrwJdXXP/IYiWmCs9V3Cg7oAy9vh8UpaJ3015cTCM9SAS7u6Lg37cos0EW7dyMxEREdENEOtROzJ3KOPB+AgMr0xRxt1jG34Fsa41UFPOsrW0pDBoQjRPxMLLidYTylhE6au65GaXJr0WKxLlRltCbnSuXEbk88+nfkFxQbh79+xOmoiIaAkRZS9/sO4HyI/JD3hcLMpuyY3F05uyEKrXYjQkBAcjwtDUN6w8ZxSjUmN4kTUKhwP43e+AfrnnAhFdv86hTrxZ8ab035b/XPnzii70D8t101MiQ7EiST0/ztDHIe9KkGBlUhJQXDz7EyciIlqiokOjkRWVpYyHd62EVysvJfcMOeEbt6FIysLevz94/12iRYRBE6J50jDQgO5hdedceatVKgMilKREQjf2RSRIDWsbG4Hq6qlfcPVqIF5tiklERETXFm+Jx5NlT+KZVc8gKUyt1SxEmvW4sywZmpAQnE+KRsugU7pA9HN5XbjUfUnuvWCzyYGTIXkDBBFNn91lx0vlL0n/TfmdbRrA5fZBpfn7rsKEgCzse7piEBJsYUZsJmKWCRER0TeilOgSe3WjLGgtyVAawvfb1e/tnuEeuPt6gLfeArzeeZkr0Uxi0IRonozPMhGNLS+2WqX74tpOlAbxizJFIT8mT47YT0WrBXbunN0JExERLWE50TlS1sn9hfcj3KDuZk+LNuPmgnjYDXoczYhHXfdQQA1nUaJLlOqSSnf19cmBE5F5QkTT4vF58MqlVzAwMhDQx+Rwjbq5aF9xEuLDjcp4Q9gKxF2qm/pF09OB/MAMMiIiIrp+K+NXwqhVv4Nd2/IxNNb/b3yJLpEp2mXvAi5fBt58M3gvXqJFgEETonlgHbFKNZv9xALM0NgCTHZcGCJD9cqxDSkboKmuAZqbp37BDRuASDXQQkRERNdP7GJflbQKz655FiadSXm8LC0SxSkR+DojAZfiIlHZaYN7XGN40fyyfWisMXxnJ/Dii4BL3XlHRJMTwcYPqj5Ak7VJeax3yImPytuVXrKiv1DBuLK1YYYw7GnWA54gvUz27GGWCRER0QzQa/UoSShRximJ4TixQu6lO+Bww+VRz4mV8+FLlxg4oUWPQROieXCq7ZRSr1k4N64B/Or0KOW+aE67JnEVcEBtiDmBwQDcdNPsTZaIiGiZiQmNwUMrH0IIQpRgiigNlBgZivdWpOFKuBnVnbaAOs41fTUYco2V5hIbHd5+W6wIz9dHIFoUjrcex9mOs8rY4fLi3fNtygJMXkIYNufEBJwbP56yD6aLl6d+0dxcIEutv05ERETfzOqk1cp9UbY2ZE0G2sJDpXH3uNK14lxYOR8WTeEZOKFFjEETonkoQXC6/bQyFrXRWwfkMh7RZgPSo+UvHqE0oRTmyjqgK0iTy61bAYtldidNRES0zOTF5OGWnFuUsU6jwV2rUmAyGfDWygxUGAwBjeFFX5NLXZek73mJKE1QF6R8ENEyJ7JLPq39NKBc7QcX22F1yH1K4sOMUlmugD4mK+5B6pnq4AswIsuEiIiIZkxaRBrizHHKeGVKFN5fkY4RnVYq0SWVqR2Xga0QgRPR44SBE1qEGDQhmmNiQWXYrS6ynL8qy2T8heHG+NXBe5mYzcCWLbM3WSIiomVsa/rWgHIEYUYd7ipLhk+vw2vFmbjgAfrGNcB0eByo7KlULxzFdzizTYgmELtQX7v0mhRs9Puyqhst/fI5stmgw92rU6DXqper2zO2o8wbB1y8OPULr1wJpKTM7uSJiIiWGbFONb4hfKRZD3NqFF4pycLAKJRy80LnUGfA97v0vf3BB3M9ZaJvjEETonlsAD/i9qKiwybdN+g0KEqOUI5lRGYg+XwtMDg49Yvt2AEY1YZcRERENLMXiGJne1JYkvJYSlQoduTHY0Svw6slWTg15JK+z/26h7vRZmuTB+3tcsYJESnEQsrrl1+HzSWfAwuX2wZxoUXeSKTVhEjByQiT2uNvRewK7EnYDLzyytSBSLHxaNeu2f8AREREy1BZYhk0IeoycnFKJNojzFLgpNWhBk3cPjd6h3sDf/n0aaC6ei6nS/SNMWhCNIearc1otbUq48vtg/CMNZItSoqQAid+my0rgCNHpn4x0fh9/frZnTAREdEyZ9Aa8FjJYzDrzQGN4fMTwjFk1OOFlVko73NM6G9ic44tCH/+OeBVgypEy92BugNoGGhQxqKsx4Erncp4d2GCFJz0S7Ak4IGCexHy2muA1Tr1C69aBcTHz97EiYiIlrFwY7hUvtZP9B0Ta1gicPKvOalwjssODSjR5SfOiZmBTYsIgyZEc+hQ4yHlvijdcWFcaa5V4xrAhxnCUHiqEfCo0foJdu8GdLrZmywRERFJokxReLDowYDG8LesTEBUqB7WUAP2J0SjqVctvTmKUVzqHutv0tsLnDs3j7MnWjgquivwdfPXylhkab1/oU3qZyKUpEZKO1f9RLDyiZLHYfz0ANDYOPULa7XAzTfP7uSJiIiWufElukQJzYLEcOl+k8WEt1bnwqPXSuNeRy8cbrl3r0JkYF+5MrcTJvoGGDQhmiOiVEd1n5qOWNM1hIGxRpcZMWbEWAzKse3uZGiqqqZ+sfR0oKxsdidMREREityYXOzI3KGMjTot7ihLlkoJnUqJRc2IB71DTuX4iGcEFzovwOlxAl98Abjl73yi5Xwu/PaVtwM2EH16qVNp/J4QbsLOAjVTRJQAebT4UURdrJbLegSzdSsQHT17kyciIiIUxBYEZF+vHFdi/pDThwt7V8GrkwMnLYMtE1/g4EE2hadFg0ETojlyuPFwwEXi8fo+Zbw2U73I0/qA1ecmSWUcX6/59tvln0RERDRnbs66GdlR2cpYLPLeXBAPj1aDrzISUNdjD+hvMugcxKm2UxjobgZOqD3NiJZT/5LKnkr89vxv8fPTP4fTqwYWTzX2o65nSLpv0mtxZ1kydONKe+zN3YvMfh/w8cfB3yQ3l71MiIiI5oBWo5V6m/glR5qUDcBtAw5UGQxoWJUpjduH2uH2XrVpqKsLuHRpbidNdIMYNCGaA51DnajoqVDGYlGlZ2w3amKECZkxaqR+e6cRpn61MeYEa9YAKSmzO2EiIiKaQOx8f3Dlg1IZTb/S1EisSAzHhaRo9Bj1qOq0wT3Wr8zfDPN8x3k0vv8CRh1XlSkgWqJEptXR5qP4v8f/L14qfwl1/XUBx5v7hnGktkcehAC3FSchMlRt/F4cX4xNofnAq68G35EaEwM89BCg4WUtERHRXJfoEiVr12aom4BPN/ajtSgVrlCDtHFCZJlOwGwTWiR4dkk0Bw43BWaZnBiXZbIpO0b6ohH0Dhc2VMk77iZlMgF79szuZImIiGhKImBydX+TPUWJiAwz4VBmIoZdXlxstWLI6QnocVLffhlfvvj/nVjfmWgJsY5Y8UnNJ/jfR/83Pqn9BP0j/QHHxXlwS/8wPrzYrvSC3Zwdi6w4i/KcOHMc7jGtQsivfw0ECzQajcDjjwOhatN4IiIiml2JYYnIispSxoXJ4TAb5H67Nd029Lt8aCzNkMattlYpeBKgrw84f35uJ010Axg0IZplPcM9uNSlph829g6jc3BEuh8fZkT2uIvEHdVuhI2qu+wm2LkTsKjPJyIiormXHZ2NnVk7lbFBp8FdZcloTI1Bp8UEl8eHS21WdI193/t5j36Nn37+/8NH1R9hYGRgHmZONLNEEERklYi65W9cfgP/fPyfcbTlaEAZLsHj9eFSqxUvHG/C66db4BgrY5cZa5E2EPkZNHo8OZwH4wsvA3b71G8sNhw9+CAQr/ZAISIiormxNX2rcl+n0WB1epR0X2yIONPUj/YVKXCajXB5XVLllQlEvz+PusGIaCGSQ4FENKu9TMQO08l6mWwcyzIJ8Y0i70QNVluTAOMULyQuCjdsmKNZExERUTA3Zd6EJmsTavtrpXFsmBFPbMrCxeERJB6tlC4aRTlOkXGSFWuBRhMCrceHlNNVOG4Mwcm2k1IJItFQU5wneH1eaSeeuB+qC0VSWBJiQtVsVKL51ufok3r0iADJkGtIypoSARP/ee5kbCNunG+xorzVGtDvR4i1GKSyXP5/4xqPF081RSK66di1JyMyrwsKvvmHIiIiouuWH5OPeHM8uoe7pXFZWiRONPTJmyTaB7E5J1bqbbLiaBWaB5ul89qAc1qrFTh7lmtctKAxaEI0yxeXF7suKuOWfgfarQ7lQjEvIQxalwcrv7yMgr4QhCeGT/1it90GaLVzMW0iIiKaRn+TB4oewC/P/FIpQRRm0mHj3pUY7ehDSL18Edllc8Lu9Erf+aEGLZKr29GRn4zB+AjpHGH8ecJkpcAyIjOQGZmJzKhMJFgSpPclmmuiL897Ve/B45verlCRZXW6qR9VnUPSpqHxkiJMWJ0RhfyEcGg18gKKyebA3WfsyBidxrluaSmwbduNfRAiIiL6xkQARGSbvFP5jjQ26bUoSYnAueYBKXBysXUA5rwkZFxsAoaGpbWxWHNs4IscOgSsXg3og1RbIZpHvOoimkVfNX0VUL/xeH2vcn9DdgxC7U6s/fAsYlv7pMWQKRUWArm5sz1dIiIiug4WgwXfWv0tJFoSlce0Wg2Md65CbnwYxtaDYXd5cL5lQGoSb3d4UHC0SsoyvRaxm/9y92V8VPMRfnbqZ/gfR/4HPq39lH1RaE41W5ulRZFrBUxEcKS+x443TrfgxRNNqOywKQETkWlVmBSORzek47GNGShMilACJqGDDuw+UI8in1qma0ppacA998jluYiIiGjelCaWSht8/NZkRCtfz+earXADaFgt9z4R2SYT2GzAqVNzNl+i68VME6JZImqVn+s4p4xbBxxSpokQbTZgvWYUZR+cgcHhQrQpGhHGiMlfSKcD9u2bq2kTERHRdYgyReG7a7+L96vex4XOC9JjgwmRCClORXF1hxQocXrkDRR9dpd0C++zIyQuEr4teddVfmvYPYwjzUdQ1VuFp8ueRqQpctY+F5EgNv+If9vjNwG1DTjQP+yC0+3DiMeLEbcPTrdXyqoSj48ndp6uSouSynZYjJNceo6O4ubTvVgbUXDtLKqiIuC++7gjlYiIaAHQaXTYlLoJB+oPSOPIUD3yEsJR3WnDsMuDKx026HMSkSmyTawDGHQOTlz3Er1NxPd7lNwThWghYdCEaJZ83fR1YJZJnZplcodFi7WfnIfGKx+fMstELKTcey8QHT37EyYiIqIbYtAacH/h/UiLSMPHNR9L3/+163OxobUPpWmR6LQ60TE4AvfY975txAN8Vo6Xh7xwmI3wjY5CJJ7IP0cRYdIjNSoUKVGhSI0ORdhVi809wz148eKL+M6a78Com6oZGtE3d7rtNDrtagPXy22D+PRyxzV/L8pswNqMKBQlR0CvnRgMCUEI8mPzscMahVQcR4g/LWuq8+Hdu4Ht25lhQkREtICsT1mPw02HpYbvwrqMaCloIpxu7EdxSoSUbSJK0ovM1eKE4sAXcDqBt94CvvUtkZY6Hx+BaEoMmhDNAlFO42zHWWXcYR1BU9+wdD9eG4K7LzcpAROxQ1XcJjAYgAcfBFasmLuJExER0Q0RGSMbUzciOSwZr156FbYIoLEsE9ln66XAR3KkCd1DTrRbR6SG2AavD1uutODtlRkTXqtnyCndREkv/869tGgzNmbFINIs77IXC9mvX34dj5c+zj4nNCtEZtPn9Z8rY/Hv9nC13KtnKimRoVibGY2ceAs0kwQ4jFoj1iSvkf5bidGFA//yL8EDIaGhwEMPsUwtERHRAhSqD8WapDU43npcGidFmqSNP6LSisg+FWU7Q7LikXHBAvR3SyVmxe8EaGwEjhyRN0cQLSAMmhDNghOtJwLqPp+o71Puf3fQBpNTVHeUieauE0REAI8/DiQnz/5kiYiIaMakR6bjB+t/gDcuv4HGEh8S6zphtg5LPR0SI0xICDeif9iNdqsDxX2DqBq0ozE+Ulpg1o6tHVtH3KJqkcLqcMPqsKKhx47HNqYj3CQHTqr7qvFJzSe4Pf/2efq0tJQdrD8Ih0ftn3OsrhcOt1e6nxFjRnFKJEx6DYw6rfRTlOISt8nEhsZiU9omrEpcpWZHHT4MWK1TTyApCXj0UWZcExERLWBb0rdIa2CjkE9e12VGS0ETf7ZJTnwY6tbloGz/Ram3SUFswcQX+fxzICcHSEmZ6+kTTYlBE6IZJtIST7aeVMaidnldz5B0v2jYgY19VmVHnajnOCHLRARKRMBEBE6IiIho0RFNMZ9Z9Qwudl3EOZsWGW8fDMhIibEYpJtQ4nPi5JZM+HTqYrPT40X7wIh0wSlunYMj8PpGpYby75xrwyPr02HQydklYmdfrDlW2rlPNFM6hjpwqk1tztorZT7JAQ6dNgS3rkxUgnfBZERmYGv6VmmBJCAjamhIDppMRZwHf+c7cuY1ERERLVhiTUuU3SrvKpfG2XEWqY+vyDQR57FioxDSYtGel4SQmnbp3MCkMwW+iM8HvPkm8IMfsHcZLRjM5SeaYaL5+/hdeWca+6WfOq8P3+7qDyhVILJMAhrAFhYCzz7LgAkREdEiJ77fyxLL8PS9f42CXQ8h0jh50/bQoRHknaiBdmwHvyB27mfFWbAtL04KkHx3e7ZUoksQZbs+Km+Xep/4fVT9Eap7q+fgU9FyMDo6Kv2b8u8YFeMvqrqln8KGzJigARPRr2Rl/Er8wdo/kPruFMYVTiwhd/Ag4ApsGh9gzx4GTIiIiBYJsUFi/DmwyDbxO14nV16p2ZiH4TAjGgYaJn+Rnh7g009nf7JE08SgCdEMEo1fjzYfVcZ2pwcVHYPS/R1tPcjV+AJ2ocaExqi/nJcHPPIILxCJiIiWEHHhmPLgt7EmewvWJq9FvDl+wnNSqtqx+fVjyDzXAN24Ep5+ZoMO965OhXEsu0TUhz5UpfaWEIvbr11+DZ1DasNuoht1qfsSGq2Nyri2ewjNY735IkL1AQshZr1ZCoqIeuZb0rbg/sL78Wdb/gyPFD+CtIi0yd+gqws4c2bqCYis67KyGfxERERENJtSwlOQFZWljAuTw5UNFg29drQNOOA16FCxowgd9k6pb9qkTp4EqqrmatpEQbE8F9EMquiuQP+InFkinGsekMppxA85cM/AIHTRZuVYekS6mmUi0g/vugvQMI5JRES05ISFAbfcgoj335fKF4i+Z0OuIemCUezKF+cD4v9Wt2rg7rSiMT8B57PN6AiRy3sKopzXXWUpeOtsq5RlIs4xROmDVelRSnnQl8pfwg/X/3BiyQOiaRL/jj6tVXd5erw+fFnVo4x35MdDp1XPV+/IvwMlCSXX9yZiF+n4pj1X27cveHN4IiIiWnC2pW9Tskh0Gg02Zcdgf4W8oedoXS8eXJuGwYRINJRlILGmEUXxRZO/0DvvAH/4h4DFMpfTJ5qAK7REM0SULPi6+Wtl7PL4cKHVipDRUdxR24aUcHUBw6g1It4ybqfp7t1A1FW9TYiIiGjpWLcOSJN33us0Oqn+s9iVlxyejKSwJCSGJUrnBinGOGxp8uGHR134c812RBvUsl7pMWbsLkxQxl9UdUnN4f0GRgZwoO7AHH8wWkq+avoKg045S9rfwNU24laav+fGWwLKzBbHF1/fG9TUyLepiFK1WepOVSIiIloc8mLyArJMi5IjlPKyImPVn7XaWJaJarMDdpd6DhvAbmeZLloQGDQhmiGijEGbrU0ZX2qzwun2Yk1bH0q9XqVhqyC+SJTazikpwKZN8zFlIiIimiti5/z1ZJW6XAj//Ct857QPsQNq74eS1EilPJLYrP9heTv67Opx0bxbNPEmul69w7040nxEGQ+OuHGyUa5DLrKhbi6IV7KkRWbU7fm3B/bmuxbR5DXYIoj4b+PWW7/BJyAiIqL5Is4JdmfvVsZaTQg258QqY5FtIjYbj2o1qLipCHX2lqlf7Px5oFEtFUq0pIIm//iP/4itW7fCbDYjapo76MV/PH/913+N5ORkhIaG4pZbbkF1NZta0uLwdZOaZeLzjeJM0wDCnW7sbOhASpSaZSJ2l4pdpcrF4d13sywXERHRcpCUJGeXXofwXhuePO5A3qk6pVn89rw45MWHKZmtX1R2KU26RX8TqYl3sPJHRFcR/14+qP5AKh3nd7i6Bx6v/O9odVoUYsOMyrH1KeulDKnreAPggw/kfiZT2bABiFUXV4iIiGhxyY7KDuhtsiIpXCoxK4i+Jo1j2SaOiFAcKY6QytVO6cMP5Q0XRPNk1lZqXS4XHn74YfzoRz+a9u/80z/9E37yk5/gZz/7GY4fPw6LxYJ9+/ZhZGRktqZJNCO67F2o7lMDfNVdQ1Ipgz217UgwaKUGrn6iFIcInEi2bJGbXRIREdHysH078PjjQGLitH8lxhiJ3e0mbHj7BKLa+6WdfPtKkhAx1mCzqW8Ytd32gOzXy92XZ2X6tDSVd5Wjrr9OGTf22lHdaZPuh+q12JQToxwL1YViV/au63uDr74CTp+e+rjJBNx88w3MnIiIiBYKcY66K0s9R9CEXJVtUitnmwjt+Uk4H+Wc+sU6O4ETJ2Z3wkTzETT5u7/7O/zZn/0ZSktLp/V88R/N//k//wf/5b/8F9x7770oKyvDb3/7W7S1teHtt9+erWkSzYijzUcD/i2fbuxDZv8QCnusSI4KVY6JUgap4anyIDoa2LlzPqZLRERE82nFCuCHPwSeeELpc3ItYtNFvjYBqz69gNimHui1GuwoiFOOH6rqlpp2+4lm3qKpN9G1ONwOfFzzsTIW/44OXlEzQkRmk0mvVcai9IZZb57+G4gSGweu0WtHBEzM1/GaREREtCBlRmVK/U388hPCEDeWrdo5OII6fz++kBAcWmmB1Stnn0zq4EFgKEg2CtEsWjA1gerr69HR0SGV5PKLjIzEpk2bcPSouiBNtNDYnDZc6LygjFv6HeixOnBLbTssBh0iTGqWiWjyatSNlTYQdc318g5RIiIiWmZEL4iCAuC73wW+/W0gN/eav5ITnYMoQwRWHrqM8O5B5MaHSc3h/f0nRNNuP6vTGlA6lGgqB+oPwO5WM5VONvRhwCE3f0+JCsXKlAjlmCjJtS5l3fRfvK4OeOed4M8RJblEaS4iIiJaEsZnm4jsky25k2ebOMNMOJorl++alNMJfPbZ7E6WaKEHTUTAREi8qlSBGPuPTcbpdGJwcDDgRjSXvmz8Et5Ruca4cKqxH2vb+xA/PILkKFNAg8z0iHT5TlnZtBZHiIiIaIkT5wlZWcDTTwPPPAPExAR5agjyY/Oh9fhQeuAiQodGsHNcc27RtFuUB/X7uvlr9DvUQArR1ZqtzTjVdkoZ99ldODkWfBMlNfYUJgScy96Rfwc0IdO8hBTXcK+8ErweucEAPPQQoFM3GREREdHilhqRihWxK5RxTpwFiRFyr9+eIadU0t7veLoGfZYg5xZsCk+LIWjyl3/5l9JJc7DblStXMJd+/OMfSxkp/lt6+tiiNNEcaLe143SbWp+52+ZEd7sV2xu7YNRpEDvW8EqIDY2FxWABjEbg1lvnacZERES0YOXkAKIf4E03AZrJT9PDDGFSqS7DiBtl+y8iUa/BqrRI6Zho2i2ad/uJpt6iTBfRZLw+L96vel8Zi12fByo64fPJuz/XZUYHNH9fm7wWGZEZ03txqxV44QV5h+hUxL/xRx5hfz8iIqIlaHz/M7FevPWqbBPfWLbJqFaDL4rUsvaTYlN4WuhBkz//8z9HRUVF0FuOuNi7AUlJSdLPTtHoZxwx9h+bzF/91V/BarUqt+bm5ht6f6LrJS4sP6r5CKMY+0M/OopD1d3YWd8Bk8eLpMirskwi09WazeHh8zVtIiIiWshE6c49e+SeJ1NsBsqOyoZOo4PZOozSA+XYmhktNesWqjptaOlXa0NX9FSgtq92zqZPi8exlmPotKvXXhXtNrQOOKT7EaF6bMxWs55ED5NbctQyykF5PMDLLwM2uZH8lO6+G8hTa54TEaJ/tOoAAE28SURBVBHR0iFKehbHFyvjjBgzUsd6/vYPu6RzVr8LYXb0ZCVM/WJirXj/fvkcg2ghBk3i4+NRWFgY9GYQKdY3IDs7WwqOHBjXJFCU2jp+/Di2bNky5e8ZjUZEREQE3IjmQnlXOZqsTcq4pmsInoYelHX2S1kmieFy6qEQbghHpDESiIsDNm2apxkTERHRopGQAHznO8DGjRMO6bV6KXAiRHZZsfp4dcDuvS8qu5VsAUFs8hBZJ0R+AyMD+KLhC2XscHlxuLpbGe9ekQC9Vr1U3Je7b/rN38WiRnt78Ofs2gWsWXMDMyciIqLFYmfWToRA3kwsNhVvzlHPV4/X9SnZJsL+PG3wvr9HjgA/+Qlw+jTgVUvkEy26niZNTU04d+6c9NPr9Ur3xW1oSK1bJ4Isb731lvIfz5/+6Z/iH/7hH/Duu+/i4sWLeOaZZ5CSkoL77rtvtqZJdENcXldAuQu314dDVd24tbZNGmfGWqDRqFkmopSBlHVy++2AVt4JSkRERBSUOHe47TZx0jzhkCjRZdFbpPsJ9V24p70HCeFGpVb0xTar8tye4R4crD84hxOnhUxkR39Q9QHcPrX/zVc13XC45QWI/MRwZMXJ/7YEEaArSyyb3otXVgLHjgV/ztq1wI4dNzh7IiIiWiziLfEoTSxVxmnRoQHZJtWd6hrxFU87OtdNPOcNIPpYv/ce8NOfAhcusGQXLc6gyV//9V9jzZo1+Ju/+RspUCLui9upU2qjwcrKSqmklt9/+k//CX/yJ3+C73//+9iwYYP0ex9//DFMJnXHPtFCcKjxEGwuNZXwVEM/shu7kGJzIDJUj2izGh2PMkUhzhwnL3iw+TsRERFdD9H34cEHgdTUSZvC+2VeasHjZvXU/khtL+xONbvkSPMRqek30fnO86juq1bGrf0OXGoblO4bdBrsLIhXjmlDtLiz4M6AkrNBFzLefjv4c/LzgTvvlAOCREREtOTtyNwRkG2yaXy2Sb3a20T4LGEIiFWPT6mvD3jzTeCllwCXa3YmTsverAVNnnvuOWkX09W3nTt3Ks8R429/+9vKWPzH81//639FR0cHRkZGsH//fhQUFMzWFIluSO9wL442H1XGVocb5TVduLm+U/oayIq1BFxY5sXkIUSkGO7bN08zJiIiokVNnEc88QQQHR3wsNiYEW9WF7hvutCADRFytonT7cX+ik7pfFsQPdjeuvKWlC1Ly9egcxAf13ysjD1en/TvxG9bbhwsRp0y3p6xXd78cy1ip+cbbwAOuSfKpBITgYcfZtY1ERHRMiLOI8Znm6RHhyJlLNukzx6YbVIzWI+2m1ZP/8Wrq4HXXhMLzDM7aaLZDJoQLVXiQtM7qtZPFGW5bqrtgMXtkZq/hxrUC8HU8FSEGcKAbdsmLHQQERERTZvFAjz1FBAqX2T65cbkQhMin9Lr3F78sK0L4Vp580Z9jx2X2+UMAqHP0YcDdWr/QFpeRADt/ar3MeIZUR47WtcrlccQEiNMKE2LVI7Fhsbipsybpvfihw4BjY3BA38PPQTcYP9LIiIiWjrZJpuzA7NN/Jt8hM81jcD27dcXODl/fmYnTMSgCdH1qeypDChn0Nhrh6umE6s7+qRmmanR6kKGXqNHVlQWEBl5fX/wiYiIiCYjyhU8/jigUzMBTDoTMiMz1acM2PEnLnVR/MuqbthG1N4Vx1uPo76/fg4nTQvFhc4LqOqtUsbtVgfONPVL97WaEOxdmQjNuGxpUZZLp1H/rU2poQH48svgz7njDiBezYoiIiKi5ZVtUpJQoozTY0KREjku26RrXLZJXw1aNhbK57wJCdN7g48/BmxqCX2imcCgCdE0NQw04I2KN5Sx1zeKw5c7cVtVqzTOiDFDJ+qOj8mOzoZeO1aWS+yuIyIiIvqmMjKAu+8OeCg9Ml3ObB2zsb0Xt0POinV5fPjsslqmS3j7yttwepxzOGmabzanLbAsl8//70Ieb8qORWyYXNpNEI3fc6Jzgr+o+OWKCuD114OXxSgtBVZfR6kNIiIiWga9TWKUY8frArNNvmj4AlixAvjhD+XefjHqcyc1MgJ88AHLdNGMYtCEaBqqe6vx+wu/D6gDfr55AIVVrYh1OBFm1CEuTC03IBYuksOS5cbvRUXzNGsiIiJaklatAtasUYaiPFdhXKFyISp8q60baV45cNLUN4wLrVblmNVpxSe1n8zxpGm+y3I5PGq/keN1fdLOTiEh3IT1mdEB57G3590e/EVbW0UTS+CVV4AhdXfoBKI87V13sfE7ERHRMhdviQ/INhEbj5PHsk16J8s2GWwBxMZksfnij/4IuOcewGye+g2uXAEuXZrdD0HLCoMmRNdwqesSXip/CR6fR3msvmcIVy60YEtTtzTOigts/p4fk48QUbOZF4lEREQ0G0S5o3ElC8RCt8hy9TP5fPiLvn5oRYNuAIerezAw1rtCONN+RtoUQktfeVc5KnsrlXHn4AhONcpluTSiLFdxovTT766CuxCqD+ydoxgYkBu+/+IXwXuYCKLhu2j8blQzWIiIiGj5mtDbJEi2ycH6g4HnFGvXyoGTYD76CLDbZ2HmtBwxaEIUxNn2s3j98uvwjcoLDkJVpw3vnWvD3soWaEdHkRhhlDJN/BItiYg0RQK7drH5OxEREc0OUfrzkUcCGmunR6QjwhihjDOdLjw7KNd39nh9+PRyJ3zjLkbfq3qPZbqWuCHXED6s/jCgvOynl9RybZuyYhA3riyX2AEqspYmdfEi8NOfyj+n45ZbgJSUb/gJiIiIaCllmxQnFF+VbWJSsk1qxmWb1PbXoq6/LvAFCguBEjVbZQIRMBH9TYhmAIMmRFM42XoS71S+g1GoiwuXWq34qLwdpe19SBscRqzFgMxYi3JcG6KV6z8nJQGbN8/TzImIiGhZiIuTs1rHiB17YsFblOvyu31wCCtH5MBI24ADZ5sGlGODzkF8VvfZHE+a5ooIjIj+NePLcp2o70WvXf73EB9mxPosdYenRW/BHfl3TP5ionfJm28Cbvf03jw/n+fCRERENMHNmTdf1dskVjl2pLYXPp+6BvdZ7WcB2SeS228PXqZLbO6oVDNsiW4UgyZEk2iyNgXsyhPONPXjs4pOWEbc2FXfgYRwI/ISwqAZV35LBEyMepOcMjiuKTwRERHRrCgrk8sVjDHrzQENvLWaEPxJ/wD0Prm/yZHawDJdp9pOoWGgYY4nTXPhROsJqSa4X9fgCE42jJXlChFluZKkfx9+dxbcKf37maC5WS7JNd3mqtnZctNWlqglIiKia2SbZMaYkTLW26R/2IVLbYPKsfahdlzsuirD1WKRy9QG8/77gEPdNEJ0I7iqS3QVUYrr3cp3lQwTEdUWtRUPVXUj0ebA0+drkWXRI/uqPiYp4SnSTdpVx1IERERENFfEjrvERGWYGp6KKFOUMk72evGsbUgpz/TZZbU8kyDOe9zeaWYQ0KLQOdQZkEXk9vrwUXmHUp5tQ1YM4sPVslzF8cVYGb9y4gv19gIvvQR41N5+UxKLGCLz6emnAZNcaoOIiIjoWr1NtufHKceO1vXC5VFL5H9e/3lAj2FJcbFcqmsqNhvwYeBGaKLrxaAJ0VUquivQM9yjjM83D0h/tMs6+qSASXGoToqEjw+YpEWkyc3fo6LkXiZEREREc9nfRDTc1ukCynSJsqF++6xDKBor09U64MDFVqtyrM/Rh4MN45pt0qImFhberHgzYIFBbP4RuzeFhHATNmarZblEdsmkZblEXfDf/x4YHg7+huLf3U03Af/u3wHr1zPbmoiIiIJKsCSgLLFMGadEhUqVXIRhl0eq9OI3MDIgZc8GEOtxd94ZfJOGKNM13T5sRJPgGS3ROGLX5eGmwwG78k7W9GBfdSvuqGpFblQo0qIDAyZZUVnIjc6VHxO768Y1ZCUiIiKas/4me/YoQ5POhNyY3MAyXX0D0HnlnXtf1fTANqJmlxxtPorWwdY5njTNhv11+9Fp71TGdd1DSpBMp9XgtpLAslwiYGIxqD36JC4X8OKLQL+6aDGp0lLgT/5E/rdnVDNXiIiIiILZlb0LOo284UfYlhunlL8/3dgPu3Pc5o/GQ3C4ryq3FR4O7NsX/E0++ACwqhuFiK4HgyZE44i6zx1DHeq4pgsPnK7BmvY+xIUZkBwl11n0E8ESETSRAiYlJXLTSyIiIqL5sGkTkJ6uDJPDkhFpjFTGKT4vnhm0SfdF2YPPr3QpZbpEWdJ3Kt+Bd6z3CS1OtX21ONZyTBmLBQdRjs3v5vx4xFjUDT4lCSVSaa4APp/cw6T1GkG0rVvl3iWR6r8xIiIioukQpWQ3pW5SxtEWA0pTI5UNzMfre5VjI56RgA3OitWrgby8qd9kZAR4++3p92UjGodBE6Ipsky0g8NY+dYppNqGlXTB8QpiC5Aema7u7rxWIyoiIiKi2STKIt17b0CZrhVxK6AJUU/5b7cNIX9YLtNV32NHZaccRBG67F2TX5DSojDsHsZbV94KOLf99HInHG45EJYTH4aS1AjluAio3Zl/Z0AGtdS75M03gcrK4G8mNgvdeussfAoiIiJaLm7KvAmhOnWtbVNODPRa+bz1Yusg+uxyaVHheMtxqVRXAHEOc889QGjgel2A+nrg6NFZmD0tdQyaEI1psjZJN0Hn8iDxzVMwji0qRJsNMBvUtMH0iHS56buQkwM8+yxgNs/PxImIiIj8xEaO3bsD+lWIrFg/vSYE/66/H3qvvJD+RWW3VDva73DjYSl4QouLzWnD8+eex5BrSHnsXPMAGnvt0n1xHntrUaISIBHNV+8vuh+h+nGLDE6nXJKrvDz4m2VmAvfdJy9UEBEREd0gUU5WNIX3E+cr6zOjlc0fX9eo/Ya9o16pBKk/S1oRESGXyg/mwAGgU828JZoOBk2Ixvh3VoZ4fSj6vBz2JjUVMHVclonYralkmGzfDjz1FGC5qg40ERER0XzZvBlISwvY7BFuCFfHoz58r70HIaOjGHF7pcDJ+AvSd668A9+o3PuEFr5uezd+eeaXAX1MeoacUt8av73FiQg1aJXxtoxtAcE0DA0Bzz0H1NVdOyj32GNKNhMRERHRN7EhdYNUqstvbWY0LGOblmu7h9A6oPYyKe8qx3tV700sJ1tcDKxaNfWbiM1CovSoyKglmiYGTYgAtNnapH4mos5hwdEqjNZ0wumRFwsiQ/UIM+kC6oMbQsOARx8FbrlFLoVBREREtFCIcxORCXBVmS6RXeC3y+vG7Y3yIntVp026KPVrtbVKJRBo4WscaMSvzv4KVqfa5NQ24sYHF9rh9ck7MdekRyMrVt3gI7Kld2XtUl+krw/41a+A9vbgbxYWJm8WClYCg4iIiOg6iGbwe7L3KGNRnmtLbqwyPlzdHZBdcqb9DH5/4fcTG8PffjsQpQZfJujqAj75hP1NaNq42ks0VopCyLjYhKTqdrT2q398x/cyEYsNqTmrgO9/Hygqmpe5EhEREV2TyAjYpS6MhxnCkBGZEXBBer/DgfWtcjaCaAovsk78Pq//HH2OvjmeNF2Py92X8bsLv5Oao47PMHnlZDP6h+Ua4HFhRmzLUxce9Bo9Hih6AFrNWNaJCJSIgEl/f/A3MxiAJ54IvhhBREREdANKEkrUEvgAViZHINZikO53WEdw4EqXshlEqB+olzaNBJyrmkzA/fcHLx968qRcqouBE5oGBk1o2RMlDSp6KpBQ14mcM/XoH3YrDTPDjDpEjMsyicwuhPkP/728EEFERES0kG3ZElCmKzMqU+px4hcXZsCD7b0o6LHC7vTgq2q1nJPb58a7le9OrBtN887usuOj6o/w2qXX4PGpZSaa+4bx6qlmDDk9Srb03WUp0I01VBVuy7sNceax89jWVrkkl13uezIlkVny9NNAirqYQURERDRTRFb0rTm3KmONJgQ3FcQr4/JWK94+1xqwwadnuAe/OP0LKes2oO/atm3B3+yrr4CDBxk4oWti0ISWva+avkJkxwAKv7oiLQyMr5coepn4G2Y6LSak//D/AxiN8zhbIiIiouso0/XQQ0rvNdGXrTCuUDksznFy4sy4v6oFqYN2lLdZ0dQ3rBxvGGiQSiDQwuD2uqXs6J8c/wmOtx7HKNSL/coOG9461wrXWHnZxAgTHt2QjkizXnmO+N9+bfJaedDdDbzwgtz8PZjISOA73wHSx/r5EREREc2C7Ohs5MfkK2NRWvS24iRoNSHK5hCRTTswlk0rODwOKeu22dqsvpDItE5KCv5mhw4BX345C5+ClhIGTWhZ67J3oeHS1yg9cBEa3ygGRzzSTkvBbNAiauxC06PXYuTRBxGbOK5hJhEREdFCJ8opibJKevmcJsIYgbQINfvEqNMiJyoUD15qRKLNgQMVnXB71Sbwn9Z+ikHn4LxMnWS+UZ8UvBLBkgP1B+D0qoEOseHndGM/Pipvh2+sbEV2nAUPrUuDeayJqpBgScC9K+6VNwMNDAC/+x0wrAbIJpWQAHz3u0C8utOTiIiIaLaIjFijVt2oXJgcgQfXpiFUL5cVFeVHXz7ZHFBSX2TdvnLpFbXHiVYLPPigcu47pS++YOCEgmLQhJYtUf/5vS9+jtJPz0E3luI3PstE9DIRF5ajISG4tKsEG9fcNY+zJSIiIrpBqalyxslY9mx2VDZMOpNyOCHciERtCL59tgbbztXjXLnaEFws0L9f9T7LdM0TEbD6zdnfSKXSbC5bwDHRv+Td821Sg1S/ktRIqSSX6Fnjlx6Rjm+v/jZC9aHA0BDw298Cg9cIhGVkAM8+C0REzPyHIiIiIppErDkWD618CNoQbcDa3GMbMhAz1uNElOh682yLlGXrN+QakjaWKMSGD3HuK7KugxFlukTghOe5NAkGTWhZEhf+H3/1HDLe/QI6l0eJWA863NJ9k06jNJ2q3FqA2JINSA5Pntc5ExEREd2wFSuA22+X7oom4CtiVwSW6YoPg6h+UNbZj81vn0DE4SvQjp0jVfVW4fnzz6NlsGXepr8cWUes+PXZX6N5cFzJCQC2ETc+u9yBF443or5H7UeyOScWewoTpDrg40tyPbPqGbmXjcMhZ5j0jWuaOtW/FdHDRPQyISIiIppD+bH56rnLGFFu9JH16ciIkR8TTeE/vdyBPrtaqutU26nAMl3ifObhh6cXOPnNb4Curln4NLSYMWhCy9LxM+/B8tIb0I+V4hKR6pquoQlZJo1lmejIT8ZNGTfN42yJiIiIZsDGjcDWrdLd6NBoJIepG0JE2YP0aPlCVO/1QfvFFWx44xgS6jqV/ia/PPNLvHrpVfQO987TB1g+xI7J357/LQZGBpTHnB4vvqrpwXNHGnCpbVDZFBlm1OH2kmQpaOLvxSdsSNmAR4ofgV6rB1wu4MUXgU75f88pFRUBjz567ZIWRERERLMkMyoTf7D2DxBnjlMeM+m1uHd1KoqSI5TAyedXOgOyod+reg9enzfwvEaU6rpW4KSpCfjZz4ADBwC3vJmaiEETWnbqq07A9ot/gd7pVv7QVnXapJ9CtNmA+HAjOnITUb8mC5mRmdIfbCIiIqJF79ZbgZUrpbu5MbkwaOXMWiE50gSLUe6D4XB70dFqReGXl5F5vlF5zuXuy/jpyZ/inSvvoN2mlvGimTPsHpYCJr2O3oBSXM8facSphj7lnNWg02BbXhy+vTULK5LCA15jT/Ye3JF/BzQhGsDjAV55BWgOzFiZICdnegsLRERERLMsJjQG313zXamsrJ9oCr+7MAERofLmjpZ+ByrabQF9i4+2HA18oeJi4IEHlDK1U/L5gMOHgX/9V6CmZoY/DS1GPCOmZcXaVo+Gf/476EfkFD4RkRZlDYZdXiVynZtgQXdOIiq3FUp/VPfk7JnnWRMRERHNEHHBeP/9QGYmdBodCmILxh0KQU6cBf5Lyo7BEZxvHkD44StIudwc0Jj8bMdZ/L+n/1/86syvcKHzgtSEk2am597vL/xeuuj3EyVk3zzTiuGxcmliwWBtRjSe3ZqNDVkx0I3rXyJqgN9feD9uyrxJzjrxeoFXXwVqa4O/cVoa8NhjgE5tHk9EREQ0n0Q/tqfKnsK65HXKY6Jv2+4VCcr4UHU3HGNresIXDV+g39Ef+EIlJdMLnAj9/cDvfw/s389eJ8scgya0bHgcdlz85/+MELta+7lz0Cnt3BM0ISEoSAxDf1YCKrYXYlQTgu0Z25ERmTGPsyYiIiKaYaL0kuhZsXUr4sITkWBRLzxFpklGrFpD2unxSSVMnW+eAU7VT3gp0W/jzYo38b+P/m8cqDsAp0c+r6Lr5/K68OLFF9Fma1MeE/323jzdogRMEiNMeGZLFnYUxCPUoDZJFdIi0qRSFquSVqk7Jl9/HaiqCv7GCQnAk08CBjXriIiIiGghEL347iq4C1lRWcpjWXEWFCSGK+X2ReDET2zkeb/q/YCyXZLSUuC++6YXOBG++go4cmSGPgUtRgya0PIwOooL//a38HS2BzTRbOxTAyi58RYM5ybi8s0rMarVICc6B7uzd8/ThImIiIhmkcgo2LsX+KM/Qs72u6HXqD0skiNDUZIaiQiT+pjd5YHx3TM4/+FFNPUNT7gQtbvtONx0GP968l/RZG2a04+yFIgL/FfKXwn4/53d6cEbZ1pgG+vBFxdmxP1rUhE5VpLCLzY0VupdIkpYJIcnqwGTt94CKiqCv3F0NJu+ExER0YImsmdF4ERk1PrdXBAvlSoVKtoH0dw3rByr7a9FeVf5xBdatQp46ikgMnJ6byyyTa5cmYFPQIsRgya0LDS/9TwGL5xUxi6PD1WdQ0qmnajhHbIiGZd3FksBk0hjJB4selCuA01ERES0VMXEwPT400j80/8H1gT1AlI0Fy9KDkdhUjjMYxkNYl/e+uPVOLe/Ar892oizTf3S7r7xrE4rfnf+d6jpYy3o6XJ73Xi5/GXpAt9PlJkQAROrw6303BMBE1FK1s+it+DO/Dvxhxv+ECvjV6pN4MUJ7rvvAhcvBn/j8HDgmWfkn0REREQLmGgKL8qPjs+O3p6nNor//EoXPF6fMv645mOpT9wEubnSpiFs2XLtPm7inOqNN4B29vFbjrgiTEve8JnjaHr/BWUsdkaKMhPusT+m4SYdwopTUb6rGD6tRopci916FoNlHmdNRERENHfyynYi68/+DnXbizE6tvYuFuGjzAaUpkYiLz4MRp0G2tFRPHC5EZk17Th2uQO//Koe+ys60TtW7lRw+9x46eJLuNLDnXnTDZiMDzKJQNSbZ1vQZ5d78ImMnwfWpkqLA36iKeq/2/TvsCF1g1S2QuF0Au+9B5w7F/yNzWY5YCIyTYiIiIgWAVFCX2TY+olzVLEJ2t8D7mRDf0AWtCh7KsqfTiBKku7bB3z/+0BqavA3dbuBF18EBgdn8JPQYhAyOqHI2+I2ODiIyMhIWK1WREREzPd0aJ6Ntrbi/I//PQaGepTHWgccStqeaCCVWZaKy3evg08nX3DeXXA31qWoTaaIiIiIlotB5yDOvfcL+N5/b8Ix3+go+u0uqUG8bcQDjyYE1bERuJQQhYbYcOwrTUH+WH1pQWTsiqbkpYmlc/wpFgdxES+CS/UDaq8Yp8eLt862osM6Io1FoOSRdemINKsludIj0vH0qqdh0BoCgyXHjwNHjwIOR/A3FqW4vvUtIClpFj4VERER0expGGjAc+eeU8aiT/GLx5uk81StJgRPbspAjMWoHBel958ofQI6jbr5JIAoaXrqFPDxx/L9qSQnA88+yx5wyyhuwKAJLV1DQ2j67/8FdY3nAmpDl7dZlbJcOdmxqH1kC5xjf1DXJK3BPSvuUcsbEBERES1D/fvfR+ubz6FnWN14Mp5oTN456ES3zSldpA7rtTiXHAPL3lKsyFCzF0IQIp1brUleM4ezXzxN38WF//gMExEw6RyUAyahei0eXp+OGIt6cZ4UloRvr/42TDp5VyVGRuRgybFj1w6WCEajHDBJSZmFT0VEREQ0+96+8jbOdahrfV/X9OBkQ590PypUL50/jc/QLYorwsPFDwcvwX/yJPDBB8HfuLAQeOSRa5f1oiURN+D/yrT0iIhIZSWG/u0naGi6oDzs9Y2iukvtY5IYY0HLXWuVgElKeAruLLiTARMiIiJa9qJvuQslD/5I2lAier1dzWzQITvOgrWZUYgPM8Ls9mJrUzeyf3cYPSfVzIlRjOKdynfwZcOXUrPz5c7mtOF8x3k8f+75awZMHlibFhAwiTfH4+myp9WAiehZ8s//DBw8OL2AidgZKZqfMmBCREREi9je3L0w683KeGN2jHLONOBwS+dU4/vuVfRU4P2q96Vy/VPasAHYvDn4G4um8J9+Kq870pLHTBNaWrq6pJQ6X20NzrSfwZBrSDlU32NXLkQtBi00j25Cd36yNDZqjfjh+h8iOpR1nYmIiIgk4jLho48wevw4BkYG0GprnTTzRFxONPQOK+dZgqYkDd47VyubU4SY0BjcmnMrCuMKl9UmlS57lxQoEX1LOu2dE45LPUzOtKDL5lQCJg+uS0NcWOD/755d/SzCjeHy/y6HDwOffz79Sej1csAkM3NmPhQRERHRPBKZJiLjxM824sarJ5thc8qbdFIiQ3H/2lSpLL/f1vSt0rnolOehojzXyy8DVVXB33zPHuAmtSk9LR4sz8WgyfIzPAx88YVch9DnQ21fLZoHm5XDoiFUZYdNuq8JAcJuKUbXTYXKcVFve1XSqnmZOhEREdGCJS4V3nlHaSw+4hlBu60d7UPtAY01xSVFU98w2sd6cQhJ8WFw7VmJ9sI0jIoTsDGZkZnYl7dPyvJdyobdw/i09lMpYCIybibjcMkBk+4hp5LB8+DaVMSOC5iITJ9n1zyLKFOU/L/HJ5/I5bimS6cDnngCyMn55h+KiIiIaAEQ556i1Gl1X7XyWJ/dhddONcMxlmWSFWvB3atSpF4nfiJosi1j29QvLPrE/frXQOfEjS4B7roLWL9+Bj4JzSUGTRg0WV4qK+WLeRE4EQESRz/Od55XDru9PlxoGYDbK/9TNxWnwvrwJuXivTi+GA+tfGhZ7XgkIiIimjZxuSCCJgcOSD3jBN+oTyov1WRtGve0UbQOONDSr5aKig83IrIgETV7SjESNlZWakxpQil2ZO5AvCUeS02fow8vXHgBvY7eSY+Lhu/NfcM4VtcnNTD1B0weWhdYkksESkQPEylg4vXK57wX1PKz15SaCtx9N5u+ExER0ZIjNvOIpvAdQx3KY12DI3j9TAtcHrmpe0FiOG4rSYJmbM1P9Nt7quwp5MbkTv3CVivwi18o572TEq/30ENAcfEMfiKabQyaMGiyPIgLR3HxfuSI8pDT48SptlNw+9zSWPzzruy0YWBYHiM+HO7v74LXqJeGEcYI/Gj9jxCqD52fz0BERES0WIiSBXV18qJ9RQXgdqNxoBH1A2oPE6F9wIHGPnkziyB298XFh8N65yoMZCUEPFdcuBYnFEvBkwRL4LHFqtnajJfKX5IyTfx8o6PoGnSisdeOxt5htA+OBNTVtogMk6sCJtGmaHxr9bfkgInbDbz22rXLRYwPluzcCeTlyRf1REREREuQKMv/67O/ljas+LX0D0t9TURvY6E0NRK7CxOUzdKiH8oP1v0AkaaJffsUra3Ab34DeIL05NNq5Wze3CABGFpQGDRh0GTpE1Hf118HmtUSXOKfsqhpaHValcc6rCNo6LVL93ujLDA9exO00RblIv2ZVc8gOzp7Hj4AERER0SImSheIZpgnT6Ll8jGpX8d4Ypef6HMiggV+Rp0GI5vz4Ny9EtBpJ7xkUVyRFDxJDpd7zi0k4jxT7GYUgRCHxyH9FGNtiFa64BYBDnEBLhqNvlnxZkDTe1Em9r3zbVLJiMlEmPRSze1oc2DARGSYRIoklOpq4MwZoK3t2hNNS5ODJeLincESIiIiWgZE771fnfkVbC65LL9Q3zOE9863K+eiO1ckYHV6lHI8LSJN6hen1Uw8Jw2obPPKK/LGoakYDMC3viVvWKEFj0ETBk2WNnHh+OabgEMt/SDU9dcFlIgYcnpwqc0Ku06Lw5mJyLytBFkJ4QENoPbm7p3TqRMREREtKeJS4uxZtL7xPKpb1fKogiiLIHb6+Ruc+w0lRqL/nnWIS4uatDzqmqQ1uC3vNhh1al+PubrgFuUdrCNWaROOGPvv2132KfuS+Bm1RqnPy/jntQ048O75NqnZ+3giQJIZa5Zu6dFm6PxNSkdHkWkNwSP61bA0tgFdXdObvEYjl+FavZrBEiIiIlp2uu3dUsaJ2Nzid6V9EB9fkkt3aTQheHhdGpIj1Uozm1I34fb824O/8PnzwFtvBX9OaCjwzDNA8sLb+EOBGDRh0GRpEv9UP/8cOHx4wqHe4V5c7LqojD1eH863DeJIQhS+zkhAYU6cFFX2SwpLwh+s/QPoNLo5mz4RERHRkmW3o+nVX6D+y3cmBBfsTo/UJN7qcKsBFa0Gg8lRiCpOhSk/Efa4iIBm8fHmeDxa8ijizHFB31b0VrnUdUnKNhb9Q7w+r7RjUJzjiSwQ8VOv1Uuvkx2VjbyYvIBgjMPtwIXOCzjTfgad9ms0/LxOVZ02fHKpQykNIUpvrUmPRkasGZGhcqnY8SI7BrDqfAe2abOvL2AkGr0//DCwYsVMTp+IiIhoUWkdbMXz55+XNrH4Harqxpmmful+uFGHJzZlItSgZpeIHsclCSXBX/jYMeDjj4M/x2QCnnwSSE//hp+CZhODJgyaLD0iFe6996SdjFcTpRFEHxN/GQTxT/qAV4MXE2LQZzYiKdKEh9elS/W0BXHxLGoXLsWmo0RERETzqfX8V6h+/n8BfWpdaf/52YDDjabeYTiuyrqwGHVIigtDSE48OvOS0J0VL2VLiMyN+wrvQ1F80aTvVd9fj09qPwlo/nktIpCSFZUlBU/E713qvhRQSmsmiM96urEfX9X0KI9lxJhxZ1kyjJOUJQsddCDnVC3yOt1SfxeDVi3TdU3iAv3xx4HMzJmaPhEREdGiJarQvHDhBXhH5fNNsXnljTMtUvavkBlrwX2rU5RsZ3He9f1137/mRh1pE/ehQ8GfI0p1ifOybLYBWKgYNGHQZOk1fBfluC5dmnR34dn2s0rdQp9Wg3czEvCcK0S62DbptXhiU4ZUK9rv7oK7sS5l3Zx+BCIiIqLlwjMyjOrf/h/0HD2gXLD6iUuPXrsLrf2OCcGTMKMO+Ylh8MRFoH5tNnrSY6Xzue0Z27E7ezc0IRolw/jT2k9R2Vs5a59BlBazjbgxOOKRfopsGTFfh9uHEZf46ZVKbum1GilrRNxi9Bqk+Lzo6RzEue5hWE16jOh1WJkcgT1FicoGnrH/R8A47EJBVS9W1g8hMTRO6mMyWbmyKYWFAU89BSQlzcr/D4iIiIgWI5FB/G7luwHl+1883ohhl3zuuTknVrqNz3D+7trvwqQzTf2iYvn8gw+AU6eunQH8yCNAQcEMfBKaaQyaMGiydHg8wKuvAlVVkx6u7q1Gq61Vuj8cEYoDa3Px69p+6YJcuHd1KrLj5MbvQlliGe4vvP/6LkiJiIiI6PqMjsJx4giaXvoZ2geaJzk8KjVGbx1wKBewgk4TgvyEcESa9bDFhkvBk76UaBh1JoTqQ6Xsk57hngnBGKfHi9ouO1xenxSc0IaEyD81IVIDepF5LAIcUxkakXvh1XYPSWXEnJ6JDT/1Xi8sLg/CXR6EOd0Ic3kQ7XAixuFCjMOJCKdafswvISEc8RkxGAkzwWvQwWh3ItFjQhrCEauPlJrH39B5aXQ08PTTQEzM9f8uERER0RImzjPfqXxHKt/q19w3jDfPtkixD4QA969OlbJOxgdOHi99HDGhMcGr4Lz+OnD58rV7zT34IFBcPCOfh2YOgyYMmiwNLhfw0ktAff2UtQqr+6ql+53ZCTi3Lge/O9smRZCFDVkx2JYXF/AH8Hvrvnd9JQ+IiIiI6MZ1dsL2u1+hvvYU+hyBJbsEcSnSP+xGY689IFCRHmNGSqRJCijYYsJgiwvHcKRZuTktJqkHim90FJdaB3G0ricg+HI1ETzJijJhI0ax2jmCxAE7tE4PehxuNFtH0GV3wocQ+ESwZXQUWp8POt/o2M0Ho8cLo1edn4hz6DQaqeSDmMPVxPGcuDDEhwf2JkkOS0Z+bL6SNXPdjEZg3TrgppvkpqNERERENIHb68Yvz/wyoGfdifo+HKmVy6eGjlWmCR9XmUZsZnmk+BGplGvQzd0icHLlSvAJiJPBVauAjRuBlJQZ+EQ0Exg0YdBk8RsZAV54AWieuDNREBfdFzsvwqsNQc3GPLTkJ+Htc21Sk1EhNSoUD65Ng2asDIJeo5dqFLKPCREREdEcczql3nTWU1+j0do4afDE4/WhpnsIA8Nqtka02YDcBIsUnLjaaEgIuj0+VA6MoNc7KjWWH9FppduwXothgw4OnU66LzJCcvtsyBoYgmEs8GEx6OD2+qTMlKuJzBRxM+i0Yz/lm8hUEZkw4qc4xRQBHXEp5faOSpkuI6J0l9srva4Iloy/CBdEI/qMyIzrzyxJSADy8+WbaC6qndgXhYiIiIgCiZKuPz/9czi9TmksztvePd+G+h67NI61GHDP6lSpzKqf2NhyV8FdWJu8NngbgbffBi5enN5ExPmbCJ6sXMnzuHnGoAmDJoub1Qq8+KK0M3Eydpddqk/o0gIXbynFQFIUvqzsxtnmfum42aDDk5sypKaifg8UPSCV5iIiIiKieSAuOWpqgBMnYK28iKbuavQ6eq96yqhUrqulX27UKYj+dOKC1l9qS9w0ImBiG5EyVMaLsRikQIt4HREKkX6OAk63V3quCGZMxaDVSIEOcRPvOZNCEILCuEIkhiVO/5dEI9Ft24DVq4HIyBmdDxEREdFyUdFdgVcuvaKMxQaXF483YXDErWSc3L0qBSlRgRm8W9K24NbcW6fODhalut5/Hzhz5vr60e3YAWzYIGei0Jxj0IRBk8WrtVUuyTU0NOlhl9clBUyGNB5cuLUMg/ERuNAygM+vdEnHxUX0/WtSpZIOfuuS1+HuFXfP2UcgIiIioiDERWZ7O3oqTqP85Afw1NdC55LLqwr9wy7UdA1Jpa+mQ2yUyYw1I+KqzI7xxCWP3eVFv90lvb6/lFe0WY+EcBOizPpZ6Xmn0+hQHF+M6NDo6f2CmMPatcCuXfKFNRERERF9I5/UfIKjLUeVsTgXfOdcGwaGXdJYbMrZU5iIlSmB68hFcUV4aOVD0Gqm2FAjltQ/+QQ4duz6JlRUBNx/v7xJhpZf0OQf//Ef8cEHH+DcuXMwGAwYGBi45u98+9vfxvPPPx/w2L59+/Dxxx9P+30ZNFnEKiqAN98E3BObaApenxfnO8+jJ8SBC3tXYSgmTKp/Lcpy+f8Z31KUiJJUdTdeUlgS/mDtH0gXrERERES0sIjzuwNVn6Dp8zeRcaERhrFdf2IXYFWnLWifEpEdIjbKxIUZrjvgIbJOxG/ogjSH9xPN5406o9QXT5R81Wv10k9xfun2ueFwOzDiGYHDI/8UxLFESyIyozKn308vJ0dc/ACJ15GRQkRERETXPN98/vzzaLI2KY+Jc80PLrZLDeL91mVGS72RxYZsv9KEUtxfdP/UGSdiPfLgQeDQoeubVFIS8PjjzChejkGTv/mbv0FUVBRaWlrwq1/9atpBk87OTvzmN79RHjMajYiOnubOLAZNFifxT/DIEeCzz6Z8yrB7GDV9NWgPseP8vlVSA9DeISdeOdUM11jT0LUZ0dhRoPYsMelMUh+TmNCYOfkYRERERHRjyrvK8d7FN5B4qQEZ5c1S5om4TBFBExHgEFkn/pvHNyr1GIkLM0o7A8cTF7QiyOEb9Uk376hX+jldobpQJIcnI8IYIZ1LioDHtJu2azTwRUdJF7+aYQfQ3y/3c5mKyCQR1yviormkBMjOZqkGIiIiolkw5BrCc+eeQ8+w3AheEOeVX1Z1SxVs/HLiwnB7aZLUw258BRvR5yToJh2xrrl/v5xRPV3iXPCxx4C0tBv4RLRogyZ+zz33HP70T/902kET8by3RTOdG8SgySIj+pZ89dWUzZPEbr2GgQZ0DnViOMwoBUxGwkMx7PLg5ZPNGHS4lT9qd61KVqLB4uL2qbKnkBOdM6cfh4iIiIhuTJe9C6+UvwKrtRNpl5qRXN0O41jZhOlIsCRI534i2OEnLnUGnYNS/xTRDNTulht/jifOG+PN8VKwJNIYOfUFsckkX9yGh6s/xfVGbKx8i4qSAifj3hwYGZGDJ+JayOORf0fsKBS/p2MmNBEREdFcERuyX730qrTOON755gF8UdWtVLFJjzbj3tUpARnJosfJ3ty9wQMnHR3A118Dly5NP3gizgfvvRcoLb3BT0XLJmgiAiainJfILtm9ezf+4R/+AbHiImSaGDRZBET5LfEH5NQpoKVl0qc4PU40WhvRbmvHKEZhiwmTmr67zEZ4vD68caYV7Va5SWh8mBEPr0+Xdhz6iQjw+pT1c/aRiIiIiOibExtm3qt8D5e6L0lBB6PdCbN1OPA2OAyDwy0uZKTfEVkhudG5iDRdu7yBI8SL1ngjOpMjYE+ORVJ4CvKjcxGqNQJer3yBq9XKF7Djb3o9gxxERERES6BU14fVH+J0++mAx0X5f1Guy1/NJifOgjvLUgKymndm7ZRu12SzAadPy+ueU/RsnmD7dmD37sANODTjFm3Q5OWXX4bZbEZ2djZqa2vxn//zf0ZYWBiOHj0Krbh4mYTT6ZRu4z98eno6gyYLkfhDcfgwcP68vOtuCmIXYEVPBTw+uSFoT3osKnashFevlWoNfl3Tg47BEaXx52Mb0hE+rvGniP7uy9s3Bx+IiIiIiGaDqDld1VuFbns3nF6ntKFGBFT89/UaHZJN8dgYW4bC8GyEiOsBcX45PKze7Hb5pxATA+TlARkZclCEiIiIiJYlsRR+vPW41CBebNT2E5uz3zzTKpWGFQoSw3FbSVJAj5M92XuwLWPb9Mq3ig05ly8Dole3OC+9FnGe+uCD7HOyGIMmf/mXf4n/9t/+W9DnVFRUoLCw8IaCJlerq6tDbm4u9u/fjz179kz6nL/927/F3/3d3014nEGTBdjk/d13AYecHRKsLENFd4XyR6t5ZRrq1ueidXAER2t70dyvNmgSaXIPr0tDYoRagmFF7Ao8WvLo9GtPExERERERERER0bJS3VuN1y+/Lm3K8RObtd8+1yr1OxFKUiKxpyghoCxXbGisFDgpSyyDTjONTGSrFXjpJbl817WEhgL33QesWHGDn4rmJWjS3d2N3t7eoM/JycmRymvNRNBEiI+Pl0p0/eAHP5j0ODNNFjiXC/jkEzkt7RpE3xKRYSKMhoSgenM+ziXH4EhtLxp6AyOyMRYDdhcmIC3arDyWFJaE76z5jtSwk4iIiIiIiIiIiCjY5u3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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "For more information on these optimizations you can refer to the [distance profile notebook](distance_profiles.ipynb) for the theory, and to the [analysis of the speedups provided by similarity search module](code_speed.ipynb) for a comparison of their performance." + "from aeon.similarity_search.collection import RandomProjectionIndexANN\n", + "\n", + "X_fit = X[:-2]\n", + "# we use a single series for this example but it will be converted into a collection\n", + "# as this is a collection estimators.\n", + "X_predict = X[-1]\n", + "index = RandomProjectionIndexANN().fit(X_fit)\n", + "indexes, distances = index.predict(X_predict, k=3)\n", + "# as X_predict is converted to a collection, we select the first returns\n", + "# to obtain its results\n", + "indexes = indexes[0]\n", + "distances = distances[0]\n", + "\n", + "for i in range(len(indexes)):\n", + " print(f\"match {i} : {indexes[i]} with distance {distances[i]}\")\n", + " # A bit of hacking of the function defined for series estimator to show best mathces\n", + " plot_best_matches(X_fit[indexes[i]], X_predict, 0, [0], X_predict.shape[1])" ] }, { "cell_type": "markdown", - "id": "4149c40f", + "id": "c4c7a34a-3620-475c-96b8-a9bb605d09c3", "metadata": {}, "source": [ - "# Series search\n", - "For series search, we are not interest in exploring the relationship of the input dataset `X` (given in `fit`) and a single query, but to all queries of size `query_length` that exists in another time series `T`. For example, with using again our simple GunPoint dataset:" + "You can then play with the different parameter of the estimator to affect the speed vs accuracy of the index, for example increasing ```n_hash_funcs``` from the default 128 to 512, and considering larger vectors (``V`` of shape ``(n_channels, L)``) for the hash functions by tuning ```hash_func_coverage``` (a float between 0 and 1, with 0.25 as default) such as ```L = n_timepoints * hash_func_coverage```:" ] }, { "cell_type": "code", "execution_count": 11, - "id": "d510c4cc", + "id": "1b22b743-5710-4691-b740-8edaa3bbac2e", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "match 0 : 17 with distance 12.0\n" + ] + }, { "data": { - "image/png": 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", + "image/png": 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" + "
" ] }, "metadata": {}, @@ -470,45 +612,56 @@ "name": "stdout", "output_type": "stream", "text": [ - "Index of the 20-th query best matches : [[195 26]]\n" + "match 1 : 190 with distance 13.0\n" ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "from aeon.similarity_search import SeriesSearch\n", + "index = RandomProjectionIndexANN(n_hash_funcs=512, hash_func_coverage=0.75).fit(X_fit)\n", + "indexes, distances = index.predict(X_predict, k=2)\n", "\n", - "query_length = 35\n", - "estimator = SeriesSearch(distance=\"euclidean\").fit(X_train) # X_test is a 3D array\n", - "mp, ip = estimator.predict(X_test, query_length) # X_test is a 2D array\n", - "plot_matrix_profile(X_test, mp, 0)\n", - "print(f\"Index of the 20-th query best matches : {ip[20]}\")" + "indexes = indexes[0]\n", + "distances = distances[0]\n", + "\n", + "for i in range(len(indexes)):\n", + " print(f\"match {i} : {indexes[i]} with distance {distances[i]}\")\n", + " # A bit of hacking of the function defined for series estimator to show best mathces\n", + " plot_best_matches(X_fit[indexes[i]], X_predict, 0, [0], X_predict.shape[1])" ] }, { "cell_type": "markdown", - "id": "0dca5122", + "id": "7828c48c-abdb-4807-bc94-d9b8414b5282", "metadata": {}, "source": [ - "Notice that we find the same best match for the 20-ith query, which was the query that we used for `QuerySearch` !\n", - "\n", - "`SeriesSearch` returns two lists, `mp` and `ip`, which respectively contain the distances to the best matches of all queries of size `query_length` in `X_test` (the `i-th` query being `X_test[:, i : i + query_length]`) and the indexes of these best matches in `X_train` in the `(ix_case, ix_timepoint)` format, such as `X_train[ix_case, :, ix_timepoint : ix_timepoint + query_length]` will be the matching subsquence.\n", - "\n", - "Most of the options (`k`, `threshold`, `inverse_distance`, etc.) from `QuerySearch` are also available for `SeriesSearch`." + "This type of method is mostly interesting where speed of the search is paramount, or when the dataset size grows large (> 10k samples)." ] }, { - "cell_type": "code", - "execution_count": null, - "id": "ff23faf5-2941-441a-8c4c-0cf66eaca121", + "cell_type": "markdown", + "id": "1610adf3-5cb1-466e-9cad-fb248148fd5a", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "## References\n", + "[1] Patrick Schäfer and Ulf Leser. 2022. Motiflets: Simple and Accurate Detection\n", + " of Motifs in Time Series. Proc. VLDB Endow. 16, 4 (December 2022), 725–737." + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (Spyder)", - "language": "python3", + "display_name": "Python 3 (ipykernel)", + "language": "python", "name": "python3" }, "language_info": { @@ -521,7 +674,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.11.11" } }, "nbformat": 4, From cf0d47839d8acb2cdfe7ae43c458f65baf66000b Mon Sep 17 00:00:00 2001 From: Aadya Chinubhai <77720426+aadya940@users.noreply.github.com> Date: Sun, 11 May 2025 23:45:06 +0530 Subject: [PATCH 088/216] [ENH] Adapt the DCNN Networks to use Weight Norm Wrappers (#2628) * adapt the dcnn networks to use weight norm wrappers and remove l2 regularization * Automatic `pre-commit` fixes * add custom object * Automatic `pre-commit` fixes * fix trial --------- Co-authored-by: Matthew Middlehurst --- aeon/clustering/deep_learning/_ae_dcnn.py | 3 ++- aeon/networks/_ae_dcnn.py | 29 +++++++++++++--------- aeon/networks/_dcnn.py | 30 +++++++++++++---------- aeon/utils/networks/weight_norm.py | 1 + 4 files changed, 37 insertions(+), 26 deletions(-) diff --git a/aeon/clustering/deep_learning/_ae_dcnn.py b/aeon/clustering/deep_learning/_ae_dcnn.py index 75f8eacfbe..19ac76d081 100644 --- a/aeon/clustering/deep_learning/_ae_dcnn.py +++ b/aeon/clustering/deep_learning/_ae_dcnn.py @@ -296,7 +296,8 @@ def _fit(self, X): try: self.model_ = tf.keras.models.load_model( - self.file_path + self.file_name_ + ".keras", compile=False + self.file_path + self.file_name_ + ".keras", + compile=False, ) if not self.save_best_model: os.remove(self.file_path + self.file_name_ + ".keras") diff --git a/aeon/networks/_ae_dcnn.py b/aeon/networks/_ae_dcnn.py index da953ec717..ea475d0161 100644 --- a/aeon/networks/_ae_dcnn.py +++ b/aeon/networks/_ae_dcnn.py @@ -241,20 +241,25 @@ def _dcnn_layer( ): import tensorflow as tf + from aeon.utils.networks.weight_norm import _WeightNormalization + _add = tf.keras.layers.Conv1D(_num_filters, kernel_size=1)(_inputs) - x = tf.keras.layers.Conv1D( - _num_filters, - kernel_size=_kernel_size, - dilation_rate=_dilation_rate, - padding=_padding_encoder, - kernel_regularizer="l2", + x = _WeightNormalization( + tf.keras.layers.Conv1D( + _num_filters, + kernel_size=_kernel_size, + dilation_rate=_dilation_rate, + padding=_padding_encoder, + ) )(_inputs) - x = tf.keras.layers.Conv1D( - _num_filters, - kernel_size=_kernel_size, - dilation_rate=_dilation_rate, - padding=_padding_encoder, - kernel_regularizer="l2", + x = _WeightNormalization( + tf.keras.layers.Conv1D( + _num_filters, + kernel_size=_kernel_size, + dilation_rate=_dilation_rate, + padding=_padding_encoder, + activation=_activation, + ) )(x) output = tf.keras.layers.Add()([x, _add]) output = tf.keras.layers.Activation(_activation)(output) diff --git a/aeon/networks/_dcnn.py b/aeon/networks/_dcnn.py index 243340c30e..051ce7d07e 100644 --- a/aeon/networks/_dcnn.py +++ b/aeon/networks/_dcnn.py @@ -146,21 +146,25 @@ def _dcnn_layer( ): import tensorflow as tf + from aeon.utils.networks.weight_norm import _WeightNormalization + _add = tf.keras.layers.Conv1D(_n_filters, kernel_size=1)(_inputs) - x = tf.keras.layers.Conv1D( - _n_filters, - kernel_size=_kernel_size, - dilation_rate=_dilation_rate, - padding=_padding, - kernel_regularizer="l2", + x = _WeightNormalization( + tf.keras.layers.Conv1D( + _n_filters, + kernel_size=_kernel_size, + dilation_rate=_dilation_rate, + padding=_padding, + ) )(_inputs) - x = tf.keras.layers.Conv1D( - _n_filters, - kernel_size=_kernel_size, - dilation_rate=_dilation_rate, - padding="causal", - kernel_regularizer="l2", - activation=_activation, + x = _WeightNormalization( + tf.keras.layers.Conv1D( + _n_filters, + kernel_size=_kernel_size, + dilation_rate=_dilation_rate, + padding=_padding, + activation=_activation, + ) )(x) output = tf.keras.layers.Add()([x, _add]) output = tf.keras.layers.Activation(_activation)(output) diff --git a/aeon/utils/networks/weight_norm.py b/aeon/utils/networks/weight_norm.py index 459cfd7104..c4825c1c91 100644 --- a/aeon/utils/networks/weight_norm.py +++ b/aeon/utils/networks/weight_norm.py @@ -5,6 +5,7 @@ if _check_soft_dependencies(["tensorflow"], severity="none"): import tensorflow as tf + @tf.keras.utils.register_keras_serializable(package="aeon") class _WeightNormalization(tf.keras.layers.Wrapper): """Apply weight normalization to a Keras layer.""" From 17d508180116132973552d57e56efe0907678054 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Mon, 12 May 2025 12:14:04 +0100 Subject: [PATCH 089/216] [GOV] Remove inactive developers (#2776) * inactive devs * logo fix --- .../utilities/generate_developer_tables.py | 10 ---------- docs/about.md | 20 ++++++++++++++++--- docs/about/code_of_conduct_workgroup.md | 8 -------- docs/about/core_developers.md | 12 ----------- docs/about/infrastructure_workgroup.md | 4 ---- 5 files changed, 17 insertions(+), 37 deletions(-) diff --git a/.github/utilities/generate_developer_tables.py b/.github/utilities/generate_developer_tables.py index afe39cf0d3..f03aa84831 100755 --- a/.github/utilities/generate_developer_tables.py +++ b/.github/utilities/generate_developer_tables.py @@ -72,9 +72,6 @@ def get_contributors(auth): iw = {c["login"] for c in iw} rmw = {c["login"] for c in rmw} - # add missing contributors with GitHub accounts - cocw |= {"KatieBuc"} - # get profiles from GitHub cocw = [get_profile(login, auth) for login in cocw] cw = [get_profile(login, auth) for login in cw] @@ -112,13 +109,6 @@ def get_profile(login, auth): if profile["name"] is None: profile["name"] = profile["login"] - # fix missing names - missing_names = { - "KatieBuc": "Katie Buchhorn", - } - if profile["name"] in missing_names: - profile["name"] = missing_names[profile["name"]] - return profile diff --git a/docs/about.md b/docs/about.md index b0e70db9cb..ca89665849 100644 --- a/docs/about.md +++ b/docs/about.md @@ -49,11 +49,26 @@ The core developers push forward `aeon`'s development and maintain the package. ```{include} about/core_developers.md ``` +#### Former Core Developers + +The following developers were part of the `aeon` core developer team at some +point. + +
Previous aeon core developers +

+ +- {user}`GuzalBulatova` 2025 +- {user}`lmmentel` 2025 +- {user}`aiwalter` 2025 + +

+
+ ## Affiliation `aeon` is an affiliated project of [NumFOCUS](https://numfocus.org/). -![https://numfocus.org/](images/other_logos/numfocus-logo.png){w=400px} +[![NumFOCUS logo](images/other_logos/numfocus-logo.png){w=400px}](https://numfocus.org/) ## History @@ -131,14 +146,13 @@ We would also like to thank [GitHub Actions](https://github.com/features/actions and [ReadtheDocs](https://readthedocs.org) for the free compute time on their servers and documentation hosting. - ## Pre-fork Acknowledgements
sktime v0.16.0 core developers

The following listed contributors were part of the `sktime` core developer team at some -point prior to the split of the project. +point prior to the 2023 split of the project. - {user}`abostrom` - {user}`ayushmaanseth` diff --git a/docs/about/code_of_conduct_workgroup.md b/docs/about/code_of_conduct_workgroup.md index b7f46f9af9..34b913e17e 100644 --- a/docs/about/code_of_conduct_workgroup.md +++ b/docs/about/code_of_conduct_workgroup.md @@ -1,18 +1,10 @@

-
-
-

Guzal

-
- diff --git a/docs/about/core_developers.md b/docs/about/core_developers.md index 46c109b1ef..285b55a38e 100644 --- a/docs/about/core_developers.md +++ b/docs/about/core_developers.md @@ -25,14 +25,6 @@

Antoine Guillaume

-
-

Guzal

-
- - @@ -52,8 +44,4 @@

Leonidas Tsaprounis

- diff --git a/docs/about/infrastructure_workgroup.md b/docs/about/infrastructure_workgroup.md index 5be9f21d41..4feb661016 100644 --- a/docs/about/infrastructure_workgroup.md +++ b/docs/about/infrastructure_workgroup.md @@ -12,8 +12,4 @@

Leonidas Tsaprounis

- From 02b6c52079828b8d546d34ef2a4175f75b0c3329 Mon Sep 17 00:00:00 2001 From: "aeon-actions-bot[bot]" <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Date: Mon, 12 May 2025 12:38:11 +0100 Subject: [PATCH 090/216] Automated `pre-commit` hook update (#2792) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 8ebd40a9b0..62dab7f167 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -29,7 +29,7 @@ repos: args: [ "--create", "--python-folders", "aeon" ] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.11.8 + rev: v0.11.9 hooks: - id: ruff args: [ "--fix"] From 13726403f15a22a82cb64951ea1b272ad6a95a84 Mon Sep 17 00:00:00 2001 From: Somto Onyekwelu <117727947+SomtoOnyekwelu@users.noreply.github.com> Date: Tue, 13 May 2025 11:18:39 +0100 Subject: [PATCH 091/216] Add SomtoOnyekwelu to .all-contributorsrc (#2788) --- .all-contributorsrc | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/.all-contributorsrc b/.all-contributorsrc index 95453ca9e6..c709456395 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -2701,6 +2701,15 @@ "contributions": [ "code" ] + }, + { + "login": "SomtoOnyekwelu", + "name": "Somtochukwu Benedict Onyekwelu", + "avatar_url": "https://avatars.githubusercontent.com/u/117727947?v=4", + "profile": "https://github.com/SomtoOnyekwelu", + "contributions": [ + "doc" + ] } ], "commitType": "docs" From 01850a7b5b9774b67acc6e4448b96edd09d8174a Mon Sep 17 00:00:00 2001 From: "aeon-actions-bot[bot]" <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Date: Tue, 13 May 2025 11:21:16 +0100 Subject: [PATCH 092/216] Automated `CONTRIBUTORS.md` update (#2794) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> --- CONTRIBUTORS.md | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md index 18945d9902..2103194799 100644 --- a/CONTRIBUTORS.md +++ b/CONTRIBUTORS.md @@ -1,7 +1,7 @@ # Contributors -[![All Contributors](https://img.shields.io/badge/all_contributors-264-orange.svg)](#contributors) +[![All Contributors](https://img.shields.io/badge/all_contributors-265-orange.svg)](#contributors) This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! @@ -264,6 +264,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Solomon Botchway
Solomon Botchway
🚧 + Somtochukwu Benedict Onyekwelu
Somtochukwu Benedict Onyekwelu
📖 Stanislav Khrapov
Stanislav Khrapov
💻 Stijn Rotman
Stijn Rotman
💻 Svea Marie Meyer
Svea Marie Meyer
📖 💻 @@ -271,9 +272,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d TNTran92
TNTran92
💻 Taiwo Owoseni
Taiwo Owoseni
💻 Tanish Yelgoe
Tanish Yelgoe
💻 - Thach Le Nguyen
Thach Le Nguyen
💻 ⚠️ + Thach Le Nguyen
Thach Le Nguyen
💻 ⚠️ TheMathcompay Widget Factory Team
TheMathcompay Widget Factory Team
📖 Thomas Buckley-Houston
Thomas Buckley-Houston
🐛 Tom Xu
Tom Xu
💻 📖 @@ -281,9 +282,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Tony Bagnall
Tony Bagnall
💻 💼 📖 🎨 📋 🔍 🤔 📆 💬 👀 📢 🔣 Tvisha Vedant
Tvisha Vedant
💻 Utkarsh Kumar
Utkarsh Kumar
💻 📖 - Utsav Kumar Tiwari
Utsav Kumar Tiwari
💻 📖 + Utsav Kumar Tiwari
Utsav Kumar Tiwari
💻 📖 Vedant
Vedant
📖 Viktor Dremov
Viktor Dremov
💻 ViktorKaz
ViktorKaz
💻 📖 🎨 @@ -291,9 +292,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Wayne Adams
Wayne Adams
📖 William Templier
William Templier
📖 William Zeng
William Zeng
🐛 - William Zheng
William Zheng
💻 ⚠️ + William Zheng
William Zheng
💻 ⚠️ Yair Beer
Yair Beer
💻 Yash Lamba
Yash Lamba
💻 Yi-Xuan Xu
Yi-Xuan Xu
💻 ⚠️ 🚧 📖 @@ -301,9 +302,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d aa25desh
aa25desh
💻 🐛 abandus
abandus
🤔 💻 adoherty21
adoherty21
🐛 - alexbanwell1
alexbanwell1
💻 🎨 📖 + alexbanwell1
alexbanwell1
💻 🎨 📖 bethrice44
bethrice44
🐛 💻 👀 ⚠️ big-o
big-o
💻 ⚠️ 🎨 🤔 👀 ✅ 🧑‍🏫 bobbys
bobbys
💻 @@ -311,9 +312,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d btrtts
btrtts
📖 chizzi25
chizzi25
📝 chrisholder
chrisholder
💻 ⚠️ 📖 🎨 💡 🐛 - danbartl
danbartl
🐛 💻 👀 📢 ⚠️ ✅ 📹 + danbartl
danbartl
🐛 💻 👀 📢 ⚠️ ✅ 📹 hamzahiqb
hamzahiqb
🚇 hiqbal2
hiqbal2
📖 jesellier
jesellier
💻 @@ -321,9 +322,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d julu98
julu98
🐛 kkoziara
kkoziara
💻 🐛 matteogales
matteogales
💻 🎨 🤔 - neuron283
neuron283
💻 + neuron283
neuron283
💻 nileenagp
nileenagp
💻 oleskiewicz
oleskiewicz
💻 📖 ⚠️ pabworks
pabworks
💻 ⚠️ @@ -331,9 +332,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d raishubham1
raishubham1
📖 simone-pignotti
simone-pignotti
💻 🐛 sophijka
sophijka
📖 🚧 - sri1419
sri1419
💻 + sri1419
sri1419
💻 tensorflow-as-tf
tensorflow-as-tf
💻 vNtzYy
vNtzYy
🐛 ved pawar
ved pawar
📖 @@ -341,6 +342,8 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d vincent-nich12
vincent-nich12
💻 vollmersj
vollmersj
📖 xiaobenbenecho
xiaobenbenecho
💻 + + xiaopu222
xiaopu222
📖 From 5c9281163444bfff513bd7ce62e6c407407dfb69 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Patrick=20Sch=C3=A4fer?= Date: Thu, 15 May 2025 09:47:17 +0200 Subject: [PATCH 093/216] [BUG] Fix a bug in ClaSP Series Transformer when using floatXX as Input (where XX != 64) (#2774) * Automatic conversion to float64. * remove print --- aeon/transformations/series/_clasp.py | 11 ++++++++++- aeon/transformations/series/tests/test_clasp.py | 17 +++++++++++++++++ 2 files changed, 27 insertions(+), 1 deletion(-) create mode 100644 aeon/transformations/series/tests/test_clasp.py diff --git a/aeon/transformations/series/_clasp.py b/aeon/transformations/series/_clasp.py index dfdfd18d57..b826dcaa81 100644 --- a/aeon/transformations/series/_clasp.py +++ b/aeon/transformations/series/_clasp.py @@ -479,9 +479,18 @@ def _transform(self, X, y=None): "Period-Length is larger than size of the time series", stacklevel=1 ) + if X.dtype != np.float64: + warnings.warn( + f"dtype is {X.dtype} but should be {np.float64}. " + f"Will apply conversion to float64 now", + stacklevel=1, + ) + scoring_metric_call = self._check_scoring_metric(self.scoring_metric) - X = X.flatten() + # The input has to be of type float64 + X = X.flatten().astype(np.float64) + Xt, _ = clasp( X, self.window_length, diff --git a/aeon/transformations/series/tests/test_clasp.py b/aeon/transformations/series/tests/test_clasp.py new file mode 100644 index 0000000000..7d6741f31b --- /dev/null +++ b/aeon/transformations/series/tests/test_clasp.py @@ -0,0 +1,17 @@ +"""Test ClaSP series transformer.""" + +import numpy as np + +from aeon.transformations.series import ClaSPTransformer + + +def test_clasp(): + """Test ClaSP series transformer returned size.""" + for dtype in [np.float64, np.float32, np.float16]: + series = np.arange(100, dtype=dtype) + clasp = ClaSPTransformer() + profile = clasp.fit_transform(series) + + m = len(series) - clasp.window_length + 1 + assert np.float64 == profile.dtype + assert m == len(profile) From 4ea649c662a953b9a1277681d968adf450f59431 Mon Sep 17 00:00:00 2001 From: Ramana Raja <83065061+Ramana-Raja@users.noreply.github.com> Date: Thu, 15 May 2025 13:17:49 +0530 Subject: [PATCH 094/216] [DOC] Changed SummaryClusterer's summary_stat from "percentiles" to "quantiles" (#2778) * changed summary_stats * changed summary_stats * changed summary_stats * changed summary_stats for regression and classification --- aeon/classification/feature_based/_summary.py | 4 ++-- aeon/clustering/feature_based/_summary.py | 4 ++-- aeon/regression/feature_based/_summary.py | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/aeon/classification/feature_based/_summary.py b/aeon/classification/feature_based/_summary.py index b6e0056392..9965d9308b 100644 --- a/aeon/classification/feature_based/_summary.py +++ b/aeon/classification/feature_based/_summary.py @@ -24,12 +24,12 @@ class SummaryClassifier(BaseClassifier): Parameters ---------- - summary_stats : ["default", "percentiles", "bowley", "tukey"], default="default" + summary_stats : ["default", "quantiles", "bowley", "tukey"], default="default" The summary statistics to compute. The options are as follows, with float denoting the percentile value extracted from the series: - "default": mean, std, min, max, 0.25, 0.5, 0.75 - - "percentiles": 0.215, 0.887, 0.25, 0.5, 0.75, 0.9113, 0.9785 + - "quantiles": 0.0215, 0.0887, 0.25, 0.5, 0.75, 0.9113, 0.9785 - "bowley": min, max, 0.1, 0.25, 0.5, 0.75, 0.9 - "tukey": min, max, 0.125, 0.25, 0.5, 0.75, 0.875 estimator : sklearn classifier, default=None diff --git a/aeon/clustering/feature_based/_summary.py b/aeon/clustering/feature_based/_summary.py index 309d3ac92f..682852103d 100644 --- a/aeon/clustering/feature_based/_summary.py +++ b/aeon/clustering/feature_based/_summary.py @@ -24,12 +24,12 @@ class SummaryClusterer(BaseClusterer): Parameters ---------- - summary_stats : ["default", "percentiles", "bowley", "tukey"], default="default" + summary_stats : ["default", "quantiles", "bowley", "tukey"], default="default" The summary statistics to compute. The options are as follows, with float denoting the percentile value extracted from the series: - "default": mean, std, min, max, 0.25, 0.5, 0.75 - - "percentiles": 0.215, 0.887, 0.25, 0.5, 0.75, 0.9113, 0.9785 + - "quantiles": 0.0215, 0.0887, 0.25, 0.5, 0.75, 0.9113, 0.9785 - "bowley": min, max, 0.1, 0.25, 0.5, 0.75, 0.9 - "tukey": min, max, 0.125, 0.25, 0.5, 0.75, 0.875 estimator : sklearn clusterer, default=None diff --git a/aeon/regression/feature_based/_summary.py b/aeon/regression/feature_based/_summary.py index 52f06ee8e2..4c5750562b 100644 --- a/aeon/regression/feature_based/_summary.py +++ b/aeon/regression/feature_based/_summary.py @@ -24,12 +24,12 @@ class SummaryRegressor(BaseRegressor): Parameters ---------- - summary_stats : ["default", "percentiles", "bowley", "tukey"], default="default" + summary_stats : ["default", "quantiles", "bowley", "tukey"], default="default" The summary statistics to compute. The options are as follows, with float denoting the percentile value extracted from the series: - "default": mean, std, min, max, 0.25, 0.5, 0.75 - - "percentiles": 0.215, 0.887, 0.25, 0.5, 0.75, 0.9113, 0.9785 + - "quantiles": 0.0215, 0.0887, 0.25, 0.5, 0.75, 0.9113, 0.9785 - "bowley": min, max, 0.1, 0.25, 0.5, 0.75, 0.9 - "tukey": min, max, 0.125, 0.25, 0.5, 0.75, 0.875 estimator : sklearn regressor, default=None From a668f6dd6fddaa6195ccca1ffbe0df862d726627 Mon Sep 17 00:00:00 2001 From: Balgopal Moharana <99070111+lucifer4073@users.noreply.github.com> Date: Thu, 15 May 2025 20:04:52 +0530 Subject: [PATCH 095/216] [ENH] Test coverage for AEAttentionBiGRUNetwork Improved (#2540) * test file for ae_abgru added * soft depedency of tf resolved * aeabgru test file changed, bug resolved * aeabgru file reverted to original state --- aeon/networks/tests/test_ae_abgru.py | 281 +++++++++++++++++++++++++++ 1 file changed, 281 insertions(+) create mode 100644 aeon/networks/tests/test_ae_abgru.py diff --git a/aeon/networks/tests/test_ae_abgru.py b/aeon/networks/tests/test_ae_abgru.py new file mode 100644 index 0000000000..e894095b5a --- /dev/null +++ b/aeon/networks/tests/test_ae_abgru.py @@ -0,0 +1,281 @@ +"""Tests for the Attention Bidirectional Network.""" + +import pytest + +from aeon.networks import AEAttentionBiGRUNetwork +from aeon.utils.validation._dependencies import _check_soft_dependencies + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("latent_space_dim", [64, 128, 256]) +def test_aeattentionbigrunetwork_latent_space(latent_space_dim): + """Test AEAttentionBiGRUNetwork with different latent space dimensions.""" + import tensorflow as tf + + input_shape = (1000, 5) + aeattentionbigru = AEAttentionBiGRUNetwork(latent_space_dim=latent_space_dim) + encoder, decoder = aeattentionbigru.build_network(input_shape) + + # Check instance types + assert isinstance(encoder, tf.keras.models.Model) + assert isinstance(decoder, tf.keras.models.Model) + + # Check encoder output shape matches the specified latent_space_dim + dummy_input = tf.keras.layers.Input(shape=input_shape) + encoder_output = encoder(dummy_input) + assert encoder_output.shape[-1] == latent_space_dim + + # Check decoder input shape matches the specified latent_space_dim + decoder_input_shape = decoder.input_shape + assert decoder_input_shape[-1] == latent_space_dim + + # Test the full autoencoder pipeline + autoencoder = tf.keras.models.Model( + inputs=encoder.inputs, outputs=decoder(encoder.outputs[0]) + ) + assert isinstance(autoencoder, tf.keras.models.Model) + + # Check output shape matches input shape + output = autoencoder(dummy_input) + assert output.shape[1:] == input_shape + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("n_layers_decoder", [1, 2, 3, 2.5, "1"]) +def test_aeattentionbigrunetwork_n_layers_decoder(n_layers_decoder): + """Test AEAttentionBiGRUNetwork with different number of decoder layers.""" + import tensorflow as tf + + if not isinstance(n_layers_decoder, int): + with pytest.raises(TypeError): + aeattentionbigru = AEAttentionBiGRUNetwork( + n_layers_decoder=n_layers_decoder + ) + encoder, decoder = aeattentionbigru.build_network((1000, 5)) + else: + aeattentionbigru = AEAttentionBiGRUNetwork(n_layers_decoder=n_layers_decoder) + encoder, decoder = aeattentionbigru.build_network((1000, 5)) + assert isinstance(encoder, tf.keras.models.Model) + assert isinstance(decoder, tf.keras.models.Model) + + # Count the number of Bidirectional layers in the decoder + bidirectional_layers = [ + layer + for layer in decoder.layers + if isinstance(layer, tf.keras.layers.Bidirectional) + ] + assert len(bidirectional_layers) == n_layers_decoder, ( + f"Expected {n_layers_decoder} Bidirectional layers in decoder, " + f"but found {len(bidirectional_layers)}" + ) + + # Check if all Bidirectional layers contain GRU as expected + for i, layer in enumerate(bidirectional_layers): + assert isinstance( + layer.forward_layer, tf.keras.layers.GRU + ), f"Layer {i} is not using GRU as expected" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("n_layers_encoder", [1, 2, 3, 3.5, "2"]) +def test_aeattentionbigrunetwork_n_layers_encoder(n_layers_encoder): + """Test AEAttentionBiGRUNetwork with different number of encoder layers.""" + import tensorflow as tf + + if not isinstance(n_layers_encoder, int): + with pytest.raises(TypeError): + aeattentionbigru = AEAttentionBiGRUNetwork( + n_layers_encoder=n_layers_encoder + ) + encoder, decoder = aeattentionbigru.build_network((1000, 5)) + else: + aeattentionbigru = AEAttentionBiGRUNetwork(n_layers_encoder=n_layers_encoder) + encoder, decoder = aeattentionbigru.build_network((1000, 5)) + assert isinstance(encoder, tf.keras.models.Model) + assert isinstance(decoder, tf.keras.models.Model) + + # Count the number of GRU layers in the encoder + gru_layers = [ + layer for layer in encoder.layers if isinstance(layer, tf.keras.layers.GRU) + ] + + # Each encoder layer has 2 GRUs (forward and backward) + assert len(gru_layers) == 2 * n_layers_encoder, ( + f"Expected {2 * n_layers_encoder} GRU layers in encoder, " + f"but found {len(gru_layers)}" + ) + + # Count attention layers + attention_layers = [ + layer + for layer in encoder.layers + if isinstance(layer, tf.keras.layers.Attention) + ] + assert len(attention_layers) == n_layers_encoder, ( + f"Expected {n_layers_encoder} Attention layers, " + f"but found {len(attention_layers)}" + ) + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "activation_decoder", + ["relu", "tanh", "sigmoid", ["relu", "tanh"], ["sigmoid", "relu", "tanh"]], +) +def test_aeattentionbigrunetwork_activation_decoder(activation_decoder): + """Test AEAttentionBiGRUNetwork with different decoder activations.""" + import tensorflow as tf + + n_layers = 3 if isinstance(activation_decoder, list) else 1 + if isinstance(activation_decoder, list) and len(activation_decoder) != n_layers: + with pytest.raises(ValueError): + aeattentionbigru = AEAttentionBiGRUNetwork( + activation_encoder="relu", + activation_decoder=activation_decoder, + n_layers_decoder=n_layers, + ) + encoder, decoder = aeattentionbigru.build_network((1000, 5)) + else: + aeattentionbigru = AEAttentionBiGRUNetwork( + activation_encoder="relu", + activation_decoder=activation_decoder, + n_layers_decoder=n_layers, + ) + encoder, decoder = aeattentionbigru.build_network((1000, 5)) + assert isinstance(encoder, tf.keras.models.Model) + assert isinstance(decoder, tf.keras.models.Model) + + # Check that the activations are correctly set in the Bidirectional GRU layers + bidirectional_layers = [ + layer + for layer in decoder.layers + if isinstance(layer, tf.keras.layers.Bidirectional) + ] + + expected_activations = ( + activation_decoder + if isinstance(activation_decoder, list) + else [activation_decoder] * n_layers + ) + + for i, layer in enumerate(bidirectional_layers): + assert ( + layer.forward_layer.activation.__name__ + == expected_activations[n_layers - i - 1] + ), ( + f"Layer {i} has activation {layer.forward_layer.activation.__name__}, " + f"expected {expected_activations[n_layers-i-1]}" + ) + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "activation_encoder", + ["relu", "tanh", "sigmoid", ["relu", "tanh"], ["sigmoid", "relu", "tanh"]], +) +def test_aeattentionbigrunetwork_activation_encoder(activation_encoder): + """Test AEAttentionBiGRUNetwork with different encoder activations.""" + import tensorflow as tf + + n_layers = 3 if isinstance(activation_encoder, list) else 1 + if isinstance(activation_encoder, list) and len(activation_encoder) != n_layers: + with pytest.raises(ValueError): + aeattentionbigru = AEAttentionBiGRUNetwork( + activation_encoder=activation_encoder, + activation_decoder="relu", + n_layers_encoder=n_layers, + ) + encoder, decoder = aeattentionbigru.build_network((1000, 5)) + else: + aeattentionbigru = AEAttentionBiGRUNetwork( + activation_encoder=activation_encoder, + activation_decoder="relu", + n_layers_encoder=n_layers, + ) + encoder, decoder = aeattentionbigru.build_network((1000, 5)) + assert isinstance(encoder, tf.keras.models.Model) + assert isinstance(decoder, tf.keras.models.Model) + + # Extract GRU layers from the encoder + gru_layers = [ + layer for layer in encoder.layers if isinstance(layer, tf.keras.layers.GRU) + ] + + expected_activations = ( + activation_encoder + if isinstance(activation_encoder, list) + else [activation_encoder] * n_layers + ) + + # Check activations for forward GRU layers (first half of GRU layers) + for i in range(n_layers): + assert gru_layers[i * 2].activation.__name__ == expected_activations[i], ( + f"Forward GRU layer {i} has activation " + f"{gru_layers[i*2].activation.__name__}, expected " + f"{expected_activations[i]}" + ) + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("temporal_latent_space", [False, True]) +def test_aeattentionbigrunetwork_temporal_latent_space(temporal_latent_space): + """Test AEAttentionBiGRUNetwork with temporal latent space enabled/disabled.""" + import tensorflow as tf + + input_shape = (1000, 5) + latent_space_dim = 64 + aeattentionbigru = AEAttentionBiGRUNetwork( + temporal_latent_space=temporal_latent_space, latent_space_dim=latent_space_dim + ) + encoder, decoder = aeattentionbigru.build_network(input_shape) + assert isinstance(encoder, tf.keras.models.Model) + assert isinstance(decoder, tf.keras.models.Model) + + # Check the encoder output shape based on temporal_latent_space setting + dummy_input = tf.keras.layers.Input(shape=input_shape) + encoder_output = encoder(dummy_input) + + if temporal_latent_space: + assert len(encoder_output.shape) == 3 + assert encoder_output.shape[-1] == latent_space_dim + assert encoder_output.shape[1] == input_shape[0] # Time dimension preserved + else: + assert len(encoder_output.shape) == 2 + assert encoder_output.shape[-1] == latent_space_dim + + # Check decoder input shape + if temporal_latent_space: + assert len(decoder.input_shape) == 3 + assert decoder.input_shape[-1] == latent_space_dim + else: + assert len(decoder.input_shape) == 2 + assert decoder.input_shape[-1] == latent_space_dim + + # Check for specific layers based on temporal_latent_space setting + if not temporal_latent_space: + # Should have a RepeatVector layer in decoder + repeat_vector_layers = [ + layer + for layer in decoder.layers + if isinstance(layer, tf.keras.layers.RepeatVector) + ] + assert len(repeat_vector_layers) == 1 + assert repeat_vector_layers[0].n == input_shape[0] From bb36e9168a9392e8873f8c292a9ce3ab75e1ad2e Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Fri, 16 May 2025 15:02:12 +0100 Subject: [PATCH 096/216] [BUG] Better error message for Rotation Forest with no usable attributes (#2798) * better error message for rotf failure * Update _rotation_forest_classifier.py * Update _rotation_forest_regressor.py --- .../sklearn/_rotation_forest_classifier.py | 4 +++ .../tests/test_rotation_forest_classifier.py | 15 ++++++++--- .../sklearn/_rotation_forest_regressor.py | 4 +++ .../tests/test_rotation_forest_regressor.py | 25 +++++++++++++++++++ 4 files changed, 44 insertions(+), 4 deletions(-) diff --git a/aeon/classification/sklearn/_rotation_forest_classifier.py b/aeon/classification/sklearn/_rotation_forest_classifier.py index f9a77d0fc8..e89776d0ca 100644 --- a/aeon/classification/sklearn/_rotation_forest_classifier.py +++ b/aeon/classification/sklearn/_rotation_forest_classifier.py @@ -327,6 +327,10 @@ def _fit_rotf(self, X, y, save_transformed_data: bool = False): # remove useless attributes self._useful_atts = ~np.all(X[1:] == X[:-1], axis=0) X = X[:, self._useful_atts] + if sum(self._useful_atts) == 0: + raise ValueError( + "All attributes in X contain the same value.", + ) self._n_atts = X.shape[1] diff --git a/aeon/classification/sklearn/tests/test_rotation_forest_classifier.py b/aeon/classification/sklearn/tests/test_rotation_forest_classifier.py index bbaea82166..07549e2fb7 100644 --- a/aeon/classification/sklearn/tests/test_rotation_forest_classifier.py +++ b/aeon/classification/sklearn/tests/test_rotation_forest_classifier.py @@ -6,7 +6,6 @@ from aeon.classification.sklearn import RotationForestClassifier from aeon.datasets import load_unit_test -from aeon.testing.data_generation import make_example_3d_numpy def test_rotf_output(): @@ -88,8 +87,10 @@ def test_rotf_fit_predict(): def test_rotf_input(): """Test RotF with incorrect input.""" rotf = RotationForestClassifier() - X2 = rotf._check_X(np.random.random((10, 1, 100))) - assert X2.shape == (10, 100) + + X = rotf._check_X(np.random.random((10, 1, 100))) + assert X.shape == (10, 100) + with pytest.raises( ValueError, match="RotationForestClassifier is not a time series classifier" ): @@ -98,4 +99,10 @@ def test_rotf_input(): ValueError, match="RotationForestClassifier is not a time series classifier" ): rotf._check_X([[1, 2, 3], [4, 5], [6, 7, 8]]) - X, y = make_example_3d_numpy() + + X2 = np.zeros((10, 10)) + y = np.zeros(10) + y[0:5] = 1 + + with pytest.raises(ValueError, match="All attributes in X contain the same value."): + rotf.fit_predict(X2, y) diff --git a/aeon/regression/sklearn/_rotation_forest_regressor.py b/aeon/regression/sklearn/_rotation_forest_regressor.py index b4ff8e682e..e187181714 100644 --- a/aeon/regression/sklearn/_rotation_forest_regressor.py +++ b/aeon/regression/sklearn/_rotation_forest_regressor.py @@ -241,6 +241,10 @@ def _fit_rotf(self, X, y, save_transformed_data: bool = False): # remove useless attributes self._useful_atts = ~np.all(X[1:] == X[:-1], axis=0) X = X[:, self._useful_atts] + if sum(self._useful_atts) == 0: + raise ValueError( + "All attributes in X contain the same value.", + ) self._n_atts = X.shape[1] diff --git a/aeon/regression/sklearn/tests/test_rotation_forest_regressor.py b/aeon/regression/sklearn/tests/test_rotation_forest_regressor.py index 9a89bbcbed..d089e363ab 100644 --- a/aeon/regression/sklearn/tests/test_rotation_forest_regressor.py +++ b/aeon/regression/sklearn/tests/test_rotation_forest_regressor.py @@ -3,6 +3,7 @@ __maintainer__ = ["MatthewMiddlehurst"] import numpy as np +import pytest from sklearn.metrics import mean_squared_error from sklearn.tree import DecisionTreeRegressor @@ -84,3 +85,27 @@ def test_rotf_fit_predict(): y_pred = rotf.predict(X_train) assert isinstance(y_pred, np.ndarray) assert len(y_pred) == len(y_train) + + +def test_rotf_input(): + """Test RotF with incorrect input.""" + rotf = RotationForestRegressor() + + X = rotf._check_X(np.random.random((10, 1, 100))) + assert X.shape == (10, 100) + + with pytest.raises( + ValueError, match="RotationForestRegressor is not a time series regressor" + ): + rotf._check_X(np.random.random((10, 10, 100))) + with pytest.raises( + ValueError, match="RotationForestRegressor is not a time series regressor" + ): + rotf._check_X([[1, 2, 3], [4, 5], [6, 7, 8]]) + + X2 = np.zeros((10, 10)) + y = np.zeros(10) + y[0:5] = 1 + + with pytest.raises(ValueError, match="All attributes in X contain the same value."): + rotf.fit_predict(X2, y) From 2d32e0f0544f5a826086a2c3e2aab80214e7c657 Mon Sep 17 00:00:00 2001 From: Massimiliano <117172198+nMaax@users.noreply.github.com> Date: Fri, 16 May 2025 16:03:25 +0200 Subject: [PATCH 097/216] [DOC] Inconsistent double ticks in segmentation package, public docstring only (#2622) * double ticks EAggloSegmenter docstring * double ticks HMMSegmenter docstring * double ticks InformationGainSegmenter docstring * double ticks for other EAggloSegmenter docstring and removed extra whitespace --------- Co-authored-by: Matthew Middlehurst --- aeon/segmentation/_eagglo.py | 12 ++++++------ aeon/segmentation/_hmm.py | 4 ++-- aeon/segmentation/_igts.py | 8 ++++---- 3 files changed, 12 insertions(+), 12 deletions(-) diff --git a/aeon/segmentation/_eagglo.py b/aeon/segmentation/_eagglo.py index 74a585ed4b..e481ab95ec 100644 --- a/aeon/segmentation/_eagglo.py +++ b/aeon/segmentation/_eagglo.py @@ -31,25 +31,25 @@ class EAggloSegmenter(BaseSegmenter): ---------- member : array_like (default=None) Assigns points to the initial cluster membership, therefore the first - dimension should be the same as for data. If `None` it will be initialized + dimension should be the same as for data. If ``None`` it will be initialized to dummy vector where each point is assigned to separate cluster. alpha : float (default=1.0) Fixed constant alpha in (0, 2] used in the divergence measure, as the alpha-th absolute moment, see equation (4) in [1]_. penalty : str or callable or None (default=None) Function that defines a penalization of the sequence of goodness-of-fit - statistic, when overfitting is a concern. If `None` not penalty is applied. - Could also be an existing penalty name, either `len_penalty` or - `mean_diff_penalty`. + statistic, when overfitting is a concern. If ``None`` not penalty is applied. + Could also be an existing penalty name, either ``len_penalty`` or + ``mean_diff_penalty``. Attributes ---------- merged_ : array_like - 2D `array_like` outlining which clusters were merged_ at each step. + 2D ``array_like`` outlining which clusters were merged_ at each step. gof_ : float goodness-of-fit statistic for current clsutering. cluster_ : array_like - 1D `array_like` specifying which cluster each row of input data + 1D ``array_like`` specifying which cluster each row of input data X belongs to. Notes diff --git a/aeon/segmentation/_hmm.py b/aeon/segmentation/_hmm.py index 6b82960303..483ef0c980 100644 --- a/aeon/segmentation/_hmm.py +++ b/aeon/segmentation/_hmm.py @@ -50,7 +50,7 @@ class HMMSegmenter(BaseSegmenter): calculated - these are both nxm matrices, where n is the number of hidden states and m is the number of observations. The transition probability matrices record the probability of the most likely - sequence which has observation `m` being assigned to hidden state n. + sequence which has observation ``m`` being assigned to hidden state n. The transition_id matrix records the step before hidden state n that proceeds it in the most likely path. This logic is mostly carried out by helper function _calculate_trans_mats. @@ -76,7 +76,7 @@ class HMMSegmenter(BaseSegmenter): probability of transitioning from state i to state j.) initial_probs: 1D np.ndarray, shape = [num hidden states], optional A array of probabilities that the sequence of hidden states starts in each - of the hidden states. If passed, should be of length `n` the number of + of the hidden states. If passed, should be of length ``n`` the number of hidden states and should match the length of both the emission funcs list and the transition_prob_mat. The initial probs should be reflective of prior beliefs. If none is passed will each hidden state will be diff --git a/aeon/segmentation/_igts.py b/aeon/segmentation/_igts.py index b59bbcfbbf..1bd673b33b 100644 --- a/aeon/segmentation/_igts.py +++ b/aeon/segmentation/_igts.py @@ -279,7 +279,7 @@ class InformationGainSegmenter(BaseSegmenter): GTS uses top-down search method to greedily find the next change point location that creates the maximum information gain. Once this is found, it - repeats the process until it finds `k_max` splits of the time series. + repeats the process until it finds ``k_max`` splits of the time series. .. note:: @@ -295,8 +295,8 @@ class InformationGainSegmenter(BaseSegmenter): step: : int, default=5 Step size, or stride for selecting candidate locations of change points. - Fox example a `step=5` would produce candidates [0, 5, 10, ...]. Has the same - meaning as `step` in `range` function. + Fox example a ``step=5`` would produce candidates [0, 5, 10, ...]. Has the same + meaning as ``step`` in ``range`` function. Attributes ---------- @@ -304,7 +304,7 @@ class InformationGainSegmenter(BaseSegmenter): Locations of change points as integer indexes. By convention change points include the identity segmentation, i.e. first and last index + 1 values. - intermediate_results_: list of `ChangePointResult` + intermediate_results_: list of ``ChangePointResult`` Intermediate segmentation results for each k value, where k=1, 2, ..., k_max Notes From 9ece5af7b774200cc59a0c3b728b45834bbc7192 Mon Sep 17 00:00:00 2001 From: Ali El Hadi ISMAIL FAWAZ <54309336+hadifawaz1999@users.noreply.github.com> Date: Fri, 16 May 2025 18:04:29 +0200 Subject: [PATCH 098/216] put lower bound keras (#2808) --- pyproject.toml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 06408e7b9d..4c4981a9f5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -70,12 +70,14 @@ all_extras = [ "statsmodels>=0.12.1", "stumpy>=1.5.1", "tensorflow>=2.14; python_version < '3.13'", + "keras>=3.6.0; python_version < '3.13'", "torch>=1.13.1", "tsfresh>=0.20.0", "tslearn>=0.5.2", ] dl = [ "tensorflow>=2.14; python_version < '3.13'", + "keras>=3.6.0; python_version < '3.13'", ] unstable_extras = [ # requires gcc and fftw to be installed for Windows and some other OS (see http://www.fftw.org/index.html) From 7f4b38c2360c6b7619be87887bef9e0b3a56005a Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Fri, 16 May 2025 18:52:01 +0100 Subject: [PATCH 099/216] [REF] Refactor smoothing transformers into a sub-package (#2783) * mockup * init * decorator * correct import and tag * base docs * wrappers * tests * docs and imports * test params * register * big refactor * smoothing refactor * ad stuff * fixes * fixes * no longer required --- .readthedocs.yml | 2 +- aeon/transformations/series/_dft.py | 59 +++---------- aeon/transformations/series/_exp_smoothing.py | 55 +++--------- aeon/transformations/series/_gauss.py | 43 +++------ .../transformations/series/_moving_average.py | 51 +++-------- aeon/transformations/series/_sg.py | 43 +++------ aeon/transformations/series/_siv.py | 53 +++-------- .../series/smoothing/__init__.py | 17 ++++ aeon/transformations/series/smoothing/_dfa.py | 84 ++++++++++++++++++ .../series/smoothing/_exp_smoothing.py | 84 ++++++++++++++++++ .../series/smoothing/_gauss.py | 67 ++++++++++++++ .../series/smoothing/_moving_average.py | 67 ++++++++++++++ aeon/transformations/series/smoothing/_rms.py | 87 +++++++++++++++++++ aeon/transformations/series/smoothing/_sg.py | 68 +++++++++++++++ .../series/smoothing/tests/__init__.py | 1 + .../series/{ => smoothing}/tests/test_dft.py | 12 +-- .../tests/test_exp_smoothing.py | 23 ++--- .../{ => smoothing}/tests/test_gauss.py | 22 +---- .../tests/test_moving_average.py | 23 ++--- .../tests/test_rms.py} | 20 +---- .../series/{ => smoothing}/tests/test_sg.py | 20 +---- docs/api_reference/transformations.rst | 15 ++++ .../transformations/smoothing_filters.ipynb | 42 ++++----- 23 files changed, 621 insertions(+), 337 deletions(-) create mode 100644 aeon/transformations/series/smoothing/__init__.py create mode 100644 aeon/transformations/series/smoothing/_dfa.py create mode 100644 aeon/transformations/series/smoothing/_exp_smoothing.py create mode 100644 aeon/transformations/series/smoothing/_gauss.py create mode 100644 aeon/transformations/series/smoothing/_moving_average.py create mode 100644 aeon/transformations/series/smoothing/_rms.py create mode 100644 aeon/transformations/series/smoothing/_sg.py create mode 100644 aeon/transformations/series/smoothing/tests/__init__.py rename aeon/transformations/series/{ => smoothing}/tests/test_dft.py (73%) rename aeon/transformations/series/{ => smoothing}/tests/test_exp_smoothing.py (72%) rename aeon/transformations/series/{ => smoothing}/tests/test_gauss.py (60%) rename aeon/transformations/series/{ => smoothing}/tests/test_moving_average.py (52%) rename aeon/transformations/series/{tests/test_siv.py => smoothing/tests/test_rms.py} (64%) rename aeon/transformations/series/{ => smoothing}/tests/test_sg.py (64%) diff --git a/.readthedocs.yml b/.readthedocs.yml index 9005cc0fc1..9abd56f587 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -13,7 +13,7 @@ python: build: os: ubuntu-24.04 tools: - python: "3.10" + python: "3.11" sphinx: configuration: docs/conf.py diff --git a/aeon/transformations/series/_dft.py b/aeon/transformations/series/_dft.py index 1f603c39af..d29ff92d8f 100644 --- a/aeon/transformations/series/_dft.py +++ b/aeon/transformations/series/_dft.py @@ -4,12 +4,20 @@ __all__ = ["DFTSeriesTransformer"] -import numpy as np +from deprecated.sphinx import deprecated -from aeon.transformations.series.base import BaseSeriesTransformer +from aeon.transformations.series.smoothing import DiscreteFourierApproximation -class DFTSeriesTransformer(BaseSeriesTransformer): +# TODO: Remove in v1.3.0 +@deprecated( + version="1.2.0", + reason="DFTSeriesTransformer is deprecated and will be removed in v1.3.0. " + "Please use DiscreteFourierApproximation from " + "transformations.series.smoothing instead.", + category=FutureWarning, +) +class DFTSeriesTransformer(DiscreteFourierApproximation): """Filter a times series using Discrete Fourier Approximation (DFT). Parameters @@ -42,47 +50,4 @@ class DFTSeriesTransformer(BaseSeriesTransformer): (2, 100) """ - _tags = { - "capability:multivariate": True, - "X_inner_type": "np.ndarray", - "fit_is_empty": True, - } - - def __init__(self, r=0.5, sort=False): - self.r = r - self.sort = sort - super().__init__(axis=1) - - def _transform(self, X, y=None): - """Transform X and return a transformed version. - - Parameters - ---------- - X : np.ndarray - time series in shape (n_channels, n_timepoints) - y : ignored argument for interface compatibility - - Returns - ------- - transformed version of X - """ - # Compute DFT - dft = np.fft.fft(X) - - # Mask array of terms to keep and number of terms to keep - mask = np.zeros_like(dft, dtype=bool) - keep = max(int(self.r * dft.shape[1]), 1) - - # If sort is set, sort the indices by the decreasing dft amplitude - if self.sort: - sorted_indices = np.argsort(np.abs(dft))[:, ::-1] - for i in range(dft.shape[0]): - mask[i, sorted_indices[i, 0:keep]] = True - # Else, keep the first terms - else: - mask[:, 0:keep] = True - - # Invert DFT with masked terms - X_ = np.fft.ifft(dft * mask).real - - return X_ + pass diff --git a/aeon/transformations/series/_exp_smoothing.py b/aeon/transformations/series/_exp_smoothing.py index 5566769463..56739e4491 100644 --- a/aeon/transformations/series/_exp_smoothing.py +++ b/aeon/transformations/series/_exp_smoothing.py @@ -3,14 +3,21 @@ __maintainer__ = ["Datadote"] __all__ = ["ExpSmoothingSeriesTransformer"] -from typing import Union -import numpy as np +from deprecated.sphinx import deprecated -from aeon.transformations.series.base import BaseSeriesTransformer +from aeon.transformations.series.smoothing import ExponentialSmoothing -class ExpSmoothingSeriesTransformer(BaseSeriesTransformer): +# TODO: Remove in v1.3.0 +@deprecated( + version="1.2.0", + reason="ExpSmoothingSeriesTransformer is deprecated and will be removed in v1.3.0. " + "Please use ExponentialSmoothing from " + "transformations.series.smoothing instead.", + category=FutureWarning, +) +class ExpSmoothingSeriesTransformer(ExponentialSmoothing): """Filter a time series using exponential smoothing. - Exponential smoothing (EXP) is a generalisaton of moving average smoothing that @@ -54,42 +61,4 @@ class ExpSmoothingSeriesTransformer(BaseSeriesTransformer): [10. 9.5 8.75 7.875]] """ - _tags = { - "capability:multivariate": True, - "X_inner_type": "np.ndarray", - "fit_is_empty": True, - } - - def __init__( - self, alpha: float = 0.2, window_size: Union[int, float, None] = None - ) -> None: - if not 0 <= alpha <= 1: - raise ValueError(f"alpha must be in range [0, 1], got {alpha}") - if window_size is not None and window_size <= 0: - raise ValueError(f"window_size must be > 0, got {window_size}") - super().__init__(axis=1) - self.alpha = alpha if window_size is None else 2.0 / (window_size + 1) - self.window_size = window_size - - def _transform(self, X, y=None): - """Transform X and return a transformed version. - - private _transform containing core logic, called from transform - - Parameters - ---------- - X : np.ndarray - Data to be transformed - y : ignored argument for interface compatibility - Additional data, e.g., labels for transformation - - Returns - ------- - Xt: 2D np.ndarray - transformed version of X - """ - Xt = np.zeros_like(X, dtype="float") - Xt[:, 0] = X[:, 0] - for i in range(1, Xt.shape[1]): - Xt[:, i] = self.alpha * X[:, i] + (1 - self.alpha) * Xt[:, i - 1] - return Xt + pass diff --git a/aeon/transformations/series/_gauss.py b/aeon/transformations/series/_gauss.py index 863d8cf6b9..a596cf74e1 100644 --- a/aeon/transformations/series/_gauss.py +++ b/aeon/transformations/series/_gauss.py @@ -4,12 +4,20 @@ __all__ = ["GaussSeriesTransformer"] -from scipy.ndimage import gaussian_filter1d +from deprecated.sphinx import deprecated -from aeon.transformations.series.base import BaseSeriesTransformer +from aeon.transformations.series.smoothing import GaussianFilter -class GaussSeriesTransformer(BaseSeriesTransformer): +# TODO: Remove in v1.3.0 +@deprecated( + version="1.2.0", + reason="GaussSeriesTransformer is deprecated and will be removed in v1.3.0. " + "Please use GaussianFilter from " + "transformations.series.smoothing instead.", + category=FutureWarning, +) +class GaussSeriesTransformer(GaussianFilter): """Filter a times series using Gaussian filter. Parameters @@ -45,31 +53,4 @@ class GaussSeriesTransformer(BaseSeriesTransformer): (2, 100) """ - _tags = { - "capability:multivariate": True, - "X_inner_type": "np.ndarray", - "fit_is_empty": True, - } - - def __init__(self, sigma=1, order=0): - self.sigma = sigma - self.order = order - super().__init__(axis=1) - - def _transform(self, X, y=None): - """Transform X and return a transformed version. - - Parameters - ---------- - X : np.ndarray - time series in shape (n_channels, n_timepoints) - y : ignored argument for interface compatibility - - Returns - ------- - transformed version of X - """ - # Compute Gaussian filter - X_ = gaussian_filter1d(X, self.sigma, self.axis, self.order) - - return X_ + pass diff --git a/aeon/transformations/series/_moving_average.py b/aeon/transformations/series/_moving_average.py index 7e9993946e..930e1a6ecb 100644 --- a/aeon/transformations/series/_moving_average.py +++ b/aeon/transformations/series/_moving_average.py @@ -3,12 +3,21 @@ __maintainer__ = ["Datadote"] __all__ = ["MovingAverageSeriesTransformer"] -import numpy as np -from aeon.transformations.series.base import BaseSeriesTransformer +from deprecated.sphinx import deprecated +from aeon.transformations.series.smoothing import MovingAverage -class MovingAverageSeriesTransformer(BaseSeriesTransformer): + +# TODO: Remove in v1.3.0 +@deprecated( + version="1.2.0", + reason="MovingAverageSeriesTransformer is deprecated and will be removed in " + "v1.3.0. Please use MovingAverage from " + "transformations.series.smoothing instead.", + category=FutureWarning, +) +class MovingAverageSeriesTransformer(MovingAverage): """Calculate the moving average of an array of numbers. Slides a window across the input array, and returns the averages for each window. @@ -41,38 +50,4 @@ class MovingAverageSeriesTransformer(BaseSeriesTransformer): [[-2.5 -1.5 -0.5 0.5 1.5 2.5]] """ - _tags = { - "capability:multivariate": True, - "X_inner_type": "np.ndarray", - "fit_is_empty": True, - } - - def __init__(self, window_size: int = 5) -> None: - super().__init__(axis=0) - if window_size <= 0: - raise ValueError(f"window_size must be > 0, got {window_size}") - self.window_size = window_size - - def _transform(self, X, y=None): - """Transform X and return a transformed version. - - private _transform containing core logic, called from transform - - Parameters - ---------- - X : np.ndarray - Data to be transformed - y : ignored argument for interface compatibility - Additional data, e.g., labels for transformation - - Returns - ------- - Xt: 2D np.ndarray - transformed version of X - """ - csum = np.cumsum(X, axis=0) - csum[self.window_size :, :] = ( - csum[self.window_size :, :] - csum[: -self.window_size, :] - ) - Xt = csum[self.window_size - 1 :, :] / self.window_size - return Xt + pass diff --git a/aeon/transformations/series/_sg.py b/aeon/transformations/series/_sg.py index 19000cc0e8..1f1db1761a 100644 --- a/aeon/transformations/series/_sg.py +++ b/aeon/transformations/series/_sg.py @@ -4,12 +4,20 @@ __all__ = ["SGSeriesTransformer"] -from scipy.signal import savgol_filter +from deprecated.sphinx import deprecated -from aeon.transformations.series.base import BaseSeriesTransformer +from aeon.transformations.series.smoothing import SavitzkyGolayFilter -class SGSeriesTransformer(BaseSeriesTransformer): +# TODO: Remove in v1.3.0 +@deprecated( + version="1.2.0", + reason="SGSeriesTransformer is deprecated and will be removed in v1.3.0. " + "Please use SavitzkyGolayFilter from " + "transformations.series.smoothing instead.", + category=FutureWarning, +) +class SGSeriesTransformer(SavitzkyGolayFilter): """Filter a times series using Savitzky-Golay (SG). Parameters @@ -45,31 +53,4 @@ class SGSeriesTransformer(BaseSeriesTransformer): (2, 100) """ - _tags = { - "capability:multivariate": True, - "X_inner_type": "np.ndarray", - "fit_is_empty": True, - } - - def __init__(self, window_length=5, polyorder=2): - self.window_length = window_length - self.polyorder = polyorder - super().__init__(axis=1) - - def _transform(self, X, y=None): - """Transform X and return a transformed version. - - Parameters - ---------- - X : np.ndarray - time series in shape (n_channels, n_timepoints) - y : ignored argument for interface compatibility - - Returns - ------- - transformed version of X - """ - # Compute SG - X_ = savgol_filter(X, self.window_length, self.polyorder) - - return X_ + pass diff --git a/aeon/transformations/series/_siv.py b/aeon/transformations/series/_siv.py index 1bb2ad3e0a..f343df8f04 100644 --- a/aeon/transformations/series/_siv.py +++ b/aeon/transformations/series/_siv.py @@ -4,13 +4,20 @@ __all__ = ["SIVSeriesTransformer"] -import numpy as np -from scipy.ndimage import median_filter +from deprecated.sphinx import deprecated -from aeon.transformations.series.base import BaseSeriesTransformer +from aeon.transformations.series.smoothing import RecursiveMedianSieve -class SIVSeriesTransformer(BaseSeriesTransformer): +# TODO: Remove in v1.3.0 +@deprecated( + version="1.2.0", + reason="SIVSeriesTransformer is deprecated and will be removed in v1.3.0. " + "Please use RecursiveMedianSieve from " + "transformations.series.smoothing instead.", + category=FutureWarning, +) +class SIVSeriesTransformer(RecursiveMedianSieve): """Filter a times series using Recursive Median Sieve (SIV). Parameters @@ -48,40 +55,4 @@ class SIVSeriesTransformer(BaseSeriesTransformer): (2, 100) """ - _tags = { - "capability:multivariate": True, - "X_inner_type": "np.ndarray", - "fit_is_empty": True, - } - - def __init__(self, window_length=None): - self.window_length = window_length - super().__init__(axis=1) - - def _transform(self, X, y=None): - """Transform X and return a transformed version. - - Parameters - ---------- - X : np.ndarray - time series in shape (n_channels, n_timepoints) - y : ignored argument for interface compatibility - - Returns - ------- - transformed version of X - """ - window_length = self.window_length - if window_length is None: - window_length = [3, 5, 7] - if not isinstance(window_length, list): - window_length = [window_length] - - # Compute SIV - X_ = X - - for w in window_length: - footprint = np.ones((1, w)) - X_ = median_filter(X_, footprint=footprint) - - return X_ + pass diff --git a/aeon/transformations/series/smoothing/__init__.py b/aeon/transformations/series/smoothing/__init__.py new file mode 100644 index 0000000000..52ebcc3c8e --- /dev/null +++ b/aeon/transformations/series/smoothing/__init__.py @@ -0,0 +1,17 @@ +"""Series smoothing transformers.""" + +__all__ = [ + "DiscreteFourierApproximation", + "ExponentialSmoothing", + "GaussianFilter", + "MovingAverage", + "SavitzkyGolayFilter", + "RecursiveMedianSieve", +] + +from aeon.transformations.series.smoothing._dfa import DiscreteFourierApproximation +from aeon.transformations.series.smoothing._exp_smoothing import ExponentialSmoothing +from aeon.transformations.series.smoothing._gauss import GaussianFilter +from aeon.transformations.series.smoothing._moving_average import MovingAverage +from aeon.transformations.series.smoothing._rms import RecursiveMedianSieve +from aeon.transformations.series.smoothing._sg import SavitzkyGolayFilter diff --git a/aeon/transformations/series/smoothing/_dfa.py b/aeon/transformations/series/smoothing/_dfa.py new file mode 100644 index 0000000000..a5e017ac66 --- /dev/null +++ b/aeon/transformations/series/smoothing/_dfa.py @@ -0,0 +1,84 @@ +"""Discrete Fourier Approximation filter transformation for smoothing.""" + +__maintainer__ = ["Cyril-Meyer"] +__all__ = ["DiscreteFourierApproximation"] + + +import numpy as np + +from aeon.transformations.series.base import BaseSeriesTransformer + + +class DiscreteFourierApproximation(BaseSeriesTransformer): + """Filter a times series using a Discrete Fourier Approximation. + + Smooths the series by first transforming into the frequency domain, discarding + the high frequency terms, then transforming back to the time domain. + + Parameters + ---------- + r : float, default=0.5 + Proportion of Fourier terms to retain [0, 1] + sort : bool, default=False + Sort the Fourier terms by amplitude to keep most important terms + + References + ---------- + .. [1] Cooley, J., Lewis, P., Welch, P.: The fast fourier transform and its + applications. IEEE Trans. Educ. 12(1), 27–34 (1969) + + Examples + -------- + >>> import numpy as np + >>> from aeon.transformations.series.smoothing import DiscreteFourierApproximation + >>> X = np.random.random((2, 100)) # Random series length 100 + >>> dft = DiscreteFourierApproximation() + >>> X_ = dft.fit_transform(X) + >>> X_.shape + (2, 100) + """ + + _tags = { + "capability:multivariate": True, + "X_inner_type": "np.ndarray", + "fit_is_empty": True, + } + + def __init__(self, r=0.5, sort=False): + self.r = r + self.sort = sort + super().__init__(axis=1) + + def _transform(self, X, y=None): + """Transform X and return a transformed version. + + Parameters + ---------- + X : np.ndarray + time series in shape (n_channels, n_timepoints) + y : ignored argument for interface compatibility + + Returns + ------- + transformed version of X + """ + # Compute DFT + dft = np.fft.fft(X) + + # Mask array of terms to keep and number of terms to keep + mask = np.zeros_like(dft, dtype=bool) + keep = max(int(self.r * dft.shape[1]), 1) + + # If sort is set, sort the indices by the decreasing dft amplitude + if self.sort: + sorted_indices = np.argsort(np.abs(dft))[:, ::-1] + for i in range(dft.shape[0]): + mask[i, sorted_indices[i, 0:keep]] = True + # Else, keep the first terms + else: + mask[:, 0:keep] = True + + # Invert DFT with masked terms + X_ = np.fft.ifft(dft * mask).real + + return X_ diff --git a/aeon/transformations/series/smoothing/_exp_smoothing.py b/aeon/transformations/series/smoothing/_exp_smoothing.py new file mode 100644 index 0000000000..7b944a7854 --- /dev/null +++ b/aeon/transformations/series/smoothing/_exp_smoothing.py @@ -0,0 +1,84 @@ +"""Exponential smoothing transformation.""" + +__maintainer__ = ["Datadote"] +__all__ = ["ExponentialSmoothing"] + +from typing import Union + +import numpy as np + +from aeon.transformations.series.base import BaseSeriesTransformer + + +class ExponentialSmoothing(BaseSeriesTransformer): + """Filter a time series using exponential smoothing. + + - Exponential smoothing (EXP) is a generalisaton of moving average smoothing that + assigns a decaying weight to each element rather than averaging over a window. + - Assume time series T = [t_0, ..., t_j], and smoothed values S = [s_0, ..., s_j] + - Then, s_0 = t_0 and s_j = alpha * t_j + (1 - alpha) * s_j-1 + where 0 ≤ alpha ≤ 1. If window_size is given, alpha is overwritten, and set as + alpha = 2. / (window_size + 1) + + Parameters + ---------- + alpha: float, default=0.2 + decaying weight. Range [0, 1]. Overwritten by window_size if window_size exists + window_size: int or float or None, default=None + If window_size is specified, alpha is set to 2. / (window_size + 1) + + Examples + -------- + >>> import numpy as np + >>> from aeon.transformations.series.smoothing import ExponentialSmoothing + >>> X = np.array([-2, -1, 0, 1, 2]) + >>> transformer = ExponentialSmoothing(0.5) + >>> transformer.fit_transform(X) + array([[-2. , -1.5 , -0.75 , 0.125 , 1.0625]]) + >>> X = np.array([[1, 2, 3, 4], [10, 9, 8, 7]]) + >>> transformer.fit_transform(X) + array([[ 1. , 1.5 , 2.25 , 3.125], + [10. , 9.5 , 8.75 , 7.875]]) + """ + + _tags = { + "capability:multivariate": True, + "X_inner_type": "np.ndarray", + "fit_is_empty": True, + } + + def __init__( + self, alpha: float = 0.2, window_size: Union[int, float, None] = None + ) -> None: + self.alpha = alpha if window_size is None else 2.0 / (window_size + 1) + self.window_size = window_size + + super().__init__(axis=1) + + def _transform(self, X, y=None): + """Transform X and return a transformed version. + + private _transform containing core logic, called from transform + + Parameters + ---------- + X : np.ndarray + Data to be transformed + y : ignored argument for interface compatibility + Additional data, e.g., labels for transformation + + Returns + ------- + Xt: 2D np.ndarray + transformed version of X + """ + if not 0 <= self.alpha <= 1: + raise ValueError(f"alpha must be in range [0, 1], got {self.alpha}") + if self.window_size is not None and self.window_size <= 0: + raise ValueError(f"window_size must be > 0, got {self.window_size}") + + Xt = np.zeros_like(X, dtype="float") + Xt[:, 0] = X[:, 0] + for i in range(1, Xt.shape[1]): + Xt[:, i] = self.alpha * X[:, i] + (1 - self.alpha) * Xt[:, i - 1] + return Xt diff --git a/aeon/transformations/series/smoothing/_gauss.py b/aeon/transformations/series/smoothing/_gauss.py new file mode 100644 index 0000000000..7946f8d634 --- /dev/null +++ b/aeon/transformations/series/smoothing/_gauss.py @@ -0,0 +1,67 @@ +"""Gaussian filter transformation.""" + +__maintainer__ = ["Cyril-Meyer"] +__all__ = ["GaussianFilter"] + + +from scipy.ndimage import gaussian_filter1d + +from aeon.transformations.series.base import BaseSeriesTransformer + + +class GaussianFilter(BaseSeriesTransformer): + """Filter a time series using Gaussian filter. + + Wrapper for the SciPy ``gaussian_filter1d`` function. + + Parameters + ---------- + sigma : float, default=1 + Standard deviation for the Gaussian kernel. + order : int, default=0 + An order of 0 corresponds to convolution with a Gaussian kernel. + A positive order corresponds to convolution with that derivative of a + Gaussian. + + References + ---------- + .. [1] Chou, Y. L. "Statistical Analysis, Section 17.9." New York: Holt + International (1975). + + Examples + -------- + >>> import numpy as np + >>> from aeon.transformations.series.smoothing import GaussianFilter + >>> X = np.random.random((2, 100)) # Random series length 100 + >>> gauss = GaussianFilter(sigma=5) + >>> X_ = gauss.fit_transform(X) + >>> X_.shape + (2, 100) + """ + + _tags = { + "capability:multivariate": True, + "X_inner_type": "np.ndarray", + "fit_is_empty": True, + } + + def __init__(self, sigma=1, order=0): + self.sigma = sigma + self.order = order + + super().__init__(axis=1) + + def _transform(self, X, y=None): + """Transform X and return a transformed version. + + Parameters + ---------- + X : np.ndarray + time series in shape (n_channels, n_timepoints) + y : ignored argument for interface compatibility + + Returns + ------- + transformed version of X + """ + return gaussian_filter1d(X, self.sigma, axis=self.axis, order=self.order) diff --git a/aeon/transformations/series/smoothing/_moving_average.py b/aeon/transformations/series/smoothing/_moving_average.py new file mode 100644 index 0000000000..612b31b32b --- /dev/null +++ b/aeon/transformations/series/smoothing/_moving_average.py @@ -0,0 +1,67 @@ +"""Moving average transformation.""" + +__maintainer__ = ["Datadote"] +__all__ = ["MovingAverage"] + +import numpy as np + +from aeon.transformations.series.base import BaseSeriesTransformer + + +class MovingAverage(BaseSeriesTransformer): + """Calculate the moving average for a time series. + + Slides a window across the input array, and returns the averages for each window. + + Parameters + ---------- + window_size: int, default=5 + Number of values to average for each window. + + Examples + -------- + >>> import numpy as np + >>> from aeon.transformations.series.smoothing import MovingAverage + >>> X = np.array([-3, -2, -1, 0, 1, 2, 3]) + >>> transformer = MovingAverage(2) + >>> transformer.fit_transform(X) + array([[-2.5, -1.5, -0.5, 0.5, 1.5, 2.5]]) + """ + + _tags = { + "capability:multivariate": True, + "X_inner_type": "np.ndarray", + "fit_is_empty": True, + } + + def __init__(self, window_size: int = 5) -> None: + self.window_size = window_size + + super().__init__(axis=0) + + def _transform(self, X, y=None): + """Transform X and return a transformed version. + + private _transform containing core logic, called from transform + + Parameters + ---------- + X : np.ndarray + Data to be transformed + y : ignored argument for interface compatibility + Additional data, e.g., labels for transformation + + Returns + ------- + Xt: 2D np.ndarray + transformed version of X + """ + if self.window_size <= 0: + raise ValueError(f"window_size must be > 0, got {self.window_size}") + + csum = np.cumsum(X, axis=0) + csum[self.window_size :, :] = ( + csum[self.window_size :, :] - csum[: -self.window_size, :] + ) + Xt = csum[self.window_size - 1 :, :] / self.window_size + return Xt diff --git a/aeon/transformations/series/smoothing/_rms.py b/aeon/transformations/series/smoothing/_rms.py new file mode 100644 index 0000000000..9b9b45becb --- /dev/null +++ b/aeon/transformations/series/smoothing/_rms.py @@ -0,0 +1,87 @@ +"""Recursive Median Sieve filter transformation.""" + +__maintainer__ = ["Cyril-Meyer"] +__all__ = ["RecursiveMedianSieve"] + + +import numpy as np +from scipy.ndimage import median_filter + +from aeon.transformations.series.base import BaseSeriesTransformer + + +class RecursiveMedianSieve(BaseSeriesTransformer): + """Filter a times series using a Recursive Median Sieve. + + Parameters + ---------- + window_length : list of int or int, default=None + The filter windows lengths (recommended increasing value). + If None, defaults to [3, 5, 7]. + + Notes + ----- + Use scipy.ndimage.median_filter instead of scipy.signal.medfilt : + The more general function scipy.ndimage.median_filter has a more efficient + implementation of a median filter and therefore runs much faster. + https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.medfilt.html + + References + ---------- + .. [1] Bangham J. A. (1988). + Data-sieving hydrophobicity plots. + Analytical biochemistry, 174(1), 142–145. + https://doi.org/10.1016/0003-2697(88)90528-3 + .. [2] Yli-Harja, O., Koivisto, P., Bangham, J. A., Cawley, G., + Harvey, R., & Shmulevich, I. (2001). + Simplified implementation of the recursive median sieve. + Signal Process., 81(7), 1565–1570. + https://doi.org/10.1016/S0165-1684(01)00054-8 + + Examples + -------- + >>> import numpy as np + >>> from aeon.transformations.series.smoothing import RecursiveMedianSieve + >>> X = np.random.random((2, 100)) # Random series length 100 + >>> siv = RecursiveMedianSieve() + >>> X_ = siv.fit_transform(X) + >>> X_.shape + (2, 100) + """ + + _tags = { + "capability:multivariate": True, + "X_inner_type": "np.ndarray", + "fit_is_empty": True, + } + + def __init__(self, window_length=None): + self.window_length = window_length + + super().__init__(axis=1) + + def _transform(self, X, y=None): + """Transform X and return a transformed version. + + Parameters + ---------- + X : np.ndarray + time series in shape (n_channels, n_timepoints) + y : ignored argument for interface compatibility + + Returns + ------- + transformed version of X + """ + window_length = self.window_length + if window_length is None: + window_length = [3, 5, 7] + if not isinstance(window_length, list): + window_length = [window_length] + + X_ = X + for w in window_length: + footprint = np.ones((1, w)) + X_ = median_filter(X_, footprint=footprint) + + return X_ diff --git a/aeon/transformations/series/smoothing/_sg.py b/aeon/transformations/series/smoothing/_sg.py new file mode 100644 index 0000000000..750f5c5701 --- /dev/null +++ b/aeon/transformations/series/smoothing/_sg.py @@ -0,0 +1,68 @@ +"""Savitzky-Golay filter transformation.""" + +__maintainer__ = ["Cyril-Meyer"] +__all__ = ["SavitzkyGolayFilter"] + + +from scipy.signal import savgol_filter + +from aeon.transformations.series.base import BaseSeriesTransformer + + +class SavitzkyGolayFilter(BaseSeriesTransformer): + """Filter a times series using Savitzky-Golay (SG). + + Wrapper for the SciPy ``savgol_filter`` function. + + Parameters + ---------- + window_length : int, default=5 + The length of the filter window (i.e., the number of coefficients). + window_length must be less than or equal to the size of the input. + polyorder : int, default=2 + The order of the polynomial used to fit the samples. + polyorder must be less than window_length. + + References + ---------- + .. [1] Savitzky, A., & Golay, M. J. (1964). + Smoothing and differentiation of data by simplified least squares procedures. + Analytical chemistry, 36(8), 1627-1639. + + Examples + -------- + >>> import numpy as np + >>> from aeon.transformations.series.smoothing import SavitzkyGolayFilter + >>> X = np.random.random((2, 100)) # Random series length 100 + >>> sg = SavitzkyGolayFilter() + >>> X_ = sg.fit_transform(X) + >>> X_.shape + (2, 100) + """ + + _tags = { + "capability:multivariate": True, + "X_inner_type": "np.ndarray", + "fit_is_empty": True, + } + + def __init__(self, window_length=5, polyorder=2): + self.window_length = window_length + self.polyorder = polyorder + + super().__init__(axis=1) + + def _transform(self, X, y=None): + """Transform X and return a transformed version. + + Parameters + ---------- + X : np.ndarray + time series in shape (n_channels, n_timepoints) + y : ignored argument for interface compatibility + + Returns + ------- + transformed version of X + """ + return savgol_filter(X, self.window_length, self.polyorder) diff --git a/aeon/transformations/series/smoothing/tests/__init__.py b/aeon/transformations/series/smoothing/tests/__init__.py new file mode 100644 index 0000000000..5882e7a73b --- /dev/null +++ b/aeon/transformations/series/smoothing/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for series smoothing transformations.""" diff --git a/aeon/transformations/series/tests/test_dft.py b/aeon/transformations/series/smoothing/tests/test_dft.py similarity index 73% rename from aeon/transformations/series/tests/test_dft.py rename to aeon/transformations/series/smoothing/tests/test_dft.py index 6ffe04b78c..62cd7147a4 100644 --- a/aeon/transformations/series/tests/test_dft.py +++ b/aeon/transformations/series/smoothing/tests/test_dft.py @@ -1,12 +1,12 @@ """Tests for DFT transformation.""" -__maintainer__ = [] - import numpy as np import pytest +from aeon.transformations.series.smoothing._dfa import DiscreteFourierApproximation + -@pytest.mark.parametrize("r", [0.00, 0.25, 0.50, 0.75, 1.00]) +@pytest.mark.parametrize("r", [0.00, 0.50, 1.00]) @pytest.mark.parametrize("sort", [True, False]) def test_dft(r, sort): """Test the functionality of DFT transformation.""" @@ -23,15 +23,11 @@ def test_dft(r, sort): + 0.1 * np.sin(2 * np.pi * 8 * t) ) x12 = np.array([x1, x2]) - x12r = x12 + np.random.random((2, n_samples)) * 0.25 - - from aeon.transformations.series._dft import DFTSeriesTransformer - dft = DFTSeriesTransformer(r=r, sort=sort) + dft = DiscreteFourierApproximation(r=r, sort=sort) x_1 = dft.fit_transform(x1) x_2 = dft.fit_transform(x2) x_12 = dft.fit_transform(x12) - dft.fit_transform(x12r) np.testing.assert_almost_equal(x_1[0], x_12[0], decimal=4) np.testing.assert_almost_equal(x_2[0], x_12[1], decimal=4) diff --git a/aeon/transformations/series/tests/test_exp_smoothing.py b/aeon/transformations/series/smoothing/tests/test_exp_smoothing.py similarity index 72% rename from aeon/transformations/series/tests/test_exp_smoothing.py rename to aeon/transformations/series/smoothing/tests/test_exp_smoothing.py index cb83a4e7da..e3ca4b0267 100644 --- a/aeon/transformations/series/tests/test_exp_smoothing.py +++ b/aeon/transformations/series/smoothing/tests/test_exp_smoothing.py @@ -1,11 +1,9 @@ -"""Tests for ExpSmoothingSeriesTransformer.""" - -__maintainer__ = ["Datadote"] +"""Tests for ExponentialSmoothing.""" import numpy as np import pytest -from aeon.transformations.series._exp_smoothing import ExpSmoothingSeriesTransformer +from aeon.transformations.series.smoothing import ExponentialSmoothing TEST_DATA = [np.array([-2, -1, 0, 1, 2]), np.array([[1, 2, 3, 4], [10, 9, 8, 7]])] EXPECTED_RESULTS = [ @@ -16,7 +14,7 @@ def test_input_1d_array(): """Test inputs of dimension 1.""" - transformer = ExpSmoothingSeriesTransformer(0.5) + transformer = ExponentialSmoothing(0.5) idx_data = 0 Xt = transformer.fit_transform(TEST_DATA[idx_data]) np.testing.assert_almost_equal(Xt, EXPECTED_RESULTS[idx_data], decimal=5) @@ -24,7 +22,7 @@ def test_input_1d_array(): def test_input_2d_array(): """Test inputs of dimension 2.""" - transformer = ExpSmoothingSeriesTransformer(0.5) + transformer = ExponentialSmoothing(0.5) idx_data = 1 Xt = transformer.fit_transform(TEST_DATA[idx_data]) np.testing.assert_almost_equal(Xt, EXPECTED_RESULTS[idx_data], decimal=5) @@ -34,8 +32,8 @@ def test_input_2d_array(): def test_window_size_matches_alpha(alpha_window): """Check same output results using equivalent alpha and window_size.""" alpha, window_size = alpha_window - transformer1 = ExpSmoothingSeriesTransformer(alpha=alpha) - transformer2 = ExpSmoothingSeriesTransformer(window_size=window_size) + transformer1 = ExponentialSmoothing(alpha=alpha) + transformer2 = ExponentialSmoothing(window_size=window_size) for i in range(len(TEST_DATA)): Xt1 = transformer1.fit_transform(TEST_DATA[i]) Xt2 = transformer2.fit_transform(TEST_DATA[i]) @@ -45,16 +43,19 @@ def test_window_size_matches_alpha(alpha_window): def test_alpha_less_than_zero(): """Test alpha less than zero.""" with pytest.raises(ValueError): - ExpSmoothingSeriesTransformer(-0.5) + e = ExponentialSmoothing(-0.5) + e.fit_transform(TEST_DATA[0]) def test_alpha_greater_than_one(): """Test alpha greater than one.""" with pytest.raises(ValueError): - ExpSmoothingSeriesTransformer(2.0) + e = ExponentialSmoothing(2.0) + e.fit_transform(TEST_DATA[0]) def test_window_size_than_one(): """Test window_size < 0.""" with pytest.raises(ValueError): - ExpSmoothingSeriesTransformer(window_size=0) + e = ExponentialSmoothing(window_size=0) + e.fit_transform(TEST_DATA[0]) diff --git a/aeon/transformations/series/tests/test_gauss.py b/aeon/transformations/series/smoothing/tests/test_gauss.py similarity index 60% rename from aeon/transformations/series/tests/test_gauss.py rename to aeon/transformations/series/smoothing/tests/test_gauss.py index 6ab65ac107..52a8ae3ee8 100644 --- a/aeon/transformations/series/tests/test_gauss.py +++ b/aeon/transformations/series/smoothing/tests/test_gauss.py @@ -1,12 +1,12 @@ """Tests for Gauss transformation.""" -__maintainer__ = [] - import numpy as np import pytest +from aeon.transformations.series.smoothing import GaussianFilter + -@pytest.mark.parametrize("sigma", [0.1, 0.5, 1, 2, 5, 10]) +@pytest.mark.parametrize("sigma", [0.1, 1, 10]) @pytest.mark.parametrize("order", [0, 1, 2]) def test_gauss(sigma, order): """Test the functionality of Gauss transformation.""" @@ -23,25 +23,11 @@ def test_gauss(sigma, order): + 0.1 * np.sin(2 * np.pi * 8 * t) ) x12 = np.array([x1, x2]) - x12r = x12 + np.random.random((2, n_samples)) * 0.25 - from aeon.transformations.series._gauss import GaussSeriesTransformer - - sg = GaussSeriesTransformer(sigma=sigma, order=order) + sg = GaussianFilter(sigma=sigma, order=order) x_1 = sg.fit_transform(x1) x_2 = sg.fit_transform(x2) x_12 = sg.fit_transform(x12) - x_12_r = sg.fit_transform(x12r) - - """ - # Visualize smoothing - import matplotlib.pyplot as plt - plt.plot(x12r[0]) - plt.plot(x_12_r[0]) - plt.savefig(fname=f'Gauss_{sigma}_{order}.png') - plt.clf() - """ np.testing.assert_almost_equal(x_1[0], x_12[0], decimal=4) np.testing.assert_almost_equal(x_2[0], x_12[1], decimal=4) - assert x_12.shape == x_12_r.shape diff --git a/aeon/transformations/series/tests/test_moving_average.py b/aeon/transformations/series/smoothing/tests/test_moving_average.py similarity index 52% rename from aeon/transformations/series/tests/test_moving_average.py rename to aeon/transformations/series/smoothing/tests/test_moving_average.py index bfbf3a71bd..049de5dbaa 100644 --- a/aeon/transformations/series/tests/test_moving_average.py +++ b/aeon/transformations/series/smoothing/tests/test_moving_average.py @@ -1,26 +1,27 @@ """Tests for MovingAverageTransformer.""" -__maintainer__ = ["Datadote"] - import numpy as np import pytest -from aeon.transformations.series._moving_average import MovingAverageSeriesTransformer +from aeon.transformations.series.smoothing import MovingAverage -TEST_DATA = [np.array([-3, -2, -1, 0, 1, 2, 3]), np.array([[-3, -2, -1, 0, 1, 2, 3]])] +TEST_DATA = [ + np.array([-3, -2, -1, 0, 1, 2, 3]), + np.array([[-3, -2, -1, 0, 1, 2, 3], [3, 2, 1, 0, -1, -2, -3]]), +] EXPECTED_RESULTS = [ np.array([[-2.5, -1.5, -0.5, 0.5, 1.5, 2.5]]), - np.array([[-2.5, -1.5, -0.5, 0.5, 1.5, 2.5]]), + np.array([[-2.5, -1.5, -0.5, 0.5, 1.5, 2.5], [2.5, 1.5, 0.5, -0.5, -1.5, -2.5]]), ] def test_window_size_greater_than_zero(): """Test window sizes > 0.""" - ma = MovingAverageSeriesTransformer(window_size=1) + ma = MovingAverage(window_size=1) xt = ma.fit_transform(TEST_DATA[0]) - np.testing.assert_array_almost_equal(xt, xt, decimal=2) + np.testing.assert_array_almost_equal(xt[0], TEST_DATA[0], decimal=2) - ma = MovingAverageSeriesTransformer(window_size=2) + ma = MovingAverage(window_size=2) for i in range(len(TEST_DATA)): xt = ma.fit_transform(TEST_DATA[i]) np.testing.assert_array_almost_equal(xt, EXPECTED_RESULTS[i], decimal=2) @@ -29,10 +30,12 @@ def test_window_size_greater_than_zero(): def test_window_size_equal_zero(): """Test window size == 0.""" with pytest.raises(ValueError): - MovingAverageSeriesTransformer(window_size=0) + m = MovingAverage(window_size=0) + m.fit_transform(TEST_DATA[0]) def test_window_size_less_than_zero(): """Test window sizes < 0.""" with pytest.raises(ValueError): - MovingAverageSeriesTransformer(window_size=-1) + m = MovingAverage(window_size=-1) + m.fit_transform(TEST_DATA[0]) diff --git a/aeon/transformations/series/tests/test_siv.py b/aeon/transformations/series/smoothing/tests/test_rms.py similarity index 64% rename from aeon/transformations/series/tests/test_siv.py rename to aeon/transformations/series/smoothing/tests/test_rms.py index c8042e9c5b..a99a02981e 100644 --- a/aeon/transformations/series/tests/test_siv.py +++ b/aeon/transformations/series/smoothing/tests/test_rms.py @@ -1,10 +1,10 @@ """Tests for SIV transformation.""" -__maintainer__ = [] - import numpy as np import pytest +from aeon.transformations.series.smoothing import RecursiveMedianSieve + @pytest.mark.parametrize( "window_length", [1, 2, 3, 5, 7, 10, 11, [2, 3], [3, 5], [3, 5, 7], [3, 5, 7, 11]] @@ -24,25 +24,11 @@ def test_siv(window_length): + 0.1 * np.sin(2 * np.pi * 8 * t) ) x12 = np.array([x1, x2]) - x12r = x12 + np.random.random((2, n_samples)) * 0.25 - from aeon.transformations.series._siv import SIVSeriesTransformer - - siv = SIVSeriesTransformer(window_length=window_length) + siv = RecursiveMedianSieve(window_length=window_length) x_1 = siv.fit_transform(x1) x_2 = siv.fit_transform(x2) x_12 = siv.fit_transform(x12) - x_12_r = siv.fit_transform(x12r) - - """ - # Visualize smoothing - import matplotlib.pyplot as plt - plt.plot(x12r[0]) - plt.plot(x_12_r[0]) - plt.savefig(fname=f'SIV_{window_length}.png') - plt.clf() - """ np.testing.assert_almost_equal(x_1[0], x_12[0], decimal=4) np.testing.assert_almost_equal(x_2[0], x_12[1], decimal=4) - assert x_12.shape == x_12_r.shape diff --git a/aeon/transformations/series/tests/test_sg.py b/aeon/transformations/series/smoothing/tests/test_sg.py similarity index 64% rename from aeon/transformations/series/tests/test_sg.py rename to aeon/transformations/series/smoothing/tests/test_sg.py index 7df2970086..75604fb196 100644 --- a/aeon/transformations/series/tests/test_sg.py +++ b/aeon/transformations/series/smoothing/tests/test_sg.py @@ -1,10 +1,10 @@ """Tests for SG transformation.""" -__maintainer__ = [] - import numpy as np import pytest +from aeon.transformations.series.smoothing import SavitzkyGolayFilter + @pytest.mark.parametrize("window_length", [5, 9, 17]) @pytest.mark.parametrize("polyorder", [2, 3, 4]) @@ -23,25 +23,11 @@ def test_sg(window_length, polyorder): + 0.1 * np.sin(2 * np.pi * 8 * t) ) x12 = np.array([x1, x2]) - x12r = x12 + np.random.random((2, n_samples)) * 0.25 - from aeon.transformations.series._sg import SGSeriesTransformer - - sg = SGSeriesTransformer(window_length=window_length, polyorder=polyorder) + sg = SavitzkyGolayFilter(window_length=window_length, polyorder=polyorder) x_1 = sg.fit_transform(x1) x_2 = sg.fit_transform(x2) x_12 = sg.fit_transform(x12) - x_12_r = sg.fit_transform(x12r) - - """ - # Visualize smoothing - import matplotlib.pyplot as plt - plt.plot(x12r[0]) - plt.plot(x_12_r[0]) - plt.savefig(fname=f'SG_{window_length}_{polyorder}.png') - plt.clf() - """ np.testing.assert_almost_equal(x_1[0], x_12[0], decimal=4) np.testing.assert_almost_equal(x_2[0], x_12[1], decimal=4) - assert x_12.shape == x_12_r.shape diff --git a/docs/api_reference/transformations.rst b/docs/api_reference/transformations.rst index 2a56fd847f..138ca4ffcb 100644 --- a/docs/api_reference/transformations.rst +++ b/docs/api_reference/transformations.rst @@ -180,6 +180,21 @@ Series transforms PCASeriesTransformer WarpingSeriesTransformer +Smoothing +~~~~~~~~~ + +.. currentmodule:: aeon.transformations.series.smoothing + +.. autosummary:: + :toctree: auto_generated/ + :template: class.rst + + DiscreteFourierApproximation + ExponentialSmoothing + GaussianFilter + MovingAverage + SavitzkyGolayFilter + RecursiveMedianSieve Base ---- diff --git a/examples/transformations/smoothing_filters.ipynb b/examples/transformations/smoothing_filters.ipynb index 6a7776f04e..d0026dc329 100644 --- a/examples/transformations/smoothing_filters.ipynb +++ b/examples/transformations/smoothing_filters.ipynb @@ -76,9 +76,11 @@ " axe.legend()\n", "\n", "\n", - "def plot_transformation(transformer=None):\n", + "def plot_transformation(transformer, title=None):\n", " \"\"\"Plot transformation for each ts.\"\"\"\n", " fig, axes = plt.subplots(2, 2, figsize=(16, 8), dpi=75)\n", + " if title is not None:\n", + " fig.suptitle(title)\n", "\n", " plot_axes(\n", " axes[0, 0], x_airline, transformer.fit_transform(x_airline)[0], \"x_airline\"\n", @@ -114,9 +116,9 @@ } ], "source": [ - "from aeon.transformations.series import GaussSeriesTransformer\n", + "from aeon.transformations.series.smoothing import GaussianFilter\n", "\n", - "t = GaussSeriesTransformer()\n", + "t = GaussianFilter()\n", "plot_transformation(t)" ] }, @@ -156,13 +158,13 @@ } ], "source": [ - "from aeon.transformations.series import DFTSeriesTransformer\n", + "from aeon.transformations.series.smoothing import DiscreteFourierApproximation\n", "\n", - "t = DFTSeriesTransformer()\n", - "plot_transformation(t)\n", + "t = DiscreteFourierApproximation()\n", + "plot_transformation(t, title=\"DFA Default\")\n", "\n", - "t = DFTSeriesTransformer(r=0.1, sort=True)\n", - "plot_transformation(t)" + "t = DiscreteFourierApproximation(r=0.1, sort=True)\n", + "plot_transformation(t, title=\"DFA Sorted\")" ] }, { @@ -191,9 +193,9 @@ } ], "source": [ - "from aeon.transformations.series import SIVSeriesTransformer\n", + "from aeon.transformations.series.smoothing import RecursiveMedianSieve\n", "\n", - "t = SIVSeriesTransformer()\n", + "t = RecursiveMedianSieve()\n", "plot_transformation(t)" ] }, @@ -223,9 +225,9 @@ } ], "source": [ - "from aeon.transformations.series import SGSeriesTransformer\n", + "from aeon.transformations.series.smoothing import SavitzkyGolayFilter\n", "\n", - "t = SGSeriesTransformer()\n", + "t = SavitzkyGolayFilter()\n", "plot_transformation(t)" ] }, @@ -255,9 +257,9 @@ } ], "source": [ - "from aeon.transformations.series._moving_average import MovingAverageSeriesTransformer\n", + "from aeon.transformations.series.smoothing import MovingAverage\n", "\n", - "t = MovingAverageSeriesTransformer()\n", + "t = MovingAverage()\n", "plot_transformation(t)" ] }, @@ -287,19 +289,11 @@ } ], "source": [ - "from aeon.transformations.series._exp_smoothing import ExpSmoothingSeriesTransformer\n", + "from aeon.transformations.series.smoothing import ExponentialSmoothing\n", "\n", - "t = ExpSmoothingSeriesTransformer()\n", + "t = ExponentialSmoothing()\n", "plot_transformation(t)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "85639aea-d14f-4b41-9b1f-b72444ecc7ae", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 572c3d81a5b297a89e65ea88662b520c3d1006d0 Mon Sep 17 00:00:00 2001 From: Sebastian Schmidl <10573700+SebastianSchmidl@users.noreply.github.com> Date: Fri, 16 May 2025 19:53:24 +0200 Subject: [PATCH 100/216] [ENH] Refactor range-based metrics to restore original behavior (#2781) * feat: refactor range-based metrics to be consistent with the other AD metrics and integrate them into the AD test harness * refactor: move new range metrics methods into their old place and remove the deprecation * refactor: factor out test execution from test fixtures * refactor: move tests to new methods * fix: copy'n'paste error --- .../metrics/anomaly_detection/__init__.py | 20 +- .../{_binary.py => _range_metrics.py} | 118 ++-- ...{range_metrics.py => _range_ts_metrics.py} | 135 ++++- .../metrics/anomaly_detection/_util.py | 12 +- .../tests/test_ad_metrics.py | 22 +- .../anomaly_detection/tests/test_metrics.py | 572 ------------------ .../tests/test_range_ts_metrics.py | 240 ++++++++ 7 files changed, 442 insertions(+), 677 deletions(-) rename aeon/benchmarking/metrics/anomaly_detection/{_binary.py => _range_metrics.py} (72%) rename aeon/benchmarking/metrics/anomaly_detection/{range_metrics.py => _range_ts_metrics.py} (81%) delete mode 100644 aeon/benchmarking/metrics/anomaly_detection/tests/test_metrics.py create mode 100644 aeon/benchmarking/metrics/anomaly_detection/tests/test_range_ts_metrics.py diff --git a/aeon/benchmarking/metrics/anomaly_detection/__init__.py b/aeon/benchmarking/metrics/anomaly_detection/__init__.py index cf6ccac42c..9e1f2c3e57 100644 --- a/aeon/benchmarking/metrics/anomaly_detection/__init__.py +++ b/aeon/benchmarking/metrics/anomaly_detection/__init__.py @@ -19,11 +19,6 @@ "ts_fscore", ] -from aeon.benchmarking.metrics.anomaly_detection._binary import ( - range_f_score, - range_precision, - range_recall, -) from aeon.benchmarking.metrics.anomaly_detection._continuous import ( f_score_at_k_points, f_score_at_k_ranges, @@ -31,6 +26,16 @@ roc_auc_score, rp_rr_auc_score, ) +from aeon.benchmarking.metrics.anomaly_detection._range_metrics import ( + range_f_score, + range_precision, + range_recall, +) +from aeon.benchmarking.metrics.anomaly_detection._range_ts_metrics import ( + ts_fscore, + ts_precision, + ts_recall, +) from aeon.benchmarking.metrics.anomaly_detection._vus_metrics import ( range_pr_auc_score, range_pr_roc_auc_support, @@ -38,8 +43,3 @@ range_roc_auc_score, range_roc_vus_score, ) -from aeon.benchmarking.metrics.anomaly_detection.range_metrics import ( - ts_fscore, - ts_precision, - ts_recall, -) diff --git a/aeon/benchmarking/metrics/anomaly_detection/_binary.py b/aeon/benchmarking/metrics/anomaly_detection/_range_metrics.py similarity index 72% rename from aeon/benchmarking/metrics/anomaly_detection/_binary.py rename to aeon/benchmarking/metrics/anomaly_detection/_range_metrics.py index 085d7c04f9..7143100c04 100644 --- a/aeon/benchmarking/metrics/anomaly_detection/_binary.py +++ b/aeon/benchmarking/metrics/anomaly_detection/_range_metrics.py @@ -6,19 +6,15 @@ import warnings import numpy as np -from deprecated.sphinx import deprecated +from aeon.benchmarking.metrics.anomaly_detection._range_ts_metrics import ( + _binary_to_ranges, + _ts_precision, + _ts_recall, +) from aeon.benchmarking.metrics.anomaly_detection._util import check_y -from aeon.utils.validation._dependencies import _check_soft_dependencies -# TODO: Remove in v1.2.0 -@deprecated( - version="1.1.0", - reason="range_precision is deprecated and will be removed in v1.2.0. " - "Please use ts_precision from the range_metrics module instead.", - category=FutureWarning, -) def range_precision( y_true: np.ndarray, y_pred: np.ndarray, @@ -28,14 +24,16 @@ def range_precision( ) -> float: """Compute the range-based precision metric. - Range-based metrics were introduced by Tatbul et al. at NeurIPS 2018 [1]_. This - implementation uses the community package `prts `_ - as a soft-dependency. + Range-based metrics were introduced by Tatbul et al. at NeurIPS 2018 [1]_. Range precision is the average precision of each predicted anomaly range. For each predicted continuous anomaly range the overlap size, position, and cardinality is considered. For more details, please refer to the paper [1]_. + The `alpha` parameter for the existence reward was removed. Because precision + emphasizes prediction quality, there is no need for an existence reward and this + value should always be set to 0. + Parameters ---------- y_true : np.ndarray @@ -43,9 +41,7 @@ def range_precision( y_pred : np.ndarray Anomaly scores for each point of the time series of shape (n_instances,). alpha : float - Weight of the existence reward. Because precision by definition emphasizes on - prediction quality, there is no need for an existence reward and this value - should always be set to 0. + DEPRECATED. Default is 0 = no existence reward. cardinality : {'reciprocal', 'one', 'udf_gamma'} Cardinality type. bias : {'flat', 'front', 'middle', 'back'} @@ -64,10 +60,13 @@ def range_precision( 1920–30. 2018. http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series.pdf. """ - _check_soft_dependencies("prts", obj="range_precision", suppress_import_stdout=True) - - from prts import ts_precision - + if alpha != 0: + warnings.warn( + "The alpha parameter should not be used in range precision. This " + "parameter is removed in 1.3.0.", + stacklevel=2, + category=FutureWarning, + ) y_true, y_pred = check_y(y_true, y_pred, force_y_pred_continuous=False) if np.unique(y_pred).shape[0] == 1: warnings.warn( @@ -75,16 +74,14 @@ def range_precision( stacklevel=2, ) return 0.0 - return ts_precision(y_true, y_pred, alpha=alpha, cardinality=cardinality, bias=bias) + + y_pred_ranges = _binary_to_ranges(y_pred) + y_true_ranges = _binary_to_ranges(y_true) + return _ts_precision( + y_pred_ranges, y_true_ranges, gamma=cardinality, bias_type=bias + ) -# TODO: Remove in v1.2.0 -@deprecated( - version="1.1.0", - reason="range_recall is deprecated and will be removed in v1.2.0. " - "Please use ts_recall from the range_metrics module instead.", - category=FutureWarning, -) def range_recall( y_true: np.ndarray, y_pred: np.ndarray, @@ -94,9 +91,7 @@ def range_recall( ) -> float: """Compute the range-based recall metric. - Range-based metrics were introduced by Tatbul et al. at NeurIPS 2018 [1]_. This - implementation uses the community package `prts `_ - as a soft-dependency. + Range-based metrics were introduced by Tatbul et al. at NeurIPS 2018 [1]_. Range recall is the average recall of each real anomaly range. For each real anomaly range the overlap size, position, and cardinality with predicted anomaly @@ -132,10 +127,6 @@ def range_recall( 1920–30. 2018. http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series.pdf. """ - _check_soft_dependencies("prts", obj="range_recall", suppress_import_stdout=True) - - from prts import ts_recall - y_true, y_pred = check_y(y_true, y_pred, force_y_pred_continuous=False) if np.unique(y_pred).shape[0] == 1: warnings.warn( @@ -143,16 +134,14 @@ def range_recall( stacklevel=2, ) return 0.0 - return ts_recall(y_true, y_pred, alpha=alpha, cardinality=cardinality, bias=bias) + + y_pred_ranges = _binary_to_ranges(y_pred) + y_true_ranges = _binary_to_ranges(y_true) + return _ts_recall( + y_pred_ranges, y_true_ranges, alpha=alpha, gamma=cardinality, bias_type=bias + ) -# TODO: Remove in v1.2.0 -@deprecated( - version="1.1.0", - reason="range_f_score is deprecated and will be removed in v1.2.0. " - "Please use ts_fscore from the range_metrics module instead.", - category=FutureWarning, -) def range_f_score( y_true: np.ndarray, y_pred: np.ndarray, @@ -165,14 +154,15 @@ def range_f_score( ) -> float: """Compute the F-score using the range-based recall and precision metrics. - Range-based metrics were introduced by Tatbul et al. at NeurIPS 2018 [1]_. This - implementation uses the community package `prts `_ - as a soft-dependency. + Range-based metrics were introduced by Tatbul et al. at NeurIPS 2018 [1]_. The F-beta score is the weighted harmonic mean of precision and recall, reaching its optimal value at 1 and its worst value at 0. This implementation uses the range-based precision and range-based recall as basis. + The `p_alpha` parameter for the potential existance reward in the calculation of + range-based precision was removed. `p_alpha` should always be set to 0, anyway. + Parameters ---------- y_true : np.ndarray @@ -183,8 +173,7 @@ def range_f_score( F-score beta determines the weight of recall in the combined score. beta < 1 lends more weight to precision, while beta > 1 favors recall. p_alpha : float - Weight of the existence reward for the range-based precision. For most - when - not all - cases, `p_alpha` should be set to 0. + DEPRECATED. Default is 0 = no existence reward for precision. r_alpha : float Weight of the existence reward. If 0: no existence reward, if 1: only existence reward. @@ -208,9 +197,13 @@ def range_f_score( 1920–30. 2018. http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series.pdf. """ - _check_soft_dependencies("prts", obj="range_recall", suppress_import_stdout=True) - - from prts import ts_fscore + if p_alpha != 0: + warnings.warn( + "The p_alpha parameter should not be used. This parameter is removed " + "in 1.3.0.", + stacklevel=2, + category=FutureWarning, + ) y_true, y_pred = check_y(y_true, y_pred, force_y_pred_continuous=False) if np.unique(y_pred).shape[0] == 1: @@ -219,13 +212,18 @@ def range_f_score( stacklevel=2, ) return 0.0 - return ts_fscore( - y_true, - y_pred, - beta=beta, - p_alpha=p_alpha, - r_alpha=r_alpha, - cardinality=cardinality, - p_bias=p_bias, - r_bias=r_bias, - ) + + y_pred_ranges = _binary_to_ranges(y_pred) + y_true_ranges = _binary_to_ranges(y_true) + + precision = _ts_precision(y_pred_ranges, y_true_ranges, cardinality, p_bias) + recall = _ts_recall(y_pred_ranges, y_true_ranges, cardinality, r_bias, r_alpha) + + if precision + recall > 0: + fscore = ((1 + beta**2) * (precision * recall)) / ( + beta**2 * (precision + recall) + ) + else: + fscore = 0.0 + + return fscore diff --git a/aeon/benchmarking/metrics/anomaly_detection/range_metrics.py b/aeon/benchmarking/metrics/anomaly_detection/_range_ts_metrics.py similarity index 81% rename from aeon/benchmarking/metrics/anomaly_detection/range_metrics.py rename to aeon/benchmarking/metrics/anomaly_detection/_range_ts_metrics.py index 9084188f59..12ad2c0eaa 100644 --- a/aeon/benchmarking/metrics/anomaly_detection/range_metrics.py +++ b/aeon/benchmarking/metrics/anomaly_detection/_range_ts_metrics.py @@ -1,9 +1,17 @@ """Calculate Precision, Recall, and F1-Score for time series anomaly detection.""" __maintainer__ = [] -__all__ = ["ts_precision", "ts_recall", "ts_fscore"] +__all__ = [ + "ts_precision", + "ts_recall", + "ts_fscore", + "_flatten_ranges", + "_ts_precision", + "_ts_recall", +] import numpy as np +from deprecated.sphinx import deprecated def _flatten_ranges(ranges): @@ -36,7 +44,7 @@ def _flatten_ranges(ranges): return ranges -def udf_gamma_def(overlap_count): +def _udf_gamma_def(overlap_count): """User-defined gamma function. Should return a gamma value > 1. Parameters @@ -109,8 +117,8 @@ def _gamma_select(cardinality, gamma): elif gamma == "reciprocal": return 1 / cardinality if cardinality > 1 else 1.0 elif gamma == "udf_gamma": - if udf_gamma_def(cardinality) is not None: - return 1.0 / udf_gamma_def(cardinality) + if _udf_gamma_def(cardinality) is not None: + return 1.0 / _udf_gamma_def(cardinality) else: raise ValueError("udf_gamma must be provided for 'udf_gamma' gamma type.") else: @@ -218,6 +226,13 @@ def _binary_to_ranges(binary_sequence): return ranges +# TODO: remove in v1.3.0 +@deprecated( + version="1.2.0", + reason="ts_precision is deprecated and will be removed in v1.3.0. " + "Please use range_precision instead.", + category=FutureWarning, +) def ts_precision(y_pred, y_real, gamma="one", bias_type="flat"): """ Calculate Precision for time series anomaly detection. @@ -271,11 +286,11 @@ def ts_precision(y_pred, y_real, gamma="one", bias_type="flat"): # Check if inputs are binary or range-based is_binary = False if isinstance(y_pred, (list, tuple, np.ndarray)) and isinstance( - y_pred[0], (int, np.integer) + y_pred[0], (int, bool, np.integer) ): is_binary = True - elif isinstance(y_real, (list, tuple, np.ndarray)) and isinstance( - y_real[0], (int, np.integer) + if isinstance(y_real, (list, tuple, np.ndarray)) and isinstance( + y_real[0], (int, bool, np.integer) ): is_binary = True @@ -298,21 +313,54 @@ def ts_precision(y_pred, y_real, gamma="one", bias_type="flat"): y_pred_ranges = y_pred y_real_ranges = y_real - if gamma not in ["reciprocal", "one"]: - raise ValueError("Invalid gamma type for precision. Use 'reciprocal' or 'one'.") - # Flattening y_pred and y_real to resolve nested lists flat_y_pred = _flatten_ranges(y_pred_ranges) flat_y_real = _flatten_ranges(y_real_ranges) + return _ts_precision(flat_y_pred, flat_y_real, gamma, bias_type) + + +def _ts_precision(y_pred_ranges, y_real_ranges, gamma="one", bias_type="flat"): + """ + Implement range-based precision for time series anomaly detection. + + Parameters + ---------- + y_pred : list of predicted anomaly ranges: each tuple in the list represents an + interval [start, end) of a detected anomaly. + y_real : list of true anomaly ranges: each tuple in the list represents an interval + [start, end) of a true anomaly. + bias_type : str, default="flat" + Type of bias to apply. Should be one of ["flat", "front", "middle", "back"]. + gamma : str, default="one" + Cardinality type. Should be one of ["reciprocal", "one"]. + + Returns + ------- + float + Range-based precision + """ + if gamma not in ["reciprocal", "one"]: + raise ValueError("Invalid gamma type for precision. Use 'reciprocal' or 'one'.") + + if bias_type not in [ + "flat", + "front", + "middle", + "back", + ]: + raise ValueError( + "Invalid bias type. Choose from ['flat', 'front', 'middle', 'back']." + ) + total_overlap_reward = 0.0 total_cardinality = 0 - for pred_range in flat_y_pred: + for pred_range in y_pred_ranges: overlap_set = set() cardinality = 0 - for real_start, real_end in flat_y_real: + for real_start, real_end in y_real_ranges: overlap_start = max(pred_range[0], real_start) overlap_end = min(pred_range[1], real_end) @@ -333,6 +381,13 @@ def ts_precision(y_pred, y_real, gamma="one", bias_type="flat"): return precision +# TODO: remove in v1.3.0 +@deprecated( + version="1.2.0", + reason="ts_recall is deprecated and will be removed in v1.3.0. " + "Please use range_recall instead.", + category=FutureWarning, +) def ts_recall(y_pred, y_real, gamma="one", bias_type="flat", alpha=0.0): """ Calculate Recall for time series anomaly detection. @@ -385,11 +440,11 @@ def ts_recall(y_pred, y_real, gamma="one", bias_type="flat", alpha=0.0): """ is_binary = False if isinstance(y_pred, (list, tuple, np.ndarray)) and isinstance( - y_pred[0], (int, np.integer) + y_pred[0], (int, bool, np.integer) ): is_binary = True - elif isinstance(y_real, (list, tuple, np.ndarray)) and isinstance( - y_real[0], (int, np.integer) + if isinstance(y_real, (list, tuple, np.ndarray)) and isinstance( + y_real[0], (int, bool, np.integer) ): is_binary = True @@ -415,14 +470,51 @@ def ts_recall(y_pred, y_real, gamma="one", bias_type="flat", alpha=0.0): # Flattening y_pred and y_real to resolve nested lists flat_y_pred = _flatten_ranges(y_pred_ranges) flat_y_real = _flatten_ranges(y_real_ranges) + return _ts_recall(flat_y_pred, flat_y_real, gamma, bias_type, alpha) + + +def _ts_recall(y_pred_ranges, y_real_ranges, gamma="one", bias_type="flat", alpha=0.0): + """ + Implement range-based recall for time series anomaly detection. + + Parameters + ---------- + y_pred : list of predicted anomaly ranges: each tuple in the list represents an + interval [start, end) of a detected anomaly. + y_real : list of true anomaly ranges: each tuple in the list represents an interval + [start, end) of a true anomaly. + gamma : str, default="one" + Cardinality type. Should be one of ["reciprocal", "one", "udf_gamma"]. + bias_type : str, default="flat" + Type of bias to apply. Should be one of ["flat", "front", "middle", "back"]. + alpha : float, default: 0.0 + Weight for existence reward in recall calculation. + + Returns + ------- + float + Range-based recall + """ + if gamma not in ["reciprocal", "one", "udf_gamma"]: + raise ValueError("Invalid gamma type for precision. Use 'reciprocal' or 'one'.") + + if bias_type not in [ + "flat", + "front", + "middle", + "back", + ]: + raise ValueError( + "Invalid bias type. Choose from ['flat', 'front', 'middle', 'back']." + ) total_overlap_reward = 0.0 - for real_range in flat_y_real: + for real_range in y_real_ranges: overlap_set = set() cardinality = 0 - for pred_range in flat_y_pred: + for pred_range in y_pred_ranges: overlap_start = max(real_range[0], pred_range[0]) overlap_end = min(real_range[1], pred_range[1]) @@ -444,10 +536,17 @@ def ts_recall(y_pred, y_real, gamma="one", bias_type="flat", alpha=0.0): recall_score = alpha * existence_reward + (1 - alpha) * overlap_reward total_overlap_reward += recall_score - recall = total_overlap_reward / len(flat_y_real) if flat_y_real else 0.0 + recall = total_overlap_reward / len(y_real_ranges) if y_real_ranges else 0.0 return recall +# TODO: remove in v1.3.0 +@deprecated( + version="1.2.0", + reason="ts_fscore is deprecated and will be removed in v1.3.0. " + "Please use range_f_score instead.", + category=FutureWarning, +) def ts_fscore( y_pred, y_real, diff --git a/aeon/benchmarking/metrics/anomaly_detection/_util.py b/aeon/benchmarking/metrics/anomaly_detection/_util.py index 9cf2bfc0e2..2d196d8d74 100644 --- a/aeon/benchmarking/metrics/anomaly_detection/_util.py +++ b/aeon/benchmarking/metrics/anomaly_detection/_util.py @@ -56,8 +56,8 @@ def check_y( # check labels if ( force_y_pred_continuous - and y_true.dtype == np.float64 - and y_pred.dtype == np.int_ + and np.issubdtype(y_true.dtype, np.floating) + and np.issubdtype(y_pred.dtype, np.integer) ): warnings.warn( "Assuming that y_true and y_score where permuted, because their" @@ -81,14 +81,16 @@ def check_y( y_pred = column_or_1d(y_pred) check_consistent_length([y_true, y_pred]) - if not force_y_pred_continuous and y_pred.dtype not in [np.int_, np.bool_]: + if not force_y_pred_continuous and not ( + np.issubdtype(y_pred.dtype, np.integer) or np.issubdtype(y_pred.dtype, bool) + ): raise ValueError( "When using metrics other than AUC/VUS-metrics that need binary " "(0 or 1) scores (like Precision, Recall or F1-Score), the scores must " "be integers and should only contain the values {0, 1}. Please " "consider applying a threshold to the scores!" ) - elif force_y_pred_continuous and y_pred.dtype != np.float64: + elif force_y_pred_continuous and not np.issubdtype(y_pred.dtype, np.floating): raise ValueError( "When using continuous scoring metrics, the scores must be floats!" ) @@ -111,7 +113,7 @@ def check_y( else: penalize_mask = penalize_mask | nan_mask y_pred[penalize_mask] = (~np.array(y_true[penalize_mask], dtype=bool)).astype( - np.int_ + np.int32 ) assert_all_finite(y_pred) diff --git a/aeon/benchmarking/metrics/anomaly_detection/tests/test_ad_metrics.py b/aeon/benchmarking/metrics/anomaly_detection/tests/test_ad_metrics.py index b7dca56380..fd14aa3919 100644 --- a/aeon/benchmarking/metrics/anomaly_detection/tests/test_ad_metrics.py +++ b/aeon/benchmarking/metrics/anomaly_detection/tests/test_ad_metrics.py @@ -7,16 +7,18 @@ f_score_at_k_points, f_score_at_k_ranges, pr_auc_score, - range_f_score, range_pr_auc_score, range_pr_vus_score, - range_precision, - range_recall, range_roc_auc_score, range_roc_vus_score, roc_auc_score, rp_rr_auc_score, ) +from aeon.benchmarking.metrics.anomaly_detection._range_metrics import ( + range_f_score, + range_precision, + range_recall, +) from aeon.testing.data_generation import make_example_1d_numpy from aeon.utils.validation._dependencies import _check_soft_dependencies @@ -32,17 +34,14 @@ f_score_at_k_points, ] continuous_metrics = [*pr_metrics, *other_metrics, *range_metrics] -binary_metrics = [] +binary_metrics = [ + range_f_score, + range_precision, + range_recall, +] if _check_soft_dependencies("prts", severity="none"): pr_metrics.append(rp_rr_auc_score) - range_metrics.extend( - [ - range_recall, - range_precision, - range_f_score, - ] - ) other_metrics.extend( [ f_score_at_k_ranges, @@ -55,7 +54,6 @@ f_score_at_k_ranges, ] ) - binary_metrics = [range_recall, range_precision, range_f_score] metrics = [*pr_metrics, *range_metrics, *other_metrics] diff --git a/aeon/benchmarking/metrics/anomaly_detection/tests/test_metrics.py b/aeon/benchmarking/metrics/anomaly_detection/tests/test_metrics.py deleted file mode 100644 index 0fbbe16fa3..0000000000 --- a/aeon/benchmarking/metrics/anomaly_detection/tests/test_metrics.py +++ /dev/null @@ -1,572 +0,0 @@ -"""Test cases for the range-based anomaly detection metrics.""" - -import numpy as np - -from aeon.benchmarking.metrics.anomaly_detection.range_metrics import ( - ts_fscore, - ts_precision, - ts_recall, -) - - -def test_single_overlapping_range(): - """Test for single overlapping range.""" - y_pred = np.array([0, 1, 1, 1, 1, 0, 0]) - y_real = np.array([0, 0, 1, 1, 1, 1, 1]) - expected_precision = 0.750000 - expected_recall = 0.600000 - expected_f1 = 0.666667 - - precision = ts_precision(y_pred, y_real, gamma="one", bias_type="flat") - recall = ts_recall( - y_pred, - y_real, - gamma="one", - bias_type="flat", - alpha=0.0, - ) - f1_score = ts_fscore( - y_pred, - y_real, - gamma="one", - p_bias="flat", - r_bias="flat", - p_alpha=0.0, - r_alpha=0.0, - ) - - np.testing.assert_almost_equal( - precision, - expected_precision, - decimal=6, - err_msg=( - f"Precision failed for single overlapping range! " - f"Expected={expected_precision}, Got={precision}" - ), - ) - np.testing.assert_almost_equal( - recall, - expected_recall, - decimal=6, - err_msg=( - f"Recall failed for single overlapping range! " - f"Expected={expected_recall}, Got={recall}" - ), - ) - np.testing.assert_almost_equal( - f1_score, - expected_f1, - decimal=6, - err_msg=( - f"F1-Score failed for single overlapping range! " - f"Expected={expected_f1}, Got={f1_score}" - ), - ) - - -def test_multiple_non_overlapping_ranges(): - """Test for multiple non-overlapping ranges.""" - y_pred = np.array([0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0]) - y_real = np.array([0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1]) - - expected_precision = 0.000000 - expected_recall = 0.000000 - expected_f1 = 0.000000 - - precision = ts_precision(y_pred, y_real, gamma="one", bias_type="flat") - recall = ts_recall( - y_pred, - y_real, - gamma="one", - bias_type="flat", - alpha=0.0, - ) - f1_score = ts_fscore( - y_pred, - y_real, - gamma="one", - beta=1, - p_bias="flat", - r_bias="flat", - p_alpha=0.0, - r_alpha=0.0, - ) - - np.testing.assert_almost_equal( - precision, - expected_precision, - decimal=6, - err_msg=( - f"Precision failed for multiple non-overlapping ranges! " - f"Expected={expected_precision}, Got={precision}" - ), - ) - np.testing.assert_almost_equal( - recall, - expected_recall, - decimal=6, - err_msg=( - f"Recall failed for multiple non-overlapping ranges! " - f"Expected={expected_recall}, Got={recall}" - ), - ) - np.testing.assert_almost_equal( - f1_score, - expected_f1, - decimal=6, - err_msg=( - f"F1-Score failed for multiple non-overlapping ranges! " - f"Expected={expected_f1}, Got={f1_score}" - ), - ) - - -def test_multiple_overlapping_ranges(): - """Test for multiple overlapping ranges.""" - y_pred = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]) - y_real = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]) - - expected_precision = 0.666667 - expected_recall = 0.400000 - expected_f1 = 0.500000 - - precision = ts_precision(y_pred, y_real, gamma="one", bias_type="flat") - recall = ts_recall( - y_pred, - y_real, - gamma="one", - bias_type="flat", - alpha=0.0, - ) - f1_score = ts_fscore( - y_pred, - y_real, - gamma="one", - beta=1, - p_bias="flat", - r_bias="flat", - p_alpha=0.0, - r_alpha=0.0, - ) - - np.testing.assert_almost_equal( - precision, - expected_precision, - decimal=6, - err_msg=( - f"Precision failed for multiple overlapping ranges! " - f"Expected={expected_precision}, Got={precision}" - ), - ) - np.testing.assert_almost_equal( - recall, - expected_recall, - decimal=6, - err_msg=( - f"Recall failed for multiple overlapping ranges! " - f"Expected={expected_recall}, Got={recall}" - ), - ) - np.testing.assert_almost_equal( - f1_score, - expected_f1, - decimal=6, - err_msg=( - f"F1-Score failed for multiple overlapping ranges! " - f"Expected={expected_f1}, Got={f1_score}" - ), - ) - - -def test_nested_lists_of_predictions(): - """Test for nested lists of predictions.""" - y_pred = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1]) - y_real = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0]) - - expected_precision = 0.555556 - expected_recall = 0.566667 - expected_f1 = 0.561056 - - precision = ts_precision(y_pred, y_real, gamma="one", bias_type="flat") - recall = ts_recall( - y_pred, - y_real, - gamma="one", - bias_type="flat", - alpha=0.0, - ) - f1_score = ts_fscore( - y_pred, - y_real, - gamma="one", - beta=1, - p_bias="flat", - r_bias="flat", - p_alpha=0.0, - r_alpha=0.0, - ) - - np.testing.assert_almost_equal( - precision, - expected_precision, - decimal=6, - err_msg=( - f"Precision failed for nested lists of predictions! " - f"Expected={expected_precision}, Got={precision}" - ), - ) - np.testing.assert_almost_equal( - recall, - expected_recall, - decimal=6, - err_msg=( - f"Recall failed for nested lists of predictions! " - f"Expected={expected_recall}, Got={recall}" - ), - ) - np.testing.assert_almost_equal( - f1_score, - expected_f1, - decimal=6, - err_msg=( - f"F1-Score failed for nested lists of predictions! " - f"Expected={expected_f1}, Got={f1_score}" - ), - ) - - -def test_all_encompassing_range(): - """Test for all encompassing range.""" - y_pred = np.array([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) - y_real = np.array([0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0]) - - expected_precision = 0.600000 - expected_recall = 1.000000 - expected_f1 = 0.750000 - - precision = ts_precision(y_pred, y_real, gamma="one", bias_type="flat") - recall = ts_recall( - y_pred, - y_real, - gamma="one", - bias_type="flat", - alpha=0.0, - ) - f1_score = ts_fscore( - y_pred, - y_real, - gamma="one", - beta=1, - p_bias="flat", - r_bias="flat", - p_alpha=0.0, - r_alpha=0.0, - ) - - np.testing.assert_almost_equal( - precision, - expected_precision, - decimal=6, - err_msg=( - f"Precision failed for all encompassing range! " - f"Expected={expected_precision}, Got={precision}" - ), - ) - np.testing.assert_almost_equal( - recall, - expected_recall, - decimal=6, - err_msg=( - f"Recall failed for all encompassing range! " - f"Expected={expected_recall}, Got={recall}" - ), - ) - np.testing.assert_almost_equal( - f1_score, - expected_f1, - decimal=6, - err_msg=( - f"F1-Score failed for all encompassing range! " - f"Expected={expected_f1}, Got={f1_score}" - ), - ) - - -def test_range_based_input(): - """Test with input being range-based or bianry-based.""" - y_pred_range = [(1, 2)] - y_true_range = [(1, 1)] - y_pred_binary = np.array([0, 1, 1, 0]) - y_true_binary = np.array([0, 1, 0, 0]) - - expected_precision = 0.5 - expected_recall = 1.000000 - expected_f1 = 0.666667 - - # for range-based input - precision_range = ts_precision( - y_pred_range, y_true_range, gamma="reciprocal", bias_type="flat" - ) - recall_range = ts_recall( - y_pred_range, - y_true_range, - gamma="reciprocal", - bias_type="flat", - alpha=0.0, - ) - f1_score_range = ts_fscore( - y_pred_range, - y_true_range, - gamma="reciprocal", - p_bias="flat", - r_bias="flat", - p_alpha=0.0, - r_alpha=0.0, - ) - - np.testing.assert_almost_equal( - precision_range, - expected_precision, - decimal=6, - err_msg=( - f"Precision mismatch: " - f"ts_precision={precision_range} vs" - f"expected_precision_range={expected_precision}" - ), - ) - np.testing.assert_almost_equal( - recall_range, - expected_recall, - decimal=6, - err_msg=( - f"Recall mismatch: " - f"ts_recall={recall_range} vs expected_recall_range={expected_recall}" - ), - ) - np.testing.assert_almost_equal( - f1_score_range, - expected_f1, - decimal=6, - err_msg=( - f"F1-Score mismatch: " - f"ts_fscore={f1_score_range} vs expected_f_score_range={expected_f1}" - ), - ) - - # for binary input - precision_binary = ts_precision( - y_pred_binary, y_true_binary, gamma="reciprocal", bias_type="flat" - ) - recall_binary = ts_recall( - y_pred_binary, - y_true_binary, - gamma="reciprocal", - bias_type="flat", - alpha=0.0, - ) - f1_score_binary = ts_fscore( - y_pred_binary, - y_true_binary, - gamma="reciprocal", - p_bias="flat", - r_bias="flat", - p_alpha=0.0, - r_alpha=0.0, - ) - - np.testing.assert_almost_equal( - precision_binary, - expected_precision, - decimal=6, - err_msg=( - f"Precision mismatch: " - f"ts_precision={precision_range} vs " - f"expected_precision_binary={expected_precision}" - ), - ) - np.testing.assert_almost_equal( - recall_binary, - expected_recall, - decimal=6, - err_msg=( - f"Recall mismatch: " - f"ts_recall={recall_range} vs expected_recall_binary={expected_recall}" - ), - ) - np.testing.assert_almost_equal( - f1_score_binary, - expected_f1, - decimal=6, - err_msg=( - f"F1-Score mismatch: " - f"ts_fscore={f1_score_range} vs expected_f_score_binary={expected_f1}" - ), - ) - - -def test_multiple_overlapping_ranges_with_gamma_reciprocal(): - """Test for multiple overlapping ranges with gamma=reciprocal.""" - y_pred = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]) - y_real = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]) - expected_precision = 0.666667 - expected_recall = 0.200000 - expected_f1 = 0.307692 - - precision = ts_precision(y_pred, y_real, gamma="reciprocal", bias_type="flat") - recall = ts_recall( - y_pred, - y_real, - gamma="reciprocal", - bias_type="flat", - alpha=0.0, - ) - f1_score = ts_fscore( - y_pred, - y_real, - gamma="reciprocal", - beta=1, - p_bias="flat", - r_bias="flat", - p_alpha=0.0, - r_alpha=0.0, - ) - - np.testing.assert_almost_equal( - precision, - expected_precision, - decimal=6, - err_msg=( - f"Precision failed for multiple overlapping ranges! " - f"Expected={expected_precision}, Got={precision}" - ), - ) - np.testing.assert_almost_equal( - recall, - expected_recall, - decimal=6, - err_msg=( - f"Recall failed for multiple overlapping ranges! " - f"Expected={expected_recall}, Got={recall}" - ), - ) - np.testing.assert_almost_equal( - f1_score, - expected_f1, - decimal=6, - err_msg=( - f"F1-Score failed for multiple overlapping ranges! " - f"Expected={expected_f1}, Got={f1_score}" - ), - ) - - -def test_multiple_overlapping_ranges_with_bias_middle(): - """Test for multiple overlapping ranges with bias_type=middle.""" - y_pred = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]) - y_real = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]) - expected_precision = 0.750000 - expected_recall = 0.333333 - expected_f1 = 0.461538 - - precision = ts_precision(y_pred, y_real, gamma="one", bias_type="middle") - recall = ts_recall( - y_pred, - y_real, - gamma="one", - bias_type="middle", - alpha=0.0, - ) - f1_score = ts_fscore( - y_pred, - y_real, - gamma="one", - beta=1, - p_bias="middle", - r_bias="middle", - p_alpha=0.0, - r_alpha=0.0, - ) - - np.testing.assert_almost_equal( - precision, - expected_precision, - decimal=6, - err_msg=( - f"Precision failed for multiple overlapping ranges! " - f"Expected={expected_precision}, Got={precision}" - ), - ) - np.testing.assert_almost_equal( - recall, - expected_recall, - decimal=6, - err_msg=( - f"Recall failed for multiple overlapping ranges! " - f"Expected={expected_recall}, Got={recall}" - ), - ) - np.testing.assert_almost_equal( - f1_score, - expected_f1, - decimal=6, - err_msg=( - f"F1-Score failed for multiple overlapping ranges! " - f"Expected={expected_f1}, Got={f1_score}" - ), - ) - - -def test_multiple_overlapping_ranges_with_bias_middle_gamma_reciprocal(): - """Test for multiple overlapping ranges with bias_type=middle, gamma=reciprocal.""" - y_pred = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]) - y_real = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]) - expected_precision = 0.750000 - expected_recall = 0.166667 - expected_f1 = 0.272727 - - precision = ts_precision(y_pred, y_real, gamma="reciprocal", bias_type="middle") - recall = ts_recall( - y_pred, - y_real, - gamma="reciprocal", - bias_type="middle", - alpha=0.0, - ) - f1_score = ts_fscore( - y_pred, - y_real, - gamma="reciprocal", - beta=1, - p_bias="middle", - r_bias="middle", - p_alpha=0.0, - r_alpha=0.0, - ) - - np.testing.assert_almost_equal( - precision, - expected_precision, - decimal=6, - err_msg=( - f"Precision failed for multiple overlapping ranges! " - f"Expected={expected_precision}, Got={precision}" - ), - ) - np.testing.assert_almost_equal( - recall, - expected_recall, - decimal=6, - err_msg=( - f"Recall failed for multiple overlapping ranges! " - f"Expected={expected_recall}, Got={recall}" - ), - ) - np.testing.assert_almost_equal( - f1_score, - expected_f1, - decimal=6, - err_msg=( - f"F1-Score failed for multiple overlapping ranges! " - f"Expected={expected_f1}, Got={f1_score}" - ), - ) diff --git a/aeon/benchmarking/metrics/anomaly_detection/tests/test_range_ts_metrics.py b/aeon/benchmarking/metrics/anomaly_detection/tests/test_range_ts_metrics.py new file mode 100644 index 0000000000..0d2eb9eccd --- /dev/null +++ b/aeon/benchmarking/metrics/anomaly_detection/tests/test_range_ts_metrics.py @@ -0,0 +1,240 @@ +"""Test cases for the range-based anomaly detection metrics.""" + +import numpy as np + +from aeon.benchmarking.metrics.anomaly_detection._range_metrics import ( + range_f_score, + range_precision, + range_recall, +) +from aeon.benchmarking.metrics.anomaly_detection._range_ts_metrics import ( + ts_fscore, + ts_precision, + ts_recall, +) + + +def _execute_test_case( + y_true: np.ndarray, + y_pred: np.ndarray, + expected_precision: float, + expected_recall: float, + expected_f1: float, + name: str = "test case", + cardinality: str = "one", + bias: str = "flat", + floating_precision: int = 6, +) -> None: + precision = range_precision(y_true, y_pred, cardinality=cardinality, bias=bias) + recall = range_recall(y_true, y_pred, cardinality=cardinality, bias=bias) + f1_score = range_f_score( + y_true, + y_pred, + cardinality=cardinality, + p_bias=bias, + r_bias=bias, + p_alpha=0.0, + r_alpha=0.0, + beta=1.0, + ) + + np.testing.assert_almost_equal( + precision, + expected_precision, + decimal=floating_precision, + err_msg=( + f"Precision failed for {name}! " + f"Expected={expected_precision}, Got={precision}" + ), + ) + np.testing.assert_almost_equal( + recall, + expected_recall, + decimal=floating_precision, + err_msg=( + f"Recall failed for {name}! " f"Expected={expected_recall}, Got={recall}" + ), + ) + np.testing.assert_almost_equal( + f1_score, + expected_f1, + decimal=floating_precision, + err_msg=( + f"F1-Score failed for {name}! " f"Expected={expected_f1}, Got={f1_score}" + ), + ) + + +def test_single_overlapping_range(): + """Test for single overlapping range.""" + _execute_test_case( + y_true=np.array([0, 0, 1, 1, 1, 1, 1]), + y_pred=np.array([0, 1, 1, 1, 1, 0, 0]), + expected_precision=0.750000, + expected_recall=0.600000, + expected_f1=0.666667, + name="single overlapping range", + ) + + +def test_multiple_non_overlapping_ranges(): + """Test for multiple non-overlapping ranges.""" + _execute_test_case( + y_true=np.array([0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1]), + y_pred=np.array([0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0]), + expected_precision=0.000000, + expected_recall=0.000000, + expected_f1=0.000000, + name="multiple non-overlapping range", + ) + + +def test_multiple_overlapping_ranges(): + """Test for multiple overlapping ranges.""" + _execute_test_case( + y_true=np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]), + y_pred=np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]), + expected_precision=0.666667, + expected_recall=0.400000, + expected_f1=0.500000, + name="multiple overlapping ranges", + ) + + +def test_nested_lists_of_predictions(): + """Test for nested lists of predictions.""" + _execute_test_case( + y_true=np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0]), + y_pred=np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1]), + expected_precision=0.555556, + expected_recall=0.566667, + expected_f1=0.561056, + name="nested lists of predictions", + ) + + +def test_all_encompassing_range(): + """Test for all encompassing range.""" + _execute_test_case( + y_true=np.array([0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0]), + y_pred=np.array([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]), + expected_precision=0.600000, + expected_recall=1.000000, + expected_f1=0.750000, + name="all encompassing range", + ) + + +def test_multiple_overlapping_ranges_with_gamma_reciprocal(): + """Test for multiple overlapping ranges with gamma=reciprocal.""" + _execute_test_case( + y_true=np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]), + y_pred=np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]), + expected_precision=0.666667, + expected_recall=0.200000, + expected_f1=0.307692, + name="multiple overlapping ranges with reciprocal cardinality", + cardinality="reciprocal", + ) + + +def test_multiple_overlapping_ranges_with_bias_middle(): + """Test for multiple overlapping ranges with bias_type=middle.""" + _execute_test_case( + y_true=np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]), + y_pred=np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]), + expected_precision=0.750000, + expected_recall=0.333333, + expected_f1=0.461538, + name="multiple overlapping ranges with middle bias", + bias="middle", + ) + + +def test_multiple_overlapping_ranges_with_bias_middle_gamma_reciprocal(): + """Test for multiple overlapping ranges with bias_type=middle, gamma=reciprocal.""" + _execute_test_case( + y_true=np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]), + y_pred=np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]), + expected_precision=0.750000, + expected_recall=0.166667, + expected_f1=0.272727, + name="multiple overlapping ranges with middle bias and reciprocal cardinality", + cardinality="reciprocal", + bias="middle", + ) + + +# TODO: remove in v1.3.0 +def test_range_based_input(): + """Test with input being range-based or binary-based.""" + y_pred_range = [(1, 2)] + y_true_range = [(1, 1)] + y_pred_binary = np.array([0, 1, 1, 0]) + y_true_binary = np.array([0, 1, 0, 0]) + + expected_precision = 0.5 + expected_recall = 1.000000 + expected_f1 = 0.666667 + + # for range-based input + precision_range = ts_precision( + y_pred_range, y_true_range, gamma="reciprocal", bias_type="flat" + ) + recall_range = ts_recall( + y_pred_range, + y_true_range, + gamma="reciprocal", + bias_type="flat", + alpha=0.0, + ) + f1_score_range = ts_fscore( + y_pred_range, + y_true_range, + gamma="reciprocal", + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision_range, + expected_precision, + decimal=6, + err_msg=( + f"Precision mismatch: " + f"ts_precision={precision_range} vs" + f"expected_precision_range={expected_precision}" + ), + ) + np.testing.assert_almost_equal( + recall_range, + expected_recall, + decimal=6, + err_msg=( + f"Recall mismatch: " + f"ts_recall={recall_range} vs expected_recall_range={expected_recall}" + ), + ) + np.testing.assert_almost_equal( + f1_score_range, + expected_f1, + decimal=6, + err_msg=( + f"F1-Score mismatch: " + f"ts_fscore={f1_score_range} vs expected_f_score_range={expected_f1}" + ), + ) + + # for binary input + _execute_test_case( + y_true=y_true_binary, + y_pred=y_pred_binary, + expected_precision=expected_precision, + expected_recall=expected_recall, + expected_f1=expected_f1, + name="binary input is inconsistent with range input", + cardinality="reciprocal", + bias="flat", + ) From 90ea9a6ac73f44b4333636351111da567f1683e6 Mon Sep 17 00:00:00 2001 From: steenrotsman <78110080+steenrotsman@users.noreply.github.com> Date: Fri, 16 May 2025 19:57:57 +0200 Subject: [PATCH 101/216] [ENH]Use n_jobs parameter in KNeighborsTimeSeriesClassifier. (#2687) * Use n_jobs parameter in KNeighborsTimeSeriesClassifier. * Don't convert proba dtype. * Remove is_fitted checks, update docstring. * Implement parallel backend param. * Correct keyword argument for Parallel. * Add check_n_jobs. --------- Co-authored-by: stijn --- .../distance_based/_time_series_neighbors.py | 61 +++++++++++-------- 1 file changed, 36 insertions(+), 25 deletions(-) diff --git a/aeon/classification/distance_based/_time_series_neighbors.py b/aeon/classification/distance_based/_time_series_neighbors.py index ded113b69e..fdf588b118 100644 --- a/aeon/classification/distance_based/_time_series_neighbors.py +++ b/aeon/classification/distance_based/_time_series_neighbors.py @@ -13,9 +13,11 @@ from typing import Callable, Union import numpy as np +from joblib import Parallel, delayed from aeon.classification.base import BaseClassifier from aeon.distances import get_distance_function +from aeon.utils.validation import check_n_jobs WEIGHTS_SUPPORTED = ["uniform", "distance"] @@ -46,11 +48,15 @@ class KNeighborsTimeSeriesClassifier(BaseClassifier): n_timepoints)`` as input and returns a float. distance_params : dict, default = None Dictionary for metric parameters for the case that distance is a str. - n_jobs : int, default = None + n_jobs : int, default = 1 The number of parallel jobs to run for neighbors search. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. - for more details. Parameter for compatibility purposes, still unimplemented. + parallel_backend : str, ParallelBackendBase instance or None, default=None + Specify the parallelisation backend implementation in joblib, if None + a ‘prefer’ value of “threads” is used by default. Valid options are + “loky”, “multiprocessing”, “threading” or a custom backend. + See the joblib Parallel documentation for more details. Raises ------ @@ -85,11 +91,13 @@ def __init__( n_neighbors: int = 1, weights: Union[str, Callable] = "uniform", n_jobs: int = 1, + parallel_backend: str = None, ) -> None: self.distance = distance self.distance_params = distance_params self.n_neighbors = n_neighbors self.n_jobs = n_jobs + self.parallel_backend = parallel_backend self._distance_params = distance_params if self._distance_params is None: @@ -140,16 +148,11 @@ def _predict_proba(self, X): The class probabilities of the input samples. Classes are ordered by lexicographic order. """ - preds = np.zeros((len(X), len(self.classes_))) - for i in range(len(X)): - idx, weights = self._kneighbors(X[i]) - for id, w in zip(idx, weights): - predicted_class = self.y_[id] - preds[i, predicted_class] += w - - preds[i] = preds[i] / np.sum(preds[i]) - - return preds + n_jobs = check_n_jobs(self.n_jobs) + preds = Parallel(n_jobs=n_jobs, backend=self.parallel_backend)( + delayed(self._proba_row)(x) for x in X + ) + return np.array(preds) def _predict(self, X): """ @@ -167,19 +170,27 @@ def _predict(self, X): y : array of shape (n_cases) Class labels for each data sample. """ - self._check_is_fitted() - - preds = np.empty(len(X), dtype=self.classes_.dtype) - for i in range(len(X)): - scores = np.zeros(len(self.classes_)) - idx, weights = self._kneighbors(X[i]) - for id, w in zip(idx, weights): - predicted_class = self.y_[id] - scores[predicted_class] += w - - preds[i] = self.classes_[np.argmax(scores)] - - return preds + n_jobs = check_n_jobs(self.n_jobs) + preds = Parallel(n_jobs=n_jobs, backend=self.parallel_backend)( + delayed(self._predict_row)(x) for x in X + ) + return np.array(preds, dtype=self.classes_.dtype) + + def _proba_row(self, x): + scores = self._predict_scores(x) + return scores / np.sum(scores) + + def _predict_row(self, x): + scores = self._predict_scores(x) + return self.classes_[np.argmax(scores)] + + def _predict_scores(self, x): + scores = np.zeros(len(self.classes_)) + idx, weights = self._kneighbors(x) + for id, weight in zip(idx, weights): + predicted_class = self.y_[id] + scores[predicted_class] += weight + return scores def _kneighbors(self, X): """ From b33e6c7ca0d7fd84102f078326509f5ec05bb5ff Mon Sep 17 00:00:00 2001 From: Adarsh Dubey Date: Sun, 18 May 2025 00:29:18 +0530 Subject: [PATCH 102/216] [ENH] add informational tags back to estimator docs (#2652) * feat: add new tags information for anomaly detection * feat: add tag information in given anomaly modles * fix: add new anomaly detector tags in mock anomaly detector class * fix: use anomaly-detector instead of estimator for new tags * update and add basic testing * fixes --------- Co-authored-by: MatthewMiddlehurst --- aeon/anomaly_detection/base.py | 3 + .../distance_based/_cblof.py | 3 + .../distance_based/_kmeans.py | 3 + .../distance_based/_left_stampi.py | 2 + aeon/anomaly_detection/distance_based/_lof.py | 3 + .../distance_based/_merlin.py | 5 ++ .../distance_based/_one_class_svm.py | 2 + .../distance_based/_stomp.py | 2 + .../distribution_based/_copod.py | 3 + .../distribution_based/_dwt_mlead.py | 2 + .../outlier_detection/_iforest.py | 3 + .../outlier_detection/_pyodadapter.py | 3 + .../outlier_detection/_stray.py | 2 + .../anomaly_detection/whole_series/_rockad.py | 2 + .../_yield_anomaly_detection_checks.py | 87 ++++++++++--------- .../_yield_classification_checks.py | 2 +- .../_yield_regression_checks.py | 2 +- .../_mock_anomaly_detectors.py | 3 + aeon/testing/testing_config.py | 1 + aeon/testing/testing_data.py | 27 +++--- aeon/utils/tags/_tags.py | 23 +++++ 21 files changed, 131 insertions(+), 52 deletions(-) diff --git a/aeon/anomaly_detection/base.py b/aeon/anomaly_detection/base.py index 2e333cf755..689c0188ee 100644 --- a/aeon/anomaly_detection/base.py +++ b/aeon/anomaly_detection/base.py @@ -79,6 +79,9 @@ class BaseAnomalyDetector(BaseSeriesEstimator): "X_inner_type": "np.ndarray", # One of VALID_SERIES_INNER_TYPES "fit_is_empty": True, "requires_y": False, + "learning_type:unsupervised": False, + "learning_type:semi_supervised": False, + "learning_type:supervised": False, } def __init__(self, axis): diff --git a/aeon/anomaly_detection/distance_based/_cblof.py b/aeon/anomaly_detection/distance_based/_cblof.py index 18bb044c14..e4470efcc7 100644 --- a/aeon/anomaly_detection/distance_based/_cblof.py +++ b/aeon/anomaly_detection/distance_based/_cblof.py @@ -81,6 +81,9 @@ class CBLOF(PyODAdapter): "capability:missing_values": False, "fit_is_empty": False, "python_dependencies": ["pyod"], + "anomaly_output_type": "anomaly_scores", + "learning_type:unsupervised": True, + "learning_type:semi_supervised": True, } def __init__( diff --git a/aeon/anomaly_detection/distance_based/_kmeans.py b/aeon/anomaly_detection/distance_based/_kmeans.py index bb8f188a1d..e4e7cc4217 100644 --- a/aeon/anomaly_detection/distance_based/_kmeans.py +++ b/aeon/anomaly_detection/distance_based/_kmeans.py @@ -81,6 +81,9 @@ class KMeansAD(BaseAnomalyDetector): "capability:multivariate": True, "capability:missing_values": False, "fit_is_empty": False, + "anomaly_output_type": "anomaly_scores", + "learning_type:unsupervised": True, + "learning_type:semi_supervised": True, } def __init__( diff --git a/aeon/anomaly_detection/distance_based/_left_stampi.py b/aeon/anomaly_detection/distance_based/_left_stampi.py index 43078ce021..59c8d1be83 100644 --- a/aeon/anomaly_detection/distance_based/_left_stampi.py +++ b/aeon/anomaly_detection/distance_based/_left_stampi.py @@ -69,6 +69,8 @@ class LeftSTAMPi(BaseAnomalyDetector): "fit_is_empty": False, "cant_pickle": True, "python_dependencies": ["stumpy"], + "anomaly_output_type": "anomaly_scores", + "learning_type:unsupervised": True, } def __init__( diff --git a/aeon/anomaly_detection/distance_based/_lof.py b/aeon/anomaly_detection/distance_based/_lof.py index 2c3615d906..bc7369026b 100644 --- a/aeon/anomaly_detection/distance_based/_lof.py +++ b/aeon/anomaly_detection/distance_based/_lof.py @@ -75,6 +75,9 @@ class LOF(PyODAdapter): "capability:multithreading": True, "fit_is_empty": False, "python_dependencies": ["pyod"], + "anomaly_output_type": "anomaly_scores", + "learning_type:unsupervised": True, + "learning_type:semi_supervised": True, } def __init__( diff --git a/aeon/anomaly_detection/distance_based/_merlin.py b/aeon/anomaly_detection/distance_based/_merlin.py index b63224acd5..08cc6fffe1 100644 --- a/aeon/anomaly_detection/distance_based/_merlin.py +++ b/aeon/anomaly_detection/distance_based/_merlin.py @@ -51,6 +51,11 @@ class MERLIN(BaseAnomalyDetector): False, False, False, False, False, False, False]) """ + _tags = { + "anomaly_output_type": "binary", + "learning_type:unsupervised": True, + } + def __init__(self, min_length=5, max_length=50, max_iterations=500): self.min_length = min_length self.max_length = max_length diff --git a/aeon/anomaly_detection/distance_based/_one_class_svm.py b/aeon/anomaly_detection/distance_based/_one_class_svm.py index 9e654ee326..c0e8f0556e 100644 --- a/aeon/anomaly_detection/distance_based/_one_class_svm.py +++ b/aeon/anomaly_detection/distance_based/_one_class_svm.py @@ -90,6 +90,8 @@ class OneClassSVM(BaseAnomalyDetector): "capability:multivariate": True, "capability:missing_values": False, "fit_is_empty": False, + "anomaly_output_type": "anomaly_scores", + "learning_type:semi_supervised": True, } def __init__( diff --git a/aeon/anomaly_detection/distance_based/_stomp.py b/aeon/anomaly_detection/distance_based/_stomp.py index 3f8be36432..9b0f551033 100644 --- a/aeon/anomaly_detection/distance_based/_stomp.py +++ b/aeon/anomaly_detection/distance_based/_stomp.py @@ -61,6 +61,8 @@ class STOMP(BaseAnomalyDetector): "capability:missing_values": False, "fit_is_empty": True, "python_dependencies": ["stumpy"], + "anomaly_output_type": "anomaly_scores", + "learning_type:unsupervised": True, } def __init__( diff --git a/aeon/anomaly_detection/distribution_based/_copod.py b/aeon/anomaly_detection/distribution_based/_copod.py index bd2af0e084..9a4fe0b667 100644 --- a/aeon/anomaly_detection/distribution_based/_copod.py +++ b/aeon/anomaly_detection/distribution_based/_copod.py @@ -37,6 +37,9 @@ class COPOD(PyODAdapter): "capability:multithreading": True, "fit_is_empty": False, "python_dependencies": ["pyod"], + "anomaly_output_type": "anomaly_scores", + "learning_type:unsupervised": True, + "learning_type:semi_supervised": True, } def __init__(self, n_jobs: int = 1, window_size: int = 10, stride: int = 1): diff --git a/aeon/anomaly_detection/distribution_based/_dwt_mlead.py b/aeon/anomaly_detection/distribution_based/_dwt_mlead.py index cb0de0c015..fed0de5f46 100644 --- a/aeon/anomaly_detection/distribution_based/_dwt_mlead.py +++ b/aeon/anomaly_detection/distribution_based/_dwt_mlead.py @@ -91,6 +91,8 @@ class DWT_MLEAD(BaseAnomalyDetector): "capability:univariate": True, "capability:multivariate": False, "capability:missing_values": False, + "anomaly_output_type": "anomaly_scores", + "learning_type:unsupervised": True, } def __init__( diff --git a/aeon/anomaly_detection/outlier_detection/_iforest.py b/aeon/anomaly_detection/outlier_detection/_iforest.py index f13152d0e7..b37696fc43 100644 --- a/aeon/anomaly_detection/outlier_detection/_iforest.py +++ b/aeon/anomaly_detection/outlier_detection/_iforest.py @@ -81,6 +81,9 @@ class IsolationForest(PyODAdapter): "capability:multithreading": True, "fit_is_empty": False, "python_dependencies": ["pyod"], + "anomaly_output_type": "anomaly_scores", + "learning_type:unsupervised": True, + "learning_type:semi_supervised": True, } def __init__( diff --git a/aeon/anomaly_detection/outlier_detection/_pyodadapter.py b/aeon/anomaly_detection/outlier_detection/_pyodadapter.py index 5a068857c6..bdde293a2f 100644 --- a/aeon/anomaly_detection/outlier_detection/_pyodadapter.py +++ b/aeon/anomaly_detection/outlier_detection/_pyodadapter.py @@ -75,6 +75,9 @@ class PyODAdapter(BaseAnomalyDetector): "capability:missing_values": False, "fit_is_empty": False, "python_dependencies": ["pyod"], + "anomaly_output_type": "anomaly_scores", + "learning_type:unsupervised": True, + "learning_type:semi_supervised": True, } def __init__( diff --git a/aeon/anomaly_detection/outlier_detection/_stray.py b/aeon/anomaly_detection/outlier_detection/_stray.py index e7512e2d24..bc4fd6dd4d 100644 --- a/aeon/anomaly_detection/outlier_detection/_stray.py +++ b/aeon/anomaly_detection/outlier_detection/_stray.py @@ -68,6 +68,8 @@ class STRAY(BaseAnomalyDetector): "capability:multivariate": True, "capability:missing_values": True, "X_inner_type": "np.ndarray", + "anomaly_output_type": "binary", + "learning_type:unsupervised": True, } def __init__( diff --git a/aeon/anomaly_detection/whole_series/_rockad.py b/aeon/anomaly_detection/whole_series/_rockad.py index 603a8732a3..55a3cad39b 100644 --- a/aeon/anomaly_detection/whole_series/_rockad.py +++ b/aeon/anomaly_detection/whole_series/_rockad.py @@ -69,6 +69,8 @@ class ROCKAD(BaseAnomalyDetector): "capability:missing_values": False, "capability:multithreading": True, "fit_is_empty": False, + "anomaly_output_type": "anomaly_scores", + "learning_type:semi_supervised": True, } def __init__( diff --git a/aeon/testing/estimator_checking/_yield_anomaly_detection_checks.py b/aeon/testing/estimator_checking/_yield_anomaly_detection_checks.py index 5f2f05aaa9..363db48e23 100644 --- a/aeon/testing/estimator_checking/_yield_anomaly_detection_checks.py +++ b/aeon/testing/estimator_checking/_yield_anomaly_detection_checks.py @@ -6,10 +6,7 @@ from aeon.base._base import _clone_estimator from aeon.base._base_series import VALID_SERIES_INNER_TYPES -from aeon.testing.data_generation import ( - make_example_1d_numpy, - make_example_2d_numpy_series, -) +from aeon.testing.testing_data import FULL_TEST_DATA_DICT def _yield_anomaly_detection_checks(estimator_class, estimator_instances, datatypes): @@ -20,10 +17,19 @@ def _yield_anomaly_detection_checks(estimator_class, estimator_instances, dataty ) # test class instances - for _, estimator in enumerate(estimator_instances): - # no data needed - yield partial(check_anomaly_detector_univariate, estimator=estimator) - yield partial(check_anomaly_detector_multivariate, estimator=estimator) + for i, estimator in enumerate(estimator_instances): + # data type irrelevant + yield partial( + check_anomaly_detector_learning_types, + estimator=estimator, + datatype=datatypes[i][0], + ) + + # test all data types + for datatype in datatypes[i]: + yield partial( + check_anomaly_detector_output, estimator=estimator, datatype=datatype + ) def check_anomaly_detector_overrides_and_tags(estimator_class): @@ -53,41 +59,44 @@ def check_anomaly_detector_overrides_and_tags(estimator_class): assert multi or uni -labels = np.zeros(15, dtype=np.int_) -labels[np.random.choice(15, 5)] = 1 -uv_series = make_example_1d_numpy(n_timepoints=15, random_state=0) -uv_series[labels == 1] += 1 -mv_series = make_example_2d_numpy_series(n_timepoints=15, n_channels=2, random_state=0) -mv_series[:, labels == 1] += 1 +def check_anomaly_detector_learning_types(estimator, datatype): + """Test anomaly detector learning types.""" + unsupervised = estimator.get_tag("learning_type:unsupervised") + semisup = estimator.get_tag("learning_type:semi_supervised") + supervised = estimator.get_tag("learning_type:supervised") + assert ( + unsupervised or semisup or supervised + ), "At least one learning type must be True" -def check_anomaly_detector_univariate(estimator): - """Test the anomaly detector on univariate data.""" - import pytest +def check_anomaly_detector_output(estimator, datatype): + """Test the anomaly detector output on valid data.""" estimator = _clone_estimator(estimator) - if estimator.get_tag(tag_name="capability:univariate"): - pred = estimator.fit_predict(uv_series, labels) - assert isinstance(pred, np.ndarray) - assert pred.shape == (15,) - assert issubclass(pred.dtype.type, (np.integer, np.floating, np.bool_)) - else: - with pytest.raises(ValueError, match="Univariate data not supported"): - estimator.fit_predict(uv_series, labels) - - -def check_anomaly_detector_multivariate(estimator): - """Test the anomaly detector on multivariate data.""" - import pytest - - estimator = _clone_estimator(estimator) + estimator.fit( + FULL_TEST_DATA_DICT[datatype]["train"][0], + FULL_TEST_DATA_DICT[datatype]["train"][1], + ) - if estimator.get_tag(tag_name="capability:multivariate"): - pred = estimator.fit_predict(mv_series, labels) - assert isinstance(pred, np.ndarray) - assert pred.shape == (15,) - assert issubclass(pred.dtype.type, (np.integer, np.floating, np.bool_)) + y_pred = estimator.predict(FULL_TEST_DATA_DICT[datatype]["test"][0]) + assert isinstance(y_pred, np.ndarray) + assert len(y_pred) == FULL_TEST_DATA_DICT[datatype]["test"][0].shape[1] + + ot = estimator.get_tag("anomaly_output_type") + if ot == "anomaly_scores": + assert np.issubdtype(y_pred.dtype, np.floating) or np.issubdtype( + y_pred.dtype, np.integer + ), "y_pred must be of floating point or int type" + assert not np.array_equal( + np.unique(y_pred), [0, 1] + ), "y_pred cannot contain only 0s and 1s" + elif ot == "binary": + assert np.issubdtype(y_pred.dtype, np.integer) or np.issubdtype( + y_pred.dtype, np.bool_ + ), "y_pred must be of int or bool type for binary output" + assert all( + val in [0, 1] for val in np.unique(y_pred) + ), "y_pred must contain only 0s, 1s, True, or False" else: - with pytest.raises(ValueError, match="Multivariate data not supported"): - estimator.fit_predict(mv_series, labels) + raise ValueError(f"Unknown anomaly output type: {ot}") diff --git a/aeon/testing/estimator_checking/_yield_classification_checks.py b/aeon/testing/estimator_checking/_yield_classification_checks.py index 1ab7b4842a..0591119f7a 100644 --- a/aeon/testing/estimator_checking/_yield_classification_checks.py +++ b/aeon/testing/estimator_checking/_yield_classification_checks.py @@ -73,7 +73,7 @@ def _yield_classification_checks(estimator_class, estimator_instances, datatypes yield partial( check_classifier_train_estimate, estimator=estimator, - datatype=datatypes[0][0], + datatype=datatypes[i][0], ) if isinstance(estimator, BaseDeepClassifier): diff --git a/aeon/testing/estimator_checking/_yield_regression_checks.py b/aeon/testing/estimator_checking/_yield_regression_checks.py index 73bba3afaf..06fe479654 100644 --- a/aeon/testing/estimator_checking/_yield_regression_checks.py +++ b/aeon/testing/estimator_checking/_yield_regression_checks.py @@ -68,7 +68,7 @@ def _yield_regression_checks(estimator_class, estimator_instances, datatypes): yield partial( check_regressor_train_estimate, estimator=estimator, - datatype=datatypes[0][0], + datatype=datatypes[i][0], ) if isinstance(estimator, BaseDeepRegressor): diff --git a/aeon/testing/mock_estimators/_mock_anomaly_detectors.py b/aeon/testing/mock_estimators/_mock_anomaly_detectors.py index 4ec14d35fa..ed0ba2488d 100644 --- a/aeon/testing/mock_estimators/_mock_anomaly_detectors.py +++ b/aeon/testing/mock_estimators/_mock_anomaly_detectors.py @@ -19,6 +19,9 @@ class MockAnomalyDetector(BaseAnomalyDetector): _tags = { "capability:missing_values": True, "capability:multivariate": True, + "anomaly_output_type": "anomaly_scores", + "learning_type:unsupervised": True, + "learning_type:semi_supervised": True, } def __init__(self): diff --git a/aeon/testing/testing_config.py b/aeon/testing/testing_config.py index b17b9626d1..3e47dcf08f 100644 --- a/aeon/testing/testing_config.py +++ b/aeon/testing/testing_config.py @@ -52,6 +52,7 @@ "SAST": ["check_fit_deterministic"], "RSAST": ["check_fit_deterministic"], "MatrixProfile": ["check_fit_deterministic", "check_persistence_via_pickle"], + "LeftSTAMPi": ["check_anomaly_detector_output"], # missed in legacy testing, changes state in predict/transform "FLUSSSegmenter": ["check_non_state_changing_method"], "InformationGainSegmenter": ["check_non_state_changing_method"], diff --git a/aeon/testing/testing_data.py b/aeon/testing/testing_data.py index 3337f83b0c..f3360d93cb 100644 --- a/aeon/testing/testing_data.py +++ b/aeon/testing/testing_data.py @@ -13,7 +13,6 @@ from aeon.similarity_search.collection import BaseCollectionSimilaritySearch from aeon.similarity_search.series import BaseSeriesSimilaritySearch from aeon.testing.data_generation import ( - make_example_1d_numpy, make_example_2d_dataframe_collection, make_example_2d_numpy_collection, make_example_2d_numpy_series, @@ -642,11 +641,14 @@ # Series testing data -X_series = make_example_1d_numpy( - n_timepoints=40, random_state=data_rng.randint(np.iinfo(np.int32).max) +X_series = make_example_2d_numpy_series( + n_timepoints=40, + n_channels=1, + axis=1, + random_state=data_rng.randint(np.iinfo(np.int32).max), ) -X_series2 = X_series[20:40] -X_series = X_series[:20] +X_series2 = X_series[:, 20:40] +X_series = X_series[:, :20] UNIVARIATE_SERIES_NONE = {"train": (X_series, None), "test": (X_series2, None)} X_series_mv = make_example_2d_numpy_series( @@ -662,13 +664,16 @@ "test": (X_series_mv2, None), } -X_series_mi = make_example_1d_numpy( - n_timepoints=40, random_state=data_rng.randint(np.iinfo(np.int32).max) +X_series_mi = make_example_2d_numpy_series( + n_timepoints=40, + n_channels=1, + axis=1, + random_state=data_rng.randint(np.iinfo(np.int32).max), ) -X_series_mi2 = X_series_mi[20:40] -X_series_mi2[data_rng.choice(20, 1)] = np.nan -X_series_mi = X_series_mi[:20] -X_series_mi[data_rng.choice(20, 2)] = np.nan +X_series_mi2 = X_series_mi[:, 20:40] +X_series_mi2[:, data_rng.choice(20, 1)] = np.nan +X_series_mi = X_series_mi[:, :20] +X_series_mi[:, data_rng.choice(20, 2)] = np.nan MISSING_VALUES_SERIES_NONE = { "train": (X_series_mi, None), "test": (X_series_mi2, None), diff --git a/aeon/utils/tags/_tags.py b/aeon/utils/tags/_tags.py index 2c132902e4..a4f9e04152 100644 --- a/aeon/utils/tags/_tags.py +++ b/aeon/utils/tags/_tags.py @@ -169,4 +169,27 @@ class : identifier for the base class of objects this tag applies to "unordered attributes are columns. Series indicates a single series output " "and collection indicates output is a collection of time series.", }, + "anomaly_output_type": { + "class": "anomaly-detector", + "type": ("str", ["anomaly_scores", "binary"]), + "description": "The format of the anomaly detector output. anomaly_scores " + "estimators provide a numeric score on how anomalous the point/case is. binary" + "estimators provide a binary classification of whether the point/case is " + "anomalous or not.", + }, + "learning_type:unsupervised": { + "class": "anomaly-detector", + "type": "bool", + "description": "Does the anomaly detector support unsupervised learning?", + }, + "learning_type:semi_supervised": { + "class": "anomaly-detector", + "type": "bool", + "description": "Does the anomaly detector support semi-supervised learning?", + }, + "learning_type:supervised": { + "class": "anomaly-detector", + "type": "bool", + "description": "Does the anomaly detector support supervised learning?", + }, } From 41c14ac51209b717533bf78ad82d1ae009d2548b Mon Sep 17 00:00:00 2001 From: Kevin Shah <161136814+kevinzb56@users.noreply.github.com> Date: Sun, 18 May 2025 16:13:12 +0530 Subject: [PATCH 103/216] [DOC] Documentation improvement of BaseSeriesEstimator and BaseAeonEstimator (#2612) * Added the docs for base series and base classes * Updated documentation for base classes --------- Co-authored-by: Matthew Middlehurst --- aeon/base/_base.py | 33 ++++++++++++++++++++++++++++++++- aeon/base/_base_series.py | 32 ++++++++++++++++++++++++++++---- 2 files changed, 60 insertions(+), 5 deletions(-) diff --git a/aeon/base/_base.py b/aeon/base/_base.py index 5a336c7397..7f886952de 100644 --- a/aeon/base/_base.py +++ b/aeon/base/_base.py @@ -1,4 +1,35 @@ -"""Base class template for aeon estimators.""" +""" +Base class for Aeon estimators. + +Provides a framework for defining estimators in Aeon, managing state, cloning, +and dynamic tagging. + +Class Name: BaseEstimator + +Main Methods: + - reset(keep) - Resets estimator while preserving attributes. + - clone(random_state) - Creates a copy of the estimator. + - get_class_tags() / get_class_tag(tag_name) - Retrieves static class tags. + - get_tags() / get_tag(tag_name) - Retrieves all or specific tags. + - set_tags(**tag_dict) - Modifies dynamic tags. + - get_fitted_params(deep) - Extracts fitted parameters. + - _check_is_fitted() - Ensures estimator is fitted before use. + - _create_test_instance() - Generates test instances. + +Attributes +---------- + - is_fitted - Indicates if `fit()` has been called. + - _tags - Static metadata dictionary. + - _tags_dynamic - Stores dynamic tag modifications. + +Sklearn Compatibility: + - Implements `__sklearn_is_fitted__()` for compatibility. + - `_validate_data()` and `get_metadata_routing()` + raise NotImplementedError. + +Helper Methods: + - _clone_estimator(base_estimator, random_state) - Clones an estimator. +""" __maintainer__ = ["MatthewMiddlehurst", "TonyBagnall"] __all__ = ["BaseAeonEstimator"] diff --git a/aeon/base/_base_series.py b/aeon/base/_base_series.py index f46091142a..e3a382f3db 100644 --- a/aeon/base/_base_series.py +++ b/aeon/base/_base_series.py @@ -1,7 +1,31 @@ -"""Base class for estimators that fit single time series. - -This time series can be univariate or multivariate. The time series can potentially -contain missing values. +""" +Base class for single time series estimators (univariate/multivariate). + +Class Name: BaseSeriesEstimator + +Methods +------- + - _preprocess_series(X, axis, store_metadata) + Validates and converts input `X` before fitting. + - _check_X(X, axis) + Ensures `X` is a valid type and format. + - _convert_X(X, axis) + Converts `X` to the required internal format. + +Attributes +---------- + - metadata_ + Stores input series metadata. + - axis + Defines time axis for input data. + - _tags + Specifies estimator capabilities (e.g., univariate, multivariate). + +Inherited Methods: + - get_params() + Returns hyperparameters. + - get_fitted_params() + Returns learned parameters. """ __maintainer__ = ["TonyBagnall", "MatthewMiddlehurst"] From 8a6cfaf9dbaff173ee6c12a1e7397ad799962262 Mon Sep 17 00:00:00 2001 From: Balgopal Moharana <99070111+lucifer4073@users.noreply.github.com> Date: Sun, 18 May 2025 16:15:29 +0530 Subject: [PATCH 104/216] [ENH] HydraTransformer Output changed (#2656) * output type added * hydra wflow modified * hydra test file added * pr_pytest reverted * soft dep check added * soft dep corrected * docstring corrected, enhanced test cases --- .../collection/convolution_based/_hydra.py | 23 +++- .../convolution_based/tests/test_hydra.py | 112 ++++++++++++++++++ 2 files changed, 133 insertions(+), 2 deletions(-) create mode 100644 aeon/transformations/collection/convolution_based/tests/test_hydra.py diff --git a/aeon/transformations/collection/convolution_based/_hydra.py b/aeon/transformations/collection/convolution_based/_hydra.py index 34ae1ced0d..75db8e3f32 100644 --- a/aeon/transformations/collection/convolution_based/_hydra.py +++ b/aeon/transformations/collection/convolution_based/_hydra.py @@ -4,6 +4,7 @@ __all__ = ["HydraTransformer"] import numpy as np +import pandas as pd from aeon.transformations.collection import BaseCollectionTransformer from aeon.utils.validation import check_n_jobs @@ -38,6 +39,12 @@ class HydraTransformer(BaseCollectionTransformer): If `RandomState` instance, random_state is the random number generator; If `None`, the random number generator is the `RandomState` instance used by `np.random`. + output_type : str, default='tensor' + The output type of the transformer. + Can be either 'tensor' or 'numpy' or 'dataframe'. + If 'tensor', the output will be a PyTorch tensor. If 'numpy', the output + will be a NumPy array. If 'dataframe', the output will be a pandas DataFrame. + See Also -------- @@ -76,13 +83,20 @@ class HydraTransformer(BaseCollectionTransformer): } def __init__( - self, n_kernels=8, n_groups=64, max_num_channels=8, n_jobs=1, random_state=None + self, + n_kernels=8, + n_groups=64, + max_num_channels=8, + n_jobs=1, + random_state=None, + output_type="tensor", ): self.n_kernels = n_kernels self.n_groups = n_groups self.max_num_channels = max_num_channels self.n_jobs = n_jobs self.random_state = random_state + self.output_type = output_type super().__init__() @@ -104,7 +118,12 @@ def _fit(self, X, y=None): ) def _transform(self, X, y=None): - return self._hydra(torch.tensor(X).float()) + transformed = self._hydra(torch.tensor(X).float()) + if (self.output_type == "numpy") or (self.output_type == "dataframe"): + transformed = transformed.detach().numpy() + if self.output_type == "dataframe": + transformed = pd.DataFrame(transformed) + return transformed if _check_soft_dependencies("torch", severity="none"): diff --git a/aeon/transformations/collection/convolution_based/tests/test_hydra.py b/aeon/transformations/collection/convolution_based/tests/test_hydra.py new file mode 100644 index 0000000000..59da35a8d5 --- /dev/null +++ b/aeon/transformations/collection/convolution_based/tests/test_hydra.py @@ -0,0 +1,112 @@ +"""Hydra tests.""" + +import numpy as np +import pytest + +from aeon.transformations.collection.convolution_based._hydra import HydraTransformer +from aeon.utils.validation._dependencies import _check_soft_dependencies + + +@pytest.mark.skipif( + not _check_soft_dependencies("torch", severity="none"), + reason="Skip test if torch not available", +) +def test_hydra_output_types(): + """Test HydraTransformer output_type parameter.""" + # Create a simple dataset + X = np.random.random(size=(10, 3, 20)) + + # Test tensor output (default) + hydra_tensor = HydraTransformer(random_state=42) + hydra_tensor.fit(X) + tensor_output = hydra_tensor.transform(X) + + # Check that output is a torch tensor + import torch + + assert isinstance(tensor_output, torch.Tensor) + + # Test numpy output + hydra_numpy = HydraTransformer(random_state=42, output_type="numpy") + hydra_numpy.fit(X) + numpy_output = hydra_numpy.transform(X) + + # Check that output is a numpy array + assert isinstance(numpy_output, np.ndarray) + + # Test dataframe output + hydra_df = HydraTransformer(random_state=42, output_type="dataframe") + hydra_df.fit(X) + df_output = hydra_df.transform(X) + + # Check that output is a pandas DataFrame + import pandas as pd + + assert isinstance(df_output, pd.DataFrame) + + # Check that all outputs have the same shape + assert tensor_output.shape == numpy_output.shape + assert numpy_output.shape == df_output.shape + + tensor_np = tensor_output.detach().cpu().numpy() + df_np = df_output.to_numpy() + assert np.allclose(tensor_np[:5], numpy_output[:5], atol=1e-6) + assert np.allclose(numpy_output[:5], df_np[:5], atol=1e-6) + + +@pytest.mark.skipif( + not _check_soft_dependencies("torch", severity="none"), + reason="Skip test if torch not available", +) +def test_hydra_short_series(): + """Test HydraTransformer with very short time series.""" + # Create a dataset with short time series (less than kernel length) + X = np.random.random(size=(10, 2, 8)) + + hydra = HydraTransformer(random_state=42) + # Should still work with short series, but will use padding + hydra.fit(X) + output = hydra.transform(X) + + # Check that output has the expected number of samples + assert output.shape[0] == 10 + + +@pytest.mark.skipif( + not _check_soft_dependencies("torch", severity="none"), + reason="Skip test if torch not available", +) +def test_hydra_parameter_values(): + """Test HydraTransformer with different parameter values.""" + X = np.random.random(size=(10, 3, 20)) + + # Test with different numbers of kernels and groups + hydra = HydraTransformer(n_kernels=4, n_groups=32, random_state=42) + hydra.fit(X) + output = hydra.transform(X) + + # Expected feature count: num_dilations * divisor * h * k + # where divisor = min(2, g), h = g // divisor + # For a 20-length time series, num_dilations should be around 2-3 + # With n_groups=32, divisor=2, h=16, n_kernels=4 + # So expected features would be approximately 2 * 2 * 16 * 4 = 256 + # But this is approximate since dilations depend on series length + assert output.shape[1] > 0 + + +@pytest.mark.skipif( + not _check_soft_dependencies("torch", severity="none"), + reason="Skip test if torch not available", +) +def test_hydra_univariate(): + """Test HydraTransformer with univariate data.""" + # Create a univariate dataset + X = np.random.random(size=(10, 1, 20)) + + hydra = HydraTransformer(random_state=42) + hydra.fit(X) + output = hydra.transform(X) + + # Check that output has the expected number of samples + assert output.shape[0] == 10 + assert output.shape[1] > 0 From 06f7a58557c42c82a80bbd184571049fb835bc56 Mon Sep 17 00:00:00 2001 From: Kavya Rambhia <161142013+kavya-r30@users.noreply.github.com> Date: Sun, 18 May 2025 16:20:17 +0530 Subject: [PATCH 105/216] [DOC] Improved Hidalgo Segmentation Notebook (#2616) * Improved Hidalgo Segmentation Notebook * Updated hidalgo notebook --------- Co-authored-by: Matthew Middlehurst --- .../segmentation/hidalgo_segmentation.ipynb | 178 +++++++++++++++--- 1 file changed, 149 insertions(+), 29 deletions(-) diff --git a/examples/segmentation/hidalgo_segmentation.ipynb b/examples/segmentation/hidalgo_segmentation.ipynb index cfa22b8e5c..f4603fc3a4 100644 --- a/examples/segmentation/hidalgo_segmentation.ipynb +++ b/examples/segmentation/hidalgo_segmentation.ipynb @@ -2,46 +2,172 @@ "cells": [ { "cell_type": "markdown", + "metadata": {}, "source": [ - "# Hidalgo segmentation" - ], - "metadata": { - "collapsed": false - } + "# Hidalgo Segmentation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hidalgo (Heterogeneous Intrinsic Dimensionality Algorithm) is a method for detecting change points in time series data. It identifies segments where statistical properties of the data shift, allowing for better understanding of structural changes in the series. It is particularly useful in applications like anomaly detection, financial market analysis, and sensor monitoring for discriminating regions with different local intrinsic dimensionality.\n", + "\n", + "Hidalgo Segmentation is used for\n", + "\n", + "- **Unsupervised Learning**: Hidalgo does not require labeled data for segmentation, making it suitable for exploratory data analysis.\n", + "- **High-Dimensional Data**: It can handle high-dimensional time series data effectively.\n", + "- **Intrinsic Dimensionality**: By focusing on local intrinsic dimensionality, Hidalgo can identify regions with different complexities within the data.\n", + "\n", + "Applications\n", + "\n", + "- **Change Point Detection**: Identifying points in time where the statistical properties of a time series change.\n", + "- **Anomaly Detection**: Detecting unusual patterns or outliers in time series data.\n", + "- **Feature Extraction**: Segmenting data to extract meaningful features for further analysis or modeling." + ] }, { "cell_type": "code", - "execution_count": 1, - "outputs": [ - { - "data": { - "text/plain": "array([ 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1,\n 1, -1, -1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n dtype=int64)" - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": 50, + "metadata": {}, + "outputs": [], "source": [ "import numpy as np\n", "\n", "from aeon.segmentation import HidalgoSegmenter\n", + "from aeon.visualisation.learning_task._segmentation import (\n", + " plot_series_with_change_points,\n", + ")\n", + "from aeon.visualisation.series._series import plot_series" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Generate Synthetic Data\n", "\n", + "We create a synthetic dataset with 100 time points, each having 3 channels. The data is constructed such that the first 60 time points have higher values in the second and third channels, and the remaining time points have lower values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "X = np.random.rand(100, 3)\n", "X[:60, 1:] += 10\n", "X[60:, 1:] = 0\n", - "hidalgo = HidalgoSegmenter(K=2, burn_in=0.8, n_iter=1000, seed=10)\n", "\n", - "hidalgo.fit_predict(X, axis=0)" + "time = np.arange(len(X))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 Visualize Synthetic Data" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = plot_series(X.T, labels=[\"Channel 0\", \"Channel 1\", \"Channel 2\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Apply Hidalgo Segmentation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `HidalgoSegmenter` is initialized with various parameters that control the behavior of the algorithm. These parameters include the number of manifolds (`K`), local homogeneity level (`zeta`), number of points for local interaction (`q`), number of Gibbs sampling iterations (`n_iter`), and others. These parameters allow to customize the segmentation process according to data and requirements, while the `fit_predict` method is used to fit the Hidalgo model to the data and predict the segmentation labels using Neighborhood Calculation and Gibbs Sampling" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found change points: [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", + " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n" + ] + } + ], + "source": [ + "hidalgo = HidalgoSegmenter(K=2, q=3, n_iter=2000, burn_in=0.8, seed=10)\n", + "\n", + "labels = hidalgo.fit_predict(X, axis=0)\n", + "print(\"Found change points:\", labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Visualize Segmentation Output" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } ], - "metadata": { - "collapsed": false - } + "source": [ + "_ = plot_series_with_change_points(\n", + " X, np.where(np.diff(labels) != 0)[0], \"Segmentation Result\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "[1] Allegra, Michele, et al. \"Data segmentation based on the local intrinsic dimension.\" [Scientific reports 10.1 (2020): 1-12.](https://www.nature.com/articles/s41598-020-72222-0)" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3.9.13 ('aeon-dev')", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -55,13 +181,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "3672c9971f48dc8b5371767a83a5c009b8758f695819d57d51811323dc5c559e" - } + "version": "3.12.3" } }, "nbformat": 4, From 4b8aa59c194825fd99f58a1f037fc9405654a26c Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Sun, 18 May 2025 12:02:51 +0100 Subject: [PATCH 106/216] new devs and workflow consolidation (#2797) --- .../utilities/generate_developer_tables.py | 29 +++- .../workflows/periodic_github_maintenace.yml | 35 ----- .github/workflows/precommit_autoupdate.yml | 39 ----- .github/workflows/scorecard.yml | 46 ------ .../workflows/weekly_github_maintenance.yml | 133 ++++++++++++++++++ docs/about.md | 11 +- docs/about/supporting_developers.md | 15 ++ 7 files changed, 181 insertions(+), 127 deletions(-) delete mode 100644 .github/workflows/periodic_github_maintenace.yml delete mode 100644 .github/workflows/precommit_autoupdate.yml delete mode 100644 .github/workflows/scorecard.yml create mode 100644 .github/workflows/weekly_github_maintenance.yml create mode 100644 docs/about/supporting_developers.md diff --git a/.github/utilities/generate_developer_tables.py b/.github/utilities/generate_developer_tables.py index f03aa84831..cbce50c830 100755 --- a/.github/utilities/generate_developer_tables.py +++ b/.github/utilities/generate_developer_tables.py @@ -11,6 +11,7 @@ generate_authors_table.py """ +import os import sys import time from os import path @@ -19,7 +20,7 @@ import requests LOGO_URL = "https://avatars2.githubusercontent.com/u/78909809" -REPO_FOLDER = Path(path.abspath(__file__)).parent.parent +REPO_FOLDER = Path(path.abspath(__file__)).parent.parent.parent def get(url, auth): @@ -53,12 +54,14 @@ def get_contributors(auth): iw_slug = "aeon-infrastructure-workgroup" rmw = [] rmw_slug = "aeon-release-management-workgroup" + sd = [] + sd_slug = "aeon-supporting-developers" entry_point = "https://api.github.com/orgs/aeon-toolkit/" for team_slug, lst in zip( - (cocw_slug, cw_slug, cd_slug, fw_slug, iw_slug, rmw_slug), - (cocw, cw, cd, fw, iw, rmw), + (cocw_slug, cw_slug, cd_slug, fw_slug, iw_slug, rmw_slug, sd_slug), + (cocw, cw, cd, fw, iw, rmw, sd), ): for page in range(5): # 5 pages, 30 per page reply = get(f"{entry_point}teams/{team_slug}/members?page={page}", auth) @@ -71,6 +74,7 @@ def get_contributors(auth): fw = {c["login"] for c in fw} iw = {c["login"] for c in iw} rmw = {c["login"] for c in rmw} + sd = {c["login"] for c in sd} # get profiles from GitHub cocw = [get_profile(login, auth) for login in cocw] @@ -79,6 +83,7 @@ def get_contributors(auth): fw = [get_profile(login, auth) for login in fw] iw = [get_profile(login, auth) for login in iw] rmw = [get_profile(login, auth) for login in rmw] + sd = [get_profile(login, auth) for login in sd] # sort by last name cocw = sorted(cocw, key=key) @@ -87,6 +92,7 @@ def get_contributors(auth): fw = sorted(fw, key=key) iw = sorted(iw, key=key) rmw = sorted(rmw, key=key) + sd = sorted(sd, key=key) return ( cocw, @@ -95,6 +101,7 @@ def get_contributors(auth): fw, iw, rmw, + sd, ) @@ -140,9 +147,11 @@ def generate_table(contributors): if __name__ == "__main__": - print("access token:", file=sys.stderr) # noqa: T201 - token = input() - auth = ("user", token) + auth = os.getenv("GITHUB_TOKEN") + if auth is None: + print("access token:", file=sys.stderr) # noqa: T201 + token = input() + auth = ("user", token) ( cocw, @@ -151,6 +160,7 @@ def generate_table(contributors): fw, iw, rmw, + sd, ) = get_contributors(auth) with open( @@ -190,3 +200,10 @@ def generate_table(contributors): encoding="utf-8", ) as rst_file: rst_file.write(generate_table(rmw)) + + with open( + REPO_FOLDER / "docs" / "about" / "supporting_developers.md", + "w+", + encoding="utf-8", + ) as rst_file: + rst_file.write(generate_table(sd)) diff --git a/.github/workflows/periodic_github_maintenace.yml b/.github/workflows/periodic_github_maintenace.yml deleted file mode 100644 index 952150313b..0000000000 --- a/.github/workflows/periodic_github_maintenace.yml +++ /dev/null @@ -1,35 +0,0 @@ -name: GitHub Maintenance - -on: - schedule: - # every Monday at 01:00 AM UTC - - cron: "0 1 * * 1" - workflow_dispatch: - -permissions: - issues: write - contents: write - -jobs: - stale_branches: - runs-on: ubuntu-24.04 - - steps: - - name: Create app token - uses: actions/create-github-app-token@v2 - id: app-token - with: - app-id: ${{ vars.PR_APP_ID }} - private-key: ${{ secrets.PR_APP_KEY }} - - - name: Stale Branches - uses: crs-k/stale-branches@v7.0.1 - with: - repo-token: ${{ steps.app-token.outputs.token }} - days-before-stale: 140 - days-before-delete: 175 - comment-updates: true - tag-committer: true - stale-branch-label: "stale branch" - compare-branches: "info" - pr-check: true diff --git a/.github/workflows/precommit_autoupdate.yml b/.github/workflows/precommit_autoupdate.yml deleted file mode 100644 index a670feaf2f..0000000000 --- a/.github/workflows/precommit_autoupdate.yml +++ /dev/null @@ -1,39 +0,0 @@ -name: Update pre-commit Hooks - -on: - schedule: - # every Monday at 12:30 AM UTC - - cron: "30 0 * * 1" - workflow_dispatch: - -jobs: - pre-commit-auto-update: - runs-on: ubuntu-24.04 - - steps: - - uses: actions/checkout@v4 - - - name: Setup Python 3.11 - uses: actions/setup-python@v5 - with: - python-version: "3.11" - - - uses: browniebroke/pre-commit-autoupdate-action@v1.0.0 - - - if: always() - name: Create app token - uses: actions/create-github-app-token@v2 - id: app-token - with: - app-id: ${{ vars.PR_APP_ID }} - private-key: ${{ secrets.PR_APP_KEY }} - - - if: always() - uses: peter-evans/create-pull-request@v7 - with: - token: ${{ steps.app-token.outputs.token }} - commit-message: "Automated `pre-commit` hook update" - branch: pre-commit-hooks-update - title: "[MNT] Automated `pre-commit` hook update" - body: "Automated weekly update to `.pre-commit-config.yaml` hook versions." - labels: maintenance, full pre-commit, no changelog diff --git a/.github/workflows/scorecard.yml b/.github/workflows/scorecard.yml deleted file mode 100644 index 3c57528fc5..0000000000 --- a/.github/workflows/scorecard.yml +++ /dev/null @@ -1,46 +0,0 @@ -name: Scorecard supply-chain security - -on: - branch_protection_rule: - schedule: - - cron: '30 1 * * 6' - push: - branches: - - main - -permissions: read-all - -jobs: - analysis: - name: Scorecard analysis - runs-on: ubuntu-24.04 - permissions: - # Needed to upload the results to code-scanning dashboard. - security-events: write - # Needed to publish results and get a badge (see publish_results below). - id-token: write - - steps: - - name: Checkout code - uses: actions/checkout@v4 - with: - persist-credentials: false - - - name: Run analysis - uses: ossf/scorecard-action@v2.4.1 - with: - results_file: results.sarif - results_format: sarif - publish_results: true - - - name: Upload artifact - uses: actions/upload-artifact@v4 - with: - name: SARIF file - path: results.sarif - retention-days: 5 - - - name: Upload to code-scanning - uses: github/codeql-action/upload-sarif@v3 - with: - sarif_file: results.sarif diff --git a/.github/workflows/weekly_github_maintenance.yml b/.github/workflows/weekly_github_maintenance.yml new file mode 100644 index 0000000000..58ea91dd17 --- /dev/null +++ b/.github/workflows/weekly_github_maintenance.yml @@ -0,0 +1,133 @@ +name: Weekly GitHub Maintenance + +on: + schedule: + # every Monday at 12:30 AM UTC + - cron: "30 0 * * 1" + workflow_dispatch: + +jobs: + pre-commit-auto-update: + runs-on: ubuntu-24.04 + + steps: + - name: Checkout main + uses: actions/checkout@v4 + + - name: Setup Python 3.11 + uses: actions/setup-python@v5 + with: + python-version: "3.11" + + - uses: browniebroke/pre-commit-autoupdate-action@v1.0.0 + + - if: always() + name: Create app token + uses: actions/create-github-app-token@v2 + id: app-token + with: + app-id: ${{ vars.PR_APP_ID }} + private-key: ${{ secrets.PR_APP_KEY }} + + - if: always() + uses: peter-evans/create-pull-request@v7 + with: + token: ${{ steps.app-token.outputs.token }} + commit-message: "Automated `pre-commit` hook update" + branch: pre-commit-hooks-update + title: "[MNT] Automated `pre-commit` hook update" + body: "Automated weekly update to `.pre-commit-config.yaml` hook versions." + labels: maintenance, full pre-commit, no changelog + + teams-table-auto-update: + runs-on: ubuntu-24.04 + + steps: + - name: Checkout main + uses: actions/checkout@v4 + with: + sparse-checkout: .github/utilities + + - name: Setup Python 3.11 + uses: actions/setup-python@v5 + with: + python-version: "3.11" + + - name: Create app token + uses: actions/create-github-app-token@v2 + id: app-token + with: + app-id: ${{ vars.PR_APP_ID }} + private-key: ${{ secrets.PR_APP_KEY }} + + - name: Process comment edit + id: label_out + run: python .github/utilities/generate_developer_tables.py + env: + GITHUB_TOKEN: ${{ steps.app-token.outputs.token }} + + - uses: peter-evans/create-pull-request@v7 + with: + token: ${{ steps.app-token.outputs.token }} + commit-message: "Automated teams tables update" + branch: webpage-teams-update + title: "[DOC] Automated website teams tables update" + body: "Automated weekly update to the website teams tables." + labels: documentation, no changelog + + github-security-scorecard: + name: Scorecard analysis + runs-on: ubuntu-24.04 + permissions: + # Needed to upload the results to code-scanning dashboard. + security-events: write + # Needed to publish results and get a badge (see publish_results below). + id-token: write + + steps: + - name: Checkout code + uses: actions/checkout@v4 + with: + persist-credentials: false + + - name: Run analysis + uses: ossf/scorecard-action@v2.4.1 + with: + results_file: results.sarif + results_format: sarif + publish_results: true + + - name: Upload artifact + uses: actions/upload-artifact@v4 + with: + name: SARIF file + path: results.sarif + retention-days: 5 + + - name: Upload to code-scanning + uses: github/codeql-action/upload-sarif@v3 + with: + sarif_file: results.sarif + + stale_branches: + runs-on: ubuntu-24.04 + + steps: + - name: Create app token + uses: actions/create-github-app-token@v2 + id: app-token + with: + app-id: ${{ vars.PR_APP_ID }} + private-key: ${{ secrets.PR_APP_KEY }} + + - name: Stale Branches + uses: crs-k/stale-branches@v7.0.1 + with: + repo-token: ${{ steps.app-token.outputs.token }} + days-before-stale: 180 + days-before-delete: 210 + comment-updates: true + tag-committer: true + stale-branch-label: "stale branch" + compare-branches: "info" + pr-check: true diff --git a/docs/about.md b/docs/about.md index ca89665849..798e6e8d44 100644 --- a/docs/about.md +++ b/docs/about.md @@ -42,9 +42,18 @@ The release management workgroup is responsible for managing the project's relea ```{include} about/release_management_workgroup.md ``` +### Supporting Developers + +Supporting developers have been provided write access to the main repository to aid +with package contributions and maintenance. + +```{include} about/supporting_developers.md +``` + ### Core Developers -The core developers push forward `aeon`'s development and maintain the package. +Core developers push forward `aeon`'s development, maintain the package and build +our community. ```{include} about/core_developers.md ``` diff --git a/docs/about/supporting_developers.md b/docs/about/supporting_developers.md new file mode 100644 index 0000000000..db9464c531 --- /dev/null +++ b/docs/about/supporting_developers.md @@ -0,0 +1,15 @@ + +
+ + +
+
+

Patrick

+
+
From b87c252402932c25a8e69f18f064a73a82d04ca8 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Sun, 18 May 2025 12:31:33 +0100 Subject: [PATCH 107/216] all branches (#2809) --- .github/workflows/pr_core_dep_import.yml | 2 -- .github/workflows/pr_examples.yml | 2 -- .github/workflows/pr_precommit.yml | 2 -- .github/workflows/pr_pytest.yml | 2 -- .github/workflows/pr_typecheck.yml | 2 -- 5 files changed, 10 deletions(-) diff --git a/.github/workflows/pr_core_dep_import.yml b/.github/workflows/pr_core_dep_import.yml index dc1965deb6..429ee28fa5 100644 --- a/.github/workflows/pr_core_dep_import.yml +++ b/.github/workflows/pr_core_dep_import.yml @@ -5,8 +5,6 @@ on: branches: - main pull_request: - branches: - - main paths: - "aeon/**" - ".github/workflows/**" diff --git a/.github/workflows/pr_examples.yml b/.github/workflows/pr_examples.yml index cf32ccd3c1..cd36250b33 100644 --- a/.github/workflows/pr_examples.yml +++ b/.github/workflows/pr_examples.yml @@ -5,8 +5,6 @@ on: branches: - main pull_request: - branches: - - main paths: - "examples/**" - "aeon/**" diff --git a/.github/workflows/pr_precommit.yml b/.github/workflows/pr_precommit.yml index 547b4c6db6..95a232282e 100644 --- a/.github/workflows/pr_precommit.yml +++ b/.github/workflows/pr_precommit.yml @@ -5,8 +5,6 @@ on: branches: - main pull_request_target: - branches: - - main concurrency: group: ${{ github.workflow }}-${{ github.head_ref || github.ref }} diff --git a/.github/workflows/pr_pytest.yml b/.github/workflows/pr_pytest.yml index 4b5679f76d..cf1baee900 100644 --- a/.github/workflows/pr_pytest.yml +++ b/.github/workflows/pr_pytest.yml @@ -5,8 +5,6 @@ on: branches: - main pull_request: - branches: - - main paths: - "aeon/**" - ".github/workflows/**" diff --git a/.github/workflows/pr_typecheck.yml b/.github/workflows/pr_typecheck.yml index f6082ac585..13ba9bec73 100644 --- a/.github/workflows/pr_typecheck.yml +++ b/.github/workflows/pr_typecheck.yml @@ -5,8 +5,6 @@ on: branches: - main pull_request: - branches: - - main paths: - "aeon/**" From 32318d8ff91e82a0d3ecc101b69c4e4254560392 Mon Sep 17 00:00:00 2001 From: "aeon-actions-bot[bot]" <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Date: Mon, 19 May 2025 11:12:39 +0100 Subject: [PATCH 108/216] Automated `pre-commit` hook update (#2820) Co-authored-by: TonyBagnall <9594042+TonyBagnall@users.noreply.github.com> --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 62dab7f167..b438f853d1 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -29,7 +29,7 @@ repos: args: [ "--create", "--python-folders", "aeon" ] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.11.9 + rev: v0.11.10 hooks: - id: ruff args: [ "--fix"] From afd8dfbdf8203f33c8966b6ae58c30e1882ef03a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 19 May 2025 11:14:25 +0100 Subject: [PATCH 109/216] [MNT] Bump crs-k/stale-branches in the github-actions group (#2821) Bumps the github-actions group with 1 update: [crs-k/stale-branches](https://github.com/crs-k/stale-branches). Updates `crs-k/stale-branches` from 7.0.1 to 8.1.1 - [Release notes](https://github.com/crs-k/stale-branches/releases) - [Commits](https://github.com/crs-k/stale-branches/compare/v7.0.1...v8.1.1) --- updated-dependencies: - dependency-name: crs-k/stale-branches dependency-version: 8.1.1 dependency-type: direct:production update-type: version-update:semver-major dependency-group: github-actions ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/weekly_github_maintenance.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/weekly_github_maintenance.yml b/.github/workflows/weekly_github_maintenance.yml index 58ea91dd17..9de93ccb46 100644 --- a/.github/workflows/weekly_github_maintenance.yml +++ b/.github/workflows/weekly_github_maintenance.yml @@ -121,7 +121,7 @@ jobs: private-key: ${{ secrets.PR_APP_KEY }} - name: Stale Branches - uses: crs-k/stale-branches@v7.0.1 + uses: crs-k/stale-branches@v8.1.1 with: repo-token: ${{ steps.app-token.outputs.token }} days-before-stale: 180 From 7be99dc8824c1f4e8deeeaa3c5eb8b786b6dda51 Mon Sep 17 00:00:00 2001 From: Sebastian Schmidl <10573700+SebastianSchmidl@users.noreply.github.com> Date: Tue, 20 May 2025 11:42:14 +0200 Subject: [PATCH 110/216] fix: indexing in kdtw (#2826) --- aeon/clustering/_kernel_k_means.py | 10 +++--- aeon/clustering/tests/test_kernel_k_means.py | 37 +++++++++++++++----- 2 files changed, 34 insertions(+), 13 deletions(-) diff --git a/aeon/clustering/_kernel_k_means.py b/aeon/clustering/_kernel_k_means.py index 062b06ebc8..311228c823 100644 --- a/aeon/clustering/_kernel_k_means.py +++ b/aeon/clustering/_kernel_k_means.py @@ -26,7 +26,7 @@ def _kdtw_lk(x, y, local_kernel): diagonal_weights = np.zeros(max(x_timepoints, y_timepoints)) min_timepoints = min(x_timepoints, y_timepoints) - diagonal_weights[1] = 1.0 + diagonal_weights[0] = 1.0 for i in range(1, min_timepoints): diagonal_weights[i] = local_kernel[i - 1, i - 1] @@ -34,12 +34,12 @@ def _kdtw_lk(x, y, local_kernel): cumulative_dp_diag[0, 0] = 1 for i in range(1, x_timepoints): - cost_matrix[i, 1] = cost_matrix[i - 1, 1] * local_kernel[i - 1, 2] - cumulative_dp_diag[i, 1] = cumulative_dp_diag[i - 1, 1] * diagonal_weights[i] + cost_matrix[i, 0] = cost_matrix[i - 1, 0] * local_kernel[i - 1, 0] + cumulative_dp_diag[i, 0] = cumulative_dp_diag[i - 1, 0] * diagonal_weights[i] for j in range(1, y_timepoints): - cost_matrix[1, j] = cost_matrix[1, j - 1] * local_kernel[2, j - 1] - cumulative_dp_diag[1, j] = cumulative_dp_diag[1, j - 1] * diagonal_weights[j] + cost_matrix[0, j] = cost_matrix[0, j - 1] * local_kernel[0, j - 1] + cumulative_dp_diag[0, j] = cumulative_dp_diag[0, j - 1] * diagonal_weights[j] for i in range(1, x_timepoints): for j in range(1, y_timepoints): diff --git a/aeon/clustering/tests/test_kernel_k_means.py b/aeon/clustering/tests/test_kernel_k_means.py index 36a761a469..dd765fcad8 100644 --- a/aeon/clustering/tests/test_kernel_k_means.py +++ b/aeon/clustering/tests/test_kernel_k_means.py @@ -3,7 +3,7 @@ import numpy as np import pytest -from aeon.clustering._kernel_k_means import TimeSeriesKernelKMeans +from aeon.clustering._kernel_k_means import TimeSeriesKernelKMeans, _kdtw from aeon.datasets import load_basic_motions from aeon.utils.validation._dependencies import _check_estimator_deps @@ -19,18 +19,18 @@ expected_results_kdtw = [0, 2, 0, 0, 0] +max_train = 5 + +X_train, y_train = load_basic_motions(split="train") +X_test, y_test = load_basic_motions(split="test") + @pytest.mark.skipif( not _check_estimator_deps(TimeSeriesKernelKMeans, severity="none"), reason="skip test if required soft dependencies not available", ) -def test_kernel_k_means(): - """Test implementation of kernel k means.""" - max_train = 5 - - X_train, y_train = load_basic_motions(split="train") - X_test, y_test = load_basic_motions(split="test") - +def test_kernel_k_means_gak(): + """Test implementation of kernel k means with GAK kernel.""" kernel_kmeans = TimeSeriesKernelKMeans(random_state=1, n_clusters=3) kernel_kmeans.fit(X_train[0:max_train]) test_shape_result = kernel_kmeans.predict(X_test[0:max_train]) @@ -44,6 +44,13 @@ def test_kernel_k_means(): for val in proba: assert np.count_nonzero(val == 1.0) == 1 + +@pytest.mark.skipif( + not _check_estimator_deps(TimeSeriesKernelKMeans, severity="none"), + reason="skip test if required soft dependencies not available", +) +def test_kernel_k_means_kdtw(): + """Test implementation of kernel k means with KDTW kernel.""" kernel_kmeans_kdtw = TimeSeriesKernelKMeans( kernel="kdtw", random_state=1, @@ -61,3 +68,17 @@ def test_kernel_k_means(): for val in kdtw_proba: assert np.count_nonzero(val == 1.0) == 1 + + +def test_kdtw_kernel_univariate(): + """Test kdtw kernel for univariate time series.""" + # expected value created with the original (Matlab) code from: + # https://people.irisa.fr/Pierre-Francois.Marteau/REDK/KDTW/KDTW.html + x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=np.float64).reshape(-1, 1) + y = np.array([5, 6, 7, 8, 9, 1, 2], dtype=np.float64).reshape(-1, 1) + sigma = 0.125 + epsilon = 1e-20 + expected_distance = 1.2814e-102 + + distance = _kdtw(x, y, sigma=sigma, epsilon=epsilon) + np.testing.assert_allclose(expected_distance, distance, rtol=1e-4, atol=1e-106) From 7153d635b2ce5a0155aeb0b0e56814f53db30e95 Mon Sep 17 00:00:00 2001 From: Ramana Raja <83065061+Ramana-Raja@users.noreply.github.com> Date: Wed, 21 May 2025 18:13:45 +0530 Subject: [PATCH 111/216] [ENH] Added test cases for feature based clustering (#2690) * Added test cases for feature based clustering * Automatic `pre-commit` fixes * added docstring * Automatic `pre-commit` fixes * added . * added . * updated tsfresh * Automatic `pre-commit` fixes * added pytest _check_soft_dependencies for tsfresh * Automatic `pre-commit` fixes * added more testing * Automatic `pre-commit` fixes * added docs * added docs * added pytest * Automatic `pre-commit` fixes * improved docs * Automatic `pre-commit` fixes * improved test cases * changes made as requested by moderators * changes made as requested by moderators * Automatic `pre-commit` fixes * changed "percentiles" to "quantiles" --------- Co-authored-by: Ramana-Raja Co-authored-by: Matthew Middlehurst Co-authored-by: Tony Bagnall --- .../feature_based/tests/__init__.py | 1 + .../feature_based/tests/test_catch22.py | 88 +++++++++++++++++++ .../feature_based/tests/test_summary.py | 48 ++++++++++ .../feature_based/tests/test_tsfresh.py | 64 ++++++++++++++ 4 files changed, 201 insertions(+) create mode 100644 aeon/clustering/feature_based/tests/__init__.py create mode 100644 aeon/clustering/feature_based/tests/test_catch22.py create mode 100644 aeon/clustering/feature_based/tests/test_summary.py create mode 100644 aeon/clustering/feature_based/tests/test_tsfresh.py diff --git a/aeon/clustering/feature_based/tests/__init__.py b/aeon/clustering/feature_based/tests/__init__.py new file mode 100644 index 0000000000..d6e8de2247 --- /dev/null +++ b/aeon/clustering/feature_based/tests/__init__.py @@ -0,0 +1 @@ +"""Feature Based learning clustering tests.""" diff --git a/aeon/clustering/feature_based/tests/test_catch22.py b/aeon/clustering/feature_based/tests/test_catch22.py new file mode 100644 index 0000000000..e91ea01982 --- /dev/null +++ b/aeon/clustering/feature_based/tests/test_catch22.py @@ -0,0 +1,88 @@ +"""Tests for Catch22 Clusterer.""" + +import numpy as np +from sklearn import metrics + +from aeon.clustering.feature_based import Catch22Clusterer +from aeon.datasets import load_basic_motions, load_gunpoint + + +def test_catch22_multivariate(): + """Test Catch22 Clusterer with univariate data.""" + X_train, y_train = load_basic_motions(split="train") + X_test, y_test = load_basic_motions(split="test") + num_points = 12 + + X_train = X_train[:num_points] + y_train = y_train[:num_points] + X_test = X_test[:num_points] + y_test = y_test[:num_points] + + catach22 = Catch22Clusterer( + catch24=False, + random_state=1, + ) + train_result = catach22.fit_predict(X_train) + train_score = metrics.rand_score(y_train, train_result) + test_result = catach22.predict(X_test) + test_score = metrics.rand_score(y_test, test_result) + ari_test = metrics.adjusted_rand_score(y_test, test_result) + ari_train = metrics.adjusted_rand_score(y_train, train_result) + predict_proba = catach22.predict_proba(X_test) + + assert len(predict_proba) == 12 + assert ari_test == 0.1927353595255745 + assert ari_train == 0.09810791871058164 + assert len(predict_proba) == 12 + assert np.array_equal( + test_result, + [3, 4, 7, 7, 7, 7, 0, 7, 0, 4, 2, 2], + ) + assert np.array_equal( + train_result, + [7, 3, 0, 5, 6, 4, 7, 7, 4, 7, 1, 2], + ) + assert train_score == 0.4090909090909091 + assert test_score == 0.5 + assert test_result.shape == (12,) + assert train_result.shape == (12,) + + +def test_catch22_univariate(): + """Test Catch22 Clusterer with multivariate data.""" + X_train, y_train = load_gunpoint(split="train") + X_test, y_test = load_gunpoint(split="test") + num_points = 8 + + X_train = X_train[:num_points] + y_train = y_train[:num_points] + X_test = X_test[:num_points] + y_test = y_test[:num_points] + + catach22 = Catch22Clusterer( + catch24=False, + random_state=1, + ) + train_result = catach22.fit_predict(X_train) + train_score = metrics.rand_score(y_train, train_result) + test_result = catach22.predict(X_test) + test_score = metrics.rand_score(y_test, test_result) + ari_test = metrics.adjusted_rand_score(y_test, test_result) + ari_train = metrics.adjusted_rand_score(y_train, train_result) + predict_proba = catach22.predict_proba(X_test) + + assert len(predict_proba) == 8 + assert ari_test == 0.023255813953488372 + assert ari_train == 0.0 + assert np.array_equal( + test_result, + [3, 0, 1, 3, 7, 5, 2, 2], + ) + assert np.array_equal( + train_result, + [5, 0, 3, 7, 4, 6, 2, 1], + ) + assert train_score == 0.42857142857142855 + assert test_score == 0.5714285714285714 + assert test_result.shape == (8,) + assert train_result.shape == (8,) diff --git a/aeon/clustering/feature_based/tests/test_summary.py b/aeon/clustering/feature_based/tests/test_summary.py new file mode 100644 index 0000000000..75d9321252 --- /dev/null +++ b/aeon/clustering/feature_based/tests/test_summary.py @@ -0,0 +1,48 @@ +"""Tests for Summary Clusterer.""" + +import numpy as np + +from aeon.clustering.feature_based import SummaryClusterer +from aeon.datasets import load_basic_motions, load_gunpoint + + +def test_all_summary_stat_uni(): + """Test Summary Clusterer with all summary stat.""" + X_train, y_train = load_gunpoint(split="train") + X_test, y_test = load_gunpoint(split="test") + num_points = 8 + + X_train = X_train[:num_points] + X_test = X_test[:num_points] + summary_stats_options = ["default", "quantiles", "bowley", "tukey"] + for summary_stat in summary_stats_options: + summary = SummaryClusterer(random_state=1, summary_stats=summary_stat) + train_result = summary.fit_predict(X_train) + test_result = summary.predict(X_test) + predict_proba = summary.predict_proba(X_test) + assert len(predict_proba) == 8 + assert not np.isnan(train_result).any() + assert not np.isnan(test_result).any() + assert test_result.shape == (8,) + assert train_result.shape == (8,) + + +def test_all_summary_stat_multi(): + """Test Summary Clusterer with all summary stat.""" + X_train, y_train = load_basic_motions(split="train") + X_test, y_test = load_basic_motions(split="test") + num_points = 8 + + X_train = X_train[:num_points] + X_test = X_test[:num_points] + summary_stats_options = ["default", "quantiles", "bowley", "tukey"] + for summary_stat in summary_stats_options: + summary = SummaryClusterer(random_state=1, summary_stats=summary_stat) + train_result = summary.fit_predict(X_train) + test_result = summary.predict(X_test) + predict_proba = summary.predict_proba(X_test) + assert len(predict_proba) == 8 + assert not np.isnan(train_result).any() + assert not np.isnan(test_result).any() + assert test_result.shape == (8,) + assert train_result.shape == (8,) diff --git a/aeon/clustering/feature_based/tests/test_tsfresh.py b/aeon/clustering/feature_based/tests/test_tsfresh.py new file mode 100644 index 0000000000..4602b0bf62 --- /dev/null +++ b/aeon/clustering/feature_based/tests/test_tsfresh.py @@ -0,0 +1,64 @@ +"""Tests for TSFresh Clusterer.""" + +import numpy as np +import pytest + +from aeon.clustering.feature_based import TSFreshClusterer +from aeon.datasets import load_basic_motions, load_gunpoint +from aeon.utils.validation._dependencies import _check_soft_dependencies + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tsfresh"], severity="none"), + reason="TSFresh soft dependency unavailable.", +) +def test_all_fc_parameters_uni(): + """Test TSFresh Clusterer with all FC parameters.""" + X_train, y_train = load_gunpoint(split="train") + X_test, y_test = load_gunpoint(split="test") + num_points = 5 + + X_train = X_train[:num_points] + X_test = X_test[:num_points] + fc_parameters = ["minimal", "efficient", "comprehensive"] + for fc in fc_parameters: + tsfresh = TSFreshClusterer( + n_clusters=2, random_state=1, default_fc_parameters=fc + ) + + train_result = tsfresh.fit_predict(X_train) + test_result = tsfresh.predict(X_test) + predict_proba = tsfresh.predict_proba(X_test) + assert len(predict_proba) == 5 + assert not np.isnan(train_result).any() + assert not np.isnan(test_result).any() + assert test_result.shape == (5,) + assert train_result.shape == (5,) + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tsfresh"], severity="none"), + reason="TSFresh soft dependency unavailable.", +) +def test_all_fc_parameters_multi(): + """Test TSFresh Clusterer with all FC parameters.""" + X_train, y_train = load_basic_motions(split="train") + X_test, y_test = load_basic_motions(split="test") + num_points = 5 + + X_train = X_train[:num_points] + X_test = X_test[:num_points] + fc_parameters = ["minimal", "efficient", "comprehensive"] + for fc in fc_parameters: + tsfresh = TSFreshClusterer( + n_clusters=2, random_state=1, default_fc_parameters=fc + ) + + train_result = tsfresh.fit_predict(X_train) + test_result = tsfresh.predict(X_test) + predict_proba = tsfresh.predict_proba(X_test) + assert len(predict_proba) == 5 + assert not np.isnan(train_result).any() + assert not np.isnan(test_result).any() + assert test_result.shape == (5,) + assert train_result.shape == (5,) From ff7c94f661dea099692705bb884e6cfd2b170305 Mon Sep 17 00:00:00 2001 From: Ciph3r <142653450+adityagh006@users.noreply.github.com> Date: Thu, 22 May 2025 00:00:42 +0530 Subject: [PATCH 112/216] [DOC] Fix inconsistent double backticks in updated similarity_search module docstrings (#2618) --- aeon/similarity_search/series/_commons.py | 4 ++-- .../similarity_search/series/motifs/_stomp.py | 23 ++++++++++--------- .../series/neighbors/_dummy.py | 9 ++++---- .../series/neighbors/_mass.py | 9 ++++---- 4 files changed, 24 insertions(+), 21 deletions(-) diff --git a/aeon/similarity_search/series/_commons.py b/aeon/similarity_search/series/_commons.py index 646c38e5ff..fa3346df11 100644 --- a/aeon/similarity_search/series/_commons.py +++ b/aeon/similarity_search/series/_commons.py @@ -41,10 +41,10 @@ def fft_sliding_dot_product(X, q): Use FFT convolution to calculate the sliding window dot product. This function applies the Fast Fourier Transform (FFT) to efficiently compute - the sliding dot product between the input time series `X` and the query `q`. + the sliding dot product between the input time series ``X`` and the query ``q``. The dot product is computed for each channel individually. The sliding window approach ensures that the dot product is calculated for every possible subsequence - of `X` that matches the length of `q` + of ``X`` that matches the length of ``q`` Parameters ---------- diff --git a/aeon/similarity_search/series/motifs/_stomp.py b/aeon/similarity_search/series/motifs/_stomp.py index 0f43bbf487..e825903fdd 100644 --- a/aeon/similarity_search/series/motifs/_stomp.py +++ b/aeon/similarity_search/series/motifs/_stomp.py @@ -157,8 +157,8 @@ def _predict( A factor of the query length used to define the exclusion zone when ``allow_trivial_matches`` is set to False. For a given timestamp, the exclusion zone starts from - :math:`id_timestamp - floor(length*exclusion_factor)` and end at - :math:`id_timestamp + floor(length*exclusion_factor)`. + :math:``id_timestamp - floor(length*exclusion_factor)`` and end at + :math:``id_timestamp + floor(length*exclusion_factor)``. inverse_distance : bool If True, the matching will be made on the inverse of the distance, and thus, the farther neighbors will be returned instead of the closest ones. @@ -241,8 +241,8 @@ def compute_matrix_profile( A factor of the query length used to define the exclusion zone when ``allow_trivial_matches`` is set to False. For a given timestamp, the exclusion zone starts from - :math:`id_timestamp - floor(length * exclusion_factor)` and end at - :math:`id_timestamp + floor(length * exclusion_factor)`. + :math:``id_timestamp - floor(length * exclusion_factor)`` and end at + :math:``id_timestamp + floor(length * exclusion_factor)``. is_self_computation : bool Wheter X is equal to the series X_ given during fit. @@ -317,7 +317,8 @@ def _get_test_params(cls, parameter_set: str = "default"): params : dict or list of dict, default = {} Parameters to create testing instances of the class Each dict are parameters to construct an "interesting" test instance, i.e., - `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid test + instance. """ if parameter_set == "default": params = {"length": 3} @@ -375,9 +376,9 @@ def _stomp_normalized( The size of the exclusion zone used to prevent returning as top k candidates the ones that are close to each other (for example i and i+1). It is used to define a region between - :math:`id_timestamp - exclusion_size` and - :math:`id_timestamp + exclusion_size` which cannot be returned - as best match if :math:`id_timestamp` was already selected. By default, + :math:``id_timestamp - exclusion_size`` and + :math:``id_timestamp + exclusion_size`` which cannot be returned + as best match if :math:``id_timestamp`` was already selected. By default, the value None means that this is not used. inverse_distance : bool If True, the matching will be made on the inverse of the distance, and thus, the @@ -470,9 +471,9 @@ def _stomp( The size of the exclusion zone used to prevent returning as top k candidates the ones that are close to each other (for example i and i+1). It is used to define a region between - :math:`id_timestamp - exclusion_size` and - :math:`id_timestamp + exclusion_size` which cannot be returned - as best match if :math:`id_timestamp` was already selected. By default, + :math:``id_timestamp - exclusion_size`` and + :math:``id_timestamp + exclusion_size`` which cannot be returned + as best match if :math:``id_timestamp`` was already selected. By default, the value None means that this is not used. inverse_distance : bool If True, the matching will be made on the inverse of the distance, and thus, the diff --git a/aeon/similarity_search/series/neighbors/_dummy.py b/aeon/similarity_search/series/neighbors/_dummy.py index 399297b5c9..031f93bc22 100644 --- a/aeon/similarity_search/series/neighbors/_dummy.py +++ b/aeon/similarity_search/series/neighbors/_dummy.py @@ -81,8 +81,8 @@ def _predict( A factor of the query length used to define the exclusion zone when ``allow_neighboring_matches`` is set to False. For a given timestamp, the exclusion zone starts from - :math:`id_timestamp - floor(length * exclusion_factor)` and end at - :math:`id_timestamp + floor(length * exclusion_factor)`. + :math:``id_timestamp - floor(length * exclusion_factor)`` and end at + :math:``id_timestamp + floor(length * exclusion_factor)``. X_index : int, optional If ``X`` is a subsequence of X_, specify its starting timestamp in ``X_``. If specified, neighboring subsequences of X won't be able to match as @@ -158,7 +158,7 @@ def _get_test_params(cls, parameter_set: str = "default"): ---------- parameter_set : str, default="default" Name of the set of test parameters to return, for use in tests. If no - special parameters are defined for a value, will return `"default"` set. + special parameters are defined for a value, will return ``"default"`` set. There are currently no reserved values for transformers. Returns @@ -166,7 +166,8 @@ def _get_test_params(cls, parameter_set: str = "default"): params : dict or list of dict, default = {} Parameters to create testing instances of the class Each dict are parameters to construct an "interesting" test instance, i.e., - `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid test + instance. """ if parameter_set == "default": params = {"length": 20} diff --git a/aeon/similarity_search/series/neighbors/_mass.py b/aeon/similarity_search/series/neighbors/_mass.py index 695dce8844..f060fe9318 100644 --- a/aeon/similarity_search/series/neighbors/_mass.py +++ b/aeon/similarity_search/series/neighbors/_mass.py @@ -90,8 +90,8 @@ def _predict( A factor of the query length used to define the exclusion zone when ``allow_trivial_matches`` is set to False. For a given timestamp, the exclusion zone starts from - :math:`id_timestamp - floor(length * exclusion_factor)` and end at - :math:`id_timestamp + floor(length * exclusion_factor)`. + :math:``id_timestamp - floor(length * exclusion_factor)`` and end at + :math:``id_timestamp + floor(length * exclusion_factor)``. X_index : int, optional If ``X`` is a subsequence of X_, specify its starting timestamp in ``X_``. If specified, neighboring subsequences of X won't be able to match as @@ -178,7 +178,7 @@ def _get_test_params(cls, parameter_set: str = "default"): ---------- parameter_set : str, default="default" Name of the set of test parameters to return, for use in tests. If no - special parameters are defined for a value, will return `"default"` set. + special parameters are defined for a value, will return ``"default"`` set. There are currently no reserved values for transformers. Returns @@ -186,7 +186,8 @@ def _get_test_params(cls, parameter_set: str = "default"): params : dict or list of dict, default = {} Parameters to create testing instances of the class Each dict are parameters to construct an "interesting" test instance, i.e., - `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid test + instance. """ if parameter_set == "default": params = {"length": 20} From 3c6f78b8e5498fb6b2a54425284cb5a4ba3e935f Mon Sep 17 00:00:00 2001 From: Aryan Pola <98093778+aryanpola@users.noreply.github.com> Date: Fri, 23 May 2025 14:24:30 +0530 Subject: [PATCH 113/216] [ENH] Update `_continuous.py` functions (#2822) * update continuous, has test failure * parameters back to original * alpha added into precision * removes p_alpha deprecation * changes --- .../metrics/anomaly_detection/_continuous.py | 32 +++++-------- .../anomaly_detection/_range_metrics.py | 30 ++---------- .../anomaly_detection/_range_ts_metrics.py | 25 ++++++---- .../tests/test_ad_metrics.py | 47 +------------------ 4 files changed, 35 insertions(+), 99 deletions(-) diff --git a/aeon/benchmarking/metrics/anomaly_detection/_continuous.py b/aeon/benchmarking/metrics/anomaly_detection/_continuous.py index ce84ee3395..ade8c102b7 100644 --- a/aeon/benchmarking/metrics/anomaly_detection/_continuous.py +++ b/aeon/benchmarking/metrics/anomaly_detection/_continuous.py @@ -18,12 +18,16 @@ from sklearn.metrics import auc, f1_score, precision_recall_curve from sklearn.metrics import roc_auc_score as _roc_auc_score +from aeon.benchmarking.metrics.anomaly_detection._range_metrics import ( + range_f_score, + range_precision, + range_recall, +) from aeon.benchmarking.metrics.anomaly_detection._util import check_y from aeon.benchmarking.metrics.anomaly_detection.thresholding import ( top_k_points_threshold, top_k_ranges_threshold, ) -from aeon.utils.validation._dependencies import _check_soft_dependencies def roc_auc_score(y_true: np.ndarray, y_score: np.ndarray) -> float: @@ -142,9 +146,8 @@ def f_score_at_k_ranges( finding a threshold on the scoring that produces at least k anomalous ranges. If `k` is not specified, the number of anomalies within the ground truth is used as `k`. - This implementation uses the community package - `prts `_ as a soft-dependency to compute the - range-based F-score. + This implementation uses the aeon's implementation from `_range_metrics.py` to + compute the range-based F-score. Parameters ---------- @@ -167,12 +170,6 @@ def f_score_at_k_ranges( aeon.benchmarking.metrics.anomaly_detection.thresholding.top_k_ranges_threshold Function used to find the threshold. """ - _check_soft_dependencies( - "prts", obj="f_score_at_k_ranges", suppress_import_stdout=True - ) - - from prts import ts_fscore - y_true, y_pred = check_y(y_true, y_score, force_y_pred_continuous=True) if np.unique(y_score).shape[0] == 1: warnings.warn( @@ -183,7 +180,8 @@ def f_score_at_k_ranges( threshold = top_k_ranges_threshold(y_true, y_score, k) y_pred = y_score >= threshold - return ts_fscore(y_true, y_pred, p_alpha=1, r_alpha=1, cardinality="reciprocal") + # Corrected p_alpha to 1 to match original prts behavior expectation + return range_f_score(y_true, y_pred, p_alpha=1, r_alpha=1, cardinality="reciprocal") def rp_rr_auc_score( @@ -199,8 +197,6 @@ def rp_rr_auc_score( Computes the area under the precision recall curve when using the range-based precision and range-based recall metric introduced by Tatbul et al. at NeurIPS 2018 - [1]_. This implementation uses the community package - `prts `_ as a soft-dependency. This metric only considers the top-k predicted anomaly ranges within the scoring by finding a threshold on the scoring that produces at least k anomalous ranges. If `k` @@ -242,12 +238,6 @@ def rp_rr_auc_score( 1920–30. 2018. http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series.pdf. """ - _check_soft_dependencies( - "prts", obj="f_score_at_k_ranges", suppress_import_stdout=True - ) - - from prts import ts_precision, ts_recall - y_true, y_pred = check_y(y_true, y_score, force_y_pred_continuous=True) if np.unique(y_score).shape[0] == 1: warnings.warn( @@ -279,10 +269,10 @@ def rp_rr_auc_score( precisions = np.zeros_like(thresholds) for i, threshold in enumerate(thresholds): y_pred = (y_score >= threshold).astype(np.int64) - recalls[i] = ts_recall( + recalls[i] = range_recall( y_true, y_pred, alpha=r_alpha, cardinality=cardinality, bias=bias ) - precisions[i] = ts_precision( + precisions[i] = range_precision( y_true, y_pred, alpha=p_alpha, cardinality=cardinality, bias=bias ) # first sort by recall, then by precision to break ties diff --git a/aeon/benchmarking/metrics/anomaly_detection/_range_metrics.py b/aeon/benchmarking/metrics/anomaly_detection/_range_metrics.py index 7143100c04..6308cd9ba8 100644 --- a/aeon/benchmarking/metrics/anomaly_detection/_range_metrics.py +++ b/aeon/benchmarking/metrics/anomaly_detection/_range_metrics.py @@ -30,10 +30,6 @@ def range_precision( predicted continuous anomaly range the overlap size, position, and cardinality is considered. For more details, please refer to the paper [1]_. - The `alpha` parameter for the existence reward was removed. Because precision - emphasizes prediction quality, there is no need for an existence reward and this - value should always be set to 0. - Parameters ---------- y_true : np.ndarray @@ -41,7 +37,7 @@ def range_precision( y_pred : np.ndarray Anomaly scores for each point of the time series of shape (n_instances,). alpha : float - DEPRECATED. Default is 0 = no existence reward. + Default is 0 = no existence reward. cardinality : {'reciprocal', 'one', 'udf_gamma'} Cardinality type. bias : {'flat', 'front', 'middle', 'back'} @@ -60,13 +56,6 @@ def range_precision( 1920–30. 2018. http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series.pdf. """ - if alpha != 0: - warnings.warn( - "The alpha parameter should not be used in range precision. This " - "parameter is removed in 1.3.0.", - stacklevel=2, - category=FutureWarning, - ) y_true, y_pred = check_y(y_true, y_pred, force_y_pred_continuous=False) if np.unique(y_pred).shape[0] == 1: warnings.warn( @@ -160,9 +149,6 @@ def range_f_score( its optimal value at 1 and its worst value at 0. This implementation uses the range-based precision and range-based recall as basis. - The `p_alpha` parameter for the potential existance reward in the calculation of - range-based precision was removed. `p_alpha` should always be set to 0, anyway. - Parameters ---------- y_true : np.ndarray @@ -173,7 +159,7 @@ def range_f_score( F-score beta determines the weight of recall in the combined score. beta < 1 lends more weight to precision, while beta > 1 favors recall. p_alpha : float - DEPRECATED. Default is 0 = no existence reward for precision. + Default is 0 = no existence reward for precision. r_alpha : float Weight of the existence reward. If 0: no existence reward, if 1: only existence reward. @@ -197,14 +183,6 @@ def range_f_score( 1920–30. 2018. http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series.pdf. """ - if p_alpha != 0: - warnings.warn( - "The p_alpha parameter should not be used. This parameter is removed " - "in 1.3.0.", - stacklevel=2, - category=FutureWarning, - ) - y_true, y_pred = check_y(y_true, y_pred, force_y_pred_continuous=False) if np.unique(y_pred).shape[0] == 1: warnings.warn( @@ -216,7 +194,9 @@ def range_f_score( y_pred_ranges = _binary_to_ranges(y_pred) y_true_ranges = _binary_to_ranges(y_true) - precision = _ts_precision(y_pred_ranges, y_true_ranges, cardinality, p_bias) + precision = _ts_precision( + y_pred_ranges, y_true_ranges, cardinality, p_bias, p_alpha + ) recall = _ts_recall(y_pred_ranges, y_true_ranges, cardinality, r_bias, r_alpha) if precision + recall > 0: diff --git a/aeon/benchmarking/metrics/anomaly_detection/_range_ts_metrics.py b/aeon/benchmarking/metrics/anomaly_detection/_range_ts_metrics.py index 12ad2c0eaa..1791d36728 100644 --- a/aeon/benchmarking/metrics/anomaly_detection/_range_ts_metrics.py +++ b/aeon/benchmarking/metrics/anomaly_detection/_range_ts_metrics.py @@ -233,7 +233,7 @@ def _binary_to_ranges(binary_sequence): "Please use range_precision instead.", category=FutureWarning, ) -def ts_precision(y_pred, y_real, gamma="one", bias_type="flat"): +def ts_precision(y_pred, y_real, gamma="one", bias_type="flat", alpha=0.0): """ Calculate Precision for time series anomaly detection. @@ -317,10 +317,12 @@ def ts_precision(y_pred, y_real, gamma="one", bias_type="flat"): flat_y_pred = _flatten_ranges(y_pred_ranges) flat_y_real = _flatten_ranges(y_real_ranges) - return _ts_precision(flat_y_pred, flat_y_real, gamma, bias_type) + return _ts_precision(flat_y_pred, flat_y_real, gamma, bias_type, alpha=alpha) -def _ts_precision(y_pred_ranges, y_real_ranges, gamma="one", bias_type="flat"): +def _ts_precision( + y_pred_ranges, y_real_ranges, gamma="one", bias_type="flat", alpha=0.0 +): """ Implement range-based precision for time series anomaly detection. @@ -371,13 +373,20 @@ def _ts_precision(y_pred_ranges, y_real_ranges, gamma="one", bias_type="flat"): overlap_reward = _calculate_overlap_reward_precision( pred_range, overlap_set, bias_type ) + + existence_reward = 1.0 if overlap_set else 0.0 gamma_value = _gamma_select(cardinality, gamma) - total_overlap_reward += gamma_value * overlap_reward + + score_i = alpha * existence_reward + (1 - alpha) * ( + gamma_value * overlap_reward + ) + + total_overlap_reward += score_i + total_cardinality += 1 - precision = ( - total_overlap_reward / total_cardinality if total_cardinality > 0 else 0.0 - ) + precision = total_overlap_reward / total_cardinality if total_cardinality else 0.0 + return precision @@ -609,7 +618,7 @@ def ts_fscore( Processing Systems (NeurIPS 2018), Montréal, Canada. http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series.pdf """ - precision = ts_precision(y_pred, y_real, gamma, p_bias) + precision = ts_precision(y_pred, y_real, gamma, p_bias, alpha=p_alpha) recall = ts_recall(y_pred, y_real, gamma, r_bias, r_alpha) if precision + recall > 0: diff --git a/aeon/benchmarking/metrics/anomaly_detection/tests/test_ad_metrics.py b/aeon/benchmarking/metrics/anomaly_detection/tests/test_ad_metrics.py index fd14aa3919..e2e5364e6c 100644 --- a/aeon/benchmarking/metrics/anomaly_detection/tests/test_ad_metrics.py +++ b/aeon/benchmarking/metrics/anomaly_detection/tests/test_ad_metrics.py @@ -20,9 +20,8 @@ range_recall, ) from aeon.testing.data_generation import make_example_1d_numpy -from aeon.utils.validation._dependencies import _check_soft_dependencies -pr_metrics = [pr_auc_score] +pr_metrics = [pr_auc_score, rp_rr_auc_score] range_metrics = [ range_roc_auc_score, range_pr_auc_score, @@ -32,6 +31,7 @@ other_metrics = [ roc_auc_score, f_score_at_k_points, + f_score_at_k_ranges, ] continuous_metrics = [*pr_metrics, *other_metrics, *range_metrics] binary_metrics = [ @@ -40,21 +40,6 @@ range_recall, ] -if _check_soft_dependencies("prts", severity="none"): - pr_metrics.append(rp_rr_auc_score) - other_metrics.extend( - [ - f_score_at_k_ranges, - rp_rr_auc_score, - ] - ) - continuous_metrics.extend( - [ - rp_rr_auc_score, - f_score_at_k_ranges, - ] - ) - metrics = [*pr_metrics, *range_metrics, *other_metrics] @@ -148,10 +133,6 @@ def test_edge_cases_pr_metrics(metric): assert score <= 0.2, f"{metric.__name__}(y_true, y_inverted)={score} is not <= 0.2" -@pytest.mark.skipif( - not _check_soft_dependencies("prts", severity="none"), - reason="required soft dependency prts not available", -) def test_range_based_f1(): """Test range-based F1 score.""" y_pred = np.array([0, 1, 1, 0]) @@ -160,10 +141,6 @@ def test_range_based_f1(): np.testing.assert_almost_equal(result, 0.66666, decimal=4) -@pytest.mark.skipif( - not _check_soft_dependencies("prts", severity="none"), - reason="required soft dependency prts not available", -) def test_range_based_precision(): """Test range-based precision.""" y_pred = np.array([0, 1, 1, 0]) @@ -172,10 +149,6 @@ def test_range_based_precision(): assert result == 0.5 -@pytest.mark.skipif( - not _check_soft_dependencies("prts", severity="none"), - reason="required soft dependency prts not available", -) def test_range_based_recall(): """Test range-based recall.""" y_pred = np.array([0, 1, 1, 0]) @@ -184,10 +157,6 @@ def test_range_based_recall(): assert result == 1 -@pytest.mark.skipif( - not _check_soft_dependencies("prts", severity="none"), - reason="required soft dependency prts not available", -) def test_rf1_value_error(): """Test range-based F1 score raises ValueError on binary predictions.""" y_pred = np.array([0, 0.2, 0.7, 0]) @@ -211,10 +180,6 @@ def test_pr_curve_auc(): # np.testing.assert_almost_equal(result, 0.8333, decimal=4) -@pytest.mark.skipif( - not _check_soft_dependencies("prts", severity="none"), - reason="required soft dependency prts not available", -) def test_range_based_p_range_based_r_curve_auc(): """Test range-based precision-recall curve AUC.""" y_pred = np.array([0, 0.1, 1.0, 0.5, 0.1, 0]) @@ -223,10 +188,6 @@ def test_range_based_p_range_based_r_curve_auc(): np.testing.assert_almost_equal(result, 0.9792, decimal=4) -@pytest.mark.skipif( - not _check_soft_dependencies("prts", severity="none"), - reason="required soft dependency prts not available", -) def test_range_based_p_range_based_r_auc_perfect_hit(): """Test range-based precision-recall curve AUC with perfect hit.""" y_pred = np.array([0, 0, 0.5, 0.5, 0, 0]) @@ -235,10 +196,6 @@ def test_range_based_p_range_based_r_auc_perfect_hit(): np.testing.assert_almost_equal(result, 1.0000, decimal=4) -@pytest.mark.skipif( - not _check_soft_dependencies("prts", severity="none"), - reason="required soft dependency prts not available", -) def test_f_score_at_k_ranges(): """Test range-based F1 score at k ranges.""" y_pred = np.array([0.4, 0.1, 1.0, 0.5, 0.1, 0, 0.4, 0.5]) From b4e790680c6ed91d486e016934c8e29b2c1e8784 Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Fri, 23 May 2025 10:17:20 +0100 Subject: [PATCH 114/216] [MNT] stop segmenters changing state in predict (#2526) * remove stored variables in predict * IGTS * GGS * FLUSS * FLUSS * CLASP * CLASP * revert clasp * revert clasp * revert clasp * Update testing_config.py * Update _fluss.py --- aeon/segmentation/_ggs.py | 6 ++---- aeon/segmentation/_igts.py | 17 ++++++----------- aeon/segmentation/tests/test_igts.py | 2 -- aeon/testing/testing_config.py | 2 -- 4 files changed, 8 insertions(+), 19 deletions(-) diff --git a/aeon/segmentation/_ggs.py b/aeon/segmentation/_ggs.py index 0a1bb615af..85b08d279e 100644 --- a/aeon/segmentation/_ggs.py +++ b/aeon/segmentation/_ggs.py @@ -503,12 +503,10 @@ def _predict(self, X): dimension of X. The numerical values represent distinct segments labels for each of the data points. """ - self.change_points_ = self.ggs.find_change_points(X) + change_points_ = self.ggs.find_change_points(X) labels = np.zeros(X.shape[0], dtype=np.int32) - for i, (start, stop) in enumerate( - zip(self.change_points_[:-1], self.change_points_[1:]) - ): + for i, (start, stop) in enumerate(zip(change_points_[:-1], change_points_[1:])): labels[start:stop] = i return labels diff --git a/aeon/segmentation/_igts.py b/aeon/segmentation/_igts.py index 1bd673b33b..dd9aa39907 100644 --- a/aeon/segmentation/_igts.py +++ b/aeon/segmentation/_igts.py @@ -106,7 +106,7 @@ class _IGTS: """ Information Gain based Temporal Segmentation (GTS). - GTS is a n unsupervised method for segmenting multivariate time series + GTS is an unsupervised method for segmenting multivariate time series into non-overlapping segments by locating change points that for which the information gain is maximized. @@ -127,18 +127,13 @@ class _IGTS: Parameters ---------- - k_max: int, default=10 + k_max : int, default=10 Maximum number of change points to find. The number of segments is thus k+1. - step: : int, default=5 + step : int, default=5 Step size, or stride for selecting candidate locations of change points. Fox example a `step=5` would produce candidates [0, 5, 10, ...]. Has the same meaning as `step` in `range` function. - Attributes - ---------- - intermediate_results_: list of `ChangePointResult` - Intermediate segmentation results for each k value, where k=1, 2, ..., k_max - Notes ----- Based on the work from [1]_. @@ -366,9 +361,9 @@ def _predict(self, X, y=None) -> np.ndarray: The numerical values represent distinct segment labels for each of the data points. """ - self.change_points_ = self._igts.find_change_points(X) - self.intermediate_results_ = self._igts.intermediate_results_ - return self.to_clusters(self.change_points_[1:-1], X.shape[0]) + change_points_ = self._igts.find_change_points(X) + # self.intermediate_results_ = self._igts.intermediate_results_ + return self.to_clusters(change_points_[1:-1], X.shape[0]) def __repr__(self) -> str: """Return a string representation of the estimator.""" diff --git a/aeon/segmentation/tests/test_igts.py b/aeon/segmentation/tests/test_igts.py index 7103dca3e2..0aaa0f2de4 100644 --- a/aeon/segmentation/tests/test_igts.py +++ b/aeon/segmentation/tests/test_igts.py @@ -51,5 +51,3 @@ def test_InformationGainSegmenter(multivariate_mean_shift): [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3], dtype=int ), ) - assert igts.change_points_ == [0, 5, 10, 15, 20] - assert len(igts.intermediate_results_) == 3 diff --git a/aeon/testing/testing_config.py b/aeon/testing/testing_config.py index 3e47dcf08f..fe7f8d003b 100644 --- a/aeon/testing/testing_config.py +++ b/aeon/testing/testing_config.py @@ -55,8 +55,6 @@ "LeftSTAMPi": ["check_anomaly_detector_output"], # missed in legacy testing, changes state in predict/transform "FLUSSSegmenter": ["check_non_state_changing_method"], - "InformationGainSegmenter": ["check_non_state_changing_method"], - "GreedyGaussianSegmenter": ["check_non_state_changing_method"], "ClaSPSegmenter": ["check_non_state_changing_method"], "HMMSegmenter": ["check_non_state_changing_method"], "RSTSF": ["check_non_state_changing_method"], From 3960a9ad4e311d5612038513edf97f51a2d41714 Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Fri, 23 May 2025 11:32:26 +0100 Subject: [PATCH 115/216] refactor dummy to naive (#2831) --- aeon/forecasting/__init__.py | 4 ++-- aeon/forecasting/{_dummy.py => _naive.py} | 12 ++++++------ aeon/forecasting/base.py | 3 +++ aeon/forecasting/tests/test_base.py | 6 +++--- examples/forecasting/forecasting.ipynb | 4 ++-- 5 files changed, 16 insertions(+), 13 deletions(-) rename aeon/forecasting/{_dummy.py => _naive.py} (60%) diff --git a/aeon/forecasting/__init__.py b/aeon/forecasting/__init__.py index de203a0bcd..7a331f69e6 100644 --- a/aeon/forecasting/__init__.py +++ b/aeon/forecasting/__init__.py @@ -1,13 +1,13 @@ """Forecasters.""" __all__ = [ - "DummyForecaster", + "NaiveForecaster", "BaseForecaster", "RegressionForecaster", "ETSForecaster", ] -from aeon.forecasting._dummy import DummyForecaster from aeon.forecasting._ets import ETSForecaster +from aeon.forecasting._naive import NaiveForecaster from aeon.forecasting._regression import RegressionForecaster from aeon.forecasting.base import BaseForecaster diff --git a/aeon/forecasting/_dummy.py b/aeon/forecasting/_naive.py similarity index 60% rename from aeon/forecasting/_dummy.py rename to aeon/forecasting/_naive.py index 7525b6ccd0..8b7deedeeb 100644 --- a/aeon/forecasting/_dummy.py +++ b/aeon/forecasting/_naive.py @@ -1,24 +1,24 @@ -"""DummyForecaster always predicts the last value seen in training.""" +"""Naive Forecaster.""" from aeon.forecasting.base import BaseForecaster -class DummyForecaster(BaseForecaster): - """Dummy forecaster always predicts the last value seen in training.""" +class NaiveForecaster(BaseForecaster): + """Naive forecaster that always predicts the last value seen in training.""" def __init__(self): - """Initialize DummyForecaster.""" + """Initialize NaiveForecaster.""" self.last_value_ = None super().__init__(horizon=1, axis=1) def _fit(self, y, exog=None): - """Fit dummy forecaster.""" + """Fit Naive forecaster.""" y = y.squeeze() self.last_value_ = y[-1] return self def _predict(self, y=None, exog=None): - """Predict using dummy forecaster.""" + """Predict using Naive forecaster.""" return self.last_value_ def _forecast(self, y, exog=None): diff --git a/aeon/forecasting/base.py b/aeon/forecasting/base.py index cf2db8d80c..23950fa0c6 100644 --- a/aeon/forecasting/base.py +++ b/aeon/forecasting/base.py @@ -4,6 +4,9 @@ """ +__maintainer__ = ["TonyBagnall"] +__all__ = ["BaseForecaster"] + from abc import abstractmethod import numpy as np diff --git a/aeon/forecasting/tests/test_base.py b/aeon/forecasting/tests/test_base.py index 22c159018b..bd4e26a670 100644 --- a/aeon/forecasting/tests/test_base.py +++ b/aeon/forecasting/tests/test_base.py @@ -4,12 +4,12 @@ import pandas as pd import pytest -from aeon.forecasting import DummyForecaster +from aeon.forecasting import NaiveForecaster def test_base_forecaster(): """Test base forecaster functionality.""" - f = DummyForecaster() + f = NaiveForecaster() y = np.random.rand(50) f.fit(y) p1 = f.predict() @@ -26,7 +26,7 @@ def test_base_forecaster(): def test_convert_y(): """Test y conversion in forecasting base.""" - f = DummyForecaster() + f = NaiveForecaster() y = np.random.rand(50) with pytest.raises(ValueError, match="Input axis should be 0 or 1"): f._convert_y(y, axis=2) diff --git a/examples/forecasting/forecasting.ipynb b/examples/forecasting/forecasting.ipynb index 0e0b4ac72f..5d98504f13 100644 --- a/examples/forecasting/forecasting.ipynb +++ b/examples/forecasting/forecasting.ipynb @@ -193,9 +193,9 @@ "cell_type": "code", "source": [ "# Fit then predict\n", - "from aeon.forecasting import DummyForecaster\n", + "from aeon.forecasting import NaiveForecaster\n", "\n", - "d = DummyForecaster()\n", + "d = NaiveForecaster()\n", "print(d.get_tag(\"y_inner_type\"))\n", "d.fit(y)\n", "p = d.predict()\n", From 9331bd0be5271fee562971834374d04e5dfa3850 Mon Sep 17 00:00:00 2001 From: TinaJin0228 <60577222+TinaJin0228@users.noreply.github.com> Date: Fri, 23 May 2025 18:37:51 +0800 Subject: [PATCH 116/216] [ENH] add a difference transformer to series transformations (#2729) * add difference transformer to series transformations * add myself to .all-contributorsrc * small modification * modify according to reviews * [ENH] add informational tags back to estimator docs (#2652) * feat: add new tags information for anomaly detection * feat: add tag information in given anomaly modles * fix: add new anomaly detector tags in mock anomaly detector class * fix: use anomaly-detector instead of estimator for new tags * update and add basic testing * fixes --------- Co-authored-by: MatthewMiddlehurst * [DOC] Documentation improvement of BaseSeriesEstimator and BaseAeonEstimator (#2612) * Added the docs for base series and base classes * Updated documentation for base classes --------- Co-authored-by: Matthew Middlehurst * [ENH] HydraTransformer Output changed (#2656) * output type added * hydra wflow modified * hydra test file added * pr_pytest reverted * soft dep check added * soft dep corrected * docstring corrected, enhanced test cases * [DOC] Improved Hidalgo Segmentation Notebook (#2616) * Improved Hidalgo Segmentation Notebook * Updated hidalgo notebook --------- Co-authored-by: Matthew Middlehurst * new devs and workflow consolidation (#2797) * all branches (#2809) * Automated `pre-commit` hook update (#2820) Co-authored-by: TonyBagnall <9594042+TonyBagnall@users.noreply.github.com> * [MNT] Bump crs-k/stale-branches in the github-actions group (#2821) Bumps the github-actions group with 1 update: [crs-k/stale-branches](https://github.com/crs-k/stale-branches). Updates `crs-k/stale-branches` from 7.0.1 to 8.1.1 - [Release notes](https://github.com/crs-k/stale-branches/releases) - [Commits](https://github.com/crs-k/stale-branches/compare/v7.0.1...v8.1.1) --- updated-dependencies: - dependency-name: crs-k/stale-branches dependency-version: 8.1.1 dependency-type: direct:production update-type: version-update:semver-major dependency-group: github-actions ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * fix: indexing in kdtw (#2826) * [ENH] Added test cases for feature based clustering (#2690) * Added test cases for feature based clustering * Automatic `pre-commit` fixes * added docstring * Automatic `pre-commit` fixes * added . * added . * updated tsfresh * Automatic `pre-commit` fixes * added pytest _check_soft_dependencies for tsfresh * Automatic `pre-commit` fixes * added more testing * Automatic `pre-commit` fixes * added docs * added docs * added pytest * Automatic `pre-commit` fixes * improved docs * Automatic `pre-commit` fixes * improved test cases * changes made as requested by moderators * changes made as requested by moderators * Automatic `pre-commit` fixes * changed "percentiles" to "quantiles" --------- Co-authored-by: Ramana-Raja Co-authored-by: Matthew Middlehurst Co-authored-by: Tony Bagnall * [DOC] Fix inconsistent double backticks in updated similarity_search module docstrings (#2618) * delete some descriptions * modification to _maintainer_ --------- Signed-off-by: dependabot[bot] Co-authored-by: Matthew Middlehurst Co-authored-by: Tony Bagnall Co-authored-by: Adarsh Dubey Co-authored-by: Kevin Shah <161136814+kevinzb56@users.noreply.github.com> Co-authored-by: Balgopal Moharana <99070111+lucifer4073@users.noreply.github.com> Co-authored-by: Kavya Rambhia <161142013+kavya-r30@users.noreply.github.com> Co-authored-by: aeon-actions-bot[bot] <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Co-authored-by: TonyBagnall <9594042+TonyBagnall@users.noreply.github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Sebastian Schmidl <10573700+SebastianSchmidl@users.noreply.github.com> Co-authored-by: Ramana Raja <83065061+Ramana-Raja@users.noreply.github.com> Co-authored-by: Ramana-Raja Co-authored-by: Ciph3r <142653450+adityagh006@users.noreply.github.com> --- .all-contributorsrc | 10 ++ aeon/transformations/series/__init__.py | 2 + aeon/transformations/series/_diff.py | 92 +++++++++++++++++++ .../transformations/series/tests/test_diff.py | 37 ++++++++ 4 files changed, 141 insertions(+) create mode 100644 aeon/transformations/series/_diff.py create mode 100644 aeon/transformations/series/tests/test_diff.py diff --git a/.all-contributorsrc b/.all-contributorsrc index c709456395..ce303bd162 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -2710,6 +2710,16 @@ "contributions": [ "doc" ] + }, + { + "login": "TinaJin0228", + "name": "Tina Jin", + "avatar_url": "https://avatars.githubusercontent.com/TinaJin0228", + "profile": "https://github.com/TinaJin0228", + "contributions": [ + "code", + "doc" + ] } ], "commitType": "docs" diff --git a/aeon/transformations/series/__init__.py b/aeon/transformations/series/__init__.py index 8b71ba9fc8..01bb5faa55 100644 --- a/aeon/transformations/series/__init__.py +++ b/aeon/transformations/series/__init__.py @@ -21,6 +21,7 @@ "SIVSeriesTransformer", "PCASeriesTransformer", "WarpingSeriesTransformer", + "DifferenceTransformer", ] from aeon.transformations.series._acf import ( @@ -32,6 +33,7 @@ from aeon.transformations.series._boxcox import BoxCoxTransformer from aeon.transformations.series._clasp import ClaSPTransformer from aeon.transformations.series._dft import DFTSeriesTransformer +from aeon.transformations.series._diff import DifferenceTransformer from aeon.transformations.series._dobin import Dobin from aeon.transformations.series._exp_smoothing import ExpSmoothingSeriesTransformer from aeon.transformations.series._gauss import GaussSeriesTransformer diff --git a/aeon/transformations/series/_diff.py b/aeon/transformations/series/_diff.py new file mode 100644 index 0000000000..221987b7bd --- /dev/null +++ b/aeon/transformations/series/_diff.py @@ -0,0 +1,92 @@ +import numpy as np + +from aeon.transformations.series.base import BaseSeriesTransformer + +__maintainer__ = ["TinaJin0228"] +__all__ = ["DifferenceTransformer"] + + +class DifferenceTransformer(BaseSeriesTransformer): + """ + Calculates the n-th order difference of a time series. + + Transforms a time series X into a series Y representing the difference + calculated `order` times. + + The time series are supposed to be all in rows, + with shape (n_channels, n_timepoints) + + - Order 1: Y[t] = X[t] - X[t-1] + - Order 2: Y[t] = (X[t] - X[t-1]) - (X[t-1] - X[t-2]) = X[t] - 2*X[t-1] + X[t-2] + - ... and so on. + + The transformed series will be shorter than the input series by `order` + elements along the time axis. + + Parameters + ---------- + order : int, default=1 + The order of differencing. Must be a positive integer. + + Notes + ----- + This transformer assumes the input series does not contain NaN values where + the difference needs to be computed. + + Examples + -------- + >>> import numpy as np + >>> from aeon.transformations.series._diff import DifferenceTransformer + >>> X1 = np.array([[1, 3, 2, 5, 4, 7, 6, 9, 8, 10]]) # Shape (1, 10) + >>> dt = DifferenceTransformer() + >>> Xt1 = dt.fit_transform(X1) + >>> print(Xt1) # Shape (1, 9) + [[ 2 -1 3 -1 3 -1 3 -1 2]] + + >>> X2 = np.array([[1, 3, 2, 5, 4, 7, 6, 9, 8, 10]]) # Shape (1, 10) + >>> dt2 = DifferenceTransformer(order=2) + >>> Xt2 = dt2.fit_transform(X2) + >>> print(Xt2) # Shape (1, 8) + [[-3 4 -4 4 -4 4 -4 3]] + + >>> X3 = np.array([[1, 2, 3, 4, 5], [5, 4, 3, 2, 1]]) # Shape (2, 5) + >>> dt = DifferenceTransformer() + >>> Xt3 = dt.fit_transform(X3) + >>> print(Xt3) # Shape (2, 4) + [[ 1 1 1 1] + [-1 -1 -1 -1]] + """ + + _tags = { + "capability:multivariate": True, + "X_inner_type": "np.ndarray", + "fit_is_empty": True, + } + + def __init__(self, order=1): + self.order = order + super().__init__(axis=1) + + def _transform(self, X, y=None): + """ + Perform the n-th order differencing transformation. + + Parameters + ---------- + X : Time series to transform. With shape (n_channels, n_timepoints). + y : ignored argument for interface compatibility + + Returns + ------- + Xt : np.ndarray + """ + if not isinstance(self.order, int) or self.order < 1: + raise ValueError( + f"`order` must be a positive integer, but got {self.order}" + ) + + diff_X = np.diff(X, n=self.order, axis=1) + + Xt = diff_X + + return Xt diff --git a/aeon/transformations/series/tests/test_diff.py b/aeon/transformations/series/tests/test_diff.py new file mode 100644 index 0000000000..9f54fccf7d --- /dev/null +++ b/aeon/transformations/series/tests/test_diff.py @@ -0,0 +1,37 @@ +"""Tests for Difference transformation.""" + +import numpy as np + +from aeon.transformations.series._diff import DifferenceTransformer + + +def test_diff(): + """Tests basic first and second order differencing.""" + X = np.array([[1.0, 4.0, 9.0, 16.0, 25.0, 36.0]]) + + dt1 = DifferenceTransformer(order=1) + Xt1 = dt1.fit_transform(X) + expected1 = np.array([[3.0, 5.0, 7.0, 9.0, 11.0]]) + + np.testing.assert_allclose( + Xt1, expected1, equal_nan=True, err_msg="Value mismatch for order 1" + ) + + dt2 = DifferenceTransformer(order=2) + Xt2 = dt2.fit_transform(X) + expected2 = np.array([[2.0, 2.0, 2.0, 2.0]]) + + np.testing.assert_allclose( + Xt2, expected2, equal_nan=True, err_msg="Value mismatch for order 2" + ) + + Y = np.array([[1, 2, 3, 4], [5, 3, 1, 8]]) + + Yt1 = dt1.fit_transform(Y) + expected3 = np.array([[1, 1, 1], [-2, -2, 7]]) + np.testing.assert_allclose( + Yt1, + expected3, + equal_nan=True, + err_msg="Value mismatch for order 1,multivariate", + ) From 2b49aa74ec8d43d10d27c37d45954baf7b5ce783 Mon Sep 17 00:00:00 2001 From: "aeon-actions-bot[bot]" <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Date: Fri, 23 May 2025 11:40:03 +0100 Subject: [PATCH 117/216] Automated `CONTRIBUTORS.md` update (#2832) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> --- CONTRIBUTORS.md | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md index 2103194799..43f07002c1 100644 --- a/CONTRIBUTORS.md +++ b/CONTRIBUTORS.md @@ -1,7 +1,7 @@ # Contributors -[![All Contributors](https://img.shields.io/badge/all_contributors-265-orange.svg)](#contributors) +[![All Contributors](https://img.shields.io/badge/all_contributors-266-orange.svg)](#contributors) This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! @@ -277,13 +277,14 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Thach Le Nguyen
Thach Le Nguyen
💻 ⚠️ TheMathcompay Widget Factory Team
TheMathcompay Widget Factory Team
📖 Thomas Buckley-Houston
Thomas Buckley-Houston
🐛 + Tina Jin
Tina Jin
💻 📖 Tom Xu
Tom Xu
💻 📖 Tomasz Chodakowski
Tomasz Chodakowski
💻 📖 🐛 Tony Bagnall
Tony Bagnall
💻 💼 📖 🎨 📋 🔍 🤔 📆 💬 👀 📢 🔣 Tvisha Vedant
Tvisha Vedant
💻 - Utkarsh Kumar
Utkarsh Kumar
💻 📖 + Utkarsh Kumar
Utkarsh Kumar
💻 📖 Utsav Kumar Tiwari
Utsav Kumar Tiwari
💻 📖 Vedant
Vedant
📖 Viktor Dremov
Viktor Dremov
💻 @@ -291,9 +292,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Vyomkesh Vyas
Vyomkesh Vyas
💻 📖 💡 ⚠️ Wayne Adams
Wayne Adams
📖 William Templier
William Templier
📖 - William Zeng
William Zeng
🐛 + William Zeng
William Zeng
🐛 William Zheng
William Zheng
💻 ⚠️ Yair Beer
Yair Beer
💻 Yash Lamba
Yash Lamba
💻 @@ -301,9 +302,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Ziyao Wei
Ziyao Wei
💻 aa25desh
aa25desh
💻 🐛 abandus
abandus
🤔 💻 - adoherty21
adoherty21
🐛 + adoherty21
adoherty21
🐛 alexbanwell1
alexbanwell1
💻 🎨 📖 bethrice44
bethrice44
🐛 💻 👀 ⚠️ big-o
big-o
💻 ⚠️ 🎨 🤔 👀 ✅ 🧑‍🏫 @@ -311,9 +312,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d brett koonce
brett koonce
📖 btrtts
btrtts
📖 chizzi25
chizzi25
📝 - chrisholder
chrisholder
💻 ⚠️ 📖 🎨 💡 🐛 + chrisholder
chrisholder
💻 ⚠️ 📖 🎨 💡 🐛 danbartl
danbartl
🐛 💻 👀 📢 ⚠️ ✅ 📹 hamzahiqb
hamzahiqb
🚇 hiqbal2
hiqbal2
📖 @@ -321,9 +322,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d jschemm
jschemm
💻 julu98
julu98
🐛 kkoziara
kkoziara
💻 🐛 - matteogales
matteogales
💻 🎨 🤔 + matteogales
matteogales
💻 🎨 🤔 neuron283
neuron283
💻 nileenagp
nileenagp
💻 oleskiewicz
oleskiewicz
💻 📖 ⚠️ @@ -331,9 +332,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d patiently pending world peace
patiently pending world peace
💻 raishubham1
raishubham1
📖 simone-pignotti
simone-pignotti
💻 🐛 - sophijka
sophijka
📖 🚧 + sophijka
sophijka
📖 🚧 sri1419
sri1419
💻 tensorflow-as-tf
tensorflow-as-tf
💻 vNtzYy
vNtzYy
🐛 @@ -341,9 +342,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d vedazeren
vedazeren
💻 ⚠️ vincent-nich12
vincent-nich12
💻 vollmersj
vollmersj
📖 - xiaobenbenecho
xiaobenbenecho
💻 + xiaobenbenecho
xiaobenbenecho
💻 xiaopu222
xiaopu222
📖 From 2fac9f72938f6f571f2efff5864694213b9e4e42 Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Mon, 26 May 2025 10:58:33 +0100 Subject: [PATCH 118/216] [BUG] Fix data loader (#2810) * Update _data_loaders.py * Update _data_loaders.py * stop deleting directories --- aeon/datasets/_data_loaders.py | 12 +++++++----- aeon/datasets/tests/test_data_loaders.py | 20 ++++++++++++++++++++ 2 files changed, 27 insertions(+), 5 deletions(-) diff --git a/aeon/datasets/_data_loaders.py b/aeon/datasets/_data_loaders.py index 4bbd6f1739..9f87e1d623 100644 --- a/aeon/datasets/_data_loaders.py +++ b/aeon/datasets/_data_loaders.py @@ -468,15 +468,17 @@ def _download_and_extract(url, extract_path=None): extract_path = os.path.join(extract_path, "%s/" % file_name.split(".")[0]) try: - if not os.path.exists(extract_path): + already_exists = os.path.exists(extract_path) + if not already_exists: os.makedirs(extract_path) zipfile.ZipFile(zip_file_name, "r").extractall(extract_path) shutil.rmtree(dl_dir) return extract_path except zipfile.BadZipFile: shutil.rmtree(dl_dir) - if os.path.exists(extract_path): - shutil.rmtree(extract_path) + if not already_exists: + if os.path.exists(extract_path): + shutil.rmtree(extract_path) raise zipfile.BadZipFile( "Could not unzip dataset. Please make sure the URL is valid." ) @@ -546,7 +548,7 @@ def _load_tsc_dataset( except zipfile.BadZipFile as e: raise ValueError( f"Invalid dataset name ={name} is not available on extract path =" - f"{extract_path}. Nor is it available on {url}", + f"{extract_path} nor is it available on {url}", ) from e return _load_saved_dataset( @@ -1342,7 +1344,7 @@ def load_classification( try_zenodo = False error_str = ( f"Invalid dataset name ={name} that is not available on extract path " - f"={extract_path}. Nor is it available on " + f"={extract_path} nor is it available on " f"https://timeseriesclassification.com/ or zenodo." ) try: diff --git a/aeon/datasets/tests/test_data_loaders.py b/aeon/datasets/tests/test_data_loaders.py index a2473e206a..4f347d4bd3 100644 --- a/aeon/datasets/tests/test_data_loaders.py +++ b/aeon/datasets/tests/test_data_loaders.py @@ -6,6 +6,7 @@ import shutil import tempfile from urllib.error import URLError +from zipfile import BadZipFile import numpy as np import pandas as pd @@ -24,6 +25,7 @@ from aeon.datasets._data_loaders import ( CONNECTION_ERRORS, _alias_datatype_check, + _download_and_extract, _get_channel_strings, _load_data, _load_header_info, @@ -551,3 +553,21 @@ def test_load_tsc_dataset(): assert isinstance(X, np.ndarray) and isinstance(y, np.ndarray) with pytest.raises(ValueError, match="Invalid dataset name"): _load_tsc_dataset("FOO", split="TEST", extract_path=tmp) + + +@pytest.mark.skipif( + PR_TESTING, + reason="Only run on overnights because of intermittent fail for read/write", +) +@pytest.mark.xfail(raises=(URLError, TimeoutError, ConnectionError)) +def test_download_and_extract(): + """Test that the function does not delete a directory if already present.""" + name = "Foo" + with tempfile.TemporaryDirectory() as tmp: + extract_path = os.path.join(tmp, name) + os.makedirs(extract_path) + url = "https://timeseriesclassification.com/aeon-toolkit/%s.zip" % name + try: + _download_and_extract(url, extract_path=extract_path) + except BadZipFile: + assert os.path.exists(extract_path) From ff1c383e6a8ca7b44d288b0353f9b5627295f876 Mon Sep 17 00:00:00 2001 From: "aeon-actions-bot[bot]" <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Date: Mon, 26 May 2025 12:20:55 +0100 Subject: [PATCH 119/216] Automated `pre-commit` hook update (#2841) Co-authored-by: TonyBagnall <9594042+TonyBagnall@users.noreply.github.com> --- .pre-commit-config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index b438f853d1..2dece2f28f 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -29,13 +29,13 @@ repos: args: [ "--create", "--python-folders", "aeon" ] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.11.10 + rev: v0.11.11 hooks: - id: ruff args: [ "--fix"] - repo: https://github.com/asottile/pyupgrade - rev: v3.19.1 + rev: v3.20.0 hooks: - id: pyupgrade args: [ "--py39-plus" ] From 6d0c86f8b38764fa2b1a915c15107eef3f73e263 Mon Sep 17 00:00:00 2001 From: "allcontributors[bot]" <46447321+allcontributors[bot]@users.noreply.github.com> Date: Tue, 27 May 2025 16:54:16 +0100 Subject: [PATCH 120/216] =?UTF-8?q?=F0=9F=93=9D=20Add=20lucifer4073=20as?= =?UTF-8?q?=20a=20contributor=20for=20code=20(#2846)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 📝 Update CONTRIBUTORS.md [skip ci] * 📝 Update .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> --- .all-contributorsrc | 9 +++++++ CONTRIBUTORS.md | 61 +++++++++++++++++++++++---------------------- 2 files changed, 40 insertions(+), 30 deletions(-) diff --git a/.all-contributorsrc b/.all-contributorsrc index ce303bd162..7a262d8a2c 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -2720,6 +2720,15 @@ "code", "doc" ] + }, + { + "login": "lucifer4073", + "name": "Balgopal Moharana", + "avatar_url": "https://avatars.githubusercontent.com/u/99070111?v=4", + "profile": "https://github.com/lucifer4073", + "contributions": [ + "code" + ] } ], "commitType": "docs" diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md index 43f07002c1..8e51820eb3 100644 --- a/CONTRIBUTORS.md +++ b/CONTRIBUTORS.md @@ -1,7 +1,7 @@ # Contributors -[![All Contributors](https://img.shields.io/badge/all_contributors-266-orange.svg)](#contributors) +[![All Contributors](https://img.shields.io/badge/all_contributors-267-orange.svg)](#contributors) This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! @@ -60,10 +60,11 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Ayushmaan Seth
Ayushmaan Seth
💻 👀 ⚠️ 📖 📋 ✅ BANDASAITEJAREDDY
BANDASAITEJAREDDY
💻 📖 Badr-Eddine Marani
Badr-Eddine Marani
💻 + Balgopal Moharana
Balgopal Moharana
💻 Benedikt Heidrich
Benedikt Heidrich
💻 - Benjamin Bluhm
Benjamin Bluhm
💻 📖 💡 + Benjamin Bluhm
Benjamin Bluhm
💻 📖 💡 Bhaskar Dhariyal
Bhaskar Dhariyal
💻 ⚠️ Binay Kumar
Binay Kumar
💻 📖 ⚠️ Bohan Zhang
Bohan Zhang
💻 @@ -71,9 +72,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Brian Murphy
Brian Murphy
📖 Carlos Borrajo
Carlos Borrajo
💻 📖 Carlos Ramos Carreño
Carlos Ramos Carreño
📖 - Chang Wei Tan
Chang Wei Tan
💻 + Chang Wei Tan
Chang Wei Tan
💻 Cheuk Ting Ho
Cheuk Ting Ho
💻 Christian Kastner
Christian Kastner
💻 🐛 Christopher Dahlin
Christopher Dahlin
💻 @@ -81,9 +82,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Chuanhang Qiu
Chuanhang Qiu
💻 Ciaran Gilbert
Ciaran Gilbert
🐛 💻 📖 ⚠️ 🤔 ClaudiaSanches
ClaudiaSanches
💻 ⚠️ - Corvin Paul
Corvin Paul
📖 + Corvin Paul
Corvin Paul
📖 Cyril Meyer
Cyril Meyer
⚠️ 📖 💻 Daniel Burkhardt Cerigo
Daniel Burkhardt Cerigo
💻 Daniel L.
Daniel L.
📖 @@ -91,9 +92,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Daniel Roberts
Daniel Roberts
📖 Daniele Carli
Daniele Carli
📖 Dave Hirschfeld
Dave Hirschfeld
🚇 - David Buchaca Prats
David Buchaca Prats
💻 + David Buchaca Prats
David Buchaca Prats
💻 David Guijo-Rubio
David Guijo-Rubio
💻 🤔 Divya Tiwari
Divya Tiwari
💻 🔣 Dmitriy Valetov
Dmitriy Valetov
💻 ✅ @@ -101,9 +102,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Drishti Bhasin
Drishti Bhasin
💻 Dylan Sherry
Dylan Sherry
🚇 Emilia Rose
Emilia Rose
💻 ⚠️ - Emmanuel Ferdman
Emmanuel Ferdman
📖 + Emmanuel Ferdman
Emmanuel Ferdman
📖 Er Jie Yong
Er Jie Yong
🐛 💻 Evan Miller
Evan Miller
✅ Eyal Shafran
Eyal Shafran
💻 @@ -111,9 +112,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Felix Claessen
Felix Claessen
💻 📖 ⚠️ 🐛 Ferdinand Rewicki
Ferdinand Rewicki
💻 🐛 Florian Stinner
Florian Stinner
💻 ⚠️ - Francesco Spinnato
Francesco Spinnato
💻 + Francesco Spinnato
Francesco Spinnato
💻 Franz Kiraly
Franz Kiraly
🐛 💼 💻 📖 🎨 📋 💡 💵 🔍 🤔 🚧 🧑‍🏫 📆 💬 👀 📢 ⚠️ ✅ 📹 Freddy A Boulton
Freddy A Boulton
🚇 ⚠️ Futuer
Futuer
📖 @@ -121,9 +122,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Galina Chernikova
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💻 George Langley
George Langley
📖 George Oastler
George Oastler
💻 ⚠️ 📦 💡 📖 - Gilberto Barbosa
Gilberto Barbosa
💻 + Gilberto Barbosa
Gilberto Barbosa
💻 Grace Gao
Grace Gao
💻 🐛 Guilherme Arcencio
Guilherme Arcencio
💻 ⚠️ Guzal Bulatova
Guzal Bulatova
🐛 💻 📋 🧑‍🏫 📆 👀 ⚠️ @@ -131,9 +132,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d HaroonAzamFiza
HaroonAzamFiza
📖 Harshitha Sudhakar
Harshitha Sudhakar
📖 💻 Hedeer El Showk
Hedeer El Showk
🐛 📖 💻 - Huayi Wei
Huayi Wei
✅ + Huayi Wei
Huayi Wei
✅ Ifeanyi30
Ifeanyi30
💻 Ilja Maurer
Ilja Maurer
💻 Ilyas Moutawwakil
Ilyas Moutawwakil
💻 📖 @@ -141,9 +142,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Ishan Nangia
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🤔 Ivan Knyazev
Ivan Knyazev
📖 Jack Russon
Jack Russon
💻 - James Large
James Large
💻 📖 ⚠️ 🚇 🚧 + James Large
James Large
💻 📖 ⚠️ 🚇 🚧 James Morrill
James Morrill
💻 Jasmine Liaw
Jasmine Liaw
💻 Jason Lines
Jason Lines
💻 💼 📖 🎨 📋 🔍 🤔 📆 💬 👀 📢 💡 @@ -151,9 +152,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Jason Pong
Jason Pong
💻 ⚠️ Jaume Mateu
Jaume Mateu
💻 JonathanBechtel
JonathanBechtel
💻 🤔 ⚠️ - Joren Hammudoglu
Joren Hammudoglu
🚇 + Joren Hammudoglu
Joren Hammudoglu
🚇 Juan Orduz
Juan Orduz
✅ 📖 Julian Cooper
Julian Cooper
💻 🤔 Juliana
Juliana
💻 @@ -161,9 +162,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Kai Lion
Kai Lion
💻 ⚠️ 📖 Kavin Anand
Kavin Anand
📖 Kavya Rambhia
Kavya Rambhia
💻 - Kejsi Take
Kejsi Take
💻 + Kejsi Take
Kejsi Take
💻 Kevin Lam
Kevin Lam
💻 💡 ⚠️ Kevin Shah
Kevin Shah
📖 Kirstie Whitaker
Kirstie Whitaker
🤔 🔍 @@ -171,9 +172,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Krum Arnaudov
Krum Arnaudov
🐛 💻 Kutay Koralturk
Kutay Koralturk
💻 🐛 Leonidas Tsaprounis
Leonidas Tsaprounis
💻 🐛 🧑‍🏫 👀 - Lielle Ravid
Lielle Ravid
💻 📖 + Lielle Ravid
Lielle Ravid
💻 📖 Logan Duffy
Logan Duffy
💻 📖 ⚠️ 🐛 🤔 Lorena Pantano
Lorena Pantano
🤔 Lorenzo Toniazzi
Lorenzo Toniazzi
💻 @@ -181,9 +182,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Luca Bennett
Luca Bennett
💻 📖 ⚠️ Luis Ventura
Luis Ventura
💻 Luis Zugasti
Luis Zugasti
📖 - Lukasz Mentel
Lukasz Mentel
💻 📖 🚇 ⚠️ 🐛 🚧 🧑‍🏫 + Lukasz Mentel
Lukasz Mentel
💻 📖 🚇 ⚠️ 🐛 🚧 🧑‍🏫 Marcelo Trylesinski
Marcelo Trylesinski
📖 Marco Gorelli
Marco Gorelli
🚇 Margaret Gorlin
Margaret Gorlin
💻 💡 ⚠️ @@ -191,9 +192,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Marielle
Marielle
📖 💻 🤔 Markus Löning
Markus Löning
💻 ⚠️ 🚧 📦 👀 🚇 💡 🐛 ✅ 💼 📖 🎨 📋 🔍 🤔 📆 💬 📢 🧑‍🏫 📹 Martin Walter
Martin Walter
💻 🐛 📆 🔍 🧑‍🏫 🤔 🎨 👀 📖 📢 - Martina G. Vilas
Martina G. Vilas
👀 🤔 + Martina G. Vilas
Martina G. Vilas
👀 🤔 Matthew Middlehurst
Matthew Middlehurst
🐛 💻 🔣 📖 🎨 💡 🤔 🚇 🚧 🧑‍🏫 📣 💬 🔬 👀 ⚠️ ✅ 📢 Max Patzelt
Max Patzelt
💻 Miao Cai
Miao Cai
🐛 💻 @@ -201,9 +202,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Michael Feil
Michael Feil
💻 ⚠️ 🤔 Michal Chromcak
Michal Chromcak
💻 📖 ⚠️ ✅ Mirae Parker
Mirae Parker
💻 ⚠️ - Mohammed Saif Kazamel
Mohammed Saif Kazamel
🐛 + Mohammed Saif Kazamel
Mohammed Saif Kazamel
🐛 Morad :)
Morad :)
💻 ⚠️ 📖 Multivin12
Multivin12
💻 ⚠️ Márcio A. Freitas Jr
Márcio A. Freitas Jr
📖 @@ -211,9 +212,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Nikhil Gupta
Nikhil Gupta
💻 🐛 📖 Nikola Shahpazov
Nikola Shahpazov
📖 Nilesh Kumar
Nilesh Kumar
💻 - Nima Nooshiri
Nima Nooshiri
📖 + Nima Nooshiri
Nima Nooshiri
📖 Ninnart Fuengfusin
Ninnart Fuengfusin
💻 Noa Ben Ami
Noa Ben Ami
💻 ⚠️ 📖 Oleksandr Shchur
Oleksandr Shchur
🐛 💻 @@ -221,9 +222,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Oliver Matthews
Oliver Matthews
💻 Patrick Müller
Patrick Müller
💻 Patrick Rockenschaub
Patrick Rockenschaub
💻 🎨 🤔 ⚠️ - Patrick Schäfer
Patrick Schäfer
💻 ✅ + Patrick Schäfer
Patrick Schäfer
💻 ✅ Paul
Paul
📖 Paul Rabich
Paul Rabich
💻 Paul Yim
Paul Yim
💻 💡 ⚠️ @@ -231,9 +232,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Philipp Kortmann
Philipp Kortmann
💻 📖 Phillip Wenig
Phillip Wenig
💻 Piyush Gade
Piyush Gade
💻 👀 - Pulkit Verma
Pulkit Verma
📖 + Pulkit Verma
Pulkit Verma
📖 Quaterion
Quaterion
🐛 Rafael Ayllón-Gavilán
Rafael Ayllón-Gavilán
💻 Rakshitha Godahewa
Rakshitha Godahewa
💻 📖 @@ -241,9 +242,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d RavenRudi
RavenRudi
💻 Raya Chakravarty
Raya Chakravarty
📖 Rick van Hattem
Rick van Hattem
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Rishabh Bali
💻 + Rishabh Bali
Rishabh Bali
💻 Rishav Kumar Sinha
Rishav Kumar Sinha
📖 Rishi Kumar Ray
Rishi Kumar Ray
🚇 Riya Elizabeth John
Riya Elizabeth John
💻 ⚠️ 📖 @@ -251,9 +252,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Ryan Kuhns
Ryan Kuhns
💻 📖 ✅ 💡 🤔 👀 ⚠️ Sagar Mishra
Sagar Mishra
⚠️ Sajaysurya Ganesh
Sajaysurya Ganesh
💻 📖 🎨 💡 🤔 ⚠️ ✅ - Saransh Chopra
Saransh Chopra
📖 🚇 + Saransh Chopra
Saransh Chopra
📖 🚇 Satya Prakash Pattnaik
Satya Prakash Pattnaik
📖 Saurabh Dasgupta
Saurabh Dasgupta
💻 Sebastiaan Koel
Sebastiaan Koel
💻 📖 @@ -261,9 +262,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Sebastian Schmidl
Sebastian Schmidl
🐛 💻 📖 🔬 ⚠️ 👀 🔣 Sharathchenna
Sharathchenna
💻 Shivansh Subramanian
Shivansh Subramanian
📖 💻 - Solomon Botchway
Solomon Botchway
🚧 + Solomon Botchway
Solomon Botchway
🚧 Somtochukwu Benedict Onyekwelu
Somtochukwu Benedict Onyekwelu
📖 Stanislav Khrapov
Stanislav Khrapov
💻 Stijn Rotman
Stijn Rotman
💻 @@ -271,9 +272,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Sylvain Combettes
Sylvain Combettes
💻 🐛 TNTran92
TNTran92
💻 Taiwo Owoseni
Taiwo Owoseni
💻 - Tanish Yelgoe
Tanish Yelgoe
💻 + Tanish Yelgoe
Tanish Yelgoe
💻 Thach Le Nguyen
Thach Le Nguyen
💻 ⚠️ TheMathcompay Widget Factory Team
TheMathcompay Widget Factory Team
📖 Thomas Buckley-Houston
Thomas Buckley-Houston
🐛 @@ -281,9 +282,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Tom Xu
Tom Xu
💻 📖 Tomasz Chodakowski
Tomasz Chodakowski
💻 📖 🐛 Tony Bagnall
Tony Bagnall
💻 💼 📖 🎨 📋 🔍 🤔 📆 💬 👀 📢 🔣 - Tvisha Vedant
Tvisha Vedant
💻 + Tvisha Vedant
Tvisha Vedant
💻 Utkarsh Kumar
Utkarsh Kumar
💻 📖 Utsav Kumar Tiwari
Utsav Kumar Tiwari
💻 📖 Vedant
Vedant
📖 @@ -291,9 +292,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d ViktorKaz
ViktorKaz
💻 📖 🎨 Vyomkesh Vyas
Vyomkesh Vyas
💻 📖 💡 ⚠️ Wayne Adams
Wayne Adams
📖 - William Templier
William Templier
📖 + William Templier
William Templier
📖 William Zeng
William Zeng
🐛 William Zheng
William Zheng
💻 ⚠️ Yair Beer
Yair Beer
💻 @@ -301,9 +302,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Yi-Xuan Xu
Yi-Xuan Xu
💻 ⚠️ 🚧 📖 Ziyao Wei
Ziyao Wei
💻 aa25desh
aa25desh
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abandus
🤔 💻 + abandus
abandus
🤔 💻 adoherty21
adoherty21
🐛 alexbanwell1
alexbanwell1
💻 🎨 📖 bethrice44
bethrice44
🐛 💻 👀 ⚠️ @@ -311,9 +312,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d bobbys
bobbys
💻 brett koonce
brett koonce
📖 btrtts
btrtts
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chizzi25
📝 + chizzi25
chizzi25
📝 chrisholder
chrisholder
💻 ⚠️ 📖 🎨 💡 🐛 danbartl
danbartl
🐛 💻 👀 📢 ⚠️ ✅ 📹 hamzahiqb
hamzahiqb
🚇 @@ -321,9 +322,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d jesellier
jesellier
💻 jschemm
jschemm
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julu98
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💻 🐛 + kkoziara
kkoziara
💻 🐛 matteogales
matteogales
💻 🎨 🤔 neuron283
neuron283
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nileenagp
💻 @@ -331,9 +332,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d pabworks
pabworks
💻 ⚠️ patiently pending world peace
patiently pending world peace
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raishubham1
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💻 🐛 + simone-pignotti
simone-pignotti
💻 🐛 sophijka
sophijka
📖 🚧 sri1419
sri1419
💻 tensorflow-as-tf
tensorflow-as-tf
💻 @@ -341,9 +342,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d ved pawar
ved pawar
📖 vedazeren
vedazeren
💻 ⚠️ vincent-nich12
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vollmersj
📖 + vollmersj
vollmersj
📖 xiaobenbenecho
xiaobenbenecho
💻 xiaopu222
xiaopu222
📖 From 38690fa580524a1af9a0e2d49133049df3d98933 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Patrick=20Sch=C3=A4fer?= Date: Wed, 28 May 2025 13:11:02 +0200 Subject: [PATCH 121/216] [BUG] Adds test case for using csr_matrix with pickle - invalidates #2210 (#2843) * Extend test parameter set to validate if csx_matrix works with pickle. * Invalidates bug #2210 --- .../collection/dictionary_based/_sfa_fast.py | 23 +++++++++++++------ 1 file changed, 16 insertions(+), 7 deletions(-) diff --git a/aeon/transformations/collection/dictionary_based/_sfa_fast.py b/aeon/transformations/collection/dictionary_based/_sfa_fast.py index 0244d73572..42c1b5d42b 100644 --- a/aeon/transformations/collection/dictionary_based/_sfa_fast.py +++ b/aeon/transformations/collection/dictionary_based/_sfa_fast.py @@ -747,13 +747,22 @@ def _get_test_params(cls, parameter_set="default"): `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. """ # small window size for testing - params = { - "word_length": 4, - "window_size": 4, - "return_sparse": False, - "feature_selection": "chi2", - "alphabet_size": 2, - } + params = [ + { + "word_length": 4, + "window_size": 4, + "return_sparse": False, + "feature_selection": "chi2", + "alphabet_size": 2, + }, + { + "word_length": 4, + "window_size": 4, + "return_sparse": True, + "feature_selection": "chi2", + "alphabet_size": 2, + }, + ] return params def __getstate__(self): From 4ac26335f0dac93e29b9b22c44b0a1cd217e1663 Mon Sep 17 00:00:00 2001 From: Balgopal Moharana <99070111+lucifer4073@users.noreply.github.com> Date: Thu, 29 May 2025 21:37:08 +0530 Subject: [PATCH 122/216] [DOC] Docstring improved for dummy regressor (#2839) * Docstring improved for dummy regressor * convo resolved --- aeon/regression/_dummy.py | 77 +++++++++++++++++++++++++-------------- 1 file changed, 49 insertions(+), 28 deletions(-) diff --git a/aeon/regression/_dummy.py b/aeon/regression/_dummy.py index 322dcbf01d..87f984213b 100644 --- a/aeon/regression/_dummy.py +++ b/aeon/regression/_dummy.py @@ -3,44 +3,53 @@ __maintainer__ = ["MatthewMiddlehurst"] __all__ = ["DummyRegressor"] -import numpy as np from sklearn.dummy import DummyRegressor as SklearnDummyRegressor from aeon.regression.base import BaseRegressor class DummyRegressor(BaseRegressor): - """ - DummyRegressor makes predictions that ignore the input features. + """Dummy regressor that makes predictions ignoring input features. - This regressor is a wrapper for the scikit-learn DummyClassifier that serves as a - simple baseline to compare against other more complex regressors. - The specific behaviour of the baseline is selected with the ``strategy`` parameter. + This regressor serves as a simple baseline to compare against other, more + complex regressors. It is a wrapper for scikit-learn's DummyRegressor that + has been adapted for aeon's time series regression framework. The specific + behavior is controlled by the ``strategy`` parameter. All strategies make predictions that ignore the input feature values passed as the ``X`` argument to ``fit`` and ``predict``. The predictions, however, typically depend on values observed in the ``y`` parameter passed to ``fit``. - Function-identical to ``sklearn.dummy.DummyRegressor``, which is called inside. - Parameters ---------- strategy : {"mean", "median", "quantile", "constant"}, default="mean" - Strategy to use to generate predictions. - * "mean": always predicts the mean of the training set - * "median": always predicts the median of the training set - * "quantile": always predicts a specified quantile of the training set, - provided with the quantile parameter. - * "constant": always predicts a constant value that is provided by - the user. - constant : int or float or array-like of shape (n_outputs,), default=None - The explicit constant as predicted by the "constant" strategy. This - parameter is useful only for the "constant" strategy. + Strategy to use to generate predictions: + + - "mean": always predicts the mean of the training set + - "median": always predicts the median of the training set + - "quantile": always predicts a specified quantile of the training set, + provided with the ``quantile`` parameter + - "constant": always predicts a constant value provided by the user + + constant : int, float or array-like of shape (n_outputs,), default=None + The explicit constant value predicted by the "constant" strategy. + This parameter is only used when ``strategy="constant"``. + quantile : float in [0.0, 1.0], default=None - The quantile to predict using the "quantile" strategy. A quantile of - 0.5 corresponds to the median, while 0.0 to the minimum and 1.0 to the + The quantile to predict when using the "quantile" strategy. A quantile + of 0.5 corresponds to the median, 0.0 to the minimum, and 1.0 to the maximum. + Attributes + ---------- + sklearn_dummy_regressor : sklearn.dummy.DummyRegressor + The underlying scikit-learn DummyRegressor instance. + + Notes + ----- + Function-identical to ``sklearn.dummy.DummyRegressor``, which is called inside. + This class has been adapted to work with aeon's time series regression framework. + Examples -------- >>> from aeon.regression._dummy import DummyRegressor @@ -48,23 +57,30 @@ class DummyRegressor(BaseRegressor): >>> X_train, y_train = load_covid_3month(split="train") >>> X_test, y_test = load_covid_3month(split="test") + Using mean strategy: + >>> reg = DummyRegressor(strategy="mean") >>> reg.fit(X_train, y_train) DummyRegressor() >>> reg.predict(X_test)[:5] array([0.03689763, 0.03689763, 0.03689763, 0.03689763, 0.03689763]) + Using quantile strategy: + >>> reg = DummyRegressor(strategy="quantile", quantile=0.75) >>> reg.fit(X_train, y_train) DummyRegressor(quantile=0.75, strategy='quantile') >>> reg.predict(X_test)[:5] array([0.05559524, 0.05559524, 0.05559524, 0.05559524, 0.05559524]) + Using constant strategy: + >>> reg = DummyRegressor(strategy="constant", constant=0.5) >>> reg.fit(X_train, y_train) DummyRegressor(constant=0.5, strategy='constant') >>> reg.predict(X_test)[:5] array([0.5, 0.5, 0.5, 0.5, 0.5]) + """ _tags = { @@ -86,29 +102,34 @@ def __init__(self, strategy="mean", constant=None, quantile=None): super().__init__() def _fit(self, X, y): - """Fit the dummy regressor. + """Fit the dummy regressor to training data. Parameters ---------- - X : 3D np.ndarray of shape [n_cases, n_channels, n_timepoints] - y : array-like, shape = [n_cases] - the target values + X : np.ndarray of shape (n_cases, n_channels, n_timepoints) + The training time series data. + y : array-like of shape (n_cases,) + The target values for training. Returns ------- - self : reference to self. + self : DummyRegressor + Reference to the fitted regressor. """ self.sklearn_dummy_regressor.fit(X, y) return self - def _predict(self, X) -> np.ndarray: - """Perform regression on test vectors X. + def _predict(self, X): + """Make predictions on test data. Parameters ---------- - X : 3D np.ndarray of shape [n_cases, n_channels, n_timepoints] + X : np.ndarray of shape (n_cases, n_channels, n_timepoints) + The test time series data. Returns ------- - y : predictions of target values for X, np.ndarray + y_pred : np.ndarray of shape (n_cases,) + Predicted target values for X. """ return self.sklearn_dummy_regressor.predict(X) From 22b96772aea09627d20126440b2175df3d9ce172 Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Fri, 30 May 2025 15:00:47 +0100 Subject: [PATCH 123/216] remove numba typing (#2858) --- .../collection/convolution_based/_minirocket.py | 8 +------- .../collection/convolution_based/_multirocket.py | 9 --------- 2 files changed, 1 insertion(+), 16 deletions(-) diff --git a/aeon/transformations/collection/convolution_based/_minirocket.py b/aeon/transformations/collection/convolution_based/_minirocket.py index cdc62d42b0..313e39c076 100644 --- a/aeon/transformations/collection/convolution_based/_minirocket.py +++ b/aeon/transformations/collection/convolution_based/_minirocket.py @@ -230,7 +230,7 @@ def _static_fit(X, n_features=10_000, max_dilations_per_kernel=32, seed=None): ) -@vectorize("float32(float32,float32)", nopython=True, cache=True) +@vectorize(nopython=True, cache=True) def _PPV(a, b): if a > b: return 1 @@ -238,8 +238,6 @@ def _PPV(a, b): @njit( - "float32[:,:](float32[:,:],Tuple((int32[:],int32[:],int32[:],int32[:],float32[" - ":])), int32[:,:])", fastmath=True, parallel=True, cache=True, @@ -305,8 +303,6 @@ def _static_transform_uni(X, parameters, indices): @njit( - "float32[:,:](float32[:,:,:],Tuple((int32[:],int32[:],int32[:],int32[:],float32[" - ":])), int32[:,:])", fastmath=True, parallel=True, cache=True, @@ -384,8 +380,6 @@ def _static_transform_multi(X, parameters, indices): @njit( - "float32[:](float32[:,:,:],int32[:],int32[:],int32[:],int32[:],float32[:]," - "int32[:,:],optional(int32))", # noqa fastmath=True, parallel=False, cache=True, diff --git a/aeon/transformations/collection/convolution_based/_multirocket.py b/aeon/transformations/collection/convolution_based/_multirocket.py index 2b6dcf5a51..7b0c9bca2e 100644 --- a/aeon/transformations/collection/convolution_based/_multirocket.py +++ b/aeon/transformations/collection/convolution_based/_multirocket.py @@ -279,8 +279,6 @@ def _fit_multivariate(self, X): @njit( - "float32[:,:](float32[:,:],float32[:,:],Tuple((int32[:],int32[:],float32[:]))," - "Tuple((int32[:],int32[:],float32[:])),int32, int32[:,:],optional(int32))", fastmath=True, parallel=True, cache=True, @@ -553,10 +551,6 @@ def _transform_uni( @njit( - "float32[:,:](float32[:,:,:],float32[:,:,:]," - "Tuple((int32[:],int32[:],int32[:],int32[:],float32[:]))," - "Tuple((int32[:],int32[:],int32[:],int32[:],float32[:])),int32, int32[:,:]," - "optional(int32))", fastmath=True, parallel=True, cache=True, @@ -875,7 +869,6 @@ def _transform_multi( @njit( - "float32[:](float32[:,:],int32[:],int32[:],float32[:], int32[:,:],optional(int32))", fastmath=True, parallel=False, cache=True, @@ -945,8 +938,6 @@ def _fit_biases_univariate( @njit( - "float32[:](float32[:,:,:],int32[:],int32[:],int32[:],int32[:],float32[:], " - "int32[:,:],optional(int32))", fastmath=True, parallel=False, cache=True, From 45d8d8f92d420847a1da57719f336df4a7653999 Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Fri, 30 May 2025 15:04:09 +0100 Subject: [PATCH 124/216] [DOC] Regression forecaster docstring (#2837) * docstring * docstring --- aeon/forecasting/_regression.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/aeon/forecasting/_regression.py b/aeon/forecasting/_regression.py index 2330073afc..5f82162cb7 100644 --- a/aeon/forecasting/_regression.py +++ b/aeon/forecasting/_regression.py @@ -1,8 +1,8 @@ """Window-based regression forecaster. General purpose forecaster to use with any scikit learn or aeon compatible -regressor. Simply forms a collection of windows from the time series and trains to -predict the next +regressor. Simply forms a collection of series using windowing from the time series +to form ``X`` and trains to predict the next ``horizon`` points ahead. """ import numpy as np @@ -16,9 +16,9 @@ class RegressionForecaster(BaseForecaster): Regression based forecasting. Container for forecaster that reduces forecasting to regression through a - window. Form a collection of sub series of length `window` through a sliding - winodw to form X, take `horizon` points ahead to form `y`, then apply an aeon or - sklearn regressor. + window. Form a collection of sub-series of length ``window`` through a sliding + window to form training collection ``X``, take ``horizon`` points ahead to form + ``y``, then apply an aeon or sklearn regressor. Parameters @@ -27,8 +27,7 @@ class RegressionForecaster(BaseForecaster): The window prior to the current time point to use in forecasting. So if horizon is one, forecaster will train using points $i$ to $window+i-1$ to predict value $window+i$. If horizon is 4, forecaster will used points $i$ - to $window+i-1$ to predict value $window+i+3$. If None, the algorithm will - internally determine what data to use to predict `horizon` steps ahead. + to $window+i-1$ to predict value $window+i+3$. horizon : int, default =1 The number of time steps ahead to forecast. If horizon is one, the forecaster will learn to predict one point ahead From bdbcd8948a05d9bbfdfcacf9f61a9e287d771464 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Fri, 30 May 2025 23:01:49 +0100 Subject: [PATCH 125/216] [ENH] Whole-series anomaly detection (#2326) * mockup * init * decorator * correct import and tag * base docs * wrappers * tests * docs and imports * test params * register * big refactor * smoothing refactor * Revert "smoothing refactor" This reverts commit c245ccbd0c1481b188a9094ccd7ff493613a89cd. * fixes * final bits and docs for refactor * fixes * testing fixes * Update aeon/anomaly_detection/collection/base.py Co-authored-by: Sebastian Schmidl <10573700+SebastianSchmidl@users.noreply.github.com> * Update docs/api_reference/anomaly_detection.rst Co-authored-by: Sebastian Schmidl <10573700+SebastianSchmidl@users.noreply.github.com> * Review changes * docs * import --------- Co-authored-by: Sebastian Schmidl <10573700+SebastianSchmidl@users.noreply.github.com> --- aeon/anomaly_detection/base.py | 284 +++-------------- aeon/anomaly_detection/collection/__init__.py | 11 + .../collection/_classification.py | 67 ++++ .../collection/_outlier_detection.py | 62 ++++ aeon/anomaly_detection/collection/base.py | 257 ++++++++++++++++ .../distance_based/__init__.py | 19 -- .../distribution_based/__init__.py | 9 - .../outlier_detection/__init__.py | 11 - aeon/anomaly_detection/series/__init__.py | 9 + .../_pyodadapter.py | 4 +- aeon/anomaly_detection/series/base.py | 288 ++++++++++++++++++ .../series/distance_based/__init__.py | 19 ++ .../{ => series}/distance_based/_cblof.py | 2 +- .../{ => series}/distance_based/_kmeans.py | 6 +- .../distance_based/_left_stampi.py | 8 +- .../{ => series}/distance_based/_lof.py | 2 +- .../{ => series}/distance_based/_merlin.py | 6 +- .../distance_based}/_rockad.py | 4 +- .../{ => series}/distance_based/_stomp.py | 6 +- .../distance_based/tests/__init__.py | 0 .../distance_based/tests/test_cblof.py | 2 +- .../distance_based/tests/test_kmeans.py | 2 +- .../distance_based/tests/test_left_stampi.py | 2 +- .../distance_based/tests/test_lof.py | 2 +- .../distance_based/tests/test_merlin.py | 2 +- .../distance_based}/tests/test_rockad.py | 2 +- .../distance_based/tests/test_stomp.py | 2 +- .../series/distribution_based/__init__.py | 9 + .../{ => series}/distribution_based/_copod.py | 2 +- .../distribution_based/_dwt_mlead.py | 6 +- .../distribution_based/tests/__init__.py | 0 .../distribution_based/tests/test_copod.py | 2 +- .../tests/test_dwt_mlead.py | 2 +- .../series/outlier_detection/__init__.py | 11 + .../outlier_detection/_iforest.py | 2 +- .../outlier_detection}/_one_class_svm.py | 4 +- .../{ => series}/outlier_detection/_stray.py | 6 +- .../outlier_detection/tests/__init__.py | 0 .../outlier_detection/tests/test_iforest.py | 2 +- .../tests/test_one_class_svm.py | 2 +- .../outlier_detection/tests/test_stray.py | 2 +- .../{ => series}/tests/__init__.py | 0 .../{ => series}/tests/test_base.py | 0 .../tests/test_pyod_adapter.py | 2 +- .../whole_series/__init__.py | 7 - .../whole_series/tests/__init__.py | 1 - aeon/classification/base.py | 2 - .../_yield_anomaly_detection_checks.py | 62 +--- ...eld_collection_anomaly_detection_checks.py | 81 +++++ .../_yield_estimator_checks.py | 18 ++ .../_yield_series_anomaly_detection_checks.py | 73 +++++ .../_mock_anomaly_detectors.py | 4 +- aeon/testing/testing_config.py | 2 +- aeon/testing/testing_data.py | 6 +- aeon/utils/base/_identifier.py | 6 +- aeon/utils/base/_register.py | 16 +- aeon/utils/base/tests/test_identifier.py | 2 +- aeon/utils/tags/tests/test_discovery.py | 6 +- aeon/utils/tests/test_discovery.py | 4 +- docs/api_reference/anomaly_detection.rst | 70 +++-- docs/developer_guide/adding_typehints.md | 12 +- .../anomaly_detection/anomaly_detection.ipynb | 12 +- 62 files changed, 1082 insertions(+), 442 deletions(-) create mode 100644 aeon/anomaly_detection/collection/__init__.py create mode 100644 aeon/anomaly_detection/collection/_classification.py create mode 100644 aeon/anomaly_detection/collection/_outlier_detection.py create mode 100644 aeon/anomaly_detection/collection/base.py delete mode 100644 aeon/anomaly_detection/distance_based/__init__.py delete mode 100644 aeon/anomaly_detection/distribution_based/__init__.py delete mode 100644 aeon/anomaly_detection/outlier_detection/__init__.py create mode 100644 aeon/anomaly_detection/series/__init__.py rename aeon/anomaly_detection/{outlier_detection => series}/_pyodadapter.py (98%) create mode 100644 aeon/anomaly_detection/series/base.py create mode 100644 aeon/anomaly_detection/series/distance_based/__init__.py rename aeon/anomaly_detection/{ => series}/distance_based/_cblof.py (98%) rename aeon/anomaly_detection/{ => series}/distance_based/_kmeans.py (97%) rename aeon/anomaly_detection/{ => series}/distance_based/_left_stampi.py (95%) rename aeon/anomaly_detection/{ => series}/distance_based/_lof.py (98%) rename aeon/anomaly_detection/{ => series}/distance_based/_merlin.py (97%) rename aeon/anomaly_detection/{whole_series => series/distance_based}/_rockad.py (98%) rename aeon/anomaly_detection/{ => series}/distance_based/_stomp.py (96%) rename aeon/anomaly_detection/{ => series}/distance_based/tests/__init__.py (100%) rename aeon/anomaly_detection/{ => series}/distance_based/tests/test_cblof.py (97%) rename aeon/anomaly_detection/{ => series}/distance_based/tests/test_kmeans.py (95%) rename aeon/anomaly_detection/{ => series}/distance_based/tests/test_left_stampi.py (99%) rename aeon/anomaly_detection/{ => series}/distance_based/tests/test_lof.py (99%) rename aeon/anomaly_detection/{ => series}/distance_based/tests/test_merlin.py (96%) rename aeon/anomaly_detection/{whole_series => series/distance_based}/tests/test_rockad.py (96%) rename aeon/anomaly_detection/{ => series}/distance_based/tests/test_stomp.py (95%) create mode 100644 aeon/anomaly_detection/series/distribution_based/__init__.py rename aeon/anomaly_detection/{ => series}/distribution_based/_copod.py (97%) rename aeon/anomaly_detection/{ => series}/distribution_based/_dwt_mlead.py (98%) rename aeon/anomaly_detection/{ => series}/distribution_based/tests/__init__.py (100%) rename aeon/anomaly_detection/{ => series}/distribution_based/tests/test_copod.py (96%) rename aeon/anomaly_detection/{ => series}/distribution_based/tests/test_dwt_mlead.py (95%) create mode 100644 aeon/anomaly_detection/series/outlier_detection/__init__.py rename aeon/anomaly_detection/{ => series}/outlier_detection/_iforest.py (98%) rename aeon/anomaly_detection/{distance_based => series/outlier_detection}/_one_class_svm.py (98%) rename aeon/anomaly_detection/{ => series}/outlier_detection/_stray.py (96%) rename aeon/anomaly_detection/{ => series}/outlier_detection/tests/__init__.py (100%) rename aeon/anomaly_detection/{ => series}/outlier_detection/tests/test_iforest.py (98%) rename aeon/anomaly_detection/{distance_based => series/outlier_detection}/tests/test_one_class_svm.py (95%) rename aeon/anomaly_detection/{ => series}/outlier_detection/tests/test_stray.py (98%) rename aeon/anomaly_detection/{ => series}/tests/__init__.py (100%) rename aeon/anomaly_detection/{ => series}/tests/test_base.py (100%) rename aeon/anomaly_detection/{outlier_detection => series}/tests/test_pyod_adapter.py (98%) delete mode 100644 aeon/anomaly_detection/whole_series/__init__.py delete mode 100644 aeon/anomaly_detection/whole_series/tests/__init__.py create mode 100644 aeon/testing/estimator_checking/_yield_collection_anomaly_detection_checks.py create mode 100644 aeon/testing/estimator_checking/_yield_series_anomaly_detection_checks.py diff --git a/aeon/anomaly_detection/base.py b/aeon/anomaly_detection/base.py index 689c0188ee..28e6dbc5b9 100644 --- a/aeon/anomaly_detection/base.py +++ b/aeon/anomaly_detection/base.py @@ -4,79 +4,16 @@ __all__ = ["BaseAnomalyDetector"] from abc import abstractmethod -from typing import final import numpy as np -import pandas as pd -from aeon.base import BaseSeriesEstimator -from aeon.base._base_series import VALID_SERIES_INPUT_TYPES +from aeon.base import BaseAeonEstimator -class BaseAnomalyDetector(BaseSeriesEstimator): - """Base class for anomaly detection algorithms. - - Anomaly detection algorithms are used to identify anomalous subsequences in time - series data. These algorithms take a series of length m and return a boolean, int or - float array of length m, where each element indicates whether the corresponding - subsequence is anomalous or its anomaly score. - - Input and internal data format (where m is the number of time points and d is the - number of channels): - Univariate series (default): - np.ndarray, shape ``(m,)``, ``(m, 1)`` or ``(1, m)`` depending on axis. - This is converted to a 2D np.ndarray internally. - pd.DataFrame, shape ``(m, 1)`` or ``(1, m)`` depending on axis. - pd.Series, shape ``(m,)``. - Multivariate series: - np.ndarray array, shape ``(m, d)`` or ``(d, m)`` depending on axis. - pd.DataFrame ``(m, d)`` or ``(d, m)`` depending on axis. - - Output data format (one of the following): - Anomaly scores (default): - np.ndarray, shape ``(m,)`` of type float. For each point of the input time - series, the anomaly score is a float value indicating the degree of - anomalousness. The higher the score, the more anomalous the point. - Binary classification: - np.ndarray, shape ``(m,)`` of type bool or int. For each point of the input - time series, the output is a boolean or integer value indicating whether the - point is anomalous (``True``/``1``) or not (``False``/``0``). - - Detector learning types: - Unsupervised (default): - Unsupervised detectors do not require any training data and can directly be - used on the target time series. Their tags are set to ``fit_is_empty=True`` - and ``requires_y=False``. You would usually call the ``fit_predict`` method - on these detectors. - Semi-supervised: - Semi-supervised detectors require a training step on a time series without - anomalies (normal behaving time series). The target value ``y`` would - consist of only zeros. Thus, these algorithms have logic in the ``fit`` - method, but do not require the target values. Their tags are set to - ``fit_is_empty=False`` and ``requires_y=False``. You would usually first - call the ``fit`` method on the training data and then the ``predict`` - method for your target time series. - Supervised: - Supervised detectors require a training step on a time series with known - anomalies (anomalies should be present and must be annotated). The detector - implements the ``fit`` method, and the target value ``y`` consists of zeros - and ones. Their tags are, thus, set to ``fit_is_empty=False`` and - ``requires_y=True``. You would usually first call the ``fit`` method on the - training data and then the ``predict`` method for your target time series. - - Parameters - ---------- - axis : int - The time point axis of the input series if it is 2D. If ``axis==0``, it is - assumed each column is a time series and each row is a time point. i.e. the - shape of the data is ``(n_timepoints, n_channels)``. ``axis==1`` indicates - the time series are in rows, i.e. the shape of the data is - ``(n_channels, n_timepoints)``. - Setting this class variable will convert the input data to the chosen axis. - """ +class BaseAnomalyDetector(BaseAeonEstimator): + """Anomaly detection base class.""" _tags = { - "X_inner_type": "np.ndarray", # One of VALID_SERIES_INNER_TYPES "fit_is_empty": True, "requires_y": False, "learning_type:unsupervised": False, @@ -84,213 +21,74 @@ class BaseAnomalyDetector(BaseSeriesEstimator): "learning_type:supervised": False, } - def __init__(self, axis): - super().__init__(axis=axis) + def __init__(self): + super().__init__() - @final - def fit(self, X, y=None, axis=1): - """Fit time series anomaly detector to X. + @abstractmethod + def fit(self, X, y=None): + """Fit anomaly detector to X, optionally to y. + + State change: + Changes state to "fitted". - If the tag ``fit_is_empty`` is true, this just sets the ``is_fitted`` tag to - true. Otherwise, it checks ``self`` can handle ``X``, formats ``X`` into - the structure required by ``self`` then passes ``X`` (and possibly ``y``) to - ``_fit``. + Writes to self: + _is_fitted : flag is set to True. Parameters ---------- - X : one of aeon.base._base_series.VALID_SERIES_INPUT_TYPES - The time series to fit the model to. - A valid aeon time series data structure. See - aeon.base._base_series.VALID_SERIES_INPUT_TYPES for aeon supported types. - y : one of aeon.base._base_series.VALID_SERIES_INPUT_TYPES, default=None - The target values for the time series. - A valid aeon time series data structure. See - aeon.base._base_series.VALID_SERIES_INPUT_TYPES for aeon supported types. - axis : int - The time point axis of the input series if it is 2D. If ``axis==0``, it is - assumed each column is a time series and each row is a time point. i.e. the - shape of the data is ``(n_timepoints, n_channels)``. ``axis==1`` indicates - the time series are in rows, i.e. the shape of the data is - ``(n_channels, n_timepoints)``. + X : Series or Collection, any supported type + Data to fit anomaly detector to, of python type as follows: + Series: 2D np.ndarray shape (n_channels, n_timepoints) + Collection: 3D np.ndarray shape (n_cases, n_channels, n_timepoints) + or list of 2D np.ndarray, case i has shape (n_channels, n_timepoints_i) + y : Series, default=None + Additional data, e.g., labels for anomaly detector. Returns ------- BaseAnomalyDetector The fitted estimator, reference to self. """ - if self.get_tag("fit_is_empty"): - self.is_fitted = True - return self - - if self.get_tag("requires_y"): - if y is None: - raise ValueError("Tag requires_y is true, but fit called with y=None") - - # reset estimator at the start of fit - self.reset() + ... - X = self._preprocess_series(X, axis, True) - if y is not None: - y = self._check_y(y) - - self._fit(X=X, y=y) - - # this should happen last - self.is_fitted = True - return self - - @final - def predict(self, X, axis=1) -> np.ndarray: + @abstractmethod + def predict(self, X) -> np.ndarray: """Find anomalies in X. Parameters ---------- - X : one of aeon.base._base_series.VALID_SERIES_INPUT_TYPES - The time series to fit the model to. - A valid aeon time series data structure. See - aeon.base._base_series.VALID_SERIES_INPUT_TYPES for aeon supported types. - axis : int, default=1 - The time point axis of the input series if it is 2D. If ``axis==0``, it is - assumed each column is a time series and each row is a time point. i.e. the - shape of the data is ``(n_timepoints, n_channels)``. ``axis==1`` indicates - the time series are in rows, i.e. the shape of the data is - ``(n_channels, n_timepoints)``. + X : Series or Collection, any supported type + Data to fit anomaly detector to, of python type as follows: + Series: 2D np.ndarray shape (n_channels, n_timepoints) + Collection: 3D np.ndarray shape (n_cases, n_channels, n_timepoints) + or list of 2D np.ndarray, case i has shape (n_channels, n_timepoints_i) Returns ------- np.ndarray A boolean, int or float array of length len(X), where each element indicates - whether the corresponding subsequence is anomalous or its anomaly score. + whether the corresponding subsequence/case is anomalous or its anomaly + score. """ - fit_empty = self.get_tag("fit_is_empty") - if not fit_empty: - self._check_is_fitted() + ... - X = self._preprocess_series(X, axis, False) - - return self._predict(X) - - @final - def fit_predict(self, X, y=None, axis=1) -> np.ndarray: + @abstractmethod + def fit_predict(self, X, y=None) -> np.ndarray: """Fit time series anomaly detector and find anomalies for X. Parameters ---------- - X : one of aeon.base._base_series.VALID_SERIES_INPUT_TYPES - The time series to fit the model to. - A valid aeon time series data structure. See - aeon.base._base_series.VALID_INPUT_TYPES for aeon supported types. - y : one of aeon.base._base_series.VALID_SERIES_INPUT_TYPES, default=None - The target values for the time series. - A valid aeon time series data structure. See - aeon.base._base_series.VALID_SERIES_INPUT_TYPES for aeon supported types. - axis : int, default=1 - The time point axis of the input series if it is 2D. If ``axis==0``, it is - assumed each column is a time series and each row is a time point. i.e. the - shape of the data is ``(n_timepoints, n_channels)``. ``axis==1`` indicates - the time series are in rows, i.e. the shape of the data is - ``(n_channels, n_timepoints)``. + X : Series or Collection, any supported type + Data to fit anomaly detector to, of python type as follows: + Series: 2D np.ndarray shape (n_channels, n_timepoints) + Collection: 3D np.ndarray shape (n_cases, n_channels, n_timepoints) + or list of 2D np.ndarray, case i has shape (n_channels, n_timepoints_i) Returns ------- np.ndarray A boolean, int or float array of length len(X), where each element indicates - whether the corresponding subsequence is anomalous or its anomaly score. + whether the corresponding subsequence/case is anomalous or its anomaly + score. """ - if self.get_tag("requires_y"): - if y is None: - raise ValueError("Tag requires_y is true, but fit called with y=None") - - # reset estimator at the start of fit - self.reset() - - X = self._preprocess_series(X, axis, True) - - if self.get_tag("fit_is_empty"): - self.is_fitted = True - return self._predict(X) - - if y is not None: - y = self._check_y(y) - - pred = self._fit_predict(X, y) - - # this should happen last - self.is_fitted = True - return pred - - def _fit(self, X, y): - return self - - @abstractmethod - def _predict(self, X) -> np.ndarray: ... - - def _fit_predict(self, X, y): - self._fit(X, y) - return self._predict(X) - - def _check_y(self, y: VALID_SERIES_INPUT_TYPES) -> np.ndarray: - # Remind user if y is not required for this estimator on failure - req_msg = ( - f"{self.__class__.__name__} does not require a y input." - if self.get_tag("requires_y") - else "" - ) - new_y = y - - # must be a valid input type, see VALID_SERIES_INPUT_TYPES in - # BaseSeriesEstimator - if isinstance(y, np.ndarray): - # check valid shape - if y.ndim > 1: - raise ValueError( - "Error in input type for y: y input as np.ndarray should be 1D." - + req_msg - ) - - # check valid dtype - fail = False - if issubclass(y.dtype.type, np.integer): - new_y = y.astype(bool) - fail = not np.array_equal(y, new_y) - elif not issubclass(y.dtype.type, np.bool_): - fail = True - - if fail: - raise ValueError( - "Error in input type for y: y input type must be an integer array " - "containing 0 and 1 or a boolean array." + req_msg - ) - elif isinstance(y, pd.Series): - # check series is of boolean dtype - if not pd.api.types.is_bool_dtype(y): - raise ValueError( - "Error in input type for y: y input as pd.Series must have a " - "boolean dtype." + req_msg - ) - - new_y = y.values - elif isinstance(y, pd.DataFrame): - # only accept size 1 dataframe - if y.shape[1] > 1: - raise ValueError( - "Error in input type for y: y input as pd.DataFrame should have a " - "single column series." - ) - - # check column is of boolean dtype - if not all(pd.api.types.is_bool_dtype(y[col]) for col in y.columns): - raise ValueError( - "Error in input type for y: y input as pd.DataFrame must have a " - "boolean dtype." + req_msg - ) - - new_y = y.squeeze().values - else: - raise ValueError( - f"Error in input type for y: it should be one of " - f"{VALID_SERIES_INPUT_TYPES}, saw {type(y)}" - ) - - new_y = new_y.astype(bool) - return new_y + ... diff --git a/aeon/anomaly_detection/collection/__init__.py b/aeon/anomaly_detection/collection/__init__.py new file mode 100644 index 0000000000..4fc14ffd1f --- /dev/null +++ b/aeon/anomaly_detection/collection/__init__.py @@ -0,0 +1,11 @@ +"""Whole-series anomaly detection methods.""" + +__all__ = [ + "BaseCollectionAnomalyDetector", + "ClassificationAdapter", + "OutlierDetectionAdapter", +] + +from aeon.anomaly_detection.collection._classification import ClassificationAdapter +from aeon.anomaly_detection.collection._outlier_detection import OutlierDetectionAdapter +from aeon.anomaly_detection.collection.base import BaseCollectionAnomalyDetector diff --git a/aeon/anomaly_detection/collection/_classification.py b/aeon/anomaly_detection/collection/_classification.py new file mode 100644 index 0000000000..b068d23bf8 --- /dev/null +++ b/aeon/anomaly_detection/collection/_classification.py @@ -0,0 +1,67 @@ +"""Adapter to use classification algorithms for collection anomaly detection.""" + +__maintainer__ = [] +__all__ = ["ClassificationAdapter"] + +from sklearn.base import ClassifierMixin +from sklearn.ensemble import RandomForestClassifier + +from aeon.anomaly_detection.collection.base import BaseCollectionAnomalyDetector +from aeon.base._base import _clone_estimator +from aeon.classification.feature_based import SummaryClassifier + + +class ClassificationAdapter(BaseCollectionAnomalyDetector): + """ + Basic classifier adapter for collection anomaly detection. + + This class wraps a classification algorithm to be used as an anomaly detector. + Anomaly labels are required for training. + + Parameters + ---------- + classifier : aeon classifier or ClassifierMixin + The classification algorithm to be adapted. + random_state : int, RandomState instance or None, default=None + If `int`, random_state is the seed used by the random number generator; + If `RandomState` instance, random_state is the random number generator; + If `None`, the random number generator is the `RandomState` instance used + by `np.random`. + """ + + _tags = { + "fit_is_empty": False, + "requires_y": True, + "anomaly_output_type": "binary", + "learning_type:supervised": True, + } + + def __init__(self, classifier, random_state=None): + self.classifier = classifier + self.random_state = random_state + + super().__init__() + + def _fit(self, X, y=None): + if not isinstance(self.classifier, ClassifierMixin): + raise ValueError( + "The estimator must be an aeon classification algorithm " + "or class that implements the ClassifierMixin interface." + ) + + self.classifier_ = _clone_estimator( + self.classifier, random_state=self.random_state + ) + self.classifier_.fit(X, y) + return self + + def _predict(self, X): + return self.classifier_.predict(X) + + @classmethod + def _get_test_params(cls, parameter_set="default"): + return { + "classifier": SummaryClassifier( + estimator=RandomForestClassifier(n_estimators=5) + ) + } diff --git a/aeon/anomaly_detection/collection/_outlier_detection.py b/aeon/anomaly_detection/collection/_outlier_detection.py new file mode 100644 index 0000000000..27d7f430ae --- /dev/null +++ b/aeon/anomaly_detection/collection/_outlier_detection.py @@ -0,0 +1,62 @@ +"""Adapter to use outlier detection algorithms for collection anomaly detection.""" + +__maintainer__ = [] +__all__ = ["OutlierDetectionAdapter"] + +from sklearn.base import OutlierMixin +from sklearn.ensemble import IsolationForest + +from aeon.anomaly_detection.collection.base import BaseCollectionAnomalyDetector +from aeon.base._base import _clone_estimator + + +class OutlierDetectionAdapter(BaseCollectionAnomalyDetector): + """ + Basic outlier detection adapter for collection anomaly detection. + + This class wraps an sklearn outlier detection algorithm to be used as an anomaly + detector. + + Parameters + ---------- + detector : OutlierMixin + The outlier detection algorithm to be adapted. + random_state : int, RandomState instance or None, default=None + If `int`, random_state is the seed used by the random number generator; + If `RandomState` instance, random_state is the random number generator; + If `None`, the random number generator is the `RandomState` instance used + by `np.random`. + """ + + _tags = { + "X_inner_type": "numpy2D", + "fit_is_empty": False, + "anomaly_output_type": "binary", + "learning_type:semi_supervised": True, + } + + def __init__(self, detector, random_state=None): + self.detector = detector + self.random_state = random_state + + super().__init__() + + def _fit(self, X, y=None): + if not isinstance(self.detector, OutlierMixin): + raise ValueError( + "The estimator must be an outlier detection algorithm " + "that implements the OutlierMixin interface." + ) + + self.detector_ = _clone_estimator(self.detector, random_state=self.random_state) + self.detector_.fit(X, y) + return self + + def _predict(self, X): + pred = self.detector_.predict(X) + pred[pred == -1] = 0 + return pred + + @classmethod + def _get_test_params(cls, parameter_set="default"): + return {"detector": IsolationForest(n_estimators=3)} diff --git a/aeon/anomaly_detection/collection/base.py b/aeon/anomaly_detection/collection/base.py new file mode 100644 index 0000000000..f7a046d5bf --- /dev/null +++ b/aeon/anomaly_detection/collection/base.py @@ -0,0 +1,257 @@ +""" +Abstract base class for whole-series/collection anomaly detectors. + + class name: BaseCollectionAnomalyDetector + +Defining methods: + fitting - fit(self, X, y) + predicting - predict(self, X) + +Data validation: + data processing - _preprocess_collection(self, X, store_metadata=True) + shape verification - _check_shape(self, X) + +State: + fitted model/strategy - by convention, any attributes ending in "_" + fitted state flag - is_fitted + train input metadata - metadata_ + resetting state - reset(self) + +Tags: + default estimator tags - _tags + tag retrieval - get_tag(self, tag_name) + tag setting - set_tag(self, tag_name, value) +""" + +__maintainer__ = ["MatthewMiddlehurst"] +__all__ = ["BaseCollectionAnomalyDetector"] + +from abc import abstractmethod +from typing import final + +import numpy as np +import pandas as pd + +from aeon.anomaly_detection.base import BaseAnomalyDetector +from aeon.base import BaseCollectionEstimator + + +class BaseCollectionAnomalyDetector(BaseCollectionEstimator, BaseAnomalyDetector): + """ + Abstract base class for collection anomaly detectors. + + The base detector specifies the methods and method signatures that all + collection anomaly detectors have to implement. Attributes with an underscore + suffix are set in the method fit. + + Attributes + ---------- + is_fitted : bool + True if the estimator has been fitted, False otherwise. + Unused if ``"fit_is_empty"`` tag is set to True. + metadata_ : dict + Dictionary containing metadata about the `fit` input data. + _tags_dynamic : dict + Dictionary containing dynamic tag values which have been set at runtime. + """ + + def __init__(self): + super().__init__() + + @final + def fit(self, X, y=None): + """Fit collection anomaly detector to training data. + + Parameters + ---------- + X : np.ndarray or list + Input data, any number of channels, equal length series of shape ``( + n_cases, n_channels, n_timepoints)`` + or 2D np.array (univariate, equal length series) of shape + ``(n_cases, n_timepoints)`` + or list of numpy arrays (any number of channels, unequal length series) + of shape ``[n_cases]``, 2D np.array ``(n_channels, n_timepoints_i)``, + where ``n_timepoints_i`` is length of series ``i``. Other types are + allowed and converted into one of the above. + + Different estimators have different capabilities to handle different + types of input. If ``self.get_tag("capability:multivariate")`` is False, + they cannot handle multivariate series, so either ``n_channels == 1`` is + true or X is 2D of shape ``(n_cases, n_timepoints)``. If ``self.get_tag( + "capability:unequal_length")`` is False, they cannot handle unequal + length input. In both situations, a ``ValueError`` is raised if X has a + characteristic that the estimator does not have the capability for is + passed. + y : np.ndarray + 1D np.array of int, of shape ``(n_cases)`` - anomaly labels + (ground truth) for fitting indices corresponding to instance indices in X. + + Returns + ------- + self : BaseCollectionAnomalyDetector + Reference to self. + + Notes + ----- + Changes state by creating a fitted model that updates attributes + ending in "_" and sets is_fitted flag to True. + """ + if self.get_tag("fit_is_empty"): + self.is_fitted = True + return self + + if self.get_tag("requires_y"): + if y is None: + raise ValueError("Tag requires_y is true, but fit called with y=None") + + # reset estimator at the start of fit + self.reset() + + X = self._preprocess_collection(X) + if y is not None: + y = self._check_y(y, self.metadata_["n_cases"]) + + self._fit(X, y) + + # this should happen last + self.is_fitted = True + return self + + @final + def predict(self, X): + """Predicts anomalies for time series in X. + + Parameters + ---------- + X : np.ndarray or list + Input data, any number of channels, equal length series of shape ``( + n_cases, n_channels, n_timepoints)`` + or 2D np.array (univariate, equal length series) of shape + ``(n_cases, n_timepoints)`` + or list of numpy arrays (any number of channels, unequal length series) + of shape ``[n_cases]``, 2D np.array ``(n_channels, n_timepoints_i)``, + where ``n_timepoints_i`` is length of series ``i`` + other types are allowed and converted into one of the above. + + Different estimators have different capabilities to handle different + types of input. If ``self.get_tag("capability:multivariate")`` is False, + they cannot handle multivariate series, so either ``n_channels == 1`` is + true or X is 2D of shape ``(n_cases, n_timepoints)``. If ``self.get_tag( + "capability:unequal_length")`` is False, they cannot handle unequal + length input. In both situations, a ``ValueError`` is raised if X has a + characteristic that the estimator does not have the capability for is + passed. + + Returns + ------- + predictions : np.ndarray + 1D np.array of float, of shape (n_cases) - predicted anomalies or anomaly + scores for each time series in X. + Indices correspond to instance indices in X. + """ + fit_empty = self.get_tag("fit_is_empty") + if not fit_empty: + self._check_is_fitted() + + X = self._preprocess_collection(X, store_metadata=False) + # Check if X has the correct shape seen during fitting + self._check_shape(X) + + return self._predict(X) + + @final + def fit_predict(self, X, y=None, axis=1) -> np.ndarray: + """Fit time series anomaly detector and find anomalies for X. + + Parameters + ---------- + X : np.ndarray or list + Input data, any number of channels, equal length series of shape ``( + n_cases, n_channels, n_timepoints)`` + or 2D np.array (univariate, equal length series) of shape + ``(n_cases, n_timepoints)`` + or list of numpy arrays (any number of channels, unequal length series) + of shape ``[n_cases]``, 2D np.array ``(n_channels, n_timepoints_i)``, + where ``n_timepoints_i`` is length of series ``i``. Other types are + allowed and converted into one of the above. + + Different estimators have different capabilities to handle different + types of input. If ``self.get_tag("capability:multivariate")`` is False, + they cannot handle multivariate series, so either ``n_channels == 1`` is + true or X is 2D of shape ``(n_cases, n_timepoints)``. If ``self.get_tag( + "capability:unequal_length")`` is False, they cannot handle unequal + length input. In both situations, a ``ValueError`` is raised if X has a + characteristic that the estimator does not have the capability for is + passed. + y : np.ndarray + 1D np.array of int, of shape ``(n_cases)`` - anomaly labels + (ground truth) for fitting indices corresponding to instance indices in X. + + Returns + ------- + predictions : np.ndarray + 1D np.array of float, of shape (n_cases) - predicted anomalies or anomaly + scores for each time series in X. + Indices correspond to instance indices in X. + """ + if self.get_tag("requires_y"): + if y is None: + raise ValueError("Tag requires_y is true, but fit called with y=None") + + # reset estimator at the start of fit + self.reset() + + X = self._preprocess_series(X, axis, store_metadata=True) + + if self.get_tag("fit_is_empty"): + self.is_fitted = True + return self._predict(X) + + if y is not None: + y = self._check_y(y) + + pred = self._fit_predict(X, y) + + # this should happen last + self.is_fitted = True + return pred + + def _fit(self, X, y): + return self + + @abstractmethod + def _predict(self, X): ... + + def _fit_predict(self, X, y): + self._fit(X, y) + return self._predict(X) + + def _check_y(self, y, n_cases): + """Check y input is valid. + + Must be 1-dimensional and contain only 0s (no anomaly) and 1s (anomaly). + Must match the number of cases in X. + """ + if not isinstance(y, (pd.Series, np.ndarray)): + raise TypeError( + f"y must be a np.array or a pd.Series, but found type: {type(y)}" + ) + if isinstance(y, np.ndarray) and y.ndim > 1: + raise TypeError(f"y must be 1-dimensional, found {y.ndim} dimensions") + + if not np.bitwise_or(y == 0, y == 1).all(): + raise ValueError( + "y input must only contain 0 (not anomalous) or 1 (anomalous) values." + ) + + # Check matching number of labels + n_labels = y.shape[0] + if n_cases != n_labels: + raise ValueError( + f"Mismatch in number of cases. Found X = {n_cases} and y = {n_labels}" + ) + + if isinstance(y, pd.Series): + y = pd.Series.to_numpy(y) + + return y diff --git a/aeon/anomaly_detection/distance_based/__init__.py b/aeon/anomaly_detection/distance_based/__init__.py deleted file mode 100644 index 5eb342b780..0000000000 --- a/aeon/anomaly_detection/distance_based/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -"""Distance basedTime Series Anomaly Detection.""" - -__all__ = [ - "CBLOF", - "KMeansAD", - "LeftSTAMPi", - "LOF", - "MERLIN", - "OneClassSVM", - "STOMP", -] - -from aeon.anomaly_detection.distance_based._cblof import CBLOF -from aeon.anomaly_detection.distance_based._kmeans import KMeansAD -from aeon.anomaly_detection.distance_based._left_stampi import LeftSTAMPi -from aeon.anomaly_detection.distance_based._lof import LOF -from aeon.anomaly_detection.distance_based._merlin import MERLIN -from aeon.anomaly_detection.distance_based._one_class_svm import OneClassSVM -from aeon.anomaly_detection.distance_based._stomp import STOMP diff --git a/aeon/anomaly_detection/distribution_based/__init__.py b/aeon/anomaly_detection/distribution_based/__init__.py deleted file mode 100644 index e52a7512ba..0000000000 --- a/aeon/anomaly_detection/distribution_based/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -"""Distribution based Time Series Anomaly Detection.""" - -__all__ = [ - "COPOD", - "DWT_MLEAD", -] - -from aeon.anomaly_detection.distribution_based._copod import COPOD -from aeon.anomaly_detection.distribution_based._dwt_mlead import DWT_MLEAD diff --git a/aeon/anomaly_detection/outlier_detection/__init__.py b/aeon/anomaly_detection/outlier_detection/__init__.py deleted file mode 100644 index ad9b7868e5..0000000000 --- a/aeon/anomaly_detection/outlier_detection/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -"""Time Series Outlier Detection.""" - -__all__ = [ - "IsolationForest", - "PyODAdapter", - "STRAY", -] - -from aeon.anomaly_detection.outlier_detection._iforest import IsolationForest -from aeon.anomaly_detection.outlier_detection._pyodadapter import PyODAdapter -from aeon.anomaly_detection.outlier_detection._stray import STRAY diff --git a/aeon/anomaly_detection/series/__init__.py b/aeon/anomaly_detection/series/__init__.py new file mode 100644 index 0000000000..a4d2052d1c --- /dev/null +++ b/aeon/anomaly_detection/series/__init__.py @@ -0,0 +1,9 @@ +"""Single series Time Series Anomaly Detection.""" + +__all__ = [ + "BaseSeriesAnomalyDetector", + "PyODAdapter", +] + +from aeon.anomaly_detection.series._pyodadapter import PyODAdapter +from aeon.anomaly_detection.series.base import BaseSeriesAnomalyDetector diff --git a/aeon/anomaly_detection/outlier_detection/_pyodadapter.py b/aeon/anomaly_detection/series/_pyodadapter.py similarity index 98% rename from aeon/anomaly_detection/outlier_detection/_pyodadapter.py rename to aeon/anomaly_detection/series/_pyodadapter.py index bdde293a2f..67da96359a 100644 --- a/aeon/anomaly_detection/outlier_detection/_pyodadapter.py +++ b/aeon/anomaly_detection/series/_pyodadapter.py @@ -10,7 +10,7 @@ import numpy as np from sklearn import clone -from aeon.anomaly_detection.base import BaseAnomalyDetector +from aeon.anomaly_detection.series.base import BaseSeriesAnomalyDetector from aeon.utils.validation._dependencies import _check_soft_dependencies from aeon.utils.windowing import reverse_windowing, sliding_windows @@ -18,7 +18,7 @@ from pyod.models.base import BaseDetector -class PyODAdapter(BaseAnomalyDetector): +class PyODAdapter(BaseSeriesAnomalyDetector): """Adapter for PyOD anomaly detection models to be used in the Aeon framework. This adapter allows the use of PyOD models in the Aeon framework. The adapter diff --git a/aeon/anomaly_detection/series/base.py b/aeon/anomaly_detection/series/base.py new file mode 100644 index 0000000000..ccd3649cd7 --- /dev/null +++ b/aeon/anomaly_detection/series/base.py @@ -0,0 +1,288 @@ +"""Abstract base class for series time series anomaly detectors.""" + +__maintainer__ = ["MatthewMiddlehurst"] +__all__ = ["BaseSeriesAnomalyDetector"] + +from abc import abstractmethod +from typing import final + +import numpy as np +import pandas as pd + +from aeon.anomaly_detection.base import BaseAnomalyDetector +from aeon.base import BaseSeriesEstimator +from aeon.base._base_series import VALID_SERIES_INPUT_TYPES + + +class BaseSeriesAnomalyDetector(BaseSeriesEstimator, BaseAnomalyDetector): + """Base class for series anomaly detection algorithms. + + Anomaly detection algorithms are used to identify anomalous subsequences in time + series data. These algorithms take a series of length m and return a boolean, int or + float array of length m, where each element indicates whether the corresponding + subsequence is anomalous or its anomaly score. + + Input and internal data format (where m is the number of time points and d is the + number of channels): + Univariate series (default): + np.ndarray, shape ``(m,)``, ``(m, 1)`` or ``(1, m)`` depending on axis. + This is converted to a 2D np.ndarray internally. + pd.DataFrame, shape ``(m, 1)`` or ``(1, m)`` depending on axis. + pd.Series, shape ``(m,)``. + Multivariate series: + np.ndarray array, shape ``(m, d)`` or ``(d, m)`` depending on axis. + pd.DataFrame ``(m, d)`` or ``(d, m)`` depending on axis. + + Output data format (one of the following): + Anomaly scores (default): + np.ndarray, shape ``(m,)`` of type float. For each point of the input time + series, the anomaly score is a float value indicating the degree of + anomalousness. The higher the score, the more anomalous the point. + Binary classification: + np.ndarray, shape ``(m,)`` of type bool or int. For each point of the input + time series, the output is a boolean or integer value indicating whether the + point is anomalous (``True``/``1``) or not (``False``/``0``). + + Detector learning types: + Unsupervised (default): + Unsupervised detectors do not require any training data and can directly be + used on the target time series. Their tags are set to ``fit_is_empty=True`` + and ``requires_y=False``. You would usually call the ``fit_predict`` method + on these detectors. + Semi-supervised: + Semi-supervised detectors require a training step on a time series without + anomalies (normal behaving time series). The target value ``y`` would + consist of only zeros. Thus, these algorithms have logic in the ``fit`` + method, but do not require the target values. Their tags are set to + ``fit_is_empty=False`` and ``requires_y=False``. You would usually first + call the ``fit`` method on the training data and then the ``predict`` + method for your target time series. + Supervised: + Supervised detectors require a training step on a time series with known + anomalies (anomalies should be present and must be annotated). The detector + implements the ``fit`` method, and the target value ``y`` consists of zeros + and ones. Their tags are, thus, set to ``fit_is_empty=False`` and + ``requires_y=True``. You would usually first call the ``fit`` method on the + training data and then the ``predict`` method for your target time series. + + Parameters + ---------- + axis : int + The time point axis of the input series if it is 2D. If ``axis==0``, it is + assumed each column is a time series and each row is a time point. i.e. the + shape of the data is ``(n_timepoints, n_channels)``. ``axis==1`` indicates + the time series are in rows, i.e. the shape of the data is + ``(n_channels, n_timepoints)``. + Setting this class variable will convert the input data to the chosen axis. + """ + + def __init__(self, axis): + super().__init__(axis=axis) + + @final + def fit(self, X, y=None, axis=1): + """Fit time series anomaly detector to X. + + If the tag ``fit_is_empty`` is true, this just sets the ``is_fitted`` tag to + true. Otherwise, it checks ``self`` can handle ``X``, formats ``X`` into + the structure required by ``self`` then passes ``X`` (and possibly ``y``) to + ``_fit``. + + Parameters + ---------- + X : one of aeon.base._base_series.VALID_SERIES_INPUT_TYPES + The time series to fit the model to. + A valid aeon time series data structure. See + aeon.base._base_series.VALID_SERIES_INPUT_TYPES for aeon supported types. + y : one of aeon.base._base_series.VALID_SERIES_INPUT_TYPES, default=None + The target values for the time series. + A valid aeon time series data structure. See + aeon.base._base_series.VALID_SERIES_INPUT_TYPES for aeon supported types. + axis : int + The time point axis of the input series if it is 2D. If ``axis==0``, it is + assumed each column is a time series and each row is a time point. i.e. the + shape of the data is ``(n_timepoints, n_channels)``. ``axis==1`` indicates + the time series are in rows, i.e. the shape of the data is + ``(n_channels, n_timepoints)``. + + Returns + ------- + BaseSeriesAnomalyDetector + The fitted estimator, reference to self. + """ + if self.get_tag("fit_is_empty"): + self.is_fitted = True + return self + + if self.get_tag("requires_y"): + if y is None: + raise ValueError("Tag requires_y is true, but fit called with y=None") + + # reset estimator at the start of fit + self.reset() + + X = self._preprocess_series(X, axis, True) + if y is not None: + y = self._check_y(y) + + self._fit(X=X, y=y) + + # this should happen last + self.is_fitted = True + return self + + @final + def predict(self, X, axis=1) -> np.ndarray: + """Find anomalies in X. + + Parameters + ---------- + X : one of aeon.base._base_series.VALID_SERIES_INPUT_TYPES + The time series to fit the model to. + A valid aeon time series data structure. See + aeon.base._base_series.VALID_SERIES_INPUT_TYPES for aeon supported types. + axis : int, default=1 + The time point axis of the input series if it is 2D. If ``axis==0``, it is + assumed each column is a time series and each row is a time point. i.e. the + shape of the data is ``(n_timepoints, n_channels)``. ``axis==1`` indicates + the time series are in rows, i.e. the shape of the data is + ``(n_channels, n_timepoints)``. + + Returns + ------- + np.ndarray + A boolean, int or float array of length len(X), where each element indicates + whether the corresponding subsequence is anomalous or its anomaly score. + """ + fit_empty = self.get_tag("fit_is_empty") + if not fit_empty: + self._check_is_fitted() + + X = self._preprocess_series(X, axis, False) + + return self._predict(X) + + @final + def fit_predict(self, X, y=None, axis=1) -> np.ndarray: + """Fit time series anomaly detector and find anomalies for X. + + Parameters + ---------- + X : one of aeon.base._base_series.VALID_SERIES_INPUT_TYPES + The time series to fit the model to. + A valid aeon time series data structure. See + aeon.base._base_series.VALID_INPUT_TYPES for aeon supported types. + y : one of aeon.base._base_series.VALID_SERIES_INPUT_TYPES, default=None + The target values for the time series. + A valid aeon time series data structure. See + aeon.base._base_series.VALID_SERIES_INPUT_TYPES for aeon supported types. + axis : int, default=1 + The time point axis of the input series if it is 2D. If ``axis==0``, it is + assumed each column is a time series and each row is a time point. i.e. the + shape of the data is ``(n_timepoints, n_channels)``. ``axis==1`` indicates + the time series are in rows, i.e. the shape of the data is + ``(n_channels, n_timepoints)``. + + Returns + ------- + np.ndarray + A boolean, int or float array of length len(X), where each element indicates + whether the corresponding subsequence is anomalous or its anomaly score. + """ + if self.get_tag("requires_y"): + if y is None: + raise ValueError("Tag requires_y is true, but fit called with y=None") + + # reset estimator at the start of fit + self.reset() + + X = self._preprocess_series(X, axis, True) + + if self.get_tag("fit_is_empty"): + self.is_fitted = True + return self._predict(X) + + if y is not None: + y = self._check_y(y) + + pred = self._fit_predict(X, y) + + # this should happen last + self.is_fitted = True + return pred + + def _fit(self, X, y): + return self + + @abstractmethod + def _predict(self, X) -> np.ndarray: ... + + def _fit_predict(self, X, y): + self._fit(X, y) + return self._predict(X) + + def _check_y(self, y: VALID_SERIES_INPUT_TYPES) -> np.ndarray: + # Remind user if y is not required for this estimator on failure + req_msg = ( + f"{self.__class__.__name__} does not require a y input." + if self.get_tag("requires_y") + else "" + ) + new_y = y + + # must be a valid input type, see VALID_SERIES_INPUT_TYPES in + # BaseSeriesEstimator + if isinstance(y, np.ndarray): + # check valid shape + if y.ndim > 1: + raise ValueError( + "Error in input type for y: y input as np.ndarray should be 1D." + + req_msg + ) + + # check valid dtype + fail = False + if issubclass(y.dtype.type, np.integer): + new_y = y.astype(bool) + fail = not np.array_equal(y, new_y) + elif not issubclass(y.dtype.type, np.bool_): + fail = True + + if fail: + raise ValueError( + "Error in input type for y: y input type must be an integer array " + "containing 0 and 1 or a boolean array." + req_msg + ) + elif isinstance(y, pd.Series): + # check series is of boolean dtype + if not pd.api.types.is_bool_dtype(y): + raise ValueError( + "Error in input type for y: y input as pd.Series must have a " + "boolean dtype." + req_msg + ) + + new_y = y.values + elif isinstance(y, pd.DataFrame): + # only accept size 1 dataframe + if y.shape[1] > 1: + raise ValueError( + "Error in input type for y: y input as pd.DataFrame should have a " + "single column series." + ) + + # check column is of boolean dtype + if not all(pd.api.types.is_bool_dtype(y[col]) for col in y.columns): + raise ValueError( + "Error in input type for y: y input as pd.DataFrame must have a " + "boolean dtype." + req_msg + ) + + new_y = y.squeeze().values + else: + raise ValueError( + f"Error in input type for y: it should be one of " + f"{VALID_SERIES_INPUT_TYPES}, saw {type(y)}" + ) + + new_y = new_y.astype(bool) + return new_y diff --git a/aeon/anomaly_detection/series/distance_based/__init__.py b/aeon/anomaly_detection/series/distance_based/__init__.py new file mode 100644 index 0000000000..479ea78819 --- /dev/null +++ b/aeon/anomaly_detection/series/distance_based/__init__.py @@ -0,0 +1,19 @@ +"""Distance-based Time Series Anomaly Detection.""" + +__all__ = [ + "CBLOF", + "KMeansAD", + "LeftSTAMPi", + "LOF", + "MERLIN", + "STOMP", + "ROCKAD", +] + +from aeon.anomaly_detection.series.distance_based._cblof import CBLOF +from aeon.anomaly_detection.series.distance_based._kmeans import KMeansAD +from aeon.anomaly_detection.series.distance_based._left_stampi import LeftSTAMPi +from aeon.anomaly_detection.series.distance_based._lof import LOF +from aeon.anomaly_detection.series.distance_based._merlin import MERLIN +from aeon.anomaly_detection.series.distance_based._rockad import ROCKAD +from aeon.anomaly_detection.series.distance_based._stomp import STOMP diff --git a/aeon/anomaly_detection/distance_based/_cblof.py b/aeon/anomaly_detection/series/distance_based/_cblof.py similarity index 98% rename from aeon/anomaly_detection/distance_based/_cblof.py rename to aeon/anomaly_detection/series/distance_based/_cblof.py index e4470efcc7..61c59d4e61 100644 --- a/aeon/anomaly_detection/distance_based/_cblof.py +++ b/aeon/anomaly_detection/series/distance_based/_cblof.py @@ -7,7 +7,7 @@ import numpy as np -from aeon.anomaly_detection.outlier_detection._pyodadapter import PyODAdapter +from aeon.anomaly_detection.series._pyodadapter import PyODAdapter from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/distance_based/_kmeans.py b/aeon/anomaly_detection/series/distance_based/_kmeans.py similarity index 97% rename from aeon/anomaly_detection/distance_based/_kmeans.py rename to aeon/anomaly_detection/series/distance_based/_kmeans.py index e4e7cc4217..2f9447ca46 100644 --- a/aeon/anomaly_detection/distance_based/_kmeans.py +++ b/aeon/anomaly_detection/series/distance_based/_kmeans.py @@ -8,11 +8,11 @@ import numpy as np from sklearn.cluster import KMeans -from aeon.anomaly_detection.base import BaseAnomalyDetector +from aeon.anomaly_detection.series.base import BaseSeriesAnomalyDetector from aeon.utils.windowing import reverse_windowing, sliding_windows -class KMeansAD(BaseAnomalyDetector): +class KMeansAD(BaseSeriesAnomalyDetector): """KMeans anomaly detector. The k-Means anomaly detector uses k-Means clustering to detect anomalies in time @@ -65,7 +65,7 @@ class KMeansAD(BaseAnomalyDetector): Examples -------- >>> import numpy as np - >>> from aeon.anomaly_detection.distance_based import KMeansAD + >>> from aeon.anomaly_detection.series.distance_based import KMeansAD >>> X = np.array([1, 2, 3, 4, 1, 2, 3, 3, 2, 8, 9, 8, 1, 2, 3, 4], dtype=np.float64) >>> detector = KMeansAD(n_clusters=3, window_size=4, stride=1, random_state=0) >>> detector.fit_predict(X) diff --git a/aeon/anomaly_detection/distance_based/_left_stampi.py b/aeon/anomaly_detection/series/distance_based/_left_stampi.py similarity index 95% rename from aeon/anomaly_detection/distance_based/_left_stampi.py rename to aeon/anomaly_detection/series/distance_based/_left_stampi.py index 59c8d1be83..fa4ceed226 100644 --- a/aeon/anomaly_detection/distance_based/_left_stampi.py +++ b/aeon/anomaly_detection/series/distance_based/_left_stampi.py @@ -6,11 +6,11 @@ import numpy as np -from aeon.anomaly_detection.base import BaseAnomalyDetector +from aeon.anomaly_detection.series.base import BaseSeriesAnomalyDetector from aeon.utils.windowing import reverse_windowing -class LeftSTAMPi(BaseAnomalyDetector): +class LeftSTAMPi(BaseSeriesAnomalyDetector): """LeftSTAMPi anomaly detector. LeftSTAMPi [1]_ calculates the left matrix profile of a time series, @@ -43,8 +43,8 @@ class LeftSTAMPi(BaseAnomalyDetector): Calculate the anomaly score for the complete time series at once. Internally,this is applying the incremental approach outlined below. - >>> import numpy as np # doctest: +SKIP - >>> from aeon.anomaly_detection.distance_based import LeftSTAMPi # doctest: +SKIP + >>> import numpy as np + >>> from aeon.anomaly_detection.series.distance_based import LeftSTAMPi >>> X = np.random.default_rng(42).random((10)) # doctest: +SKIP >>> detector = LeftSTAMPi(window_size=3, n_init_train=3) # doctest: +SKIP >>> detector.fit_predict(X) # doctest: +SKIP diff --git a/aeon/anomaly_detection/distance_based/_lof.py b/aeon/anomaly_detection/series/distance_based/_lof.py similarity index 98% rename from aeon/anomaly_detection/distance_based/_lof.py rename to aeon/anomaly_detection/series/distance_based/_lof.py index bc7369026b..8d92a7d529 100644 --- a/aeon/anomaly_detection/distance_based/_lof.py +++ b/aeon/anomaly_detection/series/distance_based/_lof.py @@ -7,7 +7,7 @@ import numpy as np -from aeon.anomaly_detection.outlier_detection._pyodadapter import PyODAdapter +from aeon.anomaly_detection.series._pyodadapter import PyODAdapter from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/distance_based/_merlin.py b/aeon/anomaly_detection/series/distance_based/_merlin.py similarity index 97% rename from aeon/anomaly_detection/distance_based/_merlin.py rename to aeon/anomaly_detection/series/distance_based/_merlin.py index 08cc6fffe1..aa749383ab 100644 --- a/aeon/anomaly_detection/distance_based/_merlin.py +++ b/aeon/anomaly_detection/series/distance_based/_merlin.py @@ -8,13 +8,13 @@ import numpy as np from numba import njit -from aeon.anomaly_detection.base import BaseAnomalyDetector +from aeon.anomaly_detection.series.base import BaseSeriesAnomalyDetector from aeon.distances import squared_distance from aeon.utils.numba.general import AEON_NUMBA_STD_THRESHOLD from aeon.utils.numba.stats import mean, std -class MERLIN(BaseAnomalyDetector): +class MERLIN(BaseSeriesAnomalyDetector): """MERLIN anomaly detector. MERLIN is a discord discovery algorithm that uses a sliding window to find the @@ -43,7 +43,7 @@ class MERLIN(BaseAnomalyDetector): Examples -------- >>> import numpy as np - >>> from aeon.anomaly_detection.distance_based import MERLIN + >>> from aeon.anomaly_detection.series.distance_based import MERLIN >>> X = np.array([1, 2, 3, 4, 1, 2, 3, 4, 2, 3, 4, 5, 1, 2, 3, 4]) >>> detector = MERLIN(min_length=4, max_length=5) >>> detector.fit_predict(X) diff --git a/aeon/anomaly_detection/whole_series/_rockad.py b/aeon/anomaly_detection/series/distance_based/_rockad.py similarity index 98% rename from aeon/anomaly_detection/whole_series/_rockad.py rename to aeon/anomaly_detection/series/distance_based/_rockad.py index 55a3cad39b..696392286c 100644 --- a/aeon/anomaly_detection/whole_series/_rockad.py +++ b/aeon/anomaly_detection/series/distance_based/_rockad.py @@ -10,12 +10,12 @@ from sklearn.preprocessing import PowerTransformer from sklearn.utils import resample -from aeon.anomaly_detection.base import BaseAnomalyDetector +from aeon.anomaly_detection.series.base import BaseSeriesAnomalyDetector from aeon.transformations.collection.convolution_based import Rocket from aeon.utils.windowing import reverse_windowing, sliding_windows -class ROCKAD(BaseAnomalyDetector): +class ROCKAD(BaseSeriesAnomalyDetector): """ ROCKET-based Anomaly Detector (ROCKAD). diff --git a/aeon/anomaly_detection/distance_based/_stomp.py b/aeon/anomaly_detection/series/distance_based/_stomp.py similarity index 96% rename from aeon/anomaly_detection/distance_based/_stomp.py rename to aeon/anomaly_detection/series/distance_based/_stomp.py index 9b0f551033..a1f7ab2ab5 100644 --- a/aeon/anomaly_detection/distance_based/_stomp.py +++ b/aeon/anomaly_detection/series/distance_based/_stomp.py @@ -7,11 +7,11 @@ import numpy as np -from aeon.anomaly_detection.base import BaseAnomalyDetector +from aeon.anomaly_detection.series.base import BaseSeriesAnomalyDetector from aeon.utils.windowing import reverse_windowing -class STOMP(BaseAnomalyDetector): +class STOMP(BaseSeriesAnomalyDetector): """STOMP anomaly detector. STOMP calculates the matrix profile of a time series which is the distance to the @@ -38,7 +38,7 @@ class STOMP(BaseAnomalyDetector): Examples -------- >>> import numpy as np - >>> from aeon.anomaly_detection.distance_based import STOMP # doctest: +SKIP + >>> from aeon.anomaly_detection.series.distance_based import STOMP >>> X = np.random.default_rng(42).random((10, 2), dtype=np.float64) >>> detector = STOMP(X, window_size=2) # doctest: +SKIP >>> detector.fit_predict(X, axis=0) # doctest: +SKIP diff --git a/aeon/anomaly_detection/distance_based/tests/__init__.py b/aeon/anomaly_detection/series/distance_based/tests/__init__.py similarity index 100% rename from aeon/anomaly_detection/distance_based/tests/__init__.py rename to aeon/anomaly_detection/series/distance_based/tests/__init__.py diff --git a/aeon/anomaly_detection/distance_based/tests/test_cblof.py b/aeon/anomaly_detection/series/distance_based/tests/test_cblof.py similarity index 97% rename from aeon/anomaly_detection/distance_based/tests/test_cblof.py rename to aeon/anomaly_detection/series/distance_based/tests/test_cblof.py index d1472af6a2..ce7579734f 100644 --- a/aeon/anomaly_detection/distance_based/tests/test_cblof.py +++ b/aeon/anomaly_detection/series/distance_based/tests/test_cblof.py @@ -3,7 +3,7 @@ import numpy as np import pytest -from aeon.anomaly_detection.distance_based import CBLOF +from aeon.anomaly_detection.series.distance_based import CBLOF from aeon.testing.data_generation import make_example_1d_numpy from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/distance_based/tests/test_kmeans.py b/aeon/anomaly_detection/series/distance_based/tests/test_kmeans.py similarity index 95% rename from aeon/anomaly_detection/distance_based/tests/test_kmeans.py rename to aeon/anomaly_detection/series/distance_based/tests/test_kmeans.py index 2647411b88..bc966929b0 100644 --- a/aeon/anomaly_detection/distance_based/tests/test_kmeans.py +++ b/aeon/anomaly_detection/series/distance_based/tests/test_kmeans.py @@ -6,7 +6,7 @@ import pytest from sklearn.utils import check_random_state -from aeon.anomaly_detection.distance_based import KMeansAD +from aeon.anomaly_detection.series.distance_based import KMeansAD def test_kmeansad_univariate(): diff --git a/aeon/anomaly_detection/distance_based/tests/test_left_stampi.py b/aeon/anomaly_detection/series/distance_based/tests/test_left_stampi.py similarity index 99% rename from aeon/anomaly_detection/distance_based/tests/test_left_stampi.py rename to aeon/anomaly_detection/series/distance_based/tests/test_left_stampi.py index 6444bccdfe..2e14928625 100644 --- a/aeon/anomaly_detection/distance_based/tests/test_left_stampi.py +++ b/aeon/anomaly_detection/series/distance_based/tests/test_left_stampi.py @@ -8,7 +8,7 @@ import numpy as np import pytest -from aeon.anomaly_detection.distance_based._left_stampi import LeftSTAMPi +from aeon.anomaly_detection.series.distance_based._left_stampi import LeftSTAMPi from aeon.testing.data_generation import make_example_1d_numpy from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/distance_based/tests/test_lof.py b/aeon/anomaly_detection/series/distance_based/tests/test_lof.py similarity index 99% rename from aeon/anomaly_detection/distance_based/tests/test_lof.py rename to aeon/anomaly_detection/series/distance_based/tests/test_lof.py index 033d11295b..a9107705fa 100644 --- a/aeon/anomaly_detection/distance_based/tests/test_lof.py +++ b/aeon/anomaly_detection/series/distance_based/tests/test_lof.py @@ -3,7 +3,7 @@ import numpy as np import pytest -from aeon.anomaly_detection.distance_based import LOF +from aeon.anomaly_detection.series.distance_based import LOF from aeon.testing.data_generation import make_example_1d_numpy from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/distance_based/tests/test_merlin.py b/aeon/anomaly_detection/series/distance_based/tests/test_merlin.py similarity index 96% rename from aeon/anomaly_detection/distance_based/tests/test_merlin.py rename to aeon/anomaly_detection/series/distance_based/tests/test_merlin.py index ccf7e3300d..0ef5aafb8a 100644 --- a/aeon/anomaly_detection/distance_based/tests/test_merlin.py +++ b/aeon/anomaly_detection/series/distance_based/tests/test_merlin.py @@ -4,7 +4,7 @@ import numpy as np -from aeon.anomaly_detection.distance_based import MERLIN +from aeon.anomaly_detection.series.distance_based import MERLIN TEST_DATA = np.array( [ diff --git a/aeon/anomaly_detection/whole_series/tests/test_rockad.py b/aeon/anomaly_detection/series/distance_based/tests/test_rockad.py similarity index 96% rename from aeon/anomaly_detection/whole_series/tests/test_rockad.py rename to aeon/anomaly_detection/series/distance_based/tests/test_rockad.py index 7d3694b2c8..51d2425505 100644 --- a/aeon/anomaly_detection/whole_series/tests/test_rockad.py +++ b/aeon/anomaly_detection/series/distance_based/tests/test_rockad.py @@ -4,7 +4,7 @@ import pytest from sklearn.utils import check_random_state -from aeon.anomaly_detection.whole_series import ROCKAD +from aeon.anomaly_detection.series.distance_based import ROCKAD def test_rockad_univariate(): diff --git a/aeon/anomaly_detection/distance_based/tests/test_stomp.py b/aeon/anomaly_detection/series/distance_based/tests/test_stomp.py similarity index 95% rename from aeon/anomaly_detection/distance_based/tests/test_stomp.py rename to aeon/anomaly_detection/series/distance_based/tests/test_stomp.py index b506c89ea0..f8225d3c7b 100644 --- a/aeon/anomaly_detection/distance_based/tests/test_stomp.py +++ b/aeon/anomaly_detection/series/distance_based/tests/test_stomp.py @@ -6,7 +6,7 @@ import pytest from sklearn.utils import check_random_state -from aeon.anomaly_detection.distance_based import STOMP +from aeon.anomaly_detection.series.distance_based import STOMP from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/series/distribution_based/__init__.py b/aeon/anomaly_detection/series/distribution_based/__init__.py new file mode 100644 index 0000000000..7338cb740d --- /dev/null +++ b/aeon/anomaly_detection/series/distribution_based/__init__.py @@ -0,0 +1,9 @@ +"""Distribution-based Time Series Anomaly Detection.""" + +__all__ = [ + "COPOD", + "DWT_MLEAD", +] + +from aeon.anomaly_detection.series.distribution_based._copod import COPOD +from aeon.anomaly_detection.series.distribution_based._dwt_mlead import DWT_MLEAD diff --git a/aeon/anomaly_detection/distribution_based/_copod.py b/aeon/anomaly_detection/series/distribution_based/_copod.py similarity index 97% rename from aeon/anomaly_detection/distribution_based/_copod.py rename to aeon/anomaly_detection/series/distribution_based/_copod.py index 9a4fe0b667..d34826e93f 100644 --- a/aeon/anomaly_detection/distribution_based/_copod.py +++ b/aeon/anomaly_detection/series/distribution_based/_copod.py @@ -7,7 +7,7 @@ import numpy as np -from aeon.anomaly_detection.outlier_detection._pyodadapter import PyODAdapter +from aeon.anomaly_detection.series._pyodadapter import PyODAdapter from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/distribution_based/_dwt_mlead.py b/aeon/anomaly_detection/series/distribution_based/_dwt_mlead.py similarity index 98% rename from aeon/anomaly_detection/distribution_based/_dwt_mlead.py rename to aeon/anomaly_detection/series/distribution_based/_dwt_mlead.py index fed0de5f46..d8da12c980 100644 --- a/aeon/anomaly_detection/distribution_based/_dwt_mlead.py +++ b/aeon/anomaly_detection/series/distribution_based/_dwt_mlead.py @@ -11,7 +11,7 @@ from numpy.lib.stride_tricks import sliding_window_view from sklearn.covariance import EmpiricalCovariance -from aeon.anomaly_detection.base import BaseAnomalyDetector +from aeon.anomaly_detection.series.base import BaseSeriesAnomalyDetector from aeon.utils.numba.wavelets import multilevel_haar_transform @@ -30,7 +30,7 @@ def _combine_alternating(xs: list[Any], ys: list[Any]) -> Iterable[Any]: yield y -class DWT_MLEAD(BaseAnomalyDetector): +class DWT_MLEAD(BaseSeriesAnomalyDetector): """DWT-MLEAD anomaly detector. DWT-MLEAD is an anomaly detection algorithm that uses the Discrete Wavelet Transform @@ -78,7 +78,7 @@ class DWT_MLEAD(BaseAnomalyDetector): Examples -------- >>> import numpy as np - >>> from aeon.anomaly_detection.distribution_based import DWT_MLEAD + >>> from aeon.anomaly_detection.series.distribution_based import DWT_MLEAD >>> X = np.array([1, 2, 3, 4, 1, 2, 3, 3, 2, 8, 9, 8, 1, 2, 3, 4], dtype=np.float64) >>> detector = DWT_MLEAD( ... start_level=1, quantile_boundary_type='percentile', quantile_epsilon=0.01 diff --git a/aeon/anomaly_detection/distribution_based/tests/__init__.py b/aeon/anomaly_detection/series/distribution_based/tests/__init__.py similarity index 100% rename from aeon/anomaly_detection/distribution_based/tests/__init__.py rename to aeon/anomaly_detection/series/distribution_based/tests/__init__.py diff --git a/aeon/anomaly_detection/distribution_based/tests/test_copod.py b/aeon/anomaly_detection/series/distribution_based/tests/test_copod.py similarity index 96% rename from aeon/anomaly_detection/distribution_based/tests/test_copod.py rename to aeon/anomaly_detection/series/distribution_based/tests/test_copod.py index 40969da0e7..8732b33c96 100644 --- a/aeon/anomaly_detection/distribution_based/tests/test_copod.py +++ b/aeon/anomaly_detection/series/distribution_based/tests/test_copod.py @@ -3,7 +3,7 @@ import numpy as np import pytest -from aeon.anomaly_detection.distribution_based import COPOD +from aeon.anomaly_detection.series.distribution_based import COPOD from aeon.testing.data_generation import make_example_1d_numpy from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/distribution_based/tests/test_dwt_mlead.py b/aeon/anomaly_detection/series/distribution_based/tests/test_dwt_mlead.py similarity index 95% rename from aeon/anomaly_detection/distribution_based/tests/test_dwt_mlead.py rename to aeon/anomaly_detection/series/distribution_based/tests/test_dwt_mlead.py index 664d715122..3a714570b3 100644 --- a/aeon/anomaly_detection/distribution_based/tests/test_dwt_mlead.py +++ b/aeon/anomaly_detection/series/distribution_based/tests/test_dwt_mlead.py @@ -6,7 +6,7 @@ import pytest from sklearn.utils import check_random_state -from aeon.anomaly_detection.distribution_based import DWT_MLEAD +from aeon.anomaly_detection.series.distribution_based import DWT_MLEAD def test_dwt_mlead_output(): diff --git a/aeon/anomaly_detection/series/outlier_detection/__init__.py b/aeon/anomaly_detection/series/outlier_detection/__init__.py new file mode 100644 index 0000000000..2eb2a248e3 --- /dev/null +++ b/aeon/anomaly_detection/series/outlier_detection/__init__.py @@ -0,0 +1,11 @@ +"""Time Series Outlier Detection.""" + +__all__ = [ + "IsolationForest", + "OneClassSVM", + "STRAY", +] + +from aeon.anomaly_detection.series.outlier_detection._iforest import IsolationForest +from aeon.anomaly_detection.series.outlier_detection._one_class_svm import OneClassSVM +from aeon.anomaly_detection.series.outlier_detection._stray import STRAY diff --git a/aeon/anomaly_detection/outlier_detection/_iforest.py b/aeon/anomaly_detection/series/outlier_detection/_iforest.py similarity index 98% rename from aeon/anomaly_detection/outlier_detection/_iforest.py rename to aeon/anomaly_detection/series/outlier_detection/_iforest.py index b37696fc43..00e550400c 100644 --- a/aeon/anomaly_detection/outlier_detection/_iforest.py +++ b/aeon/anomaly_detection/series/outlier_detection/_iforest.py @@ -7,7 +7,7 @@ import numpy as np -from aeon.anomaly_detection.outlier_detection._pyodadapter import PyODAdapter +from aeon.anomaly_detection.series._pyodadapter import PyODAdapter from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/distance_based/_one_class_svm.py b/aeon/anomaly_detection/series/outlier_detection/_one_class_svm.py similarity index 98% rename from aeon/anomaly_detection/distance_based/_one_class_svm.py rename to aeon/anomaly_detection/series/outlier_detection/_one_class_svm.py index c0e8f0556e..5161900582 100644 --- a/aeon/anomaly_detection/distance_based/_one_class_svm.py +++ b/aeon/anomaly_detection/series/outlier_detection/_one_class_svm.py @@ -7,11 +7,11 @@ import numpy as np from sklearn.svm import OneClassSVM as OCSVM -from aeon.anomaly_detection.base import BaseAnomalyDetector +from aeon.anomaly_detection.series.base import BaseSeriesAnomalyDetector from aeon.utils.windowing import reverse_windowing, sliding_windows -class OneClassSVM(BaseAnomalyDetector): +class OneClassSVM(BaseSeriesAnomalyDetector): """OneClassSVM for anomaly detection. This class implements the OneClassSVM algorithm for anomaly detection diff --git a/aeon/anomaly_detection/outlier_detection/_stray.py b/aeon/anomaly_detection/series/outlier_detection/_stray.py similarity index 96% rename from aeon/anomaly_detection/outlier_detection/_stray.py rename to aeon/anomaly_detection/series/outlier_detection/_stray.py index bc4fd6dd4d..d297ab8337 100644 --- a/aeon/anomaly_detection/outlier_detection/_stray.py +++ b/aeon/anomaly_detection/series/outlier_detection/_stray.py @@ -8,10 +8,10 @@ import numpy.typing as npt from sklearn.neighbors import NearestNeighbors -from aeon.anomaly_detection.base import BaseAnomalyDetector +from aeon.anomaly_detection.series.base import BaseSeriesAnomalyDetector -class STRAY(BaseAnomalyDetector): +class STRAY(BaseSeriesAnomalyDetector): """STRAY: robust anomaly detection in data streams with concept drift. This is based on STRAY (Search TRace AnomalY) [1]_, which is a modification @@ -54,7 +54,7 @@ class STRAY(BaseAnomalyDetector): Examples -------- - >>> from aeon.anomaly_detection.outlier_detection import STRAY + >>> from aeon.anomaly_detection.series.outlier_detection import STRAY >>> from aeon.datasets import load_airline >>> import numpy as np >>> X = load_airline() diff --git a/aeon/anomaly_detection/outlier_detection/tests/__init__.py b/aeon/anomaly_detection/series/outlier_detection/tests/__init__.py similarity index 100% rename from aeon/anomaly_detection/outlier_detection/tests/__init__.py rename to aeon/anomaly_detection/series/outlier_detection/tests/__init__.py diff --git a/aeon/anomaly_detection/outlier_detection/tests/test_iforest.py b/aeon/anomaly_detection/series/outlier_detection/tests/test_iforest.py similarity index 98% rename from aeon/anomaly_detection/outlier_detection/tests/test_iforest.py rename to aeon/anomaly_detection/series/outlier_detection/tests/test_iforest.py index a66d1003fb..07e0a085c3 100644 --- a/aeon/anomaly_detection/outlier_detection/tests/test_iforest.py +++ b/aeon/anomaly_detection/series/outlier_detection/tests/test_iforest.py @@ -4,7 +4,7 @@ import pytest from sklearn.utils import check_random_state -from aeon.anomaly_detection.outlier_detection import IsolationForest +from aeon.anomaly_detection.series.outlier_detection import IsolationForest from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/distance_based/tests/test_one_class_svm.py b/aeon/anomaly_detection/series/outlier_detection/tests/test_one_class_svm.py similarity index 95% rename from aeon/anomaly_detection/distance_based/tests/test_one_class_svm.py rename to aeon/anomaly_detection/series/outlier_detection/tests/test_one_class_svm.py index 7a3aca2042..b5975ddb08 100644 --- a/aeon/anomaly_detection/distance_based/tests/test_one_class_svm.py +++ b/aeon/anomaly_detection/series/outlier_detection/tests/test_one_class_svm.py @@ -4,7 +4,7 @@ import pytest from sklearn.utils import check_random_state -from aeon.anomaly_detection.distance_based import OneClassSVM +from aeon.anomaly_detection.series.outlier_detection import OneClassSVM def test_one_class_svm_univariate(): diff --git a/aeon/anomaly_detection/outlier_detection/tests/test_stray.py b/aeon/anomaly_detection/series/outlier_detection/tests/test_stray.py similarity index 98% rename from aeon/anomaly_detection/outlier_detection/tests/test_stray.py rename to aeon/anomaly_detection/series/outlier_detection/tests/test_stray.py index 8429a8a3c5..76ef9ef915 100644 --- a/aeon/anomaly_detection/outlier_detection/tests/test_stray.py +++ b/aeon/anomaly_detection/series/outlier_detection/tests/test_stray.py @@ -5,7 +5,7 @@ import numpy as np from sklearn.preprocessing import MinMaxScaler -from aeon.anomaly_detection.outlier_detection import STRAY +from aeon.anomaly_detection.series.outlier_detection import STRAY def test_default_1D(): diff --git a/aeon/anomaly_detection/tests/__init__.py b/aeon/anomaly_detection/series/tests/__init__.py similarity index 100% rename from aeon/anomaly_detection/tests/__init__.py rename to aeon/anomaly_detection/series/tests/__init__.py diff --git a/aeon/anomaly_detection/tests/test_base.py b/aeon/anomaly_detection/series/tests/test_base.py similarity index 100% rename from aeon/anomaly_detection/tests/test_base.py rename to aeon/anomaly_detection/series/tests/test_base.py diff --git a/aeon/anomaly_detection/outlier_detection/tests/test_pyod_adapter.py b/aeon/anomaly_detection/series/tests/test_pyod_adapter.py similarity index 98% rename from aeon/anomaly_detection/outlier_detection/tests/test_pyod_adapter.py rename to aeon/anomaly_detection/series/tests/test_pyod_adapter.py index ee75078133..aee336e089 100644 --- a/aeon/anomaly_detection/outlier_detection/tests/test_pyod_adapter.py +++ b/aeon/anomaly_detection/series/tests/test_pyod_adapter.py @@ -6,7 +6,7 @@ import pytest from sklearn.utils import check_random_state -from aeon.anomaly_detection.outlier_detection import PyODAdapter +from aeon.anomaly_detection.series import PyODAdapter from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/whole_series/__init__.py b/aeon/anomaly_detection/whole_series/__init__.py deleted file mode 100644 index 7098b8cd08..0000000000 --- a/aeon/anomaly_detection/whole_series/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -"""Whole Time Series Anomaly Detection.""" - -__all__ = [ - "ROCKAD", -] - -from aeon.anomaly_detection.whole_series._rockad import ROCKAD diff --git a/aeon/anomaly_detection/whole_series/tests/__init__.py b/aeon/anomaly_detection/whole_series/tests/__init__.py deleted file mode 100644 index 9292e8d9bd..0000000000 --- a/aeon/anomaly_detection/whole_series/tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Whole series anomaly detection tests.""" diff --git a/aeon/classification/base.py b/aeon/classification/base.py index 92d3b304a8..fa7ed89cb6 100644 --- a/aeon/classification/base.py +++ b/aeon/classification/base.py @@ -52,8 +52,6 @@ class BaseClassifier(ClassifierMixin, BaseCollectionEstimator): Number of classes (length of ``classes_``). _class_dictionary : dict Mapping of classes_ onto integers ``0 ... n_classes_-1``. - _estimator_type : string - The type of estimator. Required by some ``sklearn`` tools, set to "classifier". """ _tags = { diff --git a/aeon/testing/estimator_checking/_yield_anomaly_detection_checks.py b/aeon/testing/estimator_checking/_yield_anomaly_detection_checks.py index 363db48e23..850e4601e3 100644 --- a/aeon/testing/estimator_checking/_yield_anomaly_detection_checks.py +++ b/aeon/testing/estimator_checking/_yield_anomaly_detection_checks.py @@ -2,11 +2,7 @@ from functools import partial -import numpy as np - -from aeon.base._base import _clone_estimator -from aeon.base._base_series import VALID_SERIES_INNER_TYPES -from aeon.testing.testing_data import FULL_TEST_DATA_DICT +from aeon.utils.data_types import ALL_TIME_SERIES_TYPES def _yield_anomaly_detection_checks(estimator_class, estimator_instances, datatypes): @@ -25,33 +21,31 @@ def _yield_anomaly_detection_checks(estimator_class, estimator_instances, dataty datatype=datatypes[i][0], ) - # test all data types - for datatype in datatypes[i]: - yield partial( - check_anomaly_detector_output, estimator=estimator, datatype=datatype - ) - def check_anomaly_detector_overrides_and_tags(estimator_class): """Test compliance with the anomaly detector base class contract.""" # Test they don't override final methods, because Python does not enforce this - assert "fit" not in estimator_class.__dict__ - assert "predict" not in estimator_class.__dict__ - assert "fit_predict" not in estimator_class.__dict__ + final_methods = ["fit", "predict", "fit_predict"] + for method in final_methods: + if method in estimator_class.__dict__: + raise ValueError( + f"Anomaly detector {estimator_class} overrides the " + f"method {method}. Override _{method} instead." + ) # Test that all anomaly detectors implement abstract predict. assert "_predict" in estimator_class.__dict__ - # axis class parameter is for internal use only - assert "axis" not in estimator_class.__dict__ - # Test that fit_is_empty is correctly set fit_is_empty = estimator_class.get_class_tag(tag_name="fit_is_empty") assert not fit_is_empty == "_fit" not in estimator_class.__dict__ # Test valid tag for X_inner_type X_inner_type = estimator_class.get_class_tag(tag_name="X_inner_type") - assert X_inner_type in VALID_SERIES_INNER_TYPES + if isinstance(X_inner_type, str): + assert X_inner_type in ALL_TIME_SERIES_TYPES + else: # must be a list + assert all([t in ALL_TIME_SERIES_TYPES for t in X_inner_type]) # Must have at least one set to True multi = estimator_class.get_class_tag(tag_name="capability:multivariate") @@ -68,35 +62,3 @@ def check_anomaly_detector_learning_types(estimator, datatype): assert ( unsupervised or semisup or supervised ), "At least one learning type must be True" - - -def check_anomaly_detector_output(estimator, datatype): - """Test the anomaly detector output on valid data.""" - estimator = _clone_estimator(estimator) - - estimator.fit( - FULL_TEST_DATA_DICT[datatype]["train"][0], - FULL_TEST_DATA_DICT[datatype]["train"][1], - ) - - y_pred = estimator.predict(FULL_TEST_DATA_DICT[datatype]["test"][0]) - assert isinstance(y_pred, np.ndarray) - assert len(y_pred) == FULL_TEST_DATA_DICT[datatype]["test"][0].shape[1] - - ot = estimator.get_tag("anomaly_output_type") - if ot == "anomaly_scores": - assert np.issubdtype(y_pred.dtype, np.floating) or np.issubdtype( - y_pred.dtype, np.integer - ), "y_pred must be of floating point or int type" - assert not np.array_equal( - np.unique(y_pred), [0, 1] - ), "y_pred cannot contain only 0s and 1s" - elif ot == "binary": - assert np.issubdtype(y_pred.dtype, np.integer) or np.issubdtype( - y_pred.dtype, np.bool_ - ), "y_pred must be of int or bool type for binary output" - assert all( - val in [0, 1] for val in np.unique(y_pred) - ), "y_pred must contain only 0s, 1s, True, or False" - else: - raise ValueError(f"Unknown anomaly output type: {ot}") diff --git a/aeon/testing/estimator_checking/_yield_collection_anomaly_detection_checks.py b/aeon/testing/estimator_checking/_yield_collection_anomaly_detection_checks.py new file mode 100644 index 0000000000..8a22056871 --- /dev/null +++ b/aeon/testing/estimator_checking/_yield_collection_anomaly_detection_checks.py @@ -0,0 +1,81 @@ +"""Tests for all collection anomaly detectors.""" + +from functools import partial + +import numpy as np + +from aeon.base._base import _clone_estimator +from aeon.testing.testing_data import FULL_TEST_DATA_DICT +from aeon.utils.data_types import COLLECTIONS_DATA_TYPES +from aeon.utils.validation import get_n_cases + + +def _yield_collection_anomaly_detection_checks( + estimator_class, estimator_instances, datatypes +): + """Yield all collection anomaly detection checks for an aeon estimator.""" + # only class required + yield partial( + check_collection_detector_overrides_and_tags, estimator_class=estimator_class + ) + + # test class instances + for i, estimator in enumerate(estimator_instances): + # test all data types + for datatype in datatypes[i]: + yield partial( + check_collection_anomaly_detector_output, + estimator=estimator, + datatype=datatype, + ) + + +def check_collection_detector_overrides_and_tags(estimator_class): + """Test compliance with the detector base class contract.""" + # Test valid tag for X_inner_type + X_inner_type = estimator_class.get_class_tag(tag_name="X_inner_type") + if isinstance(X_inner_type, str): + assert X_inner_type in COLLECTIONS_DATA_TYPES + else: # must be a list + assert all([t in COLLECTIONS_DATA_TYPES for t in X_inner_type]) + + # one of X_inner_types must be capable of storing unequal length + if estimator_class.get_class_tag("capability:unequal_length"): + valid_unequal_types = ["np-list", "df-list", "pd-multiindex"] + if isinstance(X_inner_type, str): + assert X_inner_type in valid_unequal_types + else: # must be a list + assert any([t in valid_unequal_types for t in X_inner_type]) + + +def check_collection_anomaly_detector_output(estimator, datatype): + """Test the collection anomaly detector output on valid data.""" + estimator = _clone_estimator(estimator) + + estimator.fit( + FULL_TEST_DATA_DICT[datatype]["train"][0], + FULL_TEST_DATA_DICT[datatype]["train"][1], + ) + + y_pred = estimator.predict(FULL_TEST_DATA_DICT[datatype]["test"][0]) + assert isinstance(y_pred, np.ndarray) + # collections need n_cases predictions + assert len(y_pred) == get_n_cases(FULL_TEST_DATA_DICT[datatype]["test"][0]) + + ot = estimator.get_tag("anomaly_output_type") + if ot == "anomaly_scores": + assert np.issubdtype(y_pred.dtype, np.floating) or np.issubdtype( + y_pred.dtype, np.integer + ), "y_pred must be of floating point or int type" + assert not np.array_equal( + np.unique(y_pred), [0, 1] + ), "y_pred cannot contain only 0s and 1s" + elif ot == "binary": + assert np.issubdtype(y_pred.dtype, np.integer) or np.issubdtype( + y_pred.dtype, np.bool_ + ), "y_pred must be of int or bool type for binary output" + assert all( + val in [0, 1] for val in np.unique(y_pred) + ), "y_pred must contain only 0s, 1s, True, or False" + else: + raise ValueError(f"Unknown anomaly output type: {ot}") diff --git a/aeon/testing/estimator_checking/_yield_estimator_checks.py b/aeon/testing/estimator_checking/_yield_estimator_checks.py index 6cf4ee7948..9fd6d922b1 100644 --- a/aeon/testing/estimator_checking/_yield_estimator_checks.py +++ b/aeon/testing/estimator_checking/_yield_estimator_checks.py @@ -12,6 +12,8 @@ from sklearn.exceptions import NotFittedError from aeon.anomaly_detection.base import BaseAnomalyDetector +from aeon.anomaly_detection.collection.base import BaseCollectionAnomalyDetector +from aeon.anomaly_detection.series.base import BaseSeriesAnomalyDetector from aeon.base import BaseAeonEstimator from aeon.base._base import _clone_estimator from aeon.classification import BaseClassifier @@ -32,6 +34,9 @@ from aeon.testing.estimator_checking._yield_clustering_checks import ( _yield_clustering_checks, ) +from aeon.testing.estimator_checking._yield_collection_anomaly_detection_checks import ( + _yield_collection_anomaly_detection_checks, +) from aeon.testing.estimator_checking._yield_early_classification_checks import ( _yield_early_classification_checks, ) @@ -44,6 +49,9 @@ from aeon.testing.estimator_checking._yield_segmentation_checks import ( _yield_segmentation_checks, ) +from aeon.testing.estimator_checking._yield_series_anomaly_detection_checks import ( + _yield_series_anomaly_detection_checks, +) from aeon.testing.estimator_checking._yield_soft_dependency_checks import ( _yield_soft_dependency_checks, ) @@ -140,6 +148,16 @@ def _yield_all_aeon_checks( estimator_class, estimator_instances, datatypes ) + if issubclass(estimator_class, BaseSeriesAnomalyDetector): + yield from _yield_series_anomaly_detection_checks( + estimator_class, estimator_instances, datatypes + ) + + if issubclass(estimator_class, BaseCollectionAnomalyDetector): + yield from _yield_collection_anomaly_detection_checks( + estimator_class, estimator_instances, datatypes + ) + if issubclass(estimator_class, BaseTransformer): yield from _yield_transformation_checks( estimator_class, estimator_instances, datatypes diff --git a/aeon/testing/estimator_checking/_yield_series_anomaly_detection_checks.py b/aeon/testing/estimator_checking/_yield_series_anomaly_detection_checks.py new file mode 100644 index 0000000000..85368d09e7 --- /dev/null +++ b/aeon/testing/estimator_checking/_yield_series_anomaly_detection_checks.py @@ -0,0 +1,73 @@ +"""Tests for all series anomaly detectors.""" + +from functools import partial + +import numpy as np + +from aeon.base._base import _clone_estimator +from aeon.base._base_series import VALID_SERIES_INNER_TYPES +from aeon.testing.testing_data import FULL_TEST_DATA_DICT + + +def _yield_series_anomaly_detection_checks( + estimator_class, estimator_instances, datatypes +): + """Yield all anomaly detection checks for an aeon anomaly detector.""" + # only class required + yield partial( + check_series_anomaly_detector_overrides_and_tags, + estimator_class=estimator_class, + ) + + # test class instances + for i, estimator in enumerate(estimator_instances): + # test all data types + for datatype in datatypes[i]: + yield partial( + check_series_anomaly_detector_output, + estimator=estimator, + datatype=datatype, + ) + + +def check_series_anomaly_detector_overrides_and_tags(estimator_class): + """Test compliance with the anomaly detector base class contract.""" + # Test valid tag for X_inner_type + X_inner_type = estimator_class.get_class_tag(tag_name="X_inner_type") + if isinstance(X_inner_type, str): + assert X_inner_type in VALID_SERIES_INNER_TYPES + else: # must be a list + assert all([t in VALID_SERIES_INNER_TYPES for t in X_inner_type]) + + +def check_series_anomaly_detector_output(estimator, datatype): + """Test the series anomaly detector output on valid data.""" + estimator = _clone_estimator(estimator) + + estimator.fit( + FULL_TEST_DATA_DICT[datatype]["train"][0], + FULL_TEST_DATA_DICT[datatype]["train"][1], + ) + + y_pred = estimator.predict(FULL_TEST_DATA_DICT[datatype]["test"][0]) + assert isinstance(y_pred, np.ndarray) + # series need n_timepoints predictions + assert len(y_pred) == FULL_TEST_DATA_DICT[datatype]["test"][0].shape[1] + + ot = estimator.get_tag("anomaly_output_type") + if ot == "anomaly_scores": + assert np.issubdtype(y_pred.dtype, np.floating) or np.issubdtype( + y_pred.dtype, np.integer + ), "y_pred must be of floating point or int type" + assert not np.array_equal( + np.unique(y_pred), [0, 1] + ), "y_pred cannot contain only 0s and 1s" + elif ot == "binary": + assert np.issubdtype(y_pred.dtype, np.integer) or np.issubdtype( + y_pred.dtype, np.bool_ + ), "y_pred must be of int or bool type for binary output" + assert all( + val in [0, 1] for val in np.unique(y_pred) + ), "y_pred must contain only 0s, 1s, True, or False" + else: + raise ValueError(f"Unknown anomaly output type: {ot}") diff --git a/aeon/testing/mock_estimators/_mock_anomaly_detectors.py b/aeon/testing/mock_estimators/_mock_anomaly_detectors.py index ed0ba2488d..a0cb5ad949 100644 --- a/aeon/testing/mock_estimators/_mock_anomaly_detectors.py +++ b/aeon/testing/mock_estimators/_mock_anomaly_detectors.py @@ -10,10 +10,10 @@ import numpy as np -from aeon.anomaly_detection.base import BaseAnomalyDetector +from aeon.anomaly_detection.series.base import BaseSeriesAnomalyDetector -class MockAnomalyDetector(BaseAnomalyDetector): +class MockAnomalyDetector(BaseSeriesAnomalyDetector): """Mock anomaly detector.""" _tags = { diff --git a/aeon/testing/testing_config.py b/aeon/testing/testing_config.py index fe7f8d003b..c81dc60c42 100644 --- a/aeon/testing/testing_config.py +++ b/aeon/testing/testing_config.py @@ -52,7 +52,7 @@ "SAST": ["check_fit_deterministic"], "RSAST": ["check_fit_deterministic"], "MatrixProfile": ["check_fit_deterministic", "check_persistence_via_pickle"], - "LeftSTAMPi": ["check_anomaly_detector_output"], + "LeftSTAMPi": ["check_series_anomaly_detector_output"], # missed in legacy testing, changes state in predict/transform "FLUSSSegmenter": ["check_non_state_changing_method"], "ClaSPSegmenter": ["check_non_state_changing_method"], diff --git a/aeon/testing/testing_data.py b/aeon/testing/testing_data.py index f3360d93cb..7d2186398a 100644 --- a/aeon/testing/testing_data.py +++ b/aeon/testing/testing_data.py @@ -2,7 +2,8 @@ import numpy as np -from aeon.anomaly_detection.base import BaseAnomalyDetector +from aeon.anomaly_detection.collection.base import BaseCollectionAnomalyDetector +from aeon.anomaly_detection.series.base import BaseSeriesAnomalyDetector from aeon.base import BaseCollectionEstimator, BaseSeriesEstimator from aeon.classification import BaseClassifier from aeon.classification.early_classification import BaseEarlyClassifier @@ -861,6 +862,7 @@ def _get_task_for_estimator(estimator): or isinstance(estimator, BaseEarlyClassifier) or isinstance(estimator, BaseClusterer) or isinstance(estimator, BaseCollectionTransformer) + or isinstance(estimator, BaseCollectionAnomalyDetector) or isinstance(estimator, BaseCollectionSimilaritySearch) ): data_label = "Classification" @@ -869,7 +871,7 @@ def _get_task_for_estimator(estimator): data_label = "Regression" # series data with no secondary input elif ( - isinstance(estimator, BaseAnomalyDetector) + isinstance(estimator, BaseSeriesAnomalyDetector) or isinstance(estimator, BaseSegmenter) or isinstance(estimator, BaseSeriesTransformer) or isinstance(estimator, BaseForecaster) diff --git a/aeon/utils/base/_identifier.py b/aeon/utils/base/_identifier.py index 03e8d8beaf..de724156f8 100644 --- a/aeon/utils/base/_identifier.py +++ b/aeon/utils/base/_identifier.py @@ -49,12 +49,14 @@ def get_identifier(estimator): if len(identifiers) > 1 and "estimator" in identifiers: identifiers.remove("estimator") - if len(identifiers) > 1 and "series-estimator" in identifiers: - identifiers.remove("series-estimator") if len(identifiers) > 1 and "collection-estimator" in identifiers: identifiers.remove("collection-estimator") + if len(identifiers) > 1 and "series-estimator" in identifiers: + identifiers.remove("series-estimator") if len(identifiers) > 1 and "transformer" in identifiers: identifiers.remove("transformer") + if len(identifiers) > 1 and "anomaly-detector" in identifiers: + identifiers.remove("anomaly-detector") if len(identifiers) > 1 and "similarity-search" in identifiers: identifiers.remove("similarity-search") diff --git a/aeon/utils/base/_register.py b/aeon/utils/base/_register.py index 5e81e29b33..5076bd79ee 100644 --- a/aeon/utils/base/_register.py +++ b/aeon/utils/base/_register.py @@ -15,8 +15,9 @@ "VALID_ESTIMATOR_BASES", ] - from aeon.anomaly_detection.base import BaseAnomalyDetector +from aeon.anomaly_detection.collection.base import BaseCollectionAnomalyDetector +from aeon.anomaly_detection.series.base import BaseSeriesAnomalyDetector from aeon.base import BaseAeonEstimator, BaseCollectionEstimator, BaseSeriesEstimator from aeon.classification.base import BaseClassifier from aeon.classification.early_classification import BaseEarlyClassifier @@ -34,23 +35,25 @@ # all base classes BASE_CLASS_REGISTER = { # abstract - no estimator directly inherits from these - "collection-estimator": BaseCollectionEstimator, "estimator": BaseAeonEstimator, + "collection-estimator": BaseCollectionEstimator, "series-estimator": BaseSeriesEstimator, "transformer": BaseTransformer, + "anomaly-detector": BaseAnomalyDetector, "similarity-search": BaseSimilaritySearch, # estimator types - "anomaly-detector": BaseAnomalyDetector, + "collection-anomaly-detector": BaseCollectionAnomalyDetector, + "collection-similarity-search": BaseCollectionSimilaritySearch, "collection-transformer": BaseCollectionTransformer, "classifier": BaseClassifier, "clusterer": BaseClusterer, "early_classifier": BaseEarlyClassifier, + "forecaster": BaseForecaster, "regressor": BaseRegressor, "segmenter": BaseSegmenter, - "series-transformer": BaseSeriesTransformer, - "forecaster": BaseForecaster, + "series-anomaly-detector": BaseSeriesAnomalyDetector, "series-similarity-search": BaseSeriesSimilaritySearch, - "collection-similarity-search": BaseCollectionSimilaritySearch, + "series-transformer": BaseSeriesTransformer, } # base classes which are valid for estimator to directly inherit from @@ -62,6 +65,7 @@ "collection-estimator", "series-estimator", "transformer", + "anomaly-detector", "similarity-search", } } diff --git a/aeon/utils/base/tests/test_identifier.py b/aeon/utils/base/tests/test_identifier.py index 8084492599..99eed77dad 100644 --- a/aeon/utils/base/tests/test_identifier.py +++ b/aeon/utils/base/tests/test_identifier.py @@ -34,7 +34,7 @@ def test_get_identifier(): assert ( get_identifier(MockAnomalyDetector) == get_identifier(MockAnomalyDetector()) - == "anomaly-detector" + == "series-anomaly-detector" ) assert ( get_identifier(MockSeriesTransformer) diff --git a/aeon/utils/tags/tests/test_discovery.py b/aeon/utils/tags/tests/test_discovery.py index fd3d57fa16..cc9dca0e36 100644 --- a/aeon/utils/tags/tests/test_discovery.py +++ b/aeon/utils/tags/tests/test_discovery.py @@ -2,7 +2,7 @@ import pytest -from aeon.anomaly_detection.base import BaseAnomalyDetector +from aeon.anomaly_detection.series.base import BaseSeriesAnomalyDetector from aeon.classification import BaseClassifier from aeon.testing.mock_estimators import MockClassifier from aeon.testing.mock_estimators._mock_anomaly_detectors import MockAnomalyDetector @@ -42,8 +42,8 @@ def test_all_tags_for_estimator_anomaly_detection(): assert "capability:contractable" not in tags assert tags == all_tags_for_estimator(MockAnomalyDetector) - assert tags == all_tags_for_estimator(BaseAnomalyDetector) - assert tags == all_tags_for_estimator("anomaly-detector") + assert tags == all_tags_for_estimator(BaseSeriesAnomalyDetector) + assert tags == all_tags_for_estimator("series-anomaly-detector") tag_names = all_tags_for_estimator(MockAnomalyDetector(), names_only=True) assert isinstance(tag_names, list) diff --git a/aeon/utils/tests/test_discovery.py b/aeon/utils/tests/test_discovery.py index 9a0fcd6ee7..f71727320c 100644 --- a/aeon/utils/tests/test_discovery.py +++ b/aeon/utils/tests/test_discovery.py @@ -3,7 +3,7 @@ import pytest from sklearn.base import BaseEstimator -from aeon.anomaly_detection.base import BaseAnomalyDetector +from aeon.anomaly_detection.series.base import BaseSeriesAnomalyDetector from aeon.base import BaseAeonEstimator from aeon.classification import BaseClassifier, DummyClassifier from aeon.clustering import BaseClusterer @@ -62,7 +62,7 @@ def test_all_estimators_by_type(item): [ [BaseTransformer, BaseClassifier], [BaseClassifier, "segmenter"], - [BaseClassifier, BaseAnomalyDetector, BaseClusterer], + [BaseClassifier, BaseSeriesAnomalyDetector, BaseClusterer], ], ) def test_all_estimators_by_multiple_types(input): diff --git a/docs/api_reference/anomaly_detection.rst b/docs/api_reference/anomaly_detection.rst index 3e22c445b7..894f986d82 100644 --- a/docs/api_reference/anomaly_detection.rst +++ b/docs/api_reference/anomaly_detection.rst @@ -3,25 +3,38 @@ Anomaly Detection ================= -The :mod:`aeon.anomaly_detection` module contains algorithms and composition tools for time series classification. +The :mod:`aeon.anomaly_detection` module contains algorithms and composition tools for +time series anomaly detection. All detectors in `aeon` can be listed using the `aeon.utils.discovery.all_estimators` utility, using ``estimator_types="anomaly-detector"``, optionally filtered by tags. -Valid tags can be listed by calling the function `aeon.utils.discovery.all_tags_for_estimator`. +Valid tags can be listed by calling the function `aeon.utils.tags.all_tags_for_estimator`. Each detector in this module specifies its supported input data format, output data format, and learning type as an overview table in its documentation. Some detectors support multiple learning types. -.. note:: - Not all algorithm families are currently implemented. The documentation includes - placeholders for planned categories which will be supported in future. +Collection anomaly detectors +---------------------------- + +.. currentmodule:: aeon.anomaly_detection.collection + +.. autosummary:: + :toctree: auto_generated/ + :template: class.rst + + ClassificationAdapter + OutlierDetectionAdapter + BaseCollectionAnomalyDetector + +Series anomaly detectors +------------------------ Distance-based --------------- +~~~~~~~~~~~~~~ -.. currentmodule:: aeon.anomaly_detection.distance_based +.. currentmodule:: aeon.anomaly_detection.series.distance_based .. autosummary:: :toctree: auto_generated/ @@ -32,13 +45,13 @@ Distance-based LeftSTAMPi LOF MERLIN - OneClassSVM STOMP + ROCKAD Distribution-based ------------------ +~~~~~~~~~~~~~~~~~~ -.. currentmodule:: aeon.anomaly_detection.distribution_based +.. currentmodule:: aeon.anomaly_detection.series.distribution_based .. autosummary:: :toctree: auto_generated/ @@ -47,44 +60,41 @@ Distribution-based COPOD DWT_MLEAD -Encoding-based --------------- - -The algorithms for this family are not implemented yet. - -Forecasting-based ------------------ - -The algorithms for this family are not implemented yet. - Outlier-Detection ------------------ +~~~~~~~~~~~~~~~~~ -.. currentmodule:: aeon.anomaly_detection.outlier_detection +.. currentmodule:: aeon.anomaly_detection.series.outlier_detection .. autosummary:: :toctree: auto_generated/ :template: class.rst IsolationForest - PyODAdapter + OneClassSVM STRAY -Reconstruction-based --------------------- +Adapters +~~~~~~~~ + +.. currentmodule:: aeon.anomaly_detection.series + +.. autosummary:: + :toctree: auto_generated/ + :template: class.rst -The algorithms for this family are not implemented yet. + PyODAdapter -Whole-Series ------------- +Base +~~~~ -.. currentmodule:: aeon.anomaly_detection.whole_series +.. currentmodule:: aeon.anomaly_detection.series .. autosummary:: :toctree: auto_generated/ :template: class.rst - ROCKAD + BaseSeriesAnomalyDetector + Base ---- diff --git a/docs/developer_guide/adding_typehints.md b/docs/developer_guide/adding_typehints.md index 5f77ce119b..ad0a9d5fa9 100644 --- a/docs/developer_guide/adding_typehints.md +++ b/docs/developer_guide/adding_typehints.md @@ -34,17 +34,23 @@ information. The `pyod` `BaseDetector` class can now be used in type hints with these additions. ```python +"""Adapter for PyOD models""" + from __future__ import annotations -from aeon.anomaly_detection.base import BaseAnomalyDetector +__maintainer__ = [] +__all__ = ["PyODAdapter"] + +from aeon.anomaly_detection.series.base import BaseSeriesAnomalyDetector from typing import TYPE_CHECKING if TYPE_CHECKING: from pyod.models.base import BaseDetector -class PyODAdapter(BaseAnomalyDetector): + +class PyODAdapter(BaseSeriesAnomalyDetector): def __init__( - self, pyod_model: BaseDetector, window_size: int = 10, stride: int = 1 + self, pyod_model: BaseDetector, window_size: int = 10, stride: int = 1 ): self.pyod_model = pyod_model self.window_size = window_size diff --git a/examples/anomaly_detection/anomaly_detection.ipynb b/examples/anomaly_detection/anomaly_detection.ipynb index 7afd00aff8..c9011d3871 100644 --- a/examples/anomaly_detection/anomaly_detection.ipynb +++ b/examples/anomaly_detection/anomaly_detection.ipynb @@ -185,7 +185,7 @@ "metadata": {}, "outputs": [], "source": [ - "from aeon.anomaly_detection.distance_based import STOMP\n", + "from aeon.anomaly_detection.series.distance_based import STOMP\n", "from aeon.benchmarking.metrics.anomaly_detection import range_roc_auc_score\n", "\n", "detector = STOMP(window_size=200)\n", @@ -203,21 +203,21 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "743fbbaa-a7d0-4f56-993a-07453f6a9442", "metadata": {}, + "cell_type": "code", "outputs": [], + "execution_count": null, "source": [ "from pyod.models.ocsvm import OCSVM\n", "\n", - "from aeon.anomaly_detection.outlier_detection import PyODAdapter\n", + "from aeon.anomaly_detection.series import PyODAdapter\n", "from aeon.benchmarking.metrics.anomaly_detection import range_roc_auc_score\n", "\n", "detector = PyODAdapter(OCSVM(), window_size=3)\n", "y_scores = detector.fit_predict(X, axis=0)\n", "range_roc_auc_score(y, y_scores)" - ] + ], + "id": "8c89d43f2e5476e6" }, { "cell_type": "markdown", From 2f5330ee84ace92f035e654d99843a9bb6239bfa Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 2 Jun 2025 12:04:54 +0100 Subject: [PATCH 126/216] [MNT] Bump the github-actions group across 1 directory with 2 updates (#2868) Bumps the github-actions group with 2 updates in the / directory: [ossf/scorecard-action](https://github.com/ossf/scorecard-action) and [crs-k/stale-branches](https://github.com/crs-k/stale-branches). Updates `ossf/scorecard-action` from 2.4.1 to 2.4.2 - [Release notes](https://github.com/ossf/scorecard-action/releases) - [Changelog](https://github.com/ossf/scorecard-action/blob/main/RELEASE.md) - [Commits](https://github.com/ossf/scorecard-action/compare/v2.4.1...v2.4.2) Updates `crs-k/stale-branches` from 8.1.1 to 8.2.0 - [Release notes](https://github.com/crs-k/stale-branches/releases) - [Commits](https://github.com/crs-k/stale-branches/compare/v8.1.1...v8.2.0) --- updated-dependencies: - dependency-name: ossf/scorecard-action dependency-version: 2.4.2 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: github-actions - dependency-name: crs-k/stale-branches dependency-version: 8.2.0 dependency-type: direct:production update-type: version-update:semver-minor dependency-group: github-actions ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/weekly_github_maintenance.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/weekly_github_maintenance.yml b/.github/workflows/weekly_github_maintenance.yml index 9de93ccb46..a854f8e7de 100644 --- a/.github/workflows/weekly_github_maintenance.yml +++ b/.github/workflows/weekly_github_maintenance.yml @@ -91,7 +91,7 @@ jobs: persist-credentials: false - name: Run analysis - uses: ossf/scorecard-action@v2.4.1 + uses: ossf/scorecard-action@v2.4.2 with: results_file: results.sarif results_format: sarif @@ -121,7 +121,7 @@ jobs: private-key: ${{ secrets.PR_APP_KEY }} - name: Stale Branches - uses: crs-k/stale-branches@v8.1.1 + uses: crs-k/stale-branches@v8.2.0 with: repo-token: ${{ steps.app-token.outputs.token }} days-before-stale: 180 From c3cb9a032164ae55d6d6e8358d99ff3ae1d2c4a2 Mon Sep 17 00:00:00 2001 From: "aeon-actions-bot[bot]" <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Date: Mon, 2 Jun 2025 12:05:16 +0100 Subject: [PATCH 127/216] Automated `pre-commit` hook update (#2867) Co-authored-by: TonyBagnall <9594042+TonyBagnall@users.noreply.github.com> --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 2dece2f28f..5bfaa5629c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -29,7 +29,7 @@ repos: args: [ "--create", "--python-folders", "aeon" ] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.11.11 + rev: v0.11.12 hooks: - id: ruff args: [ "--fix"] From c0c66842201f489667fc4aaa3b66b478cb651ee2 Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Mon, 2 Jun 2025 19:30:11 +0100 Subject: [PATCH 128/216] [ENH] introduce revised version of ETS (#2834) * first version for correctness testing * timing * examples * new tests * refactor predict * final ETS * arima first * docstrings * docstrings * Convert error, trend and seasonality types to strings * Automatic `pre-commit` fixes * Add Type hints back in * Fix Type hints * Fix Linting issue * Automatic `pre-commit` fixes * Update test_ets to use strings for error, trend and seasonality types * Automatic `pre-commit` fixes * switch to fastmath * remove _arima, incorrectly introduced here * all int or str parameters * refactor variable name --------- Co-authored-by: alexbanwell1 <31886108+alexbanwell1@users.noreply.github.com> --- aeon/forecasting/_ets.py | 412 ++++++++++++++++++----------- aeon/forecasting/tests/test_ets.py | 123 +++++++-- 2 files changed, 353 insertions(+), 182 deletions(-) diff --git a/aeon/forecasting/_ets.py b/aeon/forecasting/_ets.py index efc99d6d47..2899e0d768 100644 --- a/aeon/forecasting/_ets.py +++ b/aeon/forecasting/_ets.py @@ -1,91 +1,102 @@ """ETSForecaster class. An implementation of the exponential smoothing statistics forecasting algorithm. -Implements additive and multiplicative error models, -None, additive and multiplicative (including damped) trend and -None, additive and multiplicative seasonality +Implements additive and multiplicative error models. """ __maintainer__ = [] -__all__ = ["ETSForecaster", "NONE", "ADDITIVE", "MULTIPLICATIVE"] +__all__ = ["ETSForecaster"] + +from typing import Union import numpy as np from numba import njit from aeon.forecasting.base import BaseForecaster -NOGIL = False -CACHE = True - -NONE = 0 -ADDITIVE = 1 -MULTIPLICATIVE = 2 +ADDITIVE = "additive" +MULTIPLICATIVE = "multiplicative" class ETSForecaster(BaseForecaster): - """Exponential Smoothing forecaster. + """Exponential Smoothing (ETS) forecaster. - An implementation of the exponential smoothing forecasting algorithm. - Implements additive and multiplicative error models, None, additive and - multiplicative (including damped) trend and None, additive and mutliplicative - seasonality. See [1]_ for a description. + Implements the ETS (Error, Trend, Seasonality) forecaster, supporting additive + and multiplicative forms of error, trend (including damped), and seasonality + components. Based on the state space model formulation of exponential + smoothing as described in Hyndman and Athanasopoulos [1]_. Parameters ---------- - error_type : int, default = 1 - Either NONE (0), ADDITIVE (1) or MULTIPLICATIVE (2). - trend_type : int, default = 0 - Either NONE (0), ADDITIVE (1) or MULTIPLICATIVE (2). - seasonality_type : int, default = 0 - Either NONE (0), ADDITIVE (1) or MULTIPLICATIVE (2). + error_type : string or int, default=1 + Type of error model: 'additive' (0) or 'multiplicative' (1) + trend_type : string, int or None, default=0 + Type of trend component: None (0), `additive' (1) or 'multiplicative' (2) + seasonality_type : string or None, default=0 + Type of seasonal component: None (0), `additive' (1) or 'multiplicative' (2) seasonal_period : int, default=1 - Length of seasonality period. If seasonality_type is NONE, this is assumed to - be 1 - alpha : float, default = 0.1 + Number of time points in a seasonal cycle. + alpha : float, default=0.1 Level smoothing parameter. - beta : float, default = 0.01 - Trend smoothing parameter. If trend_type is NONE, this is assumed to be 0.0. - gamma : float, default = 0.01 - Seasonal smoothing parameter. If seasonality is NONE, this is assumed to be - 0.0. - phi : float, default = 0.99 - Trend damping smoothing parameters - horizon : int, default = 1 - The horizon to forecast to. + beta : float, default=0.01 + Trend smoothing parameter. + gamma : float, default=0.01 + Seasonal smoothing parameter. + phi : float, default=0.99 + Trend damping parameter (used only for damped trend models). + horizon : int, default=1 + Forecasting horizon (number of time steps ahead to predict). Attributes ---------- - mean_sq_err_ : float - Mean squared error. - likelihood_ : float - Likelihood of the fitted model based on residuals. - residuals_ : arraylike - List of train set differences between fitted and actual values. - n_timpoints_ : int - Length of the series passed to fit. + forecast_val_ : float + Forecast value for the given horizon. + level_ : float + Estimated level component. + trend_ : float + Estimated trend component. + seasonality_ : array-like or None + Estimated seasonal components. + aic_ : float + Akaike Information Criterion of the fitted model. + avg_mean_sq_err_ : float + Average mean squared error of the fitted model. + residuals_ : list of float + Residuals from the fitted model. + fitted_values_ : list of float + Fitted values for the training data. + liklihood_ : float + Log-likelihood of the fitted model. + n_timepoints_ : int + Number of time points in the training series. References ---------- .. [1] R. J. Hyndman and G. Athanasopoulos, - Forecasting: Principles and Practice. Melbourne, Australia: OTexts, 2014. + Forecasting: Principles and Practice, 2nd Edition. OTexts, 2014. + https://otexts.com/fpp3/ Examples -------- >>> from aeon.forecasting import ETSForecaster >>> from aeon.datasets import load_airline >>> y = load_airline() - >>> forecaster = ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, horizon=1) + >>> forecaster = ETSForecaster( + ... alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, horizon=1, + ... error_type='additive', trend_type='multiplicative', + ... seasonality_type='multiplicative', seasonal_period=4 + ... ) >>> forecaster.fit(y) - ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8) + ETSForecaster(...) >>> forecaster.predict() - 449.9435566831507 + 366.90200486015596 """ def __init__( self, - error_type: int = ADDITIVE, - trend_type: int = NONE, - seasonality_type: int = NONE, + error_type: Union[int, str] = 1, + trend_type: Union[int, str, None] = 0, + seasonality_type: Union[int, str, None] = 0, seasonal_period: int = 1, alpha: float = 0.1, beta: float = 0.01, @@ -93,18 +104,28 @@ def __init__( phi: float = 0.99, horizon: int = 1, ): - self.error_type = error_type - self.trend_type = trend_type - self.seasonality_type = seasonality_type - self.seasonal_period = seasonal_period self.alpha = alpha self.beta = beta self.gamma = gamma self.phi = phi - self.mean_sq_err_ = 0 - self.likelihood_ = 0 + self.forecast_val_ = 0.0 + self.level_ = 0.0 + self.trend_ = 0.0 + self.seasonality_ = None + self._beta = beta + self._gamma = gamma + self.error_type = error_type + self.trend_type = trend_type + self.seasonality_type = seasonality_type + self.seasonal_period = seasonal_period + self._seasonal_period = seasonal_period + self.n_timepoints_ = 0 + self.avg_mean_sq_err_ = 0 + self.liklihood_ = 0 + self.k_ = 0 + self.aic_ = 0 self.residuals_ = [] - self.n_timpoints_ = 0 + self.fitted_values_ = [] super().__init__(horizon=horizon, axis=1) def _fit(self, y, exog=None): @@ -122,33 +143,51 @@ def _fit(self, y, exog=None): Returns ------- self - Fitted BaseForecaster. + Fitted ETSForecaster. """ - self.n_timepoints_ = len(y) - if self.error_type != MULTIPLICATIVE and self.error_type != ADDITIVE: - raise ValueError("Error must be either additive or multiplicative") - self._seasonal_period = self.seasonal_period - if self.seasonal_period < 1 or self.seasonality_type == NONE: + _validate_parameter(self.error_type, False) + _validate_parameter(self.seasonality_type, True) + _validate_parameter(self.trend_type, True) + + # Convert to string parameters to ints for numba efficiency + def _get_int(x): + if x is None: + return 0 + if x == ADDITIVE: + return 1 + if x == MULTIPLICATIVE: + return 2 + return x + + self._error_type = _get_int(self.error_type) + self._seasonality_type = _get_int(self.seasonality_type) + self._trend_type = _get_int(self.trend_type) + if self._seasonal_period < 1 or self._seasonality_type == 0: self._seasonal_period = 1 - self._beta = self.beta - if self.trend_type == NONE or self.trend_type is None: + + if self._trend_type == 0: + # Required for the equations in _update_states to work correctly self._beta = 0 - self._gamma = self.gamma - if self.seasonality_type == NONE or self.trend_type is None: + if self._seasonality_type == 0: + # Required for the equations in _update_states to work correctly self._gamma = 0 - data = np.array(y.squeeze(), dtype=np.float64) + data = y.squeeze() ( - self._level, - self._trend, - self._seasonality, + self.level_, + self.trend_, + self.seasonality_, + self.n_timepoints_, self.residuals_, - self.mean_sq_err_, - self.likelihood_, - ) = _fit_numba( + self.fitted_values_, + self.avg_mean_sq_err_, + self.liklihood_, + self.k_, + self.aic_, + ) = _numba_fit( data, - self.error_type, - self.trend_type, - self.seasonality_type, + self._error_type, + self._trend_type, + self._seasonality_type, self._seasonal_period, self.alpha, self._beta, @@ -166,7 +205,7 @@ def _predict(self, y=None, exog=None): y : np.ndarray, default = None A time series to predict the next horizon value for. If None, predict the next horizon value after series seen in fit. - exog : np.ndarray, default = None + exog : np.ndarray, default =None Optional exogenous time series data assumed to be aligned with y Returns @@ -174,40 +213,60 @@ def _predict(self, y=None, exog=None): float single prediction self.horizon steps ahead of y. """ - return _predict_numba( - self.trend_type, - self.seasonality_type, - self._level, - self._trend, - self._seasonality, + fitted_value = _predict( + self._trend_type, + self._seasonality_type, + self.level_, + self.trend_, + self.seasonality_, self.phi, self.horizon, self.n_timepoints_, - self.seasonal_period, + self._seasonal_period, + ) + return fitted_value + + def _initialise(self, data): + """ + Initialize level, trend, and seasonality values for the ETS model. + + Parameters + ---------- + data : array-like + The time series data + (should contain at least two full seasons if seasonality is specified) + """ + self.level_, self.trend_, self.seasonality_ = _initialise( + self._trend_type, self._seasonality_type, self._seasonal_period, data ) -@njit(nogil=NOGIL, cache=CACHE) -def _fit_numba( +@njit(fastmath=True, cache=True) +def _numba_fit( data, - error_type: int, - trend_type: int, - seasonality_type: int, - seasonal_period: int, - alpha: float, - beta: float, - gamma: float, - phi: float, + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, ): - n_timepoints = len(data) + n_timepoints = len(data) - seasonal_period level, trend, seasonality = _initialise( trend_type, seasonality_type, seasonal_period, data ) - mse = 0 - lhood = 0 - mul_likelihood_pt2 = 0 - res = np.zeros(n_timepoints) # 1 Less residual than data points - for t, data_item in enumerate(data[seasonal_period:]): + avg_mean_sq_err_ = 0 + liklihood_ = 0 + residuals_ = np.zeros(n_timepoints) # 1 Less residual than data points + fitted_values_ = np.zeros(n_timepoints) + for t in range(n_timepoints): + index = t + seasonal_period + s_index = t % seasonal_period + + time_point = data[index] + # Calculate level, trend, and seasonal components fitted_value, error, level, trend, seasonality[t % seasonal_period] = ( _update_states( @@ -216,39 +275,59 @@ def _fit_numba( seasonality_type, level, trend, - seasonality[t % seasonal_period], - data_item, + seasonality[s_index], + time_point, alpha, beta, gamma, phi, ) ) - res[t] = error - mse += (data_item - fitted_value) ** 2 - lhood += error * error - mul_likelihood_pt2 += np.log(np.fabs(fitted_value)) - mse /= n_timepoints - seasonal_period - lhood = (n_timepoints - seasonal_period) * np.log(lhood) - if error_type == MULTIPLICATIVE: - lhood += 2 * mul_likelihood_pt2 - return level, trend, seasonality, res, mse, lhood - - -def _predict_numba( - trend_type: int, - seasonality_type: int, - level: float, - trend: float, - seasonality: float, - phi: float, - horizon: int, - n_timepoints: int, - seasonal_period: int, + residuals_[t] = error + fitted_values_[t] = fitted_value + avg_mean_sq_err_ += (time_point - fitted_value) ** 2 + liklihood_error = error + if error_type == 2: # Multiplicative + liklihood_error *= fitted_value + liklihood_ += liklihood_error**2 + avg_mean_sq_err_ /= n_timepoints + liklihood_ = n_timepoints * np.log(liklihood_) + k_ = ( + seasonal_period * (seasonality_type != 0) + + 2 * (trend_type != 0) + + 2 + + 1 * (phi != 1) + ) + aic_ = liklihood_ + 2 * k_ - n_timepoints * np.log(n_timepoints) + return ( + level, + trend, + seasonality, + n_timepoints, + residuals_, + fitted_values_, + avg_mean_sq_err_, + liklihood_, + k_, + aic_, + ) + + +@njit(fastmath=True, cache=True) +def _predict( + trend_type, + seasonality_type, + level, + trend, + seasonality, + phi, + horizon, + n_timepoints, + seasonal_period, ): # Generate forecasts based on the final values of level, trend, and seasonals if phi == 1: # No damping case - phi_h = float(horizon) + phi_h = 1 else: # Geometric series formula for calculating phi + phi^2 + ... + phi^h phi_h = phi * (1 - phi**horizon) / (1 - phi) @@ -263,8 +342,8 @@ def _predict_numba( )[0] -@njit(nogil=NOGIL, cache=CACHE) -def _initialise(trend_type: int, seasonality_type: int, seasonal_period: int, data): +@njit(fastmath=True, cache=True) +def _initialise(trend_type, seasonality_type, seasonal_period, data): """ Initialize level, trend, and seasonality values for the ETS model. @@ -277,12 +356,12 @@ def _initialise(trend_type: int, seasonality_type: int, seasonal_period: int, da # Initial Level: Mean of the first season level = np.mean(data[:seasonal_period]) # Initial Trend - if trend_type == ADDITIVE: + if trend_type == 1: # Average difference between corresponding points in the first two seasons trend = np.mean( data[seasonal_period : 2 * seasonal_period] - data[:seasonal_period] ) - elif trend_type == MULTIPLICATIVE: + elif trend_type == 2: # Average ratio between corresponding points in the first two seasons trend = np.mean( data[seasonal_period : 2 * seasonal_period] / data[:seasonal_period] @@ -291,33 +370,33 @@ def _initialise(trend_type: int, seasonality_type: int, seasonal_period: int, da # No trend trend = 0 # Initial Seasonality - if seasonality_type == ADDITIVE: + if seasonality_type == 1: # Seasonal component is the difference # from the initial level for each point in the first season seasonality = data[:seasonal_period] - level - elif seasonality_type == MULTIPLICATIVE: + elif seasonality_type == 2: # Seasonal component is the ratio of each point in the first season # to the initial level seasonality = data[:seasonal_period] / level else: # No seasonality - seasonality = np.zeros(1) + seasonality = np.zeros(1, dtype=np.float64) return level, trend, seasonality -@njit(nogil=NOGIL, cache=CACHE) +@njit(fastmath=True, cache=True) def _update_states( - error_type: int, - trend_type: int, - seasonality_type: int, - level: float, - trend: float, - seasonality: float, + error_type, + trend_type, + seasonality_type, + level, + trend, + seasonality, data_item: int, - alpha: float, - beta: float, - gamma: float, - phi: float, + alpha, + beta, + gamma, + phi, ): """ Update level, trend, and seasonality components. @@ -338,34 +417,34 @@ def _update_states( trend_type, seasonality_type, level, trend, seasonality, phi ) # Calculate the error term (observed value - fitted value) - if error_type == MULTIPLICATIVE: + if error_type == 2: error = data_item / fitted_value - 1 # Multiplicative error else: error = data_item - fitted_value # Additive error # Update level - if error_type == MULTIPLICATIVE: + if error_type == 2: level = trend_level_combination * (1 + alpha * error) trend = damped_trend * (1 + beta * error) seasonality = curr_seasonality * (1 + gamma * error) - if seasonality_type == ADDITIVE: + if seasonality_type == 1: level += alpha * error * curr_seasonality # Add seasonality correction seasonality += gamma * error * trend_level_combination - if trend_type == ADDITIVE: + if trend_type == 1: trend += (curr_level + curr_seasonality) * beta * error else: trend += curr_seasonality / curr_level * beta * error - elif trend_type == ADDITIVE: + elif trend_type == 1: trend += curr_level * beta * error else: level_correction = 1 trend_correction = 1 seasonality_correction = 1 - if seasonality_type == MULTIPLICATIVE: + if seasonality_type == 2: # Add seasonality correction level_correction *= curr_seasonality trend_correction *= curr_seasonality seasonality_correction *= trend_level_combination - if trend_type == MULTIPLICATIVE: + if trend_type == 2: trend_correction *= curr_level level = trend_level_combination + alpha * error / level_correction trend = damped_trend + beta * error / trend_correction @@ -373,15 +452,8 @@ def _update_states( return (fitted_value, error, level, trend, seasonality) -@njit(nogil=NOGIL, cache=CACHE) -def _predict_value( - trend_type: int, - seasonality_type: int, - level: float, - trend: float, - seasonality: float, - phi: float, -): +@njit(fastmath=True, cache=True) +def _predict_value(trend_type, seasonality_type, level, trend, seasonality, phi): """ Generate various useful values, including the next fitted value. @@ -408,7 +480,7 @@ def _predict_value( """ # Apply damping parameter and # calculate commonly used combination of trend and level components - if trend_type == MULTIPLICATIVE: + if trend_type == 2: # Multiplicative damped_trend = trend**phi trend_level_combination = level * damped_trend else: # Additive trend, if no trend, then trend = 0 @@ -416,8 +488,30 @@ def _predict_value( trend_level_combination = level + damped_trend # Calculate forecast (fitted value) based on the current components - if seasonality_type == MULTIPLICATIVE: + if seasonality_type == 2: # Multiplicative fitted_value = trend_level_combination * seasonality else: # Additive seasonality, if no seasonality, then seasonality = 0 fitted_value = trend_level_combination + seasonality return fitted_value, damped_trend, trend_level_combination + + +def _validate_parameter(var, can_be_none): + valid_str = (ADDITIVE, MULTIPLICATIVE) + valid_int = (1, 2) + if can_be_none: + valid_str = (None, ADDITIVE, MULTIPLICATIVE) + valid_int = (0, 1, 2) + valid = True + if isinstance(var, str) or var is None: + if var not in valid_str: + valid = False + elif isinstance(var, int): + if var not in valid_int: + valid = False + else: + valid = False + if not valid: + raise ValueError( + f"variable must be either string or integer with values" + f" {valid_str} or {valid_int} but saw {var}" + ) diff --git a/aeon/forecasting/tests/test_ets.py b/aeon/forecasting/tests/test_ets.py index ce7513a965..f4f5f86590 100644 --- a/aeon/forecasting/tests/test_ets.py +++ b/aeon/forecasting/tests/test_ets.py @@ -1,27 +1,104 @@ -"""Test ETS forecaster.""" +"""Test ETS.""" +__maintainer__ = [] +__all__ = [] + +import numpy as np import pytest from aeon.forecasting import ETSForecaster -from aeon.testing.data_generation import make_example_1d_numpy - - -def test_ets_params(): - """Test ETS forecaster.""" - y = make_example_1d_numpy(n_timepoints=100) - forecaster = ETSForecaster(error_type=3) - with pytest.raises( - ValueError, match="Error must be either additive or " "multiplicative" - ): - forecaster.fit(y) - forecaster = ETSForecaster(seasonality_type=-3) - forecaster.fit(y) - assert forecaster._seasonal_period == 1 - forecaster = ETSForecaster(trend_type=None, seasonality_type=0, beta=1.0, gamma=1.0) - forecaster.fit(y) - assert forecaster._beta == 0 - assert forecaster._gamma == 0 - - forecaster = ETSForecaster(error_type=2, phi=1.0) - pred = forecaster.forecast(y) - assert isinstance(pred, float) +from aeon.forecasting._ets import _validate_parameter + + +def test_ets_forecaster_additive(): + """TestETSForecaster.""" + data = np.array( + [3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12] + ) # Sample seasonal data + forecaster = ETSForecaster( + alpha=0.5, + beta=0.3, + gamma=0.4, + phi=1, + horizon=1, + error_type="additive", + trend_type="additive", + seasonality_type="additive", + seasonal_period=4, + ) + forecaster.fit(data) + p = forecaster.predict() + assert np.isclose(p, 9.191190608800001) + + +def test_ets_forecaster_mult_error(): + """TestETSForecaster.""" + data = np.array( + [3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12] + ) # Sample seasonal data + forecaster = ETSForecaster( + alpha=0.7, + beta=0.6, + gamma=0.1, + phi=0.97, + horizon=1, + error_type="multiplicative", + trend_type="additive", + seasonality_type="additive", + seasonal_period=4, + ) + forecaster.fit(data) + p = forecaster.predict() + assert np.isclose(p, 16.20176819429869) + + +def test_ets_forecaster_mult_compnents(): + """TestETSForecaster.""" + data = np.array( + [3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12] + ) # Sample seasonal data + forecaster = ETSForecaster( + alpha=0.4, + beta=0.2, + gamma=0.5, + phi=0.8, + horizon=1, + error_type="additive", + trend_type="multiplicative", + seasonality_type="multiplicative", + seasonal_period=4, + ) + forecaster.fit(data) + p = forecaster.predict() + assert np.isclose(p, 12.301259229712382) + + +def test_ets_forecaster_multiplicative(): + """TestETSForecaster.""" + data = np.array( + [3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12] + ) # Sample seasonal data + forecaster = ETSForecaster( + alpha=0.7, + beta=0.5, + gamma=0.2, + phi=0.85, + horizon=1, + error_type="multiplicative", + trend_type="multiplicative", + seasonality_type="multiplicative", + seasonal_period=4, + ) + forecaster.fit(data) + p = forecaster.predict() + assert np.isclose(p, 16.811888294476528) + + +def test_incorrect_parameters(): + _validate_parameter(0, True) + _validate_parameter(None, True) + with pytest.raises(ValueError): + _validate_parameter(0, False) + _validate_parameter(None, True) + _validate_parameter(10, False) + _validate_parameter("Foo", True) From 2932628f3f611e4f756dda72dcb088b6cff5489d Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Tue, 3 Jun 2025 08:52:53 +0100 Subject: [PATCH 129/216] [DOC] Update the projects lists (#2146) * remove Theta * projects * draft * add some papers * update papers * update projects * remove completed projects * remove poorly specified/out of date projects * remove merlin projects * restructure papers by year * papers using aeon * projects list and previous projects * update papers --------- Co-authored-by: MatthewMiddlehurst --- docs/contributing.md | 2 +- docs/index.md | 2 +- docs/mentoring.md | 815 ----------------------------- docs/papers_using_aeon.md | 178 ++++--- docs/projects.md | 488 +++++++++++++++++ docs/projects/previous_projects.md | 112 ++++ 6 files changed, 711 insertions(+), 886 deletions(-) delete mode 100644 docs/mentoring.md create mode 100644 docs/projects.md create mode 100644 docs/projects/previous_projects.md diff --git a/docs/contributing.md b/docs/contributing.md index a090e1d30c..5757759e08 100644 --- a/docs/contributing.md +++ b/docs/contributing.md @@ -171,7 +171,7 @@ Mentoring and Projects +++ -```{button-ref} mentoring +```{button-ref} projects :color: primary :click-parent: :expand: diff --git a/docs/index.md b/docs/index.md index 11b558839e..a4b80ee8f0 100644 --- a/docs/index.md +++ b/docs/index.md @@ -293,7 +293,7 @@ examples.md contributing.md developer_guide.md -mentoring.md +projects.md ``` ```{toctree} diff --git a/docs/mentoring.md b/docs/mentoring.md deleted file mode 100644 index a937b42986..0000000000 --- a/docs/mentoring.md +++ /dev/null @@ -1,815 +0,0 @@ - -# Mentoring and Projects - -`aeon` runs a range of short to medium duration projects interacting with the community -and the code -base. These projects are designed for internships, usage as part of -undergraduate/postgraduate projects at academic institutions, and as options for -programs such as [Google Summer of Code (GSoC)](https://summerofcode.withgoogle.com/). - -For those interested in undertaking a project outside these scenarios, we recommend -joining the [Slack](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-22vwvut29-HDpCu~7VBUozyfL_8j3dLA) -and discussing with the project mentors. We aim to run schemes to -help new contributors to become more familiar with `aeon`, time series machine learning -research, and open-source software development. - -All the projects listed will require knowledge of Python 3 and Git/GitHub. The -majority of them will require some knowledge of machine learning and time series. - -## Current aeon projects - -This is a list of some of the projects we are interested in running in 2024. Feel -free to propose your own project ideas, but please discuss them with us first. We have -an active community of researchers and students who work on `aeon`. Please get in touch -via Slack if you are interested in any of these projects or have any questions. - -We will more widely advertise funding opportunities as and when they become available. - -Most projects can be extended, possibly into a research project that may lead to -publication. These projects are for anyone, from core devs to those completely new -to open source. We list projects by time series task - -[Classification](#classification) -1. Optimizing the Shapelet Transform for classification and similarity search -2. EEG classification with aeon-neuro (Listed for GSoC 2024) -3. Improved Proximity Forest for classification (listed for GSoC 2024) -4. Improved HIVE-COTE implementation. -5. Compare distance based classification. - -[Forecasting](#forecasting) -1. Machine Learning for Time Series Forecasting (listed in GSoC 2024) -2. Deep Learning for Time Series Forecasting -3. Implement ETS forecasters in aeon - -[Clustering](#clustering) -1. Density peaks clustering algorithm -2. Deep learning based clustering algorithms - -[Anomaly Detection](#anomaly-detection) -1. Anomaly detection with the Matrix Profile and MERLIN - -[Segmentation](#segmentation) -1. Time series segmentation - -[Transformation](#transformation) -1. Improve ROCKET family of transformers -2. Implement channel selection algorithms - -[Visualisation](#visualisation) -1. Explainable AI with the shapelet transform (Southampton intern project). - -[Regression](#regression) -1. Adapt forecasting regressors to time series extrinsic regression. -2. Adapt HIVE-COTE for regression. - -[Documentation](#documentation) -1. Improve automated API documentation - -### Classification - -#### 1. Optimizing the Shapelet Transform for Classification and Similarity Search (listed for GSoC 2024) - -Mentors : Antoine Guillaume ({user}`baraline`) and Tony Bagnall ({user}`TonyBagnall`) - -##### Related Issues -[#186](https://github.com/aeon-toolkit/aeon/issues/186) -[#324](https://github.com/aeon-toolkit/aeon/issues/324) -[#894](https://github.com/aeon-toolkit/aeon/issues/894) -[#973](https://github.com/aeon-toolkit/aeon/issues/973) -[#1184](https://github.com/aeon-toolkit/aeon/issues/1184) -[#1322](https://github.com/aeon-toolkit/aeon/issues/1322) - - -##### Description - -A shapelet is defined as a time series subsequence representing a pattern of interest -that we wish to search for in time series data. Shapelet-based algorithms can be used -for a wide range of time series tasks. In this project, we will focus on its core -application, which is to create an embedding of the input time series. - -Our goal in this project will be to optimize the code related to the shapelet -transform method, which takes as input a set of shapelets and a time series dataset, -and give as output a tabular dataset containing the features characterizing the -presence (or absence) of each shapelet in the input time series (more information -in [1] and [2]). - -Similarity search is another field of time series, which has proposed greatly optimized -algorithms (see [3] and [4]) for the task of finding the best matches of a subsequence -inside another time series. As this task is extremely similar to what is done in the -shapelet transform, we want to adapt these algorithms to the context of shapelets, -in order to achieve significant speed-ups. - -##### Project stages - -To achieve this goal, with the assistance of the mentor, we identify the following -steps for the mentee: - -1. Learn about aeon best practices, coding standards and testing policies. -2. Study the shapelet transform algorithm and how it is related to the task of -similarity search. -3. Study the similarity search algorithms for the Euclidean distance and the -computational optimization they use. -4. Propose a candidate implementation for to increase the performance of the -computations made by a single shapelet. This can be made with the help of the existing -implementation of the similarity search module in `aeon`. -5. Measure the performance of this first candidate implementation against the current -approach. -6. Implement this solution to the shapelet transform algorithm, which uses multiple -shapelets. -7. Benchmark the implementation against the original shapelet transform algorithm. -8. If time, generalize this new algorithm to the case of dilated shapelets (see [5]). - -##### Expected Outcomes - -We expect the mentee to engage with the aeon community and produce a more performant -implementation for the shapelet transform that gets accepted into the toolkit. - -##### References - -1. Hills, J., Lines, J., Baranauskas, E., Mapp, J. and Bagnall, A., 2014. -Classification of time series by shapelet transformation. Data mining and knowledge -discovery, 28, pp.851-881. -2. Bostrom, A. and Bagnall, A., 2017. Binary shapelet transform for multiclass time -series classification. Transactions on Large-Scale Data-and Knowledge-Centered Systems -XXXII: Special Issue on Big Data Analytics and Knowledge Discovery, pp.24-46. -3. Yeh, C.C.M., Zhu, Y., Ulanova, L., Begum, N., Ding, Y., Dau, H.A., Silva, D.F., -Mueen, A. and Keogh, E., 2016, December. Matrix profile I: all pairs similarity joins -for time series: a unifying view that includes motifs, discords and shapelets. In 2016 -IEEE 16th international conference on data mining (ICDM) (pp. 1317-1322). Ieee. -4. Zhu, Y., Zimmerman, Z., Senobari, N.S., Yeh, C.C.M., Funning, G., Mueen, A., Brisk, -P. and Keogh, E., 2016, December. Matrix profile ii: Exploiting a novel algorithm and -gpus to break the one hundred million barrier for time series motifs and joins. In 2016 -IEEE 16th international conference on data mining (ICDM) (pp. 739-748). IEEE. -5. Guillaume, A., Vrain, C. and Elloumi, W., 2022, June. Random dilated shapelet -transform: A new approach for time series shapelets. In International Conference on -Pattern Recognition and Artificial Intelligence (pp. 653-664). Cham: Springer -International Publishing. - -#### 2. EEG classification with aeon-neuro (Listed for GSoC 2024) - -Mentors: Tony Bagnall ({user}`TonyBagnall`) and Aiden Rushbrooke - -##### Related Issues -[#18](https://github.com/aeon-toolkit/aeon-neuro/issues/18) -[#19](https://github.com/aeon-toolkit/aeon-neuro/issues/19) -[#24](https://github.com/aeon-toolkit/aeon-neuro/issues/24) - - - -##### Description - -EEG (Electroencephalogram) data are high dimensional time series that are used in -medical, psychology and brain computer interface research. For example, EEG are -used to detect epilepsy and to control devices such as mice. There is a huge body -of work on analysing and learning from EEG, but there is a wide disparity of -tools, practices and systems used. This project will help members of the `aeon` -team who are currently researching techniques for EEG classification [1] and -developing an aeon sister toolkit, [``aeon-neuro``](https://github.com/aeon-toolkit/aeon-neuro). We will work together to -improve the structure and documentation for aeon-neuro, help integrate the -toolkit with existing EEG toolkits such as MNE [2], provide interfaces to standard data -formats such as BIDS [3] and help develop and assess a range of EEG classification -algorithms. - -##### Project stages - -1. Learn about aeon best practices, coding standards and testing policies. -2. Study the existing techniques for EEG classification. -3. Implement or wrap standard EEG processing algorithms. -4. Evaluate aeon classifiers for EEG problems. -5. Implement alternatives transformations for preprocessing EEG data. -6. Help write up results for a technical report/academic paper (depending on outcomes). - -##### Expected Outcomes - -We would expect a better documented and more integrated aeon-neuro toolkit with -better functionality and a wider appeal. - -##### References - -1. Aiden Rushbrooke, Jordan Tsigarides, Saber Sami, Anthony Bagnall, -Time Series Classification of Electroencephalography Data, IWANN 2023. -2. MNE Toolkit, https://mne.tools/stable/index.html -3. The Brain Imaging Data Structure (BIDS) standard, https://bids.neuroimaging.io/ - -#### 3. Improved Proximity Forest for classification (listed for GSoC 2024) - -Mentors: Matthew Middlehurst ({user}`MatthewMiddlehurst`) and Tony Bagnall -({user}`TonyBagnall`) - -##### Related Issues -[#159](https://github.com/aeon-toolkit/aeon/issues/159) -[#428](https://github.com/aeon-toolkit/aeon/issues/428) - - -##### Description - -Distance-based classifiers such as k-Nearest Neighbours are popular approaches to time -series classification. They primarily use elastic distance measures such as Dynamic Time -Warping (DTW) to compare two series. The Proximity Forest algorithm [1] is a -distance-based classifier for time series. The classifier creates a forest of decision -trees, where the tree splits are based on the distance between time series using -various distance measures. A recent review of time series classification algorithms [2] -found that Proximity Forest was the most accurate distance-based algorithm of those -compared. - -`aeon` previously had an implementation of the Proximity Forest algorithm, but it was -not as accurate as the original implementation (the one used in the study) and was -unstable on benchmark datasets. The goal of this project is to significantly overhaul -the previous implementation or completely re-implement Proximity Forest in `aeon` to -match the accuracy of the original algorithm. This will involve comparing against the -authors' Java implementation of the algorithm as well as alternate Python versions. -The mentors will provide results for both for alternative methods. While knowing -Java is not a requirement for this project, it could be beneficial. - -Recently, the group which published the algorithm has proposed a new version of the -Proximity Forest algorithm, Proximity Forest 2.0 [3]. This algorithm is more accurate -than the original Proximity Forest algorithm, and does not currently have an -implementation in `aeon` or elsewhere in Python. If time allows, the project could also -involve implementing and evaluating the Proximity Forest 2.0 algorithm. - -##### Project stages - -1. Learn about `aeon` best practices, coding standards and testing policies. -2. Study the Proximity Forest algorithm and previous `aeon` implementation. -3. Improve/re-implement the Proximity Forest implementation in `aeon`, with -the aim being to have an implementation that is as accurate as the original algorithm, -while remaining feasible to run. -4. Evaluate the improved implementation against the original `aeon` Proximity Forest -and the authors' Java implementation. -5. If time, implement the Proximity Forest 2.0 algorithm and repeat the above -evaluation. - -##### Expected Outcomes - -We expect the mentee engage with the aeon community and produce a high quality -implementation of the Proximity Forest algorithm(s) that gets accepted into the toolkit. - -##### References - -1. Lucas, B., Shifaz, A., Pelletier, C., O’Neill, L., Zaidi, N., Goethals, -B., Petitjean, F. and Webb, G.I., 2019. Proximity forest: an effective and scalable -distance-based classifier for time series. Data Mining and Knowledge Discovery, 33(3), -pp.607-635. -2. Middlehurst, M., Schäfer, P. and Bagnall, A., 2023. Bake off redux: a review and -experimental evaluation of recent time series classification algorithms. arXiv preprint -arXiv:2304.13029. -3. Herrmann, M., Tan, C.W., Salehi, M. and Webb, G.I., 2023. Proximity Forest 2.0: A -new effective and scalable similarity-based classifier for time series. arXiv -preprint arXiv:2304.05800. - -#### 4. Improved HIVE-COTE implementation - -Mentors: Matthew Middlehurst ({user}`MatthewMiddlehurst`) and Tony Bagnall -({user}`TonyBagnall`) - -##### Related Issues -[#663](https://github.com/aeon-toolkit/aeon/issues/663) - -##### Description - -The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) [1,2,3] -is a time series classifier that has claims to be state-of-the-art, -particularly in terms of probabilistic estimates [4]. There have been several iterations -that use different base classifiers but they all share the same design basic: -classifiers using different representations are combined in a weighted meta-ensemble -[4]. There are two HIVE-COTE implementations currently in `aeon`: HIVE-COTEV1 [2] -and HIVECOTEV2 [3]. This project will involve combining these into a single -estimator, modularising the ensemble stage and possibly experimenting with -alternative structures. This can easily develop into a research project. - -##### Project stages - -1. Learn about `aeon` best practices, coding standards and testing policies. -2. Study the HIVE-COTE algorithm and previous `aeon` implementation. -3. Combine the HIVE-COTEV1 and HIVE-COTEV2 classifiers into a single classifier - configurable into different versions through the constructor. -4. Restructure the ensemble stage to allow easy experimentation with variants. - -##### References - -1. A Bagnall, J Lines, J Hills, A Bostrom, Time-series classification with COTE: the - collective of transformation-based ensembles, IEEE Transactions on Knowledge and - Data Engineering 27 (9), 2522-2535 -2. M Middlehurst, J Large, M Flynn, J Lines, A Bostrom, A Bagnall, HIVE-COTE 2.0: a - new meta ensemble for time series classification, Machine Learning 110 (11), - 3211-3243 -3. J Lines, S Taylor, A Bagnall, Time series classification with HIVE-COTE: The - hierarchical vote collective of transformation-based ensembles, ACM Transactions - on Knowledge Discovery from Data (TKDD) 12 (5), 1-35 -4. Middlehurst, M., Schäfer, P. and Bagnall, A., 2023. Bake off redux: a review and -experimental evaluation of recent time series classification algorithms. arXiv preprint -arXiv:2304.13029. - -#### 5. Compare distance based classification and regression - -Mentors: Chris Holder ({user}`cholder`) and Tony Bagnall -({user}`TonyBagnall`) - -##### Related Issues -[#423](https://github.com/aeon-toolkit/aeon/issues/423) -[#424](https://github.com/aeon-toolkit/aeon/issues/424) -[#425](https://github.com/aeon-toolkit/aeon/issues/425) -[#426](https://github.com/aeon-toolkit/aeon/issues/426) -[#427](https://github.com/aeon-toolkit/aeon/issues/427) -[#488](https://github.com/aeon-toolkit/aeon/issues/488) - -##### Description -Distance based algorithms are popular for time series classification and regression. -However, the evaluation of distance functions for classification have not -comprehensively covered all possible uses. For example, there has not been a proper -bake off for using elastic distance with support vector machines or with tuning -distance functions and classifiers in combination. This project will combine -implementing alternative distance functions and comparing performance on the UCR -datasets. - - -### Forecasting - -#### 1. Machine Learning for Time Series Forecasting (listed in GSoC 2024) - -Mentors: Tony Bagnall ({user}`TonyBagnall`) and Matthew Middlehurst (@MatthewMiddlehurst). - -##### Related Issues -[#265](https://github.com/aeon-toolkit/aeon/issues/265) - - -##### Description - -This project will investigate algorithms for forecasting based on traditional machine -learning (tree based) and time series machine learning (transformation based). Note -this project will not involve deep learning based forecasting. It will involve -helping develop the `aeon` framework to work more transparently with ML algorithms, -evaluating regression algorithms already in `aeon`[1] for forecasting problems and -implementing at least one algorithm from the literature not already in aeon, such as -SETAR-Tree [3]. - -##### Project Stages -1. Learn about aeon best practices, coding standards and testing policies. -2. Adapt the M competition set up [2] for ML experimental framework to assess time - series regression algorithms [1]. -3. Implement a machine learning forecasting algorithm [3] - -##### Expected Outcomes - -1. Contributions to the aeon forecasting module. -2. Implementation of a machine learning forecasting algorithms. -3. Help write up results for a technical report/academic paper (depending on outcomes). - -##### Skills Required - -1. Python 3 -2. Git and GitHub -3. Some machine learning and/or forecasting background (e.g. taught courses or - practical experience) - -##### References - -1. Guijo-Rubio, D.,Middlehurst, M., Arcencio, G., Furtado, D. and Bagnall, A. -Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression, -arXiv2305.01429, 2023 -2. https://forecasters.org/resources/time-series-data/ -3. Godahewa, R., Webb, G.I., Schmidt, D. et al. SETAR-Tree: a novel and accurate -tree algorithm for global time series forecasting. Mach Learn 112, 2555–2591 (2023). -https://link.springer.com/article/10.1007/s10994-023-06316-x - -#### 2. Deep Learning for Time Series Forecasting - -Mentors: Tony Bagnall ({user}`TonyBagnall`) and Ali Ismail-Fawaz ({user} -`hadifawaz1999`) - -##### Description - -Deep learning has become incredibly popular for forecasting, see [1] for an -introduction. This project will involve taking one or more recently proposed -algorithms, implementing them in aeon, then performing an extensive experimental -comparison against traditional and machine learning algorithms. As part of this, we -will collate results from the M Competitions [2] - -##### Project Stages -1. Learn about aeon best practices, coding standards and testing policies. -2. Adapt the M competition set up [2] for deep learning. -3. Implement a deep learning forecasting algorithm after discussion with mentors. - -##### Expected Outcomes - -1. Collated M competition results and partial reproduction. -2. Extend the forecasting module to include at least one deep forecaster. - -##### References - -1. [ECML 2024 Tutorial](https://lovvge.github.io/Forecasting-Tutorial-ECML-2023/) -2. [M Competitions](https://forecasters.org/resources/time-series-data/) - - -#### 3. Implement ETS forecasters - -Mentors: Tony Bagnall ({user}`TonyBagnall`) and Leo Tsaprounis ({user}`ltsaprounis`) -Exponential smoothing (ETS) is a popular family of algorithms for forecasting, and -the ETS framework by Hyndman et al. [1] covers 30 possible models for time series -with different types of Error, Trend, and Seasonal components. -we already have an (Auto)ETS model in aeon, but it’s wrapping statsmodels. We would -like our own bespoke, optimised implementation based on the R implementation. - -##### Project Stages -1. Learn about aeon best practices, coding standards and testing policies. -2. Survey and benchmark existing implementations of ETS forecasting. -3. Implement basic implementations optimised for numba. -4. Extended implementation to include modern refinements. - - -##### References - -1. Hydman et al. [Forecasting with Exponential Smoothing The State Space Approach](https://link.springer.com/book/10.1007/978-3-540-71918-2) -2. [Smooth R Package](https://github.com/config-i1/smooth) -3. Svetunkov, [Forecasting and Analytics with the Augmented Dynamic Adaptive Model - (ADAM)](https://openforecast.org/adam/) - -### Clustering - -#### 1. Density peaks clustering algorithm - -Mentors: Tony Bagnall ({user}`TonyBagnall`) and Chris Holder (`@chrisholder`). - -##### Description - -The clustering module in `aeon`, up until now, clusters using time series -specific distance functions with partitional clustering algorithms such as k-means and -k-medoids. An alternative clustering algorithm is density peaks (DP) [1]. This -clustering -algorithm has the benefit of not having to label all cases as cluster members, which -means it can easily be adapted to anomaly detection [2]. It is -a general purpose clustering algorithm that is not available in scikit learn. This -project will implement the algorithm based on Java and matlab implementations then -compare performance against partitional clustering for time series clustering. - -##### Project Stages - -1. Research and understand how DP works. -2. Implement DP as an aeon estimator. -3. Test the implementation against other implementations for correctness. -4. Compare against alternative TSCL algorithms - -##### Expected Outcomes - -1. A well documented, tested and efficient implementation of DP -2. Possible extensions to reflect recent research [2] with specific time series - components [3]. -2. Contributions to a comparative study and paper. - -##### References -1. Rodriguez, A., & Laio, A. Clustering by Fast Search and Find of -Density Peaks. [Science](https://www.science.org/doi/10.1126/science.1242072), 344 - (6191), 1492-1496, 2014. -2. Chen, L., Gao, S. & Liu, B. An improved density peaks clustering algorithm -based on grid screening and mutual neighborhood degree for network anomaly detection. -Sci Rep 12, 1409 (2022) [DOI](https://doi.org/10.1038/s41598-021-02038-z) -3. Begum et al. A General Framework for Density Based Time Series Clustering - Exploiting a Novel Admissible Pruning Strategy, [arXiv](https://arxiv.org/ftp/arxiv/papers/1612/1612.00637.pdf) - - -#### 2. Deep learning for clustering - -Mentors: Tony Bagnall ({user}`TonyBagnall`) and Ali Ismail-Fawaz ({user} -`hadifawaz1999`) - -The clustering module in `aeon`, up until now, primarily consists of distance-based -partitional clustering algorithms. Recently, we introduced a deep clustering module, -incorporating distance-based algorithms in the latent space. - -The objective of this project is to enhance `aeon` by incorporating more deep learning -approaches for time series clustering. The specific goal is to implement and assess -InceptionTime [1] and its recent variants as a clustering algorithm, and contribute to -an ongoing collaborative effort into a bake off for clustering. More widely, there -are a broad range of deep learning clustering approaches we could consider [2]. - -##### Project Stages - -1. Research and understand clustering time series and deep learning based approaches. -2. Implement inception time as an aeon clusterer. -3. Compare performance of deep learning clusterers to distance based algorithms. - -[1] Fawaz et al. InceptionTime: Finding AlexNet for time series classification -Published: 07 September 2020 Volume 34, pages 1936–1962, (2020) -[2] Deep learning forecasting [tutorial](https://lovvge.github.io/Forecasting-Tutorial-ECML-2023/) - -### Anomaly detection - - -#### 1. Anomaly detection with the Matrix Profile and MERLIN - -Mentors: Matthew Middlehurst ({user}`MatthewMiddlehurst`) - -##### Description - -`aeon` is looking to extend its module for time series anomaly detection. The -end goal of this project is to implement the Matrix Profile [1][2] and MERLIN [3] -algorithms, but suitable framework for anomaly detection in `aeon` will need to be -designed first. The mentee will help design the API for the anomaly detection module -and implement the Matrix Profile and MERLIN algorithms. - -Usage of external libraries such as `stumpy` [4] is possible for the algorithm -implementations, or the mentee can implement the algorithms from scratch using `numba`. -There is also scope to benchmark the implementations, but as there is no existing -anomaly detection module in `aeon`, this will require some infrastructure to be -developed and is subject to time and interest. - -##### Project stages - -1. Learn about `aeon` best practices, coding standards and testing policies. -2. Familiarise yourself with similar single series experimental modules in `aeon` such -as segmentation and similarity search. -3. Help design the API for the anomaly detection module. -4. Study and implement the Matrix Profile for anomaly detection and MERLIN algorithms -using the new API. -5. If time allows and there is interest, benchmark the implementations against the -original implementations or other anomaly detection algorithms. - -##### Project Outcome - -As the anomaly detection is a new module in `aeon`, there is very little existing code -to compare against and little infrastructure to evluate anomaly detection algorithms. -The success of the project will be evaluated by the quality of the code produced and -engagement with the project and the `aeon` community. - -##### References - -1. Yeh, C.C.M., Zhu, Y., Ulanova, L., Begum, N., Ding, Y., Dau, H.A., Silva, D.F., -Mueen, A. and Keogh, E., 2016, December. Matrix profile I: all pairs similarity joins -for time series: a unifying view that includes motifs, discords and shapelets. In 2016 -IEEE 16th international conference on data mining (ICDM) (pp. 1317-1322). Ieee. -2. Lu, Y., Wu, R., Mueen, A., Zuluaga, M.A. and Keogh, E., 2022, August. -Matrix profile XXIV: scaling time series anomaly detection to trillions of datapoints -and ultra-fast arriving data streams. In Proceedings of the 28th ACM SIGKDD Conference -on Knowledge Discovery and Data Mining (pp. 1173-1182). -3. Nakamura, T., Imamura, M., Mercer, R. and Keogh, E., 2020, November. Merlin: -Parameter-free discovery of arbitrary length anomalies in massive time series archives. -In 2020 IEEE international conference on data mining (ICDM) (pp. 1190-1195). IEEE. -4. Law, S.M., 2019. STUMPY: A powerful and scalable Python library for time series data -mining. Journal of Open Source Software, 4(39), p.1504. - -### Segmentation - -#### 1. Time series segmentation - -Mentors: Tony Bagnall ({user}`TonyBagnall`) and TBC - -##### Description - -The time series segmentation module contains a range of algorithms for segmenting time -series. The goal of this project is to extend the functionality of segmentation in -`aeon` and develop tools for comparing segmentation algorithms. - -##### Project stages - -1. Learn about `aeon` best practices, coding standards and testing policies. -2. Study the existing segmentation algorithms in `aeon`. -3. Implement existing segmentation algorithms, e.g. -https://github.com/aeon-toolkit/aeon/issues/948 -4. Implement tools for comparing segmentation algorithms -5. Conduct a bake off of segmentation algorithms on a range of datasets. - -##### Project Outcome - -As with all research programming based projects, progress can be hindered by many -unforseen circumstances. Success will be measured by engagement, effort and -willingness to join the community rather than performance of the algorithms. - -##### References - -1. Allegra, M., Facco, E., Denti, F., Laio, A. and Mira, A., 2020. Data segmentation -based on the local intrinsic dimension. Scientific Reports, 10(1), p.16449. -2. Ermshaus, A., Schäfer, P. and Leser, U., 2023. ClaSP: parameter-free time series -segmentation. Data Mining and Knowledge Discovery, 37(3), pp.1262-1300. -3. Hallac, D., Nystrup, P. and Boyd, S., 2019. Greedy Gaussian segmentation of -multivariate time series. Advances in Data Analysis and Classification, 13(3), -pp.727-751. -4. Matteson, D.S. and James, N.A., 2014. A nonparametric approach for multiple change -point analysis of multivariate data. Journal of the American Statistical Association, -109(505), pp.334-345. -5. Sadri, A., Ren, Y. and Salim, F.D., 2017. Information gain-based metric for -recognizing transitions in human activities. Pervasive and Mobile Computing, 38, -pp.92-109. - -### Transformation - -#### 1. Improve ROCKET family of transformers - -Mentors: Ali Ismail-Fawaz ({user}`hadifawaz1999`) and Matthew Middlehurst -({user}`MatthewMiddlehurst`) -[#208](https://github.com/aeon-toolkit/aeon/issues/208) -[#214](https://github.com/aeon-toolkit/aeon/issues/214) -[#313](https://github.com/aeon-toolkit/aeon/issues/313) -[#1126](https://github.com/aeon-toolkit/aeon/issues/1126) -[#1248](https://github.com/aeon-toolkit/aeon/issues/1248) - - -##### Description - -The ROCKET algorithm [1] is a very fast and accurate transformation designed for time -series classification. It is based on a randomly initialised convolutional kernels that -are applied to the time series and used to extract summary statistics. ROCKET has -applications to time series classification, extrinsic regression and anomaly detection, -but as a fast and unsupervised transformation, it has potential to a wide range of -other time series tasks. - -`aeon` has implementations of the ROCKET transformation and its variants, including -MiniROCKET [2] and MultiROCKET [3]. However, these implementations have room for -improvement ([#208](https://github.com/aeon-toolkit/aeon/issues/208)). There is scope -to speed up the implementations, and the amount of varients is likely unnecessary and -could be condensed into higher quality estimators. - -This projects involves improving the existing ROCKET implementations in `aeon` or -implementing new ROCKET variants. The project will involve benchmarking to ensure that -the new implementations are as fast and accurate as the original ROCKET algorithm and -potentially to compare to other implementations ([#214](https://github.com/aeon-toolkit/aeon/issues/214)). -Besides improving the existing implementations, there is scope to implement the HYDRA -algorithm [4] or implement GPU compatible versions of the algorithms. - -##### Project Stages - -1. Learn about `aeon` best practices, coding standards and testing policies. -2. Study the ROCKET, MiniROCKET, MultiROCKET algorithms. -3. Study the existing ROCKET implementations in `aeon`. -4. Merge and tidy the ROCKET implementations, with the aim being to familiarise the -mentee with the `aeon` pull request process. -5. Implement one (or more) of the proposed ROCKET implementation improvements: - * Significantly alter the current ROCKET implementations with the goal of - speeding up the implementation on CPU processing. - * Implement a GPU version of some of the ROCKET transformers, using either - `tensorflow` or `pytorch`. - * Extend the existing ROCKET implementations to allow for the use of unequal length - series. - * Implement the HYDRA algorithm. -6. Benchmark the implementation against the original ROCKET implementations, looking at -booth speed of the transform and accuracy in a classification setting. - -##### Project Outcomes - -Success of the project will be assessed by the quality of the code produced and an -evaluation of the transformers in a classification setting. None of the implementations -should significantly degrade the performance of the original ROCKET algorithm in terms -of accuracy and speed. Regardless, effort and engagement with the project and the -`aeon` community are more important factors in evaluating success. - -##### References - -1. Dempster, A., Petitjean, F. and Webb, G.I., 2020. ROCKET: exceptionally fast and -accurate time series classification using random convolutional kernels. -Data Mining and Knowledge Discovery, 34(5), pp.1454-1495. -2. Dempster, A., Schmidt, D.F. and Webb, G.I., 2021, August. Minirocket: A very fast -(almost) deterministic transform for time series classification. In Proceedings of the -27th ACM SIGKDD conference on knowledge discovery & data mining (pp. 248-257). -3. Tan, C.W., Dempster, A., Bergmeir, C. and Webb, G.I., 2022. MultiRocket: multiple -pooling operators and transformations for fast and effective time series classification. -Data Mining and Knowledge Discovery, 36(5), pp.1623-1646. -4. Dempster, A., Schmidt, D.F. and Webb, G.I., 2023. Hydra: Competing convolutional -kernels for fast and accurate time series classification. Data Mining and Knowledge -Discovery, pp.1-27. - -#### 2. Implement channel selection algorithms - -Related issues: -[#1270](https://github.com/aeon-toolkit/aeon/issues/1270) -[#1467](https://github.com/aeon-toolkit/aeon/issues/1467) - -Channel selection in this context is the process of reducing the number of channels -in a collection of time series for classification, clustering or regression. This -project looks at filter based approaches to speed up multivariate time series -classification (MTSC) of high dimensional series. Standard approaches for -classifying high dimensional data are to -employ a filter to select a subset of attributes or to transform the data into a lower -dimensional feature space using, for example, principal component analysis. Our -focus is on dimensionality reduction through filtering. For MTSC, filtering is -generally accepted to be selecting the most important dimensions to use before -training the classifier. Dimension selection can, on average, either increase, not -change or decrease the accuracy of classification. The first case implies that the -higher dimensionality is confounding the classifier’s discriminatory power. In the -second case it is often still desirable to filter due to improved training time. In -the third case, filtering may still be desirable, depending on the trade-off between -performance (e.g. accuracy) and efficiency (e.g. train time): a small reduction in -accuracy may be acceptable if build time reduces by an order of magnitude. We -address the task of how best to select a subset of dimensions for high dimensional -data so that we can speed up and possibly improve HC2 on high dimensional -MTSC problems. -Detecting the best subset of dimensions is not a straightforward problem, -since the number of combinations to consider increases exponentially with the -number of dimensions. Selection is also made more complex by the fact that -the objective function used to assess a set of features may not generalise well -to unseen data. Furthermore, since the primary reason for filtering the dimensions -is improving the efficiency of the classifier, dimension selection strategies -themselves need to be fast. - -Currently we have the channel selection algorithms describe in [1,2] in aeon. It would -be great to include those in [3] and further work. This project will involve -experimental evaluation in addition to implementing -algorithms. We can co-ordinate the experiments with the candidate through our HPC -facilities. - -1. Implement a channel selection wrapper for the aeon toolkit (see [#1270](https://github.com/aeon-toolkit/aeon/issues/1270)) -2. Explore alternative ways of selecting channels after scoring (e.g. forward selection) -3. Use a fast classifier that can find train estimates through e.g. bagging and avoid the cross validation -4. Research, implement and evaluate alternative channel selection algorithms - -##### References -[1] Dhariyal, B. et al. Fast Channel Selection for Scalable Multivariate Time -Series Classification. AALTD, ECML-PKDD, Springer, 2021 -[2] Dhariyal, B. et al. Scalable Classifier-Agnostic Channel Selection - for Multivariate Time Series Classification", DAMI, 2023 -[3] Ruiz, A.P., Bagnall, A. Dimension Selection Strategies for Multivariate - Time Series Classification with HIVE-COTEv2.0. AALTD,ECML-PKDD 2022. - (https://doi.org/10.1007/978-3-031-24378-3_9) - -### Visualisation - -#### 1. Explainable AI with the shapelet transform (Southampton intern project). - -Mentors: TonyBagnall ({user}`TonyBagnall`) and David Guijo-Rubio -({user}`dguijo`) - -This project will focus on explainable AI for time series classification (TSC) [1], -specifically the family of algorithms based on shapelets [2]. Shapelets are small sub - certain shape of heartbeat, perhaps a short irregularity, might be useful in -predicting the medical condition. We will look at the shapelet transform classifier -[3]. This finds a large set of shapelets from the training data and uses them to -build a classifier. We want to develop tools to help us visualise the output of the -search for good shapelets to help explain why predictions are made. This project is -not tied to a specific data set. It is to develop tools to help any user of the -toolkit. It will involve learning about aeon and making contributions to open -source toolkits, familiarisation with the shapelet code and the development of a -visualisation tool to help relate shapelets back to the training data. An outline -for the project is - -Weeks 1-2: Familiarisation with open source, aeon and the visualisation module. Make -contribution for a good first issue. -Weeks 3-4: Understand the shapelet transfer algorithm, engage in ongoing discussions -for possible improvements, run experiments to create predictive models for a test data set -Weeks 5-6: Design and prototype visualisation tools for shapelets, involving a range -of summary measures and visualisation techniques, including plotting shapelets on training data, calculating frequency, measuring similarity between -Weeks 7-8: Debug, document and make PRs to merge contributions into the aeon toolkit. - -[1] Bagnall, A., Lines, J., Bostrom, A., Large, J. and Keogh, E. The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Mining and Knowledge Discovery, Volume 31, pages 606–660, (2017) -[2] Ye, L., Keogh, E. Time series shapelets: a novel technique that allows accurate, interpretable and fast classification. Data Min Knowl Disc 22, 149–182 (2011). https://doi.org/10.1007/s10618-010-0179-5 -[3] Lines, L., Davis, L., Hills, J. and Bagnall, A. A shapelet transform for time series classification, KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (2012) https://doi.org/10.1145/2339530.2339579 - -### Regression - -#### 1. Adapt forecasting regressors to time series extrinsic regression. - -Mentors: TonyBagnall ({user}`TonyBagnall`) and David Guijo-Rubio -({user}`dguijo`) - -Forecasting is often reduced to regression through the application of a sliding -window. This is a large research field that is distinct to time series extrinsic -regression, where each series is assumed to be independent. This is more of a -research project to investigate what techniques are used in forecasting for -regression based forecasting and to compare them to the time series specific -algorithms in aeon. This project would require further working up with the mentors. - - -#### 2. Adapt HIVE-COTE for regression - -Mentors: TonyBagnall ({user}`TonyBagnall`) and David Guijo-Rubio -({user}`dguijo`) - -HIVE-COTE [1] is a state of the art classifier. Adapting it for regression is an -ongoing research project for which we would welcome collaborators. Ongoing, this -needs working up. - - -### Documentation - -#### 1. Improve automated API documentation - -Mentors: Matthew Middlehurst ({user}`MatthewMiddlehurst`) - -##### Description - -`aeon` uses `sphinx` and `numpydoc` to generate API documentation from docstrings. -Many of the docstrings are incomplete or missing sections, and could be improved to -make the API documentation more useful. The goal of this project is to generally -improve the API documentation. A specific goal is to automatically generate links to -examples which use the function/class, similar to the `scikit-learn` documentation. -The way this is achieved is up to the mentee, but should include a new section in the -relevant API page. I.e., the API page for -`aeon.transformers.collection.convolution_based.Rocket` should have a section called -"Examples" which links to the examples which use the class (such as the Rocket -notebook). - -##### Project Stages - -1. Learn about `aeon` best practices and project documentation. -2. Familiarise with `sphinx` documentation generation and `numpydoc` docstring -standards. -3. Improve the API documentation for a few classes/functions and go through the Pull -Request and review process. -4. Implement a function or improve the API template to automatically generate links -to examples which use the function/class. -5. The main bulk of work is done, but the API documentation is vast and can always be -improved! If time allows, continue to enhance the API documentation through individual -docstrings, API landing page and template improvements at the mentees discretion. - -##### Project Outcomes - -Success of the project will be assessed by the quality of the documentation produced -and engagement with the project and the `aeon` community. Automatically generating -links to examples is the primary goal. diff --git a/docs/papers_using_aeon.md b/docs/papers_using_aeon.md index 30dba0fd44..ee2c142f22 100644 --- a/docs/papers_using_aeon.md +++ b/docs/papers_using_aeon.md @@ -1,87 +1,127 @@ -# Papers using Aeon + +[//]: # (Try to put references in harvard style for consistency.) + +# Papers using aeon This is a list of papers that use `aeon`. If you have a paper that uses `aeon`, please add it to this list by making a pull request. Please include a hyperlink to the paper and a link to the code in your personal GitHub or other repository. -## Challenge +If you want to reference `aeon` please reference this paper. -- Ermshaus, A., Schäfer, P., Bagnall, A., Guyet, T., Ifrim, G., Lemaire, V., ... & - Malinowski, S. (2023, September). Human Activity Segmentation Challenge@ ECML/PKDD’23. - In International Workshop on Advanced Analytics and Learning on Temporal Data - (pp. 3-13). Cham: Springer Nature Switzerland. - [Paper](https://link.springer.com/chapter/10.1007/978-3-031-49896-1_1) [Webpage/Code](https://github.com/patrickzib/human_activity_segmentation_challenge) +Middlehurst, M., Ismail-Fawaz, A., Guillaume, A., Holder, C., Guijo-Rubio, D., +Bulatova, G., Tsaprounis, L., Mentel, L., Walter, M., Schäfer, P. and Bagnall, A., 2024. +aeon: a Python toolkit for learning from time series. Journal of Machine Learning 2025. +Research, 25(289), pp.1-10. +[Paper](https://link.springer.com/chapter/10.1007/978-3-031-49896-1_1) -## Classification +## 2025 -- Middlehurst, M. and Schäfer, P. and Bagnall, A. (2024). Bake off redux: a review - and experimental evaluation of recent time series classification algorithms. - Data Mining and Knowledge Discovery, online first, open access. +- Ismail-Fawaz, A., Devanne, M., Berretti, S., Weber, J. and Forestier, G., 2025. + Establishing a unified evaluation framework for human motion generation: A + comparative analysis of metrics. Computer Vision and Image Understanding, 254, + p.104337. + [Paper](https://www.sciencedirect.com/science/article/pii/S1077314225000608) [Code](https://github.com/MSD-IRIMAS/Evaluating-HMG) +- Rewicki, F,Denzler J. and Niebling, J., Anomalous Agreement: How to find the + Ideal Number of Anomaly Classes in Correlated, Multivariate Time Series Data. + arXiv:2501.07172 + [Paper](https://arxiv.org/pdf/2501.07172) [Code](https://gitlab.com/dlr-dw/saai) +- Serramazza, D., Nguyen, T. and Ifrim, G. A short tutorial for multivariate time + series explanation using tsCaptum. Software Impacts, 22. + [Paper](https://doi.org/10.1016/j.simpa.2024.100723) +- +## 2024 + +- Bagnall, A.,Middlehurst, M., Forestier, G., Schäfer, P., Ismail-Fawaz, A., + Guillaume, A., Guijo-Rubio, D., Wei Tan, C., Dempster A. and Webb, G.I. A + hands-on introduction to time series classification and regression. Proceedings of + the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining + [Paper](https://dl.acm.org/doi/abs/10.1145/3637528.3671443) [Webpage/Code](https://aeon-tutorials.github.io/KDD-2024/) +- Dempster, A., Tan, C.W., Miller, L., Foumani, N.M., Schmidt, D.F. and Webb, G.I. + Highly Scalable Time Series Classification for Very Large Datasets. In + International Workshop on Advanced Analytics and Learning on Temporal Data. pp. + 80-95. [Paper](https://ecml-aaltd.github.io/aaltd2024/articles/Dempster_AALTD24.pdf) +- Dempster, A., Schmidt, D.F. and Webb, G.I. Quant: A minimalist interval method + for time series classification. Data Mining and Knowledge Discovery, 38(4), + pp.2377-2402. + [Paper](https://link.springer.com/article/10.1007/s10618-024-01036-9) +- Serramazza, D.I., Nguyen, T.L. and Ifrim, G. Improving the evaluation + and actionability of explanation methods for multivariate time series classification. + In Joint European Conference on Machine Learning and Knowledge Discovery in + Databases, pp. 177-195. + [Paper](https://link.springer.com/chapter/10.1007/978-3-031-70359-1_11) +- Middlehurst, M., Schäfer, P. and Bagnall, A. Bake off redux: a review and + experimental evaluation of recent time series classification algorithms. Data Mining + and Knowledge Discovery, 38(4), pp.1958-2031. [Paper](https://link.springer.com/article/10.1007/s10618-024-01022-1) [Webpage/Code](https://tsml-eval.readthedocs.io/en/stable/publications/2023/tsc_bakeoff/tsc_bakeoff_2023.html) -- Spinnato, F. and Guidotti, R. and Monreale, A. and Nanni, M. (2024). Fast, - Interpretable, and Deterministic Time Series Classification With a - Bag-of-Receptive-Fields. IEEE Access, vol. 12, (pp. 137893-137912). +- Spinnato, F., Guidotti, R., Monreale, A. and Nanni, M. Fast, Interpretable and + Deterministic Time Series Classification with a Bag-Of-Receptive-Fields. IEEE Access. [Paper](https://ieeexplore.ieee.org/document/10684604) [Code](https://github.com/fspinna/borf) -- Schäfer, P, and Leser, U. (2023). WEASEL 2.0: a random dilated dictionary transform - for fast, accurate and memory constrained time series classification. - Machine Learning, 112(12), pp.4763-4788. - [Paper](https://link.springer.com/content/pdf/10.1007/s10994-023-06395-w.pdf) [Webpage/Code](https://github.com/patrickzib/dictionary) - -## Clustering - -- Holder, C., Middlehurst, M. and Bagnall, A., (2024). A review and evaluation of - elastic distance functions for time series clustering. Knowledge and Information - Systems, 66(2), pp.765-809. +- Holder, C., Middlehurst, M. and Bagnall, A. A review and evaluation of elastic + distance functions for time series clustering. Knowledge and Information Systems, + 66(2), pp.765-809. [Paper](https://link.springer.com/article/10.1007/s10115-023-01952-0) [Webpage/Code](https://tsml-eval.readthedocs.io/en/stable/publications/2023/distance_based_clustering/distance_based_clustering.html) -- Holder, C., Guijo-Rubio, D. and Bagnall, A., (2023), September. Clustering time series - with k-medoids based algorithms. In International Workshop on Advanced Analytics and - Learning on Temporal Data (pp. 39-55). - [Paper](https://link.springer.com/chapter/10.1007/978-3-031-49896-1_4) -- Holder, Christopher & Bagnall, Anthony. (2024). - Rock the KASBA: Blazingly Fast and Accurate Time Series Clustering. - 10.48550/arXiv.2411.17838. -[Paper](https://arxiv.org/abs/2411.17838) +- Ayllón-Gavilán, R., Guijo-Rubio, D., Gutiérrez, P.A., Bagnall, A. and Hervás-Martínez, + C. Convolutional-and Deep Learning-Based Techniques for Time Series Ordinal + Classification. IEEE Transactions on Cybernetics, 55(2), pp. 537-549. + [Paper](https://ieeexplore.ieee.org/abstract/document/10769513). +- Guijo-Rubio, D., Middlehurst, M., Arcencio, G., Silva, D.F. and Bagnall, A.. + Unsupervised feature based algorithms for time series extrinsic regression. Data + Mining and Knowledge Discovery, 38(4), pp.2141-2185. + [Paper](https://link.springer.com/article/10.1007/s10618-024-01027-w) + [Webpage/Code](https://tsml-eval.readthedocs.io/en/stable/publications/2023/tser_archive_expansion/tser_archive_expansion.html) +- Miao, B., Dong, Y, Theissler, A, Lesh, A., Loftus, D. + and Lepech, M. BioSys: Efficient Quality Control System for + Manufacturing of Sustainable Biopolymer Composites. In Proceedings of the 11th ACM + International Conference on Systems for Energy-Efficient Buildings (BuildSys '24) + [Paper](https://doi.org/10.1145/3671127.3698165) +- Moore, J., Stackhouse, H. Fulcher, B. and Mahmoodian, S. Using matrix-product + states for time-series machine learning. arXiv:2412.15826 + [Paper](https://arxiv.org/abs/2412.15826) +- Holder, C. and Bagnall, B. 2024. Rock the KASBA: Blazingly Fast and Accurate Time + Series Clustering. arXiv: 2411.17838 + [Paper](https://arxiv.org/abs/2411.17838) +- Holder, C., Bagnall, B. and Lines, J. 2024. On time series clustering with k-means. + arXiv: 2410.14269 + [Paper](https://arxiv.org/abs/2410.14269) +- Ismail-Fawaz, A. 2024. Deep Learning For Time Series Analysis With Application On + Human Motion arXiv: 2502.19364 + [Paper](https://arxiv.org/abs/2502.19364) -## Regression +## 2023 -- Guijo-Rubio, D., Middlehurst, M., Arcencio, G., Silva, D. and Bagnall, A. (2024). - Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression. Data - Mining and Knowledge Discovery, online first, open access. - [Paper](https://arxiv.org/abs/2305.01429) [Webpage/Code](https://tsml-eval.readthedocs.io/en/stable/publications/2023/tser_archive_expansion/tser_archive_expansion.html) -- Middlehurst, M. and Bagnall, A., (2023), September. Extracting Features from Random - Subseries: A Hybrid Pipeline for Time Series Classification and Extrinsic Regression. +- Ayllón-Gavilán, R., Guijo-Rubio, D., Gutiérrez, P.A. and Hervás-Martínez, C., 2023, + June. A dictionary-based approach to time series ordinal classification. In + International Work-Conference on Artificial Neural Networks (pp. 541-552). Cham: + Springer Nature Switzerland. + [Paper](https://link.springer.com/chapter/10.1007/978-3-031-43078-7_44) +- Ermshaus, A., Schäfer, P., Bagnall, A., Guyet, T., Ifrim, G., Lemaire, V., ... & + Malinowski, S. (2023, September). Human Activity Segmentation Challenge@ ECML/PKDD’23. In International Workshop on Advanced Analytics and Learning on Temporal Data - (pp. 113-126). + (pp. 3-13). Cham: Springer Nature Switzerland. + [Paper](https://link.springer.com/chapter/10.1007/978-3-031-49896-1_1) [Webpage/Code](https://github.com/patrickzib/human_activity_segmentation_challenge) +- Schäfer, P. and Leser, U., 2023. WEASEL 2.0: a random dilated dictionary transform for + fast, accurate and memory constrained time series classification. Machine Learning, + 112(12), pp.4763-4788. + [Paper](https://link.springer.com/content/pdf/10.1007/s10994-023-06395-w.pdf) [Webpage/Code](https://github.com/patrickzib/dictionary) +- Middlehurst, M. and Bagnall, A., (2023), September. Middlehurst, M. and Bagnall, A., + 2023, September. Extracting features from random subseries: A hybrid pipeline for + time series classification and extrinsic regression. In International Workshop on + Advanced Analytics and Learning on Temporal Data (pp. 113-126). Cham: Springer Nature + Switzerland. [Paper](https://link.springer.com/chapter/10.1007/978-3-031-49896-1_8) [Webpage/Code](https://tsml-eval.readthedocs.io/en/stable/publications/2023/rist_pipeline/rist_pipeline.html) - -## Ordinal classification - -- Ayllón-Gavilán, R., Guijo-Rubio, D., Gutiérrez, P.A., Bagnall, A., and - Hervás-Martínez, C. Convolutional and Deep Learning based techniques for Time Series - Ordinal Classification. [ArXiV](https://arxiv.org/abs/2306.10084). -- Ayllón-Gavilán, R., Guijo-Rubio, D., Gutiérrez, P. A., and Hervás-Martínez, C. - (2024). O-Hydra: A Hybrid Convolutional and Dictionary-Based Approach to Time Series - Ordinal Classification. In Conference of the Spanish Association for Artificial - Intelligence (pp. 50-60). [Paper](https://link.springer.com/chapter/10.1007/978-3-031-62799-6_6). -- Ayllón-Gavilán, R., Guijo-Rubio, D., Gutiérrez, P.A., and Hervás-Martínez, C. (2023). - A Dictionary-Based Approach to Time Series Ordinal Classification. In: Rojas, I., - Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. - Lecture Notes in Computer Science, vol 14135. [Paper](https://link.springer.com/chapter/10.1007/978-3-031-43078-7_44). - -## Prototyping - -- Ismail-Fawaz, A. and Ismail Fawaz, H. and Petitjean, F. and Devanne, M. and Weber, - J. and Berretti, S. and Webb, GI. and Forestier, G. (2023 December "ShapeDBA: - Generating Effective Time Series Prototypes Using ShapeDTW Barycenter Averaging." - ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data. [Paper](https://doi.org/10.1007/978-3-031-49896-1_9) - [code](https://github.com/MSD-IRIMAS/ShapeDBA) +- Ismail-Fawaz, A., Ismail Fawaz, H., Petitjean, F., Devanne, M., Weber, J., Berretti, + S., Webb, G.I. and Forestier, G., 2023, September. Shapedba: Generating effective time + series prototypes using shapedtw barycenter averaging. In International Workshop on + Advanced Analytics and Learning on Temporal Data (pp. 127-142). Cham: Springer Nature + Switzerland. + [Paper](https://doi.org/10.1007/978-3-031-49896-1_9) [Code](https://github.com/MSD-IRIMAS/ShapeDBA) - Holder, C., Guijo-Rubio, D., & Bagnall, A. J. (2023). Barycentre Averaging for the Move-Split-Merge Time Series Distance Measure. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and - Knowledge Management-Volume 1:, 51-62, pp. 51-62. [Paper](https://www.scitepress.org/Link.aspx?doi=10.5220/0012164900003598) - -## Generation Evaluation - -- Ismail-Fawaz, A. and Devanne, M. and Berretti, S. and Weber, J. and Forestier, G. - (2024) May "Establishing a Unified Evaluation Framework for Human Motion - Generation: A Comparative Analysis of Metrics" [Paper](https://arxiv.org/abs/2405.07680) [code](https://github.com/MSD-IRIMAS/Evaluating-HMG) + Knowledge Management (pp. 51-62). + [Paper](https://www.scitepress.org/Link.aspx?doi=10.5220/0012164900003598) +- Holder, C., Guijo-Rubio, D. and Bagnall, A., 2023, September. Clustering time series + with k-medoids based algorithms. In International Workshop on Advanced Analytics and + Learning on Temporal Data (pp. 39-55). Cham: Springer Nature Switzerland. + [Paper](https://link.springer.com/chapter/10.1007/978-3-031-49896-1_4) diff --git a/docs/projects.md b/docs/projects.md new file mode 100644 index 0000000000..bf9d62c19b --- /dev/null +++ b/docs/projects.md @@ -0,0 +1,488 @@ + +[//]: # (Try to put references in harvard style for consistency.) + +# aeon Projects + +`aeon` runs a range of short to medium duration projects that involve +developing or using `aeon` and interacting with the community and the code +base. These projects are designed for internships, usage as part of +undergraduate/postgraduate projects at academic institutions, options for +programs such as [Google Summer of Code (GSoC)](https://summerofcode.withgoogle.com/) or just for personal side projects. +For those interested in undertaking a project outside these scenarios, we recommend +joining the [Slack](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-22vwvut29-HDpCu~7VBUozyfL_8j3dLA)and discussing with the community. + +Feel free to propose your own project ideas, but please discuss them with us first. +We have an active community of researchers and students who work on `aeon`. +Please get in touch via Slack if you are interested in any of these projects or have +any questions. We will more widely advertise funding opportunities as and when they +become available. + +All the projects listed will require knowledge of Python and Git/GitHub. The +majority of them will require some knowledge of machine learning and time series. + +## Current `aeon` projects + +This is a list of some of the projects we are interested in running (last updated +25/05/2025): + +[Classification](#classification) +1. Optimizing the Shapelet Transform for classification and similarity search +2. Improved HIVE-COTE implementation +3. Compare distance-based classification. + +[Clustering](#clustering) +1. Density peaks clustering algorithm +2. Hierarchical clustering for time series + +[Transformation](#transformation) +1. Improve ROCKET family of transformers + +[Visualisation](#visualisation) +1. Explainable AI with the shapelet transform + +[Documentation](#documentation) +1. Improve automated API documentation +2. Improve the documentation tag interactivity and testing + +[Maintenance](#maintenance) +1. Modernising the `aeon` linting and type checking workflows + +[Multi-module](#multi-module) +1. Implementing multithreading for `aeon` estimators and tools for evaluating multithreading performance + +## Classification + +### 1. Optimizing the Shapelet Transform for Classification and Similarity Search + +Contact: Antoine Guillaume ({user}`baraline`) and Tony Bagnall ({user}`TonyBagnall`) + +#### Related Issues + +[#186](https://github.com/aeon-toolkit/aeon/issues/186) +[#973](https://github.com/aeon-toolkit/aeon/issues/973) +[#1322](https://github.com/aeon-toolkit/aeon/issues/1322) + +#### Description + +A shapelet is defined as a time series subsequence representing a pattern of interest +that we wish to search for in time series data. Shapelet-based algorithms can be used +for a wide range of time series tasks. In this project, we will focus on its core +application, which is to create an embedding of the input time series. + +Our goal in this project will be to optimize the code related to the shapelet +transform method, which takes as input a set of shapelets and a time series dataset, +and give as output a tabular dataset containing the features characterizing the +presence (or absence) of each shapelet in the input time series (more information +in [1] and [2]). + +Similarity search is another field of time series, which has proposed greatly optimized +algorithms (see [3] and [4]) for the task of finding the best matches of a subsequence +inside another time series. As this task is extremely similar to what is done in the +shapelet transform, we want to adapt these algorithms to the context of shapelets, +in order to achieve significant speed-ups. + +#### Project stages + +To achieve this goal, with the assistance of the mentor, we identify the following +steps for the mentee: + +1. Learn about aeon best practices, coding standards and testing policies. +2. Study the shapelet transform algorithm and how it is related to the task of +similarity search. +3. Study the similarity search algorithms for the Euclidean distance and the +computational optimization they use. +4. Propose a candidate implementation for to increase the performance of the +computations made by a single shapelet. This can be made with the help of the existing +implementation of the similarity search module in `aeon`. +5. Measure the performance of this first candidate implementation against the current +approach. +6. Implement this solution to the shapelet transform algorithm, which uses multiple +shapelets. +7. Benchmark the implementation against the original shapelet transform algorithm. +8. If time, generalize this new algorithm to the case of dilated shapelets (see [5]). + +#### References + +1. Hills, J., Lines, J., Baranauskas, E., Mapp, J. and Bagnall, A., 2014. +Classification of time series by shapelet transformation. Data mining and knowledge +discovery, 28, pp.851-881. +2. Bostrom, A. and Bagnall, A., 2017. Binary shapelet transform for multiclass time +series classification. Transactions on Large-Scale Data-and Knowledge-Centered Systems +XXXII: Special Issue on Big Data Analytics and Knowledge Discovery, pp.24-46. +3. Yeh, C.C.M., Zhu, Y., Ulanova, L., Begum, N., Ding, Y., Dau, H.A., Silva, D.F., +Mueen, A. and Keogh, E., 2016, December. Matrix profile I: all pairs similarity joins +for time series: a unifying view that includes motifs, discords and shapelets. In 2016 +IEEE 16th international conference on data mining (ICDM) (pp. 1317-1322). Ieee. +4. Zhu, Y., Zimmerman, Z., Senobari, N.S., Yeh, C.C.M., Funning, G., Mueen, A., Brisk, +P. and Keogh, E., 2016, December. Matrix profile ii: Exploiting a novel algorithm and +gpus to break the one hundred million barrier for time series motifs and joins. In 2016 +IEEE 16th international conference on data mining (ICDM) (pp. 739-748). IEEE. +5. Guillaume, A., Vrain, C. and Elloumi, W., 2022, June. Random dilated shapelet +transform: A new approach for time series shapelets. In International Conference on +Pattern Recognition and Artificial Intelligence (pp. 653-664). Cham: Springer +International Publishing. + +### 2. Improved HIVE-COTE implementation + +Contact: Matthew Middlehurst ({user}`MatthewMiddlehurst`) and Tony Bagnall +({user}`TonyBagnall`) + +#### Related Issues + +[#663](https://github.com/aeon-toolkit/aeon/issues/663) +[#1646](https://github.com/aeon-toolkit/aeon/issues/1646) + +#### Description + +The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) [1,2,3] +is a time series classifier that has claims to be state-of-the-art, +particularly in terms of probabilistic estimates [4]. There have been several iterations +that use different base classifiers but they all share the same design basic: +classifiers using different representations are combined in a weighted meta-ensemble +[4]. There are two HIVE-COTE implementations currently in `aeon`: HIVE-COTEV1 [2] +and HIVECOTEV2 [3]. This project will involve combining these into a single +framework, modularising the ensemble stage and possibly experimenting with +alternative structures. + +#### Project stages + +1. Learn about `aeon` best practices, coding standards and testing policies. +2. Study the HIVE-COTE algorithm and previous `aeon` implementation. +3. Implement a modular HIVE-COTE or composable classification ensemble framework. +4. Convert HIVE-COTEV1 and HIVE-COTEV2 classifiers to use this framework ensuring + results remain the same. +5. Restructure the ensemble stage to allow easy experimentation with variants. + +#### References + +1. A Bagnall, J Lines, J Hills, A Bostrom, Time-series classification with COTE: the + collective of transformation-based ensembles, IEEE Transactions on Knowledge and + Data Engineering 27 (9), 2522-2535 +2. M Middlehurst, J Large, M Flynn, J Lines, A Bostrom, A Bagnall, HIVE-COTE 2.0: a + new meta ensemble for time series classification, Machine Learning 110 (11), + 3211-3243 +3. J Lines, S Taylor, A Bagnall, Time series classification with HIVE-COTE: The + hierarchical vote collective of transformation-based ensembles, ACM Transactions + on Knowledge Discovery from Data (TKDD) 12 (5), 1-35 +4. Middlehurst, M., Schäfer, P. and Bagnall, A., 2024. Bake off redux: a review and + experimental evaluation of recent time series classification algorithms. Data Mining + and Knowledge Discovery, 38(4), pp.1958-2031. + +### 3. Compare distance-based classification and regression + +Contact: Chris Holder ({user}`chrisholder`) and Tony Bagnall +({user}`TonyBagnall`) + +#### Related Issues + +[#424](https://github.com/aeon-toolkit/aeon/issues/424) +[#425](https://github.com/aeon-toolkit/aeon/issues/425) +[#426](https://github.com/aeon-toolkit/aeon/issues/426) +[#427](https://github.com/aeon-toolkit/aeon/issues/427) +[#488](https://github.com/aeon-toolkit/aeon/issues/488) + +#### Description + +Distance-based algorithms are popular for time series classification and regression. +However, the evaluation of distance functions for classification have not +comprehensively covered all possible uses. For example, there has not been a proper +bake off for using elastic distance with support vector machines or with tuning +distance functions and classifiers in combination. This project will combine +implementing alternative distance functions and comparing performance on the UCR +datasets. + +#### Project stages + +1. Learn about `aeon` best practices, coding standards and testing policies. +2. Study the distance model and the distance-based classifiers in `aeon`. +3. Read and implement alternative distance functions in the format of the + `aeon.distances` module. +4. Test the distances against previous results if available. +5. Run a mini-bakeoff of distance-based classifiers on the UCR datasets, comparing + distances using existing distance-based classifiers in `aeon` and `scikit-learn`. + +## Clustering + +### 1. Density peaks clustering algorithm + +Contact: Tony Bagnall ({user}`TonyBagnall`) and Chris Holder (`@chrisholder`). + +#### Description + +The clustering module in `aeon`, up until now, clusters using time series +specific distance functions with partitional clustering algorithms such as k-means and +k-medoids. An alternative clustering algorithm is density peaks [1]. This clustering +algorithm has the benefit of not having to label all cases as cluster members, which +means it can easily be adapted to anomaly detection [2]. It is +a general purpose clustering algorithm that is not available in scikit learn. This +project will implement the algorithm based on Java and matlab implementations then +compare performance against partitional clustering for time series clustering. + +#### Project Stages + +1. Research and understand how density peaks works. +2. Implement density peaks as an `aeon` estimator using the `aeon` distances module. +3. Test the implementation against other implementations for correctness and on + synthetic data used in the publication. +4. Compare against alternative TSCL algorithms. +5. Possible extensions to reflect recent research [2] with specific time series + components [3]. + +#### References + +1. Rodriguez, A., & Laio, A. Clustering by Fast Search and Find of + Density Peaks. [Science](https://www.science.org/doi/10.1126/science.1242072), 344 (6191), 1492-1496, 2014. +2. Chen, L., Gao, S. & Liu, B. An improved density peaks clustering algorithm + based on grid screening and mutual neighborhood degree for network anomaly detection. + Sci Rep 12, 1409 (2022) [DOI](https://doi.org/10.1038/s41598-021-02038-z) +3. Begum et al. A General Framework for Density Based Time Series Clustering + Exploiting a Novel Admissible Pruning Strategy, [arXiv](https://arxiv.org/ftp/arxiv/papers/1612/1612.00637.pdf) + +### 2. Hierarchical clustering for time series + +Contact: Tony Bagnall ({user}`TonyBagnall`) and Chris Holder (`@chrisholder`). + +#### Description + +While the aeon distances module is already extensive, there are still clusterers that +could be implemented. aeon currently has common algorithms such as KMeans and KMedoids, +but is missing Hierarchical clustering approaches. The project will involve implementing +and evaluating some of these, and ensuring they are properly integrated to use the +wide variety of functions in the distances module. + +#### Project Stages + +1. Research and understand hierarchical clustering algorithms work and different + methods. +2. Implement hierarchical clustering algorithms as an `aeon` estimator using the + `aeon` distances module. +3. Implement a dendrogram visualisation for the clustering. +4. Test the implementation visualisations and results against other implementations + for correctness + +## Transformation + +### 1. Improve ROCKET family of transformers + +Contact: Ali Ismail-Fawaz ({user}`hadifawaz1999`) and Matthew Middlehurst +({user}`MatthewMiddlehurst`) + +#### Related Issues + +[#313](https://github.com/aeon-toolkit/aeon/issues/313) +[#1126](https://github.com/aeon-toolkit/aeon/issues/1126) +[#1248](https://github.com/aeon-toolkit/aeon/issues/1248) +[#2179](https://github.com/aeon-toolkit/aeon/issues/2179) + +#### Description + +The ROCKET algorithm [1] is a very fast and accurate transformation designed for time +series classification. It is based on a randomly initialised convolutional kernels that +are applied to the time series and used to extract summary statistics. ROCKET has +applications to time series classification, extrinsic regression and anomaly detection, +but as a fast and unsupervised transformation, it has potential to a wide range of +other time series tasks. + +`aeon` has implementations of the ROCKET transformation and its variants, including +MiniROCKET [2] and MultiROCKET [3]. However, these implementations have room for +improvement. There is scope to speed up the implementations, and the amount of variants +is likely unnecessary and could be condensed into higher quality estimators. + +This projects involves improving the existing ROCKET implementations in `aeon` or +implementing new ROCKET variants. The project will involve benchmarking to ensure that +the new implementations are as fast and accurate as the original ROCKET algorithm and +potentially to compare to other implementations. +Besides improving the existing implementations, there is scope to implement a +probabilistic ridge classifier [4] for the algorithms to use or implement GPU compatible +versions of the algorithms. + +#### Project Stages + +1. Learn about `aeon` best practices, coding standards and testing policies. +2. Study the ROCKET, MiniROCKET, MultiROCKET algorithms and existing + implementations in `aeon`. +3. Merge and tidy the ROCKET implementations, with the aim being to familiarise the +mentee with the `aeon` pull request process. +4. Implement one (or more) of the proposed ROCKET implementation improvements: + * Significantly alter the current ROCKET implementations with the goal of + speeding up the implementation on CPU processing. + * Implement a GPU version of some of the ROCKET transformers, using either + `tensorflow` or `pytorch`. + * Implement probabilistic ridge classifier as a `scikit-learn` estimator. +5. Benchmark the implementation against the original ROCKET implementations, looking at +booth speed of the transform and accuracy in a classification setting. + +#### References + +1. Dempster, A., Petitjean, F. and Webb, G.I., 2020. ROCKET: exceptionally fast and +accurate time series classification using random convolutional kernels. +Data Mining and Knowledge Discovery, 34(5), pp.1454-1495. +2. Dempster, A., Schmidt, D.F. and Webb, G.I., 2021, August. Minirocket: A very fast +(almost) deterministic transform for time series classification. In Proceedings of the +27th ACM SIGKDD conference on knowledge discovery & data mining (pp. 248-257). +3. Tan, C.W., Dempster, A., Bergmeir, C. and Webb, G.I., 2022. MultiRocket: multiple +pooling operators and transformations for fast and effective time series classification. +Data Mining and Knowledge Discovery, 36(5), pp.1623-1646. +4. Dempster, A., Webb, G.I. and Schmidt, D.F., 2024. Prevalidated ridge regression is a +highly-efficient drop-in replacement for logistic regression for high-dimensional data. +arXiv preprint arXiv:2401.15610. + +## Visualisation + +### 1. Explainable AI with the shapelet transform. + +Contact: TonyBagnall ({user}`TonyBagnall`) and David Guijo-Rubio +({user}`dguijo`) + +#### Description + +This project will focus on explainable AI for time series classification (TSC) [1], +specifically the family of algorithms based on shapelets [2]. Shapelets are small sub + certain shape of heartbeat, perhaps a short irregularity, might be useful in +predicting the medical condition. We will look at the shapelet transform classifier +[3]. This finds a large set of shapelets from the training data and uses them to +build a classifier. We want to develop tools to help us visualise the output of the +search for good shapelets to help explain why predictions are made. This project is +not tied to a specific data set. It is to develop tools to help any user of the +toolkit. It will involve learning about aeon and making contributions to open +source toolkits, familiarisation with the shapelet code and the development of a +visualisation tool to help relate shapelets back to the training data. An outline +for the project is + +#### Project Stages + +1. Familiarisation with open source, aeon and the visualisation module. Make +contribution for a good first issue. +2. Understand the shapelet transfer algorithm, engage in ongoing discussions for +possible improvements, run experiments to create predictive models for a test data set +3. Design and prototype visualisation tools for shapelets, involving a range +of summary measures and visualisation techniques, including plotting shapelets on +training data, calculating frequency, measuring similarity between +4. Debug, document and make PRs to merge contributions into the aeon toolkit. + +#### References + +1. Bagnall, A., Lines, J., Bostrom, A., Large, J. and Keogh, E. The great time series +classification bake off: a review and experimental evaluation of recent algorithmic +advances. Data Mining and Knowledge Discovery, Volume 31, pages 606–660, (2017) +2. Ye, L., Keogh, E. Time series shapelets: a novel technique that allows accurate, +interpretable and fast classification. Data Min Knowl Disc 22, 149–182 (2011). +https://doi.org/10.1007/s10618-010-0179-5 +3. Lines, L., Davis, L., Hills, J. and Bagnall, A. A shapelet transform for time +series classification, KDD '12: Proceedings of the 18th ACM SIGKDD international +conference on Knowledge discovery and data mining (2012) +https://doi.org/10.1145/2339530.2339579 + +## Documentation + +### 1. Improve the documentation codebase interactions and testing + +Contact: Matthew Middlehurst ({user}`MatthewMiddlehurst`) + +#### Description + +The `aeon` documentation is a key resource for users of the toolkit. It provides +information on how to install the toolkit, how to use the toolkit, and how to +contribute to the toolkit. The `aeon` documentation is built using `sphinx` and hosted +on `readthedocs`. + +While there are always improvements that can be made to the general documentation itself +(e.g., improving the clarity of the text, adding more examples, etc.) for both webpages +and estimator docstrings, this project focuses on implementing functions to +automatically link relevant API pages together and ensure new pull requests are +accompanied by the appropriate documentation. Some examples of improvements that could +be made include: + +- Linking to examples to in API pages where the function/class is used +- Improving the [estimator overview page](https://www.aeon-toolkit.org/en/stable/estimator_overview.html) +by further integrating the tags system or adding search and filtering functionality +- Implementing workflows to ensure that new public functionality includes a valid +docstring (i.e. has a description, parameters, returns, etc. sections where relevant) + +There is a lot of potential for additional functionality, so feel free to suggest +improvements or new features outside the examples provided. + +#### Project Stages + +1. Learn about `aeon` best practices and project documentation. +2. Familiarise with `sphinx` documentation generation and `numpydoc` docstring +standards. +3. Improve the API documentation for a few classes/functions and go through the Pull +Request and review process. +4. Implement at least one new feature or improvement to the aeon documentation webpage +(outside of general text improvements) And/Or improve the aeon testing suite to ensure +that new PRs are accompanied by the appropriate documentation. + +## Maintenance + +### Modernising the `aeon` linting and type checking workflows + +Contact: Matthew Middlehurst ({user}`MatthewMiddlehurst`) + +#### Description + +This project involves updating the `aeon` linting and type checking workflows to +use modern tools and ensure that the codebase is up to date with the latest +Python standards. + +The `aeon` toolkit uses `pre-commit` to run code quality checks on all code changes +and ensure that they meet the project's standards. This includes a number of checks and +formatting tools, such as `black`, `flake8`, and `isort` (see [here](https://github.com/aeon-toolkit/aeon/blob/main/.pre-commit-config.yaml)). +Over time new tools have been released such as `ruff` and tools we previously used such +as `pydocstyle` have been deprecated. The first part of this project will involve +modernising the `pre-commit` configuration to use the latest tools. + +`aeon` contributors have been encouraged to add type hints to the codebase, but this +is a gradual process and there are still many parts of the codebase that are not fully +typed. A big issue we face in this is the current lack of automated testing to ensure +that implemented type hints are accurate. This second part project will involve +implementing robust testing utilities to help contributors and reviewers ensure that +new type hints are correct. + +Other ideas to improve the code quality testing in `aeon` pull requests or deliver +feedback from tests to contributors are welcome. + +#### Expected Outcome(s) + +1. Learn about used for code quality checks and type checking in Python. +2. Familiarise yourself with the `aeon` CI including pre-commit and GitHub Actions +workflows. +3. Update workflows for checking code quality in `aeon` pull requests +4. Implement automated testing and utilities to help contributors implement accurate +type hints for `aeon` code. + +## Multi-module + +### Implementing multithreading for `aeon` estimators and tools for evaluating multithreading performance + +Contact: Matthew Middlehurst ({user}`MatthewMiddlehurst`) + +#### Description + +Multithreading in `aeon` for estimators does not have a set structure or library that must +be used. Most algorithms which have an `n_jobs` parameter available use a mix of `Joblib` and +`numba` multithreading. Algorithms which do have the capability for multithreading have not +been thoroughly tested, and as such the efficiency of these implementations is unknown. + +As well as expanding the amount of estimators which can use multiple threads, we would +like to develop tools to evaluate whether this threading is efficient and develop +documentation for contributors which want to add multithreading to `aeon` estimators. + +#### Project Stages + +1. Investigate Python multithreading libraries and `numba` multithreading. +2. Learn about `aeon` best practices, coding standards and how current estimators use +multiple threads. +3. Write tools and testing for evaluating the efficiency of multithreaded code while +maintaining single-threaded performance. +4. If any estimators are performing poorly with multithreading, implement +improvements to the multithreading implementation. +5. Implement multithreading as a capability for currently lacking `aeon` estimators +(preferably in the `classification`, `regression` and `clustering` modules to start). + + +```{toctree} +:hidden: + +projects/previous_projects.md +``` diff --git a/docs/projects/previous_projects.md b/docs/projects/previous_projects.md new file mode 100644 index 0000000000..ae1825e4b0 --- /dev/null +++ b/docs/projects/previous_projects.md @@ -0,0 +1,112 @@ +# Previous aeon Projects + +A list of projects that have been completed in the past or are currently ongoing. + +## 2025 + +### Implementing and Evaluating Machine Learning Forecasters + +Mentors: Matthew Middlehurst ({user}`MatthewMiddlehurst`) and Tony Bagnall +({user}`TonyBagnall`) + +Mentee: Tina Jin ({user}`TinaJin0228`) + +https://summerofcode.withgoogle.com/organizations/numfocus/projects/details/MPYRSOTi + +https://medium.com/@jintina48/list/gsoc25-blog-11a0081fc6e2 + +GSoC 2025 project + +### Deep Learning for Forecasting + +Mentors: Tony Bagnall ({user}`TonyBagnall`) and Ali Ismail-Fawaz +({user}`hadifawaz1999`) and Matthew Middlehurst ({user}`MatthewMiddlehurst`) + +Mentee: Balgopal Moharana ({user}`lucifer4073`) + +https://summerofcode.withgoogle.com/organizations/numfocus/projects/details/arjEn266 + +https://medium.com/@lucifer4073/gsoc-25-journey-af8e3e0c2621 + +GSoC 2025 project + +## 2024 + +### Developing Deep Learning Framework and Implementations for Time Series Clustering + +Mentors: Ali Ismail-Fawaz ({user}`hadifawaz1999`) and Tony Bagnall +({user}`TonyBagnall`) and Matthew Middlehurst ({user}`MatthewMiddlehurst`) + +Mentee: Aadya Chinubhai ({user}`aadya940`) + +GSoC 2024 project + +https://summerofcode.withgoogle.com/programs/2024/projects/Hvd0DfkD + +https://medium.com/@aadyachinubhai + +#### Project Summary + +Time series clustering involves grouping similar time series data together based on +specific features or patterns. Deep learning algorithms have become increasingly +popular for clustering. However, the aeon's deep clustering module currently lacks +several deep learning-based algorithms. In this project the aim is to implement some of +the top performing and interesting algorithms from a recent comparison of deep learning +for time series clustering and benchmark them. This project includes further developing +the aeon deep learning networks module, making the package publicly documented for user +to explore and well tested to help the maintenance of the deep learning implemented in +the future. + +### Implement the Proximity Forest Algorithm for Time Series Classification + +Mentors: Matthew Middlehurst ({user}`MatthewMiddlehurst`) and Tony Bagnall +({user}`TonyBagnall`) and Antoine Guillaume ({user}`baraline`) + +Mentee: Divya Tiwari ({user}`itsdivya1309`) + +https://summerofcode.withgoogle.com/programs/2024/projects/8TYGhJjy + +https://medium.com/@Divya2003/ + +#### Project Summary + +This project will implement and benchmark the Proximity Forest Algorithm for Time Series +Classification in aeon. With the ever-increasing data, the applications of time series +classification are also increasing. Hence, we need classification algorithms that are +both efficient and scalable. The Proximity Forest Algorithm is the current +state-of-the-art distance-based classifier that creates an ensemble of decision trees, +where the splits are based on the similarity between time series measured using various +parameterised distance measures. Currently, a version of Proximity Forest which can +match the performance of the original implementation has not been implemented in Python. +This project aims to implement Proximity Forest in aeon for the classification of +univariate time series datasets of equal length and make it accessible for a greater +variety of users. The implementation will be benchmarked on the UCR archive to match +the results of the original Java implementation in terms of run time and accuracy. + +### Machine learning from EEG with aeon-neuro + +Mentors: Tony Bagnall ({user}`TonyBagnall`) and Matthew Middlehurst +({user}`MatthewMiddlehurst`) and Aiden Rushbrooke ({user}`AidenRushbrooke`) + +Mentee: Gabriel Riegner ({user}`griegner`) + +GSoC 2024 project + +https://summerofcode.withgoogle.com/programs/2024/projects/htrPCGOM + +https://gist.github.com/griegner/c414f77d957dea73b84dcd80d580b602 + +#### Project Summary + +Develop aeon-neuro to provide structured tools for machine learning from neural data. +This project will focus on implementing algorithms for EEG classification by building +on the multivariate classification algorithms outlined in Rushbrooke 2023. This paper +demonstrates that existing time series models implemented in aeon can successfully +classify patients from healthy individuals using frequency domain features alone, +eliminating the need for detailed time domain feature selection. In addition to +applying existing machine learning models to EEG datasets, we will further develop +aeon-neuro to be more accessible to the scientific research community by interfacing +it with existing data formatting standards (BIDs) and EEG analysis libraries (MNE). +Alongside these primary outcomes, we will adhere to best practices in research software +development, including writing well-test code, consistent documentation, and user-facing +examples/notebooks. From 06202550fda59c38210c79705d1a560f46e9ced3 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Tue, 3 Jun 2025 08:54:33 +0100 Subject: [PATCH 130/216] [DEP] Remove `prts` dependency (#2863) * remove prts from pyproject * docs * Update _continuous.py * Automatic `pre-commit` fixes --------- Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> --- .../metrics/anomaly_detection/_continuous.py | 12 +++++------- pyproject.toml | 2 -- 2 files changed, 5 insertions(+), 9 deletions(-) diff --git a/aeon/benchmarking/metrics/anomaly_detection/_continuous.py b/aeon/benchmarking/metrics/anomaly_detection/_continuous.py index ade8c102b7..da3008db76 100644 --- a/aeon/benchmarking/metrics/anomaly_detection/_continuous.py +++ b/aeon/benchmarking/metrics/anomaly_detection/_continuous.py @@ -180,7 +180,6 @@ def f_score_at_k_ranges( threshold = top_k_ranges_threshold(y_true, y_score, k) y_pred = y_score >= threshold - # Corrected p_alpha to 1 to match original prts behavior expectation return range_f_score(y_true, y_pred, p_alpha=1, r_alpha=1, cardinality="reciprocal") @@ -209,12 +208,11 @@ def rp_rr_auc_score( y_score : np.ndarray Anomaly scores for each point of the time series of shape (n_instances,). max_samples: int - The implementation of the range-based precision and recall metrics is quite - slow because it relies on the non-optimized ``prts``-package. To prevent long - runtimes caused by scorings with high precision (many thresholds), just a - specific amount of possible thresholds is sampled. This parameter controls the - maximum number of thresholds; however, too low numbers degrade the metrics' - quality. + The calculation of the range-based precision and recall metrics can be slow. + To prevent long runtimes caused by scorings with high precision + (many thresholds), a specified amount of possible thresholds is sampled. + This parameter controls the maximum number of thresholds; however, too low + numbers degrade the metrics' quality. r_alpha : float Weight of the existence reward for the range-based recall. p_alpha : float diff --git a/pyproject.toml b/pyproject.toml index 4c4981a9f5..815aa9a768 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -83,8 +83,6 @@ unstable_extras = [ # requires gcc and fftw to be installed for Windows and some other OS (see http://www.fftw.org/index.html) "mrsqm>=0.0.7,<0.1.0; platform_system != 'Windows' and python_version < '3.12'", "mrseql>=0.0.4,<0.1.0; platform_system != 'Windows' and python_version < '3.12'", - # very outdated and used code is deprecated - "prts>=1.0.0.0", # Upper bound set as <1.0.0 as 1.0 dropped support for python 3.9. We will remove # the upper bound once we also drop support for python 3.9 later in 2025. "esig>=0.9.7,<1.0.0; platform_system != 'Darwin' and python_version < '3.11'", From 3ce184b28152fd6569b8e18383ea06b3b3aac261 Mon Sep 17 00:00:00 2001 From: Ali El Hadi ISMAIL FAWAZ <54309336+hadifawaz1999@users.noreply.github.com> Date: Tue, 3 Jun 2025 10:13:22 +0200 Subject: [PATCH 131/216] [ENH] Starting Self Supervised Model with first example (#2385) * add ssl module in transformations collection * add api * add test params * fix example * restart tests * test fix params * network none * revert * import inside function * try dummy * test repr * network repr * enhance testing * fix test * replace tcnn by lite * tony's comments * fix typing * fix znorm * z_norm in test * add exp module * Update repr.py * Update base.py * Automatic `pre-commit` fixes --------- Co-authored-by: MatthewMiddlehurst Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> --- README.md | 1 + aeon/networks/base.py | 6 + aeon/networks/tests/test_network_base.py | 3 +- .../collection/self_supervised/__init__.py | 5 + .../collection/self_supervised/_trilite.py | 681 ++++++++++++++++++ .../self_supervised/tests/__init__.py | 1 + .../self_supervised/tests/test_trilite.py | 251 +++++++ aeon/utils/repr.py | 55 ++ aeon/utils/self_supervised/__init__.py | 1 + aeon/utils/self_supervised/general.py | 28 + docs/api_reference/transformations.rst | 11 + docs/index.md | 1 + 12 files changed, 1043 insertions(+), 1 deletion(-) create mode 100644 aeon/transformations/collection/self_supervised/__init__.py create mode 100644 aeon/transformations/collection/self_supervised/_trilite.py create mode 100644 aeon/transformations/collection/self_supervised/tests/__init__.py create mode 100644 aeon/transformations/collection/self_supervised/tests/test_trilite.py create mode 100644 aeon/utils/repr.py create mode 100644 aeon/utils/self_supervised/__init__.py create mode 100644 aeon/utils/self_supervised/general.py diff --git a/README.md b/README.md index 3f6eb132f1..c66d9c9a03 100644 --- a/README.md +++ b/README.md @@ -26,6 +26,7 @@ does not apply: - `segmentation` - `similarity_search` - `visualisation` +- `transformations.collection.self_supervised` | Overview | | |-----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| diff --git a/aeon/networks/base.py b/aeon/networks/base.py index e517894a49..cd249ff1e6 100644 --- a/aeon/networks/base.py +++ b/aeon/networks/base.py @@ -4,6 +4,7 @@ from abc import ABC, abstractmethod +from aeon.utils.repr import get_unchanged_and_required_params_as_str from aeon.utils.validation._dependencies import ( _check_python_version, _check_soft_dependencies, @@ -25,6 +26,11 @@ def __init__(self, soft_dependencies="tensorflow", python_version="<3.13"): _check_python_version(python_version) super().__init__() + def __repr__(self): + """Format str output like scikit-learn estimators.""" + changed_params = get_unchanged_and_required_params_as_str(self) + return f"{self.__class__.__name__}({changed_params})" + @abstractmethod def build_network(self, input_shape, **kwargs): """Construct a network and return its input and output layers. diff --git a/aeon/networks/tests/test_network_base.py b/aeon/networks/tests/test_network_base.py index 922d3cd514..542583eff5 100644 --- a/aeon/networks/tests/test_network_base.py +++ b/aeon/networks/tests/test_network_base.py @@ -20,7 +20,8 @@ def build_network(self, input_shape, **kwargs): import tensorflow as tf input_layer = tf.keras.layers.Input(input_shape) - output_layer = tf.keras.layers.Dense(units=10)(input_layer) + flatten_layer = tf.keras.layers.Flatten()(input_layer) + output_layer = tf.keras.layers.Dense(units=10)(flatten_layer) return input_layer, output_layer diff --git a/aeon/transformations/collection/self_supervised/__init__.py b/aeon/transformations/collection/self_supervised/__init__.py new file mode 100644 index 0000000000..f1b40c5d49 --- /dev/null +++ b/aeon/transformations/collection/self_supervised/__init__.py @@ -0,0 +1,5 @@ +"""Self Supervised deep learning transformers.""" + +__all__ = ["TRILITE"] + +from aeon.transformations.collection.self_supervised._trilite import TRILITE diff --git a/aeon/transformations/collection/self_supervised/_trilite.py b/aeon/transformations/collection/self_supervised/_trilite.py new file mode 100644 index 0000000000..b0ba47a879 --- /dev/null +++ b/aeon/transformations/collection/self_supervised/_trilite.py @@ -0,0 +1,681 @@ +"""TRILITE SSL transformer.""" + +from __future__ import annotations + +__maintainer__ = ["hadifawaz1999"] +__all__ = ["TRILITE"] + +import gc +import os +import sys +import time +from copy import deepcopy +from typing import TYPE_CHECKING + +import numpy as np +from sklearn.utils import check_random_state + +from aeon.networks import BaseDeepLearningNetwork +from aeon.transformations.collection import BaseCollectionTransformer +from aeon.utils.self_supervised.general import z_normalization + +if TYPE_CHECKING: + from tensorflow.keras.callbacks import Callback + from tensorflow.keras.optimizers import Optimizer + + +class TRILITE(BaseCollectionTransformer): + """TRIplet Loss In TimE (TRILITE). + + TRILITE [1]_ is a self-supervised model that learns a latent + space through the triplet loss mechanism by reducing + the loss between close samples and increasing it between + far samples. TRILITE generates the triplets using two techniques, + mixing up and masking. For each reference series (ref), a positive + representation of ref is generated by mixing it up with two other + randomly chosen time series from the dataset then masking a part + of it. The weights of the mixing up procedure are randomly chosen + for the two randomly selected series in a way that the ref still + has the highest weight. The same procedure is used to generated + the negative representation however by using another ref. + + Parameters + ---------- + alpha : float, default = 1e-2 + The value that controls the space of the triplet loss, + the smaller the value the more difficult the problem + becomes, the higher the value the more easy the problem + becomes, a balance should be found. + weight_ref_min : float, default = 0.6 + The weight of the reference series used for the triplet + generation. + percentage_mask_length : int, default = 0.2 + The percentage of time series length to calculate + the length of the masking used for the triplet + generation. Default is 20%. + use_mixing_up : bool, default = True + Wether or not to use mixing up during the triplet + generation phase. + use_masking : bool, default = True + Whether or not to use masking during the triplet + generation phase. + z_normalize_pos_neg : bool, default = True + Whether or not to z_normalize (mean 0 and std 1) + pos and neg samples after generating the triplet. + backbone_network : aeon Network, default = None + The backbone network used for the SSL model, + it can be any network from the aeon.networks + module on condition for it's structure to be + configured as "encoder", see _config attribute. + For TRILITE, the default network used is + FCNNetwork. + latent_space_dim : int, default = 128 + The size of the latent space, applied using a + fully connected layer at the end of the network's + output. + latent_space_activation : str, default = "linear" + The activation to control the range of values + of the latent space. + random_state : int, RandomState instance or None, default=None + If `int`, random_state is the seed used by the random number generator; + If `RandomState` instance, random_state is the random number generator; + If `None`, the random number generator is the `RandomState` instance used + by `np.random`. + Seeded random number generation can only be guaranteed on CPU processing, + GPU processing will be non-deterministic. + verbose : boolean, default = False + Whether to output extra information. + optimizer : keras.optimizer, default = tf.keras.optimizers.Adam() + The keras optimizer used for training. + file_path : str, default = "./" + File path to save best model. + save_best_model : bool, default = False + Whether or not to save the best model, if the + modelcheckpoint callback is used by default, + this condition, if True, will prevent the + automatic deletion of the best saved model from + file and the user can choose the file name. + save_last_model : bool, default = False + Whether or not to save the last model, last + epoch trained, using the base class method + save_last_model_to_file. + save_init_model : bool, default = False + Whether to save the initialization of the model. + best_file_name : str, default = "best_model" + The name of the file of the best model, if + save_best_model is set to False, this parameter + is discarded. + last_file_name : str, default = "last_model" + The name of the file of the last model, if + save_last_model is set to False, this parameter + is discarded. + init_file_name : str, default = "init_model" + The name of the file of the init model, if + save_init_model is set to False, + this parameter is discarded. + callbacks : keras callback or list of callbacks, + default = None + The default list of callbacks are set to + ModelCheckpoint and ReduceLROnPlateau. + batch_size : int, default = 64 + The number of samples per gradient update. + use_mini_batch_size : bool, default = False + Whether or not to use the mini batch size formula. + n_epochs : int, default = 2000 + The number of epochs to train the model. + + Notes + ----- + Adapted from the implementation from Ismail-Fawaz et. al + https://github.com/MSD-IRIMAS/TRILITE + + References + ---------- + .. [1] Ismail-Fawaz, Ali, Maxime Devanne, Jonathan Weber, + and Germain Forestier. "Enhancing time series classification + with self-supervised learning." In International Conference + on Agents and Artificial Intelligence (ICAART), pp. 40-47. + SCITEPRESS-Science and Technology Publications, 2023. + + Examples + -------- + >>> from aeon.transformations.collection.self_supervised import TRILITE + >>> from aeon.datasets import load_unit_test + >>> X_train, y_train = load_unit_test(split="train") + >>> ssl = TRILITE(latent_space_dim=2, n_epochs=5) # doctest: +SKIP + >>> ssl.fit(X_train) # doctest: +SKIP + TRILITE(...) + >>> X_train_transformed = ssl.transform(X_train) # doctest: +SKIP + """ + + _tags = { + "X_inner_type": "numpy3D", + "output_data_type": "Tabular", + "capability:multivariate": True, + "algorithm_type": "deeplearning", + "python_dependencies": "tensorflow", + "non_deterministic": True, + "cant_pickle": True, + } + + def __init__( + self, + alpha: float = 1e-2, + weight_ref_min: float = 0.6, + percentage_mask_length: float = 0.2, + use_mixing_up: bool = True, + use_masking: bool = True, + z_normalize_pos_neg: bool = True, + backbone_network: BaseDeepLearningNetwork = None, + latent_space_dim: int = 128, + latent_space_activation: str = "linear", + random_state: int | np.random.RandomState | None = None, + verbose: bool = False, + optimizer: Optimizer | None = None, + file_path: str = "./", + save_best_model: bool = False, + save_last_model: bool = False, + save_init_model: bool = False, + best_file_name: str = "best_model", + last_file_name: str = "last_model", + init_file_name: str = "init_model", + callbacks: Callback | list[Callback] | None = None, + batch_size: int = 64, + use_mini_batch_size: bool = False, + n_epochs: int = 2000, + ): + self.alpha = alpha + self.weight_ref_min = weight_ref_min + self.percentage_mask_length = percentage_mask_length + self.use_mixing_up = use_mixing_up + self.use_masking = use_masking + self.z_normalize_pos_neg = z_normalize_pos_neg + self.backbone_network = backbone_network + self.latent_space_dim = latent_space_dim + self.latent_space_activation = latent_space_activation + self.random_state = random_state + self.verbose = verbose + self.optimizer = optimizer + self.file_path = file_path + self.save_best_model = save_best_model + self.save_last_model = save_last_model + self.save_init_model = save_init_model + self.best_file_name = best_file_name + self.last_file_name = last_file_name + self.init_file_name = init_file_name + self.callbacks = callbacks + self.batch_size = batch_size + self.use_mini_batch_size = use_mini_batch_size + self.n_epochs = n_epochs + + super().__init__() + + def _fit(self, X: np.ndarray, y=None): + """Fit the SSL model on X, y is ignored. + + Parameters + ---------- + X : np.ndarray + The training input samples of shape (n_cases, n_channels, n_timepoints) + y : ignored argument for interface compatibility + + Returns + ------- + self : object + """ + import tensorflow as tf + + from aeon.networks import FCNNetwork + + if isinstance(self.backbone_network, BaseDeepLearningNetwork): + self._backbone_network = deepcopy(self.backbone_network) + elif self.backbone_network is None: + self._backbone_network = FCNNetwork() + else: + raise ValueError( + "The parameter backbone_network", "should be an aeon network." + ) + + X = X.transpose(0, 2, 1) + + self.input_shape = X.shape[1:] + self.training_model_ = self.build_model(self.input_shape) + + if self.save_init_model: + self.training_model_.save( + os.path.join(self.file_path, self.init_file_name + ".keras") + ) + + if self.verbose: + self.training_model_.summary() + + if self.use_mini_batch_size: + mini_batch_size = min(self.batch_size, X.shape[0] // 10) + else: + mini_batch_size = self.batch_size + + self.file_name_ = ( + self.best_file_name if self.save_best_model else str(time.time_ns()) + ) + + if self.callbacks is None: + self.callbacks_ = [ + tf.keras.callbacks.ReduceLROnPlateau( + monitor="loss", factor=0.5, patience=50, min_lr=0.0001 + ), + tf.keras.callbacks.ModelCheckpoint( + filepath=os.path.join(self.file_path, self.file_name_ + ".keras"), + monitor="loss", + save_best_only=True, + ), + ] + else: + self.callbacks_ = self._get_model_checkpoint_callback( + callbacks=self.callbacks, + file_path=self.file_path, + file_name=self.file_name_, + ) + + fake_y = np.zeros(shape=len(X)) + + train_dataset = tf.data.Dataset.from_tensor_slices((X, fake_y)) + train_dataset = train_dataset.shuffle(buffer_size=1024).batch(mini_batch_size) + + history = {"loss": []} + + for callback in self.callbacks_: + callback.set_model(self.training_model_) + callback.on_train_begin() + + for epoch in range(self.n_epochs): + epoch_loss = 0 + num_batches = 0 + + for step, (x_batch_train, _) in enumerate(train_dataset): + ref_batch_train, pos_batch_train, neg_batch_train = ( + self._triplet_generation(X=x_batch_train) + ) + + with tf.GradientTape() as tape: + ref_pos_neg = self.training_model_( + [ref_batch_train, pos_batch_train, neg_batch_train] + ) + loss_batch = self._triplet_loss_function( + alpha=self.alpha, ref_pos_neg=ref_pos_neg + ) + loss_mean = tf.reduce_mean(loss_batch) + + gradients = tape.gradient( + loss_mean, self.training_model_.trainable_weights + ) + self.optimizer_.apply_gradients( + zip(gradients, self.training_model_.trainable_weights) + ) + + epoch_loss += float(loss_mean) + num_batches += 1 + + for callback in self.callbacks_: + callback.on_batch_end(step, {"loss": float(loss_mean)}) + + epoch_loss /= num_batches + history["loss"].append(epoch_loss) + + if self.verbose: + sys.stdout.write( + "Training loss at epoch %d: %.4f\n" % (epoch, float(epoch_loss)) + ) + + for callback in self.callbacks_: + callback.on_epoch_end(epoch, {"loss": float(epoch_loss)}) + + for callback in self.callbacks_: + callback.on_train_end() + + self.history = history + + try: + self.model_ = tf.keras.models.load_model( + os.path.join(self.file_path, self.file_name_ + ".keras"), compile=False + ) + if not self.save_best_model: + os.remove(os.path.join(self.file_path, self.file_name_ + ".keras")) + except FileNotFoundError: + self.model_ = deepcopy(self.training_model_) + + if self.save_last_model: + self.save_last_model_to_file(file_path=self.file_path) + + gc.collect() + return self + + def _transform(self, X, y=None): + """Transform input time series using TRILITE. + + Parameters + ---------- + X : 3D np.ndarray of shape = (n_cases, n_channels, n_timepoints) + collection of time series to transform + y : ignored argument for interface compatibility + + Returns + ------- + np.ndarray (n_cases, latent_space_dim), transformed features + """ + X = X.transpose(0, 2, 1) + X_ref_pos_neg_transformed = self.model_.predict([X, X, X], self.batch_size) + + X_transformed_ = np.delete(X_ref_pos_neg_transformed, obj=[1, 2], axis=2) + + X_transformed = np.reshape( + X_transformed_, (len(X_transformed_), self.latent_space_dim) + ) + + return X_transformed + + def build_model(self, input_shape): + """Construct a compiled, un-trained, keras model that is ready for training. + + In aeon, time series are stored in numpy arrays of shape (d,m), where d + is the number of dimensions, m is the series length. Keras/tensorflow assume + data is in shape (m,d). This method also assumes (m,d). Transpose should + happen in fit. + + Parameters + ---------- + input_shape : tuple[int, int] + The shape of the data fed into the input layer, should be (m, d). + + Returns + ------- + output : a compiled Keras Model + """ + import numpy as np + import tensorflow as tf + + rng = check_random_state(self.random_state) + self.random_state_ = rng.randint(0, np.iinfo(np.int32).max) + tf.keras.utils.set_random_seed(self.random_state_) + + input_ref_layer = tf.keras.layers.Input(input_shape) + input_pos_layer = tf.keras.layers.Input(input_shape) + input_neg_layer = tf.keras.layers.Input(input_shape) + + input_layer, gap_layer = self._backbone_network.build_network( + input_shape=input_shape + ) + output_layer = tf.keras.layers.Dense( + units=self.latent_space_dim, activation=self.latent_space_activation + )(gap_layer) + + encoder_model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer) + + output_layer_ref = tf.keras.layers.Reshape(target_shape=(-1, 1))( + encoder_model(input_ref_layer) + ) + output_layer_pos = tf.keras.layers.Reshape(target_shape=(-1, 1))( + encoder_model(input_pos_layer) + ) + output_layer_neg = tf.keras.layers.Reshape(target_shape=(-1, 1))( + encoder_model(input_neg_layer) + ) + + encoder_output_layer = tf.keras.layers.Concatenate(axis=-1)( + [output_layer_ref, output_layer_pos, output_layer_neg] + ) + + model = tf.keras.models.Model( + inputs=[input_ref_layer, input_pos_layer, input_neg_layer], + outputs=encoder_output_layer, + ) + + self.optimizer_ = ( + tf.keras.optimizers.Adam() if self.optimizer is None else self.optimizer + ) + + # compile but won't be used + model.compile(loss="mse", optimizer=self.optimizer_) + + return model + + def _triplet_loss_function(self, alpha, ref_pos_neg): + """Create a triplet loss function for triplet-based training.""" + import tensorflow as tf + + ref = ref_pos_neg[:, :, 0] + pos = ref_pos_neg[:, :, 1] + neg = ref_pos_neg[:, :, 2] + + ref = tf.cast(ref, dtype=ref.dtype) + pos = tf.cast(pos, dtype=ref.dtype) + neg = tf.cast(neg, dtype=ref.dtype) + + loss_pos_ref = tf.reduce_sum(tf.square(ref - pos), axis=1) + loss_neg_ref = tf.reduce_sum(tf.square(ref - neg), axis=1) + loss_add_sub = tf.math.subtract(tf.math.add(loss_pos_ref, alpha), loss_neg_ref) + loss = tf.maximum(loss_add_sub, 0) + + return loss + + def _triplet_generation(self, X): + """Generate triplet samples (ref, pos, neg) for triplet loss training.""" + n_channels = int(X.shape[-1]) + length_TS = int(X.shape[1]) + + # define mask length + self.mask_length = int(length_TS * self.percentage_mask_length) + + # define weight for each sample in the mixing up + w_ref = np.random.choice( + np.linspace(start=self.weight_ref_min, stop=1, num=1000), size=1 + ) + w_ts = (1 - w_ref) / 2 + + # define your ref as random permutation of X + ref = np.random.permutation(X[:]) + + n = int(ref.shape[0]) + + # define positive and negative sample arrays + _pos = np.zeros(shape=ref.shape) + _neg = np.zeros(shape=ref.shape) + + all_indices = np.arange(start=0, stop=n) + + for i_ref in range(n): + # remove the sample ref from the random choice of pos-neg + all_indices_without_ref = np.delete(arr=all_indices, obj=i_ref) + + # choose a random sample used for the negative generation + index_neg = int(np.random.choice(all_indices_without_ref, size=1)) + + _ref = ref[i_ref].copy() + + # remove the index_neg from choices + all_indices_without_ref_and_not_ref = np.delete( + arr=all_indices, obj=[i_ref, index_neg] + ) + + # choose samples used for the mixing up + index_ts1_pos = int( + np.random.choice(all_indices_without_ref_and_not_ref, size=1) + ) + index_ts2_pos = int( + np.random.choice(all_indices_without_ref_and_not_ref, size=1) + ) + + index_ts1_neg = int( + np.random.choice(all_indices_without_ref_and_not_ref, size=1) + ) + index_ts2_neg = int( + np.random.choice(all_indices_without_ref_and_not_ref, size=1) + ) + + _not_ref = ref[index_neg].copy() + + _ts1_pos = ref[index_ts1_pos].copy() + _ts2_pos = ref[index_ts2_pos].copy() + + _ts1_neg = ref[index_ts1_neg].copy() + _ts2_neg = ref[index_ts2_neg].copy() + + # MixingUp + + if self.use_mixing_up and self.use_masking: + # mix up the selected series with ref to obtain pos + _pos[i_ref] = w_ref * _ref + w_ts * _ts1_pos + w_ts * _ts2_pos + # mix up the selected series with neg ref to obtain neg + _neg[i_ref] = w_ref * _not_ref + w_ts * _ts1_neg + w_ts * _ts2_neg + + # apply masking + _pos[i_ref], _neg[i_ref] = self._apply_masking( + pos=_pos[i_ref], + neg=_neg[i_ref], + n_channels=n_channels, + length_TS=length_TS, + mask_length=self.mask_length, + ) + + elif self.use_mixing_up and not self.use_masking: + # mix up the selected series with ref to obtain pos + _pos[i_ref] = w_ref * _ref + w_ts * _ts1_pos + w_ts * _ts2_pos + # mix up the selected series with neg ref to obtain neg + _neg[i_ref] = w_ref * _not_ref + w_ts * _ts1_neg + w_ts * _ts2_neg + + elif self.use_masking and not self.use_mixing_up: + # apply masking + _pos[i_ref], _neg[i_ref] = self._apply_masking( + pos=_pos[i_ref], + neg=_neg[i_ref], + n_channels=n_channels, + length_TS=length_TS, + mask_length=self.mask_length, + ) + + else: + raise ValueError( + "At least masking or mixing up", + "should be chosen to generate", + "the triplets.", + ) + if self.z_normalize_pos_neg: + # z_normalize pos and neg + _pos_normalized = z_normalization(_pos) + _neg_normalized = z_normalization(_neg) + + return ref, _pos_normalized, _neg_normalized + else: + return ref, _pos, _neg + + def _apply_masking(self, pos, neg, n_channels, length_TS, mask_length): + """Apply masking phase on pos and neg.""" + # select a random start for the mask + start_mask = int(np.random.randint(low=0, high=length_TS - mask_length, size=1)) + stop_mask = start_mask + mask_length + + # define noise on replacement on the left side of the mask + noise_pos_left = np.random.random(size=(start_mask, n_channels)) + # normalize noise + noise_pos_left /= 5 + noise_pos_left -= 0.1 + + # define noise on replacement on the left side of the mask + noise_pos_right = np.random.random(size=(length_TS - stop_mask, n_channels)) + # normalize noise + noise_pos_right /= 5 + noise_pos_right -= 0.1 + + # replace left and right side of the mask by normalized noise + pos[0:start_mask, :] = noise_pos_left + pos[stop_mask:length_TS, :] = noise_pos_right + + # repeat the same procedure for the negative sample + noise_neg_left = np.random.random(size=(start_mask, n_channels)) + noise_neg_left /= 5 + noise_neg_left -= 0.1 + noise_neg_right = np.random.random(size=(length_TS - stop_mask, n_channels)) + noise_neg_right /= 5 + noise_neg_right -= 0.1 + + neg[0:start_mask, :] = noise_neg_left + neg[stop_mask:length_TS, :] = noise_neg_right + + return pos, neg + + def save_last_model_to_file(self, file_path="./"): + """Save the last epoch of the trained deep learning model. + + Parameters + ---------- + file_path : str, default = "./" + The directory where the model will be saved + + Returns + ------- + None + """ + self.model_.save(os.path.join(file_path, self.last_file_name + ".keras")) + + def load_model(self, model_path): + """Load a pre-trained keras model instead of fitting. + + When calling this function, all functionalities can be used + such as predict, predict_proba etc. with the loaded model. + + Parameters + ---------- + model_path : str (path including model name and extension) + The directory where the model will be saved including the model + name with a ".keras" extension. + Example: model_path="path/to/file/best_model.keras" + + Returns + ------- + None + """ + import tensorflow as tf + + self.model_ = tf.keras.models.load_model(model_path) + self.is_fitted = True + + def _get_model_checkpoint_callback(self, callbacks, file_path, file_name): + import tensorflow as tf + + model_checkpoint_ = tf.keras.callbacks.ModelCheckpoint( + filepath=os.path.join(file_path, file_name + ".keras"), + monitor="loss", + save_best_only=True, + ) + + if isinstance(callbacks, list): + return callbacks + [model_checkpoint_] + else: + return [callbacks] + [model_checkpoint_] + + @classmethod + def _get_test_params(cls, parameter_set="default"): + """Return testing parameter settings for the transformer. + + Parameters + ---------- + parameter_set : str, default="default" + Name of the set of test parameters to return, for use in tests. If no + special parameters are defined for a value, will return `"default"` set. + + + Returns + ------- + params : dict or list of dict, default = {} + Parameters to create testing instances of the class + Each dict are parameters to construct an "interesting" test instance, i.e., + `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + """ + from aeon.networks import FCNNetwork + + params = { + "latent_space_dim": 2, + "backbone_network": FCNNetwork(n_layers=1, n_filters=2, kernel_size=2), + "n_epochs": 3, + } + + return [params] diff --git a/aeon/transformations/collection/self_supervised/tests/__init__.py b/aeon/transformations/collection/self_supervised/tests/__init__.py new file mode 100644 index 0000000000..4bd29e9e65 --- /dev/null +++ b/aeon/transformations/collection/self_supervised/tests/__init__.py @@ -0,0 +1 @@ +"""Self-Supervised tests.""" diff --git a/aeon/transformations/collection/self_supervised/tests/test_trilite.py b/aeon/transformations/collection/self_supervised/tests/test_trilite.py new file mode 100644 index 0000000000..03f0e40747 --- /dev/null +++ b/aeon/transformations/collection/self_supervised/tests/test_trilite.py @@ -0,0 +1,251 @@ +"""Test TRILITE Self-supervised transformer.""" + +import tempfile + +import numpy as np +import pytest + +from aeon.networks import LITENetwork +from aeon.networks.tests.test_network_base import DummyDeepNetwork +from aeon.transformations.collection.self_supervised import TRILITE +from aeon.utils.validation._dependencies import _check_soft_dependencies + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("use_mixing_up", [True, False]) +def test_trilite_use_mixing_up(use_mixing_up): + """Test TRILITE with possible mixing up setups.""" + X = np.random.random((100, 2, 5)) + with tempfile.TemporaryDirectory() as tmp: + + ssl = TRILITE( + use_mixing_up=use_mixing_up, + latent_space_dim=2, + backbone_network=DummyDeepNetwork(), + n_epochs=3, + file_path=tmp, + ) + + ssl.fit(X=X) + + X_transformed = ssl.transform(X=X) + + assert len(X_transformed.shape) == 2 + assert int(X_transformed.shape[-1]) == 2 + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("use_masking", [True, False]) +def test_trilite_use_masking(use_masking): + """Test TRILITE with possible masking setups.""" + X = np.random.random((100, 2, 5)) + with tempfile.TemporaryDirectory() as tmp: + + ssl = TRILITE( + use_masking=use_masking, + backbone_network=DummyDeepNetwork(), + latent_space_dim=2, + n_epochs=3, + file_path=tmp, + ) + + ssl.fit(X=X) + + X_transformed = ssl.transform(X=X) + + assert len(X_transformed.shape) == 2 + assert int(X_transformed.shape[-1]) == 2 + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("z_normalize_pos_neg", [True, False]) +def test_trilite_z_normalize_pos_neg(z_normalize_pos_neg): + """Test TRILITE with possible znorm pos and neg setups.""" + X = np.random.random((100, 2, 5)) + with tempfile.TemporaryDirectory() as tmp: + + ssl = TRILITE( + z_normalize_pos_neg=z_normalize_pos_neg, + backbone_network=DummyDeepNetwork(), + latent_space_dim=2, + n_epochs=3, + file_path=tmp, + ) + + ssl.fit(X=X) + + X_transformed = ssl.transform(X=X) + + assert len(X_transformed.shape) == 2 + assert int(X_transformed.shape[-1]) == 2 + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("alpha", [1e-1, 1e-2]) +def test_trilite_alpha(alpha): + """Test TRILITE with possible alpha setups.""" + X = np.random.random((100, 2, 5)) + with tempfile.TemporaryDirectory() as tmp: + + ssl = TRILITE( + alpha=alpha, + backbone_network=DummyDeepNetwork(), + latent_space_dim=2, + n_epochs=3, + file_path=tmp, + ) + + ssl.fit(X=X) + + X_transformed = ssl.transform(X=X) + + assert len(X_transformed.shape) == 2 + assert int(X_transformed.shape[-1]) == 2 + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("weight_ref_min", [0.5, 0.6]) +def test_trilite_weight_ref_min(weight_ref_min): + """Test TRILITE with possible weight_ref_min setups.""" + X = np.random.random((100, 2, 5)) + with tempfile.TemporaryDirectory() as tmp: + + ssl = TRILITE( + weight_ref_min=weight_ref_min, + backbone_network=DummyDeepNetwork(), + latent_space_dim=2, + n_epochs=3, + file_path=tmp, + ) + + ssl.fit(X=X) + + X_transformed = ssl.transform(X=X) + + assert len(X_transformed.shape) == 2 + assert int(X_transformed.shape[-1]) == 2 + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("percentage_mask_length", [0.2, 0.3]) +def test_trilite_percentage_mask_length(percentage_mask_length): + """Test TRILITE with possible percentage_mask_length setups.""" + X = np.random.random((100, 2, 5)) + with tempfile.TemporaryDirectory() as tmp: + + ssl = TRILITE( + percentage_mask_length=percentage_mask_length, + backbone_network=DummyDeepNetwork(), + latent_space_dim=2, + n_epochs=3, + file_path=tmp, + ) + + ssl.fit(X=X) + + X_transformed = ssl.transform(X=X) + + assert len(X_transformed.shape) == 2 + assert int(X_transformed.shape[-1]) == 2 + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("latent_space_dim", [2, 3]) +def test_trilite_latent_space_dim(latent_space_dim): + """Test TRILITE with possible latent_space_dim setups.""" + X = np.random.random((100, 2, 5)) + with tempfile.TemporaryDirectory() as tmp: + + ssl = TRILITE( + latent_space_dim=latent_space_dim, + backbone_network=DummyDeepNetwork(), + n_epochs=3, + file_path=tmp, + ) + + ssl.fit(X=X) + + X_transformed = ssl.transform(X=X) + + assert len(X_transformed.shape) == 2 + assert int(X_transformed.shape[-1]) == latent_space_dim + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("latent_space_activation", ["linear", "relu"]) +def test_trilite_latent_space_activation(latent_space_activation): + """Test TRILITE with possible latent_space_activation setups.""" + X = np.random.random((100, 2, 5)) + with tempfile.TemporaryDirectory() as tmp: + + ssl = TRILITE( + latent_space_activation=latent_space_activation, + backbone_network=DummyDeepNetwork(), + latent_space_dim=2, + n_epochs=3, + file_path=tmp, + ) + + ssl.fit(X=X) + + X_transformed = ssl.transform(X=X) + + assert len(X_transformed.shape) == 2 + assert int(X_transformed.shape[-1]) == 2 + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("backbone_network", [None, DummyDeepNetwork, LITENetwork]) +def test_trilite_backbone_network(backbone_network): + """Test TRILITE with possible backbone_network setups.""" + X = np.random.random((100, 2, 5)) + with tempfile.TemporaryDirectory() as tmp: + + if backbone_network is not None: + ssl = TRILITE( + backbone_network=backbone_network(), + latent_space_dim=2, + n_epochs=3, + file_path=tmp, + ) + else: + ssl = TRILITE( + backbone_network=backbone_network, + latent_space_dim=2, + n_epochs=3, + file_path=tmp, + ) + + ssl.fit(X=X) + + X_transformed = ssl.transform(X=X) + + assert len(X_transformed.shape) == 2 + assert int(X_transformed.shape[-1]) == 2 diff --git a/aeon/utils/repr.py b/aeon/utils/repr.py new file mode 100644 index 0000000000..67b3b75142 --- /dev/null +++ b/aeon/utils/repr.py @@ -0,0 +1,55 @@ +"""Utilities for class __repr__ presentation.""" + +import inspect + +from aeon.testing.utils.deep_equals import deep_equals + + +def get_unchanged_and_required_params_as_str(obj): + """ + Get object parameters as a comma delimited string. + + Collects the parameters of an object that are either required + (no default) or different from the __init__ default value. Returns + the parameter names and values as a comma delimited string. + + Parameters + ---------- + obj : object + The object to inspect. + + Returns + ------- + str + A string representation of the objects parameters and values. + """ + cls = obj.__class__ + signature = inspect.signature(cls.__init__) + + params = {} + for name, param in signature.parameters.items(): + if name == "self": + continue + + has_default = param.default is not inspect.Parameter.empty + current_val = getattr(obj, name, None) + + if not has_default: + # No default = always include + params[name] = current_val + else: + # Default exists = include if unchanged + if not deep_equals(current_val, param.default): + params[name] = current_val + + if len(params) == 0: + return "" + + param_str = [] + for k, v in params.items(): + if isinstance(v, str): + param_str.append(f"{k}='{v}'") + else: + param_str.append(f"{k}={v}") + + return ", ".join(param_str) diff --git a/aeon/utils/self_supervised/__init__.py b/aeon/utils/self_supervised/__init__.py new file mode 100644 index 0000000000..8de476b255 --- /dev/null +++ b/aeon/utils/self_supervised/__init__.py @@ -0,0 +1 @@ +"""Utils for self_supervised.""" diff --git a/aeon/utils/self_supervised/general.py b/aeon/utils/self_supervised/general.py new file mode 100644 index 0000000000..b3d2da7bfd --- /dev/null +++ b/aeon/utils/self_supervised/general.py @@ -0,0 +1,28 @@ +"""General utils for self_supervised.""" + +__all__ = ["z_normalization"] + +import numpy as np + + +def z_normalization(X, axis=1): + """Z-Normalize collection of time series. + + Parameters + ---------- + X : np.ndarray + The input collection of time series of shape + (n_cases, n_channels, n_timepoints). + axis : int, default = 1 + The axis of time, on which z-normalization + is performed. + + Returns + ------- + Normalized version of X. + """ + stds = np.std(X, axis=axis, keepdims=True) + if len(stds[stds == 0.0]) > 0: + stds[stds == 0.0] = 1.0 + return (X - X.mean(axis=axis, keepdims=True)) / stds + return (X - X.mean(axis=axis, keepdims=True)) / (X.std(axis=axis, keepdims=True)) diff --git a/docs/api_reference/transformations.rst b/docs/api_reference/transformations.rst index 138ca4ffcb..d24aa47196 100644 --- a/docs/api_reference/transformations.rst +++ b/docs/api_reference/transformations.rst @@ -126,6 +126,17 @@ Interval based SupervisedIntervals QUANTTransformer +Self Supervised +~~~~~~~~~~~~~~~ + +.. currentmodule:: aeon.transformations.collection.self_supervised + +.. autosummary:: + :toctree: auto_generated/ + :template: class.rst + + TRILITE + Shapelet based ~~~~~~~~~~~~~~ diff --git a/docs/index.md b/docs/index.md index a4b80ee8f0..745254296e 100644 --- a/docs/index.md +++ b/docs/index.md @@ -276,6 +276,7 @@ experimental modules are: - `segmentation` - `similarity_search` - `visualisation` +- `transformations.collection.self_supervised` ```{toctree} :caption: Using aeon From 889003652620cfe6cd9b3824716f7b633311b115 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Thu, 5 Jun 2025 11:20:47 +0100 Subject: [PATCH 132/216] update slack link (#2877) --- .github/ISSUE_TEMPLATE/config.yml | 2 +- .github/utilities/pr_open_commenter.py | 2 +- CODE_OF_CONDUCT.md | 2 +- README.md | 4 ++-- docs/code_of_conduct.md | 2 +- docs/conf.py | 2 +- docs/contributing.md | 4 ++-- docs/index.md | 2 +- docs/projects.md | 2 +- 9 files changed, 11 insertions(+), 11 deletions(-) diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml index 37132856e9..cf805d095a 100644 --- a/.github/ISSUE_TEMPLATE/config.yml +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -3,7 +3,7 @@ contact_links: url: https://github.com/aeon-toolkit/aeon/discussions/new/choose about: If none of these options fit, your question or topic may be better suited to a discussion. - name: "\U0001F4AC Slack" - url: https://join.slack.com/t/aeon-toolkit/shared_invite/zt-22vwvut29-HDpCu~7VBUozyfL_8j3dLA + url: https://join.slack.com/t/aeon-toolkit/shared_invite/zt-36dlmbouu-vajTShUYAHopSXUUVtHGzw about: For all other questions and general chat, come chat with the aeon community on Slack! - name: "\u2709\uFE0F Email" url: https://mailxto.com/jbp3ave49x diff --git a/.github/utilities/pr_open_commenter.py b/.github/utilities/pr_open_commenter.py index 0c3b664daf..dc6023294e 100755 --- a/.github/utilities/pr_open_commenter.py +++ b/.github/utilities/pr_open_commenter.py @@ -112,7 +112,7 @@ If our `pre-commit` code quality check fails, any trivial fixes will automatically be pushed to your PR unless it is a draft. -Don't hesitate to ask questions on the `aeon` [Slack](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-22vwvut29-HDpCu~7VBUozyfL_8j3dLA) channel if you have any. +Don't hesitate to ask questions on the `aeon` [Slack](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-36dlmbouu-vajTShUYAHopSXUUVtHGzw) channel if you have any. ### PR CI actions diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md index 50a10ab20e..090d0e8683 100644 --- a/CODE_OF_CONDUCT.md +++ b/CODE_OF_CONDUCT.md @@ -9,4 +9,4 @@ the [Python Software Foundation Code of Conduct](https://www.python.org/psf/code If you want to make a CoC report, contact the general `aeon` email [contact@aeon-toolkit.org](mailto:contact@aeon-toolkit.org) or message a member of the [Code of Conduct Workgroup (COCW)](https://www.aeon-toolkit.org/en/stable/about.html#code-of-conduct-workgroup) -directly on [Slack](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-22vwvut29-HDpCu~7VBUozyfL_8j3dLA). +directly on [Slack](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-36dlmbouu-vajTShUYAHopSXUUVtHGzw). diff --git a/README.md b/README.md index c66d9c9a03..acc652fa82 100644 --- a/README.md +++ b/README.md @@ -32,7 +32,7 @@ does not apply: 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[![!linkedin](https://img.shields.io/static/v1?logo=linkedin&label=LinkedIn&message=news&color=lightblue)](https://www.linkedin.com/company/aeon-toolkit/) [![!x-twitter](https://img.shields.io/static/v1?logo=x&label=X/Twitter&message=news&color=lightblue)](https://twitter.com/aeon_toolkit) | | **Affiliation** | [![numfocus](https://img.shields.io/badge/NumFOCUS-Affiliated%20Project-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org/sponsored-projects/affiliated-projects) | ## ⚙️ Installation @@ -127,7 +127,7 @@ clu.predict(X_test) # Assign clusters to new data [GitHub Issue Tracker]: https://github.com/aeon-toolkit/aeon/issues [GitHub Discussions]: https://github.com/aeon-toolkit/aeon/discussions -[Slack]: https://join.slack.com/t/aeon-toolkit/shared_invite/zt-22vwvut29-HDpCu~7VBUozyfL_8j3dLA +[Slack]: https://join.slack.com/t/aeon-toolkit/shared_invite/zt-36dlmbouu-vajTShUYAHopSXUUVtHGzw For enquiries about the project or collaboration, our email is [contact@aeon-toolkit.org](mailto:contact@aeon-toolkit.org). diff --git a/docs/code_of_conduct.md b/docs/code_of_conduct.md index 0c49c2b08c..65e3835ac6 100644 --- a/docs/code_of_conduct.md +++ b/docs/code_of_conduct.md @@ -37,7 +37,7 @@ If you believe someone is violating the CoC, we ask that you report it by contacting the COCW. All reports will be kept confidential. You can make a report by emailing the general `aeon` contact address [contact@aeon-toolkit.org](mailto:contact@aeon-toolkit.org), or by messaging [members of the COCW](./about.md#code-of-conduct-workgroup) -directly on [Slack](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-22vwvut29-HDpCu~7VBUozyfL_8j3dLA). +directly on [Slack](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-36dlmbouu-vajTShUYAHopSXUUVtHGzw). Thank you for reading and helping make this a welcoming, friendly community for everyone. diff --git a/docs/conf.py b/docs/conf.py index 76203f9890..e0e876bbbd 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -217,7 +217,7 @@ def find_source(): "footer_icons": [ { "name": "Slack", - "url": "https://join.slack.com/t/aeon-toolkit/shared_invite/zt-22vwvut29-HDpCu~7VBUozyfL_8j3dLA", # noqa: E501 + "url": "https://join.slack.com/t/aeon-toolkit/shared_invite/zt-36dlmbouu-vajTShUYAHopSXUUVtHGzw", # noqa: E501 "html": """ diff --git a/docs/contributing.md b/docs/contributing.md index 5757759e08..1b1003272e 100644 --- a/docs/contributing.md +++ b/docs/contributing.md @@ -38,7 +38,7 @@ stuck, please don’t hesitate to talk with us or raise an issue. Recommended steps for first time contributors, or to get started with regular contributions: -1. Say hello in the `introductions` or `contributors` channel on [Slack](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-22vwvut29-HDpCu~7VBUozyfL_8j3dLA) +1. Say hello in the `introductions` or `contributors` channel on [Slack](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-36dlmbouu-vajTShUYAHopSXUUVtHGzw) and mention which places you are interested in contributing to. 2. Get setup for development, see the [developer install instructions](developer_guide/dev_installation.md) for creating a fork of `aeon`. @@ -84,7 +84,7 @@ contributing, but if your issue to related to a specific topic i.e. documentatio testing you may find it useful. If your intended method of contribution does not fit into the above steps, please -reach out to us on [Slack](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-22vwvut29-HDpCu~7VBUozyfL_8j3dLA) +reach out to us on [Slack](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-36dlmbouu-vajTShUYAHopSXUUVtHGzw) for discussion. While GitHub contributions are the most common, it is not the only way to contribute to `aeon`. diff --git a/docs/index.md b/docs/index.md index 745254296e..e19c181484 100644 --- a/docs/index.md +++ b/docs/index.md @@ -21,7 +21,7 @@ segmentation and similarity search. **GitHub**: [github.com/aeon-toolkit/aeon](https://github.com/aeon-toolkit/aeon) -**Slack**: [aeon slack](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-22vwvut29-HDpCu~7VBUozyfL_8j3dLA) +**Slack**: [aeon slack](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-36dlmbouu-vajTShUYAHopSXUUVtHGzw) **Twitter**: [twitter/aeon-toolkit](https://twitter.com/aeon_toolkit) diff --git a/docs/projects.md b/docs/projects.md index bf9d62c19b..1337f015f2 100644 --- a/docs/projects.md +++ b/docs/projects.md @@ -9,7 +9,7 @@ base. These projects are designed for internships, usage as part of undergraduate/postgraduate projects at academic institutions, options for programs such as [Google Summer of Code (GSoC)](https://summerofcode.withgoogle.com/) or just for personal side projects. For those interested in undertaking a project outside these scenarios, we recommend -joining the [Slack](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-22vwvut29-HDpCu~7VBUozyfL_8j3dLA)and discussing with the community. +joining the [Slack](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-36dlmbouu-vajTShUYAHopSXUUVtHGzw)and discussing with the community. Feel free to propose your own project ideas, but please discuss them with us first. We have an active community of researchers and students who work on `aeon`. From ca509f467a39c25b7158170d671fe1bacfbce8e0 Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Fri, 6 Jun 2025 15:23:56 +0100 Subject: [PATCH 133/216] [BUG] Switch results loaders from http to https (#2883) * https * https in tests --- aeon/benchmarking/results_loaders.py | 6 +++--- aeon/benchmarking/tests/test_results_loaders.py | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/aeon/benchmarking/results_loaders.py b/aeon/benchmarking/results_loaders.py index b3ed1deaec..2b4ee45bca 100644 --- a/aeon/benchmarking/results_loaders.py +++ b/aeon/benchmarking/results_loaders.py @@ -228,7 +228,7 @@ def get_available_estimators( f"be one of {VALID_TASK_TYPES}" ) data = pd.read_csv( - f"http://timeseriesclassification.com/results/ReferenceResults/" + f"https://timeseriesclassification.com/results/ReferenceResults/" f"{t}/estimators.txt" ) return data.iloc[:, 0].tolist() if as_list else data @@ -241,7 +241,7 @@ def get_estimator_results( task: str = "classification", measure: str = "accuracy", remove_dataset_modifiers: bool = False, - path: str = "http://timeseriesclassification.com/results/ReferenceResults", + path: str = "https://timeseriesclassification.com/results/ReferenceResults", ): """Look for results for given estimators for a list of datasets. @@ -327,7 +327,7 @@ def get_estimator_results_as_array( task: str = "classification", measure: str = "accuracy", remove_dataset_modifiers: bool = False, - path: str = "http://timeseriesclassification.com/results/ReferenceResults", + path: str = "https://timeseriesclassification.com/results/ReferenceResults", include_missing: bool = False, ): """Look for results for given estimators for a list of datasets. diff --git a/aeon/benchmarking/tests/test_results_loaders.py b/aeon/benchmarking/tests/test_results_loaders.py index b6deaf1d82..8c44912403 100644 --- a/aeon/benchmarking/tests/test_results_loaders.py +++ b/aeon/benchmarking/tests/test_results_loaders.py @@ -85,7 +85,7 @@ def test_get_available_estimators(): ) @pytest.mark.xfail(raises=CONNECTION_ERRORS) @pytest.mark.parametrize( - "path", [data_path, "http://timeseriesclassification.com/results/ReferenceResults"] + "path", [data_path, "https://timeseriesclassification.com/results/ReferenceResults"] ) def test_get_estimator_results(path): """Test loading results returned in a dict.""" @@ -120,7 +120,7 @@ def test_get_estimator_results(path): ) @pytest.mark.xfail(raises=CONNECTION_ERRORS) @pytest.mark.parametrize( - "path", [data_path, "http://timeseriesclassification.com/results/ReferenceResults"] + "path", [data_path, "https://timeseriesclassification.com/results/ReferenceResults"] ) def test_get_estimator_results_as_array(path): """Test loading results returned in an array.""" From f8c6d8c489515e39444a80d12be5c751d964b379 Mon Sep 17 00:00:00 2001 From: Kaustubh <97254178+Kaustbh@users.noreply.github.com> Date: Sat, 7 Jun 2025 02:10:55 +0530 Subject: [PATCH 134/216] [ENH] Correct broken source links for `set_params` and `get_params` in Aeon estimator docs (#2754) * Correct broken source links for `set_params` and `get_params` in Aeon estimator docs * made necessary changes * made necessary changes * modified linkcode_resolve * added comments --- docs/conf.py | 46 +++++++++++++++++++++++++++++++++++++++------- 1 file changed, 39 insertions(+), 7 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index e0e876bbbd..89da0515ed 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -166,23 +166,55 @@ def linkcode_resolve(domain, info): def find_source(): # try to find the file and line number, based on code from numpy: # https://github.com/numpy/numpy/blob/main/doc/source/conf.py#L286 - obj = sys.modules[info["module"]] - for part in info["fullname"].split("."): - obj = getattr(obj, part) + import inspect import os - obj = inspect.unwrap(obj) + # Get the top-level object from the module name + obj = sys.modules[info["module"]] + + # Traverse dotted path (e.g., module.submodule.Class.method) + for part in info["fullname"].split("."): + obj = getattr(obj, part) - fn = inspect.getsourcefile(obj) - fn = os.path.relpath(fn, start=os.path.dirname(aeon.__file__)) + # Unwrapping decorators (if any), so we can get the true + # source function + if inspect.isfunction(obj): + obj = inspect.unwrap(obj) + + # Get the source filename + try: + fn = inspect.getsourcefile(obj) + except TypeError: + fn = None + + # If no source file is found, return None (no link) + if not fn: + return None + + # Make filename relative to the aeon source directory + startdir = Path(aeon.__file__).parent.parent + try: + fn = os.path.relpath(fn, start=startdir).replace(os.path.sep, "/") + except ValueError: + return None + + # Filter out files not in the aeon package + # (e.g., inherited from sklearn) + if not fn.startswith("aeon/"): + return None + + # Get line range of the object source, lineno = inspect.getsourcelines(obj) return fn, lineno, lineno + len(source) - 1 if domain != "py" or not info["module"]: return None try: - filename = "aeon/%s#L%d-L%d" % find_source() + result = find_source() + if not result: + return None + filename = "%s#L%d-L%d" % result except Exception: filename = info["module"].replace(".", "/") + ".py" return "https://github.com/aeon-toolkit/aeon/blob/{}/{}".format( From d28a13482a7f4d2727f0ea98342ec47a1be9e5d0 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Sun, 8 Jun 2025 11:20:37 +0100 Subject: [PATCH 135/216] open PR comment changes (#2878) --- .github/utilities/pr_open_commenter.py | 36 ++++++++++---------------- 1 file changed, 13 insertions(+), 23 deletions(-) diff --git a/.github/utilities/pr_open_commenter.py b/.github/utilities/pr_open_commenter.py index dc6023294e..f499f63f1e 100755 --- a/.github/utilities/pr_open_commenter.py +++ b/.github/utilities/pr_open_commenter.py @@ -34,22 +34,10 @@ if label == cur_label: content_labels[i] = new_label -labels = [(label.name, label.color) for label in repo.get_labels()] -title_labels = [ - f"$\\color{{#{color}}}{{\\textsf{{{label}}}}}$" - for label, color in labels - if label in title_labels -] -title_labels_new = [ - f"$\\color{{#{color}}}{{\\textsf{{{label}}}}}$" - for label, color in labels - if label in title_labels_new -] -content_labels = [ - f"$\\color{{#{color}}}{{\\textsf{{{label}}}}}$" - for label, color in labels - if label in content_labels -] +labels = [label.name for label in repo.get_labels()] +title_labels = [f"**{label}**" for label in labels if label in title_labels] +title_labels_new = [f"**{label}**" for label in labels if label in title_labels_new] +content_labels = [f"**{label}**" for label in labels if label in content_labels] title_labels_str = "" if len(title_labels) == 0: @@ -62,15 +50,13 @@ arr_str = str(title_labels_new).strip("[]").replace("'", "") title_labels_str = ( "I have added the following labels to this PR based on the title: " - f"**[ {arr_str} ]**." + f"[ {arr_str} ]." ) if len(title_labels) != len(title_labels_new): arr_str = ( str(set(title_labels) - set(title_labels_new)).strip("[]").replace("'", "") ) - title_labels_str += ( - f" The following labels were already present: **[ {arr_str} ]**" - ) + title_labels_str += f" The following labels were already present: [ {arr_str} ]" content_labels_str = "" if len(content_labels) != 0: @@ -78,14 +64,14 @@ arr_str = str(content_labels).strip("[]").replace("'", "") content_labels_str = ( "I have added the following labels to this PR based on " - f"the changes made: **[ {arr_str} ]**. Feel free " + f"the changes made: [ {arr_str} ]. Feel free " "to change these if they do not properly represent the PR." ) elif content_labels_status == "ignored": arr_str = str(content_labels).strip("[]").replace("'", "") content_labels_str = ( "I would have added the following labels to this PR " - f"based on the changes made: **[ {arr_str} ]**, " + f"based on the changes made: [ {arr_str} ], " "however some package labels are already present." ) elif content_labels_status == "large": @@ -114,7 +100,8 @@ Don't hesitate to ask questions on the `aeon` [Slack](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-36dlmbouu-vajTShUYAHopSXUUVtHGzw) channel if you have any. -### PR CI actions +
PR CI actions +

These checkboxes will add labels to enable/disable CI functionality for this PR. This may not take effect immediately, and a new commit may be required to run the new configuration. @@ -126,5 +113,8 @@ - [ ] Stop automatic `pre-commit` fixes (always disabled for drafts) - [ ] Disable numba cache loading - [ ] Push an empty commit to re-run CI checks + +

+
""" # noqa ) From f1e0b966eb5ac2b0a75abe5285601ab4ec2ea985 Mon Sep 17 00:00:00 2001 From: Balgopal Moharana <99070111+lucifer4073@users.noreply.github.com> Date: Sun, 8 Jun 2025 15:58:00 +0530 Subject: [PATCH 136/216] [ENH] Implemented Tracking differentiator-based multiview dilated characteristics (TD-MVDC) Classifier (#2535) * Files and structure of tdmvdc class added * utility functions for tdmvdc applied * unecessary files removed * Initial implementation of TDMVDC added * accuracy improved to the required 81 per cent * test params added for tdmvdc * n_jobs temporarily disabled * n_jobs test params removed * Revert "n_jobs test params removed" This reverts commit 91539099f3cd3a201b7fe68bfea80a9ae5ebc706. * multithreading capability temporarily removed from tdmvdc * multithreading added * n_jobs removed from test_param * test added for tdmvdc * test added for tdmvdc * optimized comments * Made tdmvdc uniform across all feauture based classifiers * tdmvdc tested * check constructor test exlcuded for tdmvdc * changed * init file changed * check constructor corrected tdmvdc --------- Co-authored-by: Matthew Middlehurst Co-authored-by: Tony Bagnall --- aeon/classification/feature_based/__init__.py | 2 + aeon/classification/feature_based/_tdmvdc.py | 629 ++++++++++++++++++ .../feature_based/tests/test_tdmvdc.py | 66 ++ examples/classification/feature_based.ipynb | 120 +++- 4 files changed, 793 insertions(+), 24 deletions(-) create mode 100644 aeon/classification/feature_based/_tdmvdc.py create mode 100644 aeon/classification/feature_based/tests/test_tdmvdc.py diff --git a/aeon/classification/feature_based/__init__.py b/aeon/classification/feature_based/__init__.py index 7e16c0de3c..018ec9c1ba 100644 --- a/aeon/classification/feature_based/__init__.py +++ b/aeon/classification/feature_based/__init__.py @@ -10,10 +10,12 @@ "SummaryClassifier", "TSFreshClassifier", "FreshPRINCEClassifier", + "TDMVDCClassifier", ] from aeon.classification.feature_based._catch22 import Catch22Classifier from aeon.classification.feature_based._fresh_prince import FreshPRINCEClassifier from aeon.classification.feature_based._signature_classifier import SignatureClassifier from aeon.classification.feature_based._summary import SummaryClassifier +from aeon.classification.feature_based._tdmvdc import TDMVDCClassifier from aeon.classification.feature_based._tsfresh import TSFreshClassifier diff --git a/aeon/classification/feature_based/_tdmvdc.py b/aeon/classification/feature_based/_tdmvdc.py new file mode 100644 index 0000000000..defb70bc96 --- /dev/null +++ b/aeon/classification/feature_based/_tdmvdc.py @@ -0,0 +1,629 @@ +"""Tracking Differentiator-based Multiview Dilated Characteristics (TDMVDC). + +Ensemble classifier using TSFresh features and ANOVA, +with RidgeClassifierCV and hard voting. +""" + +import numpy as np +from joblib import Parallel, delayed +from sklearn.feature_selection import f_classif +from sklearn.linear_model import RidgeClassifierCV +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import StandardScaler + +from aeon.classification.base import BaseClassifier +from aeon.transformations.collection.feature_based import TSFresh +from aeon.utils.validation import check_n_jobs + +__all__ = ["TDMVDCClassifier"] + + +class TDMVDCClassifier(BaseClassifier): + """Tracking Differentiator-based Multiview Dilated Characteristics classifier. + + The TDMVDCClassifier is an advanced ensemble classifier tailored for + time series classification tasks. It operates by transforming the + input time series data through a tracking differentiator, generating + three distinct views: the original signal, the first-order differential, + and the second-order differential. Each of these views is further processed + using a set of dilation rates, allowing the model to capture temporal + dependencies and patterns at multiple scales. + + For each dilated view, the classifier extracts a comprehensive set of + features using the TSFresh feature extraction framework. These features are + then evaluated using ANOVA F-values to determine their relevance to the + classification task. Multiple classifiers are constructed, each trained on a + different proportion of the most informative features, as determined by the + feature selection process. The ensemble of classifiers provides robust + predictions by aggregating their outputs through a hard voting mechanism, + which enhances generalization and reduces the risk of overfitting. + + The TDMVDCClassifier is highly parallelized, supporting multi-threaded feature + extraction and model training to efficiently handle large datasets. Its design + is particularly effective for time series problems where both the original and + differential characteristics of the data, as well as multiscale temporal patterns, + are important for accurate classification. + + Parameters + ---------- + default_fc_parameters : str, default="efficient" + Specifies the set of TSFresh features to extract. Options include "minimal", + "efficient", or "comprehensive". + k1 : float, default=2 + Filter parameter for the first tracking differentiator, controlling the + generation of first-order differential series. + k2 : float, default=2 + Filter parameter for the second tracking differentiator, controlling the + generation of second-order differential series. + feature_store_ratios : list, default=None + List of feature retention ratios for different feature selectors. + If None, defaults to [0.1, 0.2, 0.3, 0.4, 0.5]. + n_jobs : int, default=1 + Number of parallel jobs to run for feature extraction and model training. + "-1" uses all available processors. + parallel_backend : str, ParallelBackendBase instance or None, default=None + Specifies the parallelization backend for joblib. Options include "loky", + "multiprocessing", "threading", or a custom backend. + + Attributes + ---------- + n_classes_ : int + Number of unique classes in the training data. + classes_ : ndarray of shape (n_classes_) + Array of class labels. + clfList_ : list + List of trained classifier pipelines for each feature subset. + dList_ : ndarray + Array of dilation rates used for multiscale feature extraction. + tsFreshListR_ : list + List of TSFresh feature extractors for original signal at each dilation rate. + tsFreshListF_ : list + List of TSFresh feature extractors for the first-order differential signal + at each dilation rate. + tsFreshListS_ : list + List of TSFresh feature extractors for the second-order differential signal + at each dilation rate. + scoreRFS_ : ndarray + Array of ANOVA F-values for all extracted features, used for feature selection. + + Notes + ----- + The TDMVDCClassifier is particularly effective for time series datasets where + capturing both the original and differential dynamics, as well as multiscale + temporal features, is crucial for distinguishing between classes. Its ensemble + approach and feature selection strategy help to mitigate overfitting and improve + predictive performance on complex datasets. + + For the algorithm details, see [1]_. + + References + ---------- + .. [1] Changchun He, and Xin Huo. "Tracking Differentiator-based Multiview Dilated + Characteristics for Time Series Classification." in The 22nd IEEE International + Conference on Industrial Informatics (INDIN2024) (2024). + """ + + _tags = { + "capability:multithreading": True, + "algorithm_type": "feature", + "python_dependencies": "tsfresh", + } + + def __init__( + self, + default_fc_parameters="efficient", + k1=2, + k2=2, + feature_store_ratios=None, + n_jobs=1, + parallel_backend=None, + ): + self.default_fc_parameters = default_fc_parameters + self.k1 = k1 + self.k2 = k2 + self.feature_store_ratios = feature_store_ratios + self.n_jobs = n_jobs + self.parallel_backend = parallel_backend + super().__init__() + + def _series_set_dilation(self, seriesX, d_rate=1): + """ + Map each series of the time series set by dilation mapping. + + Should have the same dilation rate. + + Parameters + ---------- + seriesX : 3D np.ndarray of shape = [n_cases, n_channels, n_timepoints] + The set of three dimensional time series set to be dilated. + d_rate : int, default=1 + Dilation rate. + + References + ---------- + .. [1] P. Schaefer and U. Leser, "WEASEL 2.0: a random dilated dictionary + transform for fast, accurate and memory constrained time series classification" + Machine Learning, vol. 112, no. 12, pp. 4763–4788, Dec.(2024). + """ + n_cases, n_channels, _ = seriesX.shape[:] + seriesXE = np.zeros_like(seriesX) # Initializing the dilated time series set + for i in range(n_cases): + for j in range(n_channels): + series_ = [] + for d in range(d_rate): + series_.append(seriesX[i, j, d::d_rate]) + seriesXE[i, j, :] = np.hstack(series_) + return seriesXE # Return the dilated time series set + + def _fhan(self, x1, x2, r, h0): + """ + Calculate differential signal based on optimal control. + + Parameters + ---------- + x1 : float + State 1 of the observer. + x2 : float + State 2 of the observer. + r: float + Velocity factor used to control tracking speed. + h0 : float + Step size. + + References + ---------- + .. [1] J. Han, "From PID to active disturbance rejection control" IEEE Trans. + Ind. Electron., vol. 56, no. 3, pp. 900-906, Mar. (2009).. + """ + d = r * h0 + d0 = d * h0 + y = x1 + h0 * x2 # Computing the differential signal + a0 = np.sqrt(d * d + 8 * r * np.abs(y)) + if np.abs(y) > d0: + a = x2 + (a0 - d) / 2.0 * np.sign(y) + else: + a = x2 + y / h0 + if np.abs(a) <= d: # Computing the input u of observer + u = -r * a / d + else: + u = -r * np.sign(a) + return u, y # Return input u of observer, and differential signal y + + def _td(self, signal, r=100, k=3, h=1): + """ + Compute a differential signal using the tracking differentiator. + + with an adjustable filter factor. + + Parameters + ---------- + signal : 1D np.ndarray of shape = [n_timepoints] + Original time series + r : float + Velocity factor used to control tracking speed. + k: float + Filter factor. + h : float + Step size. + + References + ---------- + .. [1] J. Han, "From PID to active disturbance rejection control" IEEE Trans. + Ind. Electron., vol. 56, no. 3, pp. 900-906, Mar. (2009).. + """ + x1 = signal[0] # Initializing state 1 + x2 = -(signal[1] - signal[0]) / h # Initializing state 2 + h0 = k * h + signalTD = np.zeros(len(signal)) + dSignal = np.zeros(len(signal)) + for i in range(len(signal)): + v = signal[i] + x1k = x1 + x2k = x2 + x1 = x1k + h * x2k # Update state 1 + u, y = self._fhan( + x1k - v, x2k, r, h0 + ) # Update input u of observer and differential signal y + x2 = x2k + h * u # Update state 2 + dSignal[i] = y + signalTD[i] = x1 + dSignal = -dSignal / h0 # Scale transform + return dSignal[1:] # Return the differential signal + + def _series_transform(self, seriesX, mode=1, k1=2, k2=2): + """ + Transform each series of the time series set using a tracking differentiator. + + with an adjustable filter factor. + + Parameters + ---------- + seriesX : 3D np.ndarray of shape = [n_cases, n_channels, n_timepoints] + The set of three dimensional time series set to be dilated. + mode : int, default=1 + The flag bit of a first-order or second-order derivative is used. + Computing the first-order derivative when mode=1, + and computing the second-order derivative when mode=2 + k1 : float, default=2 + Filter factor 1 of the tracking differentiator 1. + k2 : float, default=2 + Filter factor 2 of the tracking differentiator 2. + This parameter is invalid when mode=2. + + References + ---------- + .. [1] J. Han, "From PID to active disturbance rejection control" IEEE Trans. + Ind. Electron., vol. 56, no. 3, pp. 900-906, Mar. (2009).. + """ + from sklearn.preprocessing import scale + + n_cases, n_channels, n_timepoints = seriesX.shape[:] + if mode == 1: # First-order derivative + seriesFX = np.zeros((n_cases, n_channels, n_timepoints - 1)) + for i in range(n_cases): + for j in range(n_channels): + seriesFX[i, j, :] = self._td(seriesX[i, j, :], k=k1) + seriesFX[i, j, :] = scale(seriesFX[i, j, :]) + return seriesFX # Return the first-order differential time series set + if mode == 2: # Second-order derivative + seriesSX = np.zeros((n_cases, n_channels, n_timepoints - 2)) + for i in range(n_cases): + for j in range(n_channels): + seriesF_ = self._td(seriesX[i, j, :], k=k1) + seriesSX[i, j, :] = self._td(seriesF_, k=k2) + seriesSX[i, j, :] = scale(seriesSX[i, j, :]) + return seriesSX # Return the second-order differential time series set + + def _hard_voting(self, testYList): + """ + Obtain the predicted labels by hard voting. + + to process the labels matrix from multiple classifiers. + + Parameters + ---------- + testYList : 2D np.ndarray of shape = [n_classifierss, n_cases] + """ + uniqueY = np.unique(testYList) # Holds the label for each class + n_classes = len(uniqueY) # Number of classes + n_classifiers, n_cases = testYList.shape[ + : + ] # Number of classifiers, Number of cases + testVY = np.zeros( + n_cases, int + ) # 1 * n_cases, Initializing the predicted labels + testWeightArray = np.zeros( + (n_classes, n_cases) + ) # n_classes * n_cases, Label weight matrix for samples + for i in range(n_cases): + for j in range(n_classifiers): + label_ = testYList[j, i] + index_ = np.arange(n_classes)[uniqueY == label_] + testWeightArray[ + index_, i + ] += 1 # The label weight for the sample is + 1 + for i in range(n_cases): # Predicting each sample label + testVY[i] = uniqueY[ + np.argmax(testWeightArray[:, i]) + ] # The label is predicted to be the most weighted + return testVY # return the predicted labels + + def _fit(self, X, y): + """Fit a pipeline on cases (X, y). + + Parameters + ---------- + X : 3D np.ndarray of shape = [n_cases, n_channels, n_timepoints] + The training data. + y : array-like, shape = [n_cases] + The class labels. + + Returns + ------- + self : + Reference to self. + """ + # Initialization of dilation rate parameters + n_timepoints = X.shape[2] + d_min = 0 + d_max = int(np.log2(n_timepoints - 1) - 3) + d_max = np.min([5, d_max]) + self.dList_ = 2 ** np.arange(d_min, d_max + 1) + + # Differential transformations by tracking differentiator + X_F = self._series_transform(X, mode=1, k1=self.k1) + X_S = self._series_transform(X, mode=2, k1=self.k1, k2=self.k2) + + # Feature extraction + self.tsFreshListR_ = [] + self.tsFreshListF_ = [] + self.tsFreshListS_ = [] + + # Train feature sets + RXList = [] + FXList = [] + SXList = [] + + # Use parallel processing for feature extraction + threads_to_use = check_n_jobs(self.n_jobs) + + # Extract features for each dilation rate in parallel + results = Parallel( + n_jobs=threads_to_use, backend=self.parallel_backend, prefer="threads" + )( + delayed(self._extract_features_for_dilation)(X, X_F, X_S, d_rate, y) + for d_rate in self.dList_ + ) + + # Unpack results + for tsFreshR, tsFreshF, tsFreshS, RX, FX, SX in results: + self.tsFreshListR_.append(tsFreshR) + self.tsFreshListF_.append(tsFreshF) + self.tsFreshListS_.append(tsFreshS) + RXList.append(RX) + FXList.append(FX) + SXList.append(SX) + + # Concatenating all the dilated features + RX = np.hstack(RXList) + FX = np.hstack(FXList) + SX = np.hstack(SXList) + + # Computing feature scores + self.scoreRFS_ = f_classif(np.hstack((RX, FX, SX)), y)[0] + self.scoreRFS_[np.isnan(self.scoreRFS_)] = 0 + + # Training the classifier on each view + self.clfList_ = [] + + # Train classifiers for each feature ratio in parallel + # Ensure feature_store_ratios is set + feature_store_ratios = ( + self.feature_store_ratios + if self.feature_store_ratios is not None + else [0.1, 0.2, 0.3, 0.4, 0.5] + ) + + self.clfList_ = Parallel( + n_jobs=threads_to_use, backend=self.parallel_backend, prefer="threads" + )( + delayed(self._train_classifier_for_ratio)(RX, FX, SX, y, ratio) + for ratio in feature_store_ratios + ) + + return self + + def _extract_features_for_dilation(self, X, X_F, X_S, d_rate, y): + """Extract features for a specific dilation rate. + + Parameters + ---------- + X : 3D np.ndarray + Original signal. + X_F : 3D np.ndarray + First-order differential signal. + X_S : 3D np.ndarray + Second-order differential signal. + d_rate : int + Dilation rate. + y : array-like + Class labels. + + Returns + ------- + tuple + Tuple containing feature extractors and extracted features. + """ + # Dilation Mapping + RX_E = self._series_set_dilation(X, d_rate) + FX_E = self._series_set_dilation(X_F, d_rate) + SX_E = self._series_set_dilation(X_S, d_rate) + + # Extracting the TSFresh features + tsFreshR = TSFresh( + default_fc_parameters=self.default_fc_parameters, n_jobs=self._n_jobs + ) + tsFreshR.fit(RX_E, y) + RX = np.array(tsFreshR.transform(RX_E)) + + tsFreshF = TSFresh( + default_fc_parameters=self.default_fc_parameters, n_jobs=self._n_jobs + ) + tsFreshF.fit(FX_E, y) + FX = np.array(tsFreshF.transform(FX_E)) + + tsFreshS = TSFresh( + default_fc_parameters=self.default_fc_parameters, n_jobs=self._n_jobs + ) + tsFreshS.fit(SX_E, y) + SX = np.array(tsFreshS.transform(SX_E)) + + return tsFreshR, tsFreshF, tsFreshS, RX, FX, SX + + def _train_classifier_for_ratio(self, RX, FX, SX, y, ratio): + """Train a classifier for a specific feature ratio. + + Parameters + ---------- + RX : 2D np.ndarray + Features from original signal. + FX : 2D np.ndarray + Features from first-order differential signal. + SX : 2D np.ndarray + Features from second-order differential signal. + y : array-like + Class labels. + ratio : float + Feature store ratio. + + Returns + ------- + Pipeline + Trained classifier pipeline. + """ + clf = Pipeline( + [ + ("scaler", StandardScaler()), + ("ridge", RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))), + ] + ) + + bestIndex_ = np.argsort(self.scoreRFS_)[::-1][ + 0 : int(len(self.scoreRFS_) * ratio) + ] + + clf.fit(np.hstack((RX, FX, SX))[:, bestIndex_], y) + + return clf + + def _predict(self, X): + """Predict class values of n instances in X. + + Parameters + ---------- + X : 3D np.ndarray of shape = [n_cases, n_channels, n_timepoints] + The data to make predictions for. + + Returns + ------- + y : array-like, shape = [n_cases] + Predicted class labels. + """ + X_F = self._series_transform(X, mode=1, k1=self.k1) + X_S = self._series_transform(X, mode=2, k1=self.k1, k2=self.k2) + + # Use parallel processing for feature extraction + threads_to_use = check_n_jobs(self.n_jobs) + + # Extract features for each dilation rate in parallel + results = Parallel( + n_jobs=threads_to_use, backend=self.parallel_backend, prefer="threads" + )( + delayed(self._extract_test_features_for_dilation)(X, X_F, X_S, d_rate, i) + for i, d_rate in enumerate(self.dList_) + ) + + # Unpack results + RXList = [] + FXList = [] + SXList = [] + + for RX, FX, SX in results: + RXList.append(RX) + FXList.append(FX) + SXList.append(SX) + + # Concatenating all the dilated features + RX = np.hstack(RXList) + FX = np.hstack(FXList) + SX = np.hstack(SXList) + + # Ensure feature_store_ratios is set + feature_store_ratios = ( + self.feature_store_ratios + if self.feature_store_ratios is not None + else [0.1, 0.2, 0.3, 0.4, 0.5] + ) + + # Predict in parallel for each classifier + PYList = Parallel( + n_jobs=threads_to_use, backend=self.parallel_backend, prefer="threads" + )( + delayed(self._predict_with_classifier)(RX, FX, SX, i, ratio) + for i, ratio in enumerate(feature_store_ratios) + ) + + # Convert to numpy array for voting + PYList = np.vstack(PYList) + + # Final prediction by hard voting + PYV = self._hard_voting(PYList) + + return PYV + + def _extract_test_features_for_dilation(self, X, X_F, X_S, d_rate, i): + """Extract test features for a specific dilation rate. + + Parameters + ---------- + X : 3D np.ndarray + Original signal. + X_F : 3D np.ndarray + First-order differential signal. + X_S : 3D np.ndarray + Second-order differential signal. + d_rate : int + Dilation rate. + i : int + Index of dilation rate. + + Returns + ------- + tuple + Tuple containing extracted features. + """ + # Dilation Mapping + RX_E = self._series_set_dilation(X, d_rate) + FX_E = self._series_set_dilation(X_F, d_rate) + SX_E = self._series_set_dilation(X_S, d_rate) + + # Extracting the TSFresh features + tsFreshR = self.tsFreshListR_[i] + RX = np.array(tsFreshR.transform(RX_E)) + + tsFreshF = self.tsFreshListF_[i] + FX = np.array(tsFreshF.transform(FX_E)) + + tsFreshS = self.tsFreshListS_[i] + SX = np.array(tsFreshS.transform(SX_E)) + + return RX, FX, SX + + def _predict_with_classifier(self, RX, FX, SX, i, ratio): + """Make predictions using a specific classifier. + + Parameters + ---------- + RX : 2D np.ndarray + Features from original signal. + FX : 2D np.ndarray + Features from first-order differential signal. + SX : 2D np.ndarray + Features from second-order differential signal. + i : int + Index of classifier. + ratio : float + Feature store ratio. + + Returns + ------- + ndarray + Predicted labels. + """ + clf = self.clfList_[i] + bestIndex_ = np.argsort(self.scoreRFS_)[::-1][ + 0 : int(len(self.scoreRFS_) * ratio) + ] + + return clf.predict(np.hstack((RX, FX, SX))[:, bestIndex_]) + + @classmethod + def _get_test_params(cls, parameter_set="default"): + """Return testing parameter settings for the estimator. + + Parameters + ---------- + parameter_set : str, default="default" + Name of the set of test parameters to return. + + Returns + ------- + params : dict + Parameters to create testing instances of the class. + """ + return { + "k1": 2, + "k2": 2, + "feature_store_ratios": [0.1, 0.2, 0.3, 0.4, 0.5], + "default_fc_parameters": "minimal", + } diff --git a/aeon/classification/feature_based/tests/test_tdmvdc.py b/aeon/classification/feature_based/tests/test_tdmvdc.py new file mode 100644 index 0000000000..d0775229d9 --- /dev/null +++ b/aeon/classification/feature_based/tests/test_tdmvdc.py @@ -0,0 +1,66 @@ +"""Test TDMVDC Classifier.""" + +import numpy as np +import pytest +from sklearn.metrics import accuracy_score + +from aeon.classification.feature_based import TDMVDCClassifier +from aeon.datasets import ( + load_arrow_head, + load_classification, + load_gunpoint, + load_italy_power_demand, +) +from aeon.utils.validation._dependencies import _check_soft_dependencies + + +@pytest.mark.skipif( + not _check_soft_dependencies("tsfresh", severity="none"), + reason="skip test if required soft dependency tsfresh not available", +) +def test_tdmvdc_classifier(): + """Test the TDMVDCClassifier.""" + cls = TDMVDCClassifier() + assert cls.k1 == 2 and cls.k2 == 2 + assert cls.n_jobs == 1 + + +@pytest.mark.skipif( + not _check_soft_dependencies("tsfresh", severity="none"), + reason="skip test if required soft dependency tsfresh not available", +) +@pytest.mark.parametrize( + "dataset_name,expected_accuracy", + [ + ("ArrowHead", 0.8114), + ("Beef", 0.9667), + ("BeetleFly", 0.95), + ("GunPoint", 0.993), + ("ItalyPowerDemand", 0.965), + ], +) +def check_tdmvdc_results(dataset_name, expected_accuracy): + """Check the results of TDMVDCClassifier with expected accuracy.""" + # Load the dataset + if dataset_name == "ArrowHead": + trainSignalX, trainY = load_arrow_head("TRAIN") + testSignalX, testY = load_arrow_head("TEST") + elif dataset_name == "GunPoint": + trainSignalX, trainY = load_gunpoint("TRAIN") + testSignalX, testY = load_gunpoint("TEST") + elif dataset_name == "ItalyPowerDemand": + trainSignalX, trainY = load_italy_power_demand("TRAIN") + testSignalX, testY = load_italy_power_demand("TEST") + else: + trainSignalX, trainY = load_classification(dataset_name, "TRAIN") + testSignalX, testY = load_classification(dataset_name, "TEST") + trainY, testY = trainY.astype(int), testY.astype(int) + + cls = TDMVDCClassifier(n_jobs=2) + cls.fit(trainSignalX, trainY) + preds = cls.predict(testSignalX) + acc = accuracy_score(testY, preds) + assert np.isclose( + acc, expected_accuracy, atol=0.02 + ), f"Accuracy {acc:.2f} not close to expected \ + {expected_accuracy:.2f} for {dataset_name}" diff --git a/examples/classification/feature_based.ipynb b/examples/classification/feature_based.ipynb index 2424486b0c..3ce78e547b 100644 --- a/examples/classification/feature_based.ipynb +++ b/examples/classification/feature_based.ipynb @@ -23,27 +23,37 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(67, 1, 24) (67,) (50, 1, 24) (50,)\n", + "(20, 6, 100) (20,) (20, 6, 100) (20,)\n" + ] + }, { "data": { "text/plain": [ "[('Catch22Classifier',\n", - " aeon.classification.feature_based._catch22_classifier.Catch22Classifier),\n", + " aeon.classification.feature_based._catch22.Catch22Classifier),\n", " ('FreshPRINCEClassifier',\n", " aeon.classification.feature_based._fresh_prince.FreshPRINCEClassifier),\n", " ('SignatureClassifier',\n", " aeon.classification.feature_based._signature_classifier.SignatureClassifier),\n", " ('SummaryClassifier',\n", - " aeon.classification.feature_based._summary_classifier.SummaryClassifier),\n", + " aeon.classification.feature_based._summary.SummaryClassifier),\n", + " ('TDMVDCClassifier',\n", + " aeon.classification.feature_based._tdmvdc.TDMVDCClassifier),\n", " ('TSFreshClassifier',\n", - " aeon.classification.feature_based._tsfresh_classifier.TSFreshClassifier)]" + " aeon.classification.feature_based._tsfresh.TSFreshClassifier)]" ] }, - "execution_count": 5, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -110,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -121,7 +131,7 @@ "(67, 22)" ] }, - "execution_count": 6, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -141,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -152,7 +162,7 @@ "0.84" ] }, - "execution_count": 7, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -164,6 +174,65 @@ "metrics.accuracy_score(y_test, c22_preds)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tracking Differentiator-based Multiview Dilated Characteristics (TDMVDC) Classifier\n", + "\n", + "Time Series Feature Extraction based on Scalable Hypothesis Tests classifier.\n", + "\n", + "This classifier simply transforms the input data using the TSFresh [1]_\n", + "transformer and builds a provided estimator using the transformed data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Loading time series set\n", + "from aeon.datasets import load_arrow_head\n", + "\n", + "trainSignalX, trainY = load_arrow_head(\"TRAIN\")\n", + "testSignalX, testY = load_arrow_head(\"TEST\")\n", + "trainY, testY = trainY.astype(int), testY.astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from aeon.classification.feature_based import TDMVDCClassifier\n", + "\n", + "td_mvdc = TDMVDCClassifier(n_jobs=5)\n", + "td_mvdc.fit(trainSignalX, trainY)\n", + "testPY = td_mvdc.predict(testSignalX)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy : 0.8114285714285714\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "accV = np.sum(testPY == testY) / len(testY)\n", + "print(\"Accuracy :\", accV)" + ] + }, { "attachments": {}, "cell_type": "markdown", @@ -182,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -193,7 +262,7 @@ "0.84" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -247,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -259,8 +328,9 @@ "('Catch22Classifier', )\n", "('FreshPRINCEClassifier', )\n", "('SignatureClassifier', )\n", - "('SummaryClassifier', )\n", - "('TSFreshClassifier', )\n" + "('SummaryClassifier', )\n", + "('TDMVDCClassifier', )\n", + "('TSFreshClassifier', )\n" ] } ], @@ -274,7 +344,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -285,7 +355,7 @@ "(112, 4)" ] }, - "execution_count": 3, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -296,6 +366,8 @@ "\n", "names = [t[0].replace(\"Classifier\", \"\") for t in est]\n", "names.remove(\"Summary\")\n", + "if \"TDMVDC\" in names:\n", + " names.remove(\"TDMVDC\")\n", "results, present_names = get_estimator_results_as_array(\n", " names, univariate, include_missing=False\n", ")\n", @@ -304,7 +376,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -315,13 +387,13 @@ "(
, )" ] }, - "execution_count": 4, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -338,7 +410,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -349,13 +421,13 @@ "(
, )" ] }, - "execution_count": 5, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -395,7 +467,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "aeon_cenv", "language": "python", "name": "python3" }, @@ -409,7 +481,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.11.11" } }, "nbformat": 4, From 1262254d23ac833b7b0cf927e2dfd2e3f8b9f1d9 Mon Sep 17 00:00:00 2001 From: "aeon-actions-bot[bot]" <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Date: Mon, 9 Jun 2025 10:56:44 +0100 Subject: [PATCH 137/216] Automated `pre-commit` hook update (#2888) Co-authored-by: TonyBagnall <9594042+TonyBagnall@users.noreply.github.com> --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 5bfaa5629c..b5d1532d60 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -29,7 +29,7 @@ repos: args: [ "--create", "--python-folders", "aeon" ] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.11.12 + rev: v0.11.13 hooks: - id: ruff args: [ "--fix"] From eb85272dcffa0c4c49b720d9ad5ef1823b2315d3 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 9 Jun 2025 10:57:16 +0100 Subject: [PATCH 138/216] [MNT] Update pandas requirement in the python-packages group (#2889) Updates the requirements on [pandas](https://github.com/pandas-dev/pandas) to permit the latest version. Updates `pandas` to 2.3.0 - [Release notes](https://github.com/pandas-dev/pandas/releases) - [Commits](https://github.com/pandas-dev/pandas/compare/v2.0.0...v2.3.0) --- updated-dependencies: - dependency-name: pandas dependency-version: 2.3.0 dependency-type: direct:production dependency-group: python-packages ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 815aa9a768..8e359f4158 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -50,7 +50,7 @@ dependencies = [ "numba>=0.55,<0.62.0", "numpy>=1.21.0,<2.3.0", "packaging>=20.0", - "pandas>=2.0.0,<2.3.0", + "pandas>=2.0.0,<2.4.0", "scikit-learn>=1.0.0,<1.7.0", "scipy>=1.9.0,<1.16.0", "typing-extensions>=4.6.0", From a5fdb59a30b90ec66cf8cd03eee5b01ea3460abc Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Mon, 9 Jun 2025 11:05:50 +0100 Subject: [PATCH 139/216] [MNT] Drop Python 3.9 (#2845) * drop python 3.9 * fixes * readme * tag * test skip --- .github/actions/cpu_all_extras/action.yml | 2 +- .github/workflows/fast_release.yml | 4 +-- .github/workflows/issue_assigned.yml | 4 +-- .github/workflows/issue_comment_edited.yml | 4 +-- .github/workflows/issue_comment_posted.yml | 4 +-- .github/workflows/periodic_tests.yml | 34 +++++++++---------- .github/workflows/pr_core_dep_import.yml | 4 +-- .github/workflows/pr_examples.yml | 6 ++-- .github/workflows/pr_opened.yml | 4 +-- .github/workflows/pr_precommit.yml | 4 +-- .github/workflows/pr_pytest.yml | 16 ++++----- .github/workflows/pr_typecheck.yml | 4 +-- .github/workflows/release.yml | 10 +++--- .../workflows/weekly_github_maintenance.yml | 12 ++++--- README.md | 2 +- .../feature_based/_signature_classifier.py | 1 - aeon/testing/testing_config.py | 1 + aeon/testing/tests/test_all_estimators.py | 4 +-- .../collection/signature_based/_compute.py | 1 - .../signature_based/_signature_method.py | 1 - pyproject.toml | 7 ++-- 21 files changed, 62 insertions(+), 67 deletions(-) diff --git a/.github/actions/cpu_all_extras/action.yml b/.github/actions/cpu_all_extras/action.yml index da6a93c828..91fab623aa 100644 --- a/.github/actions/cpu_all_extras/action.yml +++ b/.github/actions/cpu_all_extras/action.yml @@ -5,7 +5,7 @@ inputs: python_version: description: "Python version used" required: false - default: "3.11" + default: "3.12" additional_extras: description: "Comma-separated list of additional extras to install" required: false diff --git a/.github/workflows/fast_release.yml b/.github/workflows/fast_release.yml index 695589ee74..a4c28da6b6 100644 --- a/.github/workflows/fast_release.yml +++ b/.github/workflows/fast_release.yml @@ -11,10 +11,10 @@ jobs: steps: - uses: actions/checkout@v4 - - name: Setup Python 3.11 + - name: Setup Python 3.12 uses: actions/setup-python@v5 with: - python-version: "3.11" + python-version: "3.12" - name: Build project run: | diff --git a/.github/workflows/issue_assigned.yml b/.github/workflows/issue_assigned.yml index 343f468781..c238c6c195 100644 --- a/.github/workflows/issue_assigned.yml +++ b/.github/workflows/issue_assigned.yml @@ -25,10 +25,10 @@ jobs: with: sparse-checkout: .github/utilities - - name: Setup Python 3.11 + - name: Setup Python 3.12 uses: actions/setup-python@v5 with: - python-version: "3.11" + python-version: "3.12" - name: Install PyGithub run: pip install -Uq PyGithub diff --git a/.github/workflows/issue_comment_edited.yml b/.github/workflows/issue_comment_edited.yml index ddd9bf5520..359e15e725 100644 --- a/.github/workflows/issue_comment_edited.yml +++ b/.github/workflows/issue_comment_edited.yml @@ -26,10 +26,10 @@ jobs: with: sparse-checkout: .github/utilities - - name: Setup Python 3.11 + - name: Setup Python 3.12 uses: actions/setup-python@v5 with: - python-version: "3.11" + python-version: "3.12" - name: Install PyGithub run: pip install -Uq PyGithub diff --git a/.github/workflows/issue_comment_posted.yml b/.github/workflows/issue_comment_posted.yml index 80dfa25aab..df23c18261 100644 --- a/.github/workflows/issue_comment_posted.yml +++ b/.github/workflows/issue_comment_posted.yml @@ -14,10 +14,10 @@ jobs: with: sparse-checkout: .github/utilities - - name: Setup Python 3.11 + - name: Setup Python 3.12 uses: actions/setup-python@v5 with: - python-version: "3.11" + python-version: "3.12" - name: Install PyGithub run: pip install -Uq PyGithub diff --git a/.github/workflows/periodic_tests.yml b/.github/workflows/periodic_tests.yml index 7a18e7e10f..49d5f6c4e8 100644 --- a/.github/workflows/periodic_tests.yml +++ b/.github/workflows/periodic_tests.yml @@ -18,10 +18,10 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.11 + - name: Setup Python 3.12 uses: actions/setup-python@v5 with: - python-version: "3.11" + python-version: "3.12" - name: Run check-manifest uses: pre-commit/action@v3.0.1 @@ -35,10 +35,10 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.11 + - name: Setup Python 3.12 uses: actions/setup-python@v5 with: - python-version: "3.11" + python-version: "3.12" - name: Run pre-commit uses: pre-commit/action@v3.0.1 @@ -57,17 +57,17 @@ jobs: brew install bash /opt/homebrew/bin/bash --version - - name: Setup Python 3.11 + - name: Setup Python 3.12 uses: actions/setup-python@v5 with: - python-version: "3.11" + python-version: "3.12" - name: Use numba cache to set env variables but not restore cache uses: ./.github/actions/numba_cache with: cache_name: "run-notebook-examples" runner_os: ${{ runner.os }} - python_version: "3.11" + python_version: "3.12" restore_cache: "false" - name: Install dependencies @@ -89,7 +89,7 @@ jobs: with: path: ${{ github.workspace }}/.numba_cache # Save cache with the current date (ENV set in numba_cache action) - key: numba-run-notebook-examples-${{ runner.os }}-3.10-${{ env.CURRENT_DATE }} + key: numba-run-notebook-examples-${{ runner.os }}-3.12-${{ env.CURRENT_DATE }} test-core-imports: runs-on: ubuntu-24.04 @@ -98,10 +98,10 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.11 + - name: Setup Python 3.12 uses: actions/setup-python@v5 with: - python-version: "3.11" + python-version: "3.12" - name: Install aeon and dependencies uses: nick-fields/retry@v3 @@ -123,17 +123,17 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.11 + - name: Setup Python 3.12 uses: actions/setup-python@v5 with: - python-version: "3.11" + python-version: "3.12" - name: Use numba cache to set env variables but not restore cache uses: ./.github/actions/numba_cache with: cache_name: "test-no-soft-deps" runner_os: ${{ runner.os }} - python_version: "3.11" + python_version: "3.12" restore_cache: "false" - name: Install aeon and dependencies @@ -154,7 +154,7 @@ jobs: with: path: ${{ github.workspace }}/.numba_cache # Save cache with the current date (ENV set in numba_cache action) - key: numba-test-no-soft-deps-${{ runner.os }}-3.10-${{ env.CURRENT_DATE }} + key: numba-test-no-soft-deps-${{ runner.os }}-3.12-${{ env.CURRENT_DATE }} pytest: runs-on: ${{ matrix.os }} @@ -163,7 +163,7 @@ jobs: fail-fast: false matrix: os: [ ubuntu-24.04, macOS-14, windows-2022 ] - python-version: [ "3.9", "3.10", "3.11", "3.12", "3.13" ] + python-version: [ "3.10", "3.11", "3.12", "3.13" ] steps: - name: Checkout @@ -215,10 +215,10 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.11 + - name: Setup Python 3.12 uses: actions/setup-python@v5 with: - python-version: "3.11" + python-version: "3.12" - name: Disable Numba JIT run: echo "NUMBA_DISABLE_JIT=1" >> $GITHUB_ENV diff --git a/.github/workflows/pr_core_dep_import.yml b/.github/workflows/pr_core_dep_import.yml index 429ee28fa5..7ff7dc27fb 100644 --- a/.github/workflows/pr_core_dep_import.yml +++ b/.github/workflows/pr_core_dep_import.yml @@ -22,10 +22,10 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.11 + - name: Setup Python 3.12 uses: actions/setup-python@v5 with: - python-version: "3.11" + python-version: "3.12" - name: Install aeon and dependencies uses: nick-fields/retry@v3 diff --git a/.github/workflows/pr_examples.yml b/.github/workflows/pr_examples.yml index cd36250b33..2c9ff05ab1 100644 --- a/.github/workflows/pr_examples.yml +++ b/.github/workflows/pr_examples.yml @@ -28,10 +28,10 @@ jobs: brew install bash /opt/homebrew/bin/bash --version - - name: Setup Python 3.11 + - name: Setup Python 3.12 uses: actions/setup-python@v5 with: - python-version: "3.11" + python-version: "3.12" - if: ${{ github.event_name != 'pull_request' || !contains(github.event.pull_request.labels.*.name, 'no numba cache') }} name: Restore numba cache @@ -39,7 +39,7 @@ jobs: with: cache_name: "run-notebook-examples" runner_os: ${{ runner.os }} - python_version: "3.11" + python_version: "3.12" - uses: ./.github/actions/cpu_all_extras with: diff --git a/.github/workflows/pr_opened.yml b/.github/workflows/pr_opened.yml index f6f6e88bef..c500844d11 100644 --- a/.github/workflows/pr_opened.yml +++ b/.github/workflows/pr_opened.yml @@ -20,10 +20,10 @@ jobs: with: sparse-checkout: .github/utilities - - name: Setup Python 3.11 + - name: Setup Python 3.12 uses: actions/setup-python@v5 with: - python-version: "3.11" + python-version: "3.12" - name: Install PyGithub run: pip install -Uq PyGithub diff --git a/.github/workflows/pr_precommit.yml b/.github/workflows/pr_precommit.yml index 95a232282e..7363e44bb2 100644 --- a/.github/workflows/pr_precommit.yml +++ b/.github/workflows/pr_precommit.yml @@ -29,10 +29,10 @@ jobs: ref: ${{ github.head_ref }} token: ${{ steps.app-token.outputs.token }} - - name: Setup Python 3.11 + - name: Setup Python 3.12 uses: actions/setup-python@v5 with: - python-version: "3.11" + python-version: "3.12" - name: Get changed files uses: tj-actions/changed-files@v46.0.5 diff --git a/.github/workflows/pr_pytest.yml b/.github/workflows/pr_pytest.yml index cf1baee900..024812281d 100644 --- a/.github/workflows/pr_pytest.yml +++ b/.github/workflows/pr_pytest.yml @@ -22,10 +22,10 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.11 + - name: Setup Python 3.12 uses: actions/setup-python@v5 with: - python-version: "3.11" + python-version: "3.12" - if: ${{ github.event_name != 'pull_request' || !contains(github.event.pull_request.labels.*.name, 'no numba cache') }} name: Restore numba cache @@ -33,7 +33,7 @@ jobs: with: cache_name: "test-no-soft-deps" runner_os: ${{ runner.os }} - python_version: "3.11" + python_version: "3.12" - name: Install aeon and dependencies uses: nick-fields/retry@v3 @@ -55,15 +55,13 @@ jobs: fail-fast: false matrix: os: [ ubuntu-24.04, macOS-14, windows-2022 ] - python-version: [ "3.9", "3.10", "3.11", "3.12", "3.13" ] + python-version: [ "3.10", "3.11", "3.12", "3.13" ] # skip python versions unless the PR has the 'full pytest actions' label pr-testing: - ${{ (github.event_name == 'pull_request' && !contains(github.event.pull_request.labels.*.name, 'full pytest actions')) }} exclude: - pr-testing: true - python-version: "3.10" - - pr-testing: true - python-version: "3.12" + python-version: "3.11" steps: - name: Checkout @@ -110,10 +108,10 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.11 + - name: Setup Python 3.12 uses: actions/setup-python@v5 with: - python-version: "3.11" + python-version: "3.12" - name: Disable Numba JIT run: echo "NUMBA_DISABLE_JIT=1" >> $GITHUB_ENV diff --git a/.github/workflows/pr_typecheck.yml b/.github/workflows/pr_typecheck.yml index 13ba9bec73..9c93884feb 100644 --- a/.github/workflows/pr_typecheck.yml +++ b/.github/workflows/pr_typecheck.yml @@ -22,10 +22,10 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.11 + - name: Setup Python 3.12 uses: actions/setup-python@v5 with: - python-version: "3.11" + python-version: "3.12" - name: Install aeon, dependencies and mypy uses: nick-fields/retry@v3 diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 58d937e67e..3fe9c6fa27 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -13,10 +13,10 @@ jobs: steps: - uses: actions/checkout@v4 - - name: Setup Python 3.11 + - name: Setup Python 3.12 uses: actions/setup-python@v5 with: - python-version: "3.11" + python-version: "3.12" - uses: pre-commit/action@v3.0.1 with: @@ -29,10 +29,10 @@ jobs: steps: - uses: actions/checkout@v4 - - name: Setup Python 3.11 + - name: Setup Python 3.12 uses: actions/setup-python@v5 with: - python-version: "3.11" + python-version: "3.12" - name: Build project run: | @@ -54,7 +54,7 @@ jobs: fail-fast: false matrix: os: [ ubuntu-24.04, macOS-14, windows-2022 ] - python-version: [ "3.9", "3.10", "3.11", "3.12", "3.13" ] + python-version: [ "3.10", "3.11", "3.12", "3.13" ] steps: - name: Checkout diff --git a/.github/workflows/weekly_github_maintenance.yml b/.github/workflows/weekly_github_maintenance.yml index a854f8e7de..58c6d65a2c 100644 --- a/.github/workflows/weekly_github_maintenance.yml +++ b/.github/workflows/weekly_github_maintenance.yml @@ -14,10 +14,10 @@ jobs: - name: Checkout main uses: actions/checkout@v4 - - name: Setup Python 3.11 + - name: Setup Python 3.12 uses: actions/setup-python@v5 with: - python-version: "3.11" + python-version: "3.12" - uses: browniebroke/pre-commit-autoupdate-action@v1.0.0 @@ -48,10 +48,13 @@ jobs: with: sparse-checkout: .github/utilities - - name: Setup Python 3.11 + - name: Setup Python 3.12 uses: actions/setup-python@v5 with: - python-version: "3.11" + python-version: "3.12" + + - name: Install requests + run: pip install -Uq requests - name: Create app token uses: actions/create-github-app-token@v2 @@ -76,7 +79,6 @@ jobs: labels: documentation, no changelog github-security-scorecard: - name: Scorecard analysis runs-on: ubuntu-24.04 permissions: # Needed to upload the results to code-scanning dashboard. diff --git a/README.md b/README.md index acc652fa82..292910e3e4 100644 --- a/README.md +++ b/README.md @@ -37,7 +37,7 @@ does not apply: ## ⚙️ Installation -`aeon` requires a Python version of 3.9 or greater. Our full installation guide is +`aeon` requires a Python version of 3.10 or greater. Our full installation guide is available in our [documentation](https://www.aeon-toolkit.org/en/stable/installation.html). The easiest way to install `aeon` is via pip: diff --git a/aeon/classification/feature_based/_signature_classifier.py b/aeon/classification/feature_based/_signature_classifier.py index a3f659efcf..561f5f60c8 100644 --- a/aeon/classification/feature_based/_signature_classifier.py +++ b/aeon/classification/feature_based/_signature_classifier.py @@ -101,7 +101,6 @@ class SignatureClassifier(BaseClassifier): "capability:multivariate": True, "algorithm_type": "feature", "python_dependencies": "esig", - "python_version": "<3.11", } def __init__( diff --git a/aeon/testing/testing_config.py b/aeon/testing/testing_config.py index c81dc60c42..2838a856d1 100644 --- a/aeon/testing/testing_config.py +++ b/aeon/testing/testing_config.py @@ -53,6 +53,7 @@ "RSAST": ["check_fit_deterministic"], "MatrixProfile": ["check_fit_deterministic", "check_persistence_via_pickle"], "LeftSTAMPi": ["check_series_anomaly_detector_output"], + "SignatureClassifier": ["check_classifier_against_expected_results"], # missed in legacy testing, changes state in predict/transform "FLUSSSegmenter": ["check_non_state_changing_method"], "ClaSPSegmenter": ["check_non_state_changing_method"], diff --git a/aeon/testing/tests/test_all_estimators.py b/aeon/testing/tests/test_all_estimators.py index 192d63b1d6..a2ac68461e 100644 --- a/aeon/testing/tests/test_all_estimators.py +++ b/aeon/testing/tests/test_all_estimators.py @@ -18,9 +18,9 @@ if PR_TESTING: # only use 3 Python versions in PR i = sys.version_info.minor - if i == 9: + if i == 10: i = 0 - elif i == 11: + elif i == 12: i = 1 elif i == 13: i = 2 diff --git a/aeon/transformations/collection/signature_based/_compute.py b/aeon/transformations/collection/signature_based/_compute.py index 621017e30f..0506b87242 100644 --- a/aeon/transformations/collection/signature_based/_compute.py +++ b/aeon/transformations/collection/signature_based/_compute.py @@ -29,7 +29,6 @@ class _WindowSignatureTransform(BaseCollectionTransformer): "output_data_type": "Tabular", "capability:multivariate": True, "python_dependencies": "esig", - "python_version": "<3.11", } def __init__( diff --git a/aeon/transformations/collection/signature_based/_signature_method.py b/aeon/transformations/collection/signature_based/_signature_method.py index 7255defe43..8606fd84fc 100644 --- a/aeon/transformations/collection/signature_based/_signature_method.py +++ b/aeon/transformations/collection/signature_based/_signature_method.py @@ -43,7 +43,6 @@ class SignatureTransformer(BaseCollectionTransformer): "output_data_type": "Tabular", "capability:multivariate": True, "python_dependencies": "esig", - "python_version": "<3.11", } def __init__( diff --git a/pyproject.toml b/pyproject.toml index 8e359f4158..cf3045ca31 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,13 +38,12 @@ classifiers = [ "Operating System :: Unix", "Operating System :: MacOS", "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.13", ] -requires-python = ">=3.9,<3.14" +requires-python = ">=3.10,<3.14" dependencies = [ "deprecated>=1.2.13", "numba>=0.55,<0.62.0", @@ -59,6 +58,7 @@ dependencies = [ # soft dependencies [project.optional-dependencies] all_extras = [ + "esig>=1.0.0; platform_system != 'Darwin'", "imbalanced-learn", "matplotlib>=3.3.2", "pycatch22>=0.4.5", @@ -83,9 +83,6 @@ unstable_extras = [ # requires gcc and fftw to be installed for Windows and some other OS (see http://www.fftw.org/index.html) "mrsqm>=0.0.7,<0.1.0; platform_system != 'Windows' and python_version < '3.12'", "mrseql>=0.0.4,<0.1.0; platform_system != 'Windows' and python_version < '3.12'", - # Upper bound set as <1.0.0 as 1.0 dropped support for python 3.9. We will remove - # the upper bound once we also drop support for python 3.9 later in 2025. - "esig>=0.9.7,<1.0.0; platform_system != 'Darwin' and python_version < '3.11'", ] # development dependencies From 4f239920b5c96504a49345c0f520f540384c609c Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Mon, 9 Jun 2025 11:06:30 +0100 Subject: [PATCH 140/216] [ENH] Series transformer pipeline and datatype list tidy (#2830) * series pipeline * fixes and docs * test fix * fix --- aeon/anomaly_detection/series/base.py | 2 +- aeon/base/_base_series.py | 8 +- aeon/base/_compose.py | 9 +- aeon/base/_estimators/compose/_commons.py | 41 +++ .../compose/collection_channel_ensemble.py | 16 +- .../compose/collection_ensemble.py | 2 +- .../compose/collection_pipeline.py | 20 +- .../_estimators/compose/series_pipeline.py | 279 ++++++++++++++++++ .../compose/_channel_ensemble.py | 5 +- aeon/forecasting/base.py | 2 +- aeon/pipeline/_sklearn_to_aeon.py | 15 +- aeon/pipeline/tests/test_sklearn_to_aeon.py | 6 +- aeon/segmentation/base.py | 2 +- .../_yield_forecasting_checks.py | 2 +- .../_yield_segmentation_checks.py | 2 +- .../_yield_transformation_checks.py | 6 +- aeon/testing/utils/estimator_checks.py | 5 +- aeon/transformations/__init__.py | 8 +- .../collection/compose/_pipeline.py | 5 +- .../collection/compose/tests/test_pipeline.py | 9 +- aeon/transformations/series/__init__.py | 3 +- aeon/transformations/series/_boxcox.py | 88 +----- aeon/transformations/series/_log.py | 92 ++++++ .../series/compose/__init__.py | 9 + .../series/compose/_identity.py | 25 ++ .../series/compose/_pipeline.py | 103 +++++++ .../series/compose/tests/__init__.py | 1 + .../series/compose/tests/test_pipeline.py | 35 +++ .../series/tests/test_boxcox.py | 3 - aeon/utils/data_types.py | 111 +++++-- docs/api_reference/{base.rst => base.md} | 12 +- ...transformations.rst => transformations.md} | 117 +++++--- 32 files changed, 804 insertions(+), 239 deletions(-) create mode 100644 aeon/base/_estimators/compose/_commons.py create mode 100644 aeon/base/_estimators/compose/series_pipeline.py create mode 100644 aeon/transformations/series/_log.py create mode 100644 aeon/transformations/series/compose/__init__.py create mode 100644 aeon/transformations/series/compose/_identity.py create mode 100644 aeon/transformations/series/compose/_pipeline.py create mode 100644 aeon/transformations/series/compose/tests/__init__.py create mode 100644 aeon/transformations/series/compose/tests/test_pipeline.py rename docs/api_reference/{base.rst => base.md} (65%) rename docs/api_reference/{transformations.rst => transformations.md} (76%) diff --git a/aeon/anomaly_detection/series/base.py b/aeon/anomaly_detection/series/base.py index ccd3649cd7..4b68ee2a34 100644 --- a/aeon/anomaly_detection/series/base.py +++ b/aeon/anomaly_detection/series/base.py @@ -11,7 +11,7 @@ from aeon.anomaly_detection.base import BaseAnomalyDetector from aeon.base import BaseSeriesEstimator -from aeon.base._base_series import VALID_SERIES_INPUT_TYPES +from aeon.utils.data_types import VALID_SERIES_INPUT_TYPES class BaseSeriesAnomalyDetector(BaseSeriesEstimator, BaseAnomalyDetector): diff --git a/aeon/base/_base_series.py b/aeon/base/_base_series.py index e3a382f3db..c4fbb5aa30 100644 --- a/aeon/base/_base_series.py +++ b/aeon/base/_base_series.py @@ -37,13 +37,7 @@ import pandas as pd from aeon.base._base import BaseAeonEstimator - -# allowed input and internal data types for Series -VALID_SERIES_INNER_TYPES = [ - "np.ndarray", - "pd.DataFrame", -] -VALID_SERIES_INPUT_TYPES = [pd.DataFrame, pd.Series, np.ndarray] +from aeon.utils.data_types import VALID_SERIES_INNER_TYPES class BaseSeriesEstimator(BaseAeonEstimator): diff --git a/aeon/base/_compose.py b/aeon/base/_compose.py index 8661245806..3fd1b3ff1d 100644 --- a/aeon/base/_compose.py +++ b/aeon/base/_compose.py @@ -5,7 +5,7 @@ from abc import ABC, abstractmethod -from aeon.base import BaseAeonEstimator +from aeon.base import BaseAeonEstimator, BaseCollectionEstimator from aeon.base._base import _clone_estimator @@ -26,8 +26,11 @@ class ComposableEstimatorMixin(ABC): _fitted_estimators_attr = "estimators_" @abstractmethod - def __init__(self): - super().__init__() + def __init__(self, axis): + if isinstance(self, BaseCollectionEstimator): + super().__init__() + else: + super().__init__(axis=axis) def get_params(self, deep=True): """Get parameters for this estimator. diff --git a/aeon/base/_estimators/compose/_commons.py b/aeon/base/_estimators/compose/_commons.py new file mode 100644 index 0000000000..1a578a0af8 --- /dev/null +++ b/aeon/base/_estimators/compose/_commons.py @@ -0,0 +1,41 @@ +"""Common compose base functions.""" + +from inspect import signature + +import numpy as np + + +def _get_channel(X, key): + """Get time series channel(s) from input data X.""" + if isinstance(X, np.ndarray): + return X[:, key] + else: + li = [x[key] for x in X] + if li[0].ndim == 1: + li = [x.reshape(1, -1) for x in li] + return li + + +def _transform_args_wrapper(estimator, method_name, X, y=None, axis=None): + method = getattr(estimator, method_name) + args = list(signature(method).parameters.keys()) + + has_X = "X" in args + has_y = "y" in args + has_axis = "axis" in args + + # aeon transforms should always have X and y, other transforms i.e. sklearn may + # only have X + if has_X and has_y and has_axis: + return method(X=X, y=y, axis=axis) + elif has_X and has_y: + return method(X=X, y=y) + elif has_X and has_axis: + return method(X=X, axis=axis) + elif has_X: + return method(X=X) + else: + raise ValueError( + f"Method {method_name} of {estimator.__class__.__name__} " + "does not have the required arguments." + ) diff --git a/aeon/base/_estimators/compose/collection_channel_ensemble.py b/aeon/base/_estimators/compose/collection_channel_ensemble.py index 91ed44ef25..04cab42519 100644 --- a/aeon/base/_estimators/compose/collection_channel_ensemble.py +++ b/aeon/base/_estimators/compose/collection_channel_ensemble.py @@ -18,6 +18,7 @@ ComposableEstimatorMixin, ) from aeon.base._base import _clone_estimator +from aeon.base._estimators.compose._commons import _get_channel class BaseCollectionChannelEnsemble(ComposableEstimatorMixin, BaseCollectionEstimator): @@ -98,7 +99,7 @@ def __init__( self._ensemble, clone_estimators=False ) - super().__init__() + super().__init__(axis=1) # can handle missing values if all estimators can missing = all( @@ -241,17 +242,6 @@ def _fit(self, X, y): # fit estimators for i, (_, estimator) in enumerate(self.ensemble_): - estimator.fit(self._get_channel(X, self.channels_[i]), y) + estimator.fit(_get_channel(X, self.channels_[i]), y) return self - - @staticmethod - def _get_channel(X, key): - """Get time series channel(s) from input data X.""" - if isinstance(X, np.ndarray): - return X[:, key] - else: - li = [x[key] for x in X] - if li[0].ndim == 1: - li = [x.reshape(1, -1) for x in li] - return li diff --git a/aeon/base/_estimators/compose/collection_ensemble.py b/aeon/base/_estimators/compose/collection_ensemble.py index 11e9fbc81b..9b20e11007 100644 --- a/aeon/base/_estimators/compose/collection_ensemble.py +++ b/aeon/base/_estimators/compose/collection_ensemble.py @@ -108,7 +108,7 @@ def __init__( self._ensemble, clone_estimators=False ) - super().__init__() + super().__init__(axis=1) # can handle multivariate if all estimators can multivariate = all( diff --git a/aeon/base/_estimators/compose/collection_pipeline.py b/aeon/base/_estimators/compose/collection_pipeline.py index 1b90c6c411..73b8ce3167 100644 --- a/aeon/base/_estimators/compose/collection_pipeline.py +++ b/aeon/base/_estimators/compose/collection_pipeline.py @@ -1,4 +1,4 @@ -"""Base class for pipelines in series collection modules. +"""Base class for pipelines in collection modules. i.e. classification, regression and clustering. """ @@ -18,6 +18,7 @@ ComposableEstimatorMixin, ) from aeon.base._base import _clone_estimator +from aeon.base._estimators.compose._commons import _transform_args_wrapper class BaseCollectionPipeline(ComposableEstimatorMixin, BaseCollectionEstimator): @@ -47,11 +48,6 @@ class BaseCollectionPipeline(ComposableEstimatorMixin, BaseCollectionEstimator): Clones of transformers and the estimator which are fitted in the pipeline. Will always be in (str, estimator) format, even if transformers input is a singular transform or list of transformers. - - See Also - -------- - ClassifierPipeline : A pipeline for classification tasks. - RegressorPipeline : A pipeline for regression tasks. """ # Attribute name containing an iterable of processed (str, estimator) tuples @@ -82,7 +78,7 @@ def __init__(self, transformers, _estimator, random_state=None): ) self._steps = self._convert_estimators(self._steps, clone_estimators=False) - super().__init__() + super().__init__(axis=1) # can handle multivariate if: both estimator and all transformers can, # *or* transformer chain removes multivariate @@ -223,7 +219,7 @@ def _fit(self, X, y): # fit transforms sequentially Xt = X for i in range(len(self.steps_) - 1): - Xt = self.steps_[i][1].fit_transform(X=Xt, y=y) + Xt = _transform_args_wrapper(self.steps_[i][1], "fit_transform", Xt, y) # fit estimator self.steps_[-1][1].fit(X=Xt, y=y) @@ -243,7 +239,7 @@ def _predict(self, X) -> np.ndarray: # transform Xt = X for i in range(len(self.steps_) - 1): - Xt = self.steps_[i][1].transform(X=Xt) + Xt = _transform_args_wrapper(self.steps_[i][1], "transform", Xt) # predict return self.steps_[-1][1].predict(X=Xt) @@ -265,7 +261,7 @@ def _predict_proba(self, X) -> np.ndarray: # transform Xt = X for i in range(len(self.steps_) - 1): - Xt = self.steps_[i][1].transform(X=Xt) + Xt = _transform_args_wrapper(self.steps_[i][1], "transform", Xt) # predict return self.steps_[-1][1].predict_proba(X=Xt) @@ -285,7 +281,7 @@ def _fit_transform(self, X, y=None) -> np.ndarray: # transform Xt = X for i in range(len(self.steps_)): - Xt = self.steps_[i][1].fit_transform(X=Xt) + Xt = _transform_args_wrapper(self.steps_[i][1], "fit_transform", Xt, y) return Xt def _transform(self, X, y=None) -> np.ndarray: @@ -302,7 +298,7 @@ def _transform(self, X, y=None) -> np.ndarray: # transform Xt = X for i in range(len(self.steps_)): - Xt = self.steps_[i][1].transform(X=Xt) + Xt = _transform_args_wrapper(self.steps_[i][1], "transform", Xt, y) return Xt def _clone_steps(self): diff --git a/aeon/base/_estimators/compose/series_pipeline.py b/aeon/base/_estimators/compose/series_pipeline.py new file mode 100644 index 0000000000..40c448d8bf --- /dev/null +++ b/aeon/base/_estimators/compose/series_pipeline.py @@ -0,0 +1,279 @@ +"""Base class for pipelines in series modules. + +i.e. forecasting, anomaly detection, series transforms. +""" + +__maintainer__ = ["MatthewMiddlehurst"] +__all__ = ["BaseSeriesPipeline"] + +from abc import abstractmethod + +import numpy as np +from sklearn.base import BaseEstimator +from sklearn.utils import check_random_state + +from aeon.base import ( + BaseAeonEstimator, + BaseSeriesEstimator, + ComposableEstimatorMixin, +) +from aeon.base._base import _clone_estimator +from aeon.base._estimators.compose._commons import _transform_args_wrapper + + +class BaseSeriesPipeline(ComposableEstimatorMixin, BaseSeriesEstimator): + """Base class for composable pipelines in series based modules. + + Parameters + ---------- + transformers : aeon or sklearn transformer or list of transformers + A transform or list of transformers to use prior to fitting or predicting. + List of tuples (str, transformer) of transformers can also be passed, where + the str is used to name the transformer. + The objects are cloned prior, as such the state of the input will not be + modified by fitting the pipeline. + _estimator : aeon or sklearn estimator + A estimator to use at the end of the pipeline. + The object is cloned prior, as such the state of the input will not be modified + by fitting the pipeline. + random_state : int, RandomState instance or None, default=None + Random state used to fit the estimators. If None, no random state is set for + pipeline components (but they may still be seeded prior to input). + If `int`, random_state is the seed used by the random number generator; + If `RandomState` instance, random_state is the random number generator; + + Attributes + ---------- + steps_ : list of tuples (str, estimator) of transformers and estimator + Clones of transformers and the estimator which are fitted in the pipeline. + Will always be in (str, estimator) format, even if transformers input is a + singular transform or list of transformers. + """ + + # Attribute name containing an iterable of processed (str, estimator) tuples + # with unfitted estimators and unique names. Used in get_params and set_params + _estimators_attr = "_steps" + # Attribute name containing an iterable of fitted (str, estimator) tuples. + # Used in get_fitted_params + _fitted_estimators_attr = "steps_" + + @abstractmethod + def __init__(self, transformers, _estimator, random_state=None): + self.transformers = transformers + self._estimator = _estimator + self.random_state = random_state + + self._steps = ( + [t for t in transformers] + if isinstance(transformers, list) + else [transformers] + ) + if _estimator is not None: + self._steps.append(_estimator) + + self._check_estimators( + self._steps, + attr_name="_steps", + class_type=BaseEstimator, + ) + self._steps = self._convert_estimators(self._steps, clone_estimators=False) + + super().__init__(axis=1) + + # can handle multivariate if: both estimator and all transformers can, + # *or* transformer chain removes multivariate + multivariate_tags = [ + ( + e[1].get_tag( + "capability:multivariate", + raise_error=False, + tag_value_default=False, + ) + if isinstance(e[1], BaseAeonEstimator) + else False + ) + for e in self._steps + ] + + multivariate_rm_tag = False + for e in self._steps: + if ( + isinstance(e[1], BaseAeonEstimator) + and e[1].get_tag( + "capability:multivariate", + raise_error=False, + tag_value_default=False, + ) + and e[1].get_tag("output_data_type", raise_error=False) == "Tabular" + ): + multivariate_rm_tag = True + break + elif not isinstance(e[1], BaseAeonEstimator) or not e[1].get_tag( + "capability:multivariate", raise_error=False, tag_value_default=False + ): + break + + multivariate = all(multivariate_tags) or multivariate_rm_tag + + # can handle missing values if: both estimator and all transformers can, + # *or* transformer chain removes missing data + missing_tags = [ + ( + e[1].get_tag( + "capability:missing_values", + raise_error=False, + tag_value_default=False, + ) + if isinstance(e[1], BaseAeonEstimator) + else False + ) + for e in self._steps + ] + + missing_rm_tag = False + for e in self._steps: + if ( + isinstance(e[1], BaseAeonEstimator) + and e[1].get_tag( + "capability:missing_values", + raise_error=False, + tag_value_default=False, + ) + and e[1].get_tag( + "removes_missing_values", raise_error=False, tag_value_default=False + ) + ): + missing_rm_tag = True + break + elif not isinstance(e[1], BaseAeonEstimator) or not e[1].get_tag( + "capability:missing_values", raise_error=False, tag_value_default=False + ): + break + + missing = all(missing_tags) or missing_rm_tag + + tags_to_set = { + "capability:multivariate": multivariate, + "capability:missing_values": missing, + } + self.set_tags(**tags_to_set) + + def _fit(self, X, y): + """Fit time series estimator to training data. + + Parameters + ---------- + X : Training data of type self.get_tag("X_inner_type") + y : array-like, shape = [n_cases] - the target values + + Returns + ------- + self : reference to self. + """ + self._clone_steps() + + # fit transforms sequentially + Xt = X + for i in range(len(self.steps_) - 1): + Xt = _transform_args_wrapper( + self.steps_[i][1], "fit_transform", Xt, y=y, axis=1 + ) + # fit estimator + self.steps_[-1][1].fit(X=Xt, y=y) + + return self + + def _predict(self, X) -> np.ndarray: + """Predict labels for sequences in X. + + Parameters + ---------- + X : data not used in training, of type self.get_tag("X_inner_type") + + Returns + ------- + y : predictions of target values for X, np.ndarray + """ + # transform + Xt = X + for i in range(len(self.steps_) - 1): + Xt = _transform_args_wrapper(self.steps_[i][1], "transform", Xt, axis=1) + # predict + return self.steps_[-1][1].predict(X=Xt) + + def _predict_proba(self, X) -> np.ndarray: + """Predicts labels probabilities for sequences in X. + + Default behaviour is to call _predict and set the predicted class probability + to 1, other class probabilities to 0. Override if better estimates are + obtainable. + + Parameters + ---------- + X : data to predict y with, of type self.get_tag("X_inner_type") + + Returns + ------- + y : predictions of probabilities for target values of X, np.ndarray + """ + # transform + Xt = X + for i in range(len(self.steps_) - 1): + Xt = _transform_args_wrapper(self.steps_[i][1], "transform", Xt, axis=1) + # predict + return self.steps_[-1][1].predict_proba(X=Xt) + + def _fit_transform(self, X, y=None) -> np.ndarray: + """Fit and transform sequences in X. + + Parameters + ---------- + X : data of type self.get_tag("X_inner_type") + + Returns + ------- + Xt : transformed data + """ + self._clone_steps() + + # transform + Xt = X + for i in range(len(self.steps_)): + Xt = _transform_args_wrapper( + self.steps_[i][1], "fit_transform", Xt, y=y, axis=1 + ) + return Xt + + def _transform(self, X, y=None) -> np.ndarray: + """Transform sequences in X. + + Parameters + ---------- + X : data of type self.get_tag("X_inner_type") + + Returns + ------- + Xt : transformed data + """ + # transform + Xt = X + for i in range(len(self.steps_)): + Xt = _transform_args_wrapper( + self.steps_[i][1], "transform", Xt, y=y, axis=1 + ) + return Xt + + def _clone_steps(self): + if self.random_state is not None: + rng = check_random_state(self.random_state) + self.steps_ = [ + ( + step[0], + _clone_estimator( + step[1], random_state=rng.randint(np.iinfo(np.int32).max) + ), + ) + for step in self._steps + ] + else: + self.steps_ = [(step[0], _clone_estimator(step[1])) for step in self._steps] diff --git a/aeon/classification/compose/_channel_ensemble.py b/aeon/classification/compose/_channel_ensemble.py index 3605debe32..ce3832d25f 100644 --- a/aeon/classification/compose/_channel_ensemble.py +++ b/aeon/classification/compose/_channel_ensemble.py @@ -10,6 +10,7 @@ import numpy as np from sklearn.utils import check_random_state +from aeon.base._estimators.compose._commons import _get_channel from aeon.base._estimators.compose.collection_channel_ensemble import ( BaseCollectionChannelEnsemble, ) @@ -114,14 +115,14 @@ def _predict_proba(self, X) -> np.ndarray: # Call predict on each classifier, add the predictions to the # current probabilities for i, (_, clf) in enumerate(self.ensemble_): - preds = clf.predict(X=self._get_channel(X, self.channels_[i])) + preds = clf.predict(X=_get_channel(X, self.channels_[i])) for n in range(X.shape[0]): dists[n, self._class_dictionary[preds[n]]] += 1 else: # Call predict_proba on each classifier, then add them to the current # probabilities for i, (_, clf) in enumerate(self.ensemble_): - dists += clf.predict_proba(X=self._get_channel(X, self.channels_[i])) + dists += clf.predict_proba(X=_get_channel(X, self.channels_[i])) # Make each instances probability array sum to 1 and return y_proba = dists / dists.sum(axis=1, keepdims=True) diff --git a/aeon/forecasting/base.py b/aeon/forecasting/base.py index 23950fa0c6..46facf2ad3 100644 --- a/aeon/forecasting/base.py +++ b/aeon/forecasting/base.py @@ -13,7 +13,7 @@ import pandas as pd from aeon.base import BaseSeriesEstimator -from aeon.base._base_series import VALID_SERIES_INNER_TYPES +from aeon.utils.data_types import VALID_SERIES_INNER_TYPES class BaseForecaster(BaseSeriesEstimator): diff --git a/aeon/pipeline/_sklearn_to_aeon.py b/aeon/pipeline/_sklearn_to_aeon.py index e5639572c8..eb9f25b5a2 100644 --- a/aeon/pipeline/_sklearn_to_aeon.py +++ b/aeon/pipeline/_sklearn_to_aeon.py @@ -1,6 +1,7 @@ """Sklearn to aeon coercion utility.""" __maintainer__ = ["MatthewMiddlehurst"] +__all__ = ["sklearn_to_aeon"] from aeon.pipeline._make_pipeline import make_pipeline from aeon.transformations.collection import Tabularizer @@ -9,19 +10,19 @@ def sklearn_to_aeon(estimator): """Coerces an sklearn estimator to the aeon pipeline interface. - Creates a pipeline of two elements, the identity transformer and the estimator. - The identity transformer acts as adapter and holds aeon base class logic. + Creates a pipeline of two elements, the Tabularizer transformer and the estimator. + The Tabularizer transformer acts as adapter and holds aeon base class logic, as + well as converting aeon datatypes to a feature vector format. Multivariate series + will be concatenated into a single feature vector. Data must be of equal length. Parameters ---------- estimator : sklearn compatible estimator - can be classifier, regressor, transformer, clusterer + Can be a classifier, regressor, clusterer, or transformer. Returns ------- - pipe : aeon estimator of corresponding time series type - classifiers, regressors, clusterers are converted to time series counterparts - by flattening time series. Assumes equal length time series. - transformers are converted to time series transformer by application per series + pipe : aeon pipeline estimator + A pipeline of the Tabularizer transformer and input estimator. """ return make_pipeline(Tabularizer(), estimator) diff --git a/aeon/pipeline/tests/test_sklearn_to_aeon.py b/aeon/pipeline/tests/test_sklearn_to_aeon.py index fd47c84434..3cd1ba28cf 100644 --- a/aeon/pipeline/tests/test_sklearn_to_aeon.py +++ b/aeon/pipeline/tests/test_sklearn_to_aeon.py @@ -5,6 +5,7 @@ from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor from sklearn.preprocessing import StandardScaler +from aeon.base import BaseAeonEstimator from aeon.pipeline import sklearn_to_aeon from aeon.testing.data_generation import make_example_3d_numpy @@ -23,11 +24,12 @@ def test_sklearn_to_aeon(estimator): X, y = make_example_3d_numpy() est = sklearn_to_aeon(estimator) + + assert isinstance(est, BaseAeonEstimator) + est.fit(X, y) if hasattr(est, "predict"): est.predict(X) else: est.transform(X) - - assert est._estimator_type == getattr(estimator, "_estimator_type", "transformer") diff --git a/aeon/segmentation/base.py b/aeon/segmentation/base.py index 05f746d53c..d93cbabc5e 100644 --- a/aeon/segmentation/base.py +++ b/aeon/segmentation/base.py @@ -10,7 +10,7 @@ import pandas as pd from aeon.base import BaseSeriesEstimator -from aeon.base._base_series import VALID_SERIES_INPUT_TYPES +from aeon.utils.data_types import VALID_SERIES_INPUT_TYPES class BaseSegmenter(BaseSeriesEstimator): diff --git a/aeon/testing/estimator_checking/_yield_forecasting_checks.py b/aeon/testing/estimator_checking/_yield_forecasting_checks.py index 5c62d2f05d..e8dbaa0dd7 100644 --- a/aeon/testing/estimator_checking/_yield_forecasting_checks.py +++ b/aeon/testing/estimator_checking/_yield_forecasting_checks.py @@ -5,7 +5,7 @@ import numpy as np from aeon.base._base import _clone_estimator -from aeon.base._base_series import VALID_SERIES_INPUT_TYPES +from aeon.utils.data_types import VALID_SERIES_INPUT_TYPES def _yield_forecasting_checks(estimator_class, estimator_instances, datatypes): diff --git a/aeon/testing/estimator_checking/_yield_segmentation_checks.py b/aeon/testing/estimator_checking/_yield_segmentation_checks.py index 054ca56bd4..62d5a1d391 100644 --- a/aeon/testing/estimator_checking/_yield_segmentation_checks.py +++ b/aeon/testing/estimator_checking/_yield_segmentation_checks.py @@ -5,7 +5,7 @@ import numpy as np from aeon.base._base import _clone_estimator -from aeon.base._base_series import VALID_SERIES_INNER_TYPES +from aeon.utils.data_types import VALID_SERIES_INNER_TYPES def _yield_segmentation_checks(estimator_class, estimator_instances, datatypes): diff --git a/aeon/testing/estimator_checking/_yield_transformation_checks.py b/aeon/testing/estimator_checking/_yield_transformation_checks.py index 63538ba2dd..14bd54fa90 100644 --- a/aeon/testing/estimator_checking/_yield_transformation_checks.py +++ b/aeon/testing/estimator_checking/_yield_transformation_checks.py @@ -9,7 +9,6 @@ from sklearn.utils._testing import set_random_state from aeon.base._base import _clone_estimator -from aeon.base._base_series import VALID_SERIES_INNER_TYPES from aeon.datasets import load_basic_motions, load_unit_test from aeon.testing.expected_results.expected_transform_outputs import ( basic_motions_result, @@ -20,7 +19,7 @@ from aeon.testing.utils.estimator_checks import _run_estimator_method from aeon.transformations.collection.channel_selection.base import BaseChannelSelector from aeon.transformations.series import BaseSeriesTransformer -from aeon.utils.data_types import COLLECTIONS_DATA_TYPES +from aeon.utils.data_types import COLLECTIONS_DATA_TYPES, VALID_SERIES_INNER_TYPES def _yield_transformation_checks(estimator_class, estimator_instances, datatypes): @@ -199,4 +198,5 @@ def check_transform_inverse_transform_equivalent(estimator, datatype): if isinstance(Xit, (np.ndarray, pd.DataFrame)): Xit = Xit.squeeze() - assert deep_equals(X, Xit, ignore_index=True) + eq, msg = deep_equals(X, Xit, ignore_index=True, return_msg=True) + assert eq, msg diff --git a/aeon/testing/utils/estimator_checks.py b/aeon/testing/utils/estimator_checks.py index d556ff0249..30707837fe 100644 --- a/aeon/testing/utils/estimator_checks.py +++ b/aeon/testing/utils/estimator_checks.py @@ -2,8 +2,7 @@ __maintainer__ = ["MatthewMiddlehurst"] -import inspect -from inspect import isclass +from inspect import isclass, signature import numpy as np @@ -14,7 +13,7 @@ def _run_estimator_method(estimator, method_name, datatype, split): method = getattr(estimator, method_name) - args = inspect.getfullargspec(method)[0] + args = list(signature(method).parameters.keys()) try: # forecasting if "y" in args and "exog" in args: diff --git a/aeon/transformations/__init__.py b/aeon/transformations/__init__.py index cfb7444ccf..23421c062b 100644 --- a/aeon/transformations/__init__.py +++ b/aeon/transformations/__init__.py @@ -1 +1,7 @@ -"""Transformations.""" +"""Time series transformations.""" + +__all__ = [ + "BaseTransformer", +] + +from aeon.transformations.base import BaseTransformer diff --git a/aeon/transformations/collection/compose/_pipeline.py b/aeon/transformations/collection/compose/_pipeline.py index 796450706d..9e0acafdd8 100644 --- a/aeon/transformations/collection/compose/_pipeline.py +++ b/aeon/transformations/collection/compose/_pipeline.py @@ -49,11 +49,10 @@ class CollectionTransformerPipeline(BaseCollectionPipeline, BaseCollectionTransf Examples -------- >>> from aeon.transformations.collection import Resizer - >>> from aeon.transformations.collection.feature_based import ( - ... SevenNumberSummary) + >>> from aeon.transformations.collection.feature_based import SevenNumberSummary >>> from aeon.datasets import load_unit_test >>> from aeon.transformations.collection.compose import ( - ... CollectionTransformerPipeline) + ... CollectionTransformerPipeline) >>> X, y = load_unit_test(split="train") >>> pipeline = CollectionTransformerPipeline( ... [Resizer(length=10), SevenNumberSummary()] diff --git a/aeon/transformations/collection/compose/tests/test_pipeline.py b/aeon/transformations/collection/compose/tests/test_pipeline.py index a3a8ca2d1e..c45ddcb3a2 100644 --- a/aeon/transformations/collection/compose/tests/test_pipeline.py +++ b/aeon/transformations/collection/compose/tests/test_pipeline.py @@ -1,6 +1,4 @@ -"""Unit tests for clustering pipeline.""" - -__maintainer__ = ["MatthewMiddlehurst"] +"""Unit tests for collection transform pipeline.""" import pytest from numpy.testing import assert_array_almost_equal @@ -44,13 +42,16 @@ def test_collection_transform_pipeline(transformers): pipeline.fit(X, y) Xt = pipeline.transform(X) + pipeline2 = CollectionTransformerPipeline(transformers=transformers) + Xt2 = pipeline2.fit_transform(X, y) + if not isinstance(transformers, list): transformers = [transformers] - for t in transformers: X = t.fit_transform(X, y) assert_array_almost_equal(Xt, X) + assert_array_almost_equal(Xt2, X) def test_unequal_tag_inference(): diff --git a/aeon/transformations/series/__init__.py b/aeon/transformations/series/__init__.py index 01bb5faa55..d97d45c7ad 100644 --- a/aeon/transformations/series/__init__.py +++ b/aeon/transformations/series/__init__.py @@ -10,13 +10,13 @@ "GaussSeriesTransformer", "MatrixProfileSeriesTransformer", "MovingAverageSeriesTransformer", + "LogTransformer", "PLASeriesTransformer", "SGSeriesTransformer", "StatsModelsACF", "StatsModelsPACF", "BKFilter", "BoxCoxTransformer", - "Dobin", "ScaledLogitSeriesTransformer", "SIVSeriesTransformer", "PCASeriesTransformer", @@ -37,6 +37,7 @@ from aeon.transformations.series._dobin import Dobin from aeon.transformations.series._exp_smoothing import ExpSmoothingSeriesTransformer from aeon.transformations.series._gauss import GaussSeriesTransformer +from aeon.transformations.series._log import LogTransformer from aeon.transformations.series._matrix_profile import MatrixProfileSeriesTransformer from aeon.transformations.series._moving_average import MovingAverageSeriesTransformer from aeon.transformations.series._pca import PCASeriesTransformer diff --git a/aeon/transformations/series/_boxcox.py b/aeon/transformations/series/_boxcox.py index 4c0f20af46..a722c348eb 100644 --- a/aeon/transformations/series/_boxcox.py +++ b/aeon/transformations/series/_boxcox.py @@ -1,7 +1,7 @@ -"""Box-Cox and Log Transformations.""" +"""Box-Cox transformation.""" __maintainer__ = ["TonyBagnall"] -__all__ = ["BoxCoxTransformer", "LogTransformer"] +__all__ = ["BoxCoxTransformer"] import numpy as np from scipy import optimize, special, stats @@ -184,90 +184,6 @@ def _inverse_transform(self, X, y=None): return Xt -class LogTransformer(BaseSeriesTransformer): - """Natural logarithm transformation. - - The Natural logarithm transformation can be used to make the data more normally - distributed and stabilize its variance. - - Transforms each data point x to log(scale *(x+offset)) - - Parameters - ---------- - offset : float , default = 0 - Additive constant applied to all the data. - scale : float , default = 1 - Multiplicative scaling constant applied to all the data. - - - Notes - ----- - The log transformation is applied as :math:`ln(y)`. - - Examples - -------- - >>> from aeon.transformations.series._boxcox import LogTransformer - >>> from aeon.datasets import load_airline - >>> y = load_airline() - >>> transformer = LogTransformer() - >>> y_hat = transformer.fit_transform(y) - """ - - _tags = { - "X_inner_type": "np.ndarray", - "fit_is_empty": True, - "capability:multivariate": True, - "capability:inverse_transform": True, - } - - def __init__(self, offset=0, scale=1): - self.offset = offset - self.scale = scale - super().__init__(axis=1) - - def _transform(self, X, y=None): - """Transform X and return a transformed version. - - private _transform containing the core logic, called from transform - - Parameters - ---------- - X : 2D np.ndarray - Data to be transformed - y : ignored argument for interface compatibility - Additional data, e.g., labels for transformation - - Returns - ------- - Xt : 2D np.ndarray - transformed version of X - """ - offset = self.offset - scale = self.scale - Xt = np.log(scale * (X + offset)) - return Xt - - def _inverse_transform(self, X, y=None): - """Inverse transform X and return an inverse transformed version. - - core logic - - Parameters - ---------- - X : 2D np.ndarray - Data to be transformed - y : ignored argument for interface compatibility - Additional data, e.g., labels for transformation - - Returns - ------- - Xt : 2D np.ndarray - inverse transformed version of X - """ - Xt = (np.exp(X) / self.scale) - self.offset - return Xt - - def _make_boxcox_optimizer(bounds=None, brack=(-2.0, 2.0)): # bounds is None, use simple Brent optimisation if bounds is None: diff --git a/aeon/transformations/series/_log.py b/aeon/transformations/series/_log.py new file mode 100644 index 0000000000..45d72664f5 --- /dev/null +++ b/aeon/transformations/series/_log.py @@ -0,0 +1,92 @@ +"""Log transformation.""" + +__maintainer__ = ["TonyBagnall"] +__all__ = ["LogTransformer"] + +import numpy as np + +from aeon.transformations.series.base import BaseSeriesTransformer + + +class LogTransformer(BaseSeriesTransformer): + """Natural logarithm transformation. + + The Natural logarithm transformation can be used to make the data more normally + distributed and stabilize its variance. + + Transforms each data point x to log(scale *(x+offset)) + + Parameters + ---------- + offset : float , default = 0 + Additive constant applied to all the data. + scale : float , default = 1 + Multiplicative scaling constant applied to all the data. + + + Notes + ----- + The log transformation is applied as :math:`ln(y)`. + + Examples + -------- + >>> from aeon.transformations.series._log import LogTransformer + >>> from aeon.datasets import load_airline + >>> y = load_airline() + >>> transformer = LogTransformer() + >>> y_hat = transformer.fit_transform(y) + """ + + _tags = { + "X_inner_type": "np.ndarray", + "fit_is_empty": True, + "capability:multivariate": True, + "capability:inverse_transform": True, + } + + def __init__(self, offset=0, scale=1): + self.offset = offset + self.scale = scale + super().__init__(axis=1) + + def _transform(self, X, y=None): + """Transform X and return a transformed version. + + private _transform containing the core logic, called from transform + + Parameters + ---------- + X : 2D np.ndarray + Data to be transformed + y : ignored argument for interface compatibility + Additional data, e.g., labels for transformation + + Returns + ------- + Xt : 2D np.ndarray + transformed version of X + """ + offset = self.offset + scale = self.scale + Xt = np.log(scale * (X + offset)) + return Xt + + def _inverse_transform(self, X, y=None): + """Inverse transform X and return an inverse transformed version. + + core logic + + Parameters + ---------- + X : 2D np.ndarray + Data to be transformed + y : ignored argument for interface compatibility + Additional data, e.g., labels for transformation + + Returns + ------- + Xt : 2D np.ndarray + inverse transformed version of X + """ + Xt = (np.exp(X) / self.scale) - self.offset + return Xt diff --git a/aeon/transformations/series/compose/__init__.py b/aeon/transformations/series/compose/__init__.py new file mode 100644 index 0000000000..4855a210ee --- /dev/null +++ b/aeon/transformations/series/compose/__init__.py @@ -0,0 +1,9 @@ +"""Compositions for series transforms.""" + +__all__ = [ + "SeriesTransformerPipeline", + "SeriesId", +] + +from aeon.transformations.series.compose._identity import SeriesId +from aeon.transformations.series.compose._pipeline import SeriesTransformerPipeline diff --git a/aeon/transformations/series/compose/_identity.py b/aeon/transformations/series/compose/_identity.py new file mode 100644 index 0000000000..d9135fae36 --- /dev/null +++ b/aeon/transformations/series/compose/_identity.py @@ -0,0 +1,25 @@ +"""Identity transformer.""" + +from aeon.transformations.series import BaseSeriesTransformer +from aeon.utils.data_types import VALID_SERIES_INNER_TYPES + + +class SeriesId(BaseSeriesTransformer): + """Identity transformer, returns data unchanged in transform/inverse_transform.""" + + _tags = { + "X_inner_type": VALID_SERIES_INNER_TYPES, + "fit_is_empty": True, + "capability:inverse_transform": True, + "capability:multivariate": True, + "capability:missing_values": True, + } + + def __init__(self): + super().__init__(axis=1) + + def _transform(self, X, y=None): + return X + + def _inverse_transform(self, X, y=None): + return X diff --git a/aeon/transformations/series/compose/_pipeline.py b/aeon/transformations/series/compose/_pipeline.py new file mode 100644 index 0000000000..0f1aaef6be --- /dev/null +++ b/aeon/transformations/series/compose/_pipeline.py @@ -0,0 +1,103 @@ +"""Pipeline with series transformers.""" + +__maintainer__ = ["MatthewMiddlehurst"] +__all__ = ["SeriesTransformerPipeline"] + +from aeon.base._estimators.compose.series_pipeline import BaseSeriesPipeline +from aeon.transformations.series import BaseSeriesTransformer +from aeon.transformations.series.compose import SeriesId +from aeon.utils.data_types import VALID_SERIES_INNER_TYPES + + +class SeriesTransformerPipeline(BaseSeriesPipeline, BaseSeriesTransformer): + """Pipeline of series transformers. + + The `SeriesTransformerPipeline` compositor chains transformers. + The pipeline is constructed with a list of aeon transformers, + i.e., estimators following the BaseTransformer interface. + The transformer list can be unnamed - a simple list of transformers - + or string named - a list of pairs of string, estimator. + + For a list of transformers `trafo1`, `trafo2`, ..., `trafoN`, + the pipeline behaves as follows: + `fit(X, y)` - changes state by running `trafo1.fit_transform` on `X`, + them `trafo2.fit_transform` on the output of `trafo1.fit_transform`, etc + sequentially, with `trafo[i]` receiving the output of `trafo[i-1]`, + and then running `trafo[N].fit` with `X` being the output of `trafo[N-1]`, + and `y` identical with the input to `self.fit` + `transform(X, y)` - result is of executing `trafo1.transform`, `trafo2.transform`, + etc with `trafo[i].transform` input = output of `trafo[i-1].transform`, + then running `trafo[N].transform` on the output of `trafo[N-1].transform`, + and returning the output. + + Parameters + ---------- + transformers : aeon or sklearn transformer or list of transformers + A transform or list of transformers. + List of tuples (str, transformer) of transformers can also be passed, where + the str is used to name the transformer. + The objecst are cloned prior, as such the state of the input will not be + modified by fitting the pipeline. + + Attributes + ---------- + steps_ : list of tuples (str, estimator) of transformers + Clones of transformers which are fitted in the pipeline. + Will always be in (str, estimator) format, even if transformers input is a + singular transform or list of transformers. + + Examples + -------- + >>> from aeon.transformations.series import LogTransformer + >>> from aeon.transformations.series import MovingAverageSeriesTransformer + >>> from aeon.datasets import load_airline + >>> from aeon.transformations.series.compose import SeriesTransformerPipeline + >>> X = load_airline() + >>> pipeline = SeriesTransformerPipeline( + ... [LogTransformer(), MovingAverageSeriesTransformer()] + ... ) + >>> pipeline.fit(X) + SeriesTransformerPipeline(...) + >>> Xt = pipeline.transform(X) + """ + + _tags = { + "X_inner_type": VALID_SERIES_INNER_TYPES, + } + + def __init__(self, transformers): + if not isinstance(transformers, list): + transformers = [SeriesId(), transformers] + elif len(transformers) < 2: + transformers = [SeriesId(), transformers[0]] + + super().__init__(transformers=transformers, _estimator=None) + + @classmethod + def _get_test_params(cls, parameter_set="default"): + """Return testing parameter settings for the estimator. + + Parameters + ---------- + parameter_set : str, default="default" + Name of the set of test parameters to return, for use in tests. If no + special parameters are defined for a value, will return `"default"` set. + + Returns + ------- + params : dict or list of dict, default={} + Parameters to create testing instances of the class. + Each dict are parameters to construct an "interesting" test instance, i.e., + `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + """ + from aeon.transformations.series import ( + LogTransformer, + MovingAverageSeriesTransformer, + ) + + return { + "transformers": [ + LogTransformer(), + MovingAverageSeriesTransformer(), + ] + } diff --git a/aeon/transformations/series/compose/tests/__init__.py b/aeon/transformations/series/compose/tests/__init__.py new file mode 100644 index 0000000000..363dad1ea8 --- /dev/null +++ b/aeon/transformations/series/compose/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for composable series transformers.""" diff --git a/aeon/transformations/series/compose/tests/test_pipeline.py b/aeon/transformations/series/compose/tests/test_pipeline.py new file mode 100644 index 0000000000..29874780e7 --- /dev/null +++ b/aeon/transformations/series/compose/tests/test_pipeline.py @@ -0,0 +1,35 @@ +"""Unit tests for series transform pipeline.""" + +import pytest +from numpy.testing import assert_array_almost_equal + +from aeon.testing.data_generation import make_example_2d_numpy_series +from aeon.transformations.series import AutoCorrelationSeriesTransformer, LogTransformer +from aeon.transformations.series.compose import SeriesTransformerPipeline + + +@pytest.mark.parametrize( + "transformers", + [ + LogTransformer(), + [LogTransformer(), AutoCorrelationSeriesTransformer()], + ], +) +def test_series_transform_pipeline(transformers): + """Test the collection transform pipeline.""" + X = make_example_2d_numpy_series(n_timepoints=12) + + pipeline = SeriesTransformerPipeline(transformers=transformers) + pipeline.fit(X) + Xt = pipeline.transform(X) + + pipeline2 = SeriesTransformerPipeline(transformers=transformers) + Xt2 = pipeline2.fit_transform(X) + + if not isinstance(transformers, list): + transformers = [transformers] + for t in transformers: + X = t.fit_transform(X) + + assert_array_almost_equal(Xt, X) + assert_array_almost_equal(Xt2, X) diff --git a/aeon/transformations/series/tests/test_boxcox.py b/aeon/transformations/series/tests/test_boxcox.py index 6274924ddb..49fe0ebeef 100644 --- a/aeon/transformations/series/tests/test_boxcox.py +++ b/aeon/transformations/series/tests/test_boxcox.py @@ -1,8 +1,5 @@ """Tests for BoxCoxTransformer.""" -__maintainer__ = [] -__all__ = [] - import numpy as np import pytest from scipy.stats import boxcox diff --git a/aeon/utils/data_types.py b/aeon/utils/data_types.py index aa6b14ba49..cb5aa1d292 100644 --- a/aeon/utils/data_types.py +++ b/aeon/utils/data_types.py @@ -7,53 +7,100 @@ the index. Checks of input data are handled in the `aeon.utils.validation` module, -and conversion is handled in the `aeon.utils.conversion` module. +and conversion is handled in the `aeon.utils.conversion` module. """ +__all__ = [ + "SERIES_DATA_TYPES", + "SERIES_MULTIVARIATE_DATA_TYPES", + "VALID_SERIES_INNER_TYPES", + "VALID_SERIES_INPUT_TYPES", + "COLLECTIONS_DATA_TYPES", + "COLLECTIONS_MULTIVARIATE_DATA_TYPES", + "COLLECTIONS_UNEQUAL_DATA_TYPES", + "VALID_COLLECTIONS_INNER_TYPES", + "VALID_COLLECTIONS_INPUT_TYPES", + "HIERARCHICAL_DATA_TYPES", + "ALL_TIME_SERIES_TYPES", +] + +import numpy as np +import pandas as pd + +# SERIES + SERIES_DATA_TYPES = [ - "pd.Series", # univariate time series of shape (n_timepoints) - "pd.DataFrame", # uni/multivariate time series of shape (n_timepoints, - # n_channels) by default or (n_channels, n_timepoints) if set by axis == 1 - "np.ndarray", # uni/multivariate time series of shape (n_timepoints, - # n_channels) by default or (n_channels, n_timepoints) if set by axis ==1 + "pd.Series", # univariate 1D pandas Series time series of shape (n_timepoints) + "pd.DataFrame", # uni/multivariate 2D pandas DataFrame time series of shape + # (n_channels, n_timepoints) by default, or (n_timepoints, n_channels) if set by + # axis == 0 + "np.ndarray", # uni/multivariate 2D numpy ndarray time series of shape + # (n_channels, n_timepoints) by default, or (n_timepoints, n_channels) if set by + # axis == 0 +] + +# subset of series dtypes capable of handling multivariate time series +SERIES_MULTIVARIATE_DATA_TYPES = [ + "pd.DataFrame", + "np.ndarray", +] + +# datatypes which are valid for BaseSeriesEstimator estimators +VALID_SERIES_INNER_TYPES = [ + "np.ndarray", + "pd.DataFrame", ] +VALID_SERIES_INPUT_TYPES = [pd.Series, pd.DataFrame, np.ndarray] + +# COLLECTIONS COLLECTIONS_DATA_TYPES = [ - "numpy3D", # 3D np.ndarray of format (n_cases, n_channels, n_timepoints) - "np-list", # python list of 2D np.ndarray of length [n_cases], - # each of shape (n_channels, n_timepoints_i) - "df-list", # python list of 2D pd.DataFrames of length [n_cases], each - # of shape (n_channels, n_timepoints_i) - "numpy2D", # 2D np.ndarray of shape (n_cases, n_timepoints) - "pd-wide", # 2D pd.DataFrame of shape (n_cases, n_timepoints) - "pd-multiindex", # pd.DataFrame with MultiIndex, index [case, timepoint], - # columns [channel] -] - -# subset of collections capable of handling multivariate time series + "numpy3D", # uni/multivariate 3D numpy ndarray of shape + # (n_cases, n_channels, n_timepoints) + "np-list", # uni/multivariate length [n_cases] Python list of 2D numpy ndarray + # with shape (n_channels, n_timepoints_i) + "df-list", # uni/multivariate length [n_cases] Python list of 2D pandas DataFrame + # with shape (n_channels, n_timepoints_i) + "numpy2D", # univariate 2D numpy ndarray of shape (n_cases, n_timepoints) + "pd-wide", # univariate 2D pandas DataFrame of shape (n_cases, n_timepoints) + "pd-multiindex", # uni/multivariate pandas DataFrame with MultiIndex, + # index [case, timepoint], columns [channel] +] + +# subset of collection dtypes capable of handling multivariate time series COLLECTIONS_MULTIVARIATE_DATA_TYPES = [ - "numpy3D", # 3D np.ndarray of format (n_cases, n_channels, n_timepoints) - "np-list", # python list of 2D np.ndarray of length [n_cases], - # each of shape (n_channels, n_timepoints_i) - "df-list", # python list of 2D pd.DataFrames of length [n_cases], each - # of shape (n_channels, n_timepoints_i) - "pd-multiindex", # pd.DataFrame with MultiIndex, index [case, timepoint], - # columns [channel] + "numpy3D", + "np-list", + "df-list", + "pd-multiindex", ] -# subset of collections capable of handling unequal length time series +# subset of collection dtypes capable of handling unequal length time series COLLECTIONS_UNEQUAL_DATA_TYPES = [ - "np-list", # python list of 2D np.ndarray of length [n_cases], - # each of shape (n_channels, n_timepoints_i) - "df-list", # python list of 2D pd.DataFrames of length [n_cases], each - # of shape (n_channels, n_timepoints_i) - "pd-multiindex", # pd.DataFrame with MultiIndex, index [case, timepoint], - # columns [channel] + "np-list", + "df-list", + "pd-multiindex", +] + +# datatypes which are valid for BaseCollectionEstimator estimators +VALID_COLLECTIONS_INNER_TYPES = [ + "numpy3D", + "np-list", + "df-list", + "numpy2D", + "pd-wide", + "pd-multiindex", ] +VALID_COLLECTIONS_INPUT_TYPES = [list, pd.DataFrame, np.ndarray] + +# HIERARCHICAL + HIERARCHICAL_DATA_TYPES = ["pd_multiindex_hier"] # pd.DataFrame +# ALL + ALL_TIME_SERIES_TYPES = ( SERIES_DATA_TYPES + COLLECTIONS_DATA_TYPES + HIERARCHICAL_DATA_TYPES ) diff --git a/docs/api_reference/base.rst b/docs/api_reference/base.md similarity index 65% rename from docs/api_reference/base.rst rename to docs/api_reference/base.md index 9c315fec4c..5a717b79c5 100644 --- a/docs/api_reference/base.rst +++ b/docs/api_reference/base.md @@ -1,13 +1,10 @@ -.. _base_ref: +# Base -Base -==== +The `aeon.base` module contains abstract base classes. -The :mod:`aeon.base` module contains abstract base classes. - -Base classes ------------- +## Base classes +```{eval-rst} .. currentmodule:: aeon.base .. autosummary:: @@ -18,3 +15,4 @@ Base classes BaseCollectionEstimator BaseSeriesEstimator ComposableEstimatorMixin +``` diff --git a/docs/api_reference/transformations.rst b/docs/api_reference/transformations.md similarity index 76% rename from docs/api_reference/transformations.rst rename to docs/api_reference/transformations.md index d24aa47196..a8cabf71c5 100644 --- a/docs/api_reference/transformations.rst +++ b/docs/api_reference/transformations.md @@ -1,9 +1,6 @@ -.. _transformations_ref: +# Transformations -Transformations -=============== - -The :mod:`aeon.transformations` module contains classes for series +The `aeon.transformations` module contains classes for series transformations. The module is organised into CollectionTransformers which transform a collection of time series into a different representation and SeriesTransformers which transform single time series. @@ -11,10 +8,9 @@ transform single time series. All transformers in `aeon` can be listed using the `aeon.registry .all_estimators` utility, using `estimator_types="transformer"` tag. +## Collection transformers -Collection transformers ------------------------ - +```{eval-rst} .. currentmodule:: aeon.transformations.collection .. autosummary:: @@ -37,11 +33,11 @@ Collection transformers SimpleImputer Truncator Tabularizer +``` +### Channel selection -Channel selection -~~~~~~~~~~~~~~~~~ - +```{eval-rst} .. currentmodule:: aeon.transformations.collection.channel_selection .. autosummary:: @@ -52,11 +48,11 @@ Channel selection ElbowClassPairwise ElbowClassSum RandomChannelSelector +``` +### Compose -Compose -~~~~~~~ - +```{eval-rst} .. currentmodule:: aeon.transformations.collection.compose .. autosummary:: @@ -65,11 +61,11 @@ Compose CollectionTransformerPipeline CollectionId +``` +### Convolution based -Convolution based -~~~~~~~~~~~~~~~~~ - +```{eval-rst} .. currentmodule:: aeon.transformations.collection.convolution_based .. autosummary:: @@ -80,11 +76,21 @@ Convolution based MiniRocket MultiRocket HydraTransformer +``` +```{eval-rst} +.. currentmodule:: aeon.transformations.collection.convolution_based.rocketGPU -Dictionary-based features -~~~~~~~~~~~~~~~~~~~~~~~~~ +.. autosummary:: + :toctree: auto_generated/ + :template: class.rst + + ROCKETGPU +``` + +### Dictionary-based features +```{eval-rst} .. currentmodule:: aeon.transformations.collection.dictionary_based .. autosummary:: @@ -95,12 +101,13 @@ Dictionary-based features PAA SFA SFAFast + SFAWhole BORF +``` +### Feature based -Feature based -~~~~~~~~~~~~~ - +```{eval-rst} .. currentmodule:: aeon.transformations.collection.feature_based .. autosummary:: @@ -111,11 +118,11 @@ Feature based TSFresh TSFreshRelevant SevenNumberSummary +``` +### Interval based -Interval based -~~~~~~~~~~~~~~ - +```{eval-rst} .. currentmodule:: aeon.transformations.collection.interval_based .. autosummary:: @@ -125,10 +132,11 @@ Interval based RandomIntervals SupervisedIntervals QUANTTransformer +``` -Self Supervised -~~~~~~~~~~~~~~~ +### Self Supervised +```{eval-rst} .. currentmodule:: aeon.transformations.collection.self_supervised .. autosummary:: @@ -136,10 +144,11 @@ Self Supervised :template: class.rst TRILITE +``` -Shapelet based -~~~~~~~~~~~~~~ +### Shapelet based +```{eval-rst} .. currentmodule:: aeon.transformations.collection.shapelet_based .. autosummary:: @@ -150,10 +159,11 @@ Shapelet based RandomDilatedShapeletTransform SAST RSAST +``` -Signature based -~~~~~~~~~~~~~~~ +### Signature based +```{eval-rst} .. currentmodule:: aeon.transformations.collection.signature_based .. autosummary:: @@ -161,11 +171,11 @@ Signature based :template: class.rst SignatureTransformer +``` +## Series transforms -Series transforms ------------------ - +```{eval-rst} .. currentmodule:: aeon.transformations.series .. autosummary:: @@ -174,26 +184,35 @@ Series transforms AutoCorrelationSeriesTransformer ClaSPTransformer - DFTSeriesTransformer Dobin - ExpSmoothingSeriesTransformer - GaussSeriesTransformer MatrixProfileSeriesTransformer - MovingAverageSeriesTransformer + LogTransformer PLASeriesTransformer - SGSeriesTransformer StatsModelsACF StatsModelsPACF BKFilter BoxCoxTransformer ScaledLogitSeriesTransformer - SIVSeriesTransformer PCASeriesTransformer WarpingSeriesTransformer +``` -Smoothing -~~~~~~~~~ +### Compose +```{eval-rst} +.. currentmodule:: aeon.transformations.series.compose + +.. autosummary:: + :toctree: auto_generated/ + :template: class.rst + + SeriesTransformerPipeline + SeriesId +``` + +### Smoothing + +```{eval-rst} .. currentmodule:: aeon.transformations.series.smoothing .. autosummary:: @@ -206,10 +225,11 @@ Smoothing MovingAverage SavitzkyGolayFilter RecursiveMedianSieve +``` -Base ----- +## Base +```{eval-rst} .. currentmodule:: aeon.transformations.collection.base .. autosummary:: @@ -226,3 +246,12 @@ Base :template: class.rst BaseSeriesTransformer + +.. currentmodule:: aeon.transformations.base + +.. autosummary:: + :toctree: auto_generated/ + :template: class.rst + + BaseTransformer +``` From 139239f1df6982d6b0bf32767eb5a3cc1be0e048 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Mon, 9 Jun 2025 12:30:29 +0100 Subject: [PATCH 141/216] [ENH] Multivariate and unequal PF, speed-ups and distance classifier tidy (#2872) * pass 1 * pf change fixes and derivative function merge * test * fixes * expected result * unequal and deprecations --- .../distance_based/_elastic_ensemble.py | 39 +- .../distance_based/_proximity_forest.py | 97 ++- .../distance_based/_proximity_tree.py | 799 ++++++++++-------- .../distance_based/_time_series_neighbors.py | 9 +- .../tests/test_proximity_forest.py | 27 - .../tests/test_proximity_tree.py | 177 ++-- aeon/distances/elastic/_ddtw.py | 68 +- aeon/distances/elastic/_wddtw.py | 24 +- .../expected_classifier_outputs.py | 2 +- .../collection/signature_based/_compute.py | 1 - aeon/utils/numba/general.py | 168 +++- aeon/utils/numba/stats.py | 132 ++- aeon/utils/numba/tests/test_general.py | 85 +- aeon/utils/numba/tests/test_stats.py | 127 ++- 14 files changed, 1015 insertions(+), 740 deletions(-) delete mode 100644 aeon/classification/distance_based/tests/test_proximity_forest.py diff --git a/aeon/classification/distance_based/_elastic_ensemble.py b/aeon/classification/distance_based/_elastic_ensemble.py index c2b159f827..877401ee95 100644 --- a/aeon/classification/distance_based/_elastic_ensemble.py +++ b/aeon/classification/distance_based/_elastic_ensemble.py @@ -25,7 +25,7 @@ from aeon.classification.distance_based._time_series_neighbors import ( KNeighborsTimeSeriesClassifier, ) -from aeon.utils.numba.general import slope_derivative_2d +from aeon.utils.numba.general import slope_derivative_2d, slope_derivative_3d class ElasticEnsemble(BaseClassifier): @@ -156,20 +156,12 @@ def _fit(self, X, y): # Derivative DTW (DDTW) uses the regular DTW algorithm on data that # are transformed into derivatives. - if self._distance_measures.__contains__( - "ddtw" - ) or self._distance_measures.__contains__("wddtw"): - der_X = [] # use list to allow for unequal length - for x in X: - der_X.append(slope_derivative_2d(x)) - if isinstance(X, np.ndarray): - der_X = np.array(der_X) - else: - der_X = None + der_X = self._get_derivatives(X) self.train_accs_by_classifier_ = np.zeros(len(self._distance_measures)) self.estimators_ = [None] * len(self._distance_measures) rand = np.random.RandomState(self.random_state) + # The default EE uses all training instances for setting parameters, # and 100 parameter options per elastic measure. The # prop_train_in_param_finding and prop_of_param_options attributes of this class @@ -221,11 +213,11 @@ def _fit(self, X, y): der_param_train_x = der_X self.constituent_build_times_ = [] - if self.verbose > 0: print( # noqa: T201 f"Using{(100 * self.proportion_of_param_options)} parameter options" ) + for dm in range(0, len(self._distance_measures)): this_measure = self._distance_measures[dm] @@ -346,17 +338,7 @@ def _predict_proba(self, X) -> np.ndarray: y : array-like, shape = (n_cases, n_classes_) Predicted probabilities using the ordering in classes_. """ - if self._distance_measures.__contains__( - "ddtw" - ) or self._distance_measures.__contains__("wddtw"): - der_X = [] # use list to allow for unequal length - for x in X: - der_X.append(slope_derivative_2d(x)) - if isinstance(X, np.ndarray): - der_X = np.array(der_X) - else: - der_X = None - + der_X = self._get_derivatives(X) output_probas = [] train_sum = 0 @@ -490,6 +472,17 @@ def get_inclusive(min_val: float, max_val: float, num_vals: float): "EE does not currently support: " + str(distance_measure) ) + def _get_derivatives(self, X): + if "ddtw" in self._distance_measures or "wddtw" in self._distance_measures: + if isinstance(X, np.ndarray): + return slope_derivative_3d(X) + else: + der_X = [] + for x in X: + der_X.append(slope_derivative_2d(x)) + return der_X + return None + @classmethod def _get_test_params( cls, parameter_set: str = "default" diff --git a/aeon/classification/distance_based/_proximity_forest.py b/aeon/classification/distance_based/_proximity_forest.py index 40fca3624d..49a19e2532 100644 --- a/aeon/classification/distance_based/_proximity_forest.py +++ b/aeon/classification/distance_based/_proximity_forest.py @@ -3,11 +3,10 @@ The Proximity Forest is an ensemble of Proximity Trees. """ -from typing import Optional - +__maintainer__ = [] __all__ = ["ProximityForest"] -from typing import Union +from typing import Optional, Union import numpy as np from joblib import Parallel, delayed @@ -71,18 +70,18 @@ class ProximityForest(BaseClassifier): >>> from aeon.classification.distance_based import ProximityForest >>> X_train, y_train = load_unit_test(split="train") >>> X_test, y_test = load_unit_test(split="test") - >>> classifier = ProximityForest(n_trees = 10, n_splitters = 3) + >>> classifier = ProximityForest(n_trees=10, n_splitters=3) >>> classifier.fit(X_train, y_train) ProximityForest(...) >>> y_pred = classifier.predict(X_test) """ _tags = { - "capability:multivariate": False, - "capability:unequal_length": False, + "capability:multivariate": True, + "capability:unequal_length": True, "capability:multithreading": True, "algorithm_type": "distance", - "X_inner_type": ["numpy2D"], + "X_inner_type": ["np-list", "numpy3D"], } def __init__( @@ -102,6 +101,7 @@ def __init__( self.random_state = random_state self.n_jobs = n_jobs self.parallel_backend = parallel_backend + super().__init__() def _fit(self, X, y): @@ -109,50 +109,65 @@ def _fit(self, X, y): self.trees_ = Parallel( n_jobs=self._n_jobs, backend=self.parallel_backend, prefer="threads" )( - delayed(_fit_tree)( + delayed(self._fit_tree)( X, y, - self.n_splitters, - self.max_depth, - self.min_samples_split, check_random_state(rng.randint(np.iinfo(np.int32).max)), ) for _ in range(self.n_trees) ) + def _predict(self, X): + return np.array( + [self.classes_[int(np.argmax(prob))] for prob in self._predict_proba(X)] + ) + def _predict_proba(self, X): - classes = list(self.classes_) preds = Parallel( n_jobs=self._n_jobs, backend=self.parallel_backend, prefer="threads" - )(delayed(_predict_tree)(tree, X) for tree in self.trees_) - n_cases = X.shape[0] - votes = np.zeros((n_cases, self.n_classes_)) - for i in range(len(preds)): - predictions = np.array( - [classes.index(class_label) for class_label in preds[i]] - ) - for j in range(n_cases): - votes[j, predictions[j]] += 1 - output_probas = votes / self.n_trees - return output_probas - - def _predict(self, X): - probas = self._predict_proba(X) - idx = np.argmax(probas, axis=1) - preds = np.asarray([self.classes_[x] for x in idx]) - return preds + )(delayed(self._predict_tree)(X, tree) for tree in self.trees_) + votes = np.zeros((len(X), self.n_classes_)) + for i in range(len(preds)): + for j in range(len(X)): + votes[j, self._class_dictionary[preds[i][j]]] += 1 -def _fit_tree(X, y, n_splitters, max_depth, min_samples_split, random_state): - clf = ProximityTree( - n_splitters=n_splitters, - max_depth=max_depth, - min_samples_split=min_samples_split, - random_state=random_state, - ) - clf.fit(X, y) - return clf - + return votes / self.n_trees -def _predict_tree(tree, X): - return tree.predict(X) + def _fit_tree(self, X, y, random_state): + clf = ProximityTree( + n_splitters=self.n_splitters, + max_depth=self.max_depth, + min_samples_split=self.min_samples_split, + random_state=random_state, + ) + clf.fit(X, y) + return clf + + def _predict_tree(self, X, tree): + return tree.predict(X) + + @classmethod + def _get_test_params( + cls, parameter_set: str = "default" + ) -> Union[dict, list[dict]]: + """Return testing parameter settings for the estimator. + + Parameters + ---------- + parameter_set : str, default="default" + Name of the set of test parameters to return, for use in tests. If no + special parameters are defined for a value, will return `"default"` set. + ElasticEnsemble provides the following special sets: + "results_comparison" - used in some classifiers to compare against + previously generated results where the default set of parameters + cannot produce suitable probability estimates + + Returns + ------- + params : dict or list of dict, default={} + Parameters to create testing instances of the class. + Each dict are parameters to construct an "interesting" test instance, i.e., + `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + """ + return {"n_trees": 3, "n_splitters": 3, "max_depth": 3} diff --git a/aeon/classification/distance_based/_proximity_tree.py b/aeon/classification/distance_based/_proximity_tree.py index 86a5be86fe..afbf4a8359 100644 --- a/aeon/classification/distance_based/_proximity_tree.py +++ b/aeon/classification/distance_based/_proximity_tree.py @@ -5,51 +5,60 @@ aeon distances. """ +__maintainer__ = [] +__all__ = ["ProximityTree"] + from typing import Optional, Union import numpy as np -from numba import njit from sklearn.utils import check_random_state from aeon.classification.base import BaseClassifier from aeon.distances import distance +from aeon.utils.numba.general import ( + slope_derivative_2d, + slope_derivative_3d, + unique_count, +) +from aeon.utils.numba.stats import gini_gain, std -class _Node: +class _ProximityNode: """Proximity Tree node. Parameters ---------- - node_id: str - The id of node, root node has id 0. - _is_leaf: bool + is_leaf: bool To identify leaf nodes. - label: int, str or None - Contains the class label of leaf node, None otherwise. - splitter: dict - The splitter used to split the node. - class_distribution: dict - In case of unpure leaf node, save the class distribution to calculate - probability of each class. + class_distribution: dict or None, default=None + The class distribution for the node. Empty if not a leaf node. + splitter: tuple or None, default=None + The splitter used to split the node. Contains exemplars used, distance name and + distance parameters.Empty if leaf node. + + Attributes + ---------- children: dict - Contains the class label and the associated node, empty for leaf node. + Contains the class label as a key and child node for that class. Empty for leaf + nodes. """ def __init__( self, - node_id: str, - _is_leaf: bool, - label=None, + is_leaf, class_distribution=None, splitter=None, ): - self.node_id = node_id - self._is_leaf = _is_leaf - self.label = label + self.is_leaf = is_leaf + self.class_distribution = class_distribution self.splitter = splitter - self.class_distribution = class_distribution or {} self.children = {} + if is_leaf: + assert class_distribution is not None + else: + assert splitter is not None + class ProximityTree(BaseClassifier): """Proximity Tree classifier. @@ -102,17 +111,17 @@ class ProximityTree(BaseClassifier): >>> from aeon.classification.distance_based import ProximityTree >>> X_train, y_train = load_unit_test(split="train") >>> X_test, y_test = load_unit_test(split="test") - >>> classifier = ProximityTree(n_splitters = 3) + >>> classifier = ProximityTree(n_splitters=3) >>> classifier.fit(X_train, y_train) ProximityTree(...) >>> y_pred = classifier.predict(X_test) """ _tags = { - "capability:multivariate": False, - "capability:unequal_length": False, + "capability:unequal_length": True, + "capability:multivariate": True, "algorithm_type": "distance", - "X_inner_type": ["numpy2D"], + "X_inner_type": ["np-list", "numpy3D"], } def __init__( @@ -126,371 +135,483 @@ def __init__( self.max_depth = max_depth self.min_samples_split = min_samples_split self.random_state = random_state + super().__init__() - def _get_parameter_value(self, X): - """Generate random parameter values. + def _fit(self, X, y): + # Reuse the transform for ddtw and wddtw + der_X = self._get_derivatives(X) + rng = check_random_state(self.random_state) + self._root = self._build_tree(X, der_X, y, rng, 0) + + def _predict(self, X): + return np.array( + [self.classes_[int(np.argmax(prob))] for prob in self._predict_proba(X)] + ) + + def _predict_proba(self, X): + # Reuse the transform for ddtw and wddtw + der_X = self._get_derivatives(X) + probas = np.zeros((len(X), len(self.classes_))) + for i in range(len(X)): + probas[i] = self._traverse_tree(self._root, X[i], der_X[i]) + return probas - For a list of distance measures, generate a dictionary - of parameterized distances. + def _build_tree(self, X, der_X, y, rng, depth): + """Build the tree recursively from the root node down to the leaf nodes. Parameters ---------- - X : np.ndarray of shape (n_cases, n_timepoints) + X : np.ndarray shape (n_cases, n_channels, n_timepoints) + The training input samples. + der_X : np.ndarray shape (n_cases, n_channels, n_timepoints) + The slope derivative of the training input samples, used for ddtw and wddtw. + y : np.ndarray shape (n_cases,) + The class labels for the training input samples. + rng : np.random.RandomState + Random number generator. + depth : int + The current depth of the tree, used to limit the maximum depth of the tree. Returns ------- - distance_param : a dictionary of distances and their - parameters. + node : _ProximityNode + The current node of the tree, which contains the splitter and children + nodes. + The root node will be returned to _fit. """ - rng = check_random_state(self.random_state) + # Target value in current node + unique_classes, class_counts = unique_count(y) + class_distribution = np.zeros(len(self.classes_)) + for i, label in enumerate(unique_classes): + class_distribution[self._class_dictionary[label]] = class_counts[i] / len(X) + + if ( + # Pure node + len(unique_classes) == 1 + # If min sample splits is reached + or self.min_samples_split >= len(X) + # If max depth is reached + or (self.max_depth is not None and depth >= self.max_depth) + ): + leaf = _ProximityNode( + is_leaf=True, + class_distribution=class_distribution, + ) + return leaf - X_std = X.std() - param_ranges = { - "euclidean": {}, - "dtw": {"window": (0, 0.25)}, - "ddtw": {"window": (0, 0.25)}, - "wdtw": {"g": (0, 1)}, - "wddtw": {"g": (0, 1)}, - "erp": {"g": (X_std / 5, X_std)}, - "lcss": {"epsilon": (X_std / 5, X_std), "window": (0, 0.25)}, - } - random_params = {} - for measure, ranges in param_ranges.items(): - random_params[measure] = { - param: np.round(rng.uniform(low, high), 3) - for param, (low, high) in ranges.items() - } - # For TWE - lmbda = rng.randint(0, 9) - exponent_range = np.arange(1, 6) # Exponents from -5 to 1 (inclusive) - random_exponent = rng.choice(exponent_range) - nu = 1 / 10**random_exponent - random_params["twe"] = {"lmbda": lmbda, "nu": nu} - - # For MSM - base = 10 - # Exponents from -2 to 2 (inclusive) - exponents = np.arange(-2, 3, dtype=np.float64) - # Randomly select an index from the exponent range - random_index = rng.randint(0, len(exponents)) - c = base ** exponents[random_index] - random_params["msm"] = {"c": c} - - return random_params - - def _get_candidate_splitter(self, X, y): - """Generate candidate splitter. + # Find the best splitter + splitter, node_splits = self._get_best_splitter( + X, der_X, y, unique_classes, rng + ) - Takes a time series dataset and a set of parameterized - distance measures to create a candidate splitter, which - contains a parameterized distance measure and a set of exemplars. + # Create root node + node = _ProximityNode(is_leaf=False, splitter=splitter) + + # For each exemplar, create a branch + for i, label in enumerate(unique_classes): + child_node = self._build_tree( + ( + X[node_splits[i]] + if isinstance(X, np.ndarray) + else [X[j] for j in node_splits[i]] + ), + ( + der_X[node_splits[i]] + if isinstance(der_X, np.ndarray) + else [der_X[j] for j in node_splits[i]] + ), + y[node_splits[i]], + rng, + depth + 1, + ) + node.children[label] = child_node + + return node + + def _get_best_splitter(self, X, der_X, y, unique_classes, rng): + """Get the best splitter for the current node which maximizes the gini gain. Parameters ---------- - X : np.ndarray shape (n_cases, n_timepoints) + X : np.ndarray shape (n_cases, n_channels, n_timepoints) The training input samples. - y : np.array shape (n_cases,) or (n_cases,1) - The labels of the training samples. - parameterized_distances : dictionary - Contains the distances and their parameters. + der_X : np.ndarray shape (n_cases, n_channels, n_timepoints) + The slope derivative of the training input samples, used for ddtw and wddtw. + y : np.ndarray shape (n_cases,) + The class labels for the training input samples. + unique_classes : np.ndarray + The unique class labels in the training set. + rng : np.random.RandomState + Random number generator. Returns ------- - splitter : list of two dictionaries - A distance and its parameter values and a set of exemplars. + best_splitter : tuple + The best splitter found, containing exemplars, distance name and distance + parameters. + best_split : list of list + The best split found, containing the indices of the time series in each + class. """ - rng = check_random_state(self.random_state) - - exemplars = {} - for label in np.unique(y): - y_new = y[y == label] - X_new = X[y == label] - id = rng.randint(0, X_new.shape[0]) - exemplars[y_new[id]] = X_new[id, :] - - # Create a list with first element exemplars and second element a - # random parameterized distance measure - parameterized_distances = self._get_parameter_value(X) - n = rng.randint(0, 9) - dist = list(parameterized_distances.keys())[n] - splitter = [exemplars, {dist: parameterized_distances[dist]}] - - return splitter - - def _get_best_splitter(self, X, y): - """Get the splitter for a node which maximizes the gini gain.""" - max_gain = float("-inf") + max_gain = -np.inf best_splitter = None + best_split = None + X_std = None + cls_idx = {} + for label in unique_classes: + cls_idx[label] = np.where(y == label)[0] + for _ in range(self.n_splitters): - splitter = self._get_candidate_splitter(X, y) - labels = list(splitter[0].keys()) - measure = list(splitter[1].keys())[0] - y_subs = [[] for _ in range(len(labels))] - for j in range(X.shape[0]): - min_dist = float("inf") - sub = None - for k in range(len(labels)): + exemplars, dist_name, dist_params, X_std = self._get_candidate_splitter( + X, der_X, rng, cls_idx, X_std + ) + splits = [[] for _ in unique_classes] + + # Use the slope derivative of the time series for ddtw and wddtw + # use original distance to avoid recalculating every distance call + X_used = X + dist_used = dist_name + if dist_name == "ddtw": + X_used = der_X + dist_used = "dtw" + elif dist_name == "wddtw": + X_used = der_X + dist_used = "wdtw" + + # For each time series in the dataset, find the closest exemplar + for j in range(len(X_used)): + min_dist = np.inf + best_exemplar_idx = None + for i, label in enumerate(unique_classes): dist = distance( - X[j], - splitter[0][labels[k]], - method=measure, - **splitter[1][measure], + X_used[j], + exemplars[label], + method=dist_used, + **dist_params, ) if dist < min_dist: min_dist = dist - sub = k - y_subs[sub].append(y[j]) - y_subs = [np.array(ele, dtype=y.dtype) for ele in y_subs] + best_exemplar_idx = i + splits[best_exemplar_idx].append(j) + + # Find the gini gain for this splitter separating series by closest exemplar + # class + y_subs = [ + np.array([y[n] for n in split], dtype=y.dtype) for split in splits + ] gini_index = gini_gain(y, y_subs) if gini_index > max_gain: max_gain = gini_index - best_splitter = splitter - return best_splitter - - def _build_tree(self, X, y, depth, node_id, parent_target_value=None): - """Build the tree recursively from the root node down to the leaf nodes.""" - # If the data reaching the node is empty - if len(X) == 0: - leaf_label = parent_target_value - leaf_distribution = {} - leaf = _Node( - node_id=node_id, - _is_leaf=True, - label=leaf_label, - class_distribution=leaf_distribution, - ) - return leaf - - # Target value in current node - target_value = self._find_target_value(y) - class_distribution = { - label: count / len(y) - for label, count in zip(*np.unique(y, return_counts=True)) - } - - # Pure node - if len(np.unique(y)) == 1: - leaf_label = target_value - leaf = _Node( - node_id=node_id, - _is_leaf=True, - label=leaf_label, - class_distribution=class_distribution, - ) - return leaf + best_splitter = (exemplars, dist_name, dist_params) + best_split = splits - # If min sample splits is reached - if self.min_samples_split >= len(X): - leaf_label = target_value - leaf = _Node( - node_id=node_id, - _is_leaf=True, - label=leaf_label, - class_distribution=class_distribution, - ) - return leaf + return best_splitter, best_split - # If max depth is reached - if (self.max_depth is not None) and (depth >= self.max_depth): - leaf_label = target_value - leaf = _Node( - node_id=node_id, - _is_leaf=True, - label=leaf_label, - class_distribution=class_distribution, - ) - return leaf + def _traverse_tree(self, node, x, der_x): + """Traverse the tree to find the class distribution for a given time series. - # Find the best splitter - splitter = self._get_best_splitter(X, y) + Parameters + ---------- + node : _ProximityNode + The current node in the tree. + x : np.ndarray + The time series to classify. + der_x : np.ndarray + The slope derivative of the time series, used for ddtw and wddtw. - # Create root node - node = _Node(node_id=node_id, _is_leaf=False, splitter=splitter) + Returns + ------- + class_distribution : np.ndarray + The class distribution for the time series, i.e. the estimated probabilities + for each class. + """ + if node.is_leaf: + return node.class_distribution + else: + exemplars, dist_name, dist_params = node.splitter + + # Use the slope derivative of the time series for ddtw and wddtw + # use original distance to avoid recalculating every distance call + x_used = x + dist_used = dist_name + if dist_name == "ddtw": + x_used = der_x + dist_used = "dtw" + elif dist_name == "wddtw": + x_used = der_x + dist_used = "wdtw" - # For each exemplar split the data - labels = list(splitter[0].keys()) - measure = list(splitter[1].keys())[0] - X_child = [[] for _ in labels] - y_child = [[] for _ in labels] - for i in range(len(X)): min_dist = np.inf - id = None - for j in range(len(labels)): + best_exemplar_label = None + for label, exemplar in exemplars.items(): dist = distance( - X[i], - splitter[0][labels[j]], - method=measure, - **splitter[1][measure], + x_used, + exemplar, + method=dist_used, + **dist_params, ) if dist < min_dist: min_dist = dist - id = j - X_child[id].append(X[i]) - y_child[id].append(y[i]) - X_child = [np.array(ele) for ele in X_child] - y_child = [np.array(ele) for ele in y_child] - # For each exemplar, create a branch - for i in range(len(labels)): - child_node_id = node_id + "." + str(i) - child_node = self._build_tree( - X_child[i], - y_child[i], - depth=depth + 1, - node_id=child_node_id, - parent_target_value=target_value, - ) - node.children[labels[i]] = child_node + best_exemplar_label = label - return node + return self._traverse_tree(node.children[best_exemplar_label], x, der_x) - @staticmethod - @njit(cache=True, fastmath=True) - def _find_target_value(y): - """Get the class label of highest frequency.""" - unique_labels = list(np.unique(y)) - class_counts = [] - for i in range(len(unique_labels)): - cnt = 0 - for j in range(len(y)): - if y[j] == unique_labels[i]: - cnt += 1 - class_counts.append(cnt) - class_counts = np.array(class_counts) - # Find the index of the maximum count - max_index = np.argmax(class_counts) - mode_value = unique_labels[max_index] - # mode_count = counts[max_index] - return mode_value + def _get_candidate_splitter(self, X, der_X, rng, cls_idx, X_std): + """Generate candidate splitter. - def _fit(self, X, y): - self.root = self._build_tree( - X, y, depth=0, node_id="0", parent_target_value=None - ) + Takes a time series dataset and a set of parameterized + distance measures to create a candidate splitter, which + contains a parameterized distance measure and a set of exemplars. - def _predict(self, X): - probas = self._predict_proba(X) - predictions = np.argmax(probas, axis=1) - return np.array([self.classes_[pred] for pred in predictions]) + Parameters + ---------- + X : np.ndarray shape (n_cases, n_channels, n_timepoints) + The training input samples. + der_X : np.ndarray shape (n_cases, n_channels, n_timepoints) + The slope derivative of the training input samples, used for ddtw and wddtw. + rng : np.random.RandomState + Random number generator. + cls_idx : dict + The indices for each class label in the training set. + X_std : float or None + The standard deviation of the training set. If None, it will be + calculated from the training set. - def _predict_proba(self, X): - # Get the unique class labels - classes = list(self.classes_) - class_count = len(classes) - probas = [] + Returns + ------- + exemplars: dict + The exemplars for each class, where the key is the class label + and the value is the exemplar time series. + dist_name: str + The distance measure used for the splitter. + dist_params: dict + The parameters for the distance measure. + X_std: float or None + The standard deviation of the training set if calculated, otherwise same + as input. + """ + n = rng.randint(0, 11) + derivative = False + if n == 0: + dist_name = "euclidean" + dist_params = {} + elif n == 1: + dist_name = "dtw" + dist_params = {} + elif n == 2: + dist_name = "dtw" + dist_params = {"window": rng.uniform(0, 0.25)} + elif n == 3: + dist_name = "ddtw" + dist_params = {} + derivative = True + elif n == 4: + dist_name = "ddtw" + dist_params = {"window": rng.uniform(0, 0.25)} + derivative = True + elif n == 5: + dist_name = "wdtw" + dist_params = {"g": rng.uniform(0, 1)} + elif n == 6: + dist_name = "wddtw" + dist_params = {"g": rng.uniform(0, 1)} + derivative = True + elif n == 7: + if X_std is None: + X_std = self._get_std(X) + dist_name = "erp" + dist_params = { + "window": rng.uniform(0, 0.25), + "g": rng.uniform(X_std / 5, X_std), + } + elif n == 8: + if X_std is None: + X_std = self._get_std(X) + dist_name = "lcss" + dist_params = { + "window": rng.uniform(0, 0.25), + "epsilon": rng.uniform(X_std / 5, X_std), + } + elif n == 9: + dist_name = "msm" + dist_params = {"c": msm_params[rng.randint(0, len(msm_params))]} + elif n == 10: + dist_name = "twe" + dist_params = { + "nu": twe_nu_params[rng.randint(0, len(twe_nu_params))], + "lmbda": twe_lmbda_params[rng.randint(0, len(twe_lmbda_params))], + } + else: + raise ValueError(f"Invalid distance index {n}. Must be in range [0, 10].") - for i in range(len(X)): - # Classify the data point and find the leaf node - leaf_node = self._classify(self.root, X[i]) + exemplars = {} + for label in cls_idx.keys(): + label_idx = cls_idx[label] + id = rng.choice(label_idx) + exemplars[label] = der_X[id] if derivative else X[id] - # Create probability distribution based on class counts in the leaf node - proba = np.zeros(class_count) - for class_label, class_proba in leaf_node.class_distribution.items(): - proba[classes.index(class_label)] = class_proba - probas.append(proba) + return exemplars, dist_name, dist_params, X_std - return np.array(probas) + @staticmethod + def _get_derivatives(X): + if isinstance(X, np.ndarray): + return slope_derivative_3d(X) + else: + der_X = [] + for x in X: + der_X.append(slope_derivative_2d(x)) + return der_X - def _classify(self, treenode, x): - # Classify one data point using the proximity tree - if treenode._is_leaf: - return treenode + @staticmethod + def _get_std(X): + if isinstance(X, np.ndarray): + return std(X.flatten()) else: - measure = list(treenode.splitter[1].keys())[0] - branches = list(treenode.splitter[0].keys()) - min_dist = np.inf - id = None - for i in range(len(branches)): - dist = distance( - x, - treenode.splitter[0][branches[i]], - method=measure, - **treenode.splitter[1][measure], - ) - if dist < min_dist: - min_dist = dist - id = i - return self._classify(treenode.children[branches[id]], x) + return std(np.concatenate(X, axis=1).flatten()) + @classmethod + def _get_test_params( + cls, parameter_set: str = "default" + ) -> Union[dict, list[dict]]: + """Return testing parameter settings for the estimator. -@njit(cache=True, fastmath=True) -def gini(y) -> float: - """Get gini score at a specific node. + Parameters + ---------- + parameter_set : str, default="default" + Name of the set of test parameters to return, for use in tests. If no + special parameters are defined for a value, will return `"default"` set. + ElasticEnsemble provides the following special sets: + "results_comparison" - used in some classifiers to compare against + previously generated results where the default set of parameters + cannot produce suitable probability estimates - Parameters - ---------- - y : 1d numpy array - array of class labels - - Returns - ------- - score : float - gini score for the set of class labels (i.e. how pure they are). A - larger score means more impurity. Zero means - pure. - """ - # get number instances at node - n_instances = y.shape[0] - if n_instances > 0: - # count each class - unique_labels = list(np.unique(y)) - class_counts = [] - for i in range(len(unique_labels)): - cnt = 0 - for j in range(len(y)): - if y[j] == unique_labels[i]: - cnt += 1 - class_counts.append(cnt) - class_counts = np.array(class_counts) - # subtract class entropy from current score for each class - class_counts = np.divide(class_counts, n_instances) - class_counts = np.power(class_counts, 2) - sum = np.sum(class_counts) - return 1 - sum - else: - # y is empty, therefore considered pure - raise ValueError("y empty") - - -@njit(cache=True, fastmath=True) -def gini_gain(y, y_subs) -> float: - """Get gini score of a split, i.e. the gain from parent to children. + Returns + ------- + params : dict or list of dict, default={} + Parameters to create testing instances of the class. + Each dict are parameters to construct an "interesting" test instance, i.e., + `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + """ + return { + "n_splitters": 3, + } - Parameters - ---------- - y : 1d array - array of class labels at parent - y_subs : list of 1d array like - list of array of class labels, one array per child - - Returns - ------- - score : float - gini score of the split from parent class labels to children. Note a - higher score means better gain, - i.e. a better split - """ - # find number of instances overall - parent_n_instances = y.shape[0] - # if parent has no instances then is pure - if parent_n_instances == 0: - for child in y_subs: - if len(child) > 0: - raise ValueError("children populated but parent empty") - return 0.5 - # find gini for parent node - score = gini(y) - # sum the children's gini scores - for index in range(len(y_subs)): - child_class_labels = y_subs[index] - # ignore empty children - if len(child_class_labels) > 0: - # find gini score for this child - child_score = gini(child_class_labels) - # weight score by proportion of instances at child compared to - # parent - child_size = len(child_class_labels) - child_score *= child_size / parent_n_instances - # add to cumulative sum - score -= child_score - return score + +msm_params = [ + 0.01, + 0.01375, + 0.0175, + 0.02125, + 0.025, + 0.02875, + 0.0325, + 0.03625, + 0.04, + 0.04375, + 0.0475, + 0.05125, + 0.055, + 0.05875, + 0.0625, + 0.06625, + 0.07, + 0.07375, + 0.0775, + 0.08125, + 0.085, + 0.08875, + 0.0925, + 0.09625, + 0.1, + 0.136, + 0.172, + 0.208, + 0.244, + 0.28, + 0.316, + 0.352, + 0.388, + 0.424, + 0.46, + 0.496, + 0.532, + 0.568, + 0.604, + 0.64, + 0.676, + 0.712, + 0.748, + 0.784, + 0.82, + 0.856, + 0.892, + 0.928, + 0.964, + 1, + 1.36, + 1.72, + 2.08, + 2.44, + 2.8, + 3.16, + 3.52, + 3.88, + 4.24, + 4.6, + 4.96, + 5.32, + 5.68, + 6.04, + 6.4, + 6.76, + 7.12, + 7.48, + 7.84, + 8.2, + 8.56, + 8.92, + 9.28, + 9.64, + 10, + 13.6, + 17.2, + 20.8, + 24.4, + 28, + 31.6, + 35.2, + 38.8, + 42.4, + 46, + 49.6, + 53.2, + 56.8, + 60.4, + 64, + 67.6, + 71.2, + 74.8, + 78.4, + 82, + 85.6, + 89.2, + 92.8, + 96.4, + 100, +] + +twe_nu_params = [0.00001, 0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1] + +twe_lmbda_params = [ + 0, + 0.011111111, + 0.022222222, + 0.033333333, + 0.044444444, + 0.055555556, + 0.066666667, + 0.077777778, + 0.088888889, + 0.1, +] diff --git a/aeon/classification/distance_based/_time_series_neighbors.py b/aeon/classification/distance_based/_time_series_neighbors.py index fdf588b118..4b4d90c164 100644 --- a/aeon/classification/distance_based/_time_series_neighbors.py +++ b/aeon/classification/distance_based/_time_series_neighbors.py @@ -5,12 +5,10 @@ distances in aeon.distances. """ -from typing import Optional - __maintainer__ = [] __all__ = ["KNeighborsTimeSeriesClassifier"] -from typing import Callable, Union +from typing import Callable, Optional, Union import numpy as np from joblib import Parallel, delayed @@ -255,7 +253,4 @@ def _get_test_params( Each dict are parameters to construct an "interesting" test instance, i.e., `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. """ - # non-default distance and algorithm - params1 = {"distance": "euclidean"} - - return [params1] + return {"distance": "euclidean"} diff --git a/aeon/classification/distance_based/tests/test_proximity_forest.py b/aeon/classification/distance_based/tests/test_proximity_forest.py deleted file mode 100644 index a107938000..0000000000 --- a/aeon/classification/distance_based/tests/test_proximity_forest.py +++ /dev/null @@ -1,27 +0,0 @@ -"""Test for Proximity Forest.""" - -import pytest -from sklearn.metrics import accuracy_score - -from aeon.classification.distance_based import ProximityForest -from aeon.datasets import load_unit_test - - -def test_univariate(): - """Test that the function gives appropriate error message.""" - X, y = load_unit_test() - X_multivariate = X.reshape((-1, 2, 12)) - clf = ProximityForest(n_trees=5) - with pytest.raises(ValueError): - clf.fit(X_multivariate, y) - - -def test_proximity_forest(): - """Test the fit method of ProximityTree.""" - X_train, y_train = load_unit_test() - X_test, y_test = load_unit_test(split="test") - clf = ProximityForest(n_trees=5, n_splitters=3, max_depth=4, random_state=42) - clf.fit(X_train, y_train) - y_pred = clf.predict(X_test) - score = accuracy_score(y_test, y_pred) - assert score >= 0.9 diff --git a/aeon/classification/distance_based/tests/test_proximity_tree.py b/aeon/classification/distance_based/tests/test_proximity_tree.py index 425cb232a7..b2946f6cbb 100644 --- a/aeon/classification/distance_based/tests/test_proximity_tree.py +++ b/aeon/classification/distance_based/tests/test_proximity_tree.py @@ -1,116 +1,40 @@ """Tests for ProximityTree.""" import numpy as np -import pytest -from sklearn.metrics import accuracy_score +from numpy.testing import assert_array_equal +from sklearn.utils import check_random_state from aeon.classification.distance_based import ProximityTree -from aeon.classification.distance_based._proximity_tree import gini, gini_gain -from aeon.datasets import load_unit_test +from aeon.classification.distance_based._proximity_tree import ( + msm_params, + twe_lmbda_params, + twe_nu_params, +) +from aeon.testing.data_generation import make_example_3d_numpy -def test_gini(): - """Test the method to calculate gini.""" - # Test case: Pure node (all same class) - y_pure = np.array([1, 1, 1, 1]) - assert gini(y_pure) == 0.0 - - # Test case: Impure node with two classes - y_impure = np.array([1, 1, 2, 2]) - assert gini(y_impure) == 0.5 - - # Test case: More impure node with three classes - y_more_impure = np.array([1, 1, 2, 3]) - gini_score = 1 - ((2 / 4) ** 2 + (1 / 4) ** 2 + (1 / 4) ** 2) - assert gini(y_more_impure) == gini_score - - # Test case: All different classes - y_all_different = np.array([1, 2, 3, 4]) - gini_score_all_diff = 1 - ( - (1 / 4) ** 2 + (1 / 4) ** 2 + (1 / 4) ** 2 + (1 / 4) ** 2 - ) - assert gini(y_all_different) == gini_score_all_diff - - # Test case: Empty array - y_empty = np.array([]) - with pytest.raises(ValueError, match="y empty"): - gini(y_empty) - - -def test_gini_gain(): - """Test the method to calculate gini gain of a node.""" - # Split with non-empty children - y = np.array([1, 1, 2, 2, 4, 4, 2, 2]) - y_subs = [np.array([1, 1, 4, 4]), np.array([2, 2, 2, 2])] - score_y = 1 - ((2 / 8) ** 2 + (4 / 8) ** 2 + (2 / 8) ** 2) - score = score_y - ((4 / 8) * 0.5 + (4 / 8) * 0) - assert gini_gain(y, y_subs) == score - - # Split with an empty child - y = np.array([1, 1, 0, 0]) - y_children = [np.array([1, 1]), np.array([], dtype=y.dtype)] - score = 0.5 - ((1 / 2) * 0) - assert gini_gain(y, y_children) == score - - # When parent is empty but children non-empty - y_empty = np.array([]) - y_children = [np.array([1, 1]), np.array([], dtype=y.dtype)] - with pytest.raises(ValueError, match="children populated but parent empty"): - gini_gain(y_empty, y_children) - - -def test_get_parameter_value(): - """Test the distance parameters generated.""" - X = np.random.rand(10, 50) - random_state = 42 - tree = ProximityTree(random_state=random_state) - - params = tree._get_parameter_value(X) - - # Check if the parameters are generated for all distance measures - expected_measures = [ - "euclidean", - "dtw", - "ddtw", - "wdtw", - "wddtw", - "erp", - "lcss", - "twe", - "msm", - ] - assert set(params.keys()) == set(expected_measures) - - # Check specific parameter ranges - for measure, measure_params in params.items(): - if measure in ["dtw", "ddtw", "lcss"]: - assert 0 <= measure_params["window"] <= 0.25 - elif measure in ["wdtw", "wddtw"]: - assert 0 <= measure_params["g"] <= 1 - elif measure == "erp": - X_std = X.std() - assert X_std / 5 <= measure_params["g"] <= X_std - elif measure == "lcss": - X_std = X.std() - assert X_std / 5 <= measure_params["epsilon"] <= X_std - elif measure == "twe": - assert 0 <= measure_params["lmbda"] < 9 - assert 1e-5 <= measure_params["nu"] <= 1e-1 - elif measure == "msm": - assert measure_params["c"] in [10**i for i in range(-2, 3)] - - -def test_get_cadidate_splitter(): +def test_get_candidate_splitter(): """Test the method to generate candidate splitters.""" - X, y = load_unit_test() + X, y = make_example_3d_numpy() + cls_idx = {} + for label in np.unique(y): + cls_idx[label] = np.where(y == label)[0] clf = ProximityTree() - splitter = clf._get_candidate_splitter(X, y) - assert len(splitter) == 2 + rng = check_random_state(0) + exemplars, distance, distance_params, X_std = clf._get_candidate_splitter( + X, X, rng, cls_idx, None + ) + + assert isinstance(exemplars, dict) + assert len(exemplars) == 2 + assert all([isinstance(v, np.ndarray) for v in exemplars.values()]) - expected_measures = [ + expected_distances = [ "euclidean", + "dtw-full", "dtw", "ddtw", + "ddtw-full", "wdtw", "wddtw", "erp", @@ -118,30 +42,51 @@ def test_get_cadidate_splitter(): "twe", "msm", ] - measure = list(splitter[1].keys())[0] - assert measure in expected_measures + assert distance in expected_distances + + if distance in ["dtw", "ddtw", "lcss"]: + assert 0 <= distance_params["window"] <= 0.25 + elif distance in ["wdtw", "wddtw"]: + assert 0 <= distance_params["g"] <= 1 + elif distance == "erp": + assert X_std is not None + assert X_std / 5 <= distance_params["g"] <= X_std + elif distance == "lcss": + assert X_std is not None + assert X_std / 5 <= distance_params["epsilon"] <= X_std + elif distance == "twe": + assert distance_params["lmbda"] in twe_lmbda_params + assert distance_params["nu"] in twe_nu_params + elif distance == "msm": + assert distance_params["c"] in msm_params + elif distance == "euclidean" or distance == "dtw-full" or distance == "ddtw-full": + assert distance_params == {} + else: + raise ValueError(f"Unexpected distance: {distance}") def test_get_best_splitter(): """Test the method to get optimum splitter of a node.""" - X, y = load_unit_test() + X, y = make_example_3d_numpy() clf = ProximityTree(n_splitters=3) + rng = check_random_state(None) + unique_classes = np.unique(y) - splitter = clf._get_best_splitter(X, y) + splitter, split = clf._get_best_splitter(X, X, y, unique_classes, rng) - assert splitter is not None + assert isinstance(splitter, tuple) + assert len(splitter) == 3 - assert isinstance(splitter, list) + assert isinstance(split, list) + assert len(split) == len(unique_classes) + assert sum(len(s) for s in split) == len(y) - assert len(splitter) == 2 +def test_get_derivatives_and_std(): + """Test the methods to get derivatives and standard deviation.""" + X, y = make_example_3d_numpy() + X_list = [x for x in X] -def test_proximity_tree(): - """Test the fit method of ProximityTree.""" - X, y = load_unit_test() - clf = ProximityTree(n_splitters=3, max_depth=4, random_state=42) - clf.fit(X, y) - X_test, y_test = load_unit_test(split="train") - y_pred = clf.predict(X_test) - score = accuracy_score(y_test, y_pred) - assert score >= 0.9 + clf = ProximityTree() + assert_array_equal(clf._get_derivatives(X_list), clf._get_derivatives(X_list)) + assert clf._get_std(X_list) == clf._get_std(X_list) diff --git a/aeon/distances/elastic/_ddtw.py b/aeon/distances/elastic/_ddtw.py index a31745f2f0..559b2a3d90 100644 --- a/aeon/distances/elastic/_ddtw.py +++ b/aeon/distances/elastic/_ddtw.py @@ -16,6 +16,7 @@ ) from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.numba.general import slope_derivative_2d from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -81,15 +82,15 @@ def ddtw_distance( 2180 """ if x.ndim == 1 and y.ndim == 1: - _x = average_of_slope(x.reshape((1, x.shape[0]))) - _y = average_of_slope(y.reshape((1, y.shape[0]))) + _x = slope_derivative_2d(x.reshape((1, x.shape[0]))) + _y = slope_derivative_2d(y.reshape((1, y.shape[0]))) bounding_matrix = create_bounding_matrix( _x.shape[1], _y.shape[1], window, itakura_max_slope ) return _dtw_distance(_x, _y, bounding_matrix) if x.ndim == 2 and y.ndim == 2: - _x = average_of_slope(x) - _y = average_of_slope(y) + _x = slope_derivative_2d(x) + _y = slope_derivative_2d(y) bounding_matrix = create_bounding_matrix( _x.shape[1], _y.shape[1], window, itakura_max_slope ) @@ -152,15 +153,15 @@ def ddtw_cost_matrix( [0., 0., 0., 0., 0., 0., 0., 0.]]) """ if x.ndim == 1 and y.ndim == 1: - _x = average_of_slope(x.reshape((1, x.shape[0]))) - _y = average_of_slope(y.reshape((1, y.shape[0]))) + _x = slope_derivative_2d(x.reshape((1, x.shape[0]))) + _y = slope_derivative_2d(y.reshape((1, y.shape[0]))) bounding_matrix = create_bounding_matrix( _x.shape[1], _y.shape[1], window, itakura_max_slope ) return _dtw_cost_matrix(_x, _y, bounding_matrix) if x.ndim == 2 and y.ndim == 2: - _x = average_of_slope(x) - _y = average_of_slope(y) + _x = slope_derivative_2d(x) + _y = slope_derivative_2d(y) bounding_matrix = create_bounding_matrix( _x.shape[1], _y.shape[1], window, itakura_max_slope ) @@ -280,7 +281,7 @@ def _ddtw_pairwise_distance( X_average_of_slope = NumbaList() for i in range(n_cases): - X_average_of_slope.append(average_of_slope(X[i])) + X_average_of_slope.append(slope_derivative_2d(X[i])) for i in prange(n_cases): for j in range(i + 1, n_cases): @@ -315,11 +316,11 @@ def _ddtw_from_multiple_to_multiple_distance( # Derive the arrays before so that we dont have to redo every iteration x_average_of_slope = NumbaList() for i in range(n_cases): - x_average_of_slope.append(average_of_slope(x[i])) + x_average_of_slope.append(slope_derivative_2d(x[i])) y_average_of_slope = NumbaList() for i in range(m_cases): - y_average_of_slope.append(average_of_slope(y[i])) + y_average_of_slope.append(slope_derivative_2d(y[i])) for i in prange(n_cases): for j in range(m_cases): @@ -385,48 +386,3 @@ def ddtw_alignment_path( compute_min_return_path(cost_matrix), cost_matrix[x.shape[-1] - 3, y.shape[-1] - 3], ) - - -@njit(cache=True, fastmath=True) -def average_of_slope(q: np.ndarray) -> np.ndarray: - r"""Compute the average of a slope between points. - - Computes the average of the slope of the line through the point in question and - its left neighbour, and the slope of the line through the left neighbour and the - right neighbour. proposed in [1] for use in this context. - .. math:: - q'_(i) = \frac{{}(q_{i} - q_{i-1} + ((q_{i+1} - q_{i-1}/2)}{2} - Where q is the original time series and q' is the derived time series. - - Parameters - ---------- - q : np.ndarray (n_channels, n_timepoints) - Time series to take derivative of. - - Returns - ------- - np.ndarray (n_channels, n_timepoints - 2) - Array containing the derivative of q. - - Raises - ------ - ValueError - If the time series has less than 3 points. - - Examples - -------- - >>> import numpy as np - >>> from aeon.distances.elastic._ddtw import average_of_slope - >>> q = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]) - >>> average_of_slope(q) - array([[1., 1., 1., 1., 1., 1., 1., 1.]]) - """ - if q.shape[1] < 3: - raise ValueError("Time series must have at least 3 points.") - result = np.zeros((q.shape[0], q.shape[1] - 2)) - for i in range(q.shape[0]): - for j in range(1, q.shape[1] - 1): - result[i, j - 1] = ( - (q[i, j] - q[i, j - 1]) + (q[i, j + 1] - q[i, j - 1]) / 2.0 - ) / 2.0 - return result diff --git a/aeon/distances/elastic/_wddtw.py b/aeon/distances/elastic/_wddtw.py index 5e064a8162..bf95b1c4d1 100644 --- a/aeon/distances/elastic/_wddtw.py +++ b/aeon/distances/elastic/_wddtw.py @@ -10,10 +10,10 @@ from aeon.distances.elastic._alignment_paths import compute_min_return_path from aeon.distances.elastic._bounding_matrix import create_bounding_matrix -from aeon.distances.elastic._ddtw import average_of_slope from aeon.distances.elastic._wdtw import _wdtw_cost_matrix, _wdtw_distance from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.numba.general import slope_derivative_2d from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -86,15 +86,15 @@ def wddtw_distance( 981 """ if x.ndim == 1 and y.ndim == 1: - _x = average_of_slope(x.reshape((1, x.shape[0]))) - _y = average_of_slope(y.reshape((1, y.shape[0]))) + _x = slope_derivative_2d(x.reshape((1, x.shape[0]))) + _y = slope_derivative_2d(y.reshape((1, y.shape[0]))) bounding_matrix = create_bounding_matrix( _x.shape[1], _y.shape[1], window, itakura_max_slope ) return _wdtw_distance(_x, _y, bounding_matrix, g) if x.ndim == 2 and y.ndim == 2: - _x = average_of_slope(x) - _y = average_of_slope(y) + _x = slope_derivative_2d(x) + _y = slope_derivative_2d(y) bounding_matrix = create_bounding_matrix( _x.shape[1], _y.shape[1], window, itakura_max_slope ) @@ -156,15 +156,15 @@ def wddtw_cost_matrix( [0., 0., 0., 0., 0., 0., 0., 0.]]) """ if x.ndim == 1 and y.ndim == 1: - _x = average_of_slope(x.reshape((1, x.shape[0]))) - _y = average_of_slope(y.reshape((1, y.shape[0]))) + _x = slope_derivative_2d(x.reshape((1, x.shape[0]))) + _y = slope_derivative_2d(y.reshape((1, y.shape[0]))) bounding_matrix = create_bounding_matrix( _x.shape[1], _y.shape[1], window, itakura_max_slope ) return _wdtw_cost_matrix(_x, _y, bounding_matrix, g) if x.ndim == 2 and y.ndim == 2: - _x = average_of_slope(x) - _y = average_of_slope(y) + _x = slope_derivative_2d(x) + _y = slope_derivative_2d(y) bounding_matrix = create_bounding_matrix( _x.shape[1], _y.shape[1], window, itakura_max_slope ) @@ -284,7 +284,7 @@ def _wddtw_pairwise_distance( ) X_average_of_slope = NumbaList() for i in range(n_cases): - X_average_of_slope.append(average_of_slope(X[i])) + X_average_of_slope.append(slope_derivative_2d(X[i])) for i in prange(n_cases): for j in range(i + 1, n_cases): @@ -320,11 +320,11 @@ def _wddtw_from_multiple_to_multiple_distance( # Derive the arrays before so that we dont have to redo every iteration x_average_of_slope = NumbaList() for i in range(n_cases): - x_average_of_slope.append(average_of_slope(x[i])) + x_average_of_slope.append(slope_derivative_2d(x[i])) y_average_of_slope = NumbaList() for i in range(m_cases): - y_average_of_slope.append(average_of_slope(y[i])) + y_average_of_slope.append(slope_derivative_2d(y[i])) for i in prange(n_cases): for j in range(m_cases): diff --git a/aeon/testing/expected_results/expected_classifier_outputs.py b/aeon/testing/expected_results/expected_classifier_outputs.py index 771c47e7c6..433ddaf051 100644 --- a/aeon/testing/expected_results/expected_classifier_outputs.py +++ b/aeon/testing/expected_results/expected_classifier_outputs.py @@ -131,7 +131,7 @@ [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], - [0.6667, 0.3333], + [1.0, 0.0], [1.0, 0.0], ] ) diff --git a/aeon/transformations/collection/signature_based/_compute.py b/aeon/transformations/collection/signature_based/_compute.py index 0506b87242..c420daaa30 100644 --- a/aeon/transformations/collection/signature_based/_compute.py +++ b/aeon/transformations/collection/signature_based/_compute.py @@ -23,7 +23,6 @@ class _WindowSignatureTransform(BaseCollectionTransformer): num_intervals: int, dimension of the transformed data (default 8) """ - # default tag values for "Series-to-Primitives" _tags = { "fit_is_empty": True, "output_data_type": "Tabular", diff --git a/aeon/utils/numba/general.py b/aeon/utils/numba/general.py index 58ab9d15e9..8d166745f5 100644 --- a/aeon/utils/numba/general.py +++ b/aeon/utils/numba/general.py @@ -24,6 +24,8 @@ "generate_combinations", "get_all_subsequences", "compute_mean_stds_collection_parallel", + "prime_up_to", + "is_prime", ] @@ -63,7 +65,7 @@ def unique_count(X: np.ndarray) -> tuple[np.ndarray, np.ndarray]: """ if X.shape[0] > 0: X = np.sort(X) - unique = np.zeros(X.shape[0]) + unique = np.zeros(X.shape[0], dtype=X.dtype) unique[0] = X[0] counts = np.zeros(X.shape[0], dtype=np.int_) counts[0] = 1 @@ -77,7 +79,7 @@ def unique_count(X: np.ndarray) -> tuple[np.ndarray, np.ndarray]: else: counts[uc] += 1 return unique[: uc + 1], counts[: uc + 1] - return np.zeros(0), np.zeros(0, dtype=np.int_) + return np.zeros(0, dtype=X.dtype), np.zeros(0, dtype=np.int_) @njit(fastmath=True, cache=True) @@ -641,20 +643,37 @@ def combinations_1d(x: np.ndarray, y: np.ndarray) -> np.ndarray: @njit(fastmath=True, cache=True) def slope_derivative(X: np.ndarray) -> np.ndarray: - """Numba slope derivative transformation for a 1d numpy array. + r"""Compute the average of a slope between points. - Finds the derivative of the series, padding the first and last values so that the - length stays the same. + a.k.a. slope derivative. + + Computes the average of the slope of the line through the point in question and + its left neighbour, and the slope of the line through the left neighbour and the + right neighbour. Proposed in [1] for use in distances i.e. ddtw and wddtw. + .. math:: + q'_(i) = \frac{{}(q_{i} - q_{i-1} + ((q_{i+1} - q_{i-1}/2)}{2} + Where q is the original time series and q' is the derived time series. Parameters ---------- - X : 1d numpy array - A 1d numpy array of values + X : np.ndarray (n_timepoints) + Time series to take derivative of. Returns ------- - arr : 1d numpy array - The slope derivative of the series + np.ndarray (n_timepoints - 2) + Array containing the derivative of X. + + Raises + ------ + ValueError + If the time series length has less than 3 points. + + References + ---------- + .. [1] Keogh, Eamonn & Pazzani, Michael. (2002). Derivative Dynamic Time Warping. + First SIAM International Conference on Data Mining. + 1. 10.1137/1.9781611972719.1. Examples -------- @@ -663,31 +682,47 @@ def slope_derivative(X: np.ndarray) -> np.ndarray: >>> X = np.array([1, 2, 2, 3, 3, 3, 4, 4, 4, 4]) >>> X_der = slope_derivative(X) """ - m = len(X) - arr = np.zeros(m) - for i in range(1, m - 1): - arr[i] = ((X[i] - X[i - 1]) + ((X[i + 1] - X[i - 1]) / 2.0)) / 2.0 - arr[0] = arr[1] - arr[m - 1] = arr[m - 2] - return arr + if X.shape[0] < 3: + raise ValueError("Time series must have at least 3 points.") + result = np.zeros(X.shape[0] - 2) + for i in range(1, X.shape[0] - 1): + result[i - 1] = ((X[i] - X[i - 1]) + (X[i + 1] - X[i - 1]) / 2.0) / 2.0 + return result @njit(fastmath=True, cache=True) def slope_derivative_2d(X: np.ndarray) -> np.ndarray: - """Numba slope derivative transformation for a 2d numpy array. + r"""Compute the average of a slope between points. + + a.k.a. slope derivative. - Finds the derivative of the series, padding the first and last values so that the - length stays the same. + Computes the average of the slope of the line through the point in question and + its left neighbour, and the slope of the line through the left neighbour and the + right neighbour. Proposed in [1] for use in distances i.e. ddtw and wddtw. + .. math:: + q'_(i) = \frac{{}(q_{i} - q_{i-1} + ((q_{i+1} - q_{i-1}/2)}{2} + Where q is the original time series and q' is the derived time series. Parameters ---------- - X : 2d numpy array - A 2d numpy array of values + X : np.ndarray (n_channels, n_timepoints) + Time series to take derivative of. Returns ------- - arr : 2d numpy array - The slope derivative of each series + np.ndarray (n_channels, n_timepoints - 2) + Array containing the derivative of X. + + Raises + ------ + ValueError + If the time series length has less than 3 points. + + References + ---------- + .. [1] Keogh, Eamonn & Pazzani, Michael. (2002). Derivative Dynamic Time Warping. + First SIAM International Conference on Data Mining. + 1. 10.1137/1.9781611972719.1. Examples -------- @@ -696,7 +731,7 @@ def slope_derivative_2d(X: np.ndarray) -> np.ndarray: >>> X = np.array([[1, 2, 2, 3, 3, 3, 4, 4, 4, 4], [5, 6, 6, 7, 7, 7, 8, 8, 8, 8]]) >>> X_der = slope_derivative_2d(X) """ - arr = np.zeros(X.shape) + arr = np.zeros((X.shape[0], X.shape[1] - 2)) for i in range(X.shape[0]): arr[i] = slope_derivative(X[i]) return arr @@ -704,20 +739,37 @@ def slope_derivative_2d(X: np.ndarray) -> np.ndarray: @njit(fastmath=True, cache=True) def slope_derivative_3d(X: np.ndarray) -> np.ndarray: - """Numba slope derivative transformation for a 3d numpy array. + r"""Compute the average of a slope between points. - Finds the derivative of the series, padding the first and last values so that the - length stays the same. + a.k.a. slope derivative. + + Computes the average of the slope of the line through the point in question and + its left neighbour, and the slope of the line through the left neighbour and the + right neighbour. Proposed in [1] for use in distances i.e. ddtw and wddtw. + .. math:: + q'_(i) = \frac{{}(q_{i} - q_{i-1} + ((q_{i+1} - q_{i-1}/2)}{2} + Where q is the original time series and q' is the derived time series. Parameters ---------- - X : 3d numpy array - A 3d numpy array of values + X : np.ndarray (n_cases, n_channels, n_timepoints) + Time series to take derivative of. Returns ------- - arr : 3d numpy array - The slope derivative of each series + np.ndarray (n_cases, n_channels, n_timepoints - 2) + Array containing the derivative of X. + + Raises + ------ + ValueError + If the time series length has less than 3 points. + + References + ---------- + .. [1] Keogh, Eamonn & Pazzani, Michael. (2002). Derivative Dynamic Time Warping. + First SIAM International Conference on Data Mining. + 1. 10.1137/1.9781611972719.1. Examples -------- @@ -729,7 +781,7 @@ def slope_derivative_3d(X: np.ndarray) -> np.ndarray: ... ]) >>> X_der = slope_derivative_3d(X) """ - arr = np.zeros(X.shape) + arr = np.zeros((X.shape[0], X.shape[1], X.shape[2] - 2)) for i in range(X.shape[0]): arr[i] = slope_derivative_2d(X[i]) return arr @@ -817,3 +869,55 @@ def get_all_subsequences(X: np.ndarray, length: int, dilation: int) -> np.ndarra out_shape = (n_timepoints - (length - 1) * dilation, n_features, np.int64(length)) strides = (s1, s0, s1 * dilation) return np.lib.stride_tricks.as_strided(X, shape=out_shape, strides=strides) + + +@njit(fastmath=True, cache=True) +def prime_up_to(n: int) -> np.ndarray: + """Check if any number from 1 to n is a prime number and return the ones which are. + + Parameters + ---------- + n : int + Number up to which the search for prime number will go + + Returns + ------- + array + Prime numbers up to n + + Examples + -------- + >>> from aeon.utils.numba.stats import prime_up_to + >>> p = prime_up_to(50) + """ + is_p = np.zeros(n + 1, dtype=np.bool_) + for i in range(n + 1): + is_p[i] = is_prime(i) + return np.where(is_p)[0] + + +@njit(fastmath=True, cache=True) +def is_prime(n: int) -> bool: + """Check if the input number is a prime number. + + Parameters + ---------- + n : int + The number to test + + Returns + ------- + bool + Wheter n is a prime number + + Examples + -------- + >>> from aeon.utils.numba.stats import is_prime + >>> p = is_prime(7) + """ + if (n % 2 == 0 and n > 2) or n == 0 or n == 1: + return False + for i in range(3, int(n**0.5) + 1, 2): + if not n % i: + return False + return True diff --git a/aeon/utils/numba/stats.py b/aeon/utils/numba/stats.py index 9ecca867d1..80242b6dfd 100644 --- a/aeon/utils/numba/stats.py +++ b/aeon/utils/numba/stats.py @@ -17,7 +17,7 @@ "quantile75", "row_quantile75", "std", - "std2", + "std_with_mean", "row_std", "numba_min", "row_numba_min", @@ -30,11 +30,17 @@ "ppv", "row_ppv", "fisher_score", + "gini", + "gini_gain", + # deprecated "prime_up_to", "is_prime", + "std2", ] + import numpy as np +from deprecated.sphinx import deprecated from numba import njit import aeon.utils.numba.general as general_numba @@ -445,7 +451,7 @@ def std(X: np.ndarray) -> float: @njit(fastmath=True, cache=True) -def std2(X: np.ndarray, X_mean: float) -> float: +def std_with_mean(X: np.ndarray, X_mean: float) -> float: """Numba standard deviation function for a 1d numpy array with pre-calculated mean. Parameters @@ -463,9 +469,9 @@ def std2(X: np.ndarray, X_mean: float) -> float: Examples -------- >>> import numpy as np - >>> from aeon.utils.numba.stats import std2 + >>> from aeon.utils.numba.stats import std_with_mean >>> X = np.array([1, 2, 2, 3, 3, 3, 4, 4, 4, 4]) - >>> s = std2(X, 3) + >>> s = std_with_mean(X, 3) """ s = 0 for i in range(X.shape[0]): @@ -813,7 +819,7 @@ def fisher_score(X: np.ndarray, y: np.ndarray) -> float: data_sub = X[idx_label] mu_feat_label = mean(data_sub) - sigma_feat_label = max(std2(data_sub, mu_feat_label), 0.000001) + sigma_feat_label = max(std_with_mean(data_sub, mu_feat_label), 0.000001) accum_numerator += idx_label.shape[0] * (mu_feat_label - mu_feat) ** 2 accum_denominator += idx_label.shape[0] * sigma_feat_label**2 @@ -824,6 +830,72 @@ def fisher_score(X: np.ndarray, y: np.ndarray) -> float: return accum_numerator / accum_denominator +@njit(cache=True, fastmath=True) +def gini(y) -> float: + """Get gini score for an array of labels. + + Parameters + ---------- + y : 1d numpy array + An array of labels + + Returns + ------- + score : float + gini score for the set of labels (i.e. how pure they are). + A larger score means more impurity. 0 means pure. + """ + if y.shape[0] == 0: + raise ValueError("y is empty") + + _, counts = general_numba.unique_count(y) + proportions = counts / y.shape[0] + return 1.0 - np.sum(proportions**2) + + +@njit(cache=True, fastmath=True) +def gini_gain(y, y_subs) -> float: + """Get gini score of a split, i.e. the gain from parent to children. + + Parameters + ---------- + y : 1d array + An array of labels + y_subs : list of 1d array + List of arrays contain subsets of the labels in y. Total number of + labels must sum to len(y). + + Returns + ------- + score : float + gini score of the split from parent class labels (y) to children (y_sub). + Note a higher score means better gain. + """ + if y.shape[0] == 0: + raise ValueError("y is empty") + if sum([child.shape[0] for child in y_subs]) != y.shape[0]: + raise ValueError( + "The number of labels in y_subs must sum to the number of labels in y." + ) + + # find gini for parent node + score = gini(y) + + for child in y_subs: + # ignore empty children + if child.shape[0] > 0: + # find gini score for this child and weight score by proportion of + # instances at child compared to parent + score -= (child.shape[0] / y.shape[0]) * gini(child) + return score + + +# TODO: Remove in v1.3.0 +@deprecated( + version="1.2.0", + reason="prime_up_to has moved to aeon.utils.numba.general", + category=FutureWarning, +) @njit(fastmath=True, cache=True) def prime_up_to(n: int) -> np.ndarray: """Check if any number from 1 to n is a prime number and return the ones which are. @@ -843,12 +915,15 @@ def prime_up_to(n: int) -> np.ndarray: >>> from aeon.utils.numba.stats import prime_up_to >>> p = prime_up_to(50) """ - is_p = np.zeros(n + 1, dtype=np.bool_) - for i in range(n + 1): - is_p[i] = is_prime(i) - return np.where(is_p)[0] + return general_numba.prime_up_to(n) +# TODO: Remove in v1.3.0 +@deprecated( + version="1.2.0", + reason="is_prime has moved to aeon.utils.numba.general", + category=FutureWarning, +) @njit(fastmath=True, cache=True) def is_prime(n: int) -> bool: """Check if the input number is a prime number. @@ -868,9 +943,36 @@ def is_prime(n: int) -> bool: >>> from aeon.utils.numba.stats import is_prime >>> p = is_prime(7) """ - if (n % 2 == 0 and n > 2) or n == 0 or n == 1: - return False - for i in range(3, int(n**0.5) + 1, 2): - if not n % i: - return False - return True + return general_numba.is_prime(n) + + +# TODO: Remove in v1.3.0 +@deprecated( + version="1.2.0", + reason="std2 has been renamed to std_with_mean", + category=FutureWarning, +) +@njit(fastmath=True, cache=True) +def std2(X: np.ndarray, X_mean: float) -> float: + """Numba standard deviation function for a 1d numpy array with pre-calculated mean. + + Parameters + ---------- + X : 1d numpy array + A 1d numpy array of values + X_mean : float + The mean of the input array + + Returns + ------- + std : float + The standard deviation of the input array + + Examples + -------- + >>> import numpy as np + >>> from aeon.utils.numba.stats import std2 + >>> X = np.array([1, 2, 2, 3, 3, 3, 4, 4, 4, 4]) + >>> s = std2(X, 3) + """ + return std_with_mean(X, X_mean) diff --git a/aeon/utils/numba/tests/test_general.py b/aeon/utils/numba/tests/test_general.py index dfe5ea603f..84a12b1fdd 100644 --- a/aeon/utils/numba/tests/test_general.py +++ b/aeon/utils/numba/tests/test_general.py @@ -1,7 +1,5 @@ """Tests for numba functions.""" -__maintainer__ = [] - import numpy as np import pytest from numpy.testing import assert_array_almost_equal, assert_array_equal @@ -11,8 +9,11 @@ get_all_subsequences, get_subsequence, get_subsequence_with_mean_std, + is_prime, normalise_subsequences, + prime_up_to, sliding_mean_std_one_series, + unique_count, z_normalise_series, z_normalise_series_with_mean_std, ) @@ -20,6 +21,17 @@ DATATYPES = ["int32", "int64", "float32", "float64"] +@pytest.mark.parametrize("type", DATATYPES) +def test_unique_count(type): + """Test numba unique count.""" + a = np.array([2, 0, 2, 2, 1, 1, 0, 2, 2, 1], dtype=type) + unique_expected = [0, 1, 2] + count_expected = [2, 3, 5] + a_result = unique_count(a) + assert_array_equal(a_result[0], unique_expected) + assert_array_equal(a_result[1], count_expected) + + @pytest.mark.parametrize("type", DATATYPES) def test_z_normalise_series_with_mean_std(type): """Test z-normalization of a series using mean and standard deviation.""" @@ -150,3 +162,72 @@ def test_get_all_subsequences(dtype): dtype=dtype, ) assert_array_equal(X_subs, X_true) + + +def test_prime_up_to(): + """Test the generation of prime numbers up to a specified limit.""" + true_primes_to_100 = [ + 2, + 3, + 5, + 7, + 11, + 13, + 17, + 19, + 23, + 29, + 31, + 37, + 41, + 43, + 47, + 53, + 59, + 61, + 67, + 71, + 73, + 79, + 83, + 89, + 97, + ] + primes = prime_up_to(100) + assert_array_equal(true_primes_to_100, primes) + + +def test_is_prime(): + """Test the determination of prime numbers.""" + true_primes_to_100 = [ + 2, + 3, + 5, + 7, + 11, + 13, + 17, + 19, + 23, + 29, + 31, + 37, + 41, + 43, + 47, + 53, + 59, + 61, + 67, + 71, + 73, + 79, + 83, + 89, + 97, + ] + for n in np.arange(100): + if n in true_primes_to_100: + assert is_prime(n) + else: + assert not is_prime(n) diff --git a/aeon/utils/numba/tests/test_stats.py b/aeon/utils/numba/tests/test_stats.py index df6032e7e8..c02f696914 100644 --- a/aeon/utils/numba/tests/test_stats.py +++ b/aeon/utils/numba/tests/test_stats.py @@ -1,77 +1,68 @@ """Tests for numba utils functions related to statistical operations.""" -__maintainer__ = [] - import numpy as np -from numpy.testing import assert_array_equal +import pytest -from aeon.utils.numba.stats import is_prime, prime_up_to +from aeon.utils.numba.stats import gini, gini_gain -def test_prime_up_to(): - """Test the generation of prime numbers up to a specified limit.""" - true_primes_to_100 = [ - 2, - 3, - 5, - 7, - 11, - 13, - 17, - 19, - 23, - 29, - 31, - 37, - 41, - 43, - 47, - 53, - 59, - 61, - 67, - 71, - 73, - 79, - 83, - 89, - 97, - ] - primes = prime_up_to(100) - assert_array_equal(true_primes_to_100, primes) +def test_gini(): + """Test the method to calculate gini.""" + # Test case: Pure node (all same class) + y_pure = np.array([1, 1, 1, 1]) + assert gini(y_pure) == 0.0 + + # Test case: Impure node with two classes + y_impure = np.array([1, 1, 2, 2]) + assert gini(y_impure) == 0.5 + + # Test case: More impure node with three classes + y_more_impure = np.array([1, 1, 2, 3]) + gini_score = 1 - ((2 / 4) ** 2 + (1 / 4) ** 2 + (1 / 4) ** 2) + assert gini(y_more_impure) == gini_score + + # Test case: All different classes + y_all_different = np.array([1, 2, 3, 4]) + gini_score_all_diff = 1 - ( + (1 / 4) ** 2 + (1 / 4) ** 2 + (1 / 4) ** 2 + (1 / 4) ** 2 + ) + assert gini(y_all_different) == gini_score_all_diff + + # Test case: Empty array + y_empty = np.array([]) + with pytest.raises(ValueError, match="y is empty"): + gini(y_empty) + + +def test_gini_gain(): + """Test the method to calculate gini gain of a node.""" + # Split with mixed children + y = np.array([1, 1, 2, 2, 4, 4, 2, 2]) + y_subs = [np.array([1, 1, 4, 4]), np.array([2, 2, 2, 2])] + score_y = 1 - ((2 / 8) ** 2 + (4 / 8) ** 2 + (2 / 8) ** 2) + score = score_y - ((4 / 8) * 0.5 + (4 / 8) * 0) + assert gini_gain(y, y_subs) == score + + # Split with pure children + y = np.array([1, 1, 0, 0]) + y_children = [np.array([1, 1]), np.array([0, 0], dtype=y.dtype)] + assert gini_gain(y, y_children) == gini(y) + # Test case: Empty array + y_empty = np.array([]) + with pytest.raises(ValueError, match="y is empty"): + gini(y_empty) -def test_is_prime(): - """Test the determination of prime numbers.""" - true_primes_to_100 = [ - 2, - 3, - 5, - 7, - 11, - 13, - 17, - 19, - 23, - 29, - 31, - 37, - 41, - 43, - 47, - 53, - 59, - 61, - 67, - 71, - 73, - 79, - 83, - 89, - 97, + # When labels in y_subs do not sum to the same as y + y_empty = np.array([1, 1, 0, 0]) + y_children = [ + np.array([1, 1]), + np.array( + [ + 0, + ], + dtype=y.dtype, + ), ] - for n in np.arange(100): - if n in true_primes_to_100: - assert is_prime(n) - else: - assert not is_prime(n) + with pytest.raises(ValueError, match="labels in y_subs must sum"): + gini_gain(y_empty, y_children) From 16b848ea821b83133587220246086a228307a242 Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Mon, 9 Jun 2025 17:33:25 +0100 Subject: [PATCH 142/216] [ENH] Add two forecasting tags (#2882) * add tags * correct exog * typo --- aeon/forecasting/_ets.py | 4 ++++ aeon/forecasting/base.py | 14 ++++++++++++++ aeon/forecasting/tests/test_base.py | 4 +--- aeon/utils/tags/_tags.py | 10 ++++++++++ 4 files changed, 29 insertions(+), 3 deletions(-) diff --git a/aeon/forecasting/_ets.py b/aeon/forecasting/_ets.py index 2899e0d768..cb0fef4e0d 100644 --- a/aeon/forecasting/_ets.py +++ b/aeon/forecasting/_ets.py @@ -92,6 +92,10 @@ class ETSForecaster(BaseForecaster): 366.90200486015596 """ + _tags = { + "capability:horizon": False, + } + def __init__( self, error_type: Union[int, str] = 1, diff --git a/aeon/forecasting/base.py b/aeon/forecasting/base.py index 46facf2ad3..63262ae722 100644 --- a/aeon/forecasting/base.py +++ b/aeon/forecasting/base.py @@ -35,6 +35,8 @@ class BaseForecaster(BaseSeriesEstimator): "capability:univariate": True, "capability:multivariate": False, "capability:missing_values": False, + "capability:horizon": True, + "capability:exogenous": False, "fit_is_empty": False, "y_inner_type": "np.ndarray", } @@ -64,6 +66,18 @@ def fit(self, y, exog=None): if self.get_tag("fit_is_empty"): self.is_fitted = True return self + horizon = self.get_tag("capability:horizon") + if not horizon and self.horizon > 1: + raise ValueError( + f"Horizon is set >1, but {self.__class__.__name__} cannot handle a " + f"horizon greater than 1" + ) + exog_tag = self.get_tag("capability:exogenous") + if not exog_tag and exog is not None: + raise ValueError( + f"Exogenous variables passed but {self.__class__.__name__} cannot " + "handle exogenous variables" + ) self._check_X(y, self.axis) y = self._convert_y(y, self.axis) diff --git a/aeon/forecasting/tests/test_base.py b/aeon/forecasting/tests/test_base.py index bd4e26a670..645b392939 100644 --- a/aeon/forecasting/tests/test_base.py +++ b/aeon/forecasting/tests/test_base.py @@ -18,9 +18,7 @@ def test_base_forecaster(): p3 = f._forecast(y) assert p2 == p1 assert p3 == p2 - with pytest.raises( - NotImplementedError, match="Exogenous variables not yet " "supported" - ): + with pytest.raises(ValueError, match="Exogenous variables passed"): f.fit(y, exog=y) diff --git a/aeon/utils/tags/_tags.py b/aeon/utils/tags/_tags.py index a4f9e04152..d4a2d68a44 100644 --- a/aeon/utils/tags/_tags.py +++ b/aeon/utils/tags/_tags.py @@ -124,6 +124,16 @@ class : identifier for the base class of objects this tag applies to "type": "bool", "description": "Can the estimator limiting max fit time?", }, + "capability:exogenous": { + "class": ["forecaster"], + "type": "bool", + "description": "Can the forecaster accept exogenous arguments?", + }, + "capability:horizon": { + "class": ["forecaster"], + "type": "bool", + "description": "Can the forecaster forecast a horizon beyond one?", + }, "capability:inverse_transform": { "class": "transformer", "type": "bool", From b949eabd99e88cbf4d3bb882bbc7bb4979afb388 Mon Sep 17 00:00:00 2001 From: Ahmed Zahran <136983104+Ahmed-Zahran02@users.noreply.github.com> Date: Mon, 9 Jun 2025 20:16:45 +0300 Subject: [PATCH 143/216] [DOC] A fix to inconsistent double tick quote for the classification.distance_based module (#2627) * A fix to inconsistent double quotes * A fix to inconsistent double quotes * A fix to inconsistent double quotes * A fix to inconsistent double quotes * Revert "A fix to inconsistent double quotes" This reverts commit ff98c81a6c29b61d053e66c80f9f167b718172f5. * A fix to inconsistent double quotes * A fix to inconsistent double quotes * fix inconsistent double ticks * Automatic `pre-commit` fixes * Update _elastic_ensemble.py * Update _proximity_forest.py --------- Co-authored-by: Matthew Middlehurst Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> --- .../distance_based/_elastic_ensemble.py | 32 +++++++------ .../distance_based/_proximity_forest.py | 26 ++++++----- .../distance_based/_proximity_tree.py | 15 +++--- .../distance_based/_time_series_neighbors.py | 46 ++++++++++--------- 4 files changed, 64 insertions(+), 55 deletions(-) diff --git a/aeon/classification/distance_based/_elastic_ensemble.py b/aeon/classification/distance_based/_elastic_ensemble.py index 877401ee95..fdb3b3d7c2 100644 --- a/aeon/classification/distance_based/_elastic_ensemble.py +++ b/aeon/classification/distance_based/_elastic_ensemble.py @@ -32,14 +32,14 @@ class ElasticEnsemble(BaseClassifier): """The Elastic Ensemble (EE) of time series distance measures. The Elastic Ensemble [1]_ is an ensemble of 1-NN classifiers using elastic - distances (as defined in aeon.distances). By default, each 1-NN classifier - is tuned over 100 parameter values and the ensemble vote is weighted by + distances (as defined in ``aeon.distances``). By default, each 1-NN classifier + is tuned over ``100`` parameter values and the ensemble vote is weighted by an estimate of accuracy formed on the train set. Parameters ---------- distance_measures : str or list of str, default="all" - A list of strings identifying which distance measures to include. Valid values + A ``list`` of strings identifying which distance measures to include. Valid values are one or more of: ``euclidean``, ``dtw``, ``wdtw``, ``ddtw``, ``wddtw``, ``lcss``, ``erp``, ``msm``, ``twe``. The default value ``all`` means that all the previously listed distances are used. @@ -50,10 +50,10 @@ class ElasticEnsemble(BaseClassifier): proportion_train_for_test : float, default=1 The proportion of the train set to use in classifying new cases optional. n_jobs : int, default=1 - The number of jobs to run in parallel for both `fit` and `predict`. + The number of jobs to run in parallel for both ``fit`` and ``predict``. ``-1`` means using all processors. random_state : int, default=0 - If `int`, random_state is the seed used by the random number generator; + If ``int``, ``random_state`` is the seed used by the random number generator; verbose : int, default=0 If ``>0``, then prints out debug information. majority_vote: boolean, default = False @@ -62,7 +62,7 @@ class ElasticEnsemble(BaseClassifier): Attributes ---------- estimators_ : list - A list storing all classifiers. + A ``list`` storing all classifiers. train_accs_by_classifier_ : np.ndarray Store the train accuracies of the classifiers. constituent_build_times_ : array of float @@ -125,12 +125,12 @@ def __init__( super().__init__() def _fit(self, X, y): - """Build an ensemble of 1-NN classifiers from the training set (X, y). + """Build an ensemble of 1-NN classifiers from the training set ``(X, y)``. Parameters ---------- X : np.ndarray of shape = (n_cases, n_channels, n_timepoints) - or list of [n_cases] np.ndarray shape (n_channels, n_timepoints_i) + or list of [n_cases] np.ndarray shape ``(n_channels, n_timepoints_i)`` The training input samples. y : array-like, shape = (n_cases,) The class labels. @@ -326,7 +326,7 @@ def _fit(self, X, y): return self def _predict_proba(self, X) -> np.ndarray: - """Predict class probabilities for n instances in X. + """Predict class probabilities for ``n`` instances in ``X``. Parameters ---------- @@ -336,7 +336,7 @@ def _predict_proba(self, X) -> np.ndarray: Returns ------- y : array-like, shape = (n_cases, n_classes_) - Predicted probabilities using the ordering in classes_. + Predicted probabilities using the ordering in ``classes_``. """ der_X = self._get_derivatives(X) output_probas = [] @@ -362,7 +362,7 @@ def _predict_proba(self, X) -> np.ndarray: return output_probas def _predict(self, X) -> np.ndarray: - """Predict class values of n instances in X. + """Predict class values of ``n`` instances in ``X``. Parameters ---------- @@ -386,7 +386,7 @@ def get_metric_params(self) -> dict: Returns ------- params : dict - The distance measures and the list of their parameter values. + The distance measures and the ``list`` of their parameter values. """ return { self._distance_measures[dm]: str(self.estimators_[dm]._distance_params) @@ -493,7 +493,7 @@ def _get_test_params( ---------- parameter_set : str, default="default" Name of the set of test parameters to return, for use in tests. If no - special parameters are defined for a value, will return `"default"` set. + special parameters are defined for a value, will return "default" set. ElasticEnsemble provides the following special sets: "results_comparison" - used in some classifiers to compare against previously generated results where the default set of parameters @@ -503,8 +503,10 @@ def _get_test_params( ------- params : dict or list of dict, default={} Parameters to create testing instances of the class. - Each dict are parameters to construct an "interesting" test instance, i.e., - `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + Each ``dict`` are parameters to construct an ``interesting`` test instance + , i.e., + ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid test + instance. """ if parameter_set == "results_comparison": return { diff --git a/aeon/classification/distance_based/_proximity_forest.py b/aeon/classification/distance_based/_proximity_forest.py index 49a19e2532..afb6444225 100644 --- a/aeon/classification/distance_based/_proximity_forest.py +++ b/aeon/classification/distance_based/_proximity_forest.py @@ -27,29 +27,31 @@ class ProximityForest(BaseClassifier): Parameters ---------- n_trees: int, default = 100 - The number of trees, by default an ensemble of 100 trees is formed. + The number of trees, by default an ensemble of ``100`` trees is formed. n_splitters: int, default = 5 The number of candidate splitters to be evaluated at each node. max_depth: int, default = None - The maximum depth of the tree. If None, then nodes are expanded until all - leaves are pure or until all leaves contain less than min_samples_split samples. + The maximum depth of the tree. If ``None``, then nodes are expanded until all + leaves are pure or until all leaves contain less than ``min_samples_split`` + samples. min_samples_split: int, default = 2 The minimum number of samples required to split an internal node. random_state : int, RandomState instance or None, default=None - If `int`, random_state is the seed used by the random number generator; - If `RandomState` instance, random_state is the random number generator; - If `None`, the random number generator is the `RandomState` instance used - by `np.random`. + If ``int``, ``random_state`` is the seed used by the random number generator; + If ``RandomState`` instance, ``random_state`` is the random number generator; + If ``None``, the random number generator is the ``RandomState`` instance used + by ``np.random``. n_jobs : int, default = 1 The number of parallel jobs to run for neighbors search. - ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. + ``None`` means ``1`` unless in a :obj:``joblib.parallel_backend`` context. ``-1`` means using all processors. for more details. Parameter for compatibility purposes, still unimplemented. parallel_backend : str, ParallelBackendBase instance or None, default=None - Specify the parallelisation backend implementation in joblib, if None a 'prefer' - value of "threads" is used by default. - Valid options are "loky", "multiprocessing", "threading" or a custom backend. - See the joblib Parallel documentation for more details. + Specify the parallelisation backend implementation in ``joblib``, if ``None`` + a ``prefer`` value of ``"threads"`` is used by default. + Valid options are ``"loky"``, ``"multiprocessing"``, ``"threading"`` or a + custom backend. + See the ``joblib`` Parallel documentation for more details. Notes ----- diff --git a/aeon/classification/distance_based/_proximity_tree.py b/aeon/classification/distance_based/_proximity_tree.py index afbf4a8359..1f414abc44 100644 --- a/aeon/classification/distance_based/_proximity_tree.py +++ b/aeon/classification/distance_based/_proximity_tree.py @@ -34,7 +34,7 @@ class _ProximityNode: The class distribution for the node. Empty if not a leaf node. splitter: tuple or None, default=None The splitter used to split the node. Contains exemplars used, distance name and - distance parameters.Empty if leaf node. + distance parameters. Empty if leaf node. Attributes ---------- @@ -82,15 +82,16 @@ class ProximityTree(BaseClassifier): n_splitters: int, default = 5 The number of candidate splitters to be evaluated at each node. max_depth: int, default = None - The maximum depth of the tree. If None, then nodes are expanded until all - leaves are pure or until all leaves contain less than min_samples_split samples. + The maximum depth of the tree. If ``None``, then nodes are expanded until all + leaves are pure or until all leaves contain less than ``min_samples_split`` + samples. min_samples_split: int, default = 2 The minimum number of samples required to split an internal node. random_state : int, RandomState instance or None, default=None - If `int`, random_state is the seed used by the random number generator; - If `RandomState` instance, random_state is the random number generator; - If `None`, the random number generator is the `RandomState` instance used - by `np.random`. + If ``int``, ``random_state`` is the seed used by the random number generator; + If ``RandomState`` instance, ``random_state`` is the random number generator; + If ``None``, the random number generator is the ``RandomState`` instance used + by ``np.random``. Notes ----- diff --git a/aeon/classification/distance_based/_time_series_neighbors.py b/aeon/classification/distance_based/_time_series_neighbors.py index 4b4d90c164..540c29b0ec 100644 --- a/aeon/classification/distance_based/_time_series_neighbors.py +++ b/aeon/classification/distance_based/_time_series_neighbors.py @@ -26,29 +26,30 @@ class KNeighborsTimeSeriesClassifier(BaseClassifier): A KNN classifier which supports time series distance measures. It determines distance function through string references to numba - based distances in aeon.distances, and can also be used with callables. + based distances in ``aeon.distances``, and can also be used with callables. Parameters ---------- n_neighbors : int, default = 1 - Set k for knn. + Set ``k`` for knn. weights : str or callable, default = 'uniform' - Mechanism for weighting a vote one of: 'uniform', 'distance', or a callable + Mechanism for weighting a vote one of: ``'uniform'``, ``'distance'``, + or a callable function. distance : str or callable, default ='dtw' Distance measure between time series. - Distance metric to compute similarity between time series. A list of valid + Distance metric to compute similarity between time series. A ``list`` of valid strings for metrics can be found in the documentation for - :func:`aeon.distances.get_distance_function` or through calling - :func:`aeon.distances.get_distance_function_names`. If a - callable is passed it must be - a function that takes two 2d numpy arrays of shape ``(n_channels, - n_timepoints)`` as input and returns a float. + :func:``aeon.distances.get_distance_function`` or through calling + :func:``aeon.distances.get_distance_function_names``. If a + ``callable`` is passed it must be + a function that takes two 2d numpy arrays of shape `(n_channels, + n_timepoints)` as input and returns a ``float``. distance_params : dict, default = None - Dictionary for metric parameters for the case that distance is a str. + Dictionary for metric parameters for the case that distance is a ``str``. n_jobs : int, default = 1 The number of parallel jobs to run for neighbors search. - ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. + ``None`` means 1 unless in a :obj:``joblib.parallel_backend`` context. ``-1`` means using all processors. parallel_backend : str, ParallelBackendBase instance or None, default=None Specify the parallelisation backend implementation in joblib, if None @@ -112,14 +113,14 @@ def __init__( def _fit(self, X, y): """ - Fit the model using X as training data and y as target values. + Fit the model using ``X`` as training data and ``y`` as target values. Parameters ---------- X : 3D np.ndarray of shape = (n_cases, n_channels, n_timepoints) or list of shape [n_cases] of 2D arrays shape (n_channels,n_timepoints_i) - If the series are all equal length, a numpy3D will be passed. If unequal, - a list of 2D numpy arrays is passed, which may have different lengths. + If the series are all equal length, a ``numpy3D`` will be passed. If unequal, + a ``list`` of 2D numpy arrays is passed, which may have different lengths. y : array-like, shape = (n_cases) The class labels. """ @@ -136,8 +137,8 @@ def _predict_proba(self, X): ---------- X : 3D np.ndarray of shape = (n_cases, n_channels, n_timepoints) or list of shape[n_cases] of 2D arrays shape (n_channels,n_timepoints_i) - If the series are all equal length, a numpy3D will be passed. If - unequal, a list of 2D numpy arrays is passed, which may have + If the series are all equal length, a ``numpy3D`` will be passed. If + unequal, a ``list`` of 2D numpy arrays is passed, which may have different lengths. Returns @@ -160,7 +161,8 @@ def _predict(self, X): ---------- X : 3D np.ndarray of shape = (n_cases, n_channels, n_timepoints) or list of shape[n_cases] of 2D arrays shape (n_channels,n_timepoints_i) - If the series are all equal length, a numpy3D will be passed. If unequal, a list + If the series are all equal length, a ``numpy3D`` will be passed. If unequal, + a ``list`` of 2D numpy arrays is passed, which may have different lengths. Returns @@ -199,7 +201,7 @@ def _kneighbors(self, X): Parameters ---------- X : np.ndarray - A single time series instance if shape = (n_channels, n_timepoints) + A single time series instance if shape = ``(n_channels, n_timepoints)`` Returns ------- @@ -244,13 +246,15 @@ def _get_test_params( ---------- parameter_set : str, default="default" Name of the set of test parameters to return, for use in tests. If no - special parameters are defined for a value, will return `"default"` set. + special parameters are defined for a value, will return ``"default"`` set. Returns ------- params : dict or list of dict, default={} Parameters to create testing instances of the class. - Each dict are parameters to construct an "interesting" test instance, i.e., - `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + Each ``dict`` are parameters to construct an ``interesting`` test instance, + i.e., + ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid test + instance. """ return {"distance": "euclidean"} From ec505f99741f010985ebc64bb3012a67694e02de Mon Sep 17 00:00:00 2001 From: "allcontributors[bot]" <46447321+allcontributors[bot]@users.noreply.github.com> Date: Mon, 9 Jun 2025 18:17:41 +0100 Subject: [PATCH 144/216] =?UTF-8?q?=F0=9F=93=9D=20Add=20Ahmed-Zahran02=20a?= =?UTF-8?q?s=20a=20contributor=20for=20doc=20(#2892)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 📝 Update CONTRIBUTORS.md [skip ci] * 📝 Update .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> --- .all-contributorsrc | 9 ++++++ CONTRIBUTORS.md | 67 +++++++++++++++++++++++---------------------- 2 files changed, 43 insertions(+), 33 deletions(-) diff --git a/.all-contributorsrc b/.all-contributorsrc index 7a262d8a2c..8efaa0b03d 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -2729,6 +2729,15 @@ "contributions": [ "code" ] + }, + { + "login": "Ahmed-Zahran02", + "name": "Ahmed Zahran", + "avatar_url": "https://avatars.githubusercontent.com/u/136983104?v=4", + "profile": "https://github.com/Ahmed-Zahran02", + "contributions": [ + "doc" + ] } ], "commitType": "docs" diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md index 8e51820eb3..dbdf1ff53e 100644 --- a/CONTRIBUTORS.md +++ b/CONTRIBUTORS.md @@ -1,7 +1,7 @@ # Contributors -[![All Contributors](https://img.shields.io/badge/all_contributors-267-orange.svg)](#contributors) +[![All Contributors](https://img.shields.io/badge/all_contributors-268-orange.svg)](#contributors) This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! @@ -27,13 +27,14 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Adilson
Adilson
📖 Afzal Ansari
Afzal Ansari
💻 📖 Ahmed Bilal
Ahmed Bilal
📖 + Ahmed Zahran
Ahmed Zahran
📖 AidenRushbrooke
AidenRushbrooke
💻 ⚠️ Akash Kawle
Akash Kawle
💻 Akhil Jasson
Akhil Jasson
📖 Akshat Nayak
Akshat Nayak
💻 - Akshat Rampuria
Akshat Rampuria
📖 + Akshat Rampuria
Akshat Rampuria
📖 Aleksandr Grekov
Aleksandr Grekov
📖 Alex Hawkins-Hooker
Alex Hawkins-Hooker
💻 Alexandra Amidon
Alexandra Amidon
📝 📖 🤔 @@ -41,9 +42,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Ali Teeney
Ali Teeney
💻 Ali Yazdizadeh
Ali Yazdizadeh
📖 Alwin
Alwin
📖 💻 🚧 - An Hoang
An Hoang
🐛 💻 + An Hoang
An Hoang
🐛 💻 Andreas Kanz
Andreas Kanz
✅ André Guarnier De Mitri
André Guarnier De Mitri
💻 Angus Dempster
Angus Dempster
💻 ⚠️ ✅ @@ -51,9 +52,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Antoni Baum
Antoni Baum
💻 Aparna Sakshi
Aparna Sakshi
💻 Arelo Tanoh
Arelo Tanoh
📖 - Arepalli Yashwanth Reddy
Arepalli Yashwanth Reddy
💻 🐛 📖 + Arepalli Yashwanth Reddy
Arepalli Yashwanth Reddy
💻 🐛 📖 Arik Ermshaus
Arik Ermshaus
💻 Arnav
Arnav
💻 Aryan Pola
Aryan Pola
💻 📖 @@ -61,9 +62,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d BANDASAITEJAREDDY
BANDASAITEJAREDDY
💻 📖 Badr-Eddine Marani
Badr-Eddine Marani
💻 Balgopal Moharana
Balgopal Moharana
💻 - Benedikt Heidrich
Benedikt Heidrich
💻 + Benedikt Heidrich
Benedikt Heidrich
💻 Benjamin Bluhm
Benjamin Bluhm
💻 📖 💡 Bhaskar Dhariyal
Bhaskar Dhariyal
💻 ⚠️ Binay Kumar
Binay Kumar
💻 📖 ⚠️ @@ -71,9 +72,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Bouke Postma
Bouke Postma
💻 🐛 🤔 Brian Murphy
Brian Murphy
📖 Carlos Borrajo
Carlos Borrajo
💻 📖 - Carlos Ramos Carreño
Carlos Ramos Carreño
📖 + Carlos Ramos Carreño
Carlos Ramos Carreño
📖 Chang Wei Tan
Chang Wei Tan
💻 Cheuk Ting Ho
Cheuk Ting Ho
💻 Christian Kastner
Christian Kastner
💻 🐛 @@ -81,9 +82,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Christopher Lo
Christopher Lo
💻 🤔 Chuanhang Qiu
Chuanhang Qiu
💻 Ciaran Gilbert
Ciaran Gilbert
🐛 💻 📖 ⚠️ 🤔 - ClaudiaSanches
ClaudiaSanches
💻 ⚠️ + ClaudiaSanches
ClaudiaSanches
💻 ⚠️ Corvin Paul
Corvin Paul
📖 Cyril Meyer
Cyril Meyer
⚠️ 📖 💻 Daniel Burkhardt Cerigo
Daniel Burkhardt Cerigo
💻 @@ -91,9 +92,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Daniel Martín Martínez
Daniel Martín Martínez
📖 🐛 Daniel Roberts
Daniel Roberts
📖 Daniele Carli
Daniele Carli
📖 - Dave Hirschfeld
Dave Hirschfeld
🚇 + Dave Hirschfeld
Dave Hirschfeld
🚇 David Buchaca Prats
David Buchaca Prats
💻 David Guijo-Rubio
David Guijo-Rubio
💻 🤔 Divya Tiwari
Divya Tiwari
💻 🔣 @@ -101,9 +102,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Doug Ollerenshaw
Doug Ollerenshaw
📖 Drishti Bhasin
Drishti Bhasin
💻 Dylan Sherry
Dylan Sherry
🚇 - Emilia Rose
Emilia Rose
💻 ⚠️ + Emilia Rose
Emilia Rose
💻 ⚠️ Emmanuel Ferdman
Emmanuel Ferdman
📖 Er Jie Yong
Er Jie Yong
🐛 💻 Evan Miller
Evan Miller
✅ @@ -111,9 +112,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Federico Garza
Federico Garza
💻 💡 Felix Claessen
Felix Claessen
💻 📖 ⚠️ 🐛 Ferdinand Rewicki
Ferdinand Rewicki
💻 🐛 - Florian Stinner
Florian Stinner
💻 ⚠️ + Florian Stinner
Florian Stinner
💻 ⚠️ Francesco Spinnato
Francesco Spinnato
💻 Franz Kiraly
Franz Kiraly
🐛 💼 💻 📖 🎨 📋 💡 💵 🔍 🤔 🚧 🧑‍🏫 📆 💬 👀 📢 ⚠️ ✅ 📹 Freddy A Boulton
Freddy A Boulton
🚇 ⚠️ @@ -121,9 +122,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Gabriel Riegner
Gabriel Riegner
📖 Galina Chernikova
Galina Chernikova
💻 George Langley
George Langley
📖 - George Oastler
George Oastler
💻 ⚠️ 📦 💡 📖 + George Oastler
George Oastler
💻 ⚠️ 📦 💡 📖 Gilberto Barbosa
Gilberto Barbosa
💻 Grace Gao
Grace Gao
💻 🐛 Guilherme Arcencio
Guilherme Arcencio
💻 ⚠️ @@ -131,9 +132,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d HYang1996
HYang1996
💻 ⚠️ 📖 ✅ HaroonAzamFiza
HaroonAzamFiza
📖 Harshitha Sudhakar
Harshitha Sudhakar
📖 💻 - Hedeer El Showk
Hedeer El Showk
🐛 📖 💻 + Hedeer El Showk
Hedeer El Showk
🐛 📖 💻 Huayi Wei
Huayi Wei
✅ Ifeanyi30
Ifeanyi30
💻 Ilja Maurer
Ilja Maurer
💻 @@ -141,9 +142,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Ireoluwatomiwa
Ireoluwatomiwa
📖 Ishan Nangia
Ishan Nangia
🤔 Ivan Knyazev
Ivan Knyazev
📖 - Jack Russon
Jack Russon
💻 + Jack Russon
Jack Russon
💻 James Large
James Large
💻 📖 ⚠️ 🚇 🚧 James Morrill
James Morrill
💻 Jasmine Liaw
Jasmine Liaw
💻 @@ -151,9 +152,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Jason Mok
Jason Mok
📖 Jason Pong
Jason Pong
💻 ⚠️ Jaume Mateu
Jaume Mateu
💻 - JonathanBechtel
JonathanBechtel
💻 🤔 ⚠️ + JonathanBechtel
JonathanBechtel
💻 🤔 ⚠️ Joren Hammudoglu
Joren Hammudoglu
🚇 Juan Orduz
Juan Orduz
✅ 📖 Julian Cooper
Julian Cooper
💻 🤔 @@ -161,9 +162,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Justin Shenk
Justin Shenk
📖 Kai Lion
Kai Lion
💻 ⚠️ 📖 Kavin Anand
Kavin Anand
📖 - Kavya Rambhia
Kavya Rambhia
💻 + Kavya Rambhia
Kavya Rambhia
💻 Kejsi Take
Kejsi Take
💻 Kevin Lam
Kevin Lam
💻 💡 ⚠️ Kevin Shah
Kevin Shah
📖 @@ -171,9 +172,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Kishan Manani
Kishan Manani
💻 📖 ⚠️ 🐛 🤔 Krum Arnaudov
Krum Arnaudov
🐛 💻 Kutay Koralturk
Kutay Koralturk
💻 🐛 - Leonidas Tsaprounis
Leonidas Tsaprounis
💻 🐛 🧑‍🏫 👀 + Leonidas Tsaprounis
Leonidas Tsaprounis
💻 🐛 🧑‍🏫 👀 Lielle Ravid
Lielle Ravid
💻 📖 Logan Duffy
Logan Duffy
💻 📖 ⚠️ 🐛 🤔 Lorena Pantano
Lorena Pantano
🤔 @@ -181,9 +182,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Lovkush
Lovkush
💻 ⚠️ 🤔 🧑‍🏫 📆 Luca Bennett
Luca Bennett
💻 📖 ⚠️ Luis Ventura
Luis Ventura
💻 - Luis Zugasti
Luis Zugasti
📖 + Luis Zugasti
Luis Zugasti
📖 Lukasz Mentel
Lukasz Mentel
💻 📖 🚇 ⚠️ 🐛 🚧 🧑‍🏫 Marcelo Trylesinski
Marcelo Trylesinski
📖 Marco Gorelli
Marco Gorelli
🚇 @@ -191,9 +192,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Mariam Jabara
Mariam Jabara
💻 Marielle
Marielle
📖 💻 🤔 Markus Löning
Markus Löning
💻 ⚠️ 🚧 📦 👀 🚇 💡 🐛 ✅ 💼 📖 🎨 📋 🔍 🤔 📆 💬 📢 🧑‍🏫 📹 - Martin Walter
Martin Walter
💻 🐛 📆 🔍 🧑‍🏫 🤔 🎨 👀 📖 📢 + Martin Walter
Martin Walter
💻 🐛 📆 🔍 🧑‍🏫 🤔 🎨 👀 📖 📢 Martina G. Vilas
Martina G. Vilas
👀 🤔 Matthew Middlehurst
Matthew Middlehurst
🐛 💻 🔣 📖 🎨 💡 🤔 🚇 🚧 🧑‍🏫 📣 💬 🔬 👀 ⚠️ ✅ 📢 Max Patzelt
Max Patzelt
💻 @@ -201,9 +202,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Michael F. Mbouopda
Michael F. Mbouopda
💻 🐛 📖 Michael Feil
Michael Feil
💻 ⚠️ 🤔 Michal Chromcak
Michal Chromcak
💻 📖 ⚠️ ✅ - Mirae Parker
Mirae Parker
💻 ⚠️ + Mirae Parker
Mirae Parker
💻 ⚠️ Mohammed Saif Kazamel
Mohammed Saif Kazamel
🐛 Morad :)
Morad :)
💻 ⚠️ 📖 Multivin12
Multivin12
💻 ⚠️ @@ -211,9 +212,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Niek van der Laan
Niek van der Laan
💻 Nikhil Gupta
Nikhil Gupta
💻 🐛 📖 Nikola Shahpazov
Nikola Shahpazov
📖 - Nilesh Kumar
Nilesh Kumar
💻 + Nilesh Kumar
Nilesh Kumar
💻 Nima Nooshiri
Nima Nooshiri
📖 Ninnart Fuengfusin
Ninnart Fuengfusin
💻 Noa Ben Ami
Noa Ben Ami
💻 ⚠️ 📖 @@ -221,9 +222,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Oleksii Kachaiev
Oleksii Kachaiev
💻 ⚠️ Oliver Matthews
Oliver Matthews
💻 Patrick Müller
Patrick Müller
💻 - Patrick Rockenschaub
Patrick Rockenschaub
💻 🎨 🤔 ⚠️ + Patrick Rockenschaub
Patrick Rockenschaub
💻 🎨 🤔 ⚠️ Patrick Schäfer
Patrick Schäfer
💻 ✅ Paul
Paul
📖 Paul Rabich
Paul Rabich
💻 @@ -231,9 +232,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Philip
Philip
📖 Philipp Kortmann
Philipp Kortmann
💻 📖 Phillip Wenig
Phillip Wenig
💻 - Piyush Gade
Piyush Gade
💻 👀 + Piyush Gade
Piyush Gade
💻 👀 Pulkit Verma
Pulkit Verma
📖 Quaterion
Quaterion
🐛 Rafael Ayllón-Gavilán
Rafael Ayllón-Gavilán
💻 @@ -241,9 +242,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Ramana Raja
Ramana Raja
💻 RavenRudi
RavenRudi
💻 Raya Chakravarty
Raya Chakravarty
📖 - Rick van Hattem
Rick van Hattem
🚇 + Rick van Hattem
Rick van Hattem
🚇 Rishabh Bali
Rishabh Bali
💻 Rishav Kumar Sinha
Rishav Kumar Sinha
📖 Rishi Kumar Ray
Rishi Kumar Ray
🚇 @@ -251,9 +252,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Ronnie Llamado
Ronnie Llamado
📖 Ryan Kuhns
Ryan Kuhns
💻 📖 ✅ 💡 🤔 👀 ⚠️ Sagar Mishra
Sagar Mishra
⚠️ - Sajaysurya Ganesh
Sajaysurya Ganesh
💻 📖 🎨 💡 🤔 ⚠️ ✅ + Sajaysurya Ganesh
Sajaysurya Ganesh
💻 📖 🎨 💡 🤔 ⚠️ ✅ Saransh Chopra
Saransh Chopra
📖 🚇 Satya Prakash Pattnaik
Satya Prakash Pattnaik
📖 Saurabh Dasgupta
Saurabh Dasgupta
💻 @@ -261,9 +262,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Sebastian Hagn
Sebastian Hagn
📖 Sebastian Schmidl
Sebastian Schmidl
🐛 💻 📖 🔬 ⚠️ 👀 🔣 Sharathchenna
Sharathchenna
💻 - Shivansh Subramanian
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💻 @@ -271,9 +272,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Svea Marie Meyer
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📖 From 72944dcd5ac2c07460b2aed010543923b991c19a Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Mon, 9 Jun 2025 18:39:27 +0100 Subject: [PATCH 145/216] [BUG] Forecasting regressor fix and notebook (#2885) * stop returning an array * notebook * Create window.png * fix test and typo * remove unfinished sentence --- aeon/classification/compose/_pipeline.py | 2 +- aeon/forecasting/_regression.py | 32 +- aeon/forecasting/base.py | 3 +- aeon/forecasting/tests/test_regressor.py | 16 +- examples/forecasting/forecasting.ipynb | 214 +++---------- examples/forecasting/img/window.png | Bin 0 -> 105108 bytes examples/forecasting/regression.ipynb | 369 +++++++++++++++++++++++ 7 files changed, 441 insertions(+), 195 deletions(-) create mode 100644 examples/forecasting/img/window.png create mode 100644 examples/forecasting/regression.ipynb diff --git a/aeon/classification/compose/_pipeline.py b/aeon/classification/compose/_pipeline.py index b7473965bb..8c43f6ff9b 100644 --- a/aeon/classification/compose/_pipeline.py +++ b/aeon/classification/compose/_pipeline.py @@ -40,7 +40,7 @@ class ClassifierPipeline(BaseCollectionPipeline, BaseClassifier): A transform or list of transformers to use prior to classification. List of tuples (str, transformer) of transformers can also be passed, where the str is used to name the transformer. - The objecst are cloned prior, as such the state of the input will not be + The objects are cloned prior, as such the state of the input will not be modified by fitting the pipeline. estimator : aeon or sklearn classifier A classifier to use at the end of the pipeline. diff --git a/aeon/forecasting/_regression.py b/aeon/forecasting/_regression.py index 5f82162cb7..0106a95afb 100644 --- a/aeon/forecasting/_regression.py +++ b/aeon/forecasting/_regression.py @@ -66,6 +66,10 @@ def _fit(self, y, exog=None): else: self.regressor_ = self.regressor y = y.squeeze() + if self.window < 1 or self.window > len(y) - 3: + raise ValueError( + f" window value {self.window} is invalid for series " f"length {len(y)}" + ) X = np.lib.stride_tricks.sliding_window_view(y, window_shape=self.window) # Ignore the final horizon values: need to store these for pred with empty y X = X[: -self.horizon] @@ -91,35 +95,13 @@ def _predict(self, y=None, exog=None): Returns ------- - np.ndarray + float single prediction self.horizon steps ahead of y. """ if y is None: - return self.regressor_.predict(self.last_) + return self.regressor_.predict(self.last_)[0] last = y[:, -self.window :] - return self.regressor_.predict(last) - - def _forecast(self, y, exog=None): - """ - Forecast the next horizon steps ahead. - - Parameters - ---------- - y : np.ndarray - A time series to predict the next horizon value for. - exog : np.ndarray, default=None - Optional exogenous time series data. Included for interface - compatibility but ignored in this estimator. - - Returns - ------- - np.ndarray - single prediction self.horizon steps ahead of y. - - NOTE: deal with horizons - """ - self.fit(y, exog) - return self.predict() + return self.regressor_.predict(last)[0] @classmethod def _get_test_params(cls, parameter_set: str = "default"): diff --git a/aeon/forecasting/base.py b/aeon/forecasting/base.py index 63262ae722..3a084b6d99 100644 --- a/aeon/forecasting/base.py +++ b/aeon/forecasting/base.py @@ -111,7 +111,8 @@ def predict(self, y=None, exog=None): y = self._convert_y(y, self.axis) if exog is not None: raise NotImplementedError("Exogenous variables not yet supported") - return self._predict(y, exog) + x = self._predict(y, exog) + return x @abstractmethod def _predict(self, y=None, exog=None): ... diff --git a/aeon/forecasting/tests/test_regressor.py b/aeon/forecasting/tests/test_regressor.py index 19571d0f27..f13161c4ec 100644 --- a/aeon/forecasting/tests/test_regressor.py +++ b/aeon/forecasting/tests/test_regressor.py @@ -1,14 +1,16 @@ """Test the regression forecaster.""" +import numpy as np +import pytest from sklearn.linear_model import LinearRegression -from aeon.datasets import load_airline from aeon.forecasting import RegressionForecaster +from aeon.regression import DummyRegressor def test_regression_forecaster(): """Test the regression forecaster.""" - y = load_airline() + y = np.random.rand(100) f = RegressionForecaster(window=10) f.fit(y) p = f.predict() @@ -20,3 +22,13 @@ def test_regression_forecaster(): f2.fit(y) p2 = f2.predict() assert p == p2 + f2 = RegressionForecaster(regressor=DummyRegressor(), window=10) + f2.fit(y) + f2.predict() + + with pytest.raises(ValueError): + f = RegressionForecaster(window=-1) + f.fit(y) + with pytest.raises(ValueError): + f = RegressionForecaster(window=101) + f.fit(y) diff --git a/examples/forecasting/forecasting.ipynb b/examples/forecasting/forecasting.ipynb index 5d98504f13..8d24b659e5 100644 --- a/examples/forecasting/forecasting.ipynb +++ b/examples/forecasting/forecasting.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "metadata": {}, "cell_type": "markdown", "source": [ "# Time series forecasting with aeon\n", @@ -30,12 +31,10 @@ "we want to implement and assess the latest machine learning and deep learning\n", "forecasting for scenarios where it makes sense to use them. Our initial experimental\n", "focus will be on forecasting with long series for a single forecasting horizon.\n" - ], - "metadata": { - "collapsed": false - } + ] }, { + "metadata": {}, "cell_type": "markdown", "source": [ "## Base Class\n", @@ -53,14 +52,15 @@ " an input series.\n", "The `BaseForecaster` has three core methods: `fit`, `predict` and `forecast`. It is\n", "an abstract class, and each of these methods calls a protected method `_fit`,\n", - "`_predict` and `_forecast`.\n" - ], - "metadata": { - "collapsed": false - } + "`_predict` and `_forecast`.\n", + "\n" + ] }, { + "metadata": {}, "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "import inspect\n", "\n", @@ -73,26 +73,10 @@ " if not func[0].startswith(\"_\")\n", "]\n", "print(public_methods)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-11-16T19:20:13.050238Z", - "start_time": "2024-11-16T19:20:13.044254Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['clone', 'fit', 'forecast', 'get_fitted_params', 'get_metadata_routing', 'get_params', 'get_tag', 'get_tags', 'predict', 'reset', 'set_params', 'set_tags']\n" - ] - } - ], - "execution_count": 10 + ] }, { + "metadata": {}, "cell_type": "markdown", "source": [ " All estimators in `aeon` have tags. One specific to\n", @@ -101,51 +85,33 @@ ".ndarray` but it can also be `pd.DataFrame` or `pd.Series`. You can pass\n", "forecaster and of `SERIES_DATA_TYPES` and it will be converted to `y_inner_type` in\n", "`fit`, `predict` and `forecast`." - ], - "metadata": { - "collapsed": false - } + ] }, { + "metadata": {}, "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "from aeon.utils.data_types import SERIES_DATA_TYPES\n", "\n", "print(\" Possible data structures for input to forecaster \", SERIES_DATA_TYPES)\n", "print(\"\\n Tags for BaseForecaster: \", BaseForecaster.get_class_tags())" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-11-16T19:20:14.277081Z", - "start_time": "2024-11-16T19:20:14.262132Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Possible data structures for input to forecaster ['pd.Series', 'pd.DataFrame', 'np.ndarray']\n", - "\n", - " Tags for BaseForecaster: {'python_version': None, 'python_dependencies': None, 'cant_pickle': False, 'non_deterministic': False, 'algorithm_type': None, 'capability:missing_values': False, 'capability:multithreading': False, 'capability:univariate': True, 'capability:multivariate': False, 'X_inner_type': 'np.ndarray', 'fit_is_empty': False, 'y_inner_type': 'np.ndarray'}\n" - ] - } - ], - "execution_count": 11 + ] }, { + "metadata": {}, "cell_type": "markdown", "source": [ "We use the standard airline dataset for examples. This can be stored as a pd.Series,\n", "pd.DataFrame or np.ndarray." - ], - "metadata": { - "collapsed": false - } + ] }, { + "metadata": {}, "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "import pandas as pd\n", "\n", @@ -155,26 +121,10 @@ "print(type(y))\n", "y2 = pd.Series(y)\n", "y3 = pd.DataFrame(y)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-11-16T19:20:15.586960Z", - "start_time": "2024-11-16T19:20:15.578482Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "execution_count": 12 + ] }, { + "metadata": {}, "cell_type": "markdown", "source": [ "## DummyForecaster\n", @@ -184,13 +134,13 @@ "default the horizon is 1. It makes no difference for this forecaster. It's inner type\n", " is `np.ndarray` so all three allowable input types are internally converted to numpy\n", " arrays." - ], - "metadata": { - "collapsed": false - } + ] }, { + "metadata": {}, "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "# Fit then predict\n", "from aeon.forecasting import NaiveForecaster\n", @@ -200,52 +150,21 @@ "d.fit(y)\n", "p = d.predict()\n", "print(p)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-11-16T19:20:17.280150Z", - "start_time": "2024-11-16T19:20:17.270176Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "np.ndarray\n", - "432.0\n" - ] - } - ], - "execution_count": 13 + ] }, { + "metadata": {}, "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "# forecast is equivalent to fit_predict in other estimators\n", "p2 = d.forecast(y)\n", "print(p2)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-11-16T19:20:17.997049Z", - "start_time": "2024-11-16T19:20:17.985082Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "432.0\n" - ] - } - ], - "execution_count": 14 + ] }, { + "metadata": {}, "cell_type": "markdown", "source": [ "## Regression based forecasting\n", @@ -256,13 +175,13 @@ " series, then apply the data to new series. There will be a notebook for more details\n", " of the use of RegressionForecaster. By default it just uses a linear regressor, but\n", " our goal is to use it with `aeon` time series regressors." - ], - "metadata": { - "collapsed": false - } + ] }, { + "metadata": {}, "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "from aeon.forecasting import RegressionForecaster\n", "\n", @@ -274,74 +193,37 @@ "r2.fit(y)\n", "p = r2.predict(y)\n", "print(p)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-11-16T19:20:19.366693Z", - "start_time": "2024-11-16T19:20:19.356837Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[451.67541971]\n", - "[527.36897094]\n" - ] - } - ], - "execution_count": 15 + ] }, { + "metadata": {}, "cell_type": "markdown", "source": [ "With our set up, we can make predictions with previously unseen data, thus more\n", "closely modelling machine learning approaches. Or we can use the forecast method to\n", "fit/predict at the same time." - ], - "metadata": { - "collapsed": false - } + ] }, { + "metadata": {}, "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "p1 = r.forecast(y)\n", "p2 = r2.forecast(y)\n", "print(p1, \",\\n\", p2)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-11-16T19:21:24.486613Z", - "start_time": "2024-11-16T19:21:24.464704Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[451.67541971] ,\n", - " [527.36897094]\n" - ] - } - ], - "execution_count": 19 + ] }, { + "metadata": {}, "cell_type": "markdown", "source": [ "## Exponential Smoothing\n", "\n", - "The base exponential smoothing module is implemented in stripped down code with \n", + "The base exponential smoothing module is implemented in stripped down code with\n", "numba, and is very fast" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", @@ -350,7 +232,7 @@ "\n", "ets = ETSForecaster()\n", "ets.fit(y)\n", - "ets.predict()\n" + "ets.predict()" ], "metadata": { "collapsed": false, diff --git a/examples/forecasting/img/window.png b/examples/forecasting/img/window.png new file mode 100644 index 0000000000000000000000000000000000000000..dff287609c47f7f918754ca3f4aab63f10667513 GIT binary patch literal 105108 zcmeFY^q~BTO*}v#mK(%B zl6w!YZ-GGPAMU|a4SX%uCj7FPhH7QEEcav-r9P3iQlHFR_{Pj2?Gkb+qrIm6`tQC* zD@2@wbieTHOouDIaZ8!7K5gnW_0D|#_lW|L(NvUIjbcgOs?(?Gn>^cGE}cJnf3lke zdE98Ad!QxOES9?{HSi@%UdjBi`Qq}56MP_F`&TTxlnU(MS4Y=-ool>*U#OYZ3-k(PpJrE0@oN}uiM2s`$f+k?+3=xLF&(JBqRk|-H2>E`kJ8Of7C`_|4K)$o?vDBU5&Pa(ZvdSSa@f> z+jMK@)vH$rmx-Eg&?;BXgSvBr)xpzT6L_9D8%&(79erjl_akF+Tg2Je*Up}bJNUz; z$0RgyYC?nRHqzRU_!;K8{$pSNl6eK2)+BV<&@(V!IqJ^c*l@WRs#=9cqrQA$4h}9M zD_egwd{@^loz+s#mgzerNUp&`40uaxhhk=4 zc)`u>)xlrBe8~d{J)d-FTRrhN`o~@HViYeum1;8fUuLLWm5w?w3t06j!7?CR87vjMVPapvVkyk1+KJ(76R&Ht}NuQ7)JN>o>0KQs!1C5N5vZRO=kVOFqGWPeE?JtW#eVjkN5|t7am|{vWi0WHimt*J~Vv&%dw6~ 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Together a window and response form a training \n", + "case. Formatted like this, we can apply an `sklearn` tabular regressor or an `aeon` \n", + "time series regressor. \n", + "\n", + "The response variable is the value directly after the window (if the horizon is 1). \n", + "This is of course unknown for the last window, so we will assume that is the case we \n", + "want to use to make a forecast. " + ], + "id": "c181c85a4be9bdbc" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-07T16:02:32.093458Z", + "start_time": "2025-06-07T16:02:32.067219Z" + } + }, + "cell_type": "code", + "source": [ + "from numpy.lib.stride_tricks import sliding_window_view\n", + "\n", + "from aeon.datasets import load_airline\n", + "\n", + "y = load_airline()\n", + "X_train = sliding_window_view(y, window_shape=100)\n", + "X_train.shape" + ], + "id": "82e9fe7d601f7ad5", + "outputs": [ + { + "data": { + "text/plain": [ + "(45, 100)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 36 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-07T16:02:34.104719Z", + "start_time": "2025-06-07T16:02:33.936686Z" + } + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from aeon.datasets import load_airline\n", + "\n", + "# Window all the series\n", + "window = 50\n", + "X = sliding_window_view(y, window_shape=window)\n", + "X_train = X[:-1]\n", + "X_test = X[-1:]\n", + "y_train = y = y[window:]\n", + "# plot the whole series and three windows with an offset\n", + "# Plot full series\n", + "plt.figure(figsize=(12, 6))\n", + "plt.plot(y, label=\"Original Series\", color=\"black\")\n", + "\n", + "# Vertical offset for clarity\n", + "offset = 300 # adjust depending on your data's scale\n", + "# Indices of the windows to plot\n", + "window_indices = [0, 9, 19]\n", + "# Plot selected windowed series\n", + "for idx, i in enumerate(window_indices):\n", + " start = i\n", + " end = i + 50\n", + " plt.plot(\n", + " np.arange(start, end), X_train[i] - offset * (idx + 1), label=f\"Window {i+1}\"\n", + " )\n", + "\n", + "# Remove y-axis\n", + "plt.gca().axes.get_yaxis().set_visible(False)\n", + "\n", + "plt.legend()\n", + "plt.title(\"Airline Series with Selected Sliding Windows (1st, 10th, 200th)\")\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.grid(True)\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "id": "3c7e66fea3ec413e", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 37 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-07T16:02:35.782114Z", + "start_time": "2025-06-07T16:02:35.777553Z" + } + }, + "cell_type": "code", + "source": [ + "print(X_train.shape)\n", + "print(y_train.shape)\n", + "print(X_test.shape)" + ], + "id": "41ee1832519edeab", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(94, 50)\n", + "(94,)\n", + "(1, 50)\n" + ] + } + ], + "execution_count": 38 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "We can now build a regression model and make a prediction. This can be \n", + "a generic `sklearn` such as `LinearRegression` or `RandomForestRegressor` or any of \n", + "the large number of time series specific regressors in `aeon` such as `DrCIFRegressor`" + ], + "id": "ab970e7a82ff6f15" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-07T16:02:42.494790Z", + "start_time": "2025-06-07T16:02:37.913729Z" + } + }, + "cell_type": "code", + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "\n", + "from aeon.regression.interval_based import DrCIFRegressor\n", + "\n", + "lr = LinearRegression()\n", + "dr = DrCIFRegressor(n_estimators=10)\n", + "lr.fit(X_train, y_train)\n", + "dr.fit(X_train, y_train)\n", + "p1 = lr.predict(X_test)\n", + "p2 = dr.predict(X_test)\n", + "print(f\"LR predicts {p1} DrCIF predicts {p2}\")" + ], + "id": "5e202a07621a1167", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LR predicts [463.60934602] DrCIF predicts [425.5]\n" + ] + } + ], + "execution_count": 39 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "All of this goes on inside `RegressionForecaster`, which defaults to using \n", + "`LinearRegression` with a forecasting horizon of 1. You have to pass the window \n", + "length to use to the constructor. " + ], + "id": "7bc8af4d75fe3eb9" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-07T16:02:55.217185Z", + "start_time": "2025-06-07T16:02:51.260893Z" + } + }, + "cell_type": "code", + "source": [ + "from aeon.datasets import load_airline\n", + "from aeon.forecasting import RegressionForecaster\n", + "\n", + "airline = load_airline()\n", + "rf = RegressionForecaster(window=50)\n", + "rf.fit(airline)\n", + "p3 = rf.predict()\n", + "print(f\" Forecast for airline with linear regression = {p1} and {p3}\")\n", + "rf2 = RegressionForecaster(regressor=DrCIFRegressor(n_estimators=10), window=50)\n", + "rf2.fit(airline)\n", + "p4 = rf.predict()\n", + "print(f\" Forecast for airline with DrCIF = {p1} and {p3}\")" + ], + "id": "bdc35a7a671ee254", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Forecast for airline with linear regression = [463.60934602] and 463.6093460189972\n", + " Forecast for airline with DrCIF = [463.60934602] and 463.6093460189972\n" + ] + } + ], + "execution_count": 40 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "You can see all the available regressors in aeon like this. They are all documented \n", + "in the API and have examples " + ], + "id": "9c91e124c193963e" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-07T16:03:30.898674Z", + "start_time": "2025-06-07T16:03:08.930864Z" + } + }, + "cell_type": "code", + "source": [ + "from aeon.utils.discovery import all_estimators\n", + "\n", + "all_estimators(\"regressor\")" + ], + "id": "1df31d7824f6d7b4", + "outputs": [ + { + "data": { + "text/plain": [ + "[('CanonicalIntervalForestRegressor',\n", + " aeon.regression.interval_based._cif.CanonicalIntervalForestRegressor),\n", + " ('Catch22Regressor', aeon.regression.feature_based._catch22.Catch22Regressor),\n", + " ('DisjointCNNRegressor',\n", + " aeon.regression.deep_learning._disjoint_cnn.DisjointCNNRegressor),\n", + " ('DrCIFRegressor', aeon.regression.interval_based._drcif.DrCIFRegressor),\n", + " ('DummyRegressor', aeon.regression._dummy.DummyRegressor),\n", + " ('EncoderRegressor', aeon.regression.deep_learning._encoder.EncoderRegressor),\n", + " ('FCNRegressor', aeon.regression.deep_learning._fcn.FCNRegressor),\n", + " ('FreshPRINCERegressor',\n", + " aeon.regression.feature_based._fresh_prince.FreshPRINCERegressor),\n", + " ('HydraRegressor', aeon.regression.convolution_based._hydra.HydraRegressor),\n", + " ('InceptionTimeRegressor',\n", + " aeon.regression.deep_learning._inception_time.InceptionTimeRegressor),\n", + " ('IndividualInceptionRegressor',\n", + " aeon.regression.deep_learning._inception_time.IndividualInceptionRegressor),\n", + " ('IndividualLITERegressor',\n", + " aeon.regression.deep_learning._lite_time.IndividualLITERegressor),\n", + " ('IntervalForestRegressor',\n", + " aeon.regression.interval_based._interval_forest.IntervalForestRegressor),\n", + " ('KNeighborsTimeSeriesRegressor',\n", + " aeon.regression.distance_based._time_series_neighbors.KNeighborsTimeSeriesRegressor),\n", + " ('LITETimeRegressor',\n", + " aeon.regression.deep_learning._lite_time.LITETimeRegressor),\n", + " ('MLPRegressor', aeon.regression.deep_learning._mlp.MLPRegressor),\n", + " ('MiniRocketRegressor',\n", + " aeon.regression.convolution_based._minirocket.MiniRocketRegressor),\n", + " ('MultiRocketHydraRegressor',\n", + " aeon.regression.convolution_based._mr_hydra.MultiRocketHydraRegressor),\n", + " ('MultiRocketRegressor',\n", + " aeon.regression.convolution_based._multirocket.MultiRocketRegressor),\n", + " ('QUANTRegressor', aeon.regression.interval_based._quant.QUANTRegressor),\n", + " ('RDSTRegressor', aeon.regression.shapelet_based._rdst.RDSTRegressor),\n", + " ('RISTRegressor', aeon.regression.hybrid._rist.RISTRegressor),\n", + " ('RandomIntervalRegressor',\n", + " aeon.regression.interval_based._interval_pipelines.RandomIntervalRegressor),\n", + " ('RandomIntervalSpectralEnsembleRegressor',\n", + " aeon.regression.interval_based._rise.RandomIntervalSpectralEnsembleRegressor),\n", + " ('RegressorEnsemble', aeon.regression.compose._ensemble.RegressorEnsemble),\n", + " ('RegressorPipeline', aeon.regression.compose._pipeline.RegressorPipeline),\n", + " ('ResNetRegressor', aeon.regression.deep_learning._resnet.ResNetRegressor),\n", + " ('RocketRegressor',\n", + " aeon.regression.convolution_based._rocket.RocketRegressor),\n", + " ('SklearnRegressorWrapper',\n", + " aeon.regression.sklearn._wrapper.SklearnRegressorWrapper),\n", + " ('SummaryRegressor', aeon.regression.feature_based._summary.SummaryRegressor),\n", + " ('TSFreshRegressor', aeon.regression.feature_based._tsfresh.TSFreshRegressor),\n", + " ('TimeCNNRegressor', aeon.regression.deep_learning._cnn.TimeCNNRegressor),\n", + " ('TimeSeriesForestRegressor',\n", + " aeon.regression.interval_based._tsf.TimeSeriesForestRegressor)]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 41 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "All aeon forecasters predict a single value. If you want to forecast a range of \n", + "values ahead you should use the functions `recursive_foreacast` or `direct_forecast` \n", + "(notebooks coming soon). " + ], + "id": "939c3a82b06e1b95" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "4bf0708606952c16" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 3f6ef5fd1e0a4d47a046f924246bcad6022f86af Mon Sep 17 00:00:00 2001 From: "allcontributors[bot]" <46447321+allcontributors[bot]@users.noreply.github.com> Date: Mon, 9 Jun 2025 22:33:14 +0100 Subject: [PATCH 146/216] =?UTF-8?q?=F0=9F=93=9D=20Add=20AnaghDeshpande=20a?= =?UTF-8?q?s=20a=20contributor=20for=20doc=20(#2896)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 📝 Update CONTRIBUTORS.md [skip ci] * 📝 Update .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> --- .all-contributorsrc | 9 +++++++ CONTRIBUTORS.md | 63 +++++++++++++++++++++++---------------------- 2 files changed, 41 insertions(+), 31 deletions(-) diff --git a/.all-contributorsrc b/.all-contributorsrc index 8efaa0b03d..20f6b6696a 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -2738,6 +2738,15 @@ "contributions": [ "doc" ] + }, + { + "login": "AnaghDeshpande", + "name": "Anagh.B.Deshpande", + "avatar_url": "https://avatars.githubusercontent.com/u/125550290?v=4", + "profile": "https://github.com/AnaghDeshpande", + "contributions": [ + "doc" + ] } ], "commitType": "docs" diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md index dbdf1ff53e..5725df941a 100644 --- a/CONTRIBUTORS.md +++ b/CONTRIBUTORS.md @@ -1,7 +1,7 @@ # Contributors -[![All Contributors](https://img.shields.io/badge/all_contributors-268-orange.svg)](#contributors) +[![All Contributors](https://img.shields.io/badge/all_contributors-269-orange.svg)](#contributors) This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! @@ -45,15 +45,16 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d An Hoang
An Hoang
🐛 💻 + Anagh.B.Deshpande
Anagh.B.Deshpande
📖 Andreas Kanz
Andreas Kanz
✅ André Guarnier De Mitri
André Guarnier De Mitri
💻 Angus Dempster
Angus Dempster
💻 ⚠️ ✅ Antoine Guillaume
Antoine Guillaume
💻 📖 Antoni Baum
Antoni Baum
💻 Aparna Sakshi
Aparna Sakshi
💻 - Arelo Tanoh
Arelo Tanoh
📖 + Arelo Tanoh
Arelo Tanoh
📖 Arepalli Yashwanth Reddy
Arepalli Yashwanth Reddy
💻 🐛 📖 Arik Ermshaus
Arik Ermshaus
💻 Arnav
Arnav
💻 @@ -61,9 +62,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Ayushmaan Seth
Ayushmaan Seth
💻 👀 ⚠️ 📖 📋 ✅ BANDASAITEJAREDDY
BANDASAITEJAREDDY
💻 📖 Badr-Eddine Marani
Badr-Eddine Marani
💻 - Balgopal Moharana
Balgopal Moharana
💻 + Balgopal Moharana
Balgopal Moharana
💻 Benedikt Heidrich
Benedikt Heidrich
💻 Benjamin Bluhm
Benjamin Bluhm
💻 📖 💡 Bhaskar Dhariyal
Bhaskar Dhariyal
💻 ⚠️ @@ -71,9 +72,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Bohan Zhang
Bohan Zhang
💻 Bouke Postma
Bouke Postma
💻 🐛 🤔 Brian Murphy
Brian Murphy
📖 - Carlos Borrajo
Carlos Borrajo
💻 📖 + Carlos Borrajo
Carlos Borrajo
💻 📖 Carlos Ramos Carreño
Carlos Ramos Carreño
📖 Chang Wei Tan
Chang Wei Tan
💻 Cheuk Ting Ho
Cheuk Ting Ho
💻 @@ -81,9 +82,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Christopher Dahlin
Christopher Dahlin
💻 Christopher Lo
Christopher Lo
💻 🤔 Chuanhang Qiu
Chuanhang Qiu
💻 - Ciaran Gilbert
Ciaran Gilbert
🐛 💻 📖 ⚠️ 🤔 + Ciaran Gilbert
Ciaran Gilbert
🐛 💻 📖 ⚠️ 🤔 ClaudiaSanches
ClaudiaSanches
💻 ⚠️ Corvin Paul
Corvin Paul
📖 Cyril Meyer
Cyril Meyer
⚠️ 📖 💻 @@ -91,9 +92,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Daniel L.
Daniel L.
📖 Daniel Martín Martínez
Daniel Martín Martínez
📖 🐛 Daniel Roberts
Daniel Roberts
📖 - Daniele Carli
Daniele Carli
📖 + Daniele Carli
Daniele Carli
📖 Dave Hirschfeld
Dave Hirschfeld
🚇 David Buchaca Prats
David Buchaca Prats
💻 David Guijo-Rubio
David Guijo-Rubio
💻 🤔 @@ -101,9 +102,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Dmitriy Valetov
Dmitriy Valetov
💻 ✅ Doug Ollerenshaw
Doug Ollerenshaw
📖 Drishti Bhasin
Drishti Bhasin
💻 - Dylan Sherry
Dylan Sherry
🚇 + Dylan Sherry
Dylan Sherry
🚇 Emilia Rose
Emilia Rose
💻 ⚠️ Emmanuel Ferdman
Emmanuel Ferdman
📖 Er Jie Yong
Er Jie Yong
🐛 💻 @@ -111,9 +112,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Eyal Shafran
Eyal Shafran
💻 Federico Garza
Federico Garza
💻 💡 Felix Claessen
Felix Claessen
💻 📖 ⚠️ 🐛 - Ferdinand Rewicki
Ferdinand Rewicki
💻 🐛 + Ferdinand Rewicki
Ferdinand Rewicki
💻 🐛 Florian Stinner
Florian Stinner
💻 ⚠️ Francesco Spinnato
Francesco Spinnato
💻 Franz Kiraly
Franz Kiraly
🐛 💼 💻 📖 🎨 📋 💡 💵 🔍 🤔 🚧 🧑‍🏫 📆 💬 👀 📢 ⚠️ ✅ 📹 @@ -121,9 +122,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Futuer
Futuer
📖 Gabriel Riegner
Gabriel Riegner
📖 Galina Chernikova
Galina Chernikova
💻 - George Langley
George Langley
📖 + George Langley
George Langley
📖 George Oastler
George Oastler
💻 ⚠️ 📦 💡 📖 Gilberto Barbosa
Gilberto Barbosa
💻 Grace Gao
Grace Gao
💻 🐛 @@ -131,9 +132,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Guzal Bulatova
Guzal Bulatova
🐛 💻 📋 🧑‍🏫 📆 👀 ⚠️ HYang1996
HYang1996
💻 ⚠️ 📖 ✅ HaroonAzamFiza
HaroonAzamFiza
📖 - Harshitha Sudhakar
Harshitha Sudhakar
📖 💻 + Harshitha Sudhakar
Harshitha Sudhakar
📖 💻 Hedeer El Showk
Hedeer El Showk
🐛 📖 💻 Huayi Wei
Huayi Wei
✅ Ifeanyi30
Ifeanyi30
💻 @@ -141,9 +142,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Ilyas Moutawwakil
Ilyas Moutawwakil
💻 📖 Ireoluwatomiwa
Ireoluwatomiwa
📖 Ishan Nangia
Ishan Nangia
🤔 - Ivan Knyazev
Ivan Knyazev
📖 + Ivan Knyazev
Ivan Knyazev
📖 Jack Russon
Jack Russon
💻 James Large
James Large
💻 📖 ⚠️ 🚇 🚧 James Morrill
James Morrill
💻 @@ -151,9 +152,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Jason Lines
Jason Lines
💻 💼 📖 🎨 📋 🔍 🤔 📆 💬 👀 📢 💡 Jason Mok
Jason Mok
📖 Jason Pong
Jason Pong
💻 ⚠️ - Jaume Mateu
Jaume Mateu
💻 + Jaume Mateu
Jaume Mateu
💻 JonathanBechtel
JonathanBechtel
💻 🤔 ⚠️ Joren Hammudoglu
Joren Hammudoglu
🚇 Juan Orduz
Juan Orduz
✅ 📖 @@ -161,9 +162,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Juliana
Juliana
💻 Justin Shenk
Justin Shenk
📖 Kai Lion
Kai Lion
💻 ⚠️ 📖 - Kavin Anand
Kavin Anand
📖 + Kavin Anand
Kavin Anand
📖 Kavya Rambhia
Kavya Rambhia
💻 Kejsi Take
Kejsi Take
💻 Kevin Lam
Kevin Lam
💻 💡 ⚠️ @@ -171,9 +172,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Kirstie Whitaker
Kirstie Whitaker
🤔 🔍 Kishan Manani
Kishan Manani
💻 📖 ⚠️ 🐛 🤔 Krum Arnaudov
Krum Arnaudov
🐛 💻 - Kutay Koralturk
Kutay Koralturk
💻 🐛 + Kutay Koralturk
Kutay Koralturk
💻 🐛 Leonidas Tsaprounis
Leonidas Tsaprounis
💻 🐛 🧑‍🏫 👀 Lielle Ravid
Lielle Ravid
💻 📖 Logan Duffy
Logan Duffy
💻 📖 ⚠️ 🐛 🤔 @@ -181,9 +182,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Lorenzo Toniazzi
Lorenzo Toniazzi
💻 Lovkush
Lovkush
💻 ⚠️ 🤔 🧑‍🏫 📆 Luca Bennett
Luca Bennett
💻 📖 ⚠️ - Luis Ventura
Luis Ventura
💻 + Luis Ventura
Luis Ventura
💻 Luis Zugasti
Luis Zugasti
📖 Lukasz Mentel
Lukasz Mentel
💻 📖 🚇 ⚠️ 🐛 🚧 🧑‍🏫 Marcelo Trylesinski
Marcelo Trylesinski
📖 @@ -191,9 +192,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Margaret Gorlin
Margaret Gorlin
💻 💡 ⚠️ Mariam Jabara
Mariam Jabara
💻 Marielle
Marielle
📖 💻 🤔 - Markus Löning
Markus Löning
💻 ⚠️ 🚧 📦 👀 🚇 💡 🐛 ✅ 💼 📖 🎨 📋 🔍 🤔 📆 💬 📢 🧑‍🏫 📹 + Markus Löning
Markus Löning
💻 ⚠️ 🚧 📦 👀 🚇 💡 🐛 ✅ 💼 📖 🎨 📋 🔍 🤔 📆 💬 📢 🧑‍🏫 📹 Martin Walter
Martin Walter
💻 🐛 📆 🔍 🧑‍🏫 🤔 🎨 👀 📖 📢 Martina G. Vilas
Martina G. Vilas
👀 🤔 Matthew Middlehurst
Matthew Middlehurst
🐛 💻 🔣 📖 🎨 💡 🤔 🚇 🚧 🧑‍🏫 📣 💬 🔬 👀 ⚠️ ✅ 📢 @@ -201,9 +202,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Miao Cai
Miao Cai
🐛 💻 Michael F. Mbouopda
Michael F. Mbouopda
💻 🐛 📖 Michael Feil
Michael Feil
💻 ⚠️ 🤔 - Michal Chromcak
Michal Chromcak
💻 📖 ⚠️ ✅ + Michal Chromcak
Michal Chromcak
💻 📖 ⚠️ ✅ Mirae Parker
Mirae Parker
💻 ⚠️ Mohammed Saif Kazamel
Mohammed Saif Kazamel
🐛 Morad :)
Morad :)
💻 ⚠️ 📖 @@ -211,9 +212,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Márcio A. Freitas Jr
Márcio A. Freitas Jr
📖 Niek van der Laan
Niek van der Laan
💻 Nikhil Gupta
Nikhil Gupta
💻 🐛 📖 - Nikola Shahpazov
Nikola Shahpazov
📖 + Nikola Shahpazov
Nikola Shahpazov
📖 Nilesh Kumar
Nilesh Kumar
💻 Nima Nooshiri
Nima Nooshiri
📖 Ninnart Fuengfusin
Ninnart Fuengfusin
💻 @@ -221,9 +222,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Oleksandr Shchur
Oleksandr Shchur
🐛 💻 Oleksii Kachaiev
Oleksii Kachaiev
💻 ⚠️ Oliver Matthews
Oliver Matthews
💻 - Patrick Müller
Patrick Müller
💻 + Patrick Müller
Patrick Müller
💻 Patrick Rockenschaub
Patrick Rockenschaub
💻 🎨 🤔 ⚠️ Patrick Schäfer
Patrick Schäfer
💻 ✅ Paul
Paul
📖 @@ -231,9 +232,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Paul Yim
Paul Yim
💻 💡 ⚠️ Philip
Philip
📖 Philipp Kortmann
Philipp Kortmann
💻 📖 - Phillip Wenig
Phillip Wenig
💻 + Phillip Wenig
Phillip Wenig
💻 Piyush Gade
Piyush Gade
💻 👀 Pulkit Verma
Pulkit Verma
📖 Quaterion
Quaterion
🐛 @@ -241,9 +242,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Rakshitha Godahewa
Rakshitha Godahewa
💻 📖 Ramana Raja
Ramana Raja
💻 RavenRudi
RavenRudi
💻 - Raya Chakravarty
Raya Chakravarty
📖 + Raya Chakravarty
Raya Chakravarty
📖 Rick van Hattem
Rick van Hattem
🚇 Rishabh Bali
Rishabh Bali
💻 Rishav Kumar Sinha
Rishav Kumar Sinha
📖 @@ -251,9 +252,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Riya Elizabeth John
Riya Elizabeth John
💻 ⚠️ 📖 Ronnie Llamado
Ronnie Llamado
📖 Ryan Kuhns
Ryan Kuhns
💻 📖 ✅ 💡 🤔 👀 ⚠️ - Sagar Mishra
Sagar Mishra
⚠️ + Sagar Mishra
Sagar Mishra
⚠️ Sajaysurya Ganesh
Sajaysurya Ganesh
💻 📖 🎨 💡 🤔 ⚠️ ✅ Saransh Chopra
Saransh Chopra
📖 🚇 Satya Prakash Pattnaik
Satya Prakash Pattnaik
📖 @@ -261,9 +262,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Sebastiaan Koel
Sebastiaan Koel
💻 📖 Sebastian Hagn
Sebastian Hagn
📖 Sebastian Schmidl
Sebastian Schmidl
🐛 💻 📖 🔬 ⚠️ 👀 🔣 - Sharathchenna
Sharathchenna
💻 + Sharathchenna
Sharathchenna
💻 Shivansh Subramanian
Shivansh Subramanian
📖 💻 Solomon Botchway
Solomon Botchway
🚧 Somtochukwu Benedict Onyekwelu
Somtochukwu Benedict Onyekwelu
📖 @@ -271,9 +272,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Stijn Rotman
Stijn Rotman
💻 Svea Marie Meyer
Svea Marie Meyer
📖 💻 Sylvain Combettes
Sylvain Combettes
💻 🐛 - TNTran92
TNTran92
💻 + TNTran92
TNTran92
💻 Taiwo Owoseni
Taiwo Owoseni
💻 Tanish Yelgoe
Tanish Yelgoe
💻 Thach Le Nguyen
Thach Le Nguyen
💻 ⚠️ @@ -281,9 +282,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Thomas Buckley-Houston
Thomas Buckley-Houston
🐛 Tina Jin
Tina Jin
💻 📖 Tom Xu
Tom Xu
💻 📖 - Tomasz Chodakowski
Tomasz Chodakowski
💻 📖 🐛 + Tomasz Chodakowski
Tomasz Chodakowski
💻 📖 🐛 Tony Bagnall
Tony Bagnall
💻 💼 📖 🎨 📋 🔍 🤔 📆 💬 👀 📢 🔣 Tvisha Vedant
Tvisha Vedant
💻 Utkarsh Kumar
Utkarsh Kumar
💻 📖 @@ -291,9 +292,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Vedant
Vedant
📖 Viktor Dremov
Viktor Dremov
💻 ViktorKaz
ViktorKaz
💻 📖 🎨 - Vyomkesh Vyas
Vyomkesh Vyas
💻 📖 💡 ⚠️ + Vyomkesh Vyas
Vyomkesh Vyas
💻 📖 💡 ⚠️ Wayne Adams
Wayne Adams
📖 William Templier
William Templier
📖 William Zeng
William Zeng
🐛 @@ -301,9 +302,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Yair Beer
Yair Beer
💻 Yash Lamba
Yash Lamba
💻 Yi-Xuan Xu
Yi-Xuan Xu
💻 ⚠️ 🚧 📖 - Ziyao Wei
Ziyao Wei
💻 + Ziyao Wei
Ziyao Wei
💻 aa25desh
aa25desh
💻 🐛 abandus
abandus
🤔 💻 adoherty21
adoherty21
🐛 @@ -311,9 +312,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d bethrice44
bethrice44
🐛 💻 👀 ⚠️ big-o
big-o
💻 ⚠️ 🎨 🤔 👀 ✅ 🧑‍🏫 bobbys
bobbys
💻 - brett koonce
brett koonce
📖 + brett koonce
brett koonce
📖 btrtts
btrtts
📖 chizzi25
chizzi25
📝 chrisholder
chrisholder
💻 ⚠️ 📖 🎨 💡 🐛 @@ -321,9 +322,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d hamzahiqb
hamzahiqb
🚇 hiqbal2
hiqbal2
📖 jesellier
jesellier
💻 - jschemm
jschemm
💻 + jschemm
jschemm
💻 julu98
julu98
🐛 kkoziara
kkoziara
💻 🐛 matteogales
matteogales
💻 🎨 🤔 @@ -331,9 +332,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d nileenagp
nileenagp
💻 oleskiewicz
oleskiewicz
💻 📖 ⚠️ pabworks
pabworks
💻 ⚠️ - patiently pending world peace
patiently pending world peace
💻 + patiently pending world peace
patiently pending world peace
💻 raishubham1
raishubham1
📖 simone-pignotti
simone-pignotti
💻 🐛 sophijka
sophijka
📖 🚧 @@ -341,9 +342,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d tensorflow-as-tf
tensorflow-as-tf
💻 vNtzYy
vNtzYy
🐛 ved pawar
ved pawar
📖 - vedazeren
vedazeren
💻 ⚠️ + vedazeren
vedazeren
💻 ⚠️ vincent-nich12
vincent-nich12
💻 vollmersj
vollmersj
📖 xiaobenbenecho
xiaobenbenecho
💻 From 9e66f1691d25fcae4891683ccd01f88a7aefe8a5 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Tue, 10 Jun 2025 19:37:24 +0100 Subject: [PATCH 147/216] [GOV] Use the NumFOCUS code of conduct and rename `aeon` workgroup (#2852) * code of conduct update and readmes * typo --------- Co-authored-by: Sebastian Schmidl <10573700+SebastianSchmidl@users.noreply.github.com> --- .github/ISSUE_TEMPLATE/config.yml | 4 +- .../utilities/generate_developer_tables.py | 22 +++--- CHANGELOG.md | 2 +- CODE_OF_CONDUCT.md | 70 ++++++++++++++++--- CONTRIBUTING.md | 9 ++- GOVERNANCE.md | 2 +- docs/about.md | 8 +-- ...group.md => code_of_conduct_moderators.md} | 0 docs/code_of_conduct.md | 45 +----------- docs/governance.md | 20 +++--- 10 files changed, 101 insertions(+), 81 deletions(-) rename docs/about/{code_of_conduct_workgroup.md => code_of_conduct_moderators.md} (100%) diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml index cf805d095a..2457184a77 100644 --- a/.github/ISSUE_TEMPLATE/config.yml +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -9,5 +9,5 @@ contact_links: url: https://mailxto.com/jbp3ave49x about: Send an email to the aeon developers at contact@aeon-toolkit.org. Checked periodically. - name: "\u2709\uFE0F CoC Report" - url: https://www.aeon-toolkit.org/en/latest/code_of_conduct.html - about: Report an incident to the Code of Conduct Workgroup. + url: https://www.aeon-toolkit.org/en/stable/code_of_conduct.html + about: Report an incident to the Code of Conduct Moderators. diff --git a/.github/utilities/generate_developer_tables.py b/.github/utilities/generate_developer_tables.py index cbce50c830..992f1b7988 100755 --- a/.github/utilities/generate_developer_tables.py +++ b/.github/utilities/generate_developer_tables.py @@ -42,8 +42,8 @@ def get(url, auth): def get_contributors(auth): """Get the list of contributor profiles. Require admin rights.""" - cocw = [] - cocw_slug = "aeon-code-of-conduct-workgroup" + cocm = [] + cocm_slug = "aeon-code-of-conduct-moderators" cw = [] cw_slug = "aeon-communications-workgroup" cd = [] @@ -60,15 +60,15 @@ def get_contributors(auth): entry_point = "https://api.github.com/orgs/aeon-toolkit/" for team_slug, lst in zip( - (cocw_slug, cw_slug, cd_slug, fw_slug, iw_slug, rmw_slug, sd_slug), - (cocw, cw, cd, fw, iw, rmw, sd), + (cocm_slug, cw_slug, cd_slug, fw_slug, iw_slug, rmw_slug, sd_slug), + (cocm, cw, cd, fw, iw, rmw, sd), ): for page in range(5): # 5 pages, 30 per page reply = get(f"{entry_point}teams/{team_slug}/members?page={page}", auth) lst.extend(reply.json()) # keep only the logins - cocw = {c["login"] for c in cocw} + cocm = {c["login"] for c in cocm} cw = {c["login"] for c in cw} cd = {c["login"] for c in cd} fw = {c["login"] for c in fw} @@ -77,7 +77,7 @@ def get_contributors(auth): sd = {c["login"] for c in sd} # get profiles from GitHub - cocw = [get_profile(login, auth) for login in cocw] + cocm = [get_profile(login, auth) for login in cocm] cw = [get_profile(login, auth) for login in cw] cd = [get_profile(login, auth) for login in cd] fw = [get_profile(login, auth) for login in fw] @@ -86,7 +86,7 @@ def get_contributors(auth): sd = [get_profile(login, auth) for login in sd] # sort by last name - cocw = sorted(cocw, key=key) + cocm = sorted(cocm, key=key) cw = sorted(cw, key=key) cd = sorted(cd, key=key) fw = sorted(fw, key=key) @@ -95,7 +95,7 @@ def get_contributors(auth): sd = sorted(sd, key=key) return ( - cocw, + cocm, cw, cd, fw, @@ -154,7 +154,7 @@ def generate_table(contributors): auth = ("user", token) ( - cocw, + cocm, cw, cd, fw, @@ -164,11 +164,11 @@ def generate_table(contributors): ) = get_contributors(auth) with open( - REPO_FOLDER / "docs" / "about" / "code_of_conduct_workgroup.md", + REPO_FOLDER / "docs" / "about" / "code_of_conduct_moderators.md", "w+", encoding="utf-8", ) as rst_file: - rst_file.write(generate_table(cocw)) + rst_file.write(generate_table(cocm)) with open( REPO_FOLDER / "docs" / "about" / "communications_workgroup.md", diff --git a/CHANGELOG.md b/CHANGELOG.md index 7ac62da711..4c9f0b716e 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,6 @@ # Changelog -You can find the aeon changelog on our [website](https://www.aeon-toolkit.org/en/stable/changelog.html). +You can find the `aeon` changelog pages on our [website](https://www.aeon-toolkit.org/en/stable/changelog.html). `aeon` adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html>) and the source code for all [releases](https://github.com/aeon-toolkit/aeon/releases>) is available on GitHub. diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md index 090d0e8683..65e78adb08 100644 --- a/CODE_OF_CONDUCT.md +++ b/CODE_OF_CONDUCT.md @@ -1,12 +1,66 @@ # Code of Conduct -You can find our full Code of Conduct (CoC) on our [website](https://www.aeon-toolkit.org/en/stable/code_of_conduct.html). +We value the participation of every member in our community and want to +ensure that every contributor has an enjoyable experience. Accordingly, those who +participate in the `aeon` project are expected to show respect and courtesy to other +community members at all times. -The code of conduct aims to ensure a positive and respectful experience for all -members of the community. Community members should follow the principles set out in -the [Python Software Foundation Code of Conduct](https://www.python.org/psf/codeofconduct/). +`aeon` uses the NumFOCUS Code of Conduct (CoC). You can find the [whole document +here](https://numfocus.org/code-of-conduct). -If you want to make a CoC report, contact the general `aeon` email -[contact@aeon-toolkit.org](mailto:contact@aeon-toolkit.org) or message a member of the -[Code of Conduct Workgroup (COCW)](https://www.aeon-toolkit.org/en/stable/about.html#code-of-conduct-workgroup) -directly on [Slack](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-36dlmbouu-vajTShUYAHopSXUUVtHGzw). +Everyone participating in activities associated with the `aeon` project (whether +in-person or remote) is required to conform to this Code of Conduct regardless +of platform. + +## The Short Version + +`aeon` and NumFOCUS are dedicated to providing a harassment-free community for everyone, +regardless of gender, sexual orientation, gender identity and expression, disability, +physical appearance, body size, race, or religion. We do not tolerate harassment of +community members in any form. + +Be kind to others. Do not insult or put down others. Behave professionally. Remember +that harassment and sexist, racist, or exclusionary jokes are not appropriate for +`aeon` or NumFOCUS. + +All communication should be appropriate for a professional audience, including people +of many different backgrounds. Sexual language and imagery are not appropriate. + +Thank you for helping make this a welcoming, friendly community for all. + +## How to Report + +If you feel that the Code of Conduct has been violated, feel free to Contact a member of +the `aeon` [Code of Conduct Moderators](https://www.aeon-toolkit.org/en/stable/about.html#code-of-conduct-moderators) +directly on [Slack](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-36dlmbouu-vajTShUYAHopSXUUVtHGzw) +or through the general `aeon` email ([contact@aeon-toolkit.org](mailto:contact@aeon-toolkit.org)). + +OR + +Submit a report directly to the NumFOCUS Code of Conduct Working Group by using +[NumFOCUS Code of Conduct Reporting Form](https://numfocus.typeform.com/to/ynjGdT?typeform-source=numfocus.org). + +## Who Will Receive Your Report + +If you have contacted the `aeon` Code of Conduct Moderators, the group will decide +whether to take action directly or forward it to the NumFOCUS Code of Conduct Working +Group. The workgroup may take immediate action if the situation requires it (e.g. +deleting a post, banning a user from a channel, etc.). + +Using the NumFOCUS form, your report will be received and handled by NumFOCUS Code of +Conduct Working Group; trained and experienced contributors with diverse backgrounds. +The group is making decisions independently of `aeon`, PyData, NumFOCUS or any other +organisation. You can learn more about the current NumFOCUS group members, as well as +the reporting procedure [here](https://numfocus.org/code-of-conduct). + +## Actions Taken on Reports + +Final decision-making on any report actions lies with the `aeon` Code of Conduct +Moderators. In most cases, however, the moderators will enforce the recommendations +made by the NumFOCUS Code of Conduct Working Group. If you are unhappy with a decision +made directly by the `aeon` Code of Conduct Moderators, you can contact the NumFOCUS +Code of Conduct Working Group directly through the reporting form. + +Breaking the Code of Conduct may result in a range of actions, from a warning +to removal from `aeon` and its community spaces depending on the severity of the +action and prior history of violations. diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 82dc058c3a..6cd7028687 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,3 +1,8 @@ -# Contributing guide +# Contributing Guide -You can find our contributing guide on our [website](https://www.aeon-toolkit.org/en/stable/contributing.html). +`aeon` is a community-driven project and contributions are most welcome. We value +all kinds of contributions, not just code. Improvements to docs, bug reports, and +taking on communications or code of conduct responsibilities are all examples of +valuable contributions beyond code which help make aeon a great package. + +You can find our full contributing guide on our [website](https://www.aeon-toolkit.org/en/stable/contributing.html). diff --git a/GOVERNANCE.md b/GOVERNANCE.md index b5c198d373..0ea65677ee 100644 --- a/GOVERNANCE.md +++ b/GOVERNANCE.md @@ -9,7 +9,7 @@ engaging with the community, and steering the project. Tasks are delegated to workgroups, which are groups of core developers and contributors assigned by core developers to fulfil a specific role. -aeon's decision-making process is transparent and consensus-based. +`aeon`'s decision-making process is transparent and consensus-based. Most decisions involve seeking consensus among contributors and core developers. If consensus is not reached, a vote can be called, requiring a two-thirds majority of core developers for approval. The process emphasises transparency and open diff --git a/docs/about.md b/docs/about.md index 798e6e8d44..ada6d723f1 100644 --- a/docs/about.md +++ b/docs/about.md @@ -5,12 +5,12 @@ The roles are described in `aeon`'s [governance](governance.md) document. A list of all contributors can be found [here](contributors.md). -### Code of Conduct Workgroup +### Code of Conduct Moderators -The code of conduct workgroup maintains the code of conduct and handles any -issues related that arise. +The code of conduct moderators maintain the code of conduct and make decisions on any +code of conduct related issues. -```{include} about/code_of_conduct_workgroup.md +```{include} about/code_of_conduct_moderators.md ``` ### Communications Workgroup diff --git a/docs/about/code_of_conduct_workgroup.md b/docs/about/code_of_conduct_moderators.md similarity index 100% rename from docs/about/code_of_conduct_workgroup.md rename to docs/about/code_of_conduct_moderators.md diff --git a/docs/code_of_conduct.md b/docs/code_of_conduct.md index 65e3835ac6..1c46998a47 100644 --- a/docs/code_of_conduct.md +++ b/docs/code_of_conduct.md @@ -1,43 +1,2 @@ -# Code of Conduct - -We value the participation of every member in our community and want to -ensure that every contributor has an enjoyable experience. Accordingly, those who -participate in the `aeon` project are expected to show respect and courtesy to other -community members at all times. - -Everyone participating in activities associated with the `aeon` project (whether -in-person or remote) is required to conform to this Code of Conduct (CoC) regardless -of platform. - -It is expected that community members will abide by the principles of the [Python -Software Foundation Code of Conduct](https://www.python.org/psf/codeofconduct/) to -help maintain an open, considerate and respectful environment. - -Unacceptable behaviour and code of conduct violations are determined and dealt with -at the discretion of the `aeon` [Code of Conduct Workgroup (COCW)](./governance.md#code-of-conduct-workgroup). -Possible actions taken include warnings, mediated meeting and/or apologies, and -expulsion from the project depending on the severity of the breach. The COCW can edit -and remove comments and other contributions that are not aligned to this Code of -Conduct. - -Examples of unacceptable behaviour include (but are not limited to): -- Personal attacks, insults and threats -- Harassment of individuals or groups both publicly and privately -- Publishing private information of others, such as physical or electronic -addresses, without explicit permission -- The use of sexualised language or imagery in conversation or talks -- Jokes or insults based on stereotypes, slurs or other exclusionary language -- Continued one-on-one communication after requests to cease -- General unethical and unprofessional conduct - -Community members asked to stop any inappropriate behaviour are expected to comply -immediately. - -If you believe someone is violating the CoC, we ask that you report it by -contacting the COCW. All reports will be kept confidential. You can make a report by -emailing the general `aeon` contact address [contact@aeon-toolkit.org](mailto:contact@aeon-toolkit.org), -or by messaging [members of the COCW](./about.md#code-of-conduct-workgroup) -directly on [Slack](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-36dlmbouu-vajTShUYAHopSXUUVtHGzw). - -Thank you for reading and helping make this a welcoming, friendly community for -everyone. +```{include} ../CODE_OF_CONDUCT.md +``` diff --git a/docs/governance.md b/docs/governance.md index 5f65142a3c..5fd698bbb9 100644 --- a/docs/governance.md +++ b/docs/governance.md @@ -100,17 +100,19 @@ responsible for managing access to the `aeon` email address. To help manage GitHub discussions, the communication workgroup is given triage access to the `aeon` GitHub repository. -#### Code of Conduct Workgroup +#### Code of Conduct Moderators -The Code of Conduct Workgroup (CoCW) consists of contributors tasked with making sure -`aeon` remains a welcoming and inclusive community. CoCW responsibilities include -maintaining the `aeon` Code of Conduct (CoC) and managing reports of breaking the CoC. -CoCW members are expected to review reports of CoC violations, contact and discuss with -involved individuals and make recommendations on actions to take. +The Code of Conduct Moderators (CoCM) consists of contributors tasked with making sure +`aeon` remains a welcoming and inclusive community. CoCM responsibilities include +ensuring the Code of Conduct (CoC) remains up to date, keeping contact with the +NumFOCUS Code of Conduct Workgroup and managing reports of breaking the CoC. +CoCM members are expected to review reports of CoC violations, refer cases to the +NumFOCUS workgroup when applicable and make decisions on report actions while +consulting with the greater community. -Any CoCW members involved in a CoC report or CoCW members which have a conflict of +Any CoCM members involved in a CoC report or CoCM members which have a conflict of interest regarding the report are expected to recuse themselves from the process. The -CoCW is given triage access to the `aeon` GitHub repository to moderate discussions if +CoCM is given triage access to the `aeon` GitHub repository to moderate discussions if necessary. ## Decision-Making Process @@ -165,7 +167,7 @@ submitted to the community for discussion and comment. ## Acknowledgements -Significant portions of this document were adapted from or inspired by the following +Portions of this document were adapted from or inspired by the following projects governance documents: - [Scikit-learn](https://scikit-learn.org/stable/governance.html) From 2a5485b63a6f748af057acc28750835a88090b82 Mon Sep 17 00:00:00 2001 From: "Anagh.B.Deshpande" <125550290+AnaghDeshpande@users.noreply.github.com> Date: Wed, 11 Jun 2025 18:41:25 +0530 Subject: [PATCH 148/216] [DOC] resolved the inconsistency of double ticks for the anomaly detection module #809 (#2546) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * resolved the inconsistency of double quotes * resolved the inconsistency of double ticks * resolved the pre-commit errors occuring due to flake8 * Update _cblof.py made changes in line number 19 and 145. * Automatic `pre-commit` fixes * Update _copod.py made changes in line number 76 * Automatic `pre-commit` fixes * Update _iforest.py made changes in line number 19 * Automatic `pre-commit` fixes * Update _kmeans.py made changes in line number 178 * Automatic `pre-commit` fixes * Update _merlin.py made changes in line number 215 * Automatic `pre-commit` fixes * Update _one_class_svm.py made changes in line number 19 * Automatic `pre-commit` fixes * Update _pyodadapter.py made changes in line number 165 * Update _stomp.py made changes in line number 125 * Automatic `pre-commit` fixes * converted directives to single ticks * added double ticks to code ref * fixed link in cblof.py and some other minor changes * Update base.py --------- Co-authored-by: Patrick Schäfer Co-authored-by: Matthew Middlehurst --- aeon/anomaly_detection/series/_pyodadapter.py | 11 +++--- .../series/distance_based/_cblof.py | 32 +++++++++-------- .../series/distance_based/_kmeans.py | 5 +-- .../series/distance_based/_lof.py | 12 +++---- .../series/distance_based/_merlin.py | 5 +-- .../series/distance_based/_stomp.py | 5 +-- .../series/distribution_based/_copod.py | 11 +++--- .../series/distribution_based/_dwt_mlead.py | 5 +-- .../series/outlier_detection/_iforest.py | 36 +++++++++---------- .../outlier_detection/_one_class_svm.py | 14 ++++---- 10 files changed, 72 insertions(+), 64 deletions(-) diff --git a/aeon/anomaly_detection/series/_pyodadapter.py b/aeon/anomaly_detection/series/_pyodadapter.py index 67da96359a..41cec57897 100644 --- a/aeon/anomaly_detection/series/_pyodadapter.py +++ b/aeon/anomaly_detection/series/_pyodadapter.py @@ -40,9 +40,9 @@ class PyODAdapter(BaseSeriesAnomalyDetector): target time series with the same number of dimensions. The reference (or training) time series does not need to be clean for most PyOD models. However, knowledge in form of anomaly labels about the potential existing anomalies in the reference time - series are not used during the fitting process. Use `fit` to fit the model on the - reference time series and `predict` to detect anomalies in the target time series. - For unsupervised anomaly detection, use `fit_predict` directly on the target time + series are not used during the fitting process. Use ``fit`` to fit the model on the + reference time series and ``predict`` to detect anomalies in the target time series. + For unsupervised anomaly detection, use ``fit_predict`` directly on the target time series. @@ -160,14 +160,15 @@ def _get_test_params(cls, parameter_set="default"): ---------- parameter_set : str, default="default" Name of the set of test parameters to return, for use in tests. If no - special parameters are defined for a value, will return `"default"` set. + special parameters are defined for a value, will return ``"default"`` set. Returns ------- params : dict or list of dict, default={} Parameters to create testing instances of the class. Each dict are parameters to construct an "interesting" test instance, i.e., - `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid + test instance. """ _check_soft_dependencies(*cls._tags["python_dependencies"]) diff --git a/aeon/anomaly_detection/series/distance_based/_cblof.py b/aeon/anomaly_detection/series/distance_based/_cblof.py index 61c59d4e61..83ca9dc39b 100644 --- a/aeon/anomaly_detection/series/distance_based/_cblof.py +++ b/aeon/anomaly_detection/series/distance_based/_cblof.py @@ -16,15 +16,16 @@ class CBLOF(PyODAdapter): This class implements the CBLOF algorithm for anomaly detection using PyODAdadpter to be used in the aeon framework. All parameters are passed to - the PyOD model ``CBLOF`` except for `window_size` and `stride`, which are used to - construct the sliding windows. + the PyOD model ``CBLOF`` except for ``window_size`` and ``stride``, + which are used to construct the sliding windows. The documentation for parameters has been adapted from the - [PyOD documentation](https://pyod.readthedocs.io/en/latest/pyod.models.html#id117). - Here, `X` refers to the set of sliding windows extracted from the time series + `PyOD documentation `_ + + Here, ``X`` refers to the set of sliding windows extracted from the time series using :func:`aeon.utils.windowing.sliding_windows` with the parameters - ``window_size`` and ``stride``. The internal `X` has the shape - `(n_windows, window_size * n_channels)`. + ``window_size`` and ``stride``. The internal ``X`` has the shape + ``(n_windows, window_size * n_channels)``. Parameters ---------- @@ -52,21 +53,21 @@ class CBLOF(PyODAdapter): beta : int or float in (1,), default=5 Coefficient for deciding small and large clusters. For a list - sorted clusters by size `|C1|, \|C2|, ..., |Cn|, beta = |Ck|/|Ck-1|` + sorted clusters by size ``|C1|, |C2|, ..., |Cn|, beta = |Ck|/|Ck-1|`` use_weights : bool, default=False - If set to True, the size of clusters are used as weights in + If set to ``True``, the size of clusters are used as weights in outlier score calculation. check_estimator : bool, default=False - If set to True, check whether the base estimator is consistent with + If set to ``True``, check whether the base estimator is consistent with sklearn standard. random_state : int, np.RandomState or None, default=None - If int, random_state is the seed used by the random + If ``int``, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random - number generator; If None, the random number generator is the - RandomState instance used by `np.random`. + number generator; If ``None``, the random number generator is the + RandomState instance used by ``np.random``. window_size : int, default=10 Size of the sliding window. @@ -138,15 +139,16 @@ def _get_test_params(cls, parameter_set="default"): ---------- parameter_set : str, default="default" Name of the set of test parameters to return, for use in tests. If no - special parameters are defined for a value, will return `"default"` set. + special parameters are defined for a value, will return ``"default"`` set. Returns ------- params : dict Parameters to create testing instances of the class. Each dict are parameters to construct an "interesting" test instance, i.e., - `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. - `create_test_instance` uses the first (or only) dictionary in `params`. + ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid test + instance. ``create_test_instance`` uses the first (or only) dictionary + in ``params``. """ return { "n_clusters": 4, diff --git a/aeon/anomaly_detection/series/distance_based/_kmeans.py b/aeon/anomaly_detection/series/distance_based/_kmeans.py index 2f9447ca46..e5d2884374 100644 --- a/aeon/anomaly_detection/series/distance_based/_kmeans.py +++ b/aeon/anomaly_detection/series/distance_based/_kmeans.py @@ -171,14 +171,15 @@ def _get_test_params(cls, parameter_set="default"): ---------- parameter_set : str, default="default" Name of the set of test parameters to return, for use in tests. If no - special parameters are defined for a value, will return `"default"` set. + special parameters are defined for a value, will return ``"default"`` set. Returns ------- dict or list of dict, default={} Parameters to create testing instances of the class. Each dict are parameters to construct an "interesting" test instance, i.e., - `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid + test instance. """ return { "n_clusters": 5, diff --git a/aeon/anomaly_detection/series/distance_based/_lof.py b/aeon/anomaly_detection/series/distance_based/_lof.py index 8d92a7d529..8c7e2a77bb 100644 --- a/aeon/anomaly_detection/series/distance_based/_lof.py +++ b/aeon/anomaly_detection/series/distance_based/_lof.py @@ -19,15 +19,15 @@ class LOF(PyODAdapter): The documentation for parameters has been adapted from the [PyOD documentation](https://pyod.readthedocs.io/en/latest/pyod.models.html#id586). - Here, `X` refers to the set of sliding windows extracted from the time series + Here, ``X`` refers to the set of sliding windows extracted from the time series using :func:`aeon.utils.windowing.sliding_windows` with the parameters - ``window_size`` and ``stride``. The internal `X` has the shape - `(n_windows, window_size * n_channels)`. + ``window_size`` and ``stride``. The internal ``X`` has the shape + ``(n_windows, window_size * n_channels)``. Parameters ---------- n_neighbors : int, optional (default=20) - Number of neighbors to use by default for `kneighbors` queries. + Number of neighbors to use by default for ``kneighbors`` queries. If n_neighbors is larger than the number of samples provided, all samples will be used. algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional @@ -36,11 +36,11 @@ class LOF(PyODAdapter): - 'kd_tree' will use KDTree - 'brute' will use a brute-force search. - 'auto' will attempt to decide the most appropriate algorithm - based on the values passed to :meth:`fit` method. + based on the values passed to :meth:``fit`` method. Note: fitting on sparse input will override the setting of this parameter, using brute force. leaf_size : int, optional (default=30) - Leaf size passed to `BallTree` or `KDTree`. This can + Leaf size passed to ``BallTree`` or ``KDTree``. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem. diff --git a/aeon/anomaly_detection/series/distance_based/_merlin.py b/aeon/anomaly_detection/series/distance_based/_merlin.py index aa749383ab..444b5bf53b 100644 --- a/aeon/anomaly_detection/series/distance_based/_merlin.py +++ b/aeon/anomaly_detection/series/distance_based/_merlin.py @@ -210,13 +210,14 @@ def _get_test_params(cls, parameter_set="default"): ---------- parameter_set : str, default="default" Name of the set of test parameters to return, for use in tests. If no - special parameters are defined for a value, will return `"default"` set. + special parameters are defined for a value, will return ``"default"`` set. Returns ------- params : dict or list of dict, default={} Parameters to create testing instances of the class. Each dict are parameters to construct an "interesting" test instance, i.e., - `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid + test instance. """ return {"min_length": 4, "max_length": 7} diff --git a/aeon/anomaly_detection/series/distance_based/_stomp.py b/aeon/anomaly_detection/series/distance_based/_stomp.py index a1f7ab2ab5..b65e4068dd 100644 --- a/aeon/anomaly_detection/series/distance_based/_stomp.py +++ b/aeon/anomaly_detection/series/distance_based/_stomp.py @@ -117,14 +117,15 @@ def _get_test_params(cls, parameter_set="default"): ---------- parameter_set : str, default="default" Name of the set of test parameters to return, for use in tests. If no - special parameters are defined for a value, will return `"default"` set. + special parameters are defined for a value, will return ``"default"`` set. Returns ------- params : dict or list of dict, default={} Parameters to create testing instances of the class. Each dict are parameters to construct an "interesting" test instance, i.e., - `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid + test instance. """ return { "window_size": 10, diff --git a/aeon/anomaly_detection/series/distribution_based/_copod.py b/aeon/anomaly_detection/series/distribution_based/_copod.py index d34826e93f..b1bdc70063 100644 --- a/aeon/anomaly_detection/series/distribution_based/_copod.py +++ b/aeon/anomaly_detection/series/distribution_based/_copod.py @@ -15,8 +15,8 @@ class COPOD(PyODAdapter): """COPOD for anomaly detection. This class implements the COPOD using PyODAdadpter to be used in the aeon framework. - The parameter `n_jobs` is passed to COPOD model from PyOD, `window_size` and - `stride` are used to construct the sliding windows. + The parameter ``n_jobs`` is passed to COPOD model from PyOD, ``window_size`` and + ``stride`` are used to construct the sliding windows. Parameters ---------- @@ -69,14 +69,15 @@ def _get_test_params(cls, parameter_set="default") -> dict: ---------- parameter_set : str, default="default" Name of the set of test parameters to return, for use in tests. If no - special parameters are defined for a value, will return `"default"` set. + special parameters are defined for a value, will return ``"default"`` set. Returns ------- params : dict or list of dict, default={} Parameters to create testing instances of the class. Each dict are parameters to construct an "interesting" test instance, i.e., - `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. - `create_test_instance` uses the first (or only) dictionary in `params`. + ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid test + instance. ``create_test_instance`` uses the first (or only) dictionary + in ``params``. """ return {} diff --git a/aeon/anomaly_detection/series/distribution_based/_dwt_mlead.py b/aeon/anomaly_detection/series/distribution_based/_dwt_mlead.py index d8da12c980..899c1e9fce 100644 --- a/aeon/anomaly_detection/series/distribution_based/_dwt_mlead.py +++ b/aeon/anomaly_detection/series/distribution_based/_dwt_mlead.py @@ -237,14 +237,15 @@ def _get_test_params(cls, parameter_set="default"): ---------- parameter_set : str, default="default" Name of the set of test parameters to return, for use in tests. If no - special parameters are defined for a value, will return `"default"` set. + special parameters are defined for a value, will return ``"default"`` set. Returns ------- params : dict or list of dict, default={} Parameters to create testing instances of the class. Each dict are parameters to construct an "interesting" test instance, i.e., - `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid + test instance. """ return { "start_level": 2, diff --git a/aeon/anomaly_detection/series/outlier_detection/_iforest.py b/aeon/anomaly_detection/series/outlier_detection/_iforest.py index 00e550400c..945ba346de 100644 --- a/aeon/anomaly_detection/series/outlier_detection/_iforest.py +++ b/aeon/anomaly_detection/series/outlier_detection/_iforest.py @@ -16,15 +16,15 @@ class IsolationForest(PyODAdapter): This class implements the Isolation Forest algorithm for anomaly detection using PyODAdadpter to be used in the aeon framework. All parameters are passed to - the PyOD model ``IForest`` except for `window_size` and `stride`, which are used to - construct the sliding windows. + the PyOD model ``IForest`` except for ``window_size`` and ``stride``, + which are used to construct the sliding windows. The documentation for parameters has been adapted from the [PyOD documentation](https://pyod.readthedocs.io/en/latest/pyod.models.html#id405). - Here, `X` refers to the set of sliding windows extracted from the time series + Here, ``X`` refers to the set of sliding windows extracted from the time series using :func:`aeon.utils.windowing.sliding_windows` with the parameters - ``window_size`` and ``stride``. The internal `X` has the shape - `(n_windows, window_size * n_channels)`. + ``window_size`` and ``stride``. The internal ``X`` has the shape + ``(n_windows, window_size * n_channels)``. Parameters ---------- @@ -34,9 +34,9 @@ class IsolationForest(PyODAdapter): max_samples : int, float or "auto", default="auto" The number of samples to draw from X to train each base estimator. - - If int, then draw `max_samples` samples. - - If float, then draw `max_samples * X.shape[0]` samples. - - If "auto", then `max_samples=min(256, n_samples)`. + - If ``int``, then draw ``max_samples`` samples. + - If ``float``, then draw ``max_samples * X.shape[0]`` samples. + - If ``"auto"``, then ``max_samples=min(256, n_samples)``. If max_samples is larger than the number of samples provided, all samples will be used for all trees (no sampling). @@ -44,24 +44,24 @@ class IsolationForest(PyODAdapter): max_features : int or float, default=1.0 The number of features to draw from X to train each base estimator. - - If int, then draw `max_features` features. - - If float, then draw `max_features * X.shape[1]` features. + - If ``int``, then draw ``max_features`` features. + - If ``float``, then draw ``max_features * X.shape[1]`` features. bootstrap : bool, default=False - If True, individual trees are fit on random subsets of the training + If ``True``, individual trees are fit on random subsets of the training data sampled with replacement. If False, sampling without replacement is performed. n_jobs : int, default=1 - The number of jobs to run in parallel for both `fit` and `predict`. - If -1, then the number of jobs is set to the number of cores. + The number of jobs to run in parallel for both ``fit`` and ``predict``. + If ``-1``, then the number of jobs is set to the number of cores. random_state : int, np.RandomState or None, default=None - If int, random_state is the seed used by the random number generator; + If ``int``, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; - If None, the random number generator is the RandomState instance used - by `np.random`. + If ``None``, the random number generator is the RandomState instance used + by ``np.random``. verbose : int, default=0 Controls the verbosity of the tree building process. @@ -138,14 +138,14 @@ def _get_test_params(cls, parameter_set="default"): ---------- parameter_set : str, default="default" Name of the set of test parameters to return, for use in tests. If no - special parameters are defined for a value, will return `"default"` set. + special parameters are defined for a value, will return ``"default"`` set. Returns ------- params : dict Parameters to create testing instances of the class. Each dict are parameters to construct an "interesting" test instance, i.e., - `IsolationForest(**params)` creates a valid test instance. + ``IsolationForest(**params)`` creates a valid test instance. """ return { "n_estimators": 10, diff --git a/aeon/anomaly_detection/series/outlier_detection/_one_class_svm.py b/aeon/anomaly_detection/series/outlier_detection/_one_class_svm.py index 5161900582..1518435267 100644 --- a/aeon/anomaly_detection/series/outlier_detection/_one_class_svm.py +++ b/aeon/anomaly_detection/series/outlier_detection/_one_class_svm.py @@ -16,15 +16,15 @@ class OneClassSVM(BaseSeriesAnomalyDetector): This class implements the OneClassSVM algorithm for anomaly detection from sklearn to be used in the aeon framework. All parameters are passed to - the sklearn ``OneClassSVM`` except for `window_size` and `stride`, which are used to - construct the sliding windows. + the sklearn ``OneClassSVM`` except for ``window_size`` and ``stride``, + which are used to construct the sliding windows. The documentation for parameters has been adapted from (https://scikit-learn.org/dev/modules/generated/sklearn.svm.OneClassSVM.html). - Here, `X` refers to the set of sliding windows extracted from the time series + Here, ``X`` refers to the set of sliding windows extracted from the time series using :func:`aeon.utils.windowing.sliding_windows` with the parameters - ``window_size`` and ``stride``. The internal `X` has the shape - `(n_windows, window_size * n_channels)`. + ``window_size`` and ``stride``. The internal ``X`` has the shape + ``(n_windows, window_size * n_channels)``. Parameters ---------- @@ -43,8 +43,8 @@ class OneClassSVM(BaseSeriesAnomalyDetector): - if ``gamma='scale'`` (default) is passed then it uses 1 / (n_features * X.var()) as value of gamma, - - if 'auto', uses 1 / n_features - - if float, must be non-negative. + - if ``"auto"``, uses 1 / n_features + - if ``float``, must be non-negative. .. versionchanged:: 0.22 The default value of ``gamma`` changed from 'auto' to 'scale'. From c69fd75825c229aecafe2a289ffd8a2a68e3246f Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Wed, 11 Jun 2025 15:05:47 +0100 Subject: [PATCH 149/216] [ENH] Forecasting testing (#2891) * call forecasting checks * basic cleanup * fix * add assert error messages * remove fit_is_empty check (see #2893) * remove extra argument to fit * remove extra argument to predict * correct forecast argument * Update _yield_forecasting_checks.py * add test for predict --------- Co-authored-by: Tony Bagnall --- .../_yield_anomaly_detection_checks.py | 2 +- .../_yield_estimator_checks.py | 9 ++ .../_yield_forecasting_checks.py | 89 ++++++++++++------- 3 files changed, 68 insertions(+), 32 deletions(-) diff --git a/aeon/testing/estimator_checking/_yield_anomaly_detection_checks.py b/aeon/testing/estimator_checking/_yield_anomaly_detection_checks.py index 850e4601e3..ac5a3045f4 100644 --- a/aeon/testing/estimator_checking/_yield_anomaly_detection_checks.py +++ b/aeon/testing/estimator_checking/_yield_anomaly_detection_checks.py @@ -38,7 +38,7 @@ def check_anomaly_detector_overrides_and_tags(estimator_class): # Test that fit_is_empty is correctly set fit_is_empty = estimator_class.get_class_tag(tag_name="fit_is_empty") - assert not fit_is_empty == "_fit" not in estimator_class.__dict__ + assert fit_is_empty == ("_fit" not in estimator_class.__dict__) # Test valid tag for X_inner_type X_inner_type = estimator_class.get_class_tag(tag_name="X_inner_type") diff --git a/aeon/testing/estimator_checking/_yield_estimator_checks.py b/aeon/testing/estimator_checking/_yield_estimator_checks.py index 9fd6d922b1..c4fc3b81dd 100644 --- a/aeon/testing/estimator_checking/_yield_estimator_checks.py +++ b/aeon/testing/estimator_checking/_yield_estimator_checks.py @@ -21,6 +21,7 @@ from aeon.classification.early_classification import BaseEarlyClassifier from aeon.clustering import BaseClusterer from aeon.clustering.deep_learning.base import BaseDeepClusterer +from aeon.forecasting import BaseForecaster from aeon.regression import BaseRegressor from aeon.regression.deep_learning.base import BaseDeepRegressor from aeon.segmentation import BaseSegmenter @@ -40,6 +41,9 @@ from aeon.testing.estimator_checking._yield_early_classification_checks import ( _yield_early_classification_checks, ) +from aeon.testing.estimator_checking._yield_forecasting_checks import ( + _yield_forecasting_checks, +) from aeon.testing.estimator_checking._yield_multithreading_checks import ( _yield_multithreading_checks, ) @@ -158,6 +162,11 @@ def _yield_all_aeon_checks( estimator_class, estimator_instances, datatypes ) + if issubclass(estimator_class, BaseForecaster): + yield from _yield_forecasting_checks( + estimator_class, estimator_instances, datatypes + ) + if issubclass(estimator_class, BaseTransformer): yield from _yield_transformation_checks( estimator_class, estimator_instances, datatypes diff --git a/aeon/testing/estimator_checking/_yield_forecasting_checks.py b/aeon/testing/estimator_checking/_yield_forecasting_checks.py index e8dbaa0dd7..0a2fc3bea2 100644 --- a/aeon/testing/estimator_checking/_yield_forecasting_checks.py +++ b/aeon/testing/estimator_checking/_yield_forecasting_checks.py @@ -2,50 +2,77 @@ from functools import partial -import numpy as np - from aeon.base._base import _clone_estimator -from aeon.utils.data_types import VALID_SERIES_INPUT_TYPES +from aeon.testing.testing_data import FULL_TEST_DATA_DICT +from aeon.utils.data_types import VALID_SERIES_INNER_TYPES def _yield_forecasting_checks(estimator_class, estimator_instances, datatypes): """Yield all forecasting checks for an aeon forecaster.""" # only class required - yield partial(check_forecasting_base_functionality, estimator_class=estimator_class) + yield partial(check_forecaster_overrides_and_tags, estimator_class=estimator_class) # test class instances - for _, estimator in enumerate(estimator_instances): - # no data needed - yield partial(check_forecaster_instance, estimator=estimator) - - -def check_forecasting_base_functionality(estimator_class): - """Test compliance with the base class contract.""" - # Test they dont override final methods, because python does not enforce this - assert "fit" not in estimator_class.__dict__ - assert "predict" not in estimator_class.__dict__ - assert "forecast" not in estimator_class.__dict__ - fit_is_empty = estimator_class.get_class_tag(tag_name="fit_is_empty") - assert not fit_is_empty == "_fit" not in estimator_class.__dict__ + for i, estimator in enumerate(estimator_instances): + # test all data types + for datatype in datatypes[i]: + yield partial( + check_forecaster_output, + estimator=estimator, + datatype=datatype, + ) + + +def check_forecaster_overrides_and_tags(estimator_class): + """Test compliance with the forecaster base class contract.""" + # Test they don't override final methods, because Python does not enforce this + final_methods = ["fit", "predict", "forecast"] + for method in final_methods: + if method in estimator_class.__dict__: + raise ValueError( + f"Forecaster {estimator_class} overrides the " + f"method {method}. Override _{method} instead." + ) + + # Test that all forecasters implement abstract predict. + assert "_predict" in estimator_class.__dict__ + + # todo decide what to do with "fit_is_empty" and abstract "_fit" + # Test valid tag for X_inner_type X_inner_type = estimator_class.get_class_tag(tag_name="X_inner_type") - assert X_inner_type in VALID_SERIES_INPUT_TYPES + if isinstance(X_inner_type, str): + assert X_inner_type in VALID_SERIES_INNER_TYPES + else: # must be a list + assert all([t in VALID_SERIES_INNER_TYPES for t in X_inner_type]) + # Must have at least one set to True multi = estimator_class.get_class_tag(tag_name="capability:multivariate") uni = estimator_class.get_class_tag(tag_name="capability:univariate") - assert multi or uni + assert multi or uni, ( + "At least one of tag capability:multivariate or " + "capability:univariate must be true." + ) -def check_forecaster_instance(estimator): - """Test forecasters.""" +def check_forecaster_output(estimator, datatype): + """Test the forecaster output on valid data.""" estimator = _clone_estimator(estimator) - pass - # Sort - # Check output correct: predict should return a float - y = np.array([0.5, 0.7, 0.8, 0.9, 1.0]) - estimator.fit(y) - p = estimator.predict() - assert isinstance(p, float) - # forecast should return a float equal to fit/predict - p2 = estimator.forecast(y) - assert p == p2 + + estimator.fit( + FULL_TEST_DATA_DICT[datatype]["train"][0], + ) + y_pred = estimator.predict() + assert isinstance(y_pred, float), ( + f"predict() output should be float, got" f" {type(y_pred)}" + ) + + y_pred2 = estimator.forecast(FULL_TEST_DATA_DICT[datatype]["train"][0]) + assert y_pred == y_pred2, ( + f"predict() and forecast() output differ: {y_pred} !=" f" {y_pred2}" + ) + y_pred3 = estimator.predict(FULL_TEST_DATA_DICT[datatype]["train"][0]) + assert y_pred == y_pred3, ( + f"after fit(), predict() and predict(y_train) should be the same, but" + f"output differ: {y_pred} != {y_pred3}" + ) From a1e9dc04b79238541e9861806ee0d5a3e06d3387 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Sat, 14 Jun 2025 14:58:42 +0100 Subject: [PATCH 150/216] [MNT] Fixing CI issues and separate doctests from regular CI runners (#2902) * trying to fix CI stuff * use numba in codecov * try cov * no cov * doctest-plus * doctest-plus * addopts * dedicated doctest test * periodic --- .github/actions/cpu_all_extras/action.yml | 2 +- .github/workflows/periodic_tests.yml | 38 +++++++++++++++- .github/workflows/pr_examples.yml | 3 +- .github/workflows/pr_pytest.yml | 42 ++++++++++++++++-- .github/workflows/release.yml | 4 +- .../collection/dictionary_based/_borf.py | 4 +- pyproject.toml | 44 ++++++++++--------- 7 files changed, 104 insertions(+), 33 deletions(-) diff --git a/.github/actions/cpu_all_extras/action.yml b/.github/actions/cpu_all_extras/action.yml index 91fab623aa..dd772a0421 100644 --- a/.github/actions/cpu_all_extras/action.yml +++ b/.github/actions/cpu_all_extras/action.yml @@ -15,7 +15,7 @@ runs: using: "composite" steps: - name: Install CPU TensorFlow - if: ${{ runner.os == 'Linux' && inputs.python_version != '3.13' }} + if: ${{ runner.os == 'Linux' && inputs.python_version != '3.13.5' }} uses: nick-fields/retry@v3 with: timeout_minutes: 30 diff --git a/.github/workflows/periodic_tests.yml b/.github/workflows/periodic_tests.yml index 49d5f6c4e8..ef941711d7 100644 --- a/.github/workflows/periodic_tests.yml +++ b/.github/workflows/periodic_tests.yml @@ -163,7 +163,7 @@ jobs: fail-fast: false matrix: os: [ ubuntu-24.04, macOS-14, windows-2022 ] - python-version: [ "3.10", "3.11", "3.12", "3.13" ] + python-version: [ "3.10", "3.11", "3.12", "3.13.5" ] steps: - name: Checkout @@ -178,7 +178,7 @@ jobs: if: runner.os == 'Linux' uses: pierotofy/set-swap-space@v1.0 with: - swap-size-gb: 10 + swap-size-gb: 8 - name: Use numba cache to set env variables but not restore cache uses: ./.github/actions/numba_cache @@ -208,6 +208,40 @@ jobs: # Save cache with the current date (ENV set in numba_cache action) key: numba-pytest-${{ runner.os }}-${{ matrix.python-version}}-${{ env.CURRENT_DATE }} + doctests: + runs-on: ubuntu-24.04 + + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Setup Python 3.12 + uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - if: ${{ github.event_name != 'pull_request' || !contains(github.event.pull_request.labels.*.name, 'no numba cache') }} + name: Use numba cache to set env variables but not restore cache + uses: ./.github/actions/numba_cache + with: + cache_name: "doctests" + runner_os: ${{ runner.os }} + python_version: "3.12" + restore_cache: "false" + + - name: Install aeon and dependencies + uses: nick-fields/retry@v3 + with: + timeout_minutes: 30 + max_attempts: 3 + command: python -m pip install .[all_extras,dev] + + - name: Show dependencies + run: python -m pip list + + - name: Run tests + run: python -m pytest -n logical --doctest-only + codecov: runs-on: ubuntu-24.04 diff --git a/.github/workflows/pr_examples.yml b/.github/workflows/pr_examples.yml index 2c9ff05ab1..62035f4260 100644 --- a/.github/workflows/pr_examples.yml +++ b/.github/workflows/pr_examples.yml @@ -41,7 +41,8 @@ jobs: runner_os: ${{ runner.os }} python_version: "3.12" - - uses: ./.github/actions/cpu_all_extras + - name: Install aeon and dependencies + uses: ./.github/actions/cpu_all_extras with: additional_extras: "dev,binder" diff --git a/.github/workflows/pr_pytest.yml b/.github/workflows/pr_pytest.yml index 024812281d..77ff1d2f58 100644 --- a/.github/workflows/pr_pytest.yml +++ b/.github/workflows/pr_pytest.yml @@ -55,7 +55,7 @@ jobs: fail-fast: false matrix: os: [ ubuntu-24.04, macOS-14, windows-2022 ] - python-version: [ "3.10", "3.11", "3.12", "3.13" ] + python-version: [ "3.10", "3.11", "3.12", "3.13.5" ] # skip python versions unless the PR has the 'full pytest actions' label pr-testing: - ${{ (github.event_name == 'pull_request' && !contains(github.event.pull_request.labels.*.name, 'full pytest actions')) }} @@ -76,7 +76,7 @@ jobs: if: runner.os == 'Linux' uses: pierotofy/set-swap-space@v1.0 with: - swap-size-gb: 10 + swap-size-gb: 8 - if: ${{ github.event_name != 'pull_request' || !contains(github.event.pull_request.labels.*.name, 'no numba cache') }} name: Restore numba cache @@ -86,7 +86,8 @@ jobs: runner_os: ${{ runner.os }} python_version: ${{ matrix.python-version }} - - uses: ./.github/actions/cpu_all_extras + - name: Install aeon and dependencies + uses: ./.github/actions/cpu_all_extras with: python_version: ${{ matrix.python-version }} additional_extras: "dev" @@ -98,6 +99,38 @@ jobs: # run the full test suit if a PR has the 'full pytest actions' label run: python -m pytest -n logical --prtesting ${{ github.event_name == 'pull_request' && !contains(github.event.pull_request.labels.*.name, 'full pytest actions') }} + doctests: + runs-on: ubuntu-24.04 + + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Setup Python 3.12 + uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - if: ${{ github.event_name != 'pull_request' || !contains(github.event.pull_request.labels.*.name, 'no numba cache') }} + name: Restore numba cache + uses: ./.github/actions/numba_cache + with: + cache_name: "doctests" + runner_os: ${{ runner.os }} + python_version: "3.12" + + - name: Install aeon and dependencies + uses: ./.github/actions/cpu_all_extras + with: + additional_extras: "dev" + + - name: Show dependencies + run: python -m pip list + + - name: Run tests + run: python -m pytest -n logical --doctest-only + + codecov: # run the code coverage job if a PR has the 'codecov actions' label if: ${{ github.event_name != 'pull_request' || contains(github.event.pull_request.labels.*.name, 'codecov actions') }} @@ -116,7 +149,8 @@ jobs: - name: Disable Numba JIT run: echo "NUMBA_DISABLE_JIT=1" >> $GITHUB_ENV - - uses: ./.github/actions/cpu_all_extras + - name: Install aeon and dependencies + uses: ./.github/actions/cpu_all_extras with: additional_extras: "unstable_extras,dev" diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 3fe9c6fa27..590c4f9342 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -54,7 +54,7 @@ jobs: fail-fast: false matrix: os: [ ubuntu-24.04, macOS-14, windows-2022 ] - python-version: [ "3.10", "3.11", "3.12", "3.13" ] + python-version: [ "3.10", "3.11", "3.12", "3.13.5" ] steps: - name: Checkout @@ -69,7 +69,7 @@ jobs: if: runner.os == 'Linux' uses: pierotofy/set-swap-space@v1.0 with: - swap-size-gb: 10 + swap-size-gb: 8 - uses: actions/download-artifact@v4 with: diff --git a/aeon/transformations/collection/dictionary_based/_borf.py b/aeon/transformations/collection/dictionary_based/_borf.py index b4d7d3cc22..97efa7c65b 100644 --- a/aeon/transformations/collection/dictionary_based/_borf.py +++ b/aeon/transformations/collection/dictionary_based/_borf.py @@ -615,7 +615,7 @@ def _sax( ) global_std = np.std(a) if global_std == 0: - return np.zeros((n_windows, word_length), dtype=np.uint8) + return np.zeros((n_windows, word_length), dtype=np.uint16) seg_size = window_size // word_length n_windows = _get_n_windows( sequence_size=a.size, window_size=window_size, dilation=dilation, stride=stride @@ -646,7 +646,7 @@ def _sax( sigma_global=global_std, sigma_threshold=min_window_to_signal_std_ratio, ) - return np.digitize(out, bins).astype(np.uint8) + return np.digitize(out, bins).astype(np.uint16) @nb.njit(fastmath=True, cache=True) diff --git a/pyproject.toml b/pyproject.toml index cf3045ca31..1e66656142 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -87,17 +87,16 @@ unstable_extras = [ # development dependencies dev = [ - "backoff", - "httpx", "pre-commit", "pytest", "pytest-cov", + "pytest-doctestplus", "pytest-mock", "pytest-randomly", + "pytest-remotedata", + "pytest-rerunfailures", "pytest-timeout", "pytest-xdist[psutil]", - "pytest-rerunfailures", - "wheel", ] binder = [ "notebook", @@ -166,21 +165,24 @@ convention = "numpy" mypy_path = "aeon/" [tool.pytest.ini_options] -# ignore certain folders and pytest warnings testpaths = "aeon" -addopts = ''' - --doctest-modules - --durations 20 - --timeout 600 - --showlocals - --dist worksteal - --reruns 2 - --only-rerun "crashed while running" - --only-rerun "zipfile.BadZipFile" - --only-rerun "accessible `.keras` zip file." -''' -filterwarnings = ''' - ignore::UserWarning - ignore:numpy.dtype size changed - ignore:numpy.ufunc size changed -''' +doctest_optionflags = [ + "NORMALIZE_WHITESPACE", + "ELLIPSIS", + "FLOAT_CMP", +] +addopts = [ + "--durations=20", + "--timeout=600", + "--showlocals", + "--dist=worksteal", + "--reruns=3", + "--reruns-delay=3", + "--rerun-except=Error", + "--rerun-except=Exception", +] +filterwarnings = [ + "ignore::UserWarning", + "ignore:numpy.dtype size changed", + "ignore:numpy.ufunc size changed", +] From 3235b3576522fe77132a40a822b2f4ff0ca8751a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Patrick=20Sch=C3=A4fer?= Date: Sun, 15 Jun 2025 10:25:06 +0200 Subject: [PATCH 151/216] [BUG] Fixes a bug with SFAFast throwing an error when calling transform after fit (#2897) * Bugfix for SFA_Fast with transform called after fit --- .../collection/dictionary_based/_sfa_fast.py | 2 +- .../dictionary_based/tests/test_sfa.py | 27 ++++++++++++++++++- 2 files changed, 27 insertions(+), 2 deletions(-) diff --git a/aeon/transformations/collection/dictionary_based/_sfa_fast.py b/aeon/transformations/collection/dictionary_based/_sfa_fast.py index 42c1b5d42b..c118ce3878 100644 --- a/aeon/transformations/collection/dictionary_based/_sfa_fast.py +++ b/aeon/transformations/collection/dictionary_based/_sfa_fast.py @@ -329,7 +329,7 @@ def _fit(self, X, y=None): self: object """ # with parallel_backend("loky", inner_max_num_threads=n_jobs): - self._fit_transform(X, y, return_bag_of_words=False) + self._fit_transform(X, y, return_bag_of_words=self.return_sparse) return self def _transform(self, X, y=None): diff --git a/aeon/transformations/collection/dictionary_based/tests/test_sfa.py b/aeon/transformations/collection/dictionary_based/tests/test_sfa.py index 7304f38ee8..1ec2aba892 100644 --- a/aeon/transformations/collection/dictionary_based/tests/test_sfa.py +++ b/aeon/transformations/collection/dictionary_based/tests/test_sfa.py @@ -5,7 +5,8 @@ import numpy as np import pytest -from aeon.transformations.collection.dictionary_based._sfa import SFA +from aeon.datasets import load_unit_test +from aeon.transformations.collection.dictionary_based import SFA, SFAFast @pytest.mark.parametrize( @@ -224,3 +225,27 @@ def test_typed_dict(): word_list2 = p2.bag_to_string(p2.transform(X, y)[0][0]) assert word_list == word_list2 + + +def test_sfa_fast_transform_after_fit(): + """Test transform called after fit returns the same result as fit_transform().""" + X_train, y_train = load_unit_test(split="train") + + # Fit, then transform + sfa = SFAFast() + sfa.fit(X_train, y_train) + x = sfa.transform(X_train, y_train) + + # Fit_transform, then transform + sfa = SFAFast() + sfa.fit_transform(X_train, y_train) + y = sfa.transform(X_train, y_train) + + # Assert that the two csr_matrix are equal + assert ( + x.shape == y.shape + and x.dtype == y.dtype + and np.all(x.indices == y.indices) + and np.all(x.indptr == y.indptr) + and np.allclose(x.data, y.data) + ) From 57207e013bd515e22851870965fb3a1e7251d590 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 16 Jun 2025 10:49:49 +0100 Subject: [PATCH 152/216] [MNT] Bump stefanzweifel/git-auto-commit-action from 5 to 6 in the github-actions group (#2906) * [MNT] Bump stefanzweifel/git-auto-commit-action Bumps the github-actions group with 1 update: [stefanzweifel/git-auto-commit-action](https://github.com/stefanzweifel/git-auto-commit-action). Updates `stefanzweifel/git-auto-commit-action` from 5 to 6 - [Release notes](https://github.com/stefanzweifel/git-auto-commit-action/releases) - [Changelog](https://github.com/stefanzweifel/git-auto-commit-action/blob/master/CHANGELOG.md) - [Commits](https://github.com/stefanzweifel/git-auto-commit-action/compare/v5...v6) --- updated-dependencies: - dependency-name: stefanzweifel/git-auto-commit-action dependency-version: '6' dependency-type: direct:production update-type: version-update:semver-major dependency-group: github-actions ... Signed-off-by: dependabot[bot] * Update issue_comment_edited.yml * Update pr_precommit.yml --------- Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Matthew Middlehurst --- .github/workflows/issue_comment_edited.yml | 3 +-- .github/workflows/pr_precommit.yml | 3 +-- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/.github/workflows/issue_comment_edited.yml b/.github/workflows/issue_comment_edited.yml index 359e15e725..fadf0c1629 100644 --- a/.github/workflows/issue_comment_edited.yml +++ b/.github/workflows/issue_comment_edited.yml @@ -51,10 +51,9 @@ jobs: - if: ${{ steps.label_out.outputs.empty_commit == 'true' }} name: Push empty commit - uses: stefanzweifel/git-auto-commit-action@v5 + uses: stefanzweifel/git-auto-commit-action@v6 with: commit_message: Empty commit for CI commit_user_name: aeon-actions-bot[bot] commit_options: --allow-empty - create_branch: false skip_dirty_check: true diff --git a/.github/workflows/pr_precommit.yml b/.github/workflows/pr_precommit.yml index 7363e44bb2..ffc2e48f4b 100644 --- a/.github/workflows/pr_precommit.yml +++ b/.github/workflows/pr_precommit.yml @@ -56,8 +56,7 @@ jobs: # push fixes if pre-commit fails and PR is eligible - if: ${{ failure() && github.event_name == 'pull_request_target' && !github.event.pull_request.draft && !contains(github.event.pull_request.labels.*.name, 'stop pre-commit fixes') }} name: Push pre-commit fixes - uses: stefanzweifel/git-auto-commit-action@v5 + uses: stefanzweifel/git-auto-commit-action@v6 with: commit_message: Automatic `pre-commit` fixes commit_user_name: aeon-actions-bot[bot] - create_branch: false From 600ecad0d3b1b8f287f4e9984287f6dc912ee6b4 Mon Sep 17 00:00:00 2001 From: Balgopal Moharana <99070111+lucifer4073@users.noreply.github.com> Date: Mon, 16 Jun 2025 18:34:59 +0530 Subject: [PATCH 153/216] [ENH] Added RNN in networks (#2875) * RNN net architecture added * Fixed activation function for both str and list * RNN test mesage corrected * reverted workflow * reverted workflow * requested changes applied * convo resolved --- aeon/networks/__init__.py | 2 + aeon/networks/_rnn.py | 180 ++++++++++ aeon/networks/tests/test_rnn.py | 613 ++++++++++++++++++++++++++++++++ 3 files changed, 795 insertions(+) create mode 100644 aeon/networks/_rnn.py create mode 100644 aeon/networks/tests/test_rnn.py diff --git a/aeon/networks/__init__.py b/aeon/networks/__init__.py index b6dd2d02dd..d774abe102 100644 --- a/aeon/networks/__init__.py +++ b/aeon/networks/__init__.py @@ -18,6 +18,7 @@ "AEDRNNNetwork", "AEBiGRUNetwork", "DisjointCNNNetwork", + "RecurrentNetwork", ] from aeon.networks._ae_abgru import AEAttentionBiGRUNetwork from aeon.networks._ae_bgru import AEBiGRUNetwork @@ -34,4 +35,5 @@ from aeon.networks._lite import LITENetwork from aeon.networks._mlp import MLPNetwork from aeon.networks._resnet import ResNetNetwork +from aeon.networks._rnn import RecurrentNetwork from aeon.networks.base import BaseDeepLearningNetwork diff --git a/aeon/networks/_rnn.py b/aeon/networks/_rnn.py new file mode 100644 index 0000000000..484822f8b4 --- /dev/null +++ b/aeon/networks/_rnn.py @@ -0,0 +1,180 @@ +"""Implements a Recurrent Neural Network (RNN) for time series forecasting.""" + +__maintainer__ = [] + +from aeon.networks.base import BaseDeepLearningNetwork + + +class RecurrentNetwork(BaseDeepLearningNetwork): + """ + Implements a Recurrent Neural Network (RNN) for time series forecasting. + + This implementation provides a flexible RNN architecture that can be configured + to use different types of recurrent cells including Simple RNN, Long Short-Term + Memory (LSTM) [1], and Gated Recurrent Unit (GRU) [2]. The network supports + multiple layers, bidirectional processing, and various dropout configurations + for regularization. + + Parameters + ---------- + rnn_type : str, default='lstm' + Type of RNN cell to use ('lstm', 'gru', or 'simple'). + n_layers : int, default=1 + Number of recurrent layers. + n_units : list or int, default=64 + Number of units in each recurrent layer. If an int, the same number + of units is used in each layer. If a list, specifies the number of + units for each layer and must match the number of layers. + dropout_intermediate : float, default=0.0 + Dropout rate applied after each intermediate recurrent layer (not last layer). + dropout_output : float, default=0.0 + Dropout rate applied after the last recurrent layer. + bidirectional : bool, default=False + Whether to use bidirectional recurrent layers. + activation : str or list of str, default='tanh' + Activation function(s) for the recurrent layers. If a string, the same + activation is used for all layers. If a list, specifies activation for + each layer and must match the number of layers. + return_sequence_last : bool, default=False + Whether the last recurrent layer returns the full sequence (True) + or just the last output (False). + + References + ---------- + .. [1] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. + Neural computation, 9(8), 1735-1780. + .. [2] Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., + Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using + RNN encoder-decoder for statistical machine translation. + arXiv preprint arXiv:1406.1078. + """ + + _config = { + "python_dependencies": ["tensorflow"], + "python_version": "<3.13", + "structure": "encoder", + } + + def __init__( + self, + rnn_type="simple", + n_layers=1, + n_units=64, + dropout_intermediate=0.0, + dropout_output=0.0, + bidirectional=False, + activation="tanh", + return_sequence_last=False, + ): + super().__init__() + self.rnn_type = rnn_type.lower() + self.n_layers = n_layers + self.n_units = n_units + self.dropout_intermediate = dropout_intermediate + self.dropout_output = dropout_output + self.bidirectional = bidirectional + self.activation = activation + self.return_sequence_last = return_sequence_last + self._rnn_cell = None + + def build_network(self, input_shape, **kwargs): + """Construct a network and return its input and output layers. + + Parameters + ---------- + input_shape : tuple + The shape of the data fed into the input layer (n_timepoints, n_features) + kwargs : dict + Additional keyword arguments to be passed to the network + + Returns + ------- + input_layer : a keras layer + output_layer : a keras layer + """ + import tensorflow as tf + + # Validate parameters + if self.rnn_type not in ["lstm", "gru", "simple"]: + raise ValueError( + f"Unknown RNN type: {self.rnn_type}. Should be 'lstm', 'gru' 'simple'" + ) + + # Process n_units to a list + if isinstance(self.n_units, list): + if len(self.n_units) != self.n_layers: + raise ValueError( + f"Number of units {len(self.n_units)} should be" + f" the same as number of layers but is" + f" not: {self.n_layers}" + ) + self._n_units = self.n_units + else: + self._n_units = [self.n_units] * self.n_layers + + # Process activation to a list + if isinstance(self.activation, list): + if len(self.activation) != self.n_layers: + raise ValueError( + f"Number of activations {len(self.activation)} should be" + f" the same as number of layers but is" + f" not: {self.n_layers}" + ) + self._activation = self.activation + else: + self._activation = [self.activation] * self.n_layers + + # Select RNN cell type + if self.rnn_type == "lstm": + self._rnn_cell = tf.keras.layers.LSTM + elif self.rnn_type == "gru": + self._rnn_cell = tf.keras.layers.GRU + else: # simple + self._rnn_cell = tf.keras.layers.SimpleRNN + + # Create input layer + input_layer = tf.keras.layers.Input(shape=input_shape) + x = input_layer + + # Build RNN layers + for i in range(self.n_layers): + # Determine return_sequences for current layer + # All layers except the last must return sequences for stacking + # The last layer uses the return_sequence_last parameter + is_last_layer = i == (self.n_layers - 1) + return_sequences = (not is_last_layer) or self.return_sequence_last + + # Create the recurrent layer + if self.bidirectional: + x = tf.keras.layers.Bidirectional( + self._rnn_cell( + units=self._n_units[i], + activation=self._activation[i], + return_sequences=return_sequences, + name=f"{self.rnn_type}_{i+1}", + ) + )(x) + else: + x = self._rnn_cell( + units=self._n_units[i], + activation=self._activation[i], + return_sequences=return_sequences, + name=f"{self.rnn_type}_{i+1}", + )(x) + + # Add appropriate dropout based on layer position + if is_last_layer: + # Apply output dropout to the last layer + if self.dropout_output > 0: + x = tf.keras.layers.Dropout( + self.dropout_output, name="dropout_output" + )(x) + else: + # Apply intermediate dropout to all layers except the last + if self.dropout_intermediate > 0: + x = tf.keras.layers.Dropout( + self.dropout_intermediate, name=f"dropout_intermediate_{i+1}" + )(x) + + # Return input and output layers + return input_layer, x diff --git a/aeon/networks/tests/test_rnn.py b/aeon/networks/tests/test_rnn.py new file mode 100644 index 0000000000..2041d45431 --- /dev/null +++ b/aeon/networks/tests/test_rnn.py @@ -0,0 +1,613 @@ +"""Tests for the RecurrentNetwork.""" + +import pytest + +from aeon.networks import RecurrentNetwork +from aeon.utils.validation._dependencies import _check_soft_dependencies + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "rnn_type", ["lstm", "gru", "simple", "LSTM", "GRU", "invalid"] +) +def test_rnn_network_rnn_type(rnn_type): + """Test RecurrentNetwork with different RNN types.""" + import tensorflow as tf + + input_shape = (100, 5) + + if rnn_type == "invalid": + with pytest.raises(ValueError, match="Unknown RNN type"): + rnn_network = RecurrentNetwork(rnn_type=rnn_type) + input_layer, output_layer = rnn_network.build_network(input_shape) + else: + rnn_network = RecurrentNetwork(rnn_type=rnn_type) + input_layer, output_layer = rnn_network.build_network(input_shape) + + # Check that layers are created correctly + assert hasattr( + input_layer, "shape" + ), "Input layer should have a shape attribute" + assert hasattr( + output_layer, "shape" + ), "Output layer should have a shape attribute" + assert input_layer.dtype == tf.float32 + assert input_layer.dtype == tf.float32 + + # Create a model to test the network structure + model = tf.keras.Model(inputs=input_layer, outputs=output_layer) + + # Check that the correct RNN type is used by layer name pattern + expected_type = rnn_type.lower() + layer_names = [layer.name for layer in model.layers] + + # Find RNN layer by name pattern + rnn_layer_found = False + for name in layer_names: + if expected_type in name: + rnn_layer_found = True + break + + assert ( + rnn_layer_found + ), f"Expected {expected_type.upper()} layer not found in model" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("n_layers", [1, 2, 3, 5]) +def test_rnn_network_n_layers_valid(n_layers): + """Test RecurrentNetwork with valid number of layers.""" + import tensorflow as tf + + input_shape = (100, 5) + + rnn_network = RecurrentNetwork(rnn_type="simple", n_layers=n_layers) + input_layer, output_layer = rnn_network.build_network(input_shape) + + # Create a model to count layers + model = tf.keras.Model(inputs=input_layer, outputs=output_layer) + + # Count RNN layers by name pattern (simple by default in new implementation) + rnn_layers = [layer for layer in model.layers if "simple" in layer.name] + assert ( + len(rnn_layers) == n_layers + ), f"Expected {n_layers} SimpleRNN layers, found {len(rnn_layers)}" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("n_layers", [0, -1, 2.5, "2"]) +def test_rnn_network_n_layers_invalid(n_layers): + """Test RecurrentNetwork with invalid number of layers.""" + input_shape = (100, 5) + + # The RNN implementation doesn't validate n_layers in __init__, + # but will fail when trying to iterate in build_network + rnn_network = RecurrentNetwork(n_layers=n_layers) + + # For n_layers <= 0, range(n_layers) creates an empty range, + # which means no layers are created, but no error is raised + # The actual behavior depends on the implementation + try: + input_layer, output_layer = rnn_network.build_network(input_shape) + # If no error is raised, check that the output is still valid + assert input_layer is not None + assert output_layer is not None + except (ValueError, IndexError, TypeError): + # This is also acceptable behavior + pass + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "n_units", [32, 64, 128, [32, 64], [64, 32, 16], [32], [64, 32, 16, 8, 4]] +) +def test_rnn_network_n_units(n_units): + """Test RecurrentNetwork with different unit configurations.""" + import tensorflow as tf + + input_shape = (100, 5) + + # Determine expected number of layers + if isinstance(n_units, int): + n_layers = 1 + expected_units = [n_units] + else: + n_layers = len(n_units) + expected_units = n_units + + rnn_network = RecurrentNetwork( + rnn_type="simple", n_layers=n_layers, n_units=n_units + ) + input_layer, output_layer = rnn_network.build_network(input_shape) + + # Create a model to inspect layers + model = tf.keras.Model(inputs=input_layer, outputs=output_layer) + + # Check that units are set correctly by checking layer names and count + rnn_layers = [layer for layer in model.layers if "simple" in layer.name] + assert len(rnn_layers) == len(expected_units) + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("n_units", [[32, 64], [64, 32, 16]]) +def test_rnn_network_n_units_mismatch(n_units): + """Test RecurrentNetwork with mismatched n_units and n_layers.""" + input_shape = (100, 5) + + # Use different n_layers than length of n_units + wrong_n_layers = len(n_units) + 1 + + with pytest.raises(ValueError): + rnn_network = RecurrentNetwork(n_layers=wrong_n_layers, n_units=n_units) + input_layer, output_layer = rnn_network.build_network(input_shape) + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("dropout_intermediate", [0.1, 0.2, 0.5, 0.8]) +def test_rnn_network_dropout_intermediate_nonzero(dropout_intermediate): + """Test RecurrentNetwork with non-zero intermediate dropout rates.""" + import tensorflow as tf + + input_shape = (100, 5) + n_layers = 3 + + rnn_network = RecurrentNetwork( + rnn_type="simple", n_layers=n_layers, dropout_intermediate=dropout_intermediate + ) + input_layer, output_layer = rnn_network.build_network(input_shape) + + # Create a model to inspect layers + model = tf.keras.Model(inputs=input_layer, outputs=output_layer) + + # Count intermediate dropout layers (should be n_layers - 1) + intermediate_dropout_layers = [ + layer + for layer in model.layers + if isinstance(layer, tf.keras.layers.Dropout) and "intermediate" in layer.name + ] + expected_intermediate_dropouts = n_layers - 1 # All layers except the last + assert ( + len(intermediate_dropout_layers) == expected_intermediate_dropouts + ), f"Expected {expected_intermediate_dropouts} \ + found {len(intermediate_dropout_layers)}" + + # Check dropout rate + for layer in intermediate_dropout_layers: + assert ( + layer.rate == dropout_intermediate + ), f"Got {layer.rate}, expected {dropout_intermediate}" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("dropout_output", [0.1, 0.2, 0.5, 0.8]) +def test_rnn_network_dropout_output_nonzero(dropout_output): + """Test RecurrentNetwork with non-zero dropout rate.""" + import tensorflow as tf + + input_shape = (100, 5) + n_layers = 2 + + rnn_network = RecurrentNetwork( + rnn_type="simple", n_layers=n_layers, dropout_output=dropout_output + ) + input_layer, output_layer = rnn_network.build_network(input_shape) + + # Create a model to inspect layers + model = tf.keras.Model(inputs=input_layer, outputs=output_layer) + + # Count output dropout layers (should be 1) + output_dropout_layers = [ + layer + for layer in model.layers + if isinstance(layer, tf.keras.layers.Dropout) and "dropout_output" in layer.name + ] + assert ( + len(output_dropout_layers) == 1 + ), f"Expected 1 output Dropout layer, found {len(output_dropout_layers)}" + + # Check dropout rate + assert ( + output_dropout_layers[0].rate == dropout_output + ), f"Got {output_dropout_layers[0].rate}, expected {dropout_output}" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +def test_rnn_network_dropout_zero(): + """Test RecurrentNetwork with zero dropout rates.""" + import tensorflow as tf + + input_shape = (100, 5) + n_layers = 3 + dropout_intermediate = 0.0 + dropout_output = 0.0 + + rnn_network = RecurrentNetwork( + rnn_type="simple", + n_layers=n_layers, + dropout_intermediate=dropout_intermediate, + dropout_output=dropout_output, + ) + input_layer, output_layer = rnn_network.build_network(input_shape) + + # Create a model to inspect layers + model = tf.keras.Model(inputs=input_layer, outputs=output_layer) + + # With dropout rates = 0.0, no dropout layers should be created + dropout_layers = [ + layer for layer in model.layers if isinstance(layer, tf.keras.layers.Dropout) + ] + assert ( + len(dropout_layers) == 0 + ), f"Expected 0 Dropout layers with zero dropout rates, found {len(dropout_layers)}" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("bidirectional", [True, False]) +def test_rnn_network_bidirectional(bidirectional): + """Test RecurrentNetwork with bidirectional option.""" + import tensorflow as tf + + input_shape = (100, 5) + n_layers = 2 + + rnn_network = RecurrentNetwork( + rnn_type="simple", n_layers=n_layers, bidirectional=bidirectional + ) + input_layer, output_layer = rnn_network.build_network(input_shape) + + # Create a model to inspect layers + model = tf.keras.Model(inputs=input_layer, outputs=output_layer) + + if bidirectional: + # Check for Bidirectional layers + bidirectional_layers = [ + layer + for layer in model.layers + if isinstance(layer, tf.keras.layers.Bidirectional) + ] + assert ( + len(bidirectional_layers) == n_layers + ), f"Expected {n_layers} Bidirectional layers" + else: + # Check for regular SimpleRNN layers by name + simple_layers = [ + layer + for layer in model.layers + if "simple" in layer.name + and not isinstance(layer, tf.keras.layers.Bidirectional) + ] + assert ( + len(simple_layers) == n_layers + ), f"Expected {n_layers} SimpleRNN layers, found {len(simple_layers)}" + + # Ensure no Bidirectional layers + bidirectional_layers = [ + layer + for layer in model.layers + if isinstance(layer, tf.keras.layers.Bidirectional) + ] + assert ( + len(bidirectional_layers) == 0 + ), "Should not have Bidirectional layers when bidirectional=False" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("activation", ["tanh", "relu", "sigmoid", "linear"]) +def test_rnn_network_activation(activation): + """Test RecurrentNetwork with different activation functions.""" + import tensorflow as tf + + input_shape = (100, 5) + + rnn_network = RecurrentNetwork(rnn_type="simple", activation=activation) + input_layer, output_layer = rnn_network.build_network(input_shape) + + # Create a model to inspect layers + model = tf.keras.Model(inputs=input_layer, outputs=output_layer) + + simple_layers = [ + layer + for layer in model.layers + if "simple" in layer.name and isinstance(layer, tf.keras.layers.SimpleRNN) + ] + assert len(simple_layers) == 1 + + assert ( + simple_layers[0].activation.__name__ == activation + ), f"Got {simple_layers[0].activation.__name__}, expected {activation}" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("return_sequence_last", [True, False]) +def test_rnn_network_return_sequence_last(return_sequence_last): + """Test RecurrentNetwork with different return_sequence_last configurations.""" + import tensorflow as tf + + input_shape = (100, 5) + n_layers = 3 + + rnn_network = RecurrentNetwork( + rnn_type="simple", n_layers=n_layers, return_sequence_last=return_sequence_last + ) + input_layer, output_layer = rnn_network.build_network(input_shape) + + # Create a model to inspect layers + model = tf.keras.Model(inputs=input_layer, outputs=output_layer) + + # Check return_sequences setting + simple_layers = [ + layer + for layer in model.layers + if "simple" in layer.name and isinstance(layer, tf.keras.layers.SimpleRNN) + ] + assert len(simple_layers) == n_layers + + # Check return_sequences for each layer + for i, layer in enumerate(simple_layers): + is_last_layer = i == n_layers - 1 + expected_return_sequences = not is_last_layer or return_sequence_last + + assert ( + layer.return_sequences == expected_return_sequences + ), f"Layer {i} got {layer.return_sequences},\ + expected {expected_return_sequences}" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +def test_rnn_network_output_shape(): + """Test RecurrentNetwork output shapes.""" + import numpy as np + import tensorflow as tf + + input_shape = (50, 10) # (timesteps, features) + batch_size = 32 + + # Test with return_sequence_last=False (default) + rnn_network = RecurrentNetwork( + rnn_type="simple", n_layers=2, n_units=64, return_sequence_last=False + ) + input_layer, output_layer = rnn_network.build_network(input_shape) + + model = tf.keras.Model(inputs=input_layer, outputs=output_layer) + + # Create dummy input + dummy_input = np.random.random((batch_size,) + input_shape) + output = model(dummy_input) + + # Output should be (batch_size, n_units) when return_sequence_last=False + assert output.shape == ( + batch_size, + 64, + ), f"Expected shape (32, 64), got {output.shape}" + + # Test with return_sequence_last=True + rnn_network = RecurrentNetwork( + rnn_type="simple", n_layers=2, n_units=64, return_sequence_last=True + ) + input_layer, output_layer = rnn_network.build_network(input_shape) + + model = tf.keras.Model(inputs=input_layer, outputs=output_layer) + output = model(dummy_input) + + # Output should be (batch_size, timesteps, n_units) when return_sequence_last=True + assert output.shape == ( + batch_size, + 50, + 64, + ), f"Expected shape (32, 50, 64), got {output.shape}" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +def test_rnn_network_layer_names(): + """Test that RNN layers have correct names.""" + import tensorflow as tf + + input_shape = (100, 5) + n_layers = 3 + + rnn_network = RecurrentNetwork( + rnn_type="simple", n_layers=n_layers, dropout_intermediate=0.2 + ) + input_layer, output_layer = rnn_network.build_network(input_shape) + + model = tf.keras.Model(inputs=input_layer, outputs=output_layer) + + # Check layer names + simple_layers = [layer for layer in model.layers if "simple" in layer.name] + intermediate_dropout_layers = [ + layer for layer in model.layers if "dropout_intermediate" in layer.name + ] + + for i, layer in enumerate(simple_layers): + expected_name = f"simple_{i+1}" + assert ( + layer.name == expected_name + ), f"SimpleRNN layer {i} has name {layer.name}, expected {expected_name}" + + for i, layer in enumerate(intermediate_dropout_layers): + expected_name = f"dropout_intermediate_{i+1}" + assert ( + layer.name == expected_name + ), f"layer {i} got {layer.name}, expected {expected_name}" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +def test_rnn_network_config(): + """Test RecurrentNetwork configuration attributes.""" + rnn_network = RecurrentNetwork() + + # Check _config attributes + assert "python_dependencies" in rnn_network._config + assert "tensorflow" in rnn_network._config["python_dependencies"] + assert "python_version" in rnn_network._config + assert "structure" in rnn_network._config + assert rnn_network._config["structure"] == "encoder" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +def test_rnn_network_complex_configuration(): + """Test RecurrentNetwork with a complex configuration.""" + import tensorflow as tf + + input_shape = (200, 8) + + rnn_network = RecurrentNetwork( + rnn_type="gru", + n_layers=4, + n_units=[128, 64, 32, 16], + dropout_intermediate=0.2, + dropout_output=0.3, + bidirectional=True, + activation="relu", + return_sequence_last=True, + ) + + input_layer, output_layer = rnn_network.build_network(input_shape) + model = tf.keras.Model(inputs=input_layer, outputs=output_layer) + + # Check that all components are correctly configured + bidirectional_layers = [ + layer + for layer in model.layers + if isinstance(layer, tf.keras.layers.Bidirectional) + ] + intermediate_dropout_layers = [ + layer + for layer in model.layers + if isinstance(layer, tf.keras.layers.Dropout) and "intermediate" in layer.name + ] + output_dropout_layers = [ + layer + for layer in model.layers + if isinstance(layer, tf.keras.layers.Dropout) and "dropout_output" in layer.name + ] + + assert len(bidirectional_layers) == 4, "Should have 4 Bidirectional layers" + assert ( + len(intermediate_dropout_layers) == 3 + ), "Should have 3 intermediate Dropout layers" + assert len(output_dropout_layers) == 1, "Should have 1 output Dropout layer" + + # Check units in each layer + expected_units = [128, 64, 32, 16] + for i, layer in enumerate(bidirectional_layers): + assert ( + layer.forward_layer.units == expected_units[i] + ), f"Layer {i} has {layer.forward_layer.units} units" + + for i, layer in enumerate(bidirectional_layers): + is_last_layer = i == len(bidirectional_layers) - 1 + expected_return_sequences = ( + not is_last_layer or True + ) # return_sequence_last=True + assert ( + layer.forward_layer.return_sequences == expected_return_sequences + ), f"Layer {i} got {layer.forward_layer.return_sequences}, \ + expected {expected_return_sequences}" + + # Check activation + for layer in bidirectional_layers: + assert ( + layer.forward_layer.activation.__name__ == "relu" + ), f"Layer activation is {layer.forward_layer.activation.__name__}" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("activation", [["tanh", "relu"], ["sigmoid", "tanh", "relu"]]) +def test_rnn_network_activation_list(activation): + """Test RecurrentNetwork with list of activation functions.""" + import tensorflow as tf + + input_shape = (100, 5) + n_layers = len(activation) + + rnn_network = RecurrentNetwork( + rnn_type="simple", n_layers=n_layers, activation=activation + ) + input_layer, output_layer = rnn_network.build_network(input_shape) + + # Create a model to inspect layers + model = tf.keras.Model(inputs=input_layer, outputs=output_layer) + + # Check activation function for each layer + simple_layers = [ + layer + for layer in model.layers + if "simple" in layer.name and isinstance(layer, tf.keras.layers.SimpleRNN) + ] + assert len(simple_layers) == n_layers + + for i, layer in enumerate(simple_layers): + expected_activation = activation[i] + assert ( + layer.activation.__name__ == expected_activation + ), f"Layer {i} got {layer.activation.__name__}, \ + expected {expected_activation}" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("activation", [["tanh", "relu"], ["sigmoid", "tanh", "relu"]]) +def test_rnn_network_activation_list_mismatch(activation): + """Test RecurrentNetwork with mismatched activation list and n_layers.""" + input_shape = (100, 5) + + # Use different n_layers than length of activation list + wrong_n_layers = len(activation) + 1 + + with pytest.raises( + ValueError, + match="Number of activations .* should be the same as number of layers", + ): + rnn_network = RecurrentNetwork(n_layers=wrong_n_layers, activation=activation) + input_layer, output_layer = rnn_network.build_network(input_shape) From 430783f289178ddfb137724a3b407f94dab1b271 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Mon, 16 Jun 2025 23:10:27 +0100 Subject: [PATCH 154/216] Update README.md (#2908) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 292910e3e4..e6c8eab3d5 100644 --- a/README.md +++ b/README.md @@ -30,7 +30,7 @@ does not apply: | Overview | | |-----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| **CI/CD** | [![github-actions-release](https://img.shields.io/github/actions/workflow/status/aeon-toolkit/aeon/release.yml?logo=github&label=build%20%28release%29)](https://github.com/aeon-toolkit/aeon/actions/workflows/release.yml) [![github-actions-main](https://img.shields.io/github/actions/workflow/status/aeon-toolkit/aeon/pr_pytest.yml?logo=github&branch=main&label=build%20%28main%29)](https://github.com/aeon-toolkit/aeon/actions/workflows/pr_pytest.yml) [![github-actions-nightly](https://img.shields.io/github/actions/workflow/status/aeon-toolkit/aeon/periodic_tests.yml?logo=github&label=build%20%28nightly%29)](https://github.com/aeon-toolkit/aeon/actions/workflows/periodic_tests.yml) [![docs-main](https://img.shields.io/readthedocs/aeon-toolkit/stable?logo=readthedocs&label=docs%20%28stable%29)](https://www.aeon-toolkit.org/en/stable/) 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[![!conda](https://img.shields.io/conda/vn/conda-forge/aeon?logo=anaconda&color=blue)](https://anaconda.org/conda-forge/aeon) [![!python-versions](https://img.shields.io/pypi/pyversions/aeon?logo=python)](https://www.python.org/) [![!black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![license](https://img.shields.io/badge/license-BSD%203--Clause-green?logo=style)](https://github.com/aeon-toolkit/aeon/blob/main/LICENSE) [![binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/aeon-toolkit/aeon/main?filepath=examples) | | **Community** | [![!slack](https://img.shields.io/static/v1?logo=slack&label=Slack&message=chat&color=lightgreen)](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-36dlmbouu-vajTShUYAHopSXUUVtHGzw) [![!linkedin](https://img.shields.io/static/v1?logo=linkedin&label=LinkedIn&message=news&color=lightblue)](https://www.linkedin.com/company/aeon-toolkit/) [![!x-twitter](https://img.shields.io/static/v1?logo=x&label=X/Twitter&message=news&color=lightblue)](https://twitter.com/aeon_toolkit) | | **Affiliation** | [![numfocus](https://img.shields.io/badge/NumFOCUS-Affiliated%20Project-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org/sponsored-projects/affiliated-projects) | From c9b8a449fb83018d905bfaf406a9c6385b35ed51 Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Wed, 18 Jun 2025 07:10:01 +0100 Subject: [PATCH 155/216] [ENH] Add recursive and direct methods to forecasting base class (#2899) * base class methods * direct * recursive * base * direct_forecasting * recursive_forecasting * recursive_forecasting * recursive_forecasting * direct notebook * direct notebook * direct notebook * switch to iterative * switch to iterative * iterative * iterative notebook * append change * append change * fix regression bug * comment --- aeon/forecasting/_regression.py | 6 +- aeon/forecasting/base.py | 104 +++++++ aeon/forecasting/tests/test_base.py | 28 +- examples/forecasting/direct.ipynb | 385 +++++++++++++++++++++++++ examples/forecasting/img/direct.png | Bin 0 -> 88458 bytes examples/forecasting/img/iterative.png | Bin 0 -> 88137 bytes examples/forecasting/iterative.ipynb | 338 ++++++++++++++++++++++ 7 files changed, 857 insertions(+), 4 deletions(-) create mode 100644 examples/forecasting/direct.ipynb create mode 100644 examples/forecasting/img/direct.png create mode 100644 examples/forecasting/img/iterative.png create mode 100644 examples/forecasting/iterative.ipynb diff --git a/aeon/forecasting/_regression.py b/aeon/forecasting/_regression.py index 0106a95afb..7595b85a95 100644 --- a/aeon/forecasting/_regression.py +++ b/aeon/forecasting/_regression.py @@ -73,11 +73,11 @@ def _fit(self, y, exog=None): X = np.lib.stride_tricks.sliding_window_view(y, window_shape=self.window) # Ignore the final horizon values: need to store these for pred with empty y X = X[: -self.horizon] - # Extract y - y = y[self.window + self.horizon - 1 :] + # Extract y_train + y_train = y[self.window + self.horizon - 1 :] self.last_ = y[-self.window :] self.last_ = self.last_.reshape(1, -1) - self.regressor_.fit(X=X, y=y) + self.regressor_.fit(X=X, y=y_train) return self def _predict(self, y=None, exog=None): diff --git a/aeon/forecasting/base.py b/aeon/forecasting/base.py index 3a084b6d99..45ceb597db 100644 --- a/aeon/forecasting/base.py +++ b/aeon/forecasting/base.py @@ -8,6 +8,7 @@ __all__ = ["BaseForecaster"] from abc import abstractmethod +from typing import final import numpy as np import pandas as pd @@ -46,6 +47,7 @@ def __init__(self, horizon: int, axis: int): self.meta_ = None # Meta data related to y on the last fit super().__init__(axis) + @final def fit(self, y, exog=None): """Fit forecaster to series y. @@ -89,6 +91,7 @@ def fit(self, y, exog=None): @abstractmethod def _fit(self, y, exog=None): ... + @final def predict(self, y=None, exog=None): """Predict the next horizon steps ahead. @@ -117,6 +120,7 @@ def predict(self, y=None, exog=None): @abstractmethod def _predict(self, y=None, exog=None): ... + @final def forecast(self, y, exog=None): """Forecast the next horizon steps ahead. @@ -144,6 +148,106 @@ def _forecast(self, y, exog=None): self.fit(y, exog) return self._predict(y, exog) + @final + def direct_forecast(self, y, prediction_horizon): + """ + Make ``prediction_horizon`` ahead forecasts using a fit for each horizon. + + This is commonly called the direct strategy. The forecaster is trained to + predict one ahead, then retrained to fit two ahead etc. Not all forecasters + are capable of being used with direct forecasting. The ability to + forecast on horizons greater than 1 is indicated by the tag + "capability:horizon". If this tag is false this function raises a value + error. This method cannot be overridden. + + Parameters + ---------- + y : np.ndarray + The time series to make forecasts about. + prediction_horizon : int + The number of future time steps to forecast. + + predictions : np.ndarray + An array of shape `(prediction_horizon,)` containing the forecasts for + each horizon. + + Raises + ------ + ValueError + if ``"capability:horizon`` is False or `prediction_horizon` less than 1. + + Examples + -------- + >>> from aeon.forecasting import RegressionForecaster + >>> y = np.array([1.0, 2.0, 3.0, 4.0, 3.0, 2.0, 1.0, 2.0, 3.0, 4.0]) + >>> f = RegressionForecaster(window=3) + >>> f.direct_forecast(y,2) + array([3., 2.]) + """ + horizon = self.get_tag("capability:horizon") + if not horizon: + raise ValueError( + "This forecaster cannot be used with the direct strategy " + "because it cannot be trained with a horizon > 1." + ) + if prediction_horizon < 1: + raise ValueError( + "The `prediction_horizon` must be greater than or equal to 1." + ) + + preds = np.zeros(prediction_horizon) + for i in range(0, prediction_horizon): + self.horizon = i + 1 + preds[i] = self.forecast(y) + return preds + + def iterative_forecast(self, y, prediction_horizon): + """ + Forecast ``prediction_horizon`` prediction using a single model from `y`. + + This function implements the iterative forecasting strategy (also called + recursive or iterated). This involves a single model fit on y which is then + used to make ``prediction_horizon`` ahead using its own predictions as + inputs for future forecasts. This is done by taking + the prediction at step ``i`` and feeding it back into the model to help + predict for step ``i+1``. The basic contract of + `iterative_forecast` is that `fit` is only ever called once. + + y : np.ndarray + The time series to make forecasts about. + prediction_horizon : int + The number of future time steps to forecast. + + Returns + ------- + predictions : np.ndarray + An array of shape `(prediction_horizon,)` containing the forecasts for + each horizon. + + Raises + ------ + ValueError + if prediction_horizon` less than 1. + + Examples + -------- + >>> from aeon.forecasting import RegressionForecaster + >>> y = np.array([1.0, 2.0, 3.0, 4.0, 3.0, 2.0, 1.0, 2.0, 3.0, 4.0]) + >>> f = RegressionForecaster(window=3) + >>> f.iterative_forecast(y,2) + array([3., 2.]) + """ + if prediction_horizon < 1: + raise ValueError( + "The `prediction_horizon` must be greater than or equal to 1." + ) + preds = np.zeros(prediction_horizon) + self.fit(y) + for i in range(0, prediction_horizon): + preds[i] = self.predict(y) + y = np.append(y, preds[i]) + return preds + def _convert_y(self, y: VALID_SERIES_INNER_TYPES, axis: int): """Convert y to self.get_tag("y_inner_type").""" if axis > 1 or axis < 0: diff --git a/aeon/forecasting/tests/test_base.py b/aeon/forecasting/tests/test_base.py index 645b392939..09d39146e0 100644 --- a/aeon/forecasting/tests/test_base.py +++ b/aeon/forecasting/tests/test_base.py @@ -4,7 +4,7 @@ import pandas as pd import pytest -from aeon.forecasting import NaiveForecaster +from aeon.forecasting import NaiveForecaster, RegressionForecaster def test_base_forecaster(): @@ -39,3 +39,29 @@ def test_convert_y(): f.set_tags(**{"y_inner_type": "pd.Series"}) with pytest.raises(ValueError, match="Unsupported inner type"): f._convert_y(y, axis=1) + + +def test_direct_forecast(): + """Test direct forecasting.""" + y = np.random.rand(50) + f = RegressionForecaster(window=10) + # Direct should be the same as setting horizon manually. + preds = f.direct_forecast(y, prediction_horizon=10) + assert isinstance(preds, np.ndarray) and len(preds) == 10 + for i in range(0, 10): + f = RegressionForecaster(window=10, horizon=i + 1) + p = f.forecast(y) + assert p == preds[i] + + +def test_recursive_forecast(): + """Test recursive forecasting.""" + y = np.random.rand(50) + f = RegressionForecaster(window=4) + preds = f.iterative_forecast(y, prediction_horizon=10) + assert isinstance(preds, np.ndarray) and len(preds) == 10 + f.fit(y) + for i in range(0, 10): + p = f.predict(y) + assert p == preds[i] + y = np.append(y, p) diff --git a/examples/forecasting/direct.ipynb b/examples/forecasting/direct.ipynb new file mode 100644 index 0000000000..7ed4918bbb --- /dev/null +++ b/examples/forecasting/direct.ipynb @@ -0,0 +1,385 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "# Direct forecasting with an aeon `BaseForecaster`\n", + "\n", + "Direct forecasting involves repeatedly fitting a forecaster with increasing horizon.\n", + "Suppose you have a series of length $n$ and you want to forecast the next 20 steps. \n", + "The pseudocode for this is as follows.\n", + "\n", + "```\n", + "for i ← 1 to predictive_horizon do\n", + " f ← create forecaster for horizon i\n", + " set f.horizon ← i\n", + " call f.fit(y)\n", + " preds[i - 1] ← f.predict(y)\n", + "end for\n", + "```\n", + "Unlike [iterative forecasting](./iterative.ipynb), direct forecasting fits a new \n", + "model each time with a different horizon. You can visualise the process as \n", + "follows\n", + "\n", + "\"direct\n", + "\n", + "Not all forecasters can fit with horizon greater than 1. This capability is indicated\n", + " by the tag \"capability:horizon\". We will demonstrate direct forecasting with the airline data" + ], + "id": "d7f79f17fc5714df" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-16T17:42:04.146343Z", + "start_time": "2025-06-16T17:41:59.728235Z" + } + }, + "cell_type": "code", + "source": [ + "from aeon.datasets import load_airline\n", + "from aeon.visualisation import plot_series\n", + "\n", + "airline = load_airline()\n", + "_ = plot_series(airline)" + ], + "id": "e1a1ad0e8e30b77e", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 1 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-16T17:42:04.359026Z", + "start_time": "2025-06-16T17:42:04.195760Z" + } + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "y_train = airline[:100]\n", + "y_test = airline[100:120]\n", + "plt.plot(np.arange(0, len(y_train)), y_train, label=\"Train\", color=\"blue\")\n", + "plt.plot(\n", + " np.arange(len(y_train), len(y_train) + len(y_test)),\n", + " y_test,\n", + " label=\"Test\",\n", + " color=\"orange\",\n", + ")\n", + "plt.legend()\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.title(\"Train/Test Split of Time Series\")\n", + "plt.show()" + ], + "id": "512d5683fca427e7", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 2 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "We want to train a forecaster on the train set and forecast predictions for the\n", + "subsequent test steps. By default, `aeon` forecasters make a single prediction of the\n", + " `horizon` ahead of the training data. The `RegressionForecaster` is a window based \n", + " forecaster that by default uses linear regression. It windows across the train data \n", + " (see [regression forecaster](./regression.ipynb) for details). " + ], + "id": "d1a59c9017081cf4" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-16T17:42:04.634126Z", + "start_time": "2025-06-16T17:42:04.612193Z" + } + }, + "cell_type": "code", + "source": [ + "from aeon.forecasting import RegressionForecaster\n", + "\n", + "reg = RegressionForecaster(horizon=1, window=10)\n", + "reg.forecast(y_train)" + ], + "id": "b4c92eb8e16da6f2", + "outputs": [ + { + "data": { + "text/plain": [ + "376.10513465806844" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 3 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "If we want to predict more than one ahead, the direct strategy involves refiting the \n", + "model with different horizons using the same training data. " + ], + "id": "5b6dd96f8db8abb1" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-16T17:42:05.928694Z", + "start_time": "2025-06-16T17:42:05.868506Z" + } + }, + "cell_type": "code", + "source": [ + "y_hat = np.zeros(20)\n", + "temp = y_train.copy()\n", + "start = len(y_train)\n", + "for i in range(0, 20):\n", + " reg = RegressionForecaster(horizon=i + 1, window=10)\n", + " y_hat[i] = reg.forecast(temp)\n", + " temp = np.append(temp, y_hat[i])\n", + "print(y_hat)" + ], + "id": "b75ef36d2ca6d917", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 376.10513466 453.24184524 410.02884317 320.7916938 345.01419255\n", + " 403.67956973 422.56578606 422.96127689 439.10753893 474.27696935\n", + " 529.86945212 592.40389602 621.88188642 528.45587755 537.53416799\n", + " 615.52150133 704.15649567 781.29311407 940.19098382 1130.45046681]\n" + ] + } + ], + "execution_count": 4 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "We have a simple base class function that does this for you called `direct_forecast`", + "id": "4dde4011af897e3b" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-16T17:42:12.283250Z", + "start_time": "2025-06-16T17:42:12.108584Z" + } + }, + "cell_type": "code", + "source": [ + "forecaster = RegressionForecaster(window=10)\n", + "y_hat = forecaster.direct_forecast(y=y_train, prediction_horizon=20)\n", + "\n", + "plt.plot(np.arange(0, len(y_train)), y_train, label=\"Train\", color=\"blue\")\n", + "plt.plot(\n", + " np.arange(len(y_train), len(y_train) + len(y_test)),\n", + " y_test,\n", + " label=\"Actual\",\n", + " color=\"orange\",\n", + ")\n", + "plt.plot(\n", + " np.arange(len(y_train), len(y_train) + len(y_hat)),\n", + " y_hat,\n", + " label=\"Predicted\",\n", + " color=\"green\",\n", + " linestyle=\":\",\n", + ")\n", + "plt.legend()\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.title(\"Train/Test/Pedicted\")\n", + "plt.show()" + ], + "id": "eef4289e27cae7e5", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 5 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Differences are clearer if we plot actual vs predicted", + "id": "405d7cb89fee26cc" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-16T17:42:19.636191Z", + "start_time": "2025-06-16T17:42:19.457305Z" + } + }, + "cell_type": "code", + "source": [ + "plt.plot(\n", + " y_test,\n", + " label=\"Actual\",\n", + " color=\"orange\",\n", + ")\n", + "plt.plot(y_hat, label=\"Predicted\", color=\"green\", linestyle=\":\")\n", + "plt.legend()\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.title(\"Pedicted and Actual over the test interval\")\n", + "plt.show()" + ], + "id": "761a6afb2ab5b4d8", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 6 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "We see our regressor is consistently predicting higher than the actual values. We can\n", + " see that in the residuals. Another model or some transformation may be appropriate \n", + " for this data, we are simply showing the mechanism of direct forecasting in `aeon`." + ], + "id": "bd14b9d87ec84411" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-16T17:42:30.197819Z", + "start_time": "2025-06-16T17:42:30.174293Z" + } + }, + "cell_type": "code", + "source": [ + "residuals = y_test - y_hat\n", + "print(residuals)\n", + "mean_squared = np.mean(residuals**2)\n", + "print(\" MSE = \", mean_squared)" + ], + "id": "ac933718a232ad2e", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-21.10513466 -19.98331603 -3.94966927 8.2686585 1.3881772\n", + " 3.18872406 -0.70097463 -5.6727723 -7.79009668 -19.31041562\n", + " -31.02491562 -46.4846751 -68.33857028 -77.68696964 -48.56448463\n", + " -18.22988411 -55.45157724 -35.56987486 -40.37301295 -57.4939017 ]\n", + " MSE = 1360.1165354697248\n" + ] + } + ], + "execution_count": 7 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-16T17:42:31.360117Z", + "start_time": "2025-06-16T17:42:31.206412Z" + } + }, + "cell_type": "code", + "source": [ + "plt.plot(\n", + " residuals,\n", + " label=\"Residuals\",\n", + " color=\"blue\",\n", + ")\n", + "plt.title(\"Residuals over time\")" + ], + "id": "3143b3f645d1fd73", + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Residuals over time')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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zr-J%Q5u0kLfchuxL2t><|H^MaL=G9L*g^Ui&4NW}d8nt6h#_JoywB}$e|$;A5Ut05T=*@#A-;c!+&+sB)4AaA{hD0bL#xpS@e_ddETqu0t8a-@#@@*!=z{ k0EK|EpTBJ6f1QWb{gMJ!VL?G@61s\n", + "\n", + "\n", + "We will demonstrate direct forecasting with the airline data" + ], + "id": "d7f79f17fc5714df" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-16T16:14:58.605948Z", + "start_time": "2025-06-16T16:14:58.412664Z" + } + }, + "cell_type": "code", + "source": [ + "from aeon.datasets import load_airline\n", + "from aeon.visualisation import plot_series\n", + "\n", + "airline = load_airline()\n", + "_ = plot_series(airline)" + ], + "id": "e1a1ad0e8e30b77e", + "outputs": [ + { + "data": { + "text/plain": [ + "

" + ], + "image/png": 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QiYiIiIiIiCjkWK1WfHG+DcBQA0Rb5RtbmAemDVdBG+F4yIw2QoVYDwfQjI0JR4RKDrNoRXVLl0fnoqFjgEhEREREREREIae21YAmvQkKuQwzkqMHvb00ibmssQOiaPX18oKWIIpYm5vm8Nja3DQIomdVg3K5DJnxl18LGh4MEImIiIiIiIgo5Ej7H05LihrSZOC00ZFQymXoEkTUtrLyzRmNWon1SzLwyLJMeyWiNkKFjcuysH5JhtMhNa64vA8i28mHi+evGhERERERERFRkJECxLkpg7cvA4BKIUd6XCRON+pxulGPCaMGH7wSqsJVCtw4LRkP5GVA1yUgQRMGQRSHFNQORVYCKxCHGysQiYiIiIiIiCjkHOkOEOcMYf9DiRRccRLz4L73jy+Q9utdKG/UQ62Ue6XyUCJVIJY3sQJxuLACkYiIiIiIiIhCzuUBKjFDvo8tQKxHGYOrAV3Sm3CqO2SdOWbo13eoWIE4/FiBSEREREREREQh5ZLehBqdbR/DOUNsYQaAyYnde++xAnFA+6qbAQBTE6MQp1F7/fxSBeI5nQGdJrPXz0/9MUAkIiIiIiIiopBy5IKtfTk9LhKx3YM+hmKy1MLMyrcBlVbZAsRFaaN9cv7RkWrERdpeN7YxDw8GiEREREREREQUUi63Lw+9+hC4HCDW6LrQJVi8vq6RQqpAzEn1TYAI9GxjZoA4HBggEhEREREREVFI+cKNASoAkBClhjZCBasVKGdw5ZBBsODQOdv1zUkd5bPHyUrQIF6jRkunyWePQZcxQCQiIiIiIiKikCIFiHNTXBvwIZPJMLl7/z22MTt2uLYVJouIxCg1MuI1Pnuc/3dtOqoeXorrpybBZBah516IPsUpzEREREREREQUMvRGsz38c7WFGbC1MR+s0TFAdGJvj/ZlmUzmk8cwCBa89uUF7Cithq5LgDZChbW5aVi/JAPhKoVPHjPUMUAkIiIiIiIiCnB6kxkquRw6gwBtuAqCKEKj5q/07vjqYhusViA5OgzJMeEu3z8rsXvvPU5idkja/3CRj/Y/1JvM2F5SiYKicvvXdF0CthSVAQDW5aXzs+EDbGEmIiIiIiIiCmAGwYLtJZVI3lyI5E2FSN5ciCdKKmHgEA+3uDtARZIVL7Uwcw/EvqxWK/Z2T2DO8dEEZpVcjh2lVQ6PPVtaBZWcUZcvuHRVN23aBJlM1uvPlClT7McNBgNWrVqFuLg4REVFYeXKlaivr+91jpqaGqxYsQKRkZFITEzEunXrYDazT52IiIiIiIioL73JjG27K1BQVAZdlwDgcrXV47sruO+bG764IA1QcW3/Q8lkqQKxsQNWq9Vr6xoJTjd24FKngHClHPPcDGgHozMI9s9Cv2NdAloNjo+RZ1yOZadPn46LFy/a/5SWltqP3XfffXj77bfx2muvYc+ePbhw4QJuueUW+3GLxYIVK1bAZDJh3759ePnll/HSSy9h48aN3nk2RERERERERCMIq62874h9gIp7AVdGvAYyGdBqMKOhgxOAe9pb1QIAuHKCFmqlb96b2nAVtBEqx8ciVIgNd3yMPOPyq6lUKpGcnGz/Ex8fDwBobW3FCy+8gKeeegpLlizB/Pnz8eKLL2Lfvn04cOAAAKCwsBAnTpzAK6+8gjlz5uD6669HQUEBnnvuOZhM/NARERERERER9cRqK+8SLCKOXmwHAMwb516AGKFSYKI2AgBwmvsg9rLXx/sfAoAgilibm+bw2NrcNAii6LPHDmUuB4jl5eVISUnBpEmTcMcdd6CmpgYAcPjwYQiCgPz8fPttp0yZggkTJmD//v0AgP3792PmzJlISkqy3+a6665DW1sbjh8/7vQxjUYj2traev0hIiIiIiIiGsmsViuiw5SstvKiE/XtMFlExIYrkTY60u3zSG3MnMTcm33/Qx8GiBq1EuuXZGDjsiz7Z0MbocIjy7KwfkkGB6j4iEsB4sKFC/HSSy/hgw8+wPPPP4+qqipcffXVaG9vR11dHdRqNbRaba/7JCUloa6uDgBQV1fXKzyUjkvHnNm2bRtiY2Ptf8aPH+/KsomIiIiIiIiCiiha8bP/O4rC041YnZPq8DastnKdNEBlTkosZDKZ2+fJSmCA2FdDuxHlTbbBMotSR/n0scJVCqzLS0fdo8tR80g+zj2SjxunJiJcpfDp44Yyl2LZ66+/3v7fs2bNwsKFCzFx4kT8+9//RkREhNcXJ3nwwQdx//332//e1tbGEJGIiIiIiIhGDL3JDJVcDp1BgDZchS8utOLjM5fwSdUlHFhzNeQyGZ4trYKuS4A2QoW1uWlYvySDgYmLvjjv2QAVyeTuALGsgZOYJfvO2qoPpydFY1Sk2uePJ1UanmvpwrynPoZCLsP5R5ZBLnc/GCbnPKrr1Gq1yMrKQkVFBZYtWwaTyQSdTterCrG+vh7JyckAgOTkZHz66ae9ziFNaZZu40hYWBjCwsI8WSoRERERERFRQDIIFmwvqcSOHgHh6pxU7Pn5Inxao0N0uBLr8tKxfkkG6tqNSIwKgxVWhodusA9Q8XBC8OREDQBWIPYkDVBZlObb6sO+5o/TwmQR0aY342BNC7J92D4dyjwaidPR0YHKykqMGTMG8+fPh0qlwq5du+zHT58+jZqaGmRnZwMAsrOzcfToUTQ0NNhvU1RUhJiYGEybNs2TpRAREREREREFHb3JjG27K1BQVGYflqLrErC1uBw791bj2ow4ALZqq4pLenzjL59i7lMfcZ83N4iiFUcu2FqYPQ4QuysQzzR3wmRmGzlweYCKL/c/dEStlOOGKYkAgDeP1w/rY4cSlwLEX/7yl9izZw+qq6uxb98+fPOb34RCocB3vvMdxMbG4p577sH999+PkpISHD58GHfffTeys7Nx1VVXAQCWL1+OadOm4bvf/S6+/PJLfPjhh9iwYQNWrVrFCkMiIiIiIiIKOSq5HDtKqxwe21FaBZX88q/taaMicayuHeVNnWjuNA3XEkeMM82daDeaEaaUY0r3EBR3pcSEI1KlgEW04kwz25i7BAsO1+oAALlpw18BeNMMW1frW8edz9cgz7gUINbW1uI73/kOJk+ejNtuuw1xcXE4cOAAEhISAAC/+93vcOONN2LlypVYvHgxkpOT8frrr9vvr1Ao8M4770ChUCA7Oxt33nkn7rrrLmzZssW7z4qIiIiIiIgoCOgMgr3ysN+xLgGthsvHNGFKpMSEAwAqmhhauUra/3BmcjRUCo8aMiGXy5CVYGtjLmvka/HZOR0EixXJ0WEeTbd21/VTEqFSyHCqoQOnG9hW7gsu1Tz/85//HPB4eHg4nnvuOTz33HNObzNx4kS89957rjwsERERERER0YikDVdBG6FyGCJqI1SIDVf1+lpmvAYX2gwob9LjygnDu9dcsLs8QMWz9mXJ5MQoHLnQZguspnvllEGrtOpy+7In063dFROuwpKMeHx4uhFvHq/DA4kZw76Gkc6zyJ2IiIiIiIiI3CaIItbmpjk8tjY3DYLYe3+9jO6qt3JWvbnsCy8NUJFkde+DeJqvBfZ173843ANUevrGdFsb85vH2MbsCwwQiYiIiIiIiPxEo1Zi/ZIMbMjPhDbCVm2ojVBh47IsrF+S0W9YSma8LUBkC7NrrFarPUCc560KRHsLc2i3zIqiFfuqbROYc1Pj/LaOb0xPAgAcqGlBXZvBb+sYqTi2iYiIiIiIiMiPWg0C5o3T4twj+dCbLNCGqyCIIsJVin63lQLEcgaILrnYZkRDhwlyGTBzTLRXzjm5exBLqO+5d6qhAy1dAiJVCswZG+O3dYyNjcAV47U4dE6Ht0/U40dXTfTbWkYiViASERERERER+VFhWSNueekQvvniISRGhUGtlPerPJT0DBCtVutwLjOoHa9rw4zkaGRPHIVIJ9fWVVnxtgCxUW9CSwhPxf6sVocZydFYlhXv8XAaT0lViJzG7H2sQCQiIiIiIiLyo93lTQCAeeO0g942vTtA1HUJuNRpQrwmzJdLGxH0JjNyJ8XhzR9ciaSoMOhNZqcBrSuiw21TsS+0GXC6UY+rJqq9sNrgojeZcevsFFw9KQ7J0d67tu66ecYYPPLBaRSXN6HDaEZUGGMvb2EFIhEREREREZGfWK1W7OoOEJdmxg96+wiVAuO14QA4SGUoDIIF20sqMXZLEdIf24VxBUV4oqQSBsHilfNL+yCGYhtz32s7dot3r607piVFIT0uEkaziA9PN/htHSMRA0QiIiIiIiIiP6lo0qO21QC1Qo6c1KFNsM3sbp3lPogD05vM2La7AgVFZdB1CQBslZtbisrw+O4K6E1mjx8jS9oHMcQGqQzHtXWHTCbDTTNs05jfOl7vlzWMVAwQiYiIiIiIaEj0JjNMZhENHUaYzKLfQoKRRKo+dGVvvgwOUhkSlVyOHaVVDo89W1oFldzzSCSruwIx1KpBh+Pauuum6bYA8Z0T9RAsot/WMdIwQCQiIiIiIiI7ZyGh1K6YvLkQyZsKkby50O/tiiPB7gpbgLhkCO3LEmmQSgUDxAHpDIK9Oq7fsS4BrQbHx1wxOSEK8Ro1xBAbaDMc19Zdi1JHI16jRkuXgNKqZr+tY6ThbpJEREREREQE4HJIuKO0CrouAdoIFdbmpuH/XTMJT+45g4KiMvttpXZFAFiXl+7XwQnBShStKJECxAwXAsQQrXpzlTZcBW2EymHQpY1QITZc5fFjXDlei6qHl6KhwwSTWYQgiiP+s9BhMCNKrfT5tXWXQi7DjdOS8NKhcygqa0SeC58tco4ViEREREREROR0T7Pf76uGUhG47YrB7KuLbbjUKSAqTIErJ2iHfL/MHi3M1hCrfHOFIIpYm5vm8Nja3DQIomftrQbBgh17qzG+oBjpj+0akVW5fSuS69sNuO2Vz1BU1ojVOakO7+ONa+upO+aNxRvfvwIP52dyywUvGdmxOBEREREREQ2Jsz3NkqPDUN9uHLRdMSEqzNdLHHGk/Q8Xp8VBpRh6CDspLhIyGdBuNKOhw4SkaF57RzRqJR7Iy4BotWLn3upeVbXrl2QgXKVw+9x6kxnbSypHdFWuo4rk1TmpePn2ufjl2yfw+5UzIZfJ8GyfimVPr603LEodjW27ynH3v44E3NqCVXC/m4mIiIiIiMgrnO1pVtduREKUOmDbFYOZ1L7saotlmFKBCdoInG3pQnlTBwPEAbx1vA7zxmlR+8gy6E1mxIarIIiixyHSYENEHlqa6dH5/c1ZQLq1uBwA8MzN06FRK7EuLx0PLc1Eq0Hw2rX1lLR2aa3AyAt3/YF15kRERERERGTfL66vJr0JH1Ve8mkraCgSLCI+rroEAFjqwgAVib2NmfsgDuhfR87jlpcO4Y/7q5EQFQa1Uu6V8CiQh4h4w0AB6c691YhU2a6hRq2EWin36rX1VCBPiA5mvGpEREREREQEQRSxJjfV4bGqS5341ZIMbFyWZQ8ZtREqPLIsC+uXZAREaBBsPq3RocNoQVykCrPGxLh8/4we+yCSYyaziF3dVZ6L0+O8em5ngTswMqpygzkgDea1BzJ+lyciIiIiIiJo1Er84upJsFrRb7+4Hy6cgHCVwt6u2NBhxKhIFSqa9H5vVwxWu3u0L8vlMpfvL01irmCA6NT+s83oMFqQoFFjbkqsV88tDWjZ0qPFVyJV5aqDuGZrOCZY+0owrz2QBe+7mYiIiIiIiLzmkt6Ea3+/D/PGaXHx0eWo37QcdY8ux7q8dHtIKLUr7j/bgrRf78L/e+uEn1cdvHZ3D1BZ4uL+h5LM+CgArEAcyAenGwEA101OcCukHYhGrcR6h1W5mSOiKtdWkRyc2xb4evp2qArudzQRERERERF5xRvHLuJYXTs2F57GzTOS7VOVHVVRTUuKRpPehIM1LRAsoksThAnoNJmx/2wLAPf2PwQu74FY0aSH1WqFTObdgGwk+PBUAwBg+eREn5y/Z1Vuk96E2AgljpxvGxFVuRq1EvcvngSrDyZY+5oU7gIIyAnRwYoBIhEREREREeHfRy4AAG6bnTLobacmRtlbBL+80IYF47U+Xt3Isre6BSaLiHGx4fa9DF2VNjoSchmgN1lwsc2IlNhwL68yuF1sM+DIhTYAwPKsBJ89jlRp2KQ3Ys5Te2ARrWjYfB0UXq54HG6iaMU3XzqEtVdPwoWNy9Bu9N4E6+EghbsPLs3AxTbbJHnp6+Qe/jMRERERERFRiGtoN9r35Pv2nMEDRLlchkUTRwEA9lY3+3RtI9Gu7vblpZnxblcOqpVypI6OBACUN3V4bW0jRWF3+/L8cbFIjA7z+eNNS4qGySKipUvAkQutPn88XyutasZHlZfw/X9+AQABNWV5qDRqJQSLFTe/+CnSfr0LbQazv5cU1BggEhERERERhbj/O3oRohVYMC4Wk+KGVhGXkzYaALCvigGiq3aX28KtJW62L0syOYnZqcIyaf9D37Qv96VUyHHNJNukZykgDmb/OHIeAHDLjDFBXbUXFaaERQSa9CYcOqfz93KCGgNEIiIiIiKiEPfal93ty3PGDvk+Oam2ALG0uhlWq9Un6xqJWjpN+Py8rULN3QEqEqn9ubyRAWJPFtGKwtO2/Q+/NsV37ct9SYFwSUVwB4iCRcR/ur8n3D538IrkQHdF9xYLnzFA9AgDRCIiIiIiohB2sc2APWcuAQBunTVmyPe7YoIWKoUMF9uMqG7u8tXyRpw9Zy5BtAKTEzQYGxvh0bl6DlKhyw7X6nCpU0BMuBILJ4watseVAuFPzjTDZA7eSb+7yptwqVNAYpTa45A7EMwfHwuAAaKnGCASERERERGFsP98dRFWK3DVxFGY2L2n3lBEqBSYN9b2izn3QRy6E/XtiNeosSTT88o4tjA79sEpW/tyfmb8sE4In5EcjcQoNToFCw50T9kORv/8wta+/K1ZKVCOgAnr9grE2lZWS3sg+N8JRERERERE5LZ/d+91NpTpy31J+yAyQByc3mSGySzijrnjUPXwUvy/ayZ5fM7MhCgAtgpEUWQwIvmwu315uPY/lMhkMnvF3u4gbWPuEix441gdAOA7c4e+pUEgm50SA5VChia9CWdbWC3tLgaIREREREREIeqcrgt7q1sgkwG3zh56+7JE2gdxLwepDMggWLC9pBLJmwuR9tgujC8oxl8/q4VBsHh03tRREVDKZTCYRZxvM3hptcGtpdOEgzW26r/rJg/f/oeSvCAPEN87WY92oxkTtBHInjh87d++FKZUYGZyDABwkIoHGCASERERERGFqP98ZRuUkJs62q39+BZ1B4jH69uh6xK8uraRQm8yY9vuChQUldmvka5LwJaiMjy+uwJ6k9ntcysVcqR1t51zkIpNcXkTRCswLSkKE0YNvSXfW5Z2D1I5cLYFHUb3X1t/+ecX0kClFMjlMj+vxnsWuDFIRaoabugwwmQWPfqsjgQMEImIiIiIiELUv4/YwoJb3WhfBoCk6DBkxmtgtQL72cbskEoux47SKofHni2tgkru2a/ll/dB7PDoPCPFB93ty8uHuX1ZMilOg9RRETCLVpQGWWVum0HAOyfrAYyc9mXJAhcHqfSsGk7eVIjkzYV4oqTS46rhYMYAkYiIiIiIKARVN3fiYI0OchnwLRemL/cltTGXMkB0SGcQnFZn6roEtBo8q9zMSOAgFYnVasWH3QNUvuaH9mVJXncV4q7y4Gpj/u+xOhjNIiYnaDAnJcbfy/EqaZDK4fOtg+4X6suq4WDGAJGIiIiIiCgE/ftLW/XhNZPikBwT7vZ5FqXZ9knbVxW8U2d9SRuugjZC5fhYhAqx4Y6PDZVUgVjJABHH6tpxoc2ACJUciyfF+W0dS+37IDb6bQ3ukKYv3z53LGSykdO+DADTk6IRrpSjzWAeNGz3ddVwsArNZ01ERERERBTiDp/TIV6jxm1z3GtflkgViJ+ea4FgEb2xtBFFEEWszU1zeGxtbhoE0bNrlhHPCkTJh6dtgd216fEIVyn8tg5pEvORC224pDf5bR2uaNIbUdRdMTnS2pcB236hc8fa2pgHG6Ti66rhYMUAkYiIiIiIKIToTWYYzSIev3Eaqh5eitvc3P9QMiUxCnGRKnQJIr443+qlVY4cGrUS65dk4JFlmfZKRG2EChuXZWH9kgxo1EqPzm+vQLzUOWhr5kh3urED8Rq1X6Yv95QcE45pSVGwWoGPKoOjjfk/X16ERbRi3thYZCVE+Xs5PmEfpFKrG/B2vq4aDlaefaciIiIiIiKioCENBthRWgVdlwBthAprc9OwfkmG2xVbMpkMi1JH4+0T9SitasaVE0Z5edXBL1ylwNcmJ+KBvAy0dpkRr1FDEEWvVMlN0EZApZDBaBZxTteFiaOHf/Kwv+lNZqjkcjy0NBNP3zQdnSb/D7pYkpmAE/Ud2F1xCStneRbSD4eDNS2I16hx+wisPpQMdZCKIIpYk5uGgqKyfsekqmF1CNbjhd4zJiIiIiIiCkG+HAywqLuNeR8HqTj13X98gbRf70KNrhNqpdzjykOJUiHHpO7QMBTbmHtOy01/bBfGFxTjub3Vfp+Wa98HsTyw90GUKpI3Lp+MqoeX4u4rxvt7ST4jDVL54nwrzANst6BRK3Hf4knYkO+bquFgFZrPmoiIiIiIKMQMNhjgoaWZbp87p3uQyt7qFlit1hE3gMFTzZ0mnLnUCQCY7IP20Mz4KJxu1KO8SY/8LP+27w4nvcmM7SWVvSrFpFAcANblpfst7LkmPQ5yGXC6UY/zrV0YGxvhk8eRqi91BgHacBUEURzyc/ZFRXIgy4qPQnSYEu1GM07Ud2CWk0nTgkXEDX8+gAeWZOLCo8vQbjAjtvvajsTrMlSsQCQiIiIiIgoBvhwMsGCcFmqFHPXtRlR2B2V02eFa296QGfEajIpUe/38GQmhOUglkKflaiNUmD9OCwDYXeGbfRB7Vl8mbypE8uZCPFFSOaTqS19WJAcquVyG+eO625gH2AexqKwRB2p0+Nn/fQWVXI6EqDCvVg0HKwaIREREREQUcPQmM0xmEQ0dRpjM4oj8ZXa4+XIwQLhKYd9fbG8V25j7kvZck8ILb5MGqVQ0hlaAGOjTcpdkSm3M3g8QPQ0AAzl89SVpkMpAk5j/+cV5AMC3Zo2BQs5qasnIfEcQEREREVHQ8qSqhpwTRBFrc9McHpMGA3hC2gdxL/dB7Odwd7WTVJHmbVKAWN7U4ZPzB6pAn5a7pHsfxF3lTbBavTsh29MAMNDDV19Z0P0ZdDZIpdNkxn+P1wEAvjOCB8q4gwEiEREREREFjFBsqxsuGrUSD+Rl+GwwQA4HqTj12TlbC/MCH1cgnmnuHHA4xEhjm5ab6vCYN0JxT+WkjoJaIYfBLKKmpcur5/Y0AAz08NVXpEEqX11sg9Hc/x+l3j3ZgA6jBRNHRSB7IifK9xTaDdxERERERBRQfDnog2zh3rxxWtQ+kg+9yeLVwQCLUm2/bJ+o70BzpwmjfbDXXzBq7DCiRmcLj+b5KEAcr41AmFIOo1lEja4Lk+I0PnmcQNPYYcTqnDRYrcDOvdUBNwgkUq1E4Y+vwvzxsWjtsm3L4MqQk4HEhCmhjVA5DBGHEgAKoojVOanYWlze75gUvqpHYM1Z6ugIxEWqcKlTwFcX2nHFBG2v41L78rfnjOUwqD4YIBIRERERUcAYSlVNQlTYMK9q5Cgqa8QTH1XivsVp+O03ZgCA10KChKgwTE7Q4FKngKMX23FNepxXzhvspAEqkxM0iPFRVZdcLkN6XCQaOky42GYIugDRnUnCVqsVP/u/ozjb0oU/fmsWNuRnodUgBNS0XINgwa6KRtz80iGvhZsW0Yr1755EbtpojwLAikY91nRvaRCI4auvyGQyLBivxYenG/FZra5XgNjaJeC9Uw0AgO/MTfHTCgMXA0QiIiIiIgoYUluds6qa6DClvYrH1cCBgIM1LQCAGckxPjn/X/9nLqYlRaOlU/BqtVUwk6a9SsMbfOVPt87G7JQY6Lxc6eZr0p6nO0qrXAqxXj96ER+eboRaIUdytG1KrvSPC4FQOac3mbG9pBIFRZcDPmk7BgBYl5c+pNenb7j6aU0L3j1Zj3dP1mP/mlzIZTI82+Parc5JxQN5GYhUDxwArnvnBGpbDXjp9jkBGb76khQgHjqnw896fP2NY3UwmkVMTYzCrDG++R4ZzAL/uwkREREREYUMadCH9Et2T6tzUlFa3Yw5KbHYUXoGO0pDp2rGG8wW0b4X38IJ3t/byyBY8M6Jelz3p4N8XXo4bJ/ArPXZYxgECz441YAbX/g0qK795ZDt8ud9KCFbh9GMe988DgB4IC8dmQlRw7NgF3hjOwZH4erqnFTs+fkiHDqnQ2yECuvy0vHQ0ky0GgRo1Ep8eLoBv3rnOHbcMsvpeUsqmlBc3gSVQobEqMALX33N2SCVfx2xtS/fPpfty46M/HcGEREREREFDY1aif93zSSHgz7WXZsBlVyGZz85g4Kicg5ZcdGxunZ0ChbEhCsxJdG7gcvl4Td8Xfr6rLuFecF43+x/KF37rcXBd+3dnSS8pagM51sNSBsdiQcDdF9UT4ecOBsotbW4HDv3Vtu3CNColfYA8HxrF27722E8t+8sSiqaHJ7XarViw/unAAA/WjgRqaMj3X2KQUsapHKivh16o+3z0dhhRHG57ZrdPofty454FCA+/vjjkMlkuPfee+1fMxgMWLVqFeLi4hAVFYWVK1eivr6+1/1qamqwYsUKREZGIjExEevWrYPZHLjf1IiIiIiIaPj89P++wrxxWpzfuAz1m5aj7tHlWJeXjuhwJbInjsbOvdUO7zdQ4EDAgbO29uWFE7SQy71bXeNuEDTSXWwz4HyrATIZMCfFNwFiMF97d0K2U/UdePrjMwCAZ26egYgArbD0dMrxQK/rDieva2ZCFH5y1UQAwC/fPg5RtPa7zXsnG7D/bAsiVHI8nB+Y4auvpcSGIyUmHKIV+OKCLeB/7cuLsIhWzB8XG5AVrYHA7e8khw4dwh//+EfMmtW7LPa+++7D22+/jddeew179uzBhQsXcMstt9iPWywWrFixAiaTCfv27cPLL7+Ml156CRs3bnT/WRARERER0YhQ1tiBf3xxAd96+RDaugemqJVyexujp1U9oezTGh0A4EoftC/zdXFMGqAyNTEKUWG+2UEsmK/9YCFbdLjtmulNtn0dGzqMmDAqAv++awFWLZqIG6clDedyXSJtx+CINORkIO6+ro8uz0JMuBJfnG/D3w7X9jomilY88oGt+nBVThrGxIQP9jRGLKkiWNrW4Z9S+/KcsX5bU6BzK0Ds6OjAHXfcgT//+c8YNery/3xaW1vxwgsv4KmnnsKSJUswf/58vPjii9i3bx8OHDgAACgsLMSJEyfwyiuvYM6cObj++utRUFCA5557DiaTyeHjGY1GtLW19fpDREREREQjz8ufnQMAfG1yIpKi+/9y62lVTyg70D1A5SofBIh8XRw7LA1Q8eH+h8F87QcK2VbnpKK0qhm6LgHbSyqRvLkQyZsKMa6gCJ/X6vCbG6cP82pdo1ErsX5JBjYuy+q1HcMjyzKxfknGoANU3H1dE6LC8HB3W/fD75+yt+gCwP8dvYgjF9oQHabEr/Iy3HlaI4Y01OizczrUtHSitKoZMhnwbbYvO+VWgLhq1SqsWLEC+fn5vb5++PBhCILQ6+tTpkzBhAkTsH//fgDA/v37MXPmTCQlXf6Xguuuuw5tbW04fvy4w8fbtm0bYmNj7X/Gjx/vzrKJiIiIiCiAWUQr/vaZrWLme1c4/pnf06qeUNXSacKphg4AwMKJWq+fn6+LY4e7q5vm+3ACczBfeylkc7Tn6f3XpANW4Kk9lQ73AdxeEtj7OwJAuEqBdXnpqHt0OWofWYZzj+Tj2vT4IQ228eR1XZObhrTRkbjQZsBv99javc0WEb/bUwkAuP+aSYjTqN14RiOHfZBKrQ7/OnIBAHB12miM00b4cVWBzeUa6n/+85/4/PPPcejQoX7H6urqoFarodVqe309KSkJdXV19tv0DA+l49IxRx588EHcf//99r+3tbUxRCQiIiIiGmFKKppQ22rAqAgVvu6kNVEKHADb/m7BNHHWnz7tnjaaEa9BvCbM6+d39rqsCeHXxWq14jN7BaJv9j8Egv8zEaaUIzt1FH61JAPtRjNGR6ghiCI0aiVyJ43GbX877PB+Q51k7G9SpaFcBqT9ehcudZpQ+8iyQduHNWolfnltOkSrFTv3ujZxPlylwLYbpmJT4WnMGxcDo1nEJb0JRT/NxkcVl3BN+mivPsdgJLUwN3cK+OTMJcRr1GxfHoRLAeK5c+fwi1/8AkVFRQgPH75e+bCwMISFef9/ckREREREFDik9uVvz0kZ9JfjdXnpeGhpJuraDYjTqFHXbgz4oMSfDp7VAbANUPGVnq9LQ4cRoyJVqGzqDNnX5UKbAXXtRijkMsxOifHpY/X+TBgRp1HhnM4QFNf+bEsXVvzvpxgTE4bKB5dCrZRD3d0s2WYwD7oPYEJUcGQFyTHhSI+LRJPehDeO1uHnOamD3mdrURmuSh2N8xuXocNoRmy4CoIoDul1vXX2GORnxePpj8/grn8c6RHqp2JpZrwXnlFwi9eE4YMfLURO2mg0dJiQGKWGYAncat1A4FIL8+HDh9HQ0IB58+ZBqVRCqVRiz549ePbZZ6FUKpGUlASTyQSdTtfrfvX19UhOTgYAJCcn95vKLP1dug0REREREYWWNoOA149eBAB830n7ck8atRJqpRyvfXkRab/ehYLCMl8vMagdrJEmMHt//8OepNdl/9lmpP16F37x32M+fbxAJg1nmJ4UjchB9rvzBunaF5c3Iu3Xu7D2jaM+f0xv2FfdDACYoI3oF4wF8/6OjtwycwwA2L/XDcQgWPD8/rO45aVDOHqxrd9AqcF0ChY888kZbC0u79X+XVBUjsd3B377t68ZBAtKq5oxvqAY6Y/twviCYjz9cRUMgsXfSwtYLgWIS5cuxdGjR3HkyBH7nwULFuCOO+6w/7dKpcKuXbvs9zl9+jRqamqQnZ0NAMjOzsbRo0fR0NBgv01RURFiYmIwbdo0Lz0tIiIiIiIKJq99eRFdgoipiVG4woX94uaNi0WT3oT3TzVAFK2+W2AQs1qt9gDxqom+DRAls1Nsr8v+sy3oCtFfyKX25Xk+bF925JpJcWjSm1BSeQmX9I4HlQaSvdW292Z2av+22mDe39GRlbNsAeKeM5fQpDcOeNsPTjeg3WjGeG24W0N4VHI5dpRWOzz2bGkVVHK3RmKMCHqTGdt2V/QLV7cUlTFcHYBL75jo6GjMmDGj1x+NRoO4uDjMmDEDsbGxuOeee3D//fejpKQEhw8fxt13343s7GxcddVVAIDly5dj2rRp+O53v4svv/wSH374ITZs2IBVq1axTZmIiIiIKERJ7cvfu2I8ZDLZkO+XmzYaMeFKNOpN9sCGeqto0qO5U0C4Uo5ZY3zbSivJjNdgbGw4TBbRXmEWag537zvpywnMjqTHazA7JQYW0Yq3jjueMxBI9ne/PxY5CLedTTLeuCxrSJOMA82kOA3mdL82bx6rH/C2/+4e7PGtWSmQy4f+PVGiMwiDtn+HKlu4WuXwWKiHqwPx+lX53e9+hxtvvBErV67E4sWLkZycjNdff91+XKFQ4J133oFCoUB2djbuvPNO3HXXXdiyZYu3l0JEREREREGgokmP0qpmyGXAnfPGuXRflUKO5VkJAIB3TzYMcuvQdKC7+nDeuFiolcPzi7FMJkNeehwAoKTi0rA8ZiCxWq04XGtrYZaGNQwnV1pl/andYMZXF9sAAIscVCACvScZ129ajrpHl2NdXnpQ7O/oyC3dVYhvDPDadJrMePuELWD89pwUtx5npLV/exPDVfd4/H+Pjz76CE8//bT97+Hh4XjuuefQ3NwMvV6P119/vd/ehhMnTsR7772Hzs5ONDY24sknn4RSGVz/ckBERERERN7x1+7qw+VZCUiJdX1Y4/VTEgEA750cuKInVB2wD1AZnvZlSV6GbVBDSUXTsD5uIDin60Kj3gSlXDZsVZ89rewOEIvKmtAWwGHIwZoWiFYgdVTEgJ99aX9HV/cBDET216a8Ea1OQqx3TzZAb7IgbXSkS1s69DTS2r+9ieGqe1iXSUREREREfiOKVvz1s1oAtvZld0gB4uHaVtS1Gby2tpHi4Nnh3f9QIgWIh87p0GEMrT3FPuuuPpw5JtovlXLTkqMxJTEKJouId04EbrC+r3v/Q2fVhyPR1KRoTE2MgmCx4h0n/+ghtS/fOjvFpS0dehpp7d/exHDVPQwQiYiIiIjIbw6d06FTsCA2XImbpicPfgcHkmPCsaB7UMX7p9jG3FOn6XKL6MIJ2mF97NTRkUgdFQGzaEVpVWjtg/hZ9/6H84d5/8OegqGNWdofM5QCROByG/PrX/V/bTqMZrx70rP2ZclIa//2Foar7mGASEREREREw05vMsNkFpESG46qh5ei6CfZHv1Se/3UJADAe9wHsZfPz7fCLFoxJiYM47URw/74eZm2KsTdIdbGfLh7oM8CN9tPvUGa+Pv+qQboA7AC1CJa7ftzLkod3upYf5PamD843f+1eftEPQxmERnxtoErnhpJ7d/exHDVdQwQiYiIiIhoWBkEC7aXVCJ5cyEmbi3G+IJi2y/NgsXtc66YamtjLixrhGBh+5lE2v/wqgmj3G6F9EReui1A/CiEAkSr1YrPztlamOePG/4BKpI5KTFIGx2JLkHEB6cDL1g/Ud+ONoMZUWEKzEiO9vdyhtXsHq9N36rpfx85DwC4zYP2ZRoahquuYYBIRERERETDRm8yY9vuChQUldmnYOq6BBQUleHx3RXQm9yrlFowTosEjRrtRnPItcsO5NPuCq+Fw7z/oSQvwzaJ+fPzrU6nno40Vc2daOkSoFbI/RqMyWSyHm3MdX5bhzN7uz+nCyeMglIRWtFE79fmchtza5eA9081AvC8fZnI20LrU0pERERERH6lksuxo7TK4bFnS6ugkrv3K4pcLrMPU3mX05jtDnQPUBnu/Q8lY2MjkJWggWgFPj5zyS9rGG6HuweozBoTjTClf9shpTbmd07Uw2h2v8LXF/afDb0BKj3ZX5uTl6uv3zxeB5NFxNTEqJCryqTAxwCRiIiIiIiGjc4gOK1E03UJaDW4X6V2Q/c+iO9zH0QAQK2uC7WtBshltgpNf5GmMYfKPoj2ASp+3P9QcuV4LcbGhqPdaEZRWWBd/8sDVEJr/0OJ9Np0GC0oKrNVHUrTl2+bw/ZlCjwMEImIiIiIaNhow1X2qZf9jkWoEBvu+NhQLJ+cAIVchpMNHai61On2eUaKg93ty7PGxEAT5r+9vS7vgxgaFYjNnQLiNWq/hrYSuVyGb850PvHXX+rbjai81AmZzLY/Zyjq9docvYjmThMKu4PE22azfZkCDwNEIiIiIiInpEnBDR1GmMyi2/vz0WWCKGJtbprDY2tz0yCI7g9A0UaokNNdzcQ2ZuBgjQ6A//Y/lFybbtsH8auLbWjsMPp1Lb4kfb94OD8TVQ8vxYppif5eEgBg5cxkALb22EAZMCRVH85Ijkask39QCAXSa7O3qhm7yhuhjVBh5phoTE1i+zIFHo6YISIiIiJyQJoUvKO0CrouAdoIFdbmpmH9kgyEq/y7r1kw06iVWL8kA6LVip17q71+bW+YmoSPzzTj/VMNWO0kqAwVB+37H/o3QEyMDsOM5Ggcq2vHnspL+FYAV1fpTWao5HLoDAK04SoIojikyayB/P0iNy0OCRo1GvUm7K1uxrXdFaH+tLc7QMyeGJr7H0py0+Lw7j1XYnF6HBo7TKh6eCmrpylgMUAkIiIiIupDbzJje0klCorK7F/TdQnY0v33dXnpQwoVyLHmThPmjdPi3CP50Jss9qDGG0HLiqlJWP/uSZRUNKHTZEZkiL5OgkXEZ7U6AMBVE7V+XQtg2wfxWF07dlc0BWyA6G4IGOjfLxRyGX6SPRHzx2mxcMIoNHQYXQpHh8LV4HV/tTRAJTTblyWCRcT+sy2449Uver3nMuI1fg+eifpiCzMRERERUR++mhRMNoVlTbjlpUO49eXPkBgVBrVS7rUgY1pSFCZoI2Awi9gdInvuOXKsrh1dgghthApZ8VH+Xg7yMmxtzB9VBuZrojeZsW13BQqKyuxDfqQQ8PHdFQNuXxAM3y8eyMvA4Vodxm4pQvKmQiRvLsQTJZX26b+ekILX5M2FQzq3QbDYJ1XnhOgEZuDye25rcbnL7zkif/D/dzIiIiIiogDjy0nBBOwutw0KmDsu1uvnlslkuGGqbe+5jysDa+rsUHlj780j51sxIzkayzLjIZf7f5rrNZPiIJMBpxo6cKHV4O/l9ONJCBjo3y/0JjOe+KjSJ0GVO8Hr4dpWmCwiEqPUmBQX6fZjB7tgCJ6JeuI7koiIiIioD19OCg51VqvVXhm4NCPBJ49x2+wUvPH9K/DodZODbgCOq9VcjuhNZtw+dyze/MGVeOk7cwPiuY+KVGNuii0w/igAg11PQsBA/37hy6DKnXNLA1QWpY6GTOb/cNtfAj14JuqLASIRERERUR8DTQpenZPKX+w8cLqxAxfaDAhTyn22/9nCiaNwuFaH8QXFXm/X9CVP2mglUgA5dksR0h/bhbFbigLmuedl2IZ3BGJruSchYF27AatzUh0e83SyuDf4Mqhy59z7z0r7H4Zu+zIQ+MEzUV8MEImIiIiI+pAmBW/Iz7T/gqeNUGFDfibW5Kbhu69+jia90c+rDE67ym3VZzmpo30yJEBvMuPxIN1XzNNKMW8EkL50eR/EwKtAFEQRa3JTHR5bnZPq9NoVlzXiG385hDW5af2+X2xcloX1SzL8PnDJl0GVq+e2Wq3YWyVVIIb4AJUB/qEqEIJnor4YIBIREREROXBOZ7BPCq7btBx1jy7HfYsn4X9eOYzCsiZ888VDAVHVFWxKKmzh0ZLMeJ+cP5j3FRtqNZezPRID/blfnRYHhVyGNoMZF1q7/LqWvjRqJX5x9SSn/2jw0Hsn0dxp6nXda3VdeOCdE/jqYhu2FJ7GA3kZqHt0Oeq7v1+sy0sPiEm6vgyqBgteDebe3yMrL3WiUW+CWiHHvLHe3wM1mEj/ULVxWVZABs9EffEdSURERETkwOtHL+LB907i9jkpePXO+QAAtVKNZ785E4t2lOJSp4AvL7Rh7thY6AwCtOEqCKLIX/oGYBGtKLHvf+ibAHEoIVxCVJhPHttTUjWXo/VrI1SIUivRabK1KO8orYKuS4A2QoW1uWn4f9dMQnNXYD/36HAlin58Fa6YoEVrly0EDZTPTJPeiLzn96Pga1Nw8dHlaDMIiA1XQWcQ8Mu3T+DJr0/D0x+fwc691fbrvjonFR/++Co88sEpPPmN6QhT2sJC6RqrA6ReRwqqAFuQ3PN9s35Jhkch5yW9Catz0mC1ot+1WZObhg3vn8LTN82wD/KR9j9cMD42IMJVfwtXKbAuLx0PLc1Ea/d7ThBFXhsKSP7/Tk1EREREFIDePVkPALh6Ulyvr09NisZ7P1yIjHgNdpRW4fr/PejVX8hHsiMXWtHSJSAmXIn5PpjADAwewgXyvmJSpdiWorJ+x1bnpKK+w4i/fFqDrcXl9q9LLcrhSjnuXTwpoJ+7QbBgd2UTbnn5s4D7zPz3WB2O17VjS+Fp3Dwj2R4CJkaF4bffmI5nPznT77pLf3/ixmn28DBQSUHVg0szcbHNgIQoNUxmz4Iqq9WKH/77S9S2GvCHb83ChvwsewjWqDci/48HcPRiG+I1Ydi4PAsAsLc7QMyeGNr7H/YkBeiBFjwT9cV3JhERERFRH5f0JnulzI3Tkvodn5USg517q4Jynz1/2t29/+E1k+KgVPjmV5Fg3ldMo1biVw723ty4LAu/WpKBpOgw7Nxb7fC+2z+qhGAJ3Ocu7c+4tSgwPzP/+fIiAOBbs1P6HYsJUzq97jv3Vgd8eCjRqJUIU8rxwDsnkPbrXXj7RL1H53vl81oUlzehurkTKTHhUCvlSIgKg1opx9jYCPziatt7cVPhabx5rA4A0NRhQrxGHfL7HxIFI1YgEhERERH18f6pBohWYHZKDMZrI/odt+01V+3wvs+WVuGhpZk+XmFw2u3j/Q8B37ZrDocvz7fZ997sNFnsLY0atRINHcYBW5QFizVgn/tg+zP68zNzSW/Cru735q2zxvQ7Hsxt8Y5MSYzCf766iHdP1OOuBePdOkdjhxH3v3kcAPDIsixkxGv63eYHV07AF+dbsau8CQo5YDRb8OQ3piOxu/qRiIILA0QiIiIioj6k9uUVU/tXHwIjL1AYDiaziE/O2Ko6l/ho/0NJz33FLrYZEB+lRkunEPDhIQC8daIOj++uwM8XpWLnLTMBXG5pHHSPxDAl1Ep5QO6pNlyfGb3JDJVc7tK+pG8er4NFtGJ2SgwyE6L6HQ/mtnhHbpyWhK3F5fiwrBEmswi10vVq4M2FZbjUKWDmmGj88tp0p7d76hvT0W404+mPz+CufxwJqFCbiFzDFmYiIiIioh4Ei4gPTjUAcNy+DFwOFBweC8JAYTgcrGlBp2BBYpQaM5Kjff54GrUtTPvj/mqk/XoXnnVS/RZodnW3eV85Qdvv2FDbs6XnLrWTBsKQkuH4zBgE24CZ5M2FSN5UiOTNhXiipHLQaen/+fICAOBbDqoPgeBui3dkwTgtEqPUaDOYUVrVPOT7SdO/L7YZ8Jsbp+L171+Bv94+F6oBtiMwWUQ88wm3eyAaCRggEhERERH1sLeqGa0GMxI0alwxXuvwNiMtUBgOUjC2JCMeMpls2B539thYNOlNeMfD/d6GQ0unCZ/V6gAASx20eUvt2RuXZfXbI3H9koyACAqd8dZnRgqxGjqMMJlFewAl7bFYUFTmUlDV3GlCcfd781YH+x8CwX3dHZHLZbihu7r67RN1Q7pPz3B27JYijC8oxhfndZic2L9is6fBWtdVckYSRMEiuL7TERERERH5mDRY4IapiVDIHQddzvbZW52TyrY8J6T9D/N83L7c13WTE6GUy3CqoQMVTXqHe7UFipLKS7BagamJURgb23/vTaB3e3YgtSgPxtlnZk3u0D8zUoi1o8/+juvy0qGQydzaY/HNY3Uwi1bMHBONLAfty5Jgve7O3DgtCS8dOod3TtTjqW9MHzDU15vM2F5SiYIe08F1XQIKisohgwzr8tKdhqjc7oFo5GCASERERETUg7T/obP2ZUnPQEFnEKBRK1B4uhFfXWjDlRM5YbQnvdGMA2dbADiurPMlbYQKV6eNRknlJbx7sh6/uHrSsD6+K6QqzaVZCQPeTgprpOBFHSSNZT0/M5c6TYgJV+KTM80IG8IefM5CrC1FZYjXqLFy1hi3gqr/fNU9fXmW4+rDnoL1ujuyLDMBaoUclZc6UdaoH7CS0JMBOCNt/0iiUBa83/GIiIiIiLysrLEDZY16qBQyLBskxAEu7zWXGBWGTR+exsqXP8OTeyqHYaXB5ZOqZphFKyaOikDa6Mhhf/wV3WHwuwHexryrvBEAkD/MIetwkj4z2gglZjzxEW7434P4tEY36P0GCrGe+eQM4iLVLu+x2NJpQnH3Nb91tuP9D0eq6HAlrk2PA4BB2/uHUkXoDLd7IBo5GCASEREREXWTqg+vmRSHGBcrY+5aMB4A8MaxOpxv7fL62oKZff/DzOHd/1AiVZPuOXMJbQOEHf5U02KrBFPIZbhmUpy/l+NzESolclJHAwD+ceT8oLcfKMSqvNQJvWB2Oah683g9BIsVM5KjMSXR94N9Ao09WD85cIDoyQCckbZ/JFEoY4BIRERERNTt3RO26csrBmlfdmTmmBhcnTYaFtGKPx2o8fbSglpJ9/6HS4d5/0NJVkIUMuM1ECxWFJ5u9MsaBiOFrFeM1yLWSVgz0tw+dywA4N9HLsAiWge87WAhlkblOKjakJ+JexdPchhUXZ6+PHj78ki0YmoiAFuFsLNwFrBNpl+dk+rw2FCqCKXW9bpHl6N+03LUPboc6/LSg3b/SKJQxQCRiIiIiAhAa5eAj89cAgDcONX1ABEAft79S/afD5yFYGFrHmCbcvvFhVYAwz9ApaehVlv5i33/wxHcvtzX8qwEjIpQoa7diD2Vlwa8rSCKWJOb6vCYFGL1DaouPLoMc8fGYtGOUuzuvr4SXZeAohBtX5ZMitNgWlIULKIVH5xqcHq7XRVNWJObhg35mW5XEUqt6wlRYVAr5aw8JApCDBCJiIiIiAB8eLoRZtGKqYlRSHdzUu83Z4xBcnQY6tqNeONonZdXGJxKKppgtQLTkqIwJibcb+v4eneA+N7JhkGr3Yab1Wq178U3kvc/7EutlOOWWbbwbrA2ZpVchjW5kwYNsXoGVeFKBYrKGnGqoQM//PcRdBjN9vO9dbwOgsWKaUlRmJoUeu3LkhVTBw7WjWYLfvHfY7jm9/tw25wUVhEShTAGiEREREREuPwLtDvtyxK1Uo4fLpwAAPj9PscDH0LN7gpbZdmSzMGH0vhSbtpoxIQr0ag34dA5nV/X0texunY0dJgQqVLgqhCb4P2dObY25te/ugiT2XnV7t8On8fi5/YiO3WUSyHWb1ZMw8RREahu6cKD7520f/0/Xw59+vJI9vXptu93759qgNlB1fTz+87ibEsXWg0CJo2OZBUhUQhjgEhEREREIc8iWvFed4Dobvuy5CfZE6GQy/DxmWYcu9jmjeUFtXMtnYjXqLEkw7+DQVQKOb422bbn22BTZ4eb1L68eNJohClDq6LrmvQ4jIkJQ0uXgA9PO26jtYhWPPFRBU41dOBEXYdLIVZ0uBL/e9tsAMBze6uxp7IJbV0CzrcZEK9R49bZoR0gXjVhFEZHqtDcKeBATUuvY61dAn5dXAYA2LR8MiIZGBKFNAaIRERERBTyjl5sg0wmgzZChUWpnlWAjY2NwM3TkwEAv99X7YXV+YfeZIbJLKKhwwiTWYTeZB78Tn3ubzSLeOabM1H18FLkpfu/NXfFtEANEG3ty0v9XKXpDwq5zB7i/fPIBYe3eePoRZQ16jEqQoUfXzXR5cdYmpmAH101AVMSo2A0i1Ar5fjP9xag6uGlSB0d4dH6g51SIcf1U6TPRe8Ad/tHFbjUKWBKYhTuvmK8P5ZHRAGEASIRERERhSwpJIvXqFH18FK898OFUCo8/xFZGqbyyue1aDM4n24aqAyCBdtLKpG8uRDJmwqRvLkQT5RUwiBYXLr/mM2FSH9sF8YXFON3H58Z8v195fopiZDLgK8utqGmpdNnj+NK+CpYRHzUPUAkP8v/Ias/SG3Mbx6rg97Y+1pZrVb8pqQCALAqJxXR4e5Vwf3269Pw8aoclFY1I2VLkf196cr7eqSS9kF858TlfVsvtBrw9MdnAACP3TDFK98XiSi48bsAEREREYWkniHZhK3FGF9QjPdP1XslTLg2PQ5TE6PQYbTg306qqgKV3mTGtt0VKCgqg67LFn7qugRsKSrD47sr7GGYs5BsqPf3h3hNGLK79xh896TzqbOecDV8PVjTAr3JggSNGjOTY3yypkB35QQtJsVFolOw4J0+wzyKy5twuLYVESo51l6d5vZjyGQy7Cg9g63F5QH3vvS36yYnQCGX4UR9B85c0gMANhedRpcgYlHqKNzUXVFNRKGNASIRERERhRxnIVdBUblXwgSZTIaHlmbije9fge/MG+t2G7A/qORy7Ch1PADm2dIqqOTyAUMypVw26P39SRqS864P2pjdCU+Ly2z7Hy7NjIdcLvP6moKBTCbDt+d0tzF/0Xsa829226oPf7hwIuI1YW4/hu19Xe3wWCC8L/1pVKQaV6eNBmDbj7Pqkh5vHbd9Ph5fMRUyWWi+L4mot9D9LklEREREIWsoIZmnbpk1BodrdRhfUOxWG7C/6AyCPfzqd6xLQLvRjG27yx2GZP97sAbNnQPfv9XPLd3SkJxdFU392mU95c77Str/cElmaLYvS6Q25vdPNdrfP5/WtGB3RROUchn+3zWTPDr/YO9rf78v/e2788fhje9fgTvmjYNCLkfFg0vwyaoc5Kb5d/gREQUOBohEREREFHJ8HSboTWY8vrsiKNslteEqaCNUDo+lx0UiOkzptJLrmU/OIC5S7fT+2ggVYsMdHxsu05OjMXFUBIxmEZ9UXfLquV19X7UZBByo0QEA8kNwgEpPM8bEYEZyNEwWEa8fvQjgcvXhHfPGYsKoSI/OP9D7OhDel/727TkpOFyrw7iCIqT+2ralQ1FZY8D/gwcRDR8GiEREREQUcnwdJgxHhaOvCKKItbmO95q7b/EktHSZnIZklZc6oRfMTu+/NjcNgih6ba3ukMlk+MGVE/DG96/ANenxbrWXO9r/8WJrF6LUSpfeVx+faYZFtCI9LhKpoz0LyEYCqY35/ZP1OHNJj0+qmgEA667N8PjcA72vA+F96U96kxm/KakMyn/wIKLh494IKyIiIiKiICaFCVuKyvodk8IEtQf/1j6USrSEKPf3c/MljVqJXy3JgGi1Yufeaui6BGgjVFibm4YfXDkBcpkM2giVw+enjVBBo1Ji/RJb4PNsaVWv+69fkoFwlWK4n1I/9y2ehO0lFbj7X0dcXp+0/+OOHs9tTW4qVuekYV91M1bnpGJrcXm/+63OSUVzpwnJMeH2r0nty0tDvPpQ8t354zAjOQb5WfG4pBdQ9fBSfHmhDdOSoz0+t0Yd+O9LfxnsHzweWpo5zCsiokDEAJGIiIgoCOhNZqjkcugMArThKgiiCI2aP8q5SwoTHIVk3ggTpApHZyFboLdLHqhuxrxxWpx7JB+dJgtiu99z4SoF9CbzoOGrRq3Eurx0PLQ0E60Godf9/U1vMuOJjyp7hXxStRUArMtLd/rZ0pvM2F5SiYIez10avmO1Al+floT1SzIgl8l6hVSrc1KxJjcNX//Lp3jmphlY2D0JuqalC/EaNfJDfP9DSWJUGA7X6noFu2ty0zBvbKxX3jvhKkXAvi/9KZj/wYOIho/MarVa/b0IV7W1tSE2Nhatra2IiYnx93KIiIiIfMogWLBtd0WviidWzXiu3SCgpOISlmbFo8NowagI7wWzepMZT5RUOgzZNi7LGjCkCgTf/utneO2ri3g4PxMFX5vS77hBsODx3RVBWcllMotI3lzoNNyte3Q51ErH1adDva8U+EshVZdgwarXv8KrX1zAvHEx+O/3r0RCVBgutBmQGKWGYLE6bX0OFY7CWUkwfGaCmSefCSIKbq7kay59F3j++ecxa9YsxMTEICYmBtnZ2Xj//fftxw0GA1atWoW4uDhERUVh5cqVqK+v73WOmpoarFixApGRkUhMTMS6detgNnNPBSIiIiJH9CYztu2ucDjxlntTeeatE/W4+aVDWP7HA0iKDoNaKfdaQCFVOG5clmUPhrQRKmxYlon1SzICOgjRdQl464TtZ/hbZo5xeBupkqvu0eWo37QcdY8ux7q89IAPDwHPBugM9b4atRJqpRwJUbb3VWyECn/81mx8f8F4vP/Dq/CnA2cxZnMh0h/bhfEFxXj64zMhP6wimPcNDXbcH5KIhsKln1zGjRuHxx9/HJmZmbBarXj55Zdx00034YsvvsD06dNx33334d1338Vrr72G2NhYrF69Grfccgv27t0LALBYLFixYgWSk5Oxb98+XLx4EXfddRdUKhUee+wxnzxBIiIiomDGval8543uSa95Gb5pH+3ZLtncZUJ0mBK7yptgEQO7Aei1Ly/AaBYxPSkac1KcVyNIIajU2ujJnpHDyZP2ck/uqwlT4pmbp7vdPj3SsY3Wf7g/JBENhUv/l//617+OG264AZmZmcjKysKvf/1rREVF4cCBA2htbcULL7yAp556CkuWLMH8+fPx4osvYt++fThw4AAAoLCwECdOnMArr7yCOXPm4Prrr0dBQQGee+45mEwmp49rNBrR1tbW6w8RERFRKPCkWoqc0xvNeP9UAwBg5SzHVXbeIFWiJUWFYdkf9uPmFw/hn0cu+OzxvOGVw7UAgO8uGAeZTObn1XifJ9VWnlZqhSkV2Lm32uGxUK+y8/VkdBpYMFcVE9HwcPv/UBaLBf/85z+h1+uRnZ2Nw4cPQxAE5Ofn228zZcoUTJgwAfv37wcA7N+/HzNnzkRSUpL9Ntdddx3a2tpw/Phxp4+1bds2xMbG2v+MHz/e3WUTERERBZXBfqmO6f6lWm8yw2QW0dBhhMkssrV5EB+cbkCXICJtdOSAVXbeIpPJcHN3O/BfPq3x+eO5q+pSJz6paoZMBvzP3LH+Xo5POGsv37gsa9D2cmk4zIb8TJfvC/AfBAbCNlr/69t6H6rVsETkmMvfEY4ePYrs7GwYDAZERUXhjTfewLRp03DkyBGo1Wpotdpet09KSkJdXR0AoK6urld4KB2Xjjnz4IMP4v7777f/va2tjSEiERERhQRBFLEmN83hYIHVOanYX30J88Zp8ds9ZzhkxQWvH7X97HnLzDHDVmV31/xxePj9U9h/tgUn69sxNSl6WB7XFa98bqs+XJIRj3HaCD+vxnd6tpfXdxgxOlKF0w0dQ/q8PFZcjismjML5R5ahw2R2aZJvsE/n9iW20RIRBTaXA8TJkyfjyJEjaG1txX/+8x9873vfw549e3yxNruwsDCEhXG/CyIiIgo9GrUS9y2eBKvVip17q+2/VK/JTcPaq9PwRW0rnuSeai4xmi14p3tIiC/bl/tKjgnHiqmJeOt4PV48dA7bb5w2bI89FFar9XL78vxxfl6N70mfi6pLnZj/u89gEa0490g+Igf4vFzSm/C7j8/AYBbx6S+uxoLxWgBD3/9RqrJzNJ1bqrILlr0kfaFnsCtNsB5qOEtERL7l8v+d1Go1MjIyMH/+fGzbtg2zZ8/GM888g+TkZJhMJuh0ul63r6+vR3JyMgAgOTm531Rm6e/SbYiIiIjosjaDgKXP78O8cVpcfHSZfW+qB/LSoQ1XITdtNPdUc1FRWRPajWaMjQ3Hld0B0HC5+4oJAIC/Ha6FYAmslsxPa3Qob9IjUqVwOn15JMpJG40otQItXcKg+1O+eOgcDGYRc8fGYP64WJcfy5P26VDBNloiosDk8U+UoijCaDRi/vz5UKlU2LVrl/3Y6dOnUVNTg+zsbABAdnY2jh49ioaGBvttioqKEBMTg2nTAutfYImIiIgCwb+/vIAvLrThofdOQq3o/Uu1Qi5Dm9HMPdVc9PpXtunL35w5BnL58A4JuWFqIpKiw1DfbsR7JxsGv8Mw+mt39eE3ZyYjKix0QhuFXIafLkoFAPx+bzWsVsdTskXRij/srwYA/HxRmtut7xxWQUREwcilAPHBBx/Exx9/jOrqahw9ehQPPvggPvroI9xxxx2IjY3FPffcg/vvvx8lJSU4fPgw7r77bmRnZ+Oqq64CACxfvhzTpk3Dd7/7XXz55Zf48MMPsWHDBqxatYotykREREQOvPjpOQDA3VeMdxhYcHKpawSLiDeP2/Y/XDlz+DtgVAq5vT34xUO+H6Yy1OE6JrOIfx05DyA02pf7+sGV4xGmlOPz8634tEbn8DYfnG7AmUud0Eao8J25KR49HqvsiIgo2LgUIDY0NOCuu+7C5MmTsXTpUhw6dAgffvghli1bBgD43e9+hxtvvBErV67E4sWLkZycjNdff91+f4VCgXfeeQcKhQLZ2dm48847cdddd2HLli3efVZEREREI8DJ+nbsP9sChVyGuxY4HiDHyaWu+ajyElq6BCRGqZGbFueXNdx9he21fPdkA+raDD57HINgwfaSSiRvLkTypkIkby7EEyWVMAiWfrd9/1QDmjsFjIkJw9LMBJ+tKVDFa8Lw7dm2UPD3+6od3ub57q9//4rxA+6TSERENBK59H++F154YcDj4eHheO655/Dcc885vc3EiRPx3nvvufKwRERERCHpL93Vhzd2t7064mxy6RpOLnXo/7rbl2+akQzFMLcvS6YmRSN74ijsP9uCvx2uxbq8DK8/ht5kxvaSyl7TuwcarvO3w7b32nfmjvXbdfG3n+ek4q+Ha/GvIxfw229MQ7zm8meu6lIn3jtlazn/WfZEfy2RiIjIb7irNhEREVEAEiyiPdS5+8oJA962555qNRvyce6RfNwwJYHhYR8W0Yr/HrMFiCv9PCTk7ittVYh/+bTG6Z57Q+GsRVkll2NHaZXD+/QdrqPrNKGquQvxGjXumu+40jUUXDlhFBaMi4XJIuKFg+d6HXt+fzWsVuC6yQnITIjy0wqJiIj8hwEiERERUQB672QDGjpMSIoOw/VTEge9vbSnWqtBQNqvd2Hx7/f5tD02GO2takZDhwmjIlTIy4j361pum52CSJUCpxv1OHRO59Y5nLUodxgF1LUbhzRcR28yI1ylwH++twDVDy9Fenyku09pRPhZ9zCVP+yvhkW0BbtdggV/+bSm13EiIqJQwwCRiIiIKABJgcVd88dBpRj6j2zTk2OQGa+BYLHijwfO+mp5Qen/jtqqD78xPcmla+oLMeEq/OLqNLzx/Sswc0zMoENO+tKbzNi2uwIFRWX2oFBqUd5ZWo3EKPWAw3U0agXajbY255QtRUh/bBfGFRQ73SMxVNw+dyxGR6pwtqUL73e3LP/7yAU0dwqYOCoCK6Ym+XmFRERE/sEAkYiIiLxmqBNfaWAX2wz2/dakVldXrOkeqvLH/WdhMnOICgCIohVvdAeI3/Rz+7LkoaWZOFyrw9gtRYMOOelroBbl7R9VQrA4H66zOicVDR0mPFHiOIB8fHdFyH52I1QK3H2FbcuAV7q3EHj1C9t06p9kTwzZ/SGJiIg4PoyIiIi8Qmqn3NFjkMdaDvJwy98O18IiWrEodRSmJEa7fP+Vs8Yg5e1wXGgz4D9fXcD/zBvng1UGly/Ot0IboYJCLsPyLP9PGdabzNj+USW2FpfbvzbQkJO+dAZhwBZlwWJ1OFxnbW4afrUkAzIAO/dWO7z/s6VVeGhppntPbARYk5uK3LTRyM+Kx4VWA17//gLsKmtCbtpofy+NiIjIbxggEhERkcdcnfhKzlmtVrzY3b4sVUK5SqWQ46eLJmLjB6exo7RqxAeIepMZKrkcOoMAbbgKgij2er/pTWZMS47Gmz+4EklRYbB4MLTEWwYbcjJYgKcNV0EboXIYImojVIgKs+2JuS4vHQ8tzUSrQUBs97WJUCnQ0DH4HokJUY4nf490iVFhOFyrw93/OmIPXlfnpGL5ZP8Hz0RERP7CFmYiIiLymCsTX2lg+6pbcLpRD41agdtmp7h9nh8vnAi1Qo6DNTp8WtPixRUOv4Fa450NEpHagKXjY+37/BUFxD5/g1UQSkNOnBFE5y3Ka3PTIIi21nVpuE5CVBjUSrk9WJUCSEe0ESrEhjs+NtJJe0tuLS7v1dq9tbg8pFu7iYiI+NM8EREReczTMIQu+7CsAfEaNW6dnYLocPerNhOjw3D7HFsAudNJuBsMBgoIBxoksm13BS7pTdi2uzwg9/nzNMDTqJW4b/EkbMjPtJ9HG6HCxmVZWL8kY9CK36EGkKGG/xhCRETkGP8PSEREREPmqBLsi9pWRKmVrGbykHRt775iAqoeXoqCr032+JxrrrYFRP/68gLq2gwen2+4DRQQPvPJGSjlMqdhz6uf1yI6TIkdpdUOj/s7DPI0wBNFK1a+fAjzxmlxYeMy1G9ajrpHl2NdXvqQ9hzVqJVYvyQDG5dluRVAjlT8xxAiIiLHQvMnAyIiInKZoyEpq3NSsSY3Dfuqm7E6J7XXQAiJFIao+e+WTvlqAM38cVosSh2FfdUt+OOBs3h0ueeh5HAaqBrsrRP1uPuKCU7DngiVAi1dg4dB/trnTwrwgN5DTtbkpg7pdd93thm7Ky7h0LlW1G1abn8ernzOwlUKh3skhvLQo8H2luQ/hhARUahigEhERESDcjYkRQoMb5udggeXZkIuk7kVhoQyXw+gWZ2ThuZOAVdOGAWTWXQ6aCQQDVQNVt6oR2yE87CnS7Bg1ADHAyEM6hngNeqN0EaocLi2dUifl79/fh4AcMvMZER48PmS3gPuBJAjkVQZuqXH51HCfwwhIqJQxv/7ERER0aAGqgTbubcaWQlRiOgOQ+oeXY7ajctw7pF85KSORpiSP24MxNd7rq2cNQYfr8rB/upmp4NGAtVA+wSaRStMFudtwHfMGweD2RLw+/xJQ06sViDt17tw7e/3oepS54D3MZlFvPblBQAY8RO2hxtbu4mIiBzj/wGJiIhoUEPZFywhKsz+y3W0WoEZT3yEsy1d2P3TbFybET+cyw0qQ7227jJZROwoPdOrvdybFY6+JIgi1uSmoqDIcWu8QgaHbcA9278HOx4oxmkjMHdsDIrKmvDCpzXYev0Up7f98HQDmjsFJEeHYQk/W17H1m4iIqL+AvOnRSIiIgooru4LFh2uwtemJOKP+8/i9/uqGSAOwNd7rtkqHKsdHnu2tAoPLc306Py+pFEr8YurJ8FqtVW6OgsABwp7gikM+uHCiSgqa8JLh85h0/IsKBWOq09f/cLWvvztOSlQyGXDucSQwdZuIiKi3vh/QiIiIhqU3mTG6pxUh8ectYL+fJHt9m8cq8P51i4fri64CaKI1bmpDo95o802mKfKfnG+Fbk792L+OC0uPrrc6aRhqQ04ISoMaqW8X0XlYMcDxU3TkxGvUeNCmwHvnWpweJt2gxlvHa8DYGvTJiIiIhoODBCJiIhoQFarFQ+9dxJrctOwIT9zyPuCzRwTg6vTRsMiWvHnAzXDveygoesUsCbHtWvrioH2EQyEQSID2VFahVMNHfjXkfMIC4IA0FNqpRx3LbCFgv/r5DPzxrGL6BJEZCVoMH9c7HAuj4iIiELYyPzpi4iIiLzmfw/W4I8HarDvrA6FP16IDflZQ24F/XlOKj6pasafD57Fw/mZUDlpyQxlBcXl+PjMJfzxW7NcurZDFaxTZRs7jPhHd6vuGieDUEaiHy6cgKf2nMF7p+pRq+vCOG1Er+Ovdk9f/p+54yCTsX2ZiIiIhkfg/bRIREREAaOiSY/73zoOAPjegnFIig53qRX0mzPGIDk6DBfbjHjjaN1wLDmo1Oq68NKhczjV0AEAPmmzDdapsn8+WAOjWcSCcbG4auIofy9n2ExJjMbVaaMhWoEXD53rdayuzYDi8kYAwP/MG+uP5REREVGIYoBIREREDpktIr73jy+gN1lwbXoc7r16ksvnUCvl+OHCCQCA5/dVe3mFwe+JjyphsohYPGk0rp4U57PHkQaJ1D26HGc35OPcI/m4YWpiQA4SAQDBIuIP3e+XNVenhVyl3Q+vsn1m/vJpDUTRav/6v768ANEKLJygRUa8xl/LIyIiohDEAJGIiIjs9CYzTGYRDR1GCKIV6/IysGB8LF789hzI3Zz2+pPsiVDIZdhz5hKOXWzz8oqDV327EX8+cBYAsCE/y+ePJw0SadabkPbrXcjduRdnmzt9/rju+O+xOtS2GpAYpcZts1P8vZxh961ZKdBGqHC2pQtF3RWHQI/2ZQ5PISIiomHGAJGIiEacniGYySxCbzL7e0lBwSBYsL2kEsmbC5G8qRBjtxTh81oddv10ESaOjnT7vGNjI3Dz9GQAwO9ZhWj31J5KGMwiFk7QYmlm/LA97uyxsZidEgOzaMX2jyqH5TFd/UzuKK0CAPwkOxVhysCskvSlCJUCd3S3KL9w0DZMpbyxA4fO6aCQy/DtEAxViYiIyL8YIBIR0YjSNwRL3lyIJ0oqYRAs/l5aQNObzNi2uwIFRWXQdQkAAF2XgK3F5fjtR5Ueh7A/W5QKAHjl81q0GwRPlxv0LulN9jB1Q37WsLfoPrw0E4CtRfZCq8Gnj+XqZ/LzWh1Kq5qhlMvwk6sm+nRtgexHC23P/c3jdWjsMKKorBHxGjWWZcYjMTrMz6sjIiKiUMMAkYiIRgxnIdiWojI8vruClYgDUMnl9qqvvp4trYJK7tmPDHkZcVgxJRF/+848qBTykK8OfeaTM9CbLJiTEoMbpiYO++Nfkx6H3LTRMJpFPLnHd1WI7nwmd5ZWAwBunZ2ClNhwn60t0M1KicGts8bg399dgKgwJW6YmoSqh5fi2W/O9PfSiIiIKAQxQCQiohHD1yFYoBioHdTd9m2dQbAHPP2OdQlo9bBqUCaT4e93zsPhWh1SthQFZXWot1rj2w2CfS+7h/Mz/TIgRCaT4eF8WxXiH/dXo7HD6JPHcfUz2dhhxD+O2K7Nmtw0n6wpmPzvbbNxuFaHsVuKMOmxXRhfUIxXDtcGzWeGiIiIRg6lvxdARETkLUMJwRKigrv1T2oH3VFaBV2XAG2ECmtz0/DgkgxYAYfH1i/JGHTarjZcBW2EyuH100aoEBuu8mjdepMZv91zBluLy+1fkyrRAGBdXjo06sD9scTZdR/KtZXoTWao5HK0Gc348pfXYH91C5ZkDN/eh30tz0rAFeO1OHROh6c+PoNtN0z1+mO4+pn877E6RIcpMWtMJBZO0Hp9PcFEbzLjySD+zBAREdHIMjJKMYiIKORZrVbEhCmhjXAcdHkjBPO3gdpBy5r0brdvm8wiDtfqsDon1eHxtblpEETRo7UHc3WoN1rje+4DOL6gGOMLivFJ1SWYLJ5dV0/0rEJ8bm8VmjtNXj2/KFoRPchnUqNWoqyxw17duSwrAVUPL8Xf75jnl8rMQBLMnxkiIiIaefiTBxERBaW+7aSfdQ9e8GUI5m/OAoV4jRqT4iLdChu6BAu++dIh3PPvL7EmNw2PLMuyBz7aCBU2LsvC+iUZHlc6+bpF2pc8DXKcBZAFReV+35vz69OSMDslBh1GC/73QI3XzmsQLPjOK4dReLrR6WdydU4q9p1txqgIFX6zuwLJmwvZpttDMH9miIiIaORh3wMREQUdR+2kq3NSce/Vk7BwwijIZTI82+fYL68N/nY/Z4FCcnQYGjpMA4YNOoOAxKgwexutziBAG67CoZoWVDV34mxLJ8qb9HggLx0PL81Eq0FAbLgKgigOuUV3IL5ukfYlT1vjBwsgH+qeiOwPMpkM226YCqNZRH5WPBrajdBG2F53Vz4vfd9XB2tacLSuHRs/PIXS1bn9PpNrc9Pwq7wMnGrswI7SKrbpOhDMnxkiIiIaeULzJzIiIgpaepMZ20sqUdAdMAC2wGFrcTlkMhl+lZeOdXnpeKg7BIsKU+KDUw144O0T+P23Zvlx5Z5zFijUtRuRGKUeMGyIUiug6xLwu48rsaO0ule4uufni3C2pQsLxmvt95ECMbWXmhUEUcTa3DR7MNSTVB3qrcfytsGCnOgwJSyiFQazpVeIJogirFYrWg3mgN6b89r0ODy2qxx3/+uIW/s7Ogv09/x8ESqbOhEdpuz1mZSC6Qi1AtOTorH0D/sdntff4aq/BfNnhoiIiEYe/tRBRERBZaBqrh2lVVDK5dColVAr5UiICsOF1i6sfPkz/OHAWZQ1dgzzar1LEEWsyU3t9/UmvQlnLnVirZOptatzUtHQYcJTeypRUFTeq412a3E5du6txtSkKF8uHRq1EuuXZGBjnxbpDfmZXmmR9iUpyHFkdU4qSquboesSsL3E1oYrTZjeXlIBgyBCG6EK2L05pfbqrcXlbu3v6Kw9e2txOZ7bW42ZKdEA0OszqVbK7a8323Sdc/aZ8da2AkRERESuYIBIRERBxdXAIT0+CjdOTQIAPPOJ4+AxWGjUSqzNnYQN+Zn9AoWseI3TsOGhpZkYExOGnXurHZ53xzANZAhXKbAuLx11jy5H3ablOPdIPuaOjcUbx+p8/tiekIIcR9f9l9emQyGT4ZlPzvQLZwuKyvFsaRWa9YLD4Bfw/96cnu7v6On9pepOh8fYptvrM1O/aTnqHl2OdXnpXtlWgIiIiMgV/KdLIiIKGnqjGVFqpcv7gt27eBLeOVmPlw+dQ8HXJmN0pHo4lut1x+vacetfP8O2G6bi4qPL0dajHTSsO1Bw1CoarlKgocMYEG20UtVUYlQYXjh4Fj967SvEa9S4YUoiYp0ESYGgocOIeeO0OPdIPvQmi71FWaNWYlHqaKx8+TOH99u5txob8rPw4JJMyNB/H8Chtgn7iqf7O3p6f7bpDk76zHh7WwEiIiIiV/AnECIiCgo1LZ24+rm9KCpzPtXVWTVXXkYcZo2JQadgwZ+9OGl2uP3tcC1ONXTgxUM1CHPQDgo4bxUNxEqvuxaMx+QEDZr0JmzbXTHsj++K/3x1Ebe8dAjf+8cXSOxzbVuHEKIFaiWZp+8LT+/PNl0iIiKi4MAAkYiIApLeZIbJLKKhwwij2YJjde0wmEU8+VEFfuVi4CCTyXDv4kkAgJ17qyBY/Ncy6i6LaMXfP68FAHx3/jiX7z/QPn7+aqNVKeR44uvTAQBPf3wG1c2dw76GoXrruK3N+pr0+H7HhhqiOQt3/cnT94UgiljtYXt2oIarRERERHSZ/39yJSIi6sPZVNdPVuXAaLZAo3Y81XWgwOE7c1Pw4Hsncb7VgNe+vID/med6COdPH1U24XyrAdoIFW6cluTy/aVKLwAB1Ua7YmoilmbGY1d5Ex567yRevXO+X9YxkCa9EaVVzQCAm6b3v/bB3Ibr7H2xOicVa69OGzTkrNUZsCYnDbDa2rXdfV+xTZeIiIgosMmsVqvV34twVVtbG2JjY9Ha2oqYmBh/L4eIiLxIbzJje0klChyEMRuXZWFdXrrblVtbCsuwqfA0FoyLxcFfXA2ZTObpcofN3f/8Ai9/VosfXzURf/jWLLfPozeZoZLLewWv/q6E+/JCK+b97mNYrcDe1TnITh3t1/X09dKhc/jBv45g7tgYHL7vGoe3MQgWPL67IqDCWVf0fF/EhKvw/ql6PPTeKTx903Qsn5zo8D5WqxXX/ekAalsNeOG22Zg/ThtQ7ysiIiIiGpgr+Rp/siMiooAy2FTXh5Zmun3un2ZPxLbd5fisthV7q5uRmxbn9rmGk95oxn++ugjAvfblngKx0mt2Siy+f8V47K9ugdEswmQWoTMIvQaV+NObx2zX/hvTk53eRmrDdaUqNpD0fV/sqbyEUw0dWPfOCSzNTIBC3j9sf/9UA4rLm6BWyJEUHWZvzwYC431FRERERN7Dn+6IiCigDGWqq7sSo8NwZ3cA95eDwTNM5b/H66A3WTApLhKLUkf5ezk+se36qdjz80XYXdGE5M2FSN5UiOTNhXiipBIGweK3dXWazCgsawQA3DzDeYAIBOYeh+56ZFkWRkWocPRiO1481P+zYraIWPf2CQDA2qvTMClOM9xLJCIiIqJhxACRiIgCiq+nBT9wbQbe+P4V2HHLTNS3G2Eyi9CbzB6d09deOWwbnnLnvHFB1XbtCk2YAjv2VmFrcbk9QNZ1CdhSVIbHd1f47TUqKmtClyBi4qgIzBoTOtumjI5U45FlWQCARz44jXZD7+v/54M1ONnQgbhIlUdVwUREREQUHBggEhFRQBFEEatzUh0e88a04PHacByu1WF8QTHGbA6MKreBXGwzoKi7Au5OD9uXA5lKLsfO0mqHx54trYJK7p8fWd7snr5804zkERveOvPzRanIiNegvt2I7R9V2L/e2iXg0Q9PAwA2XTfZaeBPRERERCMHA0QiIgoonSYL1uSmYUN+pj2Y0EaosHFZFtYvyfCoLVRvMmPb7oqAq3IbyKufn4doBbInjkJG/MhtE/Vl67pEbzLDZBbR0DG0ylOLaMU7J+oBADcNsP/hSKVWyvGbFVMBAL/9qBLnW7sA2ALdJr0JUxKj8OOrJvpziUREREQ0TIJ3cx4iIhqR/njgLF79/Dx+f8tMbMjP8upACl8OaPGVVz63tS97Ojwl0Emt645CRG+0rhsEC7aXVGKHC1OS91U3o0lvwqgIFa5OC6zJ0MPl5hnJuHPeWKyclYLRkWrUtxtx/zWTMHNMDLThSqgU/LdoIiIiolDAn/qIiChgmC0i/rT/LE41dKC21eD1gRTDUeXmTV9daMOXF9qgVshx25wUfy/HpwRRxNrcNIfHXGldd1RlKFWeFhSVuVR5KrUv3zgtCcoQDcpkMhme/9YsHK7VYeyWIozZXIjxBcX4vFaHqyaOzIE+RERERNSfSz8Nb9u2DVdccQWio6ORmJiIm2++GadPn+51G4PBgFWrViEuLg5RUVFYuXIl6uvre92mpqYGK1asQGRkJBITE7Fu3TqYzYHXNkZENJjBWiJdbZkMdW+dqEdtqwEJGjVunT3G6+f39YAWb/tb9/CUFVMTMTpS7efV+JZGrcT6JRnYuCyrV+v6I8syh9y6LlUZ9pzi/Fxp9aCVp472V7RarXjzmC1A/Mb0JA+eWXDTm8zYXlLZr+1/a3F5wLb9ExEREZH3uRQg7tmzB6tWrcKBAwdQVFQEQRCwfPly6PV6+23uu+8+vP3223jttdewZ88eXLhwAbfccov9uMViwYoVK2AymbBv3z68/PLLeOmll7Bx40bvPSsiomHgKKzoOYxjsOPU3/P7qgEA9yycgDClZ+3KjgxU5bY6JzWgKhAtohVfnG9FvEaN7y4Y2e3LknCVAuvy0lH36HLUPrIM5x7JR07q6CG1yTqrMnzl81o0dBhdrjw9Ud+BykudCFPKcd3kRM+eWBBzJ3wlIiIiopHHpX6wDz74oNffX3rpJSQmJuLw4cNYvHgxWltb8cILL+DVV1/FkiVLAAAvvvgipk6digMHDuCqq65CYWEhTpw4geLiYiQlJWHOnDkoKCjAr371K2zatAlqdf8KC6PRCKPRaP97W1ubO8+ViMhrpKqcgqIy+9eklkgAuHfxJPzu4zNOj6/LS/dKS+5IcqqhHbvKmyCXAT/x0WAGqcoNsIUf0l54q3NSsSY3Dff86wj+fsd8RIf79rXRm8xQyeXQGQRou/d37Pl+0JvMUMhl+PNts5EYpYZo9elyAop0HWLDlZj72z2ouNSJ1+6aj5WzBm7hdhZ01bUbMSrS9f0V/3vsIgAgPzMeUWGh+1kdStt/QlTYMK+KiIiIiIabR/9s3NraCgAYPdq2sfjhw4chCALy8/Ptt5kyZQomTJiA/fv3AwD279+PmTNnIinpcjvQddddh7a2Nhw/ftzh42zbtg2xsbH2P+PHj/dk2UREHhuoKufvn9ciUqUY8VU73m7Pfn7fWQC2/eYmjo70xhId6lnlVr9pOeoeXY57r56Eb/31M7xzsgHf++cXED1M7Aa6NkOtXE3ZXIT0x3ZhfEExfvtR6FWuasKUuH3uWADAb3ZXwGod+DVxFnQ16U0oLmsasPLU0Xv3rePd05dnhN705Z6Cre2fiIiIiHzD7d9gRVHEvffei5ycHMyYMQMAUFdXB7VaDa1W2+u2SUlJqKurs9+mZ3goHZeOOfLggw+itbXV/ufcuXPuLpuIyCsGqsqJUCnQ0hVcwzpc5e327A6jGS9/Zvve/vNFqV5cqWMatbLXgJbRGjW2r5gGtUKOUw0dOF7f7nY4OtC1aTMI2La73OEwj227y1HfbsBjTo6H4n5za3LTEKGS47PaVuwqbxrwtgMFXdt2l+NXDvZX3JCfiTW5afjJa1+i3XD52p5v7cKhczrIZMDXp4V2gOit4TZEREREFNzc7slZtWoVjh07htLSUm+ux6GwsDCEhbE9hogChxRWOAoJuwQLRkW43jIZLAZr33anPfvvn9eizWBGRrwG+ZkJXl3vUC2cOAr/uHMectNGY0dpFXburba3OK/NTcP6JRkIVw28L+NA1yZcKce9iydhR2m1w/u++vl5PLw0CzudHH+2tAoPLc109+kFpYSoMNyzcCJ2llbhNyUVyM9y/t44Xt+O1Tmp2Fpc3u/Y1yYnwmq1Yl1eOh5amolWg4DYcBV0BgE3/eVTHKjRQWc4hHfuWQi1Uo7SqmbEa9TIStAgKTq0f/5w1vY/1M8EEREREY0MblUgrl69Gu+88w5KSkowbtzljd2Tk5NhMpmg0+l63b6+vh7Jycn22/Sdyiz9XboNEVGgazUIWJ2T6vDYHfPGwWC2DNgyaTAHbzuqt4cqWK1We/vyzxZNhFwu83iN7lo+OQE7Sqv6TZwdagXgQNfm7ZP1aDOYQ7py1R3/b/EkKOUy7CpvwqEancPb7CpvxB1//xxrctPwSJ8qw43LsrB+SQYi1cp+laeJUWF4+uYZ0KgVqG014PPzrTCZRWRPHI2qh5fij9+aNYzPNHA5avtfl5fO8JCIiIgohLj0W57VasXq1avxxhtvYPfu3UhL6/3L8fz586FSqbBr1y77106fPo2amhpkZ2cDALKzs3H06FE0NDTYb1NUVISYmBhMmzbNk+dCRDQsBIuI1a8fxZrcNGzIz3QYVsSEq7B+gJbJ+948HrR72g1lqIIr9lY346uLbYhQyfH9Bf7d41Yll2Pn3mqHx4YSjg50bcob9YiNcN5m27Ny1ZFgr1x118TRkfgfaS/Ekv7VhbouAXf/8whONXTg9/uq8YCLQdeVE0bhgx8txJ6fL8J7J+uRvLkQqb8uxviCYrz25cWg/Zx6W9/wlUOgiIiIiEKLSz/9rVq1Cq+++irefPNNREdH2/csjI2NRUREBGJjY3HPPffg/vvvx+jRoxETE4M1a9YgOzsbV111FQBg+fLlmDZtGr773e9i+/btqKurw4YNG7Bq1Sq2KRNRUHhsVzle++oizuq68O49C7EhP8veEimIoj2skKp2erZMNuqNuP7PB/D5+TaoFDI88fVpCFMonE7jDUSx4Uqvtme/fbwO8Ro1bpqRjFGRam8t0y2eTpwdqLXdLFphstj2k9vSo8VZ0rNy1dFxab85tWfzz4LSA3kZ+OvhWrxxrA6nGtoxJTHafmztG0dR22pARrwGv7zmcvu89DoN5XrNGRuL7SUVvdqfOTWdiIiIiOgyl34Lef7559Ha2oprr70WY8aMsf/517/+Zb/N7373O9x4441YuXIlFi9ejOTkZLz++uv24wqFAu+88w4UCgWys7Nx55134q677sKWLVu896yIiHzk05oWe8jwi6snIU6jHrAqp2/VztjYCDz1jRmYkxKDgq9NwRNeHEQyHD45cwnF5U1O27dX56TidGPHoBNzAdt+gUaziJ8uSkPVw0tRcN1kL6/WdZ5OnL3UaXJ6bdbmpkEhg8PK1MEqV6XjoRpiTUuOxk3Tk2C1AttLKu1ff/NoHV75/DzkMuCv35kLTZh718fWel7t8NhImZpOREREROQJmXUov+UFmLa2NsTGxqK1tRUxMTH+Xg4RhYhOkxnzfvcxyhr1uH1OCl69c77b5zrX0oU/HzzrcODDxmVZAVHxpDeZoZLL7dWRta1duOWlz2CyiChdnYNnP6nqNVRhTW4qVuek4Zrf78PXJidg83WTEaZ0XF1pECzYtrsCOwJsKIPeZMYTJZUOKwAfWZaFBwZ4XYxmC259+TO88O05gw5hka5tz8rVnucd7HgoOnC2BT/41xH8ZsVULJ+cCF2XgKgwBYrKmlDT0om1V09y+9wNHUYkbyp0erx+0/IBK0+JiIiIiIKRK/kaA0QiCmp9Qy5vBy09zx8dpsSHpxvx9Mdn8Pr3F2C0B+22JrOI5M2FTtuA6x5dDrXSf1VPjgK+1TmpWNPdXvvEjdNgsVr7hVzvn2xAQXEZin+S7TREEywintxTiYKiwAxPDYIFj++u6BWOrs5Jxdqr0yCKQKKTqbwb3j+Fx3aVIyd1FN6+ZyEiVQoGgF7W3GnC0x+f6fW+Wp2TigeXZiLCg+A50D+PRERERES+4Eq+xt9miChoGQQLtpdU+qyKzdH5V+ek4p17rkSUm62SEk/32vMlvcmM7SWVKOhRhafrErC1uBwyGbDthqm9rm/Pvea+NTsF2amj7JOMe95/S1EZwpVy3Lt40oDtog8tzfTNExsiR3tXfnzmEhY/tw8JGjV2/TQbSkXvMOlwrQ6/KakAANx3Tbq9/diVffhoYHqTGc98cqbf+2prcTnkMplHwbMgOt+bMpT3niQiIiIikvCnYSIKSnqTGdt2V6CgqMwexEkh1eO7K6A3mX1y/q3F5Xjyo0qPz+/pXnu+ZNsPrsrhsR2l1VArBv5fR4ImzOkk47dP1qPNYPbqFGdf6Lt35YRRETjfasAnVc146P1TvW5rMov4wb+OwCJa8e05Kbhl5hg/rXpk8+U+hRq1kntPEhERERENgAEiEQWlgUIubww98PX5pYonR6SKJ38ZSnWku/cvb9QjNiJww1NnshKi8JdvzwYAPPlRJf577KL92O8+PoOjF9uRoFHj2Ztn+GuJI56n78vBSJWndY8uR/2m5ah7dDnW5aX7dU9OIiIiIqJAwX9SJ6Kg5K0WYEd7KCpkQHPn4FVynrQYSxVPAPrttffLa4dnD0BHz/28zoBx2ghoI1RO94MbLOCTqisd3d8sWmGyBGe76MpZKbh3cQs+ONUAlVwOo1lES5cJq3NTMTkxCuHdFYvkGwO9r7wVPEufO7aeExERERH1xp+MiUYAvckMk1lEQ4cRJrPYr712sOPByBstwNIeh8mbC5G8qRDJmwuxvaQCeqMFsRFKn1fJ9a14Or9xGeaOjcVNf/kU7QbfvkbOnvuoSBX2VTdjdU6qw/sNpTpysOpKhQxB2y76mxVTUbo6BwdrWjBmcyFSNhdhfEExPq/V4dr0OH8vb0QL5KpdIiIiIqKRLnB/SyOiIRlskIivB434y0BDD1bnpA5aIehsUEhBUTmsVuCHCydgTW6qw0nB3qyS61nx1GE045dvn0BVcyceePcEnl85y+PzOzLYc79lZjLWL8mEXCbrVR051PeNs+rKvvfvO6hEEMWAf0+aLCKe+aT/gBhvDPKggQ31fUVERERERN4ns1qtVn8vwlWujJkmGskcBUGSR5ZlYtWiNDy3r8phCLZxWVbQhx1tBgFPflSJnXure7UAr8lNw+1/O4wXvj0HqaMjHd7XZBaRvLnQaTtk3aPLIVqteHx3xbCGFSUVTVj6h/0AgOKfZGNJZrzb53LUoqxRK4f03NVKuf3+PQM+V94vnt4/EA312pHvjMT3FRERERGRP7iSr/EnbqIgNtCgj1c/P4+Hl2YNOLX0oaWZPlydb3UYzbjhzwfxy7wMXNi4DO1GM2LDVdCbzLjz1c9RUnkJP37tS/zjzvmIDlP2CtHCFHI06o1D2uNwuKvk8jLi8dPsifjD/rP44b+P4KtfXouoMNe/VTurPP3ltZPQ0jW0/R093Q9uJO4n5629N8l9I/F9RUREREQU6PhTN1EQGyjMiFAp0NLl26ml/vRsaRX2nW3BA++cgEIuQ0JUGNRKOUZFqvGnW2djeVY8/n7HPDzzyZk++/xVos1ghnaIk4A1aiXU3cMx1Er5sFQ6/WbFNEwcFYFwlQJljR0u71+pN5mxbXcFCorK7K+/rkvAlqIy7CytRoJGHXRTkAOFN/beJCIiIiIiCjYMEImC2EBhRpdgwahBQrLo8OAsQm7pNOGJkgoAwKblWVApen8rGxsbgb/fMR87Sm171fUM0QqKyvD0J2fQpDdhTW6qw/P7eyBDdLgSf79jHvb8fBH+e6yuVwD6REklDIJlwPsPVJm6/aNKCBYOo3AXB3kQEREREVEoYoBIFMS6BIvTabl3zBsHg9niNOxYnZOKD0814tXPa4NuSvOTeyrRajBjRnI0bp8z1uFtosOU2Lm32uGxnXurMSY6HA8uyQzYScCzU2IcBqBbisrw+O6KAV+jwdpsBYs1aKcg+5s0yIPXjoiIiIiIQgl/0yEKYttLKnDv4kkA0GuQSM9BH86mlv5i8SR89++f48Xb5+A3uyuc3j/Q1Lcb8ewntuq6LV+bDLlc5vB2Q92rLlAnAavkcqcB6GD7V0qVqc4GfUSF2dqyA/W5B7pwlYLXjoiIiIiIQgoDRKIg9frRi9i2uwL/PV6Hwh9fhQ35WQ7DDGdhR6RKge03TrNXuUmkKjcAATmledvucuhNFlwxXoubpic7vd1gIVrPPQ6BwBvI4MmwDqkytefrKpHabNWQB+xzDwa8dkREREREFEr4Gw9REKprM+Anr30JAPjG9GSMjY0YcNCHo0EgMpkMGfGaAavcVPLA+hZR12bAf768CADYev0UyGSOqw+B4N+rzt1hHVarFQVFZViTm4YN+ZlssyUiIiIiIiKP8bdIoiBjtVrxw39/iUudAmanxGDz8slun8uTKrfhpDeZoZLLIYhWnF6fh8/O6bB4UtyA95H2qgP6t28Hant2T1IAKlWD9rQmN9VeRdjXS4fO4amPz+DDskZ8+KOFTitTiYiIiIiIiIaKASJ5lRT06AwCtN2BRc9qp8GOk2M9r1tMmBI/vGoizrUa8LfvzIVa6X6V4FDbfP3JIFiwvaQSO3qEgGty07BwwqhBw7Bg3qvOWQC6OicVq3PS8Nk5Ha5Jj+91n1MN7VjzxjEAwP/MHYuU2AgAbLMlIiIiIiIizzC5Ia9xFPT0rPYa7Dg55ui6rc5JxSerFiHGw4BvoCq3nnvl+YveZMb2kkoU9FifrktAQVEZZBjaHo3BvFedowD0i/M6XPP7fTjb0onin2QjO3U0ANv75DuvfI5OwYKlmfH4VV6Gn1dPREREREREI0Xw/CZNAU1vMmPb7goUFJXZq9mkYRyP765Am0EY8LjeZPbn8gOWs+u6tbgcT+054/F1k6rcNi7L6rVX3ob8TPzy2uEboKI3mWEyi2joMMJkFu3PSyWXY0dplcP7BOIejb7Qd//KeeO0SBsdiYmjItHSJcBotqChwwgrgEeXT0b2xFF4+fa5TqdTExEREREREblKZrVarf5ehKva2toQGxuL1tZWxMTE+Hs5BMBkFpG8udBhK2x6XCSOr8tzelwboULdo8s9asUdqQa6rt68blKLdKtBQFSYEh+casDv91bhrXsWIsLH1aEGwYJtuyv6VaY+kJeO5k4BE7YWO71v/ablAbFH43DrNJlhMIt4+uMz2Lm3uldl6gN5GYgKY3E5ERERERERDcyVfI2JDXnFQMM4IlQKtHQNPqwj2DmrovPEUIaceEPPKjdRtOIX/z2GXRWX8Ns9lV45vzMDVa4++0kV4jVqtyYRj3RWAM98UoWtxeX9KlOf/KiSFb1ERERERETkVQwQyStiu4dxONIlWDAqwvnxkRAESfsUJm8uRPKmQiRvLsQTJZUwCBaPzqsd4Lr66rppwpT4zY3TAACP76pAra7L43O606K8/aNKCBYRa3LTHB6X9mgMRWztJiIiIiIiouHE3zLJY0cvtqGkogmrc1IdHr9j3jgYzBasdRIErc5JhcHsWdDmT4Pt/+hJNZgg+idAu31OCnLTRqNTsOBX75706FyOwtXtJZVoMwioazcMWGEpWKx40MEejRuXZWH9koyQneA9XJWpRERERERERACnMJOHPqpowjdfOoQxMeH4ZFUO5DIZnnUyZXn9EttU2Gf7TBNek5uG7/3jCHZ+cwa0kSqo5HLoDAK04SoIohjwIdFg1WAPLc10+9xyAGty02C1Wnvtdefr6dUymQzP3DwDC57+GP/44jx+mj0RV0+Kc/k8A01RjlDKce/iSdBGqJzu8RgVZmut7juJWBDFkJ7cLVWmOrtuwV7RS0RERERERIElsJMZCjjSsA2dQUBMmBJtBjPGxIQjXqOGQo4Bg55wlaLf8Ua9EStf/gxNehPCVHJsL6nAjtLhC8q8YSjVYAlRYb2u3VDD0cd2V+D/vrqIp74xDRvys4Y1QJs7NhY/XDgBfz5Qg1/89xgO3bsYChcn+w7WorwqJxVrc9OwpUfAKJEqLNWQ26+TNDBFHeLF04IoDum6EREREREREXkDA8QRxp2QaqikVtQdfSoI967OQaRKgbAegZazoKdvEDQ2NgJ/v2Meyhs78Gz3UAiJ1AYM2ILJQK1EHKwaTKNWoNUg4Kk9Z/pNGh4oHC1r7MATJZUwWUR0CqJ9yAkwfAHa1q9NwZHzrXhoaRYEi4hLnWaX3leDhauCxeqwMjUYgmN/0qiVvG5EREREREQ0bGRWq9Xq70W4ypUx06HEIFiwbXeFSyHVUDlqRZVsXJblccBnMotI3lzoNISre3Q51MrAq6jSG804XKtDcXlTr/BTsiE/Ez+4cgL+8mmNw+POrp3VasV1fzqA4vImfG1yAt794ULIZK5V/3lLm0HAkx9VutVCPdTXVQq+e1ZYBmpgHEh43YiIiIiIiMhdruRrgZfIkFuGOsjD2TTcwfh66mswDoXoEiy4+aVD+On/HcWa3DQ84mDQx0NLM5EcE4ade6sdnsPZtXvty4soLm9CmFKOZ78502/hod5kxm/3nMHW4nK3BsQYzBanw3V6DoHRqJX2Cku1Us4QbIh43YiIiIiIiGg48LfNEWIogzwctSAPtZJsqPv8uSsYhkL0bQ8/fE6H860G1LZ24XxrFx7IS8fDDvZ/bOgwunTt9EYzthbbKj1/lZeBjHjNsDw/RzwdEPPK4fP2KdLDOQSGiIiIiIiIiLyHAeIIMVjA124045lPzri1x6DJLCJKrfRpwBfoQyGc7f+45+eLUN3chTljtfbb9t2ncLBwNCpMCYtohcFsgUouR0uXgP1rc7G3qhmL3Zh87E2eBMcXWg144J0T2Lm3Cu/cc+WwD4EhIiIiIiIiIu9gC/MIIYVUjqTHRSI6TOlyGy1gC85WvnwIRWWNQ2pFdZc0FGJjnzbgDfmZWJObBsHi2fk94aw9fGtxOXburca05KgB7y+Fo46szknF3upmtBoEbC+pRPLmQkzYWozxBcXYW93i9efiqoHeV4MFx498eAqdggXaCBXSRkey1ZaIiIiIiIgoSDFAHAG6BAsOndM5DfjuWzwJLV0DV5LpuvcY7LtH4qfndKi81InNhafxQF7/gG/jsiysX5LhlUAoXKXAurx01D26HPWbluPio8uxaOIoXP3cXnzzxc9gMvsnRByojXfHEPZ/dBaOblyWhXXXZkAhA57++Ey/gLJgiPsM+tJA4eea3FSnwfGR86146dA5AMBT35jutz0ciYiIiIiIiMhzLAMKch1GM25+8RAutBmw5+eLIJPJ+u1x+IMrJ0Aukw3cRqtWoM0g4Ld7zjhp0+1EVJgS6/LS8ZCDff68RQoipbbYcdoInG81AACOXGjFnJRY+x6EwzVx1hv7P0rhaN9rp1ErsSg1DitfPuzwfkPZZ9CXpPBTWkvP98XqnDTsP9uC/MyEXvexWq345dsnYLUC356TgqsmjvLH0omIiIiIiIjISxggBpm+gzw+O6fDhTbbII/q5k6ngzz0JrPTPQZX56SiocOEv3xa02+PxK3F5ZDJZHggLx1A/4DP1/sSzhgTg3d/eCUmJ0RhR2kVvvbng24P4uh77YYaQMaEeWf/R2fXrtXHA2o85Sj8PFbXhmt+vw8X2gw4fO9ipPcY9PLuyQbsrrBNkN52w1S/rZuIiIiIiIiIvIMBYhBxNsjj458vQm2rAXPGxtpv2zekclZJJoVwAJzukbijtAoP+7EKbu7YWGwvqXBrAIxkKBOo+waMRosFz3xS9f/bu//gqOs7j+OvTTa/DOzGYMlmlUBULD9ERomkETqcJQVbhx6KdXBSmkNG6jUBQhChtoG7QYngVC0/hpT+UaYzWFtuhFZupI3AheKFEJKiIhjARkAwpDVNNgkGQvZzf9jsdSWbH2ST7+7yfMxkxv1+vkneu/Nyw77n80MTXA7lTxnl9/s7BeOAl3A4gfrLzc8JqQ4NuylGH9a36LFfHdH/LpqqhJhotXd4tfYfJ0gvnpquUck3WVYzAAAAAAAIDhqIYaL1ylWt3/+R1vzTDMKuZgh2J9Ay2viYaNW3XA7ZWXBf7EH4cZdjvVniG+i162xAPvvgHYqy2bpszi6amq75rx/V9pz7FGWzddl87e8S7lA/gborMdFRen3eJN338gFdvurVsbpmTUx16K+tV1T6dJbKPvpMX09PtrpMAAAAAAAQBDQQQ0ygZbY9HeTR2xmCgZbRhvIsuP7uQdjda7fhYK2WTbtd68pOa03ptcu3JWnznAlyJsQM2P6PPc0ODeYek8F0qzNBu+bfrztvSdTGg7WaufWQr/ZFU0fpG3feYnWJAAAAAAAgCGgghpBAy2yf+Zfb1XDp6oDOEAzlWXD9bW5214C0R9kUa48OOMNx0zsf6yfZd0ka2P0fu5sdGsrucTu6XF6+pvSUbLL1ank5AAAAAAAIbaG1LvIG1nrlqor3ndaa0pO+ZlfnMttNBz/W8CGxSkroulEWjBmCnbPgVn3zLt/vSUqI0apv3qWV37jT0iZQZ3OzK/lTRqnh0pVuv98Zbw/42o3+SqKaPu95huNgSIy1K9Yepa8MiVOsPSosGm89LS+PieItBgAAAACAcMen+xDR3TLb9f/zkdo7AjfROmcI9lfnLLi61TN08T9mqG71DC1/8A7LZ8EFam7+JHu0Fk1N1yPbKvX+p54uv3fPh/X6Y81flT9lVJfj/zrOpaSEmAFtzkay3iwvBwAAAAAA4S30pzjdIHpqxLR3mEHZJ28gl+n2R1dLfD9v79APdryrirON+vf/ek9v/Nv9Xyx1/sf+kec9bVrx38fV3mH0Tv6ULg9BWfz19JBevh3qQnnvTAAAAAAAEBw0EENET42YIXFfLG8Nx33yguWa5qY9Slseu0ftxqhkzj3aeLBWm9752O8U5bd/kKXivaeUGGvv9rULx0NMQgHNVwAAAAAAIp/NGGOsLqKvPB6PnE6nmpqa5HA4rC4nKFqvXNVL+z/qshGz6pt3cRhFNxo/b9fLZR/5HeTR6SfZo/Xsg3dqSFzPr13nCdj/3GDkNe9ZW3uHXtx3muYrAAAAAABhpC/9tT5PDTpw4IBmzZolt9stm82mXbt2+Y0bY7Rq1SqlpqYqISFB2dnZOnXKv7HT0NCgnJwcORwOJSUlacGCBWppaelrKREllA8xCXU3xURr0zsfdzm26Z2PFRvdu5iH4yEmoSBU984EAAAAAADB0ecGYmtrqyZOnKjNmzd3Ob5+/Xpt2LBBJSUlqqioUGJiombOnKm2tjbfPTk5Ofrggw9UWlq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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 21 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-16T16:14:59.858569Z", + "start_time": "2025-06-16T16:14:59.692652Z" + } + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "y_train = airline[:100]\n", + "y_test = airline[100:120]\n", + "plt.plot(np.arange(0, len(y_train)), y_train, label=\"Train\", color=\"blue\")\n", + "plt.plot(\n", + " np.arange(len(y_train), len(y_train) + len(y_test)),\n", + " y_test,\n", + " label=\"Test\",\n", + " color=\"orange\",\n", + ")\n", + "plt.legend()\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.title(\"Train/Test Split of Time Series\")\n", + "plt.show()" + ], + "id": "512d5683fca427e7", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 22 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "We want to train a forecaster on the train set and forecast predictions for the\n", + "subsequent test steps. The `RegressionForecaster` is a window based forecaster that \n", + "by default uses linear regression to predict one step ahead. It requires a window \n", + "parameter. See the [forecasting with regression](./regression.ipynb) notebook for \n", + "details. The `forecast()` method makes a single forecast `horizon` steps ahead. " + ], + "id": "d1a59c9017081cf4" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-16T16:15:02.833697Z", + "start_time": "2025-06-16T16:15:02.824630Z" + } + }, + "cell_type": "code", + "source": [ + "from aeon.forecasting import RegressionForecaster\n", + "\n", + "reg = RegressionForecaster(horizon=1, window=10)\n", + "p1 = reg.forecast(y_train)\n", + "print(\" First forecast = \", p1)" + ], + "id": "ff55e38056585c73", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " First forecast = 376.10513465806844\n" + ] + } + ], + "execution_count": 23 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "what if we want to predict further ahead? The direct strategy, described [here](./direct.ipynb)\n", + "retrains the model for each set, changing the forecasting horizon. This can\n", + " be computationally intensive. As an alternative, the iterative \n", + " strategy uses the predicted value and predicts without refitting." + ], + "id": "2106d4593aee61de" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-16T16:15:05.268365Z", + "start_time": "2025-06-16T16:15:05.257150Z" + } + }, + "cell_type": "code", + "source": [ + "y_new = np.append(y_train, p1)\n", + "p2 = reg.predict(y_new)\n", + "y_new = np.append(y_new, p2)\n", + "p3 = reg.predict(y_new)\n", + "print(f\" second forecast = {p2} third forecast = {p3}\")" + ], + "id": "5b091fcecbd3f267", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " second forecast = 427.1861353919512 third forecast = 418.40595828752396\n" + ] + } + ], + "execution_count": 24 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "there is a function in the base class to make iterative forecasting easier.", + "id": "5e1aafb6bc8b5f98" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-16T16:15:10.611791Z", + "start_time": "2025-06-16T16:15:10.451442Z" + } + }, + "cell_type": "code", + "source": [ + "y_hat = reg.iterative_forecast(y=y_train, prediction_horizon=20)\n", + "\n", + "plt.plot(np.arange(0, len(y_train)), y_train, label=\"Train\", color=\"blue\")\n", + "plt.plot(\n", + " np.arange(len(y_train), len(y_train) + len(y_test)),\n", + " y_test,\n", + " label=\"Actual\",\n", + " color=\"orange\",\n", + ")\n", + "plt.plot(\n", + " np.arange(len(y_train), len(y_train) + len(y_hat)),\n", + " y_hat,\n", + " label=\"Predicted\",\n", + " color=\"green\",\n", + " linestyle=\":\",\n", + ")\n", + "plt.legend()\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.title(\"Train/Test/Pedicted\")\n", + "plt.show()" + ], + "id": "1b2267b78b0cdfef", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 25 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Looking closer, we can see the errors our forecaster is making.", + "id": "9bf1c1e6d500cbd7" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-16T16:15:27.178701Z", + "start_time": "2025-06-16T16:15:27.018119Z" + } + }, + "cell_type": "code", + "source": [ + "plt.plot(y_test, label=\"Actual\", color=\"orange\")\n", + "plt.plot(y_hat, label=\"Predicted iterative\", color=\"green\", linestyle=\":\")\n", + "plt.legend()\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.title(\"Pedicted and Actual over the test interval\")\n", + "plt.show()" + ], + "id": "30d6ed16e3eaa898", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 26 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "It seems to be underestimating the peaks and troughs. Contrast this to the direct \n", + "strategy which results in very different forecasts " + ], + "id": "3e97b9b31693702d" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-16T16:15:33.914963Z", + "start_time": "2025-06-16T16:15:33.788410Z" + } + }, + "cell_type": "code", + "source": [ + "y_hat2 = reg.direct_forecast(y=y_train, prediction_horizon=20)\n", + "plt.plot(y_hat2, label=\"Predicted direct\", color=\"blue\", linestyle=\":\")\n", + "plt.plot(y_test, label=\"Actual\", color=\"orange\")\n", + "plt.plot(y_hat, label=\"Predicted iterative\", color=\"green\", linestyle=\":\")\n", + "plt.show()" + ], + "id": "bc5ed962ad010b89", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 27 + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "5e283827ebb7141b" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From fc99068aab6e27333f4cea8bd673ca667a889800 Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Wed, 18 Jun 2025 21:32:28 +0100 Subject: [PATCH 156/216] [ENH] remove horizon from ETS constructor, parametrize ETS tests (#2898) * remove horizon from ETS argument, parametrize tests * remove horizon from example * docstring --- aeon/forecasting/_ets.py | 7 +- aeon/forecasting/tests/test_ets.py | 142 +++++++++++++---------------- 2 files changed, 64 insertions(+), 85 deletions(-) diff --git a/aeon/forecasting/_ets.py b/aeon/forecasting/_ets.py index cb0fef4e0d..d0aa466b7c 100644 --- a/aeon/forecasting/_ets.py +++ b/aeon/forecasting/_ets.py @@ -44,8 +44,6 @@ class ETSForecaster(BaseForecaster): Seasonal smoothing parameter. phi : float, default=0.99 Trend damping parameter (used only for damped trend models). - horizon : int, default=1 - Forecasting horizon (number of time steps ahead to predict). Attributes ---------- @@ -82,7 +80,7 @@ class ETSForecaster(BaseForecaster): >>> from aeon.datasets import load_airline >>> y = load_airline() >>> forecaster = ETSForecaster( - ... alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, horizon=1, + ... alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, ... error_type='additive', trend_type='multiplicative', ... seasonality_type='multiplicative', seasonal_period=4 ... ) @@ -106,7 +104,6 @@ def __init__( beta: float = 0.01, gamma: float = 0.01, phi: float = 0.99, - horizon: int = 1, ): self.alpha = alpha self.beta = beta @@ -130,7 +127,7 @@ def __init__( self.aic_ = 0 self.residuals_ = [] self.fitted_values_ = [] - super().__init__(horizon=horizon, axis=1) + super().__init__(horizon=1, axis=1) def _fit(self, y, exog=None): """Fit Exponential Smoothing forecaster to series y. diff --git a/aeon/forecasting/tests/test_ets.py b/aeon/forecasting/tests/test_ets.py index f4f5f86590..1c2f2ad19c 100644 --- a/aeon/forecasting/tests/test_ets.py +++ b/aeon/forecasting/tests/test_ets.py @@ -10,88 +10,70 @@ from aeon.forecasting._ets import _validate_parameter -def test_ets_forecaster_additive(): - """TestETSForecaster.""" - data = np.array( - [3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12] - ) # Sample seasonal data - forecaster = ETSForecaster( - alpha=0.5, - beta=0.3, - gamma=0.4, - phi=1, - horizon=1, - error_type="additive", - trend_type="additive", - seasonality_type="additive", - seasonal_period=4, - ) +@pytest.mark.parametrize( + "params, expected", + [ + ( + dict( + alpha=0.5, + beta=0.3, + gamma=0.4, + phi=1, + error_type="additive", + trend_type="additive", + seasonality_type="additive", + seasonal_period=4, + ), + 9.191190608800001, + ), + ( + dict( + alpha=0.7, + beta=0.6, + gamma=0.1, + phi=0.97, + error_type="multiplicative", + trend_type="additive", + seasonality_type="additive", + seasonal_period=4, + ), + 16.20176819429869, + ), + ( + dict( + alpha=0.4, + beta=0.2, + gamma=0.5, + phi=0.8, + error_type="additive", + trend_type="multiplicative", + seasonality_type="multiplicative", + seasonal_period=4, + ), + 12.301259229712382, + ), + ( + dict( + alpha=0.7, + beta=0.5, + gamma=0.2, + phi=0.85, + error_type="multiplicative", + trend_type="multiplicative", + seasonality_type="multiplicative", + seasonal_period=4, + ), + 16.811888294476528, + ), + ], +) +def test_ets_forecaster(params, expected): + """Test ETSForecaster for multiple parameter combinations.""" + data = np.array([3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12]) + forecaster = ETSForecaster(**params) forecaster.fit(data) p = forecaster.predict() - assert np.isclose(p, 9.191190608800001) - - -def test_ets_forecaster_mult_error(): - """TestETSForecaster.""" - data = np.array( - [3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12] - ) # Sample seasonal data - forecaster = ETSForecaster( - alpha=0.7, - beta=0.6, - gamma=0.1, - phi=0.97, - horizon=1, - error_type="multiplicative", - trend_type="additive", - seasonality_type="additive", - seasonal_period=4, - ) - forecaster.fit(data) - p = forecaster.predict() - assert np.isclose(p, 16.20176819429869) - - -def test_ets_forecaster_mult_compnents(): - """TestETSForecaster.""" - data = np.array( - [3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12] - ) # Sample seasonal data - forecaster = ETSForecaster( - alpha=0.4, - beta=0.2, - gamma=0.5, - phi=0.8, - horizon=1, - error_type="additive", - trend_type="multiplicative", - seasonality_type="multiplicative", - seasonal_period=4, - ) - forecaster.fit(data) - p = forecaster.predict() - assert np.isclose(p, 12.301259229712382) - - -def test_ets_forecaster_multiplicative(): - """TestETSForecaster.""" - data = np.array( - [3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12] - ) # Sample seasonal data - forecaster = ETSForecaster( - alpha=0.7, - beta=0.5, - gamma=0.2, - phi=0.85, - horizon=1, - error_type="multiplicative", - trend_type="multiplicative", - seasonality_type="multiplicative", - seasonal_period=4, - ) - forecaster.fit(data) - p = forecaster.predict() - assert np.isclose(p, 16.811888294476528) + assert np.isclose(p, expected) def test_incorrect_parameters(): From ccf5b44061e5099b8d8951c7409e9b8bb9827211 Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Wed, 18 Jun 2025 21:36:44 +0100 Subject: [PATCH 157/216] [ENH] Refactor example dataset for unequal length univariate (#2859) * deprecate PLAID * add pickup * deprecated --- aeon/datasets/__init__.py | 2 + aeon/datasets/_single_problem_loaders.py | 43 +++++ .../PickupGestureWiimoteZ_TEST.ts | 163 ++++++++++++++++ .../PickupGestureWiimoteZ_TRAIN.ts | 163 ++++++++++++++++ .../PickupGestureWiimoteZ_eq_TEST.ts | 57 ++++++ .../PickupGestureWiimoteZ_eq_TRAIN.ts | 57 ++++++ aeon/datasets/tests/test_data_loaders.py | 14 +- .../tests/test_single_problem_loaders.py | 4 +- .../collection/tests/test_pad.py | 10 +- .../collection/tests/test_truncate.py | 10 +- examples/datasets/data_loading.ipynb | 148 +++++++-------- examples/datasets/data_unequal.ipynb | 4 +- examples/datasets/load_data_from_web.ipynb | 4 +- examples/datasets/provided_data.ipynb | 177 +++++++++++------- examples/transformations/preprocessing.ipynb | 110 +++++------ .../transformations/transformations.ipynb | 34 ++-- 16 files changed, 765 insertions(+), 235 deletions(-) create mode 100644 aeon/datasets/data/PickupGestureWiimoteZ/PickupGestureWiimoteZ_TEST.ts create mode 100644 aeon/datasets/data/PickupGestureWiimoteZ/PickupGestureWiimoteZ_TRAIN.ts create mode 100644 aeon/datasets/data/PickupGestureWiimoteZ/PickupGestureWiimoteZ_eq_TEST.ts create mode 100644 aeon/datasets/data/PickupGestureWiimoteZ/PickupGestureWiimoteZ_eq_TRAIN.ts diff --git a/aeon/datasets/__init__.py b/aeon/datasets/__init__.py index 4185769f6f..a35419017e 100644 --- a/aeon/datasets/__init__.py +++ b/aeon/datasets/__init__.py @@ -25,6 +25,7 @@ "load_osuleaf", "load_italy_power_demand", "load_japanese_vowels", + "load_pickup_gesture_wiimoteZ", "load_plaid", "load_longley", "load_lynx", @@ -74,6 +75,7 @@ load_lynx, load_osuleaf, load_PBS_dataset, + load_pickup_gesture_wiimoteZ, load_plaid, load_shampoo_sales, load_solar, diff --git a/aeon/datasets/_single_problem_loaders.py b/aeon/datasets/_single_problem_loaders.py index b073823df9..cd4f6ea9b5 100644 --- a/aeon/datasets/_single_problem_loaders.py +++ b/aeon/datasets/_single_problem_loaders.py @@ -28,6 +28,7 @@ import numpy as np import pandas as pd +from deprecated.sphinx import deprecated from aeon.datasets import load_from_tsf_file from aeon.datasets._data_loaders import _load_saved_dataset, _load_tsc_dataset @@ -344,6 +345,48 @@ def load_basic_motions(split=None, return_type="numpy3d"): ) +def load_pickup_gesture_wiimoteZ(split=None): + """Load the PickupGestureWiimoteZ univariate time series classification problem. + + Example of a univariate problem with unequal length time series. + + Parameters + ---------- + split: None or one of "TRAIN", "TEST", default=None + Whether to load the train or test instances of the problem. By default it + loads both train and test instances into a single array. + + Returns + ------- + X: list of 2D np.ndarray, one for each series. + y: 1D numpy array of length len(X). The class labels for each time series + instance in X. + + Notes + ----- + Dimensionality: univariate + Series length: variable + Train cases: 50 + Test cases: 50 + Number of classes: 2 + https://timeseriesclassification.com/description.php?Dataset=PickupGestureWiimoteZ + + Examples + -------- + >>> from aeon.datasets import load_pickup_gesture_wiimoteZ + >>> X, y = load_pickup_gesture_wiimoteZ() + """ + return _load_tsc_dataset("PickupGestureWiimoteZ", split, return_type="np-list") + + +# TODO: remove in v1.3.0 +@deprecated( + version="1.2.0", + reason="load_plaid and the PLAID dataset will be removed in version 1.3. It has " + "been replaced by a smaller unequal length univariate problem " + "PickupGestureWiimoteZ.", + category=FutureWarning, +) def load_plaid(split=None): """Load the PLAID univariate time series classification problem. diff --git a/aeon/datasets/data/PickupGestureWiimoteZ/PickupGestureWiimoteZ_TEST.ts b/aeon/datasets/data/PickupGestureWiimoteZ/PickupGestureWiimoteZ_TEST.ts new file mode 100644 index 0000000000..680161f615 --- /dev/null +++ b/aeon/datasets/data/PickupGestureWiimoteZ/PickupGestureWiimoteZ_TEST.ts @@ -0,0 +1,163 @@ +## GestureWiimote +# +#The original data include 10 subjects, each perform 10 gestures 10 times. The gesture acquisition device is a Nintendo Wiimote remote controller with built-in three-axis accelerometer. Time series are of different lengths. There is no missing values. +# +#The gestures are (class label. original label - English translation): +#1. poteg – pick-up +#2. shake – shake +#3. desno – one move to the right +#4. levo – one move to the left +#5. gor – one move to up +#6. dol – one move to down +#7. kroglevo – one left circle +#8. krogdesno – one right circle +#9. suneknot – one move toward the screen +#10. sunekven – one move away from the screen +# +#We make five datasets out of these data. +# +### AllGestureWiimoteX +# +#Data is acceleration in x-axis dimension. Each subject performs a set of gestures multiple times. Classes are based on gestures (see class labels above). +# +#- Train size: 300 +# +#- Test size: 700 +# +#- Missing value: No +# +#- Number of classses: 10 +# +#- Time series length: Vary +# +#Each time series is padded with NaN to the length of the longest time series in the dataset. +# +### AllGestureWiimoteY +# +#Data is acceleration in y-axis dimension. Each subject performs a set of gestures multiple times. Classes are based on gestures (see class labels above). +# +#- Train size: 300 +# +#- Test size: 700 +# +#- Missing value: No +# +#- Number of classses: 10 +# +#- Time series length: Vary +# +#Each time series is padded with NaN to the length of the longest time series in the dataset. +# +### AllGestureWiimoteZ +# +#Data is acceleration in z-axis dimension. Classes are based on gestures (see class labels above). +# +#- Train size: 300 +# +#- Test size: 700 +# +#- Missing value: No +# +#- Number of classses: 10 +# +#- Time series length: Vary +# +#Each time series is padded with NaN to the length of the longest time series in the dataset. +# +### PickupGestureWiimoteZ +# +#Data is acceleration in z-axis dimension. Each subject performs "pick-up" gesture multiple times. Classes are based on subject. +# +#- Train size: 50 +# +#- Test size: 50 +# +#- Missing value: No +# +#- Number of classses: 10 +# +#- Time series length: Vary +# +#Each time series is padded with NaN to the length of the longest time series in the dataset. +# +### ShakeGestureWiimoteZ +# +#Data is acceleration in z-axis dimension. Each subject performs "shake" gesture multiple times. Classes are based on subject. +# +#- Train size: 50 +# +#- Test size: 50 +# +#- Missing value: No +# +#- Number of classses: 10 +# +#- Time series length: Vary +# +#There is nothing to infer from the order of examples in the train and test set. +# +#Each time series is padded with NaN to the length of the longest time series in the dataset. +# +#Data are shuffled and randomly sampled so that instances across datasets do not sychronized by dimension or subject. +# +#Data created by Guna Jože et al. (see [1]). Data edited by Hoang Anh Dau. +# +#[1] Guna, Jože, Iztok Humar, and Matevž Pogačnik. "Intuitive gesture based user identification system." Telecommunications and Signal Processing (TSP), 2012 35th International Conference on. IEEE, 2012. +# +@problemName PickupGestureWiimoteZ +@timeStamps false +@missing false +@univariate true +@equalLength false +@classLabel true 1 2 3 4 5 6 7 8 9 10 +@data 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a/aeon/datasets/data/PickupGestureWiimoteZ/PickupGestureWiimoteZ_TRAIN.ts b/aeon/datasets/data/PickupGestureWiimoteZ/PickupGestureWiimoteZ_TRAIN.ts new file mode 100644 index 0000000000..b1e5adfd8c --- /dev/null +++ b/aeon/datasets/data/PickupGestureWiimoteZ/PickupGestureWiimoteZ_TRAIN.ts @@ -0,0 +1,163 @@ +## GestureWiimote +# +#The original data include 10 subjects, each perform 10 gestures 10 times. The gesture acquisition device is a Nintendo Wiimote remote controller with built-in three-axis accelerometer. Time series are of different lengths. There is no missing values. +# +#The gestures are (class label. original label - English translation): +#1. poteg – pick-up +#2. shake – shake +#3. desno – one move to the right +#4. levo – one move to the left +#5. gor – one move to up +#6. dol – one move to down +#7. kroglevo – one left circle +#8. krogdesno – one right circle +#9. suneknot – one move toward the screen +#10. sunekven – one move away from the screen +# +#We make five datasets out of these data. +# +### AllGestureWiimoteX +# +#Data is acceleration in x-axis dimension. Each subject performs a set of gestures multiple times. Classes are based on gestures (see class labels above). +# +#- Train size: 300 +# +#- Test size: 700 +# +#- Missing value: No +# +#- Number of classses: 10 +# +#- Time series length: Vary +# +#Each time series is padded with NaN to the length of the longest time series in the dataset. +# +### AllGestureWiimoteY +# +#Data is acceleration in y-axis dimension. Each subject performs a set of gestures multiple times. Classes are based on gestures (see class labels above). +# +#- Train size: 300 +# +#- Test size: 700 +# +#- Missing value: No +# +#- Number of classses: 10 +# +#- Time series length: Vary +# +#Each time series is padded with NaN to the length of the longest time series in the dataset. +# +### AllGestureWiimoteZ +# +#Data is acceleration in z-axis dimension. Classes are based on gestures (see class labels above). +# +#- Train size: 300 +# +#- Test size: 700 +# +#- Missing value: No +# +#- Number of classses: 10 +# +#- Time series length: Vary +# +#Each time series is padded with NaN to the length of the longest time series in the dataset. +# +### PickupGestureWiimoteZ +# +#Data is acceleration in z-axis dimension. Each subject performs "pick-up" gesture multiple times. Classes are based on subject. +# +#- Train size: 50 +# +#- Test size: 50 +# +#- Missing value: No +# +#- Number of classses: 10 +# +#- Time series length: Vary +# +#Each time series is padded with NaN to the length of the longest time series in the dataset. +# +### ShakeGestureWiimoteZ +# +#Data is acceleration in z-axis dimension. Each subject performs "shake" gesture multiple times. Classes are based on subject. +# +#- Train size: 50 +# +#- Test size: 50 +# +#- Missing value: No +# +#- Number of classses: 10 +# +#- Time series length: Vary +# +#There is nothing to infer from the order of examples in the train and test set. +# +#Each time series is padded with NaN to the length of the longest time series in the dataset. +# +#Data are shuffled and randomly sampled so that instances across datasets do not sychronized by dimension or subject. +# +#Data created by Guna Jože et al. (see [1]). Data edited by Hoang Anh Dau. +# +#[1] Guna, Jože, Iztok Humar, and Matevž Pogačnik. "Intuitive gesture based user identification system." Telecommunications and Signal Processing (TSP), 2012 35th International Conference on. IEEE, 2012. +# +@problemName PickupGestureWiimoteZ +@timeStamps false +@missing false +@univariate true +@equalLength false +@classLabel true 1 2 3 4 5 6 7 8 9 10 +@data 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b/aeon/datasets/data/PickupGestureWiimoteZ/PickupGestureWiimoteZ_eq_TEST.ts @@ -0,0 +1,57 @@ +@problemName PickupGestureWiimoteZEq +@timestamps false +@univariate true +@equalLength true +@seriesLength 361 +@classLabel true 1 10 2 3 4 5 6 7 8 9 +@data 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diff --git a/aeon/datasets/data/PickupGestureWiimoteZ/PickupGestureWiimoteZ_eq_TRAIN.ts b/aeon/datasets/data/PickupGestureWiimoteZ/PickupGestureWiimoteZ_eq_TRAIN.ts new file mode 100644 index 0000000000..3b8d95b6f1 --- /dev/null +++ b/aeon/datasets/data/PickupGestureWiimoteZ/PickupGestureWiimoteZ_eq_TRAIN.ts @@ -0,0 +1,57 @@ +@problemName PickupGestureWiimoteZEq +@timestamps false +@univariate true +@equalLength true +@seriesLength 361 +@classLabel true 1 10 2 3 4 5 6 7 8 9 +@data 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diff --git a/aeon/datasets/tests/test_data_loaders.py b/aeon/datasets/tests/test_data_loaders.py index 4f347d4bd3..29d7049b9e 100644 --- a/aeon/datasets/tests/test_data_loaders.py +++ b/aeon/datasets/tests/test_data_loaders.py @@ -318,7 +318,8 @@ def test_load_from_ts_file(): Test 1. Univariate equal length (UnitTest) returns 3D numpy X, 1D numpy y 2. Multivariate equal length (BasicMotions) returns 3D numpy X, 1D numpy y - 3. Univariate and multivariate unequal length (PLAID) return X as list of numpy + 3. unequal length Univariate (PickupGestureWiimoteZ) and multivariate ( + JapaneseVowels) return X as list of numpy """ # Test 1.1: load univariate equal length (UnitTest), should return 2D array and 1D # array, test first and last data @@ -338,7 +339,8 @@ def test_load_from_ts_file(): assert X.shape == (20, 24) assert X[0][0] == 573.0 - # Test 2: load multivare equal length (BasicMotions), should return 3D array and 1D + # Test 2: load multivariate equal length (BasicMotions), should return 3D array + # and 1D # array, test first and last data. data_path = os.path.join( os.path.dirname(aeon.__file__), @@ -354,16 +356,16 @@ def test_load_from_ts_file(): assert X.shape == (40, 6, 100) and y.shape == (40,) assert X[1][2][3] == -1.898794 - # Test 3.1: load univariate unequal length (PLAID), should return a one column - # dataframe, + # Test 3.1: load univariate unequal length (PickupGestureWiimoteZ), should return + # a list up numpy arrays data_path = os.path.join( os.path.dirname(aeon.__file__), - "datasets/data/PLAID/PLAID_TRAIN.ts", + "datasets/data/PickupGestureWiimoteZ/PickupGestureWiimoteZ_TRAIN.ts", ) X, y = load_from_ts_file(full_file_path_and_name=data_path, return_meta_data=False) assert isinstance(X, list) and isinstance(y, np.ndarray) - assert len(X) == 537 and y.shape == (537,) + assert len(X) == 50 and y.shape == (50,) # Test 3.2: load multivariate unequal length (JapaneseVowels), should return a X # columns dataframe, data_path = os.path.join( diff --git a/aeon/datasets/tests/test_single_problem_loaders.py b/aeon/datasets/tests/test_single_problem_loaders.py index 6f895afeca..2c136023d6 100644 --- a/aeon/datasets/tests/test_single_problem_loaders.py +++ b/aeon/datasets/tests/test_single_problem_loaders.py @@ -20,7 +20,7 @@ load_lynx, load_osuleaf, load_PBS_dataset, - load_plaid, + load_pickup_gesture_wiimoteZ, load_shampoo_sales, load_solar, load_unit_test, @@ -39,7 +39,7 @@ load_basic_motions, ] UNEQUAL_LENGTH_PROBLEMS = [ - load_plaid, + load_pickup_gesture_wiimoteZ, load_japanese_vowels, ] diff --git a/aeon/transformations/collection/tests/test_pad.py b/aeon/transformations/collection/tests/test_pad.py index 932f7cbf7f..430f235a16 100644 --- a/aeon/transformations/collection/tests/test_pad.py +++ b/aeon/transformations/collection/tests/test_pad.py @@ -6,14 +6,20 @@ from aeon.datasets import ( load_basic_motions, load_japanese_vowels, - load_plaid, + load_pickup_gesture_wiimoteZ, load_unit_test, ) from aeon.transformations.collection import Padder @pytest.mark.parametrize( - "loader", [load_japanese_vowels, load_plaid, load_unit_test, load_basic_motions] + "loader", + [ + load_japanese_vowels, + load_pickup_gesture_wiimoteZ, + load_unit_test, + load_basic_motions, + ], ) def test_padding(loader): """Test padding to on provided datasets.""" diff --git a/aeon/transformations/collection/tests/test_truncate.py b/aeon/transformations/collection/tests/test_truncate.py index cb10289797..21d0e9a69a 100644 --- a/aeon/transformations/collection/tests/test_truncate.py +++ b/aeon/transformations/collection/tests/test_truncate.py @@ -6,14 +6,20 @@ from aeon.datasets import ( load_basic_motions, load_japanese_vowels, - load_plaid, + load_pickup_gesture_wiimoteZ, load_unit_test, ) from aeon.transformations.collection import Truncator @pytest.mark.parametrize( - "loader", [load_japanese_vowels, load_plaid, load_unit_test, load_basic_motions] + "loader", + [ + load_japanese_vowels, + load_pickup_gesture_wiimoteZ, + load_unit_test, + load_basic_motions, + ], ) def test_truncation_transformer(loader): """Test truncation to the fixed series length on provided datasets.""" diff --git a/examples/datasets/data_loading.ipynb b/examples/datasets/data_loading.ipynb index a70a401567..7716dceee8 100644 --- a/examples/datasets/data_loading.ipynb +++ b/examples/datasets/data_loading.ipynb @@ -80,16 +80,16 @@ " sensitive. We use camel case in the files for readability, but internally,\n", " everything is stripped back to lower case. Note that only one of classlabel or\n", " targetlabel can be true. If class label is true, it indicates a classification\n", - " problem, and the class values should follow the tag. Class values can be strings or integers. So, for example, the header for the PLAID dataset is\n", + " problem, and the class values should follow the tag. Class values can be strings or integers. So, for example, the header for the PickupGestureWiimoteZ dataset is\n", "\n", - " @problemName PLAID\n", + " @problemName PickupGestureWiimoteZ\n", " @missing false\n", " @univariate true\n", " @equalLength false\n", - " @classLabel true 0 1 2 3 4 5 6 7 8 9 10\n", + " @classLabel true 1 2 3 4 5 6 7 8 9 10\n", "\n", "this indicates that it is univariate (single channel per case), has no missing\n", - "values, unequal length time series and is a 11 class classification problem. For\n", + "values, unequal length time series and is a 10 class classification problem. For\n", "more detail on our provided data, see [here](provided_data.ipynb).\n", "BasicMotions data header is as follows\n", "\n", @@ -234,7 +234,6 @@ }, { "cell_type": "code", - "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:13.134330Z", @@ -243,20 +242,10 @@ "shell.execute_reply": "2020-12-19T14:32:13.811445Z" }, "ExecuteTime": { - "end_time": "2024-06-10T10:01:14.433373Z", - "start_time": "2024-06-10T10:01:14.027351Z" + "end_time": "2025-05-28T13:27:48.961642Z", + "start_time": "2025-05-28T13:27:48.127982Z" } }, - "outputs": [ - { - "data": { - "text/plain": "array([-1.9077772, -1.9048903, -1.8885626, -1.8711639, -1.8316792])" - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "import os\n", "\n", @@ -268,7 +257,20 @@ "train_x, train_y = load_from_ts_file(DATA_PATH + \"/ArrowHead/ArrowHead_TRAIN.ts\")\n", "test_x, test_y = load_from_ts_file(DATA_PATH + \"/ArrowHead/ArrowHead_TEST.ts\")\n", "test_x[0][0][:5]" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + "array([-1.9077772, -1.9048903, -1.8885626, -1.8711639, -1.8316792])" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 1 }, { "cell_type": "markdown", @@ -277,13 +279,12 @@ "Train and test partitions of the ArrowHead problem have been loaded into 3D numpy\n", "arrays with an associated array of class values. Further info on data structures is\n", "given in [this notebook](data_structures.ipynb). Datasets that are shipped with aeon\n", - "(like ArrowHead, BasicMotions and PLAID) can be more simply loaded with bespoke\n", + "(like ArrowHead and BasicMotions) can be more simply loaded with bespoke\n", "functions. More details [here](provided_data.ipynb)" ] }, { "cell_type": "code", - "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:13.828436Z", @@ -292,10 +293,18 @@ "shell.execute_reply": "2020-12-19T14:32:13.831523Z" }, "ExecuteTime": { - "end_time": "2024-06-10T10:01:14.540148Z", - "start_time": "2024-06-10T10:01:14.435147Z" + "end_time": "2025-05-28T13:27:49.303974Z", + "start_time": "2025-05-28T13:27:49.258324Z" } }, + "source": [ + "from aeon.datasets import load_arrow_head, load_basic_motions\n", + "\n", + "train_x, train_y = load_arrow_head(split=\"TRAIN\")\n", + "test_x, test_y = load_arrow_head(split=\"test\")\n", + "X, y = load_basic_motions()\n", + "print(\"Train shape = \", train_x.shape, \" test shape = \", test_x.shape)" + ], "outputs": [ { "name": "stdout", @@ -305,15 +314,7 @@ ] } ], - "source": [ - "from aeon.datasets import load_arrow_head, load_basic_motions, load_plaid\n", - "\n", - "train_x, train_y = load_arrow_head(split=\"TRAIN\")\n", - "test_x, test_y = load_arrow_head(split=\"test\")\n", - "X, y = load_basic_motions()\n", - "plaid_train, _ = load_plaid(split=\"train\")\n", - "print(\"Train shape = \", train_x.shape, \" test shape = \", test_x.shape)" - ] + "execution_count": 2 }, { "cell_type": "markdown", @@ -334,17 +335,6 @@ }, { "cell_type": "code", - "execution_count": 3, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Shape of X = (211, 1, 251)\n", - " Meta data = {'problemname': 'arrowhead', 'timestamps': False, 'missing': False, 'univariate': True, 'equallength': True, 'classlabel': True, 'targetlabel': False, 'class_values': ['0', '1', '2']}\n" - ] - } - ], "source": [ "from aeon.datasets import load_classification\n", "\n", @@ -357,10 +347,21 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-06-10T10:01:14.566858Z", - "start_time": "2024-06-10T10:01:14.541716Z" + "end_time": "2025-05-28T13:27:49.364379Z", + "start_time": "2025-05-28T13:27:49.327962Z" } - } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Shape of X = (211, 1, 251)\n", + " Meta data = {'problemname': 'arrowhead', 'timestamps': False, 'missing': False, 'univariate': True, 'equallength': True, 'classlabel': True, 'targetlabel': False, 'class_values': ['0', '1', '2']}\n" + ] + } + ], + "execution_count": 3 }, { "cell_type": "markdown", @@ -407,7 +408,6 @@ }, { "cell_type": "code", - "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:13.840562Z", @@ -416,16 +416,17 @@ "shell.execute_reply": "2020-12-19T14:32:13.869937Z" }, "ExecuteTime": { - "end_time": "2024-06-10T10:01:14.580464Z", - "start_time": "2024-06-10T10:01:14.569534Z" + "end_time": "2025-05-28T13:27:51.599554Z", + "start_time": "2025-05-28T13:27:51.569055Z" } }, - "outputs": [], "source": [ "from aeon.datasets import load_from_arff_file\n", "\n", "X, y = load_from_arff_file(os.path.join(DATA_PATH, \"ArrowHead/ArrowHead_TRAIN.arff\"))" - ] + ], + "outputs": [], + "execution_count": 4 }, { "cell_type": "markdown", @@ -445,7 +446,6 @@ }, { "cell_type": "code", - "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:13.958719Z", @@ -454,16 +454,17 @@ "shell.execute_reply": "2020-12-19T14:32:13.992003Z" }, "ExecuteTime": { - "end_time": "2024-06-10T10:01:14.610088Z", - "start_time": "2024-06-10T10:01:14.582304Z" + "end_time": "2025-05-28T13:27:53.044551Z", + "start_time": "2025-05-28T13:27:53.019464Z" } }, - "outputs": [], "source": [ "from aeon.datasets import load_from_tsv_file\n", "\n", "X, y = load_from_tsv_file(os.path.join(DATA_PATH, \"ArrowHead/ArrowHead_TRAIN.tsv\"))" - ] + ], + "outputs": [], + "execution_count": 5 }, { "cell_type": "markdown", @@ -496,16 +497,6 @@ }, { "cell_type": "code", - "outputs": [ - { - "data": { - "text/plain": "((7040, 9), (7040,))" - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "from aeon.datasets import load_from_timeeval_csv_file\n", "\n", @@ -518,23 +509,22 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-06-10T10:01:14.635824Z", - "start_time": "2024-06-10T10:01:14.612076Z" + "end_time": "2025-05-28T13:27:55.174764Z", + "start_time": "2025-05-28T13:27:55.146477Z" } }, - "execution_count": 6 - }, - { - "cell_type": "code", - "outputs": [], - "source": [], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-06-10T10:01:14.640270Z", - "start_time": "2024-06-10T10:01:14.637660Z" + "outputs": [ + { + "data": { + "text/plain": [ + "((7040, 9), (7040,))" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } - }, + ], "execution_count": 6 } ], diff --git a/examples/datasets/data_unequal.ipynb b/examples/datasets/data_unequal.ipynb index 57b0d687b7..7aee7e86aa 100644 --- a/examples/datasets/data_unequal.ipynb +++ b/examples/datasets/data_unequal.ipynb @@ -62,10 +62,10 @@ } ], "source": [ - "from aeon.datasets import load_japanese_vowels, load_plaid\n", + "from aeon.datasets import load_japanese_vowels, load_pickup_gesture_wiimoteZ\n", "\n", "j_vowels, j_labels = load_japanese_vowels()\n", - "p_vowels, p_labels = load_plaid()\n", + "p_vowels, p_labels = load_pickup_gesture_wiimoteZ()\n", "print(type(j_vowels[0].shape), \" \", type(p_vowels[0].shape))\n", "print(\"shape first =\", j_vowels[0].shape, \"shape 11th =\", j_vowels[10].shape)" ], diff --git a/examples/datasets/load_data_from_web.ipynb b/examples/datasets/load_data_from_web.ipynb index 0f3ff1ba45..75f5c28c9a 100644 --- a/examples/datasets/load_data_from_web.ipynb +++ b/examples/datasets/load_data_from_web.ipynb @@ -57,8 +57,8 @@ "[UCR/TSML Time Series Classification Archive](https://timeseriesclassification.com)\n", "hosts the UCR univariate TSC archive [1], also available from [UCR](https://www.cs.ucr.edu/~eamonn/time_series_data_2018/) and\n", "the multivariate archive [2] (previously called the UEA archive, soon to change). We\n", - "provide seven of these in the datasets/data directort: ACSF1, ArrowHead, BasicMotions,\n", - "GunPoint, ItalyPowerDemand, JapaneseVowels and PLAID. The archive is much bigger. The\n", + "provide seven of these in the datasets/data directory: ACSF1, ArrowHead, BasicMotions,\n", + "GunPoint, ItalyPowerDemand, JapaneseVowels and PickupGestureWiimoteZ. The archive is much bigger. The\n", " last batch release was for 128 univariate [1] and 33 multivariate [2]. If you just\n", " want to download them all, please go to the [website](https://timeseriesclassification.com)" ], diff --git a/examples/datasets/provided_data.ipynb b/examples/datasets/provided_data.ipynb index 8d917034b7..6546f70e24 100644 --- a/examples/datasets/provided_data.ipynb +++ b/examples/datasets/provided_data.ipynb @@ -84,8 +84,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-09-25T22:58:20.673104Z", - "start_time": "2024-09-25T22:58:20.238813Z" + "end_time": "2025-05-28T13:33:05.451007Z", + "start_time": "2025-05-28T13:33:03.638408Z" } }, "outputs": [ @@ -99,22 +99,26 @@ }, { "data": { - "text/plain": "Text(0.5, 1.0, 'First 100 observations of the first train case of the ACFS1 data, class: (9)')" + "text/plain": [ + "Text(0.5, 1.0, 'First 100 observations of the first train case of the ACFS1 data, class: (9)')" + ] }, - "execution_count": 53, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" }, { "data": { - "text/plain": "
", + "text/plain": [ + "
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" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 53 + "execution_count": 1 }, { "cell_type": "markdown", @@ -153,8 +157,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-09-25T22:58:20.861894Z", - "start_time": "2024-09-25T22:58:20.689090Z" + "end_time": "2025-05-28T13:33:07.485830Z", + "start_time": "2025-05-28T13:33:07.270860Z" } }, "outputs": [ @@ -167,22 +171,26 @@ }, { "data": { - "text/plain": "[]" + "text/plain": [ + "[]" + ] }, - "execution_count": 54, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { - "text/plain": "
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RkZEtXKwWd31mZmab9zt69CjAaUtUS0pK+N3vfkdYWBgeHh6EhIRYK7lO/l0888wzHDhwgOjoaKZOncqjjz7a7As8NTUVUCXKISEhzV5vvPEG9fX11uud7lqtceLECWpqahgxYkSLfSNHjsRkMrXIl+gJoqKiWnxRBQQENPvCSU1N5eDBgy3+3cOHDwdUvkl7HD58mD179jBz5kzS0tKsr3nz5vHtt9+2KspOrbazMGjQoBbhjMzMTOLj49Hrm3+0nfo+CgsLIz4+3vp+3LhxI7Nnz2bOnDnk5uZy7NgxNm/ejMlkalW8tPZ3uXDhwhZfcKmpqWiaRnx8fIvf2eHDh5v9vrKysrjhhhsIDAzE29ubkJAQaxKz5f1ksT8+Pr6FTa29XzrCiRMnqKio6FKJd0dsdnNz4+9//zs//PADYWFhzJkzh2eeeYb8/HzrdebOncull17KY489RnBwMBdeeCFvv/12szyq1NRUysvLCQ0NbfG7rKqqsv4uO3KtrqCdkntnEW6tXddSunyyuLMVFrsGWrWe5Lz0M9zc3Fp8cINKmr3hhhv4+uuvWb16Nffccw9PP/00v/76a7di5a0xa9YsGhsb2bp1q/VLAZSo2bhxI8nJyZw4caLVLwVbcsUVV7Blyxb+8Ic/MH78eLy9vTGZTJx99tlWT4nluNmzZ/Pll1+yevVqnn32Wf7+97/zxRdfsGTJEuuxzz77bJslx5bck9Ndy5FozbsBzT+0TSYTiYmJvPDCC60eGx0d3e493n//fQDuu+8+7rvvvhb7P//88xbelLa+ALr7xTBr1izWrl1LbW0tu3bt4uGHH7Z6Azdu3Mjhw4fx9vZmwoQJXb6HyWRCp9Pxww8/tPr7tbxPjEYjixYtoqSkhD/+8Y8kJCTg5eVFTk4ON9xwQ7P3p6PQGZvvvfdezj//fL766it+/PFH/vKXv/D000/z888/M2HCBHQ6HZ999hm//vor33zzDT/++CM33ngjzz//PL/++qv1bzU0NLRND11ISAhAh67VGSzlzqd6jQIDA3FzcyMvL6/FOZZtkZGRnbpXV7DY1V4uZH9ExMsAIjExkcTERP785z+zZcsWZs6cyWuvvcYTTzwBdE65t3fs1KlTcXV1ZePGjWzcuJE//OEPAMyZM4fXX3+dtWvXWn+2MGTIEH766acWCW7JycnW/W1hSSw+cOBAm56p0tJS1q5dy2OPPcbDDz9s3W7xopxKREQEd955J3feeSeFhYVMnDiRJ598kiVLlljv5+vre1pP2Omu1RohISF4enqSkpLSYl9ycjJ6vf60IqE1euLJLC4ujr1797JgwYJOX0/TND788EPmz5/PnXfe2WL/X//6Vz744IMW4qUzDBkyhH379mEymZqJ+NbeR7Nnz+btt9/m448/xmg0MmPGDPR6PbNmzbKKlxkzZrQp6jpCXFwcmqYRGxtr9U61xv79+zly5AjvvPMOS5cutW4/NUxssb+1921r75eOEBISgq+vLwcOHOjUeR212UJcXBwPPPAADzzwAKmpqYwfP57nn3/eKmgBzjjjDM444wyefPJJPvzwQ6699lo+/vhjbr75ZuLi4vjpp5+YOXNmh4Rre9fqDIMHD8bDw4P09PRm2/V6PYmJia02LNy2bRtDhw61ebIuYLXr1KTy/o6EjQYAFRUVGAyGZtsSExPR6/XNXJ5eXl7NSpzbw9Jro7Xj3d3dmTJlCh999BFZWVnNPC+1tbX885//JC4ujoiICOs555xzDkajkX/961/NrvXiiy+i0+na9VJMnDiR2NhYXnrppRb2WLwGli+gU12/L730UrOfjUZji3BaaGgokZGR1t/VpEmTiIuL47nnnqOqqqqFPZbeKx25Vms4OTlx1lln8fXXXzcrJS0oKODDDz9k1qxZ+Pr6tnl+W7T3f9ZRrrjiCnJycnj99ddb7KutrW1WDXQqmzdvJiMjg2XLlnHZZZe1eF155ZWsW7euU/lBp3LOOeeQn5/PJ598Yt1mMBh4+eWX8fb2btZLxvK+/Pvf/87YsWOtOSSzZ89m7dq17Ny5s9vewUsuuQQnJycee+yxFu89TdOs+RStvT81TWtW6g9KCI8fP5533nmn2XtrzZo1HDp0qEs26vV6LrroIr755ptWv4hPtdtCR22uqalp0QE2Li4OHx8f699BaWlpi/tYvJqWY6644gqMRiN//etfW9hiMBis7+uOXAs6Xirt4uLC5MmTW/3dXHbZZezYsaPZvpSUFH7++ed2+7P0JLt27UKn0zF9+vReuZ+jIJ6XAcDPP//M3XffzeWXX87w4cMxGAy89957ODk5cemll1qPmzRpEj/99BMvvPACkZGRxMbGMm3atFavGRcXh7+/P6+99ho+Pj54eXkxbdo0a37C7Nmz+dvf/oafnx+JiYmA+uIeMWIEKSkpLXrInH/++cyfP58//elPZGRkMG7cOFavXs3XX3/Nvffe22bZNqgP33//+9+cf/75jB8/nmXLlhEREUFycjIHDx7kxx9/xNfX1xprb2xsZNCgQaxevbrF01RlZSVRUVFcdtlljBs3Dm9vb3766Sd27NjB888/b73fG2+8wZIlSxg9ejTLli1j0KBB5OTksG7dOnx9ffnmm286dK22eOKJJ1izZg2zZs3izjvvxNnZmf/85z/U19e32nulI5zu/6wj/OY3v+HTTz/l9ttvZ926dcycOROj0UhycjKffvopP/74Y5tNFT/44AOcnJw499xzW91/wQUX8Kc//YmPP/64WU+gznDrrbfyn//8hxtuuIFdu3YRExPDZ599xubNm3nppZeaPQkPGzaM8PBwUlJS+O1vf2vdPmfOHGv/oe6Kl7i4OJ544gmWL19ORkYGF110ET4+PqSnp/Pll19y66238vvf/56EhATi4uL4/e9/T05ODr6+vnz++eetJrg+/fTTnHvuucyaNYsbb7yRkpISXn75ZUaPHt1CTN9www0dKgd+6qmnWL16NXPnzrWWwOfl5bFy5Uo2bdrUahPEjtp85MgRFixYwBVXXMGoUaNwdnbmyy+/pKCggKuuugqAd955h1dffZWLL76YuLg4Kisref311/H19eWcc84BVC7LbbfdxtNPP01SUhJnnXUWLi4upKamsnLlSv7xj39w2WWXdeha0PFSaYALL7yQP/3pT1RUVDR7cLjzzjt5/fXXOffcc/n973+Pi4sLL7zwAmFhYTzwwAPNrmFpc9CWGDwZizf84MGDALz33nts2rQJgD//+c/Njl2zZg0zZ87sUjffPk0vVjYJncBSktlWuV9bpdJeXl4tjj127Jh24403anFxcZq7u7sWGBiozZ8/X/vpp5+aHZecnKzNmTNH8/Dw0IDTlk1//fXX2qhRozRnZ+cWtnz33XcaoC1ZsqTZOTfffLMGaG+++WaL61VWVmr33XefFhkZqbm4uGjx8fHas88+26wcuT02bdqkLVq0SPPx8dG8vLy0sWPHNitvPH78uHbxxRdr/v7+mp+fn3b55ZdbywwfeeQRTdNUefAf/vAHbdy4cdbrjBs3Tnv11Vdb3G/Pnj3aJZdcogUFBWlubm7akCFDtCuuuEJbu3Ztp6/VGrt379YWL16seXt7a56entr8+fO1LVu2NDumM6XSmtb2/9ncuXOblfhauP7661uU3zY0NGh///vftdGjR2tubm5aQECANmnSJO2xxx7TysvLW71vQ0ODFhQUpM2ePbtd+2JjY7UJEyZomtb+30Bb9mqaphUUFGjLli3TgoODNVdXVy0xMbHV8nBN07TLL79cA7RPPvmkma2enp6aq6urVltb2+z40/1dtmXX559/rs2aNUvz8vLSvLy8tISEBO2uu+7SUlJSrMccOnRIW7hwoebt7a0FBwdrt9xyi7Z3795Wy9s///xzbeTIkZqbm5s2atQo7Ysvvmj1/+rSSy/VPDw8WrQQaI3MzExt6dKlWkhIiObm5qYNHTpUu+uuu7T6+npN01ovle6IzUVFRdpdd92lJSQkaF5eXpqfn582bdo07dNPP7VeZ/fu3drVV1+tDR48WHNzc9NCQ0O18847T9u5c2cLO//73/9qkyZN0jw8PDQfHx8tMTFRe/DBB7Xc3NxOXaujpdKapt5Tzs7O2nvvvddiX3Z2tnbZZZdpvr6+mre3t3beeedpqampLY6bNGmSFh4eftp7aZrWbin+yZSVlWmurq7aG2+80aHr9id0mtbB9qWCIAhCnyIsLIylS5fy7LPP2tuUPs9NN93EkSNHmjXb7CiVlZUEBgby0ksvcdddd/WYTS+99BLPPPMMR48e7ZXKJkdCxIsgCEI/5ODBg0yfPp1jx44NuEoUW5CVlcXw4cNZu3Zti8nSp+O7777jrrvu4siRI212K+4sjY2NxMXF8dBDD7WaAN/fEfEiCIIgCEKfQqqNBEEQBEHoU4h4EQRBEAShTyHiRRAEQRCEPoWIF0EQBEEQ+hT9rkmdyWQiNzcXHx+fATeoShAEQRD6KpqmUVlZSWRkZKsz+k6m34mX3NzcLs19EQRBEATB/mRnZ592YHC/Ey+W9t/Z2dldmv8iCIIgCELvU1FRQXR0dIcGWvY78WIJFfn6+op4EQRBEIQ+RkdSPiRhVxAEQRCEPoWIF0EQBEEQ+hQiXgRBEARB6FOIeBEEQRAEoU8h4kUQBEEQhD6FiBdBEARBEPoUIl4EQRAEQehTiHgRBEEQBKFPIeJFEARBEIQ+hYgXQRAEQRD6FCJeBEEQBEHoU4h4EQRBEAShTyHiReizmEwan+7IZlNqkb1NEQRBEHoRES9Cn8Rk0vjz1wd48PN9LH1rmwgYQRjg1DUaeXV9GtklNfY2RegFbCpeNmzYwPnnn09kZCQ6nY6vvvqq3ePXr1+PTqdr8crPz7elmUIf5MnvD/PhtiwATBrc/dFusorlQ0sQBipvbkrnmVUp/N+X++1titAL2FS8VFdXM27cOF555ZVOnZeSkkJeXp71FRoaaiMLhb5IakElb25KB+CpixMZF+1PWU0jt7y7k+p6g52tEwTBHqw+VADAlqPFlFQ32NkawdY42/LiS5YsYcmSJZ0+LzQ0FH9//w4dW19fT319vfXnioqKTt9P6Fv8+5ejAJw9Opxrpg3mzIRQzv/XJlIKKvn9yr28eu1EdDqdna0UBKG3KKyoY292GQBGk8bqg/lcNXWwfY0SbIpD5ryMHz+eiIgIFi1axObNm9s99umnn8bPz8/6io6O7iUrhZ6iwWBiY+oJjCbttMceL63hf0m5ANw5Pw6AcD93XrtuIi5OOn44kM8r69Jsaq8gCI7F2uTCZj9/f0BSDfo7DiVeIiIieO211/j888/5/PPPiY6OZt68eezevbvNc5YvX055ebn1lZ2d3YsWCz3BU98f5jdvbucfPx057bGvbziGwaQxa1gwY6P8rdsnDQnkrxeOAeD5NUdYe7jAVuYKguBg/GQOGV06MQqALWlFHCmopLKu0Z5mCTbEocTLiBEjuO2225g0aRIzZszgrbfeYsaMGbz44ottnuPm5oavr2+zl9B3KK1u4OMdKvF2xZaMZjkrdY1GjpfWUFBRB0BRVT0f71Di9M55cS2uddXUwVx3xmA0De79OIm0wqpe+BcIgmBPahoMbEpT1Ya3zIklIdwHg0njrBc3MPmJnziYW25nCwVb4FDipTWmTp1KWpqEAforH27Poq7RBEBFnYHPdh0HYEdGCWc8vZZZf1/HtKfWcteHu3ljYzr1BhPjovyYHhfU6vUePm80U2MCqaw3cOt7O6mQJy9B6LdomsafvjxAvcHE4EBPRoT5cMe8OHzdnXFx0lFvMPHU94fRtNOHpIW+hcOLl6SkJCIiIuxthmADGgwm3t2aAcDU2EBAlTt+uC2L69/aTllNIy5OOnQ6+G5fHq+ZE3XvmDeszYRcV2c9r143kUg/d46dqOa+j5Pkg0sQ+hGNRhP1BiPpRdU88d1hvtyTg5Nex1MXJ6LT6bhw/CD2PbqYnx+Yh6uTns1pxaw/csLeZgs9jE2rjaqqqpp5TdLT00lKSiIwMJDBgwezfPlycnJyePfddwF46aWXiI2NZfTo0dTV1fHGG2/w888/s3r1aluaKdiJHw7kUVBRT4iPG6//ZjJznl1HVkmNtU/DrGHBvL50Mr8cKeSOD3ajaTAs1JuzRoW1e91gbzf+85vJXPraFtYmF3Ior4LRkX698U8SBKEHMZk03t2awcgIX6YNDeKVdWn846dUGoymZsc9dsFoZsUHN9sWHejJ9TOG8PrGdP72fTJz40PQ66UKsb9gU/Gyc+dO5s+fb/35/vvvB+D6669nxYoV5OXlkZWVZd3f0NDAAw88QE5ODp6enowdO5affvqp2TWE/oGmadZeLUvPGIKfpwtPXZzIe79mYNJgVIQvDy1JwN3FibPHRPC3SxJ5bvUR/u+chA59ACVG+TEtNpCNqUXszS4X8SIIfZDvD+Tx6DeHcNLruGxiFJ/sbCrIcHHSMXFwAJdOjOKKKa1Xmd49P56Pd2STUlDJmsMFLB4d3lumCzZGp/Uzn3pFRQV+fn6Ul5dL8q4DklZYRU2DgXqDictf24qbs56tyxcQ6OXa4/d67scU/rUujSsnR/P3y8b2+PUFQbAtV7y2le0ZJc223T43jjvnx+HmrMfN2em013hmVTKvrj/KpCEBfH7HDFuZKvQAnfn+tqnnRRBOxmjSuPI/WymubiDExw2ASyYOsolwARgbpbwte4+X2eT6giDYjkO5FWzPKMFZr+OiCYP4bNdxzkkM58HFIzoV/rlhRgxvbExnV2YpuzJLmDQk0IZWC72FwyfsCv2H46U1FJvbdp+oVF2Rb5wZa7P7jY/2B+BIQSU1DTI2QBD6Eu/9mgHA4jHhPHf5OLYuP5NXrpnY6byVUF93Lp4wCIA73t/NLe/uZMcp3hyh7yHiReg1LH1XIvzcmRoTyG1zhhIf5mOz+4X6uhPh545JgwM5MjZCEPoK5TWNfLVHddK+fnoMABF+Hl0e+3Hr3KG4OusprKxnzaECrntjG1uPFveUuYIdEPEi9BoW8TJpSACf3j6d5eeMtPk9raEj89wTQRAcn5W7sqltNJIQ7sOUmIBuXy8uxJtND87nnRunMm9ECPUGEze9s0METB9GxIvQa6SaxUt8qO28Lacyzhw6krwXQegbmEwa7/2aCcDS6TE9NmQ11NeducNDeO26ScyOD6amwcj1b29nlcxB6pOIeBF6DYvnZViod6/dc7x5/pGIF0HoG/ySeoLM4hp83J25aEJkj1/f3cWJ15dOZtGoMBoMJu78YBfJ+RJW7muIeBF6BU3TrOIlPqz3xMsYc9gou6SW4qr6XruvIAgdwGSCxjr1wtz/aaPq/3T5pGg8XW1TEOvu4sS/r53IrGHBmDT4fr94X/oaIl6EXqGgop6qegNOeh0xQV69dl9fdxfiQtT99uXIgDZBcBgyNsOLo+HJMHgqElYt57MdmWxKK8LFScfS6UNsentnJz0XjFeenV9SCm16L6HnEfEi9AoWr8uQQE9cnXv3bTcu2h9nDFTv+Rwq5QlLEOxGTQkkfw/bX4f3L4FKVVGEZoRfX8X3u1txpZEHzhpBTLDtH3LmDQ8B1IONeGb7FtKkTugVUgsrgd7Nd7EwKdyV812eZ37yXih6C+7YCk7y1heEXiVvL7x3CdQUNW0bfjZc9G+0o+swfH4bi3Xb+MK3jpFTv+kVk0J93RkZ4cvhvAo2pJ7g4glRvXJfofuI50XoFXo9WbehBr66E14YzRVbzmO+0161vegIJH3QOzYIgqA4vgtWnKeEi180DJ4Os+6DK98Hz0A+b5jG0oYHqdI8GNOwF6d3zoXKgqbz09bC6wvgcM+LmnkjlPdlfYpMnu5LiHgRbI6maSSZ+6z0uHjJ2Q0lx5pvqy6Gd85XIqXiOC51xZRq3nximKf2r/8bVJ0AY2PP2iIIQuus/jPUV8CQmXDHZrhxFSx8FJxcKK6q54nvDrHVNJpVU94ArxDI3w9vLoLMrbDnffjwSsjZCV/frUJPPYgldLThyAmMpn416q9fI75zweasPlTAwdwK3Jz1zBoWfPoTOsrxneoDzt0f7tkNHgGgafDZMvVB5xEAF/wLPIO44+sK9uTWcIFPCh6VufDcMHDzg/NfgjGX9JxNgiA0pyIPsraq9Uv+C+7NJ7x/uvM4ZTWNJIT7cOGSWTBjFLx3MZRmwNtnNx3o5Ap1ZbDhWTj76R4zb+KQADxcnCitaSS9qNouoW2h84jnRbApjUYTf/8hGYBbZg8l1Ne9Zy6sabDqIdBMUFsCG19Q21PXQPov4OQGy1bByPNgyHTiBw+iHle+G3QPOJttqC+Hz26EnW/3jE2CILTk8P8ADaKmgF/LnJJ95h5Ml06MwsVJD4FD4aY1EHcmeASqMNPM38FVH6oTtv8XProGfvgj1JZ22zwXJz3Dze0bjhRUdvt6Qu8g4kWwKSt3HudYUTVBXq7cNndoz114/2dwfAfozc7Dbf+BE0dgzcPq5zNuh9AE6+GWTrvvlI6FP+XDnwth8o2ABt/eCzvf6jnbBEFo4uBXajnqolZ37ze3MBg9yLdpo3co/OZL+GM63HcAFj0O8Ytg2CIwGSDlO9j2Gry5GMqyum3iiHDV9Ts5X8RLX0HEi2BTvt+fB8Ctc4bi4+7SMxdtqIGfHlHr8x6CmNlgrIdXpsCJwypcNOv+ZqfMHxGCs17H/pxyUgqqwNkNzn0BZvxWHfDt/ZD0Uc/YJwiCojK/KWQ06sIWu8tqGjheWgvA6Ei/FvtbcOnrcOErcM5z4BMJRSnwxkJVydQNhpsHxB4R8dJnEPEi2Ix6g9E6ev7MhNCuXaT4KKy8QVUqfHS1ynPZ8jJU5IDfYJh+N5z9N+VetrDgEfDwb3aZIG83Fo4MA+DTndlqo04Hi/4KU28DNPj6Ljj2S9fsFAShJQe+ADQYNBn8o1vuNk97HxLkiZ9HBx5uPAJgwnUw9Ra4+ScIHQVVBfD2OZD6U5fNTAhXXp8UCRv1GSRhV7AZSVll1BtMBHu7dS0JLnMrfHyNymmxcGy9yncBWPQYuHhA+Bh48BgY6pRL2a31wY9XTIli1cF8vtyTwx/PTlDN8nQ6JX5qS2D/Svh0KdzyMwTFdd5eQRCaMBpg27/V+rirWj3EEjIaM6gDXpdT8RukqpY++Y3Kc/vwCpWAP3Fppy9lCRtlFFdT22DEw9Wp8/YIvYp4XgSbsfWYGjd/xtDAzk+GLUqD9y5SoiJyAlz6pkrga6wBQy1EnwGjL246XqdTQqYN4QIwJz6EUB83SqobWHv4pB4Ser2qSho0WVUzfHtf52wVBKElh79W+SieQTD+2lYPOZBrFi8dCRm1hrsfXPsZjL1Kden9329V995OEuztSqCXK5rW1JNKcGxEvAg2Y8tRJV5mxHWyPNpSSWSoU/ksN3wPiZfB1Z+oD0G/aDj3OSVYOoGzk55LJqpqh2/NuThWXNxVPB0gY1OP95IQhAGFpsHmf6j1qbeCq2erhx0we14Su+J5seDsChe/ppreAax5RJVndwKdTseIMEvSrkyY7guIeBFsQl2jkaSsMgCmxwV18KQKJRz2vAdpa0DvAuf/o+mDz9kVLnoV7t0P4Yldsuus0SrvZcOREzQaTc13Bg5VMXTNqDp6CoLQNTI3qyRaZw+Yckurh5TXNpJZXAPA6EjfVo/pMDodnPmwKsdurIa1jzffbzKqBxKt7SZ0ltCRlEv3DUS8CDZhV2YpDUYT4b7uxAS1/tQFNH2gmEzwwWWw4lzl+gWYflfruSedDUGdxLgof4K8XKmsM7Azo5UeEcPNTbGOrOryPQRhwLPjTbUcdyV4tXx4WXOogIte2QxAdKAHAV6u3b+nXg9n/12t7/0Qjv5s7rZ9ATweBM/Eqk69bSDl0n0LES+CTVh7WI2YnzEsqPV8l5oS+Oou9YHy0VWw6y3I3qa6aHoEqDyXOb/vcbuc9DrmmmeZ/Jxc0PIAi3hJWyPjAwShK1QVNs0gmnxji91phVXc9t5O0ouqCfB04dHzR/fcvaMmwfjr1PoHV8Dr81UyL2aPS+qPbfaFGRmhvD87M0pJKxQB4+iIeBF6HE3T+PFgPgCLR4e3PKCmBF6bBUnvq5+PrILvHlDrCx6BP2bArevbTb7tDgsSVOjo5+TCljujJqsEw7pyJaYEQegce94HU6NKgI8Y12L3a78cxaTB7PhgNv7xTBaYWxj0GOc+pxrimRqhLBN8B8FtG9RcJYBDX7d62thBfsyIC6K20cjt7++mut7Qs3YJPYqIF6HHOZhbQU5ZLR4uTsyJD2l5wIHPVZ8W3yhVpuxkdhmHjIRpt9ncvtnDg3HW6zh6opqMourmO/VOEH+WWt/6igpnCYLQMQz1sGuFWm/F63K8tIav9uQA8MBZI/B2s0G3DhcPuOxtWPAwjDhHlVNHjGuqTmxDvOj1Ov5x1QRCfdxIK6ziudUpPW+b0GOIeBF6nFUHlNdl7vCQ1vsl7F+pltPvhDPugKs/Vh8yl74OTj3UhbcdfN1dmBwTAMD6lFa8L1NvUcnCKd/D2sdsbo8g9As0TeWrlWUq7+XJrQzM/HfDMQwmjVnDghlvHtlhE/R6mP0AXP0R+A9W20aeD+jUWJEDX8DPT0BFbrPTQnzcePzCMQCsPliA1k6Cr2BfRLwIPc4qc8jo7DGthIxKM8zhGB2MNk9zHrZAfch0sYKoK8w2e4Q2m8u5mzFokmpBDrD5JTXsURCE9tn8Euz7BHROqi/TKeXRB3LK+WCbyje5c74dmkD6hMPg6Wr9s2VqOvWbZ6ku3icxZ3gwLk46cspqySqp6X07hQ4h4kXoUdKLqkkrrMLFScf81kYCWLwusXPAN6J3jTuJWcNU75lfjxZjOLVkGlSVxFRzCGvrK71omSD0QYqPws9PqvVznoG4+c12NxhM/H7lXowmjXPHRnS+91NPkXiZWuqdwTsMyrPhrcVQ2ZS87+nqzIRo5ZndnNbKw43gEIh4EXqUbeauuhMGB7ScVVJfBUnmsfZjr+hly5ozZpAfvu7OVNYb2GdulNWC6XcBOji2TnX8FQShdX56VCXIxi2AKTe32P36xmMk51cS6OXK4xf0YHVRZ5m0DK78AO7eAbdvhuDhUH2i6aHKjKU31ZajRfawUugAIl6EHmVnpuqdMnlIQPMd9VXwweVQckyVQo883w7WNeGk11mf/jantvEBFTAEhi9W6zvf7CXLBKGPkbkVDv8PdHo464kWu00mjQ9+zQTg/84ZSZC3W29b2IReDyPPUw0pvUNg8k1qe8oPzQ6bafbMbj1ajMkkeS+OiIgXoUfZZRYvU2ICm+/4392QtQXcfOHaz9VMEjszM94sXtp7urI8RW5/HZ6Oho+vbbdLpyAMOLb8Uy0n/AbCRrXYvT2jhNzyOnzcnTlvrP1Cxa0yYolaZm1tNhJkfLQ/Hi5OFFc3cER6vjgkIl6EHuNEZT3p5tLjiYNP8ryUZcHBr9T6tZ+pRlIOgCXvZXdmGTUNbfR0iFsAIQnKJV5fAcnfqmoKQRDU5Oj0jWp98rJWD/k6SZVGLxkTjruLg01rDhgCYWPUSJDU1dbNrs56psSqB7BNbXlmBbsi4kXoMSxel+Fh3vh5npTvsvtdQIPYuTB4mn2Ma4WYIE/CfN1oMJo4mNvGMDa9Hpb9AMtWqa6/oOYvCYIA+XuhoVJ5UsPHtthdbzDy3T41JPGi8YN627qOYfG+pHzfbPPc4aoicfXBVjpxC3ZHxIvQY+zKVG7XySeHjIyNZvFCq02r7IlOp7O2BG93nolnIAyZDnFnqp9FvAiCwuJ1GTJTNXg8hfUpJ6ioMxDm68a0oR0c0NrbjDhHLdPWqiZ7ZiytHnZkllBYUWcPy4R2EPEi9BitJuum/ABVBeAVCgnn2smytrEMY0vJb8PzcjIxs9QyY5PkvQgCQIZZvMTMbnX3zgz1QHPWqHCc9F0fqGpTIsarsumGKsjcYt08yN+D8dH+aBrWcSeC4yDiRegRqusNHDCXHE8ecpLnxdKKe/w1vdI9t7MkWMVLB5Lyoqep/hDl2ZL3IgjGRlVpBE3C/hTSCquApocEh0Svh2EL1XraT812nZuoEoy/25/X21YJp0HEi9AjbD1aTKNRIzrQg+hAj6Ydx7er5SlNqxyFEWFNYaPTtgJ39VLdd0FCR4KQmwSN1ar1QdiYVg85ekIl8A8L9e5Fw7pAG+LFEjranl7Cicr6U88S7IhNxcuGDRs4//zziYyMRKfT8dVXX532nPXr1zNx4kTc3NwYNmwYK1assKWJQg+x/oiaETRveCg6ndk9XJmvKo10+qYvfQcjLtQLJ72OyjoDeeUdiGtbnjAtsX5BGKgcW6+WQ2Yq78Up1DUayS5V7fXjQhxcvMTNV59TJ5KhLNu6OTrQk9GRvpg0+PWYdNt1JGwqXqqrqxk3bhyvvNKx9urp6emce+65zJ8/n6SkJO69915uvvlmfvzxR1uaKXQTTdNYn3ICaMrQByDb7HUJHQVujuk2dnN2YmiwF9DB0NHQeWp56GsoP247wwTB0Un+Vi3jF7W6O6O4Gk0DX3dngr1de9GwLuARAFFT1Xpa81lmoyOVd9YSAhMcA5uKlyVLlvDEE09w8cUtp4u2xmuvvUZsbCzPP/88I0eO5O677+ayyy7jxRdfbPOc+vp6Kioqmr2E3uVYUTXHS2txddIzY9hJFQWWkFHUFPsY1kGsSbsFHRAvMbNh8Aww1MKaR2xsmSA4KGVZkJekvBUjWk/Et3zZDwv1bvLGOjLx5tBRavPQUXyo+nxIOyHixZFwqJyXrVu3snDhwmbbFi9ezNatW9s85+mnn8bPz8/6io6OtrWZwilYvC5TYwPxdHVu2pG9Qy2jp9rBqo7TqaRdnQ6W/A3QwYHPIGubbY0TBEfksNnrMniGarPfCkcLVb6Lw4eMLMSbR4GkroaSdOtmS77OUfG8OBQOJV7y8/MJCwtrti0sLIyKigpqa2tbPWf58uWUl5dbX9nZ2a0eJ9iO9SnmfJcRJ32IGRogd49aj3acxnStMSK8A71eTiZiHEy4Vq3/+qqNrBIEB+bwN2rZzoyyo2ZPRZyjJ+taiBirejmZGmHdk9bNFvFy7ER16xPoBbvgUOKlK7i5ueHr69vsJfQeBRV1bE5T7bMXjDxJeObvA2M9eAapIWgOzMgI5Xk5UlBJakdCR6Cm04K5sVWDjSwTBAckd4+aBQRqyGEbWMNGfcXzArDwUbXcvxLy9gKq34u7i54Go4ns0tYfooXex6HES3h4OAUFzVsxFxQU4Ovri4eHRxtnCfbkyz05mDTVmC7WnPgKwL5P1TJ6mgq1ODBRAZ4sHBmG0aTxl68PnL5kGiByomq811AJmZttb6Qg2BujAb6+C/47D9Ag+gzwi2r1UJNJ41hRH/O8gPKqJl6u1jepXEu9XsfQYPVvkKRdx8GhxMv06dNZu3Zts21r1qxh+vTpdrJIaA9N01i5U4XpLpt00odYSTrsfEutT7vNDpZ1nkfOH4Wbs55fj5Xw5Z6c05+g18Pws9T6kVW2NU4QHIFNL8Ce99X66EvgsrfaPDS3vJa6RhOuTnqiA/rYg+fUW9Xy2HowqTCRJXQk4sVxsKl4qaqqIikpiaSkJECVQiclJZGVlQWofJWlS5daj7/99ts5duwYDz74IMnJybz66qt8+umn3HfffbY0U+giSdllHD1RjbuLnnNPHnW/7ikVN447s6m02MGJDvTk7vnDAHjws318uC3r9CdZZqKkfC/jAoT+TfYOWP83tX7Rv+Hyt8Gv9UGLJpPGy2vTABga4oWzk0M9I5+eyAng6g21pVBwABDx4ojY9F21c+dOJkyYwIQJahrv/fffz4QJE3j44YcByMvLswoZgNjYWL777jvWrFnDuHHjeP7553njjTdYvHixLc0UukByfgV/+GwfAEvGRODjbm79f/RnFS+GpvhxH+G2uXGcNzYCg0nj/77cz+e7TtPHZeg8cHJTZaOFh3vFRkHodUxG+PpO0Iww5jIYd3W7hz/6zUE+2ZmNXgf3LozvJSN7ECcXGDJDrZtnN1nFi5RLOwzOpz+k68ybN6/d/IHWuufOmzePPXv22NAqobtsTy/huje30WAwEeLjxl3z49SO4qOw8gZAg4nXq/hxH8LVWc/LV0/Az8OFD7ZlsTa5gEsntR7TVyd4KQGT+iMc/BLCRvWarYLQayR/B0VHwN0fzn2+3Ry2tMJK3t2aiU4Hz18xjrPHRLR5rEMTM1uVTKdvgOl3NSuX1jStb/St6ef0MX+eYG8ajSb+78v9NBhMzBoWzA+/m82wUB8VNvl0KdSVq06V5zxrb1O7hE6nY8HIUECVRp6WsVeo5d6PrPFxQeg3aBps+adan3ITePi3e/iqA2r68tzhIVw8oR3h7+jEzlHLjM1gNBATpMaIVNUbKKiQGUeOgIgXoVO8syWDtMIqAr1ceeWaiQR7u6kdRakqPuzkBle+D85u9jW0G8SaKwsyi2swmU6Ty5JwHrj7qUnT6b/0gnWC0Itkb4PjO8DJFaaePvl+9SFVLbp4dLitLbMt4Ynq77qhEvL24uqsZ0iQJyB5L46CiBehwxRX1fPST6kA/PHsEfh5ujTtzNqillFTwCeslbP7DlEBHjjpddQ2GimoPM2wRhf3ptJKSyWGIPQXtr2mluOuOu3fdW5ZLfuOl6PTwcKRffszAL0TDDEPYf30N7DjTWu/mrTCDvaCEmyKiBehw3yxO4eqegOjIny5fNIpYxgyzU2rhvT9snYXJz2DA9VTVnpHQkcTrlPLw9+oCgVB6A801sKR1Wrd0pSxHVYfVCGjyUMCCPHpu55XK3MfBO8wqMiB7+7nLBfVtE6Sdh0DES9Ch9A0jZW7VE+Xa88YjF5/SsJaptnzMrjvixfA2nAvvbgD4iViPISMVB2Fj8gEdKGfcOwXaKwG30GqfLgdymsa+XSnqs7r8yEjC5Hj4Xf7YMylAEyqUw9oEjZyDES8CB1i3/FyjhRU4eas57yxkc13lh+H8izQOTn8EMaOYhUvHfG86HRNM16Sv7WhVYLQi6R8p5YJ57ZbYXSkoJJzX97IobwKPFycOCexj1YYtYaLu7U0fFDRZkAT8eIgiHgROoTF67J4dDh+Hi7Nd1pCRhFjwc2nly2zDVbxUtQB8QKQYG5Yl/azcrcLQl/GZISUH9S6pRljGzz1/WGOl9YyONCTT2+bTqR/H+uoezpiZoGzO67VuQzT5VBU1UBZjcwzszciXoTT0mAw8b+kXAAun9xK+aNlts+Qmb1olW3ptHiJGK/c643Vyt0uCH2Z4zuh+gS4+akv7zYwmTR2Zao8r1eumUhilF9vWdh7uHhYP9su9DwISOjIERDxIpyWzUeLqKgzEOLjxoy44JYHZGxSy36S7wJN4iWrpAaDsQP9W3S6k8YFfGdDywShF9hvHqw6/CzVcbYNjhVVUVlnwN1Fb53O3i+JXwTAfGfVVVzEi/0R8SKclh/NjacWjw7D6dRE3dJMKE5V+S7tPKH1NcJ93XF30WMwaRwv7WAYyBI6Sv4eGmpsZ5wg2JKaEkj6UK1P+E27h+7OKgNgbJR/35th1BmGLQQgoeEA3tSIeHEA+vG7TegJDEaTtfHUktZafR81TwGPnnra7pt9Cb1eR0xQJ0NHMbPBLxpqimDj8za0ThBsyM63oLFGNWqzdJptgz1m8TJhsL/t7bInQcMgJAFnrZFLnDZKubQDIOJFaJcdGaWUVDfg7+nC1NjAlgekmcXLsAW9a1gvMDxMucGTsss6doKTC5z9tFrf8k/VdVgQ+hKGetj+X7U+/bftVhkB7MlS+S4TogNsbZl90elgys0AXO+0moPHy6g3GO1s1MBGxIvQLqsO5AGwaGQYLqe6hQ0NTcmpZrdqf2JGXBAAm9KKOn5SwnkwbBEYG2DNwzayTBBsxMGvoKoAfCJhzCXtHlpVbyClQHWb7feeF4BxV6G5ehOnz2NE7W7e2pRhb4sGNCJehDZJL6pm5S7VeGpJYiuNp45vV7M/PIMhvG9NkO4Is+JVcnJSdhkVdY0dO0mng8VPqvXU1Sp/QBD6CjvfUsvJN7aZqFtZ18iyt7dz49s70DQY5O9BmK97LxppJ9x80Jm7aV/v9CMv/5xKQcVpxocINkPEi9AqDQYTv/t4DzUNRs4YGsjc4aEtDzr4pVoOWwD6/vdWigrwZGiwF0aTxtajxR0/MWQEhCWCydDUK0MQHJ3Cw5D9q0q+t4y8aIWfDhewLuUE2zOUMB8/ELwuFibdAMA8p300NtTzz7USGrYX/e8bR+gRXvzpCPuOl+Pn4cKLV45vWWWUsRl2vKnWx17R+wb2Ehbvy6bUToSOAEZdoJaHvu5hiwTBRux8Wy1HLAHftrvkHjN3nR4Z4cuSMeHcOS+uN6xzDEISwN0PFwwM1x3vXEhZ6FFEvAgt2HK0iNd+OQrA3y9NJMLvlI6ZdeXw5e2App7Q+mG+i4XZ8SFAJ/NeAEaaxcuxdVBX0cNWCUIP01gLez9W65NvbPfQo+ZKm0snDuLf101idGQ/bEzXFjqddc5Tov4YmcU1lFRLt117IOJFaEZZTQP3f7IXTYOrpkRz9qnl0SYTfHGbmmUUEANn/80udvYWZwwNxEmvI72omqziTvRuCU2A4BEqcVeGNQqOzvEdUF8O3uEwdH67h1o8L3Eh3r1hmeNhFi8zPdXIlL3Hy+xozMBFxIvQjM92HSe/oo7YYC8ePn9UywPWPQFHfgAnN7jsrX4zy6gtfNxdmBKjykBXH8rv3MmW0FHyNz1slSD0MJb5ZDEz281fM5o0a9+jASteIsYDMN4pHYAkc68boXcR8SI042CuCnFcOnEQnq7OzXcWJjc1X7vgZRg0qZetsw+W5nw/HOikeBm+RC2PrgdjB6uVBMEeZG1Ry9OM+Mgtq6XeYMLVWc+ggH42gLGjmD0vkfXHcKOh432ghB5FxIvQjENm8TIywrflznRzT5eh82Dclb1nlJ1ZPFqVie/KLO1caWTkBFVGXl8O2dttZJ0gdBOjAbJ3qPUhM9o91JLvEhPk2TKJf6DgPxg8AnHSDIzQZbP3eBmaptnbqgGHiBfBSl2j0frhNCqyFfGSZXEt958ZRh0h3M+dieZy0B8PdsL7otc3JTOnru55wwShJ8jfq6ahu/tByMh2Dz060PNdoFnS7njnDMpqGsnsTD6c0COIeBGspBVWYTBp+Hu6EH5q0ylNg6xf1Xr0Gb1vnJ2xho72dzJ0ZJ5GS+qaHrZIEHoIS75L9Bmn7dd0zPxwMzTEy9ZWOTZm8XKml8p72ZlZak9rBiQiXgQrh/JUyGhUhC+6U2ealGVBZR7onQdMrsvJnD1GhY62pRdTXFXf8RPjzgSdHgoPQvlxG1knCN3A4lEd0n6+C0ilkZWhcwGYU/cLo3UZPPq/g6xLKbSzUQMLES+ClXbzXbK3qWXEOHD17EWrHIPoQE/GDPLFpMEa85TtDuEZCFFT1bp4XwRHw9gImZvV+uD2812gKedl6EAXL7FzYNSF6DHyiteb1NXXcdOKHawXAdNriHgRrBw+yfPSAsvT2WmqEfozXa46ktCR4KgcXQe1peAVclqPakVdI4WVyus44MNGAOc8Bx4BxBiO8tTgPZg0uO+TJHLLau1t2YBAxIsAgKZp1rBRq56XLLPnJXpaL1rlWFhCR1uOFlFe24nS5/iz1PLYejB0IuQkCLZm/0q1HH0JODm3e+gecz+TqAAPfN1bH9o4oPAOhRm/BeDSgFRGR/pSWtPIbz/ag9Ek1Ue2RsSLAEBOWS2VdQZcnHQMCz3FJVyRB4WH1PrggZesayEuxJvhYd40GjXWHu5E6Cg8UXUubaxuctELgr1pqIbk79R64uWnPXx7uhpOOi02yJZW9S3MxQtOubt59dqJeLs5syuzlJ+TJXxka0S8CAAk51UC6gva1fmUt8XBLwBN/aF6tzJdegBhGZewqjOhI51OQkeC45HygxLUATEQNfm0h287pqZITxsaaGPD+hCR49UU7spchriUc90ZQwB4a1O6fe0aAIh4EQBIKVDiJSG8lXb/Ftdy4mW9aJFjMsc8ZdrSibjDWEJH0u9FcAQ0DXatUOuJlyuB3Q51jUbrDJ9psSJerLh6Qah5jMrxnSydPgQnvY6tx4qtOYSCbRDxIgCQahYv8WGniJeiNMjdo54uRl9sB8scC0uVRU5ZLXWNxk6cOE+VmRenQfpG2xgnCB0ldTVkbFQzyiYuPe3hu7NKaTRqhPu6Mzhw4FUbtsugiWqZs5NIfw9rbpx4X2yLiBcBgJQCVQI54lTxcuAztYw7E7yCe9kqxyPA0wU/D5WsmFFc3fET3X1VUiTAR1dDzi4bWCcIHcBogNV/Uetn3K7a3Z+G7ekqZDQ1NrBlD6iBjiXkdlz9Td84MxaAz3cfZ+vRYntZ1e8R8SJgMJo4WqjEy/CTxYumnRQyOn1C30BAp9MRG6zKRNNPdEK8AFzwT4iZDQ2V8OFVYGiwgYWC0A7GRvhxORSlgEcg5ZPuYfkX+7jzg13UNrTtSZR8l3YYZBYvuXvAZGTSkAAumxSFSYN7Pt7DiUqpMLQFIl4EMktqaDCa8HBxIurkSbF5SSrM4ewBCefYzT5HY6hZvBwr6qR4cfGAqz9WwxqrC5sa/wlCb1BfBe9cANv/C8C2Yfey5D97+Wh7Nt/vz+etza2HORoMJnZnqfb3ku/SCiEjwNVbJT+fSAbg8QtHEx/qzYnKeh775qCdDeyfiHgROJKv8l2Gh3mjP3lS7H5zyGjEEnBrJZF3gGL1vHRWvAC4easQHMDRn3vQKkE4DbtWQNYWGp29uaPxXq7cMYzc8joCPFUY9N/rj7Y6+mLf8TLqDSaCvFxlLEBr6J0g2txFO+lDADxdnXnxyvGAqkwU70vPI+JF4Ig536VZsq7JCAc+V+sSMmpGbEg3xAuIeBHsw/5PAfgm5BZ+ME5laIgXDywazoYH55M4yI+qegP/XJva4rRtku9yes64Uy13vAlVJwAYM8iPCYP9MZg0Ptslc816ml4RL6+88goxMTG4u7szbdo0tm/f3uaxK1asQKfTNXu5u7u3ebzQfY6YK42aJetmblaDGN39YdhC+xjmoHTL8wIQN18t8/ZCdVEPWSUI7XDiiHq/6Z351qi6ZN+7cDi/XRCPj7sLy89JAOCDbVkt3tcW8SIho3YYthAiJ4KhFrb8w7r56ikqGfqTHVlomnTd7UlsLl4++eQT7r//fh555BF2797NuHHjWLx4MYWFbXcg9PX1JS8vz/rKzMy0tZkDmhRrmfRJLuGDX6nlqAvB2bX3jXJgYoKUeCmpbqC8phNjAiz4hEPoaEBTIwMEwdaYvS7ELWB/qQoTxQQ1lTzPiAtm/ogQDCaNZ39Mtm43GE3syrB4XqSzbpvodDDvIbW+402oUz1ezhsXgbebMxnFNWw9JpVHPYnNxcsLL7zALbfcwrJlyxg1ahSvvfYanp6evPXWW22eo9PpCA8Pt77CwsJsbeaApd5gJMP8pNWs0sgyiNHSXE2w4uXmTLiv8gamd6Zc+mQs3pdfnoEv74ACSeoTbMRJVYN1Iy+15l8MCWo+XPGhJSPR6+D7/fnWBN2DuRVUNxjxdXduvYGl0ET8WeA3GBprVOURKvfl/HGRAHy/P8+e1vU7bCpeGhoa2LVrFwsXNoUd9Ho9CxcuZOvWrW2eV1VVxZAhQ4iOjubCCy/k4MG2P9jr6+upqKho9hI6TlphFQaThp+HCxF+5vBcbRkUHlbrA3gQY3s0hY6qunaB4YvVsigF9n4IPz3aM4YJwqnk74PSDHDx4ljQHAACvVyt/YosjAj34fJJ0QDc/M5O3vs1ky92q1yNqbGBzZP5hZbodBA5Tq3n77Nunjtc9cfamVFqD6v6LTYVL0VFRRiNxhaek7CwMPLzW58NM2LECN566y2+/vpr3n//fUwmEzNmzOD48dYTnp5++mn8/Pysr+jo6B7/d/RnDuVaJkn7NCXj5ewENAgcCt4h9jPOgYmxlEt3tteL9QKz4dI3YfYD6ueMzdL3RbANaWvVMnYO6eVq9eSQ0cn8fvEIRoT5UFLdwF++OsA7W1XIXoYxdpBws3jJaxIvk4aoXKGUgsquhZmFVnG4aqPp06ezdOlSxo8fz9y5c/niiy8ICQnhP//5T6vHL1++nPLycusrOzu7ly3u2xwyz98YFeHXtDHbnFAtXpc2iTdP3u7y/BKdTs2Kmv9n1felsRqOt53ILghdxlLVNmyBtSt0zCkhIwshPm58e88s/nzuSIaHeTNmkC/nJkZw6aSo3rK2bxMxVi1P8ryE+LgxNNhLjZPKKrGTYf0PZ1tePDg4GCcnJwoKCpptLygoIDw8vEPXcHFxYcKECaSlpbW6383NDTc3t27bOlCxfPmOivRt2mhpnmbpXSC0YMJgfwB2Z5WhaVrXS0j1epX/sn8lHF0HMbN6zkhBaKiGrF/VetyZZPyswpwWz2FruDjpuXn2UG6ePbQ3LOxfhJvFS9ERaKgBV+XhmhwTwLGianZklHJmguRw9gQ29by4uroyadIk1q5da91mMplYu3Yt06dP79A1jEYj+/fvJyIiwlZmDlg0TWsWNgJUf5fjO9W6eF7aZHSkH67OekqqG8gorunexYaak3el74vQ02RsBlOjml8UOJRM83t1SBthI6Gb+ISDVwhopmZJ+JNjVOhoR7p4XnoKm4eN7r//fl5//XXeeecdDh8+zB133EF1dTXLli0DYOnSpSxfvtx6/OOPP87q1as5duwYu3fv5rrrriMzM5Obb77Z1qYOOHLKaqmoM+DipCM+1CxeCg9BQxW4+kBIgn0NdGBcnfUkDlKhtt2Z3UzEs1Qe5e6BGvlwE3oQiyCOOxN0Omt1XGw7nhehG+h0EGFJ2t1r3TzFLF72HS/v3DR6oU1sLl6uvPJKnnvuOR5++GHGjx9PUlISq1atsibxZmVlkZfXVEJWWlrKLbfcwsiRIznnnHOoqKhgy5YtjBo1ytamDjgsXpdhoT64OpvfCpaQUdRk1fZaaJOJ1tBRN8WLb6RZKGqQ/ku37RIEQDVATP5WrcedSXW9oalMOlDEi82whI5OStqNCfIk2NuNBqNJqo56iF5J2L377rvJzMykvr6ebdu2MW1aUzhi/fr1rFixwvrziy++aD02Pz+f7777jgkTJvSGmQOOw3mqOZ01ZASSrNsJJg4OAFTeS7eJX6SWB7/s/rUEoTQD3lwE5dngFQpD51uTdQM8XfDzdGn/fKHrtJK0q9PpWDgyFIDHvjko3pcewOGqjYTe41CeqpscFSHJul1h4hAlXlLyK6iqN3TvYolXqGXKKtVnRxC6iskIn1wHJcdU07RlP4C7L1nWfBfxutiUiPFqmX9AJUybefDsBIK93UgtrOKZVSn2sa0fIeJlgJJdUsOmVDVXZ4w5d4PKAvXEhk6FjYR2CfN1Z5C/ByYN9maXde9i4YkQMhKM9XDo6x6xTxig7H4X8veDux/cuAqChwFQUFEH0NSMUrANATHgF60SpbOamrEGernyzGWJALy1Od36/yF0DREvAxCjSeOBT/dS3WBkakygNZnM2mckdJT64BNOy+QY5X3ZerSbc0t0Ohh3pVrf92k3rRIGLLVl8PNf1fq85eA3yLrrRJXKdwn1kdYSNkWng9i5av2U2WVnJoRZGwR2ebCrAIh4GZB8uC2T7RkleLo68dzl43CytP2WkFGnmROvOhCvP9L2oNEOk3g5oIPMTVAmzRaFLrD1X1BTDMEjYErzCs3CCiVeQkS82J6h89TyWMsE/EEBHgDkltX2okH9DxEvA5Cfk9UX7d1nDmPwyf0eJFm308wZrsTLgZwKCiu76Qb2i2pqUrdfvC9CJzEaYPd7an3+cnBqnpTb5HmRsJHNiVUzpMjfB9XNvbKRfkq85JSKeOkOIl4GIDlmxT868qTQkKEecpPUunheOkyIjxtjo9Tv8ZeUE92/4Fhz6GjvJ2oasCB0lNTVUJWvxk2MOLfFbvG89CI+YSr8DpCxodkuq+elXMRLdxDxMsDQNM2q+Af5ezTtyN+vkkU9g9VARqHDzDN7X9b3hHgZdQE4u6tp03l7T3+8IFjY/a5ajr8anF1b7LZ4XkS89BJt5L1Emj93c8okYbc7iHgZYJTXNlLdoHoMNBMvBQfUMmKsSjgTOsy8BNW/YWPqCQxGU/cu5u4HI5aodUncFTpKRS6k/qjWJ17fYrfRpFEsCbu9S8xMtczZ3Wyz5XNXcl66h4iXAcZxs9clyMsVD9eTOugWHFLLUOlk3FnGRfkT4OlCRZ2BTWlF3b/g2KvUcv9KaJSnM6EDJH2g5ukMngHB8S12F1fXY9LUc0mgV0uvjGADLJ+lRUdU7x0zkSeJF01Cw11GxMsAw5LvYom7WikU8dJVnPQ6LpqgSlLf3ZrZ/QsOWwA+kVBdCL++2v3rCf0bk6kpUXfi0lYPsYwFCPJyw9lJPvZ7hYAYFQI21Jn7ZyksfXZqGoyU1TTax7Z+gLyLBxit5rtoWpN4CRPx0hWWTo8BYF1KIZnF3ezf4OQCCx9V6xueg4q8dg8XBjgZG6AsE9z8YNSFrR5SWCn5Lr2O3gmCh6v1wsPWze4uTgR7q/+HHAkddRkRLwMMyx9L1Mmel+oTqjcEOpkk3UVig72YOzwETYP3esL7kng5RE2Bxmr4+YnuX0/ov+x6Ry3HXg6unq0eYvG8SL5LL2PxZJ843GzzIH/lfZG8l64j4mWA0arnpeCgWgYOBRePVs4SOsINM2IA+GRHNsdLa9o8rkNxbr0eFj2u1g993SxmLghWKvObJke3ETKCJvEinpdeJtT8MFjYXLxEStJutxHxMsBoynk56QnN8oclIaNuMXd4COOj/amsN3DXh3toMLSsPMorr2XJPzay6IVfTj9SIHoauPpAQ2WTwBSEk/npMTA2QNRUiBjX5mHiebETISPVsjC52WareCmXhPyuIuJlgGEVLyd7XgrNX4ySrNst9HodL189AV93Z/Zml/HMquYfWIWVdVz7+jaS8ytJLazi6td/5Z9rU9u5oFNTw8CsX21oudAnOb4T9n6o1s9+ut1DxfNiJ0LN4qU4FYxNybmWz1/pstt1RLwMIGoaDJRUNwCnVBtJmXSPER3oyfNXjAfgzc3p7MwoAWBXZgmXvLqFY0XVDPL34MrJ0QC89NMRkvMr2r7g4OlqedJ0WkFA02DVQ2p9/LWnnQJvGV0h4qWX8YsGFy/lHSs5Zt3c1KhOxEtXEfEygLDEV33cnPHzMM89MRnhhNlDIOKlR1g0KozLJ0WhafDg5/tY/sV+Ln9tK8dLa4kO9ODDW6bx98vGck5iOCYNnvj2cNt5MIPPUMusrTIuQGgiezsc36FKcRc8ctrDm8JGMteoV9HrIWSEWj8p72WQiJduI+JlAGFpUNfM63J0HTTWgEeAjAXoQf587ihCfNw4dqKaj7ZnYdLgkgmD+O6e2QwJ8gJg+ZKRuDrp2ZRWZB2W2YJBk0DvDJV5UJbVi/8CwaHZ8bpaJl6m5uicBimVtiOWh8KT8tYGB6qcwxOV9VTVG+xhVZ9HxMsAotV8l6T31TLxCnBytoNV/RM/Txeeu3wcwd5uLB4dxie3nsELV47H171p0m90oCc3zIwB4KPt2a1fyNWzKRFT8l4EgKpCOPiVWp9yy2kPr643UGMeCSIJu3YgeopaHv3ZusnP08Xa6+XYiSp7WNXnEfEygCgwZ7aHmzs8UlMCyd+p9QnX2smq/svc4SHs/PNC/vObyUwbGtTqMWePCQdgd1ZpO6Ejc97L0bW2MFPoa+x+B0yNqg9Q5PjTHm7xuni6OuHlJg8ovU78WWqZswuqm8aHDAtVHti0QhEvXUHEywDC8iEW5msWLwc+V4lkYYntllkKtmNMpB9uznpKqhs4VtRGZ15L19T9KyH/QO8ZJzgeRgPsfFutT7m5Q6cUVKiHFuvfvdC7+EZCeCKgQeoa6+a4EG9AxEtXEfEygLB8iFldx3s/VkvxutgNV2c946L8AdiVUdr6QdFTYdRFavDeqockcXcgc+QHqMgBzyD1nugATeJFQkZ2Y/jZammZ/A0MCxXx0h1EvAwgmnle6soh1zyqfeQFdrRKmBQTAMDOzJK2D1r0ODi5QcZGOLKqlywTHI7t5kTdiUvBpWOelMKKUzyuQu8Tv1gt09Za+71YxYvkvHQJES8DiGYVB1m/qif5wKHgN8jOlg1sJg9R4mVXZhueF4CAIXDG7WpdJk0PTE6kQPovoNPD5Bs7fJqEjRyAQROVt6y+wpp4bwkbZRXX0Ghs2Y1baB8RLwMEg9FEcZW514OvG2RsUjuGzLSjVQLAxMFKvBw9UU2puYlgq0y5RX1xpW9QX2TCwCFnF3x8jVoffjb4D+7wqfmnhouF3kfvBHFnqnXzZ2+Enzterk4YTFr3J9EPQES8DBCKqxswaaDXQZCXG2RuVjtiZtnXMIEAL1fiQlTlQbveF/9oGL5Ere98qxcsExyC9A3w5llQnAY+EbDg4U6dLmEjB8HyoGj+7NXpdMRJ3kuXEfEyQLB8gIX4uOHUWAW5SWqHeF4cgqmxqpR6Q+qJ9g+ccpNaJn0I9fKB1+9prINvfgcmg/K43LGlaV5OBymolLCRQ2D5rD2+Awzq83iYVBx1GREvAwTLbJNQH3fI2gaaUbme/aPtbJkAsGhUKACrDxa03e8FYOh8CIxTsfOfTt8WXujjbHpBzcTxiYBLXgfPwE6drmmaNeclXMSLfQmOB68QMNRBjiqWsHhejp6QsFFnEfEyQCi0zjZxg0xzvkvMbDtaJJzMjLhgvFydyK+oY39OedsH6vVw7nNqfccbkPx97xgo9D5l2bDpRbV+9t/A3bfTl6ioM1DXqJJBQ6VU2r7odDBkhlo3h44s4WLpstt5RLwMEKw9XnzdIG+v2hg1xY4WCSfj7uLEvBFN3pd2iTsTpt+t1v93N9RX2tg6wS5se001kYyZ3dSosJMUmv/u/TxccHdx6knrhK5wSt5LtHnGUXapDGjsLCJeBgiFJ0+VLUpTGy3TTgWH4KzRasDejwfzT3/wgodVmXtNMez5wMaWCb1OXQXsfletz/ydemrvAvnSoM6xsIiXrG1gNFjFS0l1gwxo7CQiXgYIloTdCC8TlJunEwcPt6NFwqnMGxGKs15HamEVGW2NCrDg7AbT71Lrv74KJqPtDRR6jz3vq7ym4BEQt6DLlymQSiPHInQUuPlBYzUUHsLX3QV/TzWsNbukxs7G9S1EvAwQTpgTdodgfqp391dNkwSHwc/DhdGRKq8hpaADoaBx14BHAJRlNg3YFPo+mqZCRgDT71R5Tl1EGtQ5GHq9ec4RUHAQgMGW0JGIl04h4mWAYHkCi2g8rjYED++yK1qwHVEB6oPseEdi4K6eMNlcOr3pRTBJl85+QckxJUidXCHxim5dqlDCRo5H2Gi1LFBDVqMDJO+lK4h4GQCYTBpF5u66QXWZamNwvB0tEtoiKsADgJyOfpBNvRVcvdWcqt3v2NAyodcwt48ncqISqN1AwkYOiFW8KM9LVKD6mxfPS+cQ8TIAKKlpwGDS0OnAq/KY2ijixSEZZBYvx0s7+EHmEwZn/lmt//QIVJ6mUklwfLK2quXgM7p9qYKT+zsJjkHYGLWUsFG3EPEyALAk6wZ5uaIvTlUbg0S8OCKD/M2el7JOuJCn3goR49Wk8A+vgJJ02xgn9A4Wz8vg6d2+VEG5hI0cjtAEQAfVhVBVaA0bZYl46RS9Il5eeeUVYmJicHd3Z9q0aWzfvr3d41euXElCQgLu7u4kJiby/ffSiKs75JWrL8IwHzcoPqo2SqWRQ2LJeemUeNE7wYWvqOTdvCT4zxzIP2AbAwXbUl0ElgeM6KndulRVvYE8c86L5X0lOACuXqrNAUDBQavn5XhpbfvdtYVm2Fy8fPLJJ9x///088sgj7N69m3HjxrF48WIKCwtbPX7Lli1cffXV3HTTTezZs4eLLrqIiy66iAMH5MO4q2QUK0U/zr8WGqpA5wQBMfY1SmgVS9iorKaxc30fwsfA7ZsgcoIqsd31to0sFGyKxesSMrLTowBO5UBOOZqmpheHyERpx+KkvJdIfw90OqhtNFJU1c5UeaEZNhcvL7zwArfccgvLli1j1KhRvPbaa3h6evLWW61Pxf3HP/7B2WefzR/+8AdGjhzJX//6VyZOnMi//vUvW5vab7GMWx/rbhaMATHg7Go/g4Q28XZzxs9D9X3ocNKuBb8omHWfWs/c0sOWCb1CD+a77D+uxkyMjfLr9rWEHuakvBdXZz0R5oRqCR11HJuKl4aGBnbt2sXChQubbqjXs3DhQrZu3drqOVu3bm12PMDixYvbPL6+vp6KiopmL6E5mWbPS7w+V22QkJFDE9XZpN2TseRJFB6CmpIetEroFaz5Lt0XL3uPlwEwNsq/29cSephwi3jZDzSNCejS3/wAxabipaioCKPRSFhYWLPtYWFh5Oe33gI9Pz+/U8c//fTT+Pn5WV/R0TIl+VQsnpeoBnMiZ2iCHa0RTkeXknYteIc2JWNnb+tBqwSbY2iA/H1qvQfmju0ze17GiXhxPCxhoxMpYGy0ipesYhEvHaXPVxstX76c8vJy6ys7O9veJjkUjUaTteFZQGWy2hg+1o4WCadjUGd7vZzKELP3xTz8TegjFB5Ugxjd/ZsSOrtIaXWDNQSRKGEjx8NvMLj6qP/v4jRig83TpU83FkSwYlPxEhwcjJOTEwUFzXtPFBQUEB4e3uo54eHhnTrezc0NX1/fZi+hidyyWgwmDS8XDZeiw2qjiBeHplNddlvDOrm29VCr4KDk7FLLQZO63f16X47yusQGe1lzqAQHQq+HsFFqveAgw8N8AEjJlwnxHcWm4sXV1ZVJkyaxdu1a6zaTycTatWuZPr31HgbTp09vdjzAmjVr2jxeaB9LpdEMvzJ0hjpw8er2U51gWyxho+NdCRtBU95LXhI0yJNcnyFnt1oOmtjtS+3LLgMkWdehOWlMwAizeEk7UYXBKGM+OoLNw0b3338/r7/+Ou+88w6HDx/mjjvuoLq6mmXLlgGwdOlSli9fbj3+d7/7HatWreL5558nOTmZRx99lJ07d3L33Xfb2tR+iSXfZZpnjtoQPqZbg94E29PpEQGn4j8YfKPAZGhKABUcH6t4mdTtS+21Vhr5d/tago04qVw6KsADT1cnGgwmMorlgaMj2Pxb7Morr+S5557j4YcfZvz48SQlJbFq1SprUm5WVhZ5eXnW42fMmMGHH37If//7X8aNG8dnn33GV199xZgxY2xtar8ko0h5XkbrzTONJGTk8FjES1FVPTUNnej1YkGng6Hz1HraTz1nmGA76ivhhDknLbIHPC/mSqNx4nlxXE4ql9brdcRbQ0dVdjSq79Arj+B33303mZmZ1NfXs23bNqZNm2bdt379elasWNHs+Msvv5yUlBTq6+s5cOAA55xzTm+Y2S+xeF5iGs2ddS3j2AWHxd/TlUAv1YfnaGEXn8KGn6WWR37sIasEm5KbBGjKY+YTdrqj2yW/vI7Cynr0OhgVKTmADkvoSLWsyIGaEhKs4kXafXQEiR/0c5QLUiO46ojaIOKlTxAf6g3AkYIuJvANnQ96Fyg52jQSQnBcrMm63fe6WPq7DA/zwdPVudvXE2yEu58K8QIUHmJEuFm8dPVvfoAh4qUfYzRpZJfUEkYpLvUlaixA6Ch7myV0AEv1wZHCLn6Qufs2lUyL98Wx0TTY/5la74FhjPuszekkZOTwnBQ6sooXqTjqECJe+jH5FXU0GE1MdD6mNoQkgIu7fY0SOsTwMOV5SS3oRvw7frFapop4cWjSf1GdVl08YdxV3b7cPknW7TtYxcsBq3jJLKmhtsFoR6P6BiJe+jGZ5oZHZ3qkqQ090HJc6B0syXtdDhsBDDeLl4zNUFfeA1YJNmGLeW7bhOu6PYxR0zT250hn3T6DJYyftY1gbzeCvFzRNEjtqsd1ACHipR9j6fEyRWduTjdkhh2tETqDJWx0vLSW6s5Mlz6ZoGFqOrGpEQ580YPWCT1GYTKkrQF0cMYd3b5cVkkNZTWNuDrprU/yggMTOxv0zlCUAsVHSYhQ/2e7M0vtbJjjI+KlH5NZXI0PNQxuMCdsinjpMwR6uRLsrSqO0gq7GDrS6WD8NWo96YMeskzoUY78oJbDF/dI80hLf5eRkb64OsvHu8PjEdD0uXxkFfOGhwKw6mDrs/yEJuTd3Y/JKK5mkv4IekwQEAu+kfY2SegE8aE9EDoae6VK1D6+Qw2BExwLS2M6y0iHbvDe1gz+/KWaUjwh2r/b1xN6iRHmViDJ37MkUY3B2Z5ewonKejsa5fiIeOnHZBbXMFVvbnzVAx+OQu9icfundtXzAqpnSLy558ue93vAKqFHyU1Sy8gJ3brML0dO8JevD1JRZ2BUhC+3z43rvm1C7zBiiVpmbSXKrY5xUX6YNFh9SLwv7SHipZ+iaRoZxdUniRcJGfU14sO62evFwoRr1fLgl920SOhRqougPAvQQcS4bl1q27FiAJaMCeeb384i3E+qCvsMATEQOho0I6SuZkliBADf789r/7wBjoiXfkphZT1aYx1jdZLv0lexJO12q1waVMM6nR7Ks6EitwcsE3qE3D1qGRyv+vJ0g2Rzb5AZcUE46bs3kVqwA/GL1DJrK+eMUeJl69FiSqsb7GiUYyPipZ+SUVRNou4YrjojeIUqdS/0KYabc15yymqprGvs+oXcvNWTHcDxnT1gmdAjWMRLN0NGAIfzVEv5hAgZB9AnsTQPLT7K4CBPBgd6YtJ6wOvajxHx0k/JLK5hgt7c3yV6qqo8EfoUfp4uhPq4Ad3MewGImqyWx3d00yqhx7Ak63ZTvJTVNJBXXgcg5dF9laBhalmsPrOHBHkCqvRdaB0RL/2UjOLqJvESNcW+xghdpil01M0nMMt7QDwvjoPV89K9eUaWkFFUgAe+7i7dtUqwB0HmBOvKPKivJDpQiZdsES9tIuKln5JZXM1Efar6QcRLn6Upabe7nhfzeyB3Dxi7EYISeoaKXKjKV7lI3RyWag0ZhUvIqM/i4Q9eIWq9+CiDA8XzcjpEvPRTqgozCdeVYtI5QeR4e5sjdJHhPTEmAJRb2t0PDLVQcLAHLBO6hcXrEjISXD27dankPPXeGBUhIaM+zUmhI4t4yRTx0iYiXvohdY1GfIuTADAEjwRXL/saJHSZHhnQCKDXwyDJe3EYLOJlUA8k6+ZLsm6/wBI6Okm8SNiobUS89EOSsssYiwoZuQyRYYx9mWHmiqP8ijrKa7sZ7pGkXcehh5J1jSaNFHPOy0gRL32boHi1LE6z5rwUVTV0fbZZP0fESz9kR3qJNVlXFy35Ln0ZPw8XIswNx9K6O2k2eppaZm7pplVCt9C0HiuTTiusot5gwsPFyfq0LvRRTgob+Xm44Oehkq+zS8X70hoiXvohOzJKGK7LVj90s3OnYH/irXkv3QwdRU9TE2zLs6E0swcsE7pEWRbUloDeBcLGdOtSm9OKAJgcEyDN6fo6FvFSlAaa1pS0WyzipTVEvPQzjCaNzKwMfHW1aOh6ZFKtYF+Gh6q8F0t4oMu4eTc96Wdu7qZVQpfJNYeMwkaDs1u3LrXJLF5mDQvurlWCvQmMVdVnDZVQVSgVR6dBxEs/43BeBaENx9UP/tHd/nAU7M+YQX6AmjTbbWJmqWXGpu5fS+gaPRQyajSa+NU802imiJe+j7Mb+A9W68Wp0uvlNIh46WfsyCghVq8GeuksbkihTzNneAg6HRzKqyCvvLZ7Fxvi+OJF0zQ0TbO3GbbDkqw7qHvN6fZklVHTYCTIy5VRkqzbP7Ak7RYdEc/LaRDx0s/Yk1VGrM48Sl3ES78g0MuViYMDAPg5ubB7Fxs8DXROUJYJZdk9YF3PUtdoZOlb25nz7DoqujPPyVExGSFvr1rvpudlU+oJAGYMC0Yv+S79g9AEtSxMFvFyGkS89DOyS2tEvPRDzkwIBeDnw90UL24+TU0LHSzvRdM0/vTlATamFpFdUsuOngiTORq5e6C+Atz8VIO6brDRnO8yW0JG/QfLe6LwUFOvl9La/u2J7CIiXvoZuWW1xOpU2IjAOPsaI/QYC0Yq8bIprYjaBmP3LjZkhlpmb+umVT3Le79m8vnu49af9x4vt6M1NiJ1jVrGzQMn5y5fpqiqnr3ZZQDMjBfx0m8ItYiXw4T5qXzFBoOJ0pp+6IXsJiJe+hENBhMnKmuJ0RWoDUEiXvoLI8J8GOTvQb3BxJajRd272KBJamnJvXAAKusaeX71EQASzQnK+4+X2dEiG5H2k1oOW9ity6w6kI9Jg3FRfgzy9+gBwwSHIGSEWtYU4VZXQrC3K6AeSoXmiHjpR+SX1xGhFeOma0TTuzRlrgt9Hp1Ox/wENbhtw5ET3buYRbwUHIDGum5a1jO8uzWT8tpGhoZ48egFowHYd7y8f7nLa0ogZ5daj1vQrUt9t095V89JjOiuVYIj4eoFATFq/cRhIvyUMM0vd4y/U0dCxEs/Iqeslli9ynfRBcaC3snOFgk9iaWXx+ajxd27kF80eAaDyaAEjJ2prjfwxsZjAPz2zGGMjvTFWa+juLqB3P70oX30Z0CD0NHgN6jLlzlRWc+2dPUeEPHSDwkdpZaFyYSbu2vnVfSjv4MeQsRLP6JZvosk6/Y7zhgahE6nWsIXdOfDTKc7KXS0q2eM6wYrd2ZTWtNITJAn54+NxN3FiRHhqqvwPnNeR78gba1aDuue12XVQXPIKNrf2gtE6EeEWCqODllHg+R3t0VCP0TESz9CiRdLpZHku/Q3/D1dGROp8kG6n/di7jHiAOJlvTkMdu20ITg7qY+ksVH+AOzL6SdJuyZTj+W7fL0nB4BzE8O7a5XgiFg9L4ebPC/9yQPZQ4h46UfkltcSp8tVP4jnpV8yY1gQAJvTuhk6cpCk3UajyVoSbfm3AYyNUiJtX39J2i04ANWF4OIFg7s+6f1QbgU7M0tx1uu4cHzXQ0+CA2OpODpxmAhfVXEkOS8tEfHSj8gpq2O43lxq2s0eEoJjMjPOnPeSVtS9ZNZIs+elOBVqy7pvWBfZn1NOdYMRf08XRoY3dYltEi/lmEz9IGnX4nWJndOtkR3vbs0A4Owx4YT5uveAYYLDERyvGknWlTPYpQIQ8dIaIl76EeWlJ4jQmRt7WTo1Cv2KKTGBuDrpySuv41hRddcv5BUE/kPUuh37vVhm80yLDWzWJXZEmA8eLk5U1hlIO9HNadqOgEW8xHc9ZFRW08BXSSpkdP2MmB4wSnBInN2sFUdRRvUwmlde178q73oAES/9BE3T8C5PBcDgHQnufna2SLAFHq5OTI5RowLWdXdUwPDFarn73W5a1XW2miunpg8Narbd2UnPhMH+gJrX1aepK28SiF0skdY0jX+uTaOu0cTICF8mDwnoQQMFh8Mc9g+sUyM8ahuNlNdKo7qTEfHSTyivbWSIMQsAXaiEjPozZ40KA+DHg/ndu9Dkm9Qy5Xsoz+mmVZ2nwWBiZ0YpANPjWnaJtXxB7zIf02dJ36DK0oOGQWBsp0/XNI2/rUrmrc3pANw1Pw6dTmYZ9WvM4sWl7BiBXqpRnSTtNkfESz8hp6yWeJ1yMTqFj7KzNYItWTRaVZnszCylqKq+6xcKTVBTpjUT7FrRM8Z1gp2ZJdQ2Ggn0ciU+1LvF/kkxgebj+rh4ObpOLbvodfnf3lz+84vqg/PI+aM4b2xkT1kmOCpBQ9Wy5CjhvpZyaREvJyPipZ+QU1rLCJ15SrAk6/ZrBvl7kDjID02Dnw4VdO9iU8zel93vgLH33NKapvGPn1SYc9HIsFanIk8c7I9ep6bqFnahr82Kzenc+u5O1hwqoLy2kdSCSuoauzkXqivk7FRLy0ypTmA0afxjrfo93XPmMJbN7LznRuiDWKpFi9OsvV7E89Icm4qXkpISrr32Wnx9ffH39+emm26iqqr95Lt58+ah0+mavW6//XZbmtkvyC2rbao0krBRv8cSOlrdXfGScJ6acFxVAAUHe8CyjrH2cCHb0ktwddZzz8L4Vo/xcXdhhLkCqbPel5yyWv763WFWHyrglnd3Mu6x1Sx6cQPXv7W9dxMfG+ug4JBat/TW6QTf78/j2Ilq/DxcuGXO0B42TnBYLOKlNINIXzXAUxrVNcem4uXaa6/l4MGDrFmzhm+//ZYNGzZw6623nva8W265hby8POvrmWeesaWZ/YKighyCdaqszjrcS+i3LB6jQkeb0oqoqjd0/ULOrjBoglrPS+q+YR3AYDTxt1XJANw4M7bdwYKWvJedncx7WbE5HaNJIyrAAx+3punN29JLWHOogLpGIx9vz+LCf21i/nPr+XBbFkZblGQXHARTI3gGqbEMncBk0nhlXRoAy2bG4OPu0vP2CY6JTyQ4e4DJwAj3MkA8L6diM/Fy+PBhVq1axRtvvMG0adOYNWsWL7/8Mh9//DG5ubntnuvp6Ul4eLj15evr2+7xApgKDgNQ6RGlhnsJ/Zr4UG9igjxpMJj4JaWbgxojzeIld0/3DesA3x/IJ62wCn9PF+6Y134naEtlVWc6ClfUNfLRdhVC/euFY9j1l0UcfGwxd81X9/rbqmQueXULD32xn73Hy0kvqub/vtzPFf/ZSnV3hGBr5JqbAEZOVGMZOsHKXdkk51fi7ebMshkSLhpQ6PUQqDxtsaiRLyJemmMz8bJ161b8/f2ZPHmyddvChQvR6/Vs29Z+X4kPPviA4OBgxowZw/Lly6mpqWnz2Pr6eioqKpq9BiIeZSkANARJf5eBgE6nY7E5cXf1oW5WHfWieNE0jX+vPwrAshmx+Hm0702YOzwEZ72O5PxK0go71u/lk+3ZVNUbiA/1Zu7wEFyd9Xi5OXPb3Dj8PV04dqKaQ3kVBHm58n/nJPCX80bh4+7MrsxSfvvRnp71wFg6GHcyZFRUVc9T3yvv1D0LhuHnKV6XAYd5xEuUSVUCZpW0/T04ELGZeMnPzyc0NLTZNmdnZwIDA8nPb/vD9pprruH9999n3bp1LF++nPfee4/rrruuzeOffvpp/Pz8rK/o6M65ZvsDRpPG0Fo1HdjZEgIQ+j1njVZ5Lz8nF9JgMHX9QhbxUnBI5WjYkF+OnOBwXgWerk5cP2PIaY/393Rldrwqo/52X/seW1Di6KPtqmXATbNimyUC+7q7cP+i4YAaavjtPbO4dU4cN82K5d0bp+LmrOfn5EL++u2hrvzTWsciCCM7J16e+u4w5bWNjIzw5UZJ0h2YmPNeQhqVeDleWtO9v/N+RqfFy0MPPdQiofbUV3JycpcNuvXWW1m8eDGJiYlce+21vPvuu3z55ZccPXq01eOXL19OeXm59ZWdnd3le/dV8spqmKpTH7jeI+ba2Rqht5gQHUCwtxuVdQZrp9ou4RetcjJMjVBou6TdtMIqnjZ7E66ZOhh/T9cOnWcpDf5mb+5pk22Tsss4VlSNh4sT541rWVK8dHoM638/j89vn06EX1OuzYTBAbx05XgAVmzJ4G1zT5VuUV8FRcojahWIHWD/8XK+2JODTgdPX5JoHVYpDDDMnhePynQ8XJwwaUrACIpO/1U88MADHD58uN3X0KFDCQ8Pp7CweQdQg8FASUkJ4eEdn4Y6bdo0ANLS0lrd7+bmhq+vb7PXQKMw/SAhunIacMEpeoq9zRF6Cb1ex6KeaFin00HEeLVuo9DRyp3ZLPnHBlIKKvFxd+am2R33JiwaHYars56jJ6pJzq9s99gvdqun1LPHhON9UqLuycQEe7UqCJYkRrB8iQq7Pv7tIdaldLODcd5e1UPHdxD4hHX4tGdXK8Fz0fhBjI/2754NQt/F7HnRFaUxJMgTgMxiES8WOi1eQkJCSEhIaPfl6urK9OnTKSsrY9euXdZzf/75Z0wmk1WQdISkpCQAIiIiOmvqgKHh2EYAjrmNBBcZ1jaQWDy6qWS6W7kaNsx7qa438MR3h2k0aswfEcJ3v53dzOtxOnzdXZg3PARQpcNtUW8w8r+9KrR06cSoLtl665yhXDUlGk2D535M6V5Z9fHtatkJr8uvx4rZcOQEznod9y0c3vV7C32fkBGg00PFcSb5KtGe3p15Zv0Mm/kjR44cydlnn80tt9zC9u3b2bx5M3fffTdXXXUVkZHKnZuTk0NCQgLbt6s/8qNHj/LXv/6VXbt2kZGRwf/+9z+WLl3KnDlzGDt2rK1M7fN45/0KQEHg5NMcKfQ3ZsQF4+fhwonKerand2MGkOULNqfnxcunO7Mpr20kJsiTN66fwmDzU2RnWDBS5c+1929cl1xIeW0j4b7uTI8LavO49tDpdPzx7ARcnfUczK1g7/HyLl0HUGMBAGJmd/iUf/2sPMxXTx3cpd+T0I/wCIDBqrHhfNR3ZGaxiBcLNg2mfvDBByQkJLBgwQLOOeccZs2axX//+1/r/sbGRlJSUqzVRK6urvz000+cddZZJCQk8MADD3DppZfyzTff2NLMvo2mMahcVTTUDzrDzsYIvY2rs97qfelIQmubRJmFb+EhqOm5QYgGo4k3N6n8kZtnD8WplU66HWHCYFUyvT+nHIOx9aTFtYdVmOf8cRFdvg9AgJcr5yUqT+8Hv2Z27SKGesjcqtaHdiwPrbS6ga3m3KVbZktDOgEYeR4AYyuVEE6XsJGV1oPCPURgYCAffvhhm/tjYmKauWWjo6P55ZdfbGlS/6M0nQBjEQ2aEx5Dp9vbGsEOnDc2kk93HmfVgXwSwn14d2smz14+rnP5Ej7hEDxCJZhmboaR5/eIbasO5nO8tJZAL1cum9S1UA7AsBBvfNycqaw3cKSgilGRzXPbNE1jc5rqBTM7PqRbNgNce8ZgvtiTwzf7cvnzeaNOW9LdguM7wVALXiEQ0nr7gvSian49VszRwioumRhFSkEFRpNGQriPeF0ERcJ5sOohQkr3EEw5GUXyvrAgaex9HNNxlVN0QItlcFjXXOVC32ZGXBABni4UVzfwl68PklpYxWvrW6/Oa5fYOWp5rOceICw5KtdMHYy7i1OXr6PX6xhnFmN7slt2280oriG3vA5XJz1TzAMdu8PEwQGMCPOhrtFkLb3uFOnm32HsnFab06UXVbPohV9Y/sV+3tiUzp0f7OL7/SrpeuHIjif3Cv0c/2iIGI8OjYVOu6Rc+iREvPRxarL3AZCsDWm3zbrQf3F20nP2mOYJ7T8nF1Je08lBi5bwhiVXo5sYjCY2pSpviCVnpTtYPEl7sspa7LN4XSYM9sfDtesiyYJOp+Nmc0XUv9cfpby2k79Ly+8wtvWQ0ac7szGYNAYHehLo5UpGcQ1rzHOqFo4S8SKchNkLerbzLimXPgkRL32chlzVnK7QI076QQxgbpoVQ0K4D38+dyQJ4T40GE18f6DtypxWGTIT0KnQUWU3u/YC+3LKqagz4OvuzNgo/25fb8JgdY2k7LIW+yziZdaw4G7fx8IlE6OID/WmvLaR/27ohCervgqO71DrFm/WSRhNGl+aS7r/eHYCf1jcNIssxMeNsYP8umW30M8wJ3yP1qseZlIurZBvuz6OW4lq+lXlJ2WVA5lhoT6suncON88eykUTBgFYvyA7jGcgRIxT6z3gfdl4xCwo4oO7lUBrweJ5SSusauYJMZo0a6LrjB4UL056nVVYvLkpncKKDnYf3voKmAwQEKNep7DlaBH5FXX4ebiwYGQol0+KYniYNwALEkKbdQUWBILV1PUQrQgvaqVc2oyIl75MXQVetarCxBA8ys7GCI7CheMj0elge0YJx050bB6QFYunIOWHLt27qt7A1f/9lTs/2MWaw8p7M6cHEmgBgrzdGByoEhZP9r7syiylrKYRbzdnxkX1rNdi0agwJg72p67RxH83HDv9CYXJsOFZtX7mX1rNd/l813FAVUW5uzjh7KTnpSsncNH4SO4+c1hPmi/0BzwDVeI3MFSXR8ppGjUOFES89GUK1STpPC2QgKDu5xQI/YMIPw/mmpu6PbByL41tlBa3yqgL1fLgF11K3H1nSwZbjxXz/f58DuSoIamz4nvOGzI1ViXjrktWZdENBhMPf61Cp4tHh/d46FSn03HPAvXk+8G2LIqr6ts/4dt71ZiF+MUw5tIWu8trGlll7oZ8ciO9UZG+vHTVBKICpJpEaIVg5VmP0+WyIfVE95on9hNEvPRlzHNoUkzRRPpLZ12hib9eOAYfd2f2ZJXxj59SO35i1GSYfKNa/99vVf5GB6muN/DGRuWdcHNWHy1DQ7x69Av5nEQ1WuS7/XkYTRqvrEsjOb+SQPOEaFswd3gIY6P8qG00WnvWtEpFLmRtBZ0TnPdCq16XT3dmU9doIiHcR1r/Cx3HLF5GOOeSV17HkYJOelT7ISJe+jIFahhjshYtlUZCM6IDPXnq4kQAXlmfxtajnRjcuOhxNayxLBN2vtXh097dmklpTSNDg7344XezOXdsBA+d3bOCYtawEGtH4RVbMnhlnepI+/iFownyduvRe1nQ6XTcPV+Fc17feIxH/3ewdQ9MjmoWSehI8GvZ08Zo0nhnawYAy2bGoGtF3AhCq5jFyxRvlUfW7blb/QARL30YrZnnRcSL0Jzzx0VyxeQoNA3u+ySJ0uqGjp3o5gNTblbrHZx1lFpQyatmIXH3mcMYGuLNK9dM5KzRHR/C2hFcnfWcbb7mX789hMGkcfbocM5NtO3ss0WjwjhvbASNRo0VWzKY++x6/vFTKnWNxqaDcsxz3NqYZbTmUAHHS2vx93ThwvGDbGqv0M8IUeJlmE7lOK4X8SLipc+iaWhmz0uKFk24n4SNhJY8esFohgZ7kV9Rx+KXNnDVf7dae6+0S9hotTTnVbXHicp6lq3YQWW9gSkxAVwwLrKbVrfPeeOahIq/pwt/vWiMzb0YOp2Of10zkfdvmkbiID+q6g28+NMR7v5wT1P+Qa7Z8zJoYrNz16cUcubz67n9fSVuru5mwz5hAGL2vPjVZuOEkZ0ZpVTUdbL3UD9DxEtfpTIPfV0ZBk1PqedQ+TAUWsXT1Zl/Xj0BH3dnCivr+fVYCX/+aj+m002gtrS0L04FQ/sem4e/PsDx0lpigjz5z28m27zf0PShQYT6qBDRYxeMJsTHNuGi1pgVH8zXd83kH1eNx8VJx0+HC/g6KRc0rclLFdkkXg7mlnPH+7s5dkKVtyaE+7BsRkyv2Sv0E3yjwMUTnamRmUGVGEwaW9I6EQruh4h46asUqJBRuhZBaICPnY0RHJkxg/zY9OCZfHLrGfi4O5NRXMOG1BPtn+QXBa4+ql9JSdsN2qrqDaw1V/7865qJBHq59qTpreLspOedG6fy+tLJNvfytIZer+PC8YO450xVhfToNwcpzj4MdeXg5Gb1WhVX1XPru7uobTQyOz6YPX9ZxKp75xDqK15SoZPo9RCk8q7mB6rxGGmFA7tkWsRLX8UsXpK1aCL8JN9FaB8/TxemDQ3i8knRgEqubRedDkLN3pd2QkcbjpygwWAiJsiT0acMS7QlIyN8WTQqzK5Jr7fPi2N0pC9lNY18+e03amN4IjipIY7/Xn+UnLJaYoO9+NfVEwnoBWEn9GNCVMPEEc6qc3Z60cDutCvipa9SaK40Mg2WZF2hw/xm+hBAVStkFp+mU2foSLVsR7ysNvcsOWt0+ICrnnFx0vPsZeNw1uvQWUJG5nyX8tpG60DHh88fhZ9nJ6dSC8KpmPNeoo2qyeFp/377OSJe+ionJetKjxeho8QGezFvRAiaBl/uOc34gBCLeDnU6u5Go8kaMjprgA4THBXpy13zhzFWr0Jr35dEsDurlI+2Z1HdYCQ+1Jt5w3umw7AwwDGPCQiqU17TjAE+48jZ3gYIXcDYqIbnocJGl4rnRegEC0aGsT7lBLsyS9s/0OJ5OZHc6u5tx0qorDMQ7O3KhMEBPWxl3+GuuTGYNqsvlOcPenP0wBZrf7pb5gwdcB4pwUYEq7CRR/lRQKOoqp6qegPebgPza1w8L32R4qNgbKAad3K0YAkbCZ1ikllo7Mkqw9he1ZFFvJQcg8aWQwktU6sXJIT1yODFvopryRHcqafR2YvRiZPwcHFC0yDc150Lx/d+QrHQTwmKA50eXX0FIzyV12Ugh44GpmTr61ib00WhoRfxInSKEeE+eLk6UVVvILWwkoTwNhJtvcPAIwBqS6HoCESMte6qNxj5bp8SLxcM9C9oc3M6l6iJ/POaSVTVG9iUeoIR4b64OUsLA6GHcHZTU8pLjjHNt4iUGi8yimoYHdmzw0j7CuJ56YtYxgKYovFxdyZIqhiETuCk1zHOPFdnd2ZZ2wfqdBBqblaXt7fZrnXJhZTXNhLu684ZQ4NsY2hf4ZTmdN5uzpw9JoLYYC87GiX0S8xJu2PdCgDIGMCeFxEvfZFCS7LuYM4YGoR+ALvsha4xaYgKHZ0278XSLTZnZ7PNn+9Wyb4XTRg0oENGQNNMo8iJ7R8nCN3FLF6GOSmv50AOG4l46YuYe7ykaNHMGhZsZ2OEvshEa97LacRL9FS1PN4kXkqqG6yzVS6ZOMBn9DTWNVVjDRLxItgYs3iJbFRl+AO54kjES1+jvlJN+0WFjWYOG+Aue6FLTBjsD8CxompK2hvYOGiyWhYeUu894OukHBqNGqMjfRkeNsC7O+fvV12IPYPVJG5BsCVm8eJfkwFARpF4XoS+QqEqWy3Q/HHzDSYuxNvOBgl9EX9PV4aFqvfOc6tTmoYLnopvhPpS1kyQsxtN0/hgm3rqu2rq4N4y13E5Od9FSqIFW2Pu9eJanYcXtRRW1lPTYLCzUfZBxEtfw1ppFM3MYcHSQ0LoMg8sGo5OBx9uy2LZih3c90kSX7XSuM5o9r58+vWXfL8/n7TCKjxdnbhooFcZQVM4TfJdhN7AMxC8VNPDcR5qPlnmAA0diXjpa5grjQ5rg5kZJ/kuQtdZkhjBM5eq8uf1KSf4ck8O936SxAsneWI0TeOHUpXXElCyl7s+VJ6GC8dH4uM+wFveaxpkblbrg8+wry3CwMHcrG6qt0W8DMzQkfR56WNoBQfQoTwvi8x5C4LQVS6fHM2gAA+2p5dQXNXAe79m8s+f02gwajy0JIH/bjjGqowQznODyU5p0KgBOq6ZOsTeptuf0gyoyAG9S1NisyDYmtCRkLmJRJccYNKATdoV8dKX0DS0gkPogCMMJirA094WCf2AGXHBzDB78eLDvHn464O89stRskqq+X5/Pq7EYNS5EKBVMDOgDN9BI0mMGpiNsZqRsUktB00EV+npIvQSYar3UpwpAxi4SbsiXvoSlfno60oxaHpqfONwdZaon9CzLJ0eQ3lNI8+vOcL3+9XE6GVzR6DPmwxZW3l/QSO6yZPsbKWDYAkZxcyyrx3CwCJsjFrUqmGgA7VRnXz79SXMyboZWjgRwf72tUXot9x95jCumaYqiW6YEcNDZyegi50DgM7ibRCaPC9DZtrXDmFgEZoAgEf9CQKpkIRdoQ9gGQugRTM4UEJGgm3Q6XQ8dXEi2/+0gEcvGK0q2izehYyNKlF1oFOaCeXZoHOC6Gn2tkYYSLj5qBlHwAh9NnnlddQ1Gu1rkx0Q8dKXKDgAqGTdwYESYxdsS6iPe9MPUVPByQ2qCqA4zX5GOQrH1qvloIngJr2WhF7GHDqa4HocGJjl0iJe+gqGBkhdDcBObQRDgsTzIvQiLu5NFTXpG+xri73RNNj5plofcY59bREGJuak3YluucDAzHsR8dJXOLYeakspwp9tppESNhJ6n5NDRwOZrF/VlG1nd5h0g72tEQYiZvEyHDUqZiD2ehHx0lc4+AUA3ximYULPYPG8CL1NzGy1PLZeDSQcqGz7t1qOvUJ1PBWE3sYcNopszESPaUD2ehHx0hdorIPD3wLwrfEMAjxd8B3o3U2F3id6GvhGQW0pHPjM3tbYh4pc698i0263ry3CwCUgBlw8cTbVM0qXIZ4XwUFJWwMNldR6RLBbi2dwkCTrCnbAyRmm3arWf/33wKw6SvsJNCNETbG67gWh19E7QfwiAJY6rSGjSDwvgiOSoZphHQ2ai4Ze8l0E+zFxKbh4qsq3gZj7YuntMnSeXc0QBKbfDcCFTptpKM8bcOXSNhMvTz75JDNmzMDT0xN/f/8OnaNpGg8//DARERF4eHiwcOFCUlNTbWVi38FcIp2siwNgiIgXwV54BMD4a9X6tv/Y15beRtOaxIt01RXsTfRUtKipuOkMXOe0huOlA8v7YjPx0tDQwOWXX84dd9zR4XOeeeYZ/vnPf/Laa6+xbds2vLy8WLx4MXV1Azg5UNOs4iWpQU33lWRdwa5MXqaWqauhrsK+tvQmJcfUIEYnV9X3RhDsjG76XQBc67SWjBNVdramd7GZeHnssce47777SExM7NDxmqbx0ksv8ec//5kLL7yQsWPH8u6775Kbm8tXX31lKzMdn4pclSCpc+LXCjU8T8JGgl0JHQXBI8DYACnf29ua3sM6iHEyuMrfoOAAJJxLo86FYF0FxceP2NuaXsVhcl7S09PJz89n4cKF1m1+fn5MmzaNrVu3tnlefX09FRUVzV79igI1z0gLjie9XMU0pUGdYFd0Ohh9sVo/+KV9belNLDk+EjISHAUnF4o9hwJgzNtvZ2N6F4cRL/n5aoJtWFhYs+1hYWHWfa3x9NNP4+fnZ31FR0fb1M5ep0C9IWsCRmI0abg66wk7uW27INgDi3hJW6s8g/2dk/NdYmfb1xZBOImawFEAeJQcsrMlvUunxMtDDz2ETqdr95WcnGwrW1tl+fLllJeXW1/Z2dm9en+bY/a8FHoOAyA6wAO9XmdPiwRBTbYNHQWmRkgeAKGjwsNQmae66kZNsbc1gmDFKWIsACHVAyts5NyZgx944AFuuOGGdo8ZOnRolwwJDw8HoKCggIiICOv2goICxo8f3+Z5bm5uuLm5demefQKzeMlwjgFgiPR4ERyFkRdA4SGVuDvhWntbY1vMc8WImQ0uHva1RRBOwjd2AmyHWEM6DQYTrs4OE1CxKZ0SLyEhIYSEhNjEkNjYWMLDw1m7dq1VrFRUVLBt27ZOVSz1KxrroEiVih8wRANVkqwrOA5x8+GXv6lcEJMJ9P34Q9MiXoYvtq8dgnAK/jETABikKyI9L4fY/pY60QY2+7TJysoiKSmJrKwsjEYjSUlJJCUlUVXVVM6VkJDAl1+qhD+dTse9997LE088wf/+9z/279/P0qVLiYyM5KKLLrKVmY7NiWTVzdMjkIMVyuMi4kVwGCIngosX1BRD4UF7W2M7akvVMEawdjUVBEdB5+FPnl7lipYe22Nna3qPTnleOsPDDz/MO++8Y/15wgSlDtetW8e8efMASElJoby83HrMgw8+SHV1NbfeeitlZWXMmjWLVatW4e4+QBNU8/epZfgYMktrAak0EhwIZ1cYMkONrzj2C4R3rC1Cj7D5n5C/H857Edy8bXuvoz+rh4jgEWqmjCA4GAUew4ioLqAhZy9wgb3N6RVsJl5WrFjBihUr2j1GO2U2ik6n4/HHH+fxxx+3lVl9i1ylorWI8WQfU90TRbwIDsXQuUq8pP8CM+7unXse/RnW/EWt6/Rw8WuqfNtWHLGEjM6y3T0EoRtU+o+C6s24FfVjD+gp2Ey8CD1AbhIAVYFjqKo3ABAVIOJFcCBi56hl5hYwNoKTjaed11fC/+5p+nnfx+Dmo5JoR54P0e10vm2oVg8ERUdg5IXgFXT6+6VvhP0r1frwJd2zXRBsRfgYyIGgyhR7W9JriHhxVIyN1kqjLPfhQC7hvu64uzjZ1y5BOJmwRPAIhNoSyNkFg8+w3b0MDfDVnVCeDf6DIfEK2Pgc7Hhd7d/7Mdy7H1xOCjObTHB8B+x+Bw58Doa6pmOX/aCm87a4Tz0kfwu1ZbD+aRUyGnulCpEJggMSPHwa7ILIhgzqaipx9/Sxt0k2R8SLo1J4GIz14OZHakMIkCszjQTHQ69XoaODX0Lqmp4XLw3VsOFZMBmUJzJjI+hd4MJXYchM0ExQmgGZm6GqAPa8B1NvUedt/ifsfhcqc5uu5xMJdWWQvQ02/0OJoLJMNSU6YoIaefDxNXB0bdM5YYlw3ku2DU0JQjcYMWwERfgTrCsjadcmJs/u/15CES+OSl6SWkaMJaPEnO8ilUaCIzL8bCVejqyCBX/p/vWK0qA8CwbPgE+vVzk1Fly84Kr3m7rcLnxELbf9F374gxIszm6w/m9qiCKAqw+MPA8m3wRRk5Wg+eYeWPtY03XXPg6eQWpqdnEauHjC0PngGQhz/yizjASHRu+kp9hvDMHlm8g5uFnEi2BHzPkuRE4gtUCVl8eH2biqQhC6QvxZKnG24ACUZSlvRlc58AV8dYcK77j6QEMlOHvAxN+oUOrkZRAxruV5E3+jPDTlWfC/36ptfoOVuEk4r3koaeJSOPyNEkXe4TBoIqRvUCXfNcVKuFy7UmYYCX0Kj5gpsHcTzvlJGIwmnJ36cd8lRLw4LhbPS+R4kverYZMjwn3tZ48gtIVnIERPg6ytcORHFbbpCpv/AWseVutOrkq46Jzg8hUw4uz2z3XxgDm/hx8eVIJkxt0w5ebWu+HqdHDl+5C7W02IdnZVeS75+1VCb/Q0MLdcF4S+wqBRM2DviySY0tieXsKMYcH2NsmmiHhxRIyNkH8AgPqQsWQUqy67I8L6fxKW0EcZvtgsXla1FC8mY+uJsSez+Z9NwmX63TBvOaT8AL4RHfeATL0VYueqXiwup+kN5eLePAHX2U2FlKImd+xeguBgOEVNAiBOn8dn+4/2e/HSv/1KfZXcJJWs6+5PmiEYo0nDz8OFMN9+PMNJ6NtYyojTN8D+z9QUZoCkj+Bvg+HnJ5q2nUxNCXxzb1Pflvl/hsVPqsZzYy/vXOhGp1MDI08nXAShP+IVRJVnFACNx3fb2RjbI+LFEUlfr5axs0kpqAZgRLgPOql2EByVkBEqwdbYAJ/fBO9dDOXH4Yc/QkOVykf54Y+q3NlC4WF4eRLselv9PPchmPsH+9gvCP0AQ9h4APxK99vXkF5AxIsjkr5BLWPnklJQCUjISHBwdDq47nPlOXHxhGPr4N8zob5c5aAAbP8P/HM8/PqaGjr65W2qP0zISLjhe5i/3K7/BEHo63gMVa0KxjQeoKS64TRH921EvDgajbWQtU2tD51HSr5ZvISLeBEcHFdP5Tm5diU4ual+KgCXvw2XvqlETEUOrPoj/GMc5O0Fd39Y+hXEzLSj4YLQP3AbfiYAZ+gPk5ZbZGdrbIuIF0cje5vKd/GJhKBhVvGSIOJF6CvEzIJL31ACZsJvVGJs4mVw7z4493lw84WqfHXsOc+CT7h97RWE/kLoKMqcAvHQNVB+ZJO9rbEpUm3kaBz7RS1j51BeZyCvXLUzj5ewkdCXGHUBxGeC80nJs85uqnw5/ixY+1dzi//L7WejIPQ3dDqyA87Av+h73DN/Aa6wt0U2Q8SLo3FsnVoOncsRc75LpJ87fh42HngnCD1Naz1WQImWS1/vXVsEYYBQGz0Xir4nqnSrvU2xKRI2ciRy96iX3hnizmRnRikAYwb52dkwQRAEoS/gPWoRALGNR6HqhJ2tsR0iXhyJLS+r5ZhLwSecX48VA3DG0CA7GiUIgiD0FWKHxHDAFANA1f5v7WuMDRHx4iiUZsLBr9T6jN9iMJrYmVECiHgRBEEQOoaHqxOb3dTgUt22f7feHLIfIOLFUdj2H9CMapJteCIHciuobjDi5+EilUaCIAhChzkYcQnVmhteZSlwdK29zbEJIl4cAZMJDn6h1qfdBmANGU2NDUSvl866giAIQscYPGgQnxjnqx82/9O+xtgIES+OQN4eqMwDFy/leQG2Sb6LIAiC0AVGRvjypmEJRvSQ/gsUpdrbpB5HxIsjkPy9Wg5bAC7uGIwmdpgrjc4YGmhHwwRBEIS+RkKEDzmEsE0bpTYcW29Xe2yBiBdHIMUsXhLOVT8WVFJVb8DH3ZmEcF87GiYIgiD0NWKCvHB30bPVkKA2ZG62r0E2QMSLvSlJh8JDoHNSnUeBPVllAIyP9sdJ8l0EQRCETuCk1zEi3JftppFqQ8bmfld1JOLF3qT8oJZDZoCnChFZxMuEaH/72CQIgiD0aUaG+5CkxWHQuUJ1IRSn2dukHkXEi72xjAMwe10A9mSrfJcJgwPsYZEgCILQxxkZ4Us9rhx1NYeOMvrXoEYRL/bE0KDceQBxqsqorKaBYyeqARU2EgRBEITOMjJC5UtuNoxQG/pZ3ouIF3uSswsaq8EzCEJHA5CUXQZAbLAXAV6udjROEARB6KuMMDc3/ak2Xm3oZ3kvIl7siaV8LXYu6NV/xcnJuoIgCILQFfw8XIgK8GC3KR6jkwdU5kLubnub1WOIeLEn6b+o5dB51k0Wz8uEwf69bo4gCILQfxgf7U8dbhwNmqM27P/Mvgb1ICJe7EV9JRzfodbN4sVo0tiTZU7WjZZkXUEQBKHrWDz4q3RqUCMHPgeT0X4G9SAiXuxFxmYwGSAgBgKGAJCSX0lFnQEvVydGRsgwRkEQBKHrWCpWPyyK4//bu/e4Ju97D+CfBEhEkETkEhBQboqtioqKaG11MMFjVzut1dae1dbZ1eLO2lo33TmVdWc79rZur/W4bu3pKe1ZZ1vXqqu9rIrirYiKokWFGoWGW0CxCSB38jt/PCQ2JXKRJA+hn/frlVcen98v4ZsvMfnyPL/n9xO+I4HGGqDskMxROQeLF7lc+Kd0H5dm23W87CoAYNqYkfD24q+GiIhu3q3hAfDxUsB4TeBa7J3SzjPb5Q3KSfgNKQchgC8/k7bj0227j5VKxUtyNNczIiKigRnm44Vbui6ZPq3t+kO55OMhceqIxYscas4C9RWAty8QLZ2LFEIgv6t4mRnNlaSJiGjgrKeOcpqigWEaoPkqUHFC5qgGjsWLHL78VLqPuQPw8QUAlNU14UpjK1ReSkyO0MgYHBERDRXWQbunKhqB2FRpp3XYggdj8SKHC9ZTRteXBDhWWgdAeqMN8/GSIyoiIhpirNNunK2sR3vs96Wd1mELHozFi7tdvQSUH5O2x10f73L9lBHHuxARkXNEBQ5HoJ8KbZ0WnPdPBqAAar4AzJVyhzYgLitefvvb32L27NkYPnw4tFptnx6zatUqKBQKu1tGRoarQnQ/IYCPNwAQQMx8QBNha7LOrJs0lvO7EBGRcygUCtupo4LLSiBihtRwdod8QTmBy4qXtrY2LFu2DGvXru3X4zIyMlBdXW27bdu2zUURyuDcLkC/F/BSAf/ygm23qakNpVe6FmOM0MoUHBERDUVTreNeDCZgwg+knZ/9O/Dxz4HODtniGghvVz3xM888AwDIzs7u1+PUajV0Op0LIpKZxQLseVranvM4EBRva+JijERE5CpTusa9nCr/Glj2E+DrMuDE68Cxv0jfRTPXyBrfzRh0Y15yc3MREhKC8ePHY+3atairq+uxf2trK+rr6+1ug1JlAWAyACp/4LYn7JqsxQsXYyQiImdLjNRCoQDKrzbjSguAO18CUjdLjWfekzW2mzWoipeMjAy89dZbyMnJwXPPPYcDBw5g4cKF6Oy88YQ6W7ZsgUajsd0iIyPdGHE/nNsp3Y/LAFTD7Zqs4124GCMRETlbwDAfxAb7AwAKu75vkHg/AAVQcQwwlcsW283qV/GycePGbgNqv30rLi6+6WBWrFiBu+66C5MmTcLdd9+N3bt34/jx48jNzb3hYzZt2gSz2Wy7lZcPwl+CEMC5f0jbtyz+VpPA6QoTAB55ISIi17COe7Ee6UdAGDBmjrTtgYN3+zXmZf369Vi1alWPfWJiYgYST7fnCgoKgl6vR2pqqsM+arUaarXaaT/TJapOAWYD4OMHxH/frqmsrgmmpnaovJVI0AXIFCAREQ1lU6NGYntBBU58dfX6zok/BL46LK02Peff5AvuJvSreAkODkZwcLCrYummoqICdXV1CAsLc9vPdIlzu6T7cQtsM+paFZZ/DQCYNFoDlfegOotHRERDREqstOzM8bKvUdfYilH+amDCYmn6jupCoO4iMCpW3iD7wWXflgaDAYWFhTAYDOjs7ERhYSEKCwvR2Nho65OQkIAdO6TDVY2NjdiwYQOOHj2KsrIy5OTkYPHixYiLi0N6evqNfoxnKPlEurdeovYNH56uBgBMH8P5XYiIyDWig/wwabQGnRaBj4uM0k7/YGnOMQA4/Y58wd0ElxUvmzdvxtSpU5GVlYXGxkZMnToVU6dOxYkT1xeEKikpgdlsBgB4eXnhzJkzuOuuuzBu3DisXr0aSUlJOHTo0OA/LdQTkwG4UgIovK6vK9FFX9uIfcW1UCiAFTOjZAqQiIi+C+5KDAcAfFhYdX3nlPul+9PbpCk9PITL5nnJzs7udY4XIYRt29fXF//8p+cvFtWNfq90HzkT8NXaNb1+uBQAkDYhFNFBfm4OjIiIvkvuTAzDf31yHsfKrqLS1IzRWl8gYRGg1gDmcqDskLRgsAfgIAtXu9BVvMTZH3Wpa2zFBycrAABr5jpvkDMREZEjYRpfzBgrrZ+3+3TX0RcfX2DiEmm78G8yRdZ/LF5cqaMNKD0gbcfZX2X02qFStHZYkBihwQyuZ0RERG6waJJ0Acz+ktrrO6eslO7P7QJazDJE1X8sXlypPB9oawT8ggHdZNvuK42teCuvDADw0+/FQ6FQyBQgERF9l8yNDwIAnPzKhOa2rglgI6YDwROAjmaPGbjL4sVVhACOviJtx6UByuupfvXgJTS1dWJyhAapE0JkCpCIiL5rooP8EKYZhrZOy/U5XxQKYMZqafv469L31yDH4sVVCrKBko8ApQ8w6zHbblNTm+2oyxPfH8ejLkRE5DYKhQJz4qSjL4f1V643TF4uTaR6pQQoOyxTdH3H4sUVas4Cn26SttOygLDrp4z+cboKLe0WJOhGYN449034R0REBABz4qQJ6z7Xf2Ph42EBQOJyaXvnWuCjp4DGyzJE1zcsXpzNXAH89R7p3GHMPGBWpl3z3wukK4zunR7Joy5EROR2c2KlIy9FVWaYmtquNyQ/Cnippcumj78G/P0hmSLsHYsXZ2pvAd5eBjRUAUHjgXvesBvr8mVNA85UmOGtVGDxlHAZAyUiou+qkIBhiA/xhxDA5xe/cfQleDzw5Dlg6evSkIeyQ4DhqHyB9oDFizMVvg3UnpOuLnrgfWB4oF3z+11HXeYnhEjrShAREclgbrw0bCH3m5dMA4BfEDDpHmDKfdK/D77g5sj6hsWLs3S2A4f/IG3fvgHQRto1WywCOwsrAQD3JEW4OTgiIqLrvpcgXem6v+QyLBYHVxfd9qS0rI1+L1B50s3R9Y7Fi7OceQ8wG6SjLtN+1K25qMqMmvpW+Km8MG88B+oSEZF8ZkYHwk/lhcsNrSiqcjAxXWC0dAQGAE78r3uD6wMWL87Q2Q4c+p20Pfun0nTL35JzXjo0Nzc+GGpvL3dGR0REZEflrbSdOtpXXOu4k/UP8bM7gbYm9wTWRyxenOHkm8DVi8DwUcD0hx12sb45vsdJ6YiIaBCwnjq6YfESNRvQjgHaGoDij9wYWe9YvAxUawOQ+6y0fcdGQD2iW5ea+hZ8UWmGQgHMH8/ihYiI5DcvQTrycqbCjCpTc/cOSiWQ2DVw9/TgWrSRxctA5W0Frl0GAmOApFUOu1ir2sQILYJH8CojIiKSX8iIYZjZtcr0i5+VOO6UuEK6v7gfMFe6KbLesXgZiI5W4Nir0vb3/gPwVjnsZi1eUhN41IWIiAaPXy6aAAD44GQlCqxrHX1TYDQw5jYAAjj1V/cG1wMWLwNxbhfQVAcEjAYmLHbYpdMikH9JmgRoLpcDICKiQWRKpBb3Tpem78j6x1kIR4syWs8qnHwT6OxwX3A9YPEyEMdfl+6TVgFe3g67FBvrUd/SAT+VFyaGB7gvNiIioj74eUYC/FReKKqst59x1+qWu6QLUuorgQufuT9AB1i89FV9FbBjLfD3h4F3HwB2PgaUHwWU3g7ndbE6ekk6DDcjOhDeXkw3ERENLkH+aiztmjz1zc/LunfwVgNTVkrbx18DHB2dcTN+m/ZVW5M02rrofeD8h9JSAACQsAgYobvhw6ynjJKjR7kjSiIion77UcoYAMDe8zWo+NrBnC7WU0cX90l/wDc5GB/jRo7PdVB3fkFA2jNSBar0BhqMQPPXwG2P3/AhFovAsTLpFzwrJvCG/YiIiOQUFzICc+JG4Yi+Dn89asDGhQn2HUbFAot+B3yyESjeDVSdAh493G0NP3dh8dJXvtoeCxVHSmoaYGpqx3CVFyaO1rgkLCIiImd4MGUsjujr8O5xAx5Pi8cwn2/NBj/jx8Do6cD7q4Gxt8lWuAAsXlxqf9dqndPHBsKH412IiGgQS50QitFaX1SamvHh6Sosmx7ZvVP4FOCRA4BC3u80fqO6yKXLjXg5Rw8ASL81VOZoiIiIeualVOCBWdLYlzfzyhxfNg0Aan9ANdyNkXXH4sUF2jstePzdQjS3d2J27CjcNyNK7pCIiIh6tXxGJFTeShRV1uOkwSR3ODfE4sUFXjt0CWcqzND4+uB39yZCqVTIHRIREVGvAv1UWJwYDgB4K69M3mB6wOLFySpNzbbTRZvvvAVhGl+ZIyIiIuq7B2ePBQB8/EU1ahta5A3mBli8ONl/fngOze2dmDk2EEumjZY7HCIion6ZOFqDaVFatHcKbMsvlzsch1i8ONHuM1X49KwRXkoFfn33rVAoeLqIiIg8j/Xoy9v5X6G90yJvMA6weHGSanMz/n1HEQBg7R2xSNBxHSMiIvJMCyeGIchfjdqGVvxf3ldyh9MNixcn+cX7X8Dc3I7JERr8LC1e7nCIiIhumspbibXzYgEAv/noHD47a7S1tXdaYLHIu74RixcnOGX4Gge/vAwfLwV+v3wKJ6QjIiKP9/CcsVgxIxIWAfx02ykc0V9BbUMLVr6Wj//er5c1Ns6w6wT/c6gUALB4ymjEBvvLHA0REdHAKRQK/ObuibjS2Ia952vwcPZxBPj64HJDK4qN9fjXWWMw0k8lS2w8RDBAhromfFJUDQD48dxomaMhIiJyHm8vJbaunIq0CSFo7bDgckMrxoX6Y2fmHNkKF4BHXgbEYhF4aU8JLAK4fVwwB+kSEdGQo/b2wp9WJuG5T4thEQJPLRgPP7W85QOLl5vQ1NaB2vpWbN2vx87CKgDAY10Dm4iIiIYalbcST995i9xh2LB46aMqUzPeO1GOg19eRmG5CdaB1koF8OKyRMyKGSVvgERERN8RLhvzUlZWhtWrVyM6Ohq+vr6IjY1FVlYW2traenxcS0sLMjMzMWrUKPj7+2Pp0qWoqalxVZh9Zmpqxx/2XsBJg1S4+Pp4IS7EH688kIQl0yLkDo+IiOg7w2VHXoqLi2GxWPCXv/wFcXFxKCoqwpo1a3Dt2jW8+OKLN3zcE088gY8++gjbt2+HRqPBunXrsGTJEhw5csRVofZJgm4E7p0egWlRI3FbfBAiRsq7HDgREdF3lUII4baZZl544QW88soruHTpksN2s9mM4OBg/O1vf8M999wDQCqCJkyYgLy8PMyaNavXn1FfXw+NRgOz2YyAAA6gJSIi8gT9+f5266XSZrMZgYGBN2wvKChAe3s70tLSbPsSEhIQFRWFvLw8h49pbW1FfX293Y2IiIiGLrcVL3q9Hi+//DJ+8pOf3LCP0WiESqWCVqu12x8aGgqj0ejwMVu2bIFGo7HdIiMjnRk2ERERDTL9Ll42btwIhULR4624uNjuMZWVlcjIyMCyZcuwZs0apwUPAJs2bYLZbLbdyssH5/LdRERE5Bz9HrC7fv16rFq1qsc+MTExtu2qqirMnz8fs2fPxquvvtrj43Q6Hdra2mAymeyOvtTU1ECn0zl8jFqthlqt7nP8RERE5Nn6XbwEBwcjODi4T30rKysxf/58JCUl4Y033oBS2fOBnqSkJPj4+CAnJwdLly4FAJSUlMBgMCAlJaW/oRIREdEQ5LIxL5WVlZg3bx6ioqLw4osv4vLlyzAajXZjVyorK5GQkIBjx44BADQaDVavXo0nn3wS+/fvR0FBAR566CGkpKT06UojIiIiGvpcNs/Lnj17oNfrodfrERFhP4mb9ers9vZ2lJSUoKmpydb2+9//HkqlEkuXLkVrayvS09Pxpz/9yVVhEhERkYdx6zwv7sB5XoiIiDzPoJ3nhYiIiGigWLwQERGRR2HxQkRERB6FxQsRERF5FBYvRERE5FFcdqm0XKwXT3GBRiIiIs9h/d7uy0XQQ654aWhoAAAu0EhEROSBGhoaoNFoeuwz5OZ5sVgsqKqqwogRI6BQKJz63PX19YiMjER5eTnnkHEh5tk9mGf3YJ7dh7l2D1flWQiBhoYGhIeH97qc0JA78qJUKrvN6OtsAQEB/I/hBsyzezDP7sE8uw9z7R6uyHNvR1ysOGCXiIiIPAqLFyIiIvIoLF76Qa1WIysrC2q1Wu5QhjTm2T2YZ/dgnt2HuXaPwZDnITdgl4iIiIY2HnkhIiIij8LihYiIiDwKixciIiLyKCxeiIiIyKOweCEiIiKPwuKlj7Zu3YqxY8di2LBhSE5OxrFjx+QOyaP96le/gkKhsLslJCTY2ltaWpCZmYlRo0bB398fS5cuRU1NjYwRe46DBw/iBz/4AcLDw6FQKLBz5067diEENm/ejLCwMPj6+iItLQ0XLlyw63P16lWsXLkSAQEB0Gq1WL16NRobG934Kga/3vK8atWqbu/xjIwMuz7Mc8+2bNmCGTNmYMSIEQgJCcHdd9+NkpISuz59+awwGAxYtGgRhg8fjpCQEGzYsAEdHR3ufCmDXl9yPW/evG7v6UcffdSuj7tyzeKlD9599108+eSTyMrKwsmTJ5GYmIj09HTU1tbKHZpHu/XWW1FdXW27HT582Nb2xBNP4MMPP8T27dtx4MABVFVVYcmSJTJG6zmuXbuGxMREbN261WH7888/jz/+8Y/485//jPz8fPj5+SE9PR0tLS22PitXrsTZs2exZ88e7N69GwcPHsQjjzzirpfgEXrLMwBkZGTYvce3bdtm18489+zAgQPIzMzE0aNHsWfPHrS3t2PBggW4du2arU9vnxWdnZ1YtGgR2tra8Pnnn+PNN99EdnY2Nm/eLMdLGrT6kmsAWLNmjd17+vnnn7e1uTXXgno1c+ZMkZmZaft3Z2enCA8PF1u2bJExKs+WlZUlEhMTHbaZTCbh4+Mjtm/fbtt3/vx5AUDk5eW5KcKhAYDYsWOH7d8Wi0XodDrxwgsv2PaZTCahVqvFtm3bhBBCnDt3TgAQx48ft/X55JNPhEKhEJWVlW6L3ZN8O89CCPHggw+KxYsX3/AxzHP/1dbWCgDiwIEDQoi+fVZ8/PHHQqlUCqPRaOvzyiuviICAANHa2ureF+BBvp1rIYS44447xM9+9rMbPsadueaRl160tbWhoKAAaWlptn1KpRJpaWnIy8uTMTLPd+HCBYSHhyMmJgYrV66EwWAAABQUFKC9vd0u5wkJCYiKimLOB6i0tBRGo9EutxqNBsnJybbc5uXlQavVYvr06bY+aWlpUCqVyM/Pd3vMniw3NxchISEYP3481q5di7q6Olsb89x/ZrMZABAYGAigb58VeXl5mDRpEkJDQ2190tPTUV9fj7Nnz7oxes/y7Vxbvf322wgKCsLEiROxadMmNDU12drcmesht6q0s125cgWdnZ12vwwACA0NRXFxsUxReb7k5GRkZ2dj/PjxqK6uxjPPPIO5c+eiqKgIRqMRKpUKWq3W7jGhoaEwGo3yBDxEWPPn6P1sbTMajQgJCbFr9/b2RmBgIPPfDxkZGViyZAmio6Nx8eJF/PKXv8TChQuRl5cHLy8v5rmfLBYLHn/8ccyZMwcTJ04EgD59VhiNRofvd2sbdeco1wBw//33Y8yYMQgPD8eZM2fwi1/8AiUlJfjggw8AuDfXLF5IFgsXLrRtT548GcnJyRgzZgzee+89+Pr6yhgZkXOsWLHCtj1p0iRMnjwZsbGxyM3NRWpqqoyReabMzEwUFRXZjY0j17hRrr85HmvSpEkICwtDamoqLl68iNjYWLfGyNNGvQgKCoKXl1e30es1NTXQ6XQyRTX0aLVajBs3Dnq9HjqdDm1tbTCZTHZ9mPOBs+avp/ezTqfrNhi9o6MDV69eZf4HICYmBkFBQdDr9QCY5/5Yt24ddu/ejf379yMiIsK2vy+fFTqdzuH73dpG9m6Ua0eSk5MBwO497a5cs3jphUqlQlJSEnJycmz7LBYLcnJykJKSImNkQ0tjYyMuXryIsLAwJCUlwcfHxy7nJSUlMBgMzPkARUdHQ6fT2eW2vr4e+fn5ttympKTAZDKhoKDA1mffvn2wWCy2Dyvqv4qKCtTV1SEsLAwA89wXQgisW7cOO3bswL59+xAdHW3X3pfPipSUFHzxxRd2heKePXsQEBCAW265xT0vxAP0lmtHCgsLAcDuPe22XDt1+O8Q9c477wi1Wi2ys7PFuXPnxCOPPCK0Wq3diGrqn/Xr14vc3FxRWloqjhw5ItLS0kRQUJCora0VQgjx6KOPiqioKLFv3z5x4sQJkZKSIlJSUmSO2jM0NDSIU6dOiVOnTgkA4qWXXhKnTp0SX331lRBCiGeffVZotVqxa9cucebMGbF48WIRHR0tmpubbc+RkZEhpk6dKvLz88Xhw4dFfHy8uO++++R6SYNST3luaGgQTz31lMjLyxOlpaVi7969Ytq0aSI+Pl60tLTYnoN57tnatWuFRqMRubm5orq62nZramqy9ents6Kjo0NMnDhRLFiwQBQWFopPP/1UBAcHi02bNsnxkgat3nKt1+vFr3/9a3HixAlRWloqdu3aJWJiYsTtt99uew535prFSx+9/PLLIioqSqhUKjFz5kxx9OhRuUPyaMuXLxdhYWFCpVKJ0aNHi+XLlwu9Xm9rb25uFo899pgYOXKkGD58uPjhD38oqqurZYzYc+zfv18A6HZ78MEHhRDS5dJPP/20CA0NFWq1WqSmpoqSkhK756irqxP33Xef8Pf3FwEBAeKhhx4SDQ0NMryawaunPDc1NYkFCxaI4OBg4ePjI8aMGSPWrFnT7Q8e5rlnjvILQLzxxhu2Pn35rCgrKxMLFy4Uvr6+IigoSKxfv160t7e7+dUMbr3l2mAwiNtvv10EBgYKtVot4uLixIYNG4TZbLZ7HnflWtEVNBEREZFH4JgXIiIi8igsXoiIiMijsHghIiIij8LihYiIiDwKixciIiLyKCxeiIiIyKOweCEiIiKPwuKFiIiIPAqLFyIiIvIoLF6IiIjIo7B4ISIiIo/y/4qDjQdfSbDEAAAAAElFTkSuQmCC" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 54 + "execution_count": 2 }, { "cell_type": "markdown", @@ -214,29 +222,33 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-09-25T22:58:21.053382Z", - "start_time": "2024-09-25T22:58:20.879846Z" + "end_time": "2025-05-28T13:33:09.469963Z", + "start_time": "2025-05-28T13:33:09.304447Z" } }, "outputs": [ { "data": { - "text/plain": "[]" + "text/plain": [ + "[]" + ] }, - "execution_count": 55, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { - "text/plain": "
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" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 55 + "execution_count": 3 }, { "cell_type": "markdown", @@ -273,29 +285,33 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-09-25T22:58:21.247394Z", - "start_time": "2024-09-25T22:58:21.075323Z" + "end_time": "2025-05-28T13:33:11.355101Z", + "start_time": "2025-05-28T13:33:11.180584Z" } }, "outputs": [ { "data": { - "text/plain": "[]" + "text/plain": [ + "[]" + ] }, - "execution_count": 56, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { - "text/plain": "
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" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 56 + "execution_count": 4 }, { "cell_type": "markdown", @@ -328,29 +344,33 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-09-25T22:58:21.419932Z", - "start_time": "2024-09-25T22:58:21.266319Z" + "end_time": "2025-05-28T13:33:13.206213Z", + "start_time": "2025-05-28T13:33:13.045087Z" } }, "outputs": [ { "data": { - "text/plain": "[]" + "text/plain": [ + "[]" + ] }, - "execution_count": 57, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { - "text/plain": "
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vb+73/pycHEWj0SjPP/98iceUJDY2VgGUN954o8zPvZVWrVoV+d6pLIMHD1ZcXFyUGzduFHnst99+UwBl7969pT5fucbwvvrqKzZs2FDgdjtPPfVUgf8bs+h169aRnp5enjDKzMnJqUD9ho2NDV27duXixYtFjn344YcL1D8cOXKEc+fOcf/99xMfH09cXBxxcXGkpaUxaNAgtm/fjsFgQFEUli5dysiRI1EUxXRcXFwcoaGhJCUlFRg+Lmz58uUYDAZmz55dZIg1f1Fg/thSUlKIi4ujT58+pKenc/r0aaB0X+Nly5ZhMBgYP358gVh9fX1p0qQJW7ZsKfW5inPgwAGuX7/O1KlTC4wcDB8+nObNmxeYkqlMgwcPLjCi1rZtW1xcXEyvdUVfp5KU5bxubm5ERESwf//+SvmcK/P1riyrV69Gp9MxY8aMAvc///zzKIrCmjVrCtzfr18/WrZsWakxQMGfl5ycHOLj42ncuDFubm63fZ0nTpzI3r17uXDhgum+X3/9lcDAQPr161fg2NOnT+Pl5YWXlxctWrTgiy++YPjw4aZp6dWrV9O1a1d69+5teo6TkxOPP/44ly9f5tSpUwD06dMHvV7P7t27AXXkq0+fPvTp04cdO3YAcOLECRITE02j/wkJCWzevJnx48eb3hPi4uKIj48nNDSUc+fOFSgHALXepTSF1YU5OTmZVk+W57qTJ08u8H7WrVs3FEVh8uTJpvt0Oh2dO3cu8v6c/7W8ceMGSUlJ9OnTp9jX8XbvA3q9nnXr1jFq1CgaNGhgOq5FixaEhoaW+etitHTpUtq1a1dkJBAKvo8Xlv9zy8zMJC4uju7duwMU+Pzc3NzYu3cv165dK/Y85n7vT0hIQFEU3N3dS/zcaov33nuPjRs38p///KfYmljj1+BWU9CFlSsR69q1K4MHDy5wuxUrK6si9QfBwcHMnDmT77//Hk9PT0JDQ/nqq69KVW9UXgEBAUV+CNzd3blx40aRYwuvCj137hygJmjGN1rj7fvvvycrK4ukpCRiY2NJTEzk22+/LXLcpEmTgJtF6cW5cOECWq32tr+MTp48yejRo3F1dcXFxQUvLy9Tkmn8Gpbma3zu3DkURaFJkyZF4g0LCzPFWt7X68qVKwA0a9asyGPNmzc3PV7Z8r+pGuV/rSv6OpWkLOd9+eWXcXJyomvXrjRp0oRp06YVW29YWpX5eleWK1eu4Ofnh7Ozc4H7W7RoYXq88OdQFTIyMpg9e7apTs3T0xMvLy8SExNv+z187733Ymtra+qNlJSUxMqVK3nggQeKvJ8EBQWxYcMGNm7cyM6dO4mOjmblypWmKYwrV64U+7NQ+OvRsWNHHBwcTEmXMRHr27cvBw4cIDMz0/SYMak7f/48iqLw+uuvF3lt33jjDaDo93R5v96pqamm17Q81y3882n8ZR8YGFjk/sLvzytXrqR79+7Y2dnh4eGBl5cX33zzTbGvY2neBzIyMmjSpEmR44p7nUrrwoUL5SoWT0hI4JlnnsHHxwd7e3u8vLxMr1H+z++DDz7gxIkTBAYG0rVrV958880CCaul3vuVQvV8tc2iRYt47bXXmDx5cpHBJSPj16AsPejKVSNWVra2tsUW0H300Uc88sgjrFixgvXr1zNjxgzmzp3Lv//+W6rC0bIq6S+/4r55Cq8GMvYB+vDDD0tsjeHk5GQqqnzwwQdNxeeFVXTZd2JiIv369cPFxYU5c+YQEhKCnZ0dhw4d4uWXXy7Qs+h2X2ODwYBGo2HNmjXFfn3y1wWZ+/WqiNu91savUWW/TmU5b4sWLThz5gwrV65k7dq1LF26lK+//prZs2fz1ltvlfnaULmvtyVU1Sq8p59+mh9//JFnn32WHj164OrqikajYcKECbft8eXu7s6IESP49ddfmT17NkuWLCErK6vA6LqRo6Pjbf8wLQ1ra2u6devG9u3bOX/+PNHR0fTp0wcfHx9ycnLYu3cvO3bsoHnz5qaaQePn8cILL5Q4mtO4ceMC/y/P1zsiIoKkpCTTucpz3ZJ+Pou7P//7844dO7jrrrvo27cvX3/9NfXr18fa2poff/zRtMilNNeprgnD+PHj2b17Ny+++CLt27fHyckJg8HAnXfeWeD7dPz48fTp04e//vqL9evX8+GHH/L++++zbNkyU72zOd/7PTw80Gg0xQ5q1BYbNmxg4sSJDB8+nPnz55d4nPFrcKu6+cLMkojdSps2bWjTpg2vvfYau3fvplevXsyfP5933nkHKFtWWZVdkI3D2y4uLrd8o/Xy8sLZ2Rm9Xl+uN+SQkBAMBgOnTp0qMeHbunUr8fHxLFu2jL59+5ruv3TpUrHH3+prHBISgqIoBAcH07Rp09vGd7vXq7CGDRsCcObMGQYOHFjgsTNnzpgeL6uKvtYVfZ1KiqGs53V0dOTee+/l3nvvJTs7m3vuuYd3332XWbNmmXYXKKvKer0r4+epYcOGbNy4kZSUlAKjYsbp8/K+/iUpKeYlS5bw8MMP89FHH5nuy8zMLHWj3IkTJ3L33Xezf/9+fv31Vzp06ECrVq3KHF/Dhg05c+ZMkfuL+3r06dOH999/n40bN+Lp6Unz5s3RaDS0atWKHTt2sGPHDkaMGGE6vlGjRoCaxFVGMliShQsXApiSLnNdF9QpPzs7O9atW4etra3p/h9//LFc5/Py8sLe3t4045Ffca9TaYWEhJS5w/qNGzfYtGkTb731VoGmoMXFBlC/fn2mTp3K1KlTuX79Oh07duTdd98tsPDMXO/9VlZWhISElPg7qKbbu3cvo0ePpnPnzvz5559YWZWcOl26dAmtVluqr6mRxRpjJScnk5ubW+C+Nm3aoNVqycrKMt3n6OhY6jdLYw+cquhC3qlTJ0JCQpg3b16BpeJGxqXpOp2OMWPGsHTp0mJ/EItbBp/fqFGj0Gq1zJkzp8hf6sa/4ox/veT/qy47O5uvv/66wPGl+Rrfc8896HQ63nrrrSJ/JSqKYhrhK+3rVVjnzp3x9vZm/vz5BY5bs2YNYWFhRVY5lVZFX+uKvk7GGApfvyznLbwk3cbGhpYtW6IoCjk5OaZrQOk+z8p8vY3XrmipwLBhw9Dr9Xz55ZcF7v/kk0/QaDRFVitXVEnvFzqdrsjn+8UXXxRp91KSoUOH4unpyfvvv8+2bduKHQ0rjWHDhrFv3z727Nljui8tLY1vv/2WoKCgAiUJffr0ISsri08//ZTevXubksw+ffqwcOFCrl27ZqoPA/D29qZ///7897//JSoqqsi1S/M9fTubN2/m7bffJjg42NSGyBzXNdLpdGg0mgKv2+XLl8u9m4JOpyM0NJTly5dz9epV0/1hYWEVajI6ZswYjh49yl9//VXksZJG44p7XweK7G6h1+uL/Fx6e3vj5+dn+jm3xHt/jx49OHDgQLGfW01m/D0VFBTEypUrbzuKfPDgQVq1anXLTgKFWWxEbPPmzUyfPp1x48bRtGlTcnNzWbhwoekXmVGnTp3YuHEjH3/8MX5+fgQHB9OtW7dizxkSEoKbmxvz58/H2dkZR0dHunXrVil1J1qtlu+//56hQ4fSqlUrJk2ahL+/P5GRkWzZsgUXFxf++ecfAP7zn/+wZcsWunXrxpQpU2jZsiUJCQkcOnSIjRs3kpCQUOJ1GjduzKuvvsrbb79Nnz59uOeee7C1tWX//v34+fkxd+5cevbsibu7Ow8//DAzZsxAo9GwcOHCIj9Mpfkah4SE8M477zBr1iwuX77MqFGjcHZ25tKlS/z11188/vjjvPDCC6V+vQqztrbm/fffZ9KkSfTr14/77rvP1L4iKCio3NvgdOrUCYBXX32VCRMmYG1tzciRI8vUKLgir5MxhuK+N0t73iFDhuDr60uvXr3w8fEhLCyML7/8kuHDh5tGj8ryeVbm62289qJFi5g5cyZdunTBycmJkSNHlvrrCzBy5EgGDBjAq6++yuXLl2nXrh3r169nxYoVPPvss2XqPl0aJb0mI0aMYOHChbi6utKyZUv27NnDxo0bTS0Mbsfa2poJEybw5ZdfotPpuO+++8oV3yuvvMLvv//O0KFDmTFjBh4eHvz0009cunSJpUuXFijh6NGjB1ZWVpw5c8bUjgOgb9++fPPNNwAFEjFQF1L17t2bNm3aMGXKFBo1akRMTAx79uwhIiKCo0ePljrWNWvWcPr0aXJzc4mJiWHz5s1s2LCBhg0b8vfffxdYfFOZ172V4cOH8/HHH3PnnXdy//33c/36db766isaN27MsWPHynXOt956i7Vr19KnTx+mTp1Kbm6uqb9f4XO++eabvPXWW2zZsuWW2+e8+OKLLFmyhHHjxvHoo4/SqVMnEhIS+Pvvv5k/fz7t2rUr8hwXFxf69u3LBx98QE5ODv7+/qxfv77IKFNKSgoBAQGMHTuWdu3a4eTkxMaNG9m/f79pxNcS7/133303Cxcu5OzZs6UaDVq4cCFXrlwxLQDYvn27aWbloYceMo0Ob926lQEDBvDGG2/cdhuk7du3m9q+xMbGkpaWZjpn3759C8wgaTQa+vXrd8t9ZVNSUggNDeXGjRu8+OKLRRaXhYSE0KNHD9P/c3JyTL35yqTU6yuV2y9rLql9haOjY5FjL168qDz66KNKSEiIYmdnp3h4eCgDBgxQNm7cWOC406dPK3379lXs7e0V4LatLFasWKG0bNlSsbKyKhBLcUvdjfHlX6JvXL5e0lLdw4cPK/fcc49Sr149xdbWVmnYsKEyfvx4ZdOmTQWOi4mJUaZNm6YEBgYq1tbWiq+vrzJo0CDl22+/vWX8Rj/88IPSoUMHxdbWVnF3d1f69eunbNiwwfT4rl27lO7duyv29vaKn5+f8tJLLynr1q0rsKS+tF9jRVGUpUuXKr1791YcHR0VR0dHpXnz5sq0adOUM2fOlPlcxVm0aJHp8/Hw8FAeeOABJSIiosAxZWlfoSiK8vbbbyv+/v6KVqst0OIBKHZpd8OGDYt8/1TkdbrV92Zpzvvf//5X6du3r+l7KSQkRHnxxReVpKSkUn2ehVXm660oipKamqrcf//9ipubmwLctpVFST9jKSkpynPPPaf4+fkp1tbWSpMmTZQPP/ywQPsCRSn5dSvL9Up6TW7cuKFMmjRJ8fT0VJycnJTQ0FDl9OnTRb4nimtfYbRv3z4FUIYMGVKmz7+wCxcuKGPHjlXc3NwUOzs7pWvXrsrKlSuLPbZLly5FlsJHREQogBIYGFji+SdOnKj4+voq1tbWir+/vzJixAhlyZIlpmNu9bNWuE2RjY2N4uvrq9xxxx3KZ599ZmqpUZnXfeONNxRAiY2NLXB/cb8//ve//ylNmjRRbG1tlebNmys//vij6fn5leV9YNu2bUqnTp0UGxsbpVGjRsr8+fOLPefzzz+vaDQaJSwsrNivQX7x8fHK9OnTFX9/f8XGxkYJCAhQHn74YVNbm+J+X0ZERCijR49W3NzcFFdXV2XcuHHKtWvXCrR3yMrKUl588UWlXbt2irOzs+Lo6Ki0a9dO+frrr03nscR7f1ZWluLp6am8/fbbt/3aKMrNllLF3fL//P3zzz8KoMyfP/+25zS+ZsXd8rfHSElJUQBlwoQJtzyf8TUq6Vb4+2jNmjUKoJw7d65UXwMjjaJU06pFIYSoRo4ePUr79u35+eefeeihhywdjrCArl270rBhQxYvXmzpUKqlt99+mx9//JFz586Vqy1KcV566SV+//13zp8/X6AusCJWr17NiBEjOHr0KG3atKmUc4JaWqTRaIqdkr4V2TxRCCFK4bvvvsPJyYl77rnH0qEIC0hOTubo0aNl2kOwrnnuuedITU3ljz/+qLRzbtmyhddff73SkjDjOSdMmFCpSVhYWBgrV67k7bffLvNzZURMCCFu4Z9//uHUqVO8/vrrTJ8+nY8//tjSIQkhahFJxIQQ4haCgoKIiYkhNDSUhQsXFmlOK4QQFSGJmBBCCCGEhUiNmBBCCCGEhUgiJoQQQghhIRbf4qiqGQwGrl27hrOzc5VugSSEEEKIyqMoCikpKfj5+RW7X3VtYdZEbO7cuSxbtozTp09jb29Pz549ef/992+5y/2CBQuYNGlSgftsbW3JzMws1TWvXbtGYGBgheIWQgghhGWEh4cTEBBg6TCqjFkTsW3btjFt2jS6dOlCbm4u//d//8eQIUM4derULbemcXFxKbABa1lGtowrnMLDw3FxcSl/8EIIIYQwm+TkZAIDA2v9SmWzJmJr164t8P8FCxbg7e3NwYMHC+wBVZhGo8HX17dc1zQmbS4uLpKICSGEEDVMbS8rsuikq3EHeQ8Pj1sel5qaSsOGDQkMDOTuu+/m5MmTJR6blZVFcnJygZsQQgghRHVksUTMYDDw7LPP0qtXL1q3bl3icc2aNeOHH35gxYoV/PLLLxgMBnr27ElERESxx8+dOxdXV1fTTerDhBBCCFFdWayh61NPPcWaNWvYuXNnmYrwcnJyaNGiBffdd1+xezplZWWRlZVl+r9xjjkpKUmmJoUQQogaIjk5GVdX11r/+9si7SumT5/OypUr2b59e5lXQlhbW9OhQwfOnz9f7OO2traVujmoEEIIIURVMevUpKIoTJ8+nb/++ovNmzcTHBxc5nPo9XqOHz9O/fr1qyBCIYQQQgjzMeuI2LRp0/jtt99YsWIFzs7OREdHA+Dq6oq9vT0AEydOxN/fn7lz5wIwZ84cunfvTuPGjUlMTOTDDz/kypUrPPbYY+YMXQghhBCi0pk1Efvmm28A6N+/f4H7f/zxRx555BEArl69WqCD7o0bN5gyZQrR0dG4u7vTqVMndu/eTcuWLc0VthBCCCFElbBYsb651JViPyGEEKI2qSu/v2vv5k1CCCGEENWcJGJCCCGEEBYiiZgQQgghhIVIIiaEEEIIYSGSiAkhhBDVWLY+m59O/sT2iO3oDXpLhyMqmUU66wshhBCidJafX868A/MAqO9YnzFNxjC6yWi8HbwtHJmoDDIiJoQQQlRjl5IumT6OSoviyyNfMmTJEJ7d8iy7IndhUAwWjE5UlIyICSGEENXYtdRrALzQ+QXq2ddj8ZnFHLp+iE1XN7Hp6iYCnAIY03QMoxqPwtPe08LRirKSREwIIYSoxq6lqYlYsGswfQP6MqLRCM7fOM+Sc0v4+/zfRKRG8Nmhz/jqyFcMDBzIuGbj6OrbFa1GJr1qAumsL4QQQlRjvX7vRXJ2MsvuWkYT9yYFHsvIzWDd5XUsPruYY7HHTPc3dGnI2CZjubvx3bjbuZs75EpRV35/SyImhBBCVFMp2Sn0/L0nAP/e/y+O1o4lHnsm4QyLzy5m5cWVpOWkAWCttWZww8GMbzqeTj6d0Gg0Zom7MtSV39+SiAkhhBDV1JmEM4z9Zyxutm7smLCjVM9Jz0lnzaU1/Hn2T07FnzLd38i1EWObjuWukLtwtXWtqpArTV35/S0TyEIIIUQ1ZSzU93PyK/VzHKwdGNN0DItGLOKPEX8wpskY7K3suZh0kQ/2f8CgxYN45993yDXkVlXYogwkERNCCCGqKWOhvp9j6ROx/FrVa8WbPd9k87jNvNbtNZq5NyNLn8WiM4tYdXFVZYYqykkSMSGEEKKaKs+IWHGcbJy4t/m9LB65mMfaPAYgiVg1IYmYEEIIUU1VViJmpNFoGN14NAB7o/cSlxFXKecV5SeJmBBCCFFNVXRqsjgNXBrQ1rMtBsXA2ktrK+28onwkERNCCCGqqcoeETMa1mgYINOT1YEkYkIIIUQ1lJaTRmJWIlD5iVhoUCg6jY4T8Se4knylUs8tykYSMSGEEKIaMo6Gudi44GzjXKnn9rT3pHv97gCsvri6Us8tykYSMSGEEKIaikqLAip/NMxoeKPhAKy6tIpa3tu9WpNETAghhKiGIlMjgcot1M9vYIOB2OnsuJJ8pUAHfmFekogJIYQQ1VBVFeobOVo70j+wPwArL66skmuI25NETAghhKiGjCNi/k7+VXYN4/Tk2str0Rv0VXYdUTJJxIQQQohqKCpVrRGr71S/yq7Ry68XrrauxGXEsTd6b5VdR5RMEjEhhBCiGjI2c63KETFrnTVDGg4BZPWkpUgiJoQQQlQz6TnpJGQmAFVXI2ZknJ7ceHUjmbmZVXotUZQkYkIIIUQ1Y2xd4WztjIuNS5Veq4N3B+o71ictJ41tEduq9FqiKEnEhBBCiGrGuGKyKuvDjLQaLUODhwIyPWkJkogJIYQQ1UxVt64ozDg9uSNyB0lZSWa5plBJIiaEEEJUM5FpVd+6Ir+m7k1p4t6EHEMOG65sMMs1hcqsidjcuXPp0qULzs7OeHt7M2rUKM6cOXPb5y1evJjmzZtjZ2dHmzZtWL1ahk6FEELUXqYRsSrqql+cYcHDAFh9SX7HmpNZE7Ft27Yxbdo0/v33XzZs2EBOTg5DhgwhLS2txOfs3r2b++67j8mTJ3P48GFGjRrFqFGjOHHihBkjF0IIIczH2EPMXFOTcDMROxB9gOi0aLNdt67TKBbc6TM2NhZvb2+2bdtG3759iz3m3nvvJS0tjZUrb26/0L17d9q3b8/8+fNve43k5GRcXV1JSkrCxaVqV54IIYQQlaH/ov7EZ8azaMQiWtZrabbrPrzmYQ5dP8TMTjOZ1HqS2a5bnLry+9uiNWJJSWpBoIeHR4nH7Nmzh8GDBxe4LzQ0lD179hR7fFZWFsnJyQVuQgghRE2RmZtJfGY8YL4aMSNj0b5MT5qPxRIxg8HAs88+S69evWjdunWJx0VHR+Pj41PgPh8fH6Kjix82nTt3Lq6urqZbYGBgpcYthBBCVCVjR30HK4cq7yFW2JCGQ7DSWHE64TQXEi+Y9dp1lcUSsWnTpnHixAn++OOPSj3vrFmzSEpKMt3Cw8Mr9fxCCCFEVcpfH6bRaMx6bTc7N3r79wZg1cVVZr12XWWRRGz69OmsXLmSLVu2EBAQcMtjfX19iYmJKXBfTEwMvr6+xR5va2uLi4tLgZsQQghRU0Smmrd1RWHDGt1cPWnBMvI6w6yJmKIoTJ8+nb/++ovNmzcTHBx82+f06NGDTZs2Fbhvw4YN9OjRo6rCFEIIISzG3M1cC+sf2B8HKwciUyM5GnvUIjHUJWZNxKZNm8Yvv/zCb7/9hrOzM9HR0URHR5ORkWE6ZuLEicyaNcv0/2eeeYa1a9fy0Ucfcfr0ad58800OHDjA9OnTzRm6EEIIYRbGGjFz9hDLz97KnkENBgGw8uLK2xwtKsqsidg333xDUlIS/fv3p379+qbbokWLTMdcvXqVqKgo0/979uzJb7/9xrfffku7du1YsmQJy5cvv2WBvxBCCFFTWXpEDG5OT66/vJ4cQ47F4qgLrMx5sdLMNW/durXIfePGjWPcuHFVEJEQQghRvRgTMUvViAF0r98dDzsPEjIT2HNtD30Diu/1KSpO9poUQgghqoksfRaxGbGAZUfErLRW3Bl0JyCrJ6uaJGJCCCFENWHcWsjeyh43WzeLxmJs7rolfAvpOekWjaU2k0RMCCGEqCaMrSv8HM3fQ6ywNp5tCHQOJCM3gy3hWywaS20miZgQQghRTVSHQn0jjUZj2ghcpierjiRiQgghRDVRnRIxuLl6cve13SRkJlg4mtpJEjEhhBCimjD1EKsmiVgj10a08GiBXtGz/vJ6S4dTK0kiJoQQQlQT1W1EDG4W7cv0ZNWQREwIIYSoJkz7TDparodYYUODh6JBw5HYI0SkRFg6nFpHEjEhhBCiGsjR5xCbbvkeYoV5O3jT1bcrAGsurbFwNLWPJGJCCCFENRCdFo2Cgp3ODg87D0uHU0D+6cnS7JIjSk8SMSGEEKIaiExTpyXrO9W3eA+xwgY3HIyN1oYLSRc4c+OMpcOpVSQRE0IIIaqB6liob+Rs42zab3L1xdUWjqZ2kURMCCGEqAbyd9WvjozTk6svrcagGCwcTe0hiZgQQghRDUSlRgHVc0QMoE9AH5ytnYlJj+FgzEFLh1NrSCImhBBCVAOm1hVO1ad1RX62OlvuCLoDkJ5ilUkSMSGEEKIaqG5d9Ytj3Hty/ZX1ZOuzLRxN7SCJmBBCCGFhOYYcrqdfB6pvjRhAZ5/OeNt7k5Kdwo7IHZYOp1aQREwIIYSwsJi0GAyKARutDfXs61k6nBLptDqGBg8FZPVkZZFETAghhLCw/K0rtJrq/at5WCN1enJbxDZSs1MtHE3NV71fbSGEEKIOMLWuqMb1YUYtPFoQ7BpMlj6LjVc3WjqcGk8SMSGEEMLCjIX69R3rWziS29NoNAwPzuspJtOTFSaJmBBCCGFhxqnJ6tq6ojDj6sm90XuJy4izcDQ1myRiQgghhIVV5+2NihPoEkhbr7YYFANrLq2xdDg1miRiQgghhIXVtBExwDQ9Kc1dK0YSMSGEEMKCcg25xKTHADWjRswoNCgUnUbHyfiTXE66bOlwaixJxIQQQggLup5+Hb2ix0prhZeDl6XDKbV69vXo7tcdUDcCF+UjiZgQQghhQabWFY7Vv4dYYfmnJxVFsXA0NVPNesWFEEKIWqamFernN7DBQHwcfOhavysZuRmWDqdGsrJ0AEIIIURdVhM2+y6Jo7Uj68eur3EjedWJfOWEEEIICzKNiFXjzb5vRZKwipGvnhBCCGFBNXlqUlScWROx7du3M3LkSPz8/NBoNCxfvvyWx2/duhWNRlPkFh0dbZ6AhRBCiCpWk/aZFJXPrIlYWloa7dq146uvvirT886cOUNUVJTp5u3tXUURCiGEEOajN+iJSVN7iNWkZq6i8pi1WH/o0KEMHTq0zM/z9vbGzc2t8gMSQgghLCg2I5ZcJRcrjRVe9jWnh5ioPDWiRqx9+/bUr1+fO+64g127dt3y2KysLJKTkwvchBBCiOrIOC3p6+iLTquzcDTCEqp1Ila/fn3mz5/P0qVLWbp0KYGBgfTv359Dhw6V+Jy5c+fi6upqugUGBpoxYiGEEKL0pFBfVOs+Ys2aNaNZs2am//fs2ZMLFy7wySefsHDhwmKfM2vWLGbOnGn6f3JysiRjQgghqiVJxES1TsSK07VrV3bu3Fni47a2ttja2poxIiGEEDXZ2ZgUftt7leORSbwzqjUt6ruY7do1uZmrqBw1LhE7cuQI9evXnN3phRBCVD9ZuXrWnojm13+vsu9ygun+WcuOs+ypnmi1GrPEYawRkxWTdZdZE7HU1FTOnz9v+v+lS5c4cuQIHh4eNGjQgFmzZhEZGcnPP/8MwKeffkpwcDCtWrUiMzOT77//ns2bN7N+/Xpzhi2EEKKWuBKfxm/7rrL4QAQJadkA6LQaBrfwZue5OI6EJ/LPsWvc3d48iZFxarK+owww1FVmTcQOHDjAgAEDTP831nI9/PDDLFiwgKioKK5evWp6PDs7m+eff57IyEgcHBxo27YtGzduLHAOIYQQ4lZy9QY2hl3n171X2HEuznR/fVc7JnRpwISugfi42PHVlvN8uO4M7685zZCWvtjbVO0qRoNiICotCpARsbpMoyiKYukgqlJycjKurq4kJSXh4mK+eX8hhBCWFZWUwR/7wvlj/1VikrMA0GigX1MvHujWkAHNvLDS3WwekJmjZ9BH24hMzOD5O5ry9KAmVRpfTFoMg5cMRqfRceDBA1hpa1y1UJWqK7+/5VUXQghRaxgMCjvOx/HLv1fYFBaDIW+ooZ6jDeO7BHJflwY0qOdQ7HPtrHW8PLQ5M34/zNdbLzC+izpSVlWMhfq+jr6ShNVh8soLIYSo8eJSs1h8IILf9l0hPCHDdH+3YA8e6N6Q0FY+2FrdfqpxZNv6LNh1iUNXE/lw3RnmjWtXZTEbC/WlPqxuk0RMCCFEjaQoCvsuJfDr3qusORFFjl4d/nKxs2JMpwAe6NaAxt7OZTqnRqPh9REtGf31bpYeiuCRnkG09netivCJSlXrw6R1Rd0miZgQQogaJzUrl4n/28uhq4mm+9oFuvFAtwaMbOtXoUL7Dg3cGdXej+VHrjFn5SkWPd4djaby21lI6woBkogJIYSogVYevcahq4nYW+sY1cGPB7o1rNSRq5fubM7ak9Hsu5TAupPR3Nm68qcPpau+gGq+16QQQghRnA2nYgCY2j+Eufe0rfTpQz83ex7v0wiA91afJitXX6nnB0ytK/wcJRGryyQRE0IIUaOkZ+ey87zaD2xwS58qu84T/ULwdrblakI6P+2+XKnnNigGGRETgCRi5RadFs3nhz7nvb3vWToUIYSoU3aciyMr10CAuz3NfctWjF8WjrZWvBjaDIAvNp0nPjWr0s4dnxFPtiEbrUaLj2PVJZOi+pNErJzSc9P57vh3LDm7hLScNEuHI4QQdcbGvGnJO1r6VEkRfX5jOgbQ2t+FlKxcPtl4ttLOayzU93bwxlprXWnnFTWPJGLl1Mi1EUEuQeQYctgZudPS4QghRJ2gNyhsPn0dgDtaVP1Iklar4fXhLQH4be9VzkSnVMp5pT5MGEkiVgEDAtU9Lzdf3WzhSIQQom44dPUG8WnZuNhZ0SXYwyzX7NaoHne28sWgwDurTlEZOwNK6wphJIlYBQxsMBCAHZE7yDHkWDgaISwnOimTx346wMw/j5CrN1g6HFGLGaclBzT3xlpnvl9hs4Y1x0anZce5OLaeia3w+aRQXxhJIlYBbTzb4GHnQUp2CgdjDlo6HCEs4tDVG4z8cicbw2JYdiiyUutohChsQ776MHNqWM+RR3oFAeqoWE4F/+CQREwYSSJWATqtjv6B/QGZnhR1058Hwpnw33+JTcnCz1XdHPnrrRfYeS7OwpGJ2uhCbCoX49Kw1mno19TL7NefPrAxHo42XIhN47e9Vyt0LuOG35KICUnEKmhgoDo9uSV8S6XUDQhRE+TqDbz1z0leWnKMbL2BIS19WD+zH/d1bYCiwLOLjhCbUnlL/YWAm6Nh3RvVw9nO/CsNXeysee6OpgB8svEsSenlK0lRFMU0IubvKDVidZ0kYhXUrX437K3siU6LJiwhzNLhCFHlbqRl8/CP+/hx12UAnhnUhPkPdsLJ1orZI1rS1MeJuNQsZv55BINB/jgRlceYiA0x87Rkfvd1CaSpjxOJ6Tl8vvlcuc4RnxlPlj4LDRp8HX0rOUJR00giVkF2Vnb08usFqKNiQtRmZ2NSuPurXew6H4+DjY5vHujIc3c0RatVeznZ2+j46v6O2FmrRc3/3X7RwhGL2iIuNYtDV28AMMgMbStKYqXT8lpeO4uf91zmUlzZ+0gaR8O8HLyw1kkPsbpOErFKMKCB2sZiy1VJxEQNcu0wrHoeTiyFrNTbHr7uZDSjv9rF1YR0AtztWfpUT4a2KboRchMfZ966qxUA89af4eCVG5Ueuqh7NoddR1Ggtb8Lfm72Fo2lb1MvBjTzIkev8N7qss+EGOvDpHWFAEnEKkVf/77oNDrO3DhDREqEpcMR4vYMelg6BfZ/D0sehQ9D4I8H4NhiyEwueKhB4bON53hi4UHSsvX0aFSPv6f3pkV9lxJPP75zICPb+aE3KMz4/XC5a2mEMNoQlrdaskX1mMp7dXgLdFoNG07FsPtC2RanyIpJkZ8kYpXAzc6Njj4dAdgavtWisQhRKmF/Q/w5sHEGj0aQmwmnV8Kyx+DDxvD7fXD0D9KSEpj22yFTS4pHegbx8+SueDja3PL0Go2G90a3pmE9ByITM3h56TFZzCLKLSNbz45zau+uwS29LRyNqrG3Mw92awDA2yvD0JehHtKUiElXfYEkYpXG2GVf6sREtacosOMj9eMeU+HpQ/DkTujzAtRrDPosOLMa/noCm0+aMPbM89xrtZ2P72rAm3e1KnUTTWc7a764rwPWOg1rT0bzy79XqvCTErXZrvNxZOYY8Hezp+UtRmLN7dnBTXGxsyIsKpnFB8JL/TxjV30ZERMgiVilMSZiB2MOkpSVZOFohLiFcxsg+jhYO0K3J0GjAd82MOh1mH4AntpDeNunuUAA1uQySHeY963mc8/G/vDLGDj0M6QnlOpSbQPcePnO5gC8vSqMk9fkZ0OUnXG15OAW3lW+yXdZuDvaMGNQEwDmrT9LalZuqZ4XlZq3z6QkYgJJxCpNgHMATd2bolf0bI/YbulwhCieosCOeerHnSeBQ8G9+hTg54sO9D/Qk0GZHzDV9RuSu78I3i3BkAvnN8LfT6vTlz+PgoMLIO3W9TGTewczqLk32bkGnv79MGml/GUlBKibfG86beymXz3qw/Kb2COIYE9H4lKz+HrL+dseryiKFOuLAiQRq0QyPSmqvSu7IHwv6Gyh59MFHsrONfB/fx1n9oqT6A0Kd7f34+Pp9+Jy52swdQ9M2w8DXwOfNqDo4eIW+OcZmNcEfhqpFv6nXi9ySY1Gw4fj2uHrYsfF2DRmrzhprs9W1AJHwhOJS83G2c6Kbo3Ms8l3WdhYaZk1VB31/X7nJcIT0m95/I2sG2TkZgBQ37HoqmNR90giVomMbSx2Ru4kSy9dxUU1tD1vNKzDg+B8c3QhNiWL+7/7l9/3haPRwKyhzfn03vbYWetuPterKfR9EZ7aqdaVDXoD6rcDxQCXtqutMOY1hR+Hw77vICXa9FQPRxs+m9AerQaWHopg2SFZXSxKxzgt2b+ZeTf5Los7WvrQo1E9snMNvL/29C2PNfUQs/fCRnfrRS+ibqie39U1VEuPlvg4+JCRm8HeqL2WDkeIgiIPqqNYGh30mmG6+3hEEnd9uZMDV27gbGfFj4904Yl+IbeuxakXAn1mwhPbYcYRGPwW+HUAFLiyE1a/AB81hx+Gwr/zIfka3RrV45lB6vYwry0/wcXY2/cuE2JjmBk3+S7nyl6NRsNrI1qg0cDKY1EcvFJyDaW0rhCFSSJWiTQajWl6UjYBF9XOjo/Vf9uOB/cgAFYciWTs/N1EJWXSyMuRFdN60b9ZGdsDeARD72fh8a3wzDEY8g4EdAEUuLob1r4MH7eA/w3haYd1DG+oJz1bz/TfDpOZo6/ET1DUNpfi0jh/PRUrrRk2+T7wA7zjA191hxXT1UUp18PAYCjV01v5uTK+UyAAc1aGlbi9lyRiojArSwdQ2wxoMIA/zvzB1vCtGBQDWo3kuqIaiDml9glDA72fIyNbz/trT7Ng92UABjTz4rP7OuBS0Y2U3RuqtWc9n4akCDj1N5xaAeH/QvhetOF7+Qp40q4Jf1/vwvzlWTw7bnBFPztRS23Mt8m3q30VbwW0/we1dUtsmHo7vFC939YF/Duqf1wEdIWAzkUWuRg9H9qUlceucTQ8kb+PXmNUh6LF+KbWFdJDTOSRRKySdfHpgrO1M/GZ8RyLPUZ77/aWDkkI2PmJ+m+LkRzK8OaFn3ZwMW+PvKf6h/DCkGbotJXcFsA1QO1T1mMqJF+DsH/UpOzKbtpwjjbW5+DkbyRda41rp3HQ8m51dE2IPPnbVlSplBiIOa5+fM/3cP0UROyHyEOQlQwXt6o3I4+QvMSsMwR2Be9WoLPC29mOqQMa8+G6M7y/9jShrXyxt9EVuJRxxaSMiAkjScQqmbXOmt4BvVlzaQ2bwzdLIiYsL+EinFgCwI9WY3n7m90YFPB1seM/Y9qUfSqyPFz8oNsT6i0lBsL+5vKO3wlMPoTrjROw8QRszCv+b3k3tByl1qGJOishLZsDebVWg6u6Puxi3kr3+u2g7bib9+tz1dGx8H0QcUBNzuLPQcIF9XbsD/U4awe1RjKgM1Pqd2ataw7Hk+C7HRdNfcaMjFOT0rpCGEkiVgUGBg5kzaU1bLm6hZmdZlo6HFHX7fwUFAP7rDrx1n71R/6eDv68MbIVrg5VPN1THGcf6DoFv46TmfT1agJiNnOv4yHa5hxDE3UUoo7Cpjlqm4yh/4Gg3uaPUVjc5tPXMSjQsr4LAe4OVXux85vUf0MGFrxfZ6U2O/ZtA10mq/elJ6gLXyL2590OQlaS2hrmyi5sgH+ACFtP/tnWl7iu3+DpbAfk9RCTGjFRiCRiVaC3f2+stFZcTr7MxaSLNHJtZOmQRB2VcyMC7eFf0QEfpA2nnqMN745uw52tLd8Y08ZKyzsPDGT459b8ljyIF3rXY3r9M+r05aVt6lTRr+PhkX/Av5OlwxVmtuGU2v6kykfDDIabI2Ihg25/vIMHNLlDvRmfH3c2X2J2AOX6KQI0cTylWcaqv1oyfOKLACRlJZGeq/YZkx5iwsisleTbt29n5MiR+Pn5odFoWL58+W2fs3XrVjp27IitrS2NGzdmwYIFVR5nRTnZONHNtxsAW65Kc1dhGWdjUlj93/9Dp+Sy19Acr1b9Wf9c32qRhBk1qOfA3DFtAPhoVzw7XYbDQ8vghXPQqD/kpMGv4yDunGUDFWaVmaNn+1l1x4YhVZ2IxRyHtFh1y6/AbmV/vlYL3s2h40Nw1+cwdTeaV65yseU0AHpf+ITEmKvAzfqwenb1sLOyq7RPQdRsZk3E0tLSaNeuHV999VWpjr906RLDhw9nwIABHDlyhGeffZbHHnuMdevWVXGkFSdd9oWl6A0K/912gYc+X80dGWvV+3rN5OsHOlLPydbC0RU1oq0f93VtgKLAs4uOEJuSpY463PuLWneTHg8L74HkKEuHKsxk94U4MnL01He1o5VfFW/yfSGv1VBwH7CqpAardi4Ej3mLs7omuGrSiF00HfJNS0p9mMjPrInY0KFDeeeddxg9enSpjp8/fz7BwcF89NFHtGjRgunTpzN27Fg++eSTEp+TlZVFcnJygZsl9A/sD8Cx2GPEZdx6Lz4hKsuluDTG/3cPc9ec5kHNahw0WeT4tKPnkPHVarPkwmaPaElTHyfiUrOY+ecRtQeTrTM8sERdoZZ0FX65BzJuWDpUYQY3V0v6VP33rak+rBTTkmWg0VlzfcA8chQdTRK2kXZ46c3WFVIfJvKp1k2u9uzZw+DBBXsMhYaGsmfPnhKfM3fuXFxdXU23wMDAqg6zWD6OPrSu1xoFha3hWy0Sg6g7DAaFn3ZfZuhn2zl45Qa+ttk8Yaf+grHu/yJU4yQMwN5Gx1f3d8TOWsuOc3HM335BfcDREx76C5x81ZYCv98HORmWDVZUKYNBYWOYumdplXfTz0qFq/+qHzeu3EQMoGfPfiyyy1uFueZFriVeBKC+k9SHiZuqdSIWHR2Nj0/BH0QfHx+Sk5PJyCj+zXjWrFkkJSWZbuHh4eYItVjGvSdlelJUpYgb6Tz4v7288fdJMnMM9Aypx7peZ7DJTQGv5tBsuKVDLJUmPs68dVcrAD5af5aDV/JGv9wbwoNLwdYVru6BxZPUtgKiVjoakUhsShZOtmbY5PvKLjDkgFsD8Kj8RVVarQaPO2dx1uCPY04CkZfU3wX+jjI1KW6q1olYedja2uLi4lLgZikDA9Wl0P9e+5f0nHSLxSFqJ0VRWLT/Knd+uoPdF+Kxt9Yx5+5W/DKxDa5HvlMP6j1TLSauIcZ3DmRkOz/0BoUZvx8mKT1HfcC3Ndz/B1jZwdk1sPKZcu8LKKo3496S/Zp5YWulu83RFZR/WrKKRo1D2zXkc6dnMSgaotLVz02mJkV+1fod2tfXl5iYmAL3xcTE4OLigr29vYWiKr0QtxACnQPJNmSz69ouS4cjapGY5EweXbCfl5ceJzUrl04N3Vn9TB8m9ghCe3ghpMeBW0NoPcbSoZaJRqPhvdGtaeDhQGRiBq8uP37zwYY9YeyPoNHC4V9g01uWC1RUGWN92B0tzLDJt7FQv3D/sEqk02q4Y8hw/qcP5Zq12jHK38a1yq4nap5qnYj16NGDTZs2Fbhvw4YN9OjRw0IRlU3+TcCljYWoDIqisOJIJEM+2c6WM7HY6LT837Dm/PlED4I9HSE3G3Z/rh7c+1m1IWUN42xnzRf3dUCn1bDyWBSrj+dbLdl8GIz8TP145yew52vLBCmqxJX4NM7GpKLTahhQ1Ts+JF5Vu+RrdNCoX5VeakRbP5a43Utq3ui0777/Ven1RM1i1kQsNTWVI0eOcOTIEUBtT3HkyBGuXlV7rMyaNYuJEyeajn/yySe5ePEiL730EqdPn+brr7/mzz//5LnnnjNn2BUysIH6l9a2iG3kGqSuRZRfrt7A9N8P88wfR0jKyKGNvysrZ/Tm8b4hN/eJPPo7JEeCc31o/4BlA66AdoFuPNlPrdl5ffkJ4lOzbj7YcSIMmq1+vG4WHPvTAhGKqmAcDesW7FH1uz4YpyUDuoBd1Y5Q6bQaRnavB4CHXo/DgQVwZXeVXlPUHGZNxA4cOECHDh3o0KEDADNnzqRDhw7Mnq2+qUZFRZmSMoDg4GBWrVrFhg0baNeuHR999BHff/89oaGh5gy7Qtp7tcfd1p3k7GQOxRyydDiiBltzIppVx6Kw0mp4bnBTlk3tSVMf55sH6HNvbu7d82mwqn49w8pixqAmNPNxJj4tm9krThZ8sPdM6PaU+vHyp+DcRvMHKCpd/rYVVc4M05L5NQ1Q6x1tc/K2a1oxXVYAC8DMiVj//v1RFKXIzdgtf8GCBWzdurXIcw4fPkxWVhYXLlzgkUceMWfIFabT6ugXqA57y+pJURE7z6n96B7uGcQzg5tgrSv043tqOdy4BPYe0OkRs8dX2WytdMwb1w6dVsOq41GsPHbt5oMaDYS+B23GgSEX/nxI3ZRZ1Fg30rI5kLdStsrbVuhz4eI29eMqaFtRnJh0dYo9KrsR1/FQNw3f+h+zXFtUb9W6Rqy2yN9lX5GVXqKcdl1QE7HejT2LPmgwwI6P1I+7TwUbRzNGVnXaBLgyrX8IALNXnCQu/xSlVgt3f62ueMtJV7dCij1roUhFRW05cx29QaG5rzOBHlW8yXdk3kbddm7q7g1mYOyqb6Otz/9lT1Lv3P0FXDtsluuL6ksSMTPo4dcDO50dkamRnL0hvyhE2V2NTyfiRgZWWg1dg4vprXR2rdrw1MYZuj5m/gCr0PSBTWju60xCWjavLz9R8I8ZKxsY/7O6KXhGAiwcDUmRlgtWlJuxbUWVj4bBzWnJRv1BW8UtMvIYE7F+jZqy0dCJ9dpeoOjVKUp9jlliENWTJGJmYG9lTw8/daXn5vDNFo5G1EQ7z6ujYR0auOFoW2glpKLAjnnqx10fA3t3M0dXtWystMwb1w4rrYY1J6L551ihPSdtneD+xVCvCSRHqFshpSdYJlhRLlm5eradiQXMlYjlFeqbaVoSbm74PbxlS3xd7Hgl/SEyrd0g5gTs/NRscYjqRxIxM5E2FqIijNOSPUOKmZa8tE2darGyg+7TzByZebT2d2XagMYAzF5xguspmQUPcKwHDy0DZz+IPQ2/T4BsaaJcU+y5EE9ath4fF1ta+1Vxj62MG+rPC5itUB8w7TPZwCWAp/qHkIAL/yFvinL7B3D9tNliEdWLJGJm0i+wH1qNlrCEMKJSo27/BCHyGAwKey7EA9C7STGJ2Pa80bCOD4OTlxkjM69pAxrTsr4Liek5vPbXiaL1lm4N1K2Q7FwhfC8sfkSmfGqI/Ksltdoq3hf14jZQDODZDFwDqvZaeVKyU0jJTgHUrvr3dgnEy9mWBSmdifTqC/ps+PtpMOjNEo+oXiQRMxMPOw/ae7UHZPWkKJuw6GQS0rJxsNHRLsCt4IPh++DyDtBaQ68ZFonPXPJPUa4/FcPfR68VPcinJdz/pzo6eG4d/D1DtkKq5tRNvvMSMXNOS5pxNMxYH+Zm64ajtSN21jqe6NsI0DAj5SEUG2eI2Af7vjVbTKL6kETMjIzNXaVOTJTF7vPqaFi3YA9srAr9yBpXSrabYLa/7i2ppZ8LMwY1AdRVlNeTM4se1KA7jPtJ7Zh+9DfYMNvMUYqyOHEtiZjkLBxtdPQMqVe1F1MUuJD3h7A568PyErH8e0w+0K0hnk42HEx05FCzvCblm+bAjctmi0tUD5KImZGxTuxg9EGSs5MtHI2oKYz1Yb0Kt62IPq6ultRooXfN2W2iop7qH0JrfxeSMnL4v7+OF98SptmdcNcX6se7P1fbBIhqyTgtaZZNvuPOQVI46GygYa+qvVY+xkJ9P8ebiZi9jY4pfdTdI1682AGlYS+1Dcs/sqF9XSOJmBk1cGlAY7fG5Cq57IjYYelwRA2QnWtg70V1BWCRQv0dH6v/thoN9ULMHJnlWOvUKUprnYaNYdf563AJ7So6PACD8zYGX/8aHPndfEGKUrNIN/0GPcCminuV5WMs1M8/IgbwYPeGuDtYczE+g42NX1On1C9uVTe1F3WGJGJmZhwV23xVpifF7R0JTyQjR089Rxua++bbzijuPJz8S/2490zLBGdBzX1deCZvivLNv08SU9wUJUCvZ6DHdPXjFdPUmjpRbYQnpHM6OgWdVsPA5lW8yTdYpG0FYFqgVTgRc7S14rG8UbH/7MvG0P9V9YF1r0KyLOqqKyQRMzNjIrYzcifZ+mwLRyOqu115/cN6hNQruJps5yeAAk2Hgm9rywRnYU/2C6GNvyvJmbnMWlbCFKVGA3e8rY4aKnpY9jhkpZo/WFEs42hY54buuDnYVO3FcrPg8k714xDzJmLGETF/J/8ij03s0RAXOysuxKax2nEU+HVUu/6vel6mKOsIScTMrJVnK7ztvUnPTWdftPx1Lm7NmIgVqA9LDIdjf6gf93neAlFVD1Z5U5Q2Oi2bT19n6aESpii1WhjxKbgEqHtxrptl1jhFyczaTf/qv2oNlpMP+LSq+uvlY6wRq+9Yv8hjznbWTO6tjop9sfUyhpFfgNYKzqxS948VtZ4kYmam1WjpH9gfkOlJcWupWbkcCU8ECu0vuftzdaPr4L4Q2MUywVUTzXydeWawOkX51j8niU4qYYrS3g1Gzwc0cOhnOL3KbDGK4iWl57D3klr/aNZu+iED1ZFSM0nLSSMpKwkoOjVp9EivIJxtrTgTk8L6+Ho3/8Ba/aLsElEHSCJmAQMaqNOTW8O3YlAMlg1GVFv7LsWTa1AI9LC/uQly6nU1kQDo84LlgqtGnujbiHYBrqRk5vLKsmPFT1ECBPeBnk+rH//9NKTEmC9IUcTWs+om3019nGhYzwyb1BsL9c3YPwxutq5wsXHB2ca52GNc7a15pFcQAJ9tOo/SeyZ4tYC0WFgrI7i1nSRiFtDVtyuO1o7EZsRyMu6kpcMR1dSuvP5hvfKvltzzFeRmQkAXdURMFJii3HomlsUHIko+eOBr4NMG0uPh7+lSg2NB60+ZcVoy9bra7gWg0YCqv14+xkSsuPqw/B7tFYyjjY6wqGQ2nkuCu79UW9Mc+wPOrjdHqMJCJBGzABudDb39ewPS3FWUrEh9WMYN2P8/9eM+z5t1eqW6a+LjzMwhTQF4e+UpriVmFH+glS3c8y3obOHcejjwgxmjFEbZuQbTJt9mbVtRv53ZtwEzFuoXVx+Wn7ujDRN7BgHwxeZzKP6doPtU9cGVz0Gm9J6srSQRsxDZBFzcSlxqFqej1b3pTN3Gz2+C7BR1j7ymd1owuuppSp9GtA90IyUrl1dKWkUJ6jZIg99QP17/mtoKRJjVvxfjSc3KxdvZtui2XVXBQtOSAFFpxbeuKM5jvYOxt9ZxLCKJrWdjYcCr4B4EyRGw8c2qDVRYjCRiFtInoA9WGisuJF3gSvIVS4cjqpndeZt8t6jvQj0nW/XOiP3qvyEDZDSsGDqtRp2itNKy/Wwsi/aHl3xwt6cguJ+6im7ZFNkc3MyMbSsGmWOTb4MhXyJm3rYVcOvWFYXVc7Llwe4NAPhs4zkUa/ubO0Qc+B9c3lVlcQrLkUTMQlxsXOjs2xmQUTFR1G7jtGT+vfciDqj/BtTtlZK30tjbiRfypijfWRVGZElTlFotjPoG7Fzh2iHY9oEZo6zbFEXJ17bCDE1cY46rRe/WjhDYreqvV0hx+0zeypS+jbC10nIkPJGd5+PUWtCOD6sP7phXVWEKC5JEzIJM05PhkoiJgnYWrg/LzYLoY+rH/p0sFFXNMLl3Izo2cCM1K5dXlt5iFaWrP4z4RP14xzzpum8mJ68lE5WUib21rui2XVXBOBoW3AesqrhpbDHKmoh5O9txfzd1VOzzTefU79+eM9QHL22HjMSqCFNYkCRiFjSwgVqvcPj6YeIz4i0cjagursanE3EjAyuthq7BHuqd0cdBnw0OnmrNiCiRTqvhw3HtsLXSsuNcHL/vu8UUZesx0GY8KAbpum8mxmnJvk09sbOu4k2+Qa2tBItMS6bnpHMj6wZQ+kQM1F0jbKy07L98g38vJoBnY7U21JAL5zdWVbjCQiQRsyBfR19aeLRAQWF7xHZLhyOqiV0X1NGwDg3ccLS1Uu801ocFdJb6sFII8XLixdBmALy76hThCeklHzzsQ3ANlK77ZrLB1LbCt+ovlp2mdtQHixbqO1s742LjUurn+bjYcW/nQEAdFQOg+XD137B/KjVGYXmSiFmYsbmrtLEQRsZpyQLTNqb6sM4WiKhmmtQrmM4N3UnL1vPy0mMYDCVMURbuuh+20pxh1ikRN9I5FZWMVoN5Nvm+vBMMOeDWAOqFVP31CjG1rnC6deuK4jzZPwRrnYY9F+PZfzkBWoxQHzi/EXJK2EFC1EiSiFnYwED1r7Q91/aQnnOLv9pFnWAwKOzJWzHZu0n+RCxvRMxfErHSMk5R2llr2X0hnl/3XS354KDeN7vu/zNDuu4bKYpak1RJjW83hV0HoHNDDzwczVCvlX9a0gIjyWWtD8vP382esZ3yjYrV7wDOfpCdqtaKiVpDEjELa+reFH8nf7L0WeyJ2mPpcISFnY5OISEtGwcb3c3+SqmxkHgF0IB/R0uGV+MEezryUmhzAOauDuN68i1GEqTrvjqVd3kX7PwU/ngAPmoG7zeEda9WyunNusk3WLR/GNzc7Ls0rSuKM7V/CFZaDTvOxXEoIgmaD1MfOC3Tk7WJJGIWptFopLmrMDF20+8W7IGNVd6PZ2TetKRXM7XdgiiTR3oG0S7AlfRsPYsP3mL7o7rWdV9RIP4CHP0DVj0P8/vA3EBYMAw2vgGnV0Jq3sjgv1/BiaUVulxaVi57L6obWA9sYYZpycSrEH8ONDqLbQdmGhFzLPuIGECghwOjO6hJ3BebzkHzvOnJM2vAoK+UGIXlSSJWDRhXT26L2EauIdfC0QhLMhbqm9pWgNSHVZBWq+HB7g0BWLQ/vORaMcjruv+m+vG6VyHuXNUHaC6ZyXBhC2z7EH4dBx80gi86wl9PwP7v1fYoih6c60OLu2DIO/DoOuj1jPr8v2dUaBeCPRfiydYbaODhQCNPM27yHdBZrQO0gIpMTRpNG9AYrQa2nInlmHVrsHVV+6IZyxVEjWdl6QAEdPDugKutK4lZiRy5fsTU6FXULdm5BtOIQcFCfakPq6jhbevz1j+nuJqQzr+X4m/dv6rbk3B2LVzapnbdn7wBdNbmC7YyGAwQd0b93onYD+H7IfY0UCgJ1dmCX3u1SbDx5lpoGs2/M0QchCs74c+J8NhGsHEoc0hbz6r1Yf2aeqExR72WBdtWGBmL9SuSiAV5OjKqvT/LDkfy0cZL/NQ0FI7/qa6ebNC9skIVFiQjYtWAldaKfgH9AFh6rmLD/6LmOhKeSEaOnnqONjT3dVbvNOgh8pD6sXTULzcHGytGtlN/Gf55q62PoFDX/cM1p+t+xg04/Cv8dq9a1/V1d/j7aXUlaGwYoIBbQ2g9FoZ+AFM2w6wImLweQt+FVqOKJmEAOisY+z9w9ILrJ2HNi2UOTVEUtuZt8t2/mRk23dbnwsVt6seNLZOIZeZmkpCp/mFV3hoxoxmDmmCt07DtbCwnXPuod55eVTfrGGshScSqiQnNJqBBw8qLK9kfLUPOdZGxPqxHSL2b++/FnVU3+rZ2BO8WFoyu5ru3i7oCbc2JaJLSb7O3ZE3pup+eoCZav4yBDxvDiqnqaF5Wsvo9E9QHej8HE36HF87Bs8fUpKrbE+oODaXtNO/sC2P+BxotHP5FTfjK4GJcGhE3MrDRaemRf9uuqnLtEGQlgZ0b+HWo+usVF0Jeob6jtWOZeogVJ8jTkYd7BAEw66g3is5W7Xt3PayiYYpqQBKxaqKNVxvGNxsPwJw9c8jWZ1s4ImFuuwpvawQ368P8O4LWDF3Ia7F2Aa4083EmK9fAiqORt39Cka77KVUfZGmkxcHBBfDzKDX5+vtptbeUIRe8W8GAV+GJ7fDKVXhkpVrz1nwYOFWwQL5RP+j/f+rHq56HmFOlfuq2vNGwrsEeONiYoSLGOC3ZqL/Ffm6M9WH1HetXylTs04Oa4O5gzfFYPREeeVOSp6XnXW1gkUTsq6++IigoCDs7O7p168a+fSX/tblgwQI0Gk2Bm52dnRmjNZ8ZHWdQz64el5Mv8+OJHy0djjCjtKxcjoQnAtC7cXH1YbK/ZEVpNBrTqNii201PGuXvur/Wgl33U6/D/v/BTyNhXhP45xm4uEUtrvdtAwNfh+kHYOpu6PcS1G+nTilWtj7PqzVXuRlqvVgpt4TaetaM05Jg8bYVcDMRq+i0pJGrvTXP3aFuaP+/uJbqnZKI1QpmT8QWLVrEzJkzeeONNzh06BDt2rUjNDSU69evl/gcFxcXoqKiTLcrV66YMWLzcbFx4aUuLwHw7bFvuZp8iwaUolbZdymBXINCoIc9gR75CqEjD6r/Sn1YpRjdwR8bnZaT15I5EZl0+yfk77p/eKF5u+6nRMO+7+DH4TCvKayaqTbyVAxQv7060vX0IXhyJ/R9ATybVH1MWq3a4sPZT20NsfLZ29YpZebo2XtRbVLcr6kZErGMGzdbvlioPgwqZ8VkYfd3bUBjbyf+zmiHAS1EHVXbdIgazeyJ2Mcff8yUKVOYNGkSLVu2ZP78+Tg4OPDDDyX37NFoNPj6+ppuPj5magZoAUODh9Kjfg+yDdm8u/ddFCnGrBOM2xoVGA3LSoXredM/0rqiUrg72nBHK/X9488DpRwVM2fX/aRI+Hc+/HAnfNQcVr+grlZEUUdF75gDzxyFJ7aptV8W2LYHR08Y96Pan+v44tv2W9tzMZ6sXAP+bvY09naq+vgublOTVc9m4BpQ9dcrQWWPiAFY6bS8OrwFCbhwwKCOjnF6daWdX1iGWROx7OxsDh48yODBg28GoNUyePBg9uwpuat8amoqDRs2JDAwkLvvvpuTJ0+WeGxWVhbJyckFbjWJRqPhte6vYaO1Yfe13ay9vNbSIQkz2FXc/pLXDqu/UFwD1WJpUSkm5E1P/nU4ksycUjbFrIqu+9npal+ui9tg95fw/R3wSUtY+zJc3QMoENAVhrwLzx5XVzn2egbcgyp+7Ypq0P1mv7W1r8C1IyUeaqwP62uuthXVYFoSIDItb59Jx7LvM3krA5p507epF+v0eX+cyfRkjWfWPmJxcXHo9foiI1o+Pj6cPn262Oc0a9aMH374gbZt25KUlMS8efPo2bMnJ0+eJCCg6F87c+fO5a233qqS+M2lgUsDprSdwldHvuL9fe/Ty79XhVfdiOorLjWL09FqIXjP/CvKpD6sSvQK8cTfzZ7IxAzWnohmVIdSjFgYu+5/2z+v6/7/oMtjJR+flQLJ1yA5Mu/fQh8nRUBmYjFP1KhJTsu7ocVIi47o3FbPp9WE8cxqWPwwPL6t2Map28xZH6YoNxMxC05LAkSlRgGVOyJm9NrwFkz5rAuv8wvKld1o0hPAwaPSryPMo9o3dO3Rowc9evQw/b9nz560aNGC//73v7z99ttFjp81axYzZ840/T85OZnAwECzxFqZHm39KKsuruJy8mU+P/Q5r3V/zdIhiSqyO2+T7xb1XajnZHvzAVN9mExLViatVsO4zgF8uvEci/aHly4Rg5td99fNgnWvqa0RstOKT7iySjkSb+2otspwawBNQtXky6VyR1CqjEYDo76G//aFG5dhxTS495cCm2tfiU/jUlwaVlpNwT8yqkrcOUgKB50NNOxZ9dcrQVpOGrEZagJamTViRk19nOnTtROnDjWkpfYKhjNr0HZ4oNKvI8zDrImYp6cnOp2OmJiCNRYxMTH4+pZu6sXa2poOHTpw/nzxW23Y2tpia2tb7GM1iY3Ohte7v87k9ZP588yf3B1yN2282lg6LFEFdhvbVuT/RaUoN0fEpFC/0o3rHMhnm86x52I8V+LTaFivlFvu5O+6v3TyrY+1dQUXv3w3fzXpMn7s4ge2LgUSlxrH3h3G/QQ/hKpTZP9+Az2mmh42joZ1DnLH2c4MuxMYR8Ma9AAbM2yjVILD1w8D6miYu517lVzjucFNWXSkCy25wrV/lxAgiViNZdZEzMbGhk6dOrFp0yZGjRoFgMFgYNOmTUyfPr1U59Dr9Rw/fpxhw4ZVYaTVQ9f6Xbkr5C7+vvA3c/6dw+/Df8dKW+0HMUUZFbu/ZFKEuuGy1kptRSAqlb+bPX2aeLH9bCx/HgjnxdDmpXuiVquuolz0EORkFE2sCiRZzlX7SVQX/h0h9D11YcGG19UR3MCuAKZu+v2ammGTb4ALef3DLDwtaWzK3dmn6kaz6znZ4tNlLOxfgmfMTlJTk3FykhKWmsjsqyZnzpzJd999x08//URYWBhPPfUUaWlpTJo0CYCJEycya9bNfj1z5sxh/fr1XLx4kUOHDvHggw9y5coVHnvsFvUZtcjznZ/HxcaF0wmn+TWsbN2sRfV3NT6d8IQMrLQaugbnq/Ewjob5tAZre8sEV8vd21ktWVhyMIJcvaH0T3Txgymb1J5dDyyGkZ+pvbs6PKgWiHs1qztJmFGXx6DVaLWp7OJHIC2ezBw9e/Km3c1SH5abBZd3qh9buFD/QIzaPqOLb9WOZo8YcgdRGm/syGbjP79X6bVE1TF7Inbvvfcyb948Zs+eTfv27Tly5Ahr1641FfBfvXqVqKgo0/E3btxgypQptGjRgmHDhpGcnMzu3btp2bKluUO3CA87D2Z2UmvevjryFdFp0RaOSFQm42hYhwZuONrmG+2U+rAqN7ilN+4O1sQkZ7H9XKylw6nZNBoY+Tl4hKg1cn89wf5LcWTk6PFxsb25d2pVuvov5KSDk4/6B4yFpOekczJOXdnf2bdqf35tra3IChkKgCFsJZGJGVV6PVE1LNJZf/r06Vy5coWsrCz27t1Lt27dTI9t3bqVBQsWmP7/ySefmI6Njo5m1apVdOhgmb3DLGV0k9F08O5ARm4Gc/fOtXQ4ohIV27YCpD7MDGytdNzTUV2V+Me+UvYUEyWzc4HxP4OVHZzfQM62jwG1iat52lbkTUuGDLRo3d2R60fQK3r8HP2qZMVkYQ17jQNggOYQH64+UeXXE5VP9pqsAbQaLa93fx0rjRWbwzez5eoWS4ckKoHBoJhWTPZuki8Ry81WO2YD+MuIWFUybnm0+fR1YlOyLBxNLeDbGobNA6Bf5Ld004TRv5m56sOqR/+w/TF59WFVPBpmpGnQg1w7D9w1qcSc2MKhqzfMcl1ReSQRqyGauDdhYquJALy37z3Sc9ItHJGoqNPRKSSkZeNgo6NdgNvNB2JOQG6m2h7BEp3T65CmPs60D3Qj16Cw7FCEpcOpHTo8SFqL8egw8IXNF/T2LWXT3IpIvQ7Rx9WPGw2o+uvdwoFotT6sKgv1C9BZYdVcXbw2RHuAt1eekh1ZahhJxGqQJ9s9ib+TP9Fp0Xxz9BtLhyMqyDgt2S3YAxurfD+K+evDanJrgxrCtBH4gXD5BVYZNBr+CZjJGUMA3ppEXFY9CYYqTsYu5M0S+LYFJzNtLF6M9Jx0TsSp04PmGhEDoPlwAEJ1Bzl89QZ/H71mvmuLCpNErAaxt7Ln/7r9HwALTy3kTMIZC0ckKqLYthUg9WFmNrKdHw42Oi7GpnHgikzrVIZNF9KYmvMM2Vp7uLwDtv6nai9YTdpWHI09Sq6Si6+jLwFOZtwVIWQAWDvgp4mjleYy7685Xfrtu4TFSSJWw/QN6MsdDe9Ar+iZ8+8cDEoZlt2LaiM718C+SwlAcYX66tSG1IeZh5OtFcPbqN3sF+2Xov2Kys41sPt8HBcUf2L6va/euf1DOL+xai5oMOSrD6s+/cPMskDByNrelISOcTjCtaRMvt9x0XzXFxUiiVgN9HKXl3G0duRY7DGWnF1i6XBEORwJTyQ9W089R5uCS/vTEyDhgvqxf0fLBFcHGacnVx2LIiUzx8LR1GwHriSQlq3H08kG/z4TofOjgALLHoekyMq/YMwJSItVt4sK7Hb746vQwRi1rKCq+4cVq/kIAMY6HgHg660XuJ6caf44RJlJIlYD+Tj68HSHpwH49NCnxGXEWTgiUVbG+rAeIfXQavP95WysD6vXWDbxNaNODd0J8XIkI0fPymNRt3+CKJFxW6O+Tb3U7+3QuWrtVno8LHkU9JWc6BqnJYP7gJVN5Z67DDJyMzgWdwwwY6F+fk1DQaPDJfkcQ/3SSc/W8+E6KV+pCSQRq6EmNJtAy3otSclOYd6BeZYOR5TR7hLrw/KmJaU+zKw0Go1pVOwPmZ6skG2mbY3yiuat7WD8T+q+muH/wvrXIacSR2rO5+sfZkHHYo+Ra8jF28GbQOdA8wdg7w5BvQF4NUSdllxyKIITkUnmj0WUiSRiNZROq2N299loNVpWXVzFnmt7LB2SKKW0rFwOX00EoHdJhfr+ncwblOCejgFYaTUcDU/kTHSKpcOpkaKTMjkdnYJGA32b5Fu96NEI7v5K/XjvNzA3AL4dAKtfguNL4MZldaP7sspOUzvqQ92tD8svb3oyIHozd7XzQ1GQdhY1gCRiNVgrz1ZMaDYBgHf3vkuWXhpS1gT7LiWQa1AI9LAn0MPh5gMGQ77WFTIiZm6eTrYMaqE2H5Wi/fLZdvY6AO0C3HB3LDRN2PIuGPIuOHqBIQeuHYJ9/4Wlk+GzdjCvCfx+H+z4GC7tgKzU21/w8k71XG4NLN5zz1z7S95SXj8xwvcyq289bK207L2UwLqTMZaLSdyWJGI13NMdnsbL3osryVf43/H/WTocUQrG+rAio2EJFyAzUd0ixqeV+QMTTOjSAIC/DkeQlSvL/8tqa960ZImbfPecDi+cg2eOwZj/Qbcn1dFfrbVacH9mNWx6C34aAf8JhG96wz/PwpHfIO6c+sdKfvm76Vuw515mbibHYi1YH2bkGgB+HQCF+tGbmdKnEQBz14TJ93M1ZnX7Q0R15mTjxMtdX+aFbS/w/fHvGRo8lGDXYEuHJW5hZ4n7S+bVh9VvDzpr8wYlALXA3NfFjujkTDacimFEWz9Lh1Rj5OgN7Dynfm+b6sOKo9GAe0P11mZs3pMzIfoYhO9Tp+cjDkByBMQcV28Hf1SPs3NTGx0HdFX/NbbEsPC05PG44+QYcvCy96KhS0OLxkLz4XDtMJxexVNjH2TRgXCuxKfz8+4rTOnbyLKxiWLJiFgtMKThEHr79ybHkMO7/74r9QDVWFxqFqfz6o96htQr+KCpkav0D7MUnVbD2E5qI06Zniybw1cTScnKxd3Bmrb5t+wqDWs7COyqjpiN/wlmnoSZYTB+IfR8Ghr0UEeKMxPV5Gvre/DLPRB/HjQ6CO5bFZ9SqVWL+jCj5iPVfy9uxZEMXhzSDIDPN58jPlXKV6ojScRqAY1Gw6vdXsVWZ8ve6L2svLjS0iGJEuzJ2+S7RX0X6jnZFnww0rhiUhIxSxrfWV3xtvN8HBE3ZE/X0jLWh/Vt6oVOWwnJiItfXl3ZO/DoWpgVAY9vVTcVbzMe3PNG/psPB3u3il+vAoz1YWbd1qgkXs3AIwT02XBuA2M6BdCyvgspmbl8uvGcpaMTxZBErJYIcA7gyXZPAjDvwDySsmTJcnVkrA/rVXg0LDsdotU96qRQ37Ia1HOgZ0g9FAWWHJSNwEtra+G2FZVNZ63WP3WdAmO+g2eOqMnZ+J+r5nqllKXP4uj1o0A1ScQ0GtPek5xehU6r4fURLQH4bd9VzsXIiuDqRhKxWuThlg8T4hpCQmYCnxz8xNLhiGKUuL9k1FFQ9ODkCy7+FohM5GfsKbb4QAR6g0z13871lExOXksG1BExs7F1tmiRPsDx2ONkG7KpZ1ePYJdqUp/bIm968tx6yM2mR0g9hrT0QW9QeGdVmGVjE0VIIlaLWOuseb3H6wAsPbeUI9ePWDYgUcDV+HTCEzKw0mroGlyoa37++jBL15gIQlv54mJnRWRihmkUU5Rs+1n1a9TG3xXPwlPutVz+aUmL14cZ+XcGR2/ISobL2wH4v2EtsNZp2HY2li1nrls4QJGfJGK1TCefToxuPBqAt/a8RbY+28IRCSPjaFiHBm442hZasCz1YdWKnbWO0R3UkclFB6Ro/3aM2xqV2LaiFjsQnZeIWbJtRWFa7c2eYqdXARDk6cgjPYMAeHdVGDl6QwlPFuYmiVgtNLPTTNxt3TmfeJ45e+bIKspqwlQfVnhaEmRro2pofN705IaTMSSkyR80JdEbFHacq+L6sGoqW5/N0Vi1PsyijVyLY1w9eXq1qf/a9IFN8HC04fz1VH7fd9WCwYn8JBGrhdzs3PhP3/+g0+hYcWEFP5+ybDGrAINBYXfeiskiiVjyNUiOBI1W7SEmqoVWfq609nchW2/gr8ORlg6n2joSnkhieg4udla0D3SzdDhmdSLuBJn6TDzsPGjkWs16dAX3ARtnSI027djham/Nc4ObAPDJhrMkZVTyBuyiXCQRq6V6+vXkxS4vAvDxwY/ZHrHdwhHVbaejU0hIy8bBRke7wj2WjKNh3q3A1snssYmS3ZvXaf/P/eEyslwC47RknyZeWOnq1q8UY31YJ59O1ac+zMjKFpoOUT8+fbOl0X1dG9DE24kb6Tl8u/2ChYIT+dWtn5o65v7m9zOmyRgMioGXt7/MxcSLlg6pztqdVx/WLdgDG6tCP3am+jDZ6Lu6uaudH7ZWWs7EpHA0QlrCFGdbXuF3vzpYH5a/kWu1lK+NhZGVTssLoWqT1x92XuZ6SqYlIhP5SCJWixkbvXby6URqTirTN08nMTPR0mHVSTulPqxGcrW3Zlib+gAs2i81NYXFp2ZxLFJNUOtafViOPqf61ocZNb4DdDYQfw5iz5juHtLSh/aBbmTk6Plq83kLBihAErFaz1pnzSf9P8HfyZ/wlHBe2PYCOQapCzCn7FwD+y4lAMXsL6nPVfeFA3XJuah2jJ32/zkaRXp2roWjqV52nItDUdSdInxc7CwdjlmdjD9JRm4GbrZuhLiFWDqc4tm5QHA/9eN805MajYaX8kbFftt3lfAE2UHCkiQRqwPc7dz5fODnOFg5sDd6L+/ve9/SIdUpRyMSSc/WU8/Rhua+zgUfvH4KctLB1gU8m1omQHFL3Rt5EFTPgdSsXFYdi7J0ONVKnW5bEXOzbYVWU41/lRqnJ8MKbn3Xs7EnvRt7kqNXZOsjC6vG3z2iMjV1b8p/+vwHDRoWnVnEotOLLB1SnbHznDot2SOkHtrCe/AZ68P8O6q9f0S1o9FoGJc3Kvan9BQzMRgUtp+tm20rIF99WHXY1uhWmg0DNHDtECQVXP1rrBX763CEbH1kQfLOX4cMaDCAGR1nADB331z2Re2zcER1w+6StjUCqQ+rIcZ2CkCrgf2Xb3D+eqqlw6kWTlxLIj4tGydbKzo1dLd0OGaVY8jh8HW1pKDaFuobOftAYFf14zOrCzzUPtCN0FY+GBSYt/5MMU8W5iCJWB0zufVkhjcajl7RM3PbTMKT5S/8qpSWlcvhq4kA9L5VIib1YdWaj4sdA5t7A7BYRsWAm5t892pcD+s61rbiVPwpMnIzcLV1pYl7E0uHc3um1ZMrizz0wpBmaDWw7mQMR8MTzRuXACQRq3M0Gg1v9XyLNp5tSMpKYvrm6aRmy1/4VWXfpQRyDQqBHvYEejgUfDAjEeLy/gqVrY2qPWPR/tJDEbI9DLA1r21F/2beFo7E/IzbGnXy7lS968OMmo9Q/728EzJuFHioiY8zozsEAPDhOhkVs4Qa8B0kKputzpbPBnyGt4M3F5Mu8tL2l9Ab9JYOq1YybmtU7GjYtUPqv+5B4FjM46JaGdDcG08nW+JSs9l8um5vmpyYns2RvNGTOlkfFlND6sOM6oWAVwsw5MK5DUUefnZwE6x1Gnaej2O3bHJvdpKI1VFeDl58PuBzbHW27IjcwWeHPrN0SLWScVujIm0rQKYlaxhrnZYxnfI2At9ft6cnd5yLw6BAUx8n/NzsLR2OWeUacjkco9aHVdv+YcUxrZ78p8hDgR4O3N9V3UXi/XVnZBcJM7NIIvbVV18RFBSEnZ0d3bp1Y9++WxeNL168mObNm2NnZ0ebNm1YvXr1LY8XpdPKsxXv9HoHgB9P/siK8yssHFHtkp6dy+noZEDtqF+EFOrXOPfmTU9uPXOd6KS625F8Wx1eLRkWH0Z6bjrONs40casB9WFGxkTs/CbIySjy8LSBjbG31nE0PJENp2LMHFzdZvZEbNGiRcycOZM33niDQ4cO0a5dO0JDQ7l+vfih/t27d3PfffcxefJkDh8+zKhRoxg1ahQnTpwwc+S1053Bd/JE2ycAeGvPWxy5fsSyAdUiJ68lY1DA18UO78LNLhUFItTpDakPqzkaeTnRNcgDg6LWitVFBoOSr39YHawPy7e/pE6rs3A0ZeDXAVz8IScNLm4r8rC3sx2TegUB6gpKvUFGxczF7InYxx9/zJQpU5g0aRItW7Zk/vz5ODg48MMPPxR7/Geffcadd97Jiy++SIsWLXj77bfp2LEjX375pZkjr72mtp/KoAaDyDHk8MyWZ4hKlaaVleFY3t6EbQJciz544xJkJKjbj/i2MXNklU9v0PPS9peYvG4yf1/4m8zc6jFalGvIZfPVzUzbNI3H1j1GUlbF94sc30UdFVt8oG5uBB4WnUxsShYONjo6B9WtthVQA/aXLIlGk2/1ZNHpSYAn+obgYmfF2ZhUVhyJLPYYUfnMmohlZ2dz8OBBBg8efDMArZbBgwezZ8+eYp+zZ8+eAscDhIaGlnh8VlYWycnJBW7i1rQaLe/1fo9m7s1IyExgxpYZpOfIlhcVdSwiEYC2/sUkYhEH1X9924KVrfmCqiJHYo+w5tIa9kXv49WdrzJo8SDe3/c+F5Mss9F8dFo0Xx/5mtCloTyz5Rm2R2xnb/Refjr5U4XPPbS1L/bWOi7Hp3M8su5tBG5sW9EzpB62VjVoRKgS5BpyTf3DalR9mJExETuzBopZoOXqYM2T/dXtmj7ZeJbsXFkdbA5mTcTi4uLQ6/X4+PgUuN/Hx4fo6OhinxMdHV2m4+fOnYurq6vpFhgYWDnB13IO1g58PvBzPOw8OJ1wmtd2vYZBkR/CijieNyLWNtCt6IOmacka+GZejC1XtwDQxL0Jfo5+JGcn80vYL9y9/G4eWfsIqy6uIlufXaUx6A16tkds5+lNTxO6NJRvjn7D9fTruNu6c0fDOwD47fRvFR4Vc7S1YnBL9T1pxZFrFY67pjHVh9XBackzCWdIzUnF2dqZZu7NLB1O2TXsBXZukB4P4XuLPeSRnkF4OdsSnpAhG92bSa1bNTlr1iySkpJMt/Dwur26qSz8nPz4dMCnWGmt2HBlA/OPzrd0SDVWUkYOF+PSAGhT3IiYcWujWlAfpigKm8M3A/Bk2ydZfc9qvh70NQMCB6DVaDkYc5BXdrzC4MWD+ejAR1xJvlKp17+efp3/Hv0vQ5cNZdqmaWyN2IpBMdDFtwsf9P2AjeM2Mq/fPJq6NyUtJ42FpxZW+Jp3t/MD4J+j1+pULU1yZg4Hr6h9qPrXwUJ9Y31YR5+ONas+zEhnDU3vVD8OK9rcFcDBxoqnBzYG4PPN52WjezMwayLm6emJTqcjJqbgioyYmBh8fX2LfY6vr2+Zjre1tcXFxaXATZReB+8OvNHjDQC+OfoN6y6vs3BENdPJvCmrQA97PBxtCj6YkwlRx9SPa0EidiHxAuEp4Vhrrenl3wudVkefgD58PvBz1o1Zx9R2U/Fx8OFG1g0WnFzAiL9G8Ni6x1h3eR05+pxyXdOgGNgduZtntzzLkCVD+PLIl0SlReFq68pDLR9ixagV/BD6A0ODh2Kjs0Gr0fJkuycB+DXs1wqPivVt6oWrvTXXU7LYezG+QueqSXadi0NvUGjk5Vi0QXEdUGPrw/LL32W/hBrHCV0aEOhhT2xKFgt2XzZfbHWUWRMxGxsbOnXqxKZNm0z3GQwGNm3aRI8ePYp9To8ePQocD7Bhw4YSjxcVN6rxKCa2nAjAaztf41T8KQtHVPMcy0vE2vq7FX0w+jgYcsDBE9wamjewKrAlXJ2W7F6/O47WjgUe83X05an2T7F2zFo+H/A5ffz7oEHD3ui9vLDtBQYvGcynBz8lPKV0I9dxGXF8f/x7hi8bzhMbn2DT1U3oFT0dvTvyXu/32DRuEy91eYlGro2KPHdQg0E0dmtMak4qv4b9WqHP2cZKy7A29YG6NT1Zl9tW6A16DsWoTZhrTCPX4jQeBFZ2kHgFrhZfa21jpeW5wU0BmL/1AkkZ5fuDSZSO2acmZ86cyXfffcdPP/1EWFgYTz31FGlpaUyaNAmAiRMnMmvWLNPxzzzzDGvXruWjjz7i9OnTvPnmmxw4cIDp06ebO/Q6ZWanmfTy70WmPpMZm2cQlyHdlsvCWKhf7IrJ/PVhGo35gqoixkRsQIMBJR5jpbViQIMBfD34a9aOWcvjbR/H096ThMwE/nfifwxfNpwnNzzJpiubyDEUfNNXFIW9UWridseSO/js0GdEpEbgbO3M/c3v56+7/uKnoT8xMmQktrqSFz7kHxX75dQvJGdXbCHP3e3V6ck1J6LIyq39O1MoimIq1K+LbSvO3jhLSk4KjtaONPdobulwys/GEVqPVT9eMQ2yit/i7u72/jT1cSI5M5dvt18wY4B1j9kTsXvvvZd58+Yxe/Zs2rdvz5EjR1i7dq2pIP/q1atERd1sn9CzZ09+++03vv32W9q1a8eSJUtYvnw5rVu3NnfodYpOq+PDvh8S7BpMTHoMz2x5hix9lqXDqjGMrSvaFpeImerDOpkxoqpxPf06x+OOA9A/oH+pnuPn5MfTHZ5m/dj1fNL/E3r69URBYde1XTy79VlCl4TyxeEvOJNwhgUnFjBy+UgeW69OZeYacmnr2ZY5PeewafwmZnWbRWP3xqWO946Gd9DYrTEpOSkVHhXrGuSBr4sdyZm5bMtLUGqzszGpRCdnYmulLb5BcS1nnJbs4N0BK62VhaOpoNB31J5iCRdh/avFHqLTanh+iLog4Yedl7meUj1a0tRGFinWnz59OleuXCErK4u9e/fSrVs302Nbt25lwYIFBY4fN24cZ86cISsrixMnTjBs2DAzR1w3Ods488XAL3CxceFY7DEeXvMw/1z4p9r0iKpsiqLw08mf6L+of4Vq4+JTs4i4oXaubl1s64ras2Jya/hWANp6tcXLoWzTVdZaawY3HMx/7/gvq0ev5tHWj+Jh50FsRizfHvuWsf+M5aODanG/o7Uj9za7lyUjl/Dr8F8Z3WQ09lZl31pHq9GaGhgvPLWQlOyUMp/DdC6thpHt8qYnj9b+6UnjJt89QuphZ10DC9UryFioXyPbVhRm7w6jvlE/PrhAbWdRjCEtfWgf6EZGjp6vNp83X3x1TK1bNSkqV0OXhnzU/yNsdbacjD/J/+38P4v3iKoKmbmZzNo5i3kH5hGfGc+qi6vKfS5jb6lGXo642FkXfDD1OiReBTTg17ECEVcPxtWSAwJLnpYsjUCXQJ7r9Bwbx27kw34f0s1X/eOsZb2WvNHjDTaP28xr3V+jmUfFWwbc0fAOGrk2IiU7hd/CfqvQue5ur+49ufFUDKlZtXt1WV2uDzMoBg7GqL3/anShfn6N+kGPvBKfv5+G1KKjuhqNhpdC1Z+53/ZdJTxB+ktWBUnExG11r9+dNfes4ekOT1PfsX6BHlGT1k5i9cXVVd4jqipFpUYxcc3EAsnXuRvnyn0+U/+wYkfD8qYlvZqDXc1e0ZuancreKLUX0cDAgZVyTmudNXcG3cn3od9z+KHDLBqxiLFNx+JgXXkr9HRanWlU7OdTP5OaXXyNTGm08nOhkZcjWbkG1p8svrdhbZCalcv+ywlA3awPO3fjHMnZyThYOdCiXgtLh1N5Br4O3q0gLVZNxopZRdmzsSe9GtcjR6/w6cbyvy+KkkkiJkrFy8GLx9s+zpp71vDVoK/oH9gfrUbLgZgDvLzj5SrrEVXVDkQfYMKqCYQlhOFu685H/T4CICI1oty7Cxw1bW3kVvTBWlQftvPaTnINuQS5BBHsGlzp56/KOpzQoFCCXYNJzk7m99O/l/s8Go2Gu9upo2K1efXkngvx5OgVGng4EFSv7rat6ODdAWut9W2OrkGs7eCeb9Wt1s6uUacpi/FiqLo44a/DEZyLKf90viieJGKiTHRaHX0D+vLFwC9YN2YdT7V7Cm8H74I9otZXrEeUOSiKwh+n/2DK+ikkZCbQ3KM5f4z4gyFBQ/CyV6deziWW76+/45GJALS73YrJGs7YTX9A4AA0NWz1p06r4/G2jwPw06mfSMtJK/e57spbPbnzfBzxqbVzQYuxPqx/M68a91pXBmN9WI1uW1ES39YwaLb68br/g/iiKyTbB7oR2soHgwIfrT9r5gBrP0nERLn5Ovoytf1U1o1ZV7BHVFT5ekSZS7Y+mzf3vMm7e98lV8llaPBQfh76M35O6i/UJu5NgPJNT8YkZxKTnIVWAy39Ck09GvQQqe5Th3/NfkPPMeSwI2IHcOu2FdXZnUF30tClIUlZSRUaFQv2dKRtgCt6g8Lq41G3f0INoyiKqT6sf7O6WR9mSsRqS31YYd2nQVAfyEmHZY+Dvmi94wtDmqHRwNqT0RwNTzR/jLWYJGKiwvL3iFozZg1T2kwpdY8oc7uefp1J6yax7NwytBotMzvN5P0+7xdYgdfErfyJmLFtRVMfZxxsCk2txZ6B7BSwdgTvml1nciD6ACk5KXjYedDWs62lwykXK63VzVGxkz9VaKP7u/K2PKqN05MXYtOIuJGBjU5L90b1LB2O2Z1PPE9SVhL2Vva08mxl6XCqhlYLo+eDrataPrFjXpFDmvg4M7qDOg3/4boz5o6wVpNETFQqfyd/ZnScYeoR1aN+j2J7REWlmn/k4GjsUSasnMCx2GM42zjz9aCvmdR6UpGplqYeakfpszfKPgR/3NjI9Vb7S/p3hJq4T10+xiau/QP718w99/IMCx5GA+cGJGYlsujMonKfZ2Q7PzQaOHDlRq1bWWacluzWyKPoHxd1gLE+rL1X+9pVH1aYawAMV2tk2fbBzYVF+Tw3uCnWOg07z8ex+7w0+a4skoiJKmHsEfXtkG+L7RF157I71Q2aw7eiN1R9V/K/zv3FpLWTiM2IpbFbY/4Y/ge9/HsVe6xpRCzxHEoJe7GV5OitGrka68P8a3ahvqIopkSsslZLWoqV1oopbacAsODkgnKPivm42NEjb7Ton2O1a1SsLretAExtK2pF/7DbaTtO7bqv6GHZlCJd9wM9HLi/awMAPlh3pszvj6J4koiJKlegR1TfD+nq2xWDYmB7xHae3vw0dy67k2+OfkNMWsztT1ZGOYYc3tv7HrN3zybHkMOgBoP4ZdgvNHBpUOJzGrk1QqfRkZSVRGxG6TumK4pi6iHWtrgVkxHqG3pNL9QPSwgjOi0aeyt7utXvdvsnVHMjGo0gwCmAhMwEFp9dXO7zGLc8+rsWTU9mZOvZe8nYtqLuJWKKonAguhYX6hdn+Lxbdt2fNrAx9tY6joQnsuFU5b9n10WSiAmzsdZZc2fwnfwv9H/8M+ofHm75MG62bkSnRfP1ka8JXRrKjM0z2BGxo1JGyRIyE3h8/eOmQuxp7afxcf+Pi2xMXZitzpaGLupm3GWZnoy4kUFCWjbWOg3N6zsXfDArBa7nbZ4eULPf0I2jYT39emJnZWfhaCouf63YDyd+ICM3o1znubNVfax1Gk5Hp3AmumYv8U/JzGH9yWhmLTtGdq4Bfzd7QrycLB2W2V1IvMCNrBvY6exoXa+ObKt3m6773s52TOoVBMC89WfQG2RUrKIkERMWEeQaxAtdXmDjuI3M7TOXjt4d0St6toRvYeqmqQz/azjfHfuu3JuNn4o/xYSVEzgQcwBHa0c+H/A5T7Z7Eq2mdN/y5Vk5aRwNa+7rgq1Vobqpa4cBBVwDwdm31OesjjZfVbvpD2xQs6cl8xsRMgJ/J391VOxM+UbFXB2sTc1O/z4aWZnhVblcvYGDV27w2cZzjP1mN+3nbODxhQdZnje6d2dr3zrZtmJ/jFpO0M67Hda6WlwfVlj+rvsrpqs7guTzRN8QXOysOBuTyoojNet7vTqSRExYlK3OlhGNRvDT0J9YfvdyHmzxIM42zkSmRvL54c+5Y/EdzNw6kz3X9mBQDKU656qLq3h4zcNEpUXR0KUhvw37rcwtFsqzcvKYqZFr7a0Pi0iJ4OyNs+g0Ovr697V0OJXGWmvNlDZqrdiPJ38s936qxunJFUeuVfv6mSvxafzy7xWeWHiADm9vYMw3u/lk41kOXLmB3qAQVM+Bh7o35NuHOvHK0OaWDtcijNOSXXxqdjlBuRi77qfHFem67+pgzZP9QwD4ZONZsnNL994silf3lsCIaivELYSXu77MMx2fYd3ldSw+u5ijsUfZcGUDG65sINA5kLFNxzKq8Sg87DyKPF9v0PPpoU9ZcHIBAH38+/Cfvv/BxabsWwk1dS/7ysljeSsmi9/aqHbUhxmnJTt4d8DNzs2ywVSyu0Lu4ttj33It7RpLzi7hwZYPlvkcg5r74GijI+JGBoeuJtKpoXsVRFo+SRk57LkQx/Zzcew8F8fVQqs7Xeys6N3Ek96NvejTxJNAj7rXQT8/RVFqdyPX27G2gzHfwbf94exadZqy8yTTw4/0DOKHnZcJT8hg0f6rPNQjyFKR1niSiIlqx87Kjrsb383dje/mTMIZFp9dzKqLqwhPCeeTg5/w5eEvGdxgMOOajaOzT2c0Gg1JWUm8tP0ldl/bDcBjbR5jevvp5W6tYJyavJh0kRxDzm2XrRsMtyjUV5R8HfVr9hu6abVkLZqWNLLWWfNY28eYs2cOP5z4gXHNxmGrsy3TOextdIS28mXZ4Uj+PhJp0UQsR2/gSHgiO87GsuN8HEfDE8lfzmOl1dCxoTt9GnvSp6kXbfxd0Wnr3vRjSS4lXSIhMwFbnS1tPNtYOhzL8Gmldt1f/5radT+4L9RTR8IcbKyYMagxs1ec5PPN5xnTKaBOtjepDPJVE9VaM49mvNb9NWZ2msnay2tZfGYxJ+JPsObyGtZcXkOQSxB3hdzFsnPLiEiNwN7Knjm95nBn0J0Vuq6fkx+O1o6k5aRxNfkqIW4htzz+cnwaKZm52FppaeJTqKg5KRzSroPWCuq3q1BclpSYmcihmEOAuq1RbTQqZBTfHfuOqLQolpxdwgMtHijzOUa292PZ4UhWHY/i9REtsdKZtwJkU1gMv+8L59+L8aRmFeyQHuLlSJ8m6ohXt0b1cLKVXwElMfYPa+fVDhudjYWjsaDu0+DsOri8Q21p8eg6yKuXm9ClAd9uv0jEjQwW7L7M1P6NLRxszSQ1YqJGcLB24J4m9/D7iN9ZNGIRY5uOxcHKgcvJl/n88OdEpEbg7+TPwqELK5yEAWg1Whq7qW8qpZmeNI6GtfRzwbrwL17jaJhPa7C2p6baHrkdvaKniXsTApwDLB1OlbDWWfNYm8cA+OH4D2Tpy753ZO/Gnng42hCXms3uC/GVHeItHbxyg8k/HWBjWAypWbm4O1gzom19PhjTlt2vDGTT8/15865WDGrhI0nYbdTpacn8CnTdPwjbb3bdt7HS8txgtYxj/tYLJGVU3/2FqzNJxESN07JeS97o8Qabx2/m9e6v09arLQMDB/LH8D9o5tGs0q5TlpWTxkL9drW4f5hxk++a3sT1dkY1HoWPgw/XM66z7NyyMj/fWqdleJv6gHm3PMrVG3ht+QkABrfw4Z/pvTn42h18eX9HxncJxM+t5v4RYG6KophGxGrt/pJl4RoAIz5WP97+YYGu+6M6+DO4hTdvj2qNsyT35SKJmKixHK0dGd9sPL8O+5XPBn5W6cXjxoL90iViiUAJWxvVgvqwzNxMdl3bBdTcTb5Ly0ZnYxoV+9/x/5Gtzy7zOYyrJ9edjCYzp+p3jgBYsPsyYVHJuDlY88HYtrQJcEUrNV/lcjn5MvGZ8dhobWjrVTP3Uq10bcYW23Vfp9Xw/cNduLu9v3y/lZMkYkKUwNjC4nZTk3qDwonIZADaBRZKxHKzIeqo+nENHhHbG7WXjNwMfBx8aOnR0tLhVLl7mtyDt4M3Mekx/HXurzI/v2MDd/zd7EnNymXz6eu3f0IFRSVl8MkG9fv0lTub4+FYh2uaKoFxWrKtV9syL9io1W7TdV+UjyRiQpTAODV5Le0aqdmpJR53/noqGTl6HG10BHsWKtSPOQ76LLVbtUejqgy3ShlXSw4IHFAnGnva6GyY3HoyAN+f+L7Mo2JarYaR7Yw9xaq+4eWcf06Rlq2nYwM3xncOrPLr1Xamacm6Xh9WWOGu+6dXWzSc2kISMSFK4Grrio+DDwDnE8+XeJxxWrJVccv/jfVh/p2hhiYweoP+ZiJWy6cl8xvTdAze9t5Ep0Wz/PzyMj/fOD255XRslRYxbzlznTUnotFpNbw7uo1MD1WQoigcjM7b6LsuNnK9nfxd9/9+ukjXfVF2kogJcQvGUbFbTU8aV0y2K66jfmReUWsNrg87HnechMwEnKyd6tQvJludLY+2eRSA749/T46+bMlUc19nmvo4ka03sO5kdFWESGaOnjdWnATg0V5BtKhf9ubFoqCrKVe5nnEda6211IeVZNDsErvui7KTREyIWyhNInbUtLWRW8EHFAWu7lE/9q+5idjmcHVvyT4BferWfnvAmCZj8LT3JCotihUXVpTpuRqNhrvb+wPwdxWtnvxqy3muJqRT39WOZ/PaCIiKMW5r1MazTa3Y1L5KWNmqXfd1Nje77otyk0RMiFu43crJ7FwDYVF5hfqFR8SuHYLEq2BlDw26V2mcVamutK0ojp2VHY+2zjcqZijbqNhdeXViuy/EcT25fPtXluT89VTmb7sAwBsjW+IorQMqhXGjb6kPuw1j131Qu+7HX7BsPDWYJGJC3EL+zb+L28T5bEwK2bkGXOysaFB4b77jS9V/mw8DW6ciz60JLiZd5HLyZay0VvT2723pcCxibNOx1LOrR2RqJP9c+KdMzw30cKBDAzcMCqw8FlVpMSmKwuvLT5CjVxjQzIvQVr6Vdu66TFGUmxt9+9adafhy6z4NgvpATrra0qKM0/dCJYmYELfQyLURVhorUnJSiEmPKfK4sZFr2wC3gqsJDXo4kZeItRlnjlCrhHE0rJtvN5xsamYyWVH2VvZMaq1udvztsW/LPCp2t3H15NHKm55cceQaey7GY2ulZc7drevESlZziEiJICY9BiutFe28au52ZGZj7Lpv5wq2zpCVYumIaiRJxIS4BWudNUGuQUDxdWLHIxMBaFt4WvLyTkiNBjs3CBlUtUFWofxtK+qycU3H4WHnQWRqJCsvrCzTc4e39UOrgaPhiVyOS6twLEkZObyz6hQAMwY1IbDwSKwoN2P/sDaebbC3kp0ISsU1AB7bBA/+BQ4elo6mRpJETIjbuFXB/tFw44hYoUTsxBL135Z3g1XNbK4ZlxHHsdhjAPQP7G/ZYCzMwdqBR1o9AsB3x78j15B76yfk4+VsS6/GngD8UwmjYvPWnSEuNZsQL0em9Km5vemqI9nWqJw8m6ijY6Jc5CsnxG2UVLCfmaPnbIw6FN82/4rJ3Cw4lbfCrs1Yc4RYJbaGb0VBoXW91vg4+lg6HIu7t9m9uNu6E54SzupLZWtkaVw9ufxIZLG1hqV1JDyRX/ZeAeDtUa2xsZK38MqiKIps9C0sQn6KhbgNUyKWWDAROxWVTK5BwdPJhvqu+Za5n98EmUngXB8a9jJnqJWqLjZxvRUHawcebvUwoNaKlWVULLSVDzZWWi7EpnEqb5VtWekNCq8tP46iwOgO/vQM8SzXeUTxIlMjiUqLwkpjRXuv9pYOR9QhkogJcRvGlZOXEi8VaOp53Ng/zN+1YLH08cXqv63HgFZntjgrU3pOOv9e+xeQ+rD87mt+H262blxJvlKmFZTOdtYMau4NlL+n2MI9lzkRmYyLnRX/N6xFuc4hSmYcDWvl2QoHa6m7E+Zj1kQsISGBBx54ABcXF9zc3Jg8eTKpqSXv4QfQv39/NBpNgduTTz5ppoiFAF9HX5ytnclVcrmUfMl0f/4VkyZZqXBmjfpx6zFmjLJy7bq2i2xDNoHOgTR2a2zpcKqN/KNis3fP5uE1D7Pq4qpS7UVp3PLo76PXMBjKNj15PTmTj9arNYov3dkcL2fZiLqy7bmmNl+W+jBhbmZNxB544AFOnjzJhg0bWLlyJdu3b+fxxx+/7fOmTJlCVFSU6fbBBx+YIVohVBqNxlSwn79OzLjHZIFC/TOrITcDPELAr4M5w6xUm6+q3fTryibfZfFAiwcYFjwMnUbHoeuHeGXHKwxaPIh5++dxOelyic/r38wbZ1sropIy2X85oUzXfHtVGClZubQLdOP+rg0q+BmIwtJz0k1T8XV9YYowP7MlYmFhYaxdu5bvv/+ebt260bt3b7744gv++OMPrl279VC9g4MDvr6+ppuLi+ynJsyr8MrJtKxczseqo7lt8idixmnJNmNr7CbfOYYctkdsB2Rasjj2Vva83/d91o1Zx9T2U/Fx8CExK5GfTv3EyOUjeWzdY6y9vLbI3pR21jrubK02Xv27DKsnd5yL5Z+j19Bq4N1RrWVT7yqwNXwrGbkZBDgFSP8wYXZmS8T27NmDm5sbnTvfHPYdPHgwWq2WvXv33vK5v/76K56enrRu3ZpZs2aRnp5e4rFZWVkkJycXuAlRUfk77AOciExCUaC+qx3eznmF+mnxcEEdSaJ1zV0teTjmMMnZybjbutPeu72lw6m2fBx9eKrdU6wbs44vB35J34C+aNCwN3ovL257kcFLBvPJwU8ITwk3Pce4enLV8Siycw23vUZmjp7Xl58AYGKPIFr7F7OxvKgw4yrYYY2GyQiwMDuzbU4WHR2Nt7d3wYtbWeHh4UF0dHSJz7v//vtp2LAhfn5+HDt2jJdffpkzZ86wbNmyYo+fO3cub731VqXGLkRTj4IrJ49H3izUNzm1HAy5UL8deNXcDZiNm3z3DeiLlVb2L7wdnVZHv8B+9AvsR1RqFEvPLWXZuWXEZsTyw4kf+OHED/T068m4puPoHdwXTydb4lKz2Hk+loHNb90WZP62C1yOT8fb2Zbnh9Tc76nq7EbmDXZF7gJgePBwC0cj6qIKv8u+8sorvP/++7c8JiwsrNznz19D1qZNG+rXr8+gQYO4cOECISEhRY6fNWsWM2fONP0/OTmZwMDAcl9fCMBUsB6dFk1SVpKpUL9doNvNg47nNXGtwaNhiqLc3OS7Qd3b5Lui6jvVZ3qH6TzR7gm2h/9/e/cd31S9/3H8laTp3qV00c0oq+yWgiBQZCkXruhPXIDiZHjBvQAVuFzx3p8I4t5XRC/yAxEVBQo42MUylNEBlE5aSiedyfn9EVrkgqUjyUnbz/PxyMM0OTl510PIh+/8kTUn1rAza2fdzdfJl/BO13HucGe+SsqqtxA7lV/GG9tNGynPH98NN0e9tX6NNuWHUz9Qo9TQ1bsrEZ6yQK6wvmYXYo899hjTpk2r95iIiAj8/f05e/bsZY/X1NRQUFCAv3/DN6yNjY0FICUl5aqFmIODAw4OMqNImJebvRsBLgFkl2WTUpjCoQxTl3ddi1hRBqTvBDQterbkifMnyCrLwlHnSFxgnNpxWiy9Vk98aDzxofFklGSwNnkt65LXkVeeRx7rcOmoYUtBFzalPcjIsGFXtDwqisK8r45QVWNkSKd23NgzQKXfpPWr7Za8MUJaw4Q6ml2I+fr64uvre83j4uLiKCwsJDExkX79+gGQkJCA0WisK64aIikpCYCAAPmLSVhXZ6/OZJdlcyj3GKfOeQJ/KMRqN/gOHQweQeoENIPa2ZIDAwfKXntm0sGtA3/r+zdm9JpBwpkE1hxfw56cPWhcjvHET3PxS/RjUudJTO02tW79qo2HsvkpOR97Oy0LZVNvi8kqzeLA2QNo0DAmbIzacUQbZbXB+l27dmXMmDHcf//97N27l19++YVZs2YxefJkAgNN6+tkZmYSFRXF3r17AUhNTWXhwoUkJiZy6tQpNmzYwJQpUxg6dCjR0dHWii4EcGnm5L6s3wAI8XbGy+XiPpJ1syVbbmsYXFpNf0SwdEuam16nZ3TYaN4b/R43+66g6txQ7HAl90IubyS9wV3f3UVGSQYlFdUs3Gja1HvGsEjC2rmonLz1qm0NG+A/QLbxEqqx6jpiq1atIioqivj4eMaNG8d1113HO++8U/d8dXU1x48fr5sVaW9vz5YtWxg1ahRRUVE89thjTJo0ia+/bviK1kKYS93MyYsD9uuWrcg7DjmHQWsH3SaqlK75skuzOVpwFK1Gy/XB16sdp1W7q18/Ks+Oo+TEM8yLWYSPow/J55OZ/M1knvpmLWdLKglv58JD1185/EKYzzdp3wDSLSnUZdUpUd7e3nz22Wd/+nxYWNhlG+IGBwezY8cOa0QT4ppq95zMqzwFKPSqLcRqB+lHxoOztyrZzKF2tmRv3954O7bc36Ml6OTnRtcAd45mF1NT3JPPb/qcudvmcuTcEX6sWILeaxwv/eVRHPUtc4usluB4wXFSClPQa/WMDB2pdhzRhslek0I0UKhHKHZaOwxUoNGfp2eQJygKHLlYiPW8VdV8zVXXLSmzJa2idsujr5Iy8Xfx5/3RH+JaPRCNxoij/0a+y32VipoKlVO2Xt+cNLWGDe0wFHd7WSRcqEcKMSEaSK/VE+IWDoDOMYceQe6QdQAK0kDvDF3Gqpyw6Yoqi0jMSQRkNX1rGd/LVIjtPVVAdlE5axNzyU6ZAPkT0Gp0fJ32NdM2TSOn7M/XWRRNY1SMfHfStCesdEsKtUkhJkQjeNuFAtDOq8C0rlNtt2SXseDgqmKy5vkp8ydqlBoiPSIJcZe9DK0hyNOJmDBvFAU++uUUSzcdAzQ8NnA679zwNp4Onvx27jdu23gbibmJasdtVQ7kHiCnLAdXvStDOwxVO45o46QQE6IRNNWmZVNc3fLAaIAjF3d4aOndkrKIqyrGX+yefPvHNEoqaugR5M7dcWHEBsSy+sbVdPHqQkFFAfd9fx//Of6fy8bQiqar7ZYcGToSB52sOynUJYWYEI1QXOwDQI1dFpz6GUpzwNHTNFC/haoyVPFz5s+AdEta2409A7C7uIm3RgOLJ/ZEd/HnDm4d+GTsJ4wJG0ONUsPC3Qt5cdeLVBmq1Izc4lUbqvnh1A+AdEsK2yCFmBANpCgKp7JMMyXPV2VRdegL0xPdJoCdvYrJmmdP9h4u1FzA18mX7u26qx2nTfF2sef6zqYFse+KDb18yyzAWe/M0qFLmdN3Dho0rE1ey/Tvp5N3IU+FtK3Dz5k/U1xVjK+TLwP8BqgdRwgpxIRoqNziSvKLHFEMThgxkJZiWgyyxXdLXpwtOTx4OFqN/JVgbX+/uSd//2tPnrux61Wf12g0TO85nZXxK3HTu5GUl8TkjZM5nHfYyklbh9puyTHhY9BpZXkQoT75W1eIBjqYUQhocDCatjBKphLcAiB0kKq5msOoGNl+ZjsAw0OkW1INfu6O3BEbcs01w4Z0GMLqm1YT4RHB2fKzTN00lfUp660TspUoqy6r+/Mu3ZLCVkghJkQDHc4oAiDAybSERbLe3rTBdwv+V/WR/CPklefhonchxj9G7TjiGkLdQ1k1bhXDgodRbaxm3i/z+Mfef1BtrFY7WouwNX0rlYZKwtzD6ObdTe04QgBSiDXL71nFbD2aq3YMYSWHMk2FWDevMABO2Ouh5y0qJmq+2m7J64Kuw17Xcse5tSWu9q68Nvw1Hu71MACrjq7iwc0PUlBRoHIy21e7pdG4iHGykbqwGVKINdFPyXmMW/4TT609TEW1Qe04wsIUReFQRiEA8dpiAJIdnSCgt3qhzCAh3bStkcyWbFm0Gi0zes9g2fBlONs5sy9nH7dvvJ1jBcfUjmaz8svz2Z29G4Abw6VbUtgOKcSaaGCEDx28nMgvrWT13nS14wgLyzhfTuGFavQ6DXF5BwA4q4XCyiKVkzXd6eLTpBWlYaexY0iHIWrHEU0QHxLPqnGrCHELIassi7u/vbtuxXhxue9PfY9RMdKzXU9ZtFjYFKtu+t2a6HVaZgzryLPrDvPWjlRuj7n2YFvRch282BoW66fgnradoCA/MvV2JBcmM8C/ZU6Br13Etb9/f9lrrwXr6NWRz278jKd+fIpfsn7hyR+fZGPaRlz0LmpHuyp/Z39m9Zll9a7w2m5JGaQvbI0UYs0wqV8Qryckk1VUwZr9Z7g7LkztSMJCagfq3+qUCAUGOmmdyKSaE+dPtMhC7EL1BT49+ikgq+m3Bh4OHqyMX8nyX5fzwZEP+DHjR7Uj1ctZ78xDvR6y2vulF6dzOP8wWo2W0WGjrfa+QjSEFGLN4GCn4+Fhkcz76jfe3J7KbQNCsLeT3t7WqLZFLK7c1IrU2bcH2wt+Jfl8soqpmm5l0kpyL+QS5BrExI4T1Y4jzECn1TG331yGBA3haMFRteNcVXZZNv/+/d+8e+hdxoaPJdQ91CrvW7t22MCAgbRzameV9xSioaQQa6Zb+wfz+rYUsooqWHsgg9tjZOxBa2M0KhzJLCaQfNoXHAA0dIocCy20EDtWcIxVR1cB8FzsczjZOamcSJhTf//+9Pfvr3aMq1IUhdTCVHZm7WTR7kW8c8M7Fp+9qCgK36aZFl+Wbklhi6T5ppkc9ToeHBoJwMptKVQbjConEuZ28lwZpZU1TLTfY3ogdDCdA2MBSC5Mxqi0nGtuMBp4addLGBQDo0JHySB9YVUajYbnYp/DXmvP7uzdVplY8Pu53zlVfAoHnQPxIS13T1jRekkhZga3x4TQztWBjPPlrPs1U+04wsxql624xX6X6YGetxDiHoK91p7ymnIyS1vONf/yxJcczj+Mi96Fp2KeUjuOaINC3EN4IPoBAJbuW0pxVbFF36+2W3JY8DCbncAg2jYpxMzAyV7Hg0MjAFOrWI20irUqhzKKiNRkElGTBlo76DYBO60dkZ6mltAT50+onLBh8svzee3AawDM7jOb9s7tVU4k2qp7etxDuEc45yrO8VriaxZ7H4PRwKaTmwBZO0zYLinEzOTOgSF4u9hz+twFvj6UpXYcYUaHMor4i26n6YeOI8HZG4BOXp0AWsw4saX7llJSXUI3n25M7jJZ7TiiDbPX2TNv4DwA1pxYw8G8gxZ5n705e8krz8Pd3p3rgq6zyHsI0VxSiJmJs70d9w0x7UG4IiEFg1FROZEwhxqDkd+yCpmgvViI9bi0pVFnr85Ay2gR25m5k+9OfodWo2V+3Hx0LXh/TNE6DPAfwF8i/4KCwku7XqLGWGP29/j2pGmQ/qiwUeh1erOfXwhzkELMjKbEheHprCctr4xvDmerHUeYQUpeKZ1rUgjT5qLonaHL2LrnOnm2jBaxipoKFu1ZBMDtUbfT3ae7yomEMHm8/+N4OHhw4vyJupm85lJpqGTL6S2AdEsK2yaFmBm5OtgxfbCpVez1hGSM0irW4h06U8SEi92Smi7jwMG17rnarsn0knQqaipUydcQ7x5+lzMlZ2jv1J5ZvWepHUeIOl6OXjzW7zHAtLZddqn5/gG748wOSqtL8Xfxp69fX7OdVwhzk0LMzKYODsPN0Y4TuaV8/1uO2nFEMx3OOMd43aXZkn/UzqkdXg5eGBUjqUWpKqS7trSiND448gEAT8c+jau96zVeIYR1Teg4gb7t+1JeU86SvUvMdt7absmx4WPRauSrTtgu+dNpZu6Oeu652Cr22lZpFWvplJO/0F5TSJXeAyIvX4NIo9HY9IB9RVFYuGshNcYahgQNYWTISLUjCXEFrUbLvIHzsNPYse3MNhLSE5p9zqLKorptnqRbUtg6KcQs4N7BYbg62HEsp4QtR3PVjiOaqLLGQHThZgCqOt8EdlduUmzLhdiG1A3sz92Po86R5wY+Z/EVzIVoqo5eHZnWYxoAS/Yu4UL1hWadb8vpLVQbq+no2ZEu3l3MkFAIy5FCzAI8ne2ZOsi0h9ryhGQURVrFWqLkzHOM0ZhW03fpf/XlHmx15mRhRSH/2v8vAB7q9RBBrkEqJxKifg9EP0CQaxA5ZTm8kfRGs85V2y0pWxqJlkAKMQuZfl0EzvY6jmQWs/14ntpxRBPkJ32Lu+YCBbp2aEIHX/UYW505+eqBVzlfeZ6Onh2Z0n2K2nGEuCYnOyeei30OgE+PfsrxguNNOk9uWS77cvYBMC58nNnyCWEpUohZiLeLPXcPNLWKvbZVWsVaIq/UrwBIbT8K/mTdrUjPSDRoOFdxjnPl56wZ708l5ibyf8n/B8D8uPnotbJ+kmgZhnQYwqjQURiUi3uiGg2NPsd3J79DQaFv+74EugZaIKUQ5iWFmAXdNyQCR72WpDOF/JScr3Yc0RiVJXQp/hmA6m6T/vQwZ70zwW7BgGkDcLVVG6pZtNu0ZtikTpPo076PyomEaJynYp7CRe/CofxDrE1e2+jX13ZLSmuYaCmkELMgXzcH7oy9OFZMWsValKrfNuJIFanGAMJ7Dqr3WFsasP/x7x+TUpiCl4MXc/rOUTuOEI3W3rk9s/vMBmBZ4jLyyxv+j9i0wjSOFhzFTmPHqLBRlooohFlZrRBbvHgxgwYNwtnZGU9Pzwa9RlEU5s+fT0BAAE5OTowcOZLkZPW/7BrjwaER2Ntp2X/6PLvSbKPrSlxbeeLnAGzVDcHfw6neY2sH7KtdiGWUZPD2wbcBeHzA43g6eqqaR4immtxlMt19ulNSXcLSfUsb/LqNaRsBGBw0GC9HL0vFE8KsrFaIVVVVceutt/Lwww83+DVLly5l+fLlvPXWW+zZswcXFxdGjx5NRYXtrmL+39q7O3L7AFPX1fKtLauIbLPK8nHL+gmAU4Fjr7nsQ22LmJozJxVFYfGexVQYKojxj2F8xHjVsgjRXDqtjvlx89FqtHx38jt2Zu285msURZFuSdEiWa0Qe/HFF5k7dy49e/Zs0PGKorBs2TKef/55JkyYQHR0NJ988glZWVmsX7/esmEbQlHgt3Ww7/1rHvrQsEjsdVp2pxWwR1rFbN/v69EqBg4bw2gf3uOah9fOnEwtTG3S4GJz2Hx6Mz9n/oxeq+f5gc/LmmGixevm0407ou4AYPHuxVQaKus9/mDeQTJLM3Gyc2JY8DArJBTCPGx2jNjJkyfJyclh5MhLq4F7eHgQGxvLrl27/vR1lZWVFBcXX3aziJStsGYabJ4PpfUvTxHg4cSt/TsAsCIhxTJ5hPkc/hKArwyDie7gcc3Dg92CcdQ5UmGoIKM0w9LprlBaVcrLe18G4N4e9xLuEW71DEJYwszeM2nv1J70knTePfRuvcd+k/YNAPEh8Tjrna0RTwizsNlCLCfHtE+jn5/fZY/7+fnVPXc1S5YswcPDo+4WHBxsmYAd4yGwD1SVwo+vXPPwh4dFYqfV8HNKPomnz1smk2i+wjOQvgujomGjYSA9gzyv+RKdVkekZySgTvfk60mvc7b8LCFuIdwffb/V318IS3G1d+Xp2KcBeP/I+6QVpV31uGpjNT+c/gGQbknR8jSrEHv66afRaDT13o4dO2aurA3yzDPPUFRUVHc7c+aMZd5Io4GRL5ju7/8ACk7We3gHL2cm9a1tFZOxYjZr33sA7FWi0HoE4evm0KCXqTVz8rdzv7H62GoAnhv4HA66huUVoqUYGTKSoR2GUmOsYdHuRVedfb47azcFFQV4O3oTFxinQkohmq5Zhdhjjz3G0aNH671FREQ06dz+/v4A5OZevldjbm5u3XNX4+DggLu7+2U3i4kYBhHDwVgN2/5+zcNnDI9Ep9Ww/XgeB88UWi6XaJrsQ7DrdQA+qBlDzwZ0S9ZSY+akwWha9NKoGBkbPpZBgfUvsyFES6TRaHg29lkcdY7sy9nH12lfX3HMNydN3ZKjw0Zjp7WzdkQhmqVZhZivry9RUVH13uztr9wouSHCw8Px9/dn69atdY8VFxezZ88e4uJs6F88ta1ih9dAzuF6Dw31cWFib9Oef9IqZmMMNbBhFhhr+NV1KD8YBxDdwbPBL1dj5uTnxz/n93O/46Z348kBT1rtfYWwtiDXIB7q9RAA/9z3TworCuueu1B9gYT0BEC6JUXLZLUxYunp6SQlJZGeno7BYCApKYmkpCRKS0vrjomKimLdunWA6V9Bc+bMYdGiRWzYsIHDhw8zZcoUAgMDmThxorViX1tgb+h+M6DAlhevefjM4ZFoNbDl6FmOZBZZPJ5ooF0rIPsgOHryouEegAYN1K9VO3PyTMkZLlRfsEjEP8oty2XFrysAmNNvDu2c2ln8PYVQ05TuU+jo2ZHzledZdmBZ3ePbz2ynvKacDq4d6OXbS7V8QjSV1Qqx+fPn06dPHxYsWEBpaSl9+vShT58+7N+/v+6Y48ePU1R0qTh58sknmT17Ng888AADBgygtLSUTZs24ejoaK3YDTPiedDaQcpmOPVzvYdG+Loyvpdp/7PXZQalbchPgW1LACgbsZCk86ZxVtENGKhfy8fJBx9HHxSUPx1QbE4v73uZsuoyottFc0vnWyz+fkKoTa/VMz9uPgBrk9dyIPcAcKlbclzEOFm2RbRIVivEPvroIxRFueI2bNiwumMURWHatGl1P2s0Gl566SVycnKoqKhgy5YtdO7c2VqRG84nEvpONd3fvMC0xlg9Zg3viEYDm37L4ViOhZbXEA1jNJq6JA2VEBnPAc8xAIT6OOPh3LjNsq3VPfljxo9sPr0ZnebSopdCtAV92vdhUifT3q8Ldy8k70IeOzNNi73eGH6jmtGEaDL5G9xcrn8K9M6QuR+Obaz30E5+bozrEQDIumKq2/8+pO8CvQvKTa+yJjETgJ5BDe+WrGWNmZPlNeX8fY9pYshdXe+ii3cXi72XELZobr+5eDt6k1KYwoytM6hRaujq3ZUIz6ZNDBNCbVKImYubH8TNNN3f+pJp8Hc9Zo3oCMC3h7NJOVti6XTiagrPwJYXTPdHvsB7hw1sOJiFTqvh7oGhjT6dNWZOvn3wbTJLM/F38WdG7xkWex8hbJWHgweP938cgGMFpuWRboyQ1jDRckkhZk6DHgEnb8g/AQc/q/fQrgHujO7uh6LIWDFVKApsnGNakDd4INs9/sKS744C8PyNXYmN8Gn0Kf/YNXm1tY6a67f83/j4t48BeCbmGVk9XLRZN0XcRIx/DAAaNIwJG6NyIiGaTgoxc3J0h6Gmf6mxbQlUl9d7+OwRpi/uDQezSMsrrfdYYWaHvoCULaBzIH3Iy8z+/CBGBW7rH8y0QWFNOmWkRyRajZbzlec5V2HePUXPXjjLIwmPUKPUMDJkJCNCRpj1/EK0JBqNhnkD5+Hj6MNNETfh5+J37RcJYaOkEDO3/tPBIxhKsmDvO/Ue2iPIg/io9hgVWLkt1UoBBaVnYZNp25SK655k2teFlFTU0C/Ui5cmdm/yzCtHO0dC3EIA8w7Yr6ip4G8Jf+Ns+VkiPSJZOHih2c4tREsV5hFGwv8k8Pch115MWwhbJoWYuekdYfizpvs//QvK699Xcna8qVVsfVIm6ecsv/6UAL59AsrPo/hHMyNtEGl5ZQR4OPLWXf1wsNM169TmHrCvKAoLdi7gyLkjeDh4sGLEClztXc1ybiFaOpkxLFoD+VNsCdG3QftuUFEEPy+r99DewZ5c39kXg1Hhje0yVszijn4Nv68HjY4PfB4jIfk8jnot707p3+B9Jetj7iUs3j/yPt+e/BY7jR3/e/3/EuxuoU3shRBCqEIKMUvQ6iDetPAge96C4qx6D3/kYqvYl4kZZJyXVjGLKT8P3zwGwLGO01mYaFon7JVbetGjCctVXI05Z04mpCew/MByAJ6JfYaYgJhmn1MIIYRtkULMUjqPgeCBUFMB2/9R76H9Qr0Y3NGHGqPCfR/v50yBFGMW8cPzUJpLhUcktxy9DjAtrlu704E5dPY0FWKphanUGOtfwqQ+J86f4OmfnkZB4bYut/E/Xf7HXBGFEELYECnELEWjgRsu7j3566eQX38LyfybutPO1YFjOSVMWPkLu9PMO+uuzUtNgF8/RUHDrNJ7Ka2xY2RXPx69wbw7NQS5BeFk50SVsYr0kvQmnaOgooBHEh6hvKacWP9Ynop5yqwZhRBC2A4pxCwpZCB0HguKwbTIaz26+LuxYdZgegZ5UFBWxV3v7eHfu05ZZD2qNqeyFL7+GwDfOI1nS1k4nf1cefW2Xmi15t2bTqvR1m0A3pTuyWpDNXO3zSWzNJNgt2D+Nexf6LWN22pJCCFEyyGFmKXFzwc0cHQDZCTWe2igpxNrHopjQu9AaowK8776jWfXHaGqxmidrK1VwkIoTKdA78+T5yfi6azn3Sn9cXO0TIHT1AH7iqKweM9iDpw9gKvelddHvI6Hg3nGrgkhhLBNUohZml836HW76f6Wa28I7qjXsey23jwzNgqNBlbvTefO93aTV1JphbCtUPoe2PM2AH8rm0al1omVd/Ql1MfFYm/Z1CUsPjv2GWuT16LVaFk6dKnsnSeEEG2AFGLWMPwZ0NnDqZ8gdes1D9doNDx4fSQfTBuAm6Md+06dZ8LrP3Mks8gKYVuR6grYMAtQWGMYyk/GaObf1I3BHdtZ9G2bMnNyZ+ZOlu5bCsCj/R5lSIchFskmhBDCtkghZg2eITDgftP9LS+AsWFdjcO7tGf9zMFEtHMhq6iCW97ayYaD9S+FIf7gx1cg/wR5iicLq+9i8oBgpsQ1fjPvxqodI5ZRmkFZddk1jz9ZdJLHdzyOUTEyIXICU7pNsXREIYQQNkIKMWsZ8hg4uEPOYfjt/xr8skhfV9bNHMzwLr5UVBt5ZPWvvLzpGAajDOKvV/YhlF+WAfB89TS6hAXz0oQeTd6+qDE8HT3xdfIFIKWw/kV6iyqLeCThEUqqS+jt25v5cfOtklEIIYRtkELMWlx8YNAjpvsJC6GmqsEv9XDS897UATx0fSQAb25P5b6P91FcUW2JpC2foQZlwyw0xhq+McRw2G0ob97VD3s76/1xb0j3ZI2xhid2PMGp4lP4u/jz6vBXsdfZWyuiEEIIGyCFmDXFzQCX9nD+FBz4uFEv1Wk1PD02itcm98bBTsu243lMXPkLaXmllsnaku1agSb7IIWKC0u4l3em9Keda/O3L2qMhsyc/Nf+f7ErexdOdk6sGLGCdk6WHbsmhBDC9kghZk32LnD9k6b7O142rW/VSBN6B/HlQ4MI8HAkLa+MCSt/Ydvxs2YO2oLlp2BI+DsAL1XfzTO3DjPb9kWNca2Zk2tPrOXTo58CsPi6xUR5R1ktmxBCCNshhZi19ZsGXuFQlge7VjbpFD07eLBh1nX0D/WipKKGez/ax9s7UmXxV6OR0jUPoTNWscMQTdD193BjdIAqUeq6JguTr7gu+3P2s2jPIgBm9p7JDaE3WD2fEEII2yCFmLXp9DDiedP9ncuhLL9Jp/F1c2DV/bFMHhCMosCS744x94skKqoNZgzbshT9/DauufsoVRz5Nuxp5t7QRbUsER4R6DQ6iiqLOHvhUotlRkkGj25/lBpjDaPDRvNg9IOqZRRCCKE+KcTU0P1m8I+GqlL48Z9NPo2DnY4lN/fkpQnd0Wk1rE/K4ta3dpFdVG7GsC1DRf4p7LeZ9vb80Gkq8+4abfbtixrDXmdPqLtpqYzkQlP3ZFl1GbMTZnO+8jxdvbuycPBCmSEphBBtnBRiatBqYeQLpvv734fzp5t8Ko1Gw5S4MD6dHouXs57DmUWMX/ELiacLzJO1BVCMRk5+eD9OSjkHiGLCffNxdbBTO9ZlMyeNipGnf3qalMIU2jm1Y/mI5TjZOamcUAghhNqkEFNL5AgIHwqGKtj292afLi7Shw2zriPK34380komv7ObL/almyGobTMYFTZ/vpyuZXupVPTwlxWEtHNVOxZw+czJFb+uYPuZ7dhr7Xlt+Gv4u/irG04IIYRNkEJMLRrNpVaxQ19AzpFmnzLY25m1Dw9ibA9/qg0KT609zOsJVw4Wbw3ySipZuS2Fu/7xKTHHTVsD/db5Yfr2jVE52SW1K+xvO7ON9w6/B8ALg14g2jdazVhCCCFsiBRiagrqB90mAApsfcksp3RxsGPlHX2ZPaIjAP/84QQvbzreKooxRVHYmZrPzM8OELdkKx9/v5tXKl/AU1NGjlsP+kyer3bEy3T2NnVN1m5zdG+PexkfOV7NSEIIIWyM+gNp2roR8+HoRkj+Hk7vhNBBzT6lVqvhsVFdcHfUs/jbo7y1I5ULVTW8ML67qgPYm6rwQhVfJmbw2d500vJMRY07paxxWUoHQz5G70j8p39lmpFqQwJdAnHVu1JaXcqwDsN4pM8jakcSQghhY6QQU1u7jtD3bkj8CDYvgOk/mLotzeD+oRE4O+h4fv0RPtl1mgtVBv5xc0/sdLbfEKooCr+eKWTV7nQ2Hsqissa0UbqLvY5be/nw5NlXcc49Da7+aO9eBy62tyq9RqNhbr+5HMw7yDMxz6DT6tSOJIQQwsZolNbQZ1WP4uJiPDw8KCoqwt3dXe04V1ecDcv7QE05jH0FYu43WzEGsO7XDB5fcwiDUWFcT3+W3dbHqvsuNkZpZQ1fJWXy6e50jmYX1z3eNcCduwaGMCHaD9d1U+HEJnDwgHu+Bf8eKiYWQghhCS3i+9sMpEXMFrgHwKBZ8OMr8N0TkLIFxi8D90CznP6vfTrgpLdj9uoDfHs4h/Kq/bx5Vz8c9bbTQvN7VjGr9pxm/a+ZlFWZFqV1sNNyU3Qgdw4MoU+wJxqAr2aZijA7R7jjcynChBBCtGjSImYrDDXwyzLTHpSGKnBwh9GLoc/dZmsd23Eijwf/vZ+KaiMDI7x5b+oAVdfbqqg28M2hbFbtOc2B9MK6xyN8XbgzNpRJfYPwdLa/9ILNC0z/jzRauG0VRI2zemYhhBDW0WK+v5tJCjFbc/YofDUTMhNNP0eOgPGvgWeIWU6/J+0c0z/eT2llDb2DPfn4nhg8nK07yD3j/AU+/OUUXyZmUFReDYCdVsPoHv7cGRtCXITPlSvO71oJ3z9ruv+XFdB3ilUzCyGEsK4W9/3dRFYbKLR48WIGDRqEs7Mznp6eDXrNtGnT0Gg0l93GjBlj2aBqa98V7v0BbngJdA6QmgBvxMG+98BobPbpYyN8WHVfLJ7OepLOFDL53d3kl1aaIfi1FV6oYvE3vzPinzt4/+eTFJVXE+TpxBOju7DzmRGsvKMvgyLbXVmEHfziUhEWP1+KMCGEEK2G1VrEFixYgKenJxkZGbz//vsUFhZe8zXTpk0jNzeXDz/8sO4xBwcHvLy8Gvy+Lbqizk82tY6d2WP6OWyIqTXIO7zZpz6WU8xd7+0lv7SSCF8XVt0XS4CHZbbcqag28OEvp3hjewolFTUAxEX48MDQCIZ29kVX35IayZth9WQw1kDswzBmiVknMgghhLBNLfr7uxGs3jX50UcfMWfOnAYXYoWFhaxfv77B56+srKSy8lILT3FxMcHBwS33QhoNsPcd2PKiaVal3hniF0DMA6Y9K5vhZH4Zd767m6yiCjp4OfHZfQMJ8XE2U3DT9kNrD2Tw6uYTZBdVAKbZj0+PjWJop6u0fP23jP3w8XiovgA9b4W/vtPs31kIIUTL0FYKMZv/Vtu+fTvt27enS5cuPPzww5w7d67e45csWYKHh0fdLTg42EpJLUSrg4EPw4ydEHqdqSjZ9BR8OBbyU5p16vB2LvznoTjCfJzJOF/OrW/vJOVsSbMjK4pCwrFcxr32E09+eYjsogqCPJ343//pxTezr+P6zr7XLsLyjsOqW0y/b2Q8THhDijAhhBCtjk23iH3++ec4OzsTHh5Oamoqzz77LK6uruzatQud7upLL7S6FrE/Mhph//um2YPVZaYlHIY/B3EzTQVbE50truCu9/dwIrcUbxd7Prk3hh5BHk06V9KZQpZ8e5Q9JwsA8HDSM2t4R+6OC234chlFGfD+aCjOMG0DNWUDONjGRt5CCCGso620iDWrEHv66ad5+eWX6z3m6NGjREVF1f3cmELsv6WlpREZGcmWLVuIj49v0Gta5YU8fxq+/hukbTP9HNQfJqyE9lH1v66+U5ZVMeWDvRzOLMLN0Y6P7omhX2jDx+Kdyi/jle+P883hbADs7bTcMziMGdd3bNyszAsFpta+vGPg0wnu/R5cfBr76wghhGjhWuX391U0qxDLy8u7ZldhREQE9vaX1oJqTiEG4Ovry6JFi3jwwQcbdHyrvZCKAgc+gR+eh8pi0NnD9U/B4Dmga9raYMUV1Uz/aB/7Tp3H2V7He1P6M6hj/VsH5ZdWsnxrMp/tSafGqKDRwKS+HXj0hs4EejZy8H/VBfhkAmTsBbdAmP692ZbtEEII0bK02u/v/9Ks1Tx9fX3x9fU1V5ZrysjI4Ny5cwQEBFjtPW2WRgP9pkLHkbBxDiT/AAkL4egG03iqJqw47+6o5+N7Y3jw34n8lJzPtI/28eadfYnv6nfFsWWVNbz300ne+TG1biX84V18eWpsFFH+TfjAGKphzVRTEeboAXetlSJMCCFEq2e10c/p6ekkJSWRnp6OwWAgKSmJpKQkSktL646Jiopi3bp1AJSWlvLEE0+we/duTp06xdatW5kwYQIdO3Zk9OjR1opt+zyC4I7/wMS3TAVM9kF4Zxhs/wfUVDX6dM72drw3tT+juvlRVWPkwX8n8vXBrLrnqw1GPt19mutf2c6rW05QVmWgVwcPVt8/kA/viWlaEWY0wobZpmLSzsn0+/h1a/x5hBBCiBbGaoP1p02bxscff3zF49u2bWPYsGGmMBoNH374IdOmTaO8vJyJEyfy66+/UlhYSGBgIKNGjWLhwoX4+V3ZQvNn2krTJgAlObDxUTj+jeln9yBo18n0X/fAi7egSz87ef3pmlzVBiOPrznIV0lZaDXwj5ujcXeyY+mm46TllwEQ6uPME6O7cGPPgGvPgqzPD8/DzhWg0cHtq6GzFNpCCNHWtZXvb9niqLVRFDiyFr59AsoL6j/WzunyAs0j6A/FWiAG10Ce/yGb1fvOXPYyHxd7HonvxO0xIdjbNbNR9ZflsHme6f7EN6H3Hc07nxBCiFahrXx/SyHWWlUUQ9YBKM6G4kwozrr434v3L9Q/yaKWonOg0K4dx8vdOYsP/sERRHfrhqN38KWizcW3aWt8Ja2G9Q+Z7t/wEgz+W+PPIYQQolVqK9/fUoi1VdUVUJJ1sUDLulSgFf2hWCs727BzafXgFvCHFrXAP3SHXvyvq9/la52d+B5W3w6KAeJmwejFlvk9hRBCtEht5fu7WbMmRQumdwTvCNPtz9RUQUn25YVacZZpodXa+yU5YKyGonTT7c9odODmbyrK3AJMe0gqBoieDDcsNP/vJ4QQQrQAUoiJP2dnD16hptufMVRDae5VirXMi61rWaZiTjFc6hqt1WkUTHhdti4SQgjRZkkhJppHpwePDqbbnzEaoPTs5cWasRr6Tze9XgghhGijpBATlqfVgXuA6UY/tdMIIYQQNkP6hIQQQgghVCKFmBBCCCGESqQQE0IIIYRQiRRiQgghhBAqkUJMCCGEEEIlUogJIYQQQqhECjEhhBBCCJVIISaEEEIIoRIpxIQQQgghVCKFmBBCCCGESqQQE0IIIYRQiRRiQgghhBAqkUJMCCGEEEIldmoHsDRFUQAoLi5WOYkQQgghGqr2e7v2e7y1avWFWElJCQDBwcEqJxFCCCFEY5WUlODh4aF2DIvRKK281DQajWRlZeHm5oZGozHruYuLiwkODubMmTO4u7ub9dyi4eQ62Aa5DrZBroNtkOvQfIqiUFJSQmBgIFpt6x1J1epbxLRaLR06dLDoe7i7u8sHzQbIdbANch1sg1wH2yDXoXlac0tYrdZbYgohhBBC2DgpxIQQQgghVCKFWDM4ODiwYMECHBwc1I7Spsl1sA1yHWyDXAfbINdBNFSrH6wvhBBCCGGrpEVMCCGEEEIlUogJIYQQQqhECjEhhBBCCJVIISaEEEIIoRIpxIQQQgghVCKFWBOtXLmSsLAwHB0diY2NZe/evWpHanNeeOEFNBrNZbeoqCi1Y7V6P/74I+PHjycwMBCNRsP69esve15RFObPn09AQABOTk6MHDmS5ORkdcK2Yte6DtOmTbvi8zFmzBh1wrZSS5YsYcCAAbi5udG+fXsmTpzI8ePHLzumoqKCmTNn4uPjg6urK5MmTSI3N1elxMIWSSHWBF988QWPPvooCxYs4MCBA/Tq1YvRo0dz9uxZtaO1Od27dyc7O7vu9vPPP6sdqdUrKyujV69erFy58qrPL126lOXLl/PWW2+xZ88eXFxcGD16NBUVFVZO2rpd6zoAjBkz5rLPx+rVq62YsPXbsWMHM2fOZPfu3WzevJnq6mpGjRpFWVlZ3TFz587l66+/Zs2aNezYsYOsrCxuvvlmFVMLm6OIRouJiVFmzpxZ97PBYFACAwOVJUuWqJiq7VmwYIHSq1cvtWO0aYCybt26up+NRqPi7++vvPLKK3WPFRYWKg4ODsrq1atVSNg2/Pd1UBRFmTp1qjJhwgRV8rRVZ8+eVQBlx44diqKY/uzr9XplzZo1dcccPXpUAZRdu3apFVPYGGkRa6SqqioSExMZOXJk3WNarZaRI0eya9cuFZO1TcnJyQQGBhIREcGdd95Jenq62pHatJMnT5KTk3PZ58PDw4PY2Fj5fKhg+/bttG/fni5duvDwww9z7tw5tSO1akVFRQB4e3sDkJiYSHV19WWfh6ioKEJCQuTzIOpIIdZI+fn5GAwG/Pz8Lnvcz8+PnJwclVK1TbGxsXz00Uds2rSJN998k5MnTzJkyBBKSkrUjtZm1X4G5POhvjFjxvDJJ5+wdetWXn75ZXbs2MHYsWMxGAxqR2uVjEYjc+bMYfDgwfTo0QMwfR7s7e3x9PS87Fj5PIg/slM7gBBNNXbs2Lr70dHRxMbGEhoayn/+8x+mT5+uYjIh1Dd58uS6+z179iQ6OprIyEi2b99OfHy8islap5kzZ3LkyBEZpyoaTVrEGqldu3bodLorZr3k5ubi7++vUioB4OnpSefOnUlJSVE7SptV+xmQz4ftiYiIoF27dvL5sIBZs2axceNGtm3bRocOHeoe9/f3p6qqisLCwsuOl8+D+CMpxBrJ3t6efv36sXXr1rrHjEYjW7duJS4uTsVkorS0lNTUVAICAtSO0maFh4fj7+9/2eejuLiYPXv2yOdDZRkZGZw7d04+H2akKAqzZs1i3bp1JCQkEB4eftnz/fr1Q6/XX/Z5OH78OOnp6fJ5EHWka7IJHn30UaZOnUr//v2JiYlh2bJllJWVcc8996gdrU15/PHHGT9+PKGhoWRlZbFgwQJ0Oh2333672tFatdLS0staVU6ePElSUhLe3t6EhIQwZ84cFi1aRKdOnQgPD2fevHkEBgYyceJE9UK3QvVdB29vb1588UUmTZqEv78/qampPPnkk3Ts2JHRo0ermLp1mTlzJp999hlfffUVbm5udeO+PDw8cHJywsPDg+nTp/Poo4/i7e2Nu7s7s2fPJi4ujoEDB6qcXtgMtadttlQrVqxQQkJCFHt7eyUmJkbZvXu32pHanNtuu00JCAhQ7O3tlaCgIOW2225TUlJS1I7V6m3btk0BrrhNnTpVURTTEhbz5s1T/Pz8FAcHByU+Pl45fvy4uqFbofquw4ULF5RRo0Ypvr6+il6vV0JDQ5X7779fycnJUTt2q3K1//+A8uGHH9YdU15ersyYMUPx8vJSnJ2dlb/+9a9Kdna2eqGFzdEoiqJYv/wTQgghhBAyRkwIIYQQQiVSiAkhhBBCqEQKMSGEEEIIlUghJoQQQgihEinEhBBCCCFUIoWYEEIIIYRKpBATQgghhFCJFGJCCCGEECqRQkwIIYQQQiVSiAkhhBBCqEQKMSGEEEIIlfw/4On2ykAlntQAAAAASUVORK5CYII=" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 57 + "execution_count": 5 }, { "cell_type": "markdown", @@ -394,8 +414,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-09-25T22:58:21.705366Z", - "start_time": "2024-09-25T22:58:21.437860Z" + "end_time": "2025-05-28T13:33:15.291721Z", + "start_time": "2025-05-28T13:33:15.122303Z" } }, "outputs": [ @@ -411,22 +431,26 @@ }, { "data": { - "text/plain": "[]" + "text/plain": [ + "[]" + ] }, - "execution_count": 58, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "text/plain": "
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V/eOC7LuCvJ7C/H2aN2+eAijHjx+/ZxlFUZSePXsavO+F4fPPP1cAJTQ0tFDrXbt2rQIoP/zwQ77rw8PDFUCZMWNGgesslFN4s2bNYvPmzQa3B7m7xcHFxQWAjRs35jnN9bBWrFiBu7s7L7/8cp51d7cWDRkyhHLlyukft23bFlD/S82V+18mqE2csbGxtGjRAoDDhw/n2cbzzz9v8Lht27bcuHGDxMREg+VdunQxaA1r0KABzs7O+m0risKKFSvo3bs3iqIQGxurvwUFBZGQkJDv9u/nn3/+oXnz5rRp00a/zNHRkWeffZbLly/rT5kVtvzekzvf41wDBgzQnxIE9XTL1q1bGTx4MElJSfrXf+PGDYKCgjh//rz+tMGyZcto27Yt5cqVM3ivunTpQk5ODjt37nyo2Atar6urKykpKQX6HhRETEwMO3fuZPTo0VSuXNlg3Z2f4zs/n1lZWdy4cYPq1avj6upq8PlwdXXl5MmTnD9/Pt/tGfNeP6guY+Tk5LBp0yb69u1L1apV9cu9vb158skn2b17d57vzrhx47CwsHio7dnY2KDVavXbvnHjBo6OjtSqVSvf79Pw4cMNTjMMHDgQb29v/vnnH/2yO/dBSkoKsbGxtGrVCkVROHLkSJ46H/R9WLZsGS4uLnTt2tXgMxcQEICjoyPbtm0DbvclW7duHVlZWfm+3oLWBehbRO48jRcaGsq+ffsYNmwYWq2WgwcPEh0dzYsvvmjQ17Vnz57Url3boDtA27Zt2bVrF6BexXf06FGeffZZ3N3d9ct37dqFq6sr9erVA9TO6jqdjsGDBxvE6+XlRY0aNQzivVthfgePHDlCaGgoEyZMyNPi9aBhC+78POSeqWnfvj2XLl3Sd00pyL4ryOspzN+n3G4pdx4Tzd2iRYuwsrJi8ODB+a7Pfa13n027n0JJoJo3b06XLl0MbvdjaWmZp7+Cv78/EydOZN68ebi7uxMUFMSsWbP0H7KHcfHiRWrVqoWl5YPPVN59YMp9M+/sGxAXF8err76Kp6cndnZ2VKhQQX/1YX5xFqTO/Mrlls0tFxMTQ3x8PD/99BMVKlQwuI0aNQpQm4GNceXKFWrVqpVnee5ppCtXrhhVX0HY2toaJEVg+DrvdPdVnRcuXEBRFN57770878GUKVOA2+/B+fPn2bBhQ55yuZ9LY9+rXAWt98UXX6RmzZr06NGDSpUqMXr06Hz7tBVU7gE198ByL2lpabz//vv6/g/u7u5UqFCB+Ph4g8/nhx9+SHx8PDVr1qR+/fq88cYbBv1TjHmvH1SXMWJiYkhNTb3n51Kn0+Xp+3L358QYOp2Or7/+mho1ahi8X8eOHcv3+1yjRg2DxxqNhurVqxv0MQsLC2PkyJG4ubnp+zW1b98eyPsbUZDvw/nz50lISMDDwyPPvkhOTtbvh/bt2zNgwAA++OAD3N3deeKJJ5g/fz4ZGRlG1wXqb/SQIUPYtWuXPlnOTaZyT9/l/kbkt79q165t8BvStm1bIiIiuHDhAnv27EGj0dCyZUuDxGrXrl20bt1an9SeP38eRVGoUaNGnnhPnz593+9xYX4Hc/vLPuj7l59///2XLl264ODggKurKxUqVOCdd94Bbn8eCrLvCvJ6iuL3SVEUo19zSZScnMzq1asJCgrSd4u5W+5rNWYsr0LpA2WsO//zu9OXX37JyJEjWb16NZs2beKVV15h+vTp7Nu3z+gOosa613+xd36ABg8ezJ49e3jjjTdo1KgRjo6O6HQ6unfvnm/n5ILUWZByuXU//fTTjBgxIt+yd/Y1KqmMaSm48z83uP0eTJo0iaCgoHyfU716dX3Zrl278uabb+ZbrmbNmgWO4+4YClKvh4cHISEhbNy4kfXr17N+/Xrmz5/P8OHDWbhw4UNtuyBefvll5s+fz4QJE2jZsqV+gMqhQ4cafD7btWvHxYsX9d+zefPm8fXXXzNnzhzGjh1r1Hv9oLqK2t2fE2N88sknvPfee4wePZqPPvoINzc3tFotEyZMeODFBvnJycmha9euxMXF8dZbb1G7dm0cHBy4fv06I0eOzFNnQb4POp0ODw8P/vjjj3zX5yZgGo2G5cuXs2/fPtauXcvGjRsZPXo0X375Jfv27dP/VhWkrlxPP/0033//PYsXL2bSpEksXryYOnXq0KhRowK8G4ZyW7p37tzJpUuXaNKkCQ4ODrRt25Zvv/2W5ORkjhw5ou/Pl/vaNRoN69evz/e9ut/AvKb6Dt7p4sWLdO7cmdq1a/PVV1/h6+uLtbU1//zzD19//bX+81CQfVeQ11OYv0+5ScbNmzeL/NhbHFatWpXv1Xd3yv3H5X79t+9mkgTqfurXr0/9+vV599132bNnD61bt2bOnDl8/PHHgHHZYbVq1di/fz9ZWVmPfNXAzZs3CQ4O5oMPPuD999/XLy+MUxcPUqFCBZycnMjJyXlg615BValShbNnz+ZZfubMGf16YxXlKLy5p3SsrKwe+B5Uq1aN5OTkh36v7vU6jKnX2tqa3r1707t3b3Q6HS+++CI//vgj7733HtWrVzfqvcp97Q+6XHj58uWMGDGCL7/8Ur8sPT2d+Pj4PGXd3NwYNWoUo0aNIjk5mXbt2jF16lTGjh1r1Hv9oLqMUaFCBezt7e/5udRqtfj6+hpV5/0sX76cjh078vPPPxssj4+Pz/dH9O7vuqIoXLhwQf/Py/Hjxzl37hwLFy40uODiUU4jVatWjS1bttC6desCJYstWrSgRYsWTJs2jUWLFvHUU0+xZMkSxo4da3RdgYGBVKtWjUWLFtG1a1dOnjxpkODk/kacPXuWTp06GTz37NmzBr8hlStXpnLlyuzatYtLly7pu0i0a9eOiRMnsmzZMnJycmjXrp3Ba1cUBX9//4f6p+dB38GCyu1eceLECaN+U9auXUtGRgZr1qwxOMtwr1OP99t3BXk9hfn7VLt2bUA9bVu/fv0Cv+aS6o8//sDR0ZE+ffrcs0xoaChw/4t57lZi5sJLTEwkOzvbYFn9+vXRarUGTZkODg75HhDyM2DAAGJjY/n+++/zrDO2aTL3P6C7n/cw05oYy8LCggEDBrBixYp8D6K5Y+oY4/HHH+fAgQPs3btXvywlJYWffvoJPz8/6tSpY3SduVd5FXT/GMPDw4MOHTrw448/EhERkWf9ne/B4MGD2bt3Lxs3bsxTLj4+Ps/n7G73+owVtN67h7XQarX6g2zuZzl3vKKCvFcVKlSgXbt2/PLLL4SFhRmsu/PzaGFhkefz+d133+W5LP7u+BwdHalevbo+NmPe6wfVZQwLCwu6devG6tWrDU6LRUVFsWjRItq0aYOzs7PR9d5ve3e/X8uWLTO4BP9Ov/76K0lJSfrHy5cvJyIigh49eujrA8N9oijKI106P3jwYHJycvjoo4/yrMvOztZ/fm7evJnnteS2FOXui4LWdaennnqKI0eOMGXKlDyXfzdt2hQPDw/mzJljsL/Xr1/P6dOn84wT1bZtW7Zu3cqBAwf0CVSjRo1wcnLi008/xc7OjoCAAH35/v37Y2FhwQcffJDntSmKkuezd6eCfAcLqkmTJvj7+zNz5sw879H9jiP5fR4SEhKYP3++QbmC7LuCvJ7C/H0KCAjA2tqagwcP3vP1mYuYmBi2bNlCv3798h0YNtehQ4f0p5YLqsS0QG3dupWXXnqJQYMGUbNmTbKzs/ntt9/0yUOugIAAtmzZwldffYWPjw/+/v55Lm3ONXz4cH799VcmTpyo/9KmpKSwZcsWXnzxRZ544okCx+fs7Ey7du347LPPyMrKomLFimzatEmftRa1Tz/9lG3bthEYGMi4ceOoU6cOcXFxHD58mC1bthg1dgXA22+/zeLFi+nRowevvPIKbm5uLFy4kNDQUFasWJHvKdYHsbOzo06dOixdupSaNWvi5uZGvXr1HqrvQH5mzZpFmzZtqF+/PuPGjaNq1apERUWxd+9erl27ph+/54033mDNmjX06tWLkSNHEhAQQEpKCsePH2f58uVcvnz5vs20AQEBzJ49m48//pjq1avj4eFBp06dClzv2LFjiYuLo1OnTlSqVIkrV67w3Xff0ahRI/1/N40aNcLCwoIZM2aQkJCAjY2NfryY/Hz77be0adOGJk2a8Oyzz+Lv78/ly5f5+++/9YPO9erVi99++w0XFxfq1KnD3r172bJlS55z/nXq1KFDhw4EBATg5ubGwYMHWb58ucHl4wV9rwtSlzE+/vhjNm/eTJs2bXjxxRextLTkxx9/JCMjg88+++yh6ryXXr168eGHHzJq1ChatWrF8ePH+eOPPww6sN/Jzc2NNm3aMGrUKKKiopg5cybVq1dn3LhxgNrvp1q1akyaNInr16/j7OzMihUr8u3jV1Dt27fnueeeY/r06YSEhNCtWzesrKw4f/48y5Yt45tvvmHgwIEsXLiQH374gX79+lGtWjWSkpKYO3cuzs7OPP7440bVdaenn36aDz/8kNWrV9O6dWuDy7+trKyYMWMGo0aNon379gwbNkw/jIGfnx+vvfaaQV1t27bljz/+QKPR6E/pWVhY0KpVKzZu3EiHDh0MBsutVq0aH3/8MZMnT+by5cv07dsXJycnQkNDWblyJc8+++w9Z0coyHcQ0L+e+00botVqmT17Nr1796ZRo0aMGjUKb29vzpw5w8mTJ/NNWEDtiJ/b0vPcc8+RnJzM3Llz8fDwMPjHpCD7riCvpzB/n2xtbenWrRtbtmzhww8/NHhdx44d048Hd+HCBRISEvRniBo2bEjv3r2Nen9BTSy/++47QO03BvD999/j6uqKq6urwe/JyJEj9ceqgszisHTpUrKzs+97+g7UluLWrVvfs49Uvoy7ENBQ7qXE97r88l7DGDg4OOQpe+nSJWX06NFKtWrVFFtbW8XNzU3p2LGjsmXLFoNyZ86cUdq1a6fY2dkpwAOHNEhNTVX+97//Kf7+/oqVlZXi5eWlDBw4UH+Z9L0up1YUJc8l+deuXVP69eunuLq6Ki4uLsqgQYP0lz7eWS73ctaYmJh83687L8/kHkMEVKlSJc9ri4qKUsaPH6/4+vrqX0vnzp2Vn376SV+moMMYKIqiXLx4URk4cKDi6uqq2NraKs2bN1fWrVuX7/tQkGEMFEVR9uzZowQEBCjW1tYG78u99vvdl/7eb3/kxjx8+HDFy8tLsbKyUipWrKj06tVLWb58uUG5pKQkZfLkyUr16tUVa2trxd3dXWnVqpXyxRdfKJmZmfd9DZGRkUrPnj0VJycnBTAY0qAg9S5fvlzp1q2b4uHhoVhbWyuVK1dWnnvuOSUiIsJgO3PnzlWqVq2qWFhYFGhIgxMnTug/f7a2tkqtWrWU9957T7/+5s2byqhRoxR3d3fF0dFRCQoKUs6cOZPns/Txxx8rzZs3V1xdXRU7Ozuldu3ayrRp0/K8LwV5rwta193ut58PHz6sBAUFKY6Ojoq9vb3SsWNHZc+ePQZlHvTbc7dLly7lGdIkPT1def311xVvb2/Fzs5Oad26tbJ3716lffv2Bvs8dxiDxYsXK5MnT1Y8PDwUOzs7pWfPnnmGlTh16pTSpUsXxdHRUXF3d1fGjRunH5akIL+D+V0KryiK8tNPPykBAQGKnZ2d4uTkpNSvX1958803lfDwcP17NmzYMKVy5cqKjY2N4uHhofTq1Us5ePCg0XXdrVmzZve9/Hvp0qVK48aNFRsbG8XNzU156qmnlGvXruUpd/LkSQVQHnvsMYPlH3/8sQIYfJbvtGLFCqVNmzaKg4OD4uDgoNSuXVsZP368cvbsWX2Zu4cxKOh30N3dXWnRokW+273b7t27la5duypOTk6Kg4OD0qBBA4OhZvLbd2vWrFEaNGig2NraKn5+fsqMGTP0w+vkHgcKsu8K+noK8/fpr7/+UjQaTZ5hX+43fNHdx6yCvr+5vwf53e4eJmHAgAGKnZ1dniEl7qVFixaKh4fHfYdtiI+PV6ytrZV58+YVqM5cj5RACSGEOchNYoz9gVSU2wnUg8aoE+YlN6HL759GoY5/VbNmTeXdd999qOcX1fvr4eGhTJo0qVDr/PrrrxVvb+98x+26nxLTB0oIIYpK7rRMD9O3T5RO27Zto2XLljKn3z1YWFjw4YcfMmvWLJKTk41+flG8vydPniQtLY233nqr0OrMysriq6++4t133zX6ql6NopSSgR6EEOIue/fuZdu2bXz22Wd4eXlx6tQpo/v3bd++nY4dO7Js2bI8fYSEEGVXielELoQQhe3HH3/Uj9D83XffPdTFEUIIkR9pgRJCCCGEMJL8OyaEEEIIYSRJoIQQQgghjFQq+kDpdDrCw8NxcnIq0ulEhBBCCFF4FEUhKSkJHx8fs+ujWCoSqPDw8EKdI0sIIYQQxefq1atmN3GxUQnU9OnT+euvvzhz5gx2dna0atWKGTNmUKtWrfs+b9myZbz33ntcvnyZGjVqMGPGDP0Q9aBmoFOmTGHu3LnEx8fTunVrZs+eTY0aNQoUl5OTE6DugMKcK0sIIYQQRScxMRFfX1/9cdycGJVA7dixg/Hjx9OsWTOys7N555136NatG6dOndJPjnq3PXv2MGzYMKZPn06vXr1YtGgRffv25fDhw/o50j777DO+/fZbFi5ciL+/P++99x5BQUGcOnUKW1vbB8aVe9rO2dlZEighhBDCzJhj95tHGsYgJiYGDw8PduzYQbt27fItM2TIEFJSUli3bp1+WYsWLWjUqBFz5sxBURR8fHx4/fXX9RNDJiQk4OnpyYIFCxg6dOgD40hMTMTFxYWEhARJoIQQQggzYc7H70fqsZWQkACos5Tfy969e+nSpYvBsqCgIPbu3QtAaGgokZGRBmVcXFwIDAzUl7lbRkYGiYmJBjchhBBCiOLy0AmUTqdjwoQJtG7dWn8qLj+RkZF4enoaLPP09CQyMlK/PnfZvcrcbfr06bi4uOhv0oFcCCGEEMXpoROo8ePHc+LECZYsWVKY8RTI5MmTSUhI0N+uXr1a7DEIIYQQoux6qGEMXnrpJdatW8fOnTsfeNmhl5cXUVFRBsuioqLw8vLSr89d5u3tbVCmUaNG+dZpY2ODjY3Nw4QuhBBCCPHIjGqBUhSFl156iZUrV7J161b8/f0f+JyWLVsSHBxssGzz5s20bNkSAH9/f7y8vAzKJCYmsn//fn0ZIYQQQoiSxKgWqPHjx7No0SJWr16Nk5OTvo+Si4sLdnZ2AAwfPpyKFSsyffp0AF599VXat2/Pl19+Sc+ePVmyZAkHDx7kp59+AtRLFydMmMDHH39MjRo19MMY+Pj40Ldv30J8qUIIIYQQhcOoBGr27NkAdOjQwWD5/PnzGTlyJABhYWEGw7G3atWKRYsW8e677/LOO+9Qo0YNVq1aZdDx/M033yQlJYVnn32W+Ph42rRpw4YNGwo0BpQQQgghRHF7pHGgSgpzHkdCCCGEKKvM+fhtXjP3CSGEEEKUAJJACSGEEEIYSRIoIYQQQggjSQJl5pIzsvlj/xU2nIgwdShCCCFEmfFQA2kK04tISGPBv5dZdCCMpPRsANa/2pbHvM2rE54QQghhjiSBMjMnricwb9cl1h2LIFunXkBpbaElM0fH7O0X+XZYYxNHKIQQQpR+kkCZAZ1OYce5GObuusSeizf0ywP93RjXtipeLrb0+m43646FM7FrTfzcHUwYrRBCCFH6SQJVgqVn5bA65DrzdoVyPjoZAAuthsfrezOurT8NKrnqy3asVYFtZ2P4cedFpvdvYKKIhRBCiLJBEqgSKC4lk9/3XeHXvZeJTc4EwNHGkqHNfBnZ2o9K5ezzPGd8x+psOxvD8kPXeLVzTbxcZBR3IYQQoqhIAlWChMam8PPuSyw/dI30LB0A3i62jGrtx9DmlXG2tbrnc5v6udHc340DoXHM3XWJ93rVKa6whRBCiDJHEigTUxSFg1duMnfnJTafjiJ3Yp16FZ0Z17Yqj9f3xsqiYKNNjO9YnQOhB1i0P4yXOlannIN1EUYuhBBClF2SQJlIdo6ODScjmbsrlKNX4/XLO9X2YGxbf1pWLY9GozGqznY13KlX0ZkT1xOZv+cyE7vWLOSohRBCCAGSQBW76KR01h2N4Jd/Q7l2Mw0Aa0stA5pUZEwbf6p7OD103RqNhvEdqvPCH4dZ8G8oz7ariqON7GIhhBCisMnRtYjpdApHr8Wz7WwM285Ec/x6gn6dm4M1T7eowvCWVXB3tCmU7QXV9aJaBQcuxqTwx74rPNe+WqHUK4QQQojbJIEqAgmpWew8ryZMO87FcCMl02B9g0ouDGnmy4AmlbC1sijUbWu1Gl7oUJ1Jy44yd1coI1r5Ffo2hBBCiLJOEqhCoCgKZ6OS2Hommu1nYjgUdpOcW6OEAzjZWNK2pjsda3nQvlYFPJyKdoiBJxr58PXmc1yPT2PZoWs806JKkW5PCCGEKGskgXpIqZnZ/HvhBtvORrP9TDThCekG62t4ONKptgcdannQ1K9cga+kKwxWFlqea1+V91ef5McdFxnazLdYty+EEEKUdpJAGeFybArbzkaz9Uw0+y/FkZmj06+ztdLSqpo7HWtVoEMtD3zd8g52WZwGN/Xl2+DzXLuZxtqj4fRvUsmk8QghhBCliSRQ95GZreNAaJx6au5sNJdiUwzWVypnR6faHnSs7UHLquVLVF8jWysLxrSpyowNZ/hh+0X6NqqIVmvcsAhCCCGEyJ8kUPcRnZTO0z/v1z+21Gpo7u9Gx1pq0lStgoPRYzUVp6dbVOaH7Re4EJ3MplNRdK/nZeqQhBBCiFJBEqj7qFTOnjbV3fFxtaVTbQ9aV3fH6T7TqZQ0TrZWjGjpx/fbLvDD9gsE1fUs0QmfEEIIYS4kgXqA38cGmjqERzKqtR/zdl/i2LUEdl+IpW2NCqYOSQghhDB7cmlWKVfe0YZhzSsDMGvbBRNHI4QQQpQOkkCVAePaVsXKQsO+S3EcuhJn6nCEEEIIsycJVBng42pH/8bqMAY/bLto4miEEEII8ycJVBnxfIdqaDUQfCaaU+GJpg5HCCGEMGuSQJUR/u4OPF7fG4DZO6QVSgghhHgUkkCVIS92qA7A38fCCb1rUFAhhBBCFJwkUGVIHR9nOtX2QKfAj9IKJYQQQjw0SaDKmPEdqwGw4vA1IhLSTByNEEIIYZ4kgSpjAqq4EejvRlaOwtydoaYORwghhDBLkkCVQeM7qn2hFh8I40ZyhomjEUIIIcyPJFBlUNsa7tSv6EJaVg4L9lw2dThCCCGE2ZEEqgzSaDT6vlAL9lwmKT3LxBEJIYQQ5kUSqDKqWx0vqlVwICk9m9/3hZk6HCGEEMKsSAJVRmm1Gv24UD/vvkR6Vo6JIxJCCCHMhyRQZVifRj5UdLUjNjmTPw9eNXU4QgghhNkwOoHauXMnvXv3xsfHB41Gw6pVq+5bfuTIkWg0mjy3unXr6stMnTo1z/ratWsb/WKEcawstDzfvioAP+64RFaOzsQRCSGEEObB6AQqJSWFhg0bMmvWrAKV/+abb4iIiNDfrl69ipubG4MGDTIoV7duXYNyu3fvNjY08RAGNfXF3dGG6/FprA4JN3U4QgghhFmwNPYJPXr0oEePHgUu7+LigouLi/7xqlWruHnzJqNGjTIMxNISLy8vY8MRj8jWyoKxbf35dP0Zfth+gX6NK2Kh1Zg6LCGEEKJEK/Y+UD///DNdunShSpUqBsvPnz+Pj48PVatW5amnniIs7N5XhmVkZJCYmGhwEw/vqcDKONtacikmhU0nI00djhBCCFHiFWsCFR4ezvr16xk7dqzB8sDAQBYsWMCGDRuYPXs2oaGhtG3blqSkpHzrmT59ur5ly8XFBV9f3+IIv9RysrViRCs/AH7YfhFFUQp9G8kZ2WRmSx8rIYQQpYNGeYSjpUajYeXKlfTt27dA5adPn86XX35JeHg41tbW9ywXHx9PlSpV+OqrrxgzZkye9RkZGWRk3J6CJDExEV9fXxISEnB2djb6dQiIS8mk9adbScvK4dfRzWlXs8JD16UoChdjUjgcdpMjYTc5dOUm56OTcbSx5Pn21RjV2g97a6PPHgshhChlEhMTcXFxMcvjd7EdxRRF4ZdffuGZZ565b/IE4OrqSs2aNblw4UK+621sbLCxsSmKMMssNwdrhjWvzC//hjJr2wWjEqiUjGyOXo3n8K1k6cjVeOJT845unpSezecbz7Jgz2Ve7VyDIc18sbKQkTSEEEKYn2JLoHbs2MGFCxfybVG6W3JyMhcvXuSZZ54phshErnHt/Plt32X2h8Zx8HIcTf3c8pRRFIWwuFR9snT4SjxnIhPR3dWOaWOppWElVxpXcSWgcjkaVXZlz4UbfLn5LFfj0nh31Ql+3h3K691q8ng9b7TScV0IIYQZMTqBSk5ONmgZCg0NJSQkBDc3NypXrszkyZO5fv06v/76q8Hzfv75ZwIDA6lXr16eOidNmkTv3r2pUqUK4eHhTJkyBQsLC4YNG/YQL0k8LG8XOwY0qcSS/67yw/aL/DLSjfSsHI5dS1CTpVun5GKTM/M8t6KrHY0ruxJQpRxNKpfjMW9nrC0NW5f6Nq7I4/W9WbT/Ct9tvUBobAovLTpC/YqXeKt7bdrUcC+ulyqEEEI8EqP7QG3fvp2OHTvmWT5ixAgWLFjAyJEjuXz5Mtu3b9evS0hIwNvbm2+++YZx48blee7QoUPZuXMnN27coEKFCrRp04Zp06ZRrVq1AsVkzudQS5rQ2BQ6f7kdnQL1KjpzJiKJ7Lual6wttNSt6ExA5XI0uZUwebnYGrWd5Ixs5u26xNydl0jJVKeRaVPdnbe616Z+JZcHPFsIIURpYM7H70fqRF5SmPMOKIleXnyEtUdvD6rp4WRDk8rl1NalKq7U9XHB1sqiULYVm5zB91sv8Mf+K2TlqB/Fng28mdStFv7uDoWyDSGEECWTOR+/JYESecSlZLJo/xV83ewJqFKOiq52aDRF20fpalwqX20+x6qQ6ygKWGo1DGnmy6uda+DhbFzrlhBCCPNgzsdvSaBEiXI6IpHPNpxh29kYAOysLBjdxo/n2lfD2dbKxNEJIYQoTOZ8/JYESpRI+y/d4NMNZzgSFg+Aq70V4ztU55mWVQrt9KEQQgjTMufjtyRQosRSFIVNp6L4fONZLkQnA+DjYsuErjUZ0KSSzNknhBBmzpyP35JAiRIvR6ew4vA1vt58joiEdABqeDjyRlAtutbxLPL+WUIIIYqGOR+/JYESZiM9K4ff9l5h1vYL+pHOa3s5MbZtVXo39MbGUk7tCSGEOTHn47ckUMLsJKRl8dPOi8z/9zKpt8aQ8nCyYUQrP54KrIyr/f2nChJCCFEymPPxWxIoYbYSUrNYdCCMBXtCiUpUJ5e2s7JgYEAlxrTxx0/GkRJCiBLNnI/fkkAJs5eZrePv4+HM3RnKqYhEADQa6PqYJ2PbVqWZXznpJyWEECWQOR+/JYESpYaiKOy9eIN5u0PZeiZav7xhJRfGtq1Kj3peWFpo71ODEEKI4mTOx29JoESpdCE6iZ93h7Li8HUys3WAOuHxqNZ+DGnmi5MMyimEECZnzsdvSaBEqRabnMHv+67w294r3EjJBMDJxpKhzX0Z2dqfiq52Jo5QCCHKLnM+fksCJcqE9KwcVh65zrxdl7gYkwKAhVbD4/W9GdfWnwaVXAt1e5nZOpIzssnK0eHhZCN9sIQQIh/mfPyWBEqUKTqdwo5zMczddYk9F2/olzf3d2Nc26q0r1mBlIxskjOySUrP/Zulf6wuyyL51v2kjGz1/q1lyRnZJKZn608bAlSt4ECvBj70aehNdQ8nU7xsIYQokcz5+C0JlCizToYn8POuUNYcDSdbVzRfA60G7qy6tpcTvRv60LuBD5XL2xfJNoUQwlyY8/FbEihR5kUmpLNgz2UW7b9CYno2ALZWWpxsrXCyscTR1hInW0scbSxxsrW69Td3mZV+/e2yahlHG0vSsnLYciqKtUfD2Xk+hqyc21+3hr6u9G7gTa8GPni52Jrq5QshhMmY8/FbEighbsnM1pGSkY2jrSVWRTDcQXxqJhtPRrL2aAR7LsbqW6Y0Gmjm50bvBt70qO+Nu6NNoW9bCCFKInM+fksCJYQJxCRlsP5EBGuPhvPf5Zv65VoNtK7uTu8GPgTV9cLFvuiGW8jK0RGTlEGOTsHXTU4nCiGKnzkfvyWBEsLEwuPT+PtYBGuPhXPsWoJ+uZWFhvY1K9C7oQ9dHvPEwcayQPXpdAo3UzOJTEwnOjGDqMR0IhPTiUrMIPqO+zdSMsj99g9rXplpfeuh1ZbAqwV1Ooi7COWrq811QohSw5yP35JACVGCXI5NYd2xcNYejeBsVJJ+ua2Vlk61PejdwIcano5EJ2boE6GoxPQ7bhlEJ6Ub9LW6HysLDdk6BUWBpwIr83HfeiVryIWsNPhzBJzfCHWegP5zwVJOcQpRWpjz8VsSKCFKqHNRSaw7Gs6ao+FcvpFq9PPdHa3xdLa9dbPR3/dytsXj1mM3e2tWH73OxD+PoigwomUVpvapWzKSqIxkWDIMQnfeXla1Awz5HWxkOAghSgNzPn5LAiVECacoCifDE1l7NJy/j0eQkJqFp8utpMjJVr3vZIOXiy0et5KkCo42WFsWvCP88kPXeGO5mkSNaePPuz0fM20SlRYPfwyCawfA2hHavwU7ZkBmMvg0gaeWg0N508UnhCgU5nz8lgRKCAHA0v/CeGvFcQCea1eVt3vUNk0SlRILv/WDyGNg6wpP/wWVAuD6Ifh9IKTFgXtNdbmrb/HHJ4QoNOZ8/Jap6YUQAAxppvaBAvhx5yW+2HSWYv//KjECFvRUkyeHCjDybzV5AqgYAKM3gnMliD0HvwRBzNnijU8IIW6RBEoIofd0iyp80KcuALO2XWTmlvPFt/H4MJjfA2LOgJMPjFoPXvUMy1SoCWM2qi1Qidfhl+5w7VDxxSiEELdIAiWEMDCilR/v9aoDwDfB5/kuuBiSqNgLajJ0MxTK+cHo9eBeI/+yLpVg1Aa1RSotDhb2hovbij5GIYS4gyRQQhSV5GjYPgMO/wbhIZCdYeqICmxMG38m96gNwJebzzF7+8Wi21jUSbXlKfG62rI0ar2aRN2PQ3kYvgaqdoSsFLXD+clVRRejEELcpWAj8wkhjPfPJDi1+vZjrSW41wKv+oY3ezfTxXgfz7WvRrZO4fONZ5mx4QyWWg3j2lUt3I1cPwy/94e0m+p78fRKcKxQsOfaOMKTS+GvZ+HUKlg2EtK+gqajCzdGIYTIhyRQQhSF2Atwao16v0obiDoB6fEQfVK9HVtyu6xzpTsSqnrqX1c/0Jq+gXh8x+pk5yh8veUc0/45jYVWw+g2/oVT+ZW9astRZhJUagZPLQO7csbVYWkDA3+Bf9zg4C+w7jVIvQFtJ8mo5UKIIiUJlBBFYc+3gAI1u6utJIqinqKKPH7rdkz9e/MyJF5Tb+fW336+tdPtZCr3VuExsLIt9pfyapcaZOt0fLf1Ah+uO4WVhYZnWvo9WqUXt8LiJyE7DfzawrDFDz84ptYCen4F9u6w8zPY+jGk3ICgT0pEEiqEKJ1kHCghCltSJMysDzmZamfnKi3vXTY9Qe0DFHnidlIVfUp97t00FlChFtToBh3eBiu7onsNd1EUhc82ntX3hfqkX32eDKz8cJWd+Vs93ZaTCdW7wpDfCu+17JsDG95S7zcYAk/MAouim5BZCPFozPn4LS1QQhS2fT+oyYFvi/snTwC2LlCllXrLlZMFsecNW6oij6tXnEWfUm/nN6mnrjweK9rXcotGo+HNoFpk5+iYuyuUd1Yex1KrYXAzIweyPL5c7bOk5MBjfWDAz2BpXXiBtnhe7VO26gU4tlQd0XzQArC2L7xtCCEE0gIlROFKT4Cv60FGIgxbArV6FE69igKJ4XBlD2x8B1KiwdIWuk+HgFHF1t9HURQ+XHeK+f9eRqOBLwY2ZEBApYI9+fCvsOYVQIEGQ2+1DhXR/3DnNsGfw9VThL4t4MklxvevEkIUOXM+fksHASEK08Ff1OSpQm2oEVR49Wo04FIRGgyCF/ZA9S6Qna52mv7zGUiNK7xt3TcMDe/3qsMzLaqgKPDG8qOsDrn+4Cfumw1rXgYU9Sq5vrOLLnkCqNkNhq9SW/iu7oP5PdVTq0IIUUgkgRLF4+oBuHbQ1FEUrax02PuDer/1q0XXgdmxAjy5DLpNA60VnF4Lc9qqrVPFQKPR8EGfugxrXhmdAq8tDWHdsfB7P2HnF7DhbfV+y5fUDt/F0bm7cgt1TClHL/XKx5+7wY0iHM9KCFGmSAIlit7lf9VRpn/pDnGhpo6m6BxdrJ5ac64E9QYW7ba0Wmj1EozdDG5V1av4FvRUB+7U5RTttgGtVsO0vvUY3LQSOgVeXRLChhMRhoUUBbZ8AFs/Uh93mAzdPi7e4QU866pTv5Tzh/gr6mcw4ljxbV8IUWpJAiWKVnI0LB+tdhrWZcGuL0wdUdHQ5dwaugBoOb5wO0bfj09jeG4nNHwSFB1s/0Sd2iThWpFvWqvVML1/A/o3rkiOTuGlRUfYdPLWaTKdDta/Bbu/Uh93+1i9ctAUYzOV84Mxm9ShIFKi1UTz8r/FH4cQolSRBEoUHV0OrBgDyZHgXFFdFrIY4i6ZNq6icHqN+rpsXaHJ8OLdto0T9JsN/eeq40dd+Rdmt1ZP7RUxC62Gzwc15IlGPmTrFMYvOszWU+Gw9mU48KNaqOdX0OrlIo/lvhw9YOTfUKW12kft9/5w5A/ISDJtXEIIs2V0ArVz50569+6Nj48PGo2GVatW3bf89u3b0Wg0eW6RkYYdOmfNmoWfnx+2trYEBgZy4MABY0MTJc32TyF0J1g5wDMr1Y7PSg7s/NLUkRUuRYHdM9X7gc+pU4yYQoPB8PxO8Gmijnq+9GlYNxGy0op0sxZaDV8Oakjv+hVopYRgsWQYHPkdNFroOweajSnS7ReYrQs8vQJqPa52wF/9IszwU0/rbf8UwvarQ0gIIUQBGJ1ApaSk0LBhQ2bNmmXU886ePUtERIT+5uHhoV+3dOlSJk6cyJQpUzh8+DANGzYkKCiI6OhoY8MTJcWFLbDzc/V+72/UASA7TFYfH11cujrzhu6AiBCwtIPmz5k2FreqMHojtJ6gPj74M/zUEaJOFc32crLh4lYs173Ct9cGs9B6Bu21IWQqFoR3nQ2NhhXNdh+WlR0M/g3avaH2i9JlQ9he2D4dfukGM/xh8TDY/yPEnFOTYyGEyMcjjQOl0WhYuXIlffv2vWeZ7du307FjR27evImrq2u+ZQIDA2nWrBnff/89ADqdDl9fX15++WXefvvtB8ZhzuNIlEoJ19SrwtLi1EvWe319e93vA+HCZmj0FPT9wXQxFqZfn4BL26H5s/D456aO5raLW+Gv526PGRX0ibo/HrUfUk42XNkNJ1eqpwlTb+hXKQ4V2EILZsa1JKNCPVaPb42DTQker/fmZXXfXdoOl3aon9k7OVeEqh2gakeo2l49FSiEKDTmfPwutl+2Ro0akZGRQb169Zg6dSqtW7cGIDMzk0OHDjF58mR9Wa1WS5cuXdi7d2++dWVkZJCRkaF/nJiYWLTBi4LLyYJlo9QDkXdDCJpuuL7DZDWBOroE2r4O5auZJs7CEn5EPfhqLNRL9EuSap3UMaNWPa+2CP49ES5tg97fqqN1G0OXow6TcHKl2t8rJeb2Ovvy6qjidfuh8WtD49RsYr/dRVR0Mv9beZyvhzRCU1In9i3nBwEj1ZtOp478fmmbuk+v7FXnLwz5Q70BeNa7nVBVaQnWDiYLXQhhWkWeQHl7ezNnzhyaNm1KRkYG8+bNo0OHDuzfv58mTZoQGxtLTk4Onp6eBs/z9PTkzJkz+dY5ffp0Pvjgg6IOXTyMLVPh2gGwcYFBC/NOflspQJ3L7fwmdXygfrNNEmah+fcb9W+9AVCuimljyU/umFH7Z8PmKWqL0fUjMGCu4fQx+dHp1EEoT66EU6shOer2Orty8FhvqNtfnQz4jkEx3R0t+P7JJgz9aR+rQsJp7l/+4efNK05aLfg0Um9tXlP7joXtVZOpi9vU5CrqhHrb+z1YWINvoNoyVbWT+jythWlfgxCi2BT5Kbz8tG/fnsqVK/Pbb78RHh5OxYoV2bNnDy1b3p437M0332THjh3s378/z/Pza4Hy9fU1yybAUuX0WrXjMsCQP+CxXvmXu3YI5nVSOxm/dNB8W6FuXITvm6rDBzz/L3jVM3VE9xceog4pEXdRfe/bvan2BbpzRHCdDq79dytpWgVJd4ztZOuq7tO6/cC//QMn6f1xx0Wmrz+DtaWWv15oRb2KLkXxqopPSqza3+3Sdri4HRLCDNc7VFCn76nU1BTRCWGW5BSekZo3b87u3bsBcHd3x8LCgqioKIMyUVFReHl55ft8GxsbbGxsijxOYYS4S7BqvHq/5Uv3Tp7gVitUEJzfqHY07zeneGIsbHu+U5OnGt1KfvIEagvJczvhnzfg6CLYcesqyf4/qdOc5CZNiXdMzWLjArV7Qr3+atJkxPhW49pW5b/LcWw5Hc34RYdZ+3IbnG3vn3SVaA7uaktjvQFq5/K4S7dP94XuVE9rbn4fRv1j6kiFEMXAJONAhYSE4O3tDYC1tTUBAQEEBwfr1+t0OoKDgw1apEQJlpUOf46AjAT1lEaXqQ9+Toe31L/HlkLshSINr0gkRUHIIvV+7hVv5sDG8daYUfPUMaPC9sDM+vBzF9g3S02erJ3UyX6HLYU3zqvla3Q1enBQrVbDF4MaUtHVjis3Unlz2TFKwdzlKo1GbTltNhaG/A4v7lP7wV35FyJPmDo6IUQxMDqBSk5OJiQkhJCQEABCQ0MJCQkhLExtzp48eTLDh98eSHDmzJmsXr2aCxcucOLECSZMmMDWrVsZP368vszEiROZO3cuCxcu5PTp07zwwgukpKQwatSoR3x5olhseFvtH2JfHgbOf+CpHQAqBkDN7moLzs4SdOVaQe2fAzkZUKnZg/sSlUQNBsHzu9T9gALWjlB/EAxdBG9cgP4/Qq3uYPloLb2u9tb88FQTrCw0bDgZyfx/LxdK+CWOs4/aJwzgwE+mjUUIUSyMPoV38OBBOnbsqH88ceJEAEaMGMGCBQuIiIjQJ1OgXmX3+uuvc/36dezt7WnQoAFbtmwxqGPIkCHExMTw/vvvExkZSaNGjdiwYUOejuWiBDr2JxyaD2jUkbBdKhb8ue3fgnMb4Pifal8c9+pFFmahSk+E/35W77d5zTTTkxQGN391zKiIY+BZRx0jqQg09HXl3Z51mLLmJJ/8c5pGlV1pUrlckWzLpAKfU0+BHvsTun6gdrQXQpRaj9SJvKQw505oZi36DMztCFmpaofkTv8zvo5FQ+HcemgwRO2LYw7+/Ubt6+JeSz11o5UZkR5EUdS58v4+HoGPiy1/v9KWcg7FNF9gcVEUmNNGvUqv28emn75GCDNgzsdv+eUXDyczBZaNUJMn//bqRLEPI7cv1PFlEHu+8OIrKtkZsPfWAKCtX5HkqYA0Gg2fDqiPv7sD4QnpTPwzBJ3O7P93M6TRqIOpAvw3Tx07SwhRasmvvzCeosC61yDmDDh6wYB5Dz/+jU9jdW4yRQc7PivcOIvC0SXq5MhOPlB/sKmjMStOtlbMerIJNpZatp2NYc7OUjSdT676g9ThHm5ehvObTR2NEKIISQIljHd4oXr1nMYCBv7y6NNbtL/VCnViuTr/WEmly4E936r3W443+qo0AXV8nPnwiboAfLHxLPsu3XjAM8yMtT00eUa9L53JhSjVJIESxok4Cv+8qd7v/B74tX70On0a3W6F2lmCW6HO/A03LoCtCwSMMHU0ZmtwU1/6N6mIToGXFx8hJinjwU8yJ03HABq4GGwep6WFEA9FEihRcOkJ6nhPORnqEAStXi28unP7UB0voa1QigK7b02K3Gwc2DiZNh4zptFo+LhvPWp6OhKTlMGrS46QU5r6Q7n5q98PgANzTRuLEKLISAIlCkZRYNWLcDMUXCpD39mF24HauyHU6gkosGNG4dVbWC7vgvDDYGkLgc+bOhqzZ29tyQ9PNcHe2oI9F2/wzZYSmDQ/iubj1L8hiyAjybSxCCGKhCRQomD2zYYz60BrBYMWgL1b4W8j94q8Eysg5mzh1/8ods9U/zZ+Wp2gVzyy6h5OTO9fH4Dvtl1gx7kYE0dUiKp2hPI1IDNJvfBACFHqSAIlHuzqAdj8nno/6BN1Lrui4N0QaveixLVCRRxV+7NotOo8f6LQPNGoIk8FVkZR4LWlIUQkpJk6pMKh1d4e0uDAT2oLrhCiVJEEStxfyg1YNhJ02VC33+1TE0VFf0XeX+pAnSXBv9+of+v2V/u3iEL1Xq861PVxJi4lk5cWHSErR2fqkApHw6HqFDmx59QJh4UQpYokUOLedDr4a5w6wWz56tDnu6KftsS7QclqhYoLhZMr1futC7HTvNCztbLgh6ea4GRjyaErN/l8Ywk7ffuwbJ2h0ZPqfelMLkSpIwmUuLfdX6qnrixtYdDC4rvyLPeKvJMrIfp08WzzXvZ+rw6vUK2zmtyJIlGlvAOfD1Lf3592XmLTyUgTR1RImt1qsT23Hm5eMW0sQohCJQmUyF/oTtj2iXq/55fgVa/4tu1V/9bM9oppRydPjoEjv6v327xmujjKiO71vBnTRj1FOmnZUa7GpZo4okJQoabaoVzRwcGfTR2NEKIQSQIl8kqKhOVj1B/9Rk+rV54Vt/YloBVq/xzIToeKAeDXxjQxlDFvda9N48quJKZn8+Ifh8nILgXzyeV2Jj/8K2SVkk7yQghJoMRddDr461lIiQaPuvD456aJw6sePNYHk/WFykiC/271W2k9oej7fgkArC21fP9kE1ztrTh+PYFpf5v4FG5hqBkErpUh7aY6UKwQolSQBEoY2vMNhO4AK3t1vCdre9PFou8LtQqiThXvtg8tUEdeL1/jVqd2UVwqutrx9ZBGAPy69wprj4abNqBHpbWAZmPV+wd+lCENhCglJIESt107BFs/Vu/3mKH23zAlz7pQ5wmKvRUqOxP2/qDeb/1K4Y64LgqkYy0PxnesBsDbK45xMSbZxBE9osbPgKUdRB6HsH2mjkYIUQjkyCBU6YmwYvTt8Z4aP2PqiFS5faFOrYKok8WzzeN/QlI4OHlDgyHFs02Rx2tdahLo70ZKZg7j/zhMWqYZ94eyd4MGg9T7B34ybSxCiEIhCZRQ/TMJbl5W57nrNbPk9PnxrAN1+qr3i6MVSqe7PW1LixfB0qbotynyZWmh5bthjXF3tOFMZBL9fviXk+EJpg7r4eV2Jj+9BhIjTBuLEOKRSQIl4OhSOLZUnapkwFywczV1RIbavwVo4NRqiDxRtNs6+w/cOA82LhAwsmi3JR7Iw9mWOU83obyDNWcik+g761++33qebHMcrdyrPlRupbbyHppv6miEEI9IEqiyLu4S/D1Rvd/+bajcwrTx5MezDtTtq94vylYoRYHdX6v3m41RR5IWJtfUz42Nr7UjqK4nWTkKX2w6x8A5e82zX1TuVEgH56t97YQQZksSqLIsO1Md7ykzWf3PuN0kU0d0b7mtUKfXqB1xi8KVf+H6QbCwgRYvFM02xENxd7RhztMBfD2kIU62loRcjafnt7uY/28oOp0ZXdX2WG+1b11KtNqiKoQwW5JAlWXbpkH4YbB1hf4/qZdbl1Qej6md26HwW6Gy0iB0FwR/qD5u/BQ4ehTuNsQj02g09GtciU2vtaNtDXfSs3R8sPYUT83bz7WbZjJquYUVNB2t3j/wo2ljEUI8Eo2imP+gJImJibi4uJCQkICzs5x2KZBL2+HXvoACg3+DOn1MHFABRJ+BH1oACjy36+HnpstIgqv74fK/cGUPXD8Euix1ncYCXj4IblULLWxR+BRF4ff9YXzy92nSsnJwtLHk/d51GBRQCU1JuQDiXpKj4as66mdu3Dao2MTUEQlhMuZ8/JYEqixKiYXZrSE5Uu0o3fsbU0dUcMtHw4kV6uCWQ/8o2HNS49Sxd678q94ijoFy1yXxjl7g1xqajICq7Qs/blEkLsem8Pqyoxy6chOALo958En/+ng42Zo4sgdYMU4dLqPhk9BvtqmjEcJkzPn4LQlUcctKhzProFondWyY4qYosHiYOju8ey14drtpRxs3VsxZmBXIfVuhkqIgbI/aunT5X4jOZ/wo1ypQpbWaNFVpBeX8S87QDcIoOTqFubsu8dWmc2Tm6Chnb8W0fvV5vL63qUO7t2sHYV5ntb/dxFPg4G7qiIQwCbM6ft9FEqji9tdzcGwJuPjCkN/Bp1Hxbn//T7D+DbCwhnFb1Uurzc3yMXBi+e1WqPirarKU28J040Le57jXVBOlKrcSJpdKxR+3KFJnIhOZuPQopyISAXiikQ8f9KmLq721iSPLh6LA3I4QfgQ6vw9tXzd1REKYhFkdv+8iCVRxunYI5nW6/djSVh20stGw4tl+5AmY2wlyMqDHZxD4XPFst7DFnINZzQFFTUQTrt5VQAOe9W4lTLdu0im8TMjM1vHd1vP8sP0iOToFT2cbZgxoQIdaJXD/hyyGVc+DcyV49ShYWJo6IiGKndkcv/MhCVRxURT4pTtc3afO75adAec2qOuaPwdB09QrdIpKZir81AFiz0LN7jBsiXmfssrtQwJqx2/vhrdOx7UG30DTnB4VJUbI1Xgm/hnCpZgUAJ4MrMz/Hn8MB5sSlKRkpcPXdSE11nwu5BCikJnF8fseJIEqLidXwrKR6oSiLx9Sx4LZMQN2fKqur9wKBi8supaStRPU0Y8dveCFf82/z0V6AhxdAuWrg29zsHEydUSihEnPymHGhjPM//cyAL5udnw5qBHN/UtQch38Iez6Evzawsh1po5GiGJnFsfve5BxoIpDVjpsfl+93/pVcKkIWi10nAxDF4O1k9rp+cf2aufSwnZqza2pIzTQb475J08Ati7qKcjqnSV5EvmytbJgSu+6LBoXSEVXO67GpTHkp71M+/sU6VklZGLipqPVFtTLuyDqlKmjEUIYQRKo4rB/DsSHqa1OrV8xXFf7cXh2m9rJOSkc5veAQwsLb9sJ12DNy+r91q9CtY6FV7cQZqBVNXc2TGjL4KaV1L7bu0Lp/d1ujl8rARMTu1SC2j3V+wd+Mm0sQgijSAJV1JJjYOcX6v3OU8DaIW8Z9xowNli9qiwnE9a+AmtfVftJPQpdDvz1LKTHg08T6PTuo9UnhJlysrXis4ENmTe8Ke6ONpyPTqb/7H+Zt+uS6aeCyb2Y49hSSIs3aShCiIKTBKqobZsGmUng3QgaDLl3OVtntSNpp/cADRxaAAt6QmLEw29715fqZf3WjjDw56LtpC6EGehSx5PNr7WjRz0vsnIUPv77NKMX/seN5Ef8Z+VRVGkNHnUgKxVCCjg4rBDC5CSBKkpRJ+HwrdNx3aer/Z7uR6tVJ/R9apnax+faf/BjO7iy1/hth+2H7bc6qPf8UqYmEeKWcg7W/PBUE6b1q4eNpZbtZ2Po8c0u9lyMNU1AGg00f1a9f2Au6HSmiUMIYRS5Cq+oKAr81g8ubYPH+sCQ34x7ftwlWPK0Ooq21hK6fwrNxhZs6IG0eJjTFhLCoP5gGDD3oV6CEKXdmchEXlp0hAvRyWg08HLH6rzSuQaWFsX8v2VmCnz1mHp16ZPLoGa34t1+SZGRpM4VmJUG2enqLSsdstNu/c1dVsD1WWlqV4hqnaDbRyV7wvQyqkQevwtIEqiicm4TLBqkjvg9fv/DtQBlpsDql+DkX+rjRk9Bz6/A6j7zfCmKOl/cyb+gnJ863YltCXlPhCiB0jJz+GDtSZb8pw7I2syvHN8MbYyPq13xBrLxf7D3e6jeBZ5eUbzbLmrZGZAUAUmRt/8mhhs+TopUuzsUlZYvqePtiRKlRB6/C0gSqKKQkwWzW0HsOWj1MnT7+OHrUhTY8x1smQKKDnwaq32lXH3zL3/4N1jzktpqNXoTVAp4+G0LUYasORrOO38dJzkjGxc7Kz4f2IBudb2KL4C4S/BtE0CBlw9D+WrFt+2HlZMNKTHqFcQGyVGEYcKUFlfwOq0c1IttLG3VfxYtbcHKTv2rX2Z3e92d663swNLmjvV2EHcRNryt1t3nO2gyvGjeC/FQStzx2whGJ1A7d+7k888/59ChQ0RERLBy5Ur69u17z/J//fUXs2fPJiQkhIyMDOrWrcvUqVMJCgrSl5k6dSoffPCBwfNq1arFmTNnChRTidsBB+bCP5PAvjy8ckTtz/SoLm5TW5bS4tR6By0A/3aGZWLPq32mslLVK/7aTnz07QpRhoTdSOXlxYc5emuIgxEtqzD58cewtSqmUz9/DIbzGyHwBejxafFsU5ejnjpMu6lesZt2U+0GkB5/x9/cZQmGyzKTC74dCxtw8gJnH/Wvk/cdN/Xxv9GWrDgRTzl7ayq72au38vZUKmeHjeUj7IPtn8L26aC1guGrwK/Nw9eF2mqpoGBvXYJGtjdTJe74bQSj935KSgoNGzZk9OjR9O/f/4Hld+7cSdeuXfnkk09wdXVl/vz59O7dm/3799O4cWN9ubp167Jly5bbgVma6Qcz7SZs+0S93/GdwkmeQB2/6dntsPQpiDwOv/ZVz+m3eFHtF5WdoSZYWalqYtV6QuFsV4gypHJ5e5Y934ovNp3lp52XWLj3Cv9dvsl3TzamWgXHog8g8Fk1gQr5Qx12xKYQtpkYAREhEB4CMWfUf8LuTJAyEh+tfo0FOHreToqcve9IkLzA6VbCZFfunn04E9OzmLbuNEsP3j2v5a1NaMDL2fZ2UnUrscq97+ZgjeZ+/UPbvwUxZ9WuDUufgXHB+XaryMzWEZ2UTlRiBtGJ6UQlphOVlEFUYjrRierfqMR0EtOzeb9XHUa38X+ot0yUDkZnKT169KBHjx4FLj9z5kyDx5988gmrV69m7dq1BgmUpaUlXl7F2FxeVHZ+of5AVagNTUYWbt3lqqin5dZNUMeM2fiOOpt772/V4RIij4GdG/T76cFX/Akh8mVtqeWdxx+jZbXyTPrzKKciEun93W4+eqIeAwIqFe3Gq3YCt2rqaadjS9QLRwpKUdTTZeEhtxOmiBBIjirY860dwdYV7Fxv/73zvq2rmgTp191x/xE6Z287G807fx0nIiEdgCFNfXG2s+TKjVTC4tRbamYOEQnpRCSksz807+lARxtLfN3sqexmdyu5ctAnVxVd7dBqILbTVzhHXcQ+9ijxPw/gt7rzuJpqSdStxCg6KYO4lMwCxx1ryqEvRIlQ7M08Op2OpKQk3NwM56M6f/48Pj4+2Nra0rJlS6ZPn07lypXzrSMjI4OMjNsf3sTER/wPqrDcuAj7f1Tvd5tWNLOrW9tDvx/VgTE3vgPHl8H1w+oPLkDfH9T/AIUQj6RjLQ/+ebUtry0NYc/FG7y+7Ci7L8TyUd96OBbVpMRarTqkwYa31K4ATcfk32qjKGon7DsTpfAQSInOW1ajVWc68G4EXvXV1qI8iZFrsY8Tl5CaxYfrTrHi8DUA/Mrb89nAhnnmKlQUhRspmYTFpXI1LtUgsQq7kUpkYjrJGdmcjkjkdETeY4FWAwrqW+bBs6y2eQ/vlEvU3/saM7MmkYNh8mdtocXD2QZPZ1s8nW3wcLLV39cvc7bFqSRNTC1M4pE6kWs0mgf2gbrbZ599xqeffsqZM2fw8FAnzl2/fj3JycnUqlWLiIgIPvjgA65fv86JEydwcso7z1l+faYA059DXfIUnFlXfFfRXN4Nf45QZ3MH9Yf38c+LfrtClCE5OoXZ2y/w9Zbz5OgU/Mrb8/2TTahXsZBOz98tPQG+fAyyUmD4GvWUfOL1vC1LKTF5n6vRgnst8GmkJkw+jdSkKb8ZEExoy6ko3ll5nOikDDQaGN3an0ndamFnbXxLVnpWDtdupt1KrlIIi0u7lWClEBaXSnqWOq6WhVZDBUcbWtlf5dOEN7FWMgipOIxzjf93O0FyssXV3ur+pwNFoTLnPlDFmkAtWrSIcePGsXr1arp06XLPcvHx8VSpUoWvvvqKMWPG5FmfXwuUr6+vaXdA6C5Y2EvtD/DCHvCoXTzbTbgG615Tr7obOP/+QxwIIR7awctxvLL4COEJ6VhbaHm7R21GtfYrmoPt36/Df/PU/kM5mbf/SbqTRqt2FchNlLwbgVe9Epcs3elmSiYfrD3JqpBwAKq6O/D5oAYEVHF7wDMfjqIoxCRngALlHW2w0N7aVydXwbIR6v1eM6HpqCLZvngwc06giq0NcsmSJYwdO5Zly5bdN3kCcHV1pWbNmly4cCHf9TY2NtjY2BRFmA9Hl6OeTgMIGFl8yROok5E+taz4tidEGdXUz41/Xm3Lm8uPselUFB+uO8Wei7F8PrAh5RysC3djzcbBfz+rwwOA+o9ZhdqGLUue9dRT+mZiw4lI3l11gtjkDLQaGNe2Kq91rVmkVzhqNBo8nPL5p7JuX4h9F7Z9rF4xXb5a3quahXiAYkmgFi9ezOjRo1myZAk9e/Z8YPnk5GQuXrzIM888UwzRFYKjS9QO3DYu6pV3QohSydXemh+fCeC3fVf4+O/TbDkdTY9vdvHN0EYEVi1feBvyqA1P/gnxV263LFkV88CeheRGcgZT1pxk3TF1Xs/qHo58PrABjSuXM21g7SapVyWeWH7ryryt5jH2ligxjE6gkpOTDVqGQkNDCQkJwc3NjcqVKzN58mSuX7/Or7/+Cqin7UaMGME333xDYGAgkZGRANjZ2eHiovYhmDRpEr1796ZKlSqEh4czZcoULCwsGDZsWGG8xqKVkQzBH6r3200CB3fTxiOEKFIajYbhLf0IqFKOlxcf4VJMCsPm7uOVzjV4oUO1Rxuv6E6lYDqXv49F8P7qE9xIycRCq+G5dlV5pXON4htX6340Gnjie7gZCtcPweKhMGaz2qFeiAIwug/U9u3b6dixY57lI0aMYMGCBYwcOZLLly+zfft2ADp06MCOHTvuWR5g6NCh7Ny5kxs3blChQgXatGnDtGnTqFatYP8NmPQc6tZpsPMzddqU8QfUUXCFEGVCSkY2U9acZPkh9UoyFzsrnmjkw8CAStSv6FJmOyPHJmfw/uoT/HNc/Ye5lqcTnw9qQINKrqYNLD9JkTC3k9pRv1ondS7CoriCWuTLnPtAyVQujyLhGnzXVJ3IcvCvUOeJ4tu2EKLEWHXkOp+uP0NkYrp+WS1PJwYGVOKJxj7598MphRRFYc3RcKauOcnN1CwstRpe7FCN8Z2qF17LXFGIOAq/dFcHIm7+HDz+makjKjMkgTIxk+2Av55VB7Ss0hpG/n3PUXaFEKVfjk7h3wuxLD90jY0nI8nIvn35fIeaFRgYUIlOj3mU7ETiEUQnpfPuyhNsOqUO3PmYtzOfD2xQdMM9FLbTa2Hp0+r9nl8aN4ipeGiSQJmYSXbAtUMwrxOggWe3qZP8CiEEkJCWxd/HIlh+6CqHw+L1y13trXiioQ8DA3ypV9G5VJziUxSFlUeu88HaUySkqa1OL3dS+4NZW5rZjAi7vlT7tGos4Jm/oGoHU0dU6kkCZWLFvgMURW3uvboPGj4J/WYX/TaFEGbpYkwyKw5d46/D10vdKb7IhHT+t/I4wWfUEdDrVXTm84ENeczbvA6EeooCK59TzyzYusLYYHCvbuqoSjVJoEys2HfAyZWwbCRY2cPLh9TZxYUQ4j5yT/Etu3WKL/OOU3wda906xVfbs8S32kQnprPpVBQbT0ay9+INsnUK1hZaXu1Sg2fbVcXKomTH/0BZ6eqgyNf+g/LVYewWdd4/USQkgTKxYt0BWekwqxnEh0GHydDh7aLdnhCi1ElIy2LdsXCWH7rGkTtO8ZWzt+KJRhUZGFCJuj4l5xRfaGwKG09GsvFkpEG8AE0qu/LpgAbU9Mw77ZbZSo6GnzpC4jXwb69OzVXMcwWWFZJAmVix7oDdX8OWqeoUCy8fLNHTJgghSr4L0cmsOHyNvw5fIyrx9hRVtb2c6F7Pi3o+LtSt6IyXs22xJVSKonAyPFGfNJ2LSjZY38jXlW51PQmq60W1Co7FElOxizwOPwepcxI2G6t2LBeFThIoEyu2HZAcA982hswk6DsHGpnBQJ9CCLOQo1PYfcdVfLmn+HK5OVhT18eZOj7O1PVxoa6PM/7lHdBqCyepys7R8d/lm2w6Fcmmk1Fcj0/Tr7PUamhRtTxBdT3pWscLLxfz7LNltDP/wJInAQUe/wKajzN1RKWOJFAmVmw7YO0EODRfveJu7FbQmvm5fiFEiZSQmsU/JyL473Icp8ITOR+dTI4u70+1vbUFj3k7U8fbmbq3EquaXo4FHiohPSuH3edj2XgykuAz0cSlZOrX2VlZ0L5mBbrV9aRzbU9c7MvoKazdM2HLFPXKvKeXq4NtikIjCZSJFcsOiDoJc9qAooNR66FKq6LZjhBC3CU9K4dzUUmcDE/kZHgCJ8MTOR2RSHqWLk9ZS62G6h6O+laq3FYrJ1s1AUpMz2LbmWg2noxk+9kYUjNz9M91tbeic21Pgup60rZGBeysS+eYVUZRFFj1AhxdrM53OnYLVKhp6qhKDUmgTKzId4CiwG/94NI2dbTxwb8W/jaEEMIIOTqF0NjkW0nV7cQqPjUr3/JVytvj4WRDyNV4snJu/+x7u9gSVNeLbnU8ae7vhqW5X0VXFLIzYGEfdegat6rq8Ab2bqaOqlSQBMrEinwHnNsEiwaBhbU6352bf+FvQwghHpGiKIQnpHPyegInwhM5dSupikhINyhX3cORoFudwMvynH1GSY5R58xLCAP/djDgZ3D0MHVUZk8SKBMr0h2QkwWzW0HsOWj1CnT7qHDrF0KIIhaXksnJ8ATC49No6udWeq+cK2pRJ+HnbpB566rEcv7gGwi+zdW/Ho+BVk57GkMSKBMr0h2w/ydY/wbYu8Mrh8HWTOZ1EkIIUfgu7YCN76jJFHcdPq2doFLA7aSqYlOwczVFlGbDnBMoS1MHUKKl3YTt09X7Hd+R5EkIIcq6qu3hhX8hPQGuHYSrB+DqfvV+ZhJc2q7eANCorVK5LVS+gWofKjllWipIAnU/6Qnqhz81DpqMMHU0QgghSgpbF6jeWb0B6HIg+rSaTOUmVTdDIfqUeju0QC1nXx4qNb+dVPk0Bmt7k70M8fDkFN6DKAqk3gAH98KtVwghROmWHAPXDtxOqq4fhpwMwzJaS/BqADW6QfNnwaG8aWI1EXM+hScJlBBCCFEcsjMh8tithOpWUpUUcXu9lQM0HQUtXwJnb9PFWYzM+fgtCZQQQghhCooCCVfh8r+w7wc1uQJ1yJzGT0PrV6Gcn0lDLGrmfPyWBEoIIYQwNUWBC1tg5xfqgJ2gTh/TYDC0mVhqRz835+O3JFBCCCFESaEocOVfNZG6tO3WQg3U6QNtXwfvhiYNr7CZ8/FbEighhBCiJLp+CHZ9BWfW3V5WvSu0mwSVW5gurkJkzsdvSaCEEEKIkizqFOz+Ck6sUCe0B6jSBtq9DlU7mvW4UuZ8/JYESgghhDAHNy7CvzMhZDHobk0a7dNEbZGq2QO05jcRtDkfvyWBEkIIIcxJwjXY8x0cWgjZaeoyjzpqZ/O6/cDCfMbINufjtyRQQgghhDlKjoF9s+DAPHUaGVAnOG7zGjQcBpbWpo2vAMz5+C0JlBBCCGHO0uLhwFx1LKm0OHWZkzdU6wQVA6BSM7WFqgS2TJnz8VsSKCGEEKI0yEhW59zb8x0kRxqus7IH70ZQqemtWzNw9jFFlAbM+fgtCZQQQghRmmSlQ+gOuPafert+GDIS85Zz8oFKt1qoKjYFn0Zg7VCsoZrz8VsSKCGEEKI00+ngxvlbCdVBuH4Qok7eHhIhl8YCPOuoyVSlZmpLVfkaRXp1nzkfvyWBEkIIIcqazBQID1GTqWv/wbVDkBSet5yNC1RsoiZTFZtCtY5gaVNoYZjz8VsSKCGEEEJAwvVbCdWtW/iR28MkgNpCNfkaWNsX2ibN+fhd8rrkCyGEEKL4uVRUb3WeUB/nZEP0qVv9qA5BRlKhJk/mThIoIYQQQuRlYQneDdRbszGmjqbEMb9x34UQQgghTEwSKCGEEEIII0kCJYQQQghhJEmghBBCCCGMJAmUEEIIIYSRjE6gdu7cSe/evfHx8UGj0bBq1aoHPmf79u00adIEGxsbqlevzoIFC/KUmTVrFn5+ftja2hIYGMiBAweMDU0IIYQQolgYnUClpKTQsGFDZs2aVaDyoaGh9OzZk44dOxISEsKECRMYO3YsGzdu1JdZunQpEydOZMqUKRw+fJiGDRsSFBREdHS0seEJIYQQQhS5RxqJXKPRsHLlSvr27XvPMm+99RZ///03J06c0C8bOnQo8fHxbNiwAYDAwECaNWvG999/D4BOp8PX15eXX36Zt99++4FxmPNIpkIIIURZZc7H7yLvA7V37166dOlisCwoKIi9e/cCkJmZyaFDhwzKaLVaunTpoi9zt4yMDBITEw1uQgghhBDFpcgTqMjISDw9PQ2WeXp6kpiYSFpaGrGxseTk5ORbJjIyMt86p0+fjouLi/7m6+tbZPELIYQQQtzNLK/Cmzx5MgkJCfrb1atXTR2SEEIIIcqQIp8Lz8vLi6ioKINlUVFRODs7Y2dnh4WFBRYWFvmW8fLyyrdOGxsbbGxsiixmIYQQQoj7KfIWqJYtWxIcHGywbPPmzbRs2RIAa2trAgICDMrodDqCg4P1ZYQQQgghShKjE6jk5GRCQkIICQkB1GEKQkJCCAsLA9TTa8OHD9eXf/7557l06RJvvvkmZ86c4YcffuDPP//ktdde05eZOHEic+fOZeHChZw+fZoXXniBlJQURo0a9YgvTwghhBCi8Bl9Cu/gwYN07NhR/3jixIkAjBgxggULFhAREaFPpgD8/f35+++/ee211/jmm2+oVKkS8+bNIygoSF9myJAhxMTE8P777xMZGUmjRo3YsGFDno7lQgghhBAlwSONA1VSmPM4EkIIIURZZc7Hb7O8Ck8IIYQQwpQkgRJCCCGEMJIkUEIIIYQQRpIESgghhBDCSJJACSGEEEIYSRIoIYQQQggjSQIlhBBCCGEkSaCEEEIIIYwkCZQQQgghhJEkgRJCCCGEMJIkUEIIIYQQRpIESgghhBDCSJJACSGEEEIYSRIoIYQQQggjSQIlhBBCCGEkSaCEEEIIIYwkCZQQQgghhJEkgRJCCCGEMJIkUEIIIYQQRpIESgghhBDCSJJACSGEEEIYSRIoIYQQQggjSQIlhBBCCGEkSaCEEEIIIYwkCZQQQgghhJEkgRJCCCGEMJIkUEIIIYQQRpIESgghhBDCSJJACSGEEEIYSRIoIYQQQggjSQIlhBBCCGEkS1MHIMqGRacXEZkaSftK7WlUoREWWgtThySEEEI8NEmgRJHbcXUH0w9MB2D+ifm42brR0bcjnSt3JtA7EGsLaxNHKIQQQhhHoyiKYuogHlViYiIuLi4kJCTg7Oxs6nDEHdKy0+i3uh/Xk6/zmNtjXEu+RlJmkn69g5UD7Sq2o1OVTrSt2BYHKwcTRiuEEKI4mfPxW1qgRJH66dhPXE++jpeDFwu6L8DKwor/Iv9ja9hWtoZtJSYthvWX17P+8nqstda08GlB58qd6eDbATdbN1OHL4QQQuRLWqBEkbkUf4kBaweQrctmZoeZdK7S2WC9TtFxPPY4wWHBBF8JJiwpTL9Oq9HS2KMxnSt3pnPlzvg4+hR3+EIIIYqYOR+/JYESRUJRFMZsGsN/kf/RrlI7vu/0PRqN5r7lL8RfIDgsmK1hWzkdd9pg/WNuj+mTqWqu1e5bl7FxZuoySc1KJTU7FUVRsLO0w87SDltLW7QauVBVCCGKijkfvx8qgZo1axaff/45kZGRNGzYkO+++47mzZvnW7ZDhw7s2LEjz/LHH3+cv//+G4CRI0eycOFCg/VBQUFs2LChQPGY8w4ordZeXMs7u9/B1sKWlU+spJJTJaOefz35OlvDthIcFsyR6CPoFJ1+XRXnKnSq3Il2FdthbWFNSlYKqdmppGalkpadZvA4NTuVlKwU0rLS9MtSslP069Ky0shWsu8Zh62FrT6ZujOxyr1/582gjIUtdlZ2uNq40tSzKZZaOVsuhBB3M+fjt9EJ1NKlSxk+fDhz5swhMDCQmTNnsmzZMs6ePYuHh0ee8nFxcWRmZuof37hxg4YNGzJv3jxGjhwJqAlUVFQU8+fP15ezsbGhXLlyBYrJnHdAaZSQkUCfVX2IS4/jlcavMK7BuEeq70baDXZc28GWK1vYF7GPLF1WIUVqyMbCBg0a0nPSC7Xe8Y3G83zD5wu1TiGEKA3M+fhtdAIVGBhIs2bN+P777wHQ6XT4+vry8ssv8/bbbz/w+TNnzuT9998nIiICBwf1iquRI0cSHx/PqlWrjH8FmPcOKI0+2vsRf577E38Xf1b0XoGVhVWh1Z2cmczu67sJDgvmcPRhrLRW2FnaYW9lj4OlA/ZW9thb2qt/c+/feuxg5XB7naU9dlZ2+ufYWdrpW4l0io707HTSstP0N4PHOWmkZd1annNredYd92/dbqbf5OSNk5S3Lc/mgZsL9X0QQojSwJyP30adV8jMzOTQoUNMnjxZv0yr1dKlSxf27t1boDp+/vlnhg4dqk+ecm3fvh0PDw/KlStHp06d+Pjjjylfvny+dWRkZJCRkaF/nJiYaMzLEEXoeMxxlp1bBsC7ge8WetLgaO1Id//udPfvXqj13kmr0eoTsEeRpcui2/JuxKbFEnw1mO5+RRezEEKI4mVUD9nY2FhycnLw9PQ0WO7p6UlkZOQDn3/gwAFOnDjB2LFjDZZ3796dX3/9leDgYGbMmMGOHTvo0aMHOTk5+dYzffp0XFxc9DdfX19jXoYoIjm6HD7a9xEKCr2q9qK5d/794soKK60VA2sOBGDpmaUmjkYIIURhKtZLjH7++Wfq16+fp8P50KFD6dOnD/Xr16dv376sW7eO//77j+3bt+dbz+TJk0lISNDfrl69WgzRiwdZcnYJp+NO42TtxOtNXzd1OCXCgBoDsNBYcDDqIBduXjB1OEIIIQqJUQmUu7s7FhYWREVFGSyPiorCy8vrvs9NSUlhyZIljBkz5oHbqVq1Ku7u7ly4kP8Bx8bGBmdnZ4ObMK2Y1Bi+P6L2i3u18au427mbOKKSwcvBiw6+HQBYelZaoYQQorQwKoGytrYmICCA4OBg/TKdTkdwcDAtW7a873OXLVtGRkYGTz/99AO3c+3aNW7cuIG3t7cx4QkT+vy/z0nOSqZe+Xr601ZCNaTWEADWXlpLalaqiaMRQghRGIw+hTdx4kTmzp3LwoULOX36NC+88AIpKSmMGjUKgOHDhxt0Ms/1888/07dv3zwdw5OTk3njjTfYt28fly9fJjg4mCeeeILq1asTFBT0kC9LFKc94XtYf3k9Wo2W91q+h4XWwtQhlSiB3oH4OfuRkpXCukvrTB2OEEKIQmD06H5DhgwhJiaG999/n8jISBo1asSGDRv0HcvDwsLQag3zsrNnz7J79242bdqUpz4LCwuOHTvGwoULiY+Px8fHh27duvHRRx9hY2PzkC9LFJeMnAw+2f8JAENrDaVO+Tomjqjk0Wq0DK41mM/++4ylZ5cyqOagQhtJXQghhGnIVC7ikcw+OpsfQn7A3c6dNX3X4GTtZOqQSqSEjAS6LOtCek46v/b4lcYejU0dkhBCmJw5H79loi/x0MISw5h3bB4AbzZ7U5Kn+3CxcaGHfw8AlpxZYuJohBBCPCpJoMRDURSFT/Z/QqYukxbeLWSQyAIYUlvtTL75ymZupN0wcTRCCCEehSRQ4qFsurKJf8P/xUprxf8C/yd9egqgbvm61HevT5Yui5UXVpo6HCGEEI9AEihhtOTMZD478BkAY+qPwc/Fz7QBmZHcIQ2WnV1Gji7/kfaFEEKUfJJACaPNCplFdFo0vk6+jK0/9sFPEHpBfkE4WzsTnhLO7uu7TR2OEEKIhyQJlDDKmbgzLDqzCID/Bf4PGwsZasIYtpa29KveD5CRyYUQwpxJAiUKTKfo+GjfR+gUHd2qdKN1xdamDsksDa41GIDd13dzNUnmcRRCCHMkCdQDnI07S0h0iKnDKBFWnF/BsZhj2Fva82azN00djtmq7FyZ1j6tUVBYdm6ZqcMRQgjxECSBuo/zN88zeuNoXtjyAqdunDJ1OCZ1I+0GMw/NBOClxi/h6eBp2oDMXG5n8pXnV5KRk2HiaIQQQhhLEqj7qORUiequ1UnOSub5zc9zMf6iqUMyma8OfUViZiK13WozrPYwU4dj9tpVaoeXgxfxGfFsupx3iiMhhBAlmyRQ92FnaceszrOoW74uNzNu8uymZ8tkn5WDkQdZc3ENGjS82+JdLLVGT6Eo7mKhtWBQzUGAdCYXQghzJAnUAzhaOzKnyxyqu1YnOi2acZvGEZUSZeqwik1WThYf7/sYgAE1B9CwQkMTR1R69K/RH0utJUdjjnL6xmlThyOEEMIIkkAVgKutKz91/QlfJ1+uJ19n3OZxxKXHmTqsYvHrqV+5mHARN1s3JjSZYOpwShV3O3e6Vu4KSCuUEEKYG0mgCqiCfQXmdZuHp70noQmhPL/5eRIzE00dVpEKTw7nx2M/AjAxYCIuNi4mjqj0yR3S4J/Qf0jKTDJxNEIIIQpKEigj+Dj6MK/bPNxs3Tgdd5rxW8aTmpVq6rCKzPQD00nLTiPAM4A+1fqYOpxSKcAzgOqu1UnLTmPNxTWmDkcIIUQBSQJlJD8XP37q+hNO1k6ExITw6rZXS+Vl6NvCtrH96nYsNZa8G/iuTBZcRDQajX5Ig6Vnl6IoiokjEkIIURCSQD2EWm61mN1lNnaWduyL2McbO94gS5dl6rAKTWpWKtMPTAdgeN3hVC9X3cQRlW69qvbC3tKe0IRQ/ov8z9ThCCGEKABJoB5SwwoN+b7T91hrrdl2dRvv7n4XnaIzdViPLDYtlle2vUJESgQ+Dj481+A5U4dU6jlaO9K7Wm8AlpxdYuJohBBCFIQkUI+guXdzvu74NZYaS/4J/YeP931s1qdgDkYeZPDaweyP2I+dpR0ftP4Aeyt7U4dVJuR2Jt8Wto3o1GgTRyOEEOJBJIF6RO0qtWN6u+loNVqWnVvGlwe/NLskSqfomHd8HmM2jSEmLYZqLtVY3HMxLbxbmDq0MqNmuZo08WhCtpLNivMrTB2OEEKIB5AEqhB09+vO1JZTAVh4aiFzjs0xbUBGiE+PZ3zweL45/A06RUfvqr1Z1HMR1VyrmTq0Mie3M/nys8tLVZ86IYQojSSBKiT9avTjrWZvAfBDyA/8evJXE0f0YCHRIQxcO5Dd13djY2HDB60+YFqbaXLazkS6VOmCm60b0WnR7Li6w9ThCCGEuA9JoArR03We5qVGLwHw+cHPWXGuZJ6KURSFhScXMmrDKKJSo6jiXIU/Hv+D/jX6y3AFJmRtYU3/Gv0BGZlcCCFKOkmgCtmzDZ5lVN1RAHyw9wPWh643cUSGEjISeHXbq3xx8AuylWx6+PVgaa+l1HKrZerQBDCo5iA0aNgXsY/QhFBThyOEEOIeJIEqZBqNhtcCXmNwzcEoKLyz6x22X91u6rAAOBl7kiHrhrDt6jastFa8G/guM9rNwMHKwdShiVt8HH1oX6k9AH+e/dPE0QghhLgXSaCKgEaj4X8t/kevqr3IVrJ5ffvr7I/Yb7J4FEVh0elFPLP+Ga4nX6eSYyV+e/w3htQeIqfsSqAhtdXO5KsvrC7VUwUJIYQ5kwSqiGg1Wj5q/RGdfDuRqcvk5a0vExIdUuxxJGcmM2nHJKYfmE6WLovOlTuztPdS6pavW+yxiIJp5dOKSo6VSMpKYsPlDaYORwghRD4kgSpCllpLPm//OS29W5KWncaLwS9yJu5MsW3/TNwZhqwbwqYrm7DUWPJmszf5usPXOFs7F1sMwnhajVY/sOaSM0vMalwxRVE4E3eGa0nXTB2KEEIUKUmgipi1hTUzO86ksUdjkjKTeG7zc0XeOVhRFJafW85Tfz9FWFIY3g7eLOixgGfqPCOn7MxE3+p9sdZaczruNCdiT5g6nAcKSwxjdshseq/qzaC1g+i/pj9HY46aOiwhhCgyGsWc/r29h8TERFxcXEhISMDZuWS2riRlJjFm4xhOx53Gw96D0fVG4+vki6+TLxUdK2JtYV0o20nNSuXDfR/y96W/AWhfqT3T2kzDxcalUOoXxed/u//Hmotr6FOtD9PaTDN1OHnEpcexIXQDf1/6m2Oxx/Ksd7Fx4dfuv1LVtaoJohNCmANzOH7fiyRQxehm+k1GbRjFxYSLBss1aPBy8NInVHffHK0dC1T/hZsXmLhjIqEJoVhoLHilySuMrDsSrUYaGs3RsZhjPPXPU1hrrQkeFIyrraupQyI1K5VtV7fx96W/2RO+hxwlB1BPO7b0bknPqj1p6dOSV7a+wvHY43g5ePFbj9/wcvAyceRCiJLIXI7f+ZEEqpjdSLvBojOLuBR/iatJV7madJXU7PtfaeVm60Ylp0oGSVVlp8pUcqpEedvyaDQaVl9Yzcf7PiY9Jx0POw8+b/85TTybFNOrEkVBURSGrBvC6bjTTGo6iRF1R5gkjmxdNvsj9rPu0jqCw4JJy07Tr6tbvi69qvaiu3933O3c9ctvpt9k+PrhXE68THXX6izovkBaQYUQeZjT8ftukkCZmKIoxKXH6ZOp3FtYUhjXkq4Rlx533+fbW9rjYe/B5cTLgHoF1/S203GzdSuG6EVRW3FuBVP3TsXXyZd1/dYVW2uioiicvHGSvy/9zfrQ9dxIv6FfV8mxEj2r9qRn1Z74u/jfs47w5HCe+ecZotOiaezRmB+7/oidpV1xhC+EMBPmfPyWBKqES85M5lryNTWpSgzjatJVriWpjyNSIlBQd59Wo+XFhi8yrsE4OWVXiqRmpdJlWReSspKY02UOrSu2LtLtXU28yrrQdfxz6R99Ug7gauNKd7/u9Kzak4YVGhb4YoTzN88zYsMIkjKT6FCpA193/BpLrWURRS+EMDfmfPyWBMqMZeZkcj35OteSrlHRqSJVXaSzbmk048AMfj/9Ox18O/Bdp+8Kvf649Dg2Xt7IukvrOBZzuzO4rYUtHX070qtaL1r6tMRKa/VQ9R+OOsyzm58lIyeDftX78UGrD+RqUCEEYN7Hb0mghCjhQhNC6bOqD1qNlg39N+Dt6P3QdeXocriYcJETsSc4Hnuck7EnOXfznEFn8BbeLehVtRedKncqtGl+toVtY8L2CegUHePqj+OVJq8USr1CCPNmzsdvaUsXooTzd/En0CuQ/ZH7WXZuWYGTD0VRuJ58nRM3TnAiRk2YTsedNugEnqtO+Tr0qtqLHv49DDqDF5aOlTsypeUUpuyZwtzjcylvV56nHnuq0LcjhBDFRRIoIczAkNpD2B+5nxXnV/BCwxewssh7Oi0uPY4TsSc4GXuS47HHORF7gpsZN/OUc7ByoG75utR1r0t99/rUd69fLMMM9K/RnxtpN/j2yLd8euBT3Gzd6OHfo8i3K4QQReGhEqhZs2bx+eefExkZScOGDfnuu+9o3rx5vmUXLFjAqFGjDJbZ2NiQnp6uf6woClOmTGHu3LnEx8fTunVrZs+eTY0aNR4mPCFKnQ6+HfCw8yA6LZotYVtoX6m9fpTy3NNx15Ov53mepdaSWuVqUc+9HvXd61PPvR5+zn5YaC1M8CpgbP2xxKbFsujMIt7Z/Q4uNi608mllkliEEOJRGJ1ALV26lIkTJzJnzhwCAwOZOXMmQUFBnD17Fg8Pj3yf4+zszNmzZ/WP7+5A+tlnn/Htt9+ycOFC/P39ee+99wgKCuLUqVPY2toaG6IQpY6V1oqBNQfyw9EfmLpnKuk56egUXZ5y/i7+1CtfT58w1XKrVWij3BcGjUbDW83fUkcxv7yBCdsmMD9oPnXdZXJrIYR5MboTeWBgIM2aNeP7778HQKfT4evry8svv8zbb7+dp/yCBQuYMGEC8fHx+danKAo+Pj68/vrrTJo0CYCEhAQ8PT1ZsGABQ4cOfWBM5twJTYiCik6NpseKHmTqMgHwsPfQtyrVc69H3fJ1cbJ2MnGUBZOZk8n44PHsi9hHOZty/NrjV/xc/EwdlhCimJnz8duoFqjMzEwOHTrE5MmT9cu0Wi1dunRh796993xecnIyVapUQafT0aRJEz755BPq1lX/4wwNDSUyMpIuXbroy7u4uBAYGMjevXvzTaAyMjLIyMjQP05MTDTmZQhhljzsPfjt8d+ISomirntdPOzzb/E1B7mTbI/eOJpTN07x/Jbn+a3Hb1Swr2Dq0IQQokCMGnExNjaWnJwcPD09DZZ7enoSGRmZ73Nq1arFL7/8wurVq/n999/R6XS0atWKa9euAeifZ0yd06dPx8XFRX/z9fU15mUIYbbqlK9Dx8odzTp5yuVg5cAPnX+gslNlridf5/ktz5OUmWTqsIQQokCKfMjqli1bMnz4cBo1akT79u3566+/qFChAj/++OND1zl58mQSEhL0t6tXrxZixEKI4lLerjw/dv0Rdzt3zt08xytbXyEjJ+PBTxRCCBMzKoFyd3fHwsKCqKgog+VRUVF4eRXsMmgrKysaN27MhQsXAPTPM6ZOGxsbnJ2dDW5CCPNUyakSs7vMxtHKkYNRB3lr51vk6HJMHZYQQtyXUQmUtbU1AQEBBAcH65fpdDqCg4Np2bJlgerIycnh+PHjeHuroyn7+/vj5eVlUGdiYiL79+8vcJ1CCPNW260233b6FiutFcFhwXy8/2NKwSQJQohSzOhTeBMnTmTu3LksXLiQ06dP88ILL5CSkqIf62n48OEGncw//PBDNm3axKVLlzh8+DBPP/00V65cYezYsYB6WfOECRP4+OOPWbNmDcePH2f48OH4+PjQt2/fwnmVQogSr5lXM2a0m4EGDcvPLWf20dmmDkkIIe7J6HGghgwZQkxMDO+//z6RkZE0atSIDRs26DuBh4WFodXezstu3rzJuHHjiIyMpFy5cgQEBLBnzx7q1KmjL/Pmm2+SkpLCs88+S3x8PG3atGHDhg0yBpQQZUzXKl15t8W7fLTvI2YfnU152/IMqT3E1GEJIUQeMpmwEKLEmR0ymx+O/oAGDV+0/4Juft1MHZIQogiY8/G7yK/CE0IIYz3f8HkG1xyMgsLbu97mQMQBU4ckhBAGJIESQpQ4Go2GdwLfoWuVrmTpsnhl2yucv3ne1GEJIYSeJFBCiBLJQmvB9LbTaebVjJSsFL4/8r2pQxJCCD1JoIQQJZaNhQ3vBr4LwLar27iWdM3EEQkhhEoSKCFEiVbVtSqtfVqjoLD4zGJThyOEEIAkUEIIM/DUY08B8Nf5v0jJSjFxNEIIIQmUEMIMtK7YGj9nP5Kzkll9YbWpwxFCCEmghBAln1aj1bdCLTqzCJ2iM3FEQoiyThIoIYRZ6FOtD05WTlxJvMLu67tNHY4QooyTBEoIYRbsrezpX6M/AL+f+t3E0QghyjpJoIQQZmPYY8PQarTsjdjLxfiLpg5HCFGGSQIlhDAbFR0r0sm3EwB/nP7DxNEIIcoySaCEEGYltzP52otrSchIMHE0QoiyShIoIYRZCfAMoLZbbdJz0ll+brmpwxFClFGSQAkhzIpGo+Hpx54GYPGZxWTpskwckRCiLJIESghhdnr498DN1o2o1CiCw4JNHY4QogySBEoIYXasLawZXGswAH+cks7kQojiJwmUEMIsDak1BEutJSExIZyMPWnqcIQQZYwkUEIIs+Ru504Pvx4A/H5aBtYUQhQvSaCEEGbrqTrqkAYbLm8gJjXGxNEIIcoSSaCEEGarbvm6NPZoTLYum6Vnl5o6HCFEGSIJlBDCrOUOrLns3DIycjJMHI0QoqyQBEoIYdY6V+6Ml4MXcelx/HPpH1OHI4QoIySBEkKYNUutJcNqDwPU+fEURTFxREKIskASKCGE2RtQYwC2FracvXmWg1EHTR2OEKIMkARKCGH2XGxc6FOtD6C2QgkhRFGTBEoIUSrkdibfGraVa0nXTByNEKK0kwRKCFEqVHWtSiufVigoLD6z2NThCCFKOUmghBClRm4r1MrzK0nNSjVxNEKI0kwSKCFEqdGmYhv8nP1Iykpi9cXVpg5HCFGKSQIlhCg1tBotTz72JKB2JtcpOhNHJIQorSSBEkKUKk9UewInKyeuJF5h9/Xdpg5HCFFKSQIlhChV7K3s6VejHyBDGgghio4kUEKIUmdY7WFoNVr2hO/hYvxFU4cjhCiFJIESQpQ6lZwq0dG3IyCtUEKIoiEJlBCiVMod0mDtxbUkZCSYOBohRGkjCZQQolRq6tmU2m61Sc9JZ8X5FaYORwhRyjxUAjVr1iz8/PywtbUlMDCQAwcO3LPs3Llzadu2LeXKlaNcuXJ06dIlT/mRI0ei0WgMbt27d3+Y0IQQAgCNRqNvhVp8ZjHZumwTRySEKE2MTqCWLl3KxIkTmTJlCocPH6Zhw4YEBQURHR2db/nt27czbNgwtm3bxt69e/H19aVbt25cv37doFz37t2JiIjQ3xYvlqkYhBCPpod/D9xs3YhMiSQ4LNjU4QghShGjE6ivvvqKcePGMWrUKOrUqcOcOXOwt7fnl19+ybf8H3/8wYsvvkijRo2oXbs28+bNQ6fTERxs+GNmY2ODl5eX/lauXLmHe0VCCHGLjYUNg2oOAqQzuRCicBmVQGVmZnLo0CG6dOlyuwKtli5durB3794C1ZGamkpWVhZubm4Gy7dv346Hhwe1atXihRde4MaNG/esIyMjg8TERIObEELkZ0itIVhqLTkSfYSTsSdNHY4QopQwKoGKjY0lJycHT09Pg+Wenp5ERkYWqI633noLHx8fgySse/fu/PrrrwQHBzNjxgx27NhBjx49yMnJybeO6dOn4+Lior/5+voa8zKEEGVIBfsKdPdT+1T+fvp3E0cjhCgtivUqvE8//ZQlS5awcuVKbG1t9cuHDh1Knz59qF+/Pn379mXdunX8999/bN++Pd96Jk+eTEJCgv529erVYnoFQghz9PRjTwOw4fIGYlJjTByNEKI0MCqBcnd3x8LCgqioKIPlUVFReHl53fe5X3zxBZ9++imbNm2iQYMG9y1btWpV3N3duXDhQr7rbWxscHZ2NrgJIcS91HWvS2OPxmTrsvnz3J+mDkcIUQoYlUBZW1sTEBBg0AE8t0N4y5Yt7/m8zz77jI8++ogNGzbQtGnTB27n2rVr3LhxA29vb2PCE0KIe8od0uDPs3+SkZNh4miEEObO6FN4EydOZO7cuSxcuJDTp0/zwgsvkJKSwqhRowAYPnw4kydP1pefMWMG7733Hr/88gt+fn5ERkYSGRlJcnIyAMnJybzxxhvs27ePy5cvExwczBNPPEH16tUJCgoqpJcphCjrOlfujJeDF3HpcawPXW/qcIQQZs7oBGrIkCF88cUXvP/++zRq1IiQkBA2bNig71geFhZGRESEvvzs2bPJzMxk4MCBeHt7629ffPEFABYWFhw7dow+ffpQs2ZNxowZQ0BAALt27cLGxqaQXqYQoqyz1FoytNZQQB3SQFEUE0ckhDBnGqUU/IokJibi4uJCQkKC9IcSQtxTQkYCXZZ1IT0nnflB82nq9eAuBUKIomPOx2+ZC08IUWa42LjQu1pvQIY0EEI8GkmghBBlSm5n8m1Xt3E9+foDSgshRP4sTR2AEEIUp2qu1Xiy9pPUdquNu527qcMRQpgpSaCEEGXO5MDJDy4khBD3IafwhBBCCCGMJAmUEEIIIYSRJIESQgghhDCSJFBCCCGEEEaSBEoIIYQQwkiSQAkhhBBCGEkSKCGEEEIII0kCJYQQQghhJEmghBBCCCGMJAmUEEIIIYSRJIESQgghhDCSJFBCCCGEEEaSBEoIIYQQwkiWpg6gMCiKAkBiYqKJIxFCCCFEQeUet3OP4+akVCRQSUlJAPj6+po4EiGEEEIYKykpCRcXF1OHYRSNYo5p3110Oh3h4eE4OTmh0WgKte7ExER8fX25evUqzs7OhVq3eHiyX0ou2Tclk+yXkqss7xtFUUhKSsLHxwet1rx6FZWKFiitVkulSpWKdBvOzs5l7oNtDmS/lFyyb0om2S8lV1ndN+bW8pTLvNI9IYQQQogSQBIoIYQQQggjSQL1ADY2NkyZMgUbGxtThyLuIPul5JJ9UzLJfim5ZN+Yp1LRiVwIIYQQojhJC5QQQgghhJEkgRJCCCGEMJIkUEIIIYQQRpIESgghhBDCSJJACSGEEEIYSRKoB5g1axZ+fn7Y2toSGBjIgQMHTB1SmTZ16lQ0Go3BrXbt2qYOq0zauXMnvXv3xsfHB41Gw6pVqwzWK4rC+++/j7e3N3Z2dnTp0oXz58+bJtgy5EH7ZeTIkXm+Q927dzdNsGXI9OnTadasGU5OTnh4eNC3b1/Onj1rUCY9PZ3x48dTvnx5HB0dGTBgAFFRUSaKWDyIJFD3sXTpUiZOnMiUKVM4fPgwDRs2JCgoiOjoaFOHVqbVrVuXiIgI/W337t2mDqlMSklJoWHDhsyaNSvf9Z999hnffvstc+bMYf/+/Tg4OBAUFER6enoxR1q2PGi/AHTv3t3gO7R48eJijLBs2rFjB+PHj2ffvn1s3ryZrKwsunXrRkpKir7Ma6+9xtq1a1m2bBk7duwgPDyc/v37mzBqcV+KuKfmzZsr48eP1z/OyclRfHx8lOnTp5swqrJtypQpSsOGDU0dhrgLoKxcuVL/WKfTKV5eXsrnn3+uXxYfH6/Y2NgoixcvNkGEZdPd+0VRFGXEiBHKE088YZJ4xG3R0dEKoOzYsUNRFPX7YWVlpSxbtkxf5vTp0wqg7N2711RhivuQFqh7yMzM5NChQ3Tp0kW/TKvV0qVLF/bu3WvCyMT58+fx8fGhatWqPPXUU4SFhZk6JHGX0NBQIiMjDb4/Li4uBAYGyvenBNi+fTseHh7UqlWLF154gRs3bpg6pDInISEBADc3NwAOHTpEVlaWwXemdu3aVK5cWb4zJZQkUPcQGxtLTk4Onp6eBss9PT2JjIw0UVQiMDCQBQsWsGHDBmbPnk1oaCht27YlKSnJ1KGJO+R+R+T7U/J0796dX3/9leDgYGbMmMGOHTvo0aMHOTk5pg6tzNDpdEyYMIHWrVtTr149QP3OWFtb4+rqalBWvjMll6WpAxDCGD169NDfb9CgAYGBgVSpUoU///yTMWPGmDAyIczD0KFD9ffr169PgwYNqFatGtu3b6dz584mjKzsGD9+PCdOnJD+m2ZOWqDuwd3dHQsLizxXQERFReHl5WWiqMTdXF1dqVmzJhcuXDB1KOIOud8R+f6UfFWrVsXd3V2+Q8XkpZdeYt26dWzbto1KlSrpl3t5eZGZmUl8fLxBefnOlFySQN2DtbU1AQEBBAcH65fpdDqCg4Np2bKlCSMTd0pOTubixYt4e3ubOhRxB39/f7y8vAy+P4mJiezfv1++PyXMtWvXuHHjhnyHipiiKLz00kusXLmSrVu34u/vb7A+ICAAKysrg+/M2bNnCQsLk+9MCSWn8O5j4sSJjBgxgqZNm9K8eXNmzpxJSkoKo0aNMnVoZdakSZPo/f927h5VcTAMw/A7iBEFDQbEP0gQdAuitaDVAcUiWFmIjSKIhV0QwR24ADt7V2BraSsoduoCRMt3ioEDwxyENJOZ431BuhQPfM1NSPLxIY7jyOVykdlsJqFQSDqdTtDT3s79fv/tqcX5fJb9fi+WZYlt2zIej2WxWEipVJJCoSCe50kul5Nmsxnc6Dfw6lwsy5L5fC7tdlsymYycTieZTqdSLBal0WgEuPr7Gw6Hsl6vZbPZSDwe/3yvyTRNiUajYpqm9Ho9mUwmYlmWJBIJGY1GUq1WpVKpBLweXwr6M8B/3XK5VNu21TAMLZfLutvtgp701lzX1Ww2q4ZhaD6fV9d19Xg8Bj3rLW23WxWRP65ut6uqv35l4HmeptNpjUQiWqvV9HA4BDv6Dbw6l8fjofV6XVOplIbDYXUcR/v9vt5ut6Bnf3tfnYmI6Gq1+rzn+XzqYDDQZDKpsVhMW62WXq/X4EbjpR+qqn8/2wAAAP5fvAMFAADgEwEFAADgEwEFAADgEwEFAADgEwEFAADgEwEFAADgEwEFAADgEwEFAADgEwEFAADgEwEFAADgEwEFAADg008gf7UlRMn7LAAAAABJRU5ErkJggg==" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 58 + "execution_count": 6 }, { "cell_type": "markdown", @@ -461,41 +485,46 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-09-25T22:58:21.910360Z", - "start_time": "2024-09-25T22:58:21.726272Z" + "end_time": "2025-05-28T13:33:17.723505Z", + "start_time": "2025-05-28T13:33:17.562336Z" } }, "outputs": [ { "data": { - "text/plain": "[]" + "text/plain": [ + "[]" + ] }, - "execution_count": 59, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "text/plain": "
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" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 59 + "execution_count": 7 }, { "cell_type": "markdown", "source": [ - "### PLAID\n", - "PLAID stands for the Plug Load Appliance Identification Dataset. The data are intended for load identification research. The first version of PLAID is named PLAID1, collected in summer 2013. A second version of PLAID was collected in winter 2014 and released under the name PLAID2.\n", - "This dataset comes from PLAID1. It includes current and voltage measurements sampled at 30 kHz from 11 different appliance types present in more than 56 households in Pittsburgh, Pennsylvania, USA. Data collection took place during the summer of 2013. Each appliance type is represented by dozens of different instances of varying makes/models.\n", - "For each appliance, three to six measurements were collected for each state transition. These measurements were then post-processed to extract a few-second-long window containing both the steady-state operation and the startup transient )when available).\n", - "The classes correspond to 11 different appliance types: air\n", - "conditioner (class 0), compact flourescent lamp, fan, fridge,\n", - "hairdryer , heater, incandescent light bulb, laptop, microwave,\n", - "vacuum,washing machine (class 10). The data is univariate and unequal length." + "### PickupGestureWiimoteZ\n", + "\n", + "The time series is the Z dimension of motion data captured from a Nintendo Wiimote \n", + "remote controller with built-in three-axis accelerometer by a 10 subjects performing \n", + "a pick up task. The classification task is to identify the subject based on the \n", + "motion. The data was created by Guna Joze et al. (see [1]) and edited by Hoang \n", + "Anh Dau for the UCR archive. \n", + "\n", + "[1] Guna, Joze, Iztok Humar, and Matevz Pogacnik. \"Intuitive gesture based user identification system.\" Telecommunications and Signal Processing (TSP), 2012 35th International Conference on. IEEE, 2012." ], "metadata": { "collapsed": false @@ -504,27 +533,27 @@ { "cell_type": "code", "source": [ - "from aeon.datasets import load_plaid\n", + "from aeon.datasets import load_pickup_gesture_wiimoteZ\n", "\n", - "plaid, plaid_labels = load_plaid(split=\"train\")\n", + "wii, wii_labels = load_pickup_gesture_wiimoteZ(split=\"train\")\n", "plt.title(\n", - " f\"three train cases for PLAID, classes\"\n", - " f\"( {plaid_labels[0]}, {plaid_labels[10]}, {plaid_labels[200]})\"\n", + " f\"three train cases for WII, classes\"\n", + " f\"( {wii_labels[0]}, {wii_labels[10]}, {wii_labels[20]})\"\n", ")\n", - "print(f\" number of cases = \" f\"{len(plaid)}\")\n", - "print(f\" First case shape = \" f\"{plaid[0].shape}\")\n", - "print(f\" Tenth case shape = \" f\"{plaid[10].shape}\")\n", - "print(f\" 200th case shape = \" f\"{plaid[200].shape}\")\n", + "print(f\" number of cases = \" f\"{len(wii)}\")\n", + "print(f\" First case shape = \" f\"{wii[0].shape}\")\n", + "print(f\" Tenth case shape = \" f\"{wii[10].shape}\")\n", + "print(f\" 200th case shape = \" f\"{wii[20].shape}\")\n", "\n", - "plt.plot(plaid[0][0])\n", - "plt.plot(plaid[10][0])\n", - "plt.plot(plaid[200][0])" + "plt.plot(wii[0][0])\n", + "plt.plot(wii[10][0])\n", + "plt.plot(wii[20][0])" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-09-25T22:58:22.119236Z", - "start_time": "2024-09-25T22:58:21.932521Z" + "end_time": "2025-05-28T13:33:39.754764Z", + "start_time": "2025-05-28T13:33:39.587436Z" } }, "outputs": [ @@ -532,30 +561,34 @@ "name": "stdout", "output_type": "stream", "text": [ - " number of cases = 537\n", - " First case shape = (1, 500)\n", - " Tenth case shape = (1, 300)\n", - " 200th case shape = (1, 200)\n" + " number of cases = 50\n", + " First case shape = (1, 324)\n", + " Tenth case shape = (1, 97)\n", + " 200th case shape = (1, 108)\n" ] }, { "data": { - "text/plain": "[]" + "text/plain": [ + "[]" + ] }, - "execution_count": 60, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "text/plain": "
", - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 60 + "execution_count": 10 }, { "cell_type": "markdown", diff --git a/examples/transformations/preprocessing.ipynb b/examples/transformations/preprocessing.ipynb index cccc7ac42f..b1236ece11 100644 --- a/examples/transformations/preprocessing.ipynb +++ b/examples/transformations/preprocessing.ipynb @@ -325,17 +325,21 @@ "is_executing": true }, "ExecuteTime": { - "end_time": "2024-11-17T13:49:10.589292Z", - "start_time": "2024-11-17T13:49:10.576923Z" + "end_time": "2025-05-28T13:37:35.086084Z", + "start_time": "2025-05-28T13:36:09.244708Z" } }, "source": [ "from aeon.classification.convolution_based import RocketClassifier\n", - "from aeon.datasets import load_basic_motions, load_japanese_vowels, load_plaid\n", + "from aeon.datasets import (\n", + " load_basic_motions,\n", + " load_japanese_vowels,\n", + " load_pickup_gesture_wiimoteZ,\n", + ")\n", "from aeon.utils.validation import has_missing, is_equal_length, is_univariate" ], "outputs": [], - "execution_count": 10 + "execution_count": 2 }, { "cell_type": "markdown", @@ -373,8 +377,8 @@ "is_executing": true }, "ExecuteTime": { - "end_time": "2024-11-17T13:49:17.636972Z", - "start_time": "2024-11-17T13:49:17.599882Z" + "end_time": "2025-05-28T13:37:44.511744Z", + "start_time": "2025-05-28T13:37:44.488521Z" } }, "source": [ @@ -397,15 +401,15 @@ ] } ], - "execution_count": 11 + "execution_count": 6 }, { "cell_type": "code", "source": [ "# Unequal length univariate data\n", - "plaid_X, plaid_y = load_plaid()\n", - "X = plaid_X\n", - "print(type(plaid_X), \"\\n\", plaid_X[0].shape, \"\\n\", plaid_X[10].shape)\n", + "wii_X, wii_y = load_pickup_gesture_wiimoteZ()\n", + "X = wii_X\n", + "print(type(wii_X), \"\\n\", wii_X[0].shape, \"\\n\", wii_X[10].shape)\n", "print(\n", " f\"univariate = {is_univariate(X)}, has missing ={has_missing(X)}, equal \"\n", " f\"length = {is_equal_length(X)}\"\n", @@ -417,8 +421,8 @@ "is_executing": true }, "ExecuteTime": { - "end_time": "2024-11-17T13:49:18.797171Z", - "start_time": "2024-11-17T13:49:18.626506Z" + "end_time": "2025-05-28T13:37:45.578203Z", + "start_time": "2025-05-28T13:37:45.513828Z" } }, "outputs": [ @@ -427,19 +431,19 @@ "output_type": "stream", "text": [ " \n", - " (1, 500) \n", - " (1, 300)\n", + " (1, 324) \n", + " (1, 97)\n", "univariate = True, has missing =False, equal length = False\n" ] } ], - "execution_count": 12 + "execution_count": 7 }, { "metadata": { "ExecuteTime": { - "end_time": "2024-11-17T13:49:19.575389Z", - "start_time": "2024-11-17T13:49:19.526275Z" + "end_time": "2025-05-28T13:37:49.679258Z", + "start_time": "2025-05-28T13:37:49.639207Z" } }, "cell_type": "code", @@ -460,7 +464,7 @@ ] } ], - "execution_count": 13 + "execution_count": 8 }, { "cell_type": "markdown", @@ -470,7 +474,7 @@ { "cell_type": "code", "source": [ - "series_lengths = [array.shape[1] for array in plaid_X]\n", + "series_lengths = [array.shape[1] for array in wii_X]\n", "\n", "# Find the minimum and maximum of the second dimensions\n", "min_length = min(series_lengths)\n", @@ -483,8 +487,8 @@ "is_executing": true }, "ExecuteTime": { - "end_time": "2024-11-17T13:49:21.081698Z", - "start_time": "2024-11-17T13:49:21.061750Z" + "end_time": "2025-05-28T13:38:05.700223Z", + "start_time": "2025-05-28T13:38:05.689668Z" } }, "outputs": [ @@ -492,11 +496,11 @@ "name": "stdout", "output_type": "stream", "text": [ - " Min length = 100 max length = 1344\n" + " Min length = 29 max length = 361\n" ] } ], - "execution_count": 14 + "execution_count": 9 }, { "metadata": {}, @@ -572,7 +576,7 @@ "from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier\n", "\n", "knn = KNeighborsTimeSeriesClassifier()\n", - "model = knn.fit(plaid_X, plaid_y)" + "model = knn.fit(wii_X, wii_y)" ], "outputs": [], "execution_count": 16 @@ -589,15 +593,15 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-11-17T13:49:27.034532Z", - "start_time": "2024-11-17T13:49:27.001467Z" + "end_time": "2025-05-28T13:38:30.585470Z", + "start_time": "2025-05-28T13:38:30.565833Z" } }, "cell_type": "code", "source": [ "rc = RocketClassifier()\n", "try:\n", - " rc.fit(plaid_X, plaid_y)\n", + " rc.fit(wii_X, wii_y)\n", "except ValueError as e:\n", " print(f\"ValueError: {e}\")" ], @@ -606,11 +610,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "ValueError: Data seen by instance of RocketClassifier has unequal length series, but RocketClassifier cannot handle unequal length series. \n" + "ValueError: Data seen by instance of RocketClassifier has unequal length series, but RocketClassifier cannot handle these characteristics. \n" ] } ], - "execution_count": 17 + "execution_count": 10 }, { "cell_type": "markdown", @@ -636,8 +640,8 @@ "is_executing": true }, "ExecuteTime": { - "end_time": "2024-11-17T13:49:29.582165Z", - "start_time": "2024-11-17T13:49:29.437476Z" + "end_time": "2025-05-28T13:39:43.878568Z", + "start_time": "2025-05-28T13:39:43.857132Z" } }, "source": [ @@ -646,9 +650,9 @@ "pad = Padder()\n", "truncate = Truncator()\n", "resize = Resizer(length=600)\n", - "X2 = pad.fit_transform(plaid_X)\n", - "X3 = truncate.fit_transform(plaid_X)\n", - "X4 = resize.fit_transform(plaid_X)\n", + "X2 = pad.fit_transform(wii_X)\n", + "X3 = truncate.fit_transform(wii_X)\n", + "X4 = resize.fit_transform(wii_X)\n", "print(X2.shape, \"\\n\", X3.shape, \"\\n\", X4.shape)" ], "outputs": [ @@ -656,37 +660,37 @@ "name": "stdout", "output_type": "stream", "text": [ - "(1074, 1, 1344) \n", - " (1074, 1, 100) \n", - " (1074, 1, 600)\n" + "(100, 1, 361) \n", + " (100, 1, 29) \n", + " (100, 1, 600)\n" ] } ], - "execution_count": 18 + "execution_count": 12 }, { "metadata": { "ExecuteTime": { - "end_time": "2024-11-17T13:49:36.601769Z", - "start_time": "2024-11-17T13:49:35.625172Z" + "end_time": "2025-05-28T13:39:45.811784Z", + "start_time": "2025-05-28T13:39:44.586889Z" } }, "cell_type": "code", "source": [ "import matplotlib.pyplot as plt\n", "\n", - "plt.title(\"Before and after padding: PLAID first case (shifted up for unpadded)\")\n", - "plt.plot(plaid_X[0][0] + 10)\n", + "plt.title(\"Before and after padding: first case (shifted up for unpadded)\")\n", + "plt.plot(wii_X[0][0] + 10)\n", "plt.plot(X2[0][0])" ], "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 19, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, @@ -695,13 +699,13 @@ "text/plain": [ "
" ], - "image/png": 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" + "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 19 + "execution_count": 13 }, { "cell_type": "markdown", @@ -720,8 +724,8 @@ "# Unequal length univariate data\n", "from aeon.pipeline import make_pipeline\n", "\n", - "train_X, train_y = load_plaid(split=\"Train\")\n", - "test_X, test_y = load_plaid(split=\"Test\")\n", + "train_X, train_y = load_pickup_gesture_wiimoteZ(split=\"Train\")\n", + "test_X, test_y = load_pickup_gesture_wiimoteZ(split=\"Test\")\n", "steps = [truncate, rc]\n", "pipe = make_pipeline(steps)\n", "pipe.fit(train_X, train_y)\n", @@ -734,23 +738,23 @@ "is_executing": true }, "ExecuteTime": { - "end_time": "2024-11-17T13:50:05.966304Z", - "start_time": "2024-11-17T13:49:37.145088Z" + "end_time": "2025-05-28T13:39:55.584822Z", + "start_time": "2025-05-28T13:39:47.310020Z" } }, "outputs": [ { "data": { "text/plain": [ - "0.813780260707635" + "0.32" ] }, - "execution_count": 20, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 20 + "execution_count": 14 }, { "metadata": {}, diff --git a/examples/transformations/transformations.ipynb b/examples/transformations/transformations.ipynb index 7d88735104..261ebddedf 100644 --- a/examples/transformations/transformations.ipynb +++ b/examples/transformations/transformations.ipynb @@ -63,30 +63,34 @@ }, { "cell_type": "code", - "execution_count": 24, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Unequal length, first case (1, 500) tenth case (1, 300)\n", - "Truncated collection shape = (1074, 1, 100)\n" - ] - } - ], "source": [ - "from aeon.datasets import load_plaid\n", + "from aeon.datasets import load_pickup_gesture_wiimoteZ\n", "from aeon.transformations.collection import Truncator\n", "\n", - "X, y = load_plaid()\n", + "X, y = load_pickup_gesture_wiimoteZ()\n", "print(\" Unequal length, first case \", X[0].shape, \" tenth case \", X[10].shape)\n", "trunc = Truncator(truncated_length=100)\n", "X2 = trunc.fit_transform(X)\n", "print(\"Truncated collection shape =\", X2.shape)" ], "metadata": { - "collapsed": false - } + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-05-28T13:34:29.282584Z", + "start_time": "2025-05-28T13:34:24.749646Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Unequal length, first case (1, 324) tenth case (1, 97)\n", + "Truncated collection shape = (100, 1, 29)\n" + ] + } + ], + "execution_count": 1 }, { "cell_type": "markdown", From 8c8abe8aaed4e250c5d095741459a802028161df Mon Sep 17 00:00:00 2001 From: Patrick <55019140+pattplatt@users.noreply.github.com> Date: Thu, 19 Jun 2025 23:33:32 +0200 Subject: [PATCH 158/216] [ENH, REF]Refactored time-point based ROCKAD implementation (#2804) * mockup * init * decorator * correct import and tag * base docs * wrappers * tests * docs and imports * test params * register * big refactor * smoothing refactor * Revert "smoothing refactor" This reverts commit c245ccbd0c1481b188a9094ccd7ff493613a89cd. * fixes * final bits and docs for refactor * move predict_proba logic into inner_predict for code consistency * Added reference and example code, updated description * Adapted tests to be semi-supervised * Added semi-supervised labeling to description * moved Attributes above References and Examples, updated path of collection based rockad implementation * merge 2 * CI errors * maintainer * doctest --------- Co-authored-by: MatthewMiddlehurst Co-authored-by: MatthewMiddlehurst --- .github/workflows/periodic_tests.yml | 2 +- .github/workflows/pr_pytest.yml | 2 +- .github/workflows/release.yml | 2 +- .../series/distance_based/_rockad.py | 63 ++++++++++++------- .../distance_based/tests/test_rockad.py | 35 +++++++---- 5 files changed, 67 insertions(+), 37 deletions(-) diff --git a/.github/workflows/periodic_tests.yml b/.github/workflows/periodic_tests.yml index ef941711d7..4b9829b44e 100644 --- a/.github/workflows/periodic_tests.yml +++ b/.github/workflows/periodic_tests.yml @@ -178,7 +178,7 @@ jobs: if: runner.os == 'Linux' uses: pierotofy/set-swap-space@v1.0 with: - swap-size-gb: 8 + swap-size-gb: 4 - name: Use numba cache to set env variables but not restore cache uses: ./.github/actions/numba_cache diff --git a/.github/workflows/pr_pytest.yml b/.github/workflows/pr_pytest.yml index 77ff1d2f58..6aba9e83c4 100644 --- a/.github/workflows/pr_pytest.yml +++ b/.github/workflows/pr_pytest.yml @@ -76,7 +76,7 @@ jobs: if: runner.os == 'Linux' uses: pierotofy/set-swap-space@v1.0 with: - swap-size-gb: 8 + swap-size-gb: 4 - if: ${{ github.event_name != 'pull_request' || !contains(github.event.pull_request.labels.*.name, 'no numba cache') }} name: Restore numba cache diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 590c4f9342..02ebeea529 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -69,7 +69,7 @@ jobs: if: runner.os == 'Linux' uses: pierotofy/set-swap-space@v1.0 with: - swap-size-gb: 8 + swap-size-gb: 4 - uses: actions/download-artifact@v4 with: diff --git a/aeon/anomaly_detection/series/distance_based/_rockad.py b/aeon/anomaly_detection/series/distance_based/_rockad.py index 696392286c..1fd0fa8f30 100644 --- a/aeon/anomaly_detection/series/distance_based/_rockad.py +++ b/aeon/anomaly_detection/series/distance_based/_rockad.py @@ -1,5 +1,6 @@ """ROCKAD anomaly detector.""" +__maintainer__ = [] __all__ = ["ROCKAD"] import warnings @@ -17,14 +18,16 @@ class ROCKAD(BaseSeriesAnomalyDetector): """ - ROCKET-based Anomaly Detector (ROCKAD). + ROCKET-based Semi-Supervised Anomaly Detector (ROCKAD). + Adapted ROCKAD [1]_ version to detect anomalies on time-points. ROCKAD leverages the ROCKET transformation for feature extraction from time series data and applies the scikit learn k-nearest neighbors (k-NN) approach with bootstrap aggregation for robust anomaly detection. After windowing, the data gets transformed into the ROCKET feature space. Then the windows are compared based on the feature space by - finding the nearest neighbours. + finding the nearest neighbours. Whole-series based ROCKAD as proposed in + [1]_ can be found at aeon/anomaly_detection/collection/_rockad.py This class supports both univariate and multivariate time series and provides options for normalizing features, applying power transformations, @@ -61,6 +64,31 @@ class ROCKAD(BaseSeriesAnomalyDetector): List containing k-NN estimators used for anomaly scoring, set after fitting. power_transformer_ : PowerTransformer Transformer used to apply power transformation to the features. + + References + ---------- + .. [1] Theissler, A., Wengert, M., Gerschner, F. (2023). + ROCKAD: Transferring ROCKET to Whole Time Series Anomaly Detection. + In: Crémilleux, B., Hess, S., Nijssen, S. (eds) Advances in Intelligent + Data Analysis XXI. IDA 2023. Lecture Notes in Computer Science, + vol 13876. Springer, Cham. https://doi.org/10.1007/978-3-031-30047-9_33 + + Examples + -------- + >>> import numpy as np + >>> from aeon.anomaly_detection.series.distance_based import ROCKAD + >>> rng = np.random.default_rng(seed=42) + >>> X_train = rng.normal(loc=0.0, scale=1.0, size=(1000,)) + >>> X_test = rng.normal(loc=0.0, scale=1.0, size=(20,)) + >>> X_test[15:20] -= 5 + >>> detector = ROCKAD(window_size=15,n_estimators=10,n_kernels=10,n_neighbors=3) + >>> detector.fit(X_train) + ROCKAD(...) + >>> detector.predict(X_test) + array([0. , 0.00554713, 0.0699094 , 0.22881059, 0.32382585, + 0.43652154, 0.43652154, 0.43652154, 0.43652154, 0.43652154, + 0.43652154, 0.43652154, 0.43652154, 0.43652154, 0.43652154, + 0.52382585, 0.65200875, 0.80313368, 0.85194345, 1. ]) """ _tags = { @@ -86,7 +114,6 @@ def __init__( n_jobs=1, random_state=42, ): - self.n_estimators = n_estimators self.n_kernels = n_kernels self.normalise = normalise @@ -136,7 +163,6 @@ def _check_params(self, X: np.ndarray) -> None: ) def _inner_fit(self, X: np.ndarray) -> None: - self.rocket_transformer_ = Rocket( n_kernels=self.n_kernels, normalise=self.normalise, @@ -189,7 +215,6 @@ def _inner_fit(self, X: np.ndarray) -> None: self.list_baggers_.append(estimator) def _predict(self, X) -> np.ndarray: - _X, padding = sliding_windows( X, window_size=self.window_size, stride=self.stride, axis=0 ) @@ -209,22 +234,8 @@ def _fit_predict(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> np.ndar return point_anomaly_scores def _inner_predict(self, X: np.ndarray, padding: int) -> np.ndarray: - - anomaly_scores = self._predict_proba(X) - - point_anomaly_scores = reverse_windowing( - anomaly_scores, self.window_size, np.nanmean, self.stride, padding - ) - - point_anomaly_scores = (point_anomaly_scores - point_anomaly_scores.min()) / ( - point_anomaly_scores.max() - point_anomaly_scores.min() - ) - - return point_anomaly_scores - - def _predict_proba(self, X): """ - Predicts the probability of anomalies for the input data. + Predict the anomaly score for each time-point in the input data. Parameters ---------- @@ -259,6 +270,14 @@ def _predict_proba(self, X): y_scores[:, idx] = scores # Average the scores to get the final score for each time series - y_scores = y_scores.mean(axis=1) + anomaly_scores = y_scores.mean(axis=1) - return y_scores + point_anomaly_scores = reverse_windowing( + anomaly_scores, self.window_size, np.nanmean, self.stride, padding + ) + + point_anomaly_scores = (point_anomaly_scores - point_anomaly_scores.min()) / ( + point_anomaly_scores.max() - point_anomaly_scores.min() + ) + + return point_anomaly_scores diff --git a/aeon/anomaly_detection/series/distance_based/tests/test_rockad.py b/aeon/anomaly_detection/series/distance_based/tests/test_rockad.py index 51d2425505..16f60edff7 100644 --- a/aeon/anomaly_detection/series/distance_based/tests/test_rockad.py +++ b/aeon/anomaly_detection/series/distance_based/tests/test_rockad.py @@ -10,8 +10,9 @@ def test_rockad_univariate(): """Test ROCKAD univariate output.""" rng = check_random_state(seed=2) - series = rng.normal(size=(100,)) - series[50:58] -= 5 + train_series = rng.normal(size=(100,)) + test_series = rng.normal(size=(100,)) + test_series[50:58] -= 5 ad = ROCKAD( n_estimators=100, @@ -22,7 +23,8 @@ def test_rockad_univariate(): stride=1, ) - pred = ad.fit_predict(series, axis=0) + ad.fit(train_series, axis=0) + pred = ad.predict(test_series, axis=0) assert pred.shape == (100,) assert pred.dtype == np.float64 @@ -32,9 +34,10 @@ def test_rockad_univariate(): def test_rockad_multivariate(): """Test ROCKAD multivariate output.""" rng = check_random_state(seed=2) - series = rng.normal(size=(100, 3)) - series[50:58, 0] -= 5 - series[87:90, 1] += 0.1 + train_series = rng.normal(size=(100, 3)) + test_series = rng.normal(size=(100, 3)) + test_series[50:58, 0] -= 5 + test_series[87:90, 1] += 0.1 ad = ROCKAD( n_estimators=1000, @@ -45,7 +48,8 @@ def test_rockad_multivariate(): stride=1, ) - pred = ad.fit_predict(series, axis=0) + ad.fit(train_series, axis=0) + pred = ad.predict(test_series, axis=0) assert pred.shape == (100,) assert pred.dtype == np.float64 @@ -55,21 +59,28 @@ def test_rockad_multivariate(): def test_rockad_incorrect_input(): """Test ROCKAD incorrect input.""" rng = check_random_state(seed=2) - series = rng.normal(size=(100,)) + train_series = rng.normal(size=(100,)) + test_series = rng.normal(size=(5,)) with pytest.raises(ValueError, match="The window size must be at least 1"): ad = ROCKAD(window_size=0) - ad.fit_predict(series) + ad.fit(train_series) with pytest.raises(ValueError, match="The stride must be at least 1"): ad = ROCKAD(stride=0) - ad.fit_predict(series) + ad.fit(train_series) with pytest.raises( ValueError, match=r"Window count .* has to be larger than n_neighbors .*" ): ad = ROCKAD(stride=1, window_size=100) - ad.fit_predict(series) + ad.fit(train_series) with pytest.warns( UserWarning, match=r"Power Transform failed and thus has been disabled." ): ad = ROCKAD(stride=1, window_size=5) - ad.fit_predict(series) + ad.fit(train_series) + with pytest.raises( + ValueError, match=r"window shape cannot be larger than input array shape" + ): + ad = ROCKAD(stride=1, window_size=10) + ad.fit(train_series) + ad.predict(test_series) From 3dda595fd6a576f920a76749e6f00f6559333c25 Mon Sep 17 00:00:00 2001 From: TinaJin0228 <60577222+TinaJin0228@users.noreply.github.com> Date: Fri, 20 Jun 2025 07:14:07 +0900 Subject: [PATCH 159/216] [ENH] enhance naive forecaster with new strategies (#2869) * naive forecaster with new strategies * few modifications according to reviews * made modifications and add documentation * modifications according to reviews * try to pass the test * delete one comment * modify 1)more test cases for seasonal_last 2)raise exception for y input in _predict, and related documentation * fix typo * take y * Update _naive.py --------- Co-authored-by: Tony Bagnall Co-authored-by: MatthewMiddlehurst --- aeon/forecasting/_naive.py | 82 +++++++++++++--- aeon/forecasting/tests/test_naive.py | 66 +++++++++++++ docs/api_reference/forecasting.md | 2 +- examples/forecasting/forecasting.ipynb | 130 +++++++++++++++---------- 4 files changed, 214 insertions(+), 66 deletions(-) create mode 100644 aeon/forecasting/tests/test_naive.py diff --git a/aeon/forecasting/_naive.py b/aeon/forecasting/_naive.py index 8b7deedeeb..da242018e2 100644 --- a/aeon/forecasting/_naive.py +++ b/aeon/forecasting/_naive.py @@ -1,27 +1,79 @@ -"""Naive Forecaster.""" +"""Naive forecaster with multiple strategies.""" + +__maintainer__ = [] +__all__ = ["NaiveForecaster"] + + +import numpy as np from aeon.forecasting.base import BaseForecaster class NaiveForecaster(BaseForecaster): - """Naive forecaster that always predicts the last value seen in training.""" + """ + Naive forecaster with multiple strategies and flexible horizon. + + Parameters + ---------- + strategy : str, default="last" + The forecasting strategy to use. + Options: "last", "mean", "seasonal_last". + - "last" predicts the last value of the input series for all horizon steps. + - "mean": predicts the mean of the input series for all horizon steps. + - "seasonal_last": predicts the last season value in the training series. + Returns np.nan if the effective seasonal data is empty. + seasonal_period : int, default=1 + The seasonal period to use for the "seasonal_last" strategy. + E.g., 12 for monthly data with annual seasonality. + horizon : int, default =1 + The number of time steps ahead to forecast. If horizon is one, the forecaster + will learn to predict one point ahead. + Only relevant for "seasonal_last". + """ - def __init__(self): - """Initialize NaiveForecaster.""" - self.last_value_ = None - super().__init__(horizon=1, axis=1) + def __init__(self, strategy="last", seasonal_period=1, horizon=1): + self.strategy = strategy + self.seasonal_period = seasonal_period + + super().__init__(horizon=horizon, axis=1) def _fit(self, y, exog=None): - """Fit Naive forecaster.""" - y = y.squeeze() - self.last_value_ = y[-1] + y_squeezed = y.squeeze() + + if self.strategy == "last": + self._fitted_scalar_value = y_squeezed[-1] + elif self.strategy == "mean": + self._fitted_scalar_value = np.mean(y_squeezed) + elif self.strategy == "seasonal_last": + self._fitted_last_season = y_squeezed[-self.seasonal_period :] + else: + raise ValueError( + f"Unknown strategy: {self.strategy}. " + "Valid strategies are 'last', 'mean', 'seasonal_last'." + ) return self def _predict(self, y=None, exog=None): - """Predict using Naive forecaster.""" - return self.last_value_ + if y is None: + if self.strategy == "last" or self.strategy == "mean": + return self._fitted_scalar_value + + # For "seasonal_last" strategy + prediction_index = (self.horizon - 1) % self.seasonal_period + return self._fitted_last_season[prediction_index] + else: + y_squeezed = y.squeeze() - def _forecast(self, y, exog=None): - """Forecast using dummy forecaster.""" - y = y.squeeze() - return y[-1] + if self.strategy == "last": + return y_squeezed[-1] + elif self.strategy == "mean": + return np.mean(y_squeezed) + elif self.strategy == "seasonal_last": + period = y_squeezed[-self.seasonal_period :] + idx = (self.horizon - 1) % self.seasonal_period + return period[idx] + else: + raise ValueError( + f"Unknown strategy: {self.strategy}. " + "Valid strategies are 'last', 'mean', 'seasonal_last'." + ) diff --git a/aeon/forecasting/tests/test_naive.py b/aeon/forecasting/tests/test_naive.py new file mode 100644 index 0000000000..c0f9a98bd2 --- /dev/null +++ b/aeon/forecasting/tests/test_naive.py @@ -0,0 +1,66 @@ +"""Test Naive Forecaster.""" + +import numpy as np + +from aeon.forecasting import NaiveForecaster + + +def test_naive_forecaster_last_strategy(): + """Test NaiveForecaster with 'last' strategy.""" + sample_data = np.array([10, 20, 30, 40, 50]) + forecaster = NaiveForecaster(strategy="last", horizon=3) + forecaster.fit(sample_data) + predictions = forecaster.predict() + expected = 50 + np.testing.assert_array_equal(predictions, expected) + + +def test_naive_forecaster_mean_strategy(): + """Test NaiveForecaster with 'mean' strategy.""" + sample_data = np.array([10, 20, 30, 40, 50]) + forecaster = NaiveForecaster(strategy="mean", horizon=2) + forecaster.fit(sample_data) + predictions = forecaster.predict() + expected = 30 # Mean of [10, 20, 30, 40, 50] is 30 + np.testing.assert_array_equal(predictions, expected) + + +def test_naive_forecaster_seasonal_last_strategy(): + """Test NaiveForecaster with 'seasonal_last' strategy.""" + data = np.array([1, 2, 3, 4, 5, 6, 7, 8]) + + # Last season is [6, 7, 8] for seasonal_period = 3 + forecaster = NaiveForecaster(strategy="seasonal_last", seasonal_period=3, horizon=4) + forecaster.fit(data) + pred = forecaster.predict() + pred2 = forecaster.predict(y=data) + expected = 6 # predicts the 1-st element of the last season. + np.testing.assert_array_equal(pred, expected) + np.testing.assert_array_equal(pred2, expected) + + # Test horizon within the season length + forecaster = NaiveForecaster(strategy="seasonal_last", seasonal_period=3, horizon=2) + forecaster.fit(data) + pred = forecaster.predict() + pred2 = forecaster.predict(y=data) + expected = 7 # predicts the 2-nd element of the last season. + np.testing.assert_array_equal(pred, expected) + np.testing.assert_array_equal(pred2, expected) + + # Test horizon wrapping around to a new season + forecaster = NaiveForecaster(strategy="seasonal_last", seasonal_period=3, horizon=7) + forecaster.fit(data) + pred = forecaster.predict() + pred2 = forecaster.predict(y=data) + expected = 6 # predicts the 1-st element of the last season. + np.testing.assert_array_equal(pred, expected) + np.testing.assert_array_equal(pred2, expected) + + # Last season is now [5, 6, 7, 8] with seasonal_period = 4 + forecaster = NaiveForecaster(strategy="seasonal_last", seasonal_period=4, horizon=6) + forecaster.fit(data) + pred = forecaster.predict() + pred2 = forecaster.predict(y=data) + expected = 6 # predicts the 2nd element of the new last season. + np.testing.assert_array_equal(pred, expected) + np.testing.assert_array_equal(pred2, expected) diff --git a/docs/api_reference/forecasting.md b/docs/api_reference/forecasting.md index 338262306b..86a8cc613e 100644 --- a/docs/api_reference/forecasting.md +++ b/docs/api_reference/forecasting.md @@ -7,8 +7,8 @@ :toctree: auto_generated/ :template: class.rst - DummyForecaster BaseForecaster + NaiveForecaster RegressionForecaster ETSForecaster ``` diff --git a/examples/forecasting/forecasting.ipynb b/examples/forecasting/forecasting.ipynb index 8d24b659e5..aa7f8f3a04 100644 --- a/examples/forecasting/forecasting.ipynb +++ b/examples/forecasting/forecasting.ipynb @@ -1,8 +1,8 @@ { "cells": [ { - "metadata": {}, "cell_type": "markdown", + "metadata": {}, "source": [ "# Time series forecasting with aeon\n", "\n", @@ -34,8 +34,8 @@ ] }, { - "metadata": {}, "cell_type": "markdown", + "metadata": {}, "source": [ "## Base Class\n", "\n", @@ -57,10 +57,10 @@ ] }, { - "metadata": {}, "cell_type": "code", - "outputs": [], "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "import inspect\n", "\n", @@ -76,8 +76,8 @@ ] }, { - "metadata": {}, "cell_type": "markdown", + "metadata": {}, "source": [ " All estimators in `aeon` have tags. One specific to\n", "forecasting is `y_inner_type`. This specifies the inner type the sub class of\n", @@ -88,10 +88,10 @@ ] }, { - "metadata": {}, "cell_type": "code", - "outputs": [], "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from aeon.utils.data_types import SERIES_DATA_TYPES\n", "\n", @@ -100,18 +100,18 @@ ] }, { - "metadata": {}, "cell_type": "markdown", + "metadata": {}, "source": [ "We use the standard airline dataset for examples. This can be stored as a pd.Series,\n", "pd.DataFrame or np.ndarray." ] }, { - "metadata": {}, "cell_type": "code", - "outputs": [], "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "import pandas as pd\n", "\n", @@ -124,39 +124,49 @@ ] }, { - "metadata": {}, "cell_type": "markdown", + "metadata": {}, "source": [ - "## DummyForecaster\n", + "## NaiveForecaster\n", + "\n", + "The NaiveForecaster provides simple benchmark predictions based on common-sense strategies. It can use the `last` strategy to predict the last seen value, use the `mean` strategy to predict the mean of the training data, or use the `seasonal_last` strategy to repeat the last seasonal cycle.\n", + "By default, it uses the last strategy, which repeats the very last observation from the training series.\n", "\n", - "A dummy forecaster can illustrate the use cases for forecasting. This\n", - "forecaster simply returns the last value of the train data for the forecast. By\n", - "default the horizon is 1. It makes no difference for this forecaster. It's inner type\n", - " is `np.ndarray` so all three allowable input types are internally converted to numpy\n", - " arrays." + "The NaiveForecaster currently does not support new input time series in the `predict` function, and only predicts the next horizon value after series seen in fit." ] }, { - "metadata": {}, "cell_type": "code", - "outputs": [], - "execution_count": null, + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'y' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01maeon\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mforecasting\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m NaiveForecaster\n\u001b[32m 4\u001b[39m d = NaiveForecaster(strategy=\u001b[33m\"\u001b[39m\u001b[33mlast\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m d.fit(\u001b[43my\u001b[49m)\n\u001b[32m 6\u001b[39m p = d.predict()\n\u001b[32m 7\u001b[39m \u001b[38;5;28mprint\u001b[39m(p)\n", + "\u001b[31mNameError\u001b[39m: name 'y' is not defined" + ] + } + ], "source": [ "# Fit then predict\n", "from aeon.forecasting import NaiveForecaster\n", "\n", - "d = NaiveForecaster()\n", - "print(d.get_tag(\"y_inner_type\"))\n", + "d = NaiveForecaster(strategy=\"last\")\n", "d.fit(y)\n", "p = d.predict()\n", "print(p)" ] }, { - "metadata": {}, "cell_type": "code", - "outputs": [], "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# forecast is equivalent to fit_predict in other estimators\n", "p2 = d.forecast(y)\n", @@ -164,8 +174,28 @@ ] }, { + "cell_type": "markdown", "metadata": {}, + "source": [ + "To use a seasonal strategy, set the strategy to seasonal_last and provide the seasonal_period. The forecaster will then repeat the last full season from the training data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s = NaiveForecaster(strategy=\"seasonal_last\", horizon=2, seasonal_period=4)\n", + "s.fit(y)\n", + "p = s.predict()\n", + "print(f\"Last season: {y[-4:]}\")\n", + "print(f\"Forecast: {p}\")" + ] + }, + { "cell_type": "markdown", + "metadata": {}, "source": [ "## Regression based forecasting\n", "\n", @@ -178,10 +208,10 @@ ] }, { - "metadata": {}, "cell_type": "code", - "outputs": [], "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from aeon.forecasting import RegressionForecaster\n", "\n", @@ -196,8 +226,8 @@ ] }, { - "metadata": {}, "cell_type": "markdown", + "metadata": {}, "source": [ "With our set up, we can make predictions with previously unseen data, thus more\n", "closely modelling machine learning approaches. Or we can use the forecast method to\n", @@ -205,10 +235,10 @@ ] }, { - "metadata": {}, "cell_type": "code", - "outputs": [], "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "p1 = r.forecast(y)\n", "p2 = r2.forecast(y)\n", @@ -216,8 +246,8 @@ ] }, { - "metadata": {}, "cell_type": "markdown", + "metadata": {}, "source": [ "## Exponential Smoothing\n", "\n", @@ -227,19 +257,13 @@ }, { "cell_type": "code", - "source": [ - "from aeon.forecasting import ETSForecaster\n", - "\n", - "ets = ETSForecaster()\n", - "ets.fit(y)\n", - "ets.predict()" - ], + "execution_count": 20, "metadata": { - "collapsed": false, "ExecuteTime": { "end_time": "2024-11-16T19:21:26.225501Z", "start_time": "2024-11-16T19:21:26.204872Z" - } + }, + "collapsed": false }, "outputs": [ { @@ -253,45 +277,51 @@ "output_type": "execute_result" } ], - "execution_count": 20 + "source": [ + "from aeon.forecasting import ETSForecaster\n", + "\n", + "ets = ETSForecaster()\n", + "ets.fit(y)\n", + "ets.predict()" + ] }, { + "cell_type": "code", + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-11-16T19:21:27.095665Z", "start_time": "2024-11-16T19:21:27.077715Z" } }, - "cell_type": "code", - "source": "", "outputs": [], - "execution_count": null + "source": [] }, { - "metadata": {}, "cell_type": "code", - "outputs": [], "execution_count": null, - "source": "" + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "aeon-venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.11.9" } }, "nbformat": 4, From b8343408a15bb8ef7518b4c0f336091f4592f4bc Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Fri, 20 Jun 2025 17:08:36 +0100 Subject: [PATCH 160/216] [ENH,REF] Unequal length collection transforms (#2903) * move and update unequal transforms * unequal transforms * comment * better sorting * extra transform test * changes for review --- .github/workflows/pr_examples.yml | 2 +- aeon/classification/compose/_pipeline.py | 6 +- .../compose/tests/test_pipeline.py | 18 +- .../tests/test_sklearn_compatability.py | 4 +- aeon/clustering/compose/_pipeline.py | 6 +- .../clustering/compose/tests/test_pipeline.py | 18 +- aeon/pipeline/tests/test_make_pipeline.py | 13 +- aeon/regression/compose/_pipeline.py | 2 +- .../regression/compose/tests/test_pipeline.py | 18 +- .../_yield_transformation_checks.py | 3 + aeon/transformations/collection/__init__.py | 7 +- aeon/transformations/collection/_pad.py | 8 + .../collection/_periodogram.py | 57 +- aeon/transformations/collection/_resize.py | 8 + aeon/transformations/collection/_truncate.py | 8 + aeon/transformations/collection/base.py | 3 +- .../collection/compose/_pipeline.py | 6 +- .../collection/compose/tests/test_pipeline.py | 10 +- .../collection/tests/test_resizer.py | 18 - .../collection/tests/test_truncate.py | 78 - .../collection/unequal_length/__init__.py | 11 + .../collection/unequal_length/_commons.py | 21 + .../collection/unequal_length/_pad.py | 221 ++ .../collection/unequal_length/_resize.py | 111 + .../collection/unequal_length/_truncate.py | 138 ++ .../unequal_length/tests/__init__.py | 1 + .../{ => unequal_length}/tests/test_pad.py | 88 +- .../unequal_length/tests/test_resize.py | 83 + .../unequal_length/tests/test_truncate.py | 99 + aeon/utils/discovery.py | 4 +- examples/datasets/data_unequal.ipynb | 2 +- examples/transformations/preprocessing.ipynb | 2000 +++++++++-------- .../transformations/transformations.ipynb | 544 ++--- 33 files changed, 2151 insertions(+), 1465 deletions(-) delete mode 100644 aeon/transformations/collection/tests/test_resizer.py delete mode 100644 aeon/transformations/collection/tests/test_truncate.py create mode 100644 aeon/transformations/collection/unequal_length/__init__.py create mode 100644 aeon/transformations/collection/unequal_length/_commons.py create mode 100644 aeon/transformations/collection/unequal_length/_pad.py create mode 100644 aeon/transformations/collection/unequal_length/_resize.py create mode 100644 aeon/transformations/collection/unequal_length/_truncate.py create mode 100644 aeon/transformations/collection/unequal_length/tests/__init__.py rename aeon/transformations/collection/{ => unequal_length}/tests/test_pad.py (53%) create mode 100644 aeon/transformations/collection/unequal_length/tests/test_resize.py create mode 100644 aeon/transformations/collection/unequal_length/tests/test_truncate.py diff --git a/.github/workflows/pr_examples.yml b/.github/workflows/pr_examples.yml index 62035f4260..f03dd4825c 100644 --- a/.github/workflows/pr_examples.yml +++ b/.github/workflows/pr_examples.yml @@ -1,4 +1,4 @@ -name: PR Examples +name: PR examples on: push: diff --git a/aeon/classification/compose/_pipeline.py b/aeon/classification/compose/_pipeline.py index 8c43f6ff9b..ce176b810c 100644 --- a/aeon/classification/compose/_pipeline.py +++ b/aeon/classification/compose/_pipeline.py @@ -61,14 +61,14 @@ class ClassifierPipeline(BaseCollectionPipeline, BaseClassifier): Examples -------- - >>> from aeon.transformations.collection import Resizer + >>> from aeon.transformations.collection.unequal_length import Resizer >>> from aeon.classification.convolution_based import RocketClassifier >>> from aeon.datasets import load_unit_test >>> from aeon.classification.compose import ClassifierPipeline >>> X_train, y_train = load_unit_test(split="train") >>> X_test, y_test = load_unit_test(split="test") >>> pipeline = ClassifierPipeline( - ... Resizer(length=10), RocketClassifier(n_kernels=50) + ... Resizer(resized_length=10), RocketClassifier(n_kernels=50) ... ) >>> pipeline.fit(X_train, y_train) ClassifierPipeline(...) @@ -104,8 +104,8 @@ def _get_test_params(cls, parameter_set="default"): `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. """ from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier - from aeon.transformations.collection import Truncator from aeon.transformations.collection.feature_based import SevenNumberSummary + from aeon.transformations.collection.unequal_length import Truncator return { "transformers": [ diff --git a/aeon/classification/compose/tests/test_pipeline.py b/aeon/classification/compose/tests/test_pipeline.py index c104a3f339..660d8ad7c3 100644 --- a/aeon/classification/compose/tests/test_pipeline.py +++ b/aeon/classification/compose/tests/test_pipeline.py @@ -21,22 +21,22 @@ AutocorrelationFunctionTransformer, HOG1DTransformer, Normalizer, - Padder, Tabularizer, ) from aeon.transformations.collection.feature_based import SevenNumberSummary +from aeon.transformations.collection.unequal_length import Padder @pytest.mark.parametrize( "transformers", [ - Padder(pad_length=15), + Padder(padded_length=15), SevenNumberSummary(), - [Padder(pad_length=15), Tabularizer(), StandardScaler()], - [Padder(pad_length=15), SevenNumberSummary()], + [Padder(padded_length=15), Tabularizer(), StandardScaler()], + [Padder(padded_length=15), SevenNumberSummary()], [Tabularizer(), StandardScaler(), SevenNumberSummary()], [ - Padder(pad_length=15), + Padder(padded_length=15), SevenNumberSummary(), ], ], @@ -67,14 +67,14 @@ def test_classifier_pipeline(transformers): @pytest.mark.parametrize( "transformers", [ - [Padder(pad_length=15), Tabularizer()], + [Padder(padded_length=15), Tabularizer()], SevenNumberSummary(), [Tabularizer(), StandardScaler()], - [Padder(pad_length=15), Tabularizer(), StandardScaler()], - [Padder(pad_length=15), SevenNumberSummary()], + [Padder(padded_length=15), Tabularizer(), StandardScaler()], + [Padder(padded_length=15), SevenNumberSummary()], [Tabularizer(), StandardScaler(), SevenNumberSummary()], [ - Padder(pad_length=15), + Padder(padded_length=15), SevenNumberSummary(), ], ], diff --git a/aeon/classification/tests/test_sklearn_compatability.py b/aeon/classification/tests/test_sklearn_compatability.py index e6b6668459..34db9d359b 100644 --- a/aeon/classification/tests/test_sklearn_compatability.py +++ b/aeon/classification/tests/test_sklearn_compatability.py @@ -35,7 +35,7 @@ from aeon.classification.interval_based import CanonicalIntervalForestClassifier from aeon.testing.data_generation import make_example_3d_numpy -from aeon.transformations.collection import Resizer +from aeon.transformations.collection.unequal_length import Resizer # StratifiedGroupKFold(n_splits=2), removed because it is not available in sklearn 0.24 CROSS_VALIDATION_METHODS = [ @@ -60,7 +60,7 @@ COMPOSITE_ESTIMATORS = [ Pipeline( [ - ("transform", Resizer(length=10)), + ("transform", Resizer(resized_length=10)), ("clf", CanonicalIntervalForestClassifier._create_test_instance()), ] ), diff --git a/aeon/clustering/compose/_pipeline.py b/aeon/clustering/compose/_pipeline.py index fef3f87e0b..b9c1e880fd 100644 --- a/aeon/clustering/compose/_pipeline.py +++ b/aeon/clustering/compose/_pipeline.py @@ -60,14 +60,14 @@ class ClustererPipeline(BaseCollectionPipeline, BaseClusterer): Examples -------- - >>> from aeon.transformations.collection import Resizer + >>> from aeon.transformations.collection.unequal_length import Resizer >>> from aeon.clustering import TimeSeriesKMeans >>> from aeon.datasets import load_unit_test >>> from aeon.clustering.compose import ClustererPipeline >>> X_train, y_train = load_unit_test(split="train") >>> X_test, y_test = load_unit_test(split="test") >>> pipeline = ClustererPipeline( - ... Resizer(length=10), TimeSeriesKMeans._create_test_instance() + ... Resizer(resized_length=10), TimeSeriesKMeans._create_test_instance() ... ) >>> pipeline.fit(X_train, y_train) ClustererPipeline(...) @@ -108,8 +108,8 @@ def _get_test_params(cls, parameter_set="default"): `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. """ from aeon.clustering import TimeSeriesKMeans - from aeon.transformations.collection import Truncator from aeon.transformations.collection.feature_based import SevenNumberSummary + from aeon.transformations.collection.unequal_length import Truncator return { "transformers": [ diff --git a/aeon/clustering/compose/tests/test_pipeline.py b/aeon/clustering/compose/tests/test_pipeline.py index 2b54ee3f24..0e6bedc60d 100644 --- a/aeon/clustering/compose/tests/test_pipeline.py +++ b/aeon/clustering/compose/tests/test_pipeline.py @@ -19,22 +19,22 @@ AutocorrelationFunctionTransformer, HOG1DTransformer, Normalizer, - Padder, Tabularizer, ) from aeon.transformations.collection.feature_based import SevenNumberSummary +from aeon.transformations.collection.unequal_length import Padder @pytest.mark.parametrize( "transformers", [ - Padder(pad_length=15), + Padder(padded_length=15), SevenNumberSummary(), - [Padder(pad_length=15), Tabularizer(), StandardScaler()], - [Padder(pad_length=15), SevenNumberSummary()], + [Padder(padded_length=15), Tabularizer(), StandardScaler()], + [Padder(padded_length=15), SevenNumberSummary()], [Tabularizer(), StandardScaler(), SevenNumberSummary()], [ - Padder(pad_length=15), + Padder(padded_length=15), SevenNumberSummary(), ], ], @@ -66,14 +66,14 @@ def test_clusterer_pipeline(transformers): @pytest.mark.parametrize( "transformers", [ - [Padder(pad_length=15), Tabularizer()], + [Padder(padded_length=15), Tabularizer()], SevenNumberSummary(), [Tabularizer(), StandardScaler()], - [Padder(pad_length=15), Tabularizer(), StandardScaler()], - [Padder(pad_length=15), SevenNumberSummary()], + [Padder(padded_length=15), Tabularizer(), StandardScaler()], + [Padder(padded_length=15), SevenNumberSummary()], [Tabularizer(), StandardScaler(), SevenNumberSummary()], [ - Padder(pad_length=15), + Padder(padded_length=15), SevenNumberSummary(), ], ], diff --git a/aeon/pipeline/tests/test_make_pipeline.py b/aeon/pipeline/tests/test_make_pipeline.py index 2d569e00b8..3c9a7b18a8 100644 --- a/aeon/pipeline/tests/test_make_pipeline.py +++ b/aeon/pipeline/tests/test_make_pipeline.py @@ -12,21 +12,22 @@ from aeon.pipeline import make_pipeline from aeon.regression import DummyRegressor from aeon.testing.data_generation import make_example_3d_numpy -from aeon.transformations.collection import Padder, Tabularizer +from aeon.transformations.collection import Tabularizer from aeon.transformations.collection.feature_based import SevenNumberSummary +from aeon.transformations.collection.unequal_length import Padder @pytest.mark.parametrize( "pipeline", [ - [Padder(pad_length=15), DummyClassifier()], + [Padder(padded_length=15), DummyClassifier()], [SevenNumberSummary(), RandomForestClassifier(n_estimators=2)], - [Padder(pad_length=15), DummyRegressor()], + [Padder(padded_length=15), DummyRegressor()], [SevenNumberSummary(), RandomForestRegressor(n_estimators=2)], - [Padder(pad_length=15), TimeSeriesKMeans._create_test_instance()], + [Padder(padded_length=15), TimeSeriesKMeans._create_test_instance()], [SevenNumberSummary(), KMeans(n_clusters=2, max_iter=3)], - [Padder(pad_length=15), SevenNumberSummary()], - [Padder(pad_length=15), Tabularizer(), StandardScaler()], + [Padder(padded_length=15), SevenNumberSummary()], + [Padder(padded_length=15), Tabularizer(), StandardScaler()], ], ) def test_make_pipeline(pipeline): diff --git a/aeon/regression/compose/_pipeline.py b/aeon/regression/compose/_pipeline.py index 6f78f21be3..a560833a59 100644 --- a/aeon/regression/compose/_pipeline.py +++ b/aeon/regression/compose/_pipeline.py @@ -99,8 +99,8 @@ def _get_test_params(cls, parameter_set="default"): `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. """ from aeon.regression.distance_based import KNeighborsTimeSeriesRegressor - from aeon.transformations.collection import Truncator from aeon.transformations.collection.feature_based import SevenNumberSummary + from aeon.transformations.collection.unequal_length import Truncator return { "transformers": [ diff --git a/aeon/regression/compose/tests/test_pipeline.py b/aeon/regression/compose/tests/test_pipeline.py index 7d8360966c..01b20cd42f 100644 --- a/aeon/regression/compose/tests/test_pipeline.py +++ b/aeon/regression/compose/tests/test_pipeline.py @@ -21,22 +21,22 @@ AutocorrelationFunctionTransformer, HOG1DTransformer, Normalizer, - Padder, Tabularizer, ) from aeon.transformations.collection.feature_based import SevenNumberSummary +from aeon.transformations.collection.unequal_length import Padder @pytest.mark.parametrize( "transformers", [ - Padder(pad_length=15), + Padder(padded_length=15), SevenNumberSummary(), - [Padder(pad_length=15), Tabularizer(), StandardScaler()], - [Padder(pad_length=15), SevenNumberSummary()], + [Padder(padded_length=15), Tabularizer(), StandardScaler()], + [Padder(padded_length=15), SevenNumberSummary()], [Tabularizer(), StandardScaler(), SevenNumberSummary()], [ - Padder(pad_length=15), + Padder(padded_length=15), SevenNumberSummary(), ], ], @@ -67,14 +67,14 @@ def test_regressor_pipeline(transformers): @pytest.mark.parametrize( "transformers", [ - [Padder(pad_length=15), Tabularizer()], + [Padder(padded_length=15), Tabularizer()], SevenNumberSummary(), [Tabularizer(), StandardScaler()], - [Padder(pad_length=15), Tabularizer(), StandardScaler()], - [Padder(pad_length=15), SevenNumberSummary()], + [Padder(padded_length=15), Tabularizer(), StandardScaler()], + [Padder(padded_length=15), SevenNumberSummary()], [Tabularizer(), StandardScaler(), SevenNumberSummary()], [ - Padder(pad_length=15), + Padder(padded_length=15), SevenNumberSummary(), ], ], diff --git a/aeon/testing/estimator_checking/_yield_transformation_checks.py b/aeon/testing/estimator_checking/_yield_transformation_checks.py index 14bd54fa90..622cef290f 100644 --- a/aeon/testing/estimator_checking/_yield_transformation_checks.py +++ b/aeon/testing/estimator_checking/_yield_transformation_checks.py @@ -169,6 +169,9 @@ def check_transformer_output(estimator, datatype): Xt2 = _run_estimator_method(estimator, "fit_transform", datatype, "train") assert deep_equals(Xt, Xt2, ignore_index=True) + Xt3 = _run_estimator_method(estimator, "transform", datatype, "train") + assert deep_equals(Xt, Xt3, ignore_index=True) + def check_channel_selectors(estimator, datatype): """Test channel selectors have fit and select at least one channel.""" diff --git a/aeon/transformations/collection/__init__.py b/aeon/transformations/collection/__init__.py index 11ccc604b0..71abc15f08 100644 --- a/aeon/transformations/collection/__init__.py +++ b/aeon/transformations/collection/__init__.py @@ -13,13 +13,14 @@ "MatrixProfile", "MinMaxScaler", "Normalizer", - "Padder", "PeriodogramTransformer", - "Resizer", "SlopeTransformer", "SimpleImputer", - "Truncator", "Tabularizer", + # deprecated + "Resizer", + "Padder", + "Truncator", ] from aeon.transformations.collection._acf import AutocorrelationFunctionTransformer diff --git a/aeon/transformations/collection/_pad.py b/aeon/transformations/collection/_pad.py index f3e7f7de37..cab8dd99e0 100644 --- a/aeon/transformations/collection/_pad.py +++ b/aeon/transformations/collection/_pad.py @@ -4,6 +4,7 @@ __maintainer__ = [] import numpy as np +from deprecated.sphinx import deprecated from aeon.transformations.collection.base import BaseCollectionTransformer @@ -17,6 +18,13 @@ def _get_max_length(X): return max_length +# TODO: Remove in v1.3.0 +@deprecated( + version="1.2.0", + reason="Padder is deprecated and will be moving to " + "transformations.collection.unequal_length with additional/reworked functionality.", + category=FutureWarning, +) class Padder(BaseCollectionTransformer): """Pad unequal length time series to equal, fixed length. diff --git a/aeon/transformations/collection/_periodogram.py b/aeon/transformations/collection/_periodogram.py index 52c86526b4..70b830c926 100644 --- a/aeon/transformations/collection/_periodogram.py +++ b/aeon/transformations/collection/_periodogram.py @@ -46,8 +46,10 @@ class PeriodogramTransformer(BaseCollectionTransformer): """ _tags = { - "capability:multivariate": True, "fit_is_empty": True, + "capability:multivariate": True, + "capability:unequal_length": True, + "X_inner_type": ["np-list", "numpy3D"], } def __init__( @@ -63,23 +65,44 @@ def __init__( super().__init__() def _transform(self, X, y=None): - if self.pad_series: - kwargs = {"mode": self.pad_with} - if self.pad_with == "constant": - kwargs["constant_values"] = self.constant_value - len = int(math.pow(2, math.ceil(math.log(X.shape[2], 2))) - X.shape[2]) - X = np.pad( - X, - ( - (0, 0), - (0, 0), + if isinstance(X, np.ndarray): + if self.pad_series: + kwargs = {"mode": self.pad_with} + if self.pad_with == "constant": + kwargs["constant_values"] = self.constant_value + len = int(math.pow(2, math.ceil(math.log(X.shape[2], 2))) - X.shape[2]) + X = np.pad( + X, ( - 0, - len, + (0, 0), + (0, 0), + ( + 0, + len, + ), ), - ), - **kwargs, - ) - Xt = np.abs(np.fft.fft(X)[:, :, : int(X.shape[2] / 2)]) + **kwargs, + ) + Xt = np.abs(np.fft.fft(X)[:, :, : int(X.shape[2] / 2)]) + else: + Xt = [] + for x in X: + if self.pad_series: + len = int( + math.pow(2, math.ceil(math.log(x.shape[1], 2))) - x.shape[1] + ) + x = np.pad( + x, + ( + (0, 0), + ( + 0, + len, + ), + ), + mode=self.pad_with, + constant_values=self.constant_value, + ) + Xt.append(np.abs(np.fft.fft(x)[:, : int(x.shape[1] / 2)])) return Xt diff --git a/aeon/transformations/collection/_resize.py b/aeon/transformations/collection/_resize.py index abfd696906..a43d0a43e8 100644 --- a/aeon/transformations/collection/_resize.py +++ b/aeon/transformations/collection/_resize.py @@ -4,10 +4,18 @@ __maintainer__ = [] import numpy as np +from deprecated.sphinx import deprecated from aeon.transformations.collection.base import BaseCollectionTransformer +# TODO: Remove in v1.3.0 +@deprecated( + version="1.2.0", + reason="Resizer is deprecated and will be moving to . " + "transformations.collection.unequal_length with additional/reworked functionality.", + category=FutureWarning, +) class Resizer(BaseCollectionTransformer): """Time series interpolator/re-sampler. diff --git a/aeon/transformations/collection/_truncate.py b/aeon/transformations/collection/_truncate.py index 2c20c1e010..2be0fa0583 100644 --- a/aeon/transformations/collection/_truncate.py +++ b/aeon/transformations/collection/_truncate.py @@ -4,10 +4,18 @@ __maintainer__ = [] import numpy as np +from deprecated.sphinx import deprecated from aeon.transformations.collection.base import BaseCollectionTransformer +# TODO: Remove in v1.3.0 +@deprecated( + version="1.2.0", + reason="Truncator is deprecated and will be moving to . " + "transformations.collection.unequal_length with additional/reworked functionality.", + category=FutureWarning, +) class Truncator(BaseCollectionTransformer): """Truncate unequal length time series to a lower bounds. diff --git a/aeon/transformations/collection/base.py b/aeon/transformations/collection/base.py index 0972341771..4463f4a4ee 100644 --- a/aeon/transformations/collection/base.py +++ b/aeon/transformations/collection/base.py @@ -328,7 +328,8 @@ def _fit_transform(self, X, y=None): """ # Non-optimized default implementation; override when a better # method is possible for a given algorithm. - self._fit(X, y) + if not self.get_tag("fit_is_empty"): + self._fit(X, y) return self._transform(X, y) def _inverse_transform(self, X, y=None): diff --git a/aeon/transformations/collection/compose/_pipeline.py b/aeon/transformations/collection/compose/_pipeline.py index 9e0acafdd8..b3373e1379 100644 --- a/aeon/transformations/collection/compose/_pipeline.py +++ b/aeon/transformations/collection/compose/_pipeline.py @@ -48,14 +48,14 @@ class CollectionTransformerPipeline(BaseCollectionPipeline, BaseCollectionTransf Examples -------- - >>> from aeon.transformations.collection import Resizer + >>> from aeon.transformations.collection.unequal_length import Resizer >>> from aeon.transformations.collection.feature_based import SevenNumberSummary >>> from aeon.datasets import load_unit_test >>> from aeon.transformations.collection.compose import ( ... CollectionTransformerPipeline) >>> X, y = load_unit_test(split="train") >>> pipeline = CollectionTransformerPipeline( - ... [Resizer(length=10), SevenNumberSummary()] + ... [Resizer(resized_length=10), SevenNumberSummary()] ... ) >>> pipeline.fit(X, y) CollectionTransformerPipeline(...) @@ -91,8 +91,8 @@ def _get_test_params(cls, parameter_set="default"): Each dict are parameters to construct an "interesting" test instance, i.e., `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. """ - from aeon.transformations.collection import Truncator from aeon.transformations.collection.feature_based import SevenNumberSummary + from aeon.transformations.collection.unequal_length import Truncator return { "transformers": [ diff --git a/aeon/transformations/collection/compose/tests/test_pipeline.py b/aeon/transformations/collection/compose/tests/test_pipeline.py index c45ddcb3a2..b5ee5b6b63 100644 --- a/aeon/transformations/collection/compose/tests/test_pipeline.py +++ b/aeon/transformations/collection/compose/tests/test_pipeline.py @@ -13,23 +13,23 @@ AutocorrelationFunctionTransformer, HOG1DTransformer, Normalizer, - Padder, Tabularizer, ) from aeon.transformations.collection.compose import CollectionTransformerPipeline from aeon.transformations.collection.feature_based import SevenNumberSummary +from aeon.transformations.collection.unequal_length import Padder @pytest.mark.parametrize( "transformers", [ - Padder(pad_length=15), + Padder(padded_length=15), SevenNumberSummary(), - [Padder(pad_length=15), Tabularizer(), StandardScaler()], - [Padder(pad_length=15), SevenNumberSummary()], + [Padder(padded_length=15), Tabularizer(), StandardScaler()], + [Padder(padded_length=15), SevenNumberSummary()], [Tabularizer(), StandardScaler(), SevenNumberSummary()], [ - Padder(pad_length=15), + Padder(padded_length=15), SevenNumberSummary(), ], ], diff --git a/aeon/transformations/collection/tests/test_resizer.py b/aeon/transformations/collection/tests/test_resizer.py deleted file mode 100644 index e5e8591bd9..0000000000 --- a/aeon/transformations/collection/tests/test_resizer.py +++ /dev/null @@ -1,18 +0,0 @@ -"""Tests for Resizer.""" - -import numpy as np - -from aeon.transformations.collection import Resizer - - -def test_resizer(): - """Test Resizer resizing.""" - X_list = [] - for i in range(10): - X_list.append(np.random.rand(5, 10 + i)) - trs = Resizer(length=50) - X_new = trs.fit_transform(X_list) - assert X_new.shape == (10, 5, 50) - X_array = np.random.rand(10, 3, 30) - X_new = trs.fit_transform(X_array) - assert X_new.shape == (10, 3, 50) diff --git a/aeon/transformations/collection/tests/test_truncate.py b/aeon/transformations/collection/tests/test_truncate.py deleted file mode 100644 index 21d0e9a69a..0000000000 --- a/aeon/transformations/collection/tests/test_truncate.py +++ /dev/null @@ -1,78 +0,0 @@ -"""Test Truncator transformer.""" - -import numpy as np -import pytest - -from aeon.datasets import ( - load_basic_motions, - load_japanese_vowels, - load_pickup_gesture_wiimoteZ, - load_unit_test, -) -from aeon.transformations.collection import Truncator - - -@pytest.mark.parametrize( - "loader", - [ - load_japanese_vowels, - load_pickup_gesture_wiimoteZ, - load_unit_test, - load_basic_motions, - ], -) -def test_truncation_transformer(loader): - """Test truncation to the fixed series length on provided datasets.""" - X_train, y_train = loader(split="train") - n_cases = len(X_train) - n_channels = len(X_train[0]) - truncator = Truncator(5) - Xt = truncator.fit_transform(X_train) - assert isinstance(Xt, np.ndarray) - assert Xt.shape == (n_cases, n_channels, 5) - - -def test_truncation_equal_length(): - """Test the output dimensions after truncation. - - Truncation should not change shape of equal length unless passed parameter. - """ - X = np.random.rand(10, 2, 20) - truncator = Truncator() - Xt = truncator.fit_transform(X) - assert Xt.shape == X.shape - - -def test_truncation_parameterised_transformer(): - """Test padding to user determined length. - - Padding should change shape of equal length if passed longer value. - """ - # load data - X = np.random.rand(10, 2, 20) - truncator = Truncator(truncated_length=10) - Xt = truncator.fit_transform(X) - # Series now of length 10 - assert Xt.shape == (X.shape[0], X.shape[1], 10) - - -def test_truncation_fill_unequal_length(): - """Tes Truncator handles unequal length data correctly.""" - X = [] - for i in range(10): - X.append(np.random.random((10, 15 + i))) - truncator = Truncator(truncated_length=10) - Xt = truncator.fit_transform(X) - assert isinstance(Xt, np.ndarray) - assert Xt.shape == (len(X), X[0].shape[0], 10) - - -def test_incorrect_arguments(): - """Test Truncator with incorrect constructor arguments.""" - X = np.random.rand(10, 1, 20) - truncator = Truncator(truncated_length=30) - truncator.fit(X) - assert truncator.truncated_length_ == 20 - X2 = np.random.rand(10, 1, 10) - with pytest.raises(ValueError, match="min_length of series"): - truncator.transform(X2) diff --git a/aeon/transformations/collection/unequal_length/__init__.py b/aeon/transformations/collection/unequal_length/__init__.py new file mode 100644 index 0000000000..c25753db07 --- /dev/null +++ b/aeon/transformations/collection/unequal_length/__init__.py @@ -0,0 +1,11 @@ +"""Transformations for unequal length collections.""" + +__all__ = [ + "Padder", + "Resizer", + "Truncator", +] + +from aeon.transformations.collection.unequal_length._pad import Padder +from aeon.transformations.collection.unequal_length._resize import Resizer +from aeon.transformations.collection.unequal_length._truncate import Truncator diff --git a/aeon/transformations/collection/unequal_length/_commons.py b/aeon/transformations/collection/unequal_length/_commons.py new file mode 100644 index 0000000000..3a879496df --- /dev/null +++ b/aeon/transformations/collection/unequal_length/_commons.py @@ -0,0 +1,21 @@ +"""Common functions for unequal length transformations. + +These should ideally be incorporated into the collection data utilities in utils/ in +the future. +""" + +import numpy as np + + +def _get_min_length(X): + if isinstance(X, np.ndarray): + return X.shape[2] + else: + return min([x.shape[1] for x in X]) + + +def _get_max_length(X): + if isinstance(X, np.ndarray): + return X.shape[2] + else: + return max([x.shape[1] for x in X]) diff --git a/aeon/transformations/collection/unequal_length/_pad.py b/aeon/transformations/collection/unequal_length/_pad.py new file mode 100644 index 0000000000..c127a1cc19 --- /dev/null +++ b/aeon/transformations/collection/unequal_length/_pad.py @@ -0,0 +1,221 @@ +"""Padding transformer, pad unequal length time series to max length or fixed length.""" + +__maintainer__ = ["MatthewMiddlehurst"] +__all__ = ["Padder"] + +import numpy as np +from sklearn.utils import check_random_state + +from aeon.transformations.collection.base import BaseCollectionTransformer +from aeon.transformations.collection.unequal_length._commons import ( + _get_max_length, + _get_min_length, +) + + +class Padder(BaseCollectionTransformer): + """Pad unequal length time series to equal, fixed length. + + Pads the input dataset to either a fixed length or finds the max/min length + series across all series and channels and pads to that with zeroes. + + Parameters + ---------- + padded_length : int, "min" or "max", default="min" + Length to pad the series to. If "min", will pad the transformed series to the + shortest series seen in ``fit``. If "max", will pad to the longest series seen + in ``fit``. If an integer, will pad to that length. + Calling ``fit`` is not required if ``padded_length`` is an int. + fill_value : int, str or Callable, default=0 + Value to pad with. Can be a float or a statistic string or an numpy array for + each time series. Supported statistic strings are "mean", "median", "max", + "min". + add_noise : float or None, default=None + Add noise to the padded values of the series. + Randomly adds a value between 0 and ``add_noise`` to each padded value if + float. + Adds no noise if None. + error_on_long : bool, default=True + If True, raise an error if a series is longer than pad_length. + If False, will ignore series longer than pad_length. As the series + collection could remain unequal length, a list of numpy arrays will be returned + instead of a 3D numpy array. + random_state : int, RandomState instance or None, default=None + Only used if add_noise is True. + + If `int`, random_state is the seed used by the random number generator; + If `RandomState` instance, random_state is the random number generator; + If `None`, the random number generator is the `RandomState` instance used + by `np.random`. + + Examples + -------- + >>> from aeon.transformations.collection.unequal_length import Padder + >>> import numpy as np + >>> X = [] + >>> for i in range(10): X.append(np.random.random((4, 75 + i))) + >>> padder = Padder(padded_length=200, fill_value =42) + >>> X2 = padder.fit_transform(X) + >>> X2.shape + (10, 4, 200) + """ + + _tags = { + "X_inner_type": ["np-list", "numpy3D"], + "capability:multivariate": True, + "capability:unequal_length": True, + "removes_unequal_length": True, + } + + def __init__( + self, + padded_length="max", + fill_value=0, + add_noise=False, + error_on_long=True, + random_state=None, + ): + self.padded_length = padded_length + self.fill_value = fill_value + self.add_noise = add_noise + self.error_on_long = error_on_long + self.random_state = random_state + + super().__init__() + + self.set_tags( + **{ + "fit_is_empty": isinstance(padded_length, int), + "removes_unequal_length": error_on_long, + } + ) + + def _fit(self, X, y=None): + """Fit padding transformer to X and y. + + Calculates the max length in X unless padding length passed as an argument. + + Parameters + ---------- + X : list of [n_cases] 2D np.ndarray shape (n_channels, length_i) + where length_i can vary between time series or 3D numpy of equal length + series + y : ignored argument for interface compatibility + Additional data, e.g., labels for transformation + + Returns + ------- + self : reference to self + """ + if self.padded_length == "min": + self._padded_length = _get_min_length(X) + elif self.padded_length == "max": + self._padded_length = _get_max_length(X) + else: + raise ValueError("pad_length must be 'min', 'max' or an integer.") + + def _transform(self, X, y=None): + """Transform X and return a transformed version. + + Parameters + ---------- + X : list of [n_cases] 2D np.ndarray shape (n_channels, length_i) + where length_i can vary between time series or 3D numpy of equal length + series + y : ignored argument for interface compatibility + Additional data, e.g., labels for transformation + + Returns + ------- + Xt : numpy3D array (n_cases, n_channels, self.pad_length_) + padded time series from X. + """ + # Must call fit if pad_length is None + pad_length = ( + self.padded_length + if isinstance(self.padded_length, int) + else self._padded_length + ) + + if self.error_on_long: + max_length = _get_max_length(X) + if max_length > pad_length: + raise ValueError( + "max length of series in X is greater than the provided pad_length" + "(or greater than the series seen in fit if pad_length is None)." + ) + + # Determine if fill value is a function + func = None + if isinstance(self.fill_value, str): + if self.fill_value == "mean": + func = np.mean + elif self.fill_value == "median": + func = np.median + elif self.fill_value == "min": + func = np.min + elif self.fill_value == "max": + func = np.max + elif self.fill_value == "last": + + def last(x): + return x[-1] + + func = last + else: + raise ValueError( + "Supported str values for fill_value are {mean, median, min, " + "max, last}." + ) + elif callable(self.fill_value): + func = self.fill_value + + rng = check_random_state(self.random_state) + + # Pad the series + Xt = [] + for series in X: + if series.shape[1] >= pad_length: + Xt.append(series) + continue + + # Amount to pad + pad_width = (0, pad_length - series.shape[1]) + padded_series = [] + for channel in series: + # Pad the series channel array + p = np.pad( + channel, + pad_width, + mode="constant", + constant_values=self.fill_value if func is None else func(channel), + ) + + if self.add_noise is not None: + p[series.shape[1] :] += rng.uniform( + 0, self.add_noise, size=pad_length - series.shape[1] + ) + + padded_series.append(p) + Xt.append(np.array(padded_series)) + + return np.array(Xt) if self.error_on_long else Xt + + @classmethod + def _get_test_params(cls, parameter_set="default"): + """Return testing parameter settings for the estimator. + + Parameters + ---------- + parameter_set : str, default="default" + Name of the set of test parameters to return, for use in tests. If no + special parameters are defined for a value, will return `"default"` set. + + Returns + ------- + params : dict or list of dict, default = {} + Parameters to create testing instances of the class + Each dict are parameters to construct an "interesting" test instance, i.e., + `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + """ + return {"padded_length": 30} diff --git a/aeon/transformations/collection/unequal_length/_resize.py b/aeon/transformations/collection/unequal_length/_resize.py new file mode 100644 index 0000000000..0adee0b7b2 --- /dev/null +++ b/aeon/transformations/collection/unequal_length/_resize.py @@ -0,0 +1,111 @@ +"""Time series linear interpolation resizer for unequal length.""" + +__maintainer__ = ["MatthewMiddlehurst"] +__all__ = ["Resizer"] + +import numpy as np + +from aeon.transformations.collection.base import BaseCollectionTransformer +from aeon.transformations.collection.unequal_length._commons import ( + _get_max_length, + _get_min_length, +) + + +class Resizer(BaseCollectionTransformer): + """Resize unequal length time series to equal, fixed length. + + Resize the series using linear interpolation to either a fixed length or + finds the max/min length series across all series and channels and resizes + all series to that length. + + Parameters + ---------- + resized_length : int, "min" or "max", default="min" + Length to resize the series to. If "min", will resize the transformed series + to the shortest series seen in ``fit``. If "max", will resize to the longest + series seen in ``fit``. If an integer, will resize to that length. + Calling ``fit`` is not required if ``resized_length`` is an int. + + Examples + -------- + >>> from aeon.transformations.collection.unequal_length import Resizer + >>> import numpy as np + >>> X = [] + >>> for i in range(10): X.append(np.random.random((4, 75 + i))) + >>> resizer = Resizer(resized_length=100) + >>> X2 = resizer.fit_transform(X) + >>> X2.shape + (10, 4, 100) + """ + + _tags = { + "X_inner_type": ["np-list", "numpy3D"], + "capability:multivariate": True, + "capability:unequal_length": True, + "removes_unequal_length": True, + } + + def __init__(self, resized_length="max"): + self.resized_length = resized_length + + super().__init__() + + self.set_tags(**{"fit_is_empty": isinstance(resized_length, int)}) + + def _fit(self, X, y=None): + if self.resized_length == "min": + self._resized_length = _get_min_length(X) + elif self.resized_length == "max": + self._resized_length = _get_max_length(X) + else: + raise ValueError("resized_length must be 'min', 'max' or an integer.") + + def _transform(self, X, y=None): + """Fit a linear function on each channel of each series, then resample. + + Parameters + ---------- + X : 3D np.ndarray of shape = (n_cases, n_channels, n_timepoints) or + list size [n_cases] of 2D nump arrays, case i has shape (n_channels, + length_i). Collection of time series to transform + y : ignored argument for interface compatibility + + Returns + ------- + 3D numpy array of shape (n_cases, n_channels, self.length) + """ + length = ( + self.resized_length + if isinstance(self.resized_length, int) + else self._resized_length + ) + + Xt = [] + for x in X: + x_new = np.zeros((x.shape[0], length)) + x2 = np.linspace(0, 1, x.shape[1]) + x3 = np.linspace(0, 1, length) + for i, row in enumerate(x): + x_new[i] = np.interp(x3, x2, row) + Xt.append(x_new) + return np.array(Xt) + + @classmethod + def _get_test_params(cls, parameter_set="default"): + """Return testing parameter settings for the estimator. + + Parameters + ---------- + parameter_set : str, default="default" + Name of the set of test parameters to return, for use in tests. If no + special parameters are defined for a value, will return `"default"` set. + + Returns + ------- + params : dict or list of dict, default = {} + Parameters to create testing instances of the class + Each dict are parameters to construct an "interesting" test instance, i.e., + `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + """ + return {"resized_length": 15} diff --git a/aeon/transformations/collection/unequal_length/_truncate.py b/aeon/transformations/collection/unequal_length/_truncate.py new file mode 100644 index 0000000000..ef5fc29ea0 --- /dev/null +++ b/aeon/transformations/collection/unequal_length/_truncate.py @@ -0,0 +1,138 @@ +"""Truncation transformer, truncate unequal length collections.""" + +__maintainer__ = ["MatthewMiddlehurst"] +__all__ = ["Truncator"] + +import numpy as np + +from aeon.transformations.collection.base import BaseCollectionTransformer +from aeon.transformations.collection.unequal_length._commons import ( + _get_max_length, + _get_min_length, +) + + +class Truncator(BaseCollectionTransformer): + """Truncate unequal length time series to equal, fixed length. + + Truncates the input dataset to either a fixed series length or finds the + max/min length series across all series and channels and truncates to that. + + Parameters + ---------- + truncated_length : int, "min" or "max", default="min" + The length to truncate series to. If "min", will truncate the transformed + series to the shortest series seen in ``fit``. If "max", will truncate to the + longest series seen in ``fit``. If an integer, will truncate to that length. + Calling ``fit`` is not required if ``truncated_length`` is an int. + error_on_short : bool, default=True + If True, raise an error if a series is shorter than truncated_length. + If False, will ignore series shorter than truncated_length. As the series + collection could remain unequal length, a list of numpy arrays will be returned + instead of a 3D numpy array. + + Examples + -------- + >>> from aeon.transformations.collection.unequal_length import Truncator + >>> import numpy as np + >>> X = [] + >>> for i in range(10): X.append(np.random.random((4, 75 + i))) + >>> truncator = Truncator(truncated_length=10) + >>> X2 = truncator.fit_transform(X) + >>> X2.shape + (10, 4, 10) + """ + + _tags = { + "X_inner_type": ["np-list", "numpy3D"], + "capability:multivariate": True, + "capability:unequal_length": True, + "removes_unequal_length": True, + } + + def __init__(self, truncated_length="min", error_on_short=True): + self.truncated_length = truncated_length + self.error_on_short = error_on_short + + super().__init__() + + self.set_tags( + **{ + "fit_is_empty": isinstance(truncated_length, int), + "removes_unequal_length": error_on_short, + } + ) + + def _fit(self, X, y=None): + """Fit transformer to X and y. + + Parameters + ---------- + X : list of [n_cases] 2D np.ndarray shape (n_channels, length_i) + where length_i can vary between time series or 3D numpy of equal length + series + y : ignored argument for interface compatibility + Additional data, e.g., labels for transformation + + Returns + ------- + self : reference to self + """ + if self.truncated_length == "min": + self._truncated_length = _get_min_length(X) + elif self.truncated_length == "max": + self._truncated_length = _get_max_length(X) + else: + raise ValueError("truncated_length must be 'min', 'max' or an integer.") + + def _transform(self, X, y=None): + """Truncate X and return a transformed version. + + Parameters + ---------- + X : list of [n_cases] 2D np.ndarray shape (n_channels, length_i) + where length_i can vary between time series. + y : ignored argument for interface compatibility + + Returns + ------- + Xt : numpy3D array (n_cases, n_channels, self.truncated_length_) + truncated time series from X. + """ + # Must call fit unless truncated_length is an int + truncated_length = ( + self.truncated_length + if isinstance(self.truncated_length, int) + else self._truncated_length + ) + + if self.error_on_short: + min_length = _get_min_length(X) + if min_length < truncated_length: + raise ValueError( + "min length of series in X is less than the provided " + "truncated_length (or less than the series seen in fit if " + "truncated_length is str)." + ) + + Xt = [x[:, :truncated_length] for x in X] + return np.array(Xt) if self.error_on_short else Xt + + @classmethod + def _get_test_params(cls, parameter_set="default"): + """Return testing parameter settings for the estimator. + + Parameters + ---------- + parameter_set : str, default="default" + Name of the set of test parameters to return, for use in tests. If no + special parameters are defined for a value, will return `"default"` set. + + Returns + ------- + params : dict or list of dict, default = {} + Parameters to create testing instances of the class + Each dict are parameters to construct an "interesting" test instance, i.e., + `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + """ + return {"truncated_length": 10} diff --git a/aeon/transformations/collection/unequal_length/tests/__init__.py b/aeon/transformations/collection/unequal_length/tests/__init__.py new file mode 100644 index 0000000000..a324aba332 --- /dev/null +++ b/aeon/transformations/collection/unequal_length/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for unequal length transformations.""" diff --git a/aeon/transformations/collection/tests/test_pad.py b/aeon/transformations/collection/unequal_length/tests/test_pad.py similarity index 53% rename from aeon/transformations/collection/tests/test_pad.py rename to aeon/transformations/collection/unequal_length/tests/test_pad.py index 430f235a16..87d87ab346 100644 --- a/aeon/transformations/collection/tests/test_pad.py +++ b/aeon/transformations/collection/unequal_length/tests/test_pad.py @@ -2,6 +2,7 @@ import numpy as np import pytest +from numpy.ma.testutils import assert_array_equal from aeon.datasets import ( load_basic_motions, @@ -9,7 +10,10 @@ load_pickup_gesture_wiimoteZ, load_unit_test, ) -from aeon.transformations.collection import Padder +from aeon.testing.data_generation import make_example_3d_numpy_list +from aeon.testing.utils.deep_equals import deep_equals +from aeon.transformations.collection.unequal_length import Padder +from aeon.transformations.collection.unequal_length._commons import _get_min_length @pytest.mark.parametrize( @@ -24,12 +28,14 @@ def test_padding(loader): """Test padding to on provided datasets.""" X_train, y_train = loader(split="train") - n_cases = len(X_train) - n_channels = len(X_train[0]) - pad = Padder(pad_length=2000) + pad = Padder(padded_length=2000) Xt = pad.fit_transform(X_train) + assert isinstance(Xt, np.ndarray) - assert Xt.shape == (n_cases, n_channels, 2000) + assert Xt.shape == (len(X_train), len(X_train[0]), 2000) + + for i in range(5): + assert_array_equal(X_train[i], Xt[i, :, : len(X_train[i][0])]) def test_padding_equal_length(): @@ -50,7 +56,7 @@ def test_padding_parameterised_transformer(): """ # load data X = np.random.rand(10, 2, 20) - padding_transformer = Padder(pad_length=120) + padding_transformer = Padder(padded_length=120) X_padded = padding_transformer.fit_transform(X) # Series now of length 120 assert X_padded.shape == (X.shape[0], X.shape[1], 120) @@ -62,7 +68,7 @@ def test_padding_fixed_value(): Padding should change set a value if passed. """ X = np.random.rand(10, 2, 20) - padding_transformer = Padder(pad_length=120, fill_value=42) + padding_transformer = Padder(padded_length=120, fill_value=42) X_padded = padding_transformer.fit_transform(X) assert X_padded.shape == (X.shape[0], X.shape[1], 120) @@ -77,53 +83,65 @@ def test_padding_fill_unequal_length(): X = [] for i in range(10): X.append(np.random.random((10, 15 + i))) - padding_transformer = Padder(pad_length=120, fill_value=42) + padding_transformer = Padder(padded_length=120, fill_value=42) X_padded = padding_transformer.fit_transform(X) assert isinstance(X_padded, np.ndarray) assert X_padded.shape == (len(X), X[0].shape[0], 120) +def test_padding_min(): + """Test Truncator handles unequal length data correctly.""" + X, _ = make_example_3d_numpy_list() + X2, _ = make_example_3d_numpy_list(min_n_timepoints=6, max_n_timepoints=16) + min_length = _get_min_length(X) + + padder = Padder(padded_length="min", error_on_long=False) + padder.fit(X) + Xt = padder.transform(X2) + + assert isinstance(Xt, list) + assert len(Xt) == len(X2) + assert len(Xt[0].shape) == 2 and Xt[0].shape[0] == 1 + assert all( + ( + Xt[i].shape[1] == min_length + if X2[i].shape[1] < min_length + else Xt[i].shape[1] == X2[i].shape[1] + ) + for i in range(len(Xt)) + ) + + X3, _ = make_example_3d_numpy_list(min_n_timepoints=13, max_n_timepoints=17) + Xt2 = padder.transform(X3) + assert deep_equals(Xt2, X3) + + def test_padding_fill_too_short_pad_value(): - """Test padding unequal length shorter than longest. + """Test padding unequal length shorter than longest.""" + X = np.random.rand(2, 2, 50) - If passed a value shorter than longest, pad should bad to longest. - """ - X = [] - for i in range(10): - X.append(np.random.random((10, 15 + i))) - padding_transformer = Padder(pad_length=10, fill_value=42) - X_padded = padding_transformer.fit_transform(X) - assert isinstance(X_padded, np.ndarray) - assert X_padded.shape == (len(X), X[0].shape[0], 24) + padding_transformer = Padder(padded_length=22) + with pytest.raises(ValueError, match="max length of series"): + padding_transformer.fit(X) + padding_transformer.transform(X) @pytest.mark.parametrize( "fill_value", - ["mean", "median", "max", "min", np.random.random(size=(10, 2))], + ["mean", "median", "max", "min", "last"], ) def test_fill_value_with_string_params(fill_value): - """Test if the fill_value argument returns the correct results.""" + """Test if the fill_value string arguments run without error.""" X = np.random.rand(10, 2, 20) - padding_transformer = Padder(pad_length=120, fill_value=fill_value) + padding_transformer = Padder(padded_length=120, fill_value=fill_value) X_padded = padding_transformer.fit_transform(X) - assert X_padded.shape == (X.shape[0], X.shape[1], 120) def test_padder_incorrect_paras(): """Test Padder with incorrect parameters.""" X = np.random.rand(2, 2, 20) - padding_transformer = Padder(pad_length=22, fill_value="FOOBAR") - with pytest.raises(ValueError, match="Supported modes are mean, median, min, max"): - padding_transformer.fit_transform(X) - padding_transformer = Padder(pad_length=22, fill_value=np.array([1, 2, 3, 4])) - with pytest.raises(ValueError, match="The length of fill_value must match"): - padding_transformer.fit_transform(X) - padding_transformer = Padder(pad_length=22, fill_value=np.array([1, 2])) - with pytest.raises(ValueError, match="The fill_value argument must be"): + + padding_transformer = Padder(padded_length=22, fill_value="FOOBAR") + with pytest.raises(ValueError, match="Supported str values for fill_value are"): padding_transformer.fit_transform(X) - X2 = np.random.rand(2, 2, 50) - padding_transformer = Padder(pad_length=22) - with pytest.raises(ValueError, match="max_length of series"): - padding_transformer.fit(X) - padding_transformer.transform(X2) diff --git a/aeon/transformations/collection/unequal_length/tests/test_resize.py b/aeon/transformations/collection/unequal_length/tests/test_resize.py new file mode 100644 index 0000000000..4dc5d107da --- /dev/null +++ b/aeon/transformations/collection/unequal_length/tests/test_resize.py @@ -0,0 +1,83 @@ +"""Test Resizer transformer.""" + +import numpy as np +import pytest + +from aeon.datasets import ( + load_basic_motions, + load_japanese_vowels, + load_plaid, + load_unit_test, +) +from aeon.testing.data_generation import ( + make_example_3d_numpy, + make_example_3d_numpy_list, +) +from aeon.transformations.collection.unequal_length import Resizer +from aeon.transformations.collection.unequal_length._commons import ( + _get_max_length, + _get_min_length, +) + + +@pytest.mark.parametrize( + "loader", [load_japanese_vowels, load_plaid, load_unit_test, load_basic_motions] +) +def test_resizer_transformer(loader): + """Test resizing to the fixed series length on provided datasets.""" + X, _ = loader(split="train") + + resizer = Resizer(resized_length=10) + Xt = resizer.fit_transform(X) + + assert isinstance(Xt, np.ndarray) + assert Xt.shape == (len(X), len(X[0]), 10) + + +def test_resizer_equal_length(): + """Test the output dimensions after resizing. + + Resizing should not change shape of equal length unless passed parameter. + """ + X, _ = make_example_3d_numpy() + + resizer = Resizer() + Xt = resizer.fit_transform(X) + + assert Xt.shape == X.shape + + +def test_resizer_fill_unequal_length(): + """Test Resizer handles unequal length data correctly.""" + X, _ = make_example_3d_numpy_list() + + resizer = Resizer() + Xt = resizer.fit_transform(X) + + assert isinstance(Xt, np.ndarray) + assert Xt.shape == (10, 1, _get_max_length(X)) + + +def test_resizer_min(): + """Test Resizer handles unequal length data correctly.""" + X, _ = make_example_3d_numpy_list() + X2, _ = make_example_3d_numpy_list(min_n_timepoints=6, max_n_timepoints=16) + min_length = _get_min_length(X) + + resizer = Resizer(resized_length="min") + resizer.fit(X) + Xt = resizer.transform(X2) + + assert isinstance(Xt, np.ndarray) + assert len(Xt) == len(X2) + assert len(Xt[0].shape) == 2 and Xt[0].shape[0] == 1 + assert all((Xt[i].shape[1] == min_length) for i in range(len(Xt))) + + +def test_incorrect_arguments(): + """Test Resizer with incorrect constructor arguments.""" + X, _ = make_example_3d_numpy() + + resizer = Resizer(resized_length="invalid") + with pytest.raises(ValueError, match="resized_length must be"): + resizer.fit_transform(X) diff --git a/aeon/transformations/collection/unequal_length/tests/test_truncate.py b/aeon/transformations/collection/unequal_length/tests/test_truncate.py new file mode 100644 index 0000000000..28795e8cbf --- /dev/null +++ b/aeon/transformations/collection/unequal_length/tests/test_truncate.py @@ -0,0 +1,99 @@ +"""Test Truncator transformer.""" + +import numpy as np +import pytest + +from aeon.datasets import ( + load_basic_motions, + load_japanese_vowels, + load_plaid, + load_unit_test, +) +from aeon.testing.data_generation import ( + make_example_3d_numpy, + make_example_3d_numpy_list, +) +from aeon.testing.utils.deep_equals import deep_equals +from aeon.transformations.collection.unequal_length import Truncator +from aeon.transformations.collection.unequal_length._commons import ( + _get_max_length, + _get_min_length, +) + + +@pytest.mark.parametrize( + "loader", [load_japanese_vowels, load_plaid, load_unit_test, load_basic_motions] +) +def test_truncation_transformer(loader): + """Test truncation to the fixed series length on provided datasets.""" + X, _ = loader(split="train") + + truncator = Truncator(truncated_length=5) + Xt = truncator.fit_transform(X) + + assert isinstance(Xt, np.ndarray) + assert Xt.shape == (len(X), len(X[0]), 5) + + +def test_truncation_equal_length(): + """Test the output dimensions after truncation. + + Truncation should not change shape of equal length unless passed parameter. + """ + X, _ = make_example_3d_numpy() + + truncator = Truncator() + Xt = truncator.fit_transform(X) + + assert Xt.shape == X.shape + + +def test_truncation_fill_unequal_length(): + """Test Truncator handles unequal length data correctly.""" + X, _ = make_example_3d_numpy_list() + + truncator = Truncator() + Xt = truncator.fit_transform(X) + + assert isinstance(Xt, np.ndarray) + assert Xt.shape == (10, 1, _get_min_length(X)) + + +def test_truncation_max(): + """Test Truncator handles unequal length data correctly.""" + X, _ = make_example_3d_numpy_list() + X2, _ = make_example_3d_numpy_list(min_n_timepoints=6, max_n_timepoints=16) + max_length = _get_max_length(X) + + truncator = Truncator(truncated_length="max", error_on_short=False) + truncator.fit(X) + Xt = truncator.transform(X2) + + assert isinstance(Xt, list) + assert len(Xt) == len(X2) + assert len(Xt[0].shape) == 2 and Xt[0].shape[0] == 1 + assert all( + ( + Xt[i].shape[1] == max_length + if X2[i].shape[1] > max_length + else Xt[i].shape[1] == X2[i].shape[1] + ) + for i in range(len(Xt)) + ) + + X3, _ = make_example_3d_numpy_list(min_n_timepoints=4, max_n_timepoints=7) + Xt2 = truncator.transform(X3) + assert deep_equals(Xt2, X3) + + +def test_incorrect_arguments(): + """Test Truncator with incorrect constructor arguments.""" + X, _ = make_example_3d_numpy() + + truncator = Truncator(truncated_length="invalid") + with pytest.raises(ValueError, match="truncated_length must be"): + truncator.fit_transform(X) + + truncator = Truncator(truncated_length=20) + with pytest.raises(ValueError, match="less than the provided truncated_length"): + truncator.fit_transform(X) diff --git a/aeon/utils/discovery.py b/aeon/utils/discovery.py index 8fd4a05efe..3082fc5a86 100644 --- a/aeon/utils/discovery.py +++ b/aeon/utils/discovery.py @@ -6,7 +6,6 @@ import inspect import warnings from importlib import import_module -from operator import itemgetter from pathlib import Path from pkgutil import walk_packages @@ -97,7 +96,6 @@ def all_estimators( "datasets", "distances", "networks", - "performance_metrics", "pipeline", "testing", "utils", @@ -154,7 +152,7 @@ def all_estimators( ) # sort for reproducibility, remove names if return_names=False - estimators = sorted(set(estimators), key=itemgetter(0)) + estimators = sorted(set(estimators), key=lambda x: (x[0], str(x[1]))) if not return_names: return [est for (name, est) in estimators] else: diff --git a/examples/datasets/data_unequal.ipynb b/examples/datasets/data_unequal.ipynb index 7aee7e86aa..3139895892 100644 --- a/examples/datasets/data_unequal.ipynb +++ b/examples/datasets/data_unequal.ipynb @@ -135,7 +135,7 @@ } ], "source": [ - "from aeon.transformations.collection import Padder, Truncator\n", + "from aeon.transformations.collection.unequal_length import Padder, Truncator\n", "\n", "padder = Padder()\n", "truncator = Truncator()\n", diff --git a/examples/transformations/preprocessing.ipynb b/examples/transformations/preprocessing.ipynb index b1236ece11..d467d0a401 100644 --- a/examples/transformations/preprocessing.ipynb +++ b/examples/transformations/preprocessing.ipynb @@ -1,1001 +1,1015 @@ { "cells": [ { - "cell_type": "markdown", - "source": [ - "# Preprocessing time series with aeon\n", - "\n", - "It is common to need to preprocess time series data before applying machine learning\n", - "algorithms. So algorithms can handle these characteristics, or `aeon` transformers can be used to preprocess collections of time\n", - "series into standard format. This notebook demonstrates three common use cases\n", - "\n", - "1. [Rescaling time series](#Rescaling-time-series)\n", - "2. [Resizing time series](#Resizing-time-series)\n", - "3. [Dealing with missing values](#missing-values)\n" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## Rescaling time series\n", - "\n", - "Different levels of scale and variance can mask discriminative patterns in time\n", - "series. This is particularly true for methods that are based on distances. It common\n", - "to rescale time series to have zero mean and unit variance. For example, the data in\n", - "the `UnitTest` dataset is a subset of the [Chinatown dataset]\n", - "(https://timeseriesclassification.com/description.php?Dataset=Chinatown. These are\n", - "counts of pedestrians in Chinatown, Melbourne. The time series are of different means" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "source": [ - "import numpy as np\n", - "\n", - "from aeon.datasets import load_unit_test\n", - "\n", - "X, y = load_unit_test(split=\"Train\")\n", - "np.mean(X, axis=-1)[0:5]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-11-17T13:48:57.655001Z", - "start_time": "2024-11-17T13:48:57.631756Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[561.875 ],\n", - " [604.95833333],\n", - " [629.16666667],\n", - " [801.45833333],\n", - " [540.75 ]])" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 3 - }, - { - "cell_type": "code", - "source": [ - "np.std(X, axis=-1)[0:5]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-11-17T13:48:59.467239Z", - "start_time": "2024-11-17T13:48:59.458263Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[428.95224215],\n", - " [483.35481095],\n", - " [514.90052977],\n", - " [629.00847763],\n", - " [389.10059218]])" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 4 - }, - { - "cell_type": "markdown", - "source": [ - "We can rescale the time series in three ways:\n", - "1. Normalise: subtract the mean and divide by the standard deviation to make all\n", - "series have zero mean and unit variance." - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "source": [ - "from aeon.transformations.collection import Normalizer\n", - "\n", - "normalizer = Normalizer()\n", - "X2 = normalizer.fit_transform(X)\n", - "np.round(np.mean(X2, axis=-1)[0:5], 6)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-11-17T13:49:01.643630Z", - "start_time": "2024-11-17T13:49:01.627083Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.],\n", - " [-0.],\n", - " [ 0.],\n", - " [-0.],\n", - " [-0.]])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 5 - }, - { - "cell_type": "code", - "source": [ - "np.round(np.std(X2, axis=-1)[0:5], 6)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-11-17T13:49:02.670358Z", - "start_time": "2024-11-17T13:49:02.648594Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.],\n", - " [1.],\n", - " [1.],\n", - " [1.],\n", - " [1.]])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 6 - }, - { - "cell_type": "markdown", - "source": [ - "2. Re-center: Recentering involves subtracting the mean of each series" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "source": [ - "from aeon.transformations.collection import Centerer\n", - "\n", - "c = Centerer()\n", - "X3 = c.fit_transform(X)\n", - "np.round(np.mean(X3, axis=-1)[0:5], 6)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-11-17T13:49:04.345033Z", - "start_time": "2024-11-17T13:49:04.332065Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.],\n", - " [-0.],\n", - " [ 0.],\n", - " [-0.],\n", - " [ 0.]])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 7 - }, - { - "cell_type": "markdown", - "source": [ - "3. Min-Max: Scale the data to be between 0 and 1" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "source": [ - "from aeon.transformations.collection import MinMaxScaler\n", - "\n", - "minmax = MinMaxScaler()\n", - "X4 = minmax.fit_transform(X)\n", - "np.round(np.min(X4, axis=-1)[0:5], 6)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-11-17T13:49:06.135780Z", - "start_time": "2024-11-17T13:49:06.116831Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.],\n", - " [0.],\n", - " [0.],\n", - " [0.],\n", - " [0.]])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 8 - }, - { - "cell_type": "code", - "source": [ - "np.round(np.max(X4, axis=-1)[0:5], 6)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-11-17T13:49:07.094710Z", - "start_time": "2024-11-17T13:49:07.072733Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.],\n", - " [1.],\n", - " [1.],\n", - " [1.],\n", - " [1.]])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 9 - }, - { - "cell_type": "markdown", - "source": [ - "There is no best way to do this, although for counts such as this it is more common\n", - "to MinMax scale, so that the data still has some interpretation as proportions." - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", "metadata": {}, - "source": [ - "## Resizing time series\n", - "\n", - "Suppose we have a collections of time series with different lengths, i.e. different\n", - "number of time points. Currently, most of aeon's collection estimators\n", - "(classification, clustering or regression) require equal-length time\n", - "series. Those that can handle unequal length series are tagged with\n", - "\"capability:unequal\"." - ] - }, - { - "cell_type": "code", - "metadata": { - "execution": { - "iopub.execute_input": "2020-12-19T14:31:58.456171Z", - "iopub.status.busy": "2020-12-19T14:31:58.455565Z", - "iopub.status.idle": "2020-12-19T14:31:59.189497Z", - "shell.execute_reply": "2020-12-19T14:31:59.190005Z" - }, - "pycharm": { - "is_executing": true - }, - "ExecuteTime": { - "end_time": "2025-05-28T13:37:35.086084Z", - "start_time": "2025-05-28T13:36:09.244708Z" - } - }, - "source": [ - "from aeon.classification.convolution_based import RocketClassifier\n", - "from aeon.datasets import (\n", - " load_basic_motions,\n", - " load_japanese_vowels,\n", - " load_pickup_gesture_wiimoteZ,\n", - ")\n", - "from aeon.utils.validation import has_missing, is_equal_length, is_univariate" - ], - "outputs": [], - "execution_count": 2 - }, - { - "cell_type": "markdown", - "source": [ - "If you want to use an estimator that cannot internally handle missing values, one\n", - "option is to convert unequal length series into equal length. This can be\n", - " done through padding, truncation or resizing through fitting a function and\n", - " resampling." - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Unequal or equal length collections time series\n", - "\n", - "If a collection contains all equal length series, it will store the data in a 3D\n", - "numpy of shape `(n_cases, n_channels, n_timepoints)`. If it is unequal length, it is\n", - "stored in a list of 2D numpy arrays:" - ] - }, - { - "cell_type": "code", - "metadata": { - "execution": { - "iopub.execute_input": "2020-12-19T14:31:59.194445Z", - "iopub.status.busy": "2020-12-19T14:31:59.193903Z", - "iopub.status.idle": "2020-12-19T14:32:01.019896Z", - "shell.execute_reply": "2020-12-19T14:32:01.020463Z" - }, - "pycharm": { - "is_executing": true - }, - "ExecuteTime": { - "end_time": "2025-05-28T13:37:44.511744Z", - "start_time": "2025-05-28T13:37:44.488521Z" - } - }, - "source": [ - "# Equal length multivariate data\n", - "bm_X, bm_y = load_basic_motions()\n", - "X = bm_X\n", - "print(f\"{type(X)}, {X.shape}\")\n", - "print(\n", - " f\"univariate = {is_univariate(X)}, has missing ={has_missing(X)}, equal \"\n", - " f\"length = {is_equal_length(X)}\"\n", - ")" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ", (80, 6, 100)\n", - "univariate = False, has missing =False, equal length = True\n" - ] - } - ], - "execution_count": 6 - }, - { - "cell_type": "code", - "source": [ - "# Unequal length univariate data\n", - "wii_X, wii_y = load_pickup_gesture_wiimoteZ()\n", - "X = wii_X\n", - "print(type(wii_X), \"\\n\", wii_X[0].shape, \"\\n\", wii_X[10].shape)\n", - "print(\n", - " f\"univariate = {is_univariate(X)}, has missing ={has_missing(X)}, equal \"\n", - " f\"length = {is_equal_length(X)}\"\n", - ")" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "is_executing": true - }, - "ExecuteTime": { - "end_time": "2025-05-28T13:37:45.578203Z", - "start_time": "2025-05-28T13:37:45.513828Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \n", - " (1, 324) \n", - " (1, 97)\n", - "univariate = True, has missing =False, equal length = False\n" - ] - } - ], - "execution_count": 7 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-28T13:37:49.679258Z", - "start_time": "2025-05-28T13:37:49.639207Z" - } - }, - "cell_type": "code", - "source": [ - "vowels_X, vowels_y = load_japanese_vowels(split=\"train\")\n", - "X = vowels_X\n", - "print(\n", - " f\"univariate = {is_univariate(X)}, has missing ={has_missing(X)}, equal \"\n", - " f\"length = {is_equal_length(X)}\"\n", - ")" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "univariate = False, has missing =False, equal length = False\n" - ] - } - ], - "execution_count": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "\n" - }, - { - "cell_type": "code", - "source": [ - "series_lengths = [array.shape[1] for array in wii_X]\n", - "\n", - "# Find the minimum and maximum of the second dimensions\n", - "min_length = min(series_lengths)\n", - "max_length = max(series_lengths)\n", - "print(\" Min length = \", min_length, \" max length = \", max_length)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "is_executing": true - }, - "ExecuteTime": { - "end_time": "2025-05-28T13:38:05.700223Z", - "start_time": "2025-05-28T13:38:05.689668Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Min length = 29 max length = 361\n" - ] - } - ], - "execution_count": 9 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": [ - "There are two basic strategies for unequal length problems\n", - "1. Use an estimator that can internally handle missing values\n", - "2. Transform the data to be equal length by, for example, truncating or padding series\n", - "\n", - "Estimators with the tag `\"capability:unequal_length\": True` have the capability to\n", - "handle unequal length series. For classification, regression and\n", - "clusterign, the\n", - "current list is" - ] - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-17T13:49:23.280238Z", - "start_time": "2024-11-17T13:49:23.143830Z" - } - }, - "cell_type": "code", - "source": [ - "from aeon.utils.discovery import all_estimators\n", - "\n", - "all_estimators(\n", - " type_filter=[\"classifier\", \"regressor\", \"clusterer\"],\n", - " tag_filter={\"capability:unequal_length\": True},\n", - ")" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "[('Catch22Classifier',\n", - " aeon.classification.feature_based._catch22.Catch22Classifier),\n", - " ('Catch22Clusterer', aeon.clustering.feature_based._catch22.Catch22Clusterer),\n", - " ('Catch22Regressor', aeon.regression.feature_based._catch22.Catch22Regressor),\n", - " ('DummyClassifier', aeon.classification.dummy.DummyClassifier),\n", - " ('DummyRegressor', aeon.regression._dummy.DummyRegressor),\n", - " ('ElasticEnsemble',\n", - " aeon.classification.distance_based._elastic_ensemble.ElasticEnsemble),\n", - " ('KNeighborsTimeSeriesClassifier',\n", - " aeon.classification.distance_based._time_series_neighbors.KNeighborsTimeSeriesClassifier),\n", - " ('KNeighborsTimeSeriesRegressor',\n", - " aeon.regression.distance_based._time_series_neighbors.KNeighborsTimeSeriesRegressor),\n", - " ('RDSTClassifier', aeon.classification.shapelet_based._rdst.RDSTClassifier),\n", - " ('RDSTRegressor', aeon.regression.shapelet_based._rdst.RDSTRegressor)]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 15 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "You can pass these estimators unequal length series and they will work as expected.\n" - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-17T13:49:25.499271Z", - "start_time": "2024-11-17T13:49:25.466359Z" - } - }, - "cell_type": "code", - "source": [ - "from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier\n", - "\n", - "knn = KNeighborsTimeSeriesClassifier()\n", - "model = knn.fit(wii_X, wii_y)" - ], - "outputs": [], - "execution_count": 16 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": [ - "If time series are unequal length, collection estimators will raise an error if they\n", - "do not have the capability to handle this characteristic. If you want to use them, \n", - "you will need to preprocess the data to be equal length. " - ] - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-28T13:38:30.585470Z", - "start_time": "2025-05-28T13:38:30.565833Z" - } - }, - "cell_type": "code", - "source": [ - "rc = RocketClassifier()\n", - "try:\n", - " rc.fit(wii_X, wii_y)\n", - "except ValueError as e:\n", - " print(f\"ValueError: {e}\")" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ValueError: Data seen by instance of RocketClassifier has unequal length series, but RocketClassifier cannot handle these characteristics. \n" - ] - } - ], - "execution_count": 10 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Padding, truncating or resizing.\n", - "\n", - "We can pad, truncate or resize. By default, pad adds zeros to make all series the\n", - "length of the longest, truncate removes all values beyond the length of the shortest\n", - "and resize stretches or shrinks the series." - ] - }, - { - "cell_type": "code", - "metadata": { - "execution": { - "iopub.execute_input": "2020-12-19T14:32:01.245270Z", - "iopub.status.busy": "2020-12-19T14:32:01.244733Z", - "iopub.status.idle": "2020-12-19T14:32:02.911970Z", - "shell.execute_reply": "2020-12-19T14:32:02.912833Z" - }, - "pycharm": { - "is_executing": true - }, - "ExecuteTime": { - "end_time": "2025-05-28T13:39:43.878568Z", - "start_time": "2025-05-28T13:39:43.857132Z" - } - }, - "source": [ - "from aeon.transformations.collection import Padder, Resizer, Truncator\n", - "\n", - "pad = Padder()\n", - "truncate = Truncator()\n", - "resize = Resizer(length=600)\n", - "X2 = pad.fit_transform(wii_X)\n", - "X3 = truncate.fit_transform(wii_X)\n", - "X4 = resize.fit_transform(wii_X)\n", - "print(X2.shape, \"\\n\", X3.shape, \"\\n\", X4.shape)" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(100, 1, 361) \n", - " (100, 1, 29) \n", - " (100, 1, 600)\n" - ] - } - ], - "execution_count": 12 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-28T13:39:45.811784Z", - "start_time": "2025-05-28T13:39:44.586889Z" - } - }, - "cell_type": "code", - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "plt.title(\"Before and after padding: first case (shifted up for unpadded)\")\n", - "plt.plot(wii_X[0][0] + 10)\n", - "plt.plot(X2[0][0])" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "
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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 13 - }, - { - "cell_type": "markdown", - "source": [ - "You can put these transformers in a pipeline to apply to both train/test split\n" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "source": [ - "from sklearn.metrics import accuracy_score\n", - "\n", - "# Unequal length univariate data\n", - "from aeon.pipeline import make_pipeline\n", - "\n", - "train_X, train_y = load_pickup_gesture_wiimoteZ(split=\"Train\")\n", - "test_X, test_y = load_pickup_gesture_wiimoteZ(split=\"Test\")\n", - "steps = [truncate, rc]\n", - "pipe = make_pipeline(steps)\n", - "pipe.fit(train_X, train_y)\n", - "preds = pipe.predict(test_X)\n", - "accuracy_score(train_y, preds)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "is_executing": true - }, - "ExecuteTime": { - "end_time": "2025-05-28T13:39:55.584822Z", - "start_time": "2025-05-28T13:39:47.310020Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.32" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 14 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": [ - "## Missing Values\n", - "\n", - "Missing values are indicated by `NaN` in numpy array. You can test whether any `aeon`\n", - " data structure contains missing values using the utility function" - ] - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-17T13:50:06.076875Z", - "start_time": "2024-11-17T13:50:06.065907Z" - } - }, - "cell_type": "code", - "source": [ - "X = np.random.random(size=(10, 2, 200))\n", - "has_missing(X)" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 21 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-17T13:50:06.186109Z", - "start_time": "2024-11-17T13:50:06.180126Z" - } - }, - "cell_type": "code", - "source": [ - "X[5][0][55] = np.nan\n", - "has_missing(X)" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 22 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": [ - "There are a range of strategies for handling missing values. These include:\n", - "\n", - "1. Use an estimator that internally handles missing values. It is fairly easy for\n", - "some algorithms (such as decision trees) to internally deal with missing values,\n", - "usually be using it as a distinct series value after discretisation. We do not yet \n", - "have many estimators with this capability. Estimators that are able to internally \n", - "handle missing values are tagged with `\"capability:missing_values\": True`." - ] - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-17T13:50:06.436013Z", - "start_time": "2024-11-17T13:50:06.296331Z" - } - }, - "cell_type": "code", - "source": [ - "from aeon.utils.discovery import all_estimators\n", - "\n", - "all_estimators(\n", - " tag_filter={\"capability:missing_values\": True},\n", - ")" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "[('BORF', aeon.transformations.collection.dictionary_based._borf.BORF),\n", - " ('CollectionId',\n", - " aeon.transformations.collection.compose._identity.CollectionId),\n", - " ('DummyClassifier', aeon.classification.dummy.DummyClassifier),\n", - " ('DummyRegressor', aeon.regression._dummy.DummyRegressor),\n", - " ('RandomSegmenter', aeon.segmentation._random.RandomSegmenter),\n", - " ('STRAY', aeon.anomaly_detection._stray.STRAY),\n", - " ('SimpleImputer', aeon.transformations.collection._impute.SimpleImputer)]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 23 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": [ - "2. Removing series with missing: this is often desirable if the train set size is\n", - "large, the number of series with missing is small and the proportion of missing\n", - "values for these series is high.\n", - "\n", - "We do not yet have a transformer for this, but it is easy to implement yourself.\n", - "\n", - "3. Interpolating missing values from series: estimating the missing values from the \n", - "other values in a time series is commonly done. This is\n", - " often desirable if the train set size is small and the proportion of missing values\n", - " is low. You can do this with the transformer ``SimpleImputer``. This interpolates \n", - " each series and each channel independently. So for example a mean interpolation \n", - " of series with two channels `[[NaN,1.0,2.0,3.0],[-1.0,-2.0,-3.0,-4.0]]` would be \n", - " `[[2.0,1.0,2.0,3.0],[-1.0,-2.0,-3.0,-4.0]]`. " - ] - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-17T13:52:23.133162Z", - "start_time": "2024-11-17T13:52:23.118202Z" - } - }, - "cell_type": "code", - "source": [ - "from aeon.transformations.collection import SimpleImputer\n", - "\n", - "imput = SimpleImputer(strategy=\"mean\")\n", - "X2 = imput.fit_transform(X)\n", - "has_missing(X2)" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 26 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-17T13:52:23.825897Z", - "start_time": "2024-11-17T13:52:23.811936Z" - } - }, - "cell_type": "code", - "source": [ - "imp2 = SimpleImputer(strategy=\"median\")\n", - "X3 = imp2.fit_transform(X)\n", - "has_missing(X3)" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 27 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-17T13:52:24.602058Z", - "start_time": "2024-11-17T13:52:24.582111Z" - } - }, - "cell_type": "code", - "source": [ - "imp3 = SimpleImputer(strategy=\"constant\", fill_value=0)\n", - "X4 = imp3.fit_transform(X)\n", - "has_missing(X4)" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 28 - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "" + "cell_type": "raw", + "source": [ + "{\n", + " \"cells\": [\n", + " {\n", + " \"cell_type\": \"markdown\",\n", + " \"source\": [\n", + " \"# Preprocessing time series with aeon\\n\",\n", + " \"\\n\",\n", + " \"It is common to need to preprocess time series data before applying machine learning\\n\",\n", + " \"algorithms. So algorithms can handle these characteristics, or `aeon` transformers can be used to preprocess collections of time\\n\",\n", + " \"series into standard format. This notebook demonstrates three common use cases\\n\",\n", + " \"\\n\",\n", + " \"1. [Rescaling time series](#Rescaling-time-series)\\n\",\n", + " \"2. [Resizing time series](#Resizing-time-series)\\n\",\n", + " \"3. [Dealing with missing values](#missing-values)\\n\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false\n", + " }\n", + " },\n", + " {\n", + " \"cell_type\": \"markdown\",\n", + " \"source\": [\n", + " \"## Rescaling time series\\n\",\n", + " \"\\n\",\n", + " \"Different levels of scale and variance can mask discriminative patterns in time\\n\",\n", + " \"series. This is particularly true for methods that are based on distances. It common\\n\",\n", + " \"to rescale time series to have zero mean and unit variance. For example, the data in\\n\",\n", + " \"the `UnitTest` dataset is a subset of the [Chinatown dataset]\\n\",\n", + " \"(https://timeseriesclassification.com/description.php?Dataset=Chinatown. These are\\n\",\n", + " \"counts of pedestrians in Chinatown, Melbourne. The time series are of different means\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false\n", + " }\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"import numpy as np\\n\",\n", + " \"\\n\",\n", + " \"from aeon.datasets import load_unit_test\\n\",\n", + " \"\\n\",\n", + " \"X, y = load_unit_test(split=\\\"Train\\\")\\n\",\n", + " \"np.mean(X, axis=-1)[0:5]\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false,\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2024-11-17T13:48:57.655001Z\",\n", + " \"start_time\": \"2024-11-17T13:48:57.631756Z\"\n", + " }\n", + " },\n", + " \"outputs\": [\n", + " {\n", + " \"data\": {\n", + " \"text/plain\": [\n", + " \"array([[561.875 ],\\n\",\n", + " \" [604.95833333],\\n\",\n", + " \" [629.16666667],\\n\",\n", + " \" [801.45833333],\\n\",\n", + " \" [540.75 ]])\"\n", + " ]\n", + " },\n", + " \"execution_count\": 3,\n", + " \"metadata\": {},\n", + " \"output_type\": \"execute_result\"\n", + " }\n", + " ],\n", + " \"execution_count\": 3\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"np.std(X, axis=-1)[0:5]\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false,\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2024-11-17T13:48:59.467239Z\",\n", + " \"start_time\": \"2024-11-17T13:48:59.458263Z\"\n", + " }\n", + " },\n", + " \"outputs\": [\n", + " {\n", + " \"data\": {\n", + " \"text/plain\": [\n", + " \"array([[428.95224215],\\n\",\n", + " \" [483.35481095],\\n\",\n", + " \" [514.90052977],\\n\",\n", + " \" [629.00847763],\\n\",\n", + " \" [389.10059218]])\"\n", + " ]\n", + " },\n", + " \"execution_count\": 4,\n", + " \"metadata\": {},\n", + " \"output_type\": \"execute_result\"\n", + " }\n", + " ],\n", + " \"execution_count\": 4\n", + " },\n", + " {\n", + " \"cell_type\": \"markdown\",\n", + " \"source\": [\n", + " \"We can rescale the time series in three ways:\\n\",\n", + " \"1. Normalise: subtract the mean and divide by the standard deviation to make all\\n\",\n", + " \"series have zero mean and unit variance.\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false\n", + " }\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"from aeon.transformations.collection import Normalizer\\n\",\n", + " \"\\n\",\n", + " \"normalizer = Normalizer()\\n\",\n", + " \"X2 = normalizer.fit_transform(X)\\n\",\n", + " \"np.round(np.mean(X2, axis=-1)[0:5], 6)\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false,\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2024-11-17T13:49:01.643630Z\",\n", + " \"start_time\": \"2024-11-17T13:49:01.627083Z\"\n", + " }\n", + " },\n", + " \"outputs\": [\n", + " {\n", + " \"data\": {\n", + " \"text/plain\": [\n", + " \"array([[ 0.],\\n\",\n", + " \" [-0.],\\n\",\n", + " \" [ 0.],\\n\",\n", + " \" [-0.],\\n\",\n", + " \" [-0.]])\"\n", + " ]\n", + " },\n", + " \"execution_count\": 5,\n", + " \"metadata\": {},\n", + " \"output_type\": \"execute_result\"\n", + " }\n", + " ],\n", + " \"execution_count\": 5\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"np.round(np.std(X2, axis=-1)[0:5], 6)\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false,\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2024-11-17T13:49:02.670358Z\",\n", + " \"start_time\": \"2024-11-17T13:49:02.648594Z\"\n", + " }\n", + " },\n", + " \"outputs\": [\n", + " {\n", + " \"data\": {\n", + " \"text/plain\": [\n", + " \"array([[1.],\\n\",\n", + " \" [1.],\\n\",\n", + " \" [1.],\\n\",\n", + " \" [1.],\\n\",\n", + " \" [1.]])\"\n", + " ]\n", + " },\n", + " \"execution_count\": 6,\n", + " \"metadata\": {},\n", + " \"output_type\": \"execute_result\"\n", + " }\n", + " ],\n", + " \"execution_count\": 6\n", + " },\n", + " {\n", + " \"cell_type\": \"markdown\",\n", + " \"source\": [\n", + " \"2. Re-center: Recentering involves subtracting the mean of each series\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false\n", + " }\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"from aeon.transformations.collection import Centerer\\n\",\n", + " \"\\n\",\n", + " \"c = Centerer()\\n\",\n", + " \"X3 = c.fit_transform(X)\\n\",\n", + " \"np.round(np.mean(X3, axis=-1)[0:5], 6)\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false,\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2024-11-17T13:49:04.345033Z\",\n", + " \"start_time\": \"2024-11-17T13:49:04.332065Z\"\n", + " }\n", + " },\n", + " \"outputs\": [\n", + " {\n", + " \"data\": {\n", + " \"text/plain\": [\n", + " \"array([[ 0.],\\n\",\n", + " \" [-0.],\\n\",\n", + " \" [ 0.],\\n\",\n", + " \" [-0.],\\n\",\n", + " \" [ 0.]])\"\n", + " ]\n", + " },\n", + " \"execution_count\": 7,\n", + " \"metadata\": {},\n", + " \"output_type\": \"execute_result\"\n", + " }\n", + " ],\n", + " \"execution_count\": 7\n", + " },\n", + " {\n", + " \"cell_type\": \"markdown\",\n", + " \"source\": [\n", + " \"3. Min-Max: Scale the data to be between 0 and 1\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false\n", + " }\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"from aeon.transformations.collection import MinMaxScaler\\n\",\n", + " \"\\n\",\n", + " \"minmax = MinMaxScaler()\\n\",\n", + " \"X4 = minmax.fit_transform(X)\\n\",\n", + " \"np.round(np.min(X4, axis=-1)[0:5], 6)\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false,\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2024-11-17T13:49:06.135780Z\",\n", + " \"start_time\": \"2024-11-17T13:49:06.116831Z\"\n", + " }\n", + " },\n", + " \"outputs\": [\n", + " {\n", + " \"data\": {\n", + " \"text/plain\": [\n", + " \"array([[0.],\\n\",\n", + " \" [0.],\\n\",\n", + " \" [0.],\\n\",\n", + " \" [0.],\\n\",\n", + " \" [0.]])\"\n", + " ]\n", + " },\n", + " \"execution_count\": 8,\n", + " \"metadata\": {},\n", + " \"output_type\": \"execute_result\"\n", + " }\n", + " ],\n", + " \"execution_count\": 8\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"np.round(np.max(X4, axis=-1)[0:5], 6)\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false,\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2024-11-17T13:49:07.094710Z\",\n", + " \"start_time\": \"2024-11-17T13:49:07.072733Z\"\n", + " }\n", + " },\n", + " \"outputs\": [\n", + " {\n", + " \"data\": {\n", + " \"text/plain\": [\n", + " \"array([[1.],\\n\",\n", + " \" [1.],\\n\",\n", + " \" [1.],\\n\",\n", + " \" [1.],\\n\",\n", + " \" [1.]])\"\n", + " ]\n", + " },\n", + " \"execution_count\": 9,\n", + " \"metadata\": {},\n", + " \"output_type\": \"execute_result\"\n", + " }\n", + " ],\n", + " \"execution_count\": 9\n", + " },\n", + " {\n", + " \"cell_type\": \"markdown\",\n", + " \"source\": [\n", + " \"There is no best way to do this, although for counts such as this it is more common\\n\",\n", + " \"to MinMax scale, so that the data still has some interpretation as proportions.\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false\n", + " }\n", + " },\n", + " {\n", + " \"cell_type\": \"markdown\",\n", + " \"metadata\": {},\n", + " \"source\": [\n", + " \"## Resizing time series\\n\",\n", + " \"\\n\",\n", + " \"Suppose we have a collections of time series with different lengths, i.e. different\\n\",\n", + " \"number of time points. Currently, most of aeon's collection estimators\\n\",\n", + " \"(classification, clustering or regression) require equal-length time\\n\",\n", + " \"series. Those that can handle unequal length series are tagged with\\n\",\n", + " \"\\\"capability:unequal\\\".\"\n", + " ]\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"metadata\": {\n", + " \"execution\": {\n", + " \"iopub.execute_input\": \"2020-12-19T14:31:58.456171Z\",\n", + " \"iopub.status.busy\": \"2020-12-19T14:31:58.455565Z\",\n", + " \"iopub.status.idle\": \"2020-12-19T14:31:59.189497Z\",\n", + " \"shell.execute_reply\": \"2020-12-19T14:31:59.190005Z\"\n", + " },\n", + " \"pycharm\": {\n", + " \"is_executing\": true\n", + " },\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2025-05-28T13:37:35.086084Z\",\n", + " \"start_time\": \"2025-05-28T13:36:09.244708Z\"\n", + " }\n", + " },\n", + " \"source\": [\n", + " \"from aeon.classification.convolution_based import RocketClassifier\\n\",\n", + " \"from aeon.datasets import (\\n\",\n", + " \" load_basic_motions,\\n\",\n", + " \" load_japanese_vowels,\\n\",\n", + " \" load_pickup_gesture_wiimoteZ,\\n\",\n", + " \")\\n\",\n", + " \"from aeon.utils.validation import has_missing, is_equal_length, is_univariate\"\n", + " ],\n", + " \"outputs\": [],\n", + " \"execution_count\": 2\n", + " },\n", + " {\n", + " \"cell_type\": \"markdown\",\n", + " \"source\": [\n", + " \"If you want to use an estimator that cannot internally handle missing values, one\\n\",\n", + " \"option is to convert unequal length series into equal length. This can be\\n\",\n", + " \" done through padding, truncation or resizing through fitting a function and\\n\",\n", + " \" resampling.\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false\n", + " }\n", + " },\n", + " {\n", + " \"cell_type\": \"markdown\",\n", + " \"metadata\": {},\n", + " \"source\": [\n", + " \"### Unequal or equal length collections time series\\n\",\n", + " \"\\n\",\n", + " \"If a collection contains all equal length series, it will store the data in a 3D\\n\",\n", + " \"numpy of shape `(n_cases, n_channels, n_timepoints)`. If it is unequal length, it is\\n\",\n", + " \"stored in a list of 2D numpy arrays:\"\n", + " ]\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"metadata\": {\n", + " \"execution\": {\n", + " \"iopub.execute_input\": \"2020-12-19T14:31:59.194445Z\",\n", + " \"iopub.status.busy\": \"2020-12-19T14:31:59.193903Z\",\n", + " \"iopub.status.idle\": \"2020-12-19T14:32:01.019896Z\",\n", + " \"shell.execute_reply\": \"2020-12-19T14:32:01.020463Z\"\n", + " },\n", + " \"pycharm\": {\n", + " \"is_executing\": true\n", + " },\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2025-05-28T13:37:44.511744Z\",\n", + " \"start_time\": \"2025-05-28T13:37:44.488521Z\"\n", + " }\n", + " },\n", + " \"source\": [\n", + " \"# Equal length multivariate data\\n\",\n", + " \"bm_X, bm_y = load_basic_motions()\\n\",\n", + " \"X = bm_X\\n\",\n", + " \"print(f\\\"{type(X)}, {X.shape}\\\")\\n\",\n", + " \"print(\\n\",\n", + " \" f\\\"univariate = {is_univariate(X)}, has missing ={has_missing(X)}, equal \\\"\\n\",\n", + " \" f\\\"length = {is_equal_length(X)}\\\"\\n\",\n", + " \")\"\n", + " ],\n", + " \"outputs\": [\n", + " {\n", + " \"name\": \"stdout\",\n", + " \"output_type\": \"stream\",\n", + " \"text\": [\n", + " \", (80, 6, 100)\\n\",\n", + " \"univariate = False, has missing =False, equal length = True\\n\"\n", + " ]\n", + " }\n", + " ],\n", + " \"execution_count\": 6\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"# Unequal length univariate data\\n\",\n", + " \"wii_X, wii_y = load_pickup_gesture_wiimoteZ()\\n\",\n", + " \"X = wii_X\\n\",\n", + " \"print(type(wii_X), \\\"\\\\n\\\", wii_X[0].shape, \\\"\\\\n\\\", wii_X[10].shape)\\n\",\n", + " \"print(\\n\",\n", + " \" f\\\"univariate = {is_univariate(X)}, has missing ={has_missing(X)}, equal \\\"\\n\",\n", + " \" f\\\"length = {is_equal_length(X)}\\\"\\n\",\n", + " \")\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false,\n", + " \"pycharm\": {\n", + " \"is_executing\": true\n", + " },\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2025-05-28T13:37:45.578203Z\",\n", + " \"start_time\": \"2025-05-28T13:37:45.513828Z\"\n", + " }\n", + " },\n", + " \"outputs\": [\n", + " {\n", + " \"name\": \"stdout\",\n", + " \"output_type\": \"stream\",\n", + " \"text\": [\n", + " \" \\n\",\n", + " \" (1, 324) \\n\",\n", + " \" (1, 97)\\n\",\n", + " \"univariate = True, has missing =False, equal length = False\\n\"\n", + " ]\n", + " }\n", + " ],\n", + " \"execution_count\": 7\n", + " },\n", + " {\n", + " \"metadata\": {\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2025-05-28T13:37:49.679258Z\",\n", + " \"start_time\": \"2025-05-28T13:37:49.639207Z\"\n", + " }\n", + " },\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"vowels_X, vowels_y = load_japanese_vowels(split=\\\"train\\\")\\n\",\n", + " \"X = vowels_X\\n\",\n", + " \"print(\\n\",\n", + " \" f\\\"univariate = {is_univariate(X)}, has missing ={has_missing(X)}, equal \\\"\\n\",\n", + " \" f\\\"length = {is_equal_length(X)}\\\"\\n\",\n", + " \")\"\n", + " ],\n", + " \"outputs\": [\n", + " {\n", + " \"name\": \"stdout\",\n", + " \"output_type\": \"stream\",\n", + " \"text\": [\n", + " \"univariate = False, has missing =False, equal length = False\\n\"\n", + " ]\n", + " }\n", + " ],\n", + " \"execution_count\": 8\n", + " },\n", + " {\n", + " \"cell_type\": \"markdown\",\n", + " \"metadata\": {},\n", + " \"source\": \"\\n\"\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"series_lengths = [array.shape[1] for array in wii_X]\\n\",\n", + " \"\\n\",\n", + " \"# Find the minimum and maximum of the second dimensions\\n\",\n", + " \"min_length = min(series_lengths)\\n\",\n", + " \"max_length = max(series_lengths)\\n\",\n", + " \"print(\\\" Min length = \\\", min_length, \\\" max length = \\\", max_length)\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false,\n", + " \"pycharm\": {\n", + " \"is_executing\": true\n", + " },\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2025-05-28T13:38:05.700223Z\",\n", + " \"start_time\": \"2025-05-28T13:38:05.689668Z\"\n", + " }\n", + " },\n", + " \"outputs\": [\n", + " {\n", + " \"name\": \"stdout\",\n", + " \"output_type\": \"stream\",\n", + " \"text\": [\n", + " \" Min length = 29 max length = 361\\n\"\n", + " ]\n", + " }\n", + " ],\n", + " \"execution_count\": 9\n", + " },\n", + " {\n", + " \"metadata\": {},\n", + " \"cell_type\": \"markdown\",\n", + " \"source\": [\n", + " \"There are two basic strategies for unequal length problems\\n\",\n", + " \"1. Use an estimator that can internally handle missing values\\n\",\n", + " \"2. Transform the data to be equal length by, for example, truncating or padding series\\n\",\n", + " \"\\n\",\n", + " \"Estimators with the tag `\\\"capability:unequal_length\\\": True` have the capability to\\n\",\n", + " \"handle unequal length series. For classification, regression and\\n\",\n", + " \"clusterign, the\\n\",\n", + " \"current list is\"\n", + " ]\n", + " },\n", + " {\n", + " \"metadata\": {\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2024-11-17T13:49:23.280238Z\",\n", + " \"start_time\": \"2024-11-17T13:49:23.143830Z\"\n", + " }\n", + " },\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"from aeon.utils.discovery import all_estimators\\n\",\n", + " \"\\n\",\n", + " \"all_estimators(\\n\",\n", + " \" type_filter=[\\\"classifier\\\", \\\"regressor\\\", \\\"clusterer\\\"],\\n\",\n", + " \" tag_filter={\\\"capability:unequal_length\\\": True},\\n\",\n", + " \")\"\n", + " ],\n", + " \"outputs\": [\n", + " {\n", + " \"data\": {\n", + " \"text/plain\": [\n", + " \"[('Catch22Classifier',\\n\",\n", + " \" aeon.classification.feature_based._catch22.Catch22Classifier),\\n\",\n", + " \" ('Catch22Clusterer', aeon.clustering.feature_based._catch22.Catch22Clusterer),\\n\",\n", + " \" ('Catch22Regressor', aeon.regression.feature_based._catch22.Catch22Regressor),\\n\",\n", + " \" ('DummyClassifier', aeon.classification.dummy.DummyClassifier),\\n\",\n", + " \" ('DummyRegressor', aeon.regression._dummy.DummyRegressor),\\n\",\n", + " \" ('ElasticEnsemble',\\n\",\n", + " \" aeon.classification.distance_based._elastic_ensemble.ElasticEnsemble),\\n\",\n", + " \" ('KNeighborsTimeSeriesClassifier',\\n\",\n", + " \" aeon.classification.distance_based._time_series_neighbors.KNeighborsTimeSeriesClassifier),\\n\",\n", + " \" ('KNeighborsTimeSeriesRegressor',\\n\",\n", + " \" aeon.regression.distance_based._time_series_neighbors.KNeighborsTimeSeriesRegressor),\\n\",\n", + " \" ('RDSTClassifier', aeon.classification.shapelet_based._rdst.RDSTClassifier),\\n\",\n", + " \" ('RDSTRegressor', aeon.regression.shapelet_based._rdst.RDSTRegressor)]\"\n", + " ]\n", + " },\n", + " \"execution_count\": 15,\n", + " \"metadata\": {},\n", + " \"output_type\": \"execute_result\"\n", + " }\n", + " ],\n", + " \"execution_count\": 15\n", + " },\n", + " {\n", + " \"metadata\": {},\n", + " \"cell_type\": \"markdown\",\n", + " \"source\": \"You can pass these estimators unequal length series and they will work as expected.\\n\"\n", + " },\n", + " {\n", + " \"metadata\": {\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2024-11-17T13:49:25.499271Z\",\n", + " \"start_time\": \"2024-11-17T13:49:25.466359Z\"\n", + " }\n", + " },\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier\\n\",\n", + " \"\\n\",\n", + " \"knn = KNeighborsTimeSeriesClassifier()\\n\",\n", + " \"model = knn.fit(wii_X, wii_y)\"\n", + " ],\n", + " \"outputs\": [],\n", + " \"execution_count\": 16\n", + " },\n", + " {\n", + " \"metadata\": {},\n", + " \"cell_type\": \"markdown\",\n", + " \"source\": [\n", + " \"If time series are unequal length, collection estimators will raise an error if they\\n\",\n", + " \"do not have the capability to handle this characteristic. If you want to use them, \\n\",\n", + " \"you will need to preprocess the data to be equal length. \"\n", + " ]\n", + " },\n", + " {\n", + " \"metadata\": {\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2025-05-28T13:38:30.585470Z\",\n", + " \"start_time\": \"2025-05-28T13:38:30.565833Z\"\n", + " }\n", + " },\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"rc = RocketClassifier()\\n\",\n", + " \"try:\\n\",\n", + " \" rc.fit(wii_X, wii_y)\\n\",\n", + " \"except ValueError as e:\\n\",\n", + " \" print(f\\\"ValueError: {e}\\\")\"\n", + " ],\n", + " \"outputs\": [\n", + " {\n", + " \"name\": \"stdout\",\n", + " \"output_type\": \"stream\",\n", + " \"text\": [\n", + " \"ValueError: Data seen by instance of RocketClassifier has unequal length series, but RocketClassifier cannot handle these characteristics. \\n\"\n", + " ]\n", + " }\n", + " ],\n", + " \"execution_count\": 10\n", + " },\n", + " {\n", + " \"cell_type\": \"markdown\",\n", + " \"metadata\": {},\n", + " \"source\": [\n", + " \"### Padding, truncating or resizing.\\n\",\n", + " \"\\n\",\n", + " \"We can pad, truncate or resize. By default, pad adds zeros to make all series the\\n\",\n", + " \"length of the longest, truncate removes all values beyond the length of the shortest\\n\",\n", + " \"and resize stretches or shrinks the series.\"\n", + " ]\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"metadata\": {\n", + " \"execution\": {\n", + " \"iopub.execute_input\": \"2020-12-19T14:32:01.245270Z\",\n", + " \"iopub.status.busy\": \"2020-12-19T14:32:01.244733Z\",\n", + " \"iopub.status.idle\": \"2020-12-19T14:32:02.911970Z\",\n", + " \"shell.execute_reply\": \"2020-12-19T14:32:02.912833Z\"\n", + " },\n", + " \"pycharm\": {\n", + " \"is_executing\": true\n", + " },\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2025-05-28T13:39:43.878568Z\",\n", + " \"start_time\": \"2025-05-28T13:39:43.857132Z\"\n", + " }\n", + " },\n", + " \"source\": [\n", + " \"from aeon.transformations.collection.unequal_length import Padder, Resizer, Truncator\\n\",\n", + " \"\\n\",\n", + " \"pad = Padder()\\n\",\n", + " \"truncate = Truncator()\\n\",\n", + " \"resize = Resizer(resized_length=600)\\n\",\n", + " \"X2 = pad.fit_transform(wii_X)\\n\",\n", + " \"X3 = truncate.fit_transform(wii_X)\\n\",\n", + " \"X4 = resize.fit_transform(wii_X)\\n\",\n", + " \"print(X2.shape, \\\"\\\\n\\\", X3.shape, \\\"\\\\n\\\", X4.shape)\"\n", + " ],\n", + " \"outputs\": [\n", + " {\n", + " \"name\": \"stdout\",\n", + " \"output_type\": \"stream\",\n", + " \"text\": [\n", + " \"(100, 1, 361) \\n\",\n", + " \" (100, 1, 29) \\n\",\n", + " \" (100, 1, 600)\\n\"\n", + " ]\n", + " }\n", + " ],\n", + " \"execution_count\": 12\n", + " },\n", + " {\n", + " \"metadata\": {\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2025-05-28T13:39:45.811784Z\",\n", + " \"start_time\": \"2025-05-28T13:39:44.586889Z\"\n", + " }\n", + " },\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"import matplotlib.pyplot as plt\\n\",\n", + " \"\\n\",\n", + " \"plt.title(\\\"Before and after padding: first case (shifted up for unpadded)\\\")\\n\",\n", + " \"plt.plot(wii_X[0][0] + 10)\\n\",\n", + " \"plt.plot(X2[0][0])\"\n", + " ],\n", + " \"outputs\": [\n", + " {\n", + " \"data\": {\n", + " \"text/plain\": [\n", + " \"[]\"\n", + " ]\n", + " },\n", + " \"execution_count\": 13,\n", + " \"metadata\": {},\n", + " \"output_type\": \"execute_result\"\n", + " },\n", + " {\n", + " \"data\": {\n", + " \"text/plain\": [\n", + " \"
\"\n", + " ],\n", + " \"image/png\": 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\"\n", + " },\n", + " \"metadata\": {},\n", + " \"output_type\": \"display_data\"\n", + " }\n", + " ],\n", + " \"execution_count\": 13\n", + " },\n", + " {\n", + " \"cell_type\": \"markdown\",\n", + " \"source\": [\n", + " \"You can put these transformers in a pipeline to apply to both train/test split\\n\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false\n", + " }\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"from sklearn.metrics import accuracy_score\\n\",\n", + " \"\\n\",\n", + " \"# Unequal length univariate data\\n\",\n", + " \"from aeon.pipeline import make_pipeline\\n\",\n", + " \"\\n\",\n", + " \"train_X, train_y = load_pickup_gesture_wiimoteZ(split=\\\"Train\\\")\\n\",\n", + " \"test_X, test_y = load_pickup_gesture_wiimoteZ(split=\\\"Test\\\")\\n\",\n", + " \"steps = [truncate, rc]\\n\",\n", + " \"pipe = make_pipeline(steps)\\n\",\n", + " \"pipe.fit(train_X, train_y)\\n\",\n", + " \"preds = pipe.predict(test_X)\\n\",\n", + " \"accuracy_score(train_y, preds)\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false,\n", + " \"pycharm\": {\n", + " \"is_executing\": true\n", + " },\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2025-05-28T13:39:55.584822Z\",\n", + " \"start_time\": \"2025-05-28T13:39:47.310020Z\"\n", + " }\n", + " },\n", + " \"outputs\": [\n", + " {\n", + " \"data\": {\n", + " \"text/plain\": [\n", + " \"0.32\"\n", + " ]\n", + " },\n", + " \"execution_count\": 14,\n", + " \"metadata\": {},\n", + " \"output_type\": \"execute_result\"\n", + " }\n", + " ],\n", + " \"execution_count\": 14\n", + " },\n", + " {\n", + " \"metadata\": {},\n", + " \"cell_type\": \"markdown\",\n", + " \"source\": [\n", + " \"## Missing Values\\n\",\n", + " \"\\n\",\n", + " \"Missing values are indicated by `NaN` in numpy array. You can test whether any `aeon`\\n\",\n", + " \" data structure contains missing values using the utility function\"\n", + " ]\n", + " },\n", + " {\n", + " \"metadata\": {\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2024-11-17T13:50:06.076875Z\",\n", + " \"start_time\": \"2024-11-17T13:50:06.065907Z\"\n", + " }\n", + " },\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"X = np.random.random(size=(10, 2, 200))\\n\",\n", + " \"has_missing(X)\"\n", + " ],\n", + " \"outputs\": [\n", + " {\n", + " \"data\": {\n", + " \"text/plain\": [\n", + " \"False\"\n", + " ]\n", + " },\n", + " \"execution_count\": 21,\n", + " \"metadata\": {},\n", + " \"output_type\": \"execute_result\"\n", + " }\n", + " ],\n", + " \"execution_count\": 21\n", + " },\n", + " {\n", + " \"metadata\": {\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2024-11-17T13:50:06.186109Z\",\n", + " \"start_time\": \"2024-11-17T13:50:06.180126Z\"\n", + " }\n", + " },\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"X[5][0][55] = np.nan\\n\",\n", + " \"has_missing(X)\"\n", + " ],\n", + " \"outputs\": [\n", + " {\n", + " \"data\": {\n", + " \"text/plain\": [\n", + " \"True\"\n", + " ]\n", + " },\n", + " \"execution_count\": 22,\n", + " \"metadata\": {},\n", + " \"output_type\": \"execute_result\"\n", + " }\n", + " ],\n", + " \"execution_count\": 22\n", + " },\n", + " {\n", + " \"metadata\": {},\n", + " \"cell_type\": \"markdown\",\n", + " \"source\": [\n", + " \"There are a range of strategies for handling missing values. These include:\\n\",\n", + " \"\\n\",\n", + " \"1. Use an estimator that internally handles missing values. It is fairly easy for\\n\",\n", + " \"some algorithms (such as decision trees) to internally deal with missing values,\\n\",\n", + " \"usually be using it as a distinct series value after discretisation. We do not yet \\n\",\n", + " \"have many estimators with this capability. Estimators that are able to internally \\n\",\n", + " \"handle missing values are tagged with `\\\"capability:missing_values\\\": True`.\"\n", + " ]\n", + " },\n", + " {\n", + " \"metadata\": {\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2024-11-17T13:50:06.436013Z\",\n", + " \"start_time\": \"2024-11-17T13:50:06.296331Z\"\n", + " }\n", + " },\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"from aeon.utils.discovery import all_estimators\\n\",\n", + " \"\\n\",\n", + " \"all_estimators(\\n\",\n", + " \" tag_filter={\\\"capability:missing_values\\\": True},\\n\",\n", + " \")\"\n", + " ],\n", + " \"outputs\": [\n", + " {\n", + " \"data\": {\n", + " \"text/plain\": [\n", + " \"[('BORF', aeon.transformations.collection.dictionary_based._borf.BORF),\\n\",\n", + " \" ('CollectionId',\\n\",\n", + " \" aeon.transformations.collection.compose._identity.CollectionId),\\n\",\n", + " \" ('DummyClassifier', aeon.classification.dummy.DummyClassifier),\\n\",\n", + " \" ('DummyRegressor', aeon.regression._dummy.DummyRegressor),\\n\",\n", + " \" ('RandomSegmenter', aeon.segmentation._random.RandomSegmenter),\\n\",\n", + " \" ('STRAY', aeon.anomaly_detection._stray.STRAY),\\n\",\n", + " \" ('SimpleImputer', aeon.transformations.collection._impute.SimpleImputer)]\"\n", + " ]\n", + " },\n", + " \"execution_count\": 23,\n", + " \"metadata\": {},\n", + " \"output_type\": \"execute_result\"\n", + " }\n", + " ],\n", + " \"execution_count\": 23\n", + " },\n", + " {\n", + " \"metadata\": {},\n", + " \"cell_type\": \"markdown\",\n", + " \"source\": [\n", + " \"2. Removing series with missing: this is often desirable if the train set size is\\n\",\n", + " \"large, the number of series with missing is small and the proportion of missing\\n\",\n", + " \"values for these series is high.\\n\",\n", + " \"\\n\",\n", + " \"We do not yet have a transformer for this, but it is easy to implement yourself.\\n\",\n", + " \"\\n\",\n", + " \"3. Interpolating missing values from series: estimating the missing values from the \\n\",\n", + " \"other values in a time series is commonly done. This is\\n\",\n", + " \" often desirable if the train set size is small and the proportion of missing values\\n\",\n", + " \" is low. You can do this with the transformer ``SimpleImputer``. This interpolates \\n\",\n", + " \" each series and each channel independently. So for example a mean interpolation \\n\",\n", + " \" of series with two channels `[[NaN,1.0,2.0,3.0],[-1.0,-2.0,-3.0,-4.0]]` would be \\n\",\n", + " \" `[[2.0,1.0,2.0,3.0],[-1.0,-2.0,-3.0,-4.0]]`. \"\n", + " ]\n", + " },\n", + " {\n", + " \"metadata\": {\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2024-11-17T13:52:23.133162Z\",\n", + " \"start_time\": \"2024-11-17T13:52:23.118202Z\"\n", + " }\n", + " },\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"from aeon.transformations.collection import SimpleImputer\\n\",\n", + " \"\\n\",\n", + " \"imput = SimpleImputer(strategy=\\\"mean\\\")\\n\",\n", + " \"X2 = imput.fit_transform(X)\\n\",\n", + " \"has_missing(X2)\"\n", + " ],\n", + " \"outputs\": [\n", + " {\n", + " \"data\": {\n", + " \"text/plain\": [\n", + " \"False\"\n", + " ]\n", + " },\n", + " \"execution_count\": 26,\n", + " \"metadata\": {},\n", + " \"output_type\": \"execute_result\"\n", + " }\n", + " ],\n", + " \"execution_count\": 26\n", + " },\n", + " {\n", + " \"metadata\": {\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2024-11-17T13:52:23.825897Z\",\n", + " \"start_time\": \"2024-11-17T13:52:23.811936Z\"\n", + " }\n", + " },\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"imp2 = SimpleImputer(strategy=\\\"median\\\")\\n\",\n", + " \"X3 = imp2.fit_transform(X)\\n\",\n", + " \"has_missing(X3)\"\n", + " ],\n", + " \"outputs\": [\n", + " {\n", + " \"data\": {\n", + " \"text/plain\": [\n", + " \"False\"\n", + " ]\n", + " },\n", + " \"execution_count\": 27,\n", + " \"metadata\": {},\n", + " \"output_type\": \"execute_result\"\n", + " }\n", + " ],\n", + " \"execution_count\": 27\n", + " },\n", + " {\n", + " \"metadata\": {\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2024-11-17T13:52:24.602058Z\",\n", + " \"start_time\": \"2024-11-17T13:52:24.582111Z\"\n", + " }\n", + " },\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"imp3 = SimpleImputer(strategy=\\\"constant\\\", fill_value=0)\\n\",\n", + " \"X4 = imp3.fit_transform(X)\\n\",\n", + " \"has_missing(X4)\"\n", + " ],\n", + " \"outputs\": [\n", + " {\n", + " \"data\": {\n", + " \"text/plain\": [\n", + " \"False\"\n", + " ]\n", + " },\n", + " \"execution_count\": 28,\n", + " \"metadata\": {},\n", + " \"output_type\": \"execute_result\"\n", + " }\n", + " ],\n", + " \"execution_count\": 28\n", + " },\n", + " {\n", + " \"metadata\": {},\n", + " \"cell_type\": \"code\",\n", + " \"outputs\": [],\n", + " \"execution_count\": null,\n", + " \"source\": \"\"\n", + " }\n", + " ],\n", + " \"metadata\": {\n", + " \"kernelspec\": {\n", + " \"display_name\": \"Python 3\",\n", + " \"language\": \"python\",\n", + " \"name\": \"python3\"\n", + " },\n", + " \"language_info\": {\n", + " \"codemirror_mode\": {\n", + " \"name\": \"ipython\",\n", + " \"version\": 3\n", + " },\n", + " \"file_extension\": \".py\",\n", + " \"mimetype\": \"text/x-python\",\n", + " \"name\": \"python\",\n", + " \"nbconvert_exporter\": \"python\",\n", + " \"pygments_lexer\": \"ipython3\",\n", + " \"version\": \"3.7.8\"\n", + " }\n", + " },\n", + " \"nbformat\": 4,\n", + " \"nbformat_minor\": 4\n", + "}\n" + ], + "id": "cd7114548eda7d97" } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.8" - } - }, + "metadata": {}, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 5 } diff --git a/examples/transformations/transformations.ipynb b/examples/transformations/transformations.ipynb index 261ebddedf..41df249e78 100644 --- a/examples/transformations/transformations.ipynb +++ b/examples/transformations/transformations.ipynb @@ -1,274 +1,288 @@ { "cells": [ { - "cell_type": "markdown", + "metadata": {}, + "cell_type": "raw", "source": [ - "# Transforming time series\n", - "\n", - "Transforming time series into different data representations is fundamental to time\n", - "series machine learning. Transformation can involve extracting features that\n", - "characterize the time series, such as mean and variance or changing the series into,\n", - "for example, first order differences. We use the term transformer in the\n", - "`scikit-learn` sense, not to be confused with deep learning Transformers that employ\n", - "an attention mechanism. We call transformers that extract features\n", - "`series-to-vector` transformers and those that change the series into a different\n", - "representation that is still ordered `series-to-series` transformers.\n", - "\n", - "We further differentiate between transformers that act on a single series and those\n", - "that transform a collection of series. Single series transformers are located in\n", - "transformations/series directory and inherit from `BaseSeriesTransformer`. For\n", - "example, `AutoCorrelationSeriesTransformer` is a `series-to-series` transformer that\n", - "finds the auto correlation function for a single series." + "{\n", + " \"cells\": [\n", + " {\n", + " \"cell_type\": \"markdown\",\n", + " \"source\": [\n", + " \"# Transforming time series\\n\",\n", + " \"\\n\",\n", + " \"Transforming time series into different data representations is fundamental to time\\n\",\n", + " \"series machine learning. Transformation can involve extracting features that\\n\",\n", + " \"characterize the time series, such as mean and variance or changing the series into,\\n\",\n", + " \"for example, first order differences. We use the term transformer in the\\n\",\n", + " \"`scikit-learn` sense, not to be confused with deep learning Transformers that employ\\n\",\n", + " \"an attention mechanism. We call transformers that extract features\\n\",\n", + " \"`series-to-vector` transformers and those that change the series into a different\\n\",\n", + " \"representation that is still ordered `series-to-series` transformers.\\n\",\n", + " \"\\n\",\n", + " \"We further differentiate between transformers that act on a single series and those\\n\",\n", + " \"that transform a collection of series. Single series transformers are located in\\n\",\n", + " \"transformations/series directory and inherit from `BaseSeriesTransformer`. For\\n\",\n", + " \"example, `AutoCorrelationSeriesTransformer` is a `series-to-series` transformer that\\n\",\n", + " \"finds the auto correlation function for a single series.\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false\n", + " }\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"execution_count\": 23,\n", + " \"outputs\": [\n", + " {\n", + " \"name\": \"stdout\",\n", + " \"output_type\": \"stream\",\n", + " \"text\": [\n", + " \"[[0.96019465 0.89567531 0.83739477 0.7977347 0.78594315 0.7839188\\n\",\n", + " \" 0.78459213 0.79221505 0.8278519 0.8827128 ]]\\n\"\n", + " ]\n", + " }\n", + " ],\n", + " \"source\": [\n", + " \"from aeon.datasets import load_airline\\n\",\n", + " \"from aeon.transformations.series import AutoCorrelationSeriesTransformer\\n\",\n", + " \"\\n\",\n", + " \"series = load_airline()\\n\",\n", + " \"transformer = AutoCorrelationSeriesTransformer(n_lags=10)\\n\",\n", + " \"acf = transformer.fit_transform(series)\\n\",\n", + " \"print(acf)\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false\n", + " }\n", + " },\n", + " {\n", + " \"cell_type\": \"markdown\",\n", + " \"source\": [\n", + " \"Collection transformers are located in the transformations/collection directory and\\n\",\n", + " \"inherit from `BaseCollectionTransformer`. For example, `Truncator` truncates all time\\n\",\n", + " \" series in a collection to the same length.\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false\n", + " }\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"from aeon.datasets import load_pickup_gesture_wiimoteZ\\n\",\n", + " \"from aeon.transformations.collection.unequal_length import Truncator\\n\",\n", + " \"\\n\",\n", + " \"X, y = load_pickup_gesture_wiimoteZ()\\n\",\n", + " \"print(\\\" Unequal length, first case \\\", X[0].shape, \\\" tenth case \\\", X[10].shape)\\n\",\n", + " \"trunc = Truncator(truncated_length=100)\\n\",\n", + " \"X2 = trunc.fit_transform(X)\\n\",\n", + " \"print(\\\"Truncated collection shape =\\\", X2.shape)\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false,\n", + " \"ExecuteTime\": {\n", + " \"end_time\": \"2025-05-28T13:34:29.282584Z\",\n", + " \"start_time\": \"2025-05-28T13:34:24.749646Z\"\n", + " }\n", + " },\n", + " \"outputs\": [\n", + " {\n", + " \"name\": \"stdout\",\n", + " \"output_type\": \"stream\",\n", + " \"text\": [\n", + " \" Unequal length, first case (1, 324) tenth case (1, 97)\\n\",\n", + " \"Truncated collection shape = (100, 1, 29)\\n\"\n", + " ]\n", + " }\n", + " ],\n", + " \"execution_count\": 1\n", + " },\n", + " {\n", + " \"cell_type\": \"markdown\",\n", + " \"source\": [\n", + " \"`Truncator` is a `series-to-series` transformer\\n\",\n", + " \" that returns a new collection of time series of the same length. This can then be\\n\",\n", + " \" used, for example, by a classifier that only works with equal length series:\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false\n", + " }\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"execution_count\": 25,\n", + " \"outputs\": [\n", + " {\n", + " \"name\": \"stdout\",\n", + " \"output_type\": \"stream\",\n", + " \"text\": [\n", + " \"Data seen by instance of SummaryClassifier has unequal length series, but SummaryClassifier cannot handle unequal length series. \\n\"\n", + " ]\n", + " },\n", + " {\n", + " \"data\": {\n", + " \"text/plain\": \"SummaryClassifier()\",\n", + " \"text/html\": \"
SummaryClassifier()
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\"\n", + " },\n", + " \"execution_count\": 25,\n", + " \"metadata\": {},\n", + " \"output_type\": \"execute_result\"\n", + " }\n", + " ],\n", + " \"source\": [\n", + " \"from aeon.classification.feature_based import SummaryClassifier\\n\",\n", + " \"\\n\",\n", + " \"summary = SummaryClassifier()\\n\",\n", + " \"try:\\n\",\n", + " \" summary.fit(X, y)\\n\",\n", + " \"except ValueError as e:\\n\",\n", + " \" print(e)\\n\",\n", + " \"\\n\",\n", + " \"summary.fit(X2, y)\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false\n", + " }\n", + " },\n", + " {\n", + " \"cell_type\": \"markdown\",\n", + " \"source\": [\n", + " \"Some collection transformers are supervised, meaning they fit a transform based on\\n\",\n", + " \"the class labels. For example, the shapelet transform finds shapelets that are good\\n\",\n", + " \"at separating classes. This is a `series-to-vector` transformer that produces tabular\\n\",\n", + " \" output shape `(n_cases, n_shapelets)`.\\n\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false\n", + " }\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"execution_count\": 26,\n", + " \"outputs\": [\n", + " {\n", + " \"name\": \"stdout\",\n", + " \"output_type\": \"stream\",\n", + " \"text\": [\n", + " \"(1074, 2)\\n\"\n", + " ]\n", + " }\n", + " ],\n", + " \"source\": [\n", + " \"from aeon.transformations.collection.shapelet_based import RandomShapeletTransform\\n\",\n", + " \"\\n\",\n", + " \"st = RandomShapeletTransform(max_shapelets=10, n_shapelet_samples=100)\\n\",\n", + " \"X2 = st.fit_transform(X, y)\\n\",\n", + " \"print(X2.shape)\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false\n", + " }\n", + " },\n", + " {\n", + " \"cell_type\": \"markdown\",\n", + " \"source\": [\n", + " \"`series-to-vector` transformers produce output that is compatible with `scikit-learn`\\n\",\n", + " \" estimators\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false\n", + " }\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"execution_count\": 27,\n", + " \"outputs\": [\n", + " {\n", + " \"name\": \"stdout\",\n", + " \"output_type\": \"stream\",\n", + " \"text\": [\n", + " \"setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (1074, 1) + inhomogeneous part.\\n\"\n", + " ]\n", + " },\n", + " {\n", + " \"data\": {\n", + " \"text/plain\": \"RandomForestClassifier()\",\n", + " \"text/html\": \"
RandomForestClassifier()
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\"\n", + " },\n", + " \"execution_count\": 27,\n", + " \"metadata\": {},\n", + " \"output_type\": \"execute_result\"\n", + " }\n", + " ],\n", + " \"source\": [\n", + " \"from sklearn.ensemble import RandomForestClassifier\\n\",\n", + " \"\\n\",\n", + " \"rf = RandomForestClassifier()\\n\",\n", + " \"try:\\n\",\n", + " \" rf.fit(X, y)\\n\",\n", + " \"except ValueError as e:\\n\",\n", + " \" print(e)\\n\",\n", + " \"rf.fit(X2, y)\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false\n", + " }\n", + " },\n", + " {\n", + " \"cell_type\": \"markdown\",\n", + " \"source\": [\n", + " \"A list of all the available transformers can be found in the [API](https://www.aeon-toolkit.org/en/latest/api_reference/transformations.html). We currently have\\n\",\n", + " \"specific notebooks for the following transformers:\\n\",\n", + " \"\\n\",\n", + " \"- [preprocessing](preprocessing.ipynb)\\n\",\n", + " \"- [catch22](catch22.ipynb)\\n\",\n", + " \"- [channel selection](channel_selection.ipynb)\\n\",\n", + " \"- [mini rocket](mini_rocket.ipynb)\\n\",\n", + " \"- [resizing](resizing.ipynb)\\n\",\n", + " \"- [rocket](rocket.ipynb)\\n\",\n", + " \"- [sast](sast.ipynb)\\n\",\n", + " \"- [signature method](signature_method.ipynb)\\n\",\n", + " \"- [tsfresh](tsfresh.ipynb)\\n\",\n", + " \"\\n\"\n", + " ],\n", + " \"metadata\": {\n", + " \"collapsed\": false\n", + " }\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"execution_count\": null,\n", + " \"outputs\": [],\n", + " \"source\": [],\n", + " \"metadata\": {\n", + " \"collapsed\": false\n", + " }\n", + " }\n", + " ],\n", + " \"metadata\": {\n", + " \"kernelspec\": {\n", + " \"display_name\": \"Python 3\",\n", + " \"language\": \"python\",\n", + " \"name\": \"python3\"\n", + " },\n", + " \"language_info\": {\n", + " \"codemirror_mode\": {\n", + " \"name\": \"ipython\",\n", + " \"version\": 2\n", + " },\n", + " \"file_extension\": \".py\",\n", + " \"mimetype\": \"text/x-python\",\n", + " \"name\": \"python\",\n", + " \"nbconvert_exporter\": \"python\",\n", + " \"pygments_lexer\": \"ipython2\",\n", + " \"version\": \"2.7.6\"\n", + " }\n", + " },\n", + " \"nbformat\": 4,\n", + " \"nbformat_minor\": 0\n", + "}\n" ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 23, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0.96019465 0.89567531 0.83739477 0.7977347 0.78594315 0.7839188\n", - " 0.78459213 0.79221505 0.8278519 0.8827128 ]]\n" - ] - } - ], - "source": [ - "from aeon.datasets import load_airline\n", - "from aeon.transformations.series import AutoCorrelationSeriesTransformer\n", - "\n", - "series = load_airline()\n", - "transformer = AutoCorrelationSeriesTransformer(n_lags=10)\n", - "acf = transformer.fit_transform(series)\n", - "print(acf)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "Collection transformers are located in the transformations/collection directory and\n", - "inherit from `BaseCollectionTransformer`. For example, `Truncator` truncates all time\n", - " series in a collection to the same length." - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "source": [ - "from aeon.datasets import load_pickup_gesture_wiimoteZ\n", - "from aeon.transformations.collection import Truncator\n", - "\n", - "X, y = load_pickup_gesture_wiimoteZ()\n", - "print(\" Unequal length, first case \", X[0].shape, \" tenth case \", X[10].shape)\n", - "trunc = Truncator(truncated_length=100)\n", - "X2 = trunc.fit_transform(X)\n", - "print(\"Truncated collection shape =\", X2.shape)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2025-05-28T13:34:29.282584Z", - "start_time": "2025-05-28T13:34:24.749646Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Unequal length, first case (1, 324) tenth case (1, 97)\n", - "Truncated collection shape = (100, 1, 29)\n" - ] - } - ], - "execution_count": 1 - }, - { - "cell_type": "markdown", - "source": [ - "`Truncator` is a `series-to-series` transformer\n", - " that returns a new collection of time series of the same length. This can then be\n", - " used, for example, by a classifier that only works with equal length series:" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 25, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data seen by instance of SummaryClassifier has unequal length series, but SummaryClassifier cannot handle unequal length series. \n" - ] - }, - { - "data": { - "text/plain": "SummaryClassifier()", - "text/html": "
SummaryClassifier()
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" - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from aeon.classification.feature_based import SummaryClassifier\n", - "\n", - "summary = SummaryClassifier()\n", - "try:\n", - " summary.fit(X, y)\n", - "except ValueError as e:\n", - " print(e)\n", - "\n", - "summary.fit(X2, y)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "Some collection transformers are supervised, meaning they fit a transform based on\n", - "the class labels. For example, the shapelet transform finds shapelets that are good\n", - "at separating classes. This is a `series-to-vector` transformer that produces tabular\n", - " output shape `(n_cases, n_shapelets)`.\n" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 26, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1074, 2)\n" - ] - } - ], - "source": [ - "from aeon.transformations.collection.shapelet_based import RandomShapeletTransform\n", - "\n", - "st = RandomShapeletTransform(max_shapelets=10, n_shapelet_samples=100)\n", - "X2 = st.fit_transform(X, y)\n", - "print(X2.shape)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "`series-to-vector` transformers produce output that is compatible with `scikit-learn`\n", - " estimators" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 27, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (1074, 1) + inhomogeneous part.\n" - ] - }, - { - "data": { - "text/plain": "RandomForestClassifier()", - "text/html": "
RandomForestClassifier()
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" - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.ensemble import RandomForestClassifier\n", - "\n", - "rf = RandomForestClassifier()\n", - "try:\n", - " rf.fit(X, y)\n", - "except ValueError as e:\n", - " print(e)\n", - "rf.fit(X2, y)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "A list of all the available transformers can be found in the [API](https://www.aeon-toolkit.org/en/latest/api_reference/transformations.html). We currently have\n", - "specific notebooks for the following transformers:\n", - "\n", - "- [preprocessing](preprocessing.ipynb)\n", - "- [catch22](catch22.ipynb)\n", - "- [channel selection](channel_selection.ipynb)\n", - "- [mini rocket](mini_rocket.ipynb)\n", - "- [resizing](resizing.ipynb)\n", - "- [rocket](rocket.ipynb)\n", - "- [sast](sast.ipynb)\n", - "- [signature method](signature_method.ipynb)\n", - "- [tsfresh](tsfresh.ipynb)\n", - "\n" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false - } + "id": "62048d76702c12c8" } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, + "metadata": {}, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 5 } From 8cc8751b29f6d7beca55b72ed6c44bb73e38cab8 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Mon, 23 Jun 2025 11:17:36 +0100 Subject: [PATCH 161/216] Release v1.2.0 (#2912) * release prep * changelog --- README.md | 2 +- aeon/__init__.py | 2 +- docs/changelog.md | 1 + docs/changelogs/v1.2.md | 289 ++++++++++++++++++++++++++++++++++++++++ pyproject.toml | 2 +- 5 files changed, 293 insertions(+), 3 deletions(-) create mode 100644 docs/changelogs/v1.2.md diff --git a/README.md b/README.md index e6c8eab3d5..f84d18693d 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ We strive to provide a broad library of time series algorithms including the latest advances, offer efficient implementations using numba, and interfaces with other time series packages to provide a single framework for algorithm comparison. -The latest `aeon` release is `v1.1.0`. You can view the full changelog +The latest `aeon` release is `v1.2.0`. You can view the full changelog [here](https://www.aeon-toolkit.org/en/stable/changelog.html). Our webpage and documentation is available at https://aeon-toolkit.org. diff --git a/aeon/__init__.py b/aeon/__init__.py index f8c45d7805..50b485d4f4 100644 --- a/aeon/__init__.py +++ b/aeon/__init__.py @@ -1,3 +1,3 @@ """aeon toolkit.""" -__version__ = "1.1.0" +__version__ = "1.2.0" diff --git a/docs/changelog.md b/docs/changelog.md index f2c3d2b20b..da8de068d5 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -9,6 +9,7 @@ To stay up to date with aeon releases, subscribe to aeon [here](https://libraries.io/pypi/aeon) or follow us on [Twitter](https://twitter.com/aeon_toolbox). +- [Version 1.2.0](changelogs/v1.2.md) - [Version 1.1.0](changelogs/v1.1.md) - [Version 1.0.0](changelogs/v1.0.md) - [Version 0.11.1](changelogs/v0/v0.11.md) diff --git a/docs/changelogs/v1.2.md b/docs/changelogs/v1.2.md new file mode 100644 index 0000000000..651e51f885 --- /dev/null +++ b/docs/changelogs/v1.2.md @@ -0,0 +1,289 @@ +# v1.2.0 + +June 2025 + +## Highlights + +- `aeon` v1.2.0 drops support for Python 3.9, Python 3.10-3.13 are the current supported versions. +- Framework for whole-series anomaly detection has been implemented, and the module has been refactored +into `anomaly_detection.collection` and `anomaly_detection.series` submodules. +- `ProximityForect` and `ProximityTree` classifiers have been improved to support +unequal length and multivariate series. The classifiers are also significantly faster. +- The forecasting module has been enhanced with additional methods for direct and recursive forecasting, +as well as new algorithms such as ETS. This module is still in early development, and we expect +framework changes in future releases. +- The similarity search module has been significantly reworked to fit the `aeon` style +framework seen in other modules. See the module examples and documentation for more details. +- The start of a sub-package for self suprervised learning has been added in +`aeon.transformations.collection.self_supervised`, starting with the `TRILITE` algorithm. + +## Anomaly Detection + +### Deprecation + +- [DEP] Remove `prts` dependency ({pr}`2863`) {user}`MatthewMiddlehurst` + +### Documentation + +- [DOC] resolved the inconsistency of double ticks for the anomaly detection module #809 ({pr}`2546`) {user}`AnaghDeshpande` + +### Enhancements + +- [ENH] Refactor range-based metrics to restore original behavior ({pr}`2781`) {user}`SebastianSchmidl` +- [ENH] add informational tags back to estimator docs ({pr}`2652`) {user}`inclinedadarsh` +- [ENH] Update `_continuous.py` functions ({pr}`2822`) {user}`aryanpola` +- [ENH] Whole-series anomaly detection ({pr}`2326`) {user}`MatthewMiddlehurst` +- [ENH, REF] Refactored time-point based ROCKAD implementation ({pr}`2804`) {user}`pattplatt` + +### Refactored + +- [ENH, REF] Refactored time-point based ROCKAD implementation ({pr}`2804`) {user}`pattplatt` +- [REF] Refactor Anomaly Detection Module into Submodules by Algorithm Family ({pr}`2694`) {user}`Kaustbh` + +## Benchmarking + +### Bug Fixes + +- [BUG] Switch results loaders from http to https ({pr}`2883`) {user}`TonyBagnall` + +### Enhancements + +- [ENH] Refactor range-based metrics to restore original behavior ({pr}`2781`) {user}`SebastianSchmidl` +- [ENH] Update `_continuous.py` functions ({pr}`2822`) {user}`aryanpola` + +## Classification + +### Bug Fixes + +- [BUG] Better error message for Rotation Forest with no usable attributes ({pr}`2798`) {user}`MatthewMiddlehurst` + +### Documentation + +- [DOC] Add Raises section for invalid `weights` in KNeighborsTimeSeriesClassifier (#1766) ({pr}`2764`) {user}`SomtoOnyekwelu` +- [DOC] A fix to inconsistent double tick quote for the classification.distance_based module ({pr}`2627`) {user}`Ahmed-Zahran02` +- [DOC] Add RotationForest Classifier Notebook for Time Series Classification ({pr}`2592`) {user}`Kaustbh` + +### Enhancements + +- [ENH] Implement `load_model` function for ensemble classifiers ({pr}`2631`) {user}`inclinedadarsh` +- [ENH] Use n_jobs parameter in KNeighborsTimeSeriesClassifier. ({pr}`2687`) {user}`steenrotsman` +- [ENH] Implemented Tracking differentiator-based multiview dilated characteristics (TD-MVDC) Classifier ({pr}`2535`) {user}`lucifer4073` +- [ENH] Multivariate and unequal PF, speed-ups and distance classifier tidy ({pr}`2872`) {user}`MatthewMiddlehurst` + +## Clustering + +### Bug Fixes + +- [BUG] Fix indexing errors in kdtw of KernelKMeans ({pr}`2826`) {user}`SebastianSchmidl` + +### Documentation + +- [DOC] Changed SummaryClusterer's summary_stat from "percentiles" to "quantiles" ({pr}`2778`) {user}`Ramana-Raja` + +### Enhancements + +- [ENH] Fixes Issue Improve `_check_params` method in `kmeans.py` and `kmedoids.py` ({pr}`2682`) {user}`tanishy7777` +- [ENH] Added test cases for feature based clustering ({pr}`2690`) {user}`Ramana-Raja` + +## Datasets + +### Bug Fixes + +- [BUG] Fix data loader ({pr}`2810`) {user}`TonyBagnall` +- [ENH] Refactor example dataset for unequal length univariate ({pr}`2859`) {user}`TonyBagnall` + +### Enhancements + +- [ENH] Refactor example dataset for unequal length univariate ({pr}`2859`) {user}`TonyBagnall` + +## Distances + +### Bug Fixes + +- [BUG] Fix indexing errors in kdtw of KernelKMeans ({pr}`2826`) {user}`SebastianSchmidl` + +### Enhancements + +- [ENH] Multivariate and unequal PF, speed-ups and distance classifier tidy ({pr}`2872`) {user}`MatthewMiddlehurst` + +## Forecasting + +### Bug Fixes + +- [BUG] Forecasting regressor fix and notebook ({pr}`2885`) {user}`TonyBagnall` + +### Documentation + +- [DOC] Regression forecaster docstring ({pr}`2837`) {user}`TonyBagnall` + +### Enhancements + +- [ENH] Type hints/forecasting ({pr}`2737`) {user}`Ahmed-Zahran02` +- [ENH] refactor dummy forecaster to naive ({pr}`2831`) {user}`TonyBagnall` +- [ENH] introduce revised version of ETS ({pr}`2834`) {user}`TonyBagnall` +- [ENH] Add two forecasting tags ({pr}`2882`) {user}`TonyBagnall` +- [ENH] Forecasting testing ({pr}`2891`) {user}`MatthewMiddlehurst` +- [ENH] Add recursive and direct methods to forecasting base class ({pr}`2899`) {user}`TonyBagnall` +- [ENH] remove horizon from ETS constructor, parametrize ETS tests ({pr}`2898`) {user}`TonyBagnall` +- [ENH] enhance naive forecaster with new strategies ({pr}`2869`) {user}`TinaJin0228` + +## Networks + +### Bug Fixes + +- [BUG] Fix bug of shape variable assignment in AEAttentionBiGRUNetwork ({pr}`2771`) {user}`hadifawaz1999` + +### Enhancements + +- [ENH] Adapt the DCNN Networks to use Weight Norm Wrappers ({pr}`2628`) {user}`aadya940` +- [ENH] Test coverage for AEAttentionBiGRUNetwork Improved ({pr}`2540`) {user}`lucifer4073` +- [ENH] Added RNN in networks ({pr}`2875`) {user}`lucifer4073` + +## Regression + +### Bug Fixes + +- [BUG] Better error message for Rotation Forest with no usable attributes ({pr}`2798`) {user}`MatthewMiddlehurst` + +### Documentation + +- [DOC] Docstring improved for dummy regressor ({pr}`2839`) {user}`lucifer4073` + +### Enhancements + +- [ENH] Add type hints for deep learning regression classes ({pr}`2644`) {user}`saadaltohamy` + +## Segmentation + +### Bug Fixes + +- [BUG] Fix a bug in ClaSP Series Transformer when using floatXX as Input (where XX != 64) ({pr}`2774`) {user}`patrickzib` + +### Documentation + +- [DOC] Inconsistent double ticks in segmentation package, public docstring only ({pr}`2622`) {user}`nMaax` +- [DOC] Improved Hidalgo Segmentation Notebook ({pr}`2616`) {user}`kavya-r30` + +### Maintenance + +- [MNT] stop segmenters changing state in predict ({pr}`2526`) {user}`TonyBagnall` + +## Similarity Search + +### Documentation + +- [MNT, ENH, DOC] Rework similarity search ({pr}`2473`) {user}`baraline` +- [DOC] Fix inconsistent double backticks in similarity_search module (#809) ({pr}`2618`) {user}`adityagh006` + +### Enhancements + +- [MNT, ENH, DOC] Rework similarity search ({pr}`2473`) {user}`baraline` + +### Maintenance + +- [MNT, ENH, DOC] Rework similarity search ({pr}`2473`) {user}`baraline` + +## Transformations + +### Bug Fixes + +- [BUG] Remove typing from numba functions ({pr}`2858`) {user}`TonyBagnall` +- [BUG] Fixes a bug with SFAFast throwing an error when calling transform after fit ({pr}`2897`) {user}`patrickzib` + +### Enhancements + +- [ENH] Optimize QUANTTransformer by using shape calculation in _fit method to avoid unnecessary computations ({pr}`2727`) {user}`shinymack` +- [ENH] Add missing checks to base transformers and tidy ({pr}`2773`) {user}`MatthewMiddlehurst` +- [ENH] HydraTransformer Output changed ({pr}`2656`) {user}`lucifer4073` +- [ENH] add a difference transformer to series transformations ({pr}`2729`) {user}`TinaJin0228` +- [ENH] Starting Self Supervised Model with first example ({pr}`2385`) {user}`hadifawaz1999` +- [ENH] Series transformer pipeline and datatype list tidy ({pr}`2830`) {user}`MatthewMiddlehurst` +- [ENH,REF] Unequal length collection transforms ({pr}`2903`) {user}`MatthewMiddlehurst` + +### Refactored + +- [REF] Refactor smoothing transformers into a sub-package ({pr}`2783`) {user}`MatthewMiddlehurst` +- [ENH,REF] Unequal length collection transforms ({pr}`2903`) {user}`MatthewMiddlehurst` + +## Unit Testing + +### Bug Fixes + +- [BUG] Adds test case for using csr_matrix with pickle - invalidates #2210 ({pr}`2843`) {user}`patrickzib` + +### Documentation + +- [DOC] Fix Broken [Source] Link and Improve Documentation for suppress_output() ({pr}`2677`) {user}`Kaustbh` + +### Enhancements + +- [ENH] Forecasting testing ({pr}`2891`) {user}`MatthewMiddlehurst` + +### Maintenance + +- [MNT] stop segmenters changing state in predict ({pr}`2526`) {user}`TonyBagnall` +- [MNT] Drop Python 3.9 ({pr}`2845`) {user}`MatthewMiddlehurst` +- [MNT] Fixing CI issues and separate doctests from regular CI runners ({pr}`2902`) {user}`MatthewMiddlehurst` + +## Visualisations + +### Maintenance + +- [MNT] stop segmenters changing state in predict ({pr}`2526`) {user}`TonyBagnall` + +## Other + +### Documentation + +- [DOC] Add implementation references ({pr}`2748`) {user}`MatthewMiddlehurst` +- [DOC] Documentation improvement of BaseSeriesEstimator and BaseAeonEstimator ({pr}`2612`) {user}`kevinzb56` +- [DOC,MNT] Add supporting devs to webpage and consolidate maintenance workflows ({pr}`2797`) {user}`MatthewMiddlehurst` +- [DOC] Update the projects lists ({pr}`2146`) {user}`TonyBagnall` + +### Maintenance + +- [MNT] Use GPU installs for periodic tests ({pr}`2747`) {user}`MatthewMiddlehurst` +- [MNT] Lower bound keras to discard old bugged versions of early keras3 ({pr}`2808`) {user}`hadifawaz1999` +- [DOC,MNT] Add supporting devs to webpage and consolidate maintenance workflows ({pr}`2797`) {user}`MatthewMiddlehurst` +- [MNT] Run PR tests when merging on any branch ({pr}`2809`) {user}`MatthewMiddlehurst` +- [MNT] Update Slack link ({pr}`2877`) {user}`MatthewMiddlehurst` +- [MNT] PR open bot comment changes ({pr}`2878`) {user}`MatthewMiddlehurst` +- [MNT] codecov badge fix ({pr}`2908`) {user}`MatthewMiddlehurst` +- [MNT] Fix codeowners for AD metrics ({pr}`2784`) {user}`SebastianSchmidl` + +### Other + +- [GOV] Supporting Developer role ({pr}`2775`) {user}`MatthewMiddlehurst` +- [GOV] Remove inactive developers ({pr}`2776`) {user}`MatthewMiddlehurst` +- [ENH] Correct broken source links for `set_params` and `get_params` in Aeon estimator docs ({pr}`2754`) {user}`Kaustbh` +- [GOV] Use the NumFOCUS code of conduct and rename `aeon` workgroup ({pr}`2852`) {user}`MatthewMiddlehurst` + +## Contributors + +The following have contributed to this release through a collective 75 GitHub Pull Requests: + +{user}`aadya940`, +{user}`adityagh006`, +{user}`Ahmed-Zahran02`, +{user}`AnaghDeshpande`, +{user}`aryanpola`, +{user}`baraline`, +{user}`hadifawaz1999`, +{user}`inclinedadarsh`, +{user}`Kaustbh`, +{user}`kavya-r30`, +{user}`kevinzb56`, +{user}`lucifer4073`, +{user}`MatthewMiddlehurst`, +{user}`nMaax`, +{user}`patrickzib`, +{user}`pattplatt`, +{user}`Ramana-Raja`, +{user}`saadaltohamy`, +{user}`SebastianSchmidl`, +{user}`shinymack`, +{user}`SomtoOnyekwelu`, +{user}`steenrotsman`, +{user}`tanishy7777`, +{user}`TinaJin0228`, +{user}`TonyBagnall` diff --git a/pyproject.toml b/pyproject.toml index 1e66656142..1a04d40bd5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "aeon" -version = "1.1.0" +version = "1.2.0" description = "A toolkit for machine learning from time series" authors = [ {name = "aeon developers", email = "contact@aeon-toolkit.org"}, From 4c57abf02d8a6b6f91962a8e665b48f0a9d9636f Mon Sep 17 00:00:00 2001 From: "aeon-actions-bot[bot]" <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Date: Mon, 23 Jun 2025 16:38:47 +0100 Subject: [PATCH 162/216] Automated `pre-commit` hook update (#2914) Co-authored-by: TonyBagnall <9594042+TonyBagnall@users.noreply.github.com> --- .pre-commit-config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index b5d1532d60..6b071c9530 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -29,7 +29,7 @@ repos: args: [ "--create", "--python-folders", "aeon" ] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.11.13 + rev: v0.12.0 hooks: - id: ruff args: [ "--fix"] @@ -48,7 +48,7 @@ repos: args: [ "--profile=black", "--multi-line=3" ] - repo: https://github.com/pycqa/flake8 - rev: 7.2.0 + rev: 7.3.0 hooks: - id: flake8 additional_dependencies: [ flake8-bugbear, flake8-print, Flake8-pyproject ] From 2b6e32f596283cc1c38391f0e4b808f1abfc4156 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Mon, 23 Jun 2025 21:08:55 +0100 Subject: [PATCH 163/216] [MNT] Comments on numba caching and release CI fix (#2916) * release CI jank * comment * swap --- .github/actions/numba_cache/action.yml | 18 +++++++++++------- .github/workflows/periodic_tests.yml | 2 +- .github/workflows/pr_pytest.yml | 2 +- .github/workflows/release.yml | 10 +++++++++- aeon/testing/_cicd_numba_caching.py | 2 +- 5 files changed, 23 insertions(+), 11 deletions(-) diff --git a/.github/actions/numba_cache/action.yml b/.github/actions/numba_cache/action.yml index 808bce8459..06acc39ef4 100644 --- a/.github/actions/numba_cache/action.yml +++ b/.github/actions/numba_cache/action.yml @@ -24,19 +24,23 @@ runs: run: echo "NUMBA_CACHE_DIR=${{ github.workspace }}/.numba_cache" >> $GITHUB_ENV shell: bash - # Sets the cpu name for numba to use - - name: Set numba cpu env + # Sets the CPU name for numba to use + # This is required for reusing the cache across different runners of the same + # architecture and OS + - name: Set numba CPU env run: echo "NUMBA_CPU_NAME=generic" >> $GITHUB_ENV shell: bash - # Sets the cpu features for numba to use - - name: Set numba cpu features env + # Sets the CPU features for numba to use + # Found through a bit of trial and error to run on different CPUs found in GitHub + # runners + - name: Set numba CPU features env run: echo "NUMBA_CPU_FEATURES=+64bit +adx +aes +avx +avx2 -avx512bf16 -avx512bitalg -avx512bw -avx512cd -avx512dq -avx512er -avx512f -avx512ifma -avx512pf -avx512vbmi -avx512vbmi2 -avx512vl -avx512vnni -avx512vpopcntdq +bmi +bmi2 -cldemote -clflushopt -clwb -clzero +cmov +cx16 +cx8 -enqcmd +f16c +fma -fma4 +fsgsbase +fxsr -gfni +invpcid -lwp +lzcnt +mmx +movbe -movdir64b -movdiri -mwaitx +pclmul -pconfig -pku +popcnt -prefetchwt1 +prfchw -ptwrite -rdpid +rdrnd +rdseed +rtm +sahf -sgx -sha -shstk +sse +sse2 +sse3 +sse4.1 +sse4.2 -sse4a +ssse3 -tbm -vaes -vpclmulqdq -waitpkg -wbnoinvd -xop +xsave -xsavec +xsaveopt -xsaves" >> $GITHUB_ENV shell: bash - # Set the CICD_RUNNING env so that numba knows it is running in a CI environment - - name: Set CICD_RUNNING env - run: echo "CICD_RUNNING=1" >> $GITHUB_ENV + # Set the CACHING_CICD_RUNNING env so we know to use custom numba caching + - name: Set CACHING_CICD_RUNNING env + run: echo "CACHING_CICD_RUNNING=1" >> $GITHUB_ENV shell: bash # Get current date for cache restore diff --git a/.github/workflows/periodic_tests.yml b/.github/workflows/periodic_tests.yml index 4b9829b44e..46d99bc94a 100644 --- a/.github/workflows/periodic_tests.yml +++ b/.github/workflows/periodic_tests.yml @@ -178,7 +178,7 @@ jobs: if: runner.os == 'Linux' uses: pierotofy/set-swap-space@v1.0 with: - swap-size-gb: 4 + swap-size-gb: 6 - name: Use numba cache to set env variables but not restore cache uses: ./.github/actions/numba_cache diff --git a/.github/workflows/pr_pytest.yml b/.github/workflows/pr_pytest.yml index 6aba9e83c4..69323e47c5 100644 --- a/.github/workflows/pr_pytest.yml +++ b/.github/workflows/pr_pytest.yml @@ -76,7 +76,7 @@ jobs: if: runner.os == 'Linux' uses: pierotofy/set-swap-space@v1.0 with: - swap-size-gb: 4 + swap-size-gb: 6 - if: ${{ github.event_name != 'pull_request' || !contains(github.event.pull_request.labels.*.name, 'no numba cache') }} name: Restore numba cache diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 02ebeea529..c622d9dea8 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -69,7 +69,7 @@ jobs: if: runner.os == 'Linux' uses: pierotofy/set-swap-space@v1.0 with: - swap-size-gb: 4 + swap-size-gb: 6 - uses: actions/download-artifact@v4 with: @@ -83,6 +83,14 @@ jobs: name: Unix wheel filename run: echo "WHEELNAME=$(ls ./dist/aeon-*-none-any.whl)" >> $GITHUB_ENV + - name: Use numba cache to set env variables but not restore cache + uses: ./.github/actions/numba_cache + with: + cache_name: "release" + runner_os: ${{ runner.os }} + python_version: ${{ matrix.python-version }} + restore_cache: "false" + - if: runner.os == 'Windows' name: Windows install uses: nick-fields/retry@v3 diff --git a/aeon/testing/_cicd_numba_caching.py b/aeon/testing/_cicd_numba_caching.py index af21dbc3c9..1e4a291c6c 100644 --- a/aeon/testing/_cicd_numba_caching.py +++ b/aeon/testing/_cicd_numba_caching.py @@ -6,7 +6,7 @@ import numba.core.caching -if os.environ.get("CICD_RUNNING") == "1": # pragma: no cover +if os.environ.get("CACHING_CICD_RUNNING") == "1": # pragma: no cover def get_invalid_numba_files(): """Get the files that have been changed since the last commit. From b9380c3646dcdd1a97f3f91d71ac379c0fa0833c Mon Sep 17 00:00:00 2001 From: Aryan Pola <98093778+aryanpola@users.noreply.github.com> Date: Wed, 25 Jun 2025 03:45:42 +0530 Subject: [PATCH 164/216] [ENH] Improves `create_multi_comparison_matrix` parameters and saving (#2657) * Empty-Commit * condensed parameters and showing output * test cases updated * changes * updated examples * point 2 working * [DOC] Clarify comments regarding issue assignment limits and user notifications * condensed parameters and showing output * test cases updated * changes * dir -> save_path * Reverted _mcm.py and its test to match main * requested changes done, pdf not showing yet * changes * changes * updated docstring * changes * requested changes * example file changes * changed formats default to None --------- Co-authored-by: Matthew Middlehurst --- aeon/visualisation/results/_mcm.py | 212 +++++++++--------- aeon/visualisation/results/tests/test_mcm.py | 11 +- examples/visualisation/plotting_results.ipynb | 131 +++-------- 3 files changed, 147 insertions(+), 207 deletions(-) diff --git a/aeon/visualisation/results/_mcm.py b/aeon/visualisation/results/_mcm.py index 5a9d68f59a..1e2f56be84 100644 --- a/aeon/visualisation/results/_mcm.py +++ b/aeon/visualisation/results/_mcm.py @@ -1,4 +1,4 @@ -"""Function to createthe Multi-Comparison Matrix (MCM) results visualisation.""" +"""Function to create the Multi-Comparison Matrix (MCM) results visualisation.""" __maintainer__ = ["TonyBagnall"] @@ -16,15 +16,11 @@ def create_multi_comparison_matrix( df_results, - output_dir="./", - pdf_savename=None, - png_savename=None, - csv_savename=None, - tex_savename=None, + save_path="./mcm", + formats=None, used_statistic="Accuracy", - save_as_json=False, plot_1v1_comparisons=False, - order_win_tie_loss="higher", + higher_stat_better=True, include_pvalue=True, pvalue_test="wilcoxon", pvalue_test_params=None, @@ -32,7 +28,7 @@ def create_multi_comparison_matrix( pvalue_threshold=0.05, use_mean="mean-difference", order_stats="average-statistic", - order_better="decreasing", + order_stats_increasing=False, dataset_column=None, precision=4, load_analysis=False, @@ -45,9 +41,7 @@ def create_multi_comparison_matrix( font_size="auto", colorbar_orientation="vertical", colorbar_value=None, - win_label="r>c", - tie_label="r=c", - loss_label="rc', 'r=c', 'rc') + The tuple must contain exactly three strings, representing win, tie, and + loss outcomes for the row comparate (r) against the column comparate (c). include_legend: bool, default = True Whether or not to show the legend on the MCM. show_symetry: bool, default = True @@ -166,7 +157,11 @@ def create_multi_comparison_matrix( Example ------- >>> from aeon.visualisation import create_multi_comparison_matrix # doctest: +SKIP - >>> create_multi_comparison_matrix(df_results='results.csv') # doctest: +SKIP + >>> create_multi_comparison_matrix( + ... df_results="results.csv", + ... save_path="reports/mymcm", + ... formats=("png", "json") + ... ) # doctest: +SKIP Notes ----- @@ -184,13 +179,25 @@ def create_multi_comparison_matrix( except Exception as e: raise ValueError(f"No dataframe or valid path is given: Exception {e}") + formats = _normalize_formats(formats) + + if win_tie_loss_labels is None: + win_tie_loss_labels = ( + ("r>c", "r=c", "rc") + ) + if len(win_tie_loss_labels) != 3: + raise ValueError("win_tie_loss_labels should be a list of three strings") + win_label, tie_label, loss_label = win_tie_loss_labels + analysis = _get_analysis( df_results, - output_dir=output_dir, + save_path=save_path, + formats=formats, used_statistic=used_statistic, - save_as_json=save_as_json, plot_1v1_comparisons=plot_1v1_comparisons, - order_win_tie_loss=order_win_tie_loss, + higher_stat_better=higher_stat_better, include_pvalue=include_pvalue, pvalue_test=pvalue_test, pvalue_test_params=pvalue_test_params, @@ -198,7 +205,7 @@ def create_multi_comparison_matrix( pvalue_threshhold=pvalue_threshold, use_mean=use_mean, order_stats=order_stats, - order_better=order_better, + order_stats_increasing=order_stats_increasing, dataset_column=dataset_column, precision=precision, load_analysis=load_analysis, @@ -207,11 +214,8 @@ def create_multi_comparison_matrix( # start drawing heatmap temp = _draw( analysis, - pdf_savename=pdf_savename, - png_savename=png_savename, - tex_savename=tex_savename, - csv_savename=csv_savename, - output_dir=output_dir, + save_path=save_path, + formats=formats, row_comparates=row_comparates, col_comparates=col_comparates, excluded_row_comparates=excluded_row_comparates, @@ -222,9 +226,7 @@ def create_multi_comparison_matrix( font_size=font_size, colorbar_orientation=colorbar_orientation, colorbar_value=colorbar_value, - win_label=win_label, - tie_label=tie_label, - loss_label=loss_label, + win_tie_loss_labels=win_tie_loss_labels, include_legend=include_legend, show_symetry=show_symetry, ) @@ -233,11 +235,11 @@ def create_multi_comparison_matrix( def _get_analysis( df_results, - output_dir="./", + save_path="./", + formats=("json"), used_statistic="Score", - save_as_json=True, plot_1v1_comparisons=False, - order_win_tie_loss="higher", + higher_stat_better=True, include_pvalue=True, pvalue_test="wilcoxon", pvalue_test_params=None, @@ -245,7 +247,7 @@ def _get_analysis( pvalue_threshhold=0.05, use_mean="mean-difference", order_stats="average-statistic", - order_better="decreasing", + order_stats_increasing=False, dataset_column=None, precision=4, load_analysis=False, @@ -254,6 +256,8 @@ def _get_analysis( import matplotlib as mpl import matplotlib.pyplot as plt + formats = _normalize_formats(formats) + def _plot_1v1( x, y, @@ -262,7 +266,7 @@ def _plot_1v1( win_x, loss_x, tie, - output_directory="./", + save_path="./", min_lim: int = 0, max_lim: int = 1, scatter_size: int = 100, @@ -271,7 +275,7 @@ def _plot_1v1( fontsize: int = 20, ): save_path = os.path.join( - output_directory, + save_path, "1v1_plots", _get_keys_for_two_comparates(name_x, name_y) + ".pdf", ) @@ -332,29 +336,31 @@ def _plot_1v1( ax.legend(handles=legend_elements) - if not os.path.exists(output_directory + "1v1_plots/"): - os.mkdir(output_directory + "1v1_plots/") + if not os.path.exists(save_path + "1v1_plots/"): + os.mkdir(save_path + "1v1_plots/") plt.savefig(save_path, bbox_inches="tight") plt.savefig(save_path.replace(".pdf", ".png"), bbox_inches="tight") plt.cla() plt.clf() plt.close() - save_file = output_dir + "analysis.json" + save_file = f"{save_path}_analysis.json" if load_analysis and os.path.exists(save_file): with open(save_file) as json_file: analysis = json.load(json_file) + analysis.setdefault("order_stats_increasing", order_stats_increasing) + return analysis analysis = { "dataset-column": dataset_column, "use-mean": use_mean, "order-stats": order_stats, - "order-better": order_better, + "order_stats_increasing": order_stats_increasing, "used-statistics": used_statistic, - "order-win_tie_loss": order_win_tie_loss, + "higher_stat_better": higher_stat_better, "include-pvalue": include_pvalue, "pvalue-test": pvalue_test, "pvalue-threshold": pvalue_threshhold, @@ -386,7 +392,7 @@ def _plot_1v1( pairwise_content = _get_pairwise_content( x=x, y=y, - order_WinTieLoss=order_win_tie_loss, + higher_stat_better=higher_stat_better, include_pvalue=include_pvalue, pvalue_test=pvalue_test, pvalue_test_params=pvalue_test_params, @@ -412,7 +418,7 @@ def _plot_1v1( win_x=pairwise_content["win"], tie=pairwise_content["tie"], loss_x=pairwise_content["loss"], - output_directory=output_dir, + save_path=save_path, max_lim=max_lim, min_lim=min_lim, ) @@ -425,7 +431,7 @@ def _plot_1v1( _re_order_comparates(df_results=df_results, analysis=analysis) - if save_as_json: + if "json" in formats: with open(save_file, "w") as fjson: json.dump(analysis, fjson, cls=_NpEncoder) @@ -434,11 +440,8 @@ def _plot_1v1( def _draw( analysis, - output_dir="./", - pdf_savename=None, - png_savename=None, - csv_savename=None, - tex_savename=None, + save_path="./", + formats=None, row_comparates=None, col_comparates=None, excluded_row_comparates=None, @@ -449,15 +452,18 @@ def _draw( font_size="auto", colorbar_orientation="vertical", colorbar_value=None, - win_label="r>c", - tie_label="r=c", - loss_label="r", "$>$") latex_string = latex_string.replace("<", "$<$") - if tex_savename is not None: - with open( - f"{output_dir}/{tex_savename}.tex", "w", encoding="utf8", newline="\n" - ) as file: - file.writelines(latex_string) - # latex references: # * https://tex.stackexchange.com/a/120187 # * https://tex.stackexchange.com/a/334293 # * https://tex.stackexchange.com/a/592942 # * https://tex.stackexchange.com/a/304215 - return plt.Figure() + + parent = os.path.dirname(save_path) + if parent: + os.makedirs(parent, exist_ok=True) # check for dir existence + + if "pdf" in formats: + fig.savefig(f"{save_path}.pdf", bbox_inches="tight") + if "png" in formats: + fig.savefig(f"{save_path}.png", bbox_inches="tight") + if "csv" in formats: + df_annotations.to_csv(f"{save_path}.csv", index=False) + if "tex" in formats: + with open(f"{save_path}.tex", "w", encoding="utf8", newline="\n") as file: + file.writelines(latex_string) + + return fig def _get_keys_for_two_comparates(a, b): @@ -954,7 +951,7 @@ def _decode_results_data_frame(df, analysis): def _get_pairwise_content( x, y, - order_WinTieLoss="higher", + higher_stat_better=True, include_pvalue=True, pvalue_test="wilcoxon", pvalue_test_params=None, @@ -963,16 +960,16 @@ def _get_pairwise_content( ): content = {} - if order_WinTieLoss == "lower": - win = len(x[x < y]) - loss = len(x[x > y]) - tie = len(x[x == y]) - - else: # by default we assume higher is better + if higher_stat_better is True: win = len(x[x > y]) loss = len(x[x < y]) tie = len(x[x == y]) + else: + win = len(x[x < y]) + loss = len(x[x > y]) + tie = len(x[x == y]) + content["win"] = win content["tie"] = tie content["loss"] = loss @@ -1146,7 +1143,7 @@ def _re_order_comparates(df_results, analysis): stats.append(np.mean(pvalues)) - if analysis["order-better"] == "increasing": + if analysis["order_stats_increasing"]: ordered_indices = np.argsort(stats) else: # decreasing ordered_indices = np.argsort(stats)[::-1] @@ -1157,6 +1154,15 @@ def _re_order_comparates(df_results, analysis): ) +def _normalize_formats(formats): + """Return a list of extensions or an empty list.""" + if formats is None: + return [] + if isinstance(formats, str): + return [formats] + return list(formats) + + def _get_cell_legend( analysis, win_label="r>c", diff --git a/aeon/visualisation/results/tests/test_mcm.py b/aeon/visualisation/results/tests/test_mcm.py index 0df99a24ef..060f0f775c 100644 --- a/aeon/visualisation/results/tests/test_mcm.py +++ b/aeon/visualisation/results/tests/test_mcm.py @@ -1,5 +1,6 @@ """Test the multi-comparison-matrix visualisation.""" +import os import tempfile import numpy as np @@ -22,7 +23,7 @@ def test_mcm(): np.random.rand(10, 3), # 10 rows, 3 columns of random numbers columns=["Classifier1", "Classifier2", "Classifier3"], ) - fig = create_multi_comparison_matrix(df, save_as_json=False) + fig = create_multi_comparison_matrix(df, formats=()) assert isinstance(fig, plt.Figure) @@ -39,13 +40,11 @@ def test_mcm_file_save(): columns=["Classifier1", "Classifier2", "Classifier3"], ) with tempfile.TemporaryDirectory() as tmp: + prefix = os.path.join(tmp, "test") fig = create_multi_comparison_matrix( df, - output_dir=tmp, - pdf_savename="test1", - png_savename="test", - tex_savename="test", - save_as_json=True, + save_path=prefix, + formats=("pdf", "png", "csv", "json", "tex"), pvalue_correction="Holm", ) assert isinstance(fig, plt.Figure) diff --git a/examples/visualisation/plotting_results.ipynb b/examples/visualisation/plotting_results.ipynb index f8f8629922..54372bf6f3 100644 --- a/examples/visualisation/plotting_results.ipynb +++ b/examples/visualisation/plotting_results.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "id": "initial_id", "metadata": { "ExecuteTime": { @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "id": "bd9201e73b7ba7d7", "metadata": { "ExecuteTime": { @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "id": "da87284606d4cfd1", "metadata": { "ExecuteTime": { @@ -114,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "id": "9ba66c6d0ac437a5", "metadata": { "ExecuteTime": { @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 5, "id": "66493f5cdea9d644", "metadata": { "ExecuteTime": { @@ -144,16 +144,6 @@ "collapsed": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\aryan\\anaconda3\\envs\\aeon\\Lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", - " func(*args, **kwargs)\n", - "c:\\Users\\aryan\\anaconda3\\envs\\aeon\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", - " fig.canvas.print_figure(bytes_io, **kw)\n" - ] - }, { "data": { "image/png": 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", @@ -177,20 +167,10 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 6, "id": "a85b3be4", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\aryan\\anaconda3\\envs\\aeon\\Lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", - " func(*args, **kwargs)\n", - "c:\\Users\\aryan\\anaconda3\\envs\\aeon\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", - " fig.canvas.print_figure(bytes_io, **kw)\n" - ] - }, { "data": { "image/png": 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", @@ -214,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "id": "a0ba27ecd0bf0a4b", "metadata": { "ExecuteTime": { @@ -241,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "id": "a40d1305b6ba5e93", "metadata": { "ExecuteTime": { @@ -284,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "id": "c2944d477b66ab8d", "metadata": { "ExecuteTime": { @@ -300,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "id": "cf683faa01e14340", "metadata": { "ExecuteTime": { @@ -352,7 +332,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "id": "467f2ad0789368e8", "metadata": { "ExecuteTime": { @@ -437,12 +417,13 @@ "4 0.7 0.6 0.5 0.4" ] }, - "execution_count": 14, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "df = pd.DataFrame(classifier_accuracies, columns=classifiers)\n", @@ -451,7 +432,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "id": "ac865d14b86c42a1", "metadata": { "ExecuteTime": { @@ -460,16 +441,6 @@ } }, "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "image/png": 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", @@ -484,25 +455,16 @@ "source": [ "from aeon.visualisation import create_multi_comparison_matrix\n", "\n", - "create_multi_comparison_matrix(df, fig_size=\"8,4\")" + "create_multi_comparison_matrix(df, fig_size=\"8,4\")\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "id": "3c26d721", "metadata": {}, "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "image/png": 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", @@ -519,25 +481,16 @@ "\n", "create_multi_comparison_matrix(\n", " df, fig_size=\"8,4\", pvalue_test_params={\"alternative\": \"two-sided\"}\n", - ")" + ")\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 14, "id": "0be31bf2", "metadata": {}, "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "image/png": 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", @@ -557,28 +510,19 @@ " fig_size=\"8,4\",\n", " pvalue_test_params={\"alternative\": \"two-sided\"},\n", " pvalue_correction=\"holm\",\n", - ")" + ")\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 15, "id": "4f1e9e75", "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "
" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -595,12 +539,13 @@ " fig_size=\"5,4\",\n", " row_comparates=[\"Classifier 1\", \"Classifier 2\", \"Classifier 3\"],\n", " col_comparates=[\"Classifier 3\", \"Classifier 4\"],\n", - ")" + ")\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 16, "id": "a9919cda13fadd3", "metadata": { "ExecuteTime": { @@ -611,17 +556,7 @@ "outputs": [ { "data": { - "text/plain": [ - "
" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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vpapVq6osc+bMmZKWlpa0Y8cOpd/rz58/z3e9kiT6tvD1EAN6oVBaunSpZG9vL2lpaUkeHh7S6dOnFdd69Ogh1alTR/G8Tp06EpDj0aNHj1zLVzWgf1u9kpS1DdrEiRMla2trSVtbW6pdu7YUFBT0vi9X+EoUpF/PnDlTKlmypKSjoyOZmppKNWvWlA4ePJijzO3bt0suLi6Spqam5OrqqjTgz0+9kiT6tfD+CtK3JSlrL3pdXV1p5cqVKsuzt7dX+Xv99b3q31avJIm+LXw9ZJIkSf/lNwI9e/Zkw4YN/PTTT6xYsULpWv/+/Vm+fDk9evRg/fr1/2WzBEEQBEEQBOGz9EnW0NvZ2bFlyxZevnypSEtJSWHz5s0UL178UzTpvaWnpxco/V3LEwRBEARBEITXfZIBvYeHB8WLF2fXrl2KtF27dmFnZ4e7u7siTZIkZs2ahaOjI7q6ulSoUIEdO3YormdmZtK7d28cHBzQ1dXFxcWFhQsXKtXVs2dPWrduzZw5c7CxscHc3JwBAwa8dcCckJBA3759KVKkCEZGRnzzzTdcu3ZNcd3Hx4eKFSuydu1aHB0d0dbWVmyFtWLFClq1aoW+vj5Tp04FYPny5ZQsWRItLS1cXFzYuHGjUn255RMEQRAEQRCEvHyyXW569erFunXrFM/Xrl3LDz/8oBTz66+/sm7dOpYvX87NmzcZOnQo33//PadPnwayth4sVqwY27ZtIzg4mAkTJvDLL7+wbds2pXJOnjzJvXv3OHnyJBs2bGD9+vV5LumRJInmzZsTFRXFoUOH8Pf3x8PDg/r16xMbG6uIu3v3Ltu2bWPnzp1KBxZNnDiRVq1aERQUxA8//MDu3bsZMmQIw4cP58aNG/z000/06tUrx6mPb+YTBEEQBEEQhLf5JGvo4+PjWb16NcWKFePWrVvIZDJcXV15+PAhffr0wcTEhKVLl2JhYcGJEyfw8vJS5O/Tpw/Jycm5nio3YMAA/vnnH8VMfs+ePTl16hT37t1DXV0dgA4dOqCmpsaWLVtUlnHixAnatGlDdHQ02trainQnJydGjRpF37598fHxYfr06Tx+/BhLS0tFjEwmw9vbm/nz5yvSatSogZubGytXrlSkdejQgaSkJA4ePJhrPkEQBEEQBEF4m092sJSFhQXNmzdnw4YNihnx10/xDA4OJiUlhYYNGyrlS0tLU1qWs2LFClavXk14eDgvX74kLS2NihUrKuVxc3NTDOYh60j1oKAgAKZPn8706dOV6vX39+fFixeYm5srlfPy5Uvu3buneG5vb680mH/lzf1yQ0JC6Nu3r1JajRo1ciwPejOfIAiCIAiCILzNJz0p9ocffmDgwIEALF26VOnaq5M8Dx48SNGiRZWuvZo137ZtG0OHDmXu3Ll4eXlhaGjI7NmzuXjxolK8pqam0nOZTKYov1+/fnTo0EFxzdbWFrlcjo2NDadOncrRZhMTE8XP+vr6Kl+XqvT8HD2dW3mCIAiCIAiCkJtPelJskyZNSEtLIy0tjcaNGytdK1OmDNra2kRERODk5KT0sLOzA+Ds2bNUr16d/v374+7ujpOTk9IMen6YmZkpla2hoYGHhwdRUVFoaGjkqPv1bxHyq3Tp0vz9999KaefPn6d06dIFLkt4d6mpqfj4+JCamvqpmyIIH4zo18KXSPRrQSiYTzqgV1dXJyQkhJCQEKUlMQCGhoaMGDGCoUOHsmHDBu7du0dAQABLly5lw4YNQNaa9itXrnDkyBFu377N+PHjuXz58nu3q0GDBnh5edG6dWuOHDnCgwcPOH/+PL/++itXrlwpcHkjR45k/fr1rFixgjt37jBv3jx27drFiBEj3rutQv6lpqYyadIk8QdC+KKIfi18iUS/FoSC+aRLbgCMjIxyvTZlyhSKFCnCjBkzuH//PiYmJnh4ePDLL78AWctlAgMD6dixIzKZjM6dO9O/f3/++uuv92qTTCbj0KFDjBs3jh9++IGnT59ibW1N7dq1sbKyKnB5rVu3ZuHChcyePZvBgwfj4ODAunXrqFu37nu1UxAEQRAEQRD+811uBOFTSUxMxNjYmISEhDw/SArC50T0a+FLJPq1IBTMJ11yIwiCIAiCIAjC+/nkS26+dvfv3f3UTfhqpKamMnjQIB4/esiz184XED68w3dKfuomfDXS07Ro1nUC605poaklvnD9mE4cCfvUTfhqZGakUrrqYHqMe4y6xrNP3Zwv3o75jp+6CcJ7EktuPjExoBe+RGJAL3yJxIBe+FKJAf3nTyy5EQRBEARBEITPmBjQC4IgCIIgCMJnTAzoBUEQBEEQBOEzJgb0giAIgiAIgvAZEwN64Yvm53eRkk6lSExMVKQd9fWl3jf1KeXswpSpU3NNEwRBEARB+ByIbSsFJSNHjWLXrt107tyJqVOmKF2bMGEif27axHfftWH2rFmfqIVZatepy+PHjwHQ1tbGwsKCCuXL07lLZ6p7eSniPDzc8btwHkNDQ0Xar7+Op13btvTo0R19ff1c0wThY5AkiUN/TuLcX6tIfhFHCZeqdBiwBFt7t1zznPtrFRePb+RJ+A0Aijt50rLnNEq4VFGKi3/2mD1rxxB85S/S0l5SpKgz33uvpngpz3zXnZ6Wyu7VI7hyegvpqS9xqVifjgOWYmpZ7CO8G8KXQpIkQvwWEXZjC2kpCZhZV8T9Gx+MzJ3zlf9h6H4u/eWNjWMDqrf8XZEul2cQ4reQiFv7SEl6io5+EUqUaYtr1QHIZGr5rjszI5WgszN4GHqAzIwUihSvTsV6k9AztPlwb4IgfEJihl7IwcbGhgMHDpKSkqJIS01NZf+BA9ja2n7Clinz9h6C34XzHPM9ypzZszA0MqJ79x4sXbZMEaOlpYWlpSUymQyApKQkYmJiqFWrFlZWVhgYGKhMexdpaWkf5HUJXzbf7bM4sWs+HfovZtTCSxiZWrPkl0akJD/PNc/t66epVLcTQ347wYh55zG1tGPJuMbEP3usiEl+Hsfc4TVR19Ck/5RDjP/9Jt/1mYOuvkmB6t7xuzfXzu/hhzGbGTbnLKkpL1ju0wJ5ZuZHeT+EL8PtKyu5E7CWivV8+KbzbnT0LTi7qwfpaS/emjcp8TFBZ3/DomhlFeX+zv3rm6lYz4dG3Y9SrtZobvuv4m7ghgLVfe30VJ7c86VKs4XU7bCVjLQkzu/9EUku+rXwZRADeiEHNzc3bG1tOXLkiCLtyJEj2NjYUKZMGUWaJEn8vnIldevVo4xbWZp/24K//vpLcT0zM5MxY8ZSp27W9QYNG7Fu/XqlukaOGsVP/X5m1erVVPOqjmelykyc6EN6evpb22mgr4+lpSW2trZUqVKF6dOmMnDAABYsWMj9+/cB5SU3fn4XKV+hIgDfd+tGSadSuaYB+F+9SqfOnSnjVpYaNWsxafJkkpOTFfXXrlOXJUuXMnLUKCpUdOeXcb/mO9+yZcsZPWYM5StUpGat2mzeskXptUVGRjJ4iDcenpUoW648rVq3ITAwUHH9+PHjtGzVmtJl3Khbrx6LFi0mIyPjre+Z8GlJksTJPQtp3OkXKtb4DtsSZek2fD1pqclcPrUp13y9Rv+P2t/2x65kRaztXOk6ZBWSXE5o4HFFzNHtMzG1tKPbsLWUcKmCuVUJXN3rY2lbMt91v0xK4MLRtXz34xxc3Rtg5+ROj5EbefIgiFuBxz7umyN8tiRJ4m7AOlwr96eoU2OMLVyo1Gg2mekveXhrX9555ZlcPjyU0tWGoG9kl+N6TGQAtiUbYONQD33jYhQr1ZQi9jWJ/+dGvutOT33Og5vbKVdrLFbFa2BSxI3KTeaREBPKPxHnPvwbIgifgBjQCyq1a9uWHTt3KZ5v37GT9u3aKsXMnTefnTt3MnnSZA7/dYhevXoybPgILl7MGhDL5XKsra1ZvGghRw7/xaCBA5g7dx4HDx5SKsfPz4+IiAj+/N9GZs+ayc5du9j5Wt0F0bNnDyRJwvdYzsGHh4c7x3yPArBs6RL8LpzPNS00NJRevX6gcaNGHDx4gEWLFuJ/xR+fSZOUyly1ajXOzs7s3bOHgQMG5DvfmrVrKVe2HPv27uX7rl2ZMGEi9+7dA7K+RejSpSvR0f+w8vcVHNi/n74//ohcnnUG3JkzZxk2fAQ9enTnyOG/mDplCjt37WLZsuXv9J4J/52YqDAS46Io7dFIkaappY1TuTqEBV/IdzlpqclkZqajZ2imSAvy20/xUp6sntaB0Z2smDHAg3N/rSpQ3RF3/MnMSFeKMTG3xda+LPeDz7/Taxa+fEmJD0lJfoqVfU1FmrqGNhbFqhITeTXPvCEXF6Ota4ZD2Q4qr1vYViI64jzP47IO9Yp/GkLMkytYl6iT77rjooOQ5OlY2ddSxOgaWGFs7kzsW9onCJ8LsYZeUKl161bMnjOHR48eIZPJ8Pf3Z+GC+fhdvARAcnIya9eu5X8bN+Lh4Q5A8eLF8b/iz+YtW6hatSqampp4ew9RlGlnZ8fVqwEcOnSI5s2bKdKNjY3xmTgRdXV1SpYsSb26dTl/4QKdOnUscLtNTEwwNzfn8aPHOa5paWlhbm7+b50mWFpaAqhMW7lqNS1afEuvXr0AcChRggkTxtO5S1emTJ6MtrY2AF5eXvzYp4+ijuEjRuYrX906dfj++64A/PRTX9auW8fFixcpWbIk+/bvJzY2lt27d2FiYgJAiRL2ijqWLV9Gv59+ou133yne96He3sycNYvBgwcV+D0T/juJcVEAGJpaKaUbmRQhNjoi3+XsXTcGY/OiuLo3UKQ9i7rP2YMr+Oa7oTTuOJYHty+xfcUQNDS1qdqge77qToyLQkNDCz1DU6UYQxMrRX5BeFNq0lMAtPUslNK19cxJTnySa75nT67w4OZ26nfdn2uMc6WfSE99ztENDZGpqSPJM3GrPhw715b5rjsl6Rlq6lpo6Ri/EWNByr/5BeFzJwb0gkpmZmbUq1eXXbt2I0kS9erWxcwsezbw7t27pKam0qNnT6V86enplClTWvF806ZNbN22nSePH5OSmkp6ejqlS5dWylOqlBPq6uqK55ZFLAkNvQ3AsmXLWb5iheLakcN/vXUdvyRJijXz7+rmjRuEh4ezb1/2HxpJkpDL5Tx8+BAnJycAypUr+075XFxdFNdlMhmWlpbExMQAEBIcQpkyZRSD+TfduHGT69eDWLY8e0Y+MzOT1NRUXr58ia6u7nu9duHDuXTiTzYv7qd43n/SAYAc/VNCgnz2Wd/ts7hyagves06iqaWTXYYkp3ipSrTqOR0AOyd3IsNvcvbgCqo26K6Ie7e63//flPDliLi1l6vHf1U8r9FqddYPOfqIlGvXSk97weXDw/GoPw1tXTPVQcCj2weIuLWHKk3nY2TuTPzTYK6fnoquQRHsy7z2rXEB6n49Jr//7gShsBMDeiFX7du1w2fSZAB8fCYqXZPL5QCsXrUSKytrpWtaWloAHDx4iKnTpvPL2LG4u1dEX9+AVatXce3aNaV4DQ1NpecymQzp3/K7dOlMs2bZs/lFihTJs81xcXHExsZSzO79duSQS3I6de5Ej+49clyztc3eFUHvjcFzfvNpaij/05PJUCyp0dHRIS9yuZwhQwbTuFHjHNdefQMgFA7lq7WkhGtVxfOM9FQAEmOjMDbL7g/P459iZGKVI/+bju2Yw5GtMxg03ZeiDuWVrhmZ2WBTXPnDsrVdaQLPZS1fMzK1fmvdRqbWZGSkkfw8TmmW/nl8NA6lvRAEABvH+jSwrqB4Ls/M2hAgNekpuvrZv6NTk2NzzJy/khQfQXLiI87v66tIk6Ss3/u7FjrTqIcvBib2BJ39DZfK/bBzaQGAsYULyYmPuXV5BfZl2qKtb/nWunX0LZBnppGWkqA0S5+aHIO5jcd7vReCUFiIAb2Qq9q1aytuTq1dq5bSNScnJ7S0tHjyJJKqVauqys7lK1fw8PBQLC0BiIh4WKA2mJiY5DpTrcr6DRtQU1OjYYOGBarnTW5ubty5c1dpqcvHzPc6F1cXtm7bRnx8vMrX7ubmRtj9sPeqQ/hv6OgZoqOXvWWqJEkYmVpzK8AXO6espWoZ6WncDTpNqx9+y7Ms3x2zObx5GgOnHsbeuVKO6yXL1OCfR7eV0qIf38asSFY/Mbd2eGvdxUt5oq6hSUiAL561s9Y0J8RG8iT8Bq17z3zHd0H40mhqGaCplb0bmCRJ6OhZ8k/E35gUydoCVZ6ZxrNHFylbc5TKMgzNStLge+X7qW6en0dGehIV6oxXbCeZmZHCm7f7yWTq8O/gX9/I7q11mxYph0xNk+iIvynm3ByAl0nRJMTcpmzN0e/5bghC4SAG9EKu1NXVOXL4L8XPrzMwMKBPn95Mmz4duSSnkqcnL1684OrVAPT09Wj73XfY2xdn9+7dnDlzFju7Yuzes4fr169j956z56+8SEri6dOnpKdn8OjRQ/bs3ce2bdsYOWL4ew92f+rbl7bt2jNxog8dO3ZAV0+Pe3fv8fe5c/hMnPDB872uxbffsnz5Cvr9/DMjRoygiKUlwcHBFClihYeHO4MGDuTHvn2xsbGhadOmqKnJuBUaSmhoKMOHDXuv1y18XDKZjHqth3Bk6wwsbUtRpGgpjmydgZa2HpXrdlHEbZjTAxNzW1r1mgFkLbM58McEeo7+EzOrEiTEZq1n19Y1QEc3a2D1TWtv5gyvweEt0/Go3YHw0Euc+2sVnQf/nu+6dfWN8Wr0A7tWjUDf0Bx9QzN2rR6JbYlyuFZsgCCoIpPJcHLvReil5RiYlMDApAShl5ejrqmrWOsOcPnIcHT1rSlbcyTqGtoYW7golaOlbQSglG7j8A2hl5ehZ2SLkVkp4p/e5E7AWkqUaZfvujW1DSnh1p7rZ6ajpWOClo4J18/OwNjcBaviNT722yMI/wkxoBfy9PqBTG8aNnQo5ubmrFjxOw8fPsTQ0BA3Nzf6/5y1ZrhL586EhIQweMgQZDIZLb79lu+7duX0mdMfpG0LFixkwYKFaGlqYmFpScWKFdn4xx94eVV777JdXV3ZvOlP5s6dR6fOXZAkieLFi9P8teU/HzLf67S0tNiwfh3Tp8+gd+8+ZGZm4uTkxKR/lz3Vrl2LVStXsnjJElauWoWGhgYlSzrSob3qXSKEwqVh+1Gkp71k69IBisOdBk47ojSTHxcdoTg0B+DMgeVkZKSxelp7pbKadZ1A8+99ALB3qUzf8bvYt/4X/to0BXNrB9r9NJ8q32R/Q5afutv9NB91dQ3WzuhIWtpLXCrUp/vwdai98aFeEF7nXKkvmRkpBJ6YSFpq1uFONdusV5rJT06MRFbAzfUq1JtI8Pn5BJ6YQEpyDLoGVjiW60TpqtkbAOSn7gp1fkVNTZ2LhwaTmZGCpV11qreahUxN9GvhyyCTJEn64IXKZOzevZvWrVt/6KKVnDp1inr16hEXF6dYmrBnzx5GjBhBWFgYgwYNomLFinh7exMfH/9R2/Ku7t+7+6mbIAgf3OE7JT91EwThgztxJOxTN0EQPood8x0/dROE91TgfeijoqIYNGgQjo6OaGtrY2dnR4sWLTh+/PjbM39g1atXJzIyEmPj7JtcfvrpJ9q1a8fDhw+ZMmUKHTt25Pbt23mU8m7S09MZPXo05cqVQ19fH1tbW7p3786TJ7lv0SUIgiAIgiAIH1qBltw8ePCAGjVqYGJiwqxZsyhfvjzp6ekcOXKEAQMGcOvWrY/VTpW0tLSwts7eYeXFixdER0fTuHFjpa0N33cbv/T0dDQ1lXdiSU5O5urVq4wfP54KFSoQFxeHt7c3LVu25MqVK+9VnyAIgiAIgiDkV4Fm6Pv3749MJuPSpUu0a9cOZ2dn3NzcGDZsGH5+frnmGz16NM7Ozujp6eHo6Mj48eMVu6cAXLt2jXr16mFoaIiRkRGenp6KQXF4eDgtWrTA1NQUfX193NzcOHQo6874U6dOIZPJiI+P59SpU4r13t988w0ymYxTp06xfv36HDuF7N+/H09PT3R0dHB0dGTSpElkZGQorstkMlasWEGrVq3Q19dn6tSpOV6TsbExvr6+dOjQARcXF6pVq8bixYvx9/cnIiL/B8QIgiAIgiAIwvvI9wx9bGwshw8fZtq0aejr6+e4ntfWgoaGhqxfvx5bW1uCgoL48ccfMTQ0ZNSorC2lunbtiru7O8uXL0ddXZ3AwEDFjPiAAQNIS0vjzJkz6OvrExwcjIGBQY46qlevTmhoKC4uLuzcuZPq1atjZmbGgwcPlOKOHDnC999/z6JFi6hVqxb37t2jb9+sfXAnTszea33ixInMmDGD+fPn59jhJTcJCQnIZLICbbMoCIIgCIIgCO8j3wP6u3fvIkkSrq6uBa7k11+zT5QrUaIEw4cPZ+vWrYoBfUREBCNHjlSUXapUKUV8REQEbdu2pVy5cgA4Oqq+cUNLS0tx6JCZmZnSUpzXTZs2jTFjxtCjRw9FeVOmTGHUqFFKA/ouXbrwww8/5Ps1pqSkMGbMGLp06YKRkVG+8wmCIAiCIAjC+8j3gP7VZjjvcvz3jh07WLBgAXfv3uXFixdkZGQoDXqHDRtGnz592LhxIw0aNKB9+/aULJm1S8bgwYP5+eefOXr0KA0aNKBt27aUL18+t6reyt/fn8uXLzNt2jRFWmZmJikpKSQnJ6OnpwdApUo5D27JTXp6Op06dUIul7Ns2bJ3bpsgCIIgCIIgFFS+19CXKlUKmUxGSEhIgSrw8/OjU6dONG3alAMHDhAQEMC4ceNIS0tTxPj4+HDz5k2aN2/OiRMnKFOmDLt37wagT58+3L9/n27duhEUFESlSpVYvHhxgdrwOrlczqRJkwgMDFQ8goKCuHPnDjo6Ooo4VcuKVElPT6dDhw6EhYXh6+srZuc/Aj+/i1Tzqs5H2GH1vXXp0pVNmzZ96mYIhdTB//kwoKma0mNMF5t85b3gu57Z3l65Xr9z/TS/DarEkJa6TOhVkrMHV+Sr3Jh/whnSQoeXSYkqr8dGR7B8YkuGtjZgVEdLti0fTEZ6mspY4esUfGEhOxeUVHocWKn6xPA3Pbi5g5Nb2uZ6/emjixzf1JLdi0vz19q63L+ev9+vSYmP2b24NOmpz1VeT058wrm9P7JnSVn2r6hE4KlJyDNFvxa+HPmeoTczM6Nx48YsXbqUwYMH5xjw5nZM/blz57C3t2fcuHGKtPDw8Bxxzs7OODs7M3ToUDp37sy6deto06YNAHZ2dvTr149+/foxduxYVq1axaBBg3KUkR8eHh6Ehobi5OT0Tvlf92owf+fOHU6ePIm5ufl7l/m5SEtLQ0tL661xT548Udpx6F0cP36c+vW/yde3Q5mZmchkMtTUCrwja4HFx8dzNSCAefPm5iv+1Xv2Id4T4fNhY+/GoOm+iudq+TzIJshvP+W9Wqq89iwqjGUTmlOjSR96jtzIveBzbF06AANjS9xr5j5YArjut5dS5euiq59z8kGemcnyid9iYGzJsDlnSXoewx9ze4Ik0aH/u0+kCF8eI/NS1Ppuo+L56weh5SXy/nFsHFWfOpyU8JBze3rjULYjlZvMI+aJPwEnJqKta0bRUk3yLveeLxZFq6KpnfMwREmeybm9vdHWNaNuh62kpsRz5chIkCQq1vPJV7sFobAr0Khn2bJlZGZmUqVKFXbu3MmdO3cICQlh0aJFeHmpnklycnIiIiKCLVu2cO/ePRYtWqSYfQd4+fIlAwcO5NSpU4SHh3Pu3DkuX75M6dKlAfD29ubIkSOEhYVx9epVTpw4obj2LiZMmMAff/yh+FYgJCSErVu3Kq3zz4+MjAzatWvHlStX+PPPP8nMzCQqKoqoqCilbx++FF26dMXHZxLTpk2nUuUq9OjRM9fY58+fs23bdjp17sy3LZQHJFf8/encuQtuZcvh7uFJz569SEhIyLPuY8eP06B+fZXXduzcSUV3D06cOEHjxk0oXcaNx48fF/j1paam8tvMmdSoWYvSpcvwTf0GbNu2Pc88J0+ewtXVNdf7NWrXqcuSpUsZOWoUFSq688u4rD7WuUtX2rZtx59//vnW1w5w7NhxWrVuQ+kyblSqXIWf+/cv8OsTPh01dQ2MzawVD0MTy7fmSU9LIeTqUcpVUz2g//vgCkyLFKddvwVYFy9NjSZ98GrUi+M73/7h8vqFfZSr1kLltZCrR4mMCKbHyI3YObnj6t6A7/rM4dzh1bnO6AtfJ5lMAx19S8VDW+/tE1qZGan8E/E3tiVV/z6/f30Teoa2VKg7HiMzJxzKdqSEWztu+69+a9lP7h/Ltdx/ws+SGHuXyk3mYVLEDaviNShfeyxhN7bmOqMvCJ+bAu1D7+DgwNWrV5k2bRrDhw8nMjISS0tLPD09Wb58uco8rVq1YujQoQwcOJDU1FSaN2/O+PHj8fHxAUBdXZ2YmBi6d+/OP//8g4WFBd999x2TJk0CsmZcBwwYwKNHjzAyMqJJkybMnz//nV9w48aNOXDgAJMnT2bWrFloamri6upKnz59ClTOo0eP2LdvHwAVK1ZUunby5Enq1q37zm0srHbt3k2XLp3ZtnVLjuUvcrmc8+fPs2vXbo4cPYqVlRWtW7Vi1syZipjg4GC6detO+3btmDBhPOrqGvj5+ZGZmZlrnbdv3+HZs2dUr14915iUlBSWr/id6TOmY2pigrm5OXv37uXX8RPyfD1Tp0ymVatWAIwYOYqAgAAmTBhPaVdXHj56RFxcXJ75jx8/ToMGqv+AvLJq1WoGDhzAgP4DFGmbN21iz549bPjjD6ZOm06D+vX57rs21KpVCw0N5X+SJ0+epP+AAfT/+WfmzplNeno6J0+eyrNOoXB5+vgOv3QtioamNiVcqtKy5zQsbPI+lTE08DhGptbY2rupvH7/lh+lPRoqpZX2aMz5I2vJzEhHXUNTZb7kF/Hcu3mW7sPXqbweFnIBW/uymJhnf4NUxrMxGempPLzrj3OFenm2W/h6vIh/wMFVXqipa2FmXQG3GiMwMC6eZ57oh+fR0bPAyNxZ5fXYqACK2NdUSrOyr8WDm9uRZ6ajpq66X6elJPLs8RUqNZql8npMVADG5s7oGli9Vm5t5JlpxEXfoIhd7kvbBOFzUaABPYCNjQ1LlixhyZIluca8OdibNWsWs2Yp/0Pz9vYGsnan2bx5c65l5bVevm7dukp1mZiY5Ki7Z8+e9OzZUymtcePGNG7cON/tV6VEiRIFXtOdmppKampqjjRtbe0ClfOp2BcvzpjRo5XSwsLC2LlzF7v37OHly5c0a9aUDRvWU8nTM0f+latWUa5cOSZPnqRIc3YulSPudceOHaNWrZp5vkfp6elMnuSj9M1N/fr1qVChYp5lW1iYK17DoUOH+GPDemrUqAFA8eJ5/2FKTU3l7NmzDBo0MM84Ly8vfnzjw6KtrQ39+/9M//4/c/36dXbt3sOoUaNR19CgZcuWtG37HS7OWX/wli5bzrfNm+PtPUSR/32+oRL+WyVcqtJ9xAaKFHXmefw/HN48jTnDa/DrihsYGOU+o3n9wt5cl9sAPI+LwtDESinNyNQKeWYGLxKfYWymep3+zcuHsC1RDlNLO5XXE1WUq2doioaGFolxUbm2R/i6mFlXoHLjORiYOpCS/IxbF5dyamt7GnY7jLauaa75ntzzxbZkw1yvpyQ9xcreQilNR88CSZ5BakocuvpFVOaLenAKYwtn9AxVL2VMSXqKtp5yuVo6xqipa5Ga9DTX9gjC56TAA3rh3c2YMUPxzcMrgwcNYsiQwZ+oRQVT9t+tQ183btyvXLx0iS5dOjPul1+Ubix+U0hwCE2bNi1QnceOH6Nr1655xmj9+y3L6wwMDFSeV6BKcHAI6urqVKlSJd/tuuDnh4mpKS4uLnnGlStXNs/r5cuXp3z58vwydgxz5sxl7dq1nD9/jgP79wMQEhJCp44d8t0uoXBxq/x6fy+HQ2kvJv7gxMVjG6j/3TCVeSRJIujiAX4Yk/tEB+TccSx7giH3e02u++W+3Ca3cgEkpDzLFb4u1g51FT8b44K5jTuH19UjPGQXzh69VeaRJImo+yeo0mzhW0p/o1/z7w57efS/yHu+ua7Lz63cV23iHXbuE4TCSAzo/0Njx45l2DDlP+KPHz38RK0pOD093Rxpv/46jm3bd3DgwAFOnDhJq5Ytad26tcqZd+08BvuqPH36lJs3g6n3luVL2jo6OQYhBVlyo6NT8G9I8rPcBkBPN+d79rr79++ze88e9u7dx/Pnz+nYsQMd2rdXXM/rA5Lw+dHW0adoiXJEP76Ta8yD0EtkZqRR0q1mrjGGptY5Zsyfx0ejpq6R68x/ZkY6wVcO06jDmFzLNTK15kHoJaW05OdxZGakY2RqlUsu4WunoamHsYULL+Ie5BoTF3UNuTwdC9vct4TW0bckJVl5xjw1OQaZmgZaOiYq88gz04kKP4NL5Z/zLDcu6ppSWlpKApI8PcfMvSB8rsSA/j+kra2dY+nIs89kuU1uypQpg8/ECfwydgwnT55k167dtGzVCudSpWjdujUtWnyLpWXWTYCuri6cv3BBaflIXo4dP467e0XMzMwK3K6CLLlxcXFBLpdz6dIlxZKbvEiSxInjJ5g9Z3aB2wVZpy4fOHCQPXv3cOPGTWrUqMHIkSNo1LBhjv7h4uLC+fMXaNeu3TvVJRQu6WmpREWE5DlYv+63F7cqzVHL44RqR9dqBF08oJQWcvUo9qUq5bp+/va1k+gamGBXsmKu5TqU9uLw1ukkxEYqlu2EXD2KhqY2dk45l9EJAmTd7Po89h4WtpVzjXly/xjWDnWR5bHLk5m1O5FhJ5TS/gn/G9Mi5XJdP//0kR9a2kaYFCmTa7nm1u7curSMl0nRimU7/4SfRU1dC9MieX+LKgifCzGgFz4ILS0txb0JMTEx7Nu3j127drN02TL8r1wG4Od+/WjWrDkTJkykS5fOaGpq4ufnR9OmTVUO2o8fP06D+m/7GlW1giy5KVasGN9914bRY8Yqbop9/PgJMTExNG/eLEd80I0bJL98SZXKuf/xykvbtu3Q1tamzXdtWLF8ueKEY1UGDxpEt+7dKV68ON9+25zMzExOnT7NT337vlPdwn9r16oRlKvaAtMixXkeH83hzdNISU6kaoMeueYJ8ttP8+998iy3ZvN+nN6/lJ0rh1GjyY/cD7nAhaNr6TU69z27r1/cR/mqeS+3Ke3RCJviZdgwuzttes8i6Xksu1aPpEaTPiq3uRS+TtfPTMfGsT56hrakJscQcmkp6WkvKF7mu1zzRN4/Rplq3nmW61i+C/eubeTa6Wk4lOtIbGQAD25up2rTBbnmeXL/ODaOeX9bamVfCyMzJy4fHk75WmNIS4kn6OwMHMp2VLnNpSB8jsSAXvjgzM3N6dWrF7169eLevXuKdAcHB9avX8ecufNo811bdHR0qFChAi1a5BxkJCcnc/78BaXzCz6mKZMnM2fOXCZO9CE+Lg4bW1v6/9xPZeyxY8eoV7dujh1p8mv16lWKk5Dfplq1qixZvIglS5by+++/Y2BgQOUq7/ZBQvjvxT97zLqZXXiR+AwDY0scXKsxYv4FzK3sVcY/fXKPp0/uUtoz95v2ASysHeg/+SA7Vw7jzP5lGJvb0r7fwjz3oA/y28/3Q9fkWa6aujo/TzrA1qUDmDuiJlpaulSq25k2fea8/cUKX42XL6K49Jc3qS/j0NY1w8ymIvU67kDfqKjK+Bfx4byID8fKvlae5eob21Gj9Rqun57G/ev/Q0e/CBXrTshzD/rI+8fwbDgz1+sAMjV1arRaQ8DJCZza1gF1DR3sXFpQrtbYt79YQfhMyKTCePzmG5YtW8bs2bOJjIzEzc2NBQsWUKuW6l8Mu3btYvny5QQGBpKamoqbmxs+Pj657mqzZcsWOnfuTKtWrdizZ0+B6pUkiUmTJrFy5Uri4uKoWrUqS5cuxc1N9VZzqty/dzffsV+TI0eOMG/efI4cOfypm5JDs+bfMqB/f5Wz90KWw3fy94FFUHZ81zxuBRxnwJSDH7TciLtXWTSmPjO3ROe6JEd4uxNHwj51Ez5Lt6+uITriHDVbr/2g5cZF3+Dsju/59qfLuS7JEfJnx/y8t9IVCr+Pf5zme9q6dSve3t6MGzeOgIAAatWqRdOmTYmIiFAZf+bMGRo2bMihQ4fw9/enXr16tGjRgoCAgByx4eHhjBgxQuWHg/zUO2vWLObNm8eSJUu4fPky1tbWNGzYkOfPxUEV70tPT49Ro0Z+6mbkkJaWRpPGjalTp/anborwBTKxKEbjjrnftPqu5JkZtP95kRjMC5+EnoE1rnnctPquJHkmFepNFIN5QeAzmKGvWrUqHh4eSgdXlS5dmtatWzNjxox8leHm5kbHjh2ZMCF715PMzEzq1KlDr169OHv2LPHx8Uoz9G+rV5IkbG1t8fb2ZvS/e7OnpqZiZWXFzJkz+emnn/LVNjFDL3yJxAy98CUSM/TCl0rM0H/+CvUMfVpaGv7+/jRq1EgpvVGjRpw/fz5fZcjlcp4/f57jpsvJkydjaWlJ794598zNT71hYWFERUUpxWhra1OnTp18t00QBEEQBEEQ3lehvin22bNnZGZmYmWlvP+xlZUVUVH5O7Vw7ty5JCUl0aFD9uE8586dY82aNQQGBr5zva/+qyomPDw8X20TBEEQBEEQhPdVqGfoX1F1IqKq0wzftHnzZnx8fNi6datia8Dnz5/z/fffs2rVKiws8j5QIj/1vmvbvgR+fhcp6VSKxMREAHbs3ElFd49P3KrPV0mnUhz19f3UzRAEQRAE4TNTqGfoLSwsUFdXzzEbHx0dnWNm/E1bt26ld+/ebN++nQYNsvcyv3fvHg8ePFDaKlEulwOgoaFBaGgodnZ2b63X2toayJqpt7GxKVDbCptNmzbx28xZXPW/otiKMSkpCQ/PSlSsWJGtW7KPoL98+TKdOnfhmO9RPDzc8btwHkNDsY+vILzNnaAzHNsxh4d3/UmIjaTv+F1UqN467zzXT7Nz1XAiw29ibG5Lw3YjqdVceTvVgL93cuCPCTyLvIeFTUla9JhKxRptlGLOHFjGsR1zSIiNxMbejXY/zceprPKOXYf+nMS5v1aR/CKOEi5V6TBgCbb2+d+xS/g6PX10idv+q4iPvkFKUjTVvl1OUadGb8lzketnppEYcwcdfStcKvXFsXwXpZjHdw5z88J8khIi0Dcujlv1YRR1Ut6t7t61/3HbfxUpSdEYmZeiQp3xWBTN3tZXkiRC/BYRdmMLaSkJmFlXxP0bH4zMnT/UyxeEQqNQz9BraWnh6emJ7xuzlr6+vlSvXj3XfJs3b6Znz55s2rSJ5s2bK11zdXUlKCiIwMBAxaNly5bUq1ePwMBA7Ozs8lWvg4MD1tbWSjFpaWmcPn06z7YVRtWqVSMpKYmgoCBF2uUrV7CwsOD69eu8fPlSke538SJWVlY4ODigpaWFpaXlV/ONhCC8j7SUJIo5lqdD/8X5in8WFcayCc1xcqvJ2CVXadxxLNtXDCHg752KmPshF1g7oxNV6n/P2GWBVKn/PWtmdCTs1kVFjP/prez4fSiNO/3C2CVXcXKrydLxzYiNzt6xy3f7LE7smk+H/osZtfASRqbWLPmlESnJYscuIW+Z6cmYWLpSsZ5PvuKTEh5ybk9vLGwrU7/rflyr/Ezgqck8vpO9RXHMk6tcPDSY4q6tqd/1AMVdW3Px0GBiIwMVMQ9DD3Dt9FRcq/Snftf9WNhW5u89P5Cc+EQRc/vKSu4ErKViPR++6bwbHX0Lzu7qQXraiw/18gWh0CjUA3qAYcOGsXr1atauXUtISAhDhw4lIiKCfv2yZqnGjh1L9+7dFfGbN2+me/fuzJ07l2rVqhEVFUVUVBQJCQkA6OjoULZsWaWHiYkJhoaGlC1bFi0trXzVK5PJ8Pb2Zvr06ezevZsbN27Qs2dP9PT06NKlC58TR0dHrKysuHjxkiLt4sWLNGzQAPvixbl69epr6ZeoVrUqkHPJjSrHjh2nVes2lC7jRqXKVfi5f3/FtYSEBIaPGIm7hyduZcvR64fehD14AEBMTAxVq3mxbFn2LkOBgYG4li7D2bNn35ofspcAnTlzlkaNG1OufAV69vqB6OjoXNv76jWdPHmS5t+2oHQZN75r25bQ0NBc88yePYe2bdvlSG/W/FsWLFgIwPXr1+neoweVKlehQkV3Onfuwo0bN9/ajtff2+DgYEo6leLRo0eKNP+rV+nUuTNl3MpSo2YtJk2eTHJycq7lCp+OW+Wm/86e536a5uv+PrgC0yLFaddvAdbFS1OjSR+8GvXi+M65ipiTexbi6tGQxh3HYm3nSuOOY3GpWJ+TexYqYo7vno9Xox+o0aQP1sVL067fAkwt7Th7MOvfliRJnNyzkMadfqFije+wLVGWbsPXk5aazOVTuZ88KwgA1g51cas+PMfseW7uX9+EnqEtFeqOx8jMCYeyHSnh1o7b/qsVMXcD1lGkeA1cq/yMkVlJXKv8TBE7L+4ErFPE3Lm6lhJu7XEo2xEjMycq1B2PnoEN96//CWT167sB63Ct3J+iTo0xtnChUqPZZKa/5OGtfR/0PRCEwqDQD+g7duzIggULmDx5MhUrVuTMmTMcOnQIe/uskxYjIyOV9ob//fffycjIYMCAAdjY2CgeQ4YM+aD1AowaNQpvb2/69+9PpUqVePz4MUePHv0sl6BUrVoFPz8/xXM/v4tUrVqFKlWqcMEva7YvLS2NgIAAqlWrlq8yT548Sf8BA6hXty779+1l4x8bKFe2nOL6qFGjuREUxMrfV7Bj+zYkSaJ37z6kp6djbm7Ob7/NYNHixVwPCiIpKYlhw0fQtWsXxbkBeeV/JSUlhdVrVjNnzhw2b9pE5JMnzPjtt7e2/bffZjJ2zGj27N6FuZk5fX/qp1Tu61q2akngtWtKN0Pfvn2H0NBQWrVqCcCLpCS+a/MdWzZvYueO7ZQoUYLeffrw4sW7zxSFhobSq9cPNG7UiIMHD7Bo0UL8r/jjM2nSO5cpFB73b/lR2qOhUlppj8aE37lCZkZWXwwLuZAjpoxnI+6HZO20lZGexsM7/pT2aPRGOQ25H3wBgJioMBLjopRiNLW0cSpXh7B/YwThQ4mNCqCIfU2lNCv7WsRFByHPzOrXMVEBOU6VtbKvTWxk1uSSPDON+OgbWL1RThH7msT8G5OU+JCU5KdKMeoa2lgUq6qIEYQvSaFeQ/9K//796f/azO7r1q9fr/T81KlTBS7/zTLyUy9kzdL7+Pjg4+NT4DoLm6pVqjJt+nQyMjJISUkhODiYylWqkCmXs2HDH0DWDHlKSgrVqlXNV5lLly3n2+bN8fbO/jBVunRpAMIePODY8eNs27YVT4+sG2nnz5tLzVq18fU9RrNmTalXty4dO3Rg2LDhlC9XDm1tbUaNHJnv/ADp6elMmTxZ8UGsW7duLF6y5K1tHzR4EDVrZv0hmD17FjVq1uLoUV+Vp8O6ODvj6urKvv37GTRwIAD79u2jfPnyODg4AFDdy0spz9SpU/Dw8OTSpUt88803+Xo/37Ry1WpatPiWXr16AeBQogQTJoync5euTJk8GW1t7XcqVygcnsdFYWiifD+OkakV8swMXiQ+w9jMhkQVMYYmVjyPzbr/50XiM+TyTIxMc8YkxmXFvPqv4RsxRiZFlJblCMKHkJL0FCt75Q0pdPQskOQZpKbEoatfhJSkZ2jrKcdo61mQkvwMgNSXcUhSJjp6Ocv5J/lpVkzSU0U+5XLMlZblCMKX4rMY0AsfX7Vq1UhOTub69SASEhNwKFECC3NzqlapwogRI0lOTubixYvY2tpSvHjxfJUZEhJCp44dVF67d/ceGhoaVKxQQZFmamqKo4MD9147bGvs2DE0bdqMQ3/9xZ7duxSD1Pzm19XVVfpWxbKIJTExMW9tu4e7u+JnExOTf8u9B0C58tl1tmrVkqlTptCyZQt27NjJoIEDkSSJ/QcO0LNnD0Xcs5gYFixYwIULfjx79gy5XM7Lly958iTyrW3Jzc0bNwgPD2ffvv2KNEmSkMvlPHz4ECcnp3cuWygcVO2i9e+VvGPevK/lzef52bELFeUIwgehoq8Bstf7dY48qs7AfDNKypmWow9LolsLXyQxoBcAKFHCHmtra/z8/EhITKBKlSoAWFpaUqxYMfz9/bngdxEvr/wtt4Gs+xVyk9sBxW8OIiIiHvJPdDRyuZzHjx/j6upaoPyvdu15RSaT5Zr3bV4Vu39f9vpLA0MDAFq2aMHs2XO4ceMmKakpREZG0uLbbxVxo0aNJjY2ll9/HUdR26JoaWnRvn170tPTVNalppZV2ettzcjIUIqRS3I6de5Ej+49eJOtrU2ONOHzYmhqrZg9f+V5fDRq6hoYGJkDYKQi5kVCtGK23cDIAjU1dRJj3ygnIVoxs29kmrVjV2JsFMZm2f3mefxTjEw+rx27hMJPR9+SlH9n0V9JTY5BpqaBlo7JvzHZs/Gvx7yakdfWNUUmU89RTsrrMfqWWfmSnqKrX+S1cmJzzNoLwpeg0K+hF/471apV4+LFi1y8eImqVbOX1VSpUoWzZ/8mMDAw3+vnAVxcXDh/XvUaXKdSTmRkZBB47ZoiLS4ujrCwBziVLAlkrdkfNnwYzZs3Y9jQoYwZ+wvPnj3Ld/73ERAQqPg5ISGBsAcPcHTMKrdECXvFw8I8a2BlY2NDlcqV2bdvH/v27qN69epK5xxcuXKFHt27U69uXZydS6GlpUVsXFyu9b862fj1G3iDg0OUYtzc3Lhz565Se149Xt3cLXy+HF2rcevqMaW0kKtHsS9VCXUNTQAcSnsRkiPGF8fSWTttaWhqYVfKk1sByjt23bp6DMcyWcvAzK0dMDK1VorJSE/jbtBpHMooLxUThPdlZu1OdMQ5pbR/wv/GtEg51NSz+rW5tTv/RPz9RsxZzGyylleqqWthUqRsjnKiI85h/m+MvpEdOnqWSuXIM9N49uiiIkYQviRiQC8oeFWryhV/f0JCQqhatYoivWqVymzdupXU1FSqVc3/gH7woEHsP3CABQsWcvfuXUJDQ/l95Uoga713gwYNGPfLOK5cuUJISAjDho/AyspKcW7A3HnzeP78BRPGj6dv3x9xKlmSMWPH5jv/+1i8ZAnnzp8n9PZtRo0ajampKQ0b5l1uy5YtOXDwIH/99RetW7VSumZfvDh79uzh7t27BAYGMmz4sDy/wbC3t8fGxoZFixYTFhbGyZMnWbN2rVLMT337EhAQwMSJPgQHB2fdV3DsOD6TJr/7Cxc+mpSXL3h4L5CH9wIBiPknjIf3AhXr1PeuG8uGOdnfttRs3o/Y6HB2rhxGVEQI54+s5cLRtdRvO1wRU6/VYG5dPcrRbTOJeniLo9tmcivgGPVaZ9+3Ur/NUM4fWcP5I2uJighhx+9DiX0aQc1m2Tt21Ws9hCNbZxB4bjdPHtxg47xeaGnrUbnu57Vjl/Dfy0hLIj46mPjoYACSEx8RHx2sWKd+4+/ZXD6S3Wcdy3chOfEx105PIzH2Lg9ubufBze04e/ZRxDi59yQ6/G9CL/9OYuw9Qi//TvTD85Ry76WIKeXxA2E3tvHg5nYSY+9y7fRUkp8/weHf/exlMhlO7r0IvbScx3ePkPAslCtHR6GuqYuda8v/4q0RhP+UWHIjKFSrVo2UlBRKlnRUml2uUqUKL5KSKF68eIGWclSrVpUlixexZMlSfv/9dwwMDKhcJfvQj1kzf2PylKn0+bEv6enpVK5cmTVrVqOpqYmf30XWr9/An//bqNg1aO7cOTRr/i1//vknXbt2zTP/+xo1cgRTpkwl/MEDXEuXZuXvK946692sWVMmTZ6Murp6jsH/b7/NYNyv42nRshW2traMGD6MGb/NzLUsTU1NFiyYz4QJE2n+bQvKly/HsKHeDBw0WBHj6urK5k1/MnfuPDp17oIkSRQvXpzmzXLeuCt8ehF3rrBwdPYN0DtXZg1yqjboQffh60iIjSLutZtQLawd6D/5IDtXDuPM/mUYm9vSvt9C3Gu2VcQ4lqlOrzGbOfDHeA5snICFTUl6j92Cg2v2N2yedTqS9DyGvzZNITE2EpsSZek/+SDmVtn3ljRsP4r0tJdsXTpAcbDUwGlH0NH7/HbsEv5bcf8EcWZnV8Xz62emAWBf+jsqNZ5NSlI0yYnZ9wrpG9tRo/Uarp+exv3r/0NHvwgV606gaKkmihhzW0+qNFvIzfPzuHlhPgbGxanabBFmNhUVMXYu35KWEk+I32JSkp9iZF6KGq3WoG9UVBHjXKkvmRkpBJ6YSFpq1sFSNdusR1PL4CO+I4Lwacikd11QLHwQ91+7gVP49Pz8LtL1++8JuOqPkZHRp27OZ+vwnfdf9iQIhc2JI2GfugmC8FHsmO/4qZsgvCex5EYQBEEQBEEQPmNiQC8IgiAIgiAInzGxhl4QXlOtWlXu3b3zqZshCIIgCIKQb2KGXhAEQRAEQRA+Y2JALwiCIAiCIAifMbHkRhAEQRAEQfjkUlJSSEvLeYK6lpZWnme3CGJA/8lpyNM/dRME4YPTEr9ZhC+Qptb7n3EhCIJqKSkp2OoaEEdmjmvW1taEhYWJQX0exJ9dQRAEQRAE4ZNKS0sjjkz+0C+Jnix7RXiyJKd71D3S0tLEgD4PYkAvCIIgCIIgFAqGuproq6krnqvLMyHpEzboMyEG9IIgCIIgCEKhoK6rhrpa9gy9ulz6hK35fIgBvSAIgiAIglAoaOiro6GePUOvkXNJ/RcnJSXlvZcTiW0rBUEQBEEQhEJBXVMtx+NdLFu2DAcHB3R0dPD09OTs2bO5xp46dQqZTJbjcevWrXd9GW8ll8uZMmUKRYsWxcDAgPv37wMwfvx41qxZU+DyxIBeEARBEARBKBTUtdRzPApq69ateHt7M27cOAICAqhVqxZNmzYlIiIiz3yhoaFERkYqHqVKlXrXl/FWU6dOZf369cyaNQstLS1Ferly5Vi9enWByxMDekEQBEEQBKFQUNOQKc3Oq2nIAEhMTFR6pKam5lrGvHnz6N27N3369KF06dIsWLAAOzs7li9fnmfdRYoUwdraWvFQVy/4h4n8+uOPP1i5ciVdu3ZVqqd8+fLv9M2AGNALhdIff26mxjcNcS5bkeZt2nHp8pU84/0uXaZ5m3Y4l61IzW8a8b/NW5Sub966nXadv6dcpWqUq1SNLj1+IPDa9QLXK0kS8xctoXLNOjiXc6fj9z24fefO+79g4ashSRL7/vBhRMei9G+ux+zh9Xj84Gaeec4cWsXMobUZ0saMIW3MmDeqIWG3LuUaf2jzDH5sqMaWZd4Frjs9LZVNSwYxtK0lA1oYsGR8K2KfPnrHVyt8LSRJIujveexZWont80pxfHMHEp6F5jt/eMg+tswqztldfXJcS34exYUDQ9i1qDzb5zlzeH0TYqOyf3/np+7MjFT8j01g1+IKbJ/vwpmdP5D8PPLdX7Dw0ahpqud4ANjZ2WFsbKx4zJgxQ2X+tLQ0/P39adSokVJ6o0aNOH/+fJ51u7u7Y2NjQ/369Tl58uSHeUG5ePz4MU5OTjnS5XI56ekFP6NIDOiFQmf/wb+YPH0GA/v9xME9O6lSyZMeP/7E4ydPVMZHPHxEzx/7UaWSJwf37GRAv774TJ3OoSNHFTEXLl2i5bfN2fLHOnZv3YStrQ3dfviRqKh/ClTvilVrWL1uA5PH/8r+nduwtLCga68+vHgh9tQS8ufw1ln47pxPl4GLGbfkEsZm1swf3YiU5Oe55gm9dpoq9ToxfPYJxiw8j1kRO+aPaUzcs8c5YsNCL3Pm0CqKOZZ/p7q3Lvcm4Nwefhy3mdHzz5KS8oLFv7ZAnvkV3JkmvLNbl5YTemU1ng2n0LDbAXT1LTm5tSvpqS/emjcp4RGBJ6diWaxKjmtpKfEc+/M71NQ0qNP+D5r2Pk7Fer+iqW1UoLoDTkzi0e3DVG+xhAZddpKRnsyZnb2Qy0W/Lmw0tNVzPAAePnxIQkKC4jF27FiV+Z89e0ZmZiZWVlZK6VZWVkRFRanMY2Njw8qVK9m5cye7du3CxcWF+vXrc+bMmQ/74l7j5uamcl3/9u3bcXd3L3B5YkAvFDqr162nY7u2dO7QjlJOJZk4biw21jb8b9MWlfF/btmKrY0NE8eNpZRTSTp3aEeHtt+xcs06RcyiubPp3rUzbmVK41TSkZlTJyOXyzl3wS/f9UqSxJoNfzDw559o2rghLs6lmDtrBikvU9h74MDHfVOEL4IkSRzfvZBmnX/Bo9Z3FHUoS6+R60lLTebiiU255vtx7P+o17I/xZ0qYlPcle5DVyFJckICjivFpbx8weoZ39N96Er0DEwLXHdyUgJ/H15Lh5/mUMajAcWd3OkzeiOPHwQRfPXYh39DhC+CJEmEXlmDm9dA7JybYmLpQtVm88jMSCE8ZE+eeeXyTC4cGEzZmsPQNyme43rIxeXoGdlQtdlczG0qYmBsh7V9TQxNS+S77rTURO5f34p7vV+xLlELU6uyeDVfQMLTW/wT/vcHfjeE96Wmro6axmuPf5ejGBkZKT20tbXzLEcmkyk9lyQpR9orLi4u/Pjjj3h4eODl5cWyZcto3rw5c+bM+TAvSoWJEycycOBAZs6ciVwuZ9euXfz4449Mnz6dCRMmFLg8MaAXCpW0tDSCbgZTq0YNpfTaNavjHxCoMs/VgEBq16z+RnxNgm7czPVrq5cvU0jPyMDExDjf9T58+IinT59R67W6tLW0qFqlEv5XVbdNEF73LCqMhNgo3CplfxWsqaWNc/k63Au+kO9y0lKTycxIR9/QTCl90+KBlK/ajDIeDd6p7vDb/mRmpFPGMzvGxMKWoiXKci8476+qha9XUkIEKUlPsS5RW5GmrqFNEbuqPHvsn2fem+cXoK1nTsnynVRef3zXFzOr8pzb24/dS9w5vL4p965lf/jNT91xUUHI5elYO2TH6BpaY2zhwrPHeS/nFP5777vLjYWFBerq6jlm46Ojo3PM2uelWrVq3PmIS2pbtGjB1q1bOXToEDKZjAkTJhASEsL+/ftp2LBhgcsT+9ALhUpcXDyZmZlYWJgrpVuYm/P02TOVeZ4+e4aF+RvxFuZkZGQQGxePVRHLHHl+mzMPa6si1Kjule96o//9r6W5xRsxFrkuBxKE1yXEZv2BMTJR/qNiZFqEmH/y3n3hdTtXj8HEoqjSwP3SyS1E3LnKuKWq19bnp+7EuCg0NLXQN1Se3TcysSIxTvVX1YKQkvQUAB095d+N2noWJCfmXBb2ytNHl7l/fStNeh7ONeZF/EPuBv4Pl8p9KFNtIDGRgVw9PhE1dS0cyrbLV90vk56ipq6Flo6Jcoy+hSK/UHi8mplXPJcKdrCUlpYWnp6e+Pr60qZNG0W6r68vrVq1ync5AQEB2NjYFKjugmrcuDGNGzf+IGWJAb1QKOX4qgwJGaq/KlMZ/+8vAFXfrq1YtYZ9Bw+ydeMGdN74yi5f9Rbgazzh6+Z3/E/+t6Cf4vmgqf8uzXqzvxSgDx3eOotLp7Ywcs5JNLWyDiKJjX7IlmXeDP3tiCItV+9Qt4QEefz7E74uD27u5srR7PXLtduuz/ohRz+SVP8SBtJTX+B30JvKTWairWemMiarCDmm1uWpUHs0AKZWZUl4dpu7gf/DoWy77LgC1J0dIvp1YaSupY66ZvbwVF1W8JNihw0bRrdu3ahUqRJeXl6sXLmSiIgI+vXL+n08duxYHj9+zB9//AHAggULKFGiBG5ubqSlpfG///2PnTt3snPnzg/zolS4fPkycrmcqlWrKqVfvHgRdXV1KlWqVKDyxIBeKFRMTU1QV1fn6VPl2fiYmNgcs+evWFpY5Ji9j4mJRUNDA1MTE6X039esZemKlfy5fg2lXV0KVG8Ri6wZoKfPnirN+sfExuT4hkAQACp6tcTRNfuXdXp61jZriXFRmJhnz/wkxj/FyPTtXwUf2T6HQ5tnMGymr9JNr+F3/HkeH83U/tl/AOTyTO4EneHk3qUsP5SCsZn1W+s2MrUmIz2NpOdxSrP0z+OjKVnGq6AvX/hCFXVqiLlt9k178sysfp2S9BRdg+x+nJock2Pm/JUX8eEkJTzk7M4fFGmSJAdg62wHmvU5iaFpCXQMimBsrrwXuJF5KR7d/gsAHX3Lt9atq2+JPDONtJR4pVn61OQYLIp6Fvj1Cx+XmobyMhs1qeCrwzt27EhMTAyTJ08mMjKSsmXLcujQIezt7QGIjIxU2pM+LS2NESNG8PjxY3R1dXFzc+PgwYM0a9bs/V9QLgYMGMCoUaNyDOgfP37MzJkzuXjxYoHKEwN6oVDR0tKinFsZzp4/T5NG2csJzp47T6P636jM4+FekWMnlLeXOnvuHOXKuqGpqalIW7F6DUuW/c4fa1dRvlzZAtdrZ1cMS0sL/j53gbJlygBZvwQuXrrCmJHD3u+FC18kHT1DdPQMFc8lScLYzJpgf1+KO2UNiDLS07h9/TRt+/yWZ1lHts3m4J/TGDLjMCVclGduSrvXx2el8jas6+b8gI2dK006jkJNXR0La4e31m3v7Im6hibBV32pXKcDAPExkTx+cIO2fWa+35shfDE0tQ3Q1DZQPJckCR19S6IenMXUKut3a2ZmGtEPL1KhzhiVZRiZl6RJL1+ltKCzs0lPe4FH/UnoGdkCYFG0Eolx95TinsfeR8+oGAD6xsXfWrepdTnU1DSJenCW4q4tAHj54h8SnoVSoe4v7/t2CB9YjiU3cvk7ldO/f3/69++v8tr69euVno8aNYpRo0a9Uz3vKjg4GA8Pjxzp7u7uBAcHF7g8MaAXCp0+vXoydNRoypd1w6NiRTZv286TyEi6du4IwMw584j6J5r5s7MGIV07dWTD/zYxefpMOndox9XAQLbu2Mmiedl3p69YtYa5CxaxcN5sihW1Jfpp1rpJfT099PX181WvTCajd4/uLF2xkhL29jiUsGfJipXo6OrQ6ttv/8u3SPhMyWQy6rcZwqHNMyhStBRWRUtxaPMMtLT1qPpNF0Xcmpk9MLWw5bveWfssH946i70bJtBn7J9YWJdQrIfX1jVAR9cAHT1Dijoof0jV1tFH38hMkZ6fuvX0janZ5Ae2/z4CA0Nz9I3M2P77SIqWKKfyRltBgKy+5VKpN8F+SzE0dcDA1IFgvyWoa+hgX7q1Is7voDe6BtZUqDMGdQ0dTCxdlMrR1MnaivL1dJdKfTj2ZxtuXlhCcddviYkM5N71TVRu9Fu+69bSNsKxfEcCTk5FS9cUbR0TAk5OxdjSFSv7mh/3zREK7EMN6As7bW1t/vnnHxwdHZXSIyMj0dAo+PBcDOiFQqdF86bExcezaOlyoqOf4uxcivWrfqdY0aIARD99xpPI7ANBitsVY/2qFUye/hsb/9xEEasi+Pz6C80aZ+/UsXHTZtLS0/l5kLdSXd4D+zN08MB81QvQ78fepKSk8OukySQmJFKxQnn+t3Y1Bgb6H/EdEb4kTTqOIj3tJZsWDyDpeRyOrlUZ+tsRpZn82OgIZLLsr5lP7V9ORnoaKya3VyqrRbcJtOzu80Hr7vjzfNTUNfh9akfS017i6l6fQSPXKbaOEwRVXKv8TEZ6Cld8x5GWkoi5TUXqdvhTaSY/KfEJyAq2fMLcpgI1W6/k+pmZ3Dy/EANjOzy+mUgJt+ybHfNTt/s3E5CpaXB+b38yM1Kwsq9B1WbzUFMT/bqw+VoG9A0bNmTs2LHs3bsXY+OsHffi4+P55Zdf3mmXG5kkFfD24fwUKpOxe/duWrdu/aGLVnLq1Cnq1atHXFwcJv+uld6zZw8jRowgLCyMQYMGUbFiRby9vYmPj/+obXlXEXdCPnUTBOGDOxru+qmbIAgf3HFfcWKu8GXaPNPuUzeBxMREjI2NufVzWwy1s5fLPk9Nx3X5ThISEjAyMsqjhM/L48ePqV27NjExMYqDpAIDA7GyssLX1xc7u4L9PynwnQZRUVEMGjQIR0dHtLW1sbOzo0WLFhw/fvztmT+w6tWrExkZqfhkA/DTTz/Rrl07Hj58yJQpU+jYsSO3b9/+KPXv2rWLxo0bY2FhgUwmIzAw8KPUIwiCIAiC8DVQOlTqjdn6L0nRokW5fv06s2bNokyZMnh6erJw4UKCgoIKPJiHAi65efDgATVq1MDExIRZs2ZRvnx50tPTOXLkCAMGDODWrVsFbsD70NLSwtraWvH8xYsXREdH07hxY2xtbRXpurq671VPenq60s2VryQlJVGjRg3at2/Pjz/++F51CIIgCIIgfO3UNGSoaby2y03ml7u1qL6+Pn379v0gZRVohr5///7IZDIuXbpEu3btcHZ2xs3NjWHDhuHn55drvtGjR+Ps7Iyenh6Ojo6MHz9e6QTPa9euUa9ePQwNDTEyMsLT05MrV7JObwsPD6dFixaYmpqir6+Pm5sbhw4dArKW3MhkMuLj4zl16hSGhlnrQL/55htkMhmnTp1i/fr1iuU4r+zfvx9PT090dHRwdHRk0qRJZGRkKK7LZDJWrFhBq1at0NfXZ+rUqSpfV7du3ZgwYQINGoibxQRBEARBEN6XTF09x+NLdfv2bVauXMnUqVOZPHmy0qOg8j1DHxsby+HDh5k2bZpiV5DXvTlofp2hoSHr16/H1taWoKAgfvzxRwwNDRVbBHXt2hV3d3eWL1+Ouro6gYGBihnxAQMGkJaWxpkzZ9DX1yc4OBgDA4McdVSvXp3Q0FBcXFzYuXMn1atXx8zMjAcPHijFHTlyhO+//55FixZRq1Yt7t27p/h0NHHiREXcxIkTmTFjBvPnz0f9C+5MgiAIgiAIhYWapiZqWtmrItTkH/xWz0Jh1apV/Pzzz1hYWGBtba10wJ9MJmPChAkFKi/fA/q7d+8iSRKurgW/2e3XX39V/FyiRAmGDx/O1q1bFQP6iIgIRo4cqSi7VKnsQyQiIiJo27Yt5cqVA8ixvc8rWlpaFClSBAAzMzOlpTivmzZtGmPGjKFHjx6K8qZMmcKoUaOUBvRdunThhx9+UFmGIAiCIAiC8OHJ1NWVdtX6Umfop06dyrRp0xg9evQHKS/fA/pXm+G8yxH3O3bsYMGCBdy9e5cXL16QkZGhdKfysGHD6NOnDxs3bqRBgwa0b9+ekiVLAjB48GB+/vlnjh49SoMGDWjbti3ly5fPraq38vf35/Lly0ybNk2RlpmZSUpKCsnJyejp6QEU+MhdQRAEQRAE4f3INNSRvXYjrOwLvSk2Li6O9u3bvz0wn/K9hr5UqVLIZDJCQgq2zaKfnx+dOnWiadOmHDhwgICAAMaNG0daWpoixsfHh5s3b9K8eXNOnDhBmTJl2L17NwB9+vTh/v37dOvWjaCgICpVqsTixYsL1IbXyeVyJk2aRGBgoOIRFBTEnTt30NHRUcSpWlYkfDwbN22hcYvWuLlXxs29Mq07dObk6TP5yrt9125at++U63W/S5dp3qYdzmUrUvObRvxv85Z8lfvo8WNKuVXg+YsXOa4Fh9xi0NARVKv9Dc7l3Pmmybes3bAxX+UKX499f/jwY0M1pcfwDjb5ynvuyHqmD/LK9XrotdNM6V+Jn5vpMrZbSU7tX5GvcmP+CefnZjq8TEpUfT06gsXjWzKghQFD21qyeelgMtLTVMYKX6egv+exZVZxpceepZ75yns/aDu+G1vlej06wo8jG5qxbW4p9v9eg7sB+fu9mpTwiG1znUhPfa76euJjzuzsxfb5LuxaXAH/YxPIzBT9ujCSqau9sYa+wBsyfhbat2/P0aNHP1h5+Z6hNzMzo3HjxixdupTBgwfnGPDGx8erXEd/7tw57O3tGTdunCItPDw8R5yzszPOzs4MHTqUzp07s27dOtq0yTo4ws7Ojn79+tGvXz/Gjh3LqlWrGDRoUH6brsTDw4PQ0FCcnJzeKb/wcdhYWzF6+FBK2NsDsGP3Hn7sP5BDe3bi/NoSLFV8j5+kYf1vVF6LePiInj/2o3OHdiyYPZMrVwMYP2kyZmZmSgdPqS73BNWqVsFQxT0bQTdvYmZmyoI5M7G1tuZKQABjx/ugpqZGz25d8/mqha+BbQk3hs3MPuI+vwfZXLuwn4rVW6q89jQyjEW/NqdW0z70Gb2RuzfP8efiARiaWOJZq22e5Qae34tz+bro6ufcz1memcnicd9iYGLJ6PlneZEYw7pZPZEkiS4D330iRfjyGFs4U7fDJsVzWT779ZO7vhQtpfrQnBfxEZze2YOS5TtT7duFPHt0BX/fX9HWM8fOpVme5T6+e5QidtXQ1DbMcU0uz+TMjp5o65nToMtOUl/GcfHQMAA8GxT85kPh4/paZuidnJwYP348fn5+lCtXLsduioMHDy5QeQXatnLZsmVUr16dKlWqMHnyZMqXL09GRga+vr4sX75c5ey9k5MTERERbNmyhcqVK3Pw4EHF7DvAy5cvGTlyJO3atcPBwYFHjx5x+fJl2rbN+qPk7e1N06ZNcXZ2Ji4ujhMnTlC6dOkCvcjXTZgwgW+//RY7Ozvat2+Pmpoa169fJygoKNfdbHITGxtLREQET548ASA0NBQAa2vrXNfwC6o1+Kae0vNRw7z53+YtXA28nueAPiU1lbPnzjHCW/UHvD+3bMXWxoaJ48YCUMqpJEE3brByzbq3DuiPHjtBk0aq//B0bKc8aCpe3I6rAdc47HtMDOgFJWpqGhibFez3QXpaCsH+R2nVU/Vg4/SBFZhZFqdT/wUA2NiX5sHtKxzdPjcfA/p9eNRso/LaTf+jPIkIZtaMCEwssrb+bf/THNbN7kWbXtNUfggQvk4yNQ10DYoUKE9mRgpRD85QrtZwldfvBv4PfcOieNT3AcDYvBSxUde5dXnlWwf0j+4cxc65qcprUQ/OkBhzh5btN6JrmPVvsWK98Vw8NJzytUaq/BAgfDpqGhqoaWYPT9UyMj9haz6elStXYmBgwOnTpzl9+rTSNZlMVuABfYG+x3BwcODq1avUq1eP4cOHU7ZsWRo2bMjx48dZvny5yjytWrVi6NChDBw4kIoVK3L+/HnGjx+vuK6urk5MTAzdu3fH2dmZDh060LRpUyZNmgRkrW8fMGAApUuXpkmTJri4uLBs2bICvcjXNW7cmAMHDuDr60vlypWpVq0a8+bNw/7fmeGC2LdvH+7u7jRv3hyATp064e7uzooVqr/6Tk1NJTExUemRmia+8ntTZmYm+w4c4mXySzzcK+QZe+68H5YWFrkO+q8GBFK7ZnWltNo1axJ046bS1qlvSkhM5LK/Pw3r18s15k3PXzzH5LVDzgQBIPrJHUZ0LMqYbo6snNaZp5H335onJOA4RmbWFC3hpvL6/RA/3DyVP2y6VWpM+O0rZGTk3q+TX8Rz58ZZKnipnvm/H3yBoiXKKgbzr8rNSE8l/I7/W9stfD2ex4WxZ2kl9v9eg/P7BvAiPuc372/6J/wcOvqWGFu4qLwe8+Qq1g61lNJsHOoQG3UdeWbu/TotJYFnjy5j66R6Aibm8VWMLVwUg/mscmsjz0wlNirore0W/mPq6jkfX6CwsLBcH/fvv/3vxJsKNEMPYGNjw5IlS1iyZEmuMa9uoH1l1qxZzJo1SynN29sbyNqdZvPmzbmWldd6+bp16yrVZWJikqPunj170rNnT6W0xo0b07hx43y3Pzeqys7LjBkzFB9UXvEe2J+hgwfmu4wv2a3Q27Tp2JnU1DT09fT4fekinN+yNMr3+Ilcl9sAPH32DAtzc6U0CwtzMjIyiI2Lx6qIpcp8J0+fwcXZGVub/K139g8I5OBfh1m3Mn/rmIWvg4NrVX4YtQGrYs4kxv3DwT+n8duQGkxafQMDI/Nc8wWe30vFXAbdAAmxURhWslJKMzK1IjMzgxcJzzAxV91vgy4doqhDOcyKqD6FMCEuCiMT5XL1DU3R0NQiITYq1/YIXxdzW3eqNZuPoZkjKUlPuXlhMcf+/I6mPxxDW9c013yP7hylqFPu34ymJD1FW0/5d7KOvgWSPIPUl7HoGlipzPfk/kmMLV3QN7JVef1lUjTa+hZKaVo6Jqipa5GS9DTX9gifxpt7z3+pu9y8kpaWRlhYGCVLlkRDo8DDcoUv806DQmrs2LEkJCQoPfr3+zAnhH0JHB1K8NfeXezZtpnvO3dk+OhfuH33bq7xkiRx7GTu6+dfeXNnpuwdm3LP43vsBA2/yd/s/O07d/jx54EMHtCfWjWqvz2D8NUoV6UpnrXaUsyhHGU8GjB46gEAzh/dkGseSZK47ncg11n0V3LsOJaPncgCz++jgleLvButIr8kSe+0w5nwZbJ1rIedSzNMLF2xLlGLOm3XAxB2Y0eueSRJ4sm9YxTNZRb9lZzd+tUEW+797/Gdo28vV1V+Scr7D4HwSXwtB0slJyfTu3dv9PT0cHNzIyIiAshaO//bb78VuDwxoP8PaWtrY2RkpPTQ1tL61M0qNLS0tChhb0/5cmUZPWIYpV1dWJfHzjGB16+Tnp5OZU+PXGMsLSx4+uyZUlpMTCwaGhqY5nIYWnp6OqfP/v3WDwoAt+/epXP3H+jUoR2D+/d7a7zwddPW1aeoQzmiH9/JNSbs1iUy0tMoVbZmrjHGZtYkvjFjnhgfjbq6Bvq5zPxnZKRz8/JhKnrlvsOIsak1iXHK5SY9jyMzIx0jU9Wzo4KgoaWHsYULL+LCco2JjQxEnpmORbHKucbo6FvmmDFPTY5BpqaR68y/PDOdqLDTec786+oXyVFuWko8cnk6OnoWueQSPhWZpgYyTc3XHu8+a12YjR07lmvXrnHq1CmlXRYbNGjA1q1bC1yeGNALhZYkSaSl5b5u0vfYCb6pUyfPk3w93Cty9tx5pbSz585RrqxbjjvKX7lw8RJGRoa4lcn75uvbd+7QuVsv2rZpxahh3nnGCgJAeloqkREhGJvlvpQr8PxeylVtrnSwypscS1cj+OoxpbRg/6PYO1dCQ0N1vw4NPImugQnFnSrmXm4ZLx4/uEF8TKRSuRqa2tiXyt+2hMLXJzMjlcSYu+jo536T7KO7R7Et+U2euzyZ23oQ9eCsUlrUgzOYWZdHTV11v/4n4gKaOkaYWqm+3wTAvKgHCc9Cefnin+xyw86gpq6NmXW5XPMJn4jaG+vn87mD0udmz549LFmyhJo1ayp9A1qmTBnu3btX4PLEgF4oFGbNnc+ly1d4+Ogxt0JvM2veAvwuXaZ1y29zzeN74iQNG+Q9i961U0ceP4lk8vSZ3Ll7j607drJ1x0769u6Ve7nHT+bYdedNt+/coVO3XtSs4UWfXj2IfvqU6KdPiYmNzfuFCl+V7b+PIPTaaZ5GhnE/5CIrprQnJTmR6o165Jrnml/u21W+UufbfsREh7N1xTAiw0P4+/Ba/j68lkbtVe8eAnDtwtuX27h5NsK2eBnWzuxOxN0AQq4eZ/vKkdRq1kfscCMoBJycSnSEHy/iI4h5EsC5vf1IT3uBQ9l2ueZ5ctc3z1l0AKeK35OU+JiAE5NJiLnD/etbuX99K66Vc1+amlVu3sttrEvUxsi8FH4HvYn75wZR4X8TcGoaJSt0FjvcFEJfy5Kbp0+fUqRIzg/BSUlJ77TE8cv8HkP47DyNiWHoqDFERz/F0NAQVxdn/lizMtc16eEREYSHR1CnZo08yy1uV4z1q1YwefpvbPxzE0WsiuDz6y95bll57MQJZk/PewvTg38dISY2lj37DrBn3wFFerGitpw7eSyPnMLXJO7ZY1ZN78KLxGcYGlviWLoaYxddwNxK9a5a0U/uEf34Lm6Vcr9pH8DSxoHBUw+ybcUwTu1bhrG5LZ36L8xzy8prF/bTY8SaPMtVU1dn0LQD/LloADO9a6KppUuVbzrTvu+ct79Y4avx8nkk5/cPJO1lHNp6ZpjbetDw+z3oGxdTGf887gHP48KxdqiTZ7kGJsWp03YDAScmcyfgD3QNrPCo75PnlpWP7/pSpensPMtVU1Ondrv1+B8dx7E/v0NdQwf7Mq2pWHdcnvmET0TtjVn5L3SG/tVW7q/OVXo1iF+1ahVeXrkfKpibz2JAv2zZMmbPnk1kZCRubm4sWLCAWrVqqYzdtWsXy5cvJzAwkNTUVNzc3PDx8cmxq018fDzjxo1j165dxMXF4eDgwNy5c2nWLPsXx9vqlSSJSZMmsXLlSuLi4qhatSpLly7FzS33r/4E1d42gH7T0WMn8KpWNV8n+larUplDe3bmq9ygm8G8eJFE1Sq5r/MEGDp4oNidSHirvuNy38FLlcDze3Gt+A06ujkPM3uTS4U6jF+ev60kw+9c5WVyIs7l8x5QAZgXKc7gqfvzVa7wdarecmmB4h/f9cXKvjqaWm//fV2keDUa9zyUr3Jjo4JIT3tBEbtqb43VNypK7Xbr81Wu8IlpamY9FM+/zH3oZ8yYQZMmTQgODiYjI4OFCxdy8+ZNLly4kGNf+vwo9Etutm7dire3N+PGjSMgIIBatWrRtGlTxd3Abzpz5gwNGzbk0KFD+Pv7U69ePVq0aEFAQIAiJi0tjYYNG/LgwQN27NhBaGgoq1atomjRogWqd9asWcybN48lS5Zw+fJlrK2tadiwIc+fqz56WvhwbKytGPDTjx+83MyMDCaNH5fr+npB+JhMLYvRtPOYD16uPDODzgMW5bq+XhA+Jj1Da0pX7f/By5WkTDwaTMp1fb3wmVJTy56lV1PPev4Fql69OufPnyc5OZmSJUty9OhRrKysuHDhAp6eBb9nSSbld9P1T6Rq1ap4eHgoHVxVunRpWrduzYwZM/JVhpubGx07dmTChAkArFixgtmzZ3Pr1q1cB25vq1eSJGxtbfH29mb06NFA1sFRVlZWzJw5k59++ilfbYu4k/N0XUH43B0Nd/3UTRCED+6476NP3QRB+Cg2z1R9NsV/KTExEWNjY/7ZNg8jPd3s9OSXWHUYRkJCAkZGX8a9POnp6fTt25fx48fj6Oj4Qcos1B970tLS8Pf3p1Ej5fXOjRo14vz587nkUiaXy3n+/DlmZmaKtH379uHl5cWAAQOwsrKibNmyTJ8+nczMzHzXGxYWRlRUlFKMtrY2derUyXfbBEEQBEEQhNeoaYD6aw+1z2J1eIFoamqye/fuD1pmoR7QP3v2jMzMTKyslPc/trKyIioqf6cWzp07l6SkJDp06KBIu3//Pjt27CAzM5NDhw7x66+/MnfuXKZNm5bvel/9933aJgiCIAiCILxGabnNl7ttZZs2bdizZ88HK++z+Nij6qTP/Gzps3nzZnx8fNi7d6/S1kByuZwiRYqwcuVK1NXV8fT05MmTJ8yePVuxLCe/9b5r24S8/fHnZn5fs5an0U8pVcqJib+MoUrlSm/Nd9n/Kh2/74FLKSf+2qf86ffQkaPMXbCIiIiHFC9ux8ih3jRp1EApJirqH2bMmcupM2dJSUnFsYQ9s6ZPpVxZN9LT05mzYBEnT58h4uEjDA0NqOnlxZgRw7Cyyn3/ZUEAuH39DEe2zyH8tj8JsZH099mFe43WeeYJvXaabb8P58mDm5iY29K4w0jqtlA+wMz/7E72rp/A08h7WNqUpHWvqXjUbKMUc3LfMo5sn0NCTCS2Jdzo+PN8nMsp3+C/f+MkzhxcRfKLOBxcq9Jl0BKKlhA3+At5i354kVuXVhAbFURKUjQ126yiWKm8d2mKjvAj4ORkEp7dQdegCKWr9MPJvZtSzMPQQwT9PYcX8REYmBSnfK1RFHNuohRzJ+APbl36nZcvojG2KIX7NxMpYldVcV2SJG6cm8+9a5tIT03AzMadSg2nYGzh8uHeAOGDkzQ0kDQ0lJ5/iZycnJgyZQrnz5/H09MzxyYfgwcPLlB5hXqG3sLCAnV19Rwz3tHR0Tlmxt+0detWevfuzbZt22jQQHnQZmNjg7Ozs9KBRKVLlyYqKoq0tLR81WttbQ3wTm0T8rb/4F9Mnj6Dgf1+4uCenVSp5EmPH3/i8ZMneeZLfP6cYaPGUsMr544H/gGBDPQeznetWvLXvt1816olA7yHEXDtmiImISGBtp27oqmhwYZVv3Ps0H7GjRmFkVHWPsUvU1K4cTOYwf37cXD3Dn5fsoiwBw/o/fOAD/sGCF+k1JQkijmWp8vAxfmKfxoZxqJfm1OqbE0mLL9Ks85j2bJsCP5ns3dsuhd8gZVTO1GtwfdMWBFItQbfs3JqR+6HXFTEXD61la3Lh9K88y9MWH6VUmVrsuiXZsREZ9/gf3jrLHx3zqfLwMWMW3IJYzNr5o9uREqyuMFfyFtGejImRcrg2XBKvuJfxEdwemcPLItVoXHPQ5SpNpCrx314GJq9s82zx/6c3zeAEm7f0aTnYUq4fce5ff2JeZK9uUVEyD4Cjk+iTLWBNO55CMtiVTizowdJiY8VMbcuLSf0ymo8G06hYbcD6OpbcnJrV9JTX3y4N0D48NTUs5bZKB5f5gz96tWrMTExwd/fn5UrVzJ//nzFY8GCBQUur1AP6LW0tPD09MTX11cp3dfXl+rVVe9PDlkz8z179mTTpk00b948x/UaNWpw9+5d5HK5Iu327dvY2NigpaWVr3odHBywtrZWiklLS+P06dN5tk14u9Xr1tOxXVs6d2hHKaeSTBw3FhtrG/63aUue+X4Z70OrFs3xqFghx7W16/+gZnUvBvTri1NJRwb060sNr2qsXb9REbN85RpsrK2Z89t0KlYoj12xotSs7oV98eIAGBka8uf6NXzbrCklHR3wqFiBSePHEXTj5ls/bAhCuSpNadNrKh61vstX/OkDKzCzLE6n/guwsS9NrWZ9qNG4F0e3z1XEHNu1kDKeDWnWeSw2xV1p1nksru71ObZroSLGd+d8ajb5gVrN+mBjX5pO/RdgamnH6f1ZN/xLksTx3Qtp1vkXPGp9R1GHsvQauZ601GQuntj0Yd8E4Ytj61iP8rVGYufcNF/xdwP/h75hUTzq+2BsXoqSFTrjUK4Dty6vVMSEXlmDdYlalKk2ECNzJ8pUG4iVfQ1Cr2Sfo3Drymocy3ekZIXOGJuXwqO+D3qGttwNyPqdLkkSoVfW4OY1EDvnpphYulC12TwyM1IID9nzQd8D4cOS1NRzPL5EYWFhuT7u379f4PIK9YAeYNiwYaxevZq1a9cSEhLC0KFDiYiIoF+/rK+dx44dS/fu3RXxmzdvpnv37sydO5dq1aoRFRVFVFQUCQkJipiff/6ZmJgYhgwZwu3btzl48CDTp09nwIAB+a5XJpPh7e3N9OnT2b17Nzdu3KBnz57o6enRpUuX/+jd+fKkpaURdDOYWjWUD4yqXbM6/gGBuebbtnMX4REP8R6oemu0q4GB1K75Zpk18H9tO1PfEycoX64sPw/2xqNaTZq2+o7NW7fn2d7nz58jk8m+mDvvhcLjfogfbp7KJ2C6VWpM+O0rZGSkZ8UEX6BMjphG3AvOujE/Iz2N8Nv+lPFUvsHfzbMh925eAOBZVBgJsVG4VcqO0dTSxrl8He4FX/jgr0v4usU8uYq1g/I5MjYOdYiNuo48Mz07pkRt5ZgSdXj2JOvchczMNOKignLEWDvU4tnjrJikhAhSkp4qxahraFPErqoiRiik1NVzPr5gaWlphIaGkpGR8V7lFPqFSR07diQmJobJkycTGRlJ2bJlOXToEPb2WSctRkZGKu0N//vvv5ORkcGAAQOUBug9evRg/fr1ANjZ2XH06FGGDh1K+fLlKVq0KEOGDFFsP5mfegFGjRrFy5cv6d+/v+JgqaNHj2JoKI6SfldxcfFkZmZiYWGulG5hbs7TZ89U5gl78ICZc+azfdNGNHJZa/f02bOcZVqY8/RpdpkPHz7if5u20KdXDwb068u160FMnDodLS0t2rZplaPMlNRUfps7n1YtmmNo8PaDgAShIBJiozCspLx8z8jUiszMDF4kPMPE3IaEuCiMTHPGJMZlLQV8kfAMuTwzR4yhqRUJ/8YkxGb918jkzXKKEPOP6vM+BOFdpSQ9RVvPUilNR98CSZ5B6stYdA2ssmL0LZRitPUtSEl6CkBaciySlInOmzF6loqYV//V0XszxoLk15blCIWPXF0T+WtnC8i/0HMGkpOTGTRoEBs2bACyVoo4OjoyePBgbG1tGTOmYGeSFPoBPUD//v3p31/1zOurQforp06dyleZXl5e+Pn5vXO9kDVL7+Pjg4+PT77qFPIvx83GSMjIebNxZmYmg4eNYujgATg6lChYmW/cwCyX5JQrW5ZRw4cCULZMGW7fucvGzVtyDOjT09MZ5D0cuVzOVJ8JCMLHkOMG+3+PDVFOz9mv30xTVU6OtPzECMIHkLOrvToOJ/tCjt/3Kvp1zudSzsJz9GEVMUKhIsmUl9lIsi9zhn7s2LFcu3aNU6dO0aRJ9g3fDRo0YOLEiV/mgF74epiamqCurq40cw4QExObY4Yd4EVSEtdv3OBmSAgTJmdtOyqXy5EkCcfS5di4dhU1vKphaWHx1jKLWFpSqmRJpRinkiX564jyvRTp6ekMGDKMh48es/mPdWJ2XvgojM2sSYxVvuk+MT4adXUN9I2y+q2xqbViNv6V5/HRihl5A2ML1NTUFbPwSjH/zsgbm2Xd4J8YF4WJuc1rdT3NMbMvCO9LRz97Fv2V1OQYZGoaaOua5hnzakZeS88MmUxdRcwzxYy8jn7WtwApSU/RNbBSLueNWXuhkHl1Uuzrz79Ae/bsYevWrVSrVk1p8qRMmTLcu3evwOV9me+S8NnS0tKinFsZzr5xONfZc+fxdK+YI97QwICjB/by195dikfXzh0p6eDAX3t34V6hPAAeFSty9pxymWf+Poenu7viuaeHB/fDwpRiwh48oGhRW8XzV4P5sPBw/tywBlNTk/d8xYKgmmPpagRfPaaUFux/FHvnSmhoZH0F7VjGi2D/N2N8KVkm68Z8DU0t7J09Cbmq/KE0+OoxSrp5AWBh7YCxmTXB/tkxGelp3L5+mpJlvD746xK+bua2HkQ9OKuUFvXgDGbW5VH7d2mFua0HUeE5YyxsPQFQV9fC1LqcinLOYlE0K0bfuDg6+pZKMZmZaUQ/vKiIEQqnr+Wm2KdPnyptqf5KUlLSO307Kgb0QqHTp1dPtm7fwdYdO7lz9x6Tp//Gk8hIunbuCMDMOfMYOjLrqyg1NTVcnEspPSzMzNDW1sLFuRR6enoA9OrRjbPnzrN85Wru3rvP8pWrOXfBjx96Zu993KdndwKuXWfJ8t95EB7Onv0H2LR1O927dgYgIyODnwd7c/3GTRbOmUVmZibRT58S/fQpaWlp//G7JHxuUl6+IOJuIBF3A4Gsm1Ej7gYqto/ctWYsa2b2UMTX+bYfMdHhbF0xjMjwEP4+vJa/D6+lUfvhipj6bQYT7H+Uv7bMJDLiFn9tmUnI1WM0+G6IIqZh26Gc/WsNfx9eS2R4CFuXDyU2OoI632bf4F+/zRAObZ7B1b938zjsButm90JLW4+q34gb/IW8paclEffPTeL+uQlAUvxD4v65qdg+8trp3/A76K2Id6r4PUmJjwk4MZmEmDvcv76V+9e34lq5ryLGpdIPRIWdIeTiMhJj7hJycRlR4X/jUqm3Isa1Uh/uX9/C/etbSYi5w9Xjk0hOfIJTxe+BrH7tUqk3wX5LeXT7MPFPQ7l4aDjqGjrYl2798d8Y4Z3JZeo5Hl+iypUrc/DgQcXzV4P4VatW4eVV8MkUseRGKHRaNG9KXHw8i5YuJzr6Kc7OpVi/6neKFS0KQPTTZzyJjCxQmZU83Fk8fw5z5y9i7sJFFLcrzpL5c3GvkL3FZYXy5Vi5dBEz585n0dLlFCtWjIm/jKFNyxYAREb9g+/xkwA0baW89eCWjevxqlrlfV628IULv32FOSO+UTzftiJrYO7VsAc/jFpHfEwUsa/tDW9p48DgqQfZtmIYp/Ytw9jclk79F+JZq60ixsmtOn3HbWbP+vHs3TABS5uS9B23BcfS2YfrVK7bkReJMRz43xQSYiOxLVGWwdMOYm6VfYN/k46jSE97yabFA0h6Hoeja1WG/nYEHT1xg7+Qt9io65zc0lHxPODkZABKlG1HtWbzeJkUTVJi9ra+BibFqdN2AwEnJnMn4A90DazwqO+DnUszRYxF0UpUb7mE62fnEHR2LgYm9lRvuRRz2+xvVIuXbklqSjw3zi8kJSkaYwtnarfbgL5xMUWMa5WfyUhP4YrvONJSEjG3qUjdDn+iqS2WSRZmX8tNsTNmzKBJkyYEBweTkZHBwoULuXnzJhcuXOD06dMFLk8mZd+NInwCEXdCPnUTBOGDOxru+qmbIAgf3HHfR5+6CYLwUWyeafepm0BiYiLGxsaEXTiKoUH2qanPXyTh4NWIhISEL26L6KCgIObMmYO/vz9yuRwPDw9Gjx5NuXLlClyWmKEXBEEQBEEQCoU3l9l8SUtuhg0bxpQpU9DX1+fMmTNUr15dsW3l+xJr6AVBEARBEIRCQZKpZW1dqXh8OUPVxYsX8+LFCwDq1atHbGzsBytbzNALgiAIgiAIhUKmmgaZahpKz78UJUqUYNGiRTRq1AhJkrhw4QKmpqYqY2vXrq0yPTdfzsceQRAEQRAE4bMmV1PP8XgXy5Ytw8HBAR0dHTw9PTl79mye8adPn8bT0xMdHR0cHR1ZsWLFO9Wbl9mzZ7NmzRrq1auHTCajTZs21K1bN8ejXr16BS5bDOgFQRAEQRCEQuFDbFu5detWvL29GTduHAEBAdSqVYumTZsSERGhMj4sLIxmzZpRq1YtAgIC+OWXXxg8eDA7d+5835ejpHXr1kRFRZGYmIgkSYSGhhIXF5fj8S5LccSAXhAEQRAEQSgU5LwxoKfgA/p58+bRu3dv+vTpQ+nSpVmwYAF2dnYsX75cZfyKFSsoXrw4CxYsoHTp0vTp04cffviBOXPmvO/LUTJs2DCSkpIwMDDg5MmTODg4YGxsrPJRUF/OwqTPlOXFXZ+6CYLwwX3j1fHtQYLwmdFuVvJTN0EQvnhymdobu9xkzT0nJiYqxWlra6OtrZ0jf1paGv7+/owZM0YpvVGjRpx/4xT6Vy5cuECjRo2U0ho3bsyaNWtIT09HU/PD7IW/ePFiRo8ejb6+Pt988w2RkZEqT4t9F2JALwiCIAiCIBQKmaiT+dqs/Kuf7eyU98qfOHEiPj4+OfI/e/aMzMxMrKyslNKtrKyIiopSWWdUVJTK+IyMDJ49e4aNjc27vJQcPuZNsWJALwiCIAiCIBQKEsrLbKR/f3748KHSwVKqZudfJ5PJlMuVpBxpb4tXlf4+Zs+eTb9+/ZgxY4biptjc2pKZmVmgssWAXhAEQRAEQSgU5Kghf+0Wz1c/GxkZ5eukWAsLC9TV1XPMxkdHR+eYhX/F2tpaZbyGhgbm5uYFfQm5at26Na1bt+bFixcYGRkRGhr6wZbciJtiBUEQBEEQhEJBLqnleBSElpYWnp6e+Pr6KqX7+vpSvXp1lXm8vLxyxB89epRKlSp9sPXzrxM3xQqCIAiCIAhfrNzW0BfEsGHD6NatG5UqVcLLy4uVK1cSERFBv379ABg7diyPHz/mjz/+AKBfv34sWbKEYcOG8eOPP3LhwgXWrFnD5s2bP8yL+ldiYqLiWwZ3d3eSk5Nzjc3PtxGvEwN6QRAEQRAEoVDIRI3M12blM99hMUnHjh2JiYlh8uTJREZGUrZsWQ4dOoS9vT0AkZGRSnvSOzg4cOjQIYYOHcrSpUuxtbVl0aJFtG3b9v1f0GtMTU0VO9uYmJioXJ//aq2/WEMvCIIgCIIgfJbeXGZT0CU3r/Tv35/+/furvLZ+/focaXXq1OHq1avvVFd+nThxAjMzMwBOnjz5QcsWA3pBEARBEAShUJBLsjcG9B9ul5lPrU6dOip//hDEgF4oVLaev8aCQ38T+uQpulqa1CnjyOQODXGytsg1z/itRzh76wEPomNJeJmKjYkhTSq6MKZVXYoYGyjF7rsSzOLD57gWHklGppyiZsb0qOPBiBbZ/7COBd1h+u6TXAuPRENNjaql7JjYriGejkUVMekZmfy29xSbzwXyODYRSyN92lRxY0LbBhjq5r2VliAIwpdGkiR2r5/EqQOrSHoeR8nSVenuvYRiDm655jl5YBXnjmzkUdgNAEo4e9L+x2mULF1FEbP/zxlcObObyIhbaGrrUsqtOh1/+g2b4i4Fqjs9LZXNy0fgd3wLaWkvcfOoTw/vpZgVKfYR3g3hfWRKamS8vuTmHWfoC7s7d+6wd+9eHjx4gEwmw9HRkVatWuHo6PhO5X2Z75LwWVpz4jK9lm/nWngk1iaGZMrl7Ll8k/qTV/EkLjHXfHMPnOXyvUcY6upgZqDLg6dxrPD1o/lv65DL5Yq4hYf+ptPCTZwLDUdfWwsXW0tepqVz6uZ9RcyRa7dpM2cjfnciMNXXRVtTnWNBd2k0bTXXwyMVcT+u3MmMPSeJeBaPQxFTniYmsfTIBb6b+4dSnYIgCF+Dg5tncXj7fLoNWcykFZcwNrNm1ohGvEx+nmueW4GnqVa/E2Pnn2DC0vOYW9kxe0RjYp8+fi3mDA1a92fCsguMnnOUzMwMZo1sTOrLpALV/ecSb/zP7qH/hM38uvgsKS9fMG9sC+QFXKcsfHzvu8vN52DGjBmUKVOG0aNHs3PnTrZv386IESNwdXVlzpw571Tml/cuCZ+l1PQMfLZnbRnVurIbwfOGc3XmEAx1tHn6PIk5+8/kmndUyzo8WDKGoDlDub1gJK0rZ83K3Hz0D9cjsvaVfRQTz/htRwGY26059xeP5sLUAdxdNIrNQ7ooyvp1yxEy5XKqONlxa/5wbs4djr2FCS/T0pm04xgAV8Mes+3CdQBmd2tO4CxvNg3uDMC50HD2+Yd84HdHEASh8JIkiSM7FtLy+1+oXPs7ijmWpe/Y9aSlJHPh2KZc8/386/9o0Lo/9qUqYmvvSu8Rq5BLcoKvHlfEjJz9F7Wa9qSYgxvFnSrw45i1xPwTQdht/3zXnfwigdOH1tK5/xzKVmpAiVLu9Bu3kYdhQdzwP/Zx35z/t3ff8TVffQDHPzd7b1lIRBZi7/AYUYTatVW0RkvtVeVRe7U60FqlVFta+lDU3rE3IUYREtl779zx/HGTm1zJzVAqOO/X677ce3/nnN+5V5J77vl9z/cIFfamD+hPnTrF559/zuzZs4mPjycqKoro6Gji4uKYOXMmM2fO5MwZzWMeTd6sd0l4bd0IjiAhXZm+qXezOgA4WprR3E15OfR44CONdef370QVM2MAtLW0aOnupDqmr6uMKtt77R5SmRwjPV2uBIVRfexSXMZ/wcj1/yMjJxeAiMRU7obHANCtUS10tLUxNdSnQ103APzvPUEml3PsdmFfCr48dG3ogUH+uYoeFwRBeNPFRQWTkhhN3WadVc/p6unj2bAdj+5eLHc7OTmZyKR5GJtaaSyTlZ4CgEl+mfKcO+ThdWTSPOoVKWNp40g1l7oE3b1Q7v4J/w6ZQlLs9iZZv349o0aNYv78+VhaWqqet7KyYuHChYwYMYJ169ZVuF0xoBcqhfCEFNX9KmaFce8FMfBhRY6XJi0rh19OK2duWro7Ubuqcge2h1HxAGTm5vHnlbvYm5uSmJ7F7+dv0efrX8iTyohILNoH42J9yMrNIy41g/Ai5Wzzy2lpaWFtalShvgqCILwJUhKVV0LNLdV34TS3tFUdK48/NszE0qYqXk06lnhcoVDw29ppeNT7D9Vq1i33uZMTo9HR1cPY1PKZMnYV6p/w75DLJcVub5IrV67g5+en8bifnx+XLl2qcLtiQC9UCgoUJT+f/3R5fp3jUjPo/uVP3IuIxdOxCtvyw2AApLLCuPb1H/Xh2hcTWTeqNwC3nkZx8dFTFApNfSh8XiKRlFKuoEw5OisIgvCaunBsGx91MVXdZNI8gGI5tRUKBZJy/fVWxsFfOrGdiYt2oadvUGKZX1aNJ+zxbcbOKR7G8zznVv4tF3+wKxupXKvY7U0SExNDjRo1NB53cXEhOrriXzRFlhuhUqhubaG6H5uSrrofl6q8X8269G2QH0bF0efrXwiOTaK5W3V2Th2KjWnhLLujZeGOawXZapq6FmY3eBqXrAqtAYhNLdoH5eIrQz1dbEyN1Poak5KOo6UZcrmcxPyQoWpWFd+yWRAE4XXRqHVPXGu3UD3Oy8sBlDPhFtYOqudTk+Mws7IrVv9ZB7d/zb6ty5jxzTGcXOuXWOaXVRO4eX4fs787rZaZxtzKvsxzW1jZI83LJSMtSW2WPjU5Fve63uV5ycK/6Nkwmzct5CY7Oxs9PT2Nx3V1dcnNza1wu2JAL1QKTWpWxdrEiIT0TPZcvcvAVg2ISEzlclAYAJ3quQPQcMZKAEZ3asknnVoCcO7vYAat+o3E9Cx6N/Ni05h+GOrpqrXfoa4rS3afBODGkwg8HKpw40lhJgU3e2uqWpnhVc2Ou+Ex7L/+N1O7tSEzN48Td4IA8PFyRVtLi0713VULZPdcuctYX28O3HxAdp5U2df67i/pXRIEQXj1DI1MMTQyVT1WKBSYW9lz99oxarg3AkCal8uDgNMMGP1FqW0d2P4Vf/26hE+XH6ZmrabFjisUCn5dNYHr5/Ywa+Upqji4qB2v4uBS5rlreDRBW0eXO9eO0cJnAADJCVGEB99h4Ogvn/+NEF4KxTNhNoo3LOQG4Mcff8TExKTEY2lpmjNDlUYM6IVKQU9Hh/n9OzHhp73svXaPOlO/ITE9k/TsXGxMjZjWoy1QGAufkFaYsqz7l1vIlcqQSCSEJ6TQZekm1bGZvX3o2tATbw9nujeuzf4b9xm9cTff7D+rasvHqybeHsrtoBcP8qXvN79y7Uk4taZ8Q65USnxaJoZ6uszt+w4AjV2qMsC7Pn9cvM2MbQfZcOIywbFJALT2dKZnk9ov/w0TBEGoJCQSCb79JrFv6zLsqrljX9Wdv7YtQ8/ACO+OhVnEflj6AZY2jgz4eBmgDLPZtXkun3y+DRv7GiQnKMMMDAxNMDBSDnZ+XjmOS8d/Z/KSPRgYmqrKGJmYo6dvWK5zG5mY0+7dEfy+djomZtYYm1mxfd2nVHepR10N8frCq/Omz9A7OTmxcePGMstU1EsZ0EskEnbv3k3v3r1fRvMq/v7++Pj4kJSUhIWFBQB79uxh+vTpBAcHM2HCBBo2bMjkyZNJTk5+qX0R/rmRHZphrK/LykPneRAZh4GuDr2a1mHRwM5qITPPypUq8wgrFAquPQlXOxafWjjw/2XcAJbsPsmOi7cJikmgRhVL+rasy6dFNpXybeDB7unDWLbnFAEhkehoa/FOXVfm9e9EfefCy7kbP+6Lq501v527SXBsEjZmRvRpVpd5/TqipfVmxfsJgiCUpdvgGeTmZPHzinFkpiVRs04LZnx1RG0mPyEmFImk8O/jiT3rkObl8v28/mpt9f5gLu8Nnw/Ayb3rAVg62UetzEefbaZN1w/Lfe4h41agpa3D6gUDycvJok7jd5iy7Ce0tLVf5NsgvABSmQSpTKL2+E0SEhLyUtqVKDSt8NMgOjqaJUuWcODAASIiIrC1tVUNmt95RzmD+W8N6HNzc0lMTMTOzk61IMbOzo7hw4czceJETE1N0dHRIS0tDVtb2xd+/vnz57N9+3bCwsLQ09OjSZMmLFmyhBYtWpRdOV/W1iUvvF+C8KpFeQ981V0QhBfufLjrq+6CILwUfu1e/aA5NTUVc3Nzfj6RhJFx4SReZkYqH7xjSUpKCmZmmif3Xmfh4eE4Ojr+ownBCtUMCQmhSZMmnDx5kuXLlxMYGMjhw4fx8fFh3Lhxz92J56Wnp4e9vb1qMJ+enk5sbCy+vr44OjpiamqKoaHhPx7M5+Xllfi8h4cHq1evJjAwkHPnzlGjRg06d+5MXFzcPzqfIAiCIAjC20guL35709WpU+cfz9xXaEA/duxYJBIJV65coV+/fnh4eODl5cXUqVNLzZn52Wef4eHhgZGRETVr1mTOnDlqg+Rbt27h4+ODqakpZmZmNGnShGvXrgHw9OlTevTogaWlJcbGxnh5eXHw4EFAGXIjkUhITk7G398fU1Pl5bUOHTogkUjw9/dny5YtqnCcAvv27aNJkyYYGBhQs2ZNFixYgFQqVR2XSCSsX7+eXr16YWxszOLFi0t8XUOGDKFjx47UrFkTLy8vvv32W1JTU7l9+3ZF3lZBEARBEAQBkMklxW5vugoGy5So3DH0iYmJHD58mCVLlmBsbFzs+LOD5qJMTU3ZsmULjo6OBAYG8tFHH2FqasqMGTMAeP/992nUqBHr1q1DW1ubgIAAdHWVWUrGjRtHbm4uZ86cwdjYmHv37pW4MrhVq1Y8ePAAT09Pdu3aRatWrbCysir2jefIkSMMHTqU7777jjZt2vD48WM+/vhjAObNm6cqN2/ePJYtW8aKFSvQLkeMXW5uLhs2bMDc3JwGDRqUWV4QBEEQBEFQJ5dDka1j3ooZ+heh3AP6oKAgFAoFtWrVqvBJPv/8c9X9GjVqMG3aNHbs2KEa0IeGhvLpp5+q2nZ3L0z7FxoaSt++falXrx4ANWvWLPEcenp6qtAaKysr7O3tSyy3ZMkSZs6cyQcffKBqb9GiRcyYMUNtQD9kyBBGjBhR5mvbv38/gwYNIjMzEwcHB44dO4aNjU2Z9QRBEARBEAR1b/qi2JL897//xcrK6h+1Ue4BfcHlgGd3YyuPnTt3snLlSoKCgkhPT0cqlaotbJg6dSqjRo3i119/pWPHjvTv3x9XV+Xio4kTJ/LJJ59w9OhROnbsSN++falfv+SNJ8rj+vXrXL16lSVLChejymQysrOzyczMxMjICICmTYvnwy2Jj48PAQEBxMfHs3HjRgYMGMDly5dfyiJcQRAEQRCEN9mzcfNvwwz9rFmz/nEb5Y6hd3d3RyKRcP/+/Qqd4NKlSwwaNIiuXbuyf/9+bt68yezZs9V2wZo/fz53796lW7dunDx5kjp16rB7924ARo0axZMnT/Dz8yMwMJCmTZvy/fffV6gPRcnlchYsWEBAQIDqFhgYyKNHjzAwKNxuuqSwopIYGxvj5uZGy5Yt2bRpEzo6OmzatKnsigIAOy7cwvvzNViNmE/VMUsY8t3vBEXHl6tuVHIaxsPmsP6Y5vUb0clpfLxhF85jl2ExfB6NP1vF2iMXy92/CT/tpca4Zcg1/EVZ/OcJjPw+L/EmlcnKfR5BEITXxZ8/zWdYey2124Q+DmVXBM4e2sKCTzTvzvp3wGnmftyUkZ0MmTbYVZW2sizx0U8Z0cmArIzUko/HhPLtrJ6M6mLC2J5V+PW7iUjzKr4bp/DyyeTFb28imUzGpk2bVOsxO3TooHarqHLP0FtZWeHr68uaNWuYOHFisQFvcnJyiXH058+fx9nZmdmzZ6uee/r0abFyHh4eeHh4MGXKFAYPHsxPP/1Enz59AKhevTpjxoxhzJgxzJo1i40bNzJhwoTydl1N48aNefDgAW5ubs9VvywKhYKcnJyX0vabZtPJq0z4aS8ANapYkpi/S+z5v0O4uGRcqbnnAQ7cuI9CoaB745LDwNKzc+i85EeCohMw1NPFycaCvyPjmL71ADGp6Szo36nU9hUKBQdv/k3XRrXKTCVlY2qEi6365bLnuZolCILwOqhaw4vPvjmmelzefO43Luyj8X96lngsLiqYr2d2o323UYye/SuPAs/z88pxmFpUoVm7vqW3e34vtRq2x9C4+OeGXCbj25ndMbWowuffnyU9JYGNX3yIQqFg2KTnnyAUXg6ZTHkr+vhNNGnSJLZs2UK3bt2oW7fuPx4zVGhjqbVr19KqVSuaN2/OwoULqV+/PlKplGPHjrFu3boSZ+/d3NwIDQ1l+/btNGvWjAMHDqhm3wGysrL49NNP6devHy4uLoSHh3P16lX69lX+8k6ePJmuXbvi4eFBUlISJ0+epHbt59+Jc+7cuXTv3p3q1avTv39/tLS0uH37NoGBgRqz2ZQkIyODJUuW0LNnTxwcHEhISGDt2rWEh4fTv3//sht4y+XkSZn/P+WHQe9mXvw2cTCRSak0mrGKuLQMvt53hm+HdS+1jf3X/6aRiyPVrC1KPL7p5FWCohOUGY/mjaaekz0zfzvEd4fOs+LAWT7p1BJ7C9MS6wJcexJBVFIaPcqx82uXBp5sGF36B44gCMKbQltbBwvrkteqaZKbk82dq0fpO2JhicdP/rUea1snhk5YCUBV59oEP7jGwR3flGNA/xdN2/Qp8VjgtaNEPL3Hyq9CsbRxBGDwJ1+z8Yvh9B+1pMQvAcKrI5UrkMoUao/fRNu3b+ePP/7g3XfffSHtVShtpYuLCzdu3MDHx4dp06ZRt25dOnXqxIkTJ1i3bl2JdXr16sWUKVMYP348DRs25MKFC8yZM0d1XFtbm4SEBIYNG4aHhwcDBgyga9euLFiwAFBekhg3bhy1a9emS5cueHp6snbt2ud+wb6+vuzfv59jx47RrFkzWrZsybfffouzs3OF2tHW1ubvv/+mb9++eHh40L17d+Li4jh79ixeXl7P3b+3xY3gCBLSMwHo3awOAI6WZjR3qwbA8cBHpdZPy8rh9P0ndG+sebB97LayDTc7a+o5KT94ejdV/t9IZXJO33tS6jn2Xb+Hsb4eHbzK3kxmz7W7WI2Yj8v4L+jz9S8EhESWWUcQBOF1FR3xiIl9qzJ1UE3WLBhMbGTpf08B7t04gbmVPdVcSv6MDLp7iXrN1K+c1mvuS8iDa0ilJe8HA5CRlsyD22dp1Lrkmf+guxep5lJXNZgvaDcvL4fgh9fL7Lfw75LLCmfpZTLl4zeRnp7eC40WqdAMPYCDgwOrV69m9erVGss8m09z+fLlLF++XO25yZMnA8oX9Pvvv2tsq7R4+fbt26udy8LCoti5P/zwQz788EO153x9ffH19S13/0tiYGDAn3/+WWa5onJycoqF48jzpOjrVvi/4bUXnpCiul/FrDANqa258n5YkeMlOXb7ETl50lJnz8MTU/LbLwwPszUvvB+WkFzqOQ7c+JuO9dww0NMttZyutjb2FqZoa2nxIDKOI7ce4n/vCafmfkzDGo6l1hWEf2Lv3r18Pmeu6vHmTT/SrFmzV9gj4W3gWqcFo2f9jH11D1ISY/jr1yUsGteapVvuYGpurbHejfN7NYbbACQnRlPP0k7tOXNLO2QyKekp8VhYlxynf+vyQarXrIe1bfUSj6ckRmP+TLvGppbo6OqRkhitsT/Cq/Fs3PybGkM/bdo0Vq1axerVq19IiO7bN5J8hZYtW6a68lDgv318+Py9d15Rj14dBSV/aSr4LlXWj/b+G/dxsbWkbnXNl3xL+l5W9KnSfoEexyRwPyKWad3blNqPQa0aMM63FZbGhoDyi0avr34mJ0/KD8cvs25UyZeABeFFeOedd2jQoKHqsb29nebCgvCCNGjRVXW/es16uHt5M32IG+eO/EzXAVNLrKNQKLh5YT/j5mqewIPif5cLJ9g0/72+cf4vGrXqUXqnS/h7r1AokJT5aSP82+QyBfIiITdF779Jzp07x6lTpzh06BBeXl6q/ZcKVHTSWAzo/0WzZs1i6lT1P3byXSteUW9erepF4t5jU9JV9+NSlferWZtrrCuVyThy6yFD2zQq4xzmPIqOJza1SPspGar71aw0n2PftfvoaGvRpaFnqedws1ffc6BTfXesTYxISM8kvIwrAILwT5mYmJS40Z4g/Jv0DY2pVrMeMeGaQyWf3L+CVJqLR73/aCxjYWVfbMY8NTkWbW0dTDTM/EuleQReOUyPITM1tmtuZc/j+1fUnstIS0ImzcPMSnwJrmzelhl6CwsLVfKXF0EM6P9F+vr66Ovrqz2X9RaG2wA0qVlVNfDdc/UuA1s1ICIxlctBYQB0queuse7Zv0NIysgqc7Fqp/runLz7mMcxidx6GkUDZwf+vHIHAB1tLdqXEhu//8Z9Wns4Y2ViVOo5vtl/hgEt61PdxgKAE4FBqrUBTjaWpdYVBEF4E+Tl5hD59D6e9TUP1m+c30vDlt1KzYbj5tWSmxf2qz135+pRang2RUen5NDH+zdPYWRigbN7w1La9eavrUtJTohShe0EXj2Krq4+Lh5NSnllwqsglSqQShVqj99EP/300wtt7+0cTQqvnJ6ODvP7d2LCT3vZe+0edaZ+Q2J6JunZudiYGjGtR1uNdfdfv4+NqRHeHqUvZB7ZoRmbTikz3XRYuIGqVmYERScAMKVbG+zMS57ZjEvN4NKjUJa/X/bK840nrjD3j2NUtzbHSE+XB1HKHPrG+nqM79KqzPqCIAivm9/XTqdRqx5Y2zmRmhTL3l+XkJWZyn98P9BY58b5fbw3fH6p7XboOYZju9ewbc1U2nf/iKC7Fzl9cDNj5/ymsc7NcoTb1GvamarOdVi/ZBiDPllORmoi29d9Srvuo0SGm0pIJlMgKxJmI3tDQ25eNDGgF16ZkR2aYayvy8pD53kQGYeBrg69mtZh0cDOpeagP3Dzb7o09ES7jNzwJgb6HJk9ink7jnL41gOexiXj6ViFUR2aMc5X82D74M2/kSsUdG9Scn77oj7t0Y4/rwRyPyKOmJR0nKzN8fZwZmbv9ng4VCmzviAIwusmMS6CtYuGkJYSj5lFFVzrtGTe2ovY2Jc8yRIT8ZjYiCDqNdOcjAKgioML0784wLY1UzmxZy0W1o74TVhVasrKGxf2MWpG6Zs5amlrM/WL/fy8YhyLx/8HXX1DvN8ZzOBPvi77xQr/OrlcfUAvf0PTVgLs3LmTP/74g9DQULUNVwFu3LhRobZeiwH92rVr+eqrr4iKisLLy4uVK1fSpk3JixX9/f3x8fEp9vz9+/epVatwgLZr1y7mzJnD48ePcXV1ZcmSJcVimco6r0KhYMGCBWzYsIGkpCRatGjBmjVrRNrKChjUuiGDWjcsd/mAkEhC45Pp0aROuco7WJhWOD/8/hv3qe/sUK6QmZEdmjGyg8gqIgjC22PcvNIXtj7rxvm91G7cAQOjstd71GrYjkUby5dKMuThDbIzUqnVsF2ZZW3snJj2xb5ytSu8WjK5AlmRQbzsDR3Qf/fdd8yePZsPPviAvXv3Mnz4cB4/fszVq1cZN25chdurUB76V2HHjh1MnjyZ2bNnc/PmTdq0aUPXrl0JDQ0ttd6DBw+IiopS3dzdC2OyL168yMCBA/Hz8+PWrVv4+fkxYMAALl++XKHzLl++nG+//ZbVq1dz9epV7O3t6dSpE2lpaS/+jRAAyJPJ+W8fHzrWezk7/QJ4uzuXuYusIAiCUD5WVarR433Ni1afl0wmxW/Sdxrj64XXkzRPXuz2Jlq7di0bNmxg9erV6OnpMWPGDI4dO8bEiRNJSSk9dXdJJIryJF1/hVq0aEHjxo3VNq6qXbs2vXv3ZtmyZcXKF8zQJyUlYWFhUWKbAwcOJDU1lUOHDqme69KlC5aWlqqc+GWdV6FQ4OjoyOTJk/nss88AZZ55Ozs7vvzyS0aPHl2u15e1dUm5ygnC6yTKe+Cr7oIgvHDnw8veZE4QXkd+7V59+s7U1FTMzc2Z+G00+oaFYbc5Wal8N9WelJQUzMzenDUPRkZG3L9/H2dnZ2xtbTl27BgNGjTg0aNHtGzZkoSEhAq1V6ln6HNzc7l+/TqdO3dWe75z585cuHCh1LqNGjXCwcGBd955h1OnTqkdu3jxYrE2fX19VW2W57zBwcFER0erldHX16ddu3Zl9k0QBEEQBEEoTiaXF7u9iezt7VWDdmdnZy5dugQox5fPM9deqQf08fHxyGQy7OzU88Ta2dkRHV3y7m4ODg5s2LCBXbt28eeff+Lp6ck777zDmTNnVGWio6NLbbM85y34tyJ9EwRBEARBEDQryHJT9PYm6tChA/v2Kdd1jBw5kilTptCpUycGDhz4XPnpX4tFsSXtHKdpl09PT088PQs3A/L29iYsLIyvv/6atm0LUyGWp80XVUYQBEEQBEEom1wuR1ZkNyn5GzpDv2HDBtVrGzNmDFZWVpw7d44ePXowZsyYCrdXqQf0NjY2aGtrF5vxjo2NLTYzXpqWLVuydetW1WN7e/tS2yzPee3t7QHlTL2Dg8Nz901Qt+PCLVYePMeDyDgM9XRpV6cmCwd0KrYj67PWHrnIjyev8CQ2EXNDA3wberBwQGfsLUwBiEhMZfzmPQSGRpOQnomhrg7VrS0Y1LoBk7q2Ris/Bea98BgW7DzOtSfhRCUpFzdP696GRQML062lZeWwcNdxzj94Slh8Mhk5uVSzNqdvi3pM7dYGU0P94h0UBEF4A/196wwHt39NyMPrJCdEMWnRnzRp07v0OgGn+W3tNCKC72Jh40i3QZ/SoZf6AObq6V3s2jyX2MjH2Dq60m/UYpq2UZ+1PL5nLQe3f01KQhRVXbx4f/wKPOurZ6LbvWUB/vs3kpGWhGvtFgybvJpqLiITXWUmzZOjpS1Xe/wm0tLSUo09AAYMGMCAAQOev70X0amXRU9PjyZNmnDs2DG1548dO0arVuXftOfmzZtqg25vb+9ibR49elTVZnnO6+Ligr29vVqZ3NxcTp8+XaG+CYU2nbzK8HX/49bTKOwtTJHJ5ey5epd3Fm4kMilVY725fxxl+tYD/B0Zh5ONBek5uWw9exPfJZvIyFbmdY1Py+D0vSeYGOhRp5otWloSAsOimb39CN8eOKtqKyg6gf03/sbM0EDj+RLTM1lz5CKBodHYmBljYqBHUHQCX+71x2/1jhf3hgiCIFRyOdkZOLnWx2/S9+UqHxcVzNczu+FR7z8s/PEGPd6fxa/fT+Lq6V2qMo/uXmTNgkG07jyUxT8G0LrzUNbMH8jje4WZ6C6d3MG21VPoOfS/LPzxBh71/sPXM94lPqYwE92B35dz+H8r8Jv0PQvWX8Hcyp7l0zuTlSky0VVmcpm82O1NdfbsWYYOHYq3tzcREREA/Prrr5w7d67CbVXqGXqAqVOn4ufnR9OmTfH29mbDhg2EhoaqLkfMmjWLiIgIfvnlFwBWrlxJjRo18PLyIjc3l61bt7Jr1y527Sr8YzFp0iTatm3Ll19+Sa9evdi7dy/Hjx9XewPLOq9EImHy5MksXboUd3d33N3dWbp0KUZGRgwZMuRffIfeDDl5Uub/T/nlqHczL36bOJjIpFQazVhFXFoGX+87w7fDuherF52cxsqDyv+3SV1bs2xIVwJDo2n5+RoeRcfz48krTHr3P3hVsyV24xx08rcdT8vKwWX8F2Tm5nHxYeEHQLs6NYn6YTZmhgYY+X1eYl/1dXVYOqgLIzs0w9RQn+zcPLos28yVoDCO3n5IUkYWlsaGL/otEgRBqHQatOhKgxZdy13+5F/rsbZ1YuiElQBUda5N8INrHNzxjWoDqSM7V1G3aSd6vD8LAEfnWfwdcIYjO1cxdq5y19jD/1tBu3dH0L77KACGTlhJ4NWjnNy7jgEfKzPRHdm5ip5D/0uztu8B8PGsLUzoY8/F47/RoWf5MtEJ/z5l3Lxc7fGbaNeuXfj5+fH+++9z8+ZNcnJyAEhLS2Pp0qUcPHiwQu1V+gH9wIEDSUhIYOHChURFRVG3bl0OHjyIs7NyR7qoqCi13PC5ublMnz6diIgIDA0N8fLy4sCBA7z77ruqMq1atWL79u18/vnnzJkzB1dXV3bs2EGLFi3KfV6AGTNmkJWVxdixY1UbSx09ehRTU9N/4Z15s9wIjiAhPROA3s2Um0Y5WprR3K0aJ+485njgoxLr+d99jDT/F793M+Vl1HpO9rjaWREUncDxwEdMevc/qoF8v2+3EpWcSmhcMpm5eQC08iz8PzU30jwzX8DewpTJ3f6jemygp0sTl6pcCQpDSyJBp4wdbAVBEN5WQXcvUa+Z+j4f9Zr7cubgZqTSPHR0dAm6e5Eu/Sc/U6YzR3auAkCal0vIg+t0H/KZeplmnXh09yKgvBKQkhhN3WaFmeh09fTxbNiOR3cvigF9JSaXypFL5WqP30SLFy9m/fr1DBs2jO3bt6ueb9WqFQsXLqxwe5V+QA8wduxYxo4dW+KxLVu2qD2eMWMGM2bMKLPNfv360a9fv+c+Lyhn6efPn8/8+fPLPJ9QuvCEwk0UqpgV7iZoa668H5ZQ8iYL4YlF6xkX1jMzISg6oVi9gJBItfCdqd3aMLVbybsOl1dEYip/XrkDQP+W9UQMvSAIggbJidHUs1RfZ2ZuaYdMJiU9JR4LawdSEqMxe6aMmaUdKYnKdW1pKfHI5TLMSylT8O+zZcwtbdXCcoTKRyqToSWVqT1+Ez148EAtWUsBMzMzkpOTK9yemEoUKgUFJV9SK0jFqilvkKZUrQXtPZtxKOi7GcT/OJdd0/wwMdBj5cFzbDldvm3GS/IkJoGuyzYRk5KOt4cT3w3v9dxtCYIgvA1Kyg6Xf0RjGRQKJM9+EpSjTImZ6DR+ogiVgVwqR1bk9qbO0Ds4OBAUFFTs+XPnzlGzZs0KtycG9EKlUN3aQnU/NiVddT8uVXm/mrW5hnqFz8emZBSpp7xfzar4rnJG+np0behJh7puyBUKFu068Vx9vvwolPYLfiAoOoF3G9Vi34wPxey8IAhCKSys7FWz5wVSk2PR1tbBxNwaAHMNZcyslLPtpuY2aGlpl1rG3EqZiS65WJk4VRmhcpLLZMVuL0tSUhJ+fn6Ym5tjbm6On59fmbPjH374IRKJRO3WsmXLCp979OjRTJo0icuXLyORSIiMjGTbtm1Mnz691OgQTcSAXqgUmtSsirWJEQB7rt4FlKEsl4PCAOhUzx2AhjNW0nDGStYdU+6o1t7LFR1t5Y/x7qvKsJdbT6N4HJOorFffA4C/rt3jUVS86nyxKencCFauKM/Mya1wf3dfuUPXZZuJT8vkk04t+WPyEIz09SrcjiAIwtvEzasld64dV3vuztWj1PBsio6Obn4Z7xLKHMPdS5lBTkdXjxqeTbhzTT0T3Z1rx3H38gagioML5lb23C1SRpqXy4OA06oyQuUkk8mL3V6WIUOGEBAQwOHDhzl8+DABAQH4+fmVWa9Lly5ERUWpbhVdwArKEPHevXvj4+NDeno6bdu2ZdSoUYwePZrx48dXuL3XIoZeePPp6egwv38nJvy0l73X7lFn6jckpmeSnp2LjakR03oo48we5g/KE9KUM/D2FqZMfvc/fL3vDN8fvsChgAdEJKaiUChws7dmpE8zAPZfv8+gVb/hYGmKjYkxj6Ljyc6TAvD+fxqp+nElKIwR6/6n1rdNJ6+y+8pdqlqZcWT2KCKTUhm6egcKhQI9HW2uPQnHZ+EGVfmVH/akUQ3Hl/dmCYIgVBLZmenERBSGDcRFB/P0UQDGZlbY2Dnxx4ZZJMVHMvq/PwPQoecYju1ew7Y1U2nf/SOC7l7k9MHNjJ3zm6oN374TWTKxHft/+5LGrXtx4/xe7l4/zuffF6YY7tJ/Cj8sHYaLZ1PcvLzx37eBhJhQOvQszETn228S+7Yuw66aO/ZV3flr2zL0DIzw7igy0VVmUqkUiZZU7fHLcP/+fQ4fPsylS5dUSVE2btyIt7c3Dx48UNuk9Fn6+vqq/Yj+iSVLljB79mzu3buHXC6nTp06mJiYlF2xBGJAL1QaIzs0w1hfl5WHzvMgMg4DXR16Na3DooGdcbQsHjpTYEH/TtiZm/Djyas8iUnE3Eifvs3rsnBgZ4wNlLPmPnVdCYpJ4FFUPPciYjHS06VudXsGtWrAmE6F2Y2y8/J4Epuo1n5yZjbJmdlI83d0y5PKVDGfuVIZVx+Hq5VPy8p+Ie+HIAhCZRf84BrLpnRQPf5tzTQA/uP7AR/P+onkhGgSiixCreLgwvQvDrBtzVRO7FmLhbUjfhNWqVJWArjXbcXYub+za9Mcdm2ei62jK2Pnbce1TuHf6pYdBpKemsDenxeRnBhFNZe6TPvyADb2hVnLug2eQW5OFj+vGEdmWhI167RgxldHMDQSmegqM7lUgVxLrvYYIDVVfT8afX199PWfP8z14sWLmJubq2U4bNmyJebm5ly4cKHUAb2/vz+2trZYWFjQrl07lixZgq2tbbnOO2LEiHKV27x5c7nKFZAoFJqWFQr/hqytS151FwThhYvyHviquyAIL9z5cNdX3QVBeCn82r36hcKpqamYm5vj++EVdPUKZ6nzctM5sqV5sfLz5s37R1kGly5dypYtW3j48KHa8x4eHgwfPpxZs2aVWG/Hjh2YmJjg7OxMcHAwc+bMQSqVcv369XJ9wdDS0sLZ2ZlGjRpR2hB89+7dFXo9YoZeEARBEARBqBSeXQhbcD8sLAwzs8Kr9ZoGz/Pnz2fBggWlnuPq1atACdmUyM+EVMLzBQYOLJywqlu3Lk2bNsXZ2ZkDBw7w3nvvlXpegDFjxrB9+3aePHnCiBEjGDp0KFZWVmXWK4sY0AuCIAiCIAiVgkIqVwu5UeSnrTQzM1Mb0Gsyfvx4Bg0aVGqZGjVqcPv2bWJiYoodi4uLw86u/JmQHBwccHZ25tGjkjfAfNbatWtZsWIFf/75J5s3b2bWrFl069aNkSNH0rlz51K/TJRGDOgFQRAEQRCESkEqzQNJnvrjCrCxscHGxqbMct7e3qSkpHDlyhWaN1eG9Fy+fJmUlBRatWpV7vMlJCQQFhaGg4NDuevo6+szePBgBg8ezNOnT9myZQtjx44lLy+Pe/fuPdfCWJG2UhAEQRAEQagU/q089LVr16ZLly589NFHXLp0iUuXLvHRRx/RvXt3tQWxtWrVUsWzp6enM336dC5evEhISAj+/v706NEDGxsb+vTp81z9KMhlr1AokMufP0WnGNALgiAIgiAIlYJyh1hZkdvLy0O/bds26tWrR+fOnencuTP169fn119/VSvz4MEDUlJSANDW1iYwMJBevXrh4eHBBx98gIeHBxcvXsTUtPzZk3Jycvj999/p1KkTnp6eBAYGsnr1akJDQ0XaSkEQBEEQBOH1lpeTqjYrL5NmlFL6n7GysmLr1q2llimaicbQ0JAjR478o3OOHTuW7du34+TkxPDhw9m+fTvW1tb/qE0QaStfudvvtnvVXRCEF85jdL9X3QVBeOEi63Z91V0QhJeipqvbq+4C2dnZuLi4EB0dXeyYvb09wcHBGBgYvIKevVhaWlo4OTnRqFGjUhfA/vnnnxVqV8zQC4IgCIIgCK+UgYEBwcHB5ObmFjump6f3RgzmAYYNG/bcmWxKIwb0giAIgiAIwitnYGDwxgzcNdmyZctLaVcsihUEQRAEQRCE15gY0AuCIAiCIAjCa0wM6AVBEARBEAThNSYG9IIgCIIgCILwGhMDekEQBEEQBEF4jYksN0KlcjAylp+DwwlOz0RfW4vmVhZM9HTB2diwzLoyhYIPLwVwOzkNgOE1qzPZ00V1/HJ8EpufhPEwLYO0PCnmerrUszBltKsztc0Ld2a7EJfID0Gh/J2ajrZEQn1LMyZ41MDLvHAXuDy5nI2PQ9kfEUtMdg5Werp0sq/COA9njHXEr5Wgbvvpa6zae5K/w2Mw1NOlXT13Fvn1wM3RVmOdz3/5i7N3gwiJTiAlMwsHK3O6NPFi1gBfbC2UP4v3QqNYsfsEVx6GEJWYgkQiwdXeho+7tuHDTt5q7R2/eZ8lOw5z60k4OtpatPB0Yf7Q7jRxc1KVyZPK+OJ/R/jN/yoRCclUMTehj3dD5r3fDVPDNzvzhCAIwutMbCz1iomNpQrtDI1i0d1HAFQ1NCAlL490qQxLPV3+aN0YWwP9UuuvfRTCD0GhqsdFB/QhGZn0O3udPIUCM10dHA0NeJyWQZ5CgamODqfeaYmulhbn4hKZeP0OMgXY6uuRJ1eQlJeHgZYWv3g3xNNMOfCfGXCfQ1FxaAFOxoaEZ2YjVShobGnGphYN0HoJOWZfJ2JjqUKbjpxn/LodANSwsyYxLYPUzGyqmJtwacVnOFqZl1jPsPdEJBIJNe1tyMzJJSpRufW4l5MDV1Z+hpaWFr+euMzH32/DSF8PFztrgmMSyMxR5nBePKwn097rCMCR6/fou2QDMrkcR2tzcvOkxKdmYKini/+XU6nvUhWAD775mT/OXkdLS4KbQxWCYxLIk8poXceVo4snoKX1dl/UFRtLCW+qyrCxlPDPvN1/nYVKI1cm5/uHwQB0tLPhYPvm7G7TFGNtbZJy89j0JKzU+gFJKfz4OJTO9jYlHr+TnEZe/nfX1U3qsqN1Yz5xdwYgXSolLU8KwIq/nyBTQH0LUw61b8GB9s1wNNQnWy5n9cMQAO6lpHEoKg6AGXVc2du2Gd80qgPAjaRUTsbE/7M3Q3hj5OTlMW/rfgB6ezfg/g/zuLl6NqaG+sSlpPPVzmMa637WvzNPtyzmzro5PNq4gN7eDQC4GxrF7ZAIAKpXsWTbjOHE/raca9/N4ubq/2JupLyatePMNVVbs3/ei0wup7lnDR5smM+9H+bhbGtFVm4eC7Yp+3cjKJQ/zl4H4OuRfbm15nN+nzECgPP3HrP30u0X/O4IgiAIL4oY0AuVwt3UNJLzB9Ud8wfltgb61MsPLbgQl6SxbnqelP/eekAVfX3m1PUosUw9CzN082fNx1+/w8DzN1j36CkmOtpMr1UTK309YrJzCErPBKC9rTU6WhKMdXTwtrEE4EpCMjKFgvNF+tLRTtnXtrZW6OfPXl6I19xX4e1yPSiMhLQMAHp7NwTA0cqc5h41ADgecF9j3fnvd6dKfpiXtrYWLWsVho/p6yrDutrX9+C9Vo3Q1lb+7DlVsaJ6FeXPq15+mYiEZO6GRgHQvVlddLS1MTU04J0GtQA4dfshMpmcYzcL+1Lw5aFrUy8M9HSVfb3593O+C4IgCMLLJgb0QqUQnZWjum+VP4AAsNbXUx7PztZYd+m9IKKyslnawBMz3ZLj152NDdnQvD6Werqk5kn5OzWdPIUCWwN9PPLDaGKK9MGySB+s9JR9yJbLScrNIzq7SF/z+6clkWChpzx3VJF2hLdbeJEvd7ZF1mkUxMCHlfJFtai0rGx+OXEZgJa1XKhd3aHEcidvPeBemHLwPqJTq2J9qFJkHUhBH7Jy84hLTSc8PrlIX5XHtLS0sDY1VvY1PrFcfRUEQRD+fWJAL1RqBQs8JJQck34iOp4DkbGMdHWiiZWFxnZisnOYF/iApNw8ljeszcVOrRlaoypP0jOZcO0Ocdk5aF5MUnhEAig0lCxYjfJ2R88LRWlaoaT6uS7HWou4lDS6zVvDvdAoPKvZ8Vt+GMyzDl+7y8BlPyKXKxjbvR0jOrcqow9Ffq4loGk5VUG58vRVEARBeDXEgF6oFOwNCxe8JuTmqe4n5i/ws9OwIPZhWjoAv4aE0/LoOVoePac69mtwOJ1OXgLgj9BIQjOzMdHRxtehCkY62nR3tAOUM+8BSalqfUgs2of8+wZaWljo6eJgUJjtIyG/f3KFgpT8kKGi7Qhvt4LwF4CY/OxLAHH596vZWJRa/2FEDO1mfMvVh09p7lmD40sn4VDCItqNh8/Rb+lG0rNzmDv4Xb4Z1bfEPsSq9UH5u2Oop4uNqYl6X1OU5eRyOYlpyjC0ataFxwVBEITKRQzohUqhrrkpFvnhMsejlYtKY7JzVCkoW+cPNnqduUqvM1f5/WmEWv1smZys/FsBqUJBlkwGQFqe8t8MqYyQDOUA5V5q4eDGUEcbOwN93EyMADgVk4BUriA9T8rF/JCFFjYWaEskqr4AHMvv6+nYBHLkynO3srH6x++H8GZo6uakClnZc/EWoIxpv/wgBIBOjWoD0GDcYhqMW8y6A2dUdc/dDcJn5gqCYxLo7d2AwwvHY2Nmota+QqHgv1v2MnH9H2hrSdg8xY9ZA7uolalqbYGXkzJEZ9+VQKQyGamZWRwPUMbEd2jgiba2lqovALsvBABw4OodsvO/0HZqXBuh8tv444+0adsOdw9PXN3cuXTpconl2rZrj6ubO6tWffePz7lq1Xe4urnTtl37f9wWgKubO65u7uzctUtjmUuXLqvKaXqNz2PIkPdxdXPn0xkzXlibgvBvEAmzhUpBV0uLCR4uLLr7iBMx8bzrf4WUvDwyZTIsdXUZUbM6ACEZWQAk5w8yPnGvwSfuNdTaanBIOSgqmrbyHTtr/giNRAEMOn+DakaGPM5frOhoqE/T/FnPKbVqMuHaHe6kpNHV/7Ja2spx+eepY25KV4cqHIqK4+v7j/kjNJLwTGWMf2NLMzrYWb+090l4vejp6rBgaHfGr9vB3ku3qD16AYlpGaRn52BjZsz0vp0AeBgRC0BCarqqbrd5a8iVypBIJITHJ+M753vVsVkDutC1qRd/nL3Bij0nADAzMmDdgTNqXwrOLJ8GwJIPevHekh+49ugpnh/PV0tbOXdINwAauzkxoE0T/jh7nRmb/2TDobMExyQA0LqOKz1b1HuJ75RQUUOGvM/lK1eoWrUqZ077A3D37l2++OJLAJyqV8fKygoTE5MS69epU4cqNjbY29v/W12uNMLDw2nX3geAbVu30rJli1fcI0H458SAXqg0+jk5YKitpdxYKiMTfS0t3rGzYZKnS5k56MvSwsaSNU3r8ktwOI/SMniakYm9oT4trS0Z7eaEgbY2AP+pYsXqpnXZEBTK/fyNpVpaWzDBw0WVgx5gUX1PnIwN2RcRQ3hmNpZ6unSyt2G8R423Pge9oG6kb2uMDPRYtUe5sZSBri69WjZg8bAeGnPQA+RKlVeVFAoF1x49VTsWn6Ic+Ofmh3kBxKdmEJ+aUWJbvk3qsGfOGJbuOEzAk3B0tLR4p4En84d2V+WgB/hx0lDcHKuw7dQVgmMSsDEzoU+rhsx/v9tbn4P+dfDoUZDq/v79+zA2NtZYdv26tf9GlwRB+Je8lAG9RCJh9+7d9O7d+2U0r+Lv74+Pjw9JSUlYWFgAsGfPHqZPn05wcDATJkygYcOGTJ48meTk5JfaF+HF6FbVjm5V7TQev9W1bZltaCrTuooVrauUHQ5TnnK6WlqMda/B2GeuDghCSQa3a8bgds00Hs/aUzzsoaTnnuX3Tgv83inf7GKnRrXVwmpKoqujzZzB7zJn8LvlalOoPD6dMYM//9ytely/QUMATvufolq1asXKt23XnoiICCZOmMCkSRO5dOky7w8dCsC6dWvZvHkzt28HUrVqVWbN/IwOHTqUqx9Hjx3jq6++Jjw8HC8vL5YsXoSnp6fq+PHjJ9iwcSP3799HJpPh5uqKn99Q+vfvX2q7Bw4c5KuvvyI2No6WLVvSq2ePcvXnWTt37eKzz2aqHhe85hbNm/Pbb9tUzysUsGbtWrZu3UZ2djY+7duzcOEC1RUPuVzOz7/8wo4df/D06VMMDAxo3aoVn302g+rVqxc719Zff2XZsmUEPX6Mm6srCxbMp1GjRs/1GgShJBWecomOjmbChAnUrFkTfX19qlevTo8ePThx4sTL6F+pWrVqRVRUFObmhbNco0ePpl+/foSFhbFo0SIGDhzIw4cPX3pfRo8ejUQiYeXKlS/9XIIgCIJQlJOTE075A0mAhg0a0LBBA/Ty0+5WxMSJk4iLi0cikfDkyROmTJlarkmxuLg4Jk+egraWFgqFgps3bzJixEiyspShknv27GX0mDFcv34dIyMjbGxsuHvvHjNn/Zc1azVfMbh37x6Tp0whLCwcPT09goOD+XzO3Aq/LgBrKyvq1C78Yuvm6krDBg1wc1PfKfXQoUNs+GED+vp6pKamsvevv1j/wwbV8fnzF7B48RIePXqEs7Mz2traHDp8mP4DBhKfkFDsvCNGjiQrOxuZTMbde/eYNHkKUqm0WDlBeF4VGtCHhITQpEkTTp48yfLlywkMDOTw4cP4+Pgwbty4l9VHjfT09LC3t1elU0tPTyc2NhZfX18cHR0xNTXF0NAQW1vbf3SevLy8Uo/v2bOHy5cv4+jo+I/OIwiCIAjPY8L48YwbX/g5vGvXTnbt2vlcn3/D/Pw4cfwYq/InqNIzMrh1q+ydgnNzc1m/bh2HDx9iww8/ABAdE8Pu3corB998+y2g/LJx5rQ/p/1P0bmzch3J2rXrVAP/Z23atBm5XI6pqSnHjx3l1MkTdO7cucKvC8DHx4d1RcKNFixYwK5dO1m4cIFaOR1tbY4cOczJEyeoV0+5fuTihQsAhIWF8dvvvwPw1VfLOXzoIKf9T2Fvb09cXBy//PJLsfPOnPkZx44eYdYs5Yx9REQET58+LVZOEJ5XhQb0Y8eORSKRcOXKFfr164eHhwdeXl5MnTqVS5cuaaz32Wef4eHhgZGRETVr1mTOnDlqg+Rbt27h4+ODqakpZmZmNGnShGvXlNuWP336lB49emBpaYmxsTFeXl4cPHgQUIbcSCQSkpOT8ff3x9RUuRlKhw4dkEgk+Pv7s2XLFlU4ToF9+/bRpEkTDAwMqFmzJgsWLFD7piyRSFi/fj29evXC2NiYxYsXa3xtERERjB8/nm3btqGrq6uxnCAIgiC8Dnr37gWgNmsdnxBfZj1zc3Patm0DQNu2bVRXzx88eEh8QgKRkZEAdPbtjL6+PhKJhO7dugOQnZ3No0ePSmy34PkmTRpjY6Pcnfvdrl1KLPuitPT2xt7eHi0tLWq6KJMrFMy8BwbeUe3b8OmnM3B1c6d+g4ZER0cDEBAQUKy9PvkhyO5F39P4st9TQSivcsfQJyYmcvjwYZYsWVLiQptnB81FmZqasmXLFhwdHQkMDOSjjz7C1NSUGflpod5//30aNWrEunXr0NbWJiAgQDU4HjduHLm5uZw5cwZjY2Pu3btX4qr9Vq1a8eDBAzw9Pdm1axetWrXCysqKkJAQtXJHjhxh6NChfPfdd7Rp04bHjx/z8ccfAzBv3jxVuXnz5rFs2TJWrFiBdv6CyWfJ5XL8/Pz49NNP8fLyKvX9EwRBEITXgZmZGQA6OoWffZo2HiuqvJuPVXSTsoJzF61Xju78I2Zmhbsqa+e/DwX9KPpe1Kldu1hYU1XHqjyr4D3V1i4cdr3s1yC8Xco9oA8KCkKhUFCrVq0Kn+Tzzz9X3a9RowbTpk1jx44dqgF9aGgon376qaptd3d3VfnQ0FD69u2ruuRVs2bNEs+hp6enurRoZWWlMRXXkiVLmDlzJh988IGqvUWLFjFjxgy1Af2QIUMYMaLkHRkLfPnll+jo6DBx4sRSywmCIAjCmy45OZkzZ87Stm0bzp07R0pKCgCenh7YWFvj6OhIZGQkRw4f4YNhw9DT02P/gf0AGBgYqH32F+Xh4cG9+/e5fv0G8QkJ2Fhbc/jIkWLlii7sLS0dpYGhoep+VlZmhV9nvXp1kUgkKBQK3uv7HsM//BBQDvSvX7+uMVWoILxM5R7Ql/QNubx27tzJypUrCQoKIj09HalUqvq2CjB16lRGjRrFr7/+SseOHenfvz+urq4ATJw4kU8++YSjR4/SsWNH+vbtS/369SvchwLXr1/n6tWrLFmyRPWcTCYjOzubzMxMjIyUGws1bdq0zHZWrVrFjRs3xJbogiAIwltPT0+PMZ98glP16qr4cFtbW/r06QPAtKlTmTZ9OgG3btG2XXv09fWJiFBuEjh27CcYFhloFzVixHD2/vUXqampdOzYCSsrK6Kiop67n9ZWVlhaWpKUlMS06Z9So4YzvXr14oNhw8pV38nJiYEDB7B9+w4WL17Czz//jJGRMREREaSnp/Pll1881+SnIPwT5Y6hd3d3RyKRcP/+/Qqd4NKlSwwaNIiuXbuyf/9+bt68yezZs8nNzVWVmT9/Pnfv3qVbt26cPHmSOnXqqBbRjBo1iidPnuDn50dgYCBNmzbl+++/13S6MsnlchYsWEBAQIDqFhgYyKNHjzAwMFCVKy1/L8DZs2eJjY3FyckJHR0ddHR0ePr0KdOmTaNGjRrP3b+3zcHIWAaev0HzI+doc/wC027c42lGyQujnhWXnUPDQ2fY/jRSY5n4nFzm3H5A+xMXaXr4LH3OXGNbSITG8s9adOcRPicuItdwbXTdoxAaHDpT4k0qF9dT32bbT1/De+pyLAdMw3HoTAZ/uYmgyNhy1Y1KTMGozyTWHzyjsUx0Uioff7cNpw/+i3m/KTQav4Q1+0+Xu38T1u3A+YPZyOXyEo8v/v0ghr0nlniT5u/ALAhFValShZUrVyDL/5lq1LAhmzf9qBqo9+7di/Xr1tG4cWMyMjKIi4ujTu3afLFsKePGjtXYrpeXFytWfEu1atXIycmhatWqLHpmEStASqryioCBgQHOzk4a25NIJCxZshhnZ2fS09O5des2ERGaP0dKsmjhQj6f/V88PT2JiYklIiKCatWqMXLECFq2EBtVCf8+iaI8gXH5unbtSmBgIA8ePCg24E1OTlbF0RfNQ//NN9+wdu1aHj9+rCo7atQodu7cqTEN1uDBg8nIyOCvv/4qdmzWrFkcOHCA27dvF8tDn5ycjKWlJadOnaJ9+/YAbNmyRS0PfevWralVqxabNm3S/KaUI49+QkJCsRkCX19f/Pz8GD58uFre3dLcfrdducq9iXaGRrHornKxU1VDA1Ly8kiXyrDU0+WP1o3L3Ezqf6GRLL4bxJH2zbE3NCh2PFMqY+D5G4RmZmGgpYWtgT6hmcovC6NcqzPBw6XU9hUKBZ1OXeY/VayYX8+jxDLrHoWwPigUS11dqhmp9+Fn74Zov6VXbzxG93vVXXilNh05z/h1OwCoYWdNYloGqZnZVDE34dKKz0rdUArgxyPnmbBuBw83LqB6Fctix9OzcvCetpygyDgM9XSpamNBUGQcADP6dWbB0O6ltq9QKHAdORffxrVZN35IiWUW/36QJTsOY2NmjIu9jdqxU8umoK399m00FVm366vuglCKJUuWsvmnn5jx6XRGjx79qrvzWqnp6lZ2IaFSq9DGUmvXrqVVq1Y0b96chQsXUr9+faRSKceOHWPdunUlzt67ubkRGhrK9u3badasGQcOHFDNvgNkZWXx6aef0q9fP1xcXAgPD+fq1av07dsXgMmTJ9O1a1c8PDxISkri5MmT1K5d+uYopZk7dy7du3enevXq9O/fHy0tLW7fvk1gYGCp2WyeZW1tjbW1tdpzurq62Nvbl3sw/zbLlcn5/mEwAB3tbPimcR1is3PofeYaSbl5bHoSxqw6pf+B8Y9NoI6ZSYmDeYD/hUURmpmFBPjVuyEeZiZ8ff8xv4ZEsOVJOIOdq2KjrzlH852UNOJycvGxs9ZYpkAbWysW1Rf/7wLk5OUxb6syLri3dwN+/2wkkYkpNBy3mLiUdL7aeYwVH5f+hWf/5UAau1YvcTAPygF/UGQcEomE08unUq9GVT7bvJvv/jrFt7uP80m3tthbmpVYF+Dao1CiElPo0aLs8MUuTbzYOGlomeUE4VW7fOUKHh4ejBw58lV3RRD+dRWaYnFxceHGjRv4+Pgwbdo06tatS6dOnThx4gTr1q0rsU6vXr2YMmUK48ePp2HDhly4cIE5c+aojmtra5OQkMCwYcPw8PBgwIABdO3alQULlJfTZDIZ48aNo3bt2nTp0gVPT0/WlrIBRVl8fX3Zv38/x44do1mzZrRs2ZJvv/0WZ2fn526zvHJyckhNTVW75cpKvtz9prubmkZy/rb1HfNn/2wN9KlnocwscCEuqdT6GVIpVxKSaV/KYPtCXCIATsaGeJiZqJ1LqlBwJSG51HOciknAUFuLltYlD6qKOh4dT/Mj53jn5CXGX7vD/ZT0MusIb6brQWEkpGUA0Nu7IQCOVuY096gBwPGA0sMW07Ky8Q98SPfm9TSWOXZT2YabQxXq1VBm1OjdqgEAUpkc/9ulb6a37/JtjA306NCg7C+hey7ewnLANFyGf06fResJeBJWZh1BeBX+2ruHQwcPoKNToblKQXgjVPin3sHBgdWrV7N69WqNZZ6N4lm+fDnLly9Xe27y5MmAchHN7/kbNJSktHj59u3bq53LwsKi2Lk//PBDPsxfgV7A19cXX1/fcve/vJ5NkfmsZcuWqb6oFBjj5sQn7jWe63yvs+isHNV9K73C/P3W+TPm0dnZpdY/H5dErlxBBzsbjWWis3OKt18kvVhUVunnOBWbQCsbK/TLCC3QkUiw0ddDWwLBGVmcjUvkckISv7RsRG1zke3gbRMeX/hl1LbI/79t/pfVsDK+rB69cZ+cPGmps+cF56hSpH0788I0e2HxpZ9j/5VAOjasjYFe6Xtn6OpoY29lhraWFg/CYzh8/R6nbj/E/8spNKxZvdS6giAIwr/n7QuCfIVmzZpFSkqK2m1kTc0Ld95GBV+lJJQee34qNoFqhga4m2pevFzS17Kiz5V2jtCMLJ6kZ5YZbvOuox2n3mnJvnbN2NO2GWub1gUgV65gR2jFFlkJbwZN8wGqn+0y1lXsvxKIi501dWto3nm6pHOo/WyXcorHUXHcD4umZwvNVwAABrVrytMtSwhcO4eA1bP5a94nAOTkSfnh4NlS6wqCIAj/LjGg/xfp6+tjZmamdtN7CxeWAdgbFi54Tcgt3DU4MUeZ/ciulAWxUrmCc3GJpYbbADjkt5GQU6T9ItmVivbhWadi4tGRSGhbxarUczgbG2JWZIfg1lWssNBVXvgq6wqA8GYqGvcek5ymuh+Xf7+ajYXGulKZjCPX79G9jMF2wTliUwrbL3q/WilhYn9dvo2OthZdmpa+GZ6boy2WJkaqx50a1cY6/wt0WVcZhLfLkCHv4+rmzqf5e8sIgvDveztHk8IrV9fcVDXwPR6t3P46JjuH2/mDntYaFgMCXE9MJjVPSocyBvSt8gfjYZlZ/J2qjGk/ln8uHYmEFtYWGuueik2gkaUZ5mWEJGx+HKY2cL8Yn6RaG1DVqOTFusKbrambk2rgu+fiLQAiEpK5/CAEUA6MNTl7J4ik9Ex6lrFYtaCNx1Hx3HoSDsCf5wMA0NHWwqdByVmZQLngtnUdV6xKuboF8PWfxwjNX4cCcCLgb9XaAGfbsheKC0JlFxcXx4zPPqNZ8xbUrl0HX98ubPn553LVDQ4JYey4cTRu0pQ6XnXp2bMXf+3bp1Zm1arvcHVzL/EmlUpfxksS3mJi5YjwSuhqaTHBw4VFdx9xIiaed/2vkJKXR6ZMhqWuLiNKic89FZuApa4uDS1LT/3Xv7oDO0OVmW4+uBiglrbyw5rVVPH6z0rMyeVWUirTa7uW+Tr+FxbJdw+DcTDUx0BLm+AM5a6DhtpavO9cfPtv4c2np6vDgqHdGb9uB3sv3aL26AUkpmWQnp2DjZkx0/t20lh335VAbMyM8a5V8o7YBUb5tmbTUWWmG5+ZK9TSVk7t0xE7i5Iz3MSlpHHpQTDLR7xX5uvYeOg8c3/dT3UbS4z0dXkQocyhb2ygx/ieb2+6XaHyk8vlnD9/ntq1a2NjU/I6q4yMDAYNHkJISAgGBgY4Vq1K0OPHLFq0mPj4BKZPm6qx/ejoaAb0H0BiUhImJibY2lbh7r17TJkylcyMTAYNGqhW3srSEicn9fBasSGl8KKJGXrhlenn5MDS+p54mhoTl5ODBHjHzoafvRuWmoP+dGwCbWytyszxbqSjzeYW9elZ1Q5DbW0is7JxMTZkRm3XUnPQn45NRA7lSlc5sqYTza0tyJMrCM/KwsFQn3cdbdneqjGuZcyACm+ukb6t2TzFjwYuVYlKTEGChF4tG3Dqiyml5qA/cOUOXZvWLTPHu4mhPkcXT2SoT3OMDfR4GpuIZzU7vhr5Xqk56A9evYNcrig1g06BGf060b6eO7lSKcExCThVsWRQu6Zc+OZTald3KLO+8PK1bdceVzd3li//irlz59G4SVMaNGzE3LnzyMnJ0VgvJyeHho0a4+rmzk9btqieDw8PV80gnzlzlszMTEaP+YR27X2oW68+tWvXocM7HVmxcqXa5pDPKtrOpUuXi/V31arvVM/FxMTw2cyZeLdqTa3adWjv48P3q1c/1wz248eP+eqrr2nTpi0fDh9BbKzmjdx+/307ISEhSCQSdu38HyeOH2PkiBEAbNy4kbi4OI111/+wQTmYNzbm6JHD+J86RZf8RBtff/NNsfemvU97du3aqXbT1tau8OsThNK8FjP0a9eu5auvviIqKgovLy9WrlxJmzZtSiz74Ycf8nMJl8zq1KnD3bt3AeUv6y+//MKdO3cAaNKkCUuXLqV58+YVOq9CoWDBggVs2LCBpKQkWrRowZo1a/DyKj02VSjUraod3aralbv8/ZR0IrNyygy3KVDFQL/C+eH9YxPwNDXGUUN++6L6OTnQz0kMboTiBrdrxuB2zcpdPuBJGKFxifQoI36+gIOVeYXzw++/EkgDl6o425a+NgSUX0pG+rauUPvCq/HTli0YGxtjZmZKWFg42377DX19fWbP/m+J5fX19Xn33XfZsWMHB/YfYHh+Jrj9+w8AYGdnR+vWrUhJSeH48ePY2NjgUqMGiUlJPH36lNWr15Cdnc2smTP/Ub8TExPp268/UVFRmBgb4+rqSlBQECtXriI8PJwvv/iizDZSUlLYv38/f/65m4BbyhC3WrVqMeyDYaWmoz5zVrkLc40aNahVqxYAvl182bR5M1KplIsXL9KzZ8+S655R1m3UqBF2dsrPL1/fzhw+coSkpCQC79yhSePGqvJHjhzlwIGDmJmZ4eVVh6lTpohxgvDCVfoZ+h07djB58mRmz57NzZs3adOmDV27diU0NLTE8qtWrSIqKkp1CwsLw8rKiv79+6vK+Pv7M3jwYE6dOsXFixdxcnKic+fOREREVOi8y5cv59tvv2X16tVcvXoVe3t7OnXqRFpaGsLLIVXIGePmhLdN2bnhn1dDSzMmepa+i6wgvGh5UjmzB3ahY8NaL+0cLWvVZMHQHi+tfeHVcHR0xP/USfxPnaJHD+UVmq1bt5b6WfTee30AuBkQoPrsO3BAOaDv3asX2tramJiYcPjQQS5fusi+fX9x/txZevfqBRQO/v+JrVu3ERUVhY2NDSdPnuDA/n2szk9VvWvXn4SEPNVY9+7du0yYMJGW3q2YO28+8QkJfDJmDIcOHuTA/n2M/vjjYjvaFxUVFQ2gtkGkTZH7kZFRxeoU1o0qVte6SGhPZGRhhjNdXV2qVKlCtWpViYuLw9//NP36D1BNMArCi1LpZ+i//fZbRo4cyahRowBYuXIlR44cYd26dSxbtqxYeXNzc8zNCy9p79mzh6SkJIYPH656btu2bWp1Nm7cyM6dOzlx4gTDhg0r13kVCgUrV65k9uzZvPeeMh71559/xs7Ojt9++01sO/2S1LMwo56G+OAXZbjIry28As08nGnm8XI3uJv2XseX2r7wanTw8cHERLknQfdu3dm3bz+5eXkEBweTkJDA6tVrVGWr2Nqyft1amjZpgrOzM0+fPmX/gQN07tSJe/m7vRcM9rW1tdm79y8OHT5MZEQEuXmFGcNKC2cpr1u3lTPq8fHxNG/RUu2YQqHg1q0AatQo+Xfi+PETHDx0CENDQxYtWsjgQYMqFJde0n4zRZ+qcIh7kcoF/ejZqycffviBakxy5sxZho8YQW5uLlu3bmPZsqUVPIkgaFapZ+hzc3O5fv06nTt3Vnu+c+fOXLhwoVxtbNq0iY4dO5Z66S0zM5O8vDysrKzKfd7g4GCio6PVyujr69OuXbty900QBEEQ/qnSBrIJiYkE3Lqlut27d091rE+f3gAcOHCQ/fv3A9CgQX3c3NwAWP/DD6xbv56QkBCq2NrSsEED7PNDTORyzbucF+2PTC5T3X/2ikHBGNjE2JiGDRoUuxkYGmo8R9u2bWjbti25ubnMmTOX7j168sMPP6jNjpfG0VEZKhkfH696LiExQXXfwUFzKGXBMbW6CcXrutSooTbB2LZtGywtlVeXy9tPQSivSj2gj4+PRyaTqWLUCtjZ2REdHV1m/aioKA4dOqSaZddk5syZVK1alY4dO5b7vAX/Pm/fBEEQBOFFOHHyJOnpytS8Bw8eBEBPVxcXFxf69e3L46BHqtuZ0/6qeu/16YNEIuHu3bts++33/OcKMyAF3AwAwMXFhTOn/fnf//6gVu2yQ8KKhqKEBAcDcP78eVJTU9XKNaivTM+qraPDqlUrVQtGf/55C+8PfR/fZybVimrUqBE/bd7E2TOnmTHjU2RSKcu/+pq27dozcNBgtm3bRnYpO463bdMWgKdPn6q+5Bw6eAgAHR0dWrVqBcAvv/xKp86+dOpcuLt827bKugEBAarP+0OHDwNgaWlJvbrKDQaf/YJx7tw5kpKUezhUrVZNY98E4XlU+pAbKD77oFAoynVpbcuWLVhYWNC7d2+NZZYvX87vv/+Ov78/BgbqiyDLc97n7ZtQsoORsfwcHE5weib62lo0t7JgoqcLzsaaZ2oAtoVEsDM0irDMLEx0dWhTxYpJni7Y5KemjMnOYdGdRzxITSc5Lw99LW0cDPXp5mjLMJdqaOX/nwWlZbD6UQh3ktOIy9/kanjN6kwuElOfIZWy5uFTbiSlEJWVTZZMjp2BPr4OVRhesxrGOq/Fr5XwL9p++hqr9p7k7/AYDPV0aVfPnUV+PXBztC213pr9p/nx8DmeRMdjbmxIlyZeLPTrgb2lMuwsIiGZ8Wu3czskgoTUDAz1dKlexZJB7ZoxuZcPWlrKOZt7oVHM33aAa4+eEpWYAijDbxYPK1z0l5aVzYJtB7hw7zGhcUlk5ORSzcaCfq0bM/W9dzAtxyJx4dWIjY2lvU8HTE1MCA0LA2DI+0MwNTUttV7VqlVp3rw5ly9fJi4uDj09Pbp376Y67lmrFidPnSI4OJh27X2Q5uWRXUr2nAIGBgY0atSImzdvsuyLLzl46DC3bt1CS0tLbWZ/6ND3+eOPP4iOiaFTp864urmSkZ5BVHQ0eXl5vNenT5nnsrOzY/THHzP644+5ffs2f/65m3379zN33nwaNWpEnTp1Sqw3ePAgft+uzHTTf8BA7O3tCQkJAeCjjz5SpbtMSkriyZMnanXHjP6YA/v3k5iUhG+XrlhYWBAertwPYvq0aejpKT93tv32O199/Q2Ojo4YGhjwOL8dIyMjhn/4QZmvTRAqolLP0NvY2KCtrV1sxjs2NrbYzPizFAoFmzdvxs/PT/XL9ayvv/6apUuXcvToUerXL9zIpTzntbe3B3iuvgkl2xkaxaxbf/N3ajo2+nrIFQqOx8TzwaUAYrM1f4h89yCY5fcf8yQjEwdDA7KkMv6KiGHE5VtkSpWXe5Ny87iSkIyRjjZuJsZoSeBhWgYrHgTz05MwVVuhmVn4xyRgoqM5pVhyrpRtTyN4mJaOpZ4uRtrahGZmsfFxKDMC7r+4N0R4I2w6cp7hK34h4Ek49pZmyORy9ly8RYdZK4nMH1yXZO6v+5j+4y7+Do/BydaK9Kwcfj15mc6zvyMj//chPiUd/8BHmBjo4+XkgJaWhMCQSGb/vJdvdp9QtRUUGcf+K4GYlbLZWWJqBmv2n+Z2SCQ25iaYGOgRFBnHF/87gt9XW17Y+yG8eB8MG0bPnj1ISU3FxNiYwYMHMePTT8tVtyBeHuCdDh2wsLBQPR77yRj69OmDmZkZ6enpdOvenfffH1Kudr9a/iXNmjVFoVAQHR3NggXzi4WxWFtbs3Pn/+jXry8WlpY8ehREdk4OTZs25XMNGXpKU79+febPn8fFC+dZs/p7tSsFzzI2Nub337bx3nt9MDI0JCIiAlfXmnz++exSc9CD8vP/jz924OvbGYlEQmxsLHVq12bFt9+o5aD/5JMxeHt7k5eXR2hYGFWrVqVXz57s3bMbd3f3Cr8+QSiNRFHSypBKpEWLFjRp0oS1a9eqnqtTpw69evUqcVFsAX9/f3x8fAgMDKRu/uWvor766isWL17MkSNHaNmyZbHjZZ1XoVDg6OjIlClTmJG/3XVubi62trZ8+eWX5V4Ue/tdsUELQK5MTqdTl0jOk9LRzoZvGtchNjuH3meukSGTMcjZkVl13IrVi8/JxffUZaQKBcNcqjGtVk0epqYz4PwNFMDUWjX5wKUaUrnyx1xHSzkTnyGV0uHkJbJlctpWseL7psqfkbQ8KRLARFeHBoeUqcmenaGPz8nlQEQM/ZwcMNbRIUcmZ9SVW6pdbs929MZMt/QdZt90HqP7veouVAo5eXm4jphLQloGvb0b8PtnI4lMTKHhuMWkZeUw5t22rPi4+HsVnZSK+6i5SGVyJvXqwBfDexMYEkGLKctRKBQs+7A3k3t3QCpTfmHVyc9pnZaVTY0PPyczJ5d3m3qx63Pl36GUjCwkEjAzMsSw90Sg+Ax9dFIqv5++yijf1pgaGpCdm4fvnO+5kr/DbeTWL7A0MXqZb1elF1m366vugpq27doTERHBxAkTmDRp4qvujvAaq+la/PNVeL1U+tiAqVOn4ufnR9OmTfH29mbDhg2EhoYyZswYAGbNmkVERAS//PKLWr1NmzbRokWLEgfzy5cvZ86cOfz222/UqFFDNctuYmKiyhRQ1nklEgmTJ09m6dKluLu74+7uztKlSzEyMmLIkPLNYAiF7qamkZyn3Eiko73yUqetgT71LEy5lJDMhbikEutdjk9Cmv+dtKOdsp6HmQnVjQwJzcziQlwiH7hUUw3kJ16/Q1x2LpFZ2WTLlJd+GxXZcdZUt+xfCRt9PT4okglHX1sLL3NTbienoQVlbnglvD2uB4WRkJYBQG/vhgA4WpnT3KMGJ2494LiGKzqnbj9Amv/z2du7AQD1alTF1cGGoMg4jt+8z+TeHVQD+X5LNhCVmMLT2EQy80PFvOsU7jZrXkbIGoC9pRlTer+jemygp0sTNyeuPAhBS0uCThmbXQmCIAivTqUf0A8cOJCEhAQWLlxIVFQUdevW5eDBg6qsNVFRUcVy0qekpLBr1y5WrVpVYptr164lNzeXfv3UZ8bmzZvH/Pnzy3VegBkzZpCVlcXYsWNVG0sdPXq0zLhFobjorMKQGiu9wtlt6/wY+GgNi5uiszXV0yU0M0vtOCg3porNKdzF70OXagyv+c8WJ8Vk53AsWpntoItDFRFDL6iExxd+EbU1Nym8b6H8GxGm4YtqeHxyyfXMTQmKjCMsXr3ezSdhRCYUhu9M7fMO0/r8sxSVEQnJ7L4QAED//zQWMfSCIAiV2Gsx8hg7dixjx44t8diWIttWFzA3NyczM1NjewULX/7JeUE5Sz9//nzVlwDhxSuIB5NQsVnvwnrqjnVoSZZMxtWEZD4L+JtfgsNxNjbkvefcyj4sI4ux1+4Qn5NLQ0szPq8r4iKFQpoCGlU/n5qu5mioqKne402LyMzJ5XTgI4Z9vYWVe0/i5liF4Z1aVbzTwJOoOHotWk90UiretWvy/ScDy64k/OuKZqwRBOHtJq6hCpWCvaG+6n5CbuHmJYn5s+l2BvrF6gDYGxStVzjznpiTp7GeobY2bW2taWljgRxY+0jzboSluZWUit/FAEIzs2hna8X6ZvXE7LygpnqVwh2NY5ILc3DH5d+vZmNRYr1qReulFKmXf7+adfF6Rvp6dG3qRYeGnsjlChb+dvC5+nzp72DaffYtQZFxdGtWl/3zx4rZeUEQhEpODOiFSqGuuSkW+fHrx/PDV2Kyc1QLTVvnD3B6nblKrzNX+f2pcqvyFjaW6OTPVhaEvfydmk5YZlZ+PeVmYSdj4gnJKLxqk5CTy70UZd7mLFnhxifldSwqjo+u3CYpL4/Bzo6sbOyFobbmzDjC26mpmxPWpsrt5/dcVO6KGZGQzOX8haadGtUGoMG4xTQYt5h1B5QLsX3qe6pi1gvCXm49CedxlPJnvFNjZb2/Lt3mUUThjp2xyWncCFJmbcosElpWXn9euEnXuauJT83gk25t+WPWKIz0S84SJgiCIFQeYjpRqBR0tbSY4OHCoruPOBETz7v+V0jJyyNTJsNSV5cR+YtQQzKUA/Xk/Fl8G309PnCpxqYnYWwNieBMbCKx2TkoACcjQ/rlh9Kciklgyo17VNHXw1JPl6cZWeTk50PuUbUwzejt5FT+e+tvtb7tDI3ieHQcdgb6bGrRgNjsHD4NuI8C0JVIuJOcxrCLAarys73cqG0u1lEIoKerw4Kh3Rm/bgd7L92i9ugFJKZlkJ6dg42ZMdP7dgLgYf6gPCFV+SWzYIHqV7uO8f1f/hy6dpeI+GQUCgVujlUY5dsagH2XbzPwix9xsDLHxsyYR5FxZOf/brzv01zVjysPQxj+7TOJA46cZ/eFAKpaW3B0yUQiE1MY+tUWFAoFejraXHv0lPYzV6jKrxo9gEau1REEQRAqHzGgFyqNfk4OGGprKTeWyshEX0uLd+xsmOTpgq2GkBuACR41sNbX43+hkYRlZmOqo0NnhypM8nTBKD+ffAtrC0IzsgjJyORxegYGWtq4m5vyrqMtg50dVW3lyOSEZaovwE2TSkmTSpHlxzXnyRWqWOY8hYLAFPXtzNOlFZ/xF95cI31bY2Sgx6o9yo2lDHR16dWyAYuH9cDRylxjvQVDu2NrYcqPR84rN5YyMqBv60YsGtYT4/zfhw4NPAmKiuNRRCz3QqMx0telrrszg9o15ZN326jays7J40l0vFr7yRlZJGdkqbLp5OVJKchinCuVcfWheihaWqbmXTcFQRCEV6vS56F/04k89MKbSOShF95ElS0PvSC8KCIP/etPxNALgiAIgiAIwmtMDOgFQRAEQRAE4TUmBvSCIAiCIAiC8BoTA3pBEARBEARBeI2JAb0gCIIgCIIgvMbEgF4QBEEQBEEQXmNiQC8IgiAIgiAIrzGRh14QBEEQBEEQXmNihl4QBEEQBEEQXmNiQC8IgiAIgiAIrzExoBcEQRAEQRCE15gY0AuCIAiCIAjCa0wM6AVBEARBEAThNSYG9IIgCIIgCILwGhMDekEQBEEQBEF4jYkBvSAIgiAIgiC8xsSAXhAEQRAEQRBeY/8HvdhMSPepO08AAAAASUVORK5CYII=", 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", "text/plain": [ "
" ] @@ -638,12 +573,12 @@ " df_results=df2,\n", " excluded_col_comparates=[\"Classifier 1\"],\n", " used_statistic=\"error\",\n", - " order_better=\"increasing\",\n", - " order_win_tie_loss=\"lower\",\n", + " formats=None,\n", " fig_size=\"8,3\",\n", - " win_label=\"rc\",\n", - ")" + " higher_stat_better=False,\n", + " include_legend=False,\n", + ")\n", + "plt.show()" ] } ], From 098fdcdac442de459cd7948e3721916b74731040 Mon Sep 17 00:00:00 2001 From: Kavya Rambhia <161142013+kavya-r30@users.noreply.github.com> Date: Wed, 25 Jun 2025 22:39:17 +0530 Subject: [PATCH 165/216] [MNT] Prevents assignment on Restricted Labels (#2706) * restricted labels * set * resolve conflicts * merge assign PRs and small change --------- Co-authored-by: MatthewMiddlehurst --- .github/utilities/issue_assign.py | 38 ++++++++++++++++++++----------- 1 file changed, 25 insertions(+), 13 deletions(-) diff --git a/.github/utilities/issue_assign.py b/.github/utilities/issue_assign.py index 1696fd33fc..07f2d0e699 100755 --- a/.github/utilities/issue_assign.py +++ b/.github/utilities/issue_assign.py @@ -3,6 +3,7 @@ It checks if a comment on an issue or PR includes the trigger phrase (as defined) and a mentioned user. If it does, it assigns the issue to the mentioned user. + Users without write access can only have up to 2 open issues assigned. Users with write access (or admin) are exempt from this limit. If a non-write user already has 2 or more open issues, the bot @@ -22,20 +23,32 @@ repo = g.get_repo(repo) issue_number = context_dict["event"]["issue"]["number"] issue = repo.get_issue(number=issue_number) -comment_body = context_dict["event"]["comment"]["body"] +issue_labels = {label.name.lower() for label in issue.labels} pr = context_dict["event"]["issue"].get("pull_request") +comment_body = context_dict["event"]["comment"]["body"] commenter = context_dict["event"]["comment"]["user"]["login"] +restricted_labels = {"meta-issue"} + +# Assign tagged used to the issue if the comment includes the trigger phrase body = comment_body.lower() -if "@aeon-actions-bot" in body and not pr: - # Assign commenter if comment includes "assign me" - if "assign me" in body: - issue.add_to_assignees(commenter) - # Assign tagged used to the issue if the comment includes the trigger phrase - elif "assign" in body: +if "@aeon-actions-bot" in body and "assign" in body and not pr: + # Check if the issue has any restricted labels for auto assignment + label_intersect = issue_labels & restricted_labels + if len(label_intersect) > 0: + issue.create_comment( + f"This issue contains the following restricted label(s): " + f"{', '.join(label_intersect)}. Cannot assign to users." + ) + else: + # collect any mentioned (@username) users mentioned_users = re.findall(r"@[a-zA-Z0-9_-]+", comment_body) mentioned_users = [user[1:] for user in mentioned_users] mentioned_users.remove("aeon-actions-bot") + # Assign commenter if comment includes "assign me" + if "assign me" in body: + mentioned_users.append(commenter) + mentioned_users = set(mentioned_users) for user in mentioned_users: user_obj = g.get_user(user) @@ -53,7 +66,7 @@ issues_assigned_to_user = g.search_issues(query) assigned_count = issues_assigned_to_user.totalCount - if assigned_count >= 2: + if assigned_count >= 3: # link to issue assigned_issues_list = [ f"[#{assigned_issue.number}]({assigned_issue.html_url})" @@ -61,11 +74,10 @@ ] comment_message = ( - f"@{user}, you already have {assigned_count} " - f"open issues assigned." - "Users without write access are limited to self-assigning two" - "issues.\n\n" - "Here are the open issues assigned to you:\n" + f"@{user}, already has {assigned_count} open issues assigned." + "Users without write access are limited to self-assigning " + "three issues.\n\n" + "Here are the open issues assigned:\n" + "\n".join( f"- {issue_link}" for issue_link in assigned_issues_list ) From c7a23a5756b2c42757350d53d20fa9694d0fcc0e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 25 Jun 2025 18:11:15 +0100 Subject: [PATCH 166/216] [MNT] Update scikit-learn requirement from <1.7.0,>=1.0.0 to >=1.0.0,<1.8.0 in the python-packages group (#2907) * [MNT] Update scikit-learn requirement in the python-packages group Updates the requirements on [scikit-learn](https://github.com/scikit-learn/scikit-learn) to permit the latest version. Updates `scikit-learn` to 1.7.0 - [Release notes](https://github.com/scikit-learn/scikit-learn/releases) - [Commits](https://github.com/scikit-learn/scikit-learn/compare/1.0...1.7.0) --- updated-dependencies: - dependency-name: scikit-learn dependency-version: 1.7.0 dependency-type: direct:production dependency-group: python-packages ... Signed-off-by: dependabot[bot] * new validation * Update test_rockad.py * Update _rockad.py --------- Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: MatthewMiddlehurst --- aeon/anomaly_detection/series/distance_based/_rockad.py | 2 ++ .../series/distance_based/tests/test_rockad.py | 5 ----- aeon/classification/sklearn/_continuous_interval_tree.py | 8 +++++--- .../classification/sklearn/_rotation_forest_classifier.py | 5 +++-- aeon/regression/sklearn/_rotation_forest_regressor.py | 5 +++-- pyproject.toml | 2 +- 6 files changed, 14 insertions(+), 13 deletions(-) diff --git a/aeon/anomaly_detection/series/distance_based/_rockad.py b/aeon/anomaly_detection/series/distance_based/_rockad.py index 1fd0fa8f30..ce60a4485c 100644 --- a/aeon/anomaly_detection/series/distance_based/_rockad.py +++ b/aeon/anomaly_detection/series/distance_based/_rockad.py @@ -176,6 +176,8 @@ def _inner_fit(self, X: np.ndarray) -> None: if self.power_transform: self.power_transformer_ = PowerTransformer() + # todo check if this is still an issue with scikit-learn >= 1.7.0 + # when lower bound is raised try: Xtp = self.power_transformer_.fit_transform(Xt) diff --git a/aeon/anomaly_detection/series/distance_based/tests/test_rockad.py b/aeon/anomaly_detection/series/distance_based/tests/test_rockad.py index 16f60edff7..d7b9aca222 100644 --- a/aeon/anomaly_detection/series/distance_based/tests/test_rockad.py +++ b/aeon/anomaly_detection/series/distance_based/tests/test_rockad.py @@ -73,11 +73,6 @@ def test_rockad_incorrect_input(): ): ad = ROCKAD(stride=1, window_size=100) ad.fit(train_series) - with pytest.warns( - UserWarning, match=r"Power Transform failed and thus has been disabled." - ): - ad = ROCKAD(stride=1, window_size=5) - ad.fit(train_series) with pytest.raises( ValueError, match=r"window shape cannot be larger than input array shape" ): diff --git a/aeon/classification/sklearn/_continuous_interval_tree.py b/aeon/classification/sklearn/_continuous_interval_tree.py index d3f12feb6c..d3ce9a9b89 100644 --- a/aeon/classification/sklearn/_continuous_interval_tree.py +++ b/aeon/classification/sklearn/_continuous_interval_tree.py @@ -21,6 +21,7 @@ from sklearn.exceptions import NotFittedError from sklearn.utils import check_random_state from sklearn.utils.multiclass import check_classification_targets +from sklearn.utils.validation import validate_data class _TreeNode: @@ -374,7 +375,8 @@ def fit(self, X, y): """ # data processing X = self._check_X(X) - X, y = self._validate_data( + X, y = validate_data( + self, X=X, y=y, ensure_min_samples=2, @@ -464,8 +466,8 @@ def predict_proba(self, X): # data processing X = self._check_X(X) - X = self._validate_data( - X=X, reset=False, force_all_finite="allow-nan", accept_sparse=False + X = validate_data( + self, X=X, reset=False, force_all_finite="allow-nan", accept_sparse=False ) dists = np.zeros((X.shape[0], self.n_classes_)) diff --git a/aeon/classification/sklearn/_rotation_forest_classifier.py b/aeon/classification/sklearn/_rotation_forest_classifier.py index e89776d0ca..eab57956ae 100644 --- a/aeon/classification/sklearn/_rotation_forest_classifier.py +++ b/aeon/classification/sklearn/_rotation_forest_classifier.py @@ -19,6 +19,7 @@ from sklearn.tree import DecisionTreeClassifier from sklearn.utils import check_random_state from sklearn.utils.multiclass import check_classification_targets +from sklearn.utils.validation import validate_data from aeon.base._base import _clone_estimator from aeon.utils.validation import check_n_jobs @@ -192,7 +193,7 @@ def predict_proba(self, X) -> np.ndarray: # data processing X = self._check_X(X) - X = self._validate_data(X=X, reset=False, accept_sparse=False) + X = validate_data(self, X=X, reset=False, accept_sparse=False) # replace missing values with 0 and remove useless attributes X = X[:, self._useful_atts] @@ -299,7 +300,7 @@ def fit_predict_proba(self, X, y) -> np.ndarray: def _fit_rotf(self, X, y, save_transformed_data: bool = False): # data processing X = self._check_X(X) - X, y = self._validate_data(X=X, y=y, ensure_min_samples=2, accept_sparse=False) + X, y = validate_data(self, X=X, y=y, ensure_min_samples=2, accept_sparse=False) check_classification_targets(y) self._n_jobs = check_n_jobs(self.n_jobs) diff --git a/aeon/regression/sklearn/_rotation_forest_regressor.py b/aeon/regression/sklearn/_rotation_forest_regressor.py index e187181714..ea3b97f9d3 100644 --- a/aeon/regression/sklearn/_rotation_forest_regressor.py +++ b/aeon/regression/sklearn/_rotation_forest_regressor.py @@ -19,6 +19,7 @@ from sklearn.exceptions import NotFittedError from sklearn.tree import DecisionTreeRegressor from sklearn.utils import check_random_state +from sklearn.utils.validation import validate_data from aeon.base._base import _clone_estimator from aeon.utils.validation import check_n_jobs @@ -168,7 +169,7 @@ def predict(self, X) -> np.ndarray: # data processing X = self._check_X(X) - X = self._validate_data(X=X, reset=False, accept_sparse=False) + X = validate_data(self, X=X, reset=False, accept_sparse=False) # replace missing values with 0 and remove useless attributes X = X[:, self._useful_atts] @@ -222,7 +223,7 @@ def fit_predict(self, X, y) -> np.ndarray: def _fit_rotf(self, X, y, save_transformed_data: bool = False): # data processing X = self._check_X(X) - X, y = self._validate_data(X=X, y=y, ensure_min_samples=2, accept_sparse=False) + X, y = validate_data(self, X=X, y=y, ensure_min_samples=2, accept_sparse=False) self._label_average = np.mean(y) diff --git a/pyproject.toml b/pyproject.toml index 1a04d40bd5..6620deb194 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -50,7 +50,7 @@ dependencies = [ "numpy>=1.21.0,<2.3.0", "packaging>=20.0", "pandas>=2.0.0,<2.4.0", - "scikit-learn>=1.0.0,<1.7.0", + "scikit-learn>=1.0.0,<1.8.0", "scipy>=1.9.0,<1.16.0", "typing-extensions>=4.6.0", ] From 4ff60325b057d74c746fac1621f501b147681651 Mon Sep 17 00:00:00 2001 From: Akash Kawle <128881349+shinymack@users.noreply.github.com> Date: Thu, 26 Jun 2025 00:32:28 +0530 Subject: [PATCH 167/216] [MNT] issue-assign-bot - only users with write access should be able to assign others (#2739) * only admins can assign others * improve comment message * Automatic `pre-commit` fixes * fixes according to latest code * Automatic `pre-commit` fixes * changes according to latest code * resolve merge conflicts * Automatic `pre-commit` fixes * adjust according to latest code * merge --------- Co-authored-by: Matthew Middlehurst --- .github/utilities/issue_assign.py | 39 +++++++++++++++++-------------- 1 file changed, 22 insertions(+), 17 deletions(-) diff --git a/.github/utilities/issue_assign.py b/.github/utilities/issue_assign.py index 07f2d0e699..0610d99652 100755 --- a/.github/utilities/issue_assign.py +++ b/.github/utilities/issue_assign.py @@ -1,13 +1,8 @@ """Script for the GitHub issue self-assign bot. -It checks if a comment on an issue or PR includes the trigger -phrase (as defined) and a mentioned user. -If it does, it assigns the issue to the mentioned user. - -Users without write access can only have up to 2 open issues assigned. -Users with write access (or admin) are exempt from this limit. -If a non-write user already has 2 or more open issues, the bot -comments on the issue with links to the currently assigned open issues. +Checks if a comment on an issue or PR includes the trigger phrase and a mentioned user. +If it does, it assigns or unassigns the issue to the mentioned user if they have +permissions. """ import json @@ -27,6 +22,8 @@ pr = context_dict["event"]["issue"].get("pull_request") comment_body = context_dict["event"]["comment"]["body"] commenter = context_dict["event"]["comment"]["user"]["login"] +commenter_permission = repo.get_collaborator_permission(commenter) +has_write_permission = commenter_permission not in ["admin", "write"] restricted_labels = {"meta-issue"} @@ -50,17 +47,26 @@ mentioned_users.append(commenter) mentioned_users = set(mentioned_users) + access_error = False for user in mentioned_users: - user_obj = g.get_user(user) - permission = repo.get_collaborator_permission(user_obj) + # Can only assign others if the commenter has write access + if user != commenter and not has_write_permission: + if not access_error: + issue.create_comment( + "Cannot assign other users to issues without write access." + ) + access_error = True + continue - if permission in ["admin", "write"]: + # If the user is already assigned to this issue, remove them + if user in [assignee.login for assignee in issue.assignees]: + issue.remove_from_assignees(user) + continue + + # If the commenter has write access, just assign + if has_write_permission: issue.add_to_assignees(user) else: - # First check if the user is already assigned to this issue - if user in [assignee.login for assignee in issue.assignees]: - continue - # search for open issues only query = f"repo:{repo.full_name} is:issue is:open assignee:{user}" issues_assigned_to_user = g.search_issues(query) @@ -73,7 +79,7 @@ for assigned_issue in issues_assigned_to_user ] - comment_message = ( + issue.create_comment( f"@{user}, already has {assigned_count} open issues assigned." "Users without write access are limited to self-assigning " "three issues.\n\n" @@ -82,6 +88,5 @@ f"- {issue_link}" for issue_link in assigned_issues_list ) ) - issue.create_comment(comment_message) else: issue.add_to_assignees(user) From b5d4db9f08e268ce819326eff780df0abcdf67a3 Mon Sep 17 00:00:00 2001 From: Ramana Raja <83065061+Ramana-Raja@users.noreply.github.com> Date: Thu, 26 Jun 2025 01:13:04 +0530 Subject: [PATCH 168/216] [MNT] Created workflow for closing "AI Spam" pull requests (#2750) * added ai_spam * Automatic `pre-commit` fixes * fixed comment text * fixed comment text * updated to python 3.11 --------- Co-authored-by: Ramana-Raja --- .github/utilities/ai_spam.py | 27 ++++++++++++++++++++++ .github/workflows/ai_spam.yml | 42 +++++++++++++++++++++++++++++++++++ 2 files changed, 69 insertions(+) create mode 100644 .github/utilities/ai_spam.py create mode 100644 .github/workflows/ai_spam.yml diff --git a/.github/utilities/ai_spam.py b/.github/utilities/ai_spam.py new file mode 100644 index 0000000000..0113aaad08 --- /dev/null +++ b/.github/utilities/ai_spam.py @@ -0,0 +1,27 @@ +"""Script for handling AI Spam label on pull requests. + +Triggered when AI Spam label is added to a PR, +it adds a comment and closes the PR. +""" + +import json +import os + +from github import Github + +context_dict = json.loads(os.getenv("CONTEXT_GITHUB")) + +repo_name = context_dict["repository"] +g = Github(os.getenv("GITHUB_TOKEN")) +repo = g.get_repo(repo_name) +pr_number = context_dict["event"]["pull_request"]["number"] +pr = repo.get_pull(pr_number) +label_name = context_dict["event"]["label"]["name"] + +if label_name == "AI Spam": + comment_body = ( + "This pull request has been flagged with the **AI Spam** label.\n\n" + "This PR is being closed." + ) + pr.create_issue_comment(comment_body) + pr.edit(state="closed") diff --git a/.github/workflows/ai_spam.yml b/.github/workflows/ai_spam.yml new file mode 100644 index 0000000000..f09a1cff2d --- /dev/null +++ b/.github/workflows/ai_spam.yml @@ -0,0 +1,42 @@ +name: AI Spam Detection On PR + +on: + pull_request: + types: [labeled] + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number }} + cancel-in-progress: true + +jobs: + ai-spam-present: + if: ${{ github.event.label.name == 'AI Spam' }} + runs-on: ubuntu-24.04 + + steps: + - name: Create app token + uses: actions/create-github-app-token@v1 + id: app-token + with: + app-id: ${{ vars.PR_APP_ID }} + private-key: ${{ secrets.PR_APP_KEY }} + + - name: Checkout main + uses: actions/checkout@v4 + with: + sparse-checkout: .github/utilities + + - name: Setup Python 3.11 + uses: actions/setup-python@v5 + with: + python-version: "3.11" + + - name: Install PyGithub + run: pip install -Uq PyGithub + + - name: Process AI Spam + id: handle_spam + run: python .github/utilities/ai_spam.py + env: + CONTEXT_GITHUB: ${{ toJson(github) }} + GITHUB_TOKEN: ${{ steps.app-token.outputs.token }} From fe9078575a4256a2e1b5e94961155e6b4dd15440 Mon Sep 17 00:00:00 2001 From: Kaustubh <97254178+Kaustbh@users.noreply.github.com> Date: Thu, 26 Jun 2025 18:14:40 +0530 Subject: [PATCH 169/216] [ENH] Use `np.argpartition` for efficient top-k selection instead of `np.argsort` (#2805) * use argpartition for efficient selection instead of argsort * made the required changes --- aeon/similarity_search/series/_commons.py | 58 ++++++++++++++--------- 1 file changed, 35 insertions(+), 23 deletions(-) diff --git a/aeon/similarity_search/series/_commons.py b/aeon/similarity_search/series/_commons.py index fa3346df11..0e1df4235f 100644 --- a/aeon/similarity_search/series/_commons.py +++ b/aeon/similarity_search/series/_commons.py @@ -137,36 +137,48 @@ def _extract_top_k_from_dist_profile( top_k_distances = np.full(k, np.inf, dtype=np.float64) ub = np.full(k, np.inf) lb = np.full(k, -1.0) - # Could be optimized by using argpartition - sorted_indexes = np.argsort(dist_profile) + + remaining_indices = np.arange(len(dist_profile)) + mask = np.full(len(dist_profile), True) _current_k = 0 + if not allow_trivial_matches: - _current_j = 0 - # Until we extract k value or explore all the array or until dist is > threshold - while _current_k < k and _current_j < len(sorted_indexes): - # if we didn't insert anything or there is a conflict in lb/ub - if _current_k > 0 and np.any( - (sorted_indexes[_current_j] >= lb[:_current_k]) - & (sorted_indexes[_current_j] <= ub[:_current_k]) - ): - pass - else: - _idx = sorted_indexes[_current_j] - if dist_profile[_idx] <= threshold: - top_k_indexes[_current_k] = _idx - top_k_distances[_current_k] = dist_profile[_idx] - ub[_current_k] = min( - top_k_indexes[_current_k] + exclusion_size, - len(dist_profile), - ) - lb[_current_k] = max(top_k_indexes[_current_k] - exclusion_size, 0) + while _current_k < k and np.any(mask): + available_indices = remaining_indices[mask] + search_k = min(k, len(available_indices)) + if search_k == 0: + break + partitioned = available_indices[ + np.argpartition(dist_profile[available_indices], search_k - 1)[ + :search_k + ] + ] + sorted_indexes = partitioned[np.argsort(dist_profile[partitioned])] + + for idx in sorted_indexes: + if _current_k > 0 and np.any( + (idx >= lb[:_current_k]) & (idx <= ub[:_current_k]) + ): + continue + + if dist_profile[idx] <= threshold: + top_k_indexes[_current_k] = idx + top_k_distances[_current_k] = dist_profile[idx] + ub[_current_k] = min(idx + exclusion_size, len(dist_profile)) + lb[_current_k] = max(idx - exclusion_size, 0) _current_k += 1 else: break - _current_j += 1 + + if _current_k == k: + break + + mask[sorted_indexes] = False else: _current_k += min(k, len(dist_profile)) - dist_profile = dist_profile[sorted_indexes[:_current_k]] + partitioned = np.argpartition(dist_profile, k)[:k] + sorted_indexes = partitioned[np.argsort(dist_profile[partitioned])] + dist_profile = dist_profile[sorted_indexes] dist_profile = dist_profile[dist_profile <= threshold] _current_k = len(dist_profile) From 801363da23693a84e5699eadfd5d14614a2dda5b Mon Sep 17 00:00:00 2001 From: TinaJin0228 <60577222+TinaJin0228@users.noreply.github.com> Date: Fri, 27 Jun 2025 14:57:55 +0800 Subject: [PATCH 170/216] [EHN] Allow exogenous variables in regression forecasters (#2915) * add exogenous variable feature to base and regression forecasters * delete self._check_X for exog --------- Co-authored-by: Tony Bagnall --- aeon/forecasting/_regression.py | 72 ++++++++++++++++++------ aeon/forecasting/base.py | 22 +++++--- aeon/forecasting/tests/test_base.py | 14 +++++ aeon/forecasting/tests/test_regressor.py | 49 ++++++++++++++++ 4 files changed, 133 insertions(+), 24 deletions(-) diff --git a/aeon/forecasting/_regression.py b/aeon/forecasting/_regression.py index 7595b85a95..206d0ca05d 100644 --- a/aeon/forecasting/_regression.py +++ b/aeon/forecasting/_regression.py @@ -20,6 +20,8 @@ class RegressionForecaster(BaseForecaster): window to form training collection ``X``, take ``horizon`` points ahead to form ``y``, then apply an aeon or sklearn regressor. + If exogenous variables are provided, they are concatenated with the main series + and included in the regression windows. Parameters ---------- @@ -36,6 +38,10 @@ class RegressionForecaster(BaseForecaster): with sklearn regressors. """ + _tags = { + "capability:exogenous": True, + } + def __init__(self, window: int, horizon: int = 1, regressor=None): self.window = window self.regressor = regressor @@ -52,8 +58,7 @@ def _fit(self, y, exog=None): y : np.ndarray A time series on which to learn a forecaster to predict horizon ahead. exog : np.ndarray, default=None - Optional exogenous time series data. Included for interface - compatibility but ignored in this estimator. + Optional exogenous time series data, assumed to be aligned with y. Returns ------- @@ -65,18 +70,38 @@ def _fit(self, y, exog=None): self.regressor_ = LinearRegression() else: self.regressor_ = self.regressor - y = y.squeeze() - if self.window < 1 or self.window > len(y) - 3: + + # Combine y and exog for windowing + if exog is not None: + if exog.ndim == 1: + exog = exog.reshape(1, -1) + if exog.shape[1] != y.shape[1]: + raise ValueError("y and exog must have the same number of time points.") + combined_data = np.vstack([y, exog]) + else: + combined_data = y + + # Enforce a minimum number of training samples, currently 3 + if self.window < 1 or self.window >= combined_data.shape[1] - 3: raise ValueError( - f" window value {self.window} is invalid for series " f"length {len(y)}" + f"window value {self.window} is invalid for series length " + f"{combined_data.shape[1]}" ) - X = np.lib.stride_tricks.sliding_window_view(y, window_shape=self.window) - # Ignore the final horizon values: need to store these for pred with empty y + + # Create windowed data for X + X = np.lib.stride_tricks.sliding_window_view( + combined_data, window_shape=(combined_data.shape[0], self.window) + ) + X = X.squeeze(axis=0) + X = X[:, :, :].reshape(X.shape[0], -1) + + # Ignore the final horizon values for X X = X[: -self.horizon] - # Extract y_train - y_train = y[self.window + self.horizon - 1 :] - self.last_ = y[-self.window :] - self.last_ = self.last_.reshape(1, -1) + + # Extract y_train from the original series + y_train = y.squeeze()[self.window + self.horizon - 1 :] + + self.last_ = combined_data[:, -self.window :] self.regressor_.fit(X=X, y=y_train) return self @@ -90,8 +115,7 @@ def _predict(self, y=None, exog=None): A time series to predict the next horizon value for. If None, predict the next horizon value after series seen in fit. exog : np.ndarray, default=None - Optional exogenous time series data. Included for interface - compatibility but ignored in this estimator. + Optional exogenous time series data, assumed to be aligned with y. Returns ------- @@ -99,9 +123,25 @@ def _predict(self, y=None, exog=None): single prediction self.horizon steps ahead of y. """ if y is None: - return self.regressor_.predict(self.last_)[0] - last = y[:, -self.window :] - return self.regressor_.predict(last)[0] + # Flatten the last window to be compatible with sklearn regressors + last_window_flat = self.last_.reshape(1, -1) + return self.regressor_.predict(last_window_flat)[0] + + # Combine y and exog for prediction + if exog is not None: + if exog.ndim == 1: + exog = exog.reshape(1, -1) + if exog.shape[1] != y.shape[1]: + raise ValueError("y and exog must have the same number of time points.") + combined_data = np.vstack([y, exog]) + else: + combined_data = y + + # Extract the last window and flatten for prediction + last_window = combined_data[:, -self.window :] + last_window_flat = last_window.reshape(1, -1) + + return self.regressor_.predict(last_window_flat)[0] @classmethod def _get_test_params(cls, parameter_set: str = "default"): diff --git a/aeon/forecasting/base.py b/aeon/forecasting/base.py index 45ceb597db..5b37e7cadc 100644 --- a/aeon/forecasting/base.py +++ b/aeon/forecasting/base.py @@ -68,12 +68,14 @@ def fit(self, y, exog=None): if self.get_tag("fit_is_empty"): self.is_fitted = True return self + horizon = self.get_tag("capability:horizon") if not horizon and self.horizon > 1: raise ValueError( f"Horizon is set >1, but {self.__class__.__name__} cannot handle a " f"horizon greater than 1" ) + exog_tag = self.get_tag("capability:exogenous") if not exog_tag and exog is not None: raise ValueError( @@ -83,8 +85,10 @@ def fit(self, y, exog=None): self._check_X(y, self.axis) y = self._convert_y(y, self.axis) + if exog is not None: - raise NotImplementedError("Exogenous variables not yet supported") + exog = self._convert_y(exog, self.axis) + self.is_fitted = True return self._fit(y, exog) @@ -113,9 +117,9 @@ def predict(self, y=None, exog=None): self._check_X(y, self.axis) y = self._convert_y(y, self.axis) if exog is not None: - raise NotImplementedError("Exogenous variables not yet supported") - x = self._predict(y, exog) - return x + exog = self._convert_y(exog, self.axis) + + return self._predict(y, exog) @abstractmethod def _predict(self, y=None, exog=None): ... @@ -141,6 +145,8 @@ def forecast(self, y, exog=None): """ self._check_X(y, self.axis) y = self._convert_y(y, self.axis) + if exog is not None: + exog = self._convert_y(exog, self.axis) return self._forecast(y, exog) def _forecast(self, y, exog=None): @@ -149,7 +155,7 @@ def _forecast(self, y, exog=None): return self._predict(y, exog) @final - def direct_forecast(self, y, prediction_horizon): + def direct_forecast(self, y, prediction_horizon, exog=None): """ Make ``prediction_horizon`` ahead forecasts using a fit for each horizon. @@ -166,7 +172,8 @@ def direct_forecast(self, y, prediction_horizon): The time series to make forecasts about. prediction_horizon : int The number of future time steps to forecast. - + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y. predictions : np.ndarray An array of shape `(prediction_horizon,)` containing the forecasts for each horizon. @@ -198,7 +205,7 @@ def direct_forecast(self, y, prediction_horizon): preds = np.zeros(prediction_horizon) for i in range(0, prediction_horizon): self.horizon = i + 1 - preds[i] = self.forecast(y) + preds[i] = self.forecast(y, exog) return preds def iterative_forecast(self, y, prediction_horizon): @@ -263,7 +270,6 @@ def _convert_y(self, y: VALID_SERIES_INNER_TYPES, axis: int): if inner_names[0] == "ndarray": y = y.to_numpy() elif inner_names[0] == "DataFrame": - # converting a 1d array will create a 2d array in axis 0 format transpose = False if y.ndim == 1 and axis == 1: transpose = True diff --git a/aeon/forecasting/tests/test_base.py b/aeon/forecasting/tests/test_base.py index 09d39146e0..e6b729c62b 100644 --- a/aeon/forecasting/tests/test_base.py +++ b/aeon/forecasting/tests/test_base.py @@ -65,3 +65,17 @@ def test_recursive_forecast(): p = f.predict(y) assert p == preds[i] y = np.append(y, p) + + +def test_direct_forecast_with_exog(): + """Test direct forecasting with exogenous variables.""" + y = np.arange(50) + exog = np.arange(50) * 2 + f = RegressionForecaster(window=10) + + preds = f.direct_forecast(y, prediction_horizon=10, exog=exog) + assert isinstance(preds, np.ndarray) and len(preds) == 10 + + # Check that predictions are different from when no exog is used + preds_no_exog = f.direct_forecast(y, prediction_horizon=10) + assert not np.array_equal(preds, preds_no_exog) diff --git a/aeon/forecasting/tests/test_regressor.py b/aeon/forecasting/tests/test_regressor.py index f13161c4ec..f50519d1c9 100644 --- a/aeon/forecasting/tests/test_regressor.py +++ b/aeon/forecasting/tests/test_regressor.py @@ -32,3 +32,52 @@ def test_regression_forecaster(): with pytest.raises(ValueError): f = RegressionForecaster(window=101) f.fit(y) + + +def test_regression_forecaster_with_exog(): + """Test the regression forecaster with exogenous variables.""" + np.random.seed(0) + + n_samples = 100 + exog = np.random.rand(n_samples) * 10 + y = 2 * exog + np.random.rand(n_samples) * 0.1 + + f = RegressionForecaster(window=10) + + # Test fit and predict with exog + f.fit(y, exog=exog) + p1 = f.predict() + assert isinstance(p1, float) + + # Test that exog variable has an impact + exog_zeros = np.zeros(n_samples) + f.fit(y, exog=exog_zeros) + p2 = f.predict() + assert p1 != p2 + + # Test that forecast method works and is equivalent to fit+predict + y_new = np.arange(50, 150) + exog_new = np.arange(50, 150) * 2 + + # Manual fit + predict + f.fit(y=y_new, exog=exog_new) + p_manual = f.predict() + + # forecast() method + p_forecast = f.forecast(y=y_new, exog=exog_new) + assert p_manual == pytest.approx(p_forecast) + + +def test_regression_forecaster_with_exog_errors(): + """Test errors in regression forecaster with exogenous variables.""" + y = np.random.rand(100) + exog_short = np.random.rand(99) + f = RegressionForecaster(window=10) + + # Test for unequal length series + with pytest.raises(ValueError, match="must have the same number of time points"): + f.fit(y, exog=exog_short) + + with pytest.raises(ValueError, match="must have the same number of time points"): + f.fit(y) + f.predict(y, exog=exog_short) From 748fad0ad4702cc0a046372517b1dc29a49b97ad Mon Sep 17 00:00:00 2001 From: "aeon-actions-bot[bot]" <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Date: Mon, 30 Jun 2025 13:49:29 +0100 Subject: [PATCH 171/216] Automated `pre-commit` hook update (#2922) Co-authored-by: TonyBagnall <9594042+TonyBagnall@users.noreply.github.com> --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 6b071c9530..0306f273a1 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -29,7 +29,7 @@ repos: args: [ "--create", "--python-folders", "aeon" ] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.12.0 + rev: v0.12.1 hooks: - id: ruff args: [ "--fix"] From 8f6417374f3be5d9539cb6b9e4e3b48942bcffca Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 30 Jun 2025 13:50:49 +0100 Subject: [PATCH 172/216] [MNT] Bump actions/create-github-app-token in the github-actions group (#2923) Bumps the github-actions group with 1 update: [actions/create-github-app-token](https://github.com/actions/create-github-app-token). Updates `actions/create-github-app-token` from 1 to 2 - [Release notes](https://github.com/actions/create-github-app-token/releases) - [Commits](https://github.com/actions/create-github-app-token/compare/v1...v2) --- updated-dependencies: - dependency-name: actions/create-github-app-token dependency-version: '2' dependency-type: direct:production update-type: version-update:semver-major dependency-group: github-actions ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/ai_spam.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ai_spam.yml b/.github/workflows/ai_spam.yml index f09a1cff2d..c2d1d2751c 100644 --- a/.github/workflows/ai_spam.yml +++ b/.github/workflows/ai_spam.yml @@ -15,7 +15,7 @@ jobs: steps: - name: Create app token - uses: actions/create-github-app-token@v1 + uses: actions/create-github-app-token@v2 id: app-token with: app-id: ${{ vars.PR_APP_ID }} From 97135dac176e0f29e2355e318020ea678287b183 Mon Sep 17 00:00:00 2001 From: Balgopal Moharana <99070111+lucifer4073@users.noreply.github.com> Date: Wed, 2 Jul 2025 17:17:23 +0530 Subject: [PATCH 173/216] [ENH] Added RecurrentRegressor for time series regression (#2894) * RNN net architecture added * Fixed activation function for both str and list * RNN test mesage corrected * RNN regressor added * init updated w rnn * reverted workflow * reverted workflow * requested changes applied * updated with latest recurrent network * merged with main --- aeon/regression/deep_learning/__init__.py | 2 + aeon/regression/deep_learning/_rnn.py | 328 ++++++++++++++++++++++ 2 files changed, 330 insertions(+) create mode 100644 aeon/regression/deep_learning/_rnn.py diff --git a/aeon/regression/deep_learning/__init__.py b/aeon/regression/deep_learning/__init__.py index 030a8e40fb..a1496bef8a 100644 --- a/aeon/regression/deep_learning/__init__.py +++ b/aeon/regression/deep_learning/__init__.py @@ -12,6 +12,7 @@ "EncoderRegressor", "MLPRegressor", "DisjointCNNRegressor", + "RecurrentRegressor", ] from aeon.regression.deep_learning._cnn import TimeCNNRegressor @@ -28,4 +29,5 @@ ) from aeon.regression.deep_learning._mlp import MLPRegressor from aeon.regression.deep_learning._resnet import ResNetRegressor +from aeon.regression.deep_learning._rnn import RecurrentRegressor from aeon.regression.deep_learning.base import BaseDeepRegressor diff --git a/aeon/regression/deep_learning/_rnn.py b/aeon/regression/deep_learning/_rnn.py new file mode 100644 index 0000000000..495a906db4 --- /dev/null +++ b/aeon/regression/deep_learning/_rnn.py @@ -0,0 +1,328 @@ +"""Recurrent Neural Network (RNN) for regression.""" + +from __future__ import annotations + +__maintainer__ = [""] +__all__ = ["RecurrentRegressor"] + +import gc +import os +import time +from copy import deepcopy +from typing import TYPE_CHECKING, Any + +import numpy as np +from sklearn.utils import check_random_state + +from aeon.networks import RecurrentNetwork +from aeon.regression.deep_learning.base import BaseDeepRegressor + +if TYPE_CHECKING: + import tensorflow as tf + from tensorflow.keras.callbacks import Callback + + +class RecurrentRegressor(BaseDeepRegressor): + """ + Recurrent Neural Network (RNN) regressor. + + Adapted from the implementation used in sktime-dl for time series regression. + + Parameters + ---------- + rnn_type : str, default = "lstm" + Type of RNN layer to use. Options: "lstm", "gru", "simple_rnn" + n_layers : int, default = 1 + Number of RNN layers + n_units : int, default = 64 + Number of units in each RNN layer + dropout_rate : float, default = 0.2 + Dropout rate for regularization + bidirectional : bool, default = False + Whether to use bidirectional RNN layers + activation : str, default = "tanh" + Activation function for RNN layers + return_sequence_last : bool, default = None + Whether RNN layers should return sequences. If None, automatically determined + n_epochs : int, default = 100 + Number of epochs to train the model + batch_size : int, default = 32 + Number of samples per gradient update + use_mini_batch_size : bool, default = False + Condition on using the mini batch size formula + callbacks : keras callback or list of callbacks, default = None + The default list of callbacks are set to ModelCheckpoint and ReduceLROnPlateau + random_state : int, RandomState instance or None, default=None + If `int`, random_state is the seed used by the random number generator; + If `RandomState` instance, random_state is the random number generator; + If `None`, the random number generator is the `RandomState` instance used + by `np.random`. + file_path : str, default = './' + File path when saving model_Checkpoint callback + save_best_model : bool, default = False + Whether or not to save the best model + save_last_model : bool, default = False + Whether or not to save the last model + save_init_model : bool, default = False + Whether to save the initialization of the model + best_file_name : str, default = "best_model" + The name of the file of the best model + last_file_name : str, default = "last_model" + The name of the file of the last model + init_file_name : str, default = "init_model" + The name of the file of the init model + verbose : bool, default = False + Whether to output extra information + loss : str, default = "mean_squared_error" + The name of the keras training loss + optimizer : keras.optimizer, default = None + The keras optimizer used for training. If None, uses Adam with lr=0.001 + metrics : str or list[str], default="mean_squared_error" + The evaluation metrics to use during training + output_activation : str, default = "linear" + The output activation for the regressor + + Examples + -------- + >>> from aeon.regression.deep_learning import RecurrentRegressor + >>> from aeon.testing.data_generation import make_example_3d_numpy + >>> X, y = make_example_3d_numpy(n_cases=10, n_channels=1, n_timepoints=12, + ... return_y=True, regression_target=True, + ... random_state=0) + >>> rgs = RecurrentRegressor(n_epochs=20, batch_size=4) # doctest: +SKIP + >>> rgs.fit(X, y) # doctest: +SKIP + RecurrentRegressor(...) + """ + + def __init__( + self, + rnn_type: str = "lstm", + n_layers: int = 1, + n_units: int = 64, + dropout_intermediate: float = 0.2, + dropout_output: float = 0.2, + bidirectional: bool = False, + activation: str = "tanh", + return_sequence_last: bool | None = None, + n_epochs: int = 100, + callbacks: Callback | list[Callback] | None = None, + verbose: bool = False, + loss: str = "mean_squared_error", + output_activation: str = "linear", + metrics: str | list[str] = "mean_squared_error", + batch_size: int = 32, + use_mini_batch_size: bool = False, + random_state: int | np.random.RandomState | None = None, + file_path: str = "./", + save_best_model: bool = False, + save_last_model: bool = False, + save_init_model: bool = False, + best_file_name: str = "best_model", + last_file_name: str = "last_model", + init_file_name: str = "init_model", + optimizer: tf.keras.optimizers.Optimizer | None = None, + ): + self.rnn_type = rnn_type + self.n_layers = n_layers + self.n_units = n_units + self.dropout_intermediate = dropout_intermediate + self.dropout_output = dropout_output + self.bidirectional = bidirectional + self.activation = activation + self.return_sequence_last = return_sequence_last + self.n_epochs = n_epochs + self.callbacks = callbacks + self.verbose = verbose + self.loss = loss + self.metrics = metrics + self.use_mini_batch_size = use_mini_batch_size + self.random_state = random_state + self.output_activation = output_activation + self.file_path = file_path + self.save_best_model = save_best_model + self.save_last_model = save_last_model + self.save_init_model = save_init_model + self.best_file_name = best_file_name + self.init_file_name = init_file_name + self.optimizer = optimizer + self.history = None + + super().__init__(batch_size=batch_size, last_file_name=last_file_name) + + self._network = RecurrentNetwork( + rnn_type=self.rnn_type, + n_layers=self.n_layers, + n_units=self.n_units, + dropout_intermediate=self.dropout_intermediate, + dropout_output=self.dropout_output, + bidirectional=self.bidirectional, + activation=self.activation, + return_sequence_last=self.return_sequence_last, + ) + + def build_model( + self, input_shape: tuple[int, ...], **kwargs: Any + ) -> tf.keras.Model: + """ + Construct a compiled, un-trained, keras model that is ready for training. + + In aeon, time series are stored in numpy arrays of shape (d,m), where d + is the number of dimensions, m is the series length. Keras/tensorflow assume + data is in shape (m,d). This method also assumes (m,d). Transpose should + happen in fit. + + Parameters + ---------- + input_shape : tuple + The shape of the data fed into the input layer, should be (m,d) + + Returns + ------- + output : a compiled Keras Model + """ + import tensorflow as tf + + self.optimizer_ = ( + tf.keras.optimizers.Adam(learning_rate=0.001) + if self.optimizer is None + else self.optimizer + ) + + rng = check_random_state(self.random_state) + self.random_state_ = rng.randint(0, np.iinfo(np.int32).max) + tf.keras.utils.set_random_seed(self.random_state_) + + input_layer, output_layer = self._network.build_network(input_shape, **kwargs) + + output_layer = tf.keras.layers.Dense( + units=1, + activation=self.output_activation, + )(output_layer) + + model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer) + + model.compile( + loss=self.loss, + optimizer=self.optimizer_, + metrics=self._metrics, + ) + + return model + + def _fit(self, X: np.ndarray, y: np.ndarray) -> RecurrentRegressor: + """ + Fit the regressor on the training set (X, y). + + Parameters + ---------- + X : np.ndarray + The training input samples of shape (n_cases, n_channels, n_timepoints). + y : np.ndarray + The training data target values of shape (n_cases,). + + Returns + ------- + self : object + """ + import tensorflow as tf + + # Transpose to conform to Keras input style. + X = X.transpose(0, 2, 1) + + if isinstance(self.metrics, list): + self._metrics = self.metrics + elif isinstance(self.metrics, str): + self._metrics = [self.metrics] + + self.input_shape = X.shape[1:] + self.training_model_ = self.build_model(self.input_shape) + + if self.save_init_model: + self.training_model_.save(self.file_path + self.init_file_name + ".keras") + + if self.verbose: + self.training_model_.summary() + + self.file_name_ = ( + self.best_file_name if self.save_best_model else str(time.time_ns()) + ) + + if self.callbacks is None: + self.callbacks_ = [ + tf.keras.callbacks.ReduceLROnPlateau( + monitor="loss", factor=0.5, patience=50, min_lr=0.0001 + ), + tf.keras.callbacks.ModelCheckpoint( + filepath=self.file_path + self.file_name_ + ".keras", + monitor="loss", + save_best_only=True, + ), + ] + else: + self.callbacks_ = self._get_model_checkpoint_callback( + callbacks=self.callbacks, + file_path=self.file_path, + file_name=self.file_name_, + ) + + if self.use_mini_batch_size: + mini_batch_size = min(self.batch_size, X.shape[0] // 10) + else: + mini_batch_size = self.batch_size + + self.history = self.training_model_.fit( + X, + y, + batch_size=mini_batch_size, + epochs=self.n_epochs, + verbose=self.verbose, + callbacks=self.callbacks_, + ) + + try: + self.model_ = tf.keras.models.load_model( + self.file_path + self.file_name_ + ".keras", compile=False + ) + if not self.save_best_model: + os.remove(self.file_path + self.file_name_ + ".keras") + except FileNotFoundError: + self.model_ = deepcopy(self.training_model_) + + if self.save_last_model: + self.save_last_model_to_file(file_path=self.file_path) + + gc.collect() + return self + + @classmethod + def _get_test_params( + cls, parameter_set: str = "default" + ) -> dict[str, Any] | list[dict[str, Any]]: + """ + Return testing parameter settings for the estimator. + + Parameters + ---------- + parameter_set : str, default="default" + Name of the set of test parameters to return, for use in tests. If no + special parameters are defined for a value, will return `"default"` set. + For regressors, a "default" set of parameters should be provided for + general testing, and a "results_comparison" set for comparing against + previously recorded results if the general set does not produce suitable + probabilities to compare against. + + Returns + ------- + params : dict or list of dict, default={} + Parameters to create testing instances of the class. + Each dict are parameters to construct an "interesting" test instance, i.e., + `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + """ + param = { + "n_epochs": 10, + "batch_size": 4, + "n_layers": 1, + "n_units": 6, + "rnn_type": "lstm", + } + return [param] From 95258d2c86d71157479003160e56b9f46411922f Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Wed, 2 Jul 2025 19:29:40 +0100 Subject: [PATCH 174/216] [MNT] Add testing workflow for multithreading and make threaded estimators use `check_n_jobs` (#2913) * multithreading test * njons and workflow fix * tde and base test * _n_jobs in testing * _n_jobs in testing * more test fixes * fixes * more n_jobs * more n_jobs * more n_jobs * more n_jobs * more n_jobs * test fix * test fix * test fix * test fix * hopefully last * hopefully last again * dont enumerate * periodic test --- .github/workflows/periodic_tests.yml | 36 +++++++++- .github/workflows/pr_pytest.yml | 30 ++++++++ .../series/distance_based/_lof.py | 6 +- .../series/distance_based/_rockad.py | 7 +- .../series/distribution_based/_copod.py | 5 +- .../series/outlier_detection/_iforest.py | 5 +- aeon/base/_base_collection.py | 16 +---- aeon/base/_estimators/hybrid/base_rist.py | 10 +-- .../interval_based/base_interval_forest.py | 3 +- aeon/base/tests/test_base_collection.py | 7 -- aeon/classification/base.py | 6 +- .../convolution_based/_arsenal.py | 3 + .../convolution_based/_hydra.py | 4 +- .../convolution_based/_minirocket.py | 4 +- .../convolution_based/_mr_hydra.py | 7 +- .../convolution_based/_multirocket.py | 4 +- .../convolution_based/_rocket.py | 5 +- aeon/classification/dictionary_based/_boss.py | 7 +- .../classification/dictionary_based/_cboss.py | 2 + aeon/classification/dictionary_based/_muse.py | 3 + .../dictionary_based/_redcomets.py | 16 +++-- aeon/classification/dictionary_based/_tde.py | 3 + .../dictionary_based/_weasel.py | 2 + .../dictionary_based/_weasel_v2.py | 8 +-- .../distance_based/_elastic_ensemble.py | 3 + .../distance_based/_proximity_forest.py | 3 + .../distance_based/_time_series_neighbors.py | 9 ++- .../_probability_threshold.py | 2 + .../early_classification/_teaser.py | 2 + aeon/classification/feature_based/_catch22.py | 2 + .../feature_based/_fresh_prince.py | 2 + aeon/classification/feature_based/_summary.py | 3 + aeon/classification/feature_based/_tdmvdc.py | 6 +- aeon/classification/feature_based/_tsfresh.py | 3 + aeon/classification/hybrid/_hivecote_v1.py | 3 + aeon/classification/hybrid/_hivecote_v2.py | 3 + .../interval_based/_interval_pipelines.py | 5 +- aeon/classification/interval_based/_rstsf.py | 1 - .../ordinal_classification/_ordinal_tde.py | 3 + aeon/classification/shapelet_based/_rdst.py | 7 +- aeon/classification/shapelet_based/_rsast.py | 5 +- aeon/classification/shapelet_based/_sast.py | 4 +- aeon/classification/shapelet_based/_stc.py | 2 + .../sklearn/_rotation_forest_classifier.py | 2 +- aeon/clustering/_kernel_k_means.py | 5 +- aeon/clustering/feature_based/_catch22.py | 3 + aeon/clustering/feature_based/_summary.py | 3 + aeon/clustering/feature_based/_tsfresh.py | 3 + aeon/regression/base.py | 6 +- aeon/regression/convolution_based/_hydra.py | 4 +- .../convolution_based/_minirocket.py | 4 +- .../regression/convolution_based/_mr_hydra.py | 7 +- .../convolution_based/_multirocket.py | 4 +- aeon/regression/convolution_based/_rocket.py | 5 +- .../distance_based/_time_series_neighbors.py | 27 ++++--- aeon/regression/feature_based/_catch22.py | 3 + .../regression/feature_based/_fresh_prince.py | 2 + aeon/regression/feature_based/_summary.py | 3 + aeon/regression/feature_based/_tsfresh.py | 3 + .../interval_based/_interval_pipelines.py | 2 + aeon/regression/shapelet_based/_rdst.py | 6 +- .../sklearn/_rotation_forest_regressor.py | 3 +- aeon/segmentation/_clasp.py | 5 +- aeon/segmentation/base.py | 3 +- .../collection/neighbors/_rp_cosine_lsh.py | 3 + .../series/neighbors/_dummy.py | 3 +- .../_yield_multithreading_checks.py | 70 +++++++++++++------ aeon/testing/testing_config.py | 8 +-- .../collection/convolution_based/_hydra.py | 5 +- .../convolution_based/_minirocket.py | 7 +- .../convolution_based/_multirocket.py | 7 +- .../collection/convolution_based/_rocket.py | 7 +- .../collection/dictionary_based/_borf.py | 17 ++++- .../collection/dictionary_based/_sfa.py | 19 ++--- .../collection/dictionary_based/_sfa_fast.py | 3 + .../collection/feature_based/_catch22.py | 5 +- .../collection/feature_based/_tsfresh.py | 3 +- .../interval_based/_random_intervals.py | 3 +- .../interval_based/_supervised_intervals.py | 3 +- .../collection/shapelet_based/_rsast.py | 6 +- .../collection/shapelet_based/_sast.py | 7 +- aeon/transformations/series/_clasp.py | 5 +- 82 files changed, 394 insertions(+), 157 deletions(-) diff --git a/.github/workflows/periodic_tests.yml b/.github/workflows/periodic_tests.yml index 46d99bc94a..a06334fa21 100644 --- a/.github/workflows/periodic_tests.yml +++ b/.github/workflows/periodic_tests.yml @@ -220,8 +220,7 @@ jobs: with: python-version: "3.12" - - if: ${{ github.event_name != 'pull_request' || !contains(github.event.pull_request.labels.*.name, 'no numba cache') }} - name: Use numba cache to set env variables but not restore cache + - name: Use numba cache to set env variables but not restore cache uses: ./.github/actions/numba_cache with: cache_name: "doctests" @@ -242,6 +241,39 @@ jobs: - name: Run tests run: python -m pytest -n logical --doctest-only + multithreaded-estimators: + runs-on: ubuntu-24.04 + + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Setup Python 3.12 + uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - name: Use numba cache to set env variables but not restore cache + uses: ./.github/actions/numba_cache + with: + cache_name: "multithreaded-estimators" + runner_os: ${{ runner.os }} + python_version: "3.12" + restore_cache: "false" + + - name: Install aeon and dependencies + uses: nick-fields/retry@v3 + with: + timeout_minutes: 30 + max_attempts: 3 + command: python -m pip install .[all_extras,dev] + + - name: Show dependencies + run: python -m pip list + + - name: Run tests + run: python -m pytest aeon/testing/tests/ --enablethreading true -k "check_estimator_multithreading" + codecov: runs-on: ubuntu-24.04 diff --git a/.github/workflows/pr_pytest.yml b/.github/workflows/pr_pytest.yml index 69323e47c5..fcdc665778 100644 --- a/.github/workflows/pr_pytest.yml +++ b/.github/workflows/pr_pytest.yml @@ -130,6 +130,36 @@ jobs: - name: Run tests run: python -m pytest -n logical --doctest-only + multithreaded-estimators: + runs-on: ubuntu-24.04 + + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Setup Python 3.12 + uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - if: ${{ github.event_name != 'pull_request' || !contains(github.event.pull_request.labels.*.name, 'no numba cache') }} + name: Restore numba cache + uses: ./.github/actions/numba_cache + with: + cache_name: "multithreaded-estimators" + runner_os: ${{ runner.os }} + python_version: "3.12" + + - name: Install aeon and dependencies + uses: ./.github/actions/cpu_all_extras + with: + additional_extras: "dev" + + - name: Show dependencies + run: python -m pip list + + - name: Run tests + run: python -m pytest aeon/testing/tests/ --enablethreading true -k "check_estimator_multithreading" codecov: # run the code coverage job if a PR has the 'codecov actions' label diff --git a/aeon/anomaly_detection/series/distance_based/_lof.py b/aeon/anomaly_detection/series/distance_based/_lof.py index 8c7e2a77bb..50cfcb5406 100644 --- a/aeon/anomaly_detection/series/distance_based/_lof.py +++ b/aeon/anomaly_detection/series/distance_based/_lof.py @@ -8,6 +8,7 @@ import numpy as np from aeon.anomaly_detection.series._pyodadapter import PyODAdapter +from aeon.utils.validation import check_n_jobs from aeon.utils.validation._dependencies import _check_soft_dependencies @@ -98,6 +99,8 @@ def __init__( # Set a default contamination value internally contamination = 0.1 + self._n_jobs = check_n_jobs(n_jobs) + model = PyOD_LOF( n_neighbors=n_neighbors, algorithm=algorithm, @@ -105,7 +108,7 @@ def __init__( metric=metric, p=p, metric_params=metric_params, - n_jobs=n_jobs, + n_jobs=self._n_jobs, contamination=contamination, # Only for PyOD LOF novelty=False, # Initialize unsupervised LOF (novelty=False) ) @@ -116,6 +119,7 @@ def __init__( self.p = p self.metric_params = metric_params self.n_jobs = n_jobs + super().__init__(pyod_model=model, window_size=window_size, stride=stride) def _fit(self, X: np.ndarray, y: Union[np.ndarray, None] = None) -> None: diff --git a/aeon/anomaly_detection/series/distance_based/_rockad.py b/aeon/anomaly_detection/series/distance_based/_rockad.py index ce60a4485c..5910fb2342 100644 --- a/aeon/anomaly_detection/series/distance_based/_rockad.py +++ b/aeon/anomaly_detection/series/distance_based/_rockad.py @@ -13,6 +13,7 @@ from aeon.anomaly_detection.series.base import BaseSeriesAnomalyDetector from aeon.transformations.collection.convolution_based import Rocket +from aeon.utils.validation import check_n_jobs from aeon.utils.windowing import reverse_windowing, sliding_windows @@ -163,10 +164,12 @@ def _check_params(self, X: np.ndarray) -> None: ) def _inner_fit(self, X: np.ndarray) -> None: + self._n_jobs = check_n_jobs(self.n_jobs) + self.rocket_transformer_ = Rocket( n_kernels=self.n_kernels, normalise=self.normalise, - n_jobs=self.n_jobs, + n_jobs=self._n_jobs, random_state=self.random_state, ) # X: (n_windows, window_size) @@ -199,7 +202,7 @@ def _inner_fit(self, X: np.ndarray) -> None: # Initialize estimator estimator = NearestNeighbors( n_neighbors=self.n_neighbors, - n_jobs=self.n_jobs, + n_jobs=self._n_jobs, metric=self.metric, algorithm="kd_tree", ) diff --git a/aeon/anomaly_detection/series/distribution_based/_copod.py b/aeon/anomaly_detection/series/distribution_based/_copod.py index b1bdc70063..2265378e5d 100644 --- a/aeon/anomaly_detection/series/distribution_based/_copod.py +++ b/aeon/anomaly_detection/series/distribution_based/_copod.py @@ -8,6 +8,7 @@ import numpy as np from aeon.anomaly_detection.series._pyodadapter import PyODAdapter +from aeon.utils.validation import check_n_jobs from aeon.utils.validation._dependencies import _check_soft_dependencies @@ -46,7 +47,9 @@ def __init__(self, n_jobs: int = 1, window_size: int = 10, stride: int = 1): _check_soft_dependencies(*self._tags["python_dependencies"]) from pyod.models.copod import COPOD - model = COPOD(n_jobs=n_jobs) + self._n_jobs = check_n_jobs(n_jobs) + + model = COPOD(n_jobs=self._n_jobs) self.n_jobs = n_jobs super().__init__(model, window_size=window_size, stride=stride) diff --git a/aeon/anomaly_detection/series/outlier_detection/_iforest.py b/aeon/anomaly_detection/series/outlier_detection/_iforest.py index 945ba346de..fe7de8ff06 100644 --- a/aeon/anomaly_detection/series/outlier_detection/_iforest.py +++ b/aeon/anomaly_detection/series/outlier_detection/_iforest.py @@ -8,6 +8,7 @@ import numpy as np from aeon.anomaly_detection.series._pyodadapter import PyODAdapter +from aeon.utils.validation import check_n_jobs from aeon.utils.validation._dependencies import _check_soft_dependencies @@ -101,12 +102,14 @@ def __init__( _check_soft_dependencies(*self._tags["python_dependencies"]) from pyod.models.iforest import IForest + self._n_jobs = check_n_jobs(n_jobs) + model = IForest( n_estimators=n_estimators, max_samples=max_samples, max_features=max_features, bootstrap=bootstrap, - n_jobs=n_jobs, + n_jobs=self._n_jobs, random_state=random_state, verbose=verbose, ) diff --git a/aeon/base/_base_collection.py b/aeon/base/_base_collection.py index 4d7f4b4564..60996e7d8a 100644 --- a/aeon/base/_base_collection.py +++ b/aeon/base/_base_collection.py @@ -17,7 +17,6 @@ class name: BaseCollectionEstimator fitted model/strategy - by convention, any attributes ending in "_" fitted state flag - is_fitted (property) fitted state inspection - check_is_fitted() - """ from abc import abstractmethod @@ -30,7 +29,6 @@ class name: BaseCollectionEstimator resolve_equal_length_inner_type, resolve_unequal_length_inner_type, ) -from aeon.utils.validation import check_n_jobs from aeon.utils.validation.collection import ( get_n_cases, get_n_channels, @@ -65,7 +63,6 @@ class BaseCollectionEstimator(BaseAeonEstimator): @abstractmethod def __init__(self): self.metadata_ = {} # metadata/properties of data seen in fit - self._n_jobs = 1 super().__init__() @@ -115,21 +112,12 @@ def _preprocess_collection(self, X, store_metadata=True): """ if isinstance(X, list) and isinstance(X[0], np.ndarray): X = self._reshape_np_list(X) + meta = self._check_X(X) if len(self.metadata_) == 0 and store_metadata: self.metadata_ = meta - X = self._convert_X(X) - # This usage of n_jobs is legacy, see issue #102 - multithread = self.get_tag("capability:multithreading") - if multithread: - if hasattr(self, "n_jobs"): - self._n_jobs = check_n_jobs(self.n_jobs) - else: - raise AttributeError( - "self.n_jobs must be set if capability:multithreading is True" - ) - return X + return self._convert_X(X) def _check_X(self, X): """ diff --git a/aeon/base/_estimators/hybrid/base_rist.py b/aeon/base/_estimators/hybrid/base_rist.py index badea523d2..b223d03ebb 100644 --- a/aeon/base/_estimators/hybrid/base_rist.py +++ b/aeon/base/_estimators/hybrid/base_rist.py @@ -114,6 +114,7 @@ def __init__( def _fit(self, X, y) -> object: self.n_cases_, self.n_channels_, self.n_timepoints_ = X.shape + self._n_jobs = check_n_jobs(self.n_jobs) rng = check_random_state(self.random_state) @@ -134,10 +135,9 @@ def _fit(self, X, y) -> object: self._estimator = _clone_estimator(self._estimator, rng) - n_jobs = check_n_jobs(self.n_jobs) m = getattr(self._estimator, "n_jobs", "missing") if m != "missing": - self._estimator.n_jobs = n_jobs + self._estimator.n_jobs = self._n_jobs if self.series_transformers == "default": self._series_transformers = [ @@ -168,7 +168,7 @@ def _fit(self, X, y) -> object: if st is not None: m = getattr(st, "n_jobs", "missing") if m != "missing": - st.n_jobs = n_jobs + st.n_jobs = self._n_jobs s = st.fit_transform(X, y) else: @@ -198,7 +198,7 @@ def _fit(self, X, y) -> object: row_numba_max, row_ppv, ], - n_jobs=n_jobs, + n_jobs=self._n_jobs, ) _set_random_states(ct, rng) self._transformers.append(ct) @@ -214,7 +214,7 @@ def _fit(self, X, y) -> object: n_shapelets = self.n_shapelets st = RandomDilatedShapeletTransform( - max_shapelets=n_shapelets, n_jobs=n_jobs + max_shapelets=n_shapelets, n_jobs=self._n_jobs ) _set_random_states(st, rng) self._transformers.append(st) diff --git a/aeon/base/_estimators/interval_based/base_interval_forest.py b/aeon/base/_estimators/interval_based/base_interval_forest.py index ac2c57d5a5..c85f21a497 100644 --- a/aeon/base/_estimators/interval_based/base_interval_forest.py +++ b/aeon/base/_estimators/interval_based/base_interval_forest.py @@ -352,6 +352,7 @@ def _fit_forest(self, X, y, save_transformed_data=False): rng = check_random_state(self.random_state) self.n_cases_, self.n_channels_, self.n_timepoints_ = X.shape + self._n_jobs = check_n_jobs(self.n_jobs) self._base_estimator = self.base_estimator if self.base_estimator is None: @@ -796,8 +797,6 @@ def _fit_forest(self, X, y, save_transformed_data=False): ): raise ValueError(f"Invalid replace_nan input. Found {self.replace_nan}") - self._n_jobs = check_n_jobs(self.n_jobs) - if self.time_limit_in_minutes is not None and self.time_limit_in_minutes > 0: time_limit = self.time_limit_in_minutes * 60 start_time = time.time() diff --git a/aeon/base/tests/test_base_collection.py b/aeon/base/tests/test_base_collection.py index fff1f75f38..c68a3cebd7 100644 --- a/aeon/base/tests/test_base_collection.py +++ b/aeon/base/tests/test_base_collection.py @@ -219,16 +219,9 @@ def test_preprocess_collection(data): cls = MockClassifier() X = cls._preprocess_collection(data) - assert cls._n_jobs == 1 assert len(cls.metadata_) == 6 assert get_type(X) == "numpy3D" - tags = {"capability:multithreading": True} - cls = MockClassifier() - cls.set_tags(**tags) - with pytest.raises(AttributeError, match="self.n_jobs must be set"): - cls._preprocess_collection(data) - # Test two calls do not overwrite metadata (predict should not reset fit meta) cls = MockClassifier() cls._preprocess_collection(data) diff --git a/aeon/classification/base.py b/aeon/classification/base.py index fa7ed89cb6..f0609c9c72 100644 --- a/aeon/classification/base.py +++ b/aeon/classification/base.py @@ -592,13 +592,17 @@ def _fit_predict_default(self, X, y, method, cv_size=10): random_state = getattr(self, "random_state", None) estimator = _clone_estimator(self, random_state) + n_jobs = getattr(self, "_n_jobs", None) + if n_jobs is None: + n_jobs = getattr(self, "n_jobs", None) + return cross_val_predict( estimator, X=X, y=y, cv=cv_size, method=method, - n_jobs=self._n_jobs, + n_jobs=n_jobs, ) @staticmethod diff --git a/aeon/classification/convolution_based/_arsenal.py b/aeon/classification/convolution_based/_arsenal.py index 8a3d42ee75..2bff6e9050 100644 --- a/aeon/classification/convolution_based/_arsenal.py +++ b/aeon/classification/convolution_based/_arsenal.py @@ -22,6 +22,7 @@ MultiRocket, Rocket, ) +from aeon.utils.validation import check_n_jobs class Arsenal(BaseClassifier): @@ -276,6 +277,8 @@ def _fit_predict_proba(self, X, y) -> np.ndarray: def _fit_arsenal(self, X, y, keep_transformed_data=False): self.n_cases_, self.n_channels_, self.n_timepoints_ = X.shape + self._n_jobs = check_n_jobs(self.n_jobs) + time_limit = self.time_limit_in_minutes * 60 start_time = time.time() train_time = 0 diff --git a/aeon/classification/convolution_based/_hydra.py b/aeon/classification/convolution_based/_hydra.py index 2c890d4a69..142499ffbc 100644 --- a/aeon/classification/convolution_based/_hydra.py +++ b/aeon/classification/convolution_based/_hydra.py @@ -12,6 +12,7 @@ from aeon.classification import BaseClassifier from aeon.transformations.collection.convolution_based._hydra import HydraTransformer +from aeon.utils.validation import check_n_jobs class HydraClassifier(BaseClassifier): @@ -112,10 +113,11 @@ def __init__( super().__init__() def _fit(self, X, y): + self._n_jobs = check_n_jobs(self.n_jobs) transform = HydraTransformer( n_kernels=self.n_kernels, n_groups=self.n_groups, - n_jobs=self.n_jobs, + n_jobs=self._n_jobs, random_state=self.random_state, ) diff --git a/aeon/classification/convolution_based/_minirocket.py b/aeon/classification/convolution_based/_minirocket.py index 629c447c1c..b20324bc2f 100644 --- a/aeon/classification/convolution_based/_minirocket.py +++ b/aeon/classification/convolution_based/_minirocket.py @@ -14,6 +14,7 @@ from aeon.base._base import _clone_estimator from aeon.classification import BaseClassifier from aeon.transformations.collection.convolution_based import MiniRocket +from aeon.utils.validation import check_n_jobs class MiniRocketClassifier(BaseClassifier): @@ -127,11 +128,12 @@ def _fit(self, X, y): ending in "_" and sets is_fitted flag to True. """ self.n_cases_, self.n_channels_, self.n_timepoints_ = X.shape + self._n_jobs = check_n_jobs(self.n_jobs) self._transformer = MiniRocket( n_kernels=self.n_kernels, max_dilations_per_kernel=self.max_dilations_per_kernel, - n_jobs=self.n_jobs, + n_jobs=self._n_jobs, random_state=self.random_state, ) self._scaler = StandardScaler(with_mean=False) diff --git a/aeon/classification/convolution_based/_mr_hydra.py b/aeon/classification/convolution_based/_mr_hydra.py index 04384055ec..c9c90a034d 100644 --- a/aeon/classification/convolution_based/_mr_hydra.py +++ b/aeon/classification/convolution_based/_mr_hydra.py @@ -12,6 +12,7 @@ from aeon.classification.convolution_based._hydra import _SparseScaler from aeon.transformations.collection.convolution_based import MultiRocket from aeon.transformations.collection.convolution_based._hydra import HydraTransformer +from aeon.utils.validation import check_n_jobs class MultiRocketHydraClassifier(BaseClassifier): @@ -105,10 +106,12 @@ def __init__( super().__init__() def _fit(self, X, y): + self._n_jobs = check_n_jobs(self.n_jobs) + self._transform_hydra = HydraTransformer( n_kernels=self.n_kernels, n_groups=self.n_groups, - n_jobs=self.n_jobs, + n_jobs=self._n_jobs, random_state=self.random_state, ) Xt_hydra = self._transform_hydra.fit_transform(X) @@ -117,7 +120,7 @@ def _fit(self, X, y): Xt_hydra = self._scale_hydra.fit_transform(Xt_hydra) self._transform_multirocket = MultiRocket( - n_jobs=self.n_jobs, + n_jobs=self._n_jobs, random_state=self.random_state, ) Xt_multirocket = self._transform_multirocket.fit_transform(X) diff --git a/aeon/classification/convolution_based/_multirocket.py b/aeon/classification/convolution_based/_multirocket.py index 791cf8fe55..3e0dbf79f3 100644 --- a/aeon/classification/convolution_based/_multirocket.py +++ b/aeon/classification/convolution_based/_multirocket.py @@ -14,6 +14,7 @@ from aeon.base._base import _clone_estimator from aeon.classification import BaseClassifier from aeon.transformations.collection.convolution_based import MultiRocket +from aeon.utils.validation import check_n_jobs class MultiRocketClassifier(BaseClassifier): @@ -130,12 +131,13 @@ def _fit(self, X, y): ending in "_" and sets is_fitted flag to True. """ self.n_cases_, self.n_channels_, self.n_timepoints_ = X.shape + self._n_jobs = check_n_jobs(self.n_jobs) self._transformer = MultiRocket( n_kernels=self.n_kernels, max_dilations_per_kernel=self.max_dilations_per_kernel, n_features_per_kernel=self.n_features_per_kernel, - n_jobs=self.n_jobs, + n_jobs=self._n_jobs, random_state=self.random_state, ) self._scaler = StandardScaler(with_mean=False) diff --git a/aeon/classification/convolution_based/_rocket.py b/aeon/classification/convolution_based/_rocket.py index c6cf304c0c..7baea488bc 100644 --- a/aeon/classification/convolution_based/_rocket.py +++ b/aeon/classification/convolution_based/_rocket.py @@ -14,6 +14,7 @@ from aeon.base._base import _clone_estimator from aeon.classification import BaseClassifier from aeon.transformations.collection.convolution_based import Rocket +from aeon.utils.validation import check_n_jobs class RocketClassifier(BaseClassifier): @@ -128,9 +129,11 @@ def _fit(self, X, y): Changes state by creating a fitted model that updates attributes ending in "_" and sets is_fitted flag to True. """ + self._n_jobs = check_n_jobs(self.n_jobs) + self._transformer = Rocket( n_kernels=self.n_kernels, - n_jobs=self.n_jobs, + n_jobs=self._n_jobs, random_state=self.random_state, ) self._scaler = StandardScaler(with_mean=False) diff --git a/aeon/classification/dictionary_based/_boss.py b/aeon/classification/dictionary_based/_boss.py index 856074b226..6fa7ebe1dc 100644 --- a/aeon/classification/dictionary_based/_boss.py +++ b/aeon/classification/dictionary_based/_boss.py @@ -20,6 +20,7 @@ from aeon.classification.base import BaseClassifier from aeon.transformations.collection.dictionary_based import SFAFast +from aeon.utils.validation import check_n_jobs class BOSSEnsemble(BaseClassifier): @@ -182,6 +183,7 @@ def _fit(self, X, y, keep_train_preds=False): ending in "_" and sets is_fitted flag to True. """ self.n_cases_, _, self.n_timepoints_ = X.shape + self._n_jobs = check_n_jobs(self.n_jobs) self.estimators_ = [] @@ -213,7 +215,7 @@ def _fit(self, X, y, keep_train_preds=False): save_words=True, use_boss_distance=self.use_boss_distance, feature_selection=self.feature_selection, - n_jobs=self.n_jobs, + n_jobs=self._n_jobs, random_state=self.random_state, ) boss.fit(X, y) @@ -577,6 +579,7 @@ def _fit(self, X, y): Changes state by creating a fitted model that updates attributes ending in "_" and sets is_fitted flag to True. """ + self._n_jobs = check_n_jobs(self.n_jobs) self._transformer = SFAFast( word_length=self.word_length, alphabet_size=self.alphabet_size, @@ -585,7 +588,7 @@ def _fit(self, X, y): bigrams=False, remove_repeat_words=True, save_words=self.save_words, - n_jobs=self.n_jobs, + n_jobs=self._n_jobs, feature_selection=self.feature_selection, random_state=self.random_state, ) diff --git a/aeon/classification/dictionary_based/_cboss.py b/aeon/classification/dictionary_based/_cboss.py index efb0ede145..71fecf8672 100644 --- a/aeon/classification/dictionary_based/_cboss.py +++ b/aeon/classification/dictionary_based/_cboss.py @@ -17,6 +17,7 @@ from aeon.classification.base import BaseClassifier from aeon.classification.dictionary_based import IndividualBOSS from aeon.classification.dictionary_based._boss import pairwise_distances +from aeon.utils.validation import check_n_jobs class ContractableBOSS(BaseClassifier): @@ -204,6 +205,7 @@ def _fit(self, X, y, keep_train_preds=True): """ time_limit = self.time_limit_in_minutes * 60 self.n_cases_, _, self.n_timepoints_ = X.shape + self._n_jobs = check_n_jobs(self.n_jobs) self.estimators_ = [] self.weights_ = [] diff --git a/aeon/classification/dictionary_based/_muse.py b/aeon/classification/dictionary_based/_muse.py index 105948219d..c8ad2d43bb 100644 --- a/aeon/classification/dictionary_based/_muse.py +++ b/aeon/classification/dictionary_based/_muse.py @@ -18,6 +18,7 @@ from aeon.classification.base import BaseClassifier from aeon.transformations.collection.dictionary_based import SFAFast +from aeon.utils.validation import check_n_jobs class MUSE(BaseClassifier): @@ -196,6 +197,8 @@ def _fit(self, X, y): """ y = np.asarray(y) + self._n_jobs = check_n_jobs(self.n_jobs) + # add first order differences in each dimension to TS if self.use_first_order_differences: X = self._add_first_order_differences(X) diff --git a/aeon/classification/dictionary_based/_redcomets.py b/aeon/classification/dictionary_based/_redcomets.py index c593c8922f..76d0c98c13 100644 --- a/aeon/classification/dictionary_based/_redcomets.py +++ b/aeon/classification/dictionary_based/_redcomets.py @@ -19,6 +19,7 @@ from aeon.classification.base import BaseClassifier from aeon.transformations.collection import Normalizer from aeon.transformations.collection.dictionary_based import SAX, SFAFast +from aeon.utils.validation import check_n_jobs from aeon.utils.validation._dependencies import _check_soft_dependencies @@ -140,6 +141,7 @@ def _fit(self, X, y): Reference to self. """ self._n_channels = X.shape[1] + self._n_jobs = check_n_jobs(self.n_jobs) if self._n_channels == 1: # Univariate assert self.variant in [1, 2, 3] @@ -225,7 +227,7 @@ def _build_univariate_ensemble(self, X, y): X_smote, y_smote = SMOTE( sampling_strategy="all", k_neighbors=NearestNeighbors( - n_neighbors=min_neighbours - 1, n_jobs=self.n_jobs + n_neighbors=min_neighbours - 1, n_jobs=self._n_jobs ), random_state=self.random_state, ).fit_resample(X, y) @@ -247,7 +249,7 @@ def _build_univariate_ensemble(self, X, y): alphabet_size=a, window_size=X_smote.shape[1], binning_method="equi-width", - n_jobs=self.n_jobs, + n_jobs=self._n_jobs, random_state=self.random_state, ) for w, a in sfa_lenses @@ -261,7 +263,7 @@ def _build_univariate_ensemble(self, X, y): rf = RandomForestClassifier( n_estimators=self.n_trees, random_state=self.random_state, - n_jobs=self.n_jobs, + n_jobs=self._n_jobs, ) rf.fit(X_sfa, y_smote) @@ -269,7 +271,7 @@ def _build_univariate_ensemble(self, X, y): weight = 1 elif self.variant == 3: weight = cross_val_score( - rf, X_sfa, y_smote, cv=cv, n_jobs=self.n_jobs + rf, X_sfa, y_smote, cv=cv, n_jobs=self._n_jobs ).mean() else: @@ -286,7 +288,7 @@ def _build_univariate_ensemble(self, X, y): rf = RandomForestClassifier( n_estimators=self.n_trees, random_state=self.random_state, - n_jobs=self.n_jobs, + n_jobs=self._n_jobs, ) rf.fit(X_sax, y_smote) @@ -294,7 +296,7 @@ def _build_univariate_ensemble(self, X, y): weight = 1 elif self.variant == 3: weight = cross_val_score( - rf, X_sax, y_smote, cv=cv, n_jobs=self.n_jobs + rf, X_sax, y_smote, cv=cv, n_jobs=self._n_jobs ).mean() else: weight = None @@ -588,7 +590,7 @@ def _parallel_sax(self, sax_transforms, X): def _sax_wrapper(sax): return np.squeeze(sax.fit_transform(X)) - sax_parallel_res = Parallel(n_jobs=self.n_jobs, backend=self.parallel_backend)( + sax_parallel_res = Parallel(n_jobs=self._n_jobs, backend=self.parallel_backend)( delayed(_sax_wrapper)(sax) for sax in sax_transforms ) return sax_parallel_res diff --git a/aeon/classification/dictionary_based/_tde.py b/aeon/classification/dictionary_based/_tde.py index ba3eae07a5..84f3a571e1 100644 --- a/aeon/classification/dictionary_based/_tde.py +++ b/aeon/classification/dictionary_based/_tde.py @@ -23,6 +23,7 @@ from aeon.classification.base import BaseClassifier from aeon.transformations.collection.dictionary_based import SFA +from aeon.utils.validation import check_n_jobs class TemporalDictionaryEnsemble(BaseClassifier): @@ -247,6 +248,7 @@ def _fit(self, X, y, keep_train_preds=False): ) self.n_cases_, self.n_channels_, self.n_timepoints_ = X.shape + self._n_jobs = check_n_jobs(self.n_jobs) self.estimators_ = [] self.weights_ = [] @@ -780,6 +782,7 @@ def _fit(self, X, y): ending in "_" and sets is_fitted flag to True. """ self.n_cases_, self.n_channels_, self.n_timepoints_ = X.shape + self._n_jobs = check_n_jobs(self.n_jobs) self._class_vals = y # select dimensions using accuracy estimate if multivariate diff --git a/aeon/classification/dictionary_based/_weasel.py b/aeon/classification/dictionary_based/_weasel.py index 03b86a8c17..98579008e9 100644 --- a/aeon/classification/dictionary_based/_weasel.py +++ b/aeon/classification/dictionary_based/_weasel.py @@ -17,6 +17,7 @@ from aeon.classification.base import BaseClassifier from aeon.transformations.collection.dictionary_based import SFAFast +from aeon.utils.validation import check_n_jobs class WEASEL(BaseClassifier): @@ -195,6 +196,7 @@ def _fit(self, X, y): """ # Window length parameter space dependent on series length self.n_cases, self.n_timepoints = X.shape[0], X.shape[-1] + self._n_jobs = check_n_jobs(self.n_jobs) win_inc = self._compute_window_inc() self.max_window = int(min(self.n_timepoints, self.max_window)) diff --git a/aeon/classification/dictionary_based/_weasel_v2.py b/aeon/classification/dictionary_based/_weasel_v2.py index b8d014a089..1bf770a36c 100644 --- a/aeon/classification/dictionary_based/_weasel_v2.py +++ b/aeon/classification/dictionary_based/_weasel_v2.py @@ -16,6 +16,7 @@ from aeon.classification.base import BaseClassifier from aeon.transformations.collection.dictionary_based import SFAFast +from aeon.utils.validation import check_n_jobs # some constants on input parameters for WEASEL v2 SWITCH_SMALL_INSTANCES = 250 @@ -171,9 +172,7 @@ def _fit(self, X, y): self : Reference to self. """ - # Window length parameter space dependent on series length - - ... + self._n_jobs = check_n_jobs(self.n_jobs) self.transform = WEASELTransformerV2( min_window=self.min_window, @@ -183,7 +182,7 @@ def _fit(self, X, y): feature_selection=self.feature_selection, max_feature_count=self.max_feature_count, random_state=self.random_state, - n_jobs=self.n_jobs, + n_jobs=self._n_jobs, ) words = self.transform.fit_transform(X, y) @@ -355,6 +354,7 @@ def fit_transform(self, X, y=None): """ # Window length parameter space dependent on series length self.n_cases_, self.n_timepoints_ = X.shape[0], X.shape[-1] + XX = X.squeeze(1) # avoid overfitting with too many features diff --git a/aeon/classification/distance_based/_elastic_ensemble.py b/aeon/classification/distance_based/_elastic_ensemble.py index fdb3b3d7c2..dea1bf7b4f 100644 --- a/aeon/classification/distance_based/_elastic_ensemble.py +++ b/aeon/classification/distance_based/_elastic_ensemble.py @@ -26,6 +26,7 @@ KNeighborsTimeSeriesClassifier, ) from aeon.utils.numba.general import slope_derivative_2d, slope_derivative_3d +from aeon.utils.validation import check_n_jobs class ElasticEnsemble(BaseClassifier): @@ -139,6 +140,8 @@ def _fit(self, X, y): ------- self : object """ + self._n_jobs = check_n_jobs(self.n_jobs) + if self.distance_measures == "all": self._distance_measures = [ "dtw", diff --git a/aeon/classification/distance_based/_proximity_forest.py b/aeon/classification/distance_based/_proximity_forest.py index afb6444225..e3fbe27325 100644 --- a/aeon/classification/distance_based/_proximity_forest.py +++ b/aeon/classification/distance_based/_proximity_forest.py @@ -14,6 +14,7 @@ from aeon.classification.base import BaseClassifier from aeon.classification.distance_based._proximity_tree import ProximityTree +from aeon.utils.validation import check_n_jobs class ProximityForest(BaseClassifier): @@ -108,6 +109,8 @@ def __init__( def _fit(self, X, y): rng = check_random_state(self.random_state) + self._n_jobs = check_n_jobs(self.n_jobs) + self.trees_ = Parallel( n_jobs=self._n_jobs, backend=self.parallel_backend, prefer="threads" )( diff --git a/aeon/classification/distance_based/_time_series_neighbors.py b/aeon/classification/distance_based/_time_series_neighbors.py index 540c29b0ec..a9cf4314e4 100644 --- a/aeon/classification/distance_based/_time_series_neighbors.py +++ b/aeon/classification/distance_based/_time_series_neighbors.py @@ -126,7 +126,8 @@ def _fit(self, X, y): """ self.metric_ = get_distance_function(method=self.distance) self.X_ = X - self.classes_, self.y_ = np.unique(y, return_inverse=True) + _, self.y_ = np.unique(y, return_inverse=True) + self._n_jobs = check_n_jobs(self.n_jobs) return self def _predict_proba(self, X): @@ -147,8 +148,7 @@ def _predict_proba(self, X): The class probabilities of the input samples. Classes are ordered by lexicographic order. """ - n_jobs = check_n_jobs(self.n_jobs) - preds = Parallel(n_jobs=n_jobs, backend=self.parallel_backend)( + preds = Parallel(n_jobs=self._n_jobs, backend=self.parallel_backend)( delayed(self._proba_row)(x) for x in X ) return np.array(preds) @@ -170,8 +170,7 @@ def _predict(self, X): y : array of shape (n_cases) Class labels for each data sample. """ - n_jobs = check_n_jobs(self.n_jobs) - preds = Parallel(n_jobs=n_jobs, backend=self.parallel_backend)( + preds = Parallel(n_jobs=self._n_jobs, backend=self.parallel_backend)( delayed(self._predict_row)(x) for x in X ) return np.array(preds, dtype=self.classes_.dtype) diff --git a/aeon/classification/early_classification/_probability_threshold.py b/aeon/classification/early_classification/_probability_threshold.py index 79d2f49812..5a5ac5bf55 100644 --- a/aeon/classification/early_classification/_probability_threshold.py +++ b/aeon/classification/early_classification/_probability_threshold.py @@ -17,6 +17,7 @@ from aeon.base._base import _clone_estimator from aeon.classification.early_classification.base import BaseEarlyClassifier from aeon.classification.interval_based import DrCIFClassifier +from aeon.utils.validation import check_n_jobs class ProbabilityThresholdEarlyClassifier(BaseEarlyClassifier): @@ -127,6 +128,7 @@ def __init__( def _fit(self, X, y): self.n_cases_, self.n_channels_, self.n_timepoints_ = X.shape + self._n_jobs = check_n_jobs(self.n_jobs) self._estimator = ( DrCIFClassifier() if self.estimator is None else self.estimator diff --git a/aeon/classification/early_classification/_teaser.py b/aeon/classification/early_classification/_teaser.py index 8a01bbba3b..99a5ceff86 100644 --- a/aeon/classification/early_classification/_teaser.py +++ b/aeon/classification/early_classification/_teaser.py @@ -19,6 +19,7 @@ from aeon.base._base import _clone_estimator from aeon.classification.dictionary_based import MUSE, WEASEL from aeon.classification.early_classification.base import BaseEarlyClassifier +from aeon.utils.validation import check_n_jobs class TEASER(BaseEarlyClassifier): @@ -145,6 +146,7 @@ def __init__( def _fit(self, X, y): self.n_cases_, self.n_channels_, self.n_timepoints_ = X.shape + self._n_jobs = check_n_jobs(self.n_jobs) self._estimator = ( ( diff --git a/aeon/classification/feature_based/_catch22.py b/aeon/classification/feature_based/_catch22.py index 26a56d0a91..3ebd2392d8 100644 --- a/aeon/classification/feature_based/_catch22.py +++ b/aeon/classification/feature_based/_catch22.py @@ -12,6 +12,7 @@ from aeon.base._base import _clone_estimator from aeon.classification import BaseClassifier from aeon.transformations.collection.feature_based import Catch22 +from aeon.utils.validation import check_n_jobs class Catch22Classifier(BaseClassifier): @@ -179,6 +180,7 @@ def _fit(self, X, y): self : Reference to self. """ + self._n_jobs = check_n_jobs(self.n_jobs) self._transformer = Catch22( features=self.features, catch24=self.catch24, diff --git a/aeon/classification/feature_based/_fresh_prince.py b/aeon/classification/feature_based/_fresh_prince.py index df0c27cf36..b85471f23c 100644 --- a/aeon/classification/feature_based/_fresh_prince.py +++ b/aeon/classification/feature_based/_fresh_prince.py @@ -13,6 +13,7 @@ from aeon.classification.base import BaseClassifier from aeon.classification.sklearn import RotationForestClassifier from aeon.transformations.collection.feature_based import TSFresh +from aeon.utils.validation import check_n_jobs class FreshPRINCEClassifier(BaseClassifier): @@ -181,6 +182,7 @@ def _fit_predict_proba(self, X, y) -> np.ndarray: def _fit_fp_shared(self, X, y): self.n_cases_, self.n_channels_, self.n_timepoints_ = X.shape + self._n_jobs = check_n_jobs(self.n_jobs) self._rotf = RotationForestClassifier( n_estimators=self.n_estimators, diff --git a/aeon/classification/feature_based/_summary.py b/aeon/classification/feature_based/_summary.py index 9965d9308b..a45f9091e9 100644 --- a/aeon/classification/feature_based/_summary.py +++ b/aeon/classification/feature_based/_summary.py @@ -12,6 +12,7 @@ from aeon.base._base import _clone_estimator from aeon.classification.base import BaseClassifier from aeon.transformations.collection.feature_based import SevenNumberSummary +from aeon.utils.validation import check_n_jobs class SummaryClassifier(BaseClassifier): @@ -128,6 +129,8 @@ def _fit(self, X, y): Changes state by creating a fitted model that updates attributes ending in "_" and sets is_fitted flag to True. """ + self._n_jobs = check_n_jobs(self.n_jobs) + self.transformer_ = SevenNumberSummary( summary_stats=self.summary_stats, ) diff --git a/aeon/classification/feature_based/_tdmvdc.py b/aeon/classification/feature_based/_tdmvdc.py index defb70bc96..a9aa1600b7 100644 --- a/aeon/classification/feature_based/_tdmvdc.py +++ b/aeon/classification/feature_based/_tdmvdc.py @@ -345,11 +345,11 @@ def _fit(self, X, y): SXList = [] # Use parallel processing for feature extraction - threads_to_use = check_n_jobs(self.n_jobs) + self._n_jobs = check_n_jobs(self.n_jobs) # Extract features for each dilation rate in parallel results = Parallel( - n_jobs=threads_to_use, backend=self.parallel_backend, prefer="threads" + n_jobs=self._n_jobs, backend=self.parallel_backend, prefer="threads" )( delayed(self._extract_features_for_dilation)(X, X_F, X_S, d_rate, y) for d_rate in self.dList_ @@ -385,7 +385,7 @@ def _fit(self, X, y): ) self.clfList_ = Parallel( - n_jobs=threads_to_use, backend=self.parallel_backend, prefer="threads" + n_jobs=self._n_jobs, backend=self.parallel_backend, prefer="threads" )( delayed(self._train_classifier_for_ratio)(RX, FX, SX, y, ratio) for ratio in feature_store_ratios diff --git a/aeon/classification/feature_based/_tsfresh.py b/aeon/classification/feature_based/_tsfresh.py index 00021da5d8..ba74b5f19f 100644 --- a/aeon/classification/feature_based/_tsfresh.py +++ b/aeon/classification/feature_based/_tsfresh.py @@ -14,6 +14,7 @@ from aeon.base._base import _clone_estimator from aeon.classification.base import BaseClassifier from aeon.transformations.collection.feature_based import TSFresh, TSFreshRelevant +from aeon.utils.validation import check_n_jobs class TSFreshClassifier(BaseClassifier): @@ -135,6 +136,8 @@ def _fit(self, X, y): Changes state by creating a fitted model that updates attributes ending in "_" and sets is_fitted flag to True. """ + self._n_jobs = check_n_jobs(self.n_jobs) + self._transformer = ( TSFreshRelevant( default_fc_parameters=self.default_fc_parameters, diff --git a/aeon/classification/hybrid/_hivecote_v1.py b/aeon/classification/hybrid/_hivecote_v1.py index 22925487a6..c659b11264 100644 --- a/aeon/classification/hybrid/_hivecote_v1.py +++ b/aeon/classification/hybrid/_hivecote_v1.py @@ -20,6 +20,7 @@ TimeSeriesForestClassifier, ) from aeon.classification.shapelet_based import ShapeletTransformClassifier +from aeon.utils.validation import check_n_jobs class HIVECOTEV1(BaseClassifier): @@ -165,6 +166,8 @@ def _fit(self, X, y): self : Reference to self. """ + self._n_jobs = check_n_jobs(self.n_jobs) + if self.stc_params is None: self._stc_params = {"n_shapelet_samples": HIVECOTEV1._DEFAULT_N_SHAPELETS} if self.tsf_params is None: diff --git a/aeon/classification/hybrid/_hivecote_v2.py b/aeon/classification/hybrid/_hivecote_v2.py index f53167cc8a..090df74573 100644 --- a/aeon/classification/hybrid/_hivecote_v2.py +++ b/aeon/classification/hybrid/_hivecote_v2.py @@ -18,6 +18,7 @@ from aeon.classification.dictionary_based import TemporalDictionaryEnsemble from aeon.classification.interval_based._drcif import DrCIFClassifier from aeon.classification.shapelet_based import ShapeletTransformClassifier +from aeon.utils.validation import check_n_jobs class HIVECOTEV2(BaseClassifier): @@ -175,6 +176,8 @@ def _fit(self, X, y): Changes state by creating a fitted model that updates attributes ending in "_" and sets is_fitted flag to True. """ + self._n_jobs = check_n_jobs(self.n_jobs) + if self.stc_params is None: self._stc_params = {"n_shapelet_samples": HIVECOTEV2._DEFAULT_N_SHAPELETS} if self.drcif_params is None: diff --git a/aeon/classification/interval_based/_interval_pipelines.py b/aeon/classification/interval_based/_interval_pipelines.py index 8804f43aec..1dc3d52b9c 100644 --- a/aeon/classification/interval_based/_interval_pipelines.py +++ b/aeon/classification/interval_based/_interval_pipelines.py @@ -15,6 +15,7 @@ RandomIntervals, SupervisedIntervals, ) +from aeon.utils.validation import check_n_jobs class RandomIntervalClassifier(BaseClassifier): @@ -143,6 +144,7 @@ def _fit(self, X, y): Reference to self. """ self.n_cases_, self.n_channels_, self.n_timepoints_ = X.shape + self._n_jobs = check_n_jobs(self.n_jobs) self._transformer = RandomIntervals( n_intervals=self.n_intervals, @@ -380,6 +382,7 @@ def _fit(self, X, y): Reference to self. """ self.n_cases_, self.n_channels_, self.n_timepoints_ = X.shape + self._n_jobs = check_n_jobs(self.n_jobs) self._transformer = SupervisedIntervals( n_intervals=self.n_intervals, @@ -389,7 +392,7 @@ def _fit(self, X, y): randomised_split_point=self.randomised_split_point, normalise_for_search=self.normalise_for_search, random_state=self.random_state, - n_jobs=self.n_jobs, + n_jobs=self._n_jobs, parallel_backend=self.parallel_backend, ) diff --git a/aeon/classification/interval_based/_rstsf.py b/aeon/classification/interval_based/_rstsf.py index 9280a4a03f..1553d65fef 100644 --- a/aeon/classification/interval_based/_rstsf.py +++ b/aeon/classification/interval_based/_rstsf.py @@ -95,7 +95,6 @@ def __init__( def _fit(self, X, y): self.n_cases_, self.n_channels_, self.n_timepoints_ = X.shape - self._n_jobs = check_n_jobs(self.n_jobs) lags = int(12 * (X.shape[2] / 100.0) ** 0.25) diff --git a/aeon/classification/ordinal_classification/_ordinal_tde.py b/aeon/classification/ordinal_classification/_ordinal_tde.py index 886ff4707a..de3394bec6 100644 --- a/aeon/classification/ordinal_classification/_ordinal_tde.py +++ b/aeon/classification/ordinal_classification/_ordinal_tde.py @@ -28,6 +28,7 @@ from aeon.classification.base import BaseClassifier from aeon.classification.dictionary_based._tde import histogram_intersection from aeon.transformations.collection.dictionary_based import SFA +from aeon.utils.validation import check_n_jobs class OrdinalTDE(BaseClassifier): @@ -238,6 +239,7 @@ def _fit(self, X, y, keep_train_preds=False): ) self.n_cases_, self.n_channels_, self.n_timepoints_ = X.shape + self._n_jobs = check_n_jobs(self.n_jobs) self.estimators_ = [] self.weights_ = [] @@ -757,6 +759,7 @@ def _fit(self, X, y): ending in "_" and sets is_fitted flag to True. """ self.n_cases_, self.n_channels_, self.n_timepoints_ = X.shape + self._n_jobs = check_n_jobs(self.n_jobs) self._class_vals = y # select dimensions using accuracy estimate if multivariate diff --git a/aeon/classification/shapelet_based/_rdst.py b/aeon/classification/shapelet_based/_rdst.py index cfdbbeb571..8b510ad073 100644 --- a/aeon/classification/shapelet_based/_rdst.py +++ b/aeon/classification/shapelet_based/_rdst.py @@ -19,6 +19,7 @@ from aeon.transformations.collection.shapelet_based import ( RandomDilatedShapeletTransform, ) +from aeon.utils.validation import check_n_jobs class RDSTClassifier(BaseClassifier): @@ -186,6 +187,7 @@ def _fit(self, X, y): Changes state by creating a fitted model that updates attributes ending in "_". """ + self._n_jobs = check_n_jobs(self.n_jobs) self._transformer = RandomDilatedShapeletTransform( max_shapelets=self.max_shapelets, shapelet_lengths=self.shapelet_lengths, @@ -193,9 +195,10 @@ def _fit(self, X, y): threshold_percentiles=self.threshold_percentiles, alpha_similarity=self.alpha_similarity, use_prime_dilations=self.use_prime_dilations, - n_jobs=self.n_jobs, + n_jobs=self._n_jobs, random_state=self.random_state, ) + if self.estimator is None: self._estimator = make_pipeline( StandardScaler(with_mean=True), @@ -208,7 +211,7 @@ def _fit(self, X, y): self._estimator = _clone_estimator(self.estimator, self.random_state) m = getattr(self._estimator, "n_jobs", None) if m is not None: - self._estimator.n_jobs = self.n_jobs + self._estimator.n_jobs = self._n_jobs X_t = self._transformer.fit_transform(X, y) diff --git a/aeon/classification/shapelet_based/_rsast.py b/aeon/classification/shapelet_based/_rsast.py index 48025229b2..f8e2e00921 100644 --- a/aeon/classification/shapelet_based/_rsast.py +++ b/aeon/classification/shapelet_based/_rsast.py @@ -13,6 +13,7 @@ from aeon.base._base import _clone_estimator from aeon.classification import BaseClassifier from aeon.transformations.collection.shapelet_based import RSAST +from aeon.utils.validation import check_n_jobs class RSASTClassifier(BaseClassifier): @@ -96,12 +97,14 @@ def _fit(self, X, y): This pipeline classifier """ + self._n_jobs = check_n_jobs(self.n_jobs) + self._transformer = RSAST( self.n_random_points, self.len_method, self.nb_inst_per_class, self.seed, - self.n_jobs, + self._n_jobs, ) self._classifier = _clone_estimator( diff --git a/aeon/classification/shapelet_based/_sast.py b/aeon/classification/shapelet_based/_sast.py index c5ef836c63..3937b82333 100644 --- a/aeon/classification/shapelet_based/_sast.py +++ b/aeon/classification/shapelet_based/_sast.py @@ -18,6 +18,7 @@ from aeon.classification import BaseClassifier from aeon.transformations.collection.shapelet_based import SAST from aeon.utils.numba.general import z_normalise_series +from aeon.utils.validation import check_n_jobs class SASTClassifier(BaseClassifier): @@ -100,12 +101,13 @@ def _fit(self, X, y): This pipeline classifier """ + self._n_jobs = check_n_jobs(self.n_jobs) self._transformer = SAST( self.length_list, self.stride, self.nb_inst_per_class, self.seed, - self.n_jobs, + self._n_jobs, ) self._classifier = _clone_estimator( diff --git a/aeon/classification/shapelet_based/_stc.py b/aeon/classification/shapelet_based/_stc.py index 9bd80a8cc2..3e4f94e8b7 100644 --- a/aeon/classification/shapelet_based/_stc.py +++ b/aeon/classification/shapelet_based/_stc.py @@ -17,6 +17,7 @@ from aeon.classification.base import BaseClassifier from aeon.classification.sklearn import RotationForestClassifier from aeon.transformations.collection.shapelet_based import RandomShapeletTransform +from aeon.utils.validation import check_n_jobs class ShapeletTransformClassifier(BaseClassifier): @@ -279,6 +280,7 @@ def _fit_predict_proba(self, X, y) -> np.ndarray: def _fit_stc_shared(self, X, y): self.n_cases_, self.n_channels_, self.n_timepoints_ = X.shape + self._n_jobs = check_n_jobs(self.n_jobs) if self.time_limit_in_minutes > 0: # contracting 2/3 transform (with 1/5 of that taken away for final diff --git a/aeon/classification/sklearn/_rotation_forest_classifier.py b/aeon/classification/sklearn/_rotation_forest_classifier.py index eab57956ae..25734cd751 100644 --- a/aeon/classification/sklearn/_rotation_forest_classifier.py +++ b/aeon/classification/sklearn/_rotation_forest_classifier.py @@ -303,9 +303,9 @@ def _fit_rotf(self, X, y, save_transformed_data: bool = False): X, y = validate_data(self, X=X, y=y, ensure_min_samples=2, accept_sparse=False) check_classification_targets(y) + self.n_cases_, self.n_atts_ = X.shape self._n_jobs = check_n_jobs(self.n_jobs) - self.n_cases_, self.n_atts_ = X.shape self.classes_ = np.unique(y) self.n_classes_ = self.classes_.shape[0] self._class_dictionary = {} diff --git a/aeon/clustering/_kernel_k_means.py b/aeon/clustering/_kernel_k_means.py index 311228c823..3ef02d1dd3 100644 --- a/aeon/clustering/_kernel_k_means.py +++ b/aeon/clustering/_kernel_k_means.py @@ -8,6 +8,7 @@ from aeon.clustering.base import BaseClusterer from aeon.distances.pointwise._squared import squared_pairwise_distance +from aeon.utils.validation import check_n_jobs @njit(cache=True, fastmath=True) @@ -227,6 +228,8 @@ def _fit(self, X, y=None): """ from tslearn.clustering import KernelKMeans as TsLearnKernelKMeans + self._n_jobs = check_n_jobs(self.n_jobs) + verbose = 0 if self.verbose is True: verbose = 1 @@ -252,7 +255,7 @@ def kdtw_kernel(x, y, sigma=1.0, epsilon=1e-3): tol=self.tol, n_init=self.n_init, kernel_params=self.kernel_params, - n_jobs=self.n_jobs, + n_jobs=self._n_jobs, verbose=verbose, random_state=self.random_state, ) diff --git a/aeon/clustering/feature_based/_catch22.py b/aeon/clustering/feature_based/_catch22.py index 30fad7ff7e..e71c616830 100644 --- a/aeon/clustering/feature_based/_catch22.py +++ b/aeon/clustering/feature_based/_catch22.py @@ -12,6 +12,7 @@ from aeon.base._base import _clone_estimator from aeon.clustering import BaseClusterer from aeon.transformations.collection.feature_based import Catch22 +from aeon.utils.validation import check_n_jobs class Catch22Clusterer(BaseClusterer): @@ -146,6 +147,8 @@ def _fit(self, X, y=None): self : Reference to self. """ + self._n_jobs = check_n_jobs(self.n_jobs) + self._transformer = Catch22( features=self.features, catch24=self.catch24, diff --git a/aeon/clustering/feature_based/_summary.py b/aeon/clustering/feature_based/_summary.py index 682852103d..65d2168bfd 100644 --- a/aeon/clustering/feature_based/_summary.py +++ b/aeon/clustering/feature_based/_summary.py @@ -12,6 +12,7 @@ from aeon.base._base import _clone_estimator from aeon.clustering import BaseClusterer from aeon.transformations.collection.feature_based import SevenNumberSummary +from aeon.utils.validation import check_n_jobs class SummaryClusterer(BaseClusterer): @@ -105,6 +106,8 @@ def _fit(self, X, y=None): Changes state by creating a fitted model that updates attributes ending in "_" and sets is_fitted flag to True. """ + self._n_jobs = check_n_jobs(self.n_jobs) + self._transformer = SevenNumberSummary( summary_stats=self.summary_stats, ) diff --git a/aeon/clustering/feature_based/_tsfresh.py b/aeon/clustering/feature_based/_tsfresh.py index ed14e90a47..54bfdd05e5 100644 --- a/aeon/clustering/feature_based/_tsfresh.py +++ b/aeon/clustering/feature_based/_tsfresh.py @@ -15,6 +15,7 @@ from aeon.base._base import _clone_estimator from aeon.clustering import BaseClusterer from aeon.transformations.collection.feature_based import TSFresh +from aeon.utils.validation import check_n_jobs class TSFreshClusterer(BaseClusterer): @@ -122,6 +123,8 @@ def _fit(self, X: np.ndarray, y: Optional[np.ndarray] = None): Changes state by creating a fitted model that updates attributes ending in "_" and sets is_fitted flag to True. """ + self._n_jobs = check_n_jobs(self.n_jobs) + self._transformer = TSFresh( default_fc_parameters=self.default_fc_parameters, n_jobs=self._n_jobs, diff --git a/aeon/regression/base.py b/aeon/regression/base.py index dbe40732bb..84025decc5 100644 --- a/aeon/regression/base.py +++ b/aeon/regression/base.py @@ -333,13 +333,17 @@ def _fit_predict(self, X, y) -> np.ndarray: random_state = getattr(self, "random_state", None) estimator = _clone_estimator(self, random_state) + n_jobs = getattr(self, "_n_jobs", None) + if n_jobs is None: + n_jobs = getattr(self, "n_jobs", None) + return cross_val_predict( estimator, X=X, y=y, cv=10, method="predict", - n_jobs=self._n_jobs, + n_jobs=n_jobs, ) def _fit_setup(self, X, y): diff --git a/aeon/regression/convolution_based/_hydra.py b/aeon/regression/convolution_based/_hydra.py index 0a8c467b70..a08551dc47 100644 --- a/aeon/regression/convolution_based/_hydra.py +++ b/aeon/regression/convolution_based/_hydra.py @@ -12,6 +12,7 @@ from aeon.regression import BaseRegressor from aeon.transformations.collection.convolution_based._hydra import HydraTransformer +from aeon.utils.validation import check_n_jobs class HydraRegressor(BaseRegressor): @@ -86,10 +87,11 @@ def __init__(self, n_kernels=8, n_groups=64, n_jobs=1, random_state=None): super().__init__() def _fit(self, X, y): + self._n_jobs = check_n_jobs(self.n_jobs) transform = HydraTransformer( n_kernels=self.n_kernels, n_groups=self.n_groups, - n_jobs=self.n_jobs, + n_jobs=self._n_jobs, random_state=self.random_state, ) diff --git a/aeon/regression/convolution_based/_minirocket.py b/aeon/regression/convolution_based/_minirocket.py index 5ffccdad04..9b2a3dbedb 100644 --- a/aeon/regression/convolution_based/_minirocket.py +++ b/aeon/regression/convolution_based/_minirocket.py @@ -14,6 +14,7 @@ from aeon.base._base import _clone_estimator from aeon.regression.base import BaseRegressor from aeon.transformations.collection.convolution_based import MiniRocket +from aeon.utils.validation import check_n_jobs class MiniRocketRegressor(BaseRegressor): @@ -104,11 +105,12 @@ def _fit(self, X, y): ending in "_" and sets is_fitted flag to True. """ self.n_cases_, self.n_channels_, self.n_timepoints_ = X.shape + self._n_jobs = check_n_jobs(self.n_jobs) self._transformer = MiniRocket( n_kernels=self.n_kernels, max_dilations_per_kernel=self.max_dilations_per_kernel, - n_jobs=self.n_jobs, + n_jobs=self._n_jobs, random_state=self.random_state, ) self._scaler = StandardScaler(with_mean=False) diff --git a/aeon/regression/convolution_based/_mr_hydra.py b/aeon/regression/convolution_based/_mr_hydra.py index 162d130782..277fe9ee81 100644 --- a/aeon/regression/convolution_based/_mr_hydra.py +++ b/aeon/regression/convolution_based/_mr_hydra.py @@ -15,6 +15,7 @@ from aeon.regression.convolution_based._hydra import _SparseScaler from aeon.transformations.collection.convolution_based import MultiRocket from aeon.transformations.collection.convolution_based._hydra import HydraTransformer +from aeon.utils.validation import check_n_jobs class MultiRocketHydraRegressor(BaseRegressor): @@ -81,10 +82,12 @@ def __init__(self, n_kernels=8, n_groups=64, n_jobs=1, random_state=None): super().__init__() def _fit(self, X, y): + self._n_jobs = check_n_jobs(self.n_jobs) + self._transform_hydra = HydraTransformer( n_kernels=self.n_kernels, n_groups=self.n_groups, - n_jobs=self.n_jobs, + n_jobs=self._n_jobs, random_state=self.random_state, ) Xt_hydra = self._transform_hydra.fit_transform(X) @@ -93,7 +96,7 @@ def _fit(self, X, y): Xt_hydra = self._scale_hydra.fit_transform(Xt_hydra) self._transform_multirocket = MultiRocket( - n_jobs=self.n_jobs, + n_jobs=self._n_jobs, random_state=self.random_state, ) Xt_multirocket = self._transform_multirocket.fit_transform(X) diff --git a/aeon/regression/convolution_based/_multirocket.py b/aeon/regression/convolution_based/_multirocket.py index b62942502e..c51ac05d9a 100644 --- a/aeon/regression/convolution_based/_multirocket.py +++ b/aeon/regression/convolution_based/_multirocket.py @@ -14,6 +14,7 @@ from aeon.base._base import _clone_estimator from aeon.regression.base import BaseRegressor from aeon.transformations.collection.convolution_based import MultiRocket +from aeon.utils.validation import check_n_jobs class MultiRocketRegressor(BaseRegressor): @@ -109,12 +110,13 @@ def _fit(self, X, y): ending in "_" and sets is_fitted flag to True. """ self.n_cases_, self.n_channels_, self.n_timepoints_ = X.shape + self._n_jobs = check_n_jobs(self.n_jobs) self._transformer = MultiRocket( n_kernels=self.n_kernels, max_dilations_per_kernel=self.max_dilations_per_kernel, n_features_per_kernel=self.n_features_per_kernel, - n_jobs=self.n_jobs, + n_jobs=self._n_jobs, random_state=self.random_state, ) self._scaler = StandardScaler(with_mean=False) diff --git a/aeon/regression/convolution_based/_rocket.py b/aeon/regression/convolution_based/_rocket.py index f522763bed..ba00c32ea8 100644 --- a/aeon/regression/convolution_based/_rocket.py +++ b/aeon/regression/convolution_based/_rocket.py @@ -14,6 +14,7 @@ from aeon.base._base import _clone_estimator from aeon.regression.base import BaseRegressor from aeon.transformations.collection.convolution_based import Rocket +from aeon.utils.validation import check_n_jobs class RocketRegressor(BaseRegressor): @@ -104,9 +105,11 @@ def _fit(self, X, y): Changes state by creating a fitted model that updates attributes ending in "_" and sets is_fitted flag to True. """ + self._n_jobs = check_n_jobs(self.n_jobs) + self._transformer = Rocket( n_kernels=self.n_kernels, - n_jobs=self.n_jobs, + n_jobs=self._n_jobs, random_state=self.random_state, ) self._scaler = StandardScaler(with_mean=False) diff --git a/aeon/regression/distance_based/_time_series_neighbors.py b/aeon/regression/distance_based/_time_series_neighbors.py index 9981e2dc12..4e77fd5d49 100644 --- a/aeon/regression/distance_based/_time_series_neighbors.py +++ b/aeon/regression/distance_based/_time_series_neighbors.py @@ -13,9 +13,11 @@ from typing import Callable, Union import numpy as np +from joblib import Parallel, delayed from aeon.distances import get_distance_function from aeon.regression.base import BaseRegressor +from aeon.utils.validation import check_n_jobs WEIGHTS_SUPPORTED = ["uniform", "distance"] @@ -46,11 +48,15 @@ class KNeighborsTimeSeriesRegressor(BaseRegressor): n_timepoints)`` as input and returns a float. distance_params : dict, default = None Dictionary for metric parameters for the case that distance is a str. - n_jobs : int, default = None + n_jobs : int, default = 1 The number of parallel jobs to run for neighbors search. - ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. + ``None`` means 1 unless in a :obj:``joblib.parallel_backend`` context. ``-1`` means using all processors. - for more details. Parameter for compatibility purposes, still unimplemented. + parallel_backend : str, ParallelBackendBase instance or None, default=None + Specify the parallelisation backend implementation in joblib, if None + a ‘prefer’ value of “threads” is used by default. Valid options are + “loky”, “multiprocessing”, “threading” or a custom backend. + See the joblib Parallel documentation for more details. Examples -------- @@ -79,11 +85,13 @@ def __init__( n_neighbors: int = 1, weights: Union[str, Callable] = "uniform", n_jobs: int = 1, + parallel_backend: str = None, ) -> None: self.distance = distance self.distance_params = distance_params self.n_neighbors = n_neighbors self.n_jobs = n_jobs + self.parallel_backend = parallel_backend self._distance_params = distance_params if self._distance_params is None: @@ -114,6 +122,7 @@ def _fit(self, X, y): self.metric_ = get_distance_function(method=self.distance) self.X_ = X self.y_ = y + self._n_jobs = check_n_jobs(self.n_jobs) return self def _predict(self, X): @@ -132,12 +141,14 @@ def _predict(self, X): y : array of shape (n_cases) Output values for each data sample. """ - preds = np.empty(len(X)) - for i in range(len(X)): - idx, weights = self._kneighbors(X[i]) - preds[i] = np.average(self.y_[idx], weights=weights) + preds = Parallel(n_jobs=self._n_jobs, backend=self.parallel_backend)( + delayed(self._predict_row)(x) for x in X + ) + return np.array(preds) - return preds + def _predict_row(self, x): + idx, weights = self._kneighbors(x) + return np.average(self.y_[idx], weights=weights) def _kneighbors(self, X): """ diff --git a/aeon/regression/feature_based/_catch22.py b/aeon/regression/feature_based/_catch22.py index 1ab04ee6e1..ce69aa254a 100644 --- a/aeon/regression/feature_based/_catch22.py +++ b/aeon/regression/feature_based/_catch22.py @@ -12,6 +12,7 @@ from aeon.base._base import _clone_estimator from aeon.regression.base import BaseRegressor from aeon.transformations.collection.feature_based import Catch22 +from aeon.utils.validation import check_n_jobs class Catch22Regressor(BaseRegressor): @@ -151,6 +152,8 @@ def _fit(self, X, y): self : Reference to self. """ + self._n_jobs = check_n_jobs(self.n_jobs) + self._transformer = Catch22( features=self.features, catch24=self.catch24, diff --git a/aeon/regression/feature_based/_fresh_prince.py b/aeon/regression/feature_based/_fresh_prince.py index 4f5a4b1bcb..78897080f3 100644 --- a/aeon/regression/feature_based/_fresh_prince.py +++ b/aeon/regression/feature_based/_fresh_prince.py @@ -13,6 +13,7 @@ from aeon.regression.base import BaseRegressor from aeon.regression.sklearn import RotationForestRegressor from aeon.transformations.collection.feature_based import TSFresh +from aeon.utils.validation import check_n_jobs class FreshPRINCERegressor(BaseRegressor): @@ -161,6 +162,7 @@ def _fit_predict(self, X, y): def _fit_fp_shared(self, X, y): self.n_cases_, self.n_channels_, self.n_timepoints_ = X.shape + self._n_jobs = check_n_jobs(self.n_jobs) self._rotf = RotationForestRegressor( n_estimators=self.n_estimators, diff --git a/aeon/regression/feature_based/_summary.py b/aeon/regression/feature_based/_summary.py index 4c5750562b..0cffd584a4 100644 --- a/aeon/regression/feature_based/_summary.py +++ b/aeon/regression/feature_based/_summary.py @@ -12,6 +12,7 @@ from aeon.base._base import _clone_estimator from aeon.regression.base import BaseRegressor from aeon.transformations.collection.feature_based import SevenNumberSummary +from aeon.utils.validation import check_n_jobs class SummaryRegressor(BaseRegressor): @@ -107,6 +108,8 @@ def _fit(self, X, y): Changes state by creating a fitted model that updates attributes ending in "_" and sets is_fitted flag to True. """ + self._n_jobs = check_n_jobs(self.n_jobs) + self._transformer = SevenNumberSummary( summary_stats=self.summary_stats, ) diff --git a/aeon/regression/feature_based/_tsfresh.py b/aeon/regression/feature_based/_tsfresh.py index 0d8cb5bc00..36c98386ad 100644 --- a/aeon/regression/feature_based/_tsfresh.py +++ b/aeon/regression/feature_based/_tsfresh.py @@ -14,6 +14,7 @@ from aeon.base._base import _clone_estimator from aeon.regression.base import BaseRegressor from aeon.transformations.collection.feature_based import TSFresh, TSFreshRelevant +from aeon.utils.validation import check_n_jobs class TSFreshRegressor(BaseRegressor): @@ -115,6 +116,8 @@ def _fit(self, X, y): Changes state by creating a fitted model that updates attributes ending in "_" and sets is_fitted flag to True. """ + self._n_jobs = check_n_jobs(self.n_jobs) + self._transformer = ( TSFreshRelevant( default_fc_parameters=self.default_fc_parameters, diff --git a/aeon/regression/interval_based/_interval_pipelines.py b/aeon/regression/interval_based/_interval_pipelines.py index 42ceb467b9..64d0a8e695 100644 --- a/aeon/regression/interval_based/_interval_pipelines.py +++ b/aeon/regression/interval_based/_interval_pipelines.py @@ -12,6 +12,7 @@ from aeon.base._base import _clone_estimator from aeon.regression.base import BaseRegressor from aeon.transformations.collection.interval_based import RandomIntervals +from aeon.utils.validation import check_n_jobs class RandomIntervalRegressor(BaseRegressor): @@ -137,6 +138,7 @@ def _fit(self, X, y): Reference to self. """ self.n_cases_, self.n_channels_, self.n_timepoints_ = X.shape + self._n_jobs = check_n_jobs(self.n_jobs) self._transformer = RandomIntervals( n_intervals=self.n_intervals, diff --git a/aeon/regression/shapelet_based/_rdst.py b/aeon/regression/shapelet_based/_rdst.py index dcc77a3988..38da19021f 100644 --- a/aeon/regression/shapelet_based/_rdst.py +++ b/aeon/regression/shapelet_based/_rdst.py @@ -17,6 +17,7 @@ from aeon.transformations.collection.shapelet_based import ( RandomDilatedShapeletTransform, ) +from aeon.utils.validation import check_n_jobs class RDSTRegressor(BaseRegressor): @@ -165,6 +166,7 @@ def _fit(self, X, y): Changes state by creating a fitted model that updates attributes ending in "_". """ + self._n_jobs = check_n_jobs(self.n_jobs) self._transformer = RandomDilatedShapeletTransform( max_shapelets=self.max_shapelets, shapelet_lengths=self.shapelet_lengths, @@ -172,7 +174,7 @@ def _fit(self, X, y): threshold_percentiles=self.threshold_percentiles, alpha_similarity=self.alpha_similarity, use_prime_dilations=self.use_prime_dilations, - n_jobs=self.n_jobs, + n_jobs=self._n_jobs, random_state=self.random_state, ) if self.estimator is None: @@ -186,7 +188,7 @@ def _fit(self, X, y): self._estimator = _clone_estimator(self.estimator, self.random_state) m = getattr(self._estimator, "n_jobs", None) if m is not None: - self._estimator.n_jobs = self.n_jobs + self._estimator.n_jobs = self._n_jobs X_t = self._transformer.fit_transform(X, y) diff --git a/aeon/regression/sklearn/_rotation_forest_regressor.py b/aeon/regression/sklearn/_rotation_forest_regressor.py index ea3b97f9d3..48350efb32 100644 --- a/aeon/regression/sklearn/_rotation_forest_regressor.py +++ b/aeon/regression/sklearn/_rotation_forest_regressor.py @@ -227,9 +227,8 @@ def _fit_rotf(self, X, y, save_transformed_data: bool = False): self._label_average = np.mean(y) - self._n_jobs = check_n_jobs(self.n_jobs) - self.n_cases_, self.n_atts_ = X.shape + self._n_jobs = check_n_jobs(self.n_jobs) time_limit = self.time_limit_in_minutes * 60 start_time = time.time() diff --git a/aeon/segmentation/_clasp.py b/aeon/segmentation/_clasp.py index f214ea85bd..40f5643de0 100644 --- a/aeon/segmentation/_clasp.py +++ b/aeon/segmentation/_clasp.py @@ -12,6 +12,7 @@ from aeon.segmentation.base import BaseSegmenter from aeon.transformations.series import ClaSPTransformer +from aeon.utils.validation import check_n_jobs def find_dominant_window_sizes(X, offset=0.05): @@ -265,10 +266,12 @@ def get_fitted_params(self): return {"profiles": self.profiles, "scores": self.scores} def _run_clasp(self, X): + n_jobs = check_n_jobs(self.n_jobs) + clasp_transformer = ClaSPTransformer( window_length=self.period_length, exclusion_radius=self.exclusion_radius, - n_jobs=self.n_jobs, + n_jobs=n_jobs, ).fit(X) self.found_cps, self.profiles, self.scores = _segmentation( diff --git a/aeon/segmentation/base.py b/aeon/segmentation/base.py index d93cbabc5e..6d09d47029 100644 --- a/aeon/segmentation/base.py +++ b/aeon/segmentation/base.py @@ -147,7 +147,8 @@ def predict(self, X, axis=1): list of integers of ``len(X)`` indicating which segment each time point belongs to. """ - self._check_is_fitted() + if not self.get_tag("fit_is_empty"): + self._check_is_fitted() if axis is None: axis = self.axis X = self._preprocess_series(X, axis, False) diff --git a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py index 167ec538c6..cb143d24dc 100644 --- a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py +++ b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py @@ -5,6 +5,7 @@ from aeon.similarity_search.collection._base import BaseCollectionSimilaritySearch from aeon.utils.numba.general import AEON_NUMBA_STD_THRESHOLD, z_normalise_series_3d +from aeon.utils.validation import check_n_jobs @njit(cache=True) @@ -178,7 +179,9 @@ def _fit(self, X, y=None): """ prev_threads = get_num_threads() + self._n_jobs = check_n_jobs(self.n_jobs) set_num_threads(self._n_jobs) + rng = np.random.default_rng(self.random_state) if self.normalize: X = z_normalise_series_3d(X) diff --git a/aeon/similarity_search/series/neighbors/_dummy.py b/aeon/similarity_search/series/neighbors/_dummy.py index 031f93bc22..9abbefd1a7 100644 --- a/aeon/similarity_search/series/neighbors/_dummy.py +++ b/aeon/similarity_search/series/neighbors/_dummy.py @@ -45,7 +45,8 @@ def _fit( ): prev_threads = get_num_threads() - set_num_threads(check_n_jobs(self.n_jobs)) + self._n_jobs = check_n_jobs(self.n_jobs) + set_num_threads(self._n_jobs) self.X_subs = get_all_subsequences(self.X_, self.length, 1) if self.normalize: diff --git a/aeon/testing/estimator_checking/_yield_multithreading_checks.py b/aeon/testing/estimator_checking/_yield_multithreading_checks.py index 5d4f09a7ab..75e8fd14e2 100644 --- a/aeon/testing/estimator_checking/_yield_multithreading_checks.py +++ b/aeon/testing/estimator_checking/_yield_multithreading_checks.py @@ -1,13 +1,12 @@ import inspect from functools import partial -from numpy.testing import assert_array_almost_equal - from aeon.base._base import _clone_estimator from aeon.testing.testing_config import ( MULTITHREAD_TESTING, NON_STATE_CHANGING_METHODS_ARRAYLIKE, ) +from aeon.testing.utils.deep_equals import deep_equals from aeon.testing.utils.estimator_checks import _get_tag, _run_estimator_method from aeon.utils.validation import check_n_jobs @@ -75,41 +74,66 @@ def check_no_multithreading_param(estimator_class): def check_estimator_multithreading(estimator, datatype): """Test that multithreaded estimators store n_jobs_ and produce same results.""" + estimator_name = estimator.__class__.__name__ st_estimator = _clone_estimator(estimator, random_state=42) mt_estimator = _clone_estimator(estimator, random_state=42) + n_jobs = max(2, check_n_jobs(-2)) mt_estimator.set_params(n_jobs=n_jobs) - # fit and get results for single thread estimator - _run_estimator_method(st_estimator, "fit", datatype, "train") + tags = estimator.get_tags() + + if not tags["fit_is_empty"]: + # fit and get results for single thread estimator + _run_estimator_method(st_estimator, "fit", datatype, "train") + + # check _n_jobs attribute is set + assert hasattr(st_estimator, "_n_jobs"), ( + f"Estimator with default n_jobs {estimator_name} does not store an _n_jobs " + "attribute. It is recommended to use the " + "aeon.utils.validation.check_n_jobs function to set _n_jobs and use this " + "for any multithreading." + ) + assert st_estimator._n_jobs == 1, ( + f"Estimator with default n_jobs {estimator_name} does not store an _n_jobs " + f"attribute correctly. Expected 1, got {mt_estimator._n_jobs}." + f"It is recommended to use the aeon.utils.validation.check_n_jobs function " + f"to set _n_jobs and use this for any multithreading." + ) results = [] for method in NON_STATE_CHANGING_METHODS_ARRAYLIKE: - if hasattr(st_estimator, method) and callable(getattr(estimator, method)): + if hasattr(st_estimator, method) and callable(getattr(st_estimator, method)): output = _run_estimator_method(st_estimator, method, datatype, "test") results.append(output) - # fit multithreaded estimator - _run_estimator_method(mt_estimator, "fit", datatype, "train") + if not tags["fit_is_empty"]: + # fit multithreaded estimator + _run_estimator_method(mt_estimator, "fit", datatype, "train") - # check n_jobs_ attribute is set - assert mt_estimator.n_jobs_ == n_jobs, ( - f"Multithreaded estimator {mt_estimator} does not store n_jobs_ " - f"attribute correctly. Expected {n_jobs}, got {mt_estimator.n_jobs_}." - f"It is recommended to use the check_n_jobs function to set n_jobs_ and use" - f"this for any multithreading." - ) + # check _n_jobs attribute is set + assert hasattr(mt_estimator, "_n_jobs"), ( + f"Multithreaded estimator {estimator_name} does not store an _n_jobs " + "attribute. It is recommended to use the " + "aeon.utils.validation.check_n_jobs function to set _n_jobs and use this " + "for any multithreading." + ) + assert mt_estimator._n_jobs == n_jobs, ( + f"Multithreaded estimator {estimator_name} does not store an _n_jobs " + f"attribute correctly. Expected {n_jobs}, got {mt_estimator._n_jobs}." + f"It is recommended to use the aeon.utils.validation.check_n_jobs function " + f"to set _n_jobs and use this for any multithreading." + ) # compare results from single and multithreaded estimators i = 0 for method in NON_STATE_CHANGING_METHODS_ARRAYLIKE: - if hasattr(estimator, method) and callable(getattr(estimator, method)): - output = _run_estimator_method(estimator, method, datatype, "test") - - assert_array_almost_equal( - output, - results[i], - err_msg=f"Running {method} after fit twice with test " - f"parameters gives different results.", - ) + if hasattr(mt_estimator, method) and callable(getattr(mt_estimator, method)): + output = _run_estimator_method(mt_estimator, method, datatype, "test") + + if not tags["non_deterministic"]: + assert deep_equals(output, results[i]), ( + f"Running {method} after fit with test parameters gives different " + f"results when multithreading." + ) i += 1 diff --git a/aeon/testing/testing_config.py b/aeon/testing/testing_config.py index 2838a856d1..d199fbf59b 100644 --- a/aeon/testing/testing_config.py +++ b/aeon/testing/testing_config.py @@ -47,10 +47,10 @@ "check_save_estimators_to_file", ], # needs investigation - "SASTClassifier": ["check_fit_deterministic"], - "RSASTClassifier": ["check_fit_deterministic"], - "SAST": ["check_fit_deterministic"], - "RSAST": ["check_fit_deterministic"], + "SASTClassifier": ["check_fit_deterministic", "check_estimator_multithreading"], + "RSASTClassifier": ["check_fit_deterministic", "check_estimator_multithreading"], + "SAST": ["check_fit_deterministic", "check_estimator_multithreading"], + "RSAST": ["check_fit_deterministic", "check_estimator_multithreading"], "MatrixProfile": ["check_fit_deterministic", "check_persistence_via_pickle"], "LeftSTAMPi": ["check_series_anomaly_detector_output"], "SignatureClassifier": ["check_classifier_against_expected_results"], diff --git a/aeon/transformations/collection/convolution_based/_hydra.py b/aeon/transformations/collection/convolution_based/_hydra.py index 75db8e3f32..ca460901ad 100644 --- a/aeon/transformations/collection/convolution_based/_hydra.py +++ b/aeon/transformations/collection/convolution_based/_hydra.py @@ -101,13 +101,14 @@ def __init__( super().__init__() def _fit(self, X, y=None): + self._n_jobs = check_n_jobs(self.n_jobs) + import torch if isinstance(self.random_state, int): torch.manual_seed(self.random_state) - n_jobs = check_n_jobs(self.n_jobs) - torch.set_num_threads(n_jobs) + torch.set_num_threads(self._n_jobs) self._hydra = _HydraInternal( X.shape[2], diff --git a/aeon/transformations/collection/convolution_based/_minirocket.py b/aeon/transformations/collection/convolution_based/_minirocket.py index 313e39c076..5a3ea4bd08 100644 --- a/aeon/transformations/collection/convolution_based/_minirocket.py +++ b/aeon/transformations/collection/convolution_based/_minirocket.py @@ -10,6 +10,7 @@ from numba import get_num_threads, njit, prange, set_num_threads, vectorize from aeon.transformations.collection import BaseCollectionTransformer +from aeon.utils.validation import check_n_jobs class MiniRocket(BaseCollectionTransformer): @@ -105,6 +106,8 @@ def _fit(self, X, y=None): ------- self """ + self._n_jobs = check_n_jobs(self.n_jobs) + random_state = ( np.int32(self.random_state) if isinstance(self.random_state, int) else None ) @@ -141,10 +144,10 @@ def _transform(self, X, y=None): _, n_channels, n_timepoints = X.shape # change n_jobs dependend on value and existing cores prev_threads = get_num_threads() - if self.n_jobs < 1 or self.n_jobs > multiprocessing.cpu_count(): + if self._n_jobs < 1 or self._n_jobs > multiprocessing.cpu_count(): n_jobs = multiprocessing.cpu_count() else: - n_jobs = self.n_jobs + n_jobs = self._n_jobs set_num_threads(n_jobs) if n_channels == 1: X = X.squeeze(1) diff --git a/aeon/transformations/collection/convolution_based/_multirocket.py b/aeon/transformations/collection/convolution_based/_multirocket.py index 7b0c9bca2e..fb7a7f7ac5 100644 --- a/aeon/transformations/collection/convolution_based/_multirocket.py +++ b/aeon/transformations/collection/convolution_based/_multirocket.py @@ -5,6 +5,7 @@ from numba import get_num_threads, njit, prange, set_num_threads from aeon.transformations.collection import BaseCollectionTransformer +from aeon.utils.validation import check_n_jobs class MultiRocket(BaseCollectionTransformer): @@ -114,6 +115,8 @@ def _fit(self, X, y=None): ------- self """ + self._n_jobs = check_n_jobs(self.n_jobs) + self.random_state_ = ( np.int32(self.random_state) if isinstance(self.random_state, int) else None ) @@ -163,10 +166,10 @@ def _transform(self, X, y=None): ) # change n_jobs dependend on value and existing cores prev_threads = get_num_threads() - if self.n_jobs < 1 or self.n_jobs > multiprocessing.cpu_count(): + if self._n_jobs < 1 or self._n_jobs > multiprocessing.cpu_count(): n_jobs = multiprocessing.cpu_count() else: - n_jobs = self.n_jobs + n_jobs = self._n_jobs set_num_threads(n_jobs) X = X.astype(np.float32) diff --git a/aeon/transformations/collection/convolution_based/_rocket.py b/aeon/transformations/collection/convolution_based/_rocket.py index 6a7e104d46..1b56e0c71a 100644 --- a/aeon/transformations/collection/convolution_based/_rocket.py +++ b/aeon/transformations/collection/convolution_based/_rocket.py @@ -99,6 +99,8 @@ def _fit(self, X, y=None): ------- self """ + self._n_jobs = check_n_jobs(self.n_jobs) + if isinstance(self.random_state, int): self._random_state = self.random_state else: @@ -128,11 +130,10 @@ def _transform(self, X, y=None): if self.normalise: norm = Normalizer() X = norm.fit_transform(X) - prev_threads = get_num_threads() - n_jobs = check_n_jobs(self.n_jobs) + prev_threads = get_num_threads() + set_num_threads(self._n_jobs) - set_num_threads(n_jobs) X_ = _apply_kernels(X, self.kernels) set_num_threads(prev_threads) diff --git a/aeon/transformations/collection/dictionary_based/_borf.py b/aeon/transformations/collection/dictionary_based/_borf.py index 97efa7c65b..8b67d084c7 100644 --- a/aeon/transformations/collection/dictionary_based/_borf.py +++ b/aeon/transformations/collection/dictionary_based/_borf.py @@ -13,11 +13,13 @@ import numba as nb import numpy as np +from numba import get_num_threads from sklearn.base import BaseEstimator, TransformerMixin from sklearn.pipeline import FeatureUnion from sklearn.pipeline import make_pipeline as make_pipeline_sklearn from aeon.transformations.collection import BaseCollectionTransformer +from aeon.utils.validation import check_n_jobs class BORF(BaseCollectionTransformer): @@ -142,9 +144,13 @@ def __init__( self.transformer_weights = transformer_weights self.complexity = complexity self.densify = densify + super().__init__() def _fit(self, X, y=None): + self._n_jobs = check_n_jobs(self.n_jobs) + n_jobs_numba = check_n_jobs(self.n_jobs_numba) + time_series_length = X.shape[2] # for better computation time, this should be moved to the init, # setting time_series_length as a user parameter @@ -169,8 +175,8 @@ def _fit(self, X, y=None): dilations_min_dilation=self.dilations_min_dilation, dilations_max_dilation=self.dilations_max_dilation, min_window_to_signal_std_ratio=self.min_window_to_signal_std_ratio, - n_jobs=self.n_jobs, - n_jobs_numba=self.n_jobs_numba, + n_jobs=self._n_jobs, + n_jobs_numba=n_jobs_numba, transformer_weights=self.transformer_weights, pipeline_objects=pipeline_objects, complexity=self.complexity, @@ -303,7 +309,6 @@ def __init__( _array_to_int(np.full(self.word_length, self.alphabet_size - 1)) + 1, base=self.alphabet_size, ) - _set_n_jobs_numba(n_jobs=self.n_jobs) self.feature_names_in_ = None self.n_features_in_ = None @@ -314,6 +319,9 @@ def fit(self, X, y=None): def transform(self, X, y=None): import sparse # FIXME: can we move this outside for better performance? + prev_threads = get_num_threads() + _set_n_jobs_numba(n_jobs=self.n_jobs) + shape_ = (len(X), len(X[0]), self.n_words) out = _transform_sax_patterns( panel=X, @@ -325,6 +333,9 @@ def transform(self, X, y=None): min_window_to_signal_std_ratio=self.min_window_to_signal_std_ratio, ) # ts_idx, signal_idx, words, count + + _set_n_jobs_numba(n_jobs=prev_threads) + return sparse.COO(coords=out[:, :3].T, data=out[:, -1].T, shape=shape_) diff --git a/aeon/transformations/collection/dictionary_based/_sfa.py b/aeon/transformations/collection/dictionary_based/_sfa.py index c6429816e5..b854e83b5c 100644 --- a/aeon/transformations/collection/dictionary_based/_sfa.py +++ b/aeon/transformations/collection/dictionary_based/_sfa.py @@ -20,6 +20,7 @@ from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor from aeon.transformations.collection import BaseCollectionTransformer +from aeon.utils.validation import check_n_jobs # The binning methods to use: equi-depth, equi-width, information gain or kmeans binning_methods = { @@ -208,6 +209,8 @@ def _fit(self, X, y=None): ------- self: object """ + self._n_jobs = check_n_jobs(self.n_jobs) + if self.alphabet_size < 2: raise ValueError("Alphabet size must be an integer greater than 2") @@ -280,7 +283,7 @@ def _transform(self, X, y=None): # with warnings.catch_warnings(): # warnings.simplefilter("ignore", category=NumbaTypeSafetyWarning) - transform = Parallel(n_jobs=self.n_jobs, prefer="threads")( + transform = Parallel(n_jobs=self._n_jobs, prefer="threads")( delayed(self._transform_case)( X[i, :], supplied_dft=self.binning_dft[i] if self.keep_binning_dft else None, @@ -293,7 +296,7 @@ def _transform(self, X, y=None): self.words = np.array(list(words)) # cant pickle typed dict - if self._typed_dict and self.n_jobs != 1: + if self._typed_dict and self._n_jobs != 1: nl = [None] * len(dim) for i, pdict in enumerate(dim): ndict = ( @@ -328,7 +331,7 @@ def transform_mft(self, X): # with warnings.catch_warnings(): # warnings.simplefilter("ignore", category=NumbaTypeSafetyWarning) - transform = Parallel(n_jobs=self.n_jobs, prefer="threads")( + transform = Parallel(n_jobs=self._n_jobs, prefer="threads")( delayed(self._mft)(X[i, :]) for i in range(X.shape[0]) ) @@ -419,7 +422,7 @@ def _transform_case(self, X, supplied_dft=None): bag[skip_gram] = bag.get(skip_gram, 0) + 1 # cant pickle typed dict - if self._typed_dict and self.n_jobs != 1: + if self._typed_dict and self._n_jobs != 1: pdict = dict() for key, val in bag.items(): pdict[key] = val @@ -459,7 +462,7 @@ def transform_words(self, X): if X.ndim == 3: X = X.squeeze(1) - transform = Parallel(n_jobs=self.n_jobs, prefer="threads")( + transform = Parallel(n_jobs=self._n_jobs, prefer="threads")( delayed(self._transform_words_case)(X[i, :]) for i in range(X.shape[0]) ) @@ -813,12 +816,12 @@ def _shorten_bags(self, word_len): if self._typed_dict: warnings.simplefilter("ignore", category=NumbaTypeSafetyWarning) - dim = Parallel(n_jobs=self.n_jobs, prefer="threads")( + dim = Parallel(n_jobs=self._n_jobs, prefer="threads")( delayed(self._shorten_case)(word_len, i) for i in range(len(self.words)) ) # cant pickle typed dict - if self._typed_dict and self.n_jobs != 1: + if self._typed_dict and self._n_jobs != 1: nl = [None] * len(dim) for i, pdict in enumerate(dim): ndict = ( @@ -907,7 +910,7 @@ def _shorten_case(self, word_len, i): new_bag[skip_gram] = new_bag.get(skip_gram, 0) + 1 # cant pickle typed dict - if self._typed_dict and self.n_jobs != 1: + if self._typed_dict and self._n_jobs != 1: pdict = dict() for key, val in new_bag.items(): pdict[key] = val diff --git a/aeon/transformations/collection/dictionary_based/_sfa_fast.py b/aeon/transformations/collection/dictionary_based/_sfa_fast.py index c118ce3878..53e72e73c8 100644 --- a/aeon/transformations/collection/dictionary_based/_sfa_fast.py +++ b/aeon/transformations/collection/dictionary_based/_sfa_fast.py @@ -30,6 +30,7 @@ from aeon.transformations.collection import BaseCollectionTransformer from aeon.utils.numba.general import AEON_NUMBA_STD_THRESHOLD +from aeon.utils.validation import check_n_jobs # The binning methods to use: equi-depth, equi-width, information gain or kmeans binning_methods = { @@ -214,6 +215,8 @@ def __init__( def _fit_transform(self, X, y=None, return_bag_of_words=True): """Fit to data, then transform it.""" + self._n_jobs = check_n_jobs(self.n_jobs) + if self.alphabet_size < 2: raise ValueError("Alphabet size must be an integer greater than 2") diff --git a/aeon/transformations/collection/feature_based/_catch22.py b/aeon/transformations/collection/feature_based/_catch22.py index 0431da8df1..7baac1b3b1 100644 --- a/aeon/transformations/collection/feature_based/_catch22.py +++ b/aeon/transformations/collection/feature_based/_catch22.py @@ -222,8 +222,7 @@ def _transform(self, X, y=None): n_cases = len(X) f_idx = _verify_features(self.features, self.catch24) - - threads_to_use = check_n_jobs(self.n_jobs) + n_jobs = check_n_jobs(self.n_jobs) features = [ Catch22._DN_HistogramMode_5, @@ -289,7 +288,7 @@ def _transform(self, X, y=None): ) c22_list = Parallel( - n_jobs=threads_to_use, backend=self.parallel_backend, prefer="threads" + n_jobs=n_jobs, backend=self.parallel_backend, prefer="threads" )( delayed( self._transform_case_pycatch22 diff --git a/aeon/transformations/collection/feature_based/_tsfresh.py b/aeon/transformations/collection/feature_based/_tsfresh.py index f36047971b..89047edb3e 100644 --- a/aeon/transformations/collection/feature_based/_tsfresh.py +++ b/aeon/transformations/collection/feature_based/_tsfresh.py @@ -74,9 +74,10 @@ def __init__( def _get_extraction_params(self): """Set default parameters from tsfresh.""" - # make n_jobs compatible with scikit-learn + # make n_jobs compatible with scikit-learn. disaster area that needs reworking n_jobs = self.n_jobs self.n_jobs = check_n_jobs(self.n_jobs) + self._n_jobs = check_n_jobs(self.n_jobs) # lazy imports to avoid hard dependency from tsfresh.defaults import ( diff --git a/aeon/transformations/collection/interval_based/_random_intervals.py b/aeon/transformations/collection/interval_based/_random_intervals.py index 7d0dccc4d2..f85538b890 100644 --- a/aeon/transformations/collection/interval_based/_random_intervals.py +++ b/aeon/transformations/collection/interval_based/_random_intervals.py @@ -254,6 +254,7 @@ def _fit_setup(self, X): self._transform_features = None self.n_cases_, self.n_channels_, self.n_timepoints_ = X.shape + self._n_jobs = check_n_jobs(self.n_jobs) self._min_interval_length = self.min_interval_length if self.min_interval_length < 3: @@ -303,8 +304,6 @@ def _fit_setup(self, X): else: self._dilation = [self.dilation] - self._n_jobs = check_n_jobs(self.n_jobs) - rng = check_random_state(self.random_state) return X, rng diff --git a/aeon/transformations/collection/interval_based/_supervised_intervals.py b/aeon/transformations/collection/interval_based/_supervised_intervals.py index d247bf6739..89edb3c377 100644 --- a/aeon/transformations/collection/interval_based/_supervised_intervals.py +++ b/aeon/transformations/collection/interval_based/_supervised_intervals.py @@ -261,6 +261,7 @@ def _fit_setup(self, X, y): self.intervals_ = [] self.n_cases_, self.n_channels_, self.n_timepoints_ = X.shape + self._n_jobs = check_n_jobs(self.n_jobs) if self.n_cases_ <= 1: raise ValueError( @@ -349,8 +350,6 @@ def _fit_setup(self, X, y): else: raise ValueError("metric must be callable or 'fisher'") - self._n_jobs = check_n_jobs(self.n_jobs) - le = preprocessing.LabelEncoder() return X, le.fit_transform(y), rng diff --git a/aeon/transformations/collection/shapelet_based/_rsast.py b/aeon/transformations/collection/shapelet_based/_rsast.py index 199e52ed1c..e570c9b038 100644 --- a/aeon/transformations/collection/shapelet_based/_rsast.py +++ b/aeon/transformations/collection/shapelet_based/_rsast.py @@ -139,6 +139,7 @@ def _fit(self, X: np.ndarray, y: Union[np.ndarray, list]) -> "RSAST": This transformer """ + self._n_jobs = check_n_jobs(self.n_jobs) # 0- initialize variables and convert values in "y" to string X_ = np.reshape(X, (X.shape[0], X.shape[-1])) @@ -342,10 +343,7 @@ def _transform( X_ = np.reshape(X, (X.shape[0], X.shape[-1])) prev_threads = get_num_threads() - - n_jobs = check_n_jobs(self.n_jobs) - - set_num_threads(n_jobs) + set_num_threads(self._n_jobs) X_transformed = _apply_kernels(X_, self._kernels) # subsequence transform of X set_num_threads(prev_threads) diff --git a/aeon/transformations/collection/shapelet_based/_sast.py b/aeon/transformations/collection/shapelet_based/_sast.py index ffc513815c..8479703a2d 100644 --- a/aeon/transformations/collection/shapelet_based/_sast.py +++ b/aeon/transformations/collection/shapelet_based/_sast.py @@ -129,6 +129,8 @@ def _fit(self, X: np.ndarray, y: Union[np.ndarray, list]) -> "SAST": This transformer """ + self._n_jobs = check_n_jobs(self.n_jobs) + X_ = np.reshape(X, (X.shape[0], X.shape[-1])) self._length_list = ( self.lengths if self.lengths is not None else np.arange(3, X_.shape[1]) @@ -216,10 +218,7 @@ def _transform( X_ = np.reshape(X, (X.shape[0], X.shape[-1])) prev_threads = get_num_threads() - - n_jobs = check_n_jobs(self.n_jobs) - - set_num_threads(n_jobs) + set_num_threads(self._n_jobs) X_transformed = _apply_kernels(X_, self._kernels) # subsequence transform of X set_num_threads(prev_threads) diff --git a/aeon/transformations/series/_clasp.py b/aeon/transformations/series/_clasp.py index b826dcaa81..c76c74984c 100644 --- a/aeon/transformations/series/_clasp.py +++ b/aeon/transformations/series/_clasp.py @@ -23,6 +23,7 @@ from numba import njit, objmode, prange from aeon.transformations.series.base import BaseSeriesTransformer +from aeon.utils.validation import check_n_jobs def _sliding_window(X, m): @@ -474,6 +475,8 @@ def _transform(self, X, y=None): ClaSP of the single time series as output with length as (n-window_length+1) """ + n_jobs = check_n_jobs(self.n_jobs) + if len(X) - self.window_length < 2 * self.exclusion_radius * len(X): warnings.warn( "Period-Length is larger than size of the time series", stacklevel=1 @@ -496,7 +499,7 @@ def _transform(self, X, y=None): self.window_length, score=scoring_metric_call, exclusion_radius=self.exclusion_radius, - n_jobs=self.n_jobs, + n_jobs=n_jobs, ) return Xt From 092b577ac52fb888492475be0171f536d98795b3 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Thu, 3 Jul 2025 12:03:06 +0100 Subject: [PATCH 175/216] [DOC,MNT] Documentation configuration clean-up (#2911) * start * remove old parameter * numpydoc config * conf tidy * headers * exclude unused methods and update gsoc in about * changelog * binder button * myst config * remove extra bullet point * changelog * Automatic `pre-commit` fixes --------- Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> --- aeon/classification/deep_learning/base.py | 5 +- aeon/networks/_ae_abgru.py | 4 +- aeon/networks/_ae_dcnn.py | 4 +- docs/_templates/class_with_call.rst | 7 - docs/about.md | 8 + docs/changelogs/v1.2.md | 7 +- docs/conf.py | 358 ++++++++++------------ docs/developer_guide/coding_standards.md | 6 +- docs/papers_using_aeon.md | 2 +- pyproject.toml | 2 +- 10 files changed, 183 insertions(+), 220 deletions(-) delete mode 100644 docs/_templates/class_with_call.rst diff --git a/aeon/classification/deep_learning/base.py b/aeon/classification/deep_learning/base.py index 61ddeb3a72..a90c72c845 100644 --- a/aeon/classification/deep_learning/base.py +++ b/aeon/classification/deep_learning/base.py @@ -49,10 +49,9 @@ class BaseDeepClassifier(BaseClassifier): The name of the file of the last model, used only if save_last_model_to_file is used - Arguments - --------- + Attributes + ---------- self.model = None - """ _tags = { diff --git a/aeon/networks/_ae_abgru.py b/aeon/networks/_ae_abgru.py index ca9f0494ad..0154c841e8 100644 --- a/aeon/networks/_ae_abgru.py +++ b/aeon/networks/_ae_abgru.py @@ -64,8 +64,8 @@ def __init__( def build_network(self, input_shape, **kwargs): """Construct a network and return its input and output layers. - Arguments - --------- + Parameters + ---------- input_shape : tuple of shape = (n_timepoints (m), n_channels (d)) The shape of the data fed into the input layer. kwargs : dict diff --git a/aeon/networks/_ae_dcnn.py b/aeon/networks/_ae_dcnn.py index ea475d0161..2e2d26f370 100644 --- a/aeon/networks/_ae_dcnn.py +++ b/aeon/networks/_ae_dcnn.py @@ -85,8 +85,8 @@ def __init__( def build_network(self, input_shape): """Construct a network and return its input and output layers. - Arguments - --------- + Parameters + ---------- input_shape : tuple of shape = (n_timepoints (m), n_channels (d)) The shape of the data fed into the input layer. diff --git a/docs/_templates/class_with_call.rst b/docs/_templates/class_with_call.rst deleted file mode 100644 index 21936b6e6c..0000000000 --- a/docs/_templates/class_with_call.rst +++ /dev/null @@ -1,7 +0,0 @@ -{{ objname }} -{{ underline }} - -.. currentmodule:: {{ module }} - -.. autoclass:: {{ objname }} - :special-members: __call__ diff --git a/docs/about.md b/docs/about.md index ada6d723f1..1af1ae5ecd 100644 --- a/docs/about.md +++ b/docs/about.md @@ -124,6 +124,14 @@ organisations: - GitHub ID - Organization - Year +* - Balgopal Moharana + - {user}`lucifer4073` + - [Google Summer of Code](https://summerofcode.withgoogle.com) + - 2025 +* - Jiarong Jin + - {user}`TinaJin0228` + - [Google Summer of Code](https://summerofcode.withgoogle.com) + - 2025 * - Divya Tiwari - {user}`itsdivya1309` - [Google Summer of Code](https://summerofcode.withgoogle.com) diff --git a/docs/changelogs/v1.2.md b/docs/changelogs/v1.2.md index 651e51f885..b4930452fd 100644 --- a/docs/changelogs/v1.2.md +++ b/docs/changelogs/v1.2.md @@ -4,17 +4,18 @@ June 2025 ## Highlights -- `aeon` v1.2.0 drops support for Python 3.9, Python 3.10-3.13 are the current supported versions. +- `aeon` v1.2.0 drops support for Python 3.9, Python 3.10-3.13 are the currently supported versions. - Framework for whole-series anomaly detection has been implemented, and the module has been refactored into `anomaly_detection.collection` and `anomaly_detection.series` submodules. -- `ProximityForect` and `ProximityTree` classifiers have been improved to support +- `ProximityForest` and `ProximityTree` classifiers have been improved to support unequal length and multivariate series. The classifiers are also significantly faster. +- A new implementation for the `TD-MVDC` classifier - The forecasting module has been enhanced with additional methods for direct and recursive forecasting, as well as new algorithms such as ETS. This module is still in early development, and we expect framework changes in future releases. - The similarity search module has been significantly reworked to fit the `aeon` style framework seen in other modules. See the module examples and documentation for more details. -- The start of a sub-package for self suprervised learning has been added in +- The start of an experimental sub-package for self supervised learning has been added in `aeon.transformations.collection.self_supervised`, starting with the `TRILITE` algorithm. ## Anomaly Detection diff --git a/docs/conf.py b/docs/conf.py index 89da0515ed..fd2d36a896 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -19,6 +19,7 @@ # -- Path setup -------------------------------------------------------------- +# local sphinx extensions sys.path.append(str(Path(__file__).parent / "_sphinxext")) # If extensions (or modules to document with autodoc) are in another directory, @@ -37,29 +38,23 @@ # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ + "numpydoc", + "sphinx.ext.autosummary", + "sphinx.ext.autodoc", "sphinx.ext.autosectionlabel", "sphinx.ext.intersphinx", - "sphinx.ext.linkcode", # link to GitHub source code via linkcode_resolve() + "sphinx.ext.linkcode", "sphinxext.opengraph", - "numpydoc", - "nbsphinx", # integrates example notebooks + "nbsphinx", "sphinx_design", "sphinx_issues", "sphinx_copybutton", "versionwarning.extension", "myst_parser", - # local extensions + # local extensions (_sphinxext/) "sphinx_remove_toctrees", ] -# Notebook thumbnails -nbsphinx_thumbnails = { - "examples/02_classification": "examples/img/tsc.png", -} - -# Use bootstrap CSS from theme. -panels_add_bootstrap_css = False - # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] @@ -90,14 +85,38 @@ ".DS_Store", ] +# A boolean that decides whether module names are prepended to all object names (for +# object types where a “module” of some kind is defined), e.g. for py:function +# directives. add_module_names = False +# -- Extension configuration ------------------------------------------------- + +# -- numpydoc -- + # see http://stackoverflow.com/q/12206334/562769 +# https://github.com/numpy/numpydoc/issues/69 +# Lots of warnings numpydoc_show_class_members = True -# this is needed for some reason... -# see https://github.com/numpy/numpydoc/issues/69 + +# Whether to create a Sphinx table of contents for the lists of class methods and +# attributes. If a table of contents is made, Sphinx expects each entry to have a +# separate page. +# We remove the toctree for API for speed and do not want separate pages for this numpydoc_class_members_toctree = False +# Whether to produce plot:: directives for Examples sections that contain +# 'import matplotlib' or 'from matplotlib import'. +numpydoc_use_plots = True + +# Options for the ::plot directive: +# https://matplotlib.org/stable/api/sphinxext_plot_directive_api.html +plot_formats = ["png"] +plot_include_source = True +plot_html_show_formats = False +plot_html_show_source_link = False + +# This will produce warnings for docstring errors. numpydoc_validation_checks = { "all", "GL01", # docstring starts after opening quotes @@ -106,6 +125,11 @@ "EX01", # no examples } +# If true, '()' will be appended to :func: etc. cross-reference text. +add_function_parentheses = False + +# -- autosummary and autodoc -- + # generate autosummary even if no references autosummary_generate = True @@ -114,114 +138,82 @@ # Member-order orders the documentation in the order of how the members are defined in # the source code. autodoc_default_options = { + "class-doc-from": "class", "members": True, + "exclude-members": "get_metadata_routing, set_output", "inherited-members": True, - "member-order": "bysource", + "show-inheritance": True, } -# If true, '()' will be appended to :func: etc. cross-reference text. -add_function_parentheses = False - -# Link to GitHub repo for github_issues extension -issues_github_path = "aeon-toolkit/aeon" - -# sphinx-copybutton configuration -copybutton_exclude = ".linenos, .gp, .go" - -# sphinx-remove-toctrees (and in-built alternative) configuration -# see https://github.com/pradyunsg/furo/pull/674 -# we use an in-built alternative currently due to a bug. See sphinx_remove_toctrees.py -# extension issue https://github.com/executablebooks/sphinx-remove-toctrees/issues/9 -remove_from_toctrees = ["api_reference/auto_generated/*"] +# -- autosectionlabel -- -# MyST Parser configuration +autosectionlabel_maxdepth = 4 -# When building HTML using the sphinx.ext.mathjax (enabled by default), -# Myst-Parser injects the tex2jax_ignore (MathJax v2) and mathjax_ignore (MathJax v3) -# classes in to the top-level section of each MyST document, and adds some default -# configuration. This ensures that MathJax processes only math, identified by the -# dollarmath and amsmath extensions, or specified in math directives. We here silence -# the corresponding warning that this override happens. -suppress_warnings = ["myst.mathjax"] +# -- intersphinx -- -# "colon_fence" and "html_image" recommended by sphinx_design when using the MyST Parser -myst_enable_extensions = ["colon_fence", "html_image", "attrs_inline"] +intersphinx_mapping = { + "python": ("https://docs.python.org/3/", None), + "numpy": ("https://numpy.org/doc/stable/", None), + "scipy": ("https://docs.scipy.org/doc/scipy/", None), + "matplotlib": ("https://matplotlib.org/stable/", None), + "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None), + "joblib": ("https://joblib.readthedocs.io/en/latest/", None), + "scikit-learn": ("https://scikit-learn.org/stable/", None), +} -myst_heading_anchors = 4 +# -- nbsphinx -- +nbsphinx_execute = "never" # whether to run notebooks +nbsphinx_allow_errors = False +nbsphinx_timeout = 600 # seconds, set to -1 to disable timeout -def linkcode_resolve(domain, info): - """Return URL to source code corresponding. +# Binder launch button +current_file = "{{ env.doc2path( env.docname, base=None) }}" +binder_url = ( + f"https://mybinder.org/v2/gh/aeon-toolkit/aeon/{github_tag}?filepath={current_file}" +) - Parameters - ---------- - domain : str - info : dict +# link to original notebook +notebook_url = f"https://github.com/aeon-toolkit/aeon/tree/{github_tag}/{current_file}" - Returns - ------- - url : str - """ +# add to the bottom of each notebook page +nbsphinx_epilog = f""" +---- - def find_source(): - # try to find the file and line number, based on code from numpy: - # https://github.com/numpy/numpy/blob/main/doc/source/conf.py#L286 +Generated using nbsphinx_. The Jupyter notebook can be found here_. - import inspect - import os +|Binder|_ - # Get the top-level object from the module name - obj = sys.modules[info["module"]] +.. _here: {notebook_url} +.. _nbsphinx: https://nbsphinx.readthedocs.io/ +.. |binder| image:: https://mybinder.org/badge_logo.svg +.. _Binder: {binder_url} +""" - # Traverse dotted path (e.g., module.submodule.Class.method) - for part in info["fullname"].split("."): - obj = getattr(obj, part) +# -- sphinx_issues -- - # Unwrapping decorators (if any), so we can get the true - # source function - if inspect.isfunction(obj): - obj = inspect.unwrap(obj) +issues_github_path = "aeon-toolkit/aeon" - # Get the source filename - try: - fn = inspect.getsourcefile(obj) - except TypeError: - fn = None +# -- sphinx_copybutton -- - # If no source file is found, return None (no link) - if not fn: - return None +copybutton_exclude = ".linenos, .gp, .go" - # Make filename relative to the aeon source directory - startdir = Path(aeon.__file__).parent.parent - try: - fn = os.path.relpath(fn, start=startdir).replace(os.path.sep, "/") - except ValueError: - return None +# -- MyST parser -- - # Filter out files not in the aeon package - # (e.g., inherited from sklearn) - if not fn.startswith("aeon/"): - return None +# "colon_fence" and "html_image" recommended by sphinx_design when using the MyST Parser +myst_enable_extensions = ["colon_fence", "html_image", "attrs_inline"] - # Get line range of the object - source, lineno = inspect.getsourcelines(obj) - return fn, lineno, lineno + len(source) - 1 +myst_heading_anchors = 4 - if domain != "py" or not info["module"]: - return None - try: - result = find_source() - if not result: - return None - filename = "%s#L%d-L%d" % result - except Exception: - filename = info["module"].replace(".", "/") + ".py" - return "https://github.com/aeon-toolkit/aeon/blob/{}/{}".format( - github_tag, - filename, - ) +# -- sphinx-remove-toctrees -- +# see https://github.com/pradyunsg/furo/pull/674 +# we use an in-built alternative currently due to a bug. See sphinx_remove_toctrees.py +# extension issue https://github.com/executablebooks/sphinx-remove-toctrees/issues/9 +remove_from_toctrees = [ + "api_reference/auto_generated/*", # including all api pages significantly slows + # down the documentation build +] # -- Options for HTML output ------------------------------------------------- @@ -308,65 +300,84 @@ def find_source(): # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] -html_css_files = [ - "css/custom.css", -] +html_css_files = ["css/custom.css"] html_show_sourcelink = False -# -- Options for HTMLHelp output --------------------------------------------- +# -- Documentation functions and setup --------------------------------------- -# Output file base name for HTML help builder. -htmlhelp_basename = "aeondoc" -# -- Options for LaTeX output ------------------------------------------------ +def linkcode_resolve(domain, info): + """Return URL to source code corresponding. -latex_elements = { - # The paper size ('letterpaper' or 'a4paper'). - # 'papersize': 'letterpaper', - # The font size ('10pt', '11pt' or '12pt'). - # 'pointsize': '10pt', - # Additional stuff for the LaTeX preamble. - # 'preamble': '', - # Latex figure (float) alignment - # 'figure_align': 'htbp', -} + Parameters + ---------- + domain : str + info : dict -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, -# author, documentclass [howto, manual, or own class]). -latex_documents = [ - ( - master_doc, - "aeon.tex", - "aeon Documentation", - "aeon developers", - "manual", - ), -] + Returns + ------- + url : str + """ -# -- Options for manual page output ------------------------------------------ - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [(master_doc, "aeon", "aeon Documentation", [author], 1)] - -# -- Options for Texinfo output ---------------------------------------------- - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - ( - master_doc, - "aeon", - "aeon Documentation", - author, - "aeon", - "One line description of project.", - "Miscellaneous", - ), -] + def find_source(): + # try to find the file and line number, based on code from numpy: + # https://github.com/numpy/numpy/blob/main/doc/source/conf.py#L286 + + import inspect + import os + + # Get the top-level object from the module name + obj = sys.modules[info["module"]] + + # Traverse dotted path (e.g., module.submodule.Class.method) + for part in info["fullname"].split("."): + obj = getattr(obj, part) + + # Unwrapping decorators (if any), so we can get the true + # source function + if inspect.isfunction(obj): + obj = inspect.unwrap(obj) + + # Get the source filename + try: + fn = inspect.getsourcefile(obj) + except TypeError: + fn = None + + # If no source file is found, return None (no link) + if not fn: + return None + + # Make filename relative to the aeon source directory + startdir = Path(aeon.__file__).parent.parent + try: + fn = os.path.relpath(fn, start=startdir).replace(os.path.sep, "/") + except ValueError: + return None + + # Filter out files not in the aeon package + # (e.g., inherited from sklearn) + if not fn.startswith("aeon/"): + return None + + # Get line range of the object + source, lineno = inspect.getsourcelines(obj) + return fn, lineno, lineno + len(source) - 1 + + if domain != "py" or not info["module"]: + return None + try: + result = find_source() + if not result: + return None + filename = "%s#L%d-L%d" % result + except Exception: + filename = info["module"].replace(".", "/") + ".py" + return "https://github.com/aeon-toolkit/aeon/blob/{}/{}".format( + github_tag, + filename, + ) def _make_estimator_overview(app): @@ -612,52 +623,3 @@ def setup(app): """ app.connect("builder-inited", _make_estimator_overview) app.connect("html-page-context", _add_estimator_capabilities_table) - - -# -- Extension configuration ------------------------------------------------- - -# -- Options for nbsphinx extension --------------------------------------- -nbsphinx_execute = "never" # always # whether to run notebooks -nbsphinx_allow_errors = False # False -nbsphinx_timeout = 600 # seconds, set to -1 to disable timeout - -# add Binder launch buttom at the top -current_file = "{{ env.doc2path( env.docname, base=None) }}" - -# make sure Binder points to latest stable release, not main -binder_url = f"https://mybinder.org/v2/gh/aeon-toolkit/aeon/{github_tag}?filepath={current_file}" # noqa -nbsphinx_prolog = f""" -.. |binder| image:: https://mybinder.org/badge_logo.svg -.. _Binder: {binder_url} - -|Binder|_ -""" - -# add link to original notebook at the bottom -notebook_url = f"https://github.com/aeon-toolkit/aeon/tree/{github_tag}/{current_file}" -nbsphinx_epilog = f""" ----- - -Generated using nbsphinx_. The Jupyter notebook can be found here_. - -.. _here: {notebook_url} -.. _nbsphinx: https://nbsphinx.readthedocs.io/ -""" - -# -- Options for intersphinx extension --------------------------------------- - -# Example configuration for intersphinx: refer to the Python standard library. -intersphinx_mapping = { - "python": ("https://docs.python.org/3/", None), - "numpy": ("https://numpy.org/doc/stable/", None), - "scipy": ("https://docs.scipy.org/doc/scipy/", None), - "matplotlib": ("https://matplotlib.org/stable/", None), - "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None), - "joblib": ("https://joblib.readthedocs.io/en/latest/", None), - "scikit-learn": ("https://scikit-learn.org/stable/", None), - "statsmodels": ("https://www.statsmodels.org/stable/", None), -} - - -# -- Options for _todo extension ---------------------------------------------- -todo_include_todos = False diff --git a/docs/developer_guide/coding_standards.md b/docs/developer_guide/coding_standards.md index e5f38d6abd..2ee7743e03 100644 --- a/docs/developer_guide/coding_standards.md +++ b/docs/developer_guide/coding_standards.md @@ -13,7 +13,7 @@ example can be found [here](https://gist.github.com/nateGeorge/5455d2c57fb33c1ae - Documentation formatting using the [numpydoc](https://numpydoc.readthedocs.io/en/latest/format.html) style -### Code formatting and linting +## Code formatting and linting Our coding standards are enforced through our CI/CD workflows via [pre-commit](https://pre-commit.com/). We adhere to the code formatting standards using the following `pre-commit` hooks: @@ -35,7 +35,7 @@ quality The full `pre-commit` configuration can be found in [.pre-commit-config.yaml](https://github.com/aeon-toolkit/aeon/blob/main/.pre-commit-config.yaml). Additional configurations for some hooks can be found in the [pyproject.toml](https://github.com/aeon-toolkit/aeon/blob/main/pyproject.toml). -### `aeon` specific code formatting conventions +## `aeon` specific code formatting conventions - Use underscores to separate words in non-class names i.e.`n_cases` rather than `ncases`, `nCases` or similar. @@ -45,7 +45,7 @@ part of variable names such as `X_train` if referring to data sets. - Don’t use `import *` in the source code. It is considered harmful by the official Python recommendations. -### Using `pre-commit` +## Using `pre-commit` To set up pre-commit, follow these steps in a Python environment with the `aeon` `dev` dependencies installed. diff --git a/docs/papers_using_aeon.md b/docs/papers_using_aeon.md index ee2c142f22..f8ec7e9daf 100644 --- a/docs/papers_using_aeon.md +++ b/docs/papers_using_aeon.md @@ -29,7 +29,7 @@ Research, 25(289), pp.1-10. - Serramazza, D., Nguyen, T. and Ifrim, G. A short tutorial for multivariate time series explanation using tsCaptum. Software Impacts, 22. [Paper](https://doi.org/10.1016/j.simpa.2024.100723) -- + ## 2024 - Bagnall, A.,Middlehurst, M., Forestier, G., Schäfer, P., Ismail-Fawaz, A., diff --git a/pyproject.toml b/pyproject.toml index 6620deb194..9001628aec 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -108,7 +108,7 @@ docs = [ "sphinx-version-warning", "sphinx_issues", "sphinx-copybutton", - "sphinxext-opengraph", + "sphinxext-opengraph[social_cards]", "nbsphinx", "numpydoc", "myst-parser", From 18fc7edb7e8aaabcae2e1dcf316a192c6172089f Mon Sep 17 00:00:00 2001 From: Adarsh Dubey Date: Thu, 3 Jul 2025 16:53:42 +0530 Subject: [PATCH 176/216] [DOC] fix incorrect references of classes in getting started page (#2762) * fix: incorrect references of classes in getting started page * fix: typo in BaseSimilaritySearch --------- Co-authored-by: Matthew Middlehurst --- docs/getting_started.md | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/docs/getting_started.md b/docs/getting_started.md index ccf29cee33..1da416a1df 100644 --- a/docs/getting_started.md +++ b/docs/getting_started.md @@ -317,14 +317,15 @@ classification or clustering. Similarly to the transformation module, similarity search estimators are either defined for single series or for collection of series. The estimators are inheriting from the -[BaseSimiliaritySearch](similarity_search._base.BaseSimiliaritySearch) class, which -both [BaseSeriesSimiliaritySearch](similarity_search.series._base.BaseSeriesSimiliaritySearch) -and [BaseCollectionSimiliaritySearch](similarity_search.collection._base.BaseCollectionSimiliaritySearch) +[BaseSimilaritySearch](similarity_search._base.BaseSimilaritySearch) class, which +both [BaseSeriesSimilaritySearch](similarity_search.series._base.BaseSeriesSimilaritySearch) +and [BaseCollectionSimilaritySearch](similarity_search.collection._base.BaseCollectionSimilaritySearch) inherit from. All estimators use a `fit` `predict` interface, where `predict` outputs both the indexes of the neighbors or motifs and a distance or similarity measure linked to them. For example, using `StompMotif` to compute the matrix profile between two series : + ```{code-block} python >>> import numpy as np >>> from aeon.similarity_search.series import StompMotif @@ -333,6 +334,7 @@ For example, using `StompMotif` to compute the matrix profile between two series >>> top_k = StompMotif(4).fit(X1) # 4 is length of the motif to search >>> distances, indexes = top_k.predict(X2, k=1) ``` + Some things to note on this example : - We defined `1D` series of shape `(n_timepoints)`, but internally, series estimator @@ -350,7 +352,7 @@ and those that transform a collection. ### Transformers for Single Time Series -Transformers inheriting from the [BaseSeriesTransformer](transformations.base.BaseSeriesTransformer) +Transformers inheriting from the [BaseSeriesTransformer](transformations.series.base.BaseSeriesTransformer) in the `aeon.transformations.series` package transform a single (possibly multivariate) time series into a different time series or a feature vector. More info to follow. @@ -379,7 +381,7 @@ Most time series classification and regression algorithms are based on some form transformation into an alternative feature space. For example, we might extract some summary time series features from each series, and fit a traditional classifier or regressor on these features. For example, we could use -[Catch22](transformations.collection.feauture_based), which calculates 22 summary +[Catch22](transformations.collection.feature_based.Catch22), which calculates 22 summary statistics for each series. ```{code-block} python @@ -397,7 +399,7 @@ statistics for each series. ``` There are also series-to-series transformations, such as the -[Padder](transformations.collection) to lengthen +[Padder](transformations.collection.Padder) to lengthen series and process unequal length collections. ```{code-block} python @@ -432,7 +434,7 @@ For machine learning tasks such as classification, regression and clustering, th `scikit-learn` `make_pipeline` functionality can be used if the transformer outputs a valid input type. -The following example uses the [Catch22](transformations.collection.catch22.Catch22) +The following example uses the [Catch22](transformations.collection.feature_based.Catch22) feature extraction transformer and a random forest classifier to classify. ```{code-block} python From 0c70f47c593234fcf89a8d8b9d2f60bc2a1c92b9 Mon Sep 17 00:00:00 2001 From: "aeon-actions-bot[bot]" <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Date: Mon, 7 Jul 2025 14:05:09 +0100 Subject: [PATCH 177/216] Automated `pre-commit` hook update (#2932) Co-authored-by: TonyBagnall <9594042+TonyBagnall@users.noreply.github.com> --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 0306f273a1..4c9bf237d9 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -29,7 +29,7 @@ repos: args: [ "--create", "--python-folders", "aeon" ] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.12.1 + rev: v0.12.2 hooks: - id: ruff args: [ "--fix"] From edc972cf9ba98683b2ab24c61088f53043c7b2f2 Mon Sep 17 00:00:00 2001 From: Balgopal Moharana <99070111+lucifer4073@users.noreply.github.com> Date: Mon, 7 Jul 2025 21:07:46 +0530 Subject: [PATCH 178/216] [DOC] Add RecurrentRegressor and RecurrentNetowork to api_reference (#2935) * Recurrent Regressor added to API doc * Recurrent network added to API doc --- docs/api_reference/networks.rst | 1 + docs/api_reference/regression.rst | 1 + 2 files changed, 2 insertions(+) diff --git a/docs/api_reference/networks.rst b/docs/api_reference/networks.rst index 3eaee2c370..7e09b57396 100644 --- a/docs/api_reference/networks.rst +++ b/docs/api_reference/networks.rst @@ -29,3 +29,4 @@ Deep learning networks AEDCNNNetwork AEAttentionBiGRUNetwork AEDRNNNetwork + RecurrentNetwork diff --git a/docs/api_reference/regression.rst b/docs/api_reference/regression.rst index 5f463a8061..2192ee00c7 100644 --- a/docs/api_reference/regression.rst +++ b/docs/api_reference/regression.rst @@ -43,6 +43,7 @@ Deep learning ResNetRegressor MLPRegressor DisjointCNNRegressor + RecurrentRegressor Distance-based -------------- From c235108a83fc7c21dd9cd2a845a4897b25a9e4fb Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Thu, 10 Jul 2025 11:30:38 +0200 Subject: [PATCH 179/216] [ENH] Refactor to set forecast_ in fit and to require y in predict (#2927) * require y in predict * forecast_ * remove calls to predict * doc examples * regression forecaster rework * other tests * docstrings * refactor forecasting checks * test * test * regression notebook * test_ets * Revert "test_ets" This reverts commit 827a12a0a2fa3fec2d55d481a97aed62bd290059. * Revert "regression notebook" This reverts commit 27978022921c8c436d1ae458511ce5885d425c09. * ets * notebook * naive testing --- aeon/forecasting/_ets.py | 31 ++- aeon/forecasting/_naive.py | 44 ++--- aeon/forecasting/_regression.py | 59 ++++-- aeon/forecasting/base.py | 49 +++-- aeon/forecasting/tests/test_base.py | 26 ++- aeon/forecasting/tests/test_ets.py | 14 +- aeon/forecasting/tests/test_naive.py | 38 ++-- aeon/forecasting/tests/test_regressor.py | 68 +++++-- .../_yield_forecasting_checks.py | 16 +- examples/forecasting/forecasting.ipynb | 179 ++++++++++++------ examples/forecasting/regression.ipynb | 4 +- 11 files changed, 334 insertions(+), 194 deletions(-) diff --git a/aeon/forecasting/_ets.py b/aeon/forecasting/_ets.py index d0aa466b7c..2d3eac898b 100644 --- a/aeon/forecasting/_ets.py +++ b/aeon/forecasting/_ets.py @@ -84,9 +84,7 @@ class ETSForecaster(BaseForecaster): ... error_type='additive', trend_type='multiplicative', ... seasonality_type='multiplicative', seasonal_period=4 ... ) - >>> forecaster.fit(y) - ETSForecaster(...) - >>> forecaster.predict() + >>> forecaster.forecast(y) 366.90200486015596 """ @@ -195,9 +193,21 @@ def _get_int(x): self._gamma, self.phi, ) + self.forecast_ = _predict( + self._trend_type, + self._seasonality_type, + self.level_, + self.trend_, + self.seasonality_, + self.phi, + self.horizon, + self.n_timepoints_, + self._seasonal_period, + ) + return self - def _predict(self, y=None, exog=None): + def _predict(self, y, exog=None): """ Predict the next horizon steps ahead. @@ -214,18 +224,7 @@ def _predict(self, y=None, exog=None): float single prediction self.horizon steps ahead of y. """ - fitted_value = _predict( - self._trend_type, - self._seasonality_type, - self.level_, - self.trend_, - self.seasonality_, - self.phi, - self.horizon, - self.n_timepoints_, - self._seasonal_period, - ) - return fitted_value + return self.forecast_ def _initialise(self, data): """ diff --git a/aeon/forecasting/_naive.py b/aeon/forecasting/_naive.py index da242018e2..edb5b3ea62 100644 --- a/aeon/forecasting/_naive.py +++ b/aeon/forecasting/_naive.py @@ -41,11 +41,13 @@ def _fit(self, y, exog=None): y_squeezed = y.squeeze() if self.strategy == "last": - self._fitted_scalar_value = y_squeezed[-1] + self.forecast_ = y_squeezed[-1] elif self.strategy == "mean": - self._fitted_scalar_value = np.mean(y_squeezed) + self.forecast_ = np.mean(y_squeezed) elif self.strategy == "seasonal_last": - self._fitted_last_season = y_squeezed[-self.seasonal_period :] + season = y_squeezed[-self.seasonal_period :] + idx = (self.horizon - 1) % self.seasonal_period + self.forecast_ = season[idx] else: raise ValueError( f"Unknown strategy: {self.strategy}. " @@ -53,27 +55,19 @@ def _fit(self, y, exog=None): ) return self - def _predict(self, y=None, exog=None): - if y is None: - if self.strategy == "last" or self.strategy == "mean": - return self._fitted_scalar_value + def _predict(self, y, exog=None): + y_squeezed = y.squeeze() - # For "seasonal_last" strategy - prediction_index = (self.horizon - 1) % self.seasonal_period - return self._fitted_last_season[prediction_index] + if self.strategy == "last": + return y_squeezed[-1] + elif self.strategy == "mean": + return np.mean(y_squeezed) + elif self.strategy == "seasonal_last": + period = y_squeezed[-self.seasonal_period :] + idx = (self.horizon - 1) % self.seasonal_period + return period[idx] else: - y_squeezed = y.squeeze() - - if self.strategy == "last": - return y_squeezed[-1] - elif self.strategy == "mean": - return np.mean(y_squeezed) - elif self.strategy == "seasonal_last": - period = y_squeezed[-self.seasonal_period :] - idx = (self.horizon - 1) % self.seasonal_period - return period[idx] - else: - raise ValueError( - f"Unknown strategy: {self.strategy}. " - "Valid strategies are 'last', 'mean', 'seasonal_last'." - ) + raise ValueError( + f"Unknown strategy: {self.strategy}. " + "Valid strategies are 'last', 'mean', 'seasonal_last'." + ) diff --git a/aeon/forecasting/_regression.py b/aeon/forecasting/_regression.py index 206d0ca05d..95fc2fb141 100644 --- a/aeon/forecasting/_regression.py +++ b/aeon/forecasting/_regression.py @@ -70,9 +70,10 @@ def _fit(self, y, exog=None): self.regressor_ = LinearRegression() else: self.regressor_ = self.regressor - + self._n_exog = 0 # Combine y and exog for windowing if exog is not None: + self._n_exog = exog.shape[0] if exog.ndim == 1: exog = exog.reshape(1, -1) if exog.shape[1] != y.shape[1]: @@ -96,16 +97,18 @@ def _fit(self, y, exog=None): X = X[:, :, :].reshape(X.shape[0], -1) # Ignore the final horizon values for X - X = X[: -self.horizon] + X_train = X[: -self.horizon] # Extract y_train from the original series y_train = y.squeeze()[self.window + self.horizon - 1 :] - self.last_ = combined_data[:, -self.window :] - self.regressor_.fit(X=X, y=y_train) + self.regressor_.fit(X=X_train, y=y_train) + + last = X[[-1]] + self.forecast_ = self.regressor_.predict(last)[0] return self - def _predict(self, y=None, exog=None): + def _predict(self, y, exog=None): """ Predict the next horizon steps ahead. @@ -122,26 +125,52 @@ def _predict(self, y=None, exog=None): float single prediction self.horizon steps ahead of y. """ - if y is None: - # Flatten the last window to be compatible with sklearn regressors - last_window_flat = self.last_.reshape(1, -1) - return self.regressor_.predict(last_window_flat)[0] - + y = y[:, -self.window :] + y = y.squeeze() + # Test data compliant for regression based + if len(y) < self.window: + raise ValueError( + f" Series passed in predict length = {len(y)} but this " + f"RegressionForecaster was trained on window length = " + f"{self.window}" + ) # Combine y and exog for prediction if exog is not None: + if exog.shape[0] != self._n_exog: + raise ValueError( + f" Forecaster passed {exog.shape[0]} exogenous variables in " + f"predict but this RegressionForecaster was trained on" + f" {self._n_exog} variables in fit" + ) + if exog.ndim == 1: exog = exog.reshape(1, -1) - if exog.shape[1] != y.shape[1]: - raise ValueError("y and exog must have the same number of time points.") + if exog.shape[1] < self.window: + raise ValueError( + f" Exogenous variables passed in predict of length = {len(y)} but " + f"this RegressionForecaster was trained on window length = " + f"{self.window}" + ) + + exog = exog[:, -self.window :] combined_data = np.vstack([y, exog]) else: + if self._n_exog > 0: + raise ValueError( + f" predict passed no exogenous variables, but this " + f"RegressionForecaster was trained on {self._n_exog} exog in fit" + ) combined_data = y # Extract the last window and flatten for prediction - last_window = combined_data[:, -self.window :] - last_window_flat = last_window.reshape(1, -1) + last_window = combined_data.reshape(1, -1) + + return self.regressor_.predict(last_window)[0] - return self.regressor_.predict(last_window_flat)[0] + def _forecast(self, y, exog=None): + """Forecast values for time series X.""" + self.fit(y, exog) + return self.forecast_ @classmethod def _get_test_params(cls, parameter_set: str = "default"): diff --git a/aeon/forecasting/base.py b/aeon/forecasting/base.py index 5b37e7cadc..18e45fc7d0 100644 --- a/aeon/forecasting/base.py +++ b/aeon/forecasting/base.py @@ -96,14 +96,13 @@ def fit(self, y, exog=None): def _fit(self, y, exog=None): ... @final - def predict(self, y=None, exog=None): + def predict(self, y, exog=None): """Predict the next horizon steps ahead. Parameters ---------- - y : np.ndarray, default = None - A time series to predict the next horizon value for. If None, - predict the next horizon value after series seen in fit. + y : np.ndarray + A time series to predict the next horizon value for. exog : np.ndarray, default =None Optional exogenous time series data assumed to be aligned with y. @@ -113,28 +112,25 @@ def predict(self, y=None, exog=None): single prediction self.horizon steps ahead of y. """ self._check_is_fitted() - if y is not None: - self._check_X(y, self.axis) - y = self._convert_y(y, self.axis) + self._check_X(y, self.axis) + y = self._convert_y(y, self.axis) if exog is not None: exog = self._convert_y(exog, self.axis) - return self._predict(y, exog) @abstractmethod - def _predict(self, y=None, exog=None): ... + def _predict(self, y, exog=None): ... @final def forecast(self, y, exog=None): - """Forecast the next horizon steps ahead. + """Forecast the next horizon steps ahead of ``y``. - By default this is simply fit followed by predict. + By default this is simply fit followed by returning forecast_. Parameters ---------- - y : np.ndarray, default = None - A time series to predict the next horizon value for. If None, - predict the next horizon value after series seen in fit. + y : np.ndarray + A time series to predict the next horizon value for. exog : np.ndarray, default =None Optional exogenous time series data assumed to be aligned with y. @@ -150,9 +146,9 @@ def forecast(self, y, exog=None): return self._forecast(y, exog) def _forecast(self, y, exog=None): - """Forecast values for time series X.""" + """Forecast horizon steps ahead for time series ``y``.""" self.fit(y, exog) - return self._predict(y, exog) + return self.forecast_ @final def direct_forecast(self, y, prediction_horizon, exog=None): @@ -174,7 +170,10 @@ def direct_forecast(self, y, prediction_horizon, exog=None): The number of future time steps to forecast. exog : np.ndarray, default =None Optional exogenous time series data assumed to be aligned with y. - predictions : np.ndarray + + Returns + ------- + np.ndarray An array of shape `(prediction_horizon,)` containing the forecasts for each horizon. @@ -210,15 +209,15 @@ def direct_forecast(self, y, prediction_horizon, exog=None): def iterative_forecast(self, y, prediction_horizon): """ - Forecast ``prediction_horizon`` prediction using a single model from `y`. + Forecast ``prediction_horizon`` prediction using a single model fit on `y`. This function implements the iterative forecasting strategy (also called - recursive or iterated). This involves a single model fit on y which is then - used to make ``prediction_horizon`` ahead using its own predictions as - inputs for future forecasts. This is done by taking - the prediction at step ``i`` and feeding it back into the model to help - predict for step ``i+1``. The basic contract of - `iterative_forecast` is that `fit` is only ever called once. + recursive or iterated). This involves a single model fit on ``y`` which is then + used to make ``prediction_horizon`` ahead forecasts using its own predictions as + inputs for future forecasts. This is done by taking the prediction at step + ``i`` and feeding it back into the model to help predict for step ``i+1``. + The basic contract of `iterative_forecast` is that `fit` is only ever called + once. y : np.ndarray The time series to make forecasts about. @@ -227,7 +226,7 @@ def iterative_forecast(self, y, prediction_horizon): Returns ------- - predictions : np.ndarray + np.ndarray An array of shape `(prediction_horizon,)` containing the forecasts for each horizon. diff --git a/aeon/forecasting/tests/test_base.py b/aeon/forecasting/tests/test_base.py index e6b729c62b..b8bddb4962 100644 --- a/aeon/forecasting/tests/test_base.py +++ b/aeon/forecasting/tests/test_base.py @@ -4,7 +4,7 @@ import pandas as pd import pytest -from aeon.forecasting import NaiveForecaster, RegressionForecaster +from aeon.forecasting import BaseForecaster, NaiveForecaster, RegressionForecaster def test_base_forecaster(): @@ -12,7 +12,7 @@ def test_base_forecaster(): f = NaiveForecaster() y = np.random.rand(50) f.fit(y) - p1 = f.predict() + p1 = f.predict(y) assert p1 == y[-1] p2 = f.forecast(y) p3 = f._forecast(y) @@ -54,8 +54,8 @@ def test_direct_forecast(): assert p == preds[i] -def test_recursive_forecast(): - """Test recursive forecasting.""" +def test_iterative_forecast(): + """Test iterative forecasting.""" y = np.random.rand(50) f = RegressionForecaster(window=4) preds = f.iterative_forecast(y, prediction_horizon=10) @@ -79,3 +79,21 @@ def test_direct_forecast_with_exog(): # Check that predictions are different from when no exog is used preds_no_exog = f.direct_forecast(y, prediction_horizon=10) assert not np.array_equal(preds, preds_no_exog) + + +def test_fit_is_empty(): + """Test empty fit.""" + + class _EmptyFit(BaseForecaster): + _tags = {"fit_is_empty": True} + + def _fit(self, y): + return self + + def _predict(self, y): + return 0 + + dummy = _EmptyFit(horizon=1, axis=1) + y = np.arange(50) + dummy.fit(y) + assert dummy.is_fitted diff --git a/aeon/forecasting/tests/test_ets.py b/aeon/forecasting/tests/test_ets.py index 1c2f2ad19c..64275a2d1d 100644 --- a/aeon/forecasting/tests/test_ets.py +++ b/aeon/forecasting/tests/test_ets.py @@ -71,12 +71,12 @@ def test_ets_forecaster(params, expected): """Test ETSForecaster for multiple parameter combinations.""" data = np.array([3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12]) forecaster = ETSForecaster(**params) - forecaster.fit(data) - p = forecaster.predict() + p = forecaster.forecast(data) assert np.isclose(p, expected) def test_incorrect_parameters(): + """Test incorrect set up.""" _validate_parameter(0, True) _validate_parameter(None, True) with pytest.raises(ValueError): @@ -84,3 +84,13 @@ def test_incorrect_parameters(): _validate_parameter(None, True) _validate_parameter(10, False) _validate_parameter("Foo", True) + forecaster = ETSForecaster() + forecaster.horizon = 2 + data = np.array([3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12]) + with pytest.raises(ValueError, match="Horizon is set >1, but"): + forecaster.fit(data) + forecaster = ETSForecaster() + with pytest.raises( + ValueError, match="This forecaster cannot be used with the " "direct strategy" + ): + forecaster.direct_forecast(data, prediction_horizon=6) diff --git a/aeon/forecasting/tests/test_naive.py b/aeon/forecasting/tests/test_naive.py index c0f9a98bd2..50f1b742e8 100644 --- a/aeon/forecasting/tests/test_naive.py +++ b/aeon/forecasting/tests/test_naive.py @@ -9,8 +9,7 @@ def test_naive_forecaster_last_strategy(): """Test NaiveForecaster with 'last' strategy.""" sample_data = np.array([10, 20, 30, 40, 50]) forecaster = NaiveForecaster(strategy="last", horizon=3) - forecaster.fit(sample_data) - predictions = forecaster.predict() + predictions = forecaster.forecast(sample_data) expected = 50 np.testing.assert_array_equal(predictions, expected) @@ -19,8 +18,7 @@ def test_naive_forecaster_mean_strategy(): """Test NaiveForecaster with 'mean' strategy.""" sample_data = np.array([10, 20, 30, 40, 50]) forecaster = NaiveForecaster(strategy="mean", horizon=2) - forecaster.fit(sample_data) - predictions = forecaster.predict() + predictions = forecaster.forecast(sample_data) expected = 30 # Mean of [10, 20, 30, 40, 50] is 30 np.testing.assert_array_equal(predictions, expected) @@ -31,36 +29,36 @@ def test_naive_forecaster_seasonal_last_strategy(): # Last season is [6, 7, 8] for seasonal_period = 3 forecaster = NaiveForecaster(strategy="seasonal_last", seasonal_period=3, horizon=4) + pred = forecaster.forecast(data) forecaster.fit(data) - pred = forecaster.predict() - pred2 = forecaster.predict(y=data) + pred2 = forecaster.forecast_ + pred3 = forecaster.predict(data) expected = 6 # predicts the 1-st element of the last season. - np.testing.assert_array_equal(pred, expected) - np.testing.assert_array_equal(pred2, expected) + np.testing.assert_allclose(pred, pred2, pred3, expected) # Test horizon within the season length forecaster = NaiveForecaster(strategy="seasonal_last", seasonal_period=3, horizon=2) + pred = forecaster.forecast(data) forecaster.fit(data) - pred = forecaster.predict() - pred2 = forecaster.predict(y=data) + pred2 = forecaster.forecast_ + pred3 = forecaster.predict(data) expected = 7 # predicts the 2-nd element of the last season. - np.testing.assert_array_equal(pred, expected) - np.testing.assert_array_equal(pred2, expected) + np.testing.assert_allclose(pred, pred2, pred3, expected) # Test horizon wrapping around to a new season forecaster = NaiveForecaster(strategy="seasonal_last", seasonal_period=3, horizon=7) + pred = forecaster.forecast(data) forecaster.fit(data) - pred = forecaster.predict() - pred2 = forecaster.predict(y=data) + pred2 = forecaster.forecast_ + pred3 = forecaster.predict(data) expected = 6 # predicts the 1-st element of the last season. - np.testing.assert_array_equal(pred, expected) - np.testing.assert_array_equal(pred2, expected) + np.testing.assert_allclose(pred, pred2, pred3, expected) # Last season is now [5, 6, 7, 8] with seasonal_period = 4 forecaster = NaiveForecaster(strategy="seasonal_last", seasonal_period=4, horizon=6) + pred = forecaster.forecast(data) forecaster.fit(data) - pred = forecaster.predict() - pred2 = forecaster.predict(y=data) + pred2 = forecaster.forecast_ + pred3 = forecaster.predict(data) expected = 6 # predicts the 2nd element of the new last season. - np.testing.assert_array_equal(pred, expected) - np.testing.assert_array_equal(pred2, expected) + np.testing.assert_allclose(pred, pred2, pred3, expected) diff --git a/aeon/forecasting/tests/test_regressor.py b/aeon/forecasting/tests/test_regressor.py index f50519d1c9..0f2040d238 100644 --- a/aeon/forecasting/tests/test_regressor.py +++ b/aeon/forecasting/tests/test_regressor.py @@ -9,22 +9,23 @@ def test_regression_forecaster(): - """Test the regression forecaster.""" + """Test the regression forecaster. + + Test fit/predict and forecast are equivalent for sklearn and aeon regressors. + Test invalid window handling. + """ y = np.random.rand(100) f = RegressionForecaster(window=10) + p = f.forecast(y) f.fit(y) - p = f.predict() p2 = f.predict(y) assert p == p2 - p3 = f.forecast(y) - assert p == p3 f2 = RegressionForecaster(regressor=LinearRegression(), window=10) - f2.fit(y) - p2 = f2.predict() + p2 = f2.forecast(y) assert p == p2 f2 = RegressionForecaster(regressor=DummyRegressor(), window=10) f2.fit(y) - f2.predict() + f2.predict(y) with pytest.raises(ValueError): f = RegressionForecaster(window=-1) @@ -32,27 +33,29 @@ def test_regression_forecaster(): with pytest.raises(ValueError): f = RegressionForecaster(window=101) f.fit(y) + f = RegressionForecaster(window=10) + f.fit(y) + y_test = np.random.rand(5) + with pytest.raises(ValueError): + p = f.predict(y_test) def test_regression_forecaster_with_exog(): """Test the regression forecaster with exogenous variables.""" - np.random.seed(0) - n_samples = 100 exog = np.random.rand(n_samples) * 10 y = 2 * exog + np.random.rand(n_samples) * 0.1 - f = RegressionForecaster(window=10) # Test fit and predict with exog f.fit(y, exog=exog) - p1 = f.predict() + p1 = f.forecast_ assert isinstance(p1, float) - # Test that exog variable has an impact + # Test that exog variable have an impact exog_zeros = np.zeros(n_samples) f.fit(y, exog=exog_zeros) - p2 = f.predict() + p2 = f.forecast_ assert p1 != p2 # Test that forecast method works and is equivalent to fit+predict @@ -61,12 +64,19 @@ def test_regression_forecaster_with_exog(): # Manual fit + predict f.fit(y=y_new, exog=exog_new) - p_manual = f.predict() + p_manual = f.predict(y_new, exog=exog_new) # forecast() method p_forecast = f.forecast(y=y_new, exog=exog_new) assert p_manual == pytest.approx(p_forecast) + # Test with multivariate exog + exog_m = np.array([exog, exog_zeros]) + p1 = f.forecast(y, exog_m) + f.fit(y, exog_m) + p2 = f.predict(y, exog_m) + assert p1 == p2 + def test_regression_forecaster_with_exog_errors(): """Test errors in regression forecaster with exogenous variables.""" @@ -74,10 +84,28 @@ def test_regression_forecaster_with_exog_errors(): exog_short = np.random.rand(99) f = RegressionForecaster(window=10) - # Test for unequal length series - with pytest.raises(ValueError, match="must have the same number of time points"): + # Test for unequal length series in fit + with pytest.raises( + ValueError, match="y and exog must have the same number of time points" + ): f.fit(y, exog=exog_short) - - with pytest.raises(ValueError, match="must have the same number of time points"): - f.fit(y) - f.predict(y, exog=exog_short) + # Test for fit/predict mismatches in shape + + # If exog in fit, must have them in predict + exog_train = np.array(np.random.rand(100)) + with pytest.raises(ValueError, match="predict passed no exogenous variables"): + f.fit(y, exog=exog_train) + f.predict(y) + exog_test = np.array([np.random.rand(10), np.random.rand(10)]) + f.fit(y, exog=exog_train) + with pytest.raises(ValueError, match="Forecaster passed"): + f.predict(y, exog_test) + exog_short = np.random.rand(5) + with pytest.raises( + ValueError, match="Exogenous variables passed in predict of length" + ): + f.predict(y, exog_short) + with pytest.raises(ValueError, match="predict passed no exogenous variables"): + f.predict(y) + with pytest.raises(ValueError, match="must be greater than or equal to 1"): + f.direct_forecast(y, prediction_horizon=0) diff --git a/aeon/testing/estimator_checking/_yield_forecasting_checks.py b/aeon/testing/estimator_checking/_yield_forecasting_checks.py index 0a2fc3bea2..9c57386ac6 100644 --- a/aeon/testing/estimator_checking/_yield_forecasting_checks.py +++ b/aeon/testing/estimator_checking/_yield_forecasting_checks.py @@ -62,17 +62,17 @@ def check_forecaster_output(estimator, datatype): estimator.fit( FULL_TEST_DATA_DICT[datatype]["train"][0], ) - y_pred = estimator.predict() + assert hasattr(estimator, "forecast_"), "fit() must set the attribute forecast_" + assert isinstance(estimator.forecast_, float), "attribute forecast_ must be a float" + + y_pred = estimator.predict(FULL_TEST_DATA_DICT[datatype]["test"][0]) + assert isinstance(y_pred, float), ( - f"predict() output should be float, got" f" {type(y_pred)}" + f"predict(y) output should be float, got" f" {type(y_pred)}" ) + y_pred = estimator.forecast_ y_pred2 = estimator.forecast(FULL_TEST_DATA_DICT[datatype]["train"][0]) assert y_pred == y_pred2, ( - f"predict() and forecast() output differ: {y_pred} !=" f" {y_pred2}" - ) - y_pred3 = estimator.predict(FULL_TEST_DATA_DICT[datatype]["train"][0]) - assert y_pred == y_pred3, ( - f"after fit(), predict() and predict(y_train) should be the same, but" - f"output differ: {y_pred} != {y_pred3}" + f"estimator.forecast_ and forecast(y) output differ: {y_pred} !=" f" {y_pred2}" ) diff --git a/examples/forecasting/forecasting.ipynb b/examples/forecasting/forecasting.ipynb index aa7f8f3a04..3e2bafc622 100644 --- a/examples/forecasting/forecasting.ipynb +++ b/examples/forecasting/forecasting.ipynb @@ -58,9 +58,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-07-04T14:04:45.079757Z", + "start_time": "2025-07-04T14:04:41.363385Z" + } + }, "source": [ "import inspect\n", "\n", @@ -73,7 +76,17 @@ " if not func[0].startswith(\"_\")\n", "]\n", "print(public_methods)" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['clone', 'direct_forecast', 'fit', 'forecast', 'get_fitted_params', 'get_metadata_routing', 'get_params', 'get_tag', 'get_tags', 'iterative_forecast', 'predict', 'reset', 'set_params', 'set_tags']\n" + ] + } + ], + "execution_count": 1 }, { "cell_type": "markdown", @@ -89,15 +102,30 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-07-04T14:04:46.224571Z", + "start_time": "2025-07-04T14:04:46.203552Z" + } + }, "source": [ "from aeon.utils.data_types import SERIES_DATA_TYPES\n", "\n", "print(\" Possible data structures for input to forecaster \", SERIES_DATA_TYPES)\n", "print(\"\\n Tags for BaseForecaster: \", BaseForecaster.get_class_tags())" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Possible data structures for input to forecaster ['pd.Series', 'pd.DataFrame', 'np.ndarray']\n", + "\n", + " Tags for BaseForecaster: {'python_version': None, 'python_dependencies': None, 'cant_pickle': False, 'non_deterministic': False, 'algorithm_type': None, 'capability:missing_values': False, 'capability:multithreading': False, 'capability:univariate': True, 'capability:multivariate': False, 'X_inner_type': 'np.ndarray', 'capability:horizon': True, 'capability:exogenous': False, 'fit_is_empty': False, 'y_inner_type': 'np.ndarray'}\n" + ] + } + ], + "execution_count": 2 }, { "cell_type": "markdown", @@ -109,9 +137,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-07-04T14:04:46.412151Z", + "start_time": "2025-07-04T14:04:46.323867Z" + } + }, "source": [ "import pandas as pd\n", "\n", @@ -121,7 +152,17 @@ "print(type(y))\n", "y2 = pd.Series(y)\n", "y3 = pd.DataFrame(y)" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "execution_count": 3 }, { "cell_type": "markdown", @@ -137,41 +178,54 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'y' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01maeon\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mforecasting\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m NaiveForecaster\n\u001b[32m 4\u001b[39m d = NaiveForecaster(strategy=\u001b[33m\"\u001b[39m\u001b[33mlast\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m d.fit(\u001b[43my\u001b[49m)\n\u001b[32m 6\u001b[39m p = d.predict()\n\u001b[32m 7\u001b[39m \u001b[38;5;28mprint\u001b[39m(p)\n", - "\u001b[31mNameError\u001b[39m: name 'y' is not defined" - ] + "metadata": { + "ExecuteTime": { + "end_time": "2025-07-04T14:04:48.700995Z", + "start_time": "2025-07-04T14:04:48.693450Z" } - ], + }, "source": [ "# Fit then predict\n", "from aeon.forecasting import NaiveForecaster\n", "\n", "d = NaiveForecaster(strategy=\"last\")\n", - "d.fit(y)\n", - "p = d.predict()\n", + "p = d.forecast(y)\n", "print(p)" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "432.0\n" + ] + } + ], + "execution_count": 4 }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-07-04T14:05:28.648536Z", + "start_time": "2025-07-04T14:05:28.633476Z" + } + }, "source": [ - "# forecast is equivalent to fit_predict in other estimators\n", - "p2 = d.forecast(y)\n", - "print(p2)" - ] + "d = NaiveForecaster(strategy=\"mean\")\n", + "p = d.forecast(y)\n", + "print(p)\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "280.2986111111111\n" + ] + } + ], + "execution_count": 6 }, { "cell_type": "markdown", @@ -182,16 +236,29 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-07-04T14:05:52.382942Z", + "start_time": "2025-07-04T14:05:52.367205Z" + } + }, "source": [ "s = NaiveForecaster(strategy=\"seasonal_last\", horizon=2, seasonal_period=4)\n", - "s.fit(y)\n", - "p = s.predict()\n", + "p = s.forecast(y)\n", "print(f\"Last season: {y[-4:]}\")\n", "print(f\"Forecast: {p}\")" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Last season: [508. 461. 390. 432.]\n", + "Forecast: 461.0\n" + ] + } + ], + "execution_count": 7 }, { "cell_type": "markdown", @@ -216,8 +283,7 @@ "from aeon.forecasting import RegressionForecaster\n", "\n", "r = RegressionForecaster(window=20)\n", - "r.fit(y)\n", - "p = r.predict()\n", + "p = r.forecast(y)\n", "print(p)\n", "r2 = RegressionForecaster(window=10, horizon=5)\n", "r2.fit(y)\n", @@ -257,14 +323,19 @@ }, { "cell_type": "code", - "execution_count": 20, "metadata": { + "collapsed": false, "ExecuteTime": { - "end_time": "2024-11-16T19:21:26.225501Z", - "start_time": "2024-11-16T19:21:26.204872Z" - }, - "collapsed": false + "end_time": "2025-07-04T14:06:53.435103Z", + "start_time": "2025-07-04T14:06:49.740533Z" + } }, + "source": [ + "from aeon.forecasting import ETSForecaster\n", + "\n", + "ets = ETSForecaster()\n", + "ets.forecast(y)" + ], "outputs": [ { "data": { @@ -272,18 +343,12 @@ "460.302772481884" ] }, - "execution_count": 20, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "from aeon.forecasting import ETSForecaster\n", - "\n", - "ets = ETSForecaster()\n", - "ets.fit(y)\n", - "ets.predict()" - ] + "execution_count": 8 }, { "cell_type": "code", diff --git a/examples/forecasting/regression.ipynb b/examples/forecasting/regression.ipynb index b442c6b46f..e451051b64 100644 --- a/examples/forecasting/regression.ipynb +++ b/examples/forecasting/regression.ipynb @@ -220,11 +220,11 @@ "airline = load_airline()\n", "rf = RegressionForecaster(window=50)\n", "rf.fit(airline)\n", - "p3 = rf.predict()\n", + "p3 = rf.predict(airline)\n", "print(f\" Forecast for airline with linear regression = {p1} and {p3}\")\n", "rf2 = RegressionForecaster(regressor=DrCIFRegressor(n_estimators=10), window=50)\n", "rf2.fit(airline)\n", - "p4 = rf.predict()\n", + "p4 = rf.predict(airline)\n", "print(f\" Forecast for airline with DrCIF = {p1} and {p3}\")" ], "id": "bdc35a7a671ee254", From 6d05596eda8e5bc459653be8ea5cbeb34ce688fc Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Thu, 10 Jul 2025 14:08:52 +0200 Subject: [PATCH 180/216] [ENH] Clone estimator in direct forecast (#2936) * clone estimator * clone estimator * tests * test update --- aeon/forecasting/base.py | 6 ++++-- aeon/forecasting/tests/test_base.py | 16 ++++++++++++++-- 2 files changed, 18 insertions(+), 4 deletions(-) diff --git a/aeon/forecasting/base.py b/aeon/forecasting/base.py index 18e45fc7d0..2b065a507f 100644 --- a/aeon/forecasting/base.py +++ b/aeon/forecasting/base.py @@ -14,6 +14,7 @@ import pandas as pd from aeon.base import BaseSeriesEstimator +from aeon.base._base import _clone_estimator from aeon.utils.data_types import VALID_SERIES_INNER_TYPES @@ -203,8 +204,9 @@ def direct_forecast(self, y, prediction_horizon, exog=None): preds = np.zeros(prediction_horizon) for i in range(0, prediction_horizon): - self.horizon = i + 1 - preds[i] = self.forecast(y, exog) + f = _clone_estimator(self) + f.horizon = i + 1 + preds[i] = f.forecast(y, exog) return preds def iterative_forecast(self, y, prediction_horizon): diff --git a/aeon/forecasting/tests/test_base.py b/aeon/forecasting/tests/test_base.py index b8bddb4962..73250e4a9e 100644 --- a/aeon/forecasting/tests/test_base.py +++ b/aeon/forecasting/tests/test_base.py @@ -4,7 +4,8 @@ import pandas as pd import pytest -from aeon.forecasting import BaseForecaster, NaiveForecaster, RegressionForecaster +from aeon.forecasting import NaiveForecaster, RegressionForecaster +from aeon.forecasting.base import BaseForecaster def test_base_forecaster(): @@ -55,7 +56,7 @@ def test_direct_forecast(): def test_iterative_forecast(): - """Test iterative forecasting.""" + """Test terativeforecasting.""" y = np.random.rand(50) f = RegressionForecaster(window=4) preds = f.iterative_forecast(y, prediction_horizon=10) @@ -67,6 +68,17 @@ def test_iterative_forecast(): y = np.append(y, p) +def test_output_equivalence(): + """Test output same for one ahead forecast.""" + y = np.random.rand(50) + f = RegressionForecaster(window=4) + p1 = f.forecast(y) + p2 = f.fit(y).predict(y) + p3 = f.iterative_forecast(y, 1) + p4 = f.direct_forecast(y, 1) + assert np.allclose(p1, p2, p3[0], p4[0]) + + def test_direct_forecast_with_exog(): """Test direct forecasting with exogenous variables.""" y = np.arange(50) From c0c4efaead8a5c4540d857911496162ac9bd8b7c Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Thu, 10 Jul 2025 20:04:51 +0200 Subject: [PATCH 181/216] [ENH] Implement Time-Varying Parameter (TVP) regression forecaster using Kalman filter (#2920) * TVP * bug * refactor * test * test * add notebook * docstring * add an intercept * includes * squeeze predict * format * remove incomplete example * add test params * dummy test * dummy test * clone estimator * clone estimator * remove y is none * notebook * tests * tests * test update * remove pointless test * docstring update --- aeon/forecasting/__init__.py | 2 + aeon/forecasting/_tvp.py | 115 +++++++++++++++ aeon/forecasting/tests/test_tvp.py | 90 ++++++++++++ docs/api_reference/forecasting.md | 1 + examples/forecasting/regression.ipynb | 201 ++++++++++++++++++++++---- 5 files changed, 382 insertions(+), 27 deletions(-) create mode 100644 aeon/forecasting/_tvp.py create mode 100644 aeon/forecasting/tests/test_tvp.py diff --git a/aeon/forecasting/__init__.py b/aeon/forecasting/__init__.py index 7a331f69e6..0b134857dd 100644 --- a/aeon/forecasting/__init__.py +++ b/aeon/forecasting/__init__.py @@ -5,9 +5,11 @@ "BaseForecaster", "RegressionForecaster", "ETSForecaster", + "TVPForecaster", ] from aeon.forecasting._ets import ETSForecaster from aeon.forecasting._naive import NaiveForecaster from aeon.forecasting._regression import RegressionForecaster +from aeon.forecasting._tvp import TVPForecaster from aeon.forecasting.base import BaseForecaster diff --git a/aeon/forecasting/_tvp.py b/aeon/forecasting/_tvp.py new file mode 100644 index 0000000000..83bb9157f6 --- /dev/null +++ b/aeon/forecasting/_tvp.py @@ -0,0 +1,115 @@ +"""Time-Varying Parameter (TVP) Forecaster using Kalman filter.""" + +import numpy as np + +from aeon.forecasting.base import BaseForecaster + + +class TVPForecaster(BaseForecaster): + r"""Time-Varying Parameter (TVP) Forecaster using Kalman filter as described in [1]. + + This forecaster models the target series using a time-varying linear autoregression: + + .. math:: + + \\hat{y}_t = \beta_0,t+\beta_1,t * y_{t-1} + ... + \beta_k,t * y_{t-k} + + where the coefficients $\beta_t$ evolve based on observations $y_t$. At each + step, a weight vector is calculated based in the latest residual. This is used to + adjust the $\beta$ parameter values and the estimate of parameter variance. + + TVP can be considered as related to stochastic gradient descent (SGD) regression, + with the update weight being the dynamically calculated Kalman gain based on the + covariance of the parameters rather than a fixed learning rate. + + Parameters + ---------- + window : int + Number of autoregressive lags to use, called window to co-ordinate with + RegressionForecaster. + var : float, default=0.01 + Observation noise variance. ``var`` controls the influence of recency in the + update. A small var (such as the default 0.01) means the parameters will be + more + affected by + recent values. A large var (e.g., 1.0 or more) means the observations are + noisy, so the filter will adjust the parameters less to match recent values. + beta_var : float, default=0.01 + State evolution noise variance, applied to all coefficients at each step. Small + ``beta_var`` leads to slowly evolving parameters. + + References + ---------- + .. [1] Durbin & Koopman, Time Series Analysis by State Space Methods + Oxford University Press, 2nd Edition, 2012 + """ + + def __init__(self, window, horizon=1, var=0.01, beta_var=0.01): + self.window = window + self.var = var + self.beta_var = beta_var + super().__init__(axis=1, horizon=horizon) + + def _fit(self, y, exog=None): + y = y.squeeze() + + # Create autoregressive design matrix + X = np.lib.stride_tricks.sliding_window_view(y, window_shape=self.window) + X = X[: -self.horizon] + ones = np.ones((X.shape[0], 1)) + X = np.hstack([ones, X]) # Add intercept column + + y_train = y[self.window + self.horizon - 1 :] + + # Kalman filter initialisation + k = X.shape[1] # number of coefficients (lags + intercept) + beta = np.zeros(k) + beta_covariance = np.eye(k) + beta_var = self.beta_var * np.eye(k) + + for t in range(len(y_train)): + x_t = X[t] + y_t = y_train[t] + + # Predict covariance + beta_covariance = beta_covariance + beta_var + + # Forecast error + error_t = y_t - x_t @ beta + total_variance = x_t @ beta_covariance @ x_t + self.var + kalman_weight = beta_covariance @ x_t / total_variance + + # Update beta parameters with kalman weights times error. + beta = beta + kalman_weight * error_t + beta_covariance = ( + beta_covariance - np.outer(kalman_weight, x_t) @ beta_covariance + ) + + self._beta = beta + self._last_window = y[-self.window :] + self.forecast_ = ( + np.insert(self._last_window, 0, 1.0) @ self._beta + ) # include intercept + return self + + def _predict(self, y, exog=None): + y = y.squeeze() + x_t = np.insert(y[-self.window :], 0, 1.0) # include intercept term + y_hat = x_t @ self._beta + return y_hat + + @classmethod + def _get_test_params(cls, parameter_set: str = "default"): + """Return testing parameter settings for the estimator. + + Parameters + ---------- + parameter_set : str, default='default' + Name of the parameter set to return. + + Returns + ------- + dict + Dictionary of testing parameter settings. + """ + return {"window": 4} diff --git a/aeon/forecasting/tests/test_tvp.py b/aeon/forecasting/tests/test_tvp.py new file mode 100644 index 0000000000..5a31bee23e --- /dev/null +++ b/aeon/forecasting/tests/test_tvp.py @@ -0,0 +1,90 @@ +"""Test TVP forecaster. + +Tests include convergence properties described in Durbin & Koopman, 2012. + +""" + +import numpy as np + +from aeon.forecasting._tvp import TVPForecaster + + +def test_direct(): + """Test aeon TVP Forecaster equivalent to statsmodels.""" + expected = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]) + tvp = TVPForecaster(window=5, horizon=1, var=0.01, beta_var=0.01) + p = tvp.forecast(expected) + p2 = tvp.direct_forecast(expected, prediction_horizon=5) + assert p == p2[0] + + +def test_static_ar1_convergence_to_ols(): + """Test TVPForecaster converges to the OLS solution for a static AR(1) process.""" + # Simulate AR(1) data with constant parameters + rng = np.random.RandomState(0) + true_phi = 0.6 + true_intercept = 2.0 + noise_std = 0.5 + n = 500 + y = np.zeros(n) + # Initialize y[0] near the steady-state mean to avoid startup bias + y[0] = true_intercept / (1 - true_phi) + for t in range(1, n): + y[t] = true_intercept + true_phi * y[t - 1] + rng.normal(0, noise_std) + # Fit with beta_var=0 (no parameter drift) and observation variance = noise_var + forecaster = TVPForecaster(window=1, horizon=1, var=noise_std**2, beta_var=0.0) + forecaster.fit(y) + beta_est = forecaster._beta # [intercept, phi] estimated + # Compute static OLS estimates for comparison + X = np.vstack( + [np.ones(n - 1), y[: n - 1]] + ).T # regress y[t] on [1, y[t-1]] for t=1..n-1 + y_resp = y[1:] + beta_ols, *_ = np.linalg.lstsq(X, y_resp, rcond=None) + # The TVP forecaster (with no drift) should converge to OLS estimates + assert beta_est.shape == (2,) + # Check that estimated parameters are close to OLS solution + assert np.allclose(beta_est, beta_ols, atol=0.1) + # Also check they are close to true parameters + assert abs(beta_est[0] - true_intercept) < 0.2 + assert abs(beta_est[1] - true_phi) < 0.1 + + +def test_tvp_adapts_to_changing_coefficient(): + """Test TVP adapts its parameters when the true AR(1) coefficient changes.""" + rng = np.random.RandomState(42) + # Piecewise AR(1): phi changes from 0.2 to 0.8 at t=100, intercept remains 1.0 + n = 200 + phi1, phi2 = 0.2, 0.8 + intercept = 1.0 + noise_std = 0.05 + y = np.zeros(n) + # Start near the mean of first regime + y[0] = intercept / (1 - phi1) + # First half (t=1 to 99) with phi1 + for t in range(1, 100): + y[t] = intercept + phi1 * y[t - 1] + rng.normal(0, noise_std) + # Second half (t=100 to 199) with phi2 + for t in range(100, n): + y[t] = intercept + phi2 * y[t - 1] + rng.normal(0, noise_std) + # Fit TVPForecaster with nonzero beta_var to allow parameter drift + forecaster = TVPForecaster(window=1, horizon=1, var=noise_std**2, beta_var=0.1) + forecaster.fit(y) + beta_final = forecaster._beta + # Compute OLS on first and second half segments for reference + X1 = np.vstack([np.ones(99), y[:99]]).T + y1 = y[1:100] + beta1_ols, *_ = np.linalg.lstsq(X1, y1, rcond=None) + # use points 100..198 to predict 101..199 + X2 = np.vstack([np.ones(n - 101), y[100 : n - 1]]).T + y2 = y[101:n] + beta2_ols, *_ = np.linalg.lstsq(X2, y2, rcond=None) + # The final estimated phi should be much closer to phi2 than phi1 + estimated_intercept, estimated_phi = beta_final[0], beta_final[1] + # Validate that phi coefficient increased towards phi2 + assert estimated_phi > 0.5 # moved well above the initial ~0.2 + assert abs(estimated_phi - phi2) < 0.1 # close to the new true phi + # Validate intercept remains reasonable (around true intercept) + assert abs(estimated_intercept - intercept) < 0.5 + # Check that final phi is closer to second-half OLS estimate than first-half + assert abs(estimated_phi - beta2_ols[1]) < abs(estimated_phi - beta1_ols[1]) diff --git a/docs/api_reference/forecasting.md b/docs/api_reference/forecasting.md index 86a8cc613e..cdfae27341 100644 --- a/docs/api_reference/forecasting.md +++ b/docs/api_reference/forecasting.md @@ -11,4 +11,5 @@ NaiveForecaster RegressionForecaster ETSForecaster + TVPForecaster ``` diff --git a/examples/forecasting/regression.ipynb b/examples/forecasting/regression.ipynb index e451051b64..152cebaab8 100644 --- a/examples/forecasting/regression.ipynb +++ b/examples/forecasting/regression.ipynb @@ -4,7 +4,7 @@ "metadata": {}, "cell_type": "markdown", "source": [ - "# Forecasting using window based regression\n", + "# Forecasting using regression\n", "\n", "All aeon forecasters work by fitting on a series then making a single prediction \n", "about a future value. How they make the prediction defines the algorithm.\n", @@ -33,8 +33,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-06-07T16:02:32.093458Z", - "start_time": "2025-06-07T16:02:32.067219Z" + "end_time": "2025-07-10T10:06:22.363194Z", + "start_time": "2025-07-10T10:06:21.583141Z" } }, "cell_type": "code", @@ -55,18 +55,18 @@ "(45, 100)" ] }, - "execution_count": 36, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 36 + "execution_count": 1 }, { "metadata": { "ExecuteTime": { - "end_time": "2025-06-07T16:02:34.104719Z", - "start_time": "2025-06-07T16:02:33.936686Z" + "end_time": "2025-07-10T10:06:24.483254Z", + "start_time": "2025-07-10T10:06:22.771783Z" } }, "cell_type": "code", @@ -123,13 +123,13 @@ "output_type": "display_data" } ], - "execution_count": 37 + "execution_count": 2 }, { "metadata": { "ExecuteTime": { - "end_time": "2025-06-07T16:02:35.782114Z", - "start_time": "2025-06-07T16:02:35.777553Z" + "end_time": "2025-07-10T10:06:24.514474Z", + "start_time": "2025-07-10T10:06:24.509813Z" } }, "cell_type": "code", @@ -150,7 +150,7 @@ ] } ], - "execution_count": 38 + "execution_count": 3 }, { "metadata": {}, @@ -165,8 +165,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-06-07T16:02:42.494790Z", - "start_time": "2025-06-07T16:02:37.913729Z" + "end_time": "2025-07-10T10:07:55.008990Z", + "start_time": "2025-07-10T10:06:28.466480Z" } }, "cell_type": "code", @@ -189,11 +189,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "LR predicts [463.60934602] DrCIF predicts [425.5]\n" + "LR predicts [463.60934602] DrCIF predicts [413.2]\n" ] } ], - "execution_count": 39 + "execution_count": 4 }, { "metadata": {}, @@ -208,8 +208,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-06-07T16:02:55.217185Z", - "start_time": "2025-06-07T16:02:51.260893Z" + "end_time": "2025-07-10T10:07:57.419429Z", + "start_time": "2025-07-10T10:07:55.039066Z" } }, "cell_type": "code", @@ -238,7 +238,7 @@ ] } ], - "execution_count": 40 + "execution_count": 5 }, { "metadata": {}, @@ -252,8 +252,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-06-07T16:03:30.898674Z", - "start_time": "2025-06-07T16:03:08.930864Z" + "end_time": "2025-07-10T10:08:23.040857Z", + "start_time": "2025-07-10T10:07:57.463411Z" } }, "cell_type": "code", @@ -305,6 +305,7 @@ " aeon.regression.interval_based._interval_pipelines.RandomIntervalRegressor),\n", " ('RandomIntervalSpectralEnsembleRegressor',\n", " aeon.regression.interval_based._rise.RandomIntervalSpectralEnsembleRegressor),\n", + " ('RecurrentRegressor', aeon.regression.deep_learning._rnn.RecurrentRegressor),\n", " ('RegressorEnsemble', aeon.regression.compose._ensemble.RegressorEnsemble),\n", " ('RegressorPipeline', aeon.regression.compose._pipeline.RegressorPipeline),\n", " ('ResNetRegressor', aeon.regression.deep_learning._resnet.ResNetRegressor),\n", @@ -319,30 +320,176 @@ " aeon.regression.interval_based._tsf.TimeSeriesForestRegressor)]" ] }, - "execution_count": 41, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 41 + "execution_count": 6 }, { "metadata": {}, "cell_type": "markdown", "source": [ - "All aeon forecasters predict a single value. If you want to forecast a range of \n", - "values ahead you should use the functions `recursive_foreacast` or `direct_forecast` \n", - "(notebooks coming soon). " + "All aeon forecasters predict a single value from `fit` (next forecast value from the \n", + "train series) and from `predict` (predicted next value from test series). If you want\n", + " to forecast a prediction horizon of values ahead you should use the functions \n", + " `iterative_foreacast` or `direct_forecast`. " ], "id": "939c3a82b06e1b95" }, { "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Time-Varying Parameter (TVP) Forecaster\n", + "\n", + "The `aeon` RegressionForecaster defaults to fits a standard linear regression on the windowed series using the sklearn `LinearRegression` estimator. This fits the linear regression parameters with the standard least squares algorithm. An alternative approach in the forecasting literature is to fit the linear regression parameters adaptively, so that more recent observations can have a greater weight in setting the parameters. One way of doing this is to use a Kalman filter. This considers the correlation between parameters in addition to the forecasting error. This is implemented in `aeon` as the `TVPForecaster`.\n" + ], + "id": "2850a18027127db0" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-07-10T10:09:38.138944Z", + "start_time": "2025-07-10T10:09:38.127369Z" + } + }, "cell_type": "code", + "source": [ + "from aeon.forecasting import TVPForecaster\n", + "\n", + "tvp = TVPForecaster(window=50)\n", + "tvp.fit(y_train)\n", + "pred = tvp.predict(y_train)" + ], + "id": "b0787fa71ac848d8", "outputs": [], - "execution_count": null, + "execution_count": 13 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-07-10T10:14:25.355241Z", + "start_time": "2025-07-10T10:14:25.149557Z" + } + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "print(f\" Forecast for airline with TVP = {pred}\")\n", + "direct = tvp.direct_forecast(y_train, prediction_horizon=12)\n", + "iterative = tvp.iterative_forecast(y_train, prediction_horizon=12)\n", + "plt.plot(np.arange(0, len(y_train)), y_train, label=\"Train\", color=\"blue\")\n", + "plt.plot(\n", + " np.arange(len(y_train), len(y_train) + len(direct)),\n", + " direct,\n", + " label=\"Direct\",\n", + " color=\"green\",\n", + " linestyle=\":\",\n", + ")\n", + "plt.plot(\n", + " np.arange(len(y_train), len(y_train) + len(iterative)),\n", + " iterative,\n", + " label=\"Iterative\",\n", + " color=\"red\",\n", + " linestyle=\":\",\n", + ")\n", + "\n", + "plt.legend()\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.title(\"Iterative and Direct Forecasting with TVP\")\n", + "plt.show()" + ], + "id": "68942e2fa018848a", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Forecast for airline with TVP = 452.44604822530795\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 23 + }, + { + "metadata": {}, + "cell_type": "markdown", "source": "", - "id": "4bf0708606952c16" + "id": "495008094eee47b6" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "TVP has two parameters that control the weight given to more recent observations. These are `var` and `coeff_var`. `var` represents the variation in the data. A small value of `var`, such as the default of 0.01 indicates there is less noise in the data and recent values will have a greater effect on parameter updates. It is a bit like the learning rate used in gradient descent or reinforcement learning. A large `var` value (e.g. greater than 1.0) will mean new values will adjust the parameters less at each update.\n", + "\n", + "The second parameter, `beta_var`, estimates the variation in parameters over time. It has a similar effect on updates to `var` in that it effects how much parameters are changed at each stage, but it is modelling a different type on uncertainty. `TVPRegression` maintains an estimate of the covariance of the parameters in `fit` and this is adjusted after each update and then `beta_var` is added to perturb the matrix. Small `var` means the Kalman weights.\n", + "\n", + "Add some examples to demonstrate the difference\n" + ], + "id": "ca7da34828be1c2" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-07-10T10:16:51.355642Z", + "start_time": "2025-07-10T10:16:51.190578Z" + } + }, + "cell_type": "code", + "source": [ + "tvp2 = TVPForecaster(var=1.0, beta_var=1.0, window=50)\n", + "direct2 = tvp.direct_forecast(airline, prediction_horizon=12)\n", + "plt.plot(\n", + " np.arange(0, len(direct)),\n", + " direct,\n", + " label=\"Direct 1\",\n", + " color=\"green\",\n", + " linestyle=\":\",\n", + ")\n", + "plt.plot(\n", + " np.arange(0, len(direct2)),\n", + " direct2,\n", + " label=\"Direct 2\",\n", + " color=\"blue\",\n", + " linestyle=\":\",\n", + ")\n", + "\n", + "plt.legend()\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.title(\"Direct Forecasting with TVP\")\n", + "plt.show()" + ], + "id": "7f1b85ed426c2735", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 27 } ], "metadata": { From 73662ab62a3473777a3c1e4c84c71c0116f3718c Mon Sep 17 00:00:00 2001 From: Yaroslav Halchenko Date: Fri, 11 Jul 2025 21:30:20 +0200 Subject: [PATCH 182/216] [MNT] Add codespell support (config, workflow to detect/not fix) and make it fix a "few" typos (#2653) * Add github action to codespell main on push and PRs * Add rudimentary codespell config * Add pre-commit definition for codespell * Some skips for codespell -- lots of work todo * [DATALAD RUNCMD] run codespell throughout fixing typos automagically (but ignoring overall fail due to ambigous ones) === Do not change lines below === { "chain": [], "cmd": "codespell -w || :", "exit": 0, "extra_inputs": [], "inputs": [], "outputs": [], "pwd": "." } ^^^ Do not change lines above ^^^ * codespeed notebook and testing * incorrect typos and notebooks * more typos * config and fixes * Update similarity_search.ipynb fix notebook typos * update skip * temp typo and annotation workflow * annotations * fix typos --------- Co-authored-by: Matthew Middlehurst Co-authored-by: Antoine Guillaume --- .github/actions/numba_cache/action.yml | 2 +- .github/utilities/codespell_ignore_words.txt | 4 + .github/workflows/pr_precommit.yml | 13 + .pre-commit-config.yaml | 8 + .../series/distance_based/_kmeans.py | 2 +- .../series/distance_based/_merlin.py | 10 +- .../series/outlier_detection/_stray.py | 4 +- .../compose/collection_ensemble.py | 2 +- aeon/benchmarking/results_loaders.py | 4 +- aeon/benchmarking/stats.py | 2 +- aeon/benchmarking/tests/test_resampling.py | 4 +- aeon/classification/compose/_ensemble.py | 2 +- .../deep_learning/_disjoint_cnn.py | 18 +- aeon/classification/deep_learning/_fcn.py | 2 +- .../deep_learning/_inception_time.py | 2 +- .../deep_learning/_lite_time.py | 4 +- aeon/classification/dictionary_based/_boss.py | 4 +- .../classification/dictionary_based/_cboss.py | 2 +- .../dictionary_based/_redcomets.py | 12 +- aeon/classification/dictionary_based/_tde.py | 2 +- .../_probability_threshold.py | 4 +- .../early_classification/_teaser.py | 4 +- .../early_classification/base.py | 4 +- .../ordinal_classification/_ordinal_tde.py | 2 +- aeon/classification/shapelet_based/_sast.py | 2 +- .../sklearn/_continuous_interval_tree.py | 2 +- .../sklearn/_rotation_forest_classifier.py | 2 +- aeon/classification/tests/test_base.py | 6 +- .../tests/test_sklearn_compatability.py | 2 +- aeon/clustering/_clarans.py | 2 +- aeon/clustering/_k_medoids.py | 2 +- aeon/clustering/deep_learning/_ae_dcnn.py | 2 +- aeon/clustering/deep_learning/_ae_fcn.py | 2 +- aeon/datasets/_data_writers.py | 4 +- aeon/datasets/_single_problem_loaders.py | 2 +- aeon/datasets/_tss_data_loaders.py | 2 +- aeon/datasets/data/ACSF1/ACSF1_TEST.ts | 4 +- aeon/datasets/data/ACSF1/ACSF1_TRAIN.ts | 4 +- .../JapaneseVowels/JapaneseVowels_TEST.ts | 2 +- .../JapaneseVowels/JapaneseVowels_TRAIN.ts | 2 +- aeon/datasets/data/PLAID/PLAID_TEST.arff | 4 +- aeon/datasets/data/PLAID/PLAID_TEST.ts | 4 +- aeon/datasets/data/PLAID/PLAID_TRAIN.arff | 4 +- aeon/datasets/data/PLAID/PLAID_TRAIN.ts | 4 +- .../PickupGestureWiimoteZ_TEST.ts | 12 +- .../PickupGestureWiimoteZ_TRAIN.ts | 12 +- .../m1_yearly_dataset/m1_yearly_dataset.tsf | 2 +- aeon/distances/_distance.py | 6 +- aeon/distances/_sbd.py | 2 +- aeon/distances/elastic/_adtw.py | 2 +- aeon/distances/elastic/_ddtw.py | 2 +- aeon/distances/elastic/_dtw.py | 2 +- aeon/distances/elastic/_dtw_gi.py | 2 +- aeon/distances/elastic/_erp.py | 4 +- aeon/distances/elastic/_msm.py | 2 +- aeon/distances/elastic/_shape_dtw.py | 2 +- aeon/distances/elastic/_soft_dtw.py | 2 +- aeon/distances/elastic/_twe.py | 2 +- aeon/distances/elastic/_wddtw.py | 2 +- aeon/distances/elastic/_wdtw.py | 2 +- .../elastic/tests/test_alignment_path.py | 4 +- .../elastic/tests/test_cost_matrix.py | 4 +- aeon/distances/tests/test_distances.py | 4 +- aeon/distances/tests/test_pairwise.py | 10 +- aeon/forecasting/_ets.py | 2 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.../estimator_checking/_estimator_checking.py | 2 +- ...eld_collection_anomaly_detection_checks.py | 8 +- .../_yield_multithreading_checks.py | 2 +- .../_yield_series_anomaly_detection_checks.py | 8 +- .../mock_estimators/_mock_classifiers.py | 2 +- .../_mock_series_transformers.py | 4 +- aeon/testing/tests/__init__.py | 2 +- aeon/transformations/collection/_rescale.py | 2 +- .../channel_selection/_channel_scorer.py | 8 +- .../convolution_based/_minirocket.py | 2 +- .../convolution_based/_multirocket.py | 2 +- .../collection/dictionary_based/_sfa.py | 8 +- .../collection/dictionary_based/_sfa_whole.py | 2 +- .../collection/self_supervised/_trilite.py | 2 +- .../_dilated_shapelet_transform.py | 12 +- .../collection/shapelet_based/_sast.py | 8 +- .../tests/test_dilated_shapelet_transform.py | 2 +- .../signature_based/_augmentations.py | 2 +- .../collection/signature_based/_window.py | 2 +- .../unequal_length/tests/test_pad.py | 2 +- aeon/transformations/series/_boxcox.py | 2 +- .../transformations/series/_matrix_profile.py | 2 +- aeon/transformations/series/_pla.py | 2 +- aeon/transformations/series/_siv.py | 2 +- aeon/utils/conversion/_convert_series.py | 2 +- aeon/utils/conversion/_convert_tsf.py | 2 +- aeon/utils/discovery.py | 4 +- aeon/utils/numba/general.py | 2 +- aeon/utils/numba/stats.py | 2 +- aeon/utils/tags/_tags.py | 2 +- aeon/utils/validation/__init__.py | 2 +- aeon/utils/validation/_dependencies.py | 2 +- aeon/utils/validation/collection.py | 2 +- .../utils/validation/tests/test_collection.py | 2 +- aeon/utils/validation/tests/test_series.py | 2 +- aeon/visualisation/__init__.py | 2 +- aeon/visualisation/estimator/_shapelets.py | 114 +- .../learning_task/_segmentation.py | 2 +- .../results/_critical_difference.py | 2 +- aeon/visualisation/results/_mcm.py | 4 +- aeon/visualisation/results/_scatter.py | 28 +- docs/api_reference/data_format.md | 4 +- docs/api_reference/similarity_search.rst | 2 +- docs/changelogs/v0/v0.1.md | 2 +- docs/changelogs/v0/v0.10.md | 2 +- docs/changelogs/v0/v0.11.md | 6 +- docs/changelogs/v0/v0.6.md | 2 +- docs/changelogs/v1.0.md | 2 +- docs/changelogs/v1.1.md | 8 +- docs/conf.py | 8 +- docs/developer_guide/release.md | 2 +- docs/getting_started.md | 2 +- examples/benchmarking/benchmarking.ipynb | 2 +- examples/benchmarking/published_results.ipynb | 2 +- examples/benchmarking/reference_results.ipynb | 2 +- examples/classification/classification.ipynb | 2 +- .../classification/convolution_based.ipynb | 2 +- examples/classification/distance_based.ipynb | 2 +- examples/classification/hybrid.ipynb | 2 +- examples/classification/interval_based.ipynb | 2 +- examples/classification/shapelet_based.ipynb | 12 +- .../clustering/feature_based_clustering.ipynb | 12 +- .../clustering/partitional_clustering.ipynb | 10 +- examples/datasets/data_loading.ipynb | 2 +- examples/datasets/load_data_from_web.ipynb | 51 +- examples/datasets/provided_data.ipynb | 6 +- examples/distances/distances.ipynb | 4 +- examples/networks/deep_learning.ipynb | 26 +- .../segmentation_with_clasp.ipynb | 6 +- examples/similarity_search/code_speed.ipynb | 10 +- .../similarity_search/distance_profiles.ipynb | 2 +- .../similarity_search/similarity_search.ipynb | 27 +- examples/transformations/preprocessing.ipynb | 2000 ++++++++--------- examples/transformations/sast.ipynb | 2 +- .../transformations/signature_method.ipynb | 6 +- .../transformations/smoothing_filters.ipynb | 2 +- .../transformations/transformations.ipynb | 1389 +++++++++--- examples/transformations/tsfresh.ipynb | 7 +- pyproject.toml | 7 + 167 files changed, 2579 insertions(+), 1768 deletions(-) create mode 100644 .github/utilities/codespell_ignore_words.txt diff --git a/.github/actions/numba_cache/action.yml b/.github/actions/numba_cache/action.yml index 06acc39ef4..62a68d9981 100644 --- a/.github/actions/numba_cache/action.yml +++ b/.github/actions/numba_cache/action.yml @@ -62,6 +62,6 @@ runs: path: ${{ github.workspace }}/.numba_cache # Try restore using today's date key: numba-${{ inputs.cache_name }}-${{ inputs.runner_os }}-${{ inputs.python_version }}-${{ env.CURRENT_DATE }} - # If cant restore with today's date try another cache (without date) + # If can't restore with today's date try another cache (without date) restore-keys: | numba-${{ inputs.cache_name }}-${{ inputs.runner_os }}-${{ inputs.python_version }}- diff --git a/.github/utilities/codespell_ignore_words.txt b/.github/utilities/codespell_ignore_words.txt new file mode 100644 index 0000000000..cce6a655dd --- /dev/null +++ b/.github/utilities/codespell_ignore_words.txt @@ -0,0 +1,4 @@ +fpr +mape +recuse +strat diff --git a/.github/workflows/pr_precommit.yml b/.github/workflows/pr_precommit.yml index ffc2e48f4b..6267c529af 100644 --- a/.github/workflows/pr_precommit.yml +++ b/.github/workflows/pr_precommit.yml @@ -60,3 +60,16 @@ jobs: with: commit_message: Automatic `pre-commit` fixes commit_user_name: aeon-actions-bot[bot] + + codespell-annotations: + runs-on: ubuntu-24.04 + + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Annotate locations with typos + uses: codespell-project/codespell-problem-matcher@v1 + + - name: Codespell + uses: codespell-project/actions-codespell@v2 diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 4c9bf237d9..93a0276244 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -78,3 +78,11 @@ repos: hooks: - id: check-manifest stages: [ manual ] + + - repo: https://github.com/codespell-project/codespell + # Configuration for codespell is in pyproject.toml + rev: v2.4.1 + hooks: + - id: codespell + additional_dependencies: + - tomli # for python_version < '3.11' diff --git a/aeon/anomaly_detection/series/distance_based/_kmeans.py b/aeon/anomaly_detection/series/distance_based/_kmeans.py index e5d2884374..441b49c242 100644 --- a/aeon/anomaly_detection/series/distance_based/_kmeans.py +++ b/aeon/anomaly_detection/series/distance_based/_kmeans.py @@ -42,7 +42,7 @@ class KMeansAD(BaseSeriesAnomalyDetector): stride : int, default=1 The stride of the sliding window. The stride determines how many time points - the windows are spaced appart. A stride of 1 means that the window is moved one + the windows are spaced apart. A stride of 1 means that the window is moved one time point forward compared to the previous window. The larger the stride, the fewer windows are created, which leads to noisier anomaly scores. diff --git a/aeon/anomaly_detection/series/distance_based/_merlin.py b/aeon/anomaly_detection/series/distance_based/_merlin.py index 444b5bf53b..72023fa2b1 100644 --- a/aeon/anomaly_detection/series/distance_based/_merlin.py +++ b/aeon/anomaly_detection/series/distance_based/_merlin.py @@ -102,13 +102,13 @@ def _predict(self, X): r = 2 * np.sqrt(self.min_length) distances = np.full(len(lengths), -1.0) - indicies = np.full(len(lengths), -1) + indices = np.full(len(lengths), -1) - indicies[0], distances[0] = self._find_index(X, lengths[0], r, np.multiply, 0.5) + indices[0], distances[0] = self._find_index(X, lengths[0], r, np.multiply, 0.5) for i in range(1, min(5, len(lengths))): r = distances[i - 1] * 0.99 - indicies[i], distances[i] = self._find_index( + indices[i], distances[i] = self._find_index( X, lengths[i], r, np.multiply, 0.99 ) @@ -116,12 +116,12 @@ def _predict(self, X): m = mean(distances[i - 5 : i]) s = std(distances[i - 5 : i]) r = m - 2 * s - indicies[i], distances[i] = self._find_index( + indices[i], distances[i] = self._find_index( X, lengths[i], r, np.subtract, s ) anomalies = np.zeros(X.shape[0], dtype=bool) - for i in indicies: + for i in indices: if i > -1: anomalies[i] = True diff --git a/aeon/anomaly_detection/series/outlier_detection/_stray.py b/aeon/anomaly_detection/series/outlier_detection/_stray.py index d297ab8337..1b7a5c640f 100644 --- a/aeon/anomaly_detection/series/outlier_detection/_stray.py +++ b/aeon/anomaly_detection/series/outlier_detection/_stray.py @@ -20,7 +20,7 @@ class STRAY(BaseSeriesAnomalyDetector): ability to detect clusters of outliers in multidimensional data without requiring a model of the typical behavior of the system. However, it suffers from some limitations that affect its accuracy. STRAY is an extension of - HDoutliers that uses extreme value theory for the anomolous threshold + HDoutliers that uses extreme value theory for the anomalous threshold calculation, to deal with data streams that exhibit non-stationary behavior. Parameters @@ -39,7 +39,7 @@ class STRAY(BaseSeriesAnomalyDetector): Proportion of possible candidates for outliers. This defines the starting point for the bottom up searching algorithm. size_threshold : int, default=50 - Sample size to calculate an emperical threshold. + Sample size to calculate an empirical threshold. outlier_tail : str {"min", "max"}, default="max" Direction of the outlier tail. diff --git a/aeon/base/_estimators/compose/collection_ensemble.py b/aeon/base/_estimators/compose/collection_ensemble.py index 9b20e11007..de0d9a5820 100644 --- a/aeon/base/_estimators/compose/collection_ensemble.py +++ b/aeon/base/_estimators/compose/collection_ensemble.py @@ -42,7 +42,7 @@ class BaseCollectionEnsemble(ComposableEstimatorMixin, BaseCollectionEstimator): Only used if weights is a float. The method used to generate a performance estimation from the training data set i.e. cross-validation. If None, predictions are made using that estimators fit_predict or - fit_predict_proba methods. These are somtimes overridden for efficient + fit_predict_proba methods. These are sometimes overridden for efficient performance evaluations, i.e. out-of-bag predictions. If int or sklearn object input, the parameter is passed directly to the cv parameter of the cross_val_predict function from sklearn. diff --git a/aeon/benchmarking/results_loaders.py b/aeon/benchmarking/results_loaders.py index 2b4ee45bca..99c6533015 100644 --- a/aeon/benchmarking/results_loaders.py +++ b/aeon/benchmarking/results_loaders.py @@ -201,7 +201,7 @@ def estimator_alias(name: str) -> str: def get_available_estimators( task: str = "classification", as_list: bool = False ) -> Union[pd.DataFrame, list]: - """Get a DataFrame of estimators avialable for a specific learning task. + """Get a DataFrame of estimators available for a specific learning task. Parameters ---------- @@ -251,7 +251,7 @@ def get_estimator_results( Parameters ---------- - estimators : str ot list of str + estimators : str or list of str Estimator name or list of estimator names to search for. See get_available_estimators, aeon.benchmarking.results_loading.NAME_ALIASES or the directory at path for valid options. diff --git a/aeon/benchmarking/stats.py b/aeon/benchmarking/stats.py index db7694a4d5..87b2bde1fd 100644 --- a/aeon/benchmarking/stats.py +++ b/aeon/benchmarking/stats.py @@ -57,7 +57,7 @@ def nemenyi_test(ordered_avg_ranks, n_datasets, alpha): ordered_avg_ranks : np.array Average ranks of estimators. n_datasets : int - Mumber of datasets. + Number of datasets. alpha : float alpha level for Nemenyi test. diff --git a/aeon/benchmarking/tests/test_resampling.py b/aeon/benchmarking/tests/test_resampling.py index c7dfd9d27a..23610af18d 100644 --- a/aeon/benchmarking/tests/test_resampling.py +++ b/aeon/benchmarking/tests/test_resampling.py @@ -107,7 +107,7 @@ def test_resample_data_indices(): concat = np.concatenate((train_indices, test_indices), axis=0) assert len(np.unique(concat)) == len(concat) - # expected indicies after resampling + # expected indices after resampling np.testing.assert_array_equal( concat, [ @@ -277,7 +277,7 @@ def test_stratified_resample_data_indices(): concat = np.concatenate((train_indices, test_indices), axis=0) assert len(np.unique(concat)) == len(concat) - # expected indicies after resampling + # expected indices after resampling np.testing.assert_array_equal( concat, [ diff --git a/aeon/classification/compose/_ensemble.py b/aeon/classification/compose/_ensemble.py index af9d31f9aa..1481866c8b 100644 --- a/aeon/classification/compose/_ensemble.py +++ b/aeon/classification/compose/_ensemble.py @@ -33,7 +33,7 @@ class ClassifierEnsemble(BaseCollectionEnsemble, BaseClassifier): Only used if weights is a float. The method used to generate a performance estimation from the training data set i.e. cross-validation. If None, predictions are made using that estimators fit_predict or - fit_predict_proba methods. These are somtimes overridden for efficient + fit_predict_proba methods. These are sometimes overridden for efficient performance evaluations, i.e. out-of-bag predictions. If int or sklearn object input, the parameter is passed directly to the cv parameter of the cross_val_predict function from sklearn. diff --git a/aeon/classification/deep_learning/_disjoint_cnn.py b/aeon/classification/deep_learning/_disjoint_cnn.py index dfe857031c..f2215a7ab0 100644 --- a/aeon/classification/deep_learning/_disjoint_cnn.py +++ b/aeon/classification/deep_learning/_disjoint_cnn.py @@ -25,42 +25,42 @@ class DisjointCNNClassifier(BaseDeepClassifier): Number of 1+1D Convolution layers. n_filters : int or list of int, default = 64 Number of filters used in convolution layers. If - input is set to a list, the lenght should be the same + input is set to a list, the length should be the same as `n_layers`, if input is int the a list of the same element is created of length `n_layers`. kernel_size : int or list of int, default = [8, 5, 5, 3] Size of convolution kernel. If - input is set to a list, the lenght should be the same + input is set to a list, the length should be the same as `n_layers`, if input is int the a list of the same element is created of length `n_layers`. dilation_rate : int or list of int, default = 1 The dilation rate for convolution. If - input is set to a list, the lenght should be the same + input is set to a list, the length should be the same as `n_layers`, if input is int the a list of the same element is created of length `n_layers`. strides : int or list of int, default = 1 The strides of the convolution filter. If - input is set to a list, the lenght should be the same + input is set to a list, the length should be the same as `n_layers`, if input is int the a list of the same element is created of length `n_layers`. padding : str or list of str, default = "same" The type of padding used for convolution. If - input is set to a list, the lenght should be the same + input is set to a list, the length should be the same as `n_layers`, if input is int the a list of the same element is created of length `n_layers`. activation : str or list of str, default = "elu" Activation used after the convolution. If - input is set to a list, the lenght should be the same + input is set to a list, the length should be the same as `n_layers`, if input is int the a list of the same element is created of length `n_layers`. use_bias : bool or list of bool, default = True - Whether or not ot use bias in convolution. If - input is set to a list, the lenght should be the same + Whether or not to use bias in convolution. If + input is set to a list, the length should be the same as `n_layers`, if input is int the a list of the same element is created of length `n_layers`. kernel_initializer: str or list of str, default = "he_uniform" The initialization method of convolution layers. If - input is set to a list, the lenght should be the same + input is set to a list, the length should be the same as `n_layers`, if input is int the a list of the same element is created of length `n_layers`. pool_size: int, default = 5 diff --git a/aeon/classification/deep_learning/_fcn.py b/aeon/classification/deep_learning/_fcn.py index 2cb29bdbdc..271b638a90 100644 --- a/aeon/classification/deep_learning/_fcn.py +++ b/aeon/classification/deep_learning/_fcn.py @@ -36,7 +36,7 @@ class FCNClassifier(BaseDeepClassifier): activation : str or list of str, default = "relu" Activation used after the convolution. use_bias : bool or list of bool, default = True - Whether or not ot use bias in convolution. + Whether or not to use bias in convolution. n_epochs : int, default = 2000 The number of epochs to train the model. batch_size : int, default = 16 diff --git a/aeon/classification/deep_learning/_inception_time.py b/aeon/classification/deep_learning/_inception_time.py index eaacf44775..241e5af7b2 100644 --- a/aeon/classification/deep_learning/_inception_time.py +++ b/aeon/classification/deep_learning/_inception_time.py @@ -354,7 +354,7 @@ def _predict_proba(self, X) -> np.ndarray: def load_model(self, model_path, classes): """Load pre-trained classifiers instead of fitting. - When calling this function, all funcationalities can be used + When calling this function, all functionalities can be used such as predict, predict_proba, etc. with the loaded models. Parameters diff --git a/aeon/classification/deep_learning/_lite_time.py b/aeon/classification/deep_learning/_lite_time.py index bf3922f1d2..01d75a2465 100644 --- a/aeon/classification/deep_learning/_lite_time.py +++ b/aeon/classification/deep_learning/_lite_time.py @@ -286,7 +286,7 @@ def _predict_proba(self, X) -> np.ndarray: def load_model(self, model_path, classes): """Load pre-trained classifiers instead of fitting. - When calling this function, all funcationalities can be used + When calling this function, all functionalities can be used such as predict, predict_proba, etc. with the loaded models. Parameters @@ -448,7 +448,7 @@ class IndividualLITEClassifier(BaseDeepClassifier): References ---------- ..[1] Ismail-Fawaz et al. LITE: Light Inception with boosTing - tEchniques for Time Series Classificaion, IEEE International + tEchniques for Time Series Classification, IEEE International Conference on Data Science and Advanced Analytics, 2023. ..[2] Ismail-Fawaz, Ali, et al. "Look Into the LITE in Deep Learning for Time Series Classification." diff --git a/aeon/classification/dictionary_based/_boss.py b/aeon/classification/dictionary_based/_boss.py index 6fa7ebe1dc..3091ca09da 100644 --- a/aeon/classification/dictionary_based/_boss.py +++ b/aeon/classification/dictionary_based/_boss.py @@ -719,11 +719,11 @@ def boss_distance(X, Y, i, XX_all=None, XY_all=None): """Find the distance between two histograms. This returns the distance between first and second dictionaries, using a non- - symmetric distance measure. It is used to find the distance between historgrams + symmetric distance measure. It is used to find the distance between histograms of words. This distance function is designed for sparse matrix, represented as either a - dictionary or an arrray. It only measures the distance between counts present in + dictionary or an array. It only measures the distance between counts present in the first dictionary and the second. Hence dist(a,b) does not necessarily equal dist(b,a). diff --git a/aeon/classification/dictionary_based/_cboss.py b/aeon/classification/dictionary_based/_cboss.py index 71fecf8672..c3103771cb 100644 --- a/aeon/classification/dictionary_based/_cboss.py +++ b/aeon/classification/dictionary_based/_cboss.py @@ -28,7 +28,7 @@ class ContractableBOSS(BaseClassifier): described in [2]_. Overview: Input "n" series of length "m" and cBOSS randomly samples - `n_parameter_samples` parameter sets, evaluting each with LOOCV. It then + `n_parameter_samples` parameter sets, evaluating each with LOOCV. It then retains `max_ensemble_size` classifiers with the highest accuracy There are three primary parameters: - alpha: alphabet size diff --git a/aeon/classification/dictionary_based/_redcomets.py b/aeon/classification/dictionary_based/_redcomets.py index 76d0c98c13..d62b09d010 100644 --- a/aeon/classification/dictionary_based/_redcomets.py +++ b/aeon/classification/dictionary_based/_redcomets.py @@ -36,7 +36,7 @@ class REDCOMETS(BaseClassifier): RED CoMETS variant to use from {1, 2, 3, 4, 5, 6, 7, 8, 9} to use as per [1]_. Defaults to RED CoMETS-3. Variants 4-9 only support multivariate problems. perc_length : int or float, default=5 - Percentage of time series length used to determinne number of lenses during + Percentage of time series length used to determine number of lenses during pair selection. n_trees : int, default=100 Number of trees used by each random forest sub-classifier. @@ -185,12 +185,12 @@ def _build_univariate_ensemble(self, X, y): Returns ------- sfa_transforms : - List of ``SFAFast()`` instances with random word length and alpabet size + List of ``SFAFast()`` instances with random word length and alphabet size sfa_clfs : List of ``(RandomForestClassifier(), weight)`` tuples fitted on `SFAFast` transformed training data sax_transforms : - List of ``SAX()`` instances with random word length and alpabet size + List of ``SAX()`` instances with random word length and alphabet size sax_clfs : List of ``(RandomForestClassifier(), weight)`` tuples fitted on `SAX` transformed training data @@ -321,13 +321,13 @@ def _build_dimension_ensemble(self, X, y): Returns ------- sfa_transforms : list - List of lists of ``SFAFast()`` instances with random word length and alpabet - size + List of lists of ``SFAFast()`` instances with random word length and + alphabet size sfa_clfs : list List of lists of ``(RandomForestClassifier(), weight)`` tuples fitted on `SFAFast` transformed training data sax_transforms : list - List of lists of ``SAX()`` instances with random word length and alpabet + List of lists of ``SAX()`` instances with random word length and alphabet size sax_clfs : list List of lists ``(RandomForestClassifier(), weight)`` tuples fitted on `SAX` diff --git a/aeon/classification/dictionary_based/_tde.py b/aeon/classification/dictionary_based/_tde.py index 84f3a571e1..e4587025ec 100644 --- a/aeon/classification/dictionary_based/_tde.py +++ b/aeon/classification/dictionary_based/_tde.py @@ -618,7 +618,7 @@ class IndividualTDE(BaseClassifier): bigrams : bool, default=False Whether to record word bigrams in the SFA transform. dim_threshold : float, default=0.85 - Accuracy threshold as a propotion of the highest accuracy dimension for words + Accuracy threshold as a proportion of the highest accuracy dimension for words extracted from each dimensions. Only applicable for multivariate data. max_dims : int, default=20 Maximum number of dimensions words are extracted from. Only applicable for diff --git a/aeon/classification/early_classification/_probability_threshold.py b/aeon/classification/early_classification/_probability_threshold.py index 5a5ac5bf55..4e627ee536 100644 --- a/aeon/classification/early_classification/_probability_threshold.py +++ b/aeon/classification/early_classification/_probability_threshold.py @@ -47,7 +47,7 @@ class ProbabilityThresholdEarlyClassifier(BaseEarlyClassifier): List of integer time series time stamps to build classifiers and allow predictions at. Early predictions must have a series length that matches a value in the _classification_points List. Duplicate values will be removed, and the - full series length will be appeneded if not present. + full series length will be appended if not present. If None, will use 20 thresholds linearly spaces from 0 to the series length. n_jobs : int, default=1 The number of jobs to run in parallel for both `fit` and `predict`. @@ -73,7 +73,7 @@ class ProbabilityThresholdEarlyClassifier(BaseEarlyClassifier): state_info : 2d np.ndarray (4 columns) Information stored about input instances after the decision-making process in update/predict methods. Used in update methods to make decisions based on - the resutls of previous method calls. + the results of previous method calls. Records in order: the time stamp index, the number of consecutive decisions made, the predicted class and the series length. diff --git a/aeon/classification/early_classification/_teaser.py b/aeon/classification/early_classification/_teaser.py index 99a5ceff86..c4fa51a21c 100644 --- a/aeon/classification/early_classification/_teaser.py +++ b/aeon/classification/early_classification/_teaser.py @@ -56,7 +56,7 @@ class TEASER(BaseEarlyClassifier): List of integer time series time stamps to build classifiers and allow predictions at. Early predictions must have a series length that matches a value in the _classification_points List. Duplicate values will be removed, and the - full series length will be appeneded if not present. + full series length will be appended if not present. If None, will use 20 thresholds linearly spaces from 0 to the series length. n_jobs : int, default=1 The number of jobs to run in parallel for both `fit` and `predict`. @@ -82,7 +82,7 @@ class TEASER(BaseEarlyClassifier): state_info : 2d np.ndarray (4 columns) Information stored about input instances after the decision-making process in update/predict methods. Used in update methods to make decisions based on - the resutls of previous method calls. + the results of previous method calls. Records in order: the time stamp index, the number of consecutive decisions made, the predicted class and the series length. diff --git a/aeon/classification/early_classification/base.py b/aeon/classification/early_classification/base.py index e058449ba0..e9a29f262d 100644 --- a/aeon/classification/early_classification/base.py +++ b/aeon/classification/early_classification/base.py @@ -66,7 +66,7 @@ def __init__(self): """ An array containing the state info for each decision in X from update and - predict methods. Contains classifier dependant information for future decisions + predict methods. Contains classifier dependent information for future decisions on the data and information on when a cases decision has been made. Each row contains information for a case from the latest decision on its safety made in update/predict. Successive updates are likely to remove rows from the @@ -308,7 +308,7 @@ def get_state_info(self): Returns ------- An array containing the state info for each decision in X from update and - predict methods. Contains classifier dependant information for future decisions + predict methods. Contains classifier dependent information for future decisions on the data and information on when a cases decision has been made. Each row contains information for a case from the latest decision on its safety made in update/predict. Successive updates are likely to remove rows from the diff --git a/aeon/classification/ordinal_classification/_ordinal_tde.py b/aeon/classification/ordinal_classification/_ordinal_tde.py index de3394bec6..869770449c 100644 --- a/aeon/classification/ordinal_classification/_ordinal_tde.py +++ b/aeon/classification/ordinal_classification/_ordinal_tde.py @@ -592,7 +592,7 @@ class IndividualOrdinalTDE(BaseClassifier): bigrams : bool, default=False Whether to record word bigrams in the SFA transform. dim_threshold : float, default=0.85 - Accuracy threshold as a propotion of the highest accuracy dimension for words + Accuracy threshold as a proportion of the highest accuracy dimension for words extracted from each dimensions. Only applicable for multivariate data. max_dims : int, default=20 Maximum number of dimensions words are extracted from. Only applicable for diff --git a/aeon/classification/shapelet_based/_sast.py b/aeon/classification/shapelet_based/_sast.py index 3937b82333..a03ed48597 100644 --- a/aeon/classification/shapelet_based/_sast.py +++ b/aeon/classification/shapelet_based/_sast.py @@ -28,7 +28,7 @@ class SASTClassifier(BaseClassifier): ---------- length_list : int[], default = None an array containing the lengths of the subsequences to be generated. - If None, will be infered during fit as np.arange(3, X.shape[1]) + If None, will be inferred during fit as np.arange(3, X.shape[1]) stride : int, default = 1 the stride used when generating subsquences nb_inst_per_class : int default = 1 diff --git a/aeon/classification/sklearn/_continuous_interval_tree.py b/aeon/classification/sklearn/_continuous_interval_tree.py index d3ce9a9b89..457ad93efe 100644 --- a/aeon/classification/sklearn/_continuous_interval_tree.py +++ b/aeon/classification/sklearn/_continuous_interval_tree.py @@ -292,7 +292,7 @@ class ContinuousIntervalTree(ClassifierMixin, BaseEstimator): max_depth : int, default=sys.maxsize Maximum depth for the tree. thresholds : int, default=20 - Number of thresholds to split continous attributes on at tree nodes. + Number of thresholds to split continuous attributes on at tree nodes. random_state : int, RandomState instance or None, default=None If `int`, random_state is the seed used by the random number generator; If `RandomState` instance, random_state is the random number generator; diff --git a/aeon/classification/sklearn/_rotation_forest_classifier.py b/aeon/classification/sklearn/_rotation_forest_classifier.py index 25734cd751..1f742ec17e 100644 --- a/aeon/classification/sklearn/_rotation_forest_classifier.py +++ b/aeon/classification/sklearn/_rotation_forest_classifier.py @@ -33,7 +33,7 @@ class RotationForestClassifier(ClassifierMixin, BaseEstimator): of trees build on random portions of the data transformed using PCA. Intended as a benchmark for time series data and a base classifier for - transformation based appraoches such as ShapeletTransformClassifier, this aeon + transformation based approaches such as ShapeletTransformClassifier, this aeon implementation only works with continuous attributes. Parameters diff --git a/aeon/classification/tests/test_base.py b/aeon/classification/tests/test_base.py index 49782cab85..98159212ce 100644 --- a/aeon/classification/tests/test_base.py +++ b/aeon/classification/tests/test_base.py @@ -87,11 +87,11 @@ def test_incorrect_input(): dummy.fit(X, y) -def _assert_incorrect_X_input(dummy, correctX, correcty, X, y, msg): +def _assert_incorrect_X_input(dummy, correct_X, correct_y, X, y, msg): with pytest.raises(TypeError, match=msg): dummy.fit(X, y) - dummy.fit(correctX, correcty) + dummy.fit(correct_X, correct_y) with pytest.raises(TypeError, match=msg): dummy.predict(X) @@ -157,7 +157,7 @@ def test_univariate_equal_length_input(data): dummy = MockClassifier() _assert_fit_and_predict(dummy, X, y) - # All capabiltiies + # All capabilities dummy = MockClassifierFullTags() _assert_fit_and_predict(dummy, X, y) diff --git a/aeon/classification/tests/test_sklearn_compatability.py b/aeon/classification/tests/test_sklearn_compatability.py index 34db9d359b..b5df7a2b9d 100644 --- a/aeon/classification/tests/test_sklearn_compatability.py +++ b/aeon/classification/tests/test_sklearn_compatability.py @@ -1,4 +1,4 @@ -"""Unit tests for aeon classifier compatability with sklearn interfaces.""" +"""Unit tests for aeon classifier compatibility with sklearn interfaces.""" __maintainer__ = [] __all__ = [ diff --git a/aeon/clustering/_clarans.py b/aeon/clustering/_clarans.py index 1b4a2506bf..9764bae9ab 100644 --- a/aeon/clustering/_clarans.py +++ b/aeon/clustering/_clarans.py @@ -52,7 +52,7 @@ class TimeSeriesCLARANS(TimeSeriesKMedoids): for each set of medoids. A neighbouring solution is obtained by replacing one of the medoids with a non-medoid and seeing if total cost reduces. If not specified max_neighbours is set to 1.25% of the total number of possible - swaps (as suggested in the orginal paper). + swaps (as suggested in the original paper). n_init : int, default=5 Number of times the PAM algorithm will be run with different centroid seeds. The final result will be the best output of n_init diff --git a/aeon/clustering/_k_medoids.py b/aeon/clustering/_k_medoids.py index b2abe27aef..defbb2049a 100644 --- a/aeon/clustering/_k_medoids.py +++ b/aeon/clustering/_k_medoids.py @@ -33,7 +33,7 @@ class TimeSeriesKMedoids(BaseClusterer): PAM (Partition Around Medoids)[3]_ algorithm and is the default method used in this implementation. However, an adaptation of lloyds method classically used for k-means is also available by specifying method='alternate'. Alternate is faster but less - accurate than PAM. For a full review of varations of k-medoids for time series + accurate than PAM. For a full review of variations of k-medoids for time series see [5]_. K-medoids for time series uses a dissimilarity method to compute the distance diff --git a/aeon/clustering/deep_learning/_ae_dcnn.py b/aeon/clustering/deep_learning/_ae_dcnn.py index 19ac76d081..7ccf425dad 100644 --- a/aeon/clustering/deep_learning/_ae_dcnn.py +++ b/aeon/clustering/deep_learning/_ae_dcnn.py @@ -46,7 +46,7 @@ class AEDCNNClusterer(BaseDeepClusterer): Keras compatible Padding string for the decoder. Defaults to a list of "same" paddings. use_bias : bool or list of bool, default = True - Whether or not ot use bias in convolution. + Whether or not to use bias in convolution. n_epochs : int, default = 2000 The number of epochs to train the model. batch_size : int, default = 16 diff --git a/aeon/clustering/deep_learning/_ae_fcn.py b/aeon/clustering/deep_learning/_ae_fcn.py index 48c35f3dab..c2f0374968 100644 --- a/aeon/clustering/deep_learning/_ae_fcn.py +++ b/aeon/clustering/deep_learning/_ae_fcn.py @@ -44,7 +44,7 @@ class AEFCNClusterer(BaseDeepClusterer): activation : str or list of str, default = "relu" Activation used after the convolution. use_bias : bool or list of bool, default = True - Whether or not ot use bias in convolution. + Whether or not to use bias in convolution. n_epochs : int, default = 2000 The number of epochs to train the model. batch_size : int, default = 16 diff --git a/aeon/datasets/_data_writers.py b/aeon/datasets/_data_writers.py index 29ec83e648..dd277c6c96 100644 --- a/aeon/datasets/_data_writers.py +++ b/aeon/datasets/_data_writers.py @@ -35,7 +35,7 @@ def write_to_ts_file( header: string, default = None Optional text at the top of the file that is ignored when loading. regression: boolean, default = False - Indicate if this is a regression problem, so it is correcty specified in + Indicate if this is a regression problem, so it is correctly specified in the header since there is no definite way of inferring this from y """ if not (isinstance(X, np.ndarray) or isinstance(X, list)): @@ -139,7 +139,7 @@ def _write_header( file.write(f"@classLabel true {space_separated_class_label}\n") else: file.write("@classLabel false\n") - if regression: # or if a regresssion problem, write target label + if regression: # or if a regression problem, write target label file.write("@targetlabel true\n") file.write("@data\n") return file diff --git a/aeon/datasets/_single_problem_loaders.py b/aeon/datasets/_single_problem_loaders.py index cd4f6ea9b5..69ae780e99 100644 --- a/aeon/datasets/_single_problem_loaders.py +++ b/aeon/datasets/_single_problem_loaders.py @@ -836,7 +836,7 @@ def load_solar(return_array=True): """Get national solar estimates for GB from Sheffield Solar PV_Live API. This function calls the Sheffield Solar PV_Live API to extract national solar data - for the GB eletricity network. Note that these are estimates of the true solar + for the GB electricity network. Note that these are estimates of the true solar generation, since the true values are "behind the meter" and essentially unknown. diff --git a/aeon/datasets/_tss_data_loaders.py b/aeon/datasets/_tss_data_loaders.py index 9dcfd79bcc..da8864ae4c 100644 --- a/aeon/datasets/_tss_data_loaders.py +++ b/aeon/datasets/_tss_data_loaders.py @@ -42,7 +42,7 @@ def load_time_series_segmentation_benchmark( from one of the UEA & UCR time series classification datasets. TS are grouped by label and concatenated to create segments with distinctive temporal patterns and statistical properties. Offsets at which segments change are annotated as CPs. - Addtionally, resampling is applied to control the data resolution. Approximate, + Additionally, resampling is applied to control the data resolution. Approximate, hand-selected window sizes are provided that capture temporal patterns. If you do not specify ``extract_path``, it will set the path to diff --git a/aeon/datasets/data/ACSF1/ACSF1_TEST.ts b/aeon/datasets/data/ACSF1/ACSF1_TEST.ts index b5e282a66b..8e51e502c8 100644 --- a/aeon/datasets/data/ACSF1/ACSF1_TEST.ts +++ b/aeon/datasets/data/ACSF1/ACSF1_TEST.ts @@ -1,6 +1,6 @@ ## ACSF1 # -#The dataset is compiled from ACS-F1, the first version of the database of appliance consumption signatures. The dataset contains the power consumption of typical appliances. The recordings are characterized by long idle periods and some high bursts of enery consumption when the appliance is active. +#The dataset is compiled from ACS-F1, the first version of the database of appliance consumption signatures. The dataset contains the power consumption of typical appliances. The recordings are characterized by long idle periods and some high bursts of energy consumption when the appliance is active. # #The classes correspond to 10 categories of home appliances: mobile phones (via chargers), coffee machines, computer stations (including monitor), fridges and freezers, Hi-Fi systems (CD players), lamp (CFL), laptops (via chargers), microwave ovens, printers, and televisions (LCD or LED). # @@ -10,7 +10,7 @@ # #Missing value: No # -#Number of classses: 10 +#Number of classes: 10 # #Time series length: 1460 # diff --git a/aeon/datasets/data/ACSF1/ACSF1_TRAIN.ts b/aeon/datasets/data/ACSF1/ACSF1_TRAIN.ts index 5f8f0fbfad..24808e349f 100644 --- a/aeon/datasets/data/ACSF1/ACSF1_TRAIN.ts +++ b/aeon/datasets/data/ACSF1/ACSF1_TRAIN.ts @@ -1,6 +1,6 @@ ## ACSF1 # -#The dataset is compiled from ACS-F1, the first version of the database of appliance consumption signatures. The dataset contains the power consumption of typical appliances. The recordings are characterized by long idle periods and some high bursts of enery consumption when the appliance is active. +#The dataset is compiled from ACS-F1, the first version of the database of appliance consumption signatures. The dataset contains the power consumption of typical appliances. The recordings are characterized by long idle periods and some high bursts of energy consumption when the appliance is active. # #The classes correspond to 10 categories of home appliances: mobile phones (via chargers), coffee machines, computer stations (including monitor), fridges and freezers, Hi-Fi systems (CD players), lamp (CFL), laptops (via chargers), microwave ovens, printers, and televisions (LCD or LED). # @@ -10,7 +10,7 @@ # #Missing value: No # -#Number of classses: 10 +#Number of classes: 10 # #Time series length: 1460 # diff --git a/aeon/datasets/data/JapaneseVowels/JapaneseVowels_TEST.ts b/aeon/datasets/data/JapaneseVowels/JapaneseVowels_TEST.ts index adc85cb0db..9c46401a48 100644 --- a/aeon/datasets/data/JapaneseVowels/JapaneseVowels_TEST.ts +++ b/aeon/datasets/data/JapaneseVowels/JapaneseVowels_TEST.ts @@ -4,7 +4,7 @@ # #9 Japanese-male speakers were recorded saying the vowels 'a' and 'e'. A '12-degree linear prediction analysis' is applied to the raw recordings to obtain time-series with 12 dimensions and series lengths between 7 and 29. The classification task is to predict the speaker. Therefore, each instance is a transformed utterance, 12*29 values with a single class label attached, [1...9]. # -#The given training set is comprised of 30 utterances for each speaker, however the test set has a varied distribution based on external factors of timing and experimenal availability, between 24 and 88 instances per speaker. +#The given training set is comprised of 30 utterances for each speaker, however the test set has a varied distribution based on external factors of timing and experimental availability, between 24 and 88 instances per speaker. @problemName JapaneseVowels @timeStamps false @missing false diff --git a/aeon/datasets/data/JapaneseVowels/JapaneseVowels_TRAIN.ts b/aeon/datasets/data/JapaneseVowels/JapaneseVowels_TRAIN.ts index 31c385cec2..e50606ea9c 100644 --- a/aeon/datasets/data/JapaneseVowels/JapaneseVowels_TRAIN.ts +++ b/aeon/datasets/data/JapaneseVowels/JapaneseVowels_TRAIN.ts @@ -4,7 +4,7 @@ # #9 Japanese-male speakers were recorded saying the vowels 'a' and 'e'. A '12-degree linear prediction analysis' is applied to the raw recordings to obtain time-series with 12 dimensions and series lengths between 7 and 29. The classification task is to predict the speaker. Therefore, each instance is a transformed utterance, 12*29 values with a single class label attached, [1...9]. # -#The given training set is comprised of 30 utterances for each speaker, however the test set has a varied distribution based on external factors of timing and experimenal availability, between 24 and 88 instances per speaker. +#The given training set is comprised of 30 utterances for each speaker, however the test set has a varied distribution based on external factors of timing and experimental availability, between 24 and 88 instances per speaker. @problemName JapaneseVowels @timeStamps false @missing false diff --git a/aeon/datasets/data/PLAID/PLAID_TEST.arff b/aeon/datasets/data/PLAID/PLAID_TEST.arff index e7f1b0c887..f745b1acb0 100644 --- a/aeon/datasets/data/PLAID/PLAID_TEST.arff +++ b/aeon/datasets/data/PLAID/PLAID_TEST.arff @@ -4,11 +4,11 @@ %This dataset comes from PLAID1. It includes current and voltage measurements sampled at 30 kHz from 11 different appliance types present in more than 56 households in Pittsburgh, Pennsylvania, USA. Data collection took place during the summer of 2013. Each appliance type is represented by dozens of different instances of varying makes/models. %For each appliance, three to six measurements were collected for each state transition. These measurements were then post-processed to extract a few-second-long window containing both the steady-state operation and the startup transient )when available). %The classes correspond to 11 different appliance types: -% air conditioner (class 0), compact flourescent lamp, fan, fridge, hairdryer , heater, incandescent light bulb, laptop, microwave, vacuum,washing machine (class 10) +% air conditioner (class 0), compact fluorescent lamp, fan, fridge, hairdryer , heater, incandescent light bulb, laptop, microwave, vacuum,washing machine (class 10) %Train size: 537 %Test size: 537 %Missing value: No -%Number of classses: 11 +%Number of classes: 11 %Time series length: Vary % %We pad NaN to the end of each time series to the length of the longest time series. diff --git a/aeon/datasets/data/PLAID/PLAID_TEST.ts b/aeon/datasets/data/PLAID/PLAID_TEST.ts index 3d972f31bd..daec5296dd 100644 --- a/aeon/datasets/data/PLAID/PLAID_TEST.ts +++ b/aeon/datasets/data/PLAID/PLAID_TEST.ts @@ -4,13 +4,13 @@ #This dataset comes from PLAID1. It includes current and voltage measurements sampled at 30 kHz from 11 different appliance types present in more than 56 households in Pittsburgh, Pennsylvania, USA. Data collection took place during the summer of 2013. Each appliance type is represented by dozens of different instances of varying makes/models. #For each appliance, three to six measurements were collected for each state transition. These measurements were then post-processed to extract a few-second-long window containing both the steady-state operation and the startup transient )when available). #The classes correspond to 11 different appliance types: % air -#conditioner (class 0), compact flourescent lamp, fan, fridge, +#conditioner (class 0), compact fluorescent lamp, fan, fridge, #hairdryer , heater, incandescent light bulb, laptop, microwave, #vacuum,washing machine (class 10)% #Train size: 537 #Test size: 537 #Missing value: No -#Number of classses: 11 +#Number of classes: 11 # #Time series length: Vary #Data created by Gao, Jingkun, et al. (see [1], [2], [3]). Data edited by Patrick Schafer and Ulf Leser (see [4]). diff --git a/aeon/datasets/data/PLAID/PLAID_TRAIN.arff b/aeon/datasets/data/PLAID/PLAID_TRAIN.arff index 88524043d4..34a9d1fc03 100644 --- a/aeon/datasets/data/PLAID/PLAID_TRAIN.arff +++ b/aeon/datasets/data/PLAID/PLAID_TRAIN.arff @@ -3,11 +3,11 @@ %This dataset comes from PLAID1. It includes current and voltage measurements sampled at 30 kHz from 11 different appliance types present in more than 56 households in Pittsburgh, Pennsylvania, USA. Data collection took place during the summer of 2013. Each appliance type is represented by dozens of different instances of varying makes/models. %For each appliance, three to six measurements were collected for each state transition. These measurements were then post-processed to extract a few-second-long window containing both the steady-state operation and the startup transient )when available). %The classes correspond to 11 different appliance types: -%air conditioner (class 0), compact flourescent lamp, fan, fridge, hairdryer , heater, incandescent light bulb, laptop, microwave, vacuum,washing machine (class 10) +%air conditioner (class 0), compact fluorescent lamp, fan, fridge, hairdryer , heater, incandescent light bulb, laptop, microwave, vacuum,washing machine (class 10) %Train size: 537 %Test size: 537 %Missing value: No -%Number of classses: 11 +%Number of classes: 11 %Time series length: Vary % %We pad NaN to the end of each time series to the length of the longest time series for ARFF format, leave as it is for TS. diff --git a/aeon/datasets/data/PLAID/PLAID_TRAIN.ts b/aeon/datasets/data/PLAID/PLAID_TRAIN.ts index 72c1851bbc..857af81dca 100644 --- a/aeon/datasets/data/PLAID/PLAID_TRAIN.ts +++ b/aeon/datasets/data/PLAID/PLAID_TRAIN.ts @@ -4,14 +4,14 @@ #This dataset comes from PLAID1. It includes current and voltage measurements sampled at 30 kHz from 11 different appliance types present in more than 56 households in Pittsburgh, Pennsylvania, USA. Data collection took place during the summer of 2013. Each appliance type is represented by dozens of different instances of varying makes/models. #For each appliance, three to six measurements were collected for each state transition. These measurements were then post-processed to extract a few-second-long window containing both the steady-state operation and the startup transient )when available). #The classes correspond to 11 different appliance types: % air -#conditioner (class 0), compact flourescent lamp, fan, fridge, +#conditioner (class 0), compact fluorescent lamp, fan, fridge, #hairdryer , heater, incandescent light bulb, laptop, microwave, #vacuum,washing machine (class 10)% # #Train size: 537 #Test size: 537 #Missing value: No -#Number of classses: 11 +#Number of classes: 11 #Time series length: Vary #Data created by Gao, Jingkun, et al. (see [1], [2], [3]). Data edited by Patrick Schafer and Ulf Leser (see [4]). # diff --git a/aeon/datasets/data/PickupGestureWiimoteZ/PickupGestureWiimoteZ_TEST.ts b/aeon/datasets/data/PickupGestureWiimoteZ/PickupGestureWiimoteZ_TEST.ts index 680161f615..68af47f4ff 100644 --- a/aeon/datasets/data/PickupGestureWiimoteZ/PickupGestureWiimoteZ_TEST.ts +++ b/aeon/datasets/data/PickupGestureWiimoteZ/PickupGestureWiimoteZ_TEST.ts @@ -26,7 +26,7 @@ # #- Missing value: No # -#- Number of classses: 10 +#- Number of classes: 10 # #- Time series length: Vary # @@ -42,7 +42,7 @@ # #- Missing value: No # -#- Number of classses: 10 +#- Number of classes: 10 # #- Time series length: Vary # @@ -58,7 +58,7 @@ # #- Missing value: No # -#- Number of classses: 10 +#- Number of classes: 10 # #- Time series length: Vary # @@ -74,7 +74,7 @@ # #- Missing value: No # -#- Number of classses: 10 +#- Number of classes: 10 # #- Time series length: Vary # @@ -90,7 +90,7 @@ # #- Missing value: No # -#- Number of classses: 10 +#- Number of classes: 10 # #- Time series length: Vary # @@ -98,7 +98,7 @@ # #Each time series is padded with NaN to the length of the longest time series in the dataset. # -#Data are shuffled and randomly sampled so that instances across datasets do not sychronized by dimension or subject. +#Data are shuffled and randomly sampled so that instances across datasets do not synchronize by dimension or subject. # #Data created by Guna Jože et al. (see [1]). Data edited by Hoang Anh Dau. # diff --git a/aeon/datasets/data/PickupGestureWiimoteZ/PickupGestureWiimoteZ_TRAIN.ts b/aeon/datasets/data/PickupGestureWiimoteZ/PickupGestureWiimoteZ_TRAIN.ts index b1e5adfd8c..5c27e52124 100644 --- a/aeon/datasets/data/PickupGestureWiimoteZ/PickupGestureWiimoteZ_TRAIN.ts +++ b/aeon/datasets/data/PickupGestureWiimoteZ/PickupGestureWiimoteZ_TRAIN.ts @@ -26,7 +26,7 @@ # #- Missing value: No # -#- Number of classses: 10 +#- Number of classes: 10 # #- Time series length: Vary # @@ -42,7 +42,7 @@ # #- Missing value: No # -#- Number of classses: 10 +#- Number of classes: 10 # #- Time series length: Vary # @@ -58,7 +58,7 @@ # #- Missing value: No # -#- Number of classses: 10 +#- Number of classes: 10 # #- Time series length: Vary # @@ -74,7 +74,7 @@ # #- Missing value: No # -#- Number of classses: 10 +#- Number of classes: 10 # #- Time series length: Vary # @@ -90,7 +90,7 @@ # #- Missing value: No # -#- Number of classses: 10 +#- Number of classes: 10 # #- Time series length: Vary # @@ -98,7 +98,7 @@ # #Each time series is padded with NaN to the length of the longest time series in the dataset. # -#Data are shuffled and randomly sampled so that instances across datasets do not sychronized by dimension or subject. +#Data are shuffled and randomly sampled so that instances across datasets do not synchronize by dimension or subject. # #Data created by Guna Jože et al. (see [1]). Data edited by Hoang Anh Dau. # diff --git a/aeon/datasets/data/m1_yearly_dataset/m1_yearly_dataset.tsf b/aeon/datasets/data/m1_yearly_dataset/m1_yearly_dataset.tsf index 7a26deb2e0..d9e2866f99 100644 --- a/aeon/datasets/data/m1_yearly_dataset/m1_yearly_dataset.tsf +++ b/aeon/datasets/data/m1_yearly_dataset/m1_yearly_dataset.tsf @@ -4,7 +4,7 @@ # The last 2 digits of the starting year are missing for the 51st series and they have been replaced by zeros. # # For more details, please refer to -# Makridakis, S., Andersen, A., Carbone, R. F., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Parzen,E., Winkler, R. L., 1982. The accuracy of extrapolation (time series) methods: Results of a forecasting competition. Journal of Forecasting 1 (2), 111�153. +# Makridakis, S., Andersen, A., Carbone, R. F., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Parzen,E., Winkler, R. L., 1982. The accuracy of extrapolation (time series) methods: Results of a forecasting competition. Journal of Forecasting 1 (2), 111–153. # @relation M1 @attribute series_name string diff --git a/aeon/distances/_distance.py b/aeon/distances/_distance.py index 726eb2e836..719252a7ed 100644 --- a/aeon/distances/_distance.py +++ b/aeon/distances/_distance.py @@ -198,7 +198,7 @@ def pairwise_distance( :func:`aeon.distances.get_distance_function` or by calling the function :func:`aeon.distances.get_distance_function_names`. symmetric : bool, default=True - If True and a function is provided as the "method" paramter, then it will + If True and a function is provided as the "method" parameter, then it will compute a symmetric distance matrix where d(x, y) = d(y, x). Only the lower triangle is calculated, and the upper triangle is ignored. If False and a function is provided as the "method" parameter, then it will compute an @@ -380,7 +380,7 @@ def alignment_path( of the index in x and the index in y that have the best alignment according to the cost matrix. float - The dtw distance betweeen the two time series. + The dtw distance between the two time series. Raises ------ @@ -594,7 +594,7 @@ def get_pairwise_distance_function( Raises ------ ValueError - If mehtod is not one of the supported strings or a callable. + If method is not one of the supported strings or a callable. Examples -------- diff --git a/aeon/distances/_sbd.py b/aeon/distances/_sbd.py index 1e72d4eca6..c0fd9c0120 100644 --- a/aeon/distances/_sbd.py +++ b/aeon/distances/_sbd.py @@ -28,7 +28,7 @@ def sbd_distance(x: np.ndarray, y: np.ndarray, standardize: bool = True) -> floa x_n\}` and :math:`\mathbf{y}=\{y_1,y_2, \ldots,y_m\}`, SBD works by (optionally) first standardizing both time series using the z-score (:math:`x' = \frac{x - \mu}{\sigma}`), then computing the cross-correlation - between x and y (:math:`CC(\mathbf{x}, \mathbf{y})`), then deviding it by the + between x and y (:math:`CC(\mathbf{x}, \mathbf{y})`), then dividing it by the geometric mean of both autocorrelations of the individual sequences to normalize it to :math:`[-1, 1]` (coefficient normalization), and finally detecting the position with the maximum normalized cross-correlation: diff --git a/aeon/distances/elastic/_adtw.py b/aeon/distances/elastic/_adtw.py index f061f479ff..72d2a5be2d 100644 --- a/aeon/distances/elastic/_adtw.py +++ b/aeon/distances/elastic/_adtw.py @@ -387,7 +387,7 @@ def adtw_alignment_path( of the index in x and the index in y that have the best alignment according to the cost matrix. float - The ADTW distance betweeen the two time series. + The ADTW distance between the two time series. Raises ------ diff --git a/aeon/distances/elastic/_ddtw.py b/aeon/distances/elastic/_ddtw.py index 559b2a3d90..f7741f679e 100644 --- a/aeon/distances/elastic/_ddtw.py +++ b/aeon/distances/elastic/_ddtw.py @@ -364,7 +364,7 @@ def ddtw_alignment_path( of the index in x and the index in y that have the best alignment according to the cost matrix. float - The ddtw distance betweeen the two time series. + The ddtw distance between the two time series. Raises ------ diff --git a/aeon/distances/elastic/_dtw.py b/aeon/distances/elastic/_dtw.py index 26add187dd..ddf552cb1f 100644 --- a/aeon/distances/elastic/_dtw.py +++ b/aeon/distances/elastic/_dtw.py @@ -422,7 +422,7 @@ def dtw_alignment_path( of the index in x and the index in y that have the best alignment according to the cost matrix. float - The DTW distance betweeen the two time series. + The DTW distance between the two time series. Raises ------ diff --git a/aeon/distances/elastic/_dtw_gi.py b/aeon/distances/elastic/_dtw_gi.py index bff33e343e..f757eaa202 100644 --- a/aeon/distances/elastic/_dtw_gi.py +++ b/aeon/distances/elastic/_dtw_gi.py @@ -523,7 +523,7 @@ def dtw_gi_alignment_path( of the index in x and the index in y that have the best alignment according to the cost matrix. float - The DTW_GI distance betweeen the two time series. + The DTW_GI distance between the two time series. Raises ------ diff --git a/aeon/distances/elastic/_erp.py b/aeon/distances/elastic/_erp.py index b1a7071a7b..f5feffe6a2 100644 --- a/aeon/distances/elastic/_erp.py +++ b/aeon/distances/elastic/_erp.py @@ -133,7 +133,7 @@ def erp_cost_matrix( window : float, default=None The window to use for the bounding matrix. If None, no bounding matrix is used. - g : float, defualt=0.0 + g : float, default=0.0 The reference value to penalise gaps. The default is 0. g_arr : np.ndarray, of shape (n_channels), default=None Numpy array that must be the length of the number of channels in x and y. @@ -452,7 +452,7 @@ def erp_alignment_path( of the index in x and the index in y that have the best alignment according to the cost matrix. float - The erp distance betweeen the two time series. + The erp distance between the two time series. Raises ------ diff --git a/aeon/distances/elastic/_msm.py b/aeon/distances/elastic/_msm.py index f75ab5daf8..45f31a8c5a 100644 --- a/aeon/distances/elastic/_msm.py +++ b/aeon/distances/elastic/_msm.py @@ -541,7 +541,7 @@ def msm_alignment_path( of the index in x and the index in y that have the best alignment according to the cost matrix. float - The msm distance betweeen the two time series. + The msm distance between the two time series. Raises ------ diff --git a/aeon/distances/elastic/_shape_dtw.py b/aeon/distances/elastic/_shape_dtw.py index 894db087c3..4d5c4b1310 100644 --- a/aeon/distances/elastic/_shape_dtw.py +++ b/aeon/distances/elastic/_shape_dtw.py @@ -97,7 +97,7 @@ def _transform_subsequences( sliding_window = reach * 2 + 1 sliding_window = int(sliding_window) - # get the output dimension of the subsequence transofrmation s + # get the output dimension of the subsequence transformation dim_desc = descriptor_function(x[0, 0 : 0 + sliding_window]).shape[0] # dim_desc = 7 diff --git a/aeon/distances/elastic/_soft_dtw.py b/aeon/distances/elastic/_soft_dtw.py index 861b9cf428..be977c4bef 100644 --- a/aeon/distances/elastic/_soft_dtw.py +++ b/aeon/distances/elastic/_soft_dtw.py @@ -431,7 +431,7 @@ def soft_dtw_alignment_path( of the index in x and the index in y that have the best alignment according to the cost matrix. float - The soft-DTW distance betweeen the two time series. + The soft-DTW distance between the two time series. Raises ------ diff --git a/aeon/distances/elastic/_twe.py b/aeon/distances/elastic/_twe.py index 077172e0a2..db5a69b817 100644 --- a/aeon/distances/elastic/_twe.py +++ b/aeon/distances/elastic/_twe.py @@ -454,7 +454,7 @@ def twe_alignment_path( of the index in x and the index in y that have the best alignment according to the cost matrix. float - The twe distance betweeen the two time series. + The twe distance between the two time series. Raises ------ diff --git a/aeon/distances/elastic/_wddtw.py b/aeon/distances/elastic/_wddtw.py index bf95b1c4d1..c3967f1299 100644 --- a/aeon/distances/elastic/_wddtw.py +++ b/aeon/distances/elastic/_wddtw.py @@ -370,7 +370,7 @@ def wddtw_alignment_path( of the index in x and the index in y that have the best alignment according to the cost matrix. float - The wddtw distance betweeen the two time series. + The wddtw distance between the two time series. Raises ------ diff --git a/aeon/distances/elastic/_wdtw.py b/aeon/distances/elastic/_wdtw.py index 3573cedfc3..0923d9aee5 100644 --- a/aeon/distances/elastic/_wdtw.py +++ b/aeon/distances/elastic/_wdtw.py @@ -422,7 +422,7 @@ def wdtw_alignment_path( of the index in x and the index in y that have the best alignment according to the cost matrix. float - The WDTW distance betweeen the two time series. + The WDTW distance between the two time series. Raises ------ diff --git a/aeon/distances/elastic/tests/test_alignment_path.py b/aeon/distances/elastic/tests/test_alignment_path.py index b5f6c5cc49..3368bf7bcc 100644 --- a/aeon/distances/elastic/tests/test_alignment_path.py +++ b/aeon/distances/elastic/tests/test_alignment_path.py @@ -118,7 +118,7 @@ def test_alignment_path(dist): dist["name"] not in SINGLE_POINT_NOT_SUPPORTED_DISTANCES and dist["name"] != "lcss" ): - # Test singe point univariate of shape (1,) + # Test single point univariate of shape (1,) _validate_alignment_path_result( np.array([10.0]), np.array([15.0]), @@ -127,7 +127,7 @@ def test_alignment_path(dist): dist["alignment_path"], ) - # Test singe point univariate of shape (1, 1) + # Test single point univariate of shape (1, 1) _validate_alignment_path_result( np.array([[10.0]]), np.array([[15.0]]), diff --git a/aeon/distances/elastic/tests/test_cost_matrix.py b/aeon/distances/elastic/tests/test_cost_matrix.py index 36c75d0c02..1241e7f390 100644 --- a/aeon/distances/elastic/tests/test_cost_matrix.py +++ b/aeon/distances/elastic/tests/test_cost_matrix.py @@ -148,7 +148,7 @@ def test_cost_matrix(dist): # ============== Test single point series ============== if dist["name"] not in SINGLE_POINT_NOT_SUPPORTED_DISTANCES: - # Test singe point univariate of shape (1,) + # Test single point univariate of shape (1,) _validate_cost_matrix_result( np.array([10.0]), np.array([15.0]), @@ -157,7 +157,7 @@ def test_cost_matrix(dist): dist["cost_matrix"], ) - # Test singe point univariate of shape (1, 1) + # Test single point univariate of shape (1, 1) _validate_cost_matrix_result( np.array([[10.0]]), np.array([[15.0]]), diff --git a/aeon/distances/tests/test_distances.py b/aeon/distances/tests/test_distances.py index 4efe396ab2..0fab538888 100644 --- a/aeon/distances/tests/test_distances.py +++ b/aeon/distances/tests/test_distances.py @@ -142,7 +142,7 @@ def test_distances(dist): # ============== Test single point series ============== if dist["name"] not in SINGLE_POINT_NOT_SUPPORTED_DISTANCES: - # Test singe point univariate of shape (1,) + # Test single point univariate of shape (1,) _validate_distance_result( np.array([10.0]), np.array([15.0]), @@ -152,7 +152,7 @@ def test_distances(dist): _expected_distance_results[dist["name"]][4], ) - # Test singe point univariate of shape (1, 1) + # Test single point univariate of shape (1, 1) _validate_distance_result( np.array([[10.0]]), np.array([[15.0]]), diff --git a/aeon/distances/tests/test_pairwise.py b/aeon/distances/tests/test_pairwise.py index 14c604f8c2..8f20670de9 100644 --- a/aeon/distances/tests/test_pairwise.py +++ b/aeon/distances/tests/test_pairwise.py @@ -298,7 +298,7 @@ def test_pairwise_distance(dist): # ============== Test single point series ============== if dist["name"] not in SINGLE_POINT_NOT_SUPPORTED_DISTANCES: - # Test singe point univariate of shape (1, 1) + # Test single point univariate of shape (1, 1) _validate_pairwise_result( np.array([[10.0]]), dist["name"], @@ -399,7 +399,7 @@ def test_multiple_to_multiple_distances(dist): # ============== Test single point series ============== if dist["name"] not in SINGLE_POINT_NOT_SUPPORTED_DISTANCES: - # Test singe point univariate of shape (1,) + # Test single point univariate of shape (1,) _validate_multiple_to_multiple_result( np.array([10.0]), np.array([15.0]), @@ -408,7 +408,7 @@ def test_multiple_to_multiple_distances(dist): dist["pairwise_distance"], ) - # Test singe point univariate of shape (1, 1) + # Test single point univariate of shape (1, 1) _validate_multiple_to_multiple_result( np.array([[10.0]]), np.array([[15.0]]), @@ -514,7 +514,7 @@ def test_single_to_multiple_distances(dist): # ============== Test single point series ============== if dist["name"] not in SINGLE_POINT_NOT_SUPPORTED_DISTANCES: - # Test singe point univariate of shape (1,) compared to a collection of a + # Test single point univariate of shape (1,) compared to a collection of a # single univariate time series in the shape (n_cases, 1) _validate_single_to_multiple_result( np.array([10.0]), @@ -524,7 +524,7 @@ def test_single_to_multiple_distances(dist): dist["pairwise_distance"], ) - # Test singe point univariate of shape (1, 1) compared to a collection of a + # Test single point univariate of shape (1, 1) compared to a collection of a # single univariate time series in the shape (n_cases, 1, 1) _validate_single_to_multiple_result( np.array([[10.0]]), diff --git a/aeon/forecasting/_ets.py b/aeon/forecasting/_ets.py index 2d3eac898b..f6ec85fcaf 100644 --- a/aeon/forecasting/_ets.py +++ b/aeon/forecasting/_ets.py @@ -474,7 +474,7 @@ def _predict_value(trend_type, seasonality_type, level, trend, seasonality, phi) fitted_value : float single prediction based on the current state variables. damped_trend : float - The damping parameter combined with the trend dependant on the model type + The damping parameter combined with the trend dependent on the model type trend_level_combination : float Combination of the trend and level based on the model type. """ diff --git a/aeon/networks/_ae_fcn.py b/aeon/networks/_ae_fcn.py index 947f275355..09ac8a0b55 100644 --- a/aeon/networks/_ae_fcn.py +++ b/aeon/networks/_ae_fcn.py @@ -34,7 +34,7 @@ class AEFCNNetwork(BaseDeepLearningNetwork): activation : str or list of str, default = "relu" Activation used after the convolution. use_bias : bool or list of bool, default = True - Whether or not ot use bias in convolution. + Whether or not to use bias in convolution. Notes ----- diff --git a/aeon/networks/_ae_resnet.py b/aeon/networks/_ae_resnet.py index 7a207fd2eb..d72f663bc5 100644 --- a/aeon/networks/_ae_resnet.py +++ b/aeon/networks/_ae_resnet.py @@ -51,7 +51,7 @@ class AEResNetNetwork(BaseDeepLearningNetwork): Notes ----- - Adpated from the implementation source code + Adapted from the implementation source code https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/resnet.py References diff --git a/aeon/networks/_disjoint_cnn.py b/aeon/networks/_disjoint_cnn.py index 23da64a1a8..7f80109735 100644 --- a/aeon/networks/_disjoint_cnn.py +++ b/aeon/networks/_disjoint_cnn.py @@ -19,42 +19,42 @@ class DisjointCNNNetwork(BaseDeepLearningNetwork): Number of 1+1D Convolution layers. n_filters : int or list of int, default = 64 Number of filters used in convolution layers. If - input is set to a list, the lenght should be the same + input is set to a list, the length should be the same as `n_layers`, if input is int the a list of the same element is created of length `n_layers`. kernel_size : int or list of int, default = [8, 5, 5, 3] Size of convolution kernel. If - input is set to a list, the lenght should be the same + input is set to a list, the length should be the same as `n_layers`, if input is int the a list of the same element is created of length `n_layers`. dilation_rate : int or list of int, default = 1 The dilation rate for convolution. If - input is set to a list, the lenght should be the same + input is set to a list, the length should be the same as `n_layers`, if input is int the a list of the same element is created of length `n_layers`. strides : int or list of int, default = 1 The strides of the convolution filter. If - input is set to a list, the lenght should be the same + input is set to a list, the length should be the same as `n_layers`, if input is int the a list of the same element is created of length `n_layers`. padding : str or list of str, default = "same" The type of padding used for convolution. If - input is set to a list, the lenght should be the same + input is set to a list, the length should be the same as `n_layers`, if input is int the a list of the same element is created of length `n_layers`. activation : str or list of str, default = "elu" Activation used after the convolution. If - input is set to a list, the lenght should be the same + input is set to a list, the length should be the same as `n_layers`, if input is int the a list of the same element is created of length `n_layers`. use_bias : bool or list of bool, default = True - Whether or not ot use bias in convolution. If - input is set to a list, the lenght should be the same + Whether or not to use bias in convolution. If + input is set to a list, the length should be the same as `n_layers`, if input is int the a list of the same element is created of length `n_layers`. kernel_initializer: str or list of str, default = "he_uniform" The initialization method of convolution layers. If - input is set to a list, the lenght should be the same + input is set to a list, the length should be the same as `n_layers`, if input is int the a list of the same element is created of length `n_layers`. pool_size: int, default = 5 diff --git a/aeon/networks/_fcn.py b/aeon/networks/_fcn.py index 9c1f00504c..4f5189887e 100644 --- a/aeon/networks/_fcn.py +++ b/aeon/networks/_fcn.py @@ -29,7 +29,7 @@ class FCNNetwork(BaseDeepLearningNetwork): activation : str or list of str, default = "relu" Activation used after the convolution. use_bias : bool or list of bool, default = True - Whether or not ot use bias in convolution. + Whether or not to use bias in convolution. Notes ----- diff --git a/aeon/networks/_inception.py b/aeon/networks/_inception.py index d7768a361f..b58c08f233 100644 --- a/aeon/networks/_inception.py +++ b/aeon/networks/_inception.py @@ -133,7 +133,7 @@ def __init__( super().__init__() def hybrid_layer(self, input_tensor, input_channels, kernel_sizes=None): - """Construct the hybrid layer to compute features of cutom filters. + """Construct the hybrid layer to compute features of custom filters. Parameters ---------- @@ -272,7 +272,7 @@ def hybrid_layer(self, input_tensor, input_channels, kernel_sizes=None): hybrid_layer = tf.keras.layers.Concatenate(axis=2)( conv_list - ) # concantenate all convolution layers + ) # concatenate all convolution layers hybrid_layer = tf.keras.layers.Activation(activation="relu")( hybrid_layer ) # apply activation ReLU diff --git a/aeon/networks/_lite.py b/aeon/networks/_lite.py index caa3fe4c47..fcbe59da0a 100644 --- a/aeon/networks/_lite.py +++ b/aeon/networks/_lite.py @@ -42,7 +42,7 @@ class LITENetwork(BaseDeepLearningNetwork): References ---------- ..[1] Ismail-Fawaz et al. LITE: Light Inception with boosTing - tEchniques for Time Series Classificaion, IEEE International + tEchniques for Time Series Classification, IEEE International Conference on Data Science and Advanced Analytics, 2023. ..[2] Ismail-Fawaz, Ali, et al. "Look Into the LITE @@ -275,7 +275,7 @@ def hybrid_layer(self, input_tensor, input_channels, kernel_sizes=None): hybrid_layer = tf.keras.layers.Concatenate(axis=2)( conv_list - ) # concantenate all convolution layers + ) # concatenate all convolution layers hybrid_layer = tf.keras.layers.Activation(activation="relu")( hybrid_layer ) # apply activation ReLU diff --git a/aeon/networks/_resnet.py b/aeon/networks/_resnet.py index 4dd5eb7d9c..4315c8461a 100644 --- a/aeon/networks/_resnet.py +++ b/aeon/networks/_resnet.py @@ -45,7 +45,7 @@ class ResNetNetwork(BaseDeepLearningNetwork): Notes ----- - Adpated from the implementation source code + Adapted from the implementation source code https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/resnet.py References diff --git a/aeon/networks/tests/test_ae_resnet.py b/aeon/networks/tests/test_ae_resnet.py index b33198fd19..1ffaed96db 100644 --- a/aeon/networks/tests/test_ae_resnet.py +++ b/aeon/networks/tests/test_ae_resnet.py @@ -22,13 +22,13 @@ def test_ae_res_unit_activation( latent_space_dim, n_residual_blocks, activation, n_filters ): """Test whether AEResNetNetwork initializes correctly with different activations.""" - aer = AEResNetNetwork( + aeres = AEResNetNetwork( latent_space_dim=latent_space_dim, n_residual_blocks=n_residual_blocks, activation=activation, n_filters=n_filters, ) - encoder, decoder = aer.build_network((1000, 5)) + encoder, decoder = aeres.build_network((1000, 5)) assert encoder is not None assert decoder is not None @@ -49,12 +49,12 @@ def test_ae_res_unit_activation( ) def test_use_bias(use_bias, n_conv_per_residual_block): """Test AEResNetNetwork use_bias configurations.""" - aer = AEResNetNetwork( + aeres = AEResNetNetwork( latent_space_dim=128, use_bias=use_bias, n_conv_per_residual_block=n_conv_per_residual_block, ) - encoder, decoder = aer.build_network((1000, 5)) + encoder, decoder = aeres.build_network((1000, 5)) assert encoder is not None assert decoder is not None @@ -75,12 +75,12 @@ def test_use_bias(use_bias, n_conv_per_residual_block): ) def test_n_filters(n_filters, n_residual_blocks): """Test AEResNetNetwork n_filters configurations.""" - aer = AEResNetNetwork( + aeres = AEResNetNetwork( latent_space_dim=128, n_filters=n_filters, n_residual_blocks=n_residual_blocks, ) - encoder, decoder = aer.build_network((1000, 5)) + encoder, decoder = aeres.build_network((1000, 5)) assert encoder is not None assert decoder is not None @@ -101,12 +101,12 @@ def test_n_filters(n_filters, n_residual_blocks): ) def test_kernel_size(kernel_size, n_conv_per_residual_block): """Test AEResNetNetwork kernel_size configurations.""" - aer = AEResNetNetwork( + aeres = AEResNetNetwork( latent_space_dim=128, kernel_size=kernel_size, n_conv_per_residual_block=n_conv_per_residual_block, ) - encoder, decoder = aer.build_network((1000, 5)) + encoder, decoder = aeres.build_network((1000, 5)) assert encoder is not None assert decoder is not None @@ -126,12 +126,12 @@ def test_kernel_size(kernel_size, n_conv_per_residual_block): ) def test_strides(strides, n_conv_per_residual_block): """Test AEResNetNetwork strides configurations.""" - aer = AEResNetNetwork( + aeres = AEResNetNetwork( latent_space_dim=128, strides=strides, n_conv_per_residual_block=n_conv_per_residual_block, ) - encoder, decoder = aer.build_network((1000, 5)) + encoder, decoder = aeres.build_network((1000, 5)) assert encoder is not None assert decoder is not None @@ -151,12 +151,12 @@ def test_strides(strides, n_conv_per_residual_block): ) def test_dilation_rate(dilation_rate, n_conv_per_residual_block): """Test AEResNetNetwork dilation_rate configurations.""" - aer = AEResNetNetwork( + aeres = AEResNetNetwork( latent_space_dim=128, dilation_rate=dilation_rate, n_conv_per_residual_block=n_conv_per_residual_block, ) - encoder, decoder = aer.build_network((1000, 5)) + encoder, decoder = aeres.build_network((1000, 5)) assert encoder is not None assert decoder is not None @@ -177,11 +177,11 @@ def test_dilation_rate(dilation_rate, n_conv_per_residual_block): ) def test_padding(padding, n_conv_per_residual_block): """Test AEResNetNetwork padding configurations.""" - aer = AEResNetNetwork( + aeres = AEResNetNetwork( latent_space_dim=128, padding=padding, n_conv_per_residual_block=n_conv_per_residual_block, ) - encoder, decoder = aer.build_network((1000, 5)) + encoder, decoder = aeres.build_network((1000, 5)) assert encoder is not None assert decoder is not None diff --git a/aeon/regression/compose/_ensemble.py b/aeon/regression/compose/_ensemble.py index 2b0333ef15..d8e575d68d 100644 --- a/aeon/regression/compose/_ensemble.py +++ b/aeon/regression/compose/_ensemble.py @@ -33,7 +33,7 @@ class RegressorEnsemble(BaseCollectionEnsemble, BaseRegressor): Only used if weights is a float. The method used to generate a performance estimation from the training data set i.e. cross-validation. If None, predictions are made using that estimators fit_predict or - fit_predict_proba methods. These are somtimes overridden for efficient + fit_predict_proba methods. These are sometimes overridden for efficient performance evaluations, i.e. out-of-bag predictions. If int or sklearn object input, the parameter is passed directly to the cv parameter of the cross_val_predict function from sklearn. diff --git a/aeon/regression/deep_learning/_disjoint_cnn.py b/aeon/regression/deep_learning/_disjoint_cnn.py index ac5e61d202..ae5b45cfec 100644 --- a/aeon/regression/deep_learning/_disjoint_cnn.py +++ b/aeon/regression/deep_learning/_disjoint_cnn.py @@ -33,42 +33,42 @@ class DisjointCNNRegressor(BaseDeepRegressor): Number of 1+1D Convolution layers. n_filters : int or list of int, default = 64 Number of filters used in convolution layers. If - input is set to a list, the lenght should be the same + input is set to a list, the length should be the same as `n_layers`, if input is int the a list of the same element is created of length `n_layers`. kernel_size : int or list of int, default = [8, 5, 5, 3] Size of convolution kernel. If - input is set to a list, the lenght should be the same + input is set to a list, the length should be the same as `n_layers`, if input is int the a list of the same element is created of length `n_layers`. dilation_rate : int or list of int, default = 1 The dilation rate for convolution. If - input is set to a list, the lenght should be the same + input is set to a list, the length should be the same as `n_layers`, if input is int the a list of the same element is created of length `n_layers`. strides : int or list of int, default = 1 The strides of the convolution filter. If - input is set to a list, the lenght should be the same + input is set to a list, the length should be the same as `n_layers`, if input is int the a list of the same element is created of length `n_layers`. padding : str or list of str, default = "same" The type of padding used for convolution. If - input is set to a list, the lenght should be the same + input is set to a list, the length should be the same as `n_layers`, if input is int the a list of the same element is created of length `n_layers`. activation : str or list of str, default = "elu" Activation used after the convolution. If - input is set to a list, the lenght should be the same + input is set to a list, the length should be the same as `n_layers`, if input is int the a list of the same element is created of length `n_layers`. use_bias : bool or list of bool, default = True - Whether or not ot use bias in convolution. If - input is set to a list, the lenght should be the same + Whether or not to use bias in convolution. If + input is set to a list, the length should be the same as `n_layers`, if input is int the a list of the same element is created of length `n_layers`. kernel_initializer: str or list of str, default = "he_uniform" The initialization method of convolution layers. If - input is set to a list, the lenght should be the same + input is set to a list, the length should be the same as `n_layers`, if input is int the a list of the same element is created of length `n_layers`. pool_size: int, default = 5 diff --git a/aeon/regression/deep_learning/_fcn.py b/aeon/regression/deep_learning/_fcn.py index 082b8a7038..91b70cede2 100644 --- a/aeon/regression/deep_learning/_fcn.py +++ b/aeon/regression/deep_learning/_fcn.py @@ -44,7 +44,7 @@ class FCNRegressor(BaseDeepRegressor): activation : str or list of str, default = "relu" activation used after the convolution use_bias : bool or list of bool, default = True - whether or not ot use bias in convolution + whether or not to use bias in convolution n_epochs : int, default = 2000 the number of epochs to train the model batch_size : int, default = 16 diff --git a/aeon/regression/deep_learning/_lite_time.py b/aeon/regression/deep_learning/_lite_time.py index d21a0b391b..89c9fa1ac1 100644 --- a/aeon/regression/deep_learning/_lite_time.py +++ b/aeon/regression/deep_learning/_lite_time.py @@ -401,7 +401,7 @@ class IndividualLITERegressor(BaseDeepRegressor): References ---------- ..[1] Ismail-Fawaz et al. LITE: Light Inception with boosTing - tEchniques for Time Series Classificaion, IEEE International + tEchniques for Time Series Classification, IEEE International Conference on Data Science and Advanced Analytics, 2023. ..[2] Ismail-Fawaz, Ali, et al. "Look Into the LITE in Deep Learning for Time Series Classification." diff --git a/aeon/regression/shapelet_based/_rdst.py b/aeon/regression/shapelet_based/_rdst.py index 38da19021f..22b9137a60 100644 --- a/aeon/regression/shapelet_based/_rdst.py +++ b/aeon/regression/shapelet_based/_rdst.py @@ -49,7 +49,7 @@ class RDSTRegressor(BaseRegressor): Occurrence feature. If None, the 5th and the 10th percentiles (i.e. [5,10]) will be used. alpha_similarity : float, default=0.5 - The strenght of the alpha similarity pruning. The higher the value, the lower + The strength of the alpha similarity pruning. The higher the value, the lower the allowed number of common indexes with previously sampled shapelets when sampling a new candidate with the same dilation parameter. It can cause the number of sampled shapelets to be lower than max_shapelets if diff --git a/aeon/regression/sklearn/_rotation_forest_regressor.py b/aeon/regression/sklearn/_rotation_forest_regressor.py index 48350efb32..a1e439f41f 100644 --- a/aeon/regression/sklearn/_rotation_forest_regressor.py +++ b/aeon/regression/sklearn/_rotation_forest_regressor.py @@ -34,7 +34,7 @@ class RotationForestRegressor(RegressorMixin, BaseEstimator): transformed using PCA. Intended as a benchmark for time series data and a base regressor for - transformation based appraoches such as FreshPRINCERegressor, this aeon + transformation based approaches such as FreshPRINCERegressor, this aeon implementation only works with continuous attributes. Parameters diff --git a/aeon/segmentation/_ggs.py b/aeon/segmentation/_ggs.py index 85b08d279e..fc56ca2654 100644 --- a/aeon/segmentation/_ggs.py +++ b/aeon/segmentation/_ggs.py @@ -64,7 +64,7 @@ class _GGS: to the data by computing the approximate solution to the combinatorial problem of finding the approximate covariance-regularized maximum log-likelihood for fixed number of change points and a reagularization - strength. It follows an interative procedure + strength. It follows an iterative procedure where a new breakpoint is added and then adjusting all breakpoints to (approximately) maximize the objective. It is similar to the top-down search used in other change point detection problems. @@ -127,7 +127,7 @@ class _GGS: _intermediate_ll: list[float] = field(init=False, default_factory=list) def initialize_intermediates(self) -> None: - """Initialize the state fo the estimator.""" + """Initialize the state of the estimator.""" self._intermediate_change_points = [] self._intermediate_ll = [] @@ -388,7 +388,7 @@ class GreedyGaussianSegmenter(BaseSegmenter): to the data by computing the approximate solution to the combinatorial problem of finding the approximate covariance-regularized maximum log-likelihood for fixed number of change points and a reagularization - strength. It follows an interative procedure + strength. It follows an iterative procedure where a new breakpoint is added and then adjusting all breakpoints to (approximately) maximize the objective. It is similar to the top-down search used in other change point detection problems. diff --git a/aeon/segmentation/_hidalgo.py b/aeon/segmentation/_hidalgo.py index c70010b7bf..1eb7d6ab28 100644 --- a/aeon/segmentation/_hidalgo.py +++ b/aeon/segmentation/_hidalgo.py @@ -30,7 +30,7 @@ class HidalgoSegmenter(BaseSegmenter): distance used in the nearest neighbors part of the algorithm K : int, optional, default=2 number of manifolds used in algorithm - zeta : float, optional, defualt=0.8 + zeta : float, optional, default=0.8 "local homogeneity level" used in the algorithm, see equation (4) q : int, optional, default=3 number of points for local Z interaction, "local homogeneity range" @@ -143,7 +143,7 @@ def _get_neighbourhood_params(self, X): m : int Number of rows (timepoints) of X. mu : np.ndarray - 1D np.ndarray of length m. parameter in Pereto distribtion estimated by + 1D np.ndarray of length m. parameter in Pereto distribution estimated by ``r2/r1`` Iin : 1D np.ndarray of length m * q encodes the q neighbour index values for point index i in 0:m-1 @@ -537,15 +537,15 @@ def sample_likelihood(N, mu, p, d, Z, N_in, zeta, NN): N_in, f1 = self._update_zeta_prior(Z, N, Iin) - lik = sample_likelihood(N, mu, p, d, Z, N_in, zeta, NN) - sampling = np.append(sampling, lik) + likelihood = sample_likelihood(N, mu, p, d, Z, N_in, zeta, NN) + sampling = np.append(sampling, likelihood) return sampling def _fit(self, X, y=None): """Run the Hidalgo algorithm. - Find parameter esimates as distributions in sampling. + Find parameter estimates as distributions in sampling. Iterate through n_replicas random starts and get posterior samples with best max likelihood. @@ -568,7 +568,7 @@ def _fit(self, X, y=None): probability of posterior of z_i = k, point i can be safely assigned to manifold k if Pi > 0.8 _Z : 1D np.ndarray of length N - base-zero integer values corresponsing to segment (manifold k) + base-zero integer values corresponding to segment (manifold k) Parameters ---------- @@ -622,11 +622,11 @@ def _fit(self, X, y=None): ] sampling = sampling[idx,] - lik = np.mean(sampling[:, -1], axis=0) + likelihood = np.mean(sampling[:, -1], axis=0) - if lik > maxlik: + if likelihood > maxlik: bestsampling = sampling - maxlik = lik + maxlik = likelihood self._d = np.mean(bestsampling[:, :K], axis=0) self._derr = np.std(bestsampling[:, :K], axis=0) diff --git a/aeon/segmentation/_hmm.py b/aeon/segmentation/_hmm.py index 483ef0c980..a5513b96d4 100644 --- a/aeon/segmentation/_hmm.py +++ b/aeon/segmentation/_hmm.py @@ -80,7 +80,7 @@ class HMMSegmenter(BaseSegmenter): hidden states and should match the length of both the emission funcs list and the transition_prob_mat. The initial probs should be reflective of prior beliefs. If none is passed will each hidden state will be - assigned an equal inital prob. + assigned an equal initial prob. Attributes ---------- @@ -163,7 +163,7 @@ def _validate_init(self): or tran_mat_len != self.transition_prob_mat.shape[1] ): raise ValueError( - "Transtion Probability must be 2D square, but got an" + "Transition Probability must be 2D square, but got an" f"object of size {self.transition_prob_mat.shape}" ) # number of states should be consistent! @@ -215,7 +215,7 @@ def _calculate_trans_mats( number of hidden states and m is the number of observations. For a given observation, it should contain the probability that it could havbe been generated (ie emitted) from each of the hidden states - Each entry should be beteen 0 and 1 + Each entry should be between 0 and 1 transition_prob_mat : 2D np.ndarray, shape = [num_states, num_states] A nxn dimensional array of floats where n is the number of hidden states in the model. The jth col in the ith row @@ -243,14 +243,14 @@ def _calculate_trans_mats( trans_prob[:, 0] = np.log(initial_probs) + np.log(emi_probs[:, 0]) # trans_id is the index of the state that would have been the most - # likely preceeding state. + # likely preceding state. trans_id = np.zeros((num_states, num_obs), dtype=np.int32) # use Vertibi Algorithm to fill in trans_prob and trans_id: for i in range(1, num_obs): # adds log(transition_prob_mat) element-wise: paths = np.log(transition_prob_mat) - # adds the probabilities for the state before columsn wise: + # adds the probabilities for the state before columns wise: paths += np.stack( [trans_prob[:, i - 1] for _ in range(num_states)], axis=0 ).T @@ -295,7 +295,7 @@ def _make_emission_probs( number of hidden states and m is the number of observations. For a given observation, it contains the probability that it could havbe been generated (ie emitted) from each of the hidden states - Each entry should be beteen 0 and 1 + Each entry should be between 0 and 1 """ # assign emission probabilities from each state to each position: diff --git a/aeon/segmentation/tests/test_eagglo.py b/aeon/segmentation/tests/test_eagglo.py index d889812675..8abe0eda0d 100644 --- a/aeon/segmentation/tests/test_eagglo.py +++ b/aeon/segmentation/tests/test_eagglo.py @@ -9,9 +9,9 @@ def test_fit_default_params_univariate(): - """Test univariate data and defualt parameters. + """Test univariate data and default parameters. - These numbers are generated from the original implemention + These numbers are generated from the original implementation in R, with the following code: set.seed(1234) @@ -51,9 +51,9 @@ def test_fit_other_params_univariate(): def test_fit_default_params_multivariate(): - """Test multivariate data with defauly parameters. + """Test multivariate data with default parameters. - These numbers are generated from the original implemention + These numbers are generated from the original implementation in R, with the following code: set.seed(1234) diff --git a/aeon/similarity_search/collection/_base.py b/aeon/similarity_search/collection/_base.py index 9bd6f7cb31..47f684fcf8 100644 --- a/aeon/similarity_search/collection/_base.py +++ b/aeon/similarity_search/collection/_base.py @@ -1,4 +1,4 @@ -"""Base similiarity search for collections.""" +"""Base similarity search for collections.""" __maintainer__ = ["baraline"] __all__ = [ @@ -95,7 +95,7 @@ def predict(self, X, **kwargs): def _check_predict_series_format(self, X): """ - Check whether a series X in predict is correctly formated. + Check whether a series X in predict is correctly formatted. Parameters ---------- diff --git a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py index cb143d24dc..3cc4d98cfe 100644 --- a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py +++ b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py @@ -125,15 +125,15 @@ class RandomProjectionIndexANN(BaseCollectionSimilaritySearch): n_hash_funcs : int, optional Number of random hashing function to use to index series. The default is 128. hash_func_coverage : float, optional - A value in the interval ]0,1] which defines the size L fo the random vectors + A value in the interval ]0,1] which defines the size L of the random vectors relative to the size of the input time series. The default is 0.25. use_discrete_vectors: bool, optional, - Wheter to use dicrete vectors with values -1 or 1 as random vector. If false, + Whether to use discrete vectors with values -1 or 1 as random vector. If false, the values of the random vectors are drawn uniformly between [-1,1]. random_state: int, optional A random seed to seed the index building. The default is None. normalize: bool, optional - Wheter to z-normalize the input the series during fit and predict before + Whether to z-normalize the input the series during fit and predict before indexing them. n_jobs: int, optional Number of parallel threads to use when computing boolean hashes. @@ -231,8 +231,8 @@ def _predict( k : int, optional Number of neighbors to return for each series. The default is 1. inverse_distance : bool, optional - Wheter to inverse the computed distance, meaning that the method will return - the k most dissimilar neighbors instead of the k most similar. + Whether to inverse the computed distance, meaning that the method will + return the k most dissimilar neighbors instead of the k most similar. Returns ------- @@ -286,7 +286,7 @@ def _extract_neighors_one_series( else: current_k = 0 - # Case where we want to find more neighboors in buckets with similar hash + # Case where we want to find more neighbors in buckets with similar hash if current_k < k: dists = _bool_hamming_dist_matrix(X_bool, self._raw_index_bool_arrays) @@ -298,7 +298,7 @@ def _extract_neighors_one_series( ).max # Get top k index of keys ids = np.argpartition(dists, kth=k)[:k] - # and reoder them + # and reorder them ids = ids[np.argsort(dists[ids])] _i_bucket = 0 diff --git a/aeon/similarity_search/series/_base.py b/aeon/similarity_search/series/_base.py index 6139835e77..2acedff7cb 100644 --- a/aeon/similarity_search/series/_base.py +++ b/aeon/similarity_search/series/_base.py @@ -1,4 +1,4 @@ -"""Base similiarity search for series.""" +"""Base similarity search for series.""" __maintainer__ = ["baraline"] __all__ = ["BaseSeriesSimilaritySearch"] @@ -103,7 +103,7 @@ def _predict(self, X, **kwargs): ... def _check_predict_series_format(self, X): """ - Check wheter a series X is correctly formated regarding series given in fit. + Check whether a series X is correctly formatted regarding series given in fit. Parameters ---------- diff --git a/aeon/similarity_search/series/_commons.py b/aeon/similarity_search/series/_commons.py index 0e1df4235f..b68a9f01b5 100644 --- a/aeon/similarity_search/series/_commons.py +++ b/aeon/similarity_search/series/_commons.py @@ -11,14 +11,14 @@ def _check_X_index(X_index: int, n_timepoints: int, length: int): """ - Check wheter a X_index parameter is correctly formated and is admissible. + Check whether an X_index parameter is correctly formatted and is admissible. Parameters ---------- X_index : int Index of a timestamp in X_. n_timepoints: int - Number of timepoints in the serie X_ + Number of timepoints in the series X_ length: int Length parameter of the estimator @@ -30,7 +30,7 @@ def _check_X_index(X_index: int, n_timepoints: int, length: int): max_timepoints = n_timepoints - length if X_index >= max_timepoints or X_index < 0: raise ValueError( - "The value of X_index cannot exced the number " + "The value of X_index cannot exceed the number " "of timepoint in series given during fit. Expected a value " f"between [0, {max_timepoints - 1}] but got {X_index}" ) @@ -122,7 +122,7 @@ def _extract_top_k_from_dist_profile( ignoring the exclusion zone defined by the ``exclusion_size`` parameter. If False, the exclusion zone is applied. exclusion_size : int - The size of the exlusion size to apply when ``allow_trivial_matches`` is + The size of the exclusion size to apply when ``allow_trivial_matches`` is False. It is applied on both side of existing matches (+/- their indexes). Returns diff --git a/aeon/similarity_search/series/motifs/_stomp.py b/aeon/similarity_search/series/motifs/_stomp.py index e825903fdd..a008e1cff7 100644 --- a/aeon/similarity_search/series/motifs/_stomp.py +++ b/aeon/similarity_search/series/motifs/_stomp.py @@ -27,9 +27,9 @@ class StompMotif(BaseSeriesSimilaritySearch): """ - Estimator to extract top k motifs using STOMP, descibed in [1]_. + Estimator to extract top k motifs using STOMP, described in [1]_. - This estimators allows to perform multiple type of motif search operations by using + This estimator can perform multiple types of motif search operation by using different parameterization. We base oursleves on Figure 3 of [2]_ to establish the following list, but modify the confusing naming for some of them. We do not yet support "Learning" and "Valmod" motifs : @@ -42,8 +42,8 @@ class StompMotif(BaseSeriesSimilaritySearch): "motif_size": k, } - - for "r-motifs" (originaly named k-motifs, which was confusing as it is a range - based motif): { + - for "r-motifs" (originally named k-motifs, which was confusing as it is a + range based motif): { "motif_size":np.inf, "dist_threshold":r, "motif_extraction_method":"r_motifs" @@ -55,12 +55,13 @@ class StompMotif(BaseSeriesSimilaritySearch): The length of the motifs to extract. This is the length of the subsequence that will be used in the computations. normalize : bool - Wheter the computations between subsequences should use a z-normalied distance. + Whether the computations between subsequences should use a z-normalied + distance. Notes ----- This estimator only provides an exact computation method, faster approximate methods - also exist in the litterature. We use a squared euclidean distance instead of the + also exist in the literature. We use a squared euclidean distance instead of the euclidean distance, if you want euclidean distance results, you should square root the obtained results. @@ -129,7 +130,7 @@ def _predict( is_self_computation: Optional[bool] = False, ): """ - Exctract the motifs of X_ relative to a series X using STOMP matrix prfoile. + Extract the motifs of X_ relative to a series X using STOMP matrix prfoile. To compute self-motifs, X is set to None. @@ -172,7 +173,7 @@ def _predict( For example, if a 3-motif has distances to its matches equal to ``[0.1,0.2,0.5]`` will have a score of ``max([0.1,0.2,0.5])=0.5``. is_self_computation : bool - Wheter X is equal to the series X_ given during fit. + Whether X is equal to the series X_ given during fit. Returns ------- @@ -244,7 +245,7 @@ def compute_matrix_profile( :math:``id_timestamp - floor(length * exclusion_factor)`` and end at :math:``id_timestamp + floor(length * exclusion_factor)``. is_self_computation : bool - Wheter X is equal to the series X_ given during fit. + Whether X is equal to the series X_ given during fit. Returns ------- @@ -466,7 +467,7 @@ def _stomp( The maximum allowed distance of a candidate subsequence of X to a query subsequence from X_ for the candidate to be considered as a neighbor. allow_trivial_matches : bool - Wheter the top-k candidates can be neighboring subsequences. + Whether the top-k candidates can be neighboring subsequences. exclusion_size : int The size of the exclusion zone used to prevent returning as top k candidates the ones that are close to each other (for example i and i+1). @@ -479,7 +480,7 @@ def _stomp( If True, the matching will be made on the inverse of the distance, and thus, the worst matches to the query will be returned instead of the best ones. is_self_mp : bool - Wheter X_A == X_B. + Whether X_A == X_B. Returns ------- diff --git a/aeon/testing/estimator_checking/_estimator_checking.py b/aeon/testing/estimator_checking/_estimator_checking.py index 97eed5a0e9..b5cd21a302 100644 --- a/aeon/testing/estimator_checking/_estimator_checking.py +++ b/aeon/testing/estimator_checking/_estimator_checking.py @@ -337,7 +337,7 @@ def _get_check_estimator_ids(obj): checks. Some parameters which contain functions or methods will be obfuscated to - allow for compatability with `pytest-xdist`. This requires that IDs on each thread + allow for compatibility with `pytest-xdist`. This requires that IDs on each thread be the same, and functions can generate different IDs. Based on the `scikit-learn` `_get_check_estimator_ids` function. diff --git a/aeon/testing/estimator_checking/_yield_collection_anomaly_detection_checks.py b/aeon/testing/estimator_checking/_yield_collection_anomaly_detection_checks.py index 8a22056871..f4d9e53145 100644 --- a/aeon/testing/estimator_checking/_yield_collection_anomaly_detection_checks.py +++ b/aeon/testing/estimator_checking/_yield_collection_anomaly_detection_checks.py @@ -62,15 +62,15 @@ def check_collection_anomaly_detector_output(estimator, datatype): # collections need n_cases predictions assert len(y_pred) == get_n_cases(FULL_TEST_DATA_DICT[datatype]["test"][0]) - ot = estimator.get_tag("anomaly_output_type") - if ot == "anomaly_scores": + out_type = estimator.get_tag("anomaly_output_type") + if out_type == "anomaly_scores": assert np.issubdtype(y_pred.dtype, np.floating) or np.issubdtype( y_pred.dtype, np.integer ), "y_pred must be of floating point or int type" assert not np.array_equal( np.unique(y_pred), [0, 1] ), "y_pred cannot contain only 0s and 1s" - elif ot == "binary": + elif out_type == "binary": assert np.issubdtype(y_pred.dtype, np.integer) or np.issubdtype( y_pred.dtype, np.bool_ ), "y_pred must be of int or bool type for binary output" @@ -78,4 +78,4 @@ def check_collection_anomaly_detector_output(estimator, datatype): val in [0, 1] for val in np.unique(y_pred) ), "y_pred must contain only 0s, 1s, True, or False" else: - raise ValueError(f"Unknown anomaly output type: {ot}") + raise ValueError(f"Unknown anomaly output type: {out_type}") diff --git a/aeon/testing/estimator_checking/_yield_multithreading_checks.py b/aeon/testing/estimator_checking/_yield_multithreading_checks.py index 75e8fd14e2..512e01fe50 100644 --- a/aeon/testing/estimator_checking/_yield_multithreading_checks.py +++ b/aeon/testing/estimator_checking/_yield_multithreading_checks.py @@ -61,7 +61,7 @@ def check_multithreading_param(estimator_class): def check_no_multithreading_param(estimator_class): - """Test that estimators that cant multithread have no n_jobs parameter.""" + """Test that estimators that can't multithread have no n_jobs parameter.""" default_params = inspect.signature(estimator_class.__init__).parameters # check that the estimator does not have a n_jobs parameter diff --git a/aeon/testing/estimator_checking/_yield_series_anomaly_detection_checks.py b/aeon/testing/estimator_checking/_yield_series_anomaly_detection_checks.py index 85368d09e7..a3009c2833 100644 --- a/aeon/testing/estimator_checking/_yield_series_anomaly_detection_checks.py +++ b/aeon/testing/estimator_checking/_yield_series_anomaly_detection_checks.py @@ -54,15 +54,15 @@ def check_series_anomaly_detector_output(estimator, datatype): # series need n_timepoints predictions assert len(y_pred) == FULL_TEST_DATA_DICT[datatype]["test"][0].shape[1] - ot = estimator.get_tag("anomaly_output_type") - if ot == "anomaly_scores": + out_type = estimator.get_tag("anomaly_output_type") + if out_type == "anomaly_scores": assert np.issubdtype(y_pred.dtype, np.floating) or np.issubdtype( y_pred.dtype, np.integer ), "y_pred must be of floating point or int type" assert not np.array_equal( np.unique(y_pred), [0, 1] ), "y_pred cannot contain only 0s and 1s" - elif ot == "binary": + elif out_type == "binary": assert np.issubdtype(y_pred.dtype, np.integer) or np.issubdtype( y_pred.dtype, np.bool_ ), "y_pred must be of int or bool type for binary output" @@ -70,4 +70,4 @@ def check_series_anomaly_detector_output(estimator, datatype): val in [0, 1] for val in np.unique(y_pred) ), "y_pred must contain only 0s, 1s, True, or False" else: - raise ValueError(f"Unknown anomaly output type: {ot}") + raise ValueError(f"Unknown anomaly output type: {out_type}") diff --git a/aeon/testing/mock_estimators/_mock_classifiers.py b/aeon/testing/mock_estimators/_mock_classifiers.py index 1bf9357d60..a4139da847 100644 --- a/aeon/testing/mock_estimators/_mock_classifiers.py +++ b/aeon/testing/mock_estimators/_mock_classifiers.py @@ -95,7 +95,7 @@ def _get_test_params(cls, parameter_set="default"): class MockClassifierComposite(BaseClassifier): - """Mock classifier which contains another mock classfier.""" + """Mock classifier which contains another mock classifier.""" def __init__(self, mock=None): self.mock = mock diff --git a/aeon/testing/mock_estimators/_mock_series_transformers.py b/aeon/testing/mock_estimators/_mock_series_transformers.py index 66d62ef687..15a1e54171 100644 --- a/aeon/testing/mock_estimators/_mock_series_transformers.py +++ b/aeon/testing/mock_estimators/_mock_series_transformers.py @@ -98,7 +98,7 @@ def _transform(self, X: np.ndarray, y=None) -> np.ndarray: return X_new def _inverse_transform(self, X: np.ndarray, y=None) -> np.ndarray: - """Inverse transform X by substracting the constant and random value. + """Inverse transform X by subtracting the constant and random value. Parameters ---------- @@ -177,7 +177,7 @@ def _transform(self, X: np.ndarray, y=None) -> np.ndarray: return X_new def _inverse_transform(self, X: np.ndarray, y=None) -> np.ndarray: - """Inverse transform X by substracting the constant and random value. + """Inverse transform X by subtracting the constant and random value. Parameters ---------- diff --git a/aeon/testing/tests/__init__.py b/aeon/testing/tests/__init__.py index aaeacff24d..960c71a4e8 100644 --- a/aeon/testing/tests/__init__.py +++ b/aeon/testing/tests/__init__.py @@ -1,4 +1,4 @@ -"""Tests for the aeon package and testing module utilties.""" +"""Tests for the aeon package and testing module utilities.""" import pkgutil diff --git a/aeon/transformations/collection/_rescale.py b/aeon/transformations/collection/_rescale.py index 293aca6c86..2f820f1a83 100644 --- a/aeon/transformations/collection/_rescale.py +++ b/aeon/transformations/collection/_rescale.py @@ -83,7 +83,7 @@ class MinMaxScaler(BaseCollectionTransformer): Parameters ---------- min: float, default=0 - Minumum value of the range to scale to. + Minimum value of the range to scale to. max: float, default=1 Maximum value of the range to scale to. diff --git a/aeon/transformations/collection/channel_selection/_channel_scorer.py b/aeon/transformations/collection/channel_selection/_channel_scorer.py index 4306cdfeb6..78f4671340 100644 --- a/aeon/transformations/collection/channel_selection/_channel_scorer.py +++ b/aeon/transformations/collection/channel_selection/_channel_scorer.py @@ -21,10 +21,10 @@ class ChannelScorer(BaseChannelSelector): """Performs channel selection using a single channel classifier or regressor. - ChannelScorer uses a time series classifier or a regressor to score each channel - using an estimate of accuracy on the training data fro classifier or mean - squared error for regressor, then selects a proportion of the top - channels to keep. Can be configured through the constructor to use any time + ChannelScorer uses a time series classifier or regressor to score each channel + using an estimate of accuracy on the training data foo classifiers or mean + squared error for regressors. It then selects a proportion of the top + channels to keep. It can be configured through the constructor to use any time series estimator and could easily be adapted to use forward selection or elbow class methods. Approximately as described in [1]_. diff --git a/aeon/transformations/collection/convolution_based/_minirocket.py b/aeon/transformations/collection/convolution_based/_minirocket.py index 5a3ea4bd08..ac184aa4e6 100644 --- a/aeon/transformations/collection/convolution_based/_minirocket.py +++ b/aeon/transformations/collection/convolution_based/_minirocket.py @@ -142,7 +142,7 @@ def _transform(self, X, y=None): """ X = X.astype(np.float32) _, n_channels, n_timepoints = X.shape - # change n_jobs dependend on value and existing cores + # change n_jobs depending on value and existing cores prev_threads = get_num_threads() if self._n_jobs < 1 or self._n_jobs > multiprocessing.cpu_count(): n_jobs = multiprocessing.cpu_count() diff --git a/aeon/transformations/collection/convolution_based/_multirocket.py b/aeon/transformations/collection/convolution_based/_multirocket.py index fb7a7f7ac5..88636d4bfd 100644 --- a/aeon/transformations/collection/convolution_based/_multirocket.py +++ b/aeon/transformations/collection/convolution_based/_multirocket.py @@ -164,7 +164,7 @@ def _transform(self, X, y=None): X = (X - X.mean(axis=-1, keepdims=True)) / ( X.std(axis=-1, keepdims=True) + 1e-8 ) - # change n_jobs dependend on value and existing cores + # change n_jobs depending on value and existing cores prev_threads = get_num_threads() if self._n_jobs < 1 or self._n_jobs > multiprocessing.cpu_count(): n_jobs = multiprocessing.cpu_count() diff --git a/aeon/transformations/collection/dictionary_based/_sfa.py b/aeon/transformations/collection/dictionary_based/_sfa.py index b854e83b5c..b862c280db 100644 --- a/aeon/transformations/collection/dictionary_based/_sfa.py +++ b/aeon/transformations/collection/dictionary_based/_sfa.py @@ -295,7 +295,7 @@ def _transform(self, X, y=None): if self.save_words: self.words = np.array(list(words)) - # cant pickle typed dict + # can't pickle typed dict if self._typed_dict and self._n_jobs != 1: nl = [None] * len(dim) for i, pdict in enumerate(dim): @@ -421,7 +421,7 @@ def _transform_case(self, X, supplied_dft=None): skip_gram = (skip_gram << self.level_bits) | 0 bag[skip_gram] = bag.get(skip_gram, 0) + 1 - # cant pickle typed dict + # can't pickle typed dict if self._typed_dict and self._n_jobs != 1: pdict = dict() for key, val in bag.items(): @@ -820,7 +820,7 @@ def _shorten_bags(self, word_len): delayed(self._shorten_case)(word_len, i) for i in range(len(self.words)) ) - # cant pickle typed dict + # can't pickle typed dict if self._typed_dict and self._n_jobs != 1: nl = [None] * len(dim) for i, pdict in enumerate(dim): @@ -909,7 +909,7 @@ def _shorten_case(self, word_len, i): skip_gram = (skip_gram << self.level_bits) | 0 new_bag[skip_gram] = new_bag.get(skip_gram, 0) + 1 - # cant pickle typed dict + # can't pickle typed dict if self._typed_dict and self._n_jobs != 1: pdict = dict() for key, val in new_bag.items(): diff --git a/aeon/transformations/collection/dictionary_based/_sfa_whole.py b/aeon/transformations/collection/dictionary_based/_sfa_whole.py index 2e9f3df86a..7a12ebc93a 100644 --- a/aeon/transformations/collection/dictionary_based/_sfa_whole.py +++ b/aeon/transformations/collection/dictionary_based/_sfa_whole.py @@ -18,7 +18,7 @@ class SFAWhole(SFAFast): It is implemented as a wrapper for the SFA-Fast transformer, the latter implements subsequence-based SFA extraction. - This wrapper reduces non-needed parameters, and sets some usefull defaults for + This wrapper reduces non-needed parameters, and sets some useful defaults for lower bounding. Parameters diff --git a/aeon/transformations/collection/self_supervised/_trilite.py b/aeon/transformations/collection/self_supervised/_trilite.py index b0ba47a879..c37eb9b30a 100644 --- a/aeon/transformations/collection/self_supervised/_trilite.py +++ b/aeon/transformations/collection/self_supervised/_trilite.py @@ -54,7 +54,7 @@ class TRILITE(BaseCollectionTransformer): the length of the masking used for the triplet generation. Default is 20%. use_mixing_up : bool, default = True - Wether or not to use mixing up during the triplet + Whether or not to use mixing up during the triplet generation phase. use_masking : bool, default = True Whether or not to use masking during the triplet diff --git a/aeon/transformations/collection/shapelet_based/_dilated_shapelet_transform.py b/aeon/transformations/collection/shapelet_based/_dilated_shapelet_transform.py index 0efacf8fe0..f6ced2c159 100644 --- a/aeon/transformations/collection/shapelet_based/_dilated_shapelet_transform.py +++ b/aeon/transformations/collection/shapelet_based/_dilated_shapelet_transform.py @@ -53,9 +53,9 @@ class RandomDilatedShapeletTransform(BaseCollectionTransformer): features from each pair of shapelets and input series. Three features are extracted: - min d(S,X): the minimum value of the distance vector between a shapelet S and a time series X. - - argmin d(S,X): the location of the minumum. + - argmin d(S,X): the location of the minimum. - SO(d(S,X), threshold): The number of points in the distance vector that are - bellow the threshold parameter of the shapelet. + below the threshold parameter of the shapelet. Parameters ---------- @@ -182,7 +182,7 @@ def _fit(self, X: np.ndarray, y: Optional[Union[np.ndarray, TypingList]] = None) y: array-like or list, default=None The class values for X. If not specified, a random sample (i.e. not of the same class) will be used when computing the threshold for the Shapelet - Occurence feature. + Occurrence feature. Returns ------- @@ -837,7 +837,7 @@ def compute_shapelet_features( distance vector: - min - argmin - - Shapelet Occurence : number of point in the distance vector inferior to the + - Shapelet Occurrence : number of point in the distance vector inferior to the threshold parameter Parameters @@ -853,7 +853,7 @@ def compute_shapelet_features( Returns ------- - min, argmin, shapelet occurence + min, argmin, shapelet occurrence The three computed features as float dtypes """ _min = np.inf @@ -887,7 +887,7 @@ def compute_shapelet_dist_vector( distance vector: - min - argmin - - Shapelet Occurence : number of point in the distance vector inferior to the + - Shapelet Occurrence : number of point in the distance vector inferior to the threshold parameter Parameters diff --git a/aeon/transformations/collection/shapelet_based/_sast.py b/aeon/transformations/collection/shapelet_based/_sast.py index 8479703a2d..f7eea2fef6 100644 --- a/aeon/transformations/collection/shapelet_based/_sast.py +++ b/aeon/transformations/collection/shapelet_based/_sast.py @@ -154,12 +154,12 @@ def _fit(self, X: np.ndarray, y: Union[np.ndarray, list]) -> "SAST": # convert to int because if self. # nb_inst_per_class is float, the result of np.min() will be float cnt = np.min([self.nb_inst_per_class, X_c.shape[0]]).astype(int) - choosen = self._random_state.permutation(X_c.shape[0])[:cnt] - candidates_ts.append(X_c[choosen]) - self.kernels_generators_[c] = X_c[choosen] + chosen = self._random_state.permutation(X_c.shape[0])[:cnt] + candidates_ts.append(X_c[chosen]) + self.kernels_generators_[c] = X_c[chosen] class_values_of_candidates.extend([c] * cnt) source_series_indices.extend( - np.where(y == c)[0][choosen] + np.where(y == c)[0][chosen] ) # Record the original indices candidates_ts = np.concatenate(candidates_ts, axis=0) diff --git a/aeon/transformations/collection/shapelet_based/tests/test_dilated_shapelet_transform.py b/aeon/transformations/collection/shapelet_based/tests/test_dilated_shapelet_transform.py index 82e04dc115..07e6fa7f9d 100644 --- a/aeon/transformations/collection/shapelet_based/tests/test_dilated_shapelet_transform.py +++ b/aeon/transformations/collection/shapelet_based/tests/test_dilated_shapelet_transform.py @@ -97,7 +97,7 @@ def test_compute_shapelet_features(dtype): X_subs = get_all_subsequences(X, length, dilation) _min, _argmin, SO = compute_shapelet_features(X_subs, values, threshold) - # On some occasion, float32 precision with fasmath retruns things like + # On some occasion, float32 precision with fasmath returns things like # 2.1835059227370834e-07 instead of 0 assert_almost_equal(_min, 0.0, decimal=4) assert _argmin == 0.0 diff --git a/aeon/transformations/collection/signature_based/_augmentations.py b/aeon/transformations/collection/signature_based/_augmentations.py index 850a40f709..6ead2b4dbf 100644 --- a/aeon/transformations/collection/signature_based/_augmentations.py +++ b/aeon/transformations/collection/signature_based/_augmentations.py @@ -5,7 +5,7 @@ def _make_augmentation_pipeline(augmentation_list): - """Buids an sklearn pipeline of augmentations from a tuple of strings. + """Build an sklearn pipeline of augmentations from a tuple of strings. Parameters ---------- diff --git a/aeon/transformations/collection/signature_based/_window.py b/aeon/transformations/collection/signature_based/_window.py index 01baff18d7..a28e965680 100644 --- a/aeon/transformations/collection/signature_based/_window.py +++ b/aeon/transformations/collection/signature_based/_window.py @@ -96,7 +96,7 @@ def __call__(self, length=None): class _ExpandingSliding(_Window): def __init__(self, initial_length, start_step, end_step): - """Initilize the class. + """Initialize the class. Parameters ---------- diff --git a/aeon/transformations/collection/unequal_length/tests/test_pad.py b/aeon/transformations/collection/unequal_length/tests/test_pad.py index 87d87ab346..b935661ae5 100644 --- a/aeon/transformations/collection/unequal_length/tests/test_pad.py +++ b/aeon/transformations/collection/unequal_length/tests/test_pad.py @@ -78,7 +78,7 @@ def test_padding_fixed_value(): def test_padding_fill_unequal_length(): """Test padding unequal length longer than longest. - Padding should create a 3D numpy array and padd to given value. + Padding should create a 3D numpy array and pad to given value. """ X = [] for i in range(10): diff --git a/aeon/transformations/series/_boxcox.py b/aeon/transformations/series/_boxcox.py index a722c348eb..37f0f14550 100644 --- a/aeon/transformations/series/_boxcox.py +++ b/aeon/transformations/series/_boxcox.py @@ -90,7 +90,7 @@ class BoxCoxTransformer(BaseSeriesTransformer): References ---------- .. [1] Box, G. E. P. & Cox, D. R. (1964) An analysis of transformations, - Journal ofthe Royal Statistical Society, Series B, 26, 211-252. + Journal of the Royal Statistical Society, Series B, 26, 211-252. .. [2] V.M. Guerrero, "Time-series analysis supported by Power Transformations ", Journal of Forecasting, vol. 12, pp. 37-48, 1993. diff --git a/aeon/transformations/series/_matrix_profile.py b/aeon/transformations/series/_matrix_profile.py index 79a0a08044..00ed70b089 100644 --- a/aeon/transformations/series/_matrix_profile.py +++ b/aeon/transformations/series/_matrix_profile.py @@ -20,7 +20,7 @@ class MatrixProfileSeriesTransformer(BaseSeriesTransformer): Parameters ---------- window_length : int - Length of the sliding winodw for the matrix profile calculation. + Length of the sliding window for the matrix profile calculation. Notes ----- diff --git a/aeon/transformations/series/_pla.py b/aeon/transformations/series/_pla.py index cf1007aa29..33c907e833 100644 --- a/aeon/transformations/series/_pla.py +++ b/aeon/transformations/series/_pla.py @@ -177,7 +177,7 @@ def _top_down(self, X): return left_segment + right_segment def improvement_splitting_here(self, X, breakpoint): - """Return the SSE of two segments split at a particual point in a time series. + """Return the SSE of two segments split at a particular point in a time series. Parameters ---------- diff --git a/aeon/transformations/series/_siv.py b/aeon/transformations/series/_siv.py index f343df8f04..b34e637285 100644 --- a/aeon/transformations/series/_siv.py +++ b/aeon/transformations/series/_siv.py @@ -23,7 +23,7 @@ class SIVSeriesTransformer(RecursiveMedianSieve): Parameters ---------- window_length : list of int or int, default=[3, 5, 7] - The filter windows lenths (recommended increasing value). + The filter windows lengths (recommended increasing value). Notes ----- diff --git a/aeon/utils/conversion/_convert_series.py b/aeon/utils/conversion/_convert_series.py index 7693d52443..a198fd7d47 100644 --- a/aeon/utils/conversion/_convert_series.py +++ b/aeon/utils/conversion/_convert_series.py @@ -100,7 +100,7 @@ def convert_series(y, output_type): return y.to_numpy() if output_type == "pd.Series": if input_type == "pd.DataFrame": - if y.shape == (1, 1): # special case of single element, cant squeeze + if y.shape == (1, 1): # special case of single element, can't squeeze y = y[y.columns[0]] else: y = y.squeeze() diff --git a/aeon/utils/conversion/_convert_tsf.py b/aeon/utils/conversion/_convert_tsf.py index 2b5ef3c8b8..0ee26f4863 100644 --- a/aeon/utils/conversion/_convert_tsf.py +++ b/aeon/utils/conversion/_convert_tsf.py @@ -24,7 +24,7 @@ def _convert_tsf_to_hierarchical( tsf file metadata freq : str, optional pandas compatible time frequency, by default None - if not speciffied it's automatically mapped from the tsf frequency to a pandas + if not specified it's automatically mapped from the tsf frequency to a pandas frequency value_column_name: str, optional The name of the column that contains the values, by default "series_value" diff --git a/aeon/utils/discovery.py b/aeon/utils/discovery.py index 3082fc5a86..91036692e8 100644 --- a/aeon/utils/discovery.py +++ b/aeon/utils/discovery.py @@ -40,14 +40,14 @@ def all_estimators( type_filter: str, estimator type or list of str/type, default=None Which kind of estimators should be returned. if None, no filter is applied. - if str, type or list, returns esimators that are of one of the provided types. + if str, type or list, returns estimators that are of one of the provided types. Valid entries are available in the keys and values of utils.base.BASE_CLASS_REGISTER. i.e. "classifier" and "transformer" for str, and BaseClassifier and BaseTransformer for type. exclude_types: str, estimator type or list of str/type, default=None Which kind of estimators to exclude. Inverse of type_filter. if None, no exclusion is applied. - if str, type or list, excludes esimators that are of the provided types. + if str, type or list, excludes estimators that are of the provided types. Valid entries are available in the keys and values of utils.base.BASE_CLASS_REGISTER. i.e. "classifier" and "transformer" for str, and BaseClassifier and BaseTransformer for type. diff --git a/aeon/utils/numba/general.py b/aeon/utils/numba/general.py index 8d166745f5..1c51aab44e 100644 --- a/aeon/utils/numba/general.py +++ b/aeon/utils/numba/general.py @@ -908,7 +908,7 @@ def is_prime(n: int) -> bool: Returns ------- bool - Wheter n is a prime number + Whether n is a prime number Examples -------- diff --git a/aeon/utils/numba/stats.py b/aeon/utils/numba/stats.py index 80242b6dfd..b299bfa4cf 100644 --- a/aeon/utils/numba/stats.py +++ b/aeon/utils/numba/stats.py @@ -936,7 +936,7 @@ def is_prime(n: int) -> bool: Returns ------- bool - Wheter n is a prime number + Whether n is a prime number Examples -------- diff --git a/aeon/utils/tags/_tags.py b/aeon/utils/tags/_tags.py index d4a2d68a44..ff9274863b 100644 --- a/aeon/utils/tags/_tags.py +++ b/aeon/utils/tags/_tags.py @@ -1,6 +1,6 @@ """Register of estimator tags. -Each dictionary item corresponds to a tag with the key as its name, the contrained +Each dictionary item corresponds to a tag with the key as its name, the contained sub-dictionary has the following items: class : identifier for the base class of objects this tag applies to type : expected type of the tag value. Should be one or a list of: diff --git a/aeon/utils/validation/__init__.py b/aeon/utils/validation/__init__.py index 7e86a79a13..4bc92ac901 100644 --- a/aeon/utils/validation/__init__.py +++ b/aeon/utils/validation/__init__.py @@ -148,7 +148,7 @@ def all_inputs_are_iloc_like(args: list) -> bool: def all_inputs_are_time_like(args: list) -> bool: - """Check if all inputs in teh list are time-like.""" + """Check if all inputs in the list are time-like.""" return all([is_time_like(x) if x is not None else True for x in args]) diff --git a/aeon/utils/validation/_dependencies.py b/aeon/utils/validation/_dependencies.py index d470beb0ab..3df01ef64d 100644 --- a/aeon/utils/validation/_dependencies.py +++ b/aeon/utils/validation/_dependencies.py @@ -25,7 +25,7 @@ def _check_soft_dependencies( ---------- packages : str or list/tuple of str, or length-1-tuple containing list/tuple of str str should be package names and/or package version specifications to check. - Each str must be a PEP 440 compatibe specifier string, for a single package. + Each str must be a PEP 440 compatible specifier string, for a single package. For instance, the PEP 440 compatible package name such as "pandas"; or a package requirement specifier string such as "pandas>1.2.3". arg can be str, kwargs tuple, or tuple/list of str, following calls are valid: diff --git a/aeon/utils/validation/collection.py b/aeon/utils/validation/collection.py index 4c2fafbc65..90e2d2f451 100644 --- a/aeon/utils/validation/collection.py +++ b/aeon/utils/validation/collection.py @@ -385,7 +385,7 @@ def _is_numpy_list_multivariate( return False return True if x_dims == 2: - # As this function is used for pairwise we assume it isnt a single + # As this function is used for pairwise we assume it isn't a single # multivariate time series but two collections of univariate return False if x_dims == 1: diff --git a/aeon/utils/validation/tests/test_collection.py b/aeon/utils/validation/tests/test_collection.py index b97a55bd58..359da41662 100644 --- a/aeon/utils/validation/tests/test_collection.py +++ b/aeon/utils/validation/tests/test_collection.py @@ -159,7 +159,7 @@ def test_is_numpy_list_multivariate_single(): # 2d format tests # Equal (n_cases, n_timepoints) - # As the function is intended to be used for pairwise we assume it isnt a single + # As the function is intended to be used for pairwise we assume it isn't a single # multivariate time series but two collections of univariate x_multi_2d = make_example_2d_numpy_collection(10, 20, return_y=False) is_multivariate = _is_numpy_list_multivariate(x_multi_2d) diff --git a/aeon/utils/validation/tests/test_series.py b/aeon/utils/validation/tests/test_series.py index f7f6ed4621..ec137ff38e 100644 --- a/aeon/utils/validation/tests/test_series.py +++ b/aeon/utils/validation/tests/test_series.py @@ -87,7 +87,7 @@ def _make_hierarchical( max_timepoints : int, optional maximum time points a series can have, by default 12 min_timepoints : int, optional - minimum time points a seires can have, by default 12 + minimum time points a series can have, by default 12 same_cutoff : bool, optional If it's True all series will end at the same date, by default True n_columns : int, optional diff --git a/aeon/visualisation/__init__.py b/aeon/visualisation/__init__.py index 5276381d11..4631fe4c43 100644 --- a/aeon/visualisation/__init__.py +++ b/aeon/visualisation/__init__.py @@ -1,4 +1,4 @@ -"""Plotting utlities for time series.""" +"""Plotting utilities for time series.""" __all__ = [ # Series plotting diff --git a/aeon/visualisation/estimator/_shapelets.py b/aeon/visualisation/estimator/_shapelets.py index 8199895878..99660fb5f5 100644 --- a/aeon/visualisation/estimator/_shapelets.py +++ b/aeon/visualisation/estimator/_shapelets.py @@ -45,7 +45,7 @@ def compute_shapelet_dist_vector( distance vector: - min - argmin - - Shapelet Occurence : number of point in the distance vector inferior to the + - Shapelet Occurrence : number of point in the distance vector inferior to the threshold parameter Parameters @@ -75,14 +75,14 @@ def compute_shapelet_dist_vector( class ShapeletVisualizer: """ - A Shapelet object to use for ploting operations. + A Shapelet object to use for plotting operations. Parameters ---------- values : array, shape=(n_channels, length) Values of the shapelet. normalise : bool - Wheter the shapelet use a normalised distance. + Whether the shapelet use a normalised distance. dilation : int Dilation of the shapelet. The default is 1, which is equivalent to no dilation. @@ -90,7 +90,7 @@ class ShapeletVisualizer: Lambda threshold for Shapelet Occurrence feature. The default value is None if it is not used (used in RDST). length : int - Length of the shapelet. The default values is None, meaning length is infered + Length of the shapelet. The default values is None, meaning length is inferred from the values array. Otherwise, the values array 2nd axis will be set to this length. @@ -153,15 +153,15 @@ def plot( scatter_options : dict Options to apply to scatter plot of the shapelet values. figure_options : dict - Dictionnary of options passed to plt.figure. Only used if ax is None. + Dictionary of options passed to plt.figure. Only used if ax is None. rc_Params_options: dict - Dictionnary of options passed to plt.rcParams.update. Only used if ax is + Dictionary of options passed to plt.rcParams.update. Only used if ax is None. matplotlib_style: str Matplotlib style to be used. Only used if ax is None. custom_title_string : str If not None, use this string as title for the plot instead of the default - one based on the shapelet parametres. + one based on the shapelet parameters. Returns ------- @@ -244,13 +244,13 @@ def plot_on_X( A matplotlib axe on which to plot the figure. The default is None and will create a new figure of size figsize. scatter_options : dict - Dictionnary of options passed to the scatter plot of the shapelet values. + Dictionary of options passed to the scatter plot of the shapelet values. line_options : dict - Dictionnary of options passed to the plot of the time series values. + Dictionary of options passed to the plot of the time series values. figure_options : dict - Dictionnary of options passed to plt.figure. Only used if ax is None. + Dictionary of options passed to plt.figure. Only used if ax is None. rc_Params_options: dict - Dictionnary of options passed to plt.rcParams.update. Only used if ax is + Dictionary of options passed to plt.rcParams.update. Only used if ax is None. matplotlib_style: str Matplotlib style to be used. Only used if ax is None. @@ -259,7 +259,7 @@ def plot_on_X( ------- fig : matplotlib figure The resulting figure with S on its best match on X. A normalised - shapelet will be scalled to macth the scale of X. + shapelet will be scaled to match the scale of X. """ _check_soft_dependencies("matplotlib") @@ -358,17 +358,17 @@ def plot_distance_vector( A matplotlib axe on which to plot the figure. The default is None and will create a new figure of size figsize. show_legend : bool, optional - Wheter to show legend. Default is True + Whether to show legend. Default is True show_threshold: bool, optional - Wheter to show threshold (if it is not set to None). Default is True. + Whether to show threshold (if it is not set to None). Default is True. threshold_options : dict - Dictionnary of options passed to the line plot of the threshold. + Dictionary of options passed to the line plot of the threshold. line_options : dict - Dictionnary of options passed to the plot of the distance vector values. + Dictionary of options passed to the plot of the distance vector values. figure_options : dict - Dictionnary of options passed to plt.figure. Only used if ax is None. + Dictionary of options passed to plt.figure. Only used if ax is None. rc_Params_options: dict - Dictionnary of options passed to plt.rcParams.update. Only used if ax is + Dictionary of options passed to plt.rcParams.update. Only used if ax is None. matplotlib_style: str Matplotlib style to be used. Only used if ax is None. @@ -497,13 +497,13 @@ def plot_on_X( A matplotlib axe on which to plot the figure. The default is None and will create a new figure of size figsize. scatter_options : dict - Dictionnary of options passed to the scatter plot of the shapelet values. + Dictionary of options passed to the scatter plot of the shapelet values. line_options : dict - Dictionnary of options passed to the plot of the time series values. + Dictionary of options passed to the plot of the time series values. figure_options : dict - Dictionnary of options passed to plt.figure. Only used if ax is None. + Dictionary of options passed to plt.figure. Only used if ax is None. rc_Params_options: dict - Dictionnary of options passed to plt.rcParams.update. Only used if ax is + Dictionary of options passed to plt.rcParams.update. Only used if ax is None. matplotlib_style: str Matplotlib style to be used. Only used if ax is None. @@ -512,7 +512,7 @@ def plot_on_X( ------- fig : matplotlib figure The resulting figure with S on its best match on X. A normalised - shapelet will be scalled to macth the scale of X. + shapelet will be scaled to match the scale of X. """ return self._get_shapelet(id_shapelet).plot_on_X( @@ -559,17 +559,17 @@ def plot_distance_vector( A matplotlib axe on which to plot the figure. The default is None and will create a new figure of size figsize. show_legend : bool, optional - Wheter to show legend. Default is True + Whether to show legend. Default is True show_threshold: bool, optional - Wheter to show threshold (if it is not set to None). Default is True. + Whether to show threshold (if it is not set to None). Default is True. threshold_options : dict - Dictionnary of options passed to the line plot of the threshold. + Dictionary of options passed to the line plot of the threshold. line_options : dict - Dictionnary of options passed to the plot of the distance vector values. + Dictionary of options passed to the plot of the distance vector values. figure_options : dict - Dictionnary of options passed to plt.figure. Only used if ax is None. + Dictionary of options passed to plt.figure. Only used if ax is None. rc_Params_options: dict - Dictionnary of options passed to plt.rcParams.update. Only used if ax is + Dictionary of options passed to plt.rcParams.update. Only used if ax is None. matplotlib_style: str Matplotlib style to be used. Only used if ax is None. @@ -629,17 +629,17 @@ def plot( scatter_options : dict Options to apply to scatter plot of the shapelet values. line_options : dict - Dictionnary of options passed to plt.plot. Only used if ax is None. + Dictionary of options passed to plt.plot. Only used if ax is None. figure_options : dict - Dictionnary of options passed to plt.figure. Only used if ax is None. + Dictionary of options passed to plt.figure. Only used if ax is None. rc_Params_options: dict - Dictionnary of options passed to plt.rcParams.update. Only used if ax is + Dictionary of options passed to plt.rcParams.update. Only used if ax is None. matplotlib_style: str Matplotlib style to be used. Only used if ax is None. custom_title_string : str If not None, use this string as title for the plot instead of the default - one based on the shapelet parametres. + one based on the shapelet parameters. Returns ------- @@ -781,7 +781,7 @@ def _get_boxplot_data(self, X, mask_class_id, mask_other_class_id, id_shp): titles = [ "Boxplot of min", "Boxplot of argmin", - "Boxplot of Shapelet Occurence", + "Boxplot of Shapelet Occurrence", ] for i in range(3): box_data = [ @@ -896,21 +896,21 @@ class is selected. Sample ID to use for sample of class_id.If None, a random one from that class is selected. scatter_options : dict - Dictionnary of options passed to the scatter plot of the shapelet values. + Dictionary of options passed to the scatter plot of the shapelet values. x_plot_options : dict - Dictionnary of options passed to the plot of the time series values. + Dictionary of options passed to the plot of the time series values. shp_plot_options : dict - Dictionnary of options passed to the plot of the shapelet values. + Dictionary of options passed to the plot of the shapelet values. threshold_plot_options : dict - Dictionnary of options passed to the line plot of the threshold. + Dictionary of options passed to the line plot of the threshold. dist_plot_options : dict - Dictionnary of options passed to the plot of the distance vector values. + Dictionary of options passed to the plot of the distance vector values. figure_options : dict - Dictionnary of options passed to plt.figure. + Dictionary of options passed to plt.figure. boxplot_options : dict - Dictionnary of options passed to features boxplot. + Dictionary of options passed to features boxplot. rc_Params_options: dict - Dictionnary of options passed to plt.rcParams.update. + Dictionary of options passed to plt.rcParams.update. matplotlib_style: str Matplotlib style to be used. @@ -1099,13 +1099,13 @@ def plot_on_X( A matplotlib axe on which to plot the figure. The default is None and will create a new figure of size figsize. scatter_options : dict - Dictionnary of options passed to the scatter plot of the shapelet values. + Dictionary of options passed to the scatter plot of the shapelet values. line_options : dict - Dictionnary of options passed to the plot of the time series values. + Dictionary of options passed to the plot of the time series values. figure_options : dict - Dictionnary of options passed to plt.figure. Only used if ax is None. + Dictionary of options passed to plt.figure. Only used if ax is None. rc_Params_options: dict - Dictionnary of options passed to plt.rcParams.update. Only used if ax is + Dictionary of options passed to plt.rcParams.update. Only used if ax is None. matplotlib_style: str Matplotlib style to be used. Only used if ax is None. @@ -1114,7 +1114,7 @@ def plot_on_X( ------- fig : matplotlib figure The resulting figure with S on its best match on X. A normalised - shapelet will be scalled to macth the scale of X. + shapelet will be scaled to match the scale of X. """ return self.transformer_vis.plot_on_X( @@ -1162,17 +1162,17 @@ def plot_distance_vector( A matplotlib axe on which to plot the figure. The default is None and will create a new figure of size figsize. show_legend : bool, optional - Wheter to show legend. Default is True + Whether to show legend. Default is True show_threshold: bool, optional - Wheter to show threshold (if it is not set to None). Default is True. + Whether to show threshold (if it is not set to None). Default is True. threshold_plot_options : dict - Dictionnary of options passed to the line plot of the threshold. + Dictionary of options passed to the line plot of the threshold. dist_plot_options : dict - Dictionnary of options passed to the plot of the distance vector values. + Dictionary of options passed to the plot of the distance vector values. figure_options : dict - Dictionnary of options passed to plt.figure. Only used if ax is None. + Dictionary of options passed to plt.figure. Only used if ax is None. rc_Params_options: dict - Dictionnary of options passed to plt.rcParams.update. Only used if ax is + Dictionary of options passed to plt.rcParams.update. Only used if ax is None. matplotlib_style: str Matplotlib style to be used. Only used if ax is None. @@ -1233,17 +1233,17 @@ def plot( scatter_options : dict Options to apply to scatter plot of the shapelet values. line_options : dict - Dictionnary of options passed to plt.plot. Only used if ax is None. + Dictionary of options passed to plt.plot. Only used if ax is None. figure_options : dict - Dictionnary of options passed to plt.figure. Only used if ax is None. + Dictionary of options passed to plt.figure. Only used if ax is None. rc_Params_options: dict - Dictionnary of options passed to plt.rcParams.update. Only used if ax is + Dictionary of options passed to plt.rcParams.update. Only used if ax is None. matplotlib_style: str Matplotlib style to be used. Only used if ax is None. custom_title_string : str If not None, use this string as title for the plot instead of the default - one based on the shapelet parametres. + one based on the shapelet parameters. Returns ------- diff --git a/aeon/visualisation/learning_task/_segmentation.py b/aeon/visualisation/learning_task/_segmentation.py index 8eee43f9ff..293df00cc0 100644 --- a/aeon/visualisation/learning_task/_segmentation.py +++ b/aeon/visualisation/learning_task/_segmentation.py @@ -1,4 +1,4 @@ -"""Utility class for ploting functionality.""" +"""Utility class for plotting functionality.""" __all__ = [ "plot_series_with_change_points", diff --git a/aeon/visualisation/results/_critical_difference.py b/aeon/visualisation/results/_critical_difference.py index 7d7cb78aca..7f78a3ec58 100644 --- a/aeon/visualisation/results/_critical_difference.py +++ b/aeon/visualisation/results/_critical_difference.py @@ -47,7 +47,7 @@ def plot_critical_difference( averaging ranks over ties), then sorts estimators on average rank. It then forms cliques. The original critical difference diagrams [1]_ use the post hoc Neymeni test [4]_ to find a critical difference. However, as discussed [3]_,this post hoc - test is senstive to the estimators included in the test: "For instance the + test is sensitive to the estimators included in the test: "For instance the difference between A and B could be declared significant if the pool comprises algorithms C, D, E and not significant if the pool comprises algorithms F, G, H.". Our default option is to base cliques finding on pairwise Wilcoxon sign diff --git a/aeon/visualisation/results/_mcm.py b/aeon/visualisation/results/_mcm.py index 1e2f56be84..0cd116fde7 100644 --- a/aeon/visualisation/results/_mcm.py +++ b/aeon/visualisation/results/_mcm.py @@ -127,7 +127,7 @@ def create_multi_comparison_matrix( colormap: str, default = 'coolwarm' The colormap used in matplotlib, if set to None, no color map is used and the heatmap is turned off, no colors will be seen. - fig_size: str ot tuple of two int, default = 'auto' + fig_size: str or tuple of two int, default = 'auto' The height and width of the figure, if 'auto', use _get_fig_size function in utils.py. Note that the fig size values are in matplotlib units. font_size: int, default = 17 @@ -147,7 +147,7 @@ def create_multi_comparison_matrix( include_legend: bool, default = True Whether or not to show the legend on the MCM. show_symetry: bool, default = True - Whether or not to show the symetrical part of the heatmap. + Whether or not to show the symmetrical part of the heatmap. Returns ------- diff --git a/aeon/visualisation/results/_scatter.py b/aeon/visualisation/results/_scatter.py index 8a3bf96f05..673b8d7f83 100644 --- a/aeon/visualisation/results/_scatter.py +++ b/aeon/visualisation/results/_scatter.py @@ -229,7 +229,7 @@ def plot_pairwise_scatter( plot.get_legend().remove() # Setting text with W, T and L for each method - anc = AnchoredText( + anc_text = AnchoredText( f"{first_method} wins here\n[{wins_A}W, {ties_A}T, {losses_A}L]", loc="upper left" if not lower_better else "lower right", frameon=True, @@ -240,13 +240,13 @@ def plot_pairwise_scatter( ha="center", ), ) - anc.patch.set_boxstyle("round,pad=0.,rounding_size=0.2") - anc.patch.set_color("wheat") - anc.patch.set_edgecolor("black") - anc.patch.set_alpha(0.5) - ax.add_artist(anc) + anc_text.patch.set_boxstyle("round,pad=0.,rounding_size=0.2") + anc_text.patch.set_color("wheat") + anc_text.patch.set_edgecolor("black") + anc_text.patch.set_alpha(0.5) + ax.add_artist(anc_text) - anc = AnchoredText( + anc_text = AnchoredText( f"{second_method} wins here\n[{wins_B}W, {ties_B}T, {losses_B}L]", loc="lower right" if not lower_better else "upper left", frameon=True, @@ -257,11 +257,11 @@ def plot_pairwise_scatter( ha="center", ), ) - anc.patch.set_boxstyle("round,pad=0.,rounding_size=0.2") - anc.patch.set_color("wheat") - anc.patch.set_edgecolor("black") - anc.patch.set_alpha(0.5) - ax.add_artist(anc) + anc_text.patch.set_boxstyle("round,pad=0.,rounding_size=0.2") + anc_text.patch.set_color("wheat") + anc_text.patch.set_edgecolor("black") + anc_text.patch.set_alpha(0.5) + ax.add_artist(anc_text) # Setting title if provided. if title is not None: @@ -297,12 +297,12 @@ def plot_pairwise_scatter( )[1] ttes = f"Paired t-test for equality of means, p-value={p_value_t:.3f}" - wil = f"Wilcoxon test for equality of medians, p-value={p_value_w:.3f}" + wilcox = f"Wilcoxon test for equality of medians, p-value={p_value_w:.3f}" plt.figtext( 0.5, 0.03 if not lower_better else 0.13, - f"{wil}\n{ttes}", + f"{wilcox}\n{ttes}", fontsize=10, wrap=True, horizontalalignment="center", diff --git a/docs/api_reference/data_format.md b/docs/api_reference/data_format.md index a481117ded..c65cf10563 100644 --- a/docs/api_reference/data_format.md +++ b/docs/api_reference/data_format.md @@ -25,12 +25,12 @@ beginning with `@` in the file. 3. A dataset block. Contains a multiple collections of float values that represent the dataset. There are `n` cases each its own time series, delimited by new lines. The values for a - series are expressed in a comma `,` seperated list and the index of each value is + series are expressed in a comma `,` separated list and the index of each value is relative to its position in said list (0, 1, ..., `m`). An instance may contain 1 to `d` channels, where each channel for a case is delimited using a colon `:`. In case timestamps are present, each value in a series is enclosed within round brackets i.e. `(YYYY-MM-DD HH:mm:ss,)`. - The response variable is at the end of each case and is seperated via a colon. + The response variable is at the end of each case and is separated via a colon. Here is an extract from an examlple `.ts` file that shows portions of all three blocks: diff --git a/docs/api_reference/similarity_search.rst b/docs/api_reference/similarity_search.rst index c62b0636f3..aed9ac3748 100644 --- a/docs/api_reference/similarity_search.rst +++ b/docs/api_reference/similarity_search.rst @@ -6,7 +6,7 @@ Similarity search The :mod:`aeon.similarity_search` module contains algorithms and tools for similarity search tasks. First, we distinguish between `series` estimator and `collection` estimators, similarly to the `aeon.transformer` module. Secondly, we distinguish between -estimators used `neighbors` (with sufix SNN for subsequence nearest neighbors, or ANN +estimators used `neighbors` (with suffix SNN for subsequence nearest neighbors, or ANN for approximate nearest neighbors) search and estimators used for `motifs` search. diff --git a/docs/changelogs/v0/v0.1.md b/docs/changelogs/v0/v0.1.md index 94b329b450..894f639a9f 100644 --- a/docs/changelogs/v0/v0.1.md +++ b/docs/changelogs/v0/v0.1.md @@ -51,7 +51,7 @@ Remove TimeSeriesSVC [#105](https://github.com/scikit-time/scikit-time/pull/105) - This implementation is a wrapper of the sklearn SVC for distances. Elastic distances for time series data do not generate semi-positive definite kernels, which breaks SVC assumptions. The wrapper does not align with our outlook for the distances module and has been removed for now. Remove fit_predict in BaseClassifier [#117](https://github.com/scikit-time/scikit-time/pull/117) -- fit_predict in BaseClassifier was an inefficient alternative to simply using scikit-learn cross validation that involved coverting everything to dataframes. We think it is easier to just use scikit-learn functionality rather than reinvent the wheel (inefficiently). +- fit_predict in BaseClassifier was an inefficient alternative to simply using scikit-learn cross validation that involved converting everything to dataframes. We think it is easier to just use scikit-learn functionality rather than reinvent the wheel (inefficiently). Remove plotting [#127](https://github.com/aeon-toolkit/aeon/pull/127) - The classification plotting folder adds a single file to draw a graph for CIF added without review. While we do want functionality for plotting, there was no framework or discussion with this niche and estimator specific addition. diff --git a/docs/changelogs/v0/v0.10.md b/docs/changelogs/v0/v0.10.md index 4d5876d6ec..e1e7eeefdf 100644 --- a/docs/changelogs/v0/v0.10.md +++ b/docs/changelogs/v0/v0.10.md @@ -255,7 +255,7 @@ index page for more information. ### Deprecation -- [DEP] Remove probablistic class based metrics ({pr}`1630`) {user}`TonyBagnall` +- [DEP] Remove probabilistic class based metrics ({pr}`1630`) {user}`TonyBagnall` - [DEP] remove public VectorizedDF ({pr}`1666`) {user}`TonyBagnall` - [DEP] Remove _serialize ({pr}`1667`) {user}`TonyBagnall` - [DEP] v0.10 move ThetaLinesTransformer to legacy ({pr}`1676`) {user}`TonyBagnall` diff --git a/docs/changelogs/v0/v0.11.md b/docs/changelogs/v0/v0.11.md index 60e6debc96..d2dd9ab3d3 100644 --- a/docs/changelogs/v0/v0.11.md +++ b/docs/changelogs/v0/v0.11.md @@ -121,7 +121,7 @@ Reminder: This release will be the last 0.X minor release. Other than patches, t - [ENH] Change HC2 test to balance class ({pr}`1795`) {user}`TonyBagnall` - [ENH] at kwargs to fit_predict to allow setting of the cv_size parameter in default ({pr}`1767`) {user}`TonyBagnall` -- [ENH,MNT,DOC] Tidying up deep learning modules clasification/regression ({pr}`1826`) {user}`hadifawaz1999` +- [ENH,MNT,DOC] Tidying up deep learning modules classification/regression ({pr}`1826`) {user}`hadifawaz1999` - [ENH] Implement Proximity Forest classifier ({pr}`1729`) {user}`itsdivya1309` - [ENH] Remove BaseClassifier unnecessary declarations ({pr}`1887`) {user}`TonyBagnall` - [ENH] Composable ensemble for classification and regression ({pr}`1853`) {user}`MatthewMiddlehurst` @@ -202,7 +202,7 @@ Reminder: This release will be the last 0.X minor release. Other than patches, t ### Enhancements - [ENH] Add AEBiGRUNetwork ({pr}`1583`) {user}`aadya940` -- [ENH,MNT,DOC] Tidying up deep learning modules clasification/regression ({pr}`1826`) {user}`hadifawaz1999` +- [ENH,MNT,DOC] Tidying up deep learning modules classification/regression ({pr}`1826`) {user}`hadifawaz1999` - [ENH] Improve deep learning networks test coverage for parameters which can be list ({pr}`1851`) {user}`Cyril-Meyer` - [ENH] Improve deep learning networks test coverage ({pr}`1870`) {user}`Cyril-Meyer` - [ENH] Add AEDRNNNetwork ({pr}`1577`) {user}`aadya940` @@ -221,7 +221,7 @@ Reminder: This release will be the last 0.X minor release. Other than patches, t ### Enhancements -- [ENH,MNT,DOC] Tidying up deep learning modules clasification/regression ({pr}`1826`) {user}`hadifawaz1999` +- [ENH,MNT,DOC] Tidying up deep learning modules classification/regression ({pr}`1826`) {user}`hadifawaz1999` - [ENH] Composable ensemble for classification and regression ({pr}`1853`) {user}`MatthewMiddlehurst` - [ENH] Adds the option of using callable distances for kNN regressor ({pr}`1965`) {user}`dguijo` diff --git a/docs/changelogs/v0/v0.6.md b/docs/changelogs/v0/v0.6.md index 15461b2644..cc8b1cb1a3 100644 --- a/docs/changelogs/v0/v0.6.md +++ b/docs/changelogs/v0/v0.6.md @@ -123,7 +123,7 @@ December 2023 ### Maintenance - [MNT] Default InceptionTime to use original model and not H version ({pr}`921`) {user}`hadifawaz1999` -- [MNT] Update custom filters in H-InceptionTime and LITETime ot work with new tf versions ({pr}`949`) {user}`hadifawaz1999` +- [MNT] Update custom filters in H-InceptionTime and LITETime to work with new tf versions ({pr}`949`) {user}`hadifawaz1999` ## Regression diff --git a/docs/changelogs/v1.0.md b/docs/changelogs/v1.0.md index 2a9eaa600d..7b8e9f8d2f 100644 --- a/docs/changelogs/v1.0.md +++ b/docs/changelogs/v1.0.md @@ -472,7 +472,7 @@ writing efficient code based on array-based bespoke implementations. - [DOC] Update datasets api and data format page ({pr}`2398`) {user}`MatthewMiddlehurst` - [DOC] Documentation fixes ({pr}`2405`) {user}`MatthewMiddlehurst` - [DOC] Update .all-contributorsrc to add myself as a contributor ({pr}`2200`) {user}`alexbanwell1` -- [DOC] Seperated Notes and References ({pr}`2291`) {user}`aryanpola` +- [DOC] Separated Notes and References ({pr}`2291`) {user}`aryanpola` ### Enhancements diff --git a/docs/changelogs/v1.1.md b/docs/changelogs/v1.1.md index 55047c1b12..aa8739fb57 100644 --- a/docs/changelogs/v1.1.md +++ b/docs/changelogs/v1.1.md @@ -151,7 +151,7 @@ Make sure each dataframe in the list has channels as the first dimension and tim ### Documentation - [DOC] Base collection class docstring formatting ({pr}`2452`) {user}`TonyBagnall` -- [DOC] Inconsistent double qoutes in regression module ({pr}`2640`) {user}`Val-2608` +- [DOC] Inconsistent double quotes in regression module ({pr}`2640`) {user}`Val-2608` ### Maintenance @@ -196,7 +196,7 @@ Make sure each dataframe in the list has channels as the first dimension and tim - [MNT] Testing fixes ({pr}`2531`) {user}`MatthewMiddlehurst` - [MNT] Fix random state deep clustering checking test ({pr}`2528`) {user}`hadifawaz1999` -- [MNT] Skip some excected results tests when numba is disabled ({pr}`2639`) {user}`MatthewMiddlehurst` +- [MNT] Skip some expected results tests when numba is disabled ({pr}`2639`) {user}`MatthewMiddlehurst` - [MNT] Remove REDCOMETs from testing exclusion list ({pr}`2630`) {user}`MatthewMiddlehurst` - [MNT,ENH] Update to allow Python 3.13 ({pr}`2608`) {user}`MatthewMiddlehurst` @@ -238,7 +238,7 @@ Make sure each dataframe in the list has channels as the first dimension and tim - [ENH] Add sphinx event to add capability table to estimators' docs individually ({pr}`2468`) {user}`inclinedadarsh` - [DOC] Added search functionality for estimator overview table ({pr}`2496`) {user}`kavya-r30` - [ENH,MNT] Assign Bot (assigned issues>2) ({pr}`2702`) {user}`aryanpola` -- [MNT,ENH] Assign-bot (Allow users to type alternative phrases for assingment) ({pr}`2704`) {user}`Ramana-Raja` +- [MNT,ENH] Assign-bot (Allow users to type alternative phrases for assignment) ({pr}`2704`) {user}`Ramana-Raja` ### Maintenance @@ -251,7 +251,7 @@ Make sure each dataframe in the list has channels as the first dimension and tim - [MNT] Fix run_examples.sh exclusion ({pr}`2701`) {user}`MatthewMiddlehurst` - [MNT] Updated the release workflows ({pr}`2638`) {user}`MatthewMiddlehurst` - [ENH,MNT] Assign Bot (assigned issues>2) ({pr}`2702`) {user}`aryanpola` -- [MNT,ENH] Assign-bot (Allow users to type alternative phrases for assingment) ({pr}`2704`) {user}`Ramana-Raja` +- [MNT,ENH] Assign-bot (Allow users to type alternative phrases for assignment) ({pr}`2704`) {user}`Ramana-Raja` ### Other diff --git a/docs/conf.py b/docs/conf.py index fd2d36a896..75fb12bee7 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -401,7 +401,7 @@ def _does_not_start_with_underscore(input_string): # Initialize data dictionary with base columns data = {col: [] for col in base_columns} # Add abbreviated columns - data.update({abbrevation: [] for abbrevation in capabilities_to_include.values()}) + data.update({abbreviation: [] for abbreviation in capabilities_to_include.values()}) for estimator_name, estimator_class in all_estimators(include_sklearn=False): algorithm_type = "::".join(str(estimator_class).split(".")[1:-2]) @@ -428,14 +428,14 @@ def _does_not_start_with_underscore(input_string): data["Method family"].append("/".join(algorithm_type[1:])) else: data["Method family"].append("N/A") - for capability_name, abbrevation in capabilities_to_include.items(): + for capability_name, abbreviation in capabilities_to_include.items(): _val = tag_dict.get(f"capability:{capability_name}") # For case where tag is not included output as not supported if not _val or _val is None: - data[abbrevation].append("\u274c") + data[abbreviation].append("\u274c") else: - data[abbrevation].append("\u2705") + data[abbreviation].append("\u2705") df = pd.DataFrame(data).sort_values( by=["Module", "Method family", "Estimator name"] diff --git a/docs/developer_guide/release.md b/docs/developer_guide/release.md index 45c0d6dcf9..c8b7009577 100644 --- a/docs/developer_guide/release.md +++ b/docs/developer_guide/release.md @@ -32,7 +32,7 @@ The release process is as follows, on high-level: 4. **Create the GitHub release.** This release should create a new tag following the syntax v[MAJOR].[MINOR].[PATCH], e.g., the string `v0.10.0` for version `0.10.0`. The release name should similarly be - `aeon v0.10.0`. The GitHub release notes should contain only "hightlights", + `aeon v0.10.0`. The GitHub release notes should contain only "highlights", "new contributors" and "all contributors" sections, and otherwise link to the release notes in the changelog, following the pattern of current GitHub release notes. The full GitHub commit log between releases can also be included. diff --git a/docs/getting_started.md b/docs/getting_started.md index 1da416a1df..3debff0d7d 100644 --- a/docs/getting_started.md +++ b/docs/getting_started.md @@ -76,7 +76,7 @@ international airline passengers, 1949 to 1960, in thousands. A multivariate time series is made up of multiple series or channels, where each observation is a vector of related recordings in the same time index. An example would be a motion trace from a smartwatch with at least three dimensions (X,Y,Z -co-ordinates), or multiple financial statistics recorded over time. Single +coordinates), or multiple financial statistics recorded over time. Single multivariate series input typically follows the shape `(n_channels, n_timepoints)` by default. Algorithms may have an `axis` parameter to change this, where `axis=1` assumes the default shape and is the default setting, and `axis=0` assumes the shape diff --git a/examples/benchmarking/benchmarking.ipynb b/examples/benchmarking/benchmarking.ipynb index 6bc1f8517c..248aab80a5 100644 --- a/examples/benchmarking/benchmarking.ipynb +++ b/examples/benchmarking/benchmarking.ipynb @@ -25,7 +25,7 @@ "\n", "- [Loading published results files](./published_results.ipynb)\n", "- [Loading and using reference results](./reference_results.ipynb)\n", - "- [Regression bechmarking](./regression.ipynb)\n", + "- [Regression benchmarking](./regression.ipynb)\n", "\n", "\n", "These notebooks demonstrate usage of the `benchmarking` module." diff --git a/examples/benchmarking/published_results.ipynb b/examples/benchmarking/published_results.ipynb index f98543669b..58af682eb5 100644 --- a/examples/benchmarking/published_results.ipynb +++ b/examples/benchmarking/published_results.ipynb @@ -23,7 +23,7 @@ "has slightly changed and hence there may be small variation in cliques from published \n", "results.\n", "\n", - "The published results can be recovered from the [time series classifcation\n", + "The published results can be recovered from the [time series classification\n", "website](https://timeseriesclassification.com/results/PublishedResults/) directly or\n", "with ``aeon``." ] diff --git a/examples/benchmarking/reference_results.ipynb b/examples/benchmarking/reference_results.ipynb index 614d6f327a..f424526525 100644 --- a/examples/benchmarking/reference_results.ipynb +++ b/examples/benchmarking/reference_results.ipynb @@ -628,7 +628,7 @@ "metadata": {}, "cell_type": "markdown", "source": [ - "The `get_estimator_results` function returns the resutls as a dictionary of dictionaries, where the first key is the classifier name and the second key is the dataset name." + "The `get_estimator_results` function returns the results as a dictionary of dictionaries, where the first key is the classifier name and the second key is the dataset name." ] }, { diff --git a/examples/classification/classification.ipynb b/examples/classification/classification.ipynb index 6971838705..f1760686f1 100644 --- a/examples/classification/classification.ipynb +++ b/examples/classification/classification.ipynb @@ -55,7 +55,7 @@ "Time series can be univariate (each observation is a single value) or multivariate\n", "(each observation is a vector). For example, an ECG reading from a single\n", "sensor is a univariate series, but a motion trace of from a smart watch would be\n", - "multivariate, with at least three dimensions (x,y,z co-ordinates). The image above is\n", + "multivariate, with at least three dimensions (x,y,z coordinates). The image above is\n", " a univariate problem: each series has its own label. The dimension of the time\n", " series instance is also often called the channel. We recommend storing time series\n", " in 3D numpy array of shape `(n_cases, n_channels, n_timepoints)` and,\n", diff --git a/examples/classification/convolution_based.ipynb b/examples/classification/convolution_based.ipynb index 17d022ed96..085a181384 100644 --- a/examples/classification/convolution_based.ipynb +++ b/examples/classification/convolution_based.ipynb @@ -455,7 +455,7 @@ "from aeon.datasets.tsc_datasets import univariate\n", "\n", "names = [t[0].replace(\"Classifier\", \"\") for t in est]\n", - "names.append(\"MiniROCKET\") # Alternatve configuration of the RocketClassifier\n", + "names.append(\"MiniROCKET\") # Alternative configuration of the RocketClassifier\n", "results, present_names = get_estimator_results_as_array(\n", " names, univariate, include_missing=False\n", ")\n", diff --git a/examples/classification/distance_based.ipynb b/examples/classification/distance_based.ipynb index fec2f1627e..adb8cb3375 100644 --- a/examples/classification/distance_based.ipynb +++ b/examples/classification/distance_based.ipynb @@ -186,7 +186,7 @@ "KNeighborsTimeSeriesClassifier in aeon is configurable to use any of the distances\n", "functions in the distance module, or it can be passed a bespoke callable. You can set\n", "the number of neighbours and the weights. Weights are used in the prediction\n", - "process when neightbours differ in class values. By default all neighbours have an\n", + "process when neighbours differ in class values. By default all neighbours have an\n", "equal vote. There is an option to weight by distance, meaning closer neighbours have\n", "more weight in the vote." ] diff --git a/examples/classification/hybrid.ipynb b/examples/classification/hybrid.ipynb index 882817f168..0b650fd2bb 100644 --- a/examples/classification/hybrid.ipynb +++ b/examples/classification/hybrid.ipynb @@ -67,7 +67,7 @@ "each module found through cross-validation on the train data. The weighted\n", "probabilities from each module are summed and standardised to produce the HIVE-COTE\n", "probability prediction. HC$_\\alpha$ is not currently available in aeon, since it has\n", - "been superseeded by later versions.\n" + "been superseded by later versions.\n" ] }, { diff --git a/examples/classification/interval_based.ipynb b/examples/classification/interval_based.ipynb index 45988a0f70..3c9c6bf83e 100644 --- a/examples/classification/interval_based.ipynb +++ b/examples/classification/interval_based.ipynb @@ -6,7 +6,7 @@ "source": [ "# Interval based time series classification in aeon\n", "\n", - "Interval based approaches look at phase dependant intervals of the full series, calculating summary statistics from selected subseries to be used in classification.\n", + "Interval based approaches look at phase dependent intervals of the full series, calculating summary statistics from selected subseries to be used in classification.\n", "\n", "Current interval based approaches are implemented in aeon. Time Series Forest\n", "(TSF) \\[1\\], the Random Interval Spectral Ensemble (RISE) \\[2\\], Supervised Time\n", diff --git a/examples/classification/shapelet_based.ipynb b/examples/classification/shapelet_based.ipynb index 3384995ab5..244062613d 100644 --- a/examples/classification/shapelet_based.ipynb +++ b/examples/classification/shapelet_based.ipynb @@ -196,7 +196,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Historicaly, shapelets were first proposed as a primitive for time series machine learning [1] and were used as spliting criterion in a decision tree for time series classification. To make this work, a distance $dist$ between a shapelet $S$ and a time series $X$ was defined such as $dist(S,X) \\rightarrow \\mathbb{R}$. The Shapelet-based splitting criterion was then based on a threshold $\\lambda$ on the distance as $dist(S,X) < \\lambda$ to direct a time series $X$ to the left or right child node, with each node being defined by another shapelet.\n", + "Historicaly, shapelets were first proposed as a primitive for time series machine learning [1] and were used as splitting criterion in a decision tree for time series classification. To make this work, a distance $dist$ between a shapelet $S$ and a time series $X$ was defined such as $dist(S,X) \\rightarrow \\mathbb{R}$. The Shapelet-based splitting criterion was then based on a threshold $\\lambda$ on the distance as $dist(S,X) < \\lambda$ to direct a time series $X$ to the left or right child node, with each node being defined by another shapelet.\n", "\n", "This way of calculating the distance, which is still used today, was to compute the\n", "Euclidean distance between $S$ and all subsequences of size $l$ in $X$, and to return the minimum. More formally :\n", @@ -482,11 +482,11 @@ "\n", "Now that we understand what shapelets are and how they are used, a question is left unanswered, how do we extract them ? This process is hidden behind the `fit` method of the shapelet-based transformations. We can distinguish x types of shapelet extraction processes:\n", "\n", - "- **Exaustive search** : In this case, we extract all possible shapelet candidates (i.e. all subsequences of a given length) and evalute their quality to select the best ones. For example, after removing self-similar shapelets, we can compute the information gain of a shapelet $S$ based on $dist(S,X) < \\lambda$, as done in the shapelet-tree of [1], or as in [2] with the F-statistic of a fixed effects ANOVA.\n", + "- **Exhaustive search** : In this case, we extract all possible shapelet candidates (i.e. all subsequences of a given length) and evaluate their quality to select the best ones. For example, after removing self-similar shapelets, we can compute the information gain of a shapelet $S$ based on $dist(S,X) < \\lambda$, as done in the shapelet-tree of [1], or as in [2] with the F-statistic of a fixed effects ANOVA.\n", "\n", - "- **Heuristic-based search**: One issue of the exhaustive search is that it can take a long time to extract and evaluate all shapelet candidates in a dataset. A lot of different approaches are proposed in the literature try to approximate the result of the exaustive search while minimizing the time needed to obtain the shapelets.\n", + "- **Heuristic-based search**: One issue of the exhaustive search is that it can take a long time to extract and evaluate all shapelet candidates in a dataset. A lot of different approaches are proposed in the literature try to approximate the result of the exhaustive search while minimizing the time needed to obtain the shapelets.\n", " - For example, instead of evaluating all possible candidates, `RandomShapeletTransform` uses a parameter `n_shapelet_samples` to randomly extract a limited number of shapelets from the input. It then restricts the quality evaluation to these randomly extracted candidates to reduce the search time. It also considers the notion of self-similar shapelets during the candidate extraction to avoid sampling similar shapelets as they would be redundant.\n", - " - Others such as `SAST`[4] only select a small number of \"reference\" time series in the training data where all subsequences will be considered as shapelets without evaluating their quality. This leaves the \"feature selection\" step to the classifier that will use the transformation. `RSAST`[5] uses the same approach but also uses some statistical critera to further reduce the number of candidates extracted from these reference time series.\n", + " - Others such as `SAST`[4] only select a small number of \"reference\" time series in the training data where all subsequences will be considered as shapelets without evaluating their quality. This leaves the \"feature selection\" step to the classifier that will use the transformation. `RSAST`[5] uses the same approach but also uses some statistical criteria to further reduce the number of candidates extracted from these reference time series.\n", " - Another approach used in `RandomDilatedShapeletTransform` is to use a semi-random extraction which is guided by a masking of the input space. Once a shapelet has been randomly sampled from a time series, the neighboring points around the sampling point are removed from the list of available sampling points. This avoids extracting self-similar shapelets and improves the diversity of the extracted shapelet set. The number of neighboring points affected by this process is controlled with the `alpha_similarity` parameter.\n", "\n", "- **Shapelet generation**: This last approach takes another view at the problem: What if the best shapelets for my dataset are not present in the training data ? The goal is to use optimization methods, such as gradient descent or evolutionary algorithm, to generate shapelet values instead of extracting them from the input. The first shapelet generation method was Learning Shapelet [6], which due to the nature of the extraction, is only implemented as a classifier in aeon inside `LearningShapeletClassifier`.\n", @@ -657,7 +657,7 @@ "The distance vector $V = [v_1, \\ldots, v_{m-(l\\times \\mathbf{d})}]$ with $v_i = \\sqrt{\\sum_{j=0}^{l-1}(\\mathbf{s}_j - \\mathbf{x}_{i+(j\\times \\mathbf{d})})^2}$ (here with euclidean distance), contains all the distances between $S$ and the candidate subsequences in $X$ (subsequence of the same length, including dilation). While most shapelet-based transformers and classifiers in aeon only extract the minimum value from $V$, in `RandomDilatedShapeletTransform`, two more features are extracted :\n", "\n", "- $\\text{argmin} V$ : the location of the best match, where the location is the position of the front of the shapelet\n", - "- $\\sum_i I(v_i < \\lambda)$ : the shapelet occurence feature, which counts how many times $S$ was $\\lambda$-close to $X$ (with $I$ the identity function).\n", + "- $\\sum_i I(v_i < \\lambda)$ : the shapelet occurrence feature, which counts how many times $S$ was $\\lambda$-close to $X$ (with $I$ the identity function).\n", "\n", "These features allow us to use shapelets to discriminate, and as `RandomDilatedShapeletTransform` also make use of normalized and non-normalized shapelets, we can distinguish the following cases :\n", "\n", @@ -678,7 +678,7 @@ "\n", "## Generalization to the unequal length case\n", "\n", - "In this case, the only adaptation needed is to make sure that shapelets are not longer than any of the time series we use as input. Otherwise, this is more of an implementation problem, as theoritically, there is no issue with computing distance vectors with a shapelet and different sized time series.\n", + "In this case, the only adaptation needed is to make sure that shapelets are not longer than any of the time series we use as input. Otherwise, this is more of an implementation problem, as theoretically, there is no issue with computing distance vectors with a shapelet and different sized time series.\n", "\n", "**One point of attention in this case will be on the feature extracted**. Notably with `RDSTClassifier`, as both the `argmin` and the `ShapeletOccurence` features will be affected by varying time series length, it is up to the user to decide whether these feature should be normalized by the length of the time series from which they are extracted (e.g. `argmin/m_i`, with $m_i$ the length of time series $X_i$). If not normalized, these features could encode some information about time series length, and thus bias the classifier if this information is irrelevant.\n" ] diff --git a/examples/clustering/feature_based_clustering.ipynb b/examples/clustering/feature_based_clustering.ipynb index ce5385b778..6b20bce651 100644 --- a/examples/clustering/feature_based_clustering.ipynb +++ b/examples/clustering/feature_based_clustering.ipynb @@ -1451,18 +1451,8 @@ "\n", "[3] Christ, Maximilian, et al. “Time series feature extraction on basis of scalable hypothesis tests (tsfresh–a python package).” Neurocomputing 307 (2018): 72-77. https://www.sciencedirect.com/science/article/pii/S0925231218304843\n", "\n", - "[4] John Paparrizos, Fan Yang, and Haojun Li. 2018. Bridging the Gap: A Decade Review of Time-Series Clustering Methods. In Proceedings\n", - " of Make sure to enter the correct conference title from your rights confirmation emai (Conference acronym ’XX). ACM, New York, NY,\n", - " USA, 52 pages. https://arxiv.org/html/2412.20582v1" + "[4] Paparrizos, John, Fan Yang, and Haojun Li. \"Bridging the gap: A decade review of time-series clustering methods.\" arXiv preprint arXiv:2412.20582 (2024). https://arxiv.org/html/2412.20582v1" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e2f743d3-4cfb-49bd-bb41-ada2d9f65f08", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/examples/clustering/partitional_clustering.ipynb b/examples/clustering/partitional_clustering.ipynb index d7da495342..a24aa323d8 100644 --- a/examples/clustering/partitional_clustering.ipynb +++ b/examples/clustering/partitional_clustering.ipynb @@ -10,7 +10,7 @@ "\n", "Partition based clustering algorithms for time series are those where $k$\n", "clusters are created from $n$ time series. The aim is to cluster so that each time\n", - "series in a cluster are homogenous (similar) to each other and heterogeneous\n", + "series in a cluster are homogeneous (similar) to each other and heterogeneous\n", "(dissimilar) to those outside the cluster.\n", "\n", "Broadly speaking, clustering algorithms are either partitional or hierarchical. Partitional clustering algorithms assign (possibly probabilistic) cluster membership to each time series, usually through an iterative heuristic process of optimising some objective function that measures homogeneity. Given a dataset of $n$ time series, $D$, the partitional time series clustering problem is to partition $D$ into $k$ clusters, $C = \\{C_1, C_2, ..., C_k\\}$ where $k$ is the number of clusters.\n", @@ -109,7 +109,7 @@ "- Initialisation of cluster representatives\n", "- Assignment of series to clusters based on distance to representatives\n", "- Updating representatives to reflect new clusters\n", - "- Repeat assigment and updating until convergence" + "- Repeat assignment and updating until convergence" ] }, { @@ -139,7 +139,7 @@ " .e. the greater the distance the more likely the time series will be\n", " chosen). The remaining centres are selected using the same method, using the\n", " nearest distance to representatives already chosen to find the probability\n", - " of selection. This means that the selected representatives will proably be\n", + " of selection. This means that the selected representatives will probably be\n", " further apart from each other than randomly selected representatives." ] }, @@ -1721,7 +1721,7 @@ "3. Barycentre averaging with MSM is better than using DBA [2]\n", "4. $k$-medoids is generally better than $k$-means [3]\n", "\n", - "All the results to support these conclusions are (or will be) avaialable on the\n", + "All the results to support these conclusions are (or will be) available on the\n", "aeon companion site [tsml archive](https://www.timeseriesclassification.com/). We can\n", "download these results and recreate the performance comparison in `aeon`.\n" ] @@ -1784,7 +1784,7 @@ }, "source": [ "We have pulled down the test accuracies from the website for eight clustering\n", - "algorithms for 111 datsets. the number of above each line is the average rank of each\n", + "algorithms for 111 datasets. the number of above each line is the average rank of each\n", " algorithm" ] }, diff --git a/examples/datasets/data_loading.ipynb b/examples/datasets/data_loading.ipynb index 7716dceee8..ef2f268221 100644 --- a/examples/datasets/data_loading.ipynb +++ b/examples/datasets/data_loading.ipynb @@ -75,7 +75,7 @@ " @targetlabel \n", " @data\n", "\n", - "Note that these tags are not esssential, but they help understanding of the data.\n", + "Note that these tags are not essential, but they help understanding of the data.\n", " If they are not present they are inferred from the data. They are also not case\n", " sensitive. We use camel case in the files for readability, but internally,\n", " everything is stripped back to lower case. Note that only one of classlabel or\n", diff --git a/examples/datasets/load_data_from_web.ipynb b/examples/datasets/load_data_from_web.ipynb index 75f5c28c9a..8c6c8ac8d6 100644 --- a/examples/datasets/load_data_from_web.ipynb +++ b/examples/datasets/load_data_from_web.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "metadata": {}, "cell_type": "markdown", "source": [ "# Downloading and loading benchmarking datasets\n", @@ -19,20 +20,18 @@ "numpy if `n_timepoints` is different for different cases. Forecasting data are loaded\n", "into pd.DataFrame. Anomaly detection dataset are loaded into 2D numpy arrays of shape\n", "`(n_timepoints, n_channels)`. For more information on aeon data types see the\n", - "[data structures notebook](datsets.ipynb).\n", + "[data structures notebook](datasets.ipynb).\n", "\n", "Note that this notebook is dependent on external websites, so will not function if\n", "you are not online or the associated website is down. We use the following four\n", "functions" - ], - "metadata": { - "collapsed": false - } + ] }, { + "metadata": {}, "cell_type": "code", - "execution_count": 1, "outputs": [], + "execution_count": null, "source": [ "from aeon.datasets import (\n", " load_anomaly_detection,\n", @@ -40,14 +39,7 @@ " load_forecasting,\n", " load_regression,\n", ")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-06-17T13:07:18.978527Z", - "start_time": "2024-06-17T13:07:18.453465Z" - } - } + ] }, { "cell_type": "markdown", @@ -168,45 +160,28 @@ } }, { + "metadata": {}, "cell_type": "markdown", "source": [ "## Time Series (Extrinsic) Regression\n", "\n", "[The Monash Time Series Extrinsic Regression Archive]() [3] repo (called extrinsic to\n", - " diffentiate if from sliding window based regression) currently contains 19\n", + " differentiate if from sliding window based regression) currently contains 19\n", " regression problems in .ts format. One of these, Covid3Month, is in `datasets\\data`.\n", " We have recently expanded this repo to include 63 problems in .ts format.\n", " The usage of `load_regression` is identical to `load_classification`\n" - ], - "metadata": { - "collapsed": false - } + ] }, { + "metadata": {}, "cell_type": "code", - "execution_count": 4, - "outputs": [ - { - "data": { - "text/plain": "['AcousticContaminationMadrid',\n 'AluminiumConcentration',\n 'AppliancesEnergy',\n 'AustraliaRainfall',\n 'BIDMC32HR',\n 'BIDMC32RR',\n 'BIDMC32SpO2',\n 'BarCrawl6min',\n 'BeijingIntAirportPM25Quality',\n 'BeijingPM10Quality',\n 'BeijingPM25Quality',\n 'BenzeneConcentration',\n 'BinanceCoinSentiment',\n 'BitcoinSentiment',\n 'BoronConcentration',\n 'CalciumConcentration',\n 'CardanoSentiment',\n 'ChilledWaterPredictor',\n 'CopperConcentration',\n 'Covid19Andalusia',\n 'Covid3Month',\n 'DailyOilGasPrices',\n 'DailyTemperatureLatitude',\n 'DhakaHourlyAirQuality',\n 'ElectricMotorTemperature',\n 'ElectricityPredictor',\n 'EthereumSentiment',\n 'FloodModeling1',\n 'FloodModeling2',\n 'FloodModeling3',\n 'GasSensorArrayAcetone',\n 'GasSensorArrayEthanol',\n 'HotwaterPredictor',\n 'HouseholdPowerConsumption1',\n 'HouseholdPowerConsumption2',\n 'IEEEPPG',\n 'IronConcentration',\n 'LPGasMonitoringHomeActivity',\n 'LiveFuelMoistureContent',\n 'MadridPM10Quality',\n 'MagnesiumConcentration',\n 'ManganeseConcentration',\n 'MethaneMonitoringHomeActivity',\n 'MetroInterstateTrafficVolume',\n 'NaturalGasPricesSentiment',\n 'NewsHeadlineSentiment',\n 'NewsTitleSentiment',\n 'OccupancyDetectionLight',\n 'PPGDalia',\n 'ParkingBirmingham',\n 'PhosphorusConcentration',\n 'PotassiumConcentration',\n 'PrecipitationAndalusia',\n 'SierraNevadaMountainsSnow',\n 'SodiumConcentration',\n 'SolarRadiationAndalusia',\n 'SteamPredictor',\n 'SulphurConcentration',\n 'TetuanEnergyConsumption',\n 'VentilatorPressure',\n 'WaveDataTension',\n 'WindTurbinePower',\n 'ZincConcentration']" - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], + "execution_count": null, "source": [ "from aeon.datasets.dataset_collections import get_available_tser_datasets\n", "\n", "get_available_tser_datasets()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-06-17T13:07:19.018724Z", - "start_time": "2024-06-17T13:07:19.010510Z" - } - } + ] }, { "cell_type": "code", diff --git a/examples/datasets/provided_data.ipynb b/examples/datasets/provided_data.ipynb index 6546f70e24..bdb77aad37 100644 --- a/examples/datasets/provided_data.ipynb +++ b/examples/datasets/provided_data.ipynb @@ -881,7 +881,7 @@ "| MTS consumption/income data | `load_uschange` | multivariate |\n", "\n", " These are stored in csv format in time, value format, including a header. For\n", - " forcasting files, each column that is not an index is considered a time series. For\n", + " forecasting files, each column that is not an index is considered a time series. For\n", " example, the airline data has a single time series each row a time, value pair:\n", "\n", " Date,Passengers\n", @@ -955,7 +955,7 @@ "### Longley\n", "This mulitvariate time series dataset contains various US macroeconomic\n", " variables from 1947 to 1962 that are known to be highly collinear. This loader\n", - " returns the multivariate time series as a numpy array or a pandas DataFrame wit\n", + " returns the multivariate time series as a numpy array or a pandas DataFrame with\n", " the following columns:\n", " TOTEMP - Total employment\n", " GNPDEFL - Gross national product deflator\n", @@ -1193,7 +1193,7 @@ "cell_type": "markdown", "source": [ "### Solar\n", - "Example national solar data for the GB eletricity network extracted from the Sheffield Solar PV_Live API.\n", + "Example national solar data for the GB electricity network extracted from the Sheffield Solar PV_Live API.\n", " Note that these are estimates of the true solar\n", " generation, since the true values are \"behind the meter\" and essentially\n", " unknown. The returned data is half hourly." diff --git a/examples/distances/distances.ipynb b/examples/distances/distances.ipynb index 71513daf14..306e147300 100644 --- a/examples/distances/distances.ipynb +++ b/examples/distances/distances.ipynb @@ -275,7 +275,7 @@ "\n", "\n", "The warping path can be seen as the path of least resistance through the cost matrix\n", - "`C` found throug a dynamic programming formulation.\n", + "`C` found through a dynamic programming formulation.\n", "\n", "\n", "\n" @@ -415,7 +415,7 @@ "collapsed": false }, "source": [ - "This bounding matrix produces a corridor of allowed warping. A nice visualisation of DTW with a constained window from https://github.com/hadifawaz1999/DTW_GUI\n", + "This bounding matrix produces a corridor of allowed warping. A nice visualisation of DTW with a constrained window from https://github.com/hadifawaz1999/DTW_GUI\n", "\n", "\n", "\n", diff --git a/examples/networks/deep_learning.ipynb b/examples/networks/deep_learning.ipynb index 7cf092f7f3..87dc95c9d4 100644 --- a/examples/networks/deep_learning.ipynb +++ b/examples/networks/deep_learning.ipynb @@ -137,8 +137,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 358ms/step\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 386ms/step\n", + "\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 358ms/step\n", + "\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 386ms/step\n", "Predictions: ['2' '2' '2' '2' '2']\n", "Ground Truth: ['1' '2' '2' '1' '1']\n" ] @@ -185,8 +185,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 557ms/step\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step\n", + "\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 557ms/step\n", + "\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 1s/step\n", "Predictions: ['2' '2' '2' '2' '2']\n", "Ground Truth: ['1' '2' '2' '1' '1']\n" ] @@ -231,8 +231,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 374ms/step\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 368ms/step\n", + "\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 374ms/step\n", + "\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 368ms/step\n", "Predictions: [ -3.88514614 -0.3810918 -0.2344005 -5.31711912 -37.39011002]\n", "Ground Truth: [0. 0.07758621 0. 0. 0.15400309]\n" ] @@ -249,12 +249,12 @@ "\n", "#### Auto-Encoder Fully Convolutional Network\n", "\n", - "For this model, we adapted the idea in [7] to construct a simple auto-encoder model with the Fully Convolutional Network (FCN) [2] as backbone for the encoder and decoder architectures. Simply the decoder is the symetrical version of the encoder (FCN) with the usage of Convolution1DTranspose instead of normal Convolution1D layers. The auto-encoder is trained to correcly reconstruct the input time series in order to learn a clustering task in the latent representations (between encoder and decoder).\n", + "For this model, we adapted the idea in [7] to construct a simple auto-encoder model with the Fully Convolutional Network (FCN) [2] as backbone for the encoder and decoder architectures. Simply the decoder is the symmetrical version of the encoder (FCN) with the usage of Convolution1DTranspose instead of normal Convolution1D layers. The auto-encoder is trained to correctly reconstruct the input time series in order to learn a clustering task in the latent representations (between encoder and decoder).\n", "\n", "The AEFCNClusterer can be used in many ways, first of all, you can control whether your latent space is time series space or Euclidean space.\n", "This is done by setting the `temporal_latent_space` flag to `True` or `False`.\n", "\n", - "Given that the AEFCNClusterer learns the clustering task in the latent space, then a clustering algorithm should be defined, this is done by passing it as string value to thr `clustering_algorithm` parameter. Modifying the clustering algorithm parameters is possible throught a dictionary parameter `clustering_params`.\n", + "Given that the AEFCNClusterer learns the clustering task in the latent space, then a clustering algorithm should be defined, this is done by passing it as string value to thr `clustering_algorithm` parameter. Modifying the clustering algorithm parameters is possible through a dictionary parameter `clustering_params`.\n", "\n", "To train the model, simply call the `fit()` function which will train the auto-encoder followed by training the clustering algorithm in the latent space.\n", "\n", @@ -286,8 +286,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step\n", - "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n", + "\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 99ms/step\n", + "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 14ms/step\n", "Predictions: [1 0 1 1 0]\n", "Ground Truth: ['1' '2' '2' '1' '1']\n" ] @@ -357,8 +357,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n", - "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n", + "\u001B[1m10/10\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 15ms/step\n", + "\u001B[1m10/10\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 17ms/step\n", "['1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n", " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n", " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n", @@ -719,7 +719,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 931ms/step\n" + "\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 931ms/step\n" ] }, { diff --git a/examples/segmentation/segmentation_with_clasp.ipynb b/examples/segmentation/segmentation_with_clasp.ipynb index c5397e3d56..d4d11cd073 100644 --- a/examples/segmentation/segmentation_with_clasp.ipynb +++ b/examples/segmentation/segmentation_with_clasp.ipynb @@ -51,7 +51,7 @@ "id": "continuing-prototype", "metadata": {}, "source": [ - "The following use case shows an energy profile of electric household devices with four change points indicating different operational states or pluged household devices." + "The following use case shows an energy profile of electric household devices with four change points indicating different operational states or plugged household devices." ] }, { @@ -86,7 +86,7 @@ "id": "provincial-comment", "metadata": {}, "source": [ - "The anotated change points are around time stamps $[1090,4436,5712,7923]$, from which different electric devices are recorded." + "The annotated change points are around time stamps $[1090,4436,5712,7923]$, from which different electric devices are recorded." ] }, { @@ -328,7 +328,7 @@ "\n", "ClaSP provides two different output formats given the `fmt` input parameter:\n", "- `fmt:\"sparse\"`: provides the core change points\n", - "- `fmt:\"dense\"`: provides an inteval series giving the segmentation\n", + "- `fmt:\"dense\"`: provides an interval series giving the segmentation\n", "\n", "We have seen the output of `fmt=\"sparse\"` before. This time we return `fmt=\"dense\"`." ] diff --git a/examples/similarity_search/code_speed.ipynb b/examples/similarity_search/code_speed.ipynb index 0433b44962..386e061cf7 100644 --- a/examples/similarity_search/code_speed.ipynb +++ b/examples/similarity_search/code_speed.ipynb @@ -130,7 +130,7 @@ "\n", "The optimization here consists in keeping a rolling sum, we only compute the full sum of the `l` values for the first window `W_0`, then to obtain the sum for `W_1`, we remove `x_0` and add `x_{1+(l-1)}` from the sum of `W_0`. We can also a rolling squared sum to compute the standard deviation.\n", "\n", - "The `sliding_mean_std_one_series` function implement the computation of the means and standard deviations using these two rolling sums. The last argument indicates the dilation to apply to the subsequence, which is not used here, hence the value of 1 in the code bellow." + "The `sliding_mean_std_one_series` function implement the computation of the means and standard deviations using these two rolling sums. The last argument indicates the dilation to apply to the subsequence, which is not used here, hence the value of 1 in the code below." ] }, { @@ -206,7 +206,7 @@ "source": [ "The standard way to compute the (squared) euclidean distance between a query `Q = {q_1, ..., q_l}` and a candidate subsequence `W_i = {x_i, ..., x_{i+(l-1)}` is to compute it as $d(Q,W_i) = \\sum_j^l (x_{i+j} - q_j)^2$.\n", "\n", - "We can also express this distance as $d(Q,W_i) = Q^2 + W_i^2 - 2Q.W_i$, in our case, we can use the fact a cross corelation can be used to compute $Q*X$ to obtain the dot products between $Q$ and all $W_i$. The timing difference become even more important for large input, when it becomes worth to use a fast Fourrier transform to compute the convolution in the frequency domain. See scipy.signal.convolve documentation for more information." + "We can also express this distance as $d(Q,W_i) = Q^2 + W_i^2 - 2Q.W_i$, in our case, we can use the fact a cross correlation can be used to compute $Q*X$ to obtain the dot products between $Q$ and all $W_i$. The timing difference become even more important for large input, when it becomes worth to use a fast Fourrier transform to compute the convolution in the frequency domain. See scipy.signal.convolve documentation for more information." ] }, { @@ -275,9 +275,7 @@ "cell_type": "markdown", "id": "f10127f4-6515-4ea5-a1d9-e14671eb70be", "metadata": {}, - "source": [ - "The same reasoning holds for the normalised (squared) euclidean distance, we can use the `normalize` parameter of the two estimators to set this option. In the normalised case, the formula used to computed the normalised (squared) euclidean distance is taken from the paper [Matrix Profile I: All Pairs Similarity Joins for Time Series](https://www.cs.ucr.edu/~eamonn/PID4481997_extend_Matrix%20Profile_I.pdf), see MASS algortihm." - ] + "source": "The same reasoning holds for the normalised (squared) euclidean distance, we can use the `normalize` parameter of the two estimators to set this option. In the normalised case, the formula used to computed the normalised (squared) euclidean distance is taken from the paper [Matrix Profile I: All Pairs Similarity Joins for Time Series](https://www.cs.ucr.edu/~eamonn/PID4481997_extend_Matrix%20Profile_I.pdf), see MASS algorithm." }, { "cell_type": "code", @@ -349,7 +347,7 @@ "source": [ "## Updating the dot products used in MASS when computing matrix profiles\n", "\n", - "This is part of the STOMP algorithm, which update the dot products of the sliding query instead of recomputing it everytime. When you compute $MASS(X,q_i)$, and $q_i$ is taken from a series $Y$ such as $q_i = Y[i:i+L]$, you can compute the dot product of $q_0$, and then only update it for subsequent $q_1, ...$" + "This is part of the STOMP algorithm, which update the dot products of the sliding query instead of recomputing it every time. When you compute $MASS(X,q_i)$, and $q_i$ is taken from a series $Y$ such as $q_i = Y[i:i+L]$, you can compute the dot product of $q_0$, and then only update it for subsequent $q_1, ...$" ] }, { diff --git a/examples/similarity_search/distance_profiles.ipynb b/examples/similarity_search/distance_profiles.ipynb index d2bf3fd87f..67ccea8d64 100644 --- a/examples/similarity_search/distance_profiles.ipynb +++ b/examples/similarity_search/distance_profiles.ipynb @@ -60,7 +60,7 @@ "The main idea behind optimizing the distance profile computation, in the case where we want to obtain the exact results (i.e. make no approximation), is to avoid recomputations as much as possible. For example, when computing the mean and standard deviation of each subsequence $W_1, \\ldots, W_{m-l+1}$, instead of computing these statistics independently for each subsequence $W_i$, we can exploit the fact that these subsequences are extracted from the same time series.\n", "\n", "\n", - "Consider the case of the mean of $W_i$ expressed as $\\displaystyle{\\mu_i = \\frac{1}{l}\\sum_{j=0}^{l-1} x_{i+j}}$. Instead of completly recomputing the mean of $W_{i+1}$, we can keep a rolling sum $S$ and update it as we go from $\\mu_1$ to $\\mu_{m-l+1}$. \n", + "Consider the case of the mean of $W_i$ expressed as $\\displaystyle{\\mu_i = \\frac{1}{l}\\sum_{j=0}^{l-1} x_{i+j}}$. Instead of completely recomputing the mean of $W_{i+1}$, we can keep a rolling sum $S$ and update it as we go from $\\mu_1$ to $\\mu_{m-l+1}$. \n", "\n", "Let $\\displaystyle{S_1 = \\sum_{j=0}^{l-1} x_{1+j}}$, we compute the mean of $W_1$ as $\\displaystyle{\\mu_1 = \\frac{1}{l} S_1}$. Then we can compute $S_2$ as $S_2 = S_1 - x_1 + x_{2+l-1}$ and compute $\\displaystyle{\\mu_2 = \\frac{1}{l} S_2}$. It can be generalized as $S_i = S_{i-1} - x_{i-1} + x_{i+l-1}$. \n", "\n", diff --git a/examples/similarity_search/similarity_search.ipynb b/examples/similarity_search/similarity_search.ipynb index 6bb339f13f..43f40aa654 100644 --- a/examples/similarity_search/similarity_search.ipynb +++ b/examples/similarity_search/similarity_search.ipynb @@ -23,7 +23,7 @@ "\n", "### Other similarity search notebooks\n", "\n", - "This notebook gives an overview of similarity search module and the available estimators. The following notebooks are also avaiable to go more in depth with specific subject of similarity search in aeon:\n", + "This notebook gives an overview of similarity search module and the available estimators. The following notebooks are also available to go more in depth with specific subject of similarity search in aeon:\n", "\n", "- [The theory and math behind the similarity search estimators in aeon](distance_profiles.ipynb)\n", "- [Analysis of the performance of the estimators provided by similarity search module](code_speed.ipynb)\n", @@ -95,7 +95,7 @@ "id": "d1fd75ae-84c2-40be-95f6-bd7de409317d", "metadata": {}, "source": [ - "### A word on base clases\n", + "### A word on base classes\n", "\n", "All estimators of the similarity search module in aeon inherit from the `BaseSimilaritySearch` class, which define the some abstract methods that estimator must implement, such as `fit` and `predict` and some private function used to validate the format of the time series you will provide. Then, the two submodules `series` and `collection` also define a base class (`BaseSeriesSimilaritySearch` and `BaseCollectionSeriesSearch`) that their respective estimator will inherit from. If you ever want to extend the module or create your own estimators, these are the classes you'll want to use to define the base structure of your estimator.\n", "\n", @@ -162,13 +162,13 @@ "\n", "It takes as parameter during initialisation :\n", "- `length` : an integer giving the length of the subsequences to extract from the series. It is also the expected length of the series given in `predict`\n", - "- `normalize`: a boolean indicating wheter the subsequences should be independently z-normalized (`(X-mean(X))/std(X)`) before the distance computations. This results in a scale-independent matching.\n", + "- `normalize`: a boolean indicating whether the subsequences should be independently z-normalized (`(X-mean(X))/std(X)`) before the distance computations. This results in a scale-independent matching.\n", " \n", "To parameterize the search, additional parameters are available when calling the `predict` method:\n", "\n", "- `k` (int) : the number of nearest neighbors to return.\n", "- `dist_threshold` (float) : the maximum allowed distance for a candidate subsequence to be considered as a neighbor.\n", - "- `allow_trivial_matches` (bool) : wheter a neighbors of a match to a query can be also considered as matches (True), or if an exclusion zone is applied around each match to avoid trivial matches with their direct neighbors (False).\n", + "- `allow_trivial_matches` (bool) : whether a neighbors of a match to a query can be also considered as matches (True), or if an exclusion zone is applied around each match to avoid trivial matches with their direct neighbors (False).\n", "- `inverse_distance` (bool) : if True, the matching will be made on the inverse of the distance, and thus, the farther neighbors will be returned instead of the closest ones.\n", "- `exclusion_factor` (float): A factor of the `length` used to define the exclusion zone when `allow_trivial_matches` is set to False. For a given timestamp, the exclusion zone starts from `id_timestamp - floor(length*exclusion_factor)` and end at `id_timestamp + floor(length*exclusion_factor)`.\n", "- `X_index` (int): If series given during predict is a subsequence of series given during fit, specify its starting timestamp. If specified, neighboring subsequences of X won't be able to match as neighbors." @@ -325,7 +325,7 @@ "id": "6aecb58e-9de9-4264-959e-4180ab3fa27a", "metadata": {}, "source": [ - "When doing motif search, it's important to define the type of motif you want to extract from a series. We'll use the figure and definitions given by [1] and make some adjustement to clear out some confusion due to the naming of each method:\n", + "When doing motif search, it's important to define the type of motif you want to extract from a series. We'll use the figure and definitions given by [1] and make some adjustment to clear out some confusion due to the naming of each method:\n", "\n", "![image.png](attachment:f492cb89-5bf3-4641-8be2-a77805f20b88.png)\n", "\n", @@ -487,16 +487,15 @@ "source": [ "## 2.1 Approximate nearest neighbors with RandomProjectionIndexANN\n", "\n", - "This method uses a random projection locality sensitive hashing index based on cosine similarity. W we define a hash function as a boolean operatio such as, given a random vector ``V`` of shape ``(n_channels, L)`` and a time ser ``X`` of shape ``(n_channels, n_timeponts)`` (with ``L<=n_timepoints``), we com \n", - " ``X.V > 0`` to obtainhash of ``X``e \r\n", - " In the case where ``L 0``` instead.\n", + "This method uses a random projection locality sensitive hashing index based on cosine similarity. We define a hash function as a boolean operation such as, given a random vector ``V`` of shape ``(n_channels, L)`` and a time series ``X`` of shape ``(n_channels, n_timeponts)`` (with ``L<=n_timepoints``), we compute ``X.V > 0`` to obtain hash of ``X``\n", "\n", - "The ```RandomProjectionIndexANN``` estimators use the parameter ```n_hash_funcs``` to create that much random hash function as defined above. Each series `X` of the collection given in fit is then represented as an array of ```n_hash_funcs``` boolean, which is then hashed to a dictionnary as ``h(bool_arry): case_id_array}```.\n", + "In the case where ``L 0``` instead.\n", "\n", - "To compute the nearest neighbors of a series ``X`` given in predict, we first transform this series to a boolean array using our previously defined hash functions, and theusedthe resulting o `h(bool_aryy)``` to look at the bucket in which ``X`` falls, and consider the ```case_id_array``` as the indexes of its neighbors. If this bucket doesn't exists, we compute a distance matrix between the boolean array of ``X`` and every boolean array making the keys of the dictionnary to get similar buckets.\n", + "The ```RandomProjectionIndexANN``` estimators use the parameter ```n_hash_funcs``` to create that much random hash function as defined above. Each series `X` of the collection given in fit is then represented as an array of ```n_hash_funcs``` boolean, which is then hashed to a dictionary as ```{hash(bool_arry): case_id_array}```.\n", "\n", - "This method will not provide exact results, but will perform approximate searchs. This also ignore any temporal correlation and consider series as high dimensional points due to the cosine similarity distance.y distance.\r\n" + "To compute the nearest neighbors of a series ``X`` given in predict, we first transform this series to a boolean array using our previously defined hash functions, and then use the result of ```hash(bool_array)``` to look at the bucket in which ``X`` falls, and consider the ```case_id_array``` as the indexes of its neighbors. If this bucket doesn't exists, we compute a distance matrix between the boolean array of ``X`` and every boolean array making the keys of the dictionary to get similar buckets.\n", + "\n", + "This method will not provide exact results, but will perform approximate searches. This also ignore any temporal correlation and consider series as high dimensional points due to the cosine similarity distance.\n" ] }, { @@ -573,7 +572,7 @@ "\n", "for i in range(len(indexes)):\n", " print(f\"match {i} : {indexes[i]} with distance {distances[i]}\")\n", - " # A bit of hacking of the function defined for series estimator to show best mathces\n", + " # A bit of hacking of the function defined for series estimator to show best matches\n", " plot_best_matches(X_fit[indexes[i]], X_predict, 0, [0], X_predict.shape[1])" ] }, @@ -635,7 +634,7 @@ "\n", "for i in range(len(indexes)):\n", " print(f\"match {i} : {indexes[i]} with distance {distances[i]}\")\n", - " # A bit of hacking of the function defined for series estimator to show best mathces\n", + " # A bit of hacking of the function defined for series estimator to show best matches\n", " plot_best_matches(X_fit[indexes[i]], X_predict, 0, [0], X_predict.shape[1])" ] }, diff --git a/examples/transformations/preprocessing.ipynb b/examples/transformations/preprocessing.ipynb index d467d0a401..075ae79de4 100644 --- a/examples/transformations/preprocessing.ipynb +++ b/examples/transformations/preprocessing.ipynb @@ -1,1015 +1,1001 @@ { "cells": [ { + "cell_type": "markdown", + "source": [ + "# Preprocessing time series with aeon\n", + "\n", + "It is common to need to preprocess time series data before applying machine learning\n", + "algorithms. So algorithms can handle these characteristics, or `aeon` transformers can be used to preprocess collections of time\n", + "series into standard format. This notebook demonstrates three common use cases\n", + "\n", + "1. [Rescaling time series](#Rescaling-time-series)\n", + "2. [Resizing time series](#Resizing-time-series)\n", + "3. [Dealing with missing values](#missing-values)\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Rescaling time series\n", + "\n", + "Different levels of scale and variance can mask discriminative patterns in time\n", + "series. This is particularly true for methods that are based on distances. It common\n", + "to rescale time series to have zero mean and unit variance. For example, the data in\n", + "the `UnitTest` dataset is a subset of the [Chinatown dataset]\n", + "(https://timeseriesclassification.com/description.php?Dataset=Chinatown. These are\n", + "counts of pedestrians in Chinatown, Melbourne. The time series are of different means" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "\n", + "from aeon.datasets import load_unit_test\n", + "\n", + "X, y = load_unit_test(split=\"Train\")\n", + "np.mean(X, axis=-1)[0:5]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-06-26T14:20:51.923947Z", + "start_time": "2025-06-26T14:20:51.596622Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[561.875 ],\n", + " [604.95833333],\n", + " [629.16666667],\n", + " [801.45833333],\n", + " [540.75 ]])" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 1 + }, + { + "cell_type": "code", + "source": [ + "np.std(X, axis=-1)[0:5]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-06-26T14:20:52.071345Z", + "start_time": "2025-06-26T14:20:52.067057Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[428.95224215],\n", + " [483.35481095],\n", + " [514.90052977],\n", + " [629.00847763],\n", + " [389.10059218]])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 2 + }, + { + "cell_type": "markdown", + "source": [ + "We can rescale the time series in three ways:\n", + "1. Normalise: subtract the mean and divide by the standard deviation to make all\n", + "series have zero mean and unit variance." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "source": [ + "from aeon.transformations.collection import Normalizer\n", + "\n", + "normalizer = Normalizer()\n", + "X2 = normalizer.fit_transform(X)\n", + "np.round(np.mean(X2, axis=-1)[0:5], 6)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-06-26T14:20:52.520759Z", + "start_time": "2025-06-26T14:20:52.120495Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.],\n", + " [-0.],\n", + " [ 0.],\n", + " [-0.],\n", + " [-0.]])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 3 + }, + { + "cell_type": "code", + "source": [ + "np.round(np.std(X2, axis=-1)[0:5], 6)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-06-26T14:20:52.638775Z", + "start_time": "2025-06-26T14:20:52.635450Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.],\n", + " [1.],\n", + " [1.],\n", + " [1.],\n", + " [1.]])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 4 + }, + { + "cell_type": "markdown", + "source": [ + "2. Re-center: Recentering involves subtracting the mean of each series" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "source": [ + "from aeon.transformations.collection import Centerer\n", + "\n", + "c = Centerer()\n", + "X3 = c.fit_transform(X)\n", + "np.round(np.mean(X3, axis=-1)[0:5], 6)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-06-26T14:20:52.711527Z", + "start_time": "2025-06-26T14:20:52.707232Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.],\n", + " [-0.],\n", + " [ 0.],\n", + " [-0.],\n", + " [ 0.]])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 5 + }, + { + "cell_type": "markdown", + "source": [ + "3. Min-Max: Scale the data to be between 0 and 1" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "source": [ + "from aeon.transformations.collection import MinMaxScaler\n", + "\n", + "minmax = MinMaxScaler()\n", + "X4 = minmax.fit_transform(X)\n", + "np.round(np.min(X4, axis=-1)[0:5], 6)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-06-26T14:20:52.778785Z", + "start_time": "2025-06-26T14:20:52.774978Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.],\n", + " [0.],\n", + " [0.],\n", + " [0.],\n", + " [0.]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 6 + }, + { + "cell_type": "code", + "source": [ + "np.round(np.max(X4, axis=-1)[0:5], 6)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-06-26T14:20:52.856645Z", + "start_time": "2025-06-26T14:20:52.853030Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.],\n", + " [1.],\n", + " [1.],\n", + " [1.],\n", + " [1.]])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 7 + }, + { + "cell_type": "markdown", + "source": [ + "There is no best way to do this, although for counts such as this it is more common\n", + "to MinMax scale, so that the data still has some interpretation as proportions." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Resizing time series\n", + "\n", + "Suppose we have a collections of time series with different lengths, i.e. different\n", + "number of time points. Currently, most of aeon's collection estimators\n", + "(classification, clustering or regression) require equal-length time\n", + "series. Those that can handle unequal length series are tagged with\n", + "\"capability:unequal\"." + ] + }, + { + "cell_type": "code", + "metadata": { + "execution": { + "iopub.execute_input": "2020-12-19T14:31:58.456171Z", + "iopub.status.busy": "2020-12-19T14:31:58.455565Z", + "iopub.status.idle": "2020-12-19T14:31:59.189497Z", + "shell.execute_reply": "2020-12-19T14:31:59.190005Z" + }, + "pycharm": { + "is_executing": true + }, + "ExecuteTime": { + "end_time": "2025-06-26T14:20:54.123215Z", + "start_time": "2025-06-26T14:20:52.985764Z" + } + }, + "source": [ + "from aeon.classification.convolution_based import RocketClassifier\n", + "from aeon.datasets import (\n", + " load_basic_motions,\n", + " load_japanese_vowels,\n", + " load_pickup_gesture_wiimoteZ,\n", + ")\n", + "from aeon.utils.validation import has_missing, is_equal_length, is_univariate" + ], + "outputs": [], + "execution_count": 8 + }, + { + "cell_type": "markdown", + "source": [ + "If you want to use an estimator that cannot internally handle missing values, one\n", + "option is to convert unequal length series into equal length. This can be\n", + " done through padding, truncation or resizing through fitting a function and\n", + " resampling." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", "metadata": {}, - "cell_type": "raw", - "source": [ - "{\n", - " \"cells\": [\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"source\": [\n", - " \"# Preprocessing time series with aeon\\n\",\n", - " \"\\n\",\n", - " \"It is common to need to preprocess time series data before applying machine learning\\n\",\n", - " \"algorithms. So algorithms can handle these characteristics, or `aeon` transformers can be used to preprocess collections of time\\n\",\n", - " \"series into standard format. This notebook demonstrates three common use cases\\n\",\n", - " \"\\n\",\n", - " \"1. [Rescaling time series](#Rescaling-time-series)\\n\",\n", - " \"2. [Resizing time series](#Resizing-time-series)\\n\",\n", - " \"3. [Dealing with missing values](#missing-values)\\n\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false\n", - " }\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"source\": [\n", - " \"## Rescaling time series\\n\",\n", - " \"\\n\",\n", - " \"Different levels of scale and variance can mask discriminative patterns in time\\n\",\n", - " \"series. This is particularly true for methods that are based on distances. It common\\n\",\n", - " \"to rescale time series to have zero mean and unit variance. For example, the data in\\n\",\n", - " \"the `UnitTest` dataset is a subset of the [Chinatown dataset]\\n\",\n", - " \"(https://timeseriesclassification.com/description.php?Dataset=Chinatown. These are\\n\",\n", - " \"counts of pedestrians in Chinatown, Melbourne. The time series are of different means\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false\n", - " }\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"source\": [\n", - " \"import numpy as np\\n\",\n", - " \"\\n\",\n", - " \"from aeon.datasets import load_unit_test\\n\",\n", - " \"\\n\",\n", - " \"X, y = load_unit_test(split=\\\"Train\\\")\\n\",\n", - " \"np.mean(X, axis=-1)[0:5]\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false,\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2024-11-17T13:48:57.655001Z\",\n", - " \"start_time\": \"2024-11-17T13:48:57.631756Z\"\n", - " }\n", - " },\n", - " \"outputs\": [\n", - " {\n", - " \"data\": {\n", - " \"text/plain\": [\n", - " \"array([[561.875 ],\\n\",\n", - " \" [604.95833333],\\n\",\n", - " \" [629.16666667],\\n\",\n", - " \" [801.45833333],\\n\",\n", - " \" [540.75 ]])\"\n", - " ]\n", - " },\n", - " \"execution_count\": 3,\n", - " \"metadata\": {},\n", - " \"output_type\": \"execute_result\"\n", - " }\n", - " ],\n", - " \"execution_count\": 3\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"source\": [\n", - " \"np.std(X, axis=-1)[0:5]\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false,\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2024-11-17T13:48:59.467239Z\",\n", - " \"start_time\": \"2024-11-17T13:48:59.458263Z\"\n", - " }\n", - " },\n", - " \"outputs\": [\n", - " {\n", - " \"data\": {\n", - " \"text/plain\": [\n", - " \"array([[428.95224215],\\n\",\n", - " \" [483.35481095],\\n\",\n", - " \" [514.90052977],\\n\",\n", - " \" [629.00847763],\\n\",\n", - " \" [389.10059218]])\"\n", - " ]\n", - " },\n", - " \"execution_count\": 4,\n", - " \"metadata\": {},\n", - " \"output_type\": \"execute_result\"\n", - " }\n", - " ],\n", - " \"execution_count\": 4\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"source\": [\n", - " \"We can rescale the time series in three ways:\\n\",\n", - " \"1. Normalise: subtract the mean and divide by the standard deviation to make all\\n\",\n", - " \"series have zero mean and unit variance.\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false\n", - " }\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"source\": [\n", - " \"from aeon.transformations.collection import Normalizer\\n\",\n", - " \"\\n\",\n", - " \"normalizer = Normalizer()\\n\",\n", - " \"X2 = normalizer.fit_transform(X)\\n\",\n", - " \"np.round(np.mean(X2, axis=-1)[0:5], 6)\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false,\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2024-11-17T13:49:01.643630Z\",\n", - " \"start_time\": \"2024-11-17T13:49:01.627083Z\"\n", - " }\n", - " },\n", - " \"outputs\": [\n", - " {\n", - " \"data\": {\n", - " \"text/plain\": [\n", - " \"array([[ 0.],\\n\",\n", - " \" [-0.],\\n\",\n", - " \" [ 0.],\\n\",\n", - " \" [-0.],\\n\",\n", - " \" [-0.]])\"\n", - " ]\n", - " },\n", - " \"execution_count\": 5,\n", - " \"metadata\": {},\n", - " \"output_type\": \"execute_result\"\n", - " }\n", - " ],\n", - " \"execution_count\": 5\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"source\": [\n", - " \"np.round(np.std(X2, axis=-1)[0:5], 6)\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false,\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2024-11-17T13:49:02.670358Z\",\n", - " \"start_time\": \"2024-11-17T13:49:02.648594Z\"\n", - " }\n", - " },\n", - " \"outputs\": [\n", - " {\n", - " \"data\": {\n", - " \"text/plain\": [\n", - " \"array([[1.],\\n\",\n", - " \" [1.],\\n\",\n", - " \" [1.],\\n\",\n", - " \" [1.],\\n\",\n", - " \" [1.]])\"\n", - " ]\n", - " },\n", - " \"execution_count\": 6,\n", - " \"metadata\": {},\n", - " \"output_type\": \"execute_result\"\n", - " }\n", - " ],\n", - " \"execution_count\": 6\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"source\": [\n", - " \"2. Re-center: Recentering involves subtracting the mean of each series\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false\n", - " }\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"source\": [\n", - " \"from aeon.transformations.collection import Centerer\\n\",\n", - " \"\\n\",\n", - " \"c = Centerer()\\n\",\n", - " \"X3 = c.fit_transform(X)\\n\",\n", - " \"np.round(np.mean(X3, axis=-1)[0:5], 6)\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false,\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2024-11-17T13:49:04.345033Z\",\n", - " \"start_time\": \"2024-11-17T13:49:04.332065Z\"\n", - " }\n", - " },\n", - " \"outputs\": [\n", - " {\n", - " \"data\": {\n", - " \"text/plain\": [\n", - " \"array([[ 0.],\\n\",\n", - " \" [-0.],\\n\",\n", - " \" [ 0.],\\n\",\n", - " \" [-0.],\\n\",\n", - " \" [ 0.]])\"\n", - " ]\n", - " },\n", - " \"execution_count\": 7,\n", - " \"metadata\": {},\n", - " \"output_type\": \"execute_result\"\n", - " }\n", - " ],\n", - " \"execution_count\": 7\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"source\": [\n", - " \"3. Min-Max: Scale the data to be between 0 and 1\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false\n", - " }\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"source\": [\n", - " \"from aeon.transformations.collection import MinMaxScaler\\n\",\n", - " \"\\n\",\n", - " \"minmax = MinMaxScaler()\\n\",\n", - " \"X4 = minmax.fit_transform(X)\\n\",\n", - " \"np.round(np.min(X4, axis=-1)[0:5], 6)\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false,\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2024-11-17T13:49:06.135780Z\",\n", - " \"start_time\": \"2024-11-17T13:49:06.116831Z\"\n", - " }\n", - " },\n", - " \"outputs\": [\n", - " {\n", - " \"data\": {\n", - " \"text/plain\": [\n", - " \"array([[0.],\\n\",\n", - " \" [0.],\\n\",\n", - " \" [0.],\\n\",\n", - " \" [0.],\\n\",\n", - " \" [0.]])\"\n", - " ]\n", - " },\n", - " \"execution_count\": 8,\n", - " \"metadata\": {},\n", - " \"output_type\": \"execute_result\"\n", - " }\n", - " ],\n", - " \"execution_count\": 8\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"source\": [\n", - " \"np.round(np.max(X4, axis=-1)[0:5], 6)\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false,\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2024-11-17T13:49:07.094710Z\",\n", - " \"start_time\": \"2024-11-17T13:49:07.072733Z\"\n", - " }\n", - " },\n", - " \"outputs\": [\n", - " {\n", - " \"data\": {\n", - " \"text/plain\": [\n", - " \"array([[1.],\\n\",\n", - " \" [1.],\\n\",\n", - " \" [1.],\\n\",\n", - " \" [1.],\\n\",\n", - " \" [1.]])\"\n", - " ]\n", - " },\n", - " \"execution_count\": 9,\n", - " \"metadata\": {},\n", - " \"output_type\": \"execute_result\"\n", - " }\n", - " ],\n", - " \"execution_count\": 9\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"source\": [\n", - " \"There is no best way to do this, although for counts such as this it is more common\\n\",\n", - " \"to MinMax scale, so that the data still has some interpretation as proportions.\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false\n", - " }\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"## Resizing time series\\n\",\n", - " \"\\n\",\n", - " \"Suppose we have a collections of time series with different lengths, i.e. different\\n\",\n", - " \"number of time points. Currently, most of aeon's collection estimators\\n\",\n", - " \"(classification, clustering or regression) require equal-length time\\n\",\n", - " \"series. Those that can handle unequal length series are tagged with\\n\",\n", - " \"\\\"capability:unequal\\\".\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {\n", - " \"execution\": {\n", - " \"iopub.execute_input\": \"2020-12-19T14:31:58.456171Z\",\n", - " \"iopub.status.busy\": \"2020-12-19T14:31:58.455565Z\",\n", - " \"iopub.status.idle\": \"2020-12-19T14:31:59.189497Z\",\n", - " \"shell.execute_reply\": \"2020-12-19T14:31:59.190005Z\"\n", - " },\n", - " \"pycharm\": {\n", - " \"is_executing\": true\n", - " },\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2025-05-28T13:37:35.086084Z\",\n", - " \"start_time\": \"2025-05-28T13:36:09.244708Z\"\n", - " }\n", - " },\n", - " \"source\": [\n", - " \"from aeon.classification.convolution_based import RocketClassifier\\n\",\n", - " \"from aeon.datasets import (\\n\",\n", - " \" load_basic_motions,\\n\",\n", - " \" load_japanese_vowels,\\n\",\n", - " \" load_pickup_gesture_wiimoteZ,\\n\",\n", - " \")\\n\",\n", - " \"from aeon.utils.validation import has_missing, is_equal_length, is_univariate\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": 2\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"source\": [\n", - " \"If you want to use an estimator that cannot internally handle missing values, one\\n\",\n", - " \"option is to convert unequal length series into equal length. This can be\\n\",\n", - " \" done through padding, truncation or resizing through fitting a function and\\n\",\n", - " \" resampling.\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false\n", - " }\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"### Unequal or equal length collections time series\\n\",\n", - " \"\\n\",\n", - " \"If a collection contains all equal length series, it will store the data in a 3D\\n\",\n", - " \"numpy of shape `(n_cases, n_channels, n_timepoints)`. If it is unequal length, it is\\n\",\n", - " \"stored in a list of 2D numpy arrays:\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {\n", - " \"execution\": {\n", - " \"iopub.execute_input\": \"2020-12-19T14:31:59.194445Z\",\n", - " \"iopub.status.busy\": \"2020-12-19T14:31:59.193903Z\",\n", - " \"iopub.status.idle\": \"2020-12-19T14:32:01.019896Z\",\n", - " \"shell.execute_reply\": \"2020-12-19T14:32:01.020463Z\"\n", - " },\n", - " \"pycharm\": {\n", - " \"is_executing\": true\n", - " },\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2025-05-28T13:37:44.511744Z\",\n", - " \"start_time\": \"2025-05-28T13:37:44.488521Z\"\n", - " }\n", - " },\n", - " \"source\": [\n", - " \"# Equal length multivariate data\\n\",\n", - " \"bm_X, bm_y = load_basic_motions()\\n\",\n", - " \"X = bm_X\\n\",\n", - " \"print(f\\\"{type(X)}, {X.shape}\\\")\\n\",\n", - " \"print(\\n\",\n", - " \" f\\\"univariate = {is_univariate(X)}, has missing ={has_missing(X)}, equal \\\"\\n\",\n", - " \" f\\\"length = {is_equal_length(X)}\\\"\\n\",\n", - " \")\"\n", - " ],\n", - " \"outputs\": [\n", - " {\n", - " \"name\": \"stdout\",\n", - " \"output_type\": \"stream\",\n", - " \"text\": [\n", - " \", (80, 6, 100)\\n\",\n", - " \"univariate = False, has missing =False, equal length = True\\n\"\n", - " ]\n", - " }\n", - " ],\n", - " \"execution_count\": 6\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"source\": [\n", - " \"# Unequal length univariate data\\n\",\n", - " \"wii_X, wii_y = load_pickup_gesture_wiimoteZ()\\n\",\n", - " \"X = wii_X\\n\",\n", - " \"print(type(wii_X), \\\"\\\\n\\\", wii_X[0].shape, \\\"\\\\n\\\", wii_X[10].shape)\\n\",\n", - " \"print(\\n\",\n", - " \" f\\\"univariate = {is_univariate(X)}, has missing ={has_missing(X)}, equal \\\"\\n\",\n", - " \" f\\\"length = {is_equal_length(X)}\\\"\\n\",\n", - " \")\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false,\n", - " \"pycharm\": {\n", - " \"is_executing\": true\n", - " },\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2025-05-28T13:37:45.578203Z\",\n", - " \"start_time\": \"2025-05-28T13:37:45.513828Z\"\n", - " }\n", - " },\n", - " \"outputs\": [\n", - " {\n", - " \"name\": \"stdout\",\n", - " \"output_type\": \"stream\",\n", - " \"text\": [\n", - " \" \\n\",\n", - " \" (1, 324) \\n\",\n", - " \" (1, 97)\\n\",\n", - " \"univariate = True, has missing =False, equal length = False\\n\"\n", - " ]\n", - " }\n", - " ],\n", - " \"execution_count\": 7\n", - " },\n", - " {\n", - " \"metadata\": {\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2025-05-28T13:37:49.679258Z\",\n", - " \"start_time\": \"2025-05-28T13:37:49.639207Z\"\n", - " }\n", - " },\n", - " \"cell_type\": \"code\",\n", - " \"source\": [\n", - " \"vowels_X, vowels_y = load_japanese_vowels(split=\\\"train\\\")\\n\",\n", - " \"X = vowels_X\\n\",\n", - " \"print(\\n\",\n", - " \" f\\\"univariate = {is_univariate(X)}, has missing ={has_missing(X)}, equal \\\"\\n\",\n", - " \" f\\\"length = {is_equal_length(X)}\\\"\\n\",\n", - " \")\"\n", - " ],\n", - " \"outputs\": [\n", - " {\n", - " \"name\": \"stdout\",\n", - " \"output_type\": \"stream\",\n", - " \"text\": [\n", - " \"univariate = False, has missing =False, equal length = False\\n\"\n", - " ]\n", - " }\n", - " ],\n", - " \"execution_count\": 8\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {},\n", - " \"source\": \"\\n\"\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"source\": [\n", - " \"series_lengths = [array.shape[1] for array in wii_X]\\n\",\n", - " \"\\n\",\n", - " \"# Find the minimum and maximum of the second dimensions\\n\",\n", - " \"min_length = min(series_lengths)\\n\",\n", - " \"max_length = max(series_lengths)\\n\",\n", - " \"print(\\\" Min length = \\\", min_length, \\\" max length = \\\", max_length)\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false,\n", - " \"pycharm\": {\n", - " \"is_executing\": true\n", - " },\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2025-05-28T13:38:05.700223Z\",\n", - " \"start_time\": \"2025-05-28T13:38:05.689668Z\"\n", - " }\n", - " },\n", - " \"outputs\": [\n", - " {\n", - " \"name\": \"stdout\",\n", - " \"output_type\": \"stream\",\n", - " \"text\": [\n", - " \" Min length = 29 max length = 361\\n\"\n", - " ]\n", - " }\n", - " ],\n", - " \"execution_count\": 9\n", - " },\n", - " {\n", - " \"metadata\": {},\n", - " \"cell_type\": \"markdown\",\n", - " \"source\": [\n", - " \"There are two basic strategies for unequal length problems\\n\",\n", - " \"1. Use an estimator that can internally handle missing values\\n\",\n", - " \"2. Transform the data to be equal length by, for example, truncating or padding series\\n\",\n", - " \"\\n\",\n", - " \"Estimators with the tag `\\\"capability:unequal_length\\\": True` have the capability to\\n\",\n", - " \"handle unequal length series. For classification, regression and\\n\",\n", - " \"clusterign, the\\n\",\n", - " \"current list is\"\n", - " ]\n", - " },\n", - " {\n", - " \"metadata\": {\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2024-11-17T13:49:23.280238Z\",\n", - " \"start_time\": \"2024-11-17T13:49:23.143830Z\"\n", - " }\n", - " },\n", - " \"cell_type\": \"code\",\n", - " \"source\": [\n", - " \"from aeon.utils.discovery import all_estimators\\n\",\n", - " \"\\n\",\n", - " \"all_estimators(\\n\",\n", - " \" type_filter=[\\\"classifier\\\", \\\"regressor\\\", \\\"clusterer\\\"],\\n\",\n", - " \" tag_filter={\\\"capability:unequal_length\\\": True},\\n\",\n", - " \")\"\n", - " ],\n", - " \"outputs\": [\n", - " {\n", - " \"data\": {\n", - " \"text/plain\": [\n", - " \"[('Catch22Classifier',\\n\",\n", - " \" aeon.classification.feature_based._catch22.Catch22Classifier),\\n\",\n", - " \" ('Catch22Clusterer', aeon.clustering.feature_based._catch22.Catch22Clusterer),\\n\",\n", - " \" ('Catch22Regressor', aeon.regression.feature_based._catch22.Catch22Regressor),\\n\",\n", - " \" ('DummyClassifier', aeon.classification.dummy.DummyClassifier),\\n\",\n", - " \" ('DummyRegressor', aeon.regression._dummy.DummyRegressor),\\n\",\n", - " \" ('ElasticEnsemble',\\n\",\n", - " \" aeon.classification.distance_based._elastic_ensemble.ElasticEnsemble),\\n\",\n", - " \" ('KNeighborsTimeSeriesClassifier',\\n\",\n", - " \" aeon.classification.distance_based._time_series_neighbors.KNeighborsTimeSeriesClassifier),\\n\",\n", - " \" ('KNeighborsTimeSeriesRegressor',\\n\",\n", - " \" aeon.regression.distance_based._time_series_neighbors.KNeighborsTimeSeriesRegressor),\\n\",\n", - " \" ('RDSTClassifier', aeon.classification.shapelet_based._rdst.RDSTClassifier),\\n\",\n", - " \" ('RDSTRegressor', aeon.regression.shapelet_based._rdst.RDSTRegressor)]\"\n", - " ]\n", - " },\n", - " \"execution_count\": 15,\n", - " \"metadata\": {},\n", - " \"output_type\": \"execute_result\"\n", - " }\n", - " ],\n", - " \"execution_count\": 15\n", - " },\n", - " {\n", - " \"metadata\": {},\n", - " \"cell_type\": \"markdown\",\n", - " \"source\": \"You can pass these estimators unequal length series and they will work as expected.\\n\"\n", - " },\n", - " {\n", - " \"metadata\": {\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2024-11-17T13:49:25.499271Z\",\n", - " \"start_time\": \"2024-11-17T13:49:25.466359Z\"\n", - " }\n", - " },\n", - " \"cell_type\": \"code\",\n", - " \"source\": [\n", - " \"from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier\\n\",\n", - " \"\\n\",\n", - " \"knn = KNeighborsTimeSeriesClassifier()\\n\",\n", - " \"model = knn.fit(wii_X, wii_y)\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": 16\n", - " },\n", - " {\n", - " \"metadata\": {},\n", - " \"cell_type\": \"markdown\",\n", - " \"source\": [\n", - " \"If time series are unequal length, collection estimators will raise an error if they\\n\",\n", - " \"do not have the capability to handle this characteristic. If you want to use them, \\n\",\n", - " \"you will need to preprocess the data to be equal length. \"\n", - " ]\n", - " },\n", - " {\n", - " \"metadata\": {\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2025-05-28T13:38:30.585470Z\",\n", - " \"start_time\": \"2025-05-28T13:38:30.565833Z\"\n", - " }\n", - " },\n", - " \"cell_type\": \"code\",\n", - " \"source\": [\n", - " \"rc = RocketClassifier()\\n\",\n", - " \"try:\\n\",\n", - " \" rc.fit(wii_X, wii_y)\\n\",\n", - " \"except ValueError as e:\\n\",\n", - " \" print(f\\\"ValueError: {e}\\\")\"\n", - " ],\n", - " \"outputs\": [\n", - " {\n", - " \"name\": \"stdout\",\n", - " \"output_type\": \"stream\",\n", - " \"text\": [\n", - " \"ValueError: Data seen by instance of RocketClassifier has unequal length series, but RocketClassifier cannot handle these characteristics. \\n\"\n", - " ]\n", - " }\n", - " ],\n", - " \"execution_count\": 10\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"### Padding, truncating or resizing.\\n\",\n", - " \"\\n\",\n", - " \"We can pad, truncate or resize. By default, pad adds zeros to make all series the\\n\",\n", - " \"length of the longest, truncate removes all values beyond the length of the shortest\\n\",\n", - " \"and resize stretches or shrinks the series.\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {\n", - " \"execution\": {\n", - " \"iopub.execute_input\": \"2020-12-19T14:32:01.245270Z\",\n", - " \"iopub.status.busy\": \"2020-12-19T14:32:01.244733Z\",\n", - " \"iopub.status.idle\": \"2020-12-19T14:32:02.911970Z\",\n", - " \"shell.execute_reply\": \"2020-12-19T14:32:02.912833Z\"\n", - " },\n", - " \"pycharm\": {\n", - " \"is_executing\": true\n", - " },\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2025-05-28T13:39:43.878568Z\",\n", - " \"start_time\": \"2025-05-28T13:39:43.857132Z\"\n", - " }\n", - " },\n", - " \"source\": [\n", - " \"from aeon.transformations.collection.unequal_length import Padder, Resizer, Truncator\\n\",\n", - " \"\\n\",\n", - " \"pad = Padder()\\n\",\n", - " \"truncate = Truncator()\\n\",\n", - " \"resize = Resizer(resized_length=600)\\n\",\n", - " \"X2 = pad.fit_transform(wii_X)\\n\",\n", - " \"X3 = truncate.fit_transform(wii_X)\\n\",\n", - " \"X4 = resize.fit_transform(wii_X)\\n\",\n", - " \"print(X2.shape, \\\"\\\\n\\\", X3.shape, \\\"\\\\n\\\", X4.shape)\"\n", - " ],\n", - " \"outputs\": [\n", - " {\n", - " \"name\": \"stdout\",\n", - " \"output_type\": \"stream\",\n", - " \"text\": [\n", - " \"(100, 1, 361) \\n\",\n", - " \" (100, 1, 29) \\n\",\n", - " \" (100, 1, 600)\\n\"\n", - " ]\n", - " }\n", - " ],\n", - " \"execution_count\": 12\n", - " },\n", - " {\n", - " \"metadata\": {\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2025-05-28T13:39:45.811784Z\",\n", - " \"start_time\": \"2025-05-28T13:39:44.586889Z\"\n", - " }\n", - " },\n", - " \"cell_type\": \"code\",\n", - " \"source\": [\n", - " \"import matplotlib.pyplot as plt\\n\",\n", - " \"\\n\",\n", - " \"plt.title(\\\"Before and after padding: first case (shifted up for unpadded)\\\")\\n\",\n", - " \"plt.plot(wii_X[0][0] + 10)\\n\",\n", - " \"plt.plot(X2[0][0])\"\n", - " ],\n", - " \"outputs\": [\n", - " {\n", - " \"data\": {\n", - " \"text/plain\": [\n", - " \"[]\"\n", - " ]\n", - " },\n", - " \"execution_count\": 13,\n", - " \"metadata\": {},\n", - " \"output_type\": \"execute_result\"\n", - " },\n", - " {\n", - " \"data\": {\n", - " \"text/plain\": [\n", - " \"
\"\n", - " ],\n", - " \"image/png\": 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kJETk5Ofrpn332mQAg1q1bV+x6TB2/unOyUaNG4v79+/rpGzZsEADEBx98oJ+mO/+mT59eYp5085vKv6l9A0BYW1uLy5cv66edOnVKABDffvut0XcHDRpk8P3x48cLAOLUqVP6aaaO+dDQUFGrVi39+8TERGFtbS369+9vcDy/8847RteFyZMnCwDi0KFDBt9Xq9UG52t5HjsHDhwQAMSKFSuMPitKpVfrfP/999i6dSu2bt2KP/74A926dcNLL71k8Mtj5cqVUKvV6NWrF+7cuaN/hYSEwMHBATt37ix2HbpuTG+++abB9LfeegsA8O+//xpMDwoKQmhoqMG0lStXolOnTnBxcTFIQ8+ePaHRaLBnz55i01AwSk1LS8OdO3fQqVMnZGZm4uLFiwbzOjg4GJQcWVtbo3Xr1rh69apBnnx8fPD000/rp9nZ2eGVV14pNh2m0pObm4ukpCTUrl0bzs7ORkXhZbFy5UrUr18f9erVM9hOurrGwvuqS5cuaNCgQamX7+vriyFDhujf64qHT5w4gfj4eACASqWCUikd2hqNBklJSXBwcEDdunUN8lbabXj06FEkJiZi3LhxBm0vRo8eDbVaXeq0jxs3zuB9p06dDPbppk2bYGVlhZdfflk/TalUGvxa0gkMDIQQoly6LD/11FNGJR7Ozs64fv26UTXEwzpx4gSio6MxefJko7YEBX9FFzxOs7KycOfOHX2bj4L70tnZGYcOHcLNmzdNru/kyZOIiorCs88+i6SkJP1xmZGRgR49emDPnj3FVs/qqpFcXFwMpuvSvmHDBuTm5hab5+HDhxt8v1OnTgBgcAw8qq1btyI5ORkjR440OP8sLCzQpk0b/fl369YtnDx5EmFhYQbHcK9evcp0PpbFK6+8YlBC9Oqrr8LS0vKhupnqzsnx48cbtPHq378/6tWrZ3RN162vPPTs2dOgNLJJkyZwcnIyuV8Ln8uvvfYaABhsg4LHvK5moUuXLrh69SpSUlIAANu2bUNOTg5ee+01g/PFVAeF//77D23btjUoGfTw8DCq2irPY0d33N+5c8fk56ZUerVO69atDRrEjhw5Es2bN8fEiRMxYMAAWFtbIyoqCikpKfD09DS5DF2DqaLExMRAqVSidu3aBtO9vb3h7OxsNGZBUFCQ0TKioqJw+vTpIoupS0rDuXPn8N5772HHjh1ITU01+Ex3wOnUrFnTqJjTxcUFp0+fNshT7dq1jearW7dusenQuX//PsLDw7Fw4ULcuHHDoC6wcHrKIioqChcuXCj1djK1rYtjKs9PPPEEAKmO3tvbG1qtFl9//TV++OEHREdHG7TrKFjMWtptqDs+6tSpYzDdysoKtWrVKlW6bWxsjLaJi4uLQZ17TEwMfHx8jIqcCx+35c3UPpk2bRq2bduG1q1bo3bt2ujduzeeffZZdOjQ4aHWceXKFQAosYj37t27mD17NpYvX2507BQ8Tj/77DOEhYXBz88PISEh6NevH1544QX9/omKigIAk1VkBZdXOPgoTBSqM+/SpQueeuopzJ49G1999RW6du2KwYMH49lnn4VKpTKY19/f3+C9bl0ltbsoC10+i2p46OTkBKDoYxqAURBvLoXX5eDgAB8fn4fqDq1Lv6nrXb169bBv3z6DaZaWluVWXVV4vwLG57ZO4W0QHBwMpVJpsA3279+PmTNnIiIiApmZmQbzp6SkQK1WF7n/PDw8jI7hmJgYtGnTxigthbddeR47uvOmqOo7Uyo9OClMqVSiW7du+PrrrxEVFYWGDRtCq9XC09MTS5cuNfmd4uq1CyrthjFVF6fVatGrVy9MnTrV5Hd0N0hTkpOT0aVLFzg5OeHDDz9EcHAwbGxscPz4cUybNs3oF5uFhYXJ5RS+MD6K1157DQsXLsTkyZPRrl07/QBrI0aMeKTupVqtFo0bN8aXX35p8vPC7TPKoxfUp59+ivfffx8vvvgiPvroI7i6ukKpVGLy5Mlm7TpbFkXtUzkytU/q16+PyMhIbNiwAZs2bcLff/+NH374AR988AFmz55dbmkZNmwYDhw4gClTpqBZs2ZwcHCAVqtFnz59DPblsGHD0KlTJ6xZswZbtmzB3LlzMWfOHKxevRp9+/bVzzt37twiB2bUtZsyRRfUFr7hKBQKrFq1CgcPHsT69euxefNmvPjii/jiiy9w8OBBg2VWxHmty+eSJUvg7e1t9HnhjgbloXAjbzkoWJpakqLuE0Xl61H2a+F1XblyBT169EC9evXw5Zdfws/PD9bW1vjvv//w1Vdflev1qzyPHd15o2uLVhqyC04AqfESAKSnpwOQostt27ahQ4cOD3UzCwgIgFarRVRUlL7RGAAkJCQgOTm5VAMqBQcHIz09/aH62e/atQtJSUlYvXo1OnfurJ8eHR1d5mXpBAQE4OzZsxBCGBzgkZGRpfr+qlWrEBYWhi+++EI/LSsrq9QDaBV1AgcHB+PUqVPo0aNHmaLk0rp8+bJRni9dugQA+sZaq1atQrdu3fDrr78afDc5Odng5CjtNtQdH1FRUQa/KnJzcxEdHY2mTZuaJW8BAQHYuXMnMjMzDUpPLl++bJblPyp7e3sMHz4cw4cPR05ODoYOHYpPPvkEM2bM0I/cWVq6YvCzZ88WeU7du3cP27dvx+zZs/HBBx/op+t+4RXm4+OD8ePHY/z48UhMTESLFi3wySefoG/fvvr1OTk5PdQ5XK9ePQBFn7Nt27ZF27Zt8cknn+DPP//EqFGjsHz5crz00ktlXldpFHf+AYCnp2ex+Sx4TBdW2muIi4uL0fUiJyenyB5kUVFR6Natm/59eno6bt26hX79+pVqfQXp0h8ZGWn0Sz8yMvKRBskzlS8AZRoVuChRUVEGJZOXL1+GVqvVX7vWr1+P7Oxs/PPPPwYlMoWrwwvuv4Klt7dv3zYKoAMCAkq1n8vz2NGdNwXvvyWp9DYnheXm5mLLli2wtrbWZ2TYsGHQaDT46KOPjObPy8sr8YaqO/gL96jR/brv379/iekaNmwYIiIisHnzZqPPkpOT9QGVKbrIumAknZOTgx9++KHE9RalX79+uHnzJlatWqWflpmZiQULFpTq+xYWFkaR/bffflvqXz26HjuFDRs2DDdu3MDPP/9s9Nn9+/eRkZFRquUX5ebNmwa9q1JTU/H777+jWbNm+mjfVN5Wrlxp1MWwtNuwZcuW8PDwwI8//mjQEn7RokVmHQ01NDQUubm5BttOq9Xi+++/N5q3tF2JzaVw111ra2s0aNAAQgh9WwvduBGl2SYtWrRAUFAQ5s2bZzS/bt+ZOm8A4/NYo9EYHYuenp7w9fXVdwsPCQlBcHAwPv/8c/2PnoJ04/kUpUaNGvDz88PRo0cNpt+7d88ofbqSmdJ2638YRW3r0NBQODk54dNPPzXZBkaXTx8fHzRr1gyLFy822HZbt27Vdx0vSXBwsFFbuwULFhR5DVmwYIFBmubPn4+8vDx9D8SyaNmyJTw9PfHjjz8abOeNGzfiwoULpbqmFyU4OBgpKSkG1ei3bt0qUzfrohQ+l7/99lsA0G8DU8d8SkoKFi5caPC9nj17wsrKCt9++63BvKZ6jfbr1w8HDx7E4cOH9dNu375tVBNRnsfOsWPHoFar0bBhQ5Ofm1LpJScbN27UNwhNTEzEn3/+iaioKEyfPl1fx9WlSxeMHTsW4eHhOHnyJHr37g0rKytERUVh5cqV+Prrrw0aNRbWtGlThIWFYcGCBfoqlsOHD2Px4sUYPHiwQTRflClTpuCff/7BgAEDMHr0aISEhCAjIwNnzpzBqlWrcO3atSKLrNq3bw8XFxeEhYVh0qRJUCgUWLJkySMV57788sv47rvv8MILL+DYsWPw8fHBkiVLSt1FbsCAAViyZAnUajUaNGiAiIgIbNu2zajrW1FCQkKwYsUKvPnmm2jVqhUcHBwwcOBAPP/88/jrr78wbtw47Ny5Ex06dIBGo8HFixfx119/6cePeVhPPPEE/ve//+HIkSPw8vLCb7/9hoSEBIOTd8CAAfjwww8xZswYtG/fHmfOnMHSpUuN2oeUdhtaWVnh448/xtixY9G9e3cMHz4c0dHRWLhwYanbnJTG4MGD0bp1a7z11lu4fPky6tWrh3/++Qd3794FYPhrubRdic2ld+/e8Pb2RocOHeDl5YULFy7gu+++Q//+/eHo6AhAOiYAqfvniBEjYGVlhYEDB5oc7EqpVGL+/PkYOHAgmjVrhjFjxsDHxwcXL17EuXPnsHnzZjg5OaFz58747LPPkJubixo1amDLli1GpRdpaWmoWbMmnn76aTRt2hQODg7Ytm0bjhw5oi8ZVCqV+OWXX9C3b180bNgQY8aMQY0aNXDjxg3s3LkTTk5OWL9+fbHb4Mknn8SaNWsMStoWL16MH374AUOGDEFwcDDS0tLw888/w8nJ6aFKBEqrWbNmsLCwwJw5c5CSkgKVSqUfG2P+/Pl4/vnn0aJFC4wYMQIeHh6IjY3Fv//+iw4dOuC7774DAISHh6N///7o2LEjXnzxRdy9e1c/lo2pAK6wl156CePGjcNTTz2FXr164dSpU9i8eXOR18GcnBz06NEDw4YNQ2RkJH744Qd07NgRgwYNKnP+raysMGfOHIwZMwZdunTByJEj9V2JAwMD8cYbb5R5mTojRozAtGnTMGTIEEyaNEnfnfaJJ5545LY40dHRGDRoEPr06YOIiAj88ccfePbZZ/Wlr71794a1tTUGDhyIsWPHIj09HT///DM8PT0NfojoxkkKDw/HgAED0K9fP5w4cQIbN2402v5Tp07FkiVL0KdPH7z++uv6rsQBAQEGAZiTk1O5HTtbt27FwIEDy1aaXup+PWZmqiuxjY2NaNasmZg/f75B9yidBQsWiJCQEGFrayscHR1F48aNxdSpU8XNmzf185jqSiyEELm5uWL27NkiKChIWFlZCT8/PzFjxgyDbrxCSN3g+vfvbzLNaWlpYsaMGaJ27drC2tpauLu7i/bt24vPP//coIucKfv37xdt27YVtra2wtfXV0ydOlVs3rzZqDugqW6DunwV7qIVExMjBg0aJOzs7IS7u7t4/fXX9d2rS+pKfO/ePTFmzBjh7u4uHBwcRGhoqLh48aJRN8CiuhKnp6eLZ599Vjg7Oxt1H8vJyRFz5swRDRs2FCqVSri4uIiQkBAxe/ZskZKSop8PQLHdPwvT7ZvNmzeLJk2aCJVKJerVqydWrlxpMF9WVpZ46623hI+Pj7C1tRUdOnQQERERokuXLgbdfsu6DX/44QcRFBQkVCqVaNmypdizZ4/RMovqSmzqmDTVNfH27dvi2WefFY6OjkKtVovRo0eL/fv3CwBi+fLlRuspj67Ehfe1EEL89NNPonPnzsLNzU2oVCoRHBwspkyZYrA/hZC63NeoUUMolcpSdSvet2+f6NWrl3B0dBT29vaiSZMmBl0wr1+/LoYMGSKcnZ2FWq0WzzzzjLh586ZBN9bs7GwxZcoU0bRpU/1ymjZtKn744Qej9Z04cUIMHTpUn4+AgAAxbNgwsX379hK2ohDHjx8XAAy6yR8/flyMHDlS+Pv7C5VKJTw9PcWAAQPE0aNHS7VdC+ZDiNJ3JRZCiJ9//lnUqlVLWFhYGB2vO3fuFKGhoUKtVgsbGxsRHBwsRo8ebZAuIaTu6/Xr1xcqlUo0aNBArF69usiutIVpNBoxbdo04e7uLuzs7ERoaKi4fPlykV2Jd+/eLV555RXh4uIiHBwcxKhRo0RSUlKJ6ynq+BVCiBUrVojmzZsLlUolXF1dxahRo8T169cN5inq/CvOli1bRKNGjYS1tbWoW7eu+OOPP4rsSmzqGlZ4G+i+e/78efH0008LR0dH4eLiIiZOnGjQFVoIIf755x/RpEkTYWNjIwIDA8WcOXPEb7/9ZnQ+aTQaMXv2bP11rmvXruLs2bNG6xZCiNOnT4suXboIGxsbUaNGDfHRRx+JX3/91eQ5au5j58KFCwKA2LZtW/EbvRCFEGZsjUVUjgIDA9GoUSNs2LChspNSodauXYshQ4Zg3759D907hsyjR48e+sH6qHR0g6UdOXLkkUpNq7JZs2Zh9uzZuH37dpkahVYHkydPxp49e3Ds2LEylZzIrs0J0ePs/v37Bu81Gg2+/fZbODk5GY1OShXv008/xYoVK8zSOJKouktKSsIvv/yCjz/+uMwdJCq9zQkR5Xvttddw//59tGvXDtnZ2Vi9ejUOHDiATz/99LF9+KSctGnTxmh4cCIyzc3NrVTtl0xhcEIkI927d8cXX3yBDRs2ICsrC7Vr18a3336LiRMnVnbSiIgqDNucEBERkaywzQkRERHJCoMTIiIikhXZtTnRarW4efMmHB0dy2X4cyIiIjI/IQTS0tLg6+tb6mcZFUV2wcnNmzeNHg5HREREVUNcXNwjPwVadsGJbijsuLg4/fD1REREJG+pqanw8/PT38cfheyCE11VjpOTE4MTIiKiKsYcTTLYIJaIiIhkhcEJERERyQqDEyIiIpKVMgcne/bswcCBA+Hr6wuFQoG1a9fqP8vNzcW0adPQuHFj2Nvbw9fXFy+88AJu3rxpzjQTERFRNVbm4CQjIwNNmzbF999/b/RZZmYmjh8/jvfffx/Hjx/H6tWrERkZiUGDBpklsURERFT9PdKzdRQKBdasWYPBgwcXOc+RI0fQunVrxMTEwN/fv8RlpqamQq1WIyUlhb11iIiIqghz3r/LvStxSkoKFAoFnJ2dTX6enZ2N7Oxs/fvU1NTyThIRERHJWLk2iM3KysK0adMwcuTIIqOo8PBwqNVq/YujwxIRET3eyi04yc3NxbBhwyCEwPz584ucb8aMGUhJSdG/4uLiyitJREREVAWUS7WOLjCJiYnBjh07iq17UqlUUKlU5ZEMIiIiqoLMHpzoApOoqCjs3LkTbm5u5l4FERERVWNlDk7S09Nx+fJl/fvo6GicPHkSrq6u8PHxwdNPP43jx49jw4YN0Gg0iI+PBwC4urrC2trafCknIiKiaqnMXYl37dqFbt26GU0PCwvDrFmzEBQUZPJ7O3fuRNeuXUtcPrsS08NYffw6fNS2aBfMkjoiospQqV2Ju3btiuLimUcYNoXooRy6moQ3/zoFO2sLHH63JxxUsnvYNhERlQGfrUNV3l9HrwMAMnM0+Pc0H5VARFTVMTihKi09Ow//nbmlf68LVKh8abUCh6PvYvel20i5n1vZySGiaobl31Slbb+QgPu5GviqbZCQlo1jMfcQm5QJfze7yk5atTZ/9xXM3RwJAHB3UGHh6FZoXFNdyakiouqCJSdUpcXdzQQAdKrjgeZ+zgCAw9fuVmKKqjeNVuByYjq+2yH12HO1t8ad9GwM+ykCm8/FIyk9W//K1WgrObVEVFWx5ISqtDvpOQAANwdrONu54GjMPRyLuYunQ2pWcsqqn5w8LQZ/vx/nb0nPv2rh74zFL7bG+KXHsTfqDsYuOWYwv4/aBpvf6AwnG6vKSC4RVWEsOaEq7U669NBIdwcVQgJcAADHYu5VZpKqrd8jrukDE7WtFT58shEcbazw2+hWeK6tPyyVCoP5b6Vk4Z+TbKBMRGXHkhOq0nTBiZuDNVo8CE4uJaQjJTMXajv+YjeXy4np+Hp7FADgs6eaYFir/Ad0Wlko8fHgxvh4cGP9tF/2XsXH/17AymPX8VzbgApPLxFVbQxOqEpLelCt4+GggruDCoFudriWlInjcffQra5nJaeu6hJC4P82XsT+K3cAANfuZCI9Ow9NaqrxVCmqzAY3r4H/23gRp+KScSkhDU94OZZ3komoGmG1DlVp+SUn0sMjW/hLpSen4pIrK0nVws7IRPy05yrO3kjF2RupSM/OQwt/Zywc3QoWhapvTHF3UKHLEx4AgO0XEss7uURUzbDkhKqsPI0W9zKlMTbcHaTnNjWsocbqEzdw/mZqZSatykpMy8L8XVew5VwCAGBYy5ro29gHNpYWaBnoAiuL0v+eaVvLDdsvJrINEBGVGYMTqrLuZkhVOkoF4Gz3IDjxlZ7ncI7ByUN5b81ZbDkvBSZu9tZ4f0ADOD5kbxtdG6DjsfcghIBCUXKJCxERwOCEqjBdN2JXe5W+qqG+jxSc3Ei+j+TMHH3QQiU7cPkOtpxPgIVSgYndaiO0ofdDByYA0KiGE6wtlbibkYNrSZkIcrc3Y2qJqDpjcEJVVn434vwARG1rBT9XW8TdvY/zt1LRPtgdgNTA89T1FNzLyEEDXyd4OdlUSprlIi0rF8djk6HVSg/qTM3KxYfrzwMAnmvjjzd6PfHI61BZWqBJDTWOxtzD0Wt3GZwQUakxOKEqKykjf4yTghr6qBF39z5WHIlDcz8XWCgVmLH6DP4+Lj13x8tJhW1vdnmkUoGqbtKyE9gZedtoekNfJ7zZq67Z1hMSIA2Mdzz2Hp5p6VfyF4iIwOCEqrA7afmjwxbUwNcJm87FY93Jm4i4koQgd3scir4LC6UCDipLJKRm44ddVzCtT73KSHali7ubqQ9MGtdQQ9cUpKGvE97t3wAOKvNdFjgwHhE9DAYnVGXdTjddcvJkM19sOhuP6/cykZiWjcS0bNhZW+D7US2g0Qi89PtR/Lo3Gm2CXNH1MRsLRQiBlcekEqQOtd2w9KW25bq+8hoYjw1siao3jnNCVdLk5SewYM9VAMYlJwFu9vjv9U7YO607etTzRC0Pe6x4pR261fVEj/qe6N3ACzkaLf63+Ch2XzKu2qiuElKz0C58B755MNLrsAqoZnF3UOnbmhyPM0/pydkbKejxxW48/+sh3HvQY4uIqheWnJDsvb/2LLY+6N5qZanAq11qY92p/Ge2tAlyM/k9ta0Vfh3dymCaQqHAd8+2wFsrT2H9qZuY9c85bJ7cGdaW1T9O33D6FuJTswAAgW52CG3oXSHrbeHvgug7GTge8+ij9u6MTMTEpceRkaPB1TsZ6PTZTng5qfD1iOZoVENtphQTUWWr/ldkqtJ2RiZiycEYxKdmIT41C3F372PWP+cgBOBsZ4ULH/bRt2soLWtLJT4d0gjuDtaIvpOB0Hl78Nmmi+WUA/nYFSmN1Ppq12Bse7MLbKwsKmS9uv1z9NrDlZzkarSYs+ki+n29Fy8tPoqMHA3aBLmihrMt0rPzcOV2Bt5be1bf84iIqj6WnDykrFwNvt0Rhcj4NACAg8oSr3atjbrefIbIo9p96TaWHYpFnlaL09dTAADPtfXHwCa+GL7gIHI0WgBA93qesLV+uBuso40Vpvaph6mrTiP6TgZ+2HUF7YPd0bGOu9nyISf3czQ4FH0XADC0eQ1YlmGk10fVOih/MLasXE2ZgqL07DyMX3ocewpUvw1tUQP/N7QJBATOXE9B2G+HcTIuGaN+OYTQhl4Iax9Yqe1RcvK0+G7nZdhaWeCVzrVKNdw/mYcQAssOx2HHxUQAhsGqn6sdRrTyx+KIa+hZ3xPd63kZfX/dyRtYf+qWwXdbBbri5U61oOR+rFCPTXCSmJaFLzZfMtvyzt1KwdkbhqOQbr+QiPnPhaBjHXdcSkjDupM3MKKVP/xc7QAAUQlpWFtg2q7IRGw+Fw+t1nj5dioLjG4fiAA3e+yNuo3/ztwyms9LbYNXOteCvbUFlh2OK/Z5MrbWFni+XQCCPRwAALFJmVhxNBZDmtdAbU/DgErXaPKYiV+69X0c8Xy7QORqtPh5z1Vcv3ff4HOFAuhZ3wsu9lbYHXkbozsEwcXOCutO3sSd9Gz0aeSN5Yfj0K+xDxo8GM21oEX7ozF7w3mIAtcVdwdrTO1TD042VmhSU60PWMpaYlLYsJZ+eMLLEb9HXMPq4zfw8b/n8e+kTtXyZhJx9Q5y8rSo4WyL2p4OFbruYA8H+KptcDMlCxFXk0pdtZOQmoUxC4/g/K1U2FgpMXNgQzTwcUKTmmp98NEy0BXju9XG3M2RiLiahIirSTh1PQX/91RjKBUKLNp/DZcT0wEATraWCGsfiE1n4xGVkG60vrbBrqjv44Q/D8UiO9fwZHOwscSYDoGo6WJXZHqjEtLwx8EYnIxLxqkHx+jOi4lFju+iVCowsKkPNFph8vwui4Lnd8SVJBy8moQXOwZBbSs1QD54NQnrTt40KF1q4OuE59sGQKlUQAiBv4/fQHpWbpHnt8pKiWfb+KOetxOOxdzF6uM3kKcRUCikHwrujirsupiIsPaB+mddnYi9h1XHriNPk7/e2p4OGN0h0OBRCKfikrHyWBxy86T5utT1QL/GPkb5PHsjBcuPxOrn61jHHQOb+gKQStg+WHcWyw7HFbmdFu6/BgBYdjgWA5r4oq6XA17qVAs2VhaIScrAlJWn9T9+dLZdSMSJ2GR8NbzZQ/8YorJTCCFkVRaampoKtVqNlJQUODkZ37we1tXb6ej+xW6zLQ8AHG0sMbnnE7C3tsDq4zdw+NpdWCoVGN7KD+tO3kR6dh7cHazxTEs/aLUCSw/FIj07D2721uhZ3wsrjhZ9EgGAi50VQht6Y8XROBS1l+p5O6KGsy22Xyz54WqONpZ4to0/FFBgxZFY3MvMhaNKmlbwhnzldjo2P3i2iimd6rgj9X6u/gJsioVSAY1WINDNDg1rqPHv6VsAAEulAnlaAVsrCzzX1t/gAhV37z7WP2hLMrR5DbQOcgUAtA5yRa0HQdWXWyLxzY7LAIDNkzubpaQqOTMHXebuQsr9XHw6pDFGtvbDP6du6kvFHG2sMKKVH1zsq+5os1NWnsLKY9fxbBt/fDqkcYWvf8bqM1h2OBaj2wdi1qCGJc5/KSENo387jJspWXB3sMavYa3Q1M/Z5LwarcCms/G4lJCG73ZehkYrEBLgAisLBQ5evWswr+74K0pxn7s7qPBMy5owFbpqCpzfAGBvbYFcrUBOXvERh1Ih/UY3x1XY0cYSA5v6YvnhWGgFUMfTAb0aeCEtKw9/Ho6FxkS+Oj/hgUa+Trh6OwObzsUDKP78trO2wNAWNbDiSBxyNYbL0227ADc79G/sg/u5Giw9GGt0sweANkGu+h8XWbla/HEoxmhbDW7mC19nW/377Dwt/jgYg+xC8w1q6ouaLrY4HnsPB6/ehVIhVV36FQgkczVafLPjMm6nZcPN3hpJBRpSN/VzRodgN0RcTcKJ2GS0DHDB0w+evJ2UkYOvt0UhR6NFk5pqdKxtXLLaobY7OpiY/jgy5/37sQlOkjNzsPRQrNmWZ6lUoG8jH/i7SSdAdp4GU1edxrqT+Q01bayUyCr0C6zwtKHNayDYxC/ZTWfjceZG/sXhyWa+Bo+d12oFFkfE6EdJtVQq8L+OQXCyNd1Vc/uFBByPTS42LQUpFcCYDkFwLXBDzsjOwy/7ovUXEbWtFV7sEARLi/zLdUxSBv46et1o+QoFYGWhRE6ettj1AsC0PvUwrkstk0XzJ+OSMfj7/XCxs8Kx93qZrah10f5ozFp/Hm721uhYx91gPwJAkLs9fn+xtb4UrCrJyM5Dq0+2ITNHg5Xj2qFVoGuFp2HzuXiMXXIMgW522DWlW7HzHrhyB2OXHENaVh5qedhj0ejW+vOsJHsu3cb4pcf1QYKtlQVe6hQEGysLrD1xA1GJ6bCxUuLlB7+WdW6nZWNxxDUIAbSt5YpOdTwMlrv+1E1cfBCsFqdVoAt61vdC74beyMnTYsfFRGiLuMSev5WqD9oLn99lVfj8NnWO9W3krW80nJ6dh1/3RhsEDkoFYKlU6qcVPr/3Rt02CPZ61vdCc3/nB6WwcUWut3s9T30gkp2rwc97o3E/V2OUh651PdAq0BU3k+8Xe63uVMcdbWu5IT4lC0sOxhh8ZmtlgW9GNkevBsZVNgmpWdh96Tb6NvLGoat3cfZmCn7bF43UrDz9PBZKBTa93gl1CuyLw9F38fLvR5FyP9dkel7vUccsIypXBwxOZEpXHXLxVhpquNjiqRY18OfhWP1gYb7ONng6pCaWHY7D7bRstAx0MVl0CQCZOXn4PSIGianZaO7vjAFNfIxu1jeS72P54Vhk5WrQp5FPsdUcWbkaLImIwa0UqbeGp5MKI1r5YdWx67iZnGUwr1IB9GrghTa1jHvBnL2Rgn9O3YSlUoFhLf0QaKLIesu5eNzLzEG3ep7481As0rLy0L2eJ1zsrLE36jaeaemH9aduIiYp0+B7CoV0gSp8Yyhs7YkbqOFia9abbK5Giz7z9uDK7QwA0jYY1tIPdtaW2HwuHjeS76NRDSf8M6Fjlat7/utoHKauOo0gd3vseKtLpbTHSM/OQ/MPtyBXI7BqXDu0LLTvNFqBQ1eTcO5mKj7bfBG5GoFWgS74+YWWZX4+0uXEdKw6dh1CCAxpUQP1vKXrSMr9XCw/HIvOT3jon8FU0IHLdxCVmI5n2/gbPX05LSsXv0fEICm96K7Lvs42eL5dAFSWpSv6F0Jg7ckbsFQqTZ7fZVHw/G5Uwwntg93x56EYpGdLQUBDXycMbVHDYB1nrqdg/emb+qqZXg284KCyLPL8zsnTYumhGMTdvY+63g54JsRPfy5sPZ+ApPRs9KjvhaWHYpB6X7rh1/FywPCWfgbnzMX4VKw5cUNfNQMAwZ72GNEqvwR3Z2Qi9l66Y5TPIHc7jGztr28ztefSbey+dBtCAFYWCoP9XRrRdzLw19E4fTVeu2A3k4FNTFIGVhyJM/mjqvMT7o/deElFYXBCVA7O3kjBgj1XIQAMa1lTHyTdSrmP3l/uQVp2Hqb3rYe2tdxgZ22BOp4Osh8ITKsVGPjdPpy7mYopoXUxoVvtSkvLtFWnseJoHJrWVCN8aBPU83aEUqnA/RwNXlt2Atsu5Fcl9m/igy+eaVphPYqI6NExOCGqYD/tvoLwjYbdjWcObIAxHYIqKUWls+rYdby98hQcVZbYNaWrvqFiZUhMy0K3ubuQkSP9mm9byxXv9W+Ad9eexam4ZFhbKtGspjO61vPAuM7BVa6Eiuhxx+CEqIJl52kwYekJXIxPRZ5GID41SxY3/OJkZOeh2+e7kJiWjRl962Fsl+DKThJWHo3D9zsv41ZKlkHDRmc7K/z8QstKaQ9DRObB4ISoEmm0AoMeVJWUtvdJZfhiSyS+3XEZ/q522Ppm51K3hagIF26l4o0VJxGTlInang6YN6KZvps7EVVN5rx/PzbjnBCZi4VSgbd718WYRUfw75lb+GBAA9lVQdxIvq9/9tA7/erLKjABgPo+Ttg0uXNlJ4OIZIrD1xM9hPa1pUaxt9Oycf5WaslfqGBzNl5Edp4WbYJcEdrQuPcBEZGcseSE6CGoLC3QPtgd2y4kYPel25X60Lm0rFzMXHcOiWnSmDdaIXDgShIUCuD9AQ1k36OIiKgwBidED6lrXQ9su5CAuZsjkavR4rXudSpl6Puvt0Vh9YkbRtOHt/Tjk3qJqEpicEL0kLrV84RSAWgFMG9bFII9HPTP+ago0XcysDjiGgBgSmhd1HSRhvtWWSo5MBQRVVkMTogeUg1nWywc0xpTVp5CYlo2Vh67XuHByaf/XUCuRqBrXY9KHWCNiMicGJwQPYIuT3hg5bh26DJ3F/ZG3cbN5PsGDyt7WNl5Gpy9kYrmfs76nkDZeRrsv3wHmQ8GMYtPycLW8wmwUCrwXv/6j7xOIiK5YHBC9IgC3OzRtpYrDl69izf/OolJPeqgvrfTQz3FOCE1C5cT0zF3cyROxiXjfx2D8P6ABriTno2Xfz+KE4Ue3ggAz7cNQG3PR386MxGRXHAQNiIzOBZzFy/8elg/NLujyhJfDW+GOl4OUCoUqOFsW+JYKCn3c9H5s50GTz/VPSV16t+ncSI2GY42lmhQ4KF17o4qfDqkMdRFPI2aiKiicIRYIhk6fzMVH204j+g7GYhPNXzS86CmvvhmZPNiv7/0UAzeXXMWgPRY+Jw8LQ5F34WnowqJadmwt7bAuokdUduTI6kSkfyY8/7NQdiIzKSBrxOWvdIWu6Z0xfCWfnC0sYSDSqo5/efUTURcSSr2+6uOXQcAvNuvPpb8rw0+e7oJnO2s9OOXTOhem4EJET0WWHJCVM7eX3sWSw7GoI6nA4a0qIFF+6/hfq5G/7mv2hYjW/th1vrzsFAqEDGjOzwdbQAAV2+nY9LyE7CxtMAfL7WBjZW8hqEnItJhtQ5RFXI3Iwe9vtyNpIycEuctqvpHCMGRXolI1vjgP6IqxNXeGn+Na4cxC48g9m4mpvWphz6NvAFITzj+5N/z2Bl5G30aeuOzp5uYXAYDEyJ6nLDkhKiCZOVqcDcjx2gcFCEEYu9mwt/VjkEIEVVZLDkhqoJsrCxMDtCmUCgQ4GZfCSkiIpIn9tYhIiIiWWFwQkRERLJS5uBkz549GDhwIHx9faFQKLB27VqDz4UQ+OCDD+Dj4wNbW1v07NkTUVFR5kovERERVXNlDk4yMjLQtGlTfP/99yY//+yzz/DNN9/gxx9/xKFDh2Bvb4/Q0FBkZWWZnJ+IiIiooDI3iO3bty/69u1r8jMhBObNm4f33nsPTz75JADg999/h5eXF9auXYsRI0Y8WmqJiIio2jNrm5Po6GjEx8ejZ8+e+mlqtRpt2rRBRESEye9kZ2cjNTXV4EVERESPL7MGJ/Hx8QAALy8vg+leXl76zwoLDw+HWq3Wv/z8/MyZJCIiIqpiKr23zowZM5CSkqJ/xcXFVXaSiIiIqBKZNTjx9paG5E5ISDCYnpCQoP+sMJVKBScnJ4MXERERPb7MGpwEBQXB29sb27dv109LTU3FoUOH0K5dO3OuioiIiKqpMvfWSU9Px+XLl/Xvo6OjcfLkSbi6usLf3x+TJ0/Gxx9/jDp16iAoKAjvv/8+fH19MXjwYHOmm4iIiKqpMgcnR48eRbdu3fTv33zzTQBAWFgYFi1ahKlTpyIjIwOvvPIKkpOT0bFjR2zatAk2NjbmSzURERFVW3wqMRERET0yc96/K723DhEREVFBDE6IiIhIVhicEBERkawwOCEiIiJZYXBCREREssLghIiIiGSFwQkRERHJCoMTIiIikhUGJ0RERCQrDE6IiIhIVhicEBERkawwOCEiIiJZYXBCREREssLghIiIiGSFwQkRERHJCoMTIiIikhUGJ0RERCQrDE6IiIhIVhicEBERkawwOCEiIiJZYXBCREREssLghIiIiGSFwQkRERHJCoMTIiIikhUGJ0RERCQrDE6IiIhIVhicEBERkawwOCEiIiJZYXBCREREssLghIiIiGSFwQkRERHJCoMTIiIikhUGJ0RERCQrDE6IiIhIVhicEBERkawwOCEiIiJZYXBCREREssLghIiIiGSFwQkRERHJCoMTIiIikhUGJ0RERCQrDE6IiIhIVhicEBERkawwOCEiIiJZYXBCREREssLghIiIiGTF7MGJRqPB+++/j6CgINja2iI4OBgfffQRhBDmXhURERFVQ5bmXuCcOXMwf/58LF68GA0bNsTRo0cxZswYqNVqTJo0ydyrIyIiomrG7MHJgQMH8OSTT6J///4AgMDAQCxbtgyHDx8296qIiIioGjJ7tU779u2xfft2XLp0CQBw6tQp7Nu3D3379jU5f3Z2NlJTUw1eRERE9Pgye8nJ9OnTkZqainr16sHCwgIajQaffPIJRo0aZXL+8PBwzJ4929zJICIioirK7CUnf/31F5YuXYo///wTx48fx+LFi/H5559j8eLFJuefMWMGUlJS9K+4uDhzJ4mIiIiqEIUwczcaPz8/TJ8+HRMmTNBP+/jjj/HHH3/g4sWLJX4/NTUVarUaKSkpcHJyMmfSiIiIqJyY8/5t9pKTzMxMKJWGi7WwsIBWqzX3qoiIiKgaMnubk4EDB+KTTz6Bv78/GjZsiBMnTuDLL7/Eiy++aO5VERERUTVk9mqdtLQ0vP/++1izZg0SExPh6+uLkSNH4oMPPoC1tXWJ32e1DhERUdVjzvu32YOTR8XghIiIqOqRdZsTIiIiokfB4ISIiIhkhcEJERERyQqDEyIiIpIVBidEREQkKwxOiIiISFYYnBAREZGsMDghIiIiWWFwQkRERLLC4ISIiIhkhcEJERERyQqDEyIiIpIVBidEREQkKwxOiIiISFYYnBAREZGsMDghIiIiWWFwQkRERLLC4ISIiIhkhcEJERERyQqDEyIiIpIVBidEREQkKwxOiIiISFYYnBAREZGsMDghIiIiWWFwQkRERLLC4ISIiIhkhcEJERERyQqDEyIiIpIVBidEREQkKwxOiIiISFYYnBAREZGsMDghIiIiWWFwQkRERLLC4ISIiIhkhcEJERERyQqDEyIiIpIVBidEREQkKwxOiIiISFYYnBAREZGsMDghIiIiWWFwQkRERLLC4ISIiIhkhcEJERERyQqDEyIiIpIVBidEREQkKwxOiIiISFbKJTi5ceMGnnvuObi5ucHW1haNGzfG0aNHy2NVREREVM1YmnuB9+7dQ4cOHdCtWzds3LgRHh4eiIqKgouLi7lXRURERNWQ2YOTOXPmwM/PDwsXLtRPCwoKMvdqiIiIqJoye7XOP//8g5YtW+KZZ56Bp6cnmjdvjp9//rnI+bOzs5GammrwIiIioseX2YOTq1evYv78+ahTpw42b96MV199FZMmTcLixYtNzh8eHg61Wq1/+fn5mTtJREREVIUohBDCnAu0trZGy5YtceDAAf20SZMm4ciRI4iIiDCaPzs7G9nZ2fr3qamp8PPzQ0pKCpycnMyZNCIiIionqampUKvVZrl/m73kxMfHBw0aNDCYVr9+fcTGxpqcX6VSwcnJyeBFREREjy+zBycdOnRAZGSkwbRLly4hICDA3KsiIiKiasjswckbb7yBgwcP4tNPP8Xly5fx559/YsGCBZgwYYK5V0VERETVkNmDk1atWmHNmjVYtmwZGjVqhI8++gjz5s3DqFGjzL0qIiIiqobM3iD2UZmzQQ0RERFVDFk3iCUiIiJ6FAxOiIiISFYYnBAREZGsMDghIiIiWWFwQkRERLLC4ISIiIhkhcEJERERyQqDEyIiIpIVBidEREQkKwxOiIiISFYYnBAREZGsMDghIiIiWWFwQkRERLLC4ISIiIhkhcEJERERyQqDEyIiIpIVBidEREQkKwxOiIiISFYYnBAREZGsMDghIiIiWWFwQkRERLLC4ISIiIhkhcEJERERyQqDEyIiIpIVBidEREQkKwxOiIiISFYYnBAREZGsMDghIiIiWWFwQkRERLLC4ISIiIhkhcEJERERyQqDEyIiIpIVBidEREQkKwxOiIiISFYYnBAREZGsMDghIiIiWWFwQkRERLLC4ISIiIhkhcEJERERyQqDEyIiIpIVBidEREQkKwxOiIiISFYYnBAREZGsMDghIiIiWWFwQkRERLLC4ISIiIhkpdyDk//7v/+DQqHA5MmTy3tVREREVA2Ua3By5MgR/PTTT2jSpEl5roaIiIiqkXILTtLT0zFq1Cj8/PPPcHFxKa/VEBERUTVTbsHJhAkT0L9/f/Ts2bPY+bKzs5GammrwIiIioseXZXksdPny5Th+/DiOHDlS4rzh4eGYPXt2eSSDiIiIqiCzl5zExcXh9ddfx9KlS2FjY1Pi/DNmzEBKSor+FRcXZ+4kERERURWiEEIIcy5w7dq1GDJkCCwsLPTTNBoNFAoFlEolsrOzDT4rLDU1FWq1GikpKXBycjJn0oiIiKicmPP+bfZqnR49euDMmTMG08aMGYN69eph2rRpxQYmRERERGYPThwdHdGoUSODafb29nBzczOaTkRERFQYR4glIiIiWSmX3jqF7dq1qyJWQ0RERNUAS06IiIhIVhicEBERkawwOCEiIiJZYXBCREREssLghIiIiGSFwQkRERHJCoMTIiIikhUGJ0RERCQrDE6IiIhIVhicEBERkawwOCEiIiJZYXBCREREssLghIiIiGSFwQkRERHJCoMTIiIikhUGJ0RERCQrDE6IiIhIVhicEBERkawwOCEiIiJZYXBCREREssLghIiIiGSFwQkRERHJCoMTIiIikhUGJ0RERCQrDE6IiIhIVhicEBERkawwOCEiIiJZYXBCREREssLghIiIiGSFwQkRERHJCoMTIiIikhUGJ0RERCQrDE6IiIhIVhicEBERkawwOCEiIiJZYXBCREREssLghIiIiGSFwQkRERHJCoMTIiIikhUGJ0RERCQrDE6IiIhIVhicEBERkawwOCEiIiJZYXBCREREssLghIiIiGSFwQkRERHJCoMTIiIikhWzByfh4eFo1aoVHB0d4enpicGDByMyMtLcqyEiIqJqyuzBye7duzFhwgQcPHgQW7duRW5uLnr37o2MjAxzr4qIiIiqIYUQQpTnCm7fvg1PT0/s3r0bnTt3Nvo8Ozsb2dnZ+vepqanw8/NDSkoKnJycyjNpREREZCapqalQq9VmuX+Xe5uTlJQUAICrq6vJz8PDw6FWq/UvPz+/8k4SERERyVi5lpxotVoMGjQIycnJ2Ldvn8l5WHJCRERU9Zmz5MTSTGkyacKECTh79myRgQkAqFQqqFSq8kwGERERVSHlFpxMnDgRGzZswJ49e1CzZs3yWg0RERFVM2YPToQQeO2117BmzRrs2rULQUFB5l4FERERVWNmD04mTJiAP//8E+vWrYOjoyPi4+MBAGq1Gra2tuZeHREREVUzZm8Qq1AoTE5fuHAhRo8eXeL3zdmghh4TmXeBBV0Bj7rAs38BRRyDRERUfmTdILach00hMnZ6BZAcI71unQJ8m1V2ioiI6BHw2TpUtQkBHF+S//7EH5WXFiIiMgsGJ1S13ToJJJ7Lf3/mLyA3q9KS89hIvAgsGgD83APY+wWg1VZ2ioioGmFwQlVbTIT094k+gKMPkJUCxB2s3DRVd0IA6ycB1/YCN44C2z8EVr8E5GWX/F0iolIo10HYiMpdRqL01yUQsLYHzv4NxB0GanWtzFRVX+fWAOfXAXGHACs7oPMUYOcn0na/Gw34Ns+f16sh0Op/lZdWIqqyGJxQ1ZZxW/pr7w64Bj8ITg5Vbpqqq4RzwKoXAfGgCqfDZKDTm0CNFsCK54Gbx6VXQd5NAL9WFZ5UIqraGJxQ1ZauC048AZ+m0v9xR6Q2EErWWpqNEMDmd6TAxL8d0HAI0PJF6bNaXYFXdkmlKto8adqVHVKQePIPBidEVGYMTqhq05eceABejQAreyA7Bbh9EfBqULlpq+qSY4H4s9L/lzYBV3cBFtbAkB+larSC3IKBzm/nvw9oDyweCJxdDYSGA9Z2FZVqIqoGGJxQ1aYLThw8AQtLoGYIEL1H+tXO4OThZSQBP3UG7t8rMFEB9J1jHJiYEtARcPaXApxLm4BGQ8srpURUDbHcm6ouIQzbnACAbwvpb/zpyklTdZB5F9g+WwpM7NyBGi2BwE7AyGX5VTklUSqBuv2l/2MOlF9aiahaYskJVV3ZaUDegzFN7D2kv96Npb+3GJw8lJPLgLXj8t8/sxAI6vxwy/JrDRyazwbKRFRmLDmhqktXamJlL3UjBvIbxSacA7SayklXVZWVAmx5V/pfaQWEjHn4wAQA/NpIfxPOAtnpj54+InpsMDihqkvf3sQjf5prLSlYybsPJF2unHRVRVotsOkdIDMJcH8CePcWMHDeoy1TXQNQ+0k9fG4cNUsyiejxwGodqroK9tTRUVpIg39dPyxV7XjUlabHnwG2zZJuvnVCgW4zKjy5snJmFXBwPiAelC5lpwNJUdL/fcIBCyvzrMevDZASx4HxiKhMGJxQ1ZX+YHRYe0/D6T5NpOBk9UtSAOPxBPBXGJDzoGrh5gkgqBMQ2LFi0ysXmlxg0/T84E5HaQUMng/U7mm+dfm1Ac6uYrsTIioTBidUdWXckf4WrNYBpEHCjvwi/b+5QAlJYCfAwUu6WW6aDry0A7C0rpi0yknUFikwsfcEnvwOgEKa7lEXcAkw77r8Wkt/OTAeEZUBgxOqmuIOA7s+lf63LxScNBwiPfcl4Syw6/+kqovGw4AnvweyU4GorVI1zx9DgRF/AjZOFZ/+yiAEcGwRsGGy9L7pcOCJ0PJdZ3kMjCeEVC3l5AsEdnj05RGR7DA4Ifm7HZk/VoaFNVC3L7BsZP7n6pqG8ystgHr9pFdwDyD5GtBwKKBQAJbuwLBFwIoXpKfqbpsJDPiqonJSuS5vzw9MAKDZc+W/ToOB8Q4+enCSlw2smwCcWQlAAXScLAVADQZL6yKiaoFnM8lb5l3g195AVnL+NJ9mQOaDKp1ObwONnir6+zVDpFdBwd2BEUuB3wdJJQk1WkpdZp39zJx4mbm0Mf//wT8CnvUqZr1+bR4EJ4dLP4hbYWkJwPUjwKEfpaASACCAfQ8CyzuXgG7vmCW5RFT5GJw8isy7QHKM9L/KSXq+CD06rRZIPA9oc6W2I1nJgFMNqYvr1Z3ArZPSfIGdgB7vP9w6anUB6g8CLvwDrBsvVQ1NPArYOpspEzIjhFSdBQAjlkmlShXFr63092FHir26S3rqcXaq9N7aARj2u9QL6Nxa6ZjY/7XUwLlmK8DK1hypfjT3kwGlJaByqOyUPH5ys6QqRAjD6U41pMdcpN6S/iotjL+bkykFugW/6/5E/jhKVGEen+BEkwukJ5hvedePAGvHA7mZ+dOaPSeNDaHrhpl73/hCWXCaEEDarfxH0BdkZQfYuRY/n41z/sWvpPxZ2gL2bkWnpTBNHpAebzzd3jO/EWlOJnD/bqEZFFJbAIXCcPlajfTEWktV8evNSAJWjAJiIwynD/kRCOgAzA3Of96Lf9sis1sqff5PGmH2xjGpgeieuUDoJ4+2TLlKuiwF0hbWjzaw2sPwbyP1BEqOAZKulC2IP7kM+GeidOy4BALudYHu70k9sgCgRRiwsB8Qe0B60KCjD/DsivzB+HKz8kvZrO0BWxepaqhwTyVA+q7SQnrStSbb8LOC52NxMu5Ix+6acdJ14Klf87uzF6awABy9pf+Lug6UVsHzWwjpuC54jgkBpMXndx0HDM9lQDrnhbbo89vSJv8xEYBUmqXNRZHnvNF8D9i5A1Y2xnlITwQ0OdL/jr5FN54ubr7EC8DSYUBKrPH3lJZSlfCF9VJp6eD5UpWw7qGUWg2wqD9w87jh9+zcgJHL8xt3U4V4fIKTezHAdyElz1dWdm7SSZt2S3o8fOp1YMgCYPuHwKk/gfavAV3fkS4KG6cBJ5cC7SYAbcYBK0dLQY5JCmme9q8Bq16U6usLs7SR2kt41JV+WabeKD6tIWOA0E+li8j2j6ShxZs/L41roSxwKNy5BCwflV8qVJC9JzBssXSBWDchv3tuQT5Npfmu7gJ6fShVo6wYJQUV9foDJ5ZKjVYHzpNuljrJscCfw4G7V6S82T242DYYnH9DDe4h9bYB8kcgfVjqGsColUDUNmDpU8Chn6RqB7dg6WKuuwAqLKp+e4ZLm6W//u0q/te8ylEKJK/tlUpvShOcCCEFizsfBIsNh0o3k8I3NYUCGPAlsHIMkHpTOg9/6ws8/ZsUCK96Mf8Gq1BKx/uF9SaCagCuwVLQc26N8WcKJdB+EtB1hrTOwrR50vl9Yonh9D9KeOBhYCfph4Wp87usQsYAXacDq18GYg8B/T4Dmo4EslKlbvVXdxnOb+8plUDVaAHciZLO0ZzM4s/vpiOlc3rdBKnXl453Eym4u7ID6DkLaP2y9MNt7Xgg8j/DZdi6SPsn4EFj5tz7UgB6YX3+PB71parXgu3J8rKAfyYB59fmT3OvK83n7C+VzP0VJjW+VjlJx52OJhfISMxfx42jwPetAGtHYPAPUsPwE39IgYmFdX4j+5x0aWykxQOlH0h1C5U4Vodrg0wphBCi5NkqTmpqKtRqNVJSUuDkZMZeFElXgB/amW95SkugxQtA74+lg/PSZukCmZtRtuUolNKvysIK/3Izmq/AzbNgmhQmiipNLa9UabMwDFrEg9IPg3VaSWnT0eY+2i9AAFD7S0GDqTYRp5YDa8ZK/0+LMV81zB9PA5e3Sg+r6/YO8NcLUpAESL1Nes4E2ow1z7oqw48dpR5KfT+rnHzs/xrY+oE0hspzfxc/ryYX2PBG/o2+w+tAj1kld0POSpH2W+GbsO68KHgOFD5XtHmGpQoWqkJpKsP5Y2Uv9YTKyQTOryv6fNDkQF99UNR1oLRKlT5F/o8BU+eyKQXPb6N1PFheied8aderW16hfWGKharo+fzbSb3wCpZ0CQHsniP9MGr1PykATThnWKKj02cO0PbB86Wy04G/XzJsr1WQlZ20ruBuxaf3MWHO+/fjE5xUhFunpBKM5BjpV3+LF4DDvwA5adLntq5AyGipHUV2KuDZABj+h+lfkufWAP+8Lv0K8KgnzedeJ/9zrRbY+bF00dfmAU/0BYYuKLpb7MX/pLYVuioRlVo6SY8tzJ9WUHB3qUi64Amekykt49wa6cLe+hWpGqRg3e29GGDFc1Kde4NBUl7zsoCAjoCTDxC5SVrvyT+lXzKF+bcDnlkMOHqZzsf9ZOCXHtIvtWcWmp7nYdyOlIJXoZEuyKYuWj1nS71Dqppbp4CfOksX/rciS1c9YW6JF4Af2kolYlMuG/6qLSg7Tfr1e2W7dFPs+5n0K7y0NLnAf28Dx3+XbkhNRwAD5kklLscWA5vfldrbDPzGsBQm445Uknn7IjD0Z+ObzanlwL9v55/Lpti6SudgnV6lS2viBelcUVoan99lVfD8dg0G6vQGjv6a/wPGtZa0Dq+G0vucDKlUo2ApRK2uUqlGUef3lZ3SjTrzjvQDYvjvgG9zIDlOykdmEtBwMHD45/wHcjrVlEpndI3S87KB9ZOBU8tg0K7D0VcqsfFrLbUJ+esFaSDFwhy8pfM+oL1UXbQyrEAVsMJwf5dEkyeNg3Tkl/zgqkZL4MVNhiMkazXAlvekklVTwVDHN6UfL8TgRNa0Wqko0MpOKlHJy8k/UQtPUzmaLiLW0eRKRZ7FzZd7Xzp5SlNUr1seINULW1gZTtNRKIq+eQDShU2hLLrdiBDSS6mULkaanPzlaTXSxU6TZ9hepzTrLW9b3gMOfCv9H9AReOoXqZ3CoZ+kQNBCBUw8Yv6Bysrbf1OAwwukqrRnFlVOGoQAvmsptX3pPBXo/q7h5zER0uMF7l6VglYrO6nov27fh1tfTiYAYdyQUXf8FaW4zwuey6bozu+y0Gql476460Bp6c5lawfp3MvNyg9OdNMKy8mQ8lzw3Cvu/Nadt9b2htup8Dmf96CUpfB8hderY2p52SYCQVPLy057sG6Lh2u4mpOZX5pT0rVWU+BHy57PpOtF67FSFRoxOCEqF1otcC9autC5BedfpISQuh1H7wHcagPOAVIjul4fSr9I5exeDPBdK6lI/vk1UolYZTm/TvpFbGkjpaP1y9Lfs6ulBqS6agN7D6lRa41yaCNGZC57v5DaFjZ/Thrgkcx6/2ZLHiIdpdJ0FZtCAYSGS1UjSZfzn3aclQq8sM48v3rLy7aZ0k0/qDNQq5LrxesPkhqAXtsrNZK8tEl6CKOuPr9uf6mu36fZ4zNqL1Vd1g9Kq3PK2M6QSoXBCVFpeDcCXtoK3L4k3ez/mwpE7wYiN1bsmCFlEXvwQfsBpRRcVXYQpVBI7Q8ub5caH59ekR+YtB77oNdYMVUuRHKiq0JicFIuGJwQlVaNkPyqhnvXpNFJD3wjz+BEq5UebghIDbO9G1VuenTsXIEmzwCNnwYaPCn1ovOoKzXgrOzgiagsrB6Mj5KTWfx89FAYnBA9jJb/k4KTuENSDwlbl8pOkaHTK4CbJ6RxHLq9W/L8FU2hkMa8Iaqq9NU6JsZ6okfG4IToYTj7SV28b1+Uulg2KmGwrfJ253L+qKdCC2yfLf3f+W1pqG4iMi9W65QrBidED6t2Tyk4ObZQGtuisrpBX90N/P4kjJ4l4hIItH21MlJEVP3phr0vPCQCmUUJQy4SUZFq95T+Ru8BPq8rjdFR0TR5D9qWCGmAKrfa0surkTTQmKWqxEUQ0UNgtU65YskJ0cMK6AD4t5ceOpebIY1A2mt2xabh+GLpCc62LsD4iMoZ/ZXoccRqnXLFkhOih2VpDby4ERj24Bkwp5ZJJRkVJSsl/8F4Xd9hYEJUkXS9dbR50ujBZFYsOSF6VE/0kZ6llJ4gPTG56YhHX+b5f4CjvwE93s/vvnxhPbDn8/w67uwHT0x1rwu0HPPo6ySi0is4VH5OOmDJHwfmxOCE6FFZWktjiez7SnqY2rHF0oPJuk43fIBYSYSQni8TexCIOyhNS70BvHpAepz7hjdg1OgVAPp8Wrb1ENGjs7CSnrelyZaqdlhyaVYMTojMoes7QFq8VLUTe0B6Xd0pPSFWoQRaPA8Edix+GTeOAfvn5b+3sAbuXJICliO/ABDS+CoFuy3buQOe9cohQ0RUIms74H42e+yUAwYnROZgaQ0Mni+VoCScA7Z+IAUbN45Jn1/eBkw6Dtioi17GyaXSX0cfqR1LwhmptCTiO2l6QEeg/xccSZVILqwdpEEY2WPH7BicEJmLQiFV5wS0B2p1lQISoZVKPe5eldqLdHwDOL9Wevy6jlMNafj2M39L74f8CPi1ktqaJF4EDv8EQCFV3zAwIZIP9tgpNwxOiMqDex3pBUhVO8uGAwe+lYaVT08wnt/RB8hOAdR+QGBnaZpSCfT7DAjuLpXM+DStuPQTUcn4fJ1yw+CEqLw9EQq0eRU4NF8KTNT+gH9b6TOhkXrhpN0CLG2A/l9KQUlBdftUfJqJqGT6khNW65gbgxOi8qZQAH3CAc/6UhuU7u8DDh75n8dEAMcWAa1fAWqGVFoyiaiM9KPEslrH3BicEFUEhQIICZNehQW0k15EVLXw+TrlhiPEEhERPQxW65QbBidEREQPg9U65YbBCRER0cNgb51yU27Byffff4/AwEDY2NigTZs2OHz4cHmtioiIqOJxnJNyUy7ByYoVK/Dmm29i5syZOH78OJo2bYrQ0FAkJiaWx+qIiIgqnr5ah21OzE0hhDDxJLFH06ZNG7Rq1QrffScNu63VauHn54fXXnsN06dPN5g3Ozsb2dnZ+vepqanw8/NDSkoKnJyczJ00IiIi8zjxB7BuAuAcANTtV9mpKRt7d6Dz22ZdZGpqKtRqtVnu32bvSpyTk4Njx45hxowZ+mlKpRI9e/ZERESE0fzh4eGYPXu2uZNBRERUvhy8pL/JMdIgi1WJWx2zByfmZPbg5M6dO9BoNPDy8jKY7uXlhYsXLxrNP2PGDLz55pv697qSEyIiIlmr1Q3o97k0wnNVY+dW2SkoVqUPwqZSqaBSqSo7GURERGVjYQm0frmyU1Etmb1BrLu7OywsLJCQYPhws4SEBHh7e5t7dURERFTNmD04sba2RkhICLZv366fptVqsX37drRrxyG6iYiIqHjlUq3z5ptvIiwsDC1btkTr1q0xb948ZGRkYMyYMeWxOiIiIqpGyiU4GT58OG7fvo0PPvgA8fHxaNasGTZt2mTUSJaIiIiosHIZ5+RRmLOfNBEREVUMc96/+WwdIiIikhUGJ0RERCQrDE6IiIhIVhicEBERkawwOCEiIiJZYXBCREREssLghIiIiGSFwQkRERHJSqU/lbgw3ZhwqamplZwSIiIiKi3dfdscY7vKLjhJS0sDAPj5+VVySoiIiKis0tLSoFarH2kZshu+XqvV4ubNm3B0dIRCoTDrslNTU+Hn54e4uLjHbmj8xzXvj2u+gcc3749rvgHm/XHMu5zyLYRAWloafH19oVQ+WqsR2ZWcKJVK1KxZs1zX4eTkVOk7sbI8rnl/XPMNPL55f1zzDTDvj2Pe5ZLvRy0x0WGDWCIiIpIVBidEREQkK49VcKJSqTBz5kyoVKrKTkqFe1zz/rjmG3h88/645htg3h/HvFfXfMuuQSwRERE93h6rkhMiIiKSPwYnREREJCsMToiIiEhWGJwQERGRrDA4ISIiIll5bIKT77//HoGBgbCxsUGbNm1w+PDhyk6S2c2aNQsKhcLgVa9ePf3nWVlZmDBhAtzc3ODg4ICnnnoKCQkJlZjih7dnzx4MHDgQvr6+UCgUWLt2rcHnQgh88MEH8PHxga2tLXr27ImoqCiDee7evYtRo0bByckJzs7O+N///of09PQKzEXZlZTv0aNHGx0Dffr0MZinKuY7PDwcrVq1gqOjIzw9PTF48GBERkYazFOa4zs2Nhb9+/eHnZ0dPD09MWXKFOTl5VVkVsqsNHnv2rWr0X4fN26cwTxVMe/z589HkyZN9KOftmvXDhs3btR/Xl33eUn5rq7724B4DCxfvlxYW1uL3377TZw7d068/PLLwtnZWSQkJFR20sxq5syZomHDhuLWrVv61+3bt/Wfjxs3Tvj5+Ynt27eLo0ePirZt24r27dtXYoof3n///SfeffddsXr1agFArFmzxuDz//u//xNqtVqsXbtWnDp1SgwaNEgEBQWJ+/fv6+fp06ePaNq0qTh48KDYu3evqF27thg5cmQF56RsSsp3WFiY6NOnj8ExcPfuXYN5qmK+Q0NDxcKFC8XZs2fFyZMnRb9+/YS/v79IT0/Xz1PS8Z2XlycaNWokevbsKU6cOCH+++8/4e7uLmbMmFEZWSq10uS9S5cu4uWXXzbY7ykpKfrPq2re//nnH/Hvv/+KS5cuicjISPHOO+8IKysrcfbsWSFE9d3nJeW7uu7vgh6L4KR169ZiwoQJ+vcajUb4+vqK8PDwSkyV+c2cOVM0bdrU5GfJycnCyspKrFy5Uj/twoULAoCIiIiooBSWj8I3aa1WK7y9vcXcuXP105KTk4VKpRLLli0TQghx/vx5AUAcOXJEP8/GjRuFQqEQN27cqLC0P4qigpMnn3yyyO9Uh3wLIURiYqIAIHbv3i2EKN3x/d9//wmlUini4+P188yfP184OTmJ7Ozsis3AIyicdyGkm9Xrr79e5HeqS96FEMLFxUX88ssvj9U+FyI/30I8Hvu72lfr5OTk4NixY+jZs6d+mlKpRM+ePREREVGJKSsfUVFR8PX1Ra1atTBq1CjExsYCAI4dO4bc3FyD7VCvXj34+/tXu+0QHR2N+Ph4g7yq1Wq0adNGn9eIiAg4OzujZcuW+nl69uwJpVKJQ4cOVXiazWnXrl3w9PRE3bp18eqrryIpKUn/WXXJd0pKCgDA1dUVQOmO74iICDRu3BheXl76eUJDQ5Gamopz585VYOofTeG86yxduhTu7u5o1KgRZsyYgczMTP1n1SHvGo0Gy5cvR0ZGBtq1a/fY7PPC+dap7vtbdk8lNrc7d+5Ao9EY7CQA8PLywsWLFyspVeWjTZs2WLRoEerWrYtbt25h9uzZ6NSpE86ePYv4+HhYW1vD2dnZ4DteXl6Ij4+vnASXE11+TO1z3Wfx8fHw9PQ0+NzS0hKurq5Venv06dMHQ4cORVBQEK5cuYJ33nkHffv2RUREBCwsLKpFvrVaLSZPnowOHTqgUaNGAFCq4zs+Pt7kMaH7rCowlXcAePbZZxEQEABfX1+cPn0a06ZNQ2RkJFavXg2gauf9zJkzaNeuHbKysuDg4IA1a9agQYMGOHnyZLXe50XlG6je+1un2gcnj5O+ffvq/2/SpAnatGmDgIAA/PXXX7C1ta3ElFFFGTFihP7/xo0bo0mTJggODsauXbvQo0ePSkyZ+UyYMAFnz57Fvn37KjspFa6ovL/yyiv6/xs3bgwfHx/06NEDV65cQXBwcEUn06zq1q2LkydPIiUlBatWrUJYWBh2795d2ckqd0Xlu0GDBtV6f+tU+2odd3d3WFhYGLXgTkhIgLe3dyWlqmI4OzvjiSeewOXLl+Ht7Y2cnBwkJycbzFMdt4MuP8Xtc29vbyQmJhp8npeXh7t371ar7VGrVi24u7vj8uXLAKp+vidOnIgNGzZg586dqFmzpn56aY5vb29vk8eE7jO5KyrvprRp0wYADPZ7Vc27tbU1ateujZCQEISHh6Np06b4+uuvq/0+LyrfplSn/a1T7YMTa2trhISEYPv27fppWq0W27dvN6i/q47S09Nx5coV+Pj4ICQkBFZWVgbbITIyErGxsdVuOwQFBcHb29sgr6mpqTh06JA+r+3atUNycjKOHTumn2fHjh3QarX6E706uH79OpKSkuDj4wOg6uZbCIGJEydizZo12LFjB4KCggw+L83x3a5dO5w5c8YgONu6dSucnJz0xeVyVFLeTTl58iQAGOz3qph3U7RaLbKzs6v1PjdFl29TquX+ruwWuRVh+fLlQqVSiUWLFonz58+LV155RTg7Oxu0ZK4O3nrrLbFr1y4RHR0t9u/fL3r27Cnc3d1FYmKiEELqdufv7y927Nghjh49Ktq1ayfatWtXyal+OGlpaeLEiRPixIkTAoD48ssvxYkTJ0RMTIwQQupK7OzsLNatWydOnz4tnnzySZNdiZs3by4OHTok9u3bJ+rUqSP7LrXF5TstLU28/fbbIiIiQkRHR4tt27aJFi1aiDp16oisrCz9Mqpivl999VWhVqvFrl27DLpPZmZm6ucp6fjWda/s3bu3OHnypNi0aZPw8PCQfffKkvJ++fJl8eGHH4qjR4+K6OhosW7dOlGrVi3RuXNn/TKqat6nT58udu/eLaKjo8Xp06fF9OnThUKhEFu2bBFCVN99Xly+q/P+LuixCE6EEOLbb78V/v7+wtraWrRu3VocPHiwspNkdsOHDxc+Pj7C2tpa1KhRQwwfPlxcvnxZ//n9+/fF+PHjhYuLi7CzsxNDhgwRt27dqsQUP7ydO3cKAEavsLAwIYTUnfj9998XXl5eQqVSiR49eojIyEiDZSQlJYmRI0cKBwcH4eTkJMaMGSPS0tIqITelV1y+MzMzRe/evYWHh4ewsrISAQEB4uWXXzYKwqtivk3lGYBYuHChfp7SHN/Xrl0Tffv2Fba2tsLd3V289dZbIjc3t4JzUzYl5T02NlZ07txZuLq6CpVKJWrXri2mTJliMO6FEFUz7y+++KIICAgQ1tbWwsPDQ/To0UMfmAhRffd5cfmuzvu7IIUQQlRcOQ0RERFR8ap9mxMiIiKqWhicEBERkawwOCEiIiJZYXBCREREssLghIiIiGSFwQkRERHJCoMTIiIikhUGJ0RERCQrDE6IiIhIVhicEBERkawwOCEiIiJZ+X/Sx8LLj01S8QAAAABJRU5ErkJggg==\"\n", - " },\n", - " \"metadata\": {},\n", - " \"output_type\": \"display_data\"\n", - " }\n", - " ],\n", - " \"execution_count\": 13\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"source\": [\n", - " \"You can put these transformers in a pipeline to apply to both train/test split\\n\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false\n", - " }\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"source\": [\n", - " \"from sklearn.metrics import accuracy_score\\n\",\n", - " \"\\n\",\n", - " \"# Unequal length univariate data\\n\",\n", - " \"from aeon.pipeline import make_pipeline\\n\",\n", - " \"\\n\",\n", - " \"train_X, train_y = load_pickup_gesture_wiimoteZ(split=\\\"Train\\\")\\n\",\n", - " \"test_X, test_y = load_pickup_gesture_wiimoteZ(split=\\\"Test\\\")\\n\",\n", - " \"steps = [truncate, rc]\\n\",\n", - " \"pipe = make_pipeline(steps)\\n\",\n", - " \"pipe.fit(train_X, train_y)\\n\",\n", - " \"preds = pipe.predict(test_X)\\n\",\n", - " \"accuracy_score(train_y, preds)\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false,\n", - " \"pycharm\": {\n", - " \"is_executing\": true\n", - " },\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2025-05-28T13:39:55.584822Z\",\n", - " \"start_time\": \"2025-05-28T13:39:47.310020Z\"\n", - " }\n", - " },\n", - " \"outputs\": [\n", - " {\n", - " \"data\": {\n", - " \"text/plain\": [\n", - " \"0.32\"\n", - " ]\n", - " },\n", - " \"execution_count\": 14,\n", - " \"metadata\": {},\n", - " \"output_type\": \"execute_result\"\n", - " }\n", - " ],\n", - " \"execution_count\": 14\n", - " },\n", - " {\n", - " \"metadata\": {},\n", - " \"cell_type\": \"markdown\",\n", - " \"source\": [\n", - " \"## Missing Values\\n\",\n", - " \"\\n\",\n", - " \"Missing values are indicated by `NaN` in numpy array. You can test whether any `aeon`\\n\",\n", - " \" data structure contains missing values using the utility function\"\n", - " ]\n", - " },\n", - " {\n", - " \"metadata\": {\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2024-11-17T13:50:06.076875Z\",\n", - " \"start_time\": \"2024-11-17T13:50:06.065907Z\"\n", - " }\n", - " },\n", - " \"cell_type\": \"code\",\n", - " \"source\": [\n", - " \"X = np.random.random(size=(10, 2, 200))\\n\",\n", - " \"has_missing(X)\"\n", - " ],\n", - " \"outputs\": [\n", - " {\n", - " \"data\": {\n", - " \"text/plain\": [\n", - " \"False\"\n", - " ]\n", - " },\n", - " \"execution_count\": 21,\n", - " \"metadata\": {},\n", - " \"output_type\": \"execute_result\"\n", - " }\n", - " ],\n", - " \"execution_count\": 21\n", - " },\n", - " {\n", - " \"metadata\": {\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2024-11-17T13:50:06.186109Z\",\n", - " \"start_time\": \"2024-11-17T13:50:06.180126Z\"\n", - " }\n", - " },\n", - " \"cell_type\": \"code\",\n", - " \"source\": [\n", - " \"X[5][0][55] = np.nan\\n\",\n", - " \"has_missing(X)\"\n", - " ],\n", - " \"outputs\": [\n", - " {\n", - " \"data\": {\n", - " \"text/plain\": [\n", - " \"True\"\n", - " ]\n", - " },\n", - " \"execution_count\": 22,\n", - " \"metadata\": {},\n", - " \"output_type\": \"execute_result\"\n", - " }\n", - " ],\n", - " \"execution_count\": 22\n", - " },\n", - " {\n", - " \"metadata\": {},\n", - " \"cell_type\": \"markdown\",\n", - " \"source\": [\n", - " \"There are a range of strategies for handling missing values. These include:\\n\",\n", - " \"\\n\",\n", - " \"1. Use an estimator that internally handles missing values. It is fairly easy for\\n\",\n", - " \"some algorithms (such as decision trees) to internally deal with missing values,\\n\",\n", - " \"usually be using it as a distinct series value after discretisation. We do not yet \\n\",\n", - " \"have many estimators with this capability. Estimators that are able to internally \\n\",\n", - " \"handle missing values are tagged with `\\\"capability:missing_values\\\": True`.\"\n", - " ]\n", - " },\n", - " {\n", - " \"metadata\": {\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2024-11-17T13:50:06.436013Z\",\n", - " \"start_time\": \"2024-11-17T13:50:06.296331Z\"\n", - " }\n", - " },\n", - " \"cell_type\": \"code\",\n", - " \"source\": [\n", - " \"from aeon.utils.discovery import all_estimators\\n\",\n", - " \"\\n\",\n", - " \"all_estimators(\\n\",\n", - " \" tag_filter={\\\"capability:missing_values\\\": True},\\n\",\n", - " \")\"\n", - " ],\n", - " \"outputs\": [\n", - " {\n", - " \"data\": {\n", - " \"text/plain\": [\n", - " \"[('BORF', aeon.transformations.collection.dictionary_based._borf.BORF),\\n\",\n", - " \" ('CollectionId',\\n\",\n", - " \" aeon.transformations.collection.compose._identity.CollectionId),\\n\",\n", - " \" ('DummyClassifier', aeon.classification.dummy.DummyClassifier),\\n\",\n", - " \" ('DummyRegressor', aeon.regression._dummy.DummyRegressor),\\n\",\n", - " \" ('RandomSegmenter', aeon.segmentation._random.RandomSegmenter),\\n\",\n", - " \" ('STRAY', aeon.anomaly_detection._stray.STRAY),\\n\",\n", - " \" ('SimpleImputer', aeon.transformations.collection._impute.SimpleImputer)]\"\n", - " ]\n", - " },\n", - " \"execution_count\": 23,\n", - " \"metadata\": {},\n", - " \"output_type\": \"execute_result\"\n", - " }\n", - " ],\n", - " \"execution_count\": 23\n", - " },\n", - " {\n", - " \"metadata\": {},\n", - " \"cell_type\": \"markdown\",\n", - " \"source\": [\n", - " \"2. Removing series with missing: this is often desirable if the train set size is\\n\",\n", - " \"large, the number of series with missing is small and the proportion of missing\\n\",\n", - " \"values for these series is high.\\n\",\n", - " \"\\n\",\n", - " \"We do not yet have a transformer for this, but it is easy to implement yourself.\\n\",\n", - " \"\\n\",\n", - " \"3. Interpolating missing values from series: estimating the missing values from the \\n\",\n", - " \"other values in a time series is commonly done. This is\\n\",\n", - " \" often desirable if the train set size is small and the proportion of missing values\\n\",\n", - " \" is low. You can do this with the transformer ``SimpleImputer``. This interpolates \\n\",\n", - " \" each series and each channel independently. So for example a mean interpolation \\n\",\n", - " \" of series with two channels `[[NaN,1.0,2.0,3.0],[-1.0,-2.0,-3.0,-4.0]]` would be \\n\",\n", - " \" `[[2.0,1.0,2.0,3.0],[-1.0,-2.0,-3.0,-4.0]]`. \"\n", - " ]\n", - " },\n", - " {\n", - " \"metadata\": {\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2024-11-17T13:52:23.133162Z\",\n", - " \"start_time\": \"2024-11-17T13:52:23.118202Z\"\n", - " }\n", - " },\n", - " \"cell_type\": \"code\",\n", - " \"source\": [\n", - " \"from aeon.transformations.collection import SimpleImputer\\n\",\n", - " \"\\n\",\n", - " \"imput = SimpleImputer(strategy=\\\"mean\\\")\\n\",\n", - " \"X2 = imput.fit_transform(X)\\n\",\n", - " \"has_missing(X2)\"\n", - " ],\n", - " \"outputs\": [\n", - " {\n", - " \"data\": {\n", - " \"text/plain\": [\n", - " \"False\"\n", - " ]\n", - " },\n", - " \"execution_count\": 26,\n", - " \"metadata\": {},\n", - " \"output_type\": \"execute_result\"\n", - " }\n", - " ],\n", - " \"execution_count\": 26\n", - " },\n", - " {\n", - " \"metadata\": {\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2024-11-17T13:52:23.825897Z\",\n", - " \"start_time\": \"2024-11-17T13:52:23.811936Z\"\n", - " }\n", - " },\n", - " \"cell_type\": \"code\",\n", - " \"source\": [\n", - " \"imp2 = SimpleImputer(strategy=\\\"median\\\")\\n\",\n", - " \"X3 = imp2.fit_transform(X)\\n\",\n", - " \"has_missing(X3)\"\n", - " ],\n", - " \"outputs\": [\n", - " {\n", - " \"data\": {\n", - " \"text/plain\": [\n", - " \"False\"\n", - " ]\n", - " },\n", - " \"execution_count\": 27,\n", - " \"metadata\": {},\n", - " \"output_type\": \"execute_result\"\n", - " }\n", - " ],\n", - " \"execution_count\": 27\n", - " },\n", - " {\n", - " \"metadata\": {\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2024-11-17T13:52:24.602058Z\",\n", - " \"start_time\": \"2024-11-17T13:52:24.582111Z\"\n", - " }\n", - " },\n", - " \"cell_type\": \"code\",\n", - " \"source\": [\n", - " \"imp3 = SimpleImputer(strategy=\\\"constant\\\", fill_value=0)\\n\",\n", - " \"X4 = imp3.fit_transform(X)\\n\",\n", - " \"has_missing(X4)\"\n", - " ],\n", - " \"outputs\": [\n", - " {\n", - " \"data\": {\n", - " \"text/plain\": [\n", - " \"False\"\n", - " ]\n", - " },\n", - " \"execution_count\": 28,\n", - " \"metadata\": {},\n", - " \"output_type\": \"execute_result\"\n", - " }\n", - " ],\n", - " \"execution_count\": 28\n", - " },\n", - " {\n", - " \"metadata\": {},\n", - " \"cell_type\": \"code\",\n", - " \"outputs\": [],\n", - " \"execution_count\": null,\n", - " \"source\": \"\"\n", - " }\n", - " ],\n", - " \"metadata\": {\n", - " \"kernelspec\": {\n", - " \"display_name\": \"Python 3\",\n", - " \"language\": \"python\",\n", - " \"name\": \"python3\"\n", - " },\n", - " \"language_info\": {\n", - " \"codemirror_mode\": {\n", - " \"name\": \"ipython\",\n", - " \"version\": 3\n", - " },\n", - " \"file_extension\": \".py\",\n", - " \"mimetype\": \"text/x-python\",\n", - " \"name\": \"python\",\n", - " \"nbconvert_exporter\": \"python\",\n", - " \"pygments_lexer\": \"ipython3\",\n", - " \"version\": \"3.7.8\"\n", - " }\n", - " },\n", - " \"nbformat\": 4,\n", - " \"nbformat_minor\": 4\n", - "}\n" - ], - "id": "cd7114548eda7d97" + "source": [ + "### Unequal or equal length collections time series\n", + "\n", + "If a collection contains all equal length series, it will store the data in a 3D\n", + "numpy of shape `(n_cases, n_channels, n_timepoints)`. If it is unequal length, it is\n", + "stored in a list of 2D numpy arrays:" + ] + }, + { + "cell_type": "code", + "metadata": { + "execution": { + "iopub.execute_input": "2020-12-19T14:31:59.194445Z", + "iopub.status.busy": "2020-12-19T14:31:59.193903Z", + "iopub.status.idle": "2020-12-19T14:32:01.019896Z", + "shell.execute_reply": "2020-12-19T14:32:01.020463Z" + }, + "pycharm": { + "is_executing": true + }, + "ExecuteTime": { + "end_time": "2025-06-26T14:20:54.165217Z", + "start_time": "2025-06-26T14:20:54.154215Z" + } + }, + "source": [ + "# Equal length multivariate data\n", + "bm_X, bm_y = load_basic_motions()\n", + "X = bm_X\n", + "print(f\"{type(X)}, {X.shape}\")\n", + "print(\n", + " f\"univariate = {is_univariate(X)}, has missing ={has_missing(X)}, equal \"\n", + " f\"length = {is_equal_length(X)}\"\n", + ")" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ", (80, 6, 100)\n", + "univariate = False, has missing =False, equal length = True\n" + ] + } + ], + "execution_count": 9 + }, + { + "cell_type": "code", + "source": [ + "# Unequal length univariate data\n", + "wii_X, wii_y = load_pickup_gesture_wiimoteZ()\n", + "X = wii_X\n", + "print(type(wii_X), \"\\n\", wii_X[0].shape, \"\\n\", wii_X[10].shape)\n", + "print(\n", + " f\"univariate = {is_univariate(X)}, has missing ={has_missing(X)}, equal \"\n", + " f\"length = {is_equal_length(X)}\"\n", + ")" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "is_executing": true + }, + "ExecuteTime": { + "end_time": "2025-06-26T14:20:54.195143Z", + "start_time": "2025-06-26T14:20:54.187782Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " (1, 324) \n", + " (1, 97)\n", + "univariate = True, has missing =False, equal length = False\n" + ] + } + ], + "execution_count": 10 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-26T14:20:54.228577Z", + "start_time": "2025-06-26T14:20:54.212474Z" + } + }, + "cell_type": "code", + "source": [ + "vowels_X, vowels_y = load_japanese_vowels(split=\"train\")\n", + "X = vowels_X\n", + "print(\n", + " f\"univariate = {is_univariate(X)}, has missing ={has_missing(X)}, equal \"\n", + " f\"length = {is_equal_length(X)}\"\n", + ")" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "univariate = False, has missing =False, equal length = False\n" + ] + } + ], + "execution_count": 11 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "\n" + }, + { + "cell_type": "code", + "source": [ + "series_lengths = [array.shape[1] for array in wii_X]\n", + "\n", + "# Find the minimum and maximum of the second dimensions\n", + "min_length = min(series_lengths)\n", + "max_length = max(series_lengths)\n", + "print(\" Min length = \", min_length, \" max length = \", max_length)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "is_executing": true + }, + "ExecuteTime": { + "end_time": "2025-06-26T14:20:54.248725Z", + "start_time": "2025-06-26T14:20:54.245466Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Min length = 29 max length = 361\n" + ] + } + ], + "execution_count": 12 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "There are two basic strategies for unequal length problems\n", + "1. Use an estimator that can internally handle missing values\n", + "2. Transform the data to be equal length by, for example, truncating or padding series\n", + "\n", + "Estimators with the tag `\"capability:unequal_length\": True` have the capability to\n", + "handle unequal length series. For classification, regression and\n", + "clusterign, the\n", + "current list is" + ] + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-26T14:20:57.363914Z", + "start_time": "2025-06-26T14:20:54.266089Z" + } + }, + "cell_type": "code", + "source": [ + "from aeon.utils.discovery import all_estimators\n", + "\n", + "all_estimators(\n", + " type_filter=[\"classifier\", \"regressor\", \"clusterer\"],\n", + " tag_filter={\"capability:unequal_length\": True},\n", + ")" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "[('Catch22Classifier',\n", + " aeon.classification.feature_based._catch22.Catch22Classifier),\n", + " ('Catch22Clusterer', aeon.clustering.feature_based._catch22.Catch22Clusterer),\n", + " ('Catch22Regressor', aeon.regression.feature_based._catch22.Catch22Regressor),\n", + " ('DummyClassifier', aeon.classification.dummy.DummyClassifier),\n", + " ('DummyClusterer', aeon.clustering.dummy.DummyClusterer),\n", + " ('DummyRegressor', aeon.regression._dummy.DummyRegressor),\n", + " ('ElasticEnsemble',\n", + " aeon.classification.distance_based._elastic_ensemble.ElasticEnsemble),\n", + " ('KNeighborsTimeSeriesClassifier',\n", + " aeon.classification.distance_based._time_series_neighbors.KNeighborsTimeSeriesClassifier),\n", + " ('KNeighborsTimeSeriesRegressor',\n", + " aeon.regression.distance_based._time_series_neighbors.KNeighborsTimeSeriesRegressor),\n", + " ('ProximityForest',\n", + " aeon.classification.distance_based._proximity_forest.ProximityForest),\n", + " ('ProximityTree',\n", + " aeon.classification.distance_based._proximity_tree.ProximityTree),\n", + " ('RDSTClassifier', aeon.classification.shapelet_based._rdst.RDSTClassifier),\n", + " ('RDSTRegressor', aeon.regression.shapelet_based._rdst.RDSTRegressor)]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 13 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "You can pass these estimators unequal length series and they will work as expected.\n" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-26T14:20:57.383279Z", + "start_time": "2025-06-26T14:20:57.379786Z" + } + }, + "cell_type": "code", + "source": [ + "from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier\n", + "\n", + "knn = KNeighborsTimeSeriesClassifier()\n", + "model = knn.fit(wii_X, wii_y)" + ], + "outputs": [], + "execution_count": 14 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "If time series are unequal length, collection estimators will raise an error if they\n", + "do not have the capability to handle this characteristic. If you want to use them, \n", + "you will need to preprocess the data to be equal length. " + ] + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-26T14:20:57.402848Z", + "start_time": "2025-06-26T14:20:57.398728Z" + } + }, + "cell_type": "code", + "source": [ + "rc = RocketClassifier()\n", + "try:\n", + " rc.fit(wii_X, wii_y)\n", + "except ValueError as e:\n", + " print(f\"ValueError: {e}\")" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ValueError: Data seen by instance of RocketClassifier has unequal length series, but RocketClassifier cannot handle these characteristics. \n" + ] + } + ], + "execution_count": 15 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Padding, truncating or resizing.\n", + "\n", + "We can pad, truncate or resize. By default, pad adds zeros to make all series the\n", + "length of the longest, truncate removes all values beyond the length of the shortest\n", + "and resize stretches or shrinks the series." + ] + }, + { + "cell_type": "code", + "metadata": { + "execution": { + "iopub.execute_input": "2020-12-19T14:32:01.245270Z", + "iopub.status.busy": "2020-12-19T14:32:01.244733Z", + "iopub.status.idle": "2020-12-19T14:32:02.911970Z", + "shell.execute_reply": "2020-12-19T14:32:02.912833Z" + }, + "pycharm": { + "is_executing": true + }, + "ExecuteTime": { + "end_time": "2025-06-26T14:20:57.427657Z", + "start_time": "2025-06-26T14:20:57.419635Z" + } + }, + "source": [ + "from aeon.transformations.collection.unequal_length import Padder, Resizer, Truncator\n", + "\n", + "pad = Padder()\n", + "truncate = Truncator()\n", + "resize = Resizer(resized_length=600)\n", + "X2 = pad.fit_transform(wii_X)\n", + "X3 = truncate.fit_transform(wii_X)\n", + "X4 = resize.fit_transform(wii_X)\n", + "print(X2.shape, \"\\n\", X3.shape, \"\\n\", X4.shape)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(100, 1, 361) \n", + " (100, 1, 29) \n", + " (100, 1, 600)\n" + ] + } + ], + "execution_count": 16 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-26T14:20:57.508373Z", + "start_time": "2025-06-26T14:20:57.437625Z" + } + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.title(\"Before and after padding: first case (shifted up for unpadded)\")\n", + "plt.plot(wii_X[0][0] + 10)\n", + "plt.plot(X2[0][0])" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 17 + }, + { + "cell_type": "markdown", + "source": [ + "You can put these transformers in a pipeline to apply to both train/test split\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.metrics import accuracy_score\n", + "\n", + "# Unequal length univariate data\n", + "from aeon.pipeline import make_pipeline\n", + "\n", + "train_X, train_y = load_pickup_gesture_wiimoteZ(split=\"Train\")\n", + "test_X, test_y = load_pickup_gesture_wiimoteZ(split=\"Test\")\n", + "steps = [truncate, rc]\n", + "pipe = make_pipeline(steps)\n", + "pipe.fit(train_X, train_y)\n", + "preds = pipe.predict(test_X)\n", + "accuracy_score(train_y, preds)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "is_executing": true + }, + "ExecuteTime": { + "end_time": "2025-06-26T14:21:05.181601Z", + "start_time": "2025-06-26T14:20:57.519450Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.32" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 18 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Missing Values\n", + "\n", + "Missing values are indicated by `NaN` in numpy array. You can test whether any `aeon`\n", + " data structure contains missing values using the utility function" + ] + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-26T14:21:05.201674Z", + "start_time": "2025-06-26T14:21:05.197670Z" + } + }, + "cell_type": "code", + "source": [ + "X = np.random.random(size=(10, 2, 200))\n", + "has_missing(X)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "np.False_" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 19 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-26T14:21:05.227734Z", + "start_time": "2025-06-26T14:21:05.223970Z" + } + }, + "cell_type": "code", + "source": [ + "X[5][0][55] = np.nan\n", + "has_missing(X)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "np.True_" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 20 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "There are a range of strategies for handling missing values. These include:\n", + "\n", + "1. Use an estimator that internally handles missing values. It is fairly easy for\n", + "some algorithms (such as decision trees) to internally deal with missing values,\n", + "usually be using it as a distinct series value after discretisation. We do not yet \n", + "have many estimators with this capability. Estimators that are able to internally \n", + "handle missing values are tagged with `\"capability:missing_values\": True`." + ] + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-26T14:21:05.310052Z", + "start_time": "2025-06-26T14:21:05.250832Z" + } + }, + "cell_type": "code", + "source": [ + "from aeon.utils.discovery import all_estimators\n", + "\n", + "all_estimators(\n", + " tag_filter={\"capability:missing_values\": True},\n", + ")" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "[('BORF', aeon.transformations.collection.dictionary_based._borf.BORF),\n", + " ('CollectionId',\n", + " aeon.transformations.collection.compose._identity.CollectionId),\n", + " ('DummyClassifier', aeon.classification.dummy.DummyClassifier),\n", + " ('DummyClusterer', aeon.clustering.dummy.DummyClusterer),\n", + " ('DummyRegressor', aeon.regression._dummy.DummyRegressor),\n", + " ('RandomSegmenter', aeon.segmentation._random.RandomSegmenter),\n", + " ('STRAY', aeon.anomaly_detection.series.outlier_detection._stray.STRAY),\n", + " ('SeriesId', aeon.transformations.series.compose._identity.SeriesId),\n", + " ('SimpleImputer', aeon.transformations.collection._impute.SimpleImputer)]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 21 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "2. Removing series with missing: this is often desirable if the train set size is\n", + "large, the number of series with missing is small and the proportion of missing\n", + "values for these series is high.\n", + "\n", + "We do not yet have a transformer for this, but it is easy to implement yourself.\n", + "\n", + "3. Interpolating missing values from series: estimating the missing values from the \n", + "other values in a time series is commonly done. This is\n", + " often desirable if the train set size is small and the proportion of missing values\n", + " is low. You can do this with the transformer ``SimpleImputer``. This interpolates \n", + " each series and each channel independently. So for example a mean interpolation \n", + " of series with two channels `[[NaN,1.0,2.0,3.0],[-1.0,-2.0,-3.0,-4.0]]` would be \n", + " `[[2.0,1.0,2.0,3.0],[-1.0,-2.0,-3.0,-4.0]]`. " + ] + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-26T14:21:05.327249Z", + "start_time": "2025-06-26T14:21:05.323149Z" + } + }, + "cell_type": "code", + "source": [ + "from aeon.transformations.collection import SimpleImputer\n", + "\n", + "imputer = SimpleImputer(strategy=\"mean\")\n", + "X2 = imputer.fit_transform(X)\n", + "has_missing(X2)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "np.False_" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 22 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-26T14:21:05.345785Z", + "start_time": "2025-06-26T14:21:05.340781Z" + } + }, + "cell_type": "code", + "source": [ + "imp2 = SimpleImputer(strategy=\"median\")\n", + "X3 = imp2.fit_transform(X)\n", + "has_missing(X3)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "np.False_" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 23 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-26T14:21:05.366255Z", + "start_time": "2025-06-26T14:21:05.362121Z" + } + }, + "cell_type": "code", + "source": [ + "imp3 = SimpleImputer(strategy=\"constant\", fill_value=0)\n", + "X4 = imp3.fit_transform(X)\n", + "has_missing(X4)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "np.False_" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 24 } ], - "metadata": {}, + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + } + }, "nbformat": 4, - "nbformat_minor": 5 + "nbformat_minor": 4 } diff --git a/examples/transformations/sast.ipynb b/examples/transformations/sast.ipynb index 3e7072df36..0b5a1dc706 100644 --- a/examples/transformations/sast.ipynb +++ b/examples/transformations/sast.ipynb @@ -2149,7 +2149,7 @@ "\n", "SASTClassifier's interpretability can be achieved by visualizing the most important subsequences used for the transformation.\n", "\n", - "The importance of features can be computed by different means. In this demo, we considere the absolute values of the weights of the ridge classifier as the feature importances.\n", + "The importance of features can be computed by different means. In this demo, we consider the absolute values of the weights of the ridge classifier as the feature importances.\n", "\n", "#### 4.4.1 Interpretability of class `1`" ] diff --git a/examples/transformations/signature_method.ipynb b/examples/transformations/signature_method.ipynb index 81d6d4b866..94a7cc14d1 100644 --- a/examples/transformations/signature_method.ipynb +++ b/examples/transformations/signature_method.ipynb @@ -18,7 +18,7 @@ "## The Path Signature\n", "At the heart of the signature method is the so-called \"signature transform\".\n", "\n", - "A path $X$ of finite length in $\\textit{d}$ dimensions can be described by the mapping $X:[a, b]\\rightarrow\\mathbb{R}$ $\\!\\!^d$, or in terms of co-ordinates $X=(X^1_t, X^2_t, ...,X^d_t)$, where each coordinate $X^i_t$ is real-valued and parameterised by $t\\in[a,b]$.\n", + "A path $X$ of finite length in $\\textit{d}$ dimensions can be described by the mapping $X:[a, b]\\rightarrow\\mathbb{R}$ $\\!\\!^d$, or in terms of coordinates $X=(X^1_t, X^2_t, ...,X^d_t)$, where each coordinate $X^i_t$ is real-valued and parameterised by $t\\in[a,b]$.\n", "\n", "The **signature transform** $S$ of a path $X$ is defined as an infinite sequence of values:\n", "\n", @@ -53,7 +53,7 @@ "\n", "\n", "#### Depth > 2:\n", - "Depths larger than 2 become more difficult to visualise graphically, however the idea is similar to that of the depth 2 case where we saw that the signature produced information on whther the increase in HR or SBP appeared to be happening first, along with some numerical quantification of how much this was happening. At higher orders the signature is doing something similar, but now with three events, rather than two. The signature picks out structural information regarding the order in which events occur." + "Depths larger than 2 become more difficult to visualise graphically, however the idea is similar to that of the depth 2 case where we saw that the signature produced information on whether the increase in HR or SBP appeared to be happening first, along with some numerical quantification of how much this was happening. At higher orders the signature is doing something similar, but now with three events, rather than two. The signature picks out structural information regarding the order in which events occur." ] }, { @@ -74,7 +74,7 @@ "metadata": {}, "source": [ "## Considered Signature Variations\n", - "Again, following the work in [1] we group the variations on the siganture method conceptually into:\n", + "Again, following the work in [1] we group the variations on the signature method conceptually into:\n", "\n", "- **Augmentations** - Transformation of an input sequence or time series into one or more new sequences, so that the signature will return different information about the path.\n", "- **Windows** - Windowing operations, so that the signature can act with some locality.\n", diff --git a/examples/transformations/smoothing_filters.ipynb b/examples/transformations/smoothing_filters.ipynb index d0026dc329..c5a6766ea1 100644 --- a/examples/transformations/smoothing_filters.ipynb +++ b/examples/transformations/smoothing_filters.ipynb @@ -48,7 +48,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Generate random serie\n", + "# Generate random series\n", "np.random.seed(42)\n", "x_random = np.random.random(128) * 10\n", "\n", diff --git a/examples/transformations/transformations.ipynb b/examples/transformations/transformations.ipynb index 41df249e78..7c11f0ccab 100644 --- a/examples/transformations/transformations.ipynb +++ b/examples/transformations/transformations.ipynb @@ -1,288 +1,1119 @@ { "cells": [ { - "metadata": {}, - "cell_type": "raw", + "cell_type": "markdown", "source": [ - "{\n", - " \"cells\": [\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"source\": [\n", - " \"# Transforming time series\\n\",\n", - " \"\\n\",\n", - " \"Transforming time series into different data representations is fundamental to time\\n\",\n", - " \"series machine learning. Transformation can involve extracting features that\\n\",\n", - " \"characterize the time series, such as mean and variance or changing the series into,\\n\",\n", - " \"for example, first order differences. We use the term transformer in the\\n\",\n", - " \"`scikit-learn` sense, not to be confused with deep learning Transformers that employ\\n\",\n", - " \"an attention mechanism. We call transformers that extract features\\n\",\n", - " \"`series-to-vector` transformers and those that change the series into a different\\n\",\n", - " \"representation that is still ordered `series-to-series` transformers.\\n\",\n", - " \"\\n\",\n", - " \"We further differentiate between transformers that act on a single series and those\\n\",\n", - " \"that transform a collection of series. Single series transformers are located in\\n\",\n", - " \"transformations/series directory and inherit from `BaseSeriesTransformer`. For\\n\",\n", - " \"example, `AutoCorrelationSeriesTransformer` is a `series-to-series` transformer that\\n\",\n", - " \"finds the auto correlation function for a single series.\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false\n", - " }\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"execution_count\": 23,\n", - " \"outputs\": [\n", - " {\n", - " \"name\": \"stdout\",\n", - " \"output_type\": \"stream\",\n", - " \"text\": [\n", - " \"[[0.96019465 0.89567531 0.83739477 0.7977347 0.78594315 0.7839188\\n\",\n", - " \" 0.78459213 0.79221505 0.8278519 0.8827128 ]]\\n\"\n", - " ]\n", - " }\n", - " ],\n", - " \"source\": [\n", - " \"from aeon.datasets import load_airline\\n\",\n", - " \"from aeon.transformations.series import AutoCorrelationSeriesTransformer\\n\",\n", - " \"\\n\",\n", - " \"series = load_airline()\\n\",\n", - " \"transformer = AutoCorrelationSeriesTransformer(n_lags=10)\\n\",\n", - " \"acf = transformer.fit_transform(series)\\n\",\n", - " \"print(acf)\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false\n", - " }\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"source\": [\n", - " \"Collection transformers are located in the transformations/collection directory and\\n\",\n", - " \"inherit from `BaseCollectionTransformer`. For example, `Truncator` truncates all time\\n\",\n", - " \" series in a collection to the same length.\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false\n", - " }\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"source\": [\n", - " \"from aeon.datasets import load_pickup_gesture_wiimoteZ\\n\",\n", - " \"from aeon.transformations.collection.unequal_length import Truncator\\n\",\n", - " \"\\n\",\n", - " \"X, y = load_pickup_gesture_wiimoteZ()\\n\",\n", - " \"print(\\\" Unequal length, first case \\\", X[0].shape, \\\" tenth case \\\", X[10].shape)\\n\",\n", - " \"trunc = Truncator(truncated_length=100)\\n\",\n", - " \"X2 = trunc.fit_transform(X)\\n\",\n", - " \"print(\\\"Truncated collection shape =\\\", X2.shape)\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false,\n", - " \"ExecuteTime\": {\n", - " \"end_time\": \"2025-05-28T13:34:29.282584Z\",\n", - " \"start_time\": \"2025-05-28T13:34:24.749646Z\"\n", - " }\n", - " },\n", - " \"outputs\": [\n", - " {\n", - " \"name\": \"stdout\",\n", - " \"output_type\": \"stream\",\n", - " \"text\": [\n", - " \" Unequal length, first case (1, 324) tenth case (1, 97)\\n\",\n", - " \"Truncated collection shape = (100, 1, 29)\\n\"\n", - " ]\n", - " }\n", - " ],\n", - " \"execution_count\": 1\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"source\": [\n", - " \"`Truncator` is a `series-to-series` transformer\\n\",\n", - " \" that returns a new collection of time series of the same length. This can then be\\n\",\n", - " \" used, for example, by a classifier that only works with equal length series:\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false\n", - " }\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"execution_count\": 25,\n", - " \"outputs\": [\n", - " {\n", - " \"name\": \"stdout\",\n", - " \"output_type\": \"stream\",\n", - " \"text\": [\n", - " \"Data seen by instance of SummaryClassifier has unequal length series, but SummaryClassifier cannot handle unequal length series. \\n\"\n", - " ]\n", - " },\n", - " {\n", - " \"data\": {\n", - " \"text/plain\": \"SummaryClassifier()\",\n", - " \"text/html\": \"
SummaryClassifier()
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\"\n", - " },\n", - " \"execution_count\": 25,\n", - " \"metadata\": {},\n", - " \"output_type\": \"execute_result\"\n", - " }\n", - " ],\n", - " \"source\": [\n", - " \"from aeon.classification.feature_based import SummaryClassifier\\n\",\n", - " \"\\n\",\n", - " \"summary = SummaryClassifier()\\n\",\n", - " \"try:\\n\",\n", - " \" summary.fit(X, y)\\n\",\n", - " \"except ValueError as e:\\n\",\n", - " \" print(e)\\n\",\n", - " \"\\n\",\n", - " \"summary.fit(X2, y)\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false\n", - " }\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"source\": [\n", - " \"Some collection transformers are supervised, meaning they fit a transform based on\\n\",\n", - " \"the class labels. For example, the shapelet transform finds shapelets that are good\\n\",\n", - " \"at separating classes. This is a `series-to-vector` transformer that produces tabular\\n\",\n", - " \" output shape `(n_cases, n_shapelets)`.\\n\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false\n", - " }\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"execution_count\": 26,\n", - " \"outputs\": [\n", - " {\n", - " \"name\": \"stdout\",\n", - " \"output_type\": \"stream\",\n", - " \"text\": [\n", - " \"(1074, 2)\\n\"\n", - " ]\n", - " }\n", - " ],\n", - " \"source\": [\n", - " \"from aeon.transformations.collection.shapelet_based import RandomShapeletTransform\\n\",\n", - " \"\\n\",\n", - " \"st = RandomShapeletTransform(max_shapelets=10, n_shapelet_samples=100)\\n\",\n", - " \"X2 = st.fit_transform(X, y)\\n\",\n", - " \"print(X2.shape)\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false\n", - " }\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"source\": [\n", - " \"`series-to-vector` transformers produce output that is compatible with `scikit-learn`\\n\",\n", - " \" estimators\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false\n", - " }\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"execution_count\": 27,\n", - " \"outputs\": [\n", - " {\n", - " \"name\": \"stdout\",\n", - " \"output_type\": \"stream\",\n", - " \"text\": [\n", - " \"setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (1074, 1) + inhomogeneous part.\\n\"\n", - " ]\n", - " },\n", - " {\n", - " \"data\": {\n", - " \"text/plain\": \"RandomForestClassifier()\",\n", - " \"text/html\": \"
RandomForestClassifier()
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\"\n", - " },\n", - " \"execution_count\": 27,\n", - " \"metadata\": {},\n", - " \"output_type\": \"execute_result\"\n", - " }\n", - " ],\n", - " \"source\": [\n", - " \"from sklearn.ensemble import RandomForestClassifier\\n\",\n", - " \"\\n\",\n", - " \"rf = RandomForestClassifier()\\n\",\n", - " \"try:\\n\",\n", - " \" rf.fit(X, y)\\n\",\n", - " \"except ValueError as e:\\n\",\n", - " \" print(e)\\n\",\n", - " \"rf.fit(X2, y)\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false\n", - " }\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"source\": [\n", - " \"A list of all the available transformers can be found in the [API](https://www.aeon-toolkit.org/en/latest/api_reference/transformations.html). We currently have\\n\",\n", - " \"specific notebooks for the following transformers:\\n\",\n", - " \"\\n\",\n", - " \"- [preprocessing](preprocessing.ipynb)\\n\",\n", - " \"- [catch22](catch22.ipynb)\\n\",\n", - " \"- [channel selection](channel_selection.ipynb)\\n\",\n", - " \"- [mini rocket](mini_rocket.ipynb)\\n\",\n", - " \"- [resizing](resizing.ipynb)\\n\",\n", - " \"- [rocket](rocket.ipynb)\\n\",\n", - " \"- [sast](sast.ipynb)\\n\",\n", - " \"- [signature method](signature_method.ipynb)\\n\",\n", - " \"- [tsfresh](tsfresh.ipynb)\\n\",\n", - " \"\\n\"\n", - " ],\n", - " \"metadata\": {\n", - " \"collapsed\": false\n", - " }\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"execution_count\": null,\n", - " \"outputs\": [],\n", - " \"source\": [],\n", - " \"metadata\": {\n", - " \"collapsed\": false\n", - " }\n", - " }\n", - " ],\n", - " \"metadata\": {\n", - " \"kernelspec\": {\n", - " \"display_name\": \"Python 3\",\n", - " \"language\": \"python\",\n", - " \"name\": \"python3\"\n", - " },\n", - " \"language_info\": {\n", - " \"codemirror_mode\": {\n", - " \"name\": \"ipython\",\n", - " \"version\": 2\n", - " },\n", - " \"file_extension\": \".py\",\n", - " \"mimetype\": \"text/x-python\",\n", - " \"name\": \"python\",\n", - " \"nbconvert_exporter\": \"python\",\n", - " \"pygments_lexer\": \"ipython2\",\n", - " \"version\": \"2.7.6\"\n", - " }\n", - " },\n", - " \"nbformat\": 4,\n", - " \"nbformat_minor\": 0\n", - "}\n" + "# Transforming time series\n", + "\n", + "Transforming time series into different data representations is fundamental to time\n", + "series machine learning. Transformation can involve extracting features that\n", + "characterize the time series, such as mean and variance or changing the series into,\n", + "for example, first order differences. We use the term transformer in the\n", + "`scikit-learn` sense, not to be confused with deep learning Transformers that employ\n", + "an attention mechanism. We call transformers that extract features\n", + "`series-to-vector` transformers and those that change the series into a different\n", + "representation that is still ordered `series-to-series` transformers.\n", + "\n", + "We further differentiate between transformers that act on a single series and those\n", + "that transform a collection of series. Single series transformers are located in\n", + "transformations/series directory and inherit from `BaseSeriesTransformer`. For\n", + "example, `AutoCorrelationSeriesTransformer` is a `series-to-series` transformer that\n", + "finds the auto correlation function for a single series." ], - "id": "62048d76702c12c8" + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "source": [ + "from aeon.datasets import load_airline\n", + "from aeon.transformations.series import AutoCorrelationSeriesTransformer\n", + "\n", + "series = load_airline()\n", + "transformer = AutoCorrelationSeriesTransformer(n_lags=10)\n", + "acf = transformer.fit_transform(series)\n", + "print(acf)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-06-26T14:20:19.669624Z", + "start_time": "2025-06-26T14:20:19.665170Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.96019465 0.89567531 0.83739477 0.7977347 0.78594315 0.7839188\n", + " 0.78459213 0.79221505 0.8278519 0.8827128 ]]\n" + ] + } + ], + "execution_count": 10 + }, + { + "cell_type": "markdown", + "source": [ + "Collection transformers are located in the transformations/collection directory and\n", + "inherit from `BaseCollectionTransformer`. For example, `Truncator` truncates all time\n", + " series in a collection to the same length." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "source": [ + "from aeon.datasets import load_pickup_gesture_wiimoteZ\n", + "from aeon.transformations.collection.unequal_length import Truncator\n", + "\n", + "X, y = load_pickup_gesture_wiimoteZ()\n", + "print(\" Unequal length, first case \", X[0].shape, \" tenth case \", X[10].shape)\n", + "trunc = Truncator(truncated_length=20)\n", + "X2 = trunc.fit_transform(X)\n", + "print(\"Truncated collection shape =\", X2.shape)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-06-26T14:20:19.691898Z", + "start_time": "2025-06-26T14:20:19.683943Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Unequal length, first case (1, 324) tenth case (1, 97)\n", + "Truncated collection shape = (100, 1, 20)\n" + ] + } + ], + "execution_count": 11 + }, + { + "cell_type": "markdown", + "source": [ + "`Truncator` is a `series-to-series` transformer\n", + " that returns a new collection of time series of the same length. This can then be\n", + " used, for example, by a classifier that only works with equal length series:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "source": [ + "from aeon.classification.feature_based import SummaryClassifier\n", + "\n", + "summary = SummaryClassifier()\n", + "try:\n", + " summary.fit(X, y)\n", + "except ValueError as e:\n", + " print(e)\n", + "\n", + "summary.fit(X2, y)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-06-26T14:20:25.225599Z", + "start_time": "2025-06-26T14:20:19.708075Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data seen by instance of SummaryClassifier has unequal length series, but SummaryClassifier cannot handle these characteristics. \n" + ] + }, + { + "data": { + "text/plain": [ + "SummaryClassifier()" + ], + "text/html": [ + "
SummaryClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 12 + }, + { + "cell_type": "markdown", + "source": [ + "Some collection transformers are supervised, meaning they fit a transform based on\n", + "the class labels. For example, the shapelet transform finds shapelets that are good\n", + "at separating classes. This is a `series-to-vector` transformer that produces tabular\n", + " output shape `(n_cases, n_shapelets)`.\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "source": [ + "from aeon.transformations.collection.shapelet_based import RandomShapeletTransform\n", + "\n", + "st = RandomShapeletTransform(max_shapelets=10, n_shapelet_samples=100)\n", + "X2 = st.fit_transform(X, y)\n", + "print(X2.shape)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-06-26T14:20:30.449101Z", + "start_time": "2025-06-26T14:20:25.236203Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(100, 10)\n" + ] + } + ], + "execution_count": 13 + }, + { + "cell_type": "markdown", + "source": [ + "`series-to-vector` transformers produce output that is compatible with `scikit-learn`\n", + " estimators" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "rf = RandomForestClassifier()\n", + "try:\n", + " rf.fit(X, y)\n", + "except ValueError as e:\n", + " print(e)\n", + "rf.fit(X2, y)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-06-26T14:20:30.531392Z", + "start_time": "2025-06-26T14:20:30.465900Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (100, 1) + inhomogeneous part.\n" + ] + }, + { + "data": { + "text/plain": [ + "RandomForestClassifier()" + ], + "text/html": [ + "
RandomForestClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 14 + }, + { + "cell_type": "markdown", + "source": [ + "A list of all the available transformers can be found in the [API](https://www.aeon-toolkit.org/en/latest/api_reference/transformations.html). We currently have\n", + "specific notebooks for the following transformers:\n", + "\n", + "- [preprocessing](preprocessing.ipynb)\n", + "- [catch22](catch22.ipynb)\n", + "- [channel selection](channel_selection.ipynb)\n", + "- [mini rocket](mini_rocket.ipynb)\n", + "- [resizing](resizing.ipynb)\n", + "- [rocket](rocket.ipynb)\n", + "- [sast](sast.ipynb)\n", + "- [signature method](signature_method.ipynb)\n", + "- [tsfresh](tsfresh.ipynb)\n", + "\n" + ], + "metadata": { + "collapsed": false + } } ], - "metadata": {}, + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, "nbformat": 4, - "nbformat_minor": 5 + "nbformat_minor": 0 } diff --git a/examples/transformations/tsfresh.ipynb b/examples/transformations/tsfresh.ipynb index d1d37d1761..1198b711c1 100644 --- a/examples/transformations/tsfresh.ipynb +++ b/examples/transformations/tsfresh.ipynb @@ -6,7 +6,7 @@ "source": [ "# Feature extraction with tsfresh transformer\n", "\n", - "[tsfresh](https://tsfresh.readthedocs.io) is a tool for extacting summary features\n", + "[tsfresh](https://tsfresh.readthedocs.io) is a tool for extracting summary features\n", "from a collection of time series. It is an unsupervised transformation, and as such\n", "can easily be used as a pipeline stage in classification, clustering and regression\n", "in conjunction with a scikit-learn compatible estimator.\n", @@ -281,9 +281,8 @@ "cell_type": "markdown", "source": [ "By default, the `TSFreshClassifier` uses the supervised\n", - "`TSFreshRelevant` and the scitkit `RandomForestClassifier`.\n", - " You can\n", - "change this through the constructor" + "`TSFreshRelevant` and the scikit-learn `RandomForestClassifier`.\n", + " You can change this through the constructor." ], "metadata": { "collapsed": false diff --git a/pyproject.toml b/pyproject.toml index 9001628aec..b27cc4d00f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -186,3 +186,10 @@ filterwarnings = [ "ignore:numpy.dtype size changed", "ignore:numpy.ufunc size changed", ] + +[tool.codespell] +ignore-words = ".github/utilities/codespell_ignore_words.txt" +ignore-regex = '(^\s*"image/\S+": ".*|Binay|Lamba|Mapp|Hart|Sch"afer|SOM|COO)' +skip = ".venv,.mypy_cache,.git" +check-filenames = true +check-hidden = true From 30cfd1561371b21f7b13064ce989aab7f8ce3159 Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Sat, 12 Jul 2025 17:16:44 +0200 Subject: [PATCH 183/216] typo (#2944) --- aeon/forecasting/_tvp.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/forecasting/_tvp.py b/aeon/forecasting/_tvp.py index 83bb9157f6..a8a8e1731a 100644 --- a/aeon/forecasting/_tvp.py +++ b/aeon/forecasting/_tvp.py @@ -25,7 +25,7 @@ class TVPForecaster(BaseForecaster): Parameters ---------- window : int - Number of autoregressive lags to use, called window to co-ordinate with + Number of autoregressive lags to use, called window to coordinate with RegressionForecaster. var : float, default=0.01 Observation noise variance. ``var`` controls the influence of recency in the From 681c69ca1d56a80fd84f3e8f52f0efb622fdfcf9 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Sat, 12 Jul 2025 17:32:42 +0100 Subject: [PATCH 184/216] correct checkout (#2945) --- .github/workflows/pr_precommit.yml | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/.github/workflows/pr_precommit.yml b/.github/workflows/pr_precommit.yml index 6267c529af..ce961e0668 100644 --- a/.github/workflows/pr_precommit.yml +++ b/.github/workflows/pr_precommit.yml @@ -65,8 +65,19 @@ jobs: runs-on: ubuntu-24.04 steps: + - name: Create app token + uses: actions/create-github-app-token@v2 + id: app-token + with: + app-id: ${{ vars.PR_APP_ID }} + private-key: ${{ secrets.PR_APP_KEY }} + - name: Checkout uses: actions/checkout@v4 + with: + repository: ${{ github.event.pull_request.head.repo.full_name }} + ref: ${{ github.head_ref }} + token: ${{ steps.app-token.outputs.token }} - name: Annotate locations with typos uses: codespell-project/codespell-problem-matcher@v1 From 82905bd64d3f2804d4412fc25ad597a174e0f1f2 Mon Sep 17 00:00:00 2001 From: Saniya S Inamdar <39870781+CodeFor2001@users.noreply.github.com> Date: Sat, 12 Jul 2025 18:20:57 +0100 Subject: [PATCH 185/216] DOC: fix typos in MatrixProfileSeriesTransformer (#2940) (#2941) Co-authored-by: Tony Bagnall --- aeon/transformations/series/_matrix_profile.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/transformations/series/_matrix_profile.py b/aeon/transformations/series/_matrix_profile.py index 00ed70b089..ea078f159a 100644 --- a/aeon/transformations/series/_matrix_profile.py +++ b/aeon/transformations/series/_matrix_profile.py @@ -14,7 +14,7 @@ class MatrixProfileSeriesTransformer(BaseSeriesTransformer): z-normalized Euclidean distance between any subsequence within a time series and its nearest neighbour. - For more information on the matrix profile, see `th stumpy tutorial + For more information on the matrix profile, see `the STUMPY tutorial `_ Parameters From ec2b171eb54bd1eecd874fa2177607b47467cfa8 Mon Sep 17 00:00:00 2001 From: "allcontributors[bot]" <46447321+allcontributors[bot]@users.noreply.github.com> Date: Sat, 12 Jul 2025 19:25:13 +0100 Subject: [PATCH 186/216] =?UTF-8?q?=F0=9F=93=9D=20Add=20CodeFor2001=20as?= =?UTF-8?q?=20a=20contributor=20for=20doc=20(#2946)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 📝 Update CONTRIBUTORS.md [skip ci] * 📝 Update .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> --- .all-contributorsrc | 9 +++++++++ CONTRIBUTORS.md | 21 +++++++++++---------- 2 files changed, 20 insertions(+), 10 deletions(-) diff --git a/.all-contributorsrc b/.all-contributorsrc index 20f6b6696a..b18b856c33 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -2747,6 +2747,15 @@ "contributions": [ "doc" ] + }, + { + "login": "CodeFor2001", + "name": "Saniya S Inamdar", + "avatar_url": "https://avatars.githubusercontent.com/u/39870781?v=4", + "profile": "https://github.com/CodeFor2001", + "contributions": [ + "doc" + ] } ], "commitType": "docs" diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md index 5725df941a..9471bc39f6 100644 --- a/CONTRIBUTORS.md +++ b/CONTRIBUTORS.md @@ -1,7 +1,7 @@ # Contributors -[![All Contributors](https://img.shields.io/badge/all_contributors-269-orange.svg)](#contributors) +[![All Contributors](https://img.shields.io/badge/all_contributors-270-orange.svg)](#contributors) This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! @@ -256,14 +256,15 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Sagar Mishra
Sagar Mishra
⚠️ Sajaysurya Ganesh
Sajaysurya Ganesh
💻 📖 🎨 💡 🤔 ⚠️ ✅ + Saniya S Inamdar
Saniya S Inamdar
📖 Saransh Chopra
Saransh Chopra
📖 🚇 Satya Prakash Pattnaik
Satya Prakash Pattnaik
📖 Saurabh Dasgupta
Saurabh Dasgupta
💻 Sebastiaan Koel
Sebastiaan Koel
💻 📖 Sebastian Hagn
Sebastian Hagn
📖 - Sebastian Schmidl
Sebastian Schmidl
🐛 💻 📖 🔬 ⚠️ 👀 🔣 + Sebastian Schmidl
Sebastian Schmidl
🐛 💻 📖 🔬 ⚠️ 👀 🔣 Sharathchenna
Sharathchenna
💻 Shivansh Subramanian
Shivansh Subramanian
📖 💻 Solomon Botchway
Solomon Botchway
🚧 @@ -271,9 +272,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Stanislav Khrapov
Stanislav Khrapov
💻 Stijn Rotman
Stijn Rotman
💻 Svea Marie Meyer
Svea Marie Meyer
📖 💻 - Sylvain Combettes
Sylvain Combettes
💻 🐛 + Sylvain Combettes
Sylvain Combettes
💻 🐛 TNTran92
TNTran92
💻 Taiwo Owoseni
Taiwo Owoseni
💻 Tanish Yelgoe
Tanish Yelgoe
💻 @@ -281,9 +282,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d TheMathcompay Widget Factory Team
TheMathcompay Widget Factory Team
📖 Thomas Buckley-Houston
Thomas Buckley-Houston
🐛 Tina Jin
Tina Jin
💻 📖 - Tom Xu
Tom Xu
💻 📖 + Tom Xu
Tom Xu
💻 📖 Tomasz Chodakowski
Tomasz Chodakowski
💻 📖 🐛 Tony Bagnall
Tony Bagnall
💻 💼 📖 🎨 📋 🔍 🤔 📆 💬 👀 📢 🔣 Tvisha Vedant
Tvisha Vedant
💻 @@ -291,9 +292,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Utsav Kumar Tiwari
Utsav Kumar Tiwari
💻 📖 Vedant
Vedant
📖 Viktor Dremov
Viktor Dremov
💻 - ViktorKaz
ViktorKaz
💻 📖 🎨 + ViktorKaz
ViktorKaz
💻 📖 🎨 Vyomkesh Vyas
Vyomkesh Vyas
💻 📖 💡 ⚠️ Wayne Adams
Wayne Adams
📖 William Templier
William Templier
📖 @@ -301,9 +302,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d William Zheng
William Zheng
💻 ⚠️ Yair Beer
Yair Beer
💻 Yash Lamba
Yash Lamba
💻 - Yi-Xuan Xu
Yi-Xuan Xu
💻 ⚠️ 🚧 📖 + Yi-Xuan Xu
Yi-Xuan Xu
💻 ⚠️ 🚧 📖 Ziyao Wei
Ziyao Wei
💻 aa25desh
aa25desh
💻 🐛 abandus
abandus
🤔 💻 @@ -311,9 +312,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d alexbanwell1
alexbanwell1
💻 🎨 📖 bethrice44
bethrice44
🐛 💻 👀 ⚠️ big-o
big-o
💻 ⚠️ 🎨 🤔 👀 ✅ 🧑‍🏫 - bobbys
bobbys
💻 + bobbys
bobbys
💻 brett koonce
brett koonce
📖 btrtts
btrtts
📖 chizzi25
chizzi25
📝 @@ -321,9 +322,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d danbartl
danbartl
🐛 💻 👀 📢 ⚠️ ✅ 📹 hamzahiqb
hamzahiqb
🚇 hiqbal2
hiqbal2
📖 - jesellier
jesellier
💻 + jesellier
jesellier
💻 jschemm
jschemm
💻 julu98
julu98
🐛 kkoziara
kkoziara
💻 🐛 @@ -331,9 +332,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d neuron283
neuron283
💻 nileenagp
nileenagp
💻 oleskiewicz
oleskiewicz
💻 📖 ⚠️ - pabworks
pabworks
💻 ⚠️ + pabworks
pabworks
💻 ⚠️ patiently pending world peace
patiently pending world peace
💻 raishubham1
raishubham1
📖 simone-pignotti
simone-pignotti
💻 🐛 @@ -341,9 +342,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d sri1419
sri1419
💻 tensorflow-as-tf
tensorflow-as-tf
💻 vNtzYy
vNtzYy
🐛 - ved pawar
ved pawar
📖 + ved pawar
ved pawar
📖 vedazeren
vedazeren
💻 ⚠️ vincent-nich12
vincent-nich12
💻 vollmersj
vollmersj
📖 From 5aa01639298449d48e46f260a7b43e7eef9001fd Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Wed, 16 Jul 2025 18:13:52 +0200 Subject: [PATCH 187/216] [ENH] Rework ETS Forecaster (#2939) * iterative * iterative ETS * notebook * Fix bug in seasonality calculation * Add example for ETS iterative forecasting * ets tests * holding notebook * example * Run Itertive notebook --------- Co-authored-by: Alex Banwell --- aeon/forecasting/_ets.py | 64 ++++-- aeon/forecasting/tests/test_ets.py | 8 +- examples/forecasting/ets.ipynb | 44 ++++ examples/forecasting/iterative.ipynb | 318 +++++++++++++++++---------- 4 files changed, 293 insertions(+), 141 deletions(-) create mode 100644 examples/forecasting/ets.ipynb diff --git a/aeon/forecasting/_ets.py b/aeon/forecasting/_ets.py index f6ec85fcaf..9758d3535b 100644 --- a/aeon/forecasting/_ets.py +++ b/aeon/forecasting/_ets.py @@ -1,7 +1,8 @@ """ETSForecaster class. An implementation of the exponential smoothing statistics forecasting algorithm. -Implements additive and multiplicative error models. +Implements additive and multiplicative error models. We recommend using the AutoETS +version, but this is useful for demonstrations. """ __maintainer__ = [] @@ -85,7 +86,7 @@ class ETSForecaster(BaseForecaster): ... seasonality_type='multiplicative', seasonal_period=4 ... ) >>> forecaster.forecast(y) - 366.90200486015596 + 365.5141941111267 """ _tags = { @@ -193,7 +194,7 @@ def _get_int(x): self._gamma, self.phi, ) - self.forecast_ = _predict( + self.forecast_ = _numba_predict( self._trend_type, self._seasonality_type, self.level_, @@ -240,6 +241,29 @@ def _initialise(self, data): self._trend_type, self._seasonality_type, self._seasonal_period, data ) + def iterative_forecast(self, y, prediction_horizon): + """Forecast with ETS specific iterative method. + + Overrides the base class iterative_forecast to avoid refitting on each step. + This simply rolls the ETS model forward + """ + self.fit(y) + preds = np.zeros(prediction_horizon) + preds[0] = self.forecast_ + for i in range(1, prediction_horizon): + preds[i] = _numba_predict( + self._trend_type, + self._seasonality_type, + self.level_, + self.trend_, + self.seasonality_, + self.phi, + i + 1, + self.n_timepoints_, + self._seasonal_period, + ) + return preds + @njit(fastmath=True, cache=True) def _numba_fit( @@ -268,20 +292,18 @@ def _numba_fit( time_point = data[index] # Calculate level, trend, and seasonal components - fitted_value, error, level, trend, seasonality[t % seasonal_period] = ( - _update_states( - error_type, - trend_type, - seasonality_type, - level, - trend, - seasonality[s_index], - time_point, - alpha, - beta, - gamma, - phi, - ) + fitted_value, error, level, trend, seasonality[s_index] = _update_states( + error_type, + trend_type, + seasonality_type, + level, + trend, + seasonality[s_index], + time_point, + alpha, + beta, + gamma, + phi, ) residuals_[t] = error fitted_values_[t] = fitted_value @@ -314,7 +336,7 @@ def _numba_fit( @njit(fastmath=True, cache=True) -def _predict( +def _numba_predict( trend_type, seasonality_type, level, @@ -327,11 +349,11 @@ def _predict( ): # Generate forecasts based on the final values of level, trend, and seasonals if phi == 1: # No damping case - phi_h = 1 + phi_h = horizon else: # Geometric series formula for calculating phi + phi^2 + ... + phi^h phi_h = phi * (1 - phi**horizon) / (1 - phi) - seasonal_index = (n_timepoints + horizon) % seasonal_period + seasonal_index = (n_timepoints + horizon - 1) % seasonal_period return _predict_value( trend_type, seasonality_type, @@ -392,7 +414,7 @@ def _update_states( level, trend, seasonality, - data_item: int, + data_item, alpha, beta, gamma, diff --git a/aeon/forecasting/tests/test_ets.py b/aeon/forecasting/tests/test_ets.py index 64275a2d1d..176df75d26 100644 --- a/aeon/forecasting/tests/test_ets.py +++ b/aeon/forecasting/tests/test_ets.py @@ -24,7 +24,7 @@ seasonality_type="additive", seasonal_period=4, ), - 9.191190608800001, + 11.456563248800002, ), ( dict( @@ -37,7 +37,7 @@ seasonality_type="additive", seasonal_period=4, ), - 16.20176819429869, + 15.507105356706465, ), ( dict( @@ -50,7 +50,7 @@ seasonality_type="multiplicative", seasonal_period=4, ), - 12.301259229712382, + 13.168538863095991, ), ( dict( @@ -63,7 +63,7 @@ seasonality_type="multiplicative", seasonal_period=4, ), - 16.811888294476528, + 15.223040987015944, ), ], ) diff --git a/examples/forecasting/ets.ipynb b/examples/forecasting/ets.ipynb new file mode 100644 index 0000000000..ad9d795325 --- /dev/null +++ b/examples/forecasting/ets.ipynb @@ -0,0 +1,44 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "# ETS Forecasting\n", + "\n", + "Examples for ETS notebook here\n", + "\n", + "`aeon` has native a implementation of ETS and will soon add Auto ETS. Details on this\n", + " will be forthcoming when the implementation is stable" + ], + "id": "70dcc762ca5e1b6c" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "", + "id": "f1fa7d726479eff9" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/forecasting/iterative.ipynb b/examples/forecasting/iterative.ipynb index e98d588656..c0f8d8dd8a 100644 --- a/examples/forecasting/iterative.ipynb +++ b/examples/forecasting/iterative.ipynb @@ -1,8 +1,9 @@ { "cells": [ { - "metadata": {}, "cell_type": "markdown", + "id": "d7f79f17fc5714df", + "metadata": {}, "source": [ "# Iterative forecasting\n", "\n", @@ -24,47 +25,60 @@ "\n", "\n", "We will demonstrate direct forecasting with the airline data" - ], - "id": "d7f79f17fc5714df" + ] }, { + "cell_type": "code", + "execution_count": 1, + "id": "e1a1ad0e8e30b77e", "metadata": { "ExecuteTime": { "end_time": "2025-06-16T16:14:58.605948Z", "start_time": "2025-06-16T16:14:58.412664Z" } }, - "cell_type": "code", - "source": [ - "from aeon.datasets import load_airline\n", - "from aeon.visualisation import plot_series\n", - "\n", - "airline = load_airline()\n", - "_ = plot_series(airline)" - ], - "id": "e1a1ad0e8e30b77e", "outputs": [ { "data": { + "image/png": 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+ ] }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 21 + "source": [ + "from aeon.datasets import load_airline\n", + "from aeon.visualisation import plot_series\n", + "\n", + "airline = load_airline()\n", + "_ = plot_series(airline)" + ] }, { + "cell_type": "code", + "execution_count": 2, + "id": "512d5683fca427e7", "metadata": { "ExecuteTime": { "end_time": "2025-06-16T16:14:59.858569Z", "start_time": "2025-06-16T16:14:59.692652Z" } }, - "cell_type": "code", + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -83,113 +97,115 @@ "plt.ylabel(\"Value\")\n", "plt.title(\"Train/Test Split of Time Series\")\n", "plt.show()" - ], - "id": "512d5683fca427e7", - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 22 + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "d1a59c9017081cf4", + "metadata": {}, "source": [ "We want to train a forecaster on the train set and forecast predictions for the\n", "subsequent test steps. The `RegressionForecaster` is a window based forecaster that \n", "by default uses linear regression to predict one step ahead. It requires a window \n", "parameter. See the [forecasting with regression](./regression.ipynb) notebook for \n", "details. The `forecast()` method makes a single forecast `horizon` steps ahead. " - ], - "id": "d1a59c9017081cf4" + ] }, { + "cell_type": "code", + "execution_count": 3, + "id": "ff55e38056585c73", "metadata": { "ExecuteTime": { "end_time": "2025-06-16T16:15:02.833697Z", "start_time": "2025-06-16T16:15:02.824630Z" } }, - "cell_type": "code", - "source": [ - "from aeon.forecasting import RegressionForecaster\n", - "\n", - "reg = RegressionForecaster(horizon=1, window=10)\n", - "p1 = reg.forecast(y_train)\n", - "print(\" First forecast = \", p1)" - ], - "id": "ff55e38056585c73", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " First forecast = 376.10513465806844\n" + " First forecast = 376.1051346580682\n" ] } ], - "execution_count": 23 + "source": [ + "from aeon.forecasting import RegressionForecaster\n", + "\n", + "reg = RegressionForecaster(horizon=1, window=10)\n", + "p1 = reg.forecast(y_train)\n", + "print(\" First forecast = \", p1)" + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "2106d4593aee61de", + "metadata": {}, "source": [ "what if we want to predict further ahead? The direct strategy, described [here](./direct.ipynb)\n", "retrains the model for each set, changing the forecasting horizon. This can\n", " be computationally intensive. As an alternative, the iterative \n", " strategy uses the predicted value and predicts without refitting." - ], - "id": "2106d4593aee61de" + ] }, { + "cell_type": "code", + "execution_count": 4, + "id": "5b091fcecbd3f267", "metadata": { "ExecuteTime": { "end_time": "2025-06-16T16:15:05.268365Z", "start_time": "2025-06-16T16:15:05.257150Z" } }, - "cell_type": "code", - "source": [ - "y_new = np.append(y_train, p1)\n", - "p2 = reg.predict(y_new)\n", - "y_new = np.append(y_new, p2)\n", - "p3 = reg.predict(y_new)\n", - "print(f\" second forecast = {p2} third forecast = {p3}\")" - ], - "id": "5b091fcecbd3f267", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " second forecast = 427.1861353919512 third forecast = 418.40595828752396\n" + " second forecast = 427.18613539195076 third forecast = 418.4059582875235\n" ] } ], - "execution_count": 24 + "source": [ + "y_new = np.append(y_train, p1)\n", + "p2 = reg.predict(y_new)\n", + "y_new = np.append(y_new, p2)\n", + "p3 = reg.predict(y_new)\n", + "print(f\" second forecast = {p2} third forecast = {p3}\")" + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "there is a function in the base class to make iterative forecasting easier.", - "id": "5e1aafb6bc8b5f98" + "id": "5e1aafb6bc8b5f98", + "metadata": {}, + "source": [ + "there is a function in the base class to make iterative forecasting easier." + ] }, { + "cell_type": "code", + "execution_count": 5, + "id": "1b2267b78b0cdfef", "metadata": { "ExecuteTime": { "end_time": "2025-06-16T16:15:10.611791Z", "start_time": "2025-06-16T16:15:10.451442Z" } }, - "cell_type": "code", + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "y_hat = reg.iterative_forecast(y=y_train, prediction_horizon=20)\n", "\n", @@ -212,125 +228,195 @@ "plt.ylabel(\"Value\")\n", "plt.title(\"Train/Test/Pedicted\")\n", "plt.show()" - ], - "id": "1b2267b78b0cdfef", - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 25 + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "Looking closer, we can see the errors our forecaster is making.", - "id": "9bf1c1e6d500cbd7" + "id": "9bf1c1e6d500cbd7", + "metadata": {}, + "source": [ + "Looking closer, we can see the errors our forecaster is making." + ] }, { + "cell_type": "code", + "execution_count": 6, + "id": "30d6ed16e3eaa898", "metadata": { "ExecuteTime": { "end_time": "2025-06-16T16:15:27.178701Z", "start_time": "2025-06-16T16:15:27.018119Z" } }, - "cell_type": "code", - "source": [ - "plt.plot(y_test, label=\"Actual\", color=\"orange\")\n", - "plt.plot(y_hat, label=\"Predicted iterative\", color=\"green\", linestyle=\":\")\n", - "plt.legend()\n", - "plt.xlabel(\"Time\")\n", - "plt.ylabel(\"Value\")\n", - "plt.title(\"Pedicted and Actual over the test interval\")\n", - "plt.show()" - ], - "id": "30d6ed16e3eaa898", "outputs": [ { "data": { + "image/png": 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", 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" + ] }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 26 + "source": [ + "plt.plot(y_test, label=\"Actual\", color=\"orange\")\n", + "plt.plot(y_hat, label=\"Predicted iterative\", color=\"green\", linestyle=\":\")\n", + "plt.legend()\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.title(\"Pedicted and Actual over the test interval\")\n", + "plt.show()" + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "3e97b9b31693702d", + "metadata": {}, "source": [ "It seems to be underestimating the peaks and troughs. Contrast this to the direct \n", "strategy which results in very different forecasts " - ], - "id": "3e97b9b31693702d" + ] }, { + "cell_type": "code", + "execution_count": 7, + "id": "bc5ed962ad010b89", "metadata": { "ExecuteTime": { "end_time": "2025-06-16T16:15:33.914963Z", "start_time": "2025-06-16T16:15:33.788410Z" } }, - "cell_type": "code", + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "y_hat2 = reg.direct_forecast(y=y_train, prediction_horizon=20)\n", "plt.plot(y_hat2, label=\"Predicted direct\", color=\"blue\", linestyle=\":\")\n", "plt.plot(y_test, label=\"Actual\", color=\"orange\")\n", "plt.plot(y_hat, label=\"Predicted iterative\", color=\"green\", linestyle=\":\")\n", "plt.show()" - ], - "id": "bc5ed962ad010b89", + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5e283827ebb7141b", + "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Alpha: 0.2550646708489574, Beta: 0.02550646708489574, Gamma: 0.7448608356181274, Phi: 1.0\n", + "Alpha: 0.25508210344031595, Beta: 2.5508210344031598e-05, Gamma: 0.7448434047700282, Phi: N/A\n" + ] + }, { "data": { + "image/png": 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", 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" + ] }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 27 - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "", - "id": "5e283827ebb7141b" + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from statsmodels.tsa.exponential_smoothing.ets import ETSModel\n", + "\n", + "from aeon.datasets import load_airline\n", + "from aeon.forecasting import ETSForecaster\n", + "\n", + "airline = load_airline()\n", + "y_train = airline[:100]\n", + "y_test = airline[100:120]\n", + "\n", + "ets = ETSForecaster(\n", + " error_type=\"additive\",\n", + " trend_type=\"additive\",\n", + " seasonality_type=\"additive\",\n", + " seasonal_period=12,\n", + " alpha=0.2550646708489574,\n", + " beta=2.550646708489574e-02,\n", + " gamma=0.7448608356181274,\n", + " phi=1.0,\n", + ")\n", + "statsmodels_ets = ETSModel(\n", + " endog=y_train,\n", + " error=\"add\",\n", + " trend=\"add\",\n", + " seasonal=\"add\",\n", + " seasonal_periods=12,\n", + " damped_trend=False,\n", + ")\n", + "\n", + "ets_forecasts = ets.iterative_forecast(y_train, 20)\n", + "sm_model = statsmodels_ets.fit()\n", + "statsmodels_forecasts = sm_model.forecast(steps=20)\n", + "print(f\"Alpha: {ets.alpha}, Beta: {ets.beta}, Gamma: {ets.gamma}, Phi: {ets.phi}\")\n", + "print(\n", + " f\"Alpha: {sm_model.alpha}, Beta: {sm_model.beta if sm_model.has_trend else 'N/A'}, \\\n", + " Gamma: {sm_model.gamma if sm_model.has_seasonal else 'N/A'}, \\\n", + " Phi: {sm_model.phi if sm_model.damped_trend else 'N/A'}\"\n", + ")\n", + "# plt.plot(np.arange(0, len(y_train)), y_train, label=\"Train\", color=\"blue\")\n", + "plt.plot(\n", + " # np.arange(len(y_train), len(y_train) + len(y_test)),\n", + " y_test,\n", + " label=\"Actual\",\n", + " color=\"orange\",\n", + ")\n", + "plt.plot(\n", + " # np.arange(len(y_train), len(y_train) + len(ets_forecasts)),\n", + " ets_forecasts,\n", + " label=\"Aeon\",\n", + " color=\"red\",\n", + " linestyle=\":\",\n", + ")\n", + "plt.plot(\n", + " # np.arange(len(y_train), len(y_train) + len(statsmodels_forecasts)),\n", + " statsmodels_forecasts,\n", + " label=\"Statsmodels\",\n", + " color=\"green\",\n", + " linestyle=\":\",\n", + ")\n", + "plt.legend()\n", + "plt.show()" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "aeon-venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.13.5" } }, "nbformat": 4, From a7521c7f5db3befd9391d16012b9f0adea463f09 Mon Sep 17 00:00:00 2001 From: Antoine Guillaume Date: Sat, 19 Jul 2025 22:35:07 +0200 Subject: [PATCH 188/216] [MNT] add n_jobs parameter for sax invertion (#2953) * add n_jobs * add tag --- .../collection/dictionary_based/_sax.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/aeon/transformations/collection/dictionary_based/_sax.py b/aeon/transformations/collection/dictionary_based/_sax.py index fe49423bda..8200f804ad 100644 --- a/aeon/transformations/collection/dictionary_based/_sax.py +++ b/aeon/transformations/collection/dictionary_based/_sax.py @@ -5,10 +5,11 @@ import numpy as np import scipy.stats -from numba import njit, prange +from numba import get_num_threads, njit, prange, set_num_threads from aeon.transformations.collection import BaseCollectionTransformer from aeon.transformations.collection.dictionary_based import PAA +from aeon.utils.validation import check_n_jobs class SAX(BaseCollectionTransformer): @@ -63,6 +64,7 @@ class SAX(BaseCollectionTransformer): _tags = { "capability:multivariate": True, + "capability:multithreading": True, "fit_is_empty": True, "algorithm_type": "dictionary", } @@ -74,11 +76,12 @@ def __init__( distribution="Gaussian", distribution_params=None, znormalized=True, + n_jobs=1, ): self.n_segments = n_segments self.alphabet_size = alphabet_size self.distribution = distribution - + self.n_jobs = n_jobs self.distribution_params = distribution_params self.znormalized = znormalized @@ -182,12 +185,15 @@ def inverse_sax(self, X, original_length, y=None): sax_inverse : np.ndarray(n_cases, n_channels, n_timepoints) The inverse of sax transform """ + prev_threads = get_num_threads() + _n_jobs = check_n_jobs(self.n_jobs) + set_num_threads(_n_jobs) sax_inverse = _invert_sax_symbols( sax_symbols=X, n_timepoints=original_length, breakpoints_mid=self.breakpoints_mid, ) - + set_num_threads(prev_threads) return sax_inverse def _generate_breakpoints( From c7e74cbf31b63638f2800743d9492bfaf1f86631 Mon Sep 17 00:00:00 2001 From: "aeon-actions-bot[bot]" <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Date: Wed, 23 Jul 2025 11:01:30 +0200 Subject: [PATCH 189/216] Automated `pre-commit` hook update (#2949) Co-authored-by: TonyBagnall <9594042+TonyBagnall@users.noreply.github.com> --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 93a0276244..8ae225d0b5 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -29,7 +29,7 @@ repos: args: [ "--create", "--python-folders", "aeon" ] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.12.2 + rev: v0.12.4 hooks: - id: ruff args: [ "--fix"] From ccee1b7ff50bf57098d308eb1a7eb4b70dee6ab9 Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Sat, 26 Jul 2025 11:05:12 +0100 Subject: [PATCH 190/216] [DEP] Deprecate PLAID dataset (#2928) * remove PLAID * remove PLAID --- aeon/datasets/__init__.py | 2 - aeon/datasets/_single_problem_loaders.py | 44 - aeon/datasets/data/PLAID/PLAID_TEST.arff | 1910 ----------------- aeon/datasets/data/PLAID/PLAID_TEST.ts | 564 ----- aeon/datasets/data/PLAID/PLAID_TRAIN.arff | 1906 ---------------- aeon/datasets/data/PLAID/PLAID_TRAIN.ts | 568 ----- .../unequal_length/tests/test_resize.py | 10 +- .../unequal_length/tests/test_truncate.py | 10 +- 8 files changed, 16 insertions(+), 4998 deletions(-) delete mode 100644 aeon/datasets/data/PLAID/PLAID_TEST.arff delete mode 100644 aeon/datasets/data/PLAID/PLAID_TEST.ts delete mode 100644 aeon/datasets/data/PLAID/PLAID_TRAIN.arff delete mode 100644 aeon/datasets/data/PLAID/PLAID_TRAIN.ts diff --git a/aeon/datasets/__init__.py b/aeon/datasets/__init__.py index a35419017e..2ebe44b8dc 100644 --- a/aeon/datasets/__init__.py +++ b/aeon/datasets/__init__.py @@ -26,7 +26,6 @@ "load_italy_power_demand", "load_japanese_vowels", "load_pickup_gesture_wiimoteZ", - "load_plaid", "load_longley", "load_lynx", "load_shampoo_sales", @@ -76,7 +75,6 @@ load_osuleaf, load_PBS_dataset, load_pickup_gesture_wiimoteZ, - load_plaid, load_shampoo_sales, load_solar, load_unit_test, diff --git a/aeon/datasets/_single_problem_loaders.py b/aeon/datasets/_single_problem_loaders.py index 69ae780e99..30e6a053c6 100644 --- a/aeon/datasets/_single_problem_loaders.py +++ b/aeon/datasets/_single_problem_loaders.py @@ -3,7 +3,6 @@ __maintainer__ = [] __all__ = [ "load_airline", - "load_plaid", "load_arrow_head", "load_gunpoint", "load_osuleaf", @@ -28,7 +27,6 @@ import numpy as np import pandas as pd -from deprecated.sphinx import deprecated from aeon.datasets import load_from_tsf_file from aeon.datasets._data_loaders import _load_saved_dataset, _load_tsc_dataset @@ -379,48 +377,6 @@ def load_pickup_gesture_wiimoteZ(split=None): return _load_tsc_dataset("PickupGestureWiimoteZ", split, return_type="np-list") -# TODO: remove in v1.3.0 -@deprecated( - version="1.2.0", - reason="load_plaid and the PLAID dataset will be removed in version 1.3. It has " - "been replaced by a smaller unequal length univariate problem " - "PickupGestureWiimoteZ.", - category=FutureWarning, -) -def load_plaid(split=None): - """Load the PLAID univariate time series classification problem. - - Example of a univariate problem with unequal length time series. - - Parameters - ---------- - split: None or one of "TRAIN", "TEST", default=None - Whether to load the train or test instances of the problem. By default it - loads both train and test instances into a single array. - - Returns - ------- - X: list of 2D np.ndarray, one for each series. - y: 1D numpy array of length len(X). The class labels for each time series - instance in X. - - Notes - ----- - Dimensionality: univariate - Series length: variable - Train cases: 537 - Test cases: 537 - Number of classes: 2 - Details: https://timeseriesclassification.com/description.php?Dataset=PLAID - - Examples - -------- - >>> from aeon.datasets import load_plaid - >>> X, y = load_plaid() - """ - return _load_tsc_dataset("PLAID", split, return_type="np-list") - - def load_japanese_vowels(split=None): """Load the JapaneseVowels time series classification problem. diff --git a/aeon/datasets/data/PLAID/PLAID_TEST.arff b/aeon/datasets/data/PLAID/PLAID_TEST.arff deleted file mode 100644 index f745b1acb0..0000000000 --- a/aeon/datasets/data/PLAID/PLAID_TEST.arff +++ /dev/null @@ -1,1910 +0,0 @@ -%# PLAID dataset -% -%PLAID stands for the Plug Load Appliance Identification Dataset. The data are intended for load identification research. The first version of PLAID is named PLAID1, collected in summer 2013. A second version of PLAID was collected in winter 2014 and released under the name PLAID2. -%This dataset comes from PLAID1. It includes current and voltage measurements sampled at 30 kHz from 11 different appliance types present in more than 56 households in Pittsburgh, Pennsylvania, USA. Data collection took place during the summer of 2013. Each appliance type is represented by dozens of different instances of varying makes/models. -%For each appliance, three to six measurements were collected for each state transition. These measurements were then post-processed to extract a few-second-long window containing both the steady-state operation and the startup transient )when available). -%The classes correspond to 11 different appliance types: -% air conditioner (class 0), compact fluorescent lamp, fan, fridge, hairdryer , heater, incandescent light bulb, laptop, microwave, vacuum,washing machine (class 10) -%Train size: 537 -%Test size: 537 -%Missing value: No -%Number of classes: 11 -%Time series length: Vary -% -%We pad NaN to the end of each time series to the length of the longest time series. -% -%Data created by Gao, Jingkun, et al. (see [1], [2], [3]). Data edited by Patrick Schafer and Ulf Leser (see [4]). -% -%[1] Gao, Jingkun, et al. "PLAID: a public dataset of high-resolution electrical appliance measurements for load identification research: demo abstract." proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings. ACM, 2014. -% -%[2] https://github.com/jingkungao/PLAID -% -%[3] http://www.plaidplug.com/ -% -%[4] Schäfer, Patrick, and Ulf Leser. "Fast and accurate time series classification with weasel." Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 2017. -@Relation PLAID -@attribute att1 numeric -@attribute att2 numeric -@attribute att3 numeric -@attribute att4 numeric -@attribute att5 numeric -@attribute att6 numeric -@attribute att7 numeric -@attribute att8 numeric -@attribute att9 numeric -@attribute att10 numeric -@attribute att11 numeric -@attribute att12 numeric -@attribute att13 numeric -@attribute att14 numeric -@attribute att15 numeric -@attribute att16 numeric -@attribute att17 numeric -@attribute att18 numeric -@attribute att19 numeric -@attribute att20 numeric -@attribute att21 numeric -@attribute att22 numeric -@attribute att23 numeric -@attribute att24 numeric -@attribute att25 numeric -@attribute att26 numeric -@attribute att27 numeric -@attribute att28 numeric -@attribute att29 numeric -@attribute att30 numeric -@attribute att31 numeric -@attribute att32 numeric -@attribute att33 numeric -@attribute att34 numeric -@attribute att35 numeric -@attribute att36 numeric -@attribute att37 numeric -@attribute att38 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,1 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,2 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,2 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,2 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,2 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,2 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,4 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,4 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,4 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,6 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,6 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,6 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,6 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,6 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,6 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,6 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,6 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?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,7 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,7 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,7 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,7 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,7 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,7 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,7 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,7 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,7 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,7 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,7 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,7 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,8 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,8 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,8 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,8 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,8 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,10 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,10 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diff --git a/aeon/datasets/data/PLAID/PLAID_TEST.ts b/aeon/datasets/data/PLAID/PLAID_TEST.ts deleted file mode 100644 index daec5296dd..0000000000 --- a/aeon/datasets/data/PLAID/PLAID_TEST.ts +++ /dev/null @@ -1,564 +0,0 @@ -## PLAID dataset -# -#PLAID stands for the Plug Load Appliance Identification Dataset. The data are intended for load identification research. The first version of PLAID is named PLAID1, collected in summer 2013. A second version of PLAID was collected in winter 2014 and released under the name PLAID2. -#This dataset comes from PLAID1. It includes current and voltage measurements sampled at 30 kHz from 11 different appliance types present in more than 56 households in Pittsburgh, Pennsylvania, USA. Data collection took place during the summer of 2013. Each appliance type is represented by dozens of different instances of varying makes/models. -#For each appliance, three to six measurements were collected for each state transition. These measurements were then post-processed to extract a few-second-long window containing both the steady-state operation and the startup transient )when available). -#The classes correspond to 11 different appliance types: % air -#conditioner (class 0), compact fluorescent lamp, fan, fridge, -#hairdryer , heater, incandescent light bulb, laptop, microwave, -#vacuum,washing machine (class 10)% -#Train size: 537 -#Test size: 537 -#Missing value: No -#Number of classes: 11 -# -#Time series length: Vary -#Data created by Gao, Jingkun, et al. (see [1], [2], [3]). Data edited by Patrick Schafer and Ulf Leser (see [4]). -#[1] Gao, Jingkun, et al. "PLAID: a public dataset of high-resolution electrical appliance measurements for load identification research: demo abstract." proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings. ACM, 2014. -#[2] https://github.com/jingkungao/PLAID -#[3] http://www.plaidplug.com/ -#[4] Schäfer, Patrick, and Ulf Leser. "Fast and accurate time series classification with weasel." Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 2017. -@problemName PLAID -@timeStamps false -@missing false -@univariate true -@equalLength false -@classLabel true 0 1 2 3 4 5 6 7 8 9 10 -@data 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diff --git a/aeon/datasets/data/PLAID/PLAID_TRAIN.arff b/aeon/datasets/data/PLAID/PLAID_TRAIN.arff deleted file mode 100644 index 34a9d1fc03..0000000000 --- a/aeon/datasets/data/PLAID/PLAID_TRAIN.arff +++ /dev/null @@ -1,1906 +0,0 @@ -%# PLAID dataset -%PLAID stands for the Plug Load Appliance Identification Dataset. The data are intended for load identification research. The first version of PLAID is named PLAID1, collected in summer 2013. A second version of PLAID was collected in winter 2014 and released under the name PLAID2. -%This dataset comes from PLAID1. It includes current and voltage measurements sampled at 30 kHz from 11 different appliance types present in more than 56 households in Pittsburgh, Pennsylvania, USA. Data collection took place during the summer of 2013. Each appliance type is represented by dozens of different instances of varying makes/models. -%For each appliance, three to six measurements were collected for each state transition. These measurements were then post-processed to extract a few-second-long window containing both the steady-state operation and the startup transient )when available). -%The classes correspond to 11 different appliance types: -%air conditioner (class 0), compact fluorescent lamp, fan, fridge, hairdryer , heater, incandescent light bulb, laptop, microwave, vacuum,washing machine (class 10) -%Train size: 537 -%Test size: 537 -%Missing value: No -%Number of classes: 11 -%Time series length: Vary -% -%We pad NaN to the end of each time series to the length of the longest time series for ARFF format, leave as it is for TS. -% -%Data created by Gao, Jingkun, et al. (see [1], [2], [3]). Data edited by Patrick Schafer and Ulf Leser (see [4]). -% -%[1] Gao, Jingkun, et al. "PLAID: a public dataset of high-resolution electrical appliance measurements for load identification research: demo abstract." proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings. ACM, 2014. -%[2] https://github.com/jingkungao/PLAID -%[3] http://www.plaidplug.com/ -%[4] Schäfer, Patrick, and Ulf Leser. "Fast and accurate time series classification with weasel." Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,2 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,2 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,2 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,2 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,2 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?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,6 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,6 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,6 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?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,6 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?,?,?,?,?,?,7 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,7 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,7 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?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,7 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,7 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,7 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,7 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,7 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,7 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,7 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,7 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,7 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,7 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,8 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,8 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,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,8 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-1.1779,1.3814,1.6751,1.4048,1.1847,1.1858,1.385,1.6649,1.403,1.1722,1.1771,1.3741,1.6555,1.4014,1.1698,1.1719,1.3783,1.6701,1.4029,1.1829,1.1923,1.3893,1.6712,1.3996,1.1734,1.1732,1.3785,1.6595,1.3911,1.1677,1.1779,1.3825,1.6665,1.4088,1.1829,1.1839,1.3884,1.6784,1.4013,1.1746,1.188,1.3859,1.6642,1.393,1.1628,1.1764,1.3805,1.6557,1.3882,1.1655,1.1868,1.3915,1.6664,1.3993,1.1685,1.1993,1.3923,1.6587,1.3946,1.1559,1.1815,1.3925,1.6614,1.389,1.1624,1.1864,1.3928,1.672,1.401,1.1658,1.1929,1.4076,1.6679,1.4006,1.1645,1.2,1.4,1.6561,1.3897,1.157,1.1957,1.3941,1.6527,1.3885,1.1593,1.2044,1.412,1.6579,1.3937,1.1668,1.2043,1.3979,1.6465,1.3838,1.1477,1.1954,1.4009,1.6434,1.3759,1.153,1.2039,1.4103,1.653,1.3872,1.1655,1.2136,1.4165,1.6578,1.3751,1.1563,1.2004,1.4005,1.6451,1.3698,1.1572,1.1934,1.4097,1.6468,1.3829,1.1527,1.212,1.4133,1.6572,1.3877,1.1572,1.2002,1.4057,1.649,1.3712,1.1492,1.2009,1.4055,1.6464,1.3761,1.1498,1.2122,1.4141,1.6625,1.3877,1.1574,1.2176,1.42,1.6486,1.3805,1.1458,1.2098,1.4074,1.6428,1.3758,1.1434,1.2098,1.4151,1.6481,1.3819,1.1516,1.2176,1.4313,1.6492,1.3725,1.1447,1.2017,1.4179,1.6316,1.367,1.1415,1.2092,1.4207,1.6461,1.3752,1.1543,1.2254,1.4378,1.6719,1.3726,1.1432,1.2204,1.427,1.6488,1.3713,1.1376,1.2131,1.4258,1.6493,1.3675,1.142,1.2113,1.4296,1.6542,1.3686,1.1392,1.2328,1.4374,1.6515,1.3614,1.1327,1.2138,1.3328,193.19,285.62,230.98,252.79,306.08,317.85,281.27,206.72,245.25,276.09,230.46,161.35,101.18,69.735,69.116,75.859,64.935,46.171,41.098,53.449,67.597,62.101,46.245,41.95,52.01,66.937,66.045,55.939,62.902,84.926,99.945,87.396,69.336,67.601,79.507,86.32,70.723,48.782,41.558,51.334,64.183,57.765,40.488,40.212,60.921,81.828,75.973,61.015,62.058,80.976,95.991,83.423,64.437,59.954,68.067,73.981,61.117,41.243,36.925,53.908,77.549,76.293,65.689,69.605,86.956,97.496,82.515,62.822,58.818,68.894,76.735,63.687,42.991,36.642,48.156,62.763,57.148,42.761,45.713,67.597,86.395,78.034,62.389,63.664,81.329,92.22,76.171,54.436,47.553,54.467,62.582,53.602,35.12,33.299,56.71,82.154,78.497,64.97,66.031,81.992,92.866,77.444,56.307,49.645,58.187,67.717,57.16,37.47,31.933,44.494,60.561,58.028,45.863,50.008,69.687,85.144,74.727,59.069,61.098,77.609,86.857,70.82,49.426,42.597,50.864,61.791,53.991,38.635,44.983,73.16,94.955,83.541,65.705,64.542,79.963,89.673,73.318,51.711,44.548,52.825,62.808,53.554,35.217,31.816,47.196,66.947,63.145,51.034,54.138,72.564,87.602,77.221,62.289,63.04,75.891,82.075,65.518,43.877,37.274,47.625,60.972,56.356,46.635,57.948,84.886,98.566,82.21,63.869,64.755,79.494,85.987,68.083,46.204,40.08,49.685,60.714,52.127,34.502,33.344,53.371,73.737,68.151,55.154,58.263,77.772,92.663,79.506,62.502,61.485,72.173,77.195,60.569,38.967,33.964,47.524,67.003,65.282,57.619,67.048,88.221,99.128,82.769,65.27,64.486,75.93,81.814,64.807,43.525,37.481,47.483,59.674,51.536,35.497,39.027,61.609,80.845,71.567,57.012,59.897,80.032,93.229,77.603,58.793,55.216,63.936,69.609,55.435,35.995,33.663,53.458,77.034,73.805,64.872,71.075,88.997,98.683,81.65,63.268,60.391,69.498,74.527,58.931,38.669,34.096,46.032,59.213,52.236,39.637,46.677,69.912,85.753,74.187,60.214,65.256,84.918,93.972,75.415,54.89,49.559,57.091,64.186,52.302,34.557,37.835,64.423,87.466,79.064,66.093,69.521,87.214,96.856,79.053,59.971,55.837,64.176,70.223,55.545,35.777,32.487,45.992,61.081,56.105,45.97,53.988,74.991,88.14,74.616,60.467,65.489,83.654,90.965,70.979,48.882,42.334,51.179,61.71,51.846,38.66,49.172,78.248,95.516,81.469,66.807,71.336,89.668,95.868,75.019,54.151,49.714,58.947,65.874,52.203,33.459,32.083,48.093,66.989,61.396,51.614,58.075,77.287,88.69,74.813,61.967,67.427,82.364,85.303,64.59,42.999,38.154,49.342,61.375,54.953,47.07,61.537,87.754,98.774,81.426,66.736,71.64,87.139,90.493,68.914,47.499,43.118,53.006,61.679,49.951,32.553,33.418,55.191,74.515,66.502,54.633,59.539,78.857,91.236,76.967,62.968,65.19,75.695,77.401,58.411,37.844,35.08,49.345,67.444,63.571,57.996,69.75,90.815,99.517,81.773,67.06,69.497,81.291,83.375,62.93,42.072,37.931,48.773,59.586,49.174,33.865,40.136,63.731,80.394,68.938,56.091,61.761,83.072,94.4,77.102,60.088,58.282,66.89,70.224,53.539,34.711,34.555,56.079,78.448,73.071,65.337,73.42,92.55,100.29,81.797,65.873,65.92,75.581,77.423,58.341,38.196,34.926,47.459,59.172,50.208,38.614,47.539,70.388,83.471,70.499,58.403,65.735,85.781,93.18,74.026,56.051,53.012,60.701,65.153,50.609,33.484,39.359,67.698,88.987,78.185,65.988,71.793,91.191,98.519,78.107,59.699,57.287,66.464,70.677,53.835,34.63,33.185,47.614,61.565,54.392,45.24,54.705,75.377,86.623,72.242,60.456,68.455,87.201,91.849,70.114,49.854,45.482,53.979,61.669,49.372,37.379,50.69,54.95,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,10 -1.2284,1.5171,1.4688,1.2512,1.0772,1.2312,1.5247,1.466,1.255,1.0725,1.2425,1.5391,1.4815,1.2558,1.081,1.2468,1.5312,1.4611,1.2529,1.0762,1.2225,1.5366,1.4711,1.2625,1.0803,1.2127,1.5424,1.4796,1.2281,1.0568,1.0037,139.94,328.55,259.49,238.4,285.24,321.3,321.06,231.74,233.23,259.55,273.0,215.58,131.51,55.985,53.064,84.466,83.449,56.861,28.778,31.782,53.082,61.917,47.665,29.417,32.949,50.373,59.15,46.244,30.579,35.172,54.261,64.211,51.133,36.677,40.543,57.01,64.926,51.505,37.67,41.417,55.674,61.882,47.945,33.109,36.57,51.418,59.253,46.155,31.237,34.47,49.52,58.033,45.395,30.744,34.905,51.628,60.556,47.925,34.003,38.264,54.611,62.946,49.838,36.114,40.57,56.516,64.06,49.94,35.291,38.744,53.311,60.607,46.865,31.951,34.992,49.721,58.03,45.288,31.384,37.519,55.271,63.718,49.989,36.22,41.235,57.188,63.804,49.549,35.64,40.22,54.771,61.287,47.421,33.15,37.267,51.626,59.028,45.51,30.658,34.658,49.781,58.074,44.889,30.313,35.559,52.105,60.737,47.223,33.142,38.263,54.994,63.035,49.274,35.755,41.066,57.394,64.191,49.675,35.286,39.388,53.794,59.901,45.61,30.382,34.03,49.317,57.38,44.309,30.058,36.348,54.643,62.727,48.756,34.321,39.966,57.343,64.209,49.554,34.641,39.083,54.413,61.128,47.116,32.311,36.914,52.266,59.467,45.549,29.959,34.034,49.605,57.898,44.54,29.425,34.752,51.904,60.529,46.939,32.264,37.405,54.397,62.757,49.008,35.113,40.412,56.775,63.718,48.925,34.116,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,10 diff --git a/aeon/datasets/data/PLAID/PLAID_TRAIN.ts b/aeon/datasets/data/PLAID/PLAID_TRAIN.ts deleted file mode 100644 index 857af81dca..0000000000 --- a/aeon/datasets/data/PLAID/PLAID_TRAIN.ts +++ /dev/null @@ -1,568 +0,0 @@ -## PLAID dataset -# -#PLAID stands for the Plug Load Appliance Identification Dataset. The data are intended for load identification research. The first version of PLAID is named PLAID1, collected in summer 2013. A second version of PLAID was collected in winter 2014 and released under the name PLAID2. -#This dataset comes from PLAID1. It includes current and voltage measurements sampled at 30 kHz from 11 different appliance types present in more than 56 households in Pittsburgh, Pennsylvania, USA. Data collection took place during the summer of 2013. Each appliance type is represented by dozens of different instances of varying makes/models. -#For each appliance, three to six measurements were collected for each state transition. These measurements were then post-processed to extract a few-second-long window containing both the steady-state operation and the startup transient )when available). -#The classes correspond to 11 different appliance types: % air -#conditioner (class 0), compact fluorescent lamp, fan, fridge, -#hairdryer , heater, incandescent light bulb, laptop, microwave, -#vacuum,washing machine (class 10)% -# -#Train size: 537 -#Test size: 537 -#Missing value: No -#Number of classes: 11 -#Time series length: Vary -#Data created by Gao, Jingkun, et al. (see [1], [2], [3]). Data edited by Patrick Schafer and Ulf Leser (see [4]). -# -#[1] Gao, Jingkun, et al. "PLAID: a public dataset of high-resolution electrical appliance measurements for load identification research: demo abstract." proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings. ACM, 2014. -# -#[2] https://github.com/jingkungao/PLAID -# -#[3] http://www.plaidplug.com/ -# -#[4] Schäfer, Patrick, and Ulf Leser. "Fast and accurate time series classification with weasel." Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 2017. -@problemName PLAID -@timeStamps false -@missing false -@univariate true -@equalLength false -@classLabel true 0 1 2 3 4 5 6 7 8 9 10 -@data 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diff --git a/aeon/transformations/collection/unequal_length/tests/test_resize.py b/aeon/transformations/collection/unequal_length/tests/test_resize.py index 4dc5d107da..dc92adefe9 100644 --- a/aeon/transformations/collection/unequal_length/tests/test_resize.py +++ b/aeon/transformations/collection/unequal_length/tests/test_resize.py @@ -6,7 +6,7 @@ from aeon.datasets import ( load_basic_motions, load_japanese_vowels, - load_plaid, + load_pickup_gesture_wiimoteZ, load_unit_test, ) from aeon.testing.data_generation import ( @@ -21,7 +21,13 @@ @pytest.mark.parametrize( - "loader", [load_japanese_vowels, load_plaid, load_unit_test, load_basic_motions] + "loader", + [ + load_japanese_vowels, + load_pickup_gesture_wiimoteZ, + load_unit_test, + load_basic_motions, + ], ) def test_resizer_transformer(loader): """Test resizing to the fixed series length on provided datasets.""" diff --git a/aeon/transformations/collection/unequal_length/tests/test_truncate.py b/aeon/transformations/collection/unequal_length/tests/test_truncate.py index 28795e8cbf..ca201fc459 100644 --- a/aeon/transformations/collection/unequal_length/tests/test_truncate.py +++ b/aeon/transformations/collection/unequal_length/tests/test_truncate.py @@ -6,7 +6,7 @@ from aeon.datasets import ( load_basic_motions, load_japanese_vowels, - load_plaid, + load_pickup_gesture_wiimoteZ, load_unit_test, ) from aeon.testing.data_generation import ( @@ -22,7 +22,13 @@ @pytest.mark.parametrize( - "loader", [load_japanese_vowels, load_plaid, load_unit_test, load_basic_motions] + "loader", + [ + load_japanese_vowels, + load_pickup_gesture_wiimoteZ, + load_unit_test, + load_basic_motions, + ], ) def test_truncation_transformer(loader): """Test truncation to the fixed series length on provided datasets.""" From de241e0b97e1340d985716763660e4487903a816 Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Sat, 26 Jul 2025 11:05:29 +0100 Subject: [PATCH 191/216] [DOC] Classification notebook links (#2942) * fix links * fix links --------- Co-authored-by: Antoine Guillaume --- examples/classification/classification.ipynb | 1770 +++++++++--------- 1 file changed, 885 insertions(+), 885 deletions(-) diff --git a/examples/classification/classification.ipynb b/examples/classification/classification.ipynb index f1760686f1..51da84f4fb 100644 --- a/examples/classification/classification.ipynb +++ b/examples/classification/classification.ipynb @@ -1,931 +1,931 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "collapsed": false, - "id": "_pBlXBeTh5IG" - }, - "source": [ - "# Time Series Classification\n", - "\n", - "Time Series Classification (TSC) involves training a model from a collection\n", - " of time series (real valued, ordered, data) in order to predict a discrete target\n", - " variable. For example, we might want to build a model that can predict whether a patient\n", - " is sick based on their ECG reading, or a persons type of movement based on the trace\n", - " of the position of their hand. This notebook gives a quick guide to TSC to get you\n", - " started using aeon time series classifiers. If you can use scikit-learn, it should\n", - " be easy, because the basic usage is identical.\n", - "\n", - "\"time" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false, - "id": "weha73tPh5IH" - }, - "source": [ - "## Classification Notebooks\n", - "\n", - "This notebook gives an overview of TSC. More specific notebooks on TSC are based on\n", - "the type of representation or transformation they use:\n", - "\n", - "- [Convolution based](convolution_based.ipynb)\n", - "- [Deep learning](deep_learning.ipynb)\n", - "- [Dictionary based](dictionary_based.ipynb)\n", - "- [Distance based](distance_based.ipynb)\n", - "- [Feature based](feature_based.ipynb)\n", - "- [Interval based](interval_based.ipynb)\n", - "- [Shapelet based](shapelet_based.ipynb)\n", - "- [Hybrid](hybrid.ipynb)\n", - "- [Early classification](early_classification.ipynb)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false, - "id": "EyjESzTQh5II" - }, - "source": [ - "## Data Storage and Problem Types\n", - "\n", - "Time series can be univariate (each observation is a single value) or multivariate\n", - "(each observation is a vector). For example, an ECG reading from a single\n", - "sensor is a univariate series, but a motion trace of from a smart watch would be\n", - "multivariate, with at least three dimensions (x,y,z coordinates). The image above is\n", - " a univariate problem: each series has its own label. The dimension of the time\n", - " series instance is also often called the channel. We recommend storing time series\n", - " in 3D numpy array of shape `(n_cases, n_channels, n_timepoints)` and,\n", - " where possible, our single problem loaders will return a\n", - " 3D numpy. Unequal length classification problems are stored in a list of 2D numpy\n", - " arrays. More details on data storage can be found in the [data storage](../datasets/datasets.ipynb) notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "bjW-qRxOh5II", - "outputId": "a17f6f06-04b2-4fed-877e-92ef9680cdef" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ArrowHead series of type and shape (36, 1, 251)\n", - "Motions type of shape (40,)\n" - ] - } - ], - "source": [ - "# Plotting and data loading imports used in this notebook\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from aeon.datasets import load_arrow_head, load_basic_motions\n", - "\n", - "arrow, arrow_labels = load_arrow_head(split=\"train\")\n", - "motions, motions_labels = load_basic_motions(split=\"train\")\n", - "print(f\"ArrowHead series of type {type(arrow)} and shape {arrow.shape}\")\n", - "print(f\"Motions type {type(motions)} of shape {motions_labels.shape}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false, - "id": "pPrsdjsOh5IJ" - }, - "source": [ - "We use 3D numpy even if the data is univariate: even though classifiers\n", - "can work using a 2D array of shape `(n_cases, n_timepoints)`, this 2D shape can get\n", - "confused with single multivariate time series, which are of shape `(n_channels, n_timepoints)`.\n", - "Hence, to differentiate both cases, we enforce the 3D format `(n_cases, n_channels,\n", - "n_timepoints)` to avoid any confusion.\n", - "\n", - "If your series are unequal length, have missing values or are\n", - " sampled at irregular time intervals, you should read the note book\n", - " on [data preprocessing](../transformations/preprocessing.ipynb).\n", - "\n", - "The [TSC dataset archive](https://timeseriesclassification.com/) contains a\n", - "large number of example TSC problems that have been used thousands of times in the\n", - "literature to assess TSC algorithms. These datasets have certain characteristics that\n", - "influence what data structure we use to store them in memory.\n", - "\n", - "Most datasets in the archive contain time series all the same length. For example,\n", - "the [ArrowHead dataset](https://timeseriesclassification.com/description.php?Dataset=ArrowHead) we have just loaded consists of outlines of the images of\n", - "arrow heads. The classification of projectile points is an important topic in anthropology.\n", - "\n", - "\"arrow\n", - "\n", - "The shapes of the projectile points are converted into a sequence using the\n", - "angle-based method as described in this [blog post](https://izbicki.me/blog/converting-images-into-time-series-for-data-mining.html) about converting images into time series for data mining.\n", - "\n", - "\"from\n", - "\n", - "Each instance consists of a single time series (i.e. the problem is univariate) of\n", - "equal length and a class label based on shape distinctions such as the presence and\n", - "location of a notch in the arrow. The data set consists of 210 instances, by default split into 36 train and 175 test instances.\n", - "\n", - "The [BasicMotions dataset](https://timeseriesclassification.com/description.php?Dataset=BasicMotions) is an example of a multivariate TSC problem. It was generated\n", - " as part of a project where four students performed four activities whilst wearing a\n", - " smartwatch. The watch collects 3D accelerometer and 3D gyroscope data. Each instance\n", - " involved a subject performing one of four tasks (walking, resting, running and\n", - " badminton) for ten seconds. Time series in this data set have six dimensions or\n", - " channels." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 469 - }, - "id": "9T5zoVT9h5IJ", - "outputId": "2aa3e84a-9fdd-4cd7-fcff-4f6f8172c5ce" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.title(\n", - " f\"First and second dimensions of the first instance in BasicMotions data, \"\n", - " f\"(student {motions_labels[0]})\"\n", - ")\n", - "plt.plot(motions[0][0])\n", - "plt.plot(motions[0][1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 469 - }, - "id": "TtIuima2h5IK", - "outputId": "17310dc6-8ba5-45bb-8e2b-a07f80402bef" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.title(f\"First instance in ArrowHead data (class {arrow_labels[0]})\")\n", - "plt.plot(arrow[0, 0])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dEZoDQSTh5IK" - }, - "source": [ - "It is possible to use a standard `sklearn` classifier for univariate, equal length\n", - "classification problems, but it is unlikely to perform as well as bespoke time series\n", - " classifiers, since `sklearn` classifiers ignore the sequence information in the variables.\n", - "\n", - "To apply `sklearn` classifiers directly, the data needs to be reshaped into a 2D\n", - "numpy array. We also offer the ability to load univariate TSC problems directly in 2D\n", - " arrays although we recommend using 3D numpy of shape `(n_channels, 1, n_timepoints)\n", - " ` for univariate collections." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "qG96PKaCh5IK", - "outputId": "07ae1abe-a9d2-4e19-f515-9ca20e017177" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.72" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import accuracy_score\n", - "\n", - "rand_forest = RandomForestClassifier(n_estimators=100)\n", - "arrow2d = arrow.squeeze()\n", - "arrow_test, arrow_test_labels = load_arrow_head(split=\"test\", return_type=\"numpy2d\")\n", - "rand_forest.fit(arrow2d, arrow_labels)\n", - "y_pred = rand_forest.predict(arrow_test)\n", - "accuracy_score(arrow_test_labels, y_pred)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false, - "id": "r2T8AclIh5IK" - }, - "source": [ - "## Time Series Classifiers in aeon\n", - "\n", - "`aeon` contains the state of the art in time series classifiers in the package\n", - "`classification`. These are grouped based on the data representation used to find\n", - "discriminatory features. We provide a separate notebook for each of type:\n", - "[convolution based](convolution_based.ipynb), [deep learning](deep_learning.ipynb), [distance based](distance_based.ipynb), [dictionary based](dictionary_based.ipynb),\n", - "[feature_based](feature_based.ipynb), [hybrid](hybrid.ipynb), [interval based](interval_based.ipynb), and [shapelet based](shapelet_based.ipynb). We also\n", - "provide some\n", - "standard classifiers not available in scikit learn in the sklearn package.\n", - "We show the simplest use cases for classifiers and demonstrate how to build bespoke\n", - "pipelines for time series classification. An accurate and relatively\n", - "fast classifier is the [ROCKET](https://link.springer.com/article/10.1007/s10618-020-00701-z) classifier. ROCKET is a convolution based algorithm\n", - "described in detail in the [convolution based](convolution_based.ipynb) notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-16T19:16:46.486243Z", - "start_time": "2024-11-16T19:15:42.973051Z" - }, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "2xIrRErYh5IL", - "outputId": "372654b5-3fae-42e8-a315-da9e33ad8e38" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.76" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from aeon.classification.convolution_based import RocketClassifier\n", - "\n", - "rocket = RocketClassifier(n_kernels=2000)\n", - "rocket.fit(arrow, arrow_labels)\n", - "y_pred = rocket.predict(arrow_test)\n", - "\n", - "accuracy_score(arrow_test_labels, y_pred)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false, - "id": "t9Ngfjfzh5IL" - }, - "source": [ - "A slower but generally more accurate classifier for time series classification is\n", - "version 2 of the [HIVE-COTE](https://link.springer.com/article/10.1007/s10994-021-06057-9) algorithm.\n", - "(HC2) is described in the [hybrid notebook](hybrid.ipynb) notebook. HC2 is particularly\n", - "slow\n", - "on small problems like these examples. However, it can be\n", - "configured with an approximate maximum run time as follows (it may take a bit longer\n", - "than 12 seconds to run this cell, very short times are approximate since there is a\n", - "minimum amount of work the classifier needs to do):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "u0rqqET8h5IL", - "outputId": "b1347f40-c82b-4ecf-ec72-500b1f7f8a12" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.8685714285714285" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from aeon.classification.hybrid import HIVECOTEV2\n", - "\n", - "hc2 = HIVECOTEV2(time_limit_in_minutes=0.2)\n", - "hc2.fit(arrow, arrow_labels)\n", - "y_pred = hc2.predict(arrow_test)\n", - "\n", - "accuracy_score(arrow_test_labels, y_pred)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gTQRU2rkuPvw" - }, - "source": [ - "The LITETime Classifier is an efficient deep learning-based model for time series classification. It is designed to handle both univariate and multivariate time series data effectively, offering lightweight architecture and competitive performance. For simplicity, this notebook uses 10 epochs to demonstrate the classifier's functionality. To observe the full performance of deep learning models in aeon, it’s recommended to use the library's default epochs. The reduced epochs here simplify the demonstration and reduce runtime. Deep learning approaches for time series classification, are further described in the [deep learning notebook](./deep_learning.ipynb).\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "-nnwMXqtSzzc", - "outputId": "5ca88c72-3d6d-4d0b-90e7-b76da94aa62f" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 122ms/step\n", - "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 126ms/step\n", - "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 132ms/step\n", - "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 122ms/step\n", - "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 119ms/step\n" - ] - }, - { - "data": { - "text/plain": [ - "0.3942857142857143" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from aeon.classification.deep_learning import LITETimeClassifier\n", - "\n", - "lite_time = LITETimeClassifier(n_epochs=10, batch_size=32, random_state=42)\n", - "lite_time.fit(arrow, arrow_labels)\n", - "y_pred = lite_time.predict(arrow_test)\n", - "\n", - "accuracy_score(arrow_test_labels, y_pred)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false, - "id": "3y4vwmA1h5IL" - }, - "source": [] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "_pBlXBeTh5IG" + }, + "source": [ + "# Time Series Classification\n", + "\n", + "Time Series Classification (TSC) involves training a model from a collection\n", + " of time series (real valued, ordered, data) in order to predict a discrete target\n", + " variable. For example, we might want to build a model that can predict whether a patient\n", + " is sick based on their ECG reading, or a persons type of movement based on the trace\n", + " of the position of their hand. This notebook gives a quick guide to TSC to get you\n", + " started using aeon time series classifiers. If you can use scikit-learn, it should\n", + " be easy, because the basic usage is identical.\n", + "\n", + "\"time" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "weha73tPh5IH" + }, + "source": [ + "## Classification Notebooks\n", + "\n", + "This notebook gives an overview of TSC. More specific notebooks on TSC are based on\n", + "the type of representation or transformation they use:\n", + "\n", + "- [Convolution based](convolution_based.ipynb)\n", + "- [Deep learning](deep_learning.ipynb)\n", + "- [Dictionary based](dictionary_based.ipynb)\n", + "- [Distance based](distance_based.ipynb)\n", + "- [Feature based](feature_based.ipynb)\n", + "- [Interval based](interval_based.ipynb)\n", + "- [Shapelet based](shapelet_based.ipynb)\n", + "- [Hybrid](hybrid.ipynb)\n", + "- [Early classification](early_classification.ipynb)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "EyjESzTQh5II" + }, + "source": [ + "## Data Storage and Problem Types\n", + "\n", + "Time series can be univariate (each observation is a single value) or multivariate\n", + "(each observation is a vector). For example, an ECG reading from a single\n", + "sensor is a univariate series, but a motion trace of from a smart watch would be\n", + "multivariate, with at least three dimensions (x,y,z coordinates). The image above is\n", + " a univariate problem: each series has its own label. The dimension of the time\n", + " series instance is also often called the channel. We recommend storing time series\n", + " in 3D numpy array of shape `(n_cases, n_channels, n_timepoints)` and,\n", + " where possible, our single problem loaders will return a\n", + " 3D numpy. Unequal length classification problems are stored in a list of 2D numpy\n", + " arrays. More details on data storage can be found in the [data storage](../datasets/datasets.ipynb) notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bjW-qRxOh5II", + "outputId": "a17f6f06-04b2-4fed-877e-92ef9680cdef" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ArrowHead series of type and shape (36, 1, 251)\n", + "Motions type of shape (40,)\n" + ] + } + ], + "source": [ + "# Plotting and data loading imports used in this notebook\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from aeon.datasets import load_arrow_head, load_basic_motions\n", + "\n", + "arrow, arrow_labels = load_arrow_head(split=\"train\")\n", + "motions, motions_labels = load_basic_motions(split=\"train\")\n", + "print(f\"ArrowHead series of type {type(arrow)} and shape {arrow.shape}\")\n", + "print(f\"Motions type {type(motions)} of shape {motions_labels.shape}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "pPrsdjsOh5IJ" + }, + "source": [ + "We use 3D numpy even if the data is univariate: even though classifiers\n", + "can work using a 2D array of shape `(n_cases, n_timepoints)`, this 2D shape can get\n", + "confused with single multivariate time series, which are of shape `(n_channels, n_timepoints)`.\n", + "Hence, to differentiate both cases, we enforce the 3D format `(n_cases, n_channels,\n", + "n_timepoints)` to avoid any confusion.\n", + "\n", + "If your series are unequal length, have missing values or are\n", + " sampled at irregular time intervals, you should read the note book\n", + " on [data preprocessing](../transformations/preprocessing.ipynb).\n", + "\n", + "The [TSC dataset archive](https://timeseriesclassification.com/) contains a\n", + "large number of example TSC problems that have been used thousands of times in the\n", + "literature to assess TSC algorithms. These datasets have certain characteristics that\n", + "influence what data structure we use to store them in memory.\n", + "\n", + "Most datasets in the archive contain time series all the same length. For example,\n", + "the [ArrowHead dataset](https://timeseriesclassification.com/description.php?Dataset=ArrowHead) we have just loaded consists of outlines of the images of\n", + "arrow heads. The classification of projectile points is an important topic in anthropology.\n", + "\n", + "\"arrow\n", + "\n", + "The shapes of the projectile points are converted into a sequence using the\n", + "angle-based method as described in this [blog post](https://izbicki.me/blog/converting-images-into-time-series-for-data-mining.html) about converting images into time series for data mining.\n", + "\n", + "\"from\n", + "\n", + "Each instance consists of a single time series (i.e. the problem is univariate) of\n", + "equal length and a class label based on shape distinctions such as the presence and\n", + "location of a notch in the arrow. The data set consists of 210 instances, by default split into 36 train and 175 test instances.\n", + "\n", + "The [BasicMotions dataset](https://timeseriesclassification.com/description.php?Dataset=BasicMotions) is an example of a multivariate TSC problem. It was generated\n", + " as part of a project where four students performed four activities whilst wearing a\n", + " smartwatch. The watch collects 3D accelerometer and 3D gyroscope data. Each instance\n", + " involved a subject performing one of four tasks (walking, resting, running and\n", + " badminton) for ten seconds. Time series in this data set have six dimensions or\n", + " channels." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 469 }, + "id": "9T5zoVT9h5IJ", + "outputId": "2aa3e84a-9fdd-4cd7-fcff-4f6f8172c5ce" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "collapsed": false, - "id": "OaBVEJmnh5IM" - }, - "source": [ - "## Multivariate Classification\n", - "To use ``sklearn`` classifiers directly on multivariate data, one option is to flatten\n", - "the data so that the 3D array `(n_cases, n_channels, n_timepoints)` becomes a 2D array\n", - "of shape `(n_cases, n_channels*n_timepoints)`." + "data": { + "text/plain": [ + "[]" ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "1mfxhLaZh5IM", - "outputId": "c0a7278f-7feb-45dc-a337-e0da2bcbbf60" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.925" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "motions_test, motions_test_labels = load_basic_motions(split=\"test\")\n", - "motions2d = motions.reshape(motions.shape[0], motions.shape[1] * motions.shape[2])\n", - "motions2d_test = motions_test.reshape(\n", - " motions_test.shape[0], motions_test.shape[1] * motions_test.shape[2]\n", - ")\n", - "rand_forest.fit(motions2d, motions_labels)\n", - "y_pred = rand_forest.predict(motions2d_test)\n", - "accuracy_score(motions_test_labels, y_pred)" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\n", + " f\"First and second dimensions of the first instance in BasicMotions data, \"\n", + " f\"(student {motions_labels[0]})\"\n", + ")\n", + "plt.plot(motions[0][0])\n", + "plt.plot(motions[0][1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 469 }, + "id": "TtIuima2h5IK", + "outputId": "17310dc6-8ba5-45bb-8e2b-a07f80402bef" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "collapsed": false, - "id": "Hc2DrT2Fh5IM" - }, - "source": [ - "However, many ``aeon`` classifiers, including ROCKET and HC2, are configured to\n", - "work with multivariate input. This works exactly like univariate classification. For example:" + "data": { + "text/plain": [ + "[]" ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "yXZW8cAch5IM", - "outputId": "f3b7b3b7-8204-4e30-cca8-1f07b4d53d90" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rocket.fit(motions, motions_labels)\n", - "y_pred = rocket.predict(motions_test)\n", - "accuracy_score(motions_test_labels, y_pred)" + "data": { + "image/png": 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kGAPD6+d6S5bBnhe6Ikqlss1ZIq+++ioWLlyIH374Ab///jseeOABrFy5Ert3726V7FwJFxeXyx4TEhKCtLQ0/Pbbb/j111/x66+/4qOPPsL8+fPx8ccfd3jfyspKjBs3Dr6+vnjuueeQkJAAd3d3HDx4EH/9619bNUu2F8+lH+SXo9VqERIS0mZSAKBVwtZZ3Y2vuroaP/74IxobG9v8Av3iiy/wj3/8w+SDuq1ejM7cdjmjR48GAGzbtg3nzp3D0KFDjQ2lb775Jmpra3Ho0KEuL452McN/19tuu63dnhRDz81nn32GhQsXYubMmXjssccQEhICFxcXrFy50qQB21q6+p5ty5W+psDAwE4lxJ2h0Whw3XXXoby8HH/961+RmJgILy8v5OfnY+HChSav56GHHsL06dOxbt06/Pbbb/jb3/6GlStXYvPmzUhKSjKu5bN79278+OOP+O2333DHHXfg1Vdfxe7du1sllgbh4eEAgIKCgla3FRQUICAgoFVVxvD6O+oZpO5j8kIWM3DgQAwcOBBPPfUUdu3ahWuuuQbvvvsu/v73vwOw7i8ShUKB6dOnY/r06dBqtbjvvvvwn//8B3/729/Qs2fPdmPZunUrysrKsHbtWpOZE5mZmd2OJSEhAXv27EFLS0u7TbcJCQnYuHEjrrnmmiv6ojeXtWvXorGxEe+8806rD+P09HQ89dRT2LlzpzGx6KqYmBjjY1177bWtHt9wO6CrHkVHR2P79u04d+4cxowZAwAYO3Ysli1bhjVr1kCj0bQ506WzgoOD4ePjA41Gc9nq47fffov4+HisXbvW5H309NNPt3qNmzZtQm1trcmXZHp6eqdiiomJwZkzZ1pdf+n9u/Kebe9939nX1J7ExMRWzxccHAxfX18cO3asU49hcPToUZw+fRoff/wx5s+fb7y+vWHghIQEPPLII3jkkUdw5swZDBkyBK+++io+++wz4zFXX301rr76avzjH//AF198gXnz5uGrr75qd9p/jx49EBwcjP3797e6rb0G5szMTAQFBXX7hwZ1jMNGZHbV1dVQq9Um1w0cOBByudyk7Orl5WWVFVrLyspM/i2Xy42/mg3xGNamuTQeQ6Xi4spEc3Mz3n777W7HM2vWLJSWluLf//53q9sMz3PzzTdDo9Hg+eefb3WMWq22+sq2n332GeLj43HPPffgpptuMrk8+uij8Pb2brdK1BnDhg1DSEgI3n33XZP3yK+//oqTJ09i2rRpJsePGTMGmzdvxt69e43Jy5AhQ+Dj44N//vOf8PDwaDUU1BUuLi6YNWsWvvvuuza/bC9eVbWt98iePXuQmppqcp/rr78earUa77zzjvE6jUaDt956q1MxXX/99di9ezf27t1rEsel570r71kvL682h4E6+5raM3LkSBw7dszkv6VcLsfMmTPx448/tpkEtFf9aysWIYRxqQOD+vp64xCOQUJCAnx8fIxxVFRUtHoeQ+JxuaGjWbNm4aeffkJubq7xuk2bNuH06dPGpRYuduDAAYwcObLDx6TuY+WFzG7z5s1YunQpZs+ejd69e0OtVuPTTz81fiEYJCcnY+PGjfjXv/6FiIgIxMXFYcSIEWaP584770R5eTmuvfZaREZGIjs7G2+99RaGDBlinA49ZMgQuLi44MUXX0RVVRWUSiWuvfZajBo1Cv7+/liwYAEeeOAByGQyfPrpp10eBrrY/Pnz8cknn2DZsmXGL9+6ujps3LgR9913H2bMmIFx48bh7rvvxsqVK5GWlobJkyfDzc0NZ86cwZo1a/DGG2/gpptuMtcp6tD58+exZcsWPPDAA23erlQqkZKSgjVr1uDNN9/s1hRuNzc3vPjii7j99tsxbtw4zJ071zhVOjY2Fg8//LDJ8WPGjMHnn38OmUxmrPa4uLhg1KhR+O233zB+/Ph2p9121j//+U9s2bIFI0aMwOLFi9GvXz+Ul5fj4MGD2LhxI8rLywEAN9xwA9auXYs///nPmDZtGjIzM/Huu++iX79+qK2tNT7e9OnTcc011+CJJ55AVlYW+vXrh7Vr13a6h+Txxx/Hp59+iilTpuDBBx80TpWOiYkxWfemK+/Z5ORkfP3111i2bBmuuuoqeHt7Y/r06Z1+Te2ZMWMGnn/+efzxxx+YPHmy8foXXngBv//+O8aNG4e77roLffv2RUFBAdasWYMdO3YYG2ovlpiYiISEBDz66KPIz8+Hr68vvvvuu1bDUqdPn8bEiRNx8803o1+/fnB1dcX333+PoqIizJkzB4Cuj+ftt9/Gn//8ZyQkJKCmpgbvv/8+fH19cf3113f4mp588kmsWbMGEyZMwIMPPoja2lq8/PLLGDhwoLEPyqC4uBhHjhzBkiVLLnuuqJusPb2JbJNhqnR7Uwjbm6rs5eXV6thz586JO+64QyQkJAh3d3cREBAgJkyYIDZu3Ghy3KlTp8TYsWOFh4eHANDhtOmuPP+l04S//fZbMXnyZBESEiIUCoWIjo4Wd999tygoKDC53/vvvy/i4+OFi4uLyVTNnTt3iquvvlp4eHiIiIgI8fjjj4vffvut1XTOcePGif79+7eKZ8GCBa2mo9bX14v/+7//E3FxccLNzU2EhYWJm266SZw9e9bkuPfee08kJycLDw8P4ePjIwYOHCgef/xxcf78+XbPVVvnQAjdVOklS5a0OjYmJqbDc//qq68KAGLTpk3tHrN69WoBQPzwww/Gx5w2bVqr4wzTYNubRv/111+LpKQkoVQqRUBAgJg3b57Iy8trddzx48cFANG3b1+T6//+978LAOJvf/tbm6+zrZg6iquoqEgsWbJEREVFGf87TZw4Ubz33nvGY7RarXjhhRdETEyMUCqVIikpSfz0009t/ncvKysTf/nLX4Svr69QqVTiL3/5i3FpgctNlRZCiCNHjohx48YJd3d30aNHD/H888+L//73v62mSnf2PVtbWytuvfVW4efnJwAY4+3Ka2rPoEGDxKJFi1pdn52dLebPny+Cg4OFUqkU8fHxYsmSJcap2W1NlT5x4oSYNGmS8Pb2FkFBQWLx4sXGaf6G81ZaWiqWLFkiEhMThZeXl1CpVGLEiBHim2++MT7OwYMHxdy5c0V0dLRQKpUiJCRE3HDDDWL//v2dek3Hjh0TkydPFp6ensLPz0/MmzdPFBYWtjrunXfeEZ6enqK6urpTj0tdJxPiCn5CEhERteHTTz/FkiVLkJOT02ZFxZElJSVh/PjxxsX/yPzY80JERGY3b948REdHY9WqVVKHYlXr16/HmTNnsHz5cqlDcWisvBAREZFdYeWFiIiI7AqTFyIiIrIrTF6IiIjIrjB5ISIiIrvicIvUabVanD9/Hj4+PtwQi4iIyE4IIVBTU4OIiIg298y7mMMlL+fPn0dUVJTUYRAREVE35ObmXnYDX4dLXnx8fADoXryvr6/E0RAREVFnVFdXIyoqyvg93hGHS14MQ0W+vr5MXoiIiOxMZ1o+2LBLREREdoXJCxEREdkVJi9ERERkV5i8EBERkV1h8kJERER2hckLERER2RUmL0RERGRXmLwQERGRXWHyQkRERHaFyQsRERHZFSYvREREZFeYvBAREZFdYfJCdkurFfhmXy52nCmVOhQiIrIiJi9kl7Ragad+OIbHvzuC+R/uYQJD5OQaWzR4e2sGcsvrpQ6FrMCiycu2bdswffp0REREQCaTYd26dR0ev3XrVshkslaXwsJCS4ZJdugfv5zEF3tyAABaASz98iByyvihReSs/rsjEy+tT8eT3x+VOhSyAosmL3V1dRg8eDBWrVrVpfulp6ejoKDAeAkJCbFQhGSPzhTV4L87MgEAL/x5IAZH+aGyvgWLP9mPuia1xNERkRR+P1EEANh1tgzldc0SR0OW5mrJB586dSqmTp3a5fuFhITAz8+vU8c2NTWhqanJ+O/q6uouPx/Zl3f+OAsAmNI/DLeOiMa1iSGY/u8dSC+qwaNrDuPteUMhk8kkjpKIrKW4uhGHcysBABqtwO/HCzFneLS0QZFF2WTPy5AhQxAeHo7rrrsOO3fu7PDYlStXQqVSGS9RUVFWipLMpVmtxfYzJdBoxWWPzauox//SzgMA7puQAAAIU7nj3duGws1Fhl+PFWLVlgyLxktEtmXTqWKTf/9yjK0Gjs6mkpfw8HC8++67+O677/Ddd98hKioK48ePx8GDB9u9z/Lly1FVVWW85ObmWjFiMocXfjmJv/x3L97YePqyx76/7RzUWoHRPYMwKNLPeH1yTACenzEAAPDqhtPYdLLIUuESkY3ZqB8ymjU0EgCwK6MUp4tqUNPYImVYZEE2lbz06dMHd999N5KTkzFq1Ch8+OGHGDVqFF577bV276NUKuHr62tyIftRUdeMr/bpGm9X78oy6VlpbNEgr6IeRdWNAIDS2iZ8tU+XnN43PqHVY80ZHo3bro6GEMBDX6Uho7jWCq+AiKRU36zGjgzdbMPFY+OQGOYDtVZg8mvbMOzvG3H8fJXEEZIl2FTy0pbhw4cjI4PDAI7qi705aGzRAgCqG9X49kAeAGBfVjmuXrkJo1/cghEvbMKSLw7ig+2ZaFJrMThShZEJgW0+3oob+mN4bABqmtS469P9qOYvLyKHJYTA/31/DE1qLaIDPNEn1Af3jk+Ar7sr3FxkaFJr8cIvJyHE5Yekyb7YfPKSlpaG8PBwqcMgC2hWa/FJahYAYHhcAADddMcv9uRgwYd7UVnfAjcXGWQy4OcjBXhX36h77/ie7TbkKlzlePu2oYhQueNcSR0e/iqNH1xEDqRFo0WTWoPM0jr8/eeT+P5QPlzkMrzw54GQyWSYMaQHjjyTgs2PjIfCRY6dGWXYerpE6rDJzCw626i2ttakapKZmYm0tDQEBAQgOjoay5cvR35+Pj755BMAwOuvv464uDj0798fjY2N+OCDD7B582b8/vvvlgyTJPLrsQIUVTch2EeJ9/8yDGNf3oKc8nrjOg2jewbh/fnD8MfpYtz7+UEIAfQM8cbkfqEdPm6QtxL/+cswzHp3FzadKsaJgmr0j1BZ4yURkRlptQKfpGahb7gvRsQHYtWWDLyx8QyaNVqT4579U3+M7hVkcl1UgCcWjIrB+9sz8c9fTmFcr2DI5ZyF6Cgsmrzs378fEyZMMP572bJlAIAFCxZg9erVKCgoQE5OjvH25uZmPPLII8jPz4enpycGDRqEjRs3mjwGOQYhhHGtlvlXx0Dl6YYX/jwQn+7OglYA/cJ98cTURLi7uWDKgHD888aBeOX303jy+sROfQANjFRhRFwAtp8pxeHcKiYvRHbol2MFeObHE3CRy3DT0Eh8vf/ChAw3FxmGRvtj1tBI3HxV27NMl07oha/25SK9qAYbThYhpX+YtUInC5MJB6upV1dXQ6VSoaqqis27NiijuBb1zWo0qbWY/W4qlK5ypC6fiAAvhdmf65Xf0vHvLRm4ZVgUXrxpkNkfn4gs6+Z3U7E3q9zkunvGJeC+CQlQusqhdHW57GO8tP4U3t56Fskx/vju3lGWCpXMoCvf3xatvBBdTKMVuOU/qSira0awjxIAcOPQHhZJXABgUKSu2nI4r9Iij09ElnPifDX2ZpXDVS7DzKQe+PZAHq4fGIbHU/p0afhn4ahYfLA9EweyK3AguxzJMQEWjJqsxeYbdslx5FXUo0y/bHdJjW5V5DuuibPY8w2J8gMAnC6qQX0ztw0gsief7s4CAKQMCMMrswcjdfm1WHXr0C73rYT4uuPPST0AAPd+dhCLP9mPfZdUc8j+MHkhqzGsuxKucsfw2ADcPTYevUJ9LPZ8Ib7uCFe5QyuAY/ncNoLIXlTVt2DdId1K2gtGxgIAwlUe3d72465x8VC4ylFc04QNJ4pw2wd7kHq2zFzhkgSYvJDVGJKX5Bh/fHPPSCy/vq/Fn9M4dKTf94SIbN+aA7loaNEgMcwHV8X6X/HjJQR7Y8fjE/DxHcMxvk8wmtRaLPp4HxMYO8bkhazmjD556RViuWrLpQbrh47Y90JkH7RagU93ZwMA5o+MNdsmqyG+7hjXOxjv3paMMb2CUN+swYKP9mI990GyS0xeyGoMlZeeId5We84h+v2PmLwQ2Yc/zpQgu6wePu6umJkUYfbHd3dzwfvzh+G6fqFoVmtx3+cHcKqQw8r2hskLWYUQwpi89Aq1XvIyQD9slFvegLLaJqs9LxF1nRAC/92uW/9pdnIUPBWWmRDr7uaCd+YNxeieQdAK4JejrL7YGyYvZBVF1U2obVLDRS5DbKCX1Z7X190NCcG65zuSzw3aiGzZmv152JFRCjcXGeaPjLHoc7m6yPGnIbrKzh/pxRZ9LjI/Ji9kFYaqS0yAJxSu1n3bGfte2LRLZLNyy+vx7I/HAQCPTO6D2CDL/8gZ3zsYgO6HDSuz9oXJC1nFmeIaANbtdzEYbOh7YfJCZJOEEPjrd0dQ16zB8NgALB4Tb5XnDfF1R99wXwgBbDvDzRvtCZMXsgopmnUNLsw4quIO00Q26LuD+dh1tgzubnK8PHsQXKy4geL4Prrqy9Z0Ji/2hMkLWZwQAmn6qocUyUvfcB+4uchQXteMvIoGqz8/EbWvrLYJf//5BADgoUm9EWPFnjjgwtDRttMl0Gj548ZeMHkhi/v9RBGOn6+G0lWO0T2DLn8HM1O6uqBvuG6TL06ZJrIt3+zPQ2V9CxLDfLBotOW2C2nP0Bh/eLi5oKK+BZmldVZ/fuoeJi9kUS0aLV789RQAYPGYeIT4uksSB/teiGzTEf0PillDI+HmYv2vJDcXOXrrl284XVRj9een7mHyQha1Zn8ezpXWIdBLgbvHWacJry2Gvpfd57ghG5EtOapfwqB/D1/JYugTplv1+1Qhkxd7weSFLOqXowUAgLvGxsPH3U2yOCb0CYarXIaj+VVI5wcUkU2orL/Qh9Y/QiVZHL31G8Se5meD3WDyQhbTpNYYt56/NjFE0lgCvZWY1DcUAPDN/lxJYyEiHcNu7zGBnlB5SPfjJjFMV/VJ57CR3WDyQhaTllOJJrUWQd5KSWYZXermqyIBAN8fykezWitxNERkGDIa0EO6qgtwYdgoq6wODc0aSWOhzmHyQhaTek633fzV8QFm2xn2SoztFYwQHyXK65qx6WSR1OEQOb1j5/XJi4RDRgAQ5K1AgJcCQlxYk4psG5MXsphdZ3XJy6gE60+Pbourixw3DtVVX37S9+IQkXSO6SsvAyWuvMhkMvQJNTTtcodpe8DkhSyisUWDtJxKAMDIhEBpg7nI5P66vpdtp0vQouHQEZFUqhpakF1WDwDoHyHdTCMDw9ARp0vbByYvZBEHsivQrNEizNcdsYGeUodjNDjSD4FeCtQ0qrE/q0LqcIic0oYTRZi5aicAICrAA/5eCokj4nRpe8PkhSxi00ndFvOjegbaRL+LgYtchnH6vUw2n2LfC5G1ZRTX4u5P9yOztA7+nm54Znp/qUMCAOMq3PuzKpBRzATG1jF5IbMTQuC344UAgJT+YRJH09rERN3Q0eZTxRJHQuR83v3jLLQCGNMrCNv/ei0m6pcwkNqgHiqMSghEQ4sG93x2EHVNaqlDog4weSGzO36+GvmVDfBwc8HYXsFSh9PKmN5BcJXLcLakDlncy4TIavIq6rHuUD4A4JHJfeCtdJU4ogvkchnemJOEEB8lMopr8crv6VKHRB1g8kJmt/6YruoyrncwPBQuEkfTmq+7G4bF+gMAtqaz+kJkLe9tOwe1VmB0zyAM0W/ZYUuCfZR4bsYAAMDvx4sgBHeZtlVMXsjs1uuHjKYMsL0hI4Mx+orQTv10biKyrGP5Vfh8Tw4A4L4JCRJH076xvYPg5iJDfmUDcsrrpQ6H2sHkhcwqs7QOGcW1cHORYYLEWwJ0ZHRP3dozu8+WQc0p00QW1azW4tE1h6HRCkwbFG4zaz+1xVPhiqQoXWV2ZwZ/3NgqJi9kVnv0q+omRftLulfJ5QzooYKvuytqmtQ4ol8oi4gs4/3t53CqsAYBXgo89yfbmF3UEcPaVLvOlkocCbWHyQuZ1f5s3dopw2L8JY6kYy5ymfHX384z/IAishStVuDz3dkAgCev74tAb6XEEV3eNfrKbOrZMmi17HuxRUxeyKwO6JOXq2IDJI7k8q7ppU9e+OuKyGL2ZpXjfFUjfNxdccOgcKnD6ZQhUX7wcHNBWV0zTnPNF5vE5IXMpqSmCZn6qcdDo2278gJc6Hs5mF2J+mau6UBkCT+k6aZGTx0QBnc325t92BaFqxxXxel+gO1gZdYmMXkhszFUXXqHekPlabv9LgaxgZ4I9VWiWaPF8fPcjI3I3JrUGvx8RLcJ6swhPSSOpmvG9dbNSPz9OFfitkVMXshsDmSXAwCG2cGQEaDbSdawJDj3MyEyv63pJahuVCPUV4kR8bazQWtnGJZ62JddjuLqRomjoUsxeSGzsZdm3YsZNmNLL2Tlhcjc9mfpftBM7hcGF7nt7HHWGT38PDAkyg9CwLjdCdkOJi9kFnVNahzTTzkeFmMflRcASDQmL6y8EJlbRnEtgAs/EuzNtIG6BuOfjxZIHAldiskLmUXq2TK0aASiAjwQFeAhdTid1if0wrARlwInMq+zJboG/p4h3hJH0j2GoaO9meUoqWmSOBq6mEWTl23btmH69OmIiIiATCbDunXrLnufrVu3YujQoVAqlejZsydWr15tyRDJTLae1u0RNL53CGQy+ykPJ4R4wUUuQ02jGgVVHNcmMpfGFg1yK3TL6ycE22fyEhXgif4RvtAKYPc5rrZrSyyavNTV1WHw4MFYtWpVp47PzMzEtGnTMGHCBKSlpeGhhx7CnXfeid9++82SYdIVEkJga3oJgAsd+vZC6eqC+CAvABw6IjKnrLI6CAH4ursiyFshdTjd1j9CV501DIGRbbDofuRTp07F1KlTO338u+++i7i4OLz66qsAgL59+2LHjh147bXXkJKS0uZ9mpqa0NR0oZxXXc3GS2s7V1qHvIoGKFzkGNXTvmYUALrx+DPFtUgvqrHp/ZiI7Inhy75niLddVWMv1StE16+TUcLkxZbYVM9LamoqJk2aZHJdSkoKUlNT273PypUroVKpjJeoqChLh0mXMFRdhscFwFNh0XzYIti0S2R+Z4t1/S72OmRkYOjXOcvKi02xqeSlsLAQoaGhJteFhoaiuroaDQ0Nbd5n+fLlqKqqMl5yc3OtESpdZGu6vt+lj30NGRn0CeNaL0TmdlZfqUiw02ZdA0Pycq6kjjvQ2xD7+5l8CaVSCaXS9jf6clRF1Y3YmaFbPnti39DLHG2b+obrKi+ni2pwpqgGvULtc1onkS0xDhvZeeWlh58H3N3kaGzRIreiAXH6HjmSlk1VXsLCwlBUZLoUc1FREXx9feHhYT/Tb53J94fyoRW6hens9Y860t8Tk/qGQqMV+NsPxzhlmugKabUC50odo/Iil8sQH6R7DWzatR02lbyMHDkSmzZtMrluw4YNGDlypEQRUUeEEFizXzdMd1NypMTRXJmnp/eD0lWO3efK8f2hfKnDIbJr56sa0NiihcJFjih/+//haRg6YvJiOyyavNTW1iItLQ1paWkAdFOh09LSkJOTA0DXrzJ//nzj8ffccw/OnTuHxx9/HKdOncLbb7+Nb775Bg8//LAlw6RuSsutxNmSOri7yTHNTra6b09UgCeWTugJAHj82yP4Yk+OxBER2SetVuCtTRkAgPhgL7i62NRv5G5h8mJ7LPqu2r9/P5KSkpCUlAQAWLZsGZKSkrBixQoAQEFBgTGRAYC4uDj8/PPP2LBhAwYPHoxXX30VH3zwQbvTpEk6pwqr8di3RwAAUweEw8fd9neRvpy7xyXghkHhUGsFnvz+KL47kCd1SER255kfj+Pr/bmQy4CHJvWSOhyzMCYvnC5tMyzasDt+/PgO+wfaWj13/PjxOHTokAWjoiu1N7Mct/13D5rVWgT7KLFkQoLUIZmFwlWOt+YmQeXhhs/35GDTqSLMsvPhMCJryiiuwSep2ZDJgFdvHowpA+y7Imtw8XRpIYRdr1vjKOy/nkdW1aLR4snvj6JZrcXonkH49cEx6BniOLNzZDIZJvbVLVR3Tr8vCxF1zvpjut2Xx/UOxp+THCfxjw3UbSNS26RGUTX3OLIFTF6oSz7elYWM4loEeCmw6tahCPJ2vGnqcfqZBdll9dBqOfOIqLN+P6GbLZrSP0ziSMxL4SpHTKAnAPa92AomL9RpZbVNeH3jGQDAX6f0gcrT/vtc2hLp7wEXuQwNLRoU1XCzRqLOOF/ZgCN5VZDJgEl2uuZTRwzr1WQUczFLW8DkhTpt7cF81Dap0S/cF7OTHXcbBjcXOaIDdL+yMjl0RNQpvx/XDRkNi/FHsI/jVWTZtGtbmLxQpwghsOaAbk2XeVdHQy537IY1w4J7mWVMXogup6q+Bd/s183Oc7QhIwNOl7Ytdr89AFnHkbwqnC6qhdJVjhsGRUgdjsUZkxdWXog6dLqoBnes3oe8igZ4uLng+oGOMcPoUkxebAsrL9QphqpLSv8wqDwcs9flYsbkpZTJC1FHXvjlJPIqGhAd4Ilv7h6JCD/7X1G3LYbdsUtrm1FZ3yxxNMTkhS6rWa3F/9LOAwBmD3Oc6Y8dYfJCdHlarcCB7AoAwKpbh2JgpEriiCzHS+mKCJU7AFZfbAGTF7qsnWdLUd2oRrCPEqMSgqQOxyoMyUtOeT3UGq3E0RDZpnOltahpVMPdTW7cnd2RJXDoyGYweaHL+k2/8FRK/1C4OHijrkGYrzvc3eRQawXyKhqkDofIJh3MqQQADIr0c4g9jC6HfS+2w/HfbXRF1BqtceGpqQ6y1HdnyOUyxAZy6IioI4f0yUtStJ+kcVhLL/1q4pwuLT0mL9ShfVkVKK9rhp+nG4bHBUgdjlX1DtV9UKXlVkobCJGNOpSj63dJivKXOBLrMFRejuVXo0mtkTga58bkhTq0/lgBAOC6vqFwc4Ky8MVGJQQCAHZklEocCZHtqW1SI71It9qss1ReBkWqEOKjRGltEz7ckSV1OE7Nub6NqEsyS+uw5oBu4ampAx1z4amOjO6la05Oy61EdWOLxNEQ2Yaaxhbc/tFe3PHRPggB9PDzQKivu9RhWYW7mwuemJoIAHhr8xkUVXP7EKkweaE2Nau1ePCrQ6hv1uDq+ACM6x0idUhWF+nvifggL2i0Aqlny6QOh8gmbDxZhC3pJdibVQ4AGOIkVReDmUN6ICnaD/XNGry56YzU4TgtJi/Uptc2nsaRvCqoPNzw2i1DnGaW0aUM1ZcdZzh0RAQA5/SrTvcN98XUAWG4b3yCxBFZl1wuwwMTewHgkLKUmLxQK7vOluLdP84CAF6cNRDhKsdcMbMzxvQKBsAPKSKDs/qZNrOG9sA7tyWjf4TjLkzXnqHRugbl7LJ6lNdxtV0pMHkhE5X1zVj29WEIAcy5KgpTnGh6dFuujg+Ai1yGzNI65JTVSx0OkeQMlRfDcvnOSOXhhvhg3VIKh/MqpQ3GSTF5IRPfHshDYXUj4oK8sGJ6P6nDkZyPuxuuitX9yvr9RKHE0RBJS6MVxnWPnDl5AYAhUX4AgDT9WjdkXUxeyMTx89UAdCVhTwU3HQcuLM736zEmL+Tczlc2oEmthcJVjh7+zjucDABJhuSF60BJgskLmTihT176hvtKHIntSOmvmyZ+ILuCUyPJqRn6XWIDPZ22id9gsD55OZxXCSGEtME4ISYvZNTYojF+OPWLYPJiEKZyx1D9dNDfjrP6Qs7rLPtdjBLDfKFwlaOyvgXZ7IezOiYvZJRRXAu1VsDP0w1hTrLoVGcZh46OMnkh53VO/+PG0KzqzBSucgzQ/8jbn10hcTTOh8kLGZ0o0A0Z9Qv3hUzm3CXhS00ZoBs62pNZhrLaJomjIZIGZxqZGh6n20Lkmf8dx5b0YomjcS5MXsiI/S7tiwrwxIAevtAKYIN+l20iZ3PWWHlh8gIA901IwMj4QNQ2qbFo9T5sZQJjNUxeyOjkRZUXao2zjsiZVTe2oLhGV3XksJGOr7sbPr5jOP40OAJaATz8dRrOVzZIHZZTYPJCAAAhhHHYiJWXthmGjnadLUVVAzdqJOdySL+eSaS/B3zd3aQNxoYoXOV46aZB6B/hi4r6Ftz/5SFotJx9ZGlMXggAkF/ZgJpGNdxcZOgZwpJwWxKCvdE71BstGoFNJzl0RM5lb6Zuc9IR+j4PusDdzQVvzxsKb6UrDmRXYPMpDh9ZGpMXAgCcKqgBoPuCVrjybdEew3YJ6zl0RE5mzzndLtIj4gMkjsQ2xQR64barYwAAH+7IlDgax8dvKQIApBfpkpfEMB+JI7FtY/W7TBtWIiZyBo0tGuMePiPimLy0Z/7IGLjIZUg9V2bsISTLYPJCAIAz+uSlVyiTl44YZlnkVzagsUUjcTRE1nEwpwItGoEwX3dEB3hKHY7NivDzMPbGsfpiWUxeCACQXqSbAtmHyUuH/D3doPLQNStmldVJHA2RdezN1A0ZDY8L4BpQl3HHNXEAgO8O5iH1bJnE0TguJi8EtUaLs8W65KU3k5cOyWQyxAXppolmljB5IftWVd+C5WuP4L7PD6Chuf1KIvtdOi85xh83JUdCK4AHvjqEkhouamkJTF4I2eX1aNZo4eHmgkgn3ym2M+L1ycu5UiYvZJ+Kqhvx1d4cTH1jG77cm4tfjhbiw51tD3M0q7U4mKNb/p79Lp3z3Iz+6BXijZKaJjz743Gpw3FITF4Ipwt1/S69Q70hd/KdYjvDWHlh8kJ26JejBRi5chOeWHsU56sa4e+pGwZ9Z+vZNre+OJJXiSa1FoFeCm4L0EmeCle8dssQALqZiay+mB+TF8Jpfb8Lm3U7Jy6YyQvZry2niqEVulVyH7muN7Y9PgEDe6hQ26TGm5vOtDp+D/tdumVADxWSov2g1gp8eyBP6nAcjlWSl1WrViE2Nhbu7u4YMWIE9u7d2+6xq1evhkwmM7m4u3OHY0s6rZ9pxGbdzmHlheyZodH8oUm9cf/EXvBxd8Py6xMBAJ/vyWn1vjYkLxwy6rq5V0UDAL7elwMhuOquOVk8efn666+xbNkyPP300zh48CAGDx6MlJQUFBe3vwKhr68vCgoKjJfs7GxLh+nU0o3TpFkS7ozYQF3yUl7XjKp6bhNA9iWrrB4AEBt4YcrzqIQgTOgTDLVW4OXfThmvV2u0OJBlqLxwZd2uumFwOLyVrsgqq0fqOc48MieLJy//+te/sHjxYtx+++3o168f3n33XXh6euLDDz9s9z4ymQxhYWHGS2hoqKXDdFpNag2y9L+0ONOoc7yUrgjz1VUDMzldmuxIbZPa2H8RE2i6ueITU/tCLgN+OVpobNA9fr4adc0a+Lq7cgHLbvBUuGL64AgAul4jMh+LJi/Nzc04cOAAJk2adOEJ5XJMmjQJqamp7d6vtrYWMTExiIqKwowZM3D8ePvd2k1NTaiurja5UOdlFNdCrRVQebghXMXhuc66MHRUK3EkRJ2XrU+2A7wUxvWKDPqE+WB2chQA4M6P9+PT3dlYe1DXqzE8LoDN/N00rrduVe79WRUSR+JYLJq8lJaWQqPRtKqchIaGorCw7b1h+vTpgw8//BA//PADPvvsM2i1WowaNQp5eW03PK1cuRIqlcp4iYqKMvvrcGQnzht2kvZhM14XxBqmS3OtF7IjWaWth4wu9mhKH/QJ9UF5XTP+tu4YPk7VDdlzM8buS47R9QqlF9VwmNmMbG620ciRIzF//nwMGTIE48aNw9q1axEcHIz//Oc/bR6/fPlyVFVVGS+5ublWjti+ndDvv9EvXCVxJPall37nbe5fQvbE0Kwbe8mQkUGwjxI/PTAaT03ri96h3hjQwxfTBoZjVnKkNcN0KME+SsQHeUEI4EBOudThOAxXSz54UFAQXFxcUFRUZHJ9UVERwsLCOvUYbm5uSEpKQkZGRpu3K5VKKJXKK47VWRm+fPtF+EociX1JivYDABzMqYQQglUrsguG/jZD5bAtbi5y3DkmHneOibdWWA5vWKw/zpXWYV9WBa5NZA+nOVi08qJQKJCcnIxNmzYZr9Nqtdi0aRNGjhzZqcfQaDQ4evQowsPDLRWm0xJCmAwbUef1j1BB4SpHeV2zcfYGka3L1r9XY9oZNiLLGBarGzral8nKi7lYfNho2bJleP/99/Hxxx/j5MmTuPfee1FXV4fbb78dADB//nwsX77cePxzzz2H33//HefOncPBgwdx2223ITs7G3feeaelQ3U6+ZUNqG5Uw81Fhl4hTF66QuEqx8AeuqG2g9lsxCP7YJgdF9dB5YXM7yp98nIkr4q70ZuJRYeNAOCWW25BSUkJVqxYgcLCQgwZMgTr1683NvHm5ORALr+QQ1VUVGDx4sUoLCyEv78/kpOTsWvXLvTr18/SoTodQ9WlZ4gPFK421/5k84ZG++FAdgUO5lSwJ4BsXt3F06QDmLxYU2ygJ4K8lSitbcL+rAqM7hUkdUh2z+LJCwAsXboUS5cubfO2rVu3mvz7tddew2uvvWaFqOhkgW5xOg4Zdc/QaH8AmTiYUyl1KESXZWjW9fd0g8rT7TJHkznJZDJM6huCr/bl4tkfj+PH+0fD3c1F6rDsGn9uO7ETBVUAgH7hbNbtjqEx/gCA9MJq1DapJY6GqGM5xn4XVl2k8PiURAR5K3GmuBYvrU+XOhy7x+TFSeWW12PHmVIAug3EqOtCfd3Rw88DWgEczq2UOhyiDhVVNwIAF6OUSICXAi/dNBAA8OHOTON/D+oeJi9OSKMVeOSbw6hr1mB4bICxmYy6blisrvqSepb7lpBtK6nV9buE+HBpCalcmxhqXCCQG7teGSYvTuiLPdnYm1UOT4ULXpk9GC5c9rvbxvYKBgBsPd3+RqNEtqC4Wpe8BDN5kVQPfw8AwPnKBokjsW9MXpzQ5lO6L9ql1/ZENNd7uCJje+uSl2P51SiuYRmYbNeFyguHjaQUodIlL/kVTF6uBJMXJ5Svz/j7R7DX5UoF+ygxKFJ3Hv9IL5E4GqL2sfJiG4yVlyomL1eCyYuTEUIYM/4efh4SR+MYxuurL1uZvJANM1RemLxIK0L/uZtfyUrtlWDy4mSqGlpQ16xb4ZHJi3mMTwwBAGw/UwK1RitxNEStabQCZWzYtQmGz132vFwZJi9OJk9fdQn0UsBDwUWSzGFwpB/8Pd1Q3ajGjoxSqcMhaqWsrglaAchkuim7JJ2Ii5IXIYTE0dgvJi9OxtDvYhh3pSvnIpdhZlIPAMAnqdkSR0PUmmFbgEAvJVxd+LEvJcM6O/XNGlTWt0gcjf3iu9jJsN/FMuaPjAUAbEkvRnYZ128g21Jcw34XW+Hu5oIgb91/h3wOHXUbkxcnY/hjiWTlxazigrwwrncwhAA+ZfWFbIyh8sJ+F9vQw09XfWHfS/cxeXEyrLxYzsJRsQCAr/flIq+ivt3jOM5N1lbCyotNiWDT7hVj8uJkLvS8cHE6cxvXOxhDovxQ06TGki8OoVndeuZRQVUDpr6xHdf96w9uKUBWw8qLbTEmL1WcLt1dTF6cjDF5YeXF7ORyGd6amwRfd1cczq3ES+tPmdxeXNOIee/vwanCGpwprsXc93fjzU1nJIqWnAkrL7bF8PnLVXa7j8mLE6lvVqO8rhkAZxtZSlSAJ169eQgA4L87M7E/qxwAcCC7HDe+vQvnSuvQw88DtwyLAgC8vvE0ThVWSxUuOQnD1hVMXmzDhYXqmLx0l6vUAZD1GMZXfZSuUHm4SRyN47quXyhmJ0dizYE8PP7dEYyIC8TX+3KgFUBUgAc+WzQCMYFeqGlqwS9HC/H3n07i00XDIZNxg0yyjAvDRtzXyBb0YPJyxVh5cSKGBepYdbG8p6b1Q7CPEudK6vDlXl3icmNSD/z8wBjEBHoBAJZP7QuFixw7MkqNm2USWQKnStuW6ABdz2FJTRNqm9QSR2OfmLw4Efa7WI/K0w2vzB6MIG8lUvqH4uu7rsa/bhkCX/cLFa+oAE8svCYWAPDl3lyJIiVHV9ekRr1+SxA27NoGlaebca2XcyW1Ekdjnzhs5ESK9J3tYSqWjq1hXO9g7H9qUofHTBkQhve2ncPBnAoIITh0RGZnqLp4KlzgpeRHvq3oGeKF0tomZBTXYlCkn9Th2B1WXpyI4UMs1JfJi60YEKGC0lWO8rpmnCvlyrxkfkXVuh8t/Lu3LQnB3gCAjGJWXrqDyYsTMXyIsXRsOxSucgzW/+o6kFUhbTDkkC4kL/y7tyU9Q5i8XAkmL06ElRfblBzrDwDYn10ucSTkiIqr+Xdvi4zJC3teuoXJixPhjAPbNCxGl7wcyGblhcyPw0a2yTBslFNWjxZN69W4qWNMXpyEWqNFWa1+rQeWj23K0Ghd8nK2pA4V+kUEicylkMPFNilc5Q4vhQvUWsGd6LuByYuTKKtrhlYAchkQ6MUPMVvi76VAQrBu7RdWX8jcOGxkm2QyGRLY99JtTF6chOEDLNhHCRc5p+PamuFxgQCAbWdKJI6EHE1RDYeNbFVPzjjqNiYvTsKwtwmXB7dN1/ULAQD8frwIQgiJoyFHIYQw9ryEMXmxOYbKy9kSDht1FZMXJ1Fs3NuEQ0a2aFRCELwULiisbsTR/CqpwyEHUd2oRmOLrhmUvW62xzBczFV2u47Ji5MwrvHCDzCb5O7mgvF9LlRfiMyhWP93r/Jwg7ubi8TR0KWi9Hsc5VZwg8auYvLiJIq5q6zNm9w/FADw2/FCiSMhR1HIBepsmiF5Ka9r5gaNXcTkxUkYGnZZebFd4/uEwFUuw5niWmRxqwAygyLONLJpvu5u8PPUbdaaW14vcTT2hcmLkyhhw67NU3m4oX+ELwAgvahG4mjIEXCBOtsXbRg6YvLSJUxenIThFxgbdm1bpL/ugyyPY+BkBsUcNrJ5Uf7se+kOJi9OQKsVKK1l+dgeRPp7AADy+UFGZsBhI9sXGaD7m2flpWuYvDiB8vpmqLUCMhkQ5K2QOhzqQA998pJXwQ8yunJFHC62eRw26h4mL07A0Kwb6KWAqwv/k9uyHn76ykslKy905YqqOGxk6wzDRjlMXrrEKt9kq1atQmxsLNzd3TFixAjs3bu3w+PXrFmDxMREuLu7Y+DAgfjll1+sEabDKqjSfRGydGz7DD0vTF7oStU2qVGg73kxvK/I9hgqL3kVDVxduwssnrx8/fXXWLZsGZ5++mkcPHgQgwcPRkpKCoqLi9s8fteuXZg7dy4WLVqEQ4cOYebMmZg5cyaOHTtm6VAdVlaZLqOPDfSSOBK6HMOwUWV9C9d9oCtyLL8KQuh2Lw5mo77NivDzgEwGNLRoUFrLXeU7y+LJy7/+9S8sXrwYt99+O/r164d3330Xnp6e+PDDD9s8/o033sCUKVPw2GOPoW/fvnj++ecxdOhQ/Pvf/7Z0qA7LsN16dCB/fdk6b6UrVB66dR/YtEtX4miebpuJQZEqiSOhjihc5QjXV8U5dNR5Fk1empubceDAAUyaNOnCE8rlmDRpElJTU9u8T2pqqsnxAJCSktLu8U1NTaiurja5kKlsY+WFyYs9iGTTLpnB4bxKAMCgSD9J46DLizIOHfFvvrMsmryUlpZCo9EgNDTU5PrQ0FAUFra9BHphYWGXjl+5ciVUKpXxEhUVZZ7gHYih8hLDYSO7wKZdMocj+srLYCYvNs+QvOSUMXnpLLuferJ8+XJUVVUZL7m5uVKHZFNaNFrjgmfsebEPPbjWC12hirpm4xDEQA4b2by4IP3u0twWpNNcLfngQUFBcHFxQVGR6S65RUVFCAsLa/M+YWFhXTpeqVRCqWQzWnvOVzZArRVwd5NzdV07wVV26UodyddVXeKCvIw9VGS7eof6AADSC7ktSGdZtPKiUCiQnJyMTZs2Ga/TarXYtGkTRo4c2eZ9Ro4caXI8AGzYsKHd46ljhplGMQFekMtlEkdDnWEYNsrjsBF105HcSgBs1rUXffTJS0ZJLdQarcTR2AeLDxstW7YM77//Pj7++GOcPHkS9957L+rq6nD77bcDAObPn4/ly5cbj3/wwQexfv16vPrqqzh16hSeeeYZ7N+/H0uXLrV0qA7pQr8Lm3XtBbcIoCt12DjTyE/aQKhTIv094KlwQbNai6wyDh11hkWHjQDglltuQUlJCVasWIHCwkIMGTIE69evNzbl5uTkQC6/kEONGjUKX3zxBZ566ik8+eST6NWrF9atW4cBAwZYOlSHlFWqn2kUxH4Xe2FIXkprm1DfrIanwuJ/puRgjuhnGg1m5cUuyOUy9Ar1weHcSqQX1qJniI/UIdk8q3wqLl26tN3KydatW1tdN3v2bMyePdvCUTkHVl7sj5+nAgFeCpTXNeNscR0bLqlLCqsaUVzTBLkM6BfhK3U41EmJxuSlGtMGhUsdjs2z+9lG1DFDCZIzjexLrxBvAMDpIjbwUdcY1nfpHerDqp0d6ROmb9rl33ynMHlxYBqtQG65rm/CsH8G2QfD7IPTxfwgo645YlycjhU7e2JMXjjjqFOYvDiwwupGNGu0cHORIUI/g4XsQ+9QXeXlTFGtxJGQvTnCZl27ZEhessvr0dCskTga28fkxYFl6xc8igrwhAunSduVXobKC0vI1AVCCBzN58q69ijIW4lALwWEAM6w4npZTF4cGHeTtl+GYaO8igbUcXdp6qSc8npU1rdA4SI3/pIn+5EYrvtvdjC7QuJIbB+TFwfGmUb2K8BLgSBvBQAgo5hDR9Q5hvVd+kb4QuHKj3d7M753CABg/fG29/KjC/judmCcaWTfeoVw6Ig679PULDz1/VEAQFKUn7TBULdMHajbBmdvZjlKapokjsa2MXlxYNmGrQFYebFLhrL/GVZe6DL+OF2Cv/1wHNWNavQL98U94xKkDom6IdLfE4MjVdAK4PcTrL50hMmLgxJCsPJi53qFcq0X6pw958oAAFMHhOHH+0cjTOUucUTUXVMH6hao++VogcSR2DYmLw6quKYJjS1auMhl6OHPadL2yNC0y+nSdDmn9GuDjEoI5MxCO3f9AF3yknq2DBV1zRJHY7uYvDioLP006Uh/D7i58D+zPeqt73nJr2xATWOLxNGQLTtZUA0ASAzndgD2LjrQE9EBntAKVl07wm81B2Xod+HKuvZL5emGEB8lAPa9UPsq65tRUNUIAJwe7SAMfYo55fUSR2K7mLw4KPa7OIYLQ0f8BUZtMwwZRfp7wNfdTeJoyByi9D86c5m8tIvJi4PKLudMI0dwoWmXlRdqm3HIKIxDRo7CUDFn5aV9TF4clKHnhZUX+9ab2wTQZZwq0L03+oVzyMhRGJKXbCYv7WLy4oAaWzTGLzvDlx/ZJ27QSJdzspDNuo4mmsNGl8XkxQGl5VaiRSMQ4qNEVACnSduznvoZR4XVjahq4IwjMqXRCqTre176MnlxGIael9LaZu5t1g4mLw5oX2Y5AOCquADIZFzzwZ6pPNwQrl9wLIM7zdIlMopr0aTWwsPNhTMLHYjKww0qD13zdW4Fqy9tYfLigPbpdyQdHhsgcSRkDr2MfS8cOiJTOzNKAQDDYv25OJ2DMTbtljF5aQuTFwej0QrjdurDYv0ljobMoXeIru/FMDxAZLBDn7yM7hkkcSRkbpxx1DEmLw7mZEE1apvU8FG6cuqkgxjQQwVAt9MskUGLRovd+j2NrmHy4nC41kvHmLw4mH1Zui+4ZJaRHcbY3sGQyYATBdUoqGqQOhyLE0JACCF1GDbvUE4l6ps1CPRSoB+bdR0OKy8dY/LiYA7lVAIAhsVwyMhRBHgpMDRa999z86liiaOxrMYWDeZ/uBdjX96Cau7n1KEdZ0oAAKN6BkHOHyoOh8lLx5i8OBhDZ3pPfZ8EOYZrE0MAAJtPOm7yIoTA/31/DNvPlCK3vME4a47atl3f7zKGQ0YOybjWS0UDK5FtYPLiYM5X6oYVIvy4vosjmdhXl7zsyChFQ7NG4mgs49Pd2fjuYJ7x34fzqiSMxraV1jbhcG4lAOCaXkxeHFGoSrcpa7Nai4p6ViEvxeTFgTSrtSiuaQLA5MXR9An1QQ8/DzSptdh1tlTqcMyuprEFr/5+GgAwUN+gfDSvUsKIbNv6Y4XQCmBwpAo9+LfukJSuLgjyVgC48KOULmDy4kAKqxohBKB0lSPQSyF1OGRGMpkMExKDAQDbTpdIHI35fZKajaqGFsQHe+GZP/UHABzJq2K5vB0/HykAAFw/MFziSMiSwlW6xLSwqlHiSGwPkxcHkq/Pznv4eXBlXQdkWMtj59kyiSMxr7omNT7Yfg4AcP+1PdE/wheuchnK6ppxnh/arZTUNGFPpu49wOTFsYXpV9cuqObfwaWYvDgQ9rs4tqvjAyGT6ZaEL3KgD7M1+3NRUd+C2EBPTB8UAXc3F/QJ060qfETf10EXrD+uHzKK8jOuBUKOybA1SKETLJHQVUxeHMiF5MVd4kjIEvw8FRgQoesHcaS+l636YbB5I2Lg6qL7SBoU6QcAOJLPpt1L/XAoHwAwbWCYxJGQpRkrL6xAtsLkxYGcr2LlxdGN6hkIANiZ4RhDRy0arXFKtOG1AcCgSF2SdoRNuyZOnK/G/uwKuMplmDGkh9ThkIVdqLwwebkUkxcHkl+pe4MzeXFc1yTo+14ySh2imfVofhXqmjXw83RD34u2s7iQvFRBq7X/12kun6RmAQCmDAhDqC8rrI4uzJcNu+1h8uJAzl/UsEuO6arYAChc5CioasS50jqpw7lihr15RsQFmKwS2yfUBx5uLqhpVCOjhLtpA0BlfTPWpemGjBaMipU2GLIKQwtAQVWjQ/xYMScmLw5CCMGGXSfgoXAx7ha+xQG2CkjVz5waGR9ocr2rixxJ0X4ALuzX5cyEEHhzUwYaW7ToG+7L7T+chKG61tCiQVUDF6q7GJMXB1HV0IJ6/cqrhnFSckyT+4UCAH47XihxJFemWa3F/qwKAMDIhNarxBq+oA/oj3FWQgj8c/0pfLgzEwCwZEICl0JwEu5uLgjQr9nFpl1TTF4chGGNlyBvBdzdXCSOhizpuv66WSb7sytQWtskcTTdtz+7HA0tGgR4KdCrjb24kmMD9Mc5d/Lyv8Pn8Z8/dOvgPD29H24YFCFxRGRNYb5s2m0LkxcHkV/BISNn0cPPAwN7qCAEsPFEkdThdIsQAm9sPAMAuK5vaJu7Ig+N9oNcpttVt7gb69qs3pmJuz7Zjw0nilDV0IIzRTVobLGvfaE0WoE3NunO0wPX9sTt18RJHBFZWzinS7fJoslLeXk55s2bB19fX/j5+WHRokWore24+W78+PGQyWQml3vuuceSYToENus6F8PQ0e92mrxsOlmMPZnlULjK8cCkXm0e4+Puhj76GUhdrb7kVzbg+Z9P4vcTRVj8yX4MfvZ3XPfaNiz4cK9dNT7+crQA50rqoPJww+Kx8VKHQxII40J1bbJo8jJv3jwcP34cGzZswE8//YRt27bhrrvuuuz9Fi9ejIKCAuPlpZdesmSYDiGnnMmLM0kZoBs62pFRitomtcTRdI1ao8U/158CANxxTVyH71lD38v+Lva9rN6ZCY1WINLfAz5KV+P1ezLLseFEERpbNPhqbw5m/HsHJryyFV/syYHGxqZka7UCq7ZkAABuvyYWPu5uEkdEUjBU01l5MeV6+UO65+TJk1i/fj327duHYcOGAQDeeustXH/99XjllVcQEdH+uK2npyfCwrh6ZFdkl+mmzcYGeUkcCVlDrxBvxAZ6IqusHn+kl2DaIPvZ4+aXY4XIKK6Fn6cb7h2f0OGxw2L98enu7C6tKFzd2IIv9+YCAJ6fMQDX9AxCi0aLt7dmYNWWs/jn+lN4feMZnCioNt7nye+P4ruDefjkjuHwUlrsY7FL1hzIxanCGngrXXH7KA4XOStDzwuTF1MWq7ykpqbCz8/PmLgAwKRJkyCXy7Fnz54O7/v5558jKCgIAwYMwPLly1FfX9/usU1NTaiurja5OKMsQ/ISyOTFGchkMqToG3d/P2E/s46EEHhn61kAwO2j4qDy6LiaMK53MFzlMpwqrEFGcefWe/l6by5qm9ToFeKNcb2DoXCVw0vpirvHJcDP0w3nSupwoqAagV4KPHl9Iv52Qz/4uLviQHYF7v/ykE1UYEprm/DCL7rq1AMTe0LlyaqLs4r011Vecsrb/x50RhZLXgoLCxESEmJynaurKwICAlBY2P6H7a233orPPvsMW7ZswfLly/Hpp5/itttua/f4lStXQqVSGS9RUVFmew32QqMVyNUPG8UEcqM2ZzG5v67vZfOpYjSrtRJH0zl/nC7ByYJqeCpcsGBUzGWP9/NUYEwv3TTqn46cv+zxQgh8uTcHALBodJxJI7CvuxuWXdcbgG5Tw58eGI27xiZg0eg4fHLHcChd5dh8qhjP/3SiOy/NrF74+SSqGlrQN9wXd7BJ16kZqul5FfV283duDV1OXp544olWDbWXXk6dOtXtgO666y6kpKRg4MCBmDdvHj755BN8//33OHv2bJvHL1++HFVVVcZLbm5ut5/bXhVUNaBZo4XCRc7ZRk4kKcofQd5K1DSqjSvV2rKM4lqs1FcTbh0eDT9PRafuZ5ga/OPh85dttk3LrcS50jp4uLnghsGth6bnj4zF1kfH47t7RiJcdeFvJSnaH6/fMgQAsHpXFj7Sr6kihaN5VVh7KB8yGbDyxoHGzSrJOYX4KOHh5gKt0CUwpNPlv4pHHnkEJ0+e7PASHx+PsLAwFBebrgCqVqtRXl7epX6WESNGAAAyMjLavF2pVMLX19fk4myyy3Rv6KgAD7i0MeWUHJNcLsN1drJg3Zr9uZj6xjakF9XAx90Vi8Z0vppwXf9QKFzlOFtSh1OFNR0eu/agbvn8KQPC4N1O70pskFebCcHUgeFYPjURAPDcTyewJV2aFYxf/j0dADBzSA8MifKTJAayHTKZzFhRN3zWUzeSl+DgYCQmJnZ4USgUGDlyJCorK3HgwAHjfTdv3gytVmtMSDojLS0NABAebj8NidaWWcp+F2eV0v/ClGlb6NVoS12TGn//+SRaNAIT+gTj5/vHmFQ9LsfX3Q3jewcD0E0dbk+TWoP/HdYNLc0aGtmtWO8aG485V0VBCOCV39KtPq1697kybDtdAle5DA9P6m3V5ybbZfhsz3SA/czMxWL1yL59+2LKlClYvHgx9u7di507d2Lp0qWYM2eOcaZRfn4+EhMTsXfvXgDA2bNn8fzzz+PAgQPIysrC//73P8yfPx9jx47FoEGDLBWq3TPMNIph8uJ0RiUEQeXhhpKaJuzNtM09gL7Zn4uqhhbEBnrigwVXIbobfVkT++r65zp6jVtOFaOqoQVhvu4YmRDY7nEdkclk+OuURChc5Th+vhqH86q69Tjd9e/Nugrz3OHR3TpP5JgMfS+Gz3qy8Dovn3/+ORITEzFx4kRcf/31GD16NN577z3j7S0tLUhPTzfOJlIoFNi4cSMmT56MxMREPPLII5g1axZ+/PFHS4Zp97L0pcTYIH7YORuFq9xYfelMQ6u1qTVa/HeHrn/kzjHx3R7WTIrWrfdyNL8Kak3bTYubTuqGeaYPDr+i4VN/LwVuGKir9H6+O7vbj9NVFXXNSNX3Li0ewwXp6IJYfSKbyWEjI4suaBAQEIAvvvii3dtjY2NNyrJRUVH4448/LBmSQ2LlxbndMCgC3+zPw/pjhUgM88Enqdl4efZgm+iXWH+8EHkVDQjwUuCm5O4N5QBAz2Bv+ChdUdOkxumiWvSLMO1tE0JgZ4ZuLZgxvYKvKGYAmHd1NNYeysePR87jqRv6XXZKd3dkltZh97kynC2uxY1DI5FeVA2NViAxzIdVFzJhqLxkcdjIiG3sdk6rFcYmrlh+4DmlUQmB8Pd0Q1ldM/72w3GcKa7Fu1vbnp1nbYYelVuHR1/RhqFyuQyD9cnYodzWq+1mldXjfFUjFC5yXKXf0PFKDI32R59QHzS2aI1Tr80ps7QO1/3rDyxfexQf7MjEfZ8fwC9HdU3Xk/qGmv35yL4Zel44XfoCJi92rqimEU1qLVzlMm4N4KRcXeSYMsC0oX3zqWJU1bdIFJGOWqPFjjO6aoihZ+VKGCpJh3IqW91mqLokRfvBQ3Hlu6rLZDLcqZ8R9c7Ws6hqMO+5/GZ/LtRagegATwR4KZBVVo8N+n2qJvVj8kKmQn2VcHeTc7r0RZi82LmsUt0bOdLfg+tBOLFFo2ORGOaDp6b1RWKYD5o1WvxyrP2ZOdZwJL8K1Y1q+Lq7YlCk3xU/XlK07jHScitb3WZIXkb3DLri5zG4cWgkeoV4o6qhBe9tM18lS6MV+F4/pfuvUxLxWEof423BPkoM6qEy23ORY5DJZMbqC6dL6/Dbzs7l63eTjgrgkJEz6xnig/UPjcWdY+IxM6kHABi/IKWy/bQ+oegVZJb1hwyVl4ziWpNKiEYrjI2uo8yYvLjIZcbE4r87MlFcbZ69ZXadLUVhdSNUHm6Y2DcEs5Mj0TvUGwAwMTHEZFVgIgNOlzbF5MXOndcnLxFdWDeDHNuMIRGQyYC9WeU4V9K5/YDMpbZJjbnv7cZ9nx/AhpO6Ho6xZmigBYBAbyWi9Un6xdWXA9kVqKxvgbfSFYMjzVu1uK5fKIZG+6GxRYv3tp0zy2N+dyAPgG5WlLubC1xd5Hj9liTMHBKBpdf2NMtzkOMxNO2mX2ahRmfB5MXOGZMX9ruQXrjKA+P0i7o9suYwWtqZWmwJH+/KQuq5MvxytBDH8nWbpI7uZb5qyPA4XTPullO6adHNai1W/HAMAJDSP8zsQ6cymQwPTOwFAPh8Tw7Kapuu6PGq6luwXr8a8sUL6fWL8MXrc5IQ6c8KKrVtRLzuvb/tTInVF0+0RUxe7Fy+MXlxlzgSsiXPzxgAH3dXHMqpxBsbz1jlOeua1Phgu646oXTVfbTEB3uZ9Qv5+oG6rUV+PloAjVZg1ZYMnCqsQYB+h2hLGNc7GIMiVWho0RjXrOmub/bnorFFi8QwH5uYyk72Y2R8IJSuchRUNeJ0kXUrqraIyYudM1ReONOILhYV4IkX/jwQALBqawZSz1p+48ZPUrNRUd+C+CAv/PrgGEwbFI4nppg3oRjdM9i4ovDqXVlYtUW3Iu1zM/oj0Ftp1ucykMlkWDpBN5zz/vZzeOZ/x7tVgdFoBT5OzQIA3H5NLGQy9rZQ57m7uWCUfuVoqfbdsiVMXuyYEALnK3VNhBw2oktNHxyBm4dFQgjg4a/TUFHXbLHnOlNUg7f1icTSa3siPtgbq24disn9O78Ja2coXOWYon/M5386AbVWYEr/MEwbaNm9z67rF4obBoWjRSOwelcWxr28FW9sPIPGFk2nH2PDiSLkVTTAz9MNM4b0sGC05KgmJOqWHNjK5IXJiz2rrG9Bg/7DM0zFYSNq7Zk/9Ud8kBcKqxuR8vo2zHkv1bj2irmU1DTh9tX7UNOkxlWx/vjT4AizPv6lbhh8IVHx83TD8zMHWLyKIZPJ8O9bh+KzRSMwsIcKtU1qvLbxNJZ+ceiy/Qdb04tx7atbcc9nuk1q517hgn3kvMb31iUv+7MqUN0o7TpOUmPyYscM/S5B3kp+GFKbPBWueHNuEnzcXVFc04Td58rx1Lqj0JpxB+oVPxxDXkUDYgM98Z+/DLP4ekMj4wMR4qMbInr2T/0R7GOZ4aK2jO4VhB+WXIM35gyBm4sMG08W4Ye09veUOn6+Cvd+dhDnSnTTWxPDfHD7qFgrRUuOJjrQE/HBXlBrBXZlWH4o2JZZdG8jsqwL/S6sulD7BvRQYcfj1+JUYTXu/GQ/ssrqse1MCcb3ufJVb2ub1Nikn/nz71uHIsBLccWPeTmuLnJ8fMdw5FU0YJIZVu7tKrlchhlDeiCnrB6vbjiNZ348jlE9AxHiY/p3WFbbhLs+OYCGFg3G9ArCm3OS4G+F80OObWi0P86V1CGjuAaAeYdl7QkrL3bMkLyEc40XugyVpxtGxAdidnIUAF1zrTlsO12CZrUWsYGe6H/JZomW1DfcF9f1C5W06fWe8QnoH+GLyvoW/G3dsVbDR+9sPYv8ygbEBXnh33OHMnEhszDuMF3q3CvtMnmxY+er2KxLXfOXkTEAdLMVDLuRX4nf9WuWTO4f5nSzZ9xc5Hj5psFwlcvw2/Ei/HTkwnYMVQ0txg0dV0zvB5Wn+XelJucUY9wmwLlX2mXyYse4xgt1VVyQF8b3CYYQwPeHrmz7gBaN1jhkNNlJNxPsF+GLJfpp1E+tO4ZXfkvHwZwKfLk3B3XNGvQK8cb43uZZYZgI0P0NA7qd1J0Ze17sGNd4oe6Y2DcUW9NLcCC74ooeZ8+5ctQ0qhHkrUBStL+ZorM/Syb0xKZTRTiWX41/b8nAv7dkwFCEWjw23ukqUmRZ0fpho9LaJtQ2qeGtdM6vcVZe7Bi3BqDuSNYnGodyKqG5gllHhl2rJyaGmmXjRXulcJXj67tG4pXZgzF9cAQ83FwgBBDm644ZQyw7bZycj6+7GwL1/VPOPHTknCmbA2hWa1Fco1vlk8kLdUWfMB94KVxQ26TGmeIaJIZ1vdG2Sa3Bz/oejz/xCxpeSlfclByJm5IjUdukxo4zJegT5gulK5cwIPOLCfREWV0zskrr0T/CvJuR2gtWXuzUyYJqCAH4uLsas3CiznCRyzBYv6/OwezKbj3GllPFqGpoQZivO66ODzRfcA7AW+mKKQPCjb0JROYWG2joe3HeyguTFzu1I0O3SurV8YGQO3HJnronOUY3dNTdvpfvDuqafWcm9XDqISMiKcQGccYRkxc7teusLnkZ3TNI4kjIHg019r10PXkpr2s27q1y41Du0UNkbTH6pl1nnnHE5MUONbZosC9L96VzTU+W7KnrkqL9AADnSutQ3sUNG39Iy0eLRqB/hC96h/pYIDoi6ohx2KiUlReyIweyK9Cs1iLUV4mEYG+pwyE75OepQM8Q3Xvnld/TL7u5oIEQAp/v0S2+Nmd4tMXiI6L2GZKX4pom1DerJY5GGpxtZId26vtdrukZxDUkqNseua437vviIL7Yk4PzlQ3w91RgXO9gzEwyHQpqVmvxxNojKKlpwpyropFRXAtPhQtmcpYRkSRUnm7w83RDZX0Lssvq0Tfceltz2AomL3Zo51ndbqLXJLDfhbpv6sBwvDRrEB779gi2ppcA0K26e66kFg9f1xsymQxCCKz44RjW6ht0t5/RJc4zhkTAx51L3hNJJTbQC2n1lcguq2PyQrZPCIH0wmoAF/oWiLpr9rAo9PD3wN7McpTVNuPT3dl4c3MGmjUCT0xNxHvbzuGrfbmQy3RDTYb+mFuHx0gcOZFziw30RFpupdM27TJ5sTMlNU1obNFCLgMi/T2lDoccwKiEIIzSV/F6hXpjxQ/H8e4fZ5FTXodfjuo2Xvy/af0wrncwFn28D/3CfTEw0jkXxiKyFTFO3rTL5MXOZJfrsuwIPw8oXNlvTeY1f2Qsqupb8OqG08bE5e5x8bjjmljIZDJsfXQ8+6yIbEBskGG6tHMmL/z2szPZ+hKhYZ4/kbktvbYnbh2hm0m0cFQsnpiSaExYmLgQ2QZD5SWbw0ZkD3L0lZfoACYvZBkymQwv/HkgHprUCyE+7lKHQ0RtiNMnLwVVjWhs0cDdzbn20WLlxc7k6EuE0QHcN4Usi4kLke3y83SDr7uu/uCM1RcmL3bG0PPCYSMiIuclk8mMexw5Y98Lkxc7k8thIyIiwsV9L0xeyIbVNqlRWqtbZyOalRciIqcW58QbNDJ5sSM5+jeov6cbfLm6KRGRU2PlhexCTrm+WTeQzbpERM7OuNZLKSsvZMM4TZqIiAwMlZfzVQ1obNFIHI11WSx5+cc//oFRo0bB09MTfn5+nbqPEAIrVqxAeHg4PDw8MGnSJJw5c8ZSIdod4wJ1TF6IiJxeoJcC3kpXCAHkVThX9cViyUtzczNmz56Ne++9t9P3eemll/Dmm2/i3XffxZ49e+Dl5YWUlBQ0NjZaKky7Yqy8sFmXiMjp6aZLO+fQkcWSl2effRYPP/wwBg4c2KnjhRB4/fXX8dRTT2HGjBkYNGgQPvnkE5w/fx7r1q2zVJh2xVB54bAREREBF23Q6GRNuzbT85KZmYnCwkJMmjTJeJ1KpcKIESOQmpra7v2amppQXV1tcnFEao0W+ZUNALhAHRER6cQGOucGjTaTvBQW6nawDQ0NNbk+NDTUeFtbVq5cCZVKZbxERUVZNE6pnK9shEYroHCVI5TLthMREZx3g8YuJS9PPPEEZDJZh5dTp05ZKtY2LV++HFVVVcZLbm6uVZ/fWrL106Sj/D0gl3NnXyIiAmKddNioS7tKP/LII1i4cGGHx8THx3crkLCwMABAUVERwsPDjdcXFRVhyJAh7d5PqVRCqVR26zntSY5xTyOu8UJERDqGht38igY0q7VQuNrMgIpFdSl5CQ4ORnBwsEUCiYuLQ1hYGDZt2mRMVqqrq7Fnz54uzVhyVDls1iUioksEeyvhqXBBfbMGuRX1SAj2ljokq7BYipaTk4O0tDTk5ORAo9EgLS0NaWlpqK2tNR6TmJiI77//HoBuytdDDz2Ev//97/jf//6Ho0ePYv78+YiIiMDMmTMtFabd4EwjIiK6lEwmc8ptArpUeemKFStW4OOPPzb+OykpCQCwZcsWjB8/HgCQnp6Oqqoq4zGPP/446urqcNddd6GyshKjR4/G+vXr4e7OBtVs47ARkxciIrogNtATJwuqnWqtF4slL6tXr8bq1as7PEYIYfJvmUyG5557Ds8995ylwrJLQgjkMnkhIqI2OGPlxTk6e+xceV0zapvUAIBIfyYvRER0QZy+afdcKZMXsiGGIaMwX3e4u7lIHA0REdmSvuG+AICD2RVOs0Ejkxc7YJxpxCEjIiK6xIAIFcJV7qhr1mBnRqnU4VgFkxc7YFh8iLtJExHRpeRyGVL669ZKW3+s/RXpHQmTFztwpkg3vbxXqHPM3ycioq4xJC8bThZBrdFKHI3lMXmxA6cKdZtN9gnzlTgSIiKyRVfF+iPAS4HK+hbszSyXOhyLY/Ji4xpbNMjS97z0CfWROBoiIrJFri5yXNdXt7HxxpPFEkdjeUxebNzZklpotAIqDzeE+jr+Hk5ERNQ9w2L9AQAnC6oljsTymLzYuPTCGgBAnzAfyGTcTZqIiNrWW1+dP1NcI3EklsfkxcalF+mTFw4ZERFRB3qG6CZ1lNY2o7yuWeJoLIvJi427uPJCRETUHi+lKyL9PQAAZ4ocu/rC5MXGGZKXRCYvRER0GYaho9PFtRJHYllMXmxYVUMLCqoaAQC9OGxERESXYVgPjJUXksxp/ZsvQuUOlYebxNEQEZGt6x2ir7wweSGp7M+qAAAM6KGSOBIiIrIHxhlHRRw2IonsPlcGALg6PlDiSIiIyB70DPGGTAaU1TWjrLZJ6nAshsmLjVJrtNifpVvimckLERF1hofCBVH+uk18Tztw9YXJi406dr4adc0aqDzcONOIiIg6rbe+adeR+16YvNgow5DR8LgAyOVcWZeIiDonUb+Jr2FTX0fE5MVG7WG/CxERdUPfcF3ycqKAlReyIrVGi336mUZXxwdIHA0REdmTxHBdq0F6YTU0WiFxNJbB5MUGpRfVoLZJDR93V2P5j4iIqDNiA73g7iZHY4sWWWV1UodjEUxebNChnEoAwJAoP7iw34WIiLrARS5DH0Pfi4MOHTF5sUGG5CUpyk/SOIiIyD711c9SPVngmE27TF5s0KFcXb9LUrS/xJEQEZE9MjTtMnkhq6isb8a5Et0Y5RBWXoiIqBuYvJBVpeVWAgDigrzg76WQNhgiIrJLffTDRuerGlFV3yJxNObH5MXGXNysS0RE1B0qDzdE+nsAAI6dr5I4GvNj8mJjDJWXpGg/SeMgIiL7ZvgRbPhecSRMXmyIRitwKEffrBvFZl0iIuo+Q/JiqOg7EiYvNiS9sAbVjWp4KVzQN5ybMRIRUfcZZqym5VZACMdaaZfJiw3Zl1UOABga4w9XF/6nISKi7usf4Qs3FxlKa5uRV9EgdThmxW9IG7I3U5e8jIjjfkZERHRl3N1c0E8/ZfqQg/W9MHmxEUII7NEnL8PjuJM0ERFdOcPQkaGf0lEwebERWWX1KK1tgsJFjkGRKqnDISIiB+CoM46YvNiIvZllAHRvNHc3F4mjISIiR2BYduN4fjWa1BppgzEjJi824sKQEftdiIjIPKIDPBHgpUCzRosT5x1nqwCLJS//+Mc/MGrUKHh6esLPz69T91m4cCFkMpnJZcqUKZYK0aYY5uEnx3J9FyIiMg+ZTOaQQ0cWS16am5sxe/Zs3HvvvV2635QpU1BQUGC8fPnllxaK0HZU1jcjs1S/GWOkn7TBEBGRQ0lywMXqXC31wM8++ywAYPXq1V26n1KpRFhYmAUisl3cjJGIiCxliL7v5VCu48w4srmel61btyIkJAR9+vTBvffei7Kysg6Pb2pqQnV1tcnF3hiSF27GSERE5jY4yg8yGZBb3oDS2iapwzELm0pepkyZgk8++QSbNm3Ciy++iD/++ANTp06FRtN+h/TKlSuhUqmMl6ioKCtGbB6GUh43YyQiInPzdXdDQrA3ACDNQYaOupS8PPHEE60aai+9nDp1qtvBzJkzB3/6058wcOBAzJw5Ez/99BP27duHrVu3tnuf5cuXo6qqynjJzc3t9vNLQQiBw3mVAFh5ISIiy0hysKbdLvW8PPLII1i4cGGHx8THx19JPK0eKygoCBkZGZg4cWKbxyiVSiiVSrM9p7VlldWjsr4FClc5EsN8pQ6HiIgcUFK0P9YcyMP+7HKpQzGLLiUvwcHBCA4OtlQsreTl5aGsrAzh4eFWe05rS9M3UA3soYLC1aZG8YiIyEGMTNBtO7MvqwJltU0I9LbfH/2ABXtecnJykJaWhpycHGg0GqSlpSEtLQ21tbXGYxITE/H9998DAGpra/HYY49h9+7dyMrKwqZNmzBjxgz07NkTKSkplgpTcj8eLgAADIvh+i5ERGQZcUFeGNhDBY1W4JdjhVKHc8UslrysWLECSUlJePrpp1FbW4ukpCQkJSVh//79xmPS09NRVVUFAHBxccGRI0fwpz/9Cb1798aiRYuQnJyM7du32/WwUEcyimux+VQxZDJgzvBoqcMhIiIH9qfBEQCAH9POSxzJlZMJIYTUQZhTdXU1VCoVqqqq4Otr2z0ky9cexZd7c3Bdv1C8P3+Y1OEQEZEDK6hqwKh/boYQwM4nrkUPPw+pQzLRle9vNllIpKy2CWsP5gEAFo8xX5MzERFRW8JVHrgqVrd/3k+H7bv6wuRFIu9vz0STWovBkSpcxf2MiIjICqYN1E2A2ZJeLHEkV4bJiwRKa5vwSWoWAOD+a3tBJpNJGxARETmFMb2CAAAHsyvR0Nz+ArC2jsmLBN7bdg71zRoMilRhYt8QqcMhIiInERfkhXCVO5o1Wrte84XJi5VV1jcbqy4PX9ebVRciIrIamUyGa3rqqi87Mkoljqb7mLxY2f8On0djixaJYT4Y39t6C/4REREBwDU9dQvW7croeONjW8bkxcq+PaCbYXTzsChWXYiIyOquSdBVXo6dr0JlfbPE0XQPkxcrOl1UgyN5VXCVyzBjSITU4RARkRMK8XVHrxBvCAHsOmuf1RcmL1b0nb7qMiExxO73lSAiIvs1ppeubWGrnU6ZZvJiJVqtwLq0fADATcmREkdDRETO7NpE3UzXLekl0Grtb6F9Ji9Wcux8FYqqm+ClcMH4PmzUJSIi6QyPC4CXwgUlNU04dr5K6nC6jMmLlWw6qSvNjekVDKWri8TREBGRM1O4yo1DR5tP2d/QEZMXKzG8Oa7lonRERGQDDENHTF6oTUXVjTiaXwWZDJjQh8kLERFJb3yirvJyJK8K5ysbJI6ma5i8WIEhqx0c6YdgH84yIiIi6YX4uGO4fpfpV35PlziarmHyYgWG5GViIqsuRERkO56c1hcAsPZgPg7Y0V5HTF4sTKMV2HNOtwjQGG4HQERENmRIlB9uHqZbvuPp/x2HEPYxbZrJi4WdKqxGdaMaXgoXDIjwlTocIiIiE49PSYSXwgXH8qvtZsVdJi8Wtvucrgx3VVwAXF14uomIyLYEeSsxS7946se7sqQNppP4bWphhiGjEXGBEkdCRETUtvkjYwAAG08WIa+iXuJoLo/JiwVptQJ7s3SVl6vjAySOhoiIqG09Q3xwTc9AaAXw2e4cqcO5LCYvFpReVIPK+hZ4KlwwoIdK6nCIiIjatWBkLADg6305aGzRSBvMZTB5saAt+t06h8UGwI39LkREZMMm9g1FDz8PVNS34MfD56UOp0P8RrWQcyW1eGtTBgAgpX+oxNEQERF1zEUuw21X63pfPk7Nsulp00xeLKBFo8VDX6ehoUWDUQmBmHtVtNQhERERXdYtV0VB4SrHsfxqHMyplDqcdjF5sYD3t5/DkbwqqDzc8OrNgyGXy6QOiYiI6LICvBSYMTgCAPBJapa0wXSAyYuZ5Vc2GIeLVtzQD+EqD4kjIiIi6rwFo2IBAL8cLUBxTaO0wbSDyYuZPf/jCTS0aDA8NgA3Du0hdThERERdMqCHCkOj/dCiEfhyT67U4bSJyYsZ/XTkPNYfL4SLXIbnZvaHTMbhIiIisj+G6svne7LRotFKG0wbmLyYSUFVA/7v+2MAgHvHJSAxjPsYERGRfZo6IBxB3koU1zTh09RsqcNphcmLmfz1u6OoamjBoEgVHpzUS+pwiIiIuk3hKse94xMAAH//+QR+P15ovK1Fo4VWK+00aiYvZnAopwLbTpfAzUWG124ZwgXpiIjI7t1xTSzmXBUFrQDu//IQdmaUorimEfPe34N/b8mQNDZXSZ/dQXywPRMAMGNIDyQEe0scDRER0ZWTyWT4+8wBKK1txsaTRbhj9T74erihpKYJpwqr8ZerY+DvpZAkNpYIrlBOWT1+PVYAALhzTJzE0RAREZmPq4scq+YlYVLfEDSptSipaULvUG+sW3KNZIkLwMrLFdFqBf61IR1aAYztHcwmXSIicjhKVxe8PS8ZL64/Ba0QeHRyH3gppU0fmLx0Q32zGsXVTVi1JQPr0nSbV92nb2wiIiJyNApXOf52Qz+pwzBi8tJJ5ysb8M3+XGw7XYK03EoYGq3lMuCV2YNxdXygtAESERE5CYv1vGRlZWHRokWIi4uDh4cHEhIS8PTTT6O5ubnD+zU2NmLJkiUIDAyEt7c3Zs2ahaKiIkuF2WmV9S14feMZHMzRJS4ebi7oGeKNd25Lxo1DI6UOj4iIyGlYrPJy6tQpaLVa/Oc//0HPnj1x7NgxLF68GHV1dXjllVfavd/DDz+Mn3/+GWvWrIFKpcLSpUtx4403YufOnZYKtVMSw3xw87BIDI32x+heQYj095Q0HiIiImclE0JYbaWZl19+Ge+88w7OnTvX5u1VVVUIDg7GF198gZtuugmALgnq27cvUlNTcfXVV1/2Oaqrq6FSqVBVVQVfXzbQEhER2YOufH9bdap0VVUVAgIC2r39wIEDaGlpwaRJk4zXJSYmIjo6GqmpqW3ep6mpCdXV1SYXIiIiclxWS14yMjLw1ltv4e677273mMLCQigUCvj5+ZlcHxoaisLCwjbvs3LlSqhUKuMlKirKnGETERGRjely8vLEE09AJpN1eDl16pTJffLz8zFlyhTMnj0bixcvNlvwALB8+XJUVVUZL7m5trl9NxEREZlHlxt2H3nkESxcuLDDY+Lj443///z585gwYQJGjRqF9957r8P7hYWFobm5GZWVlSbVl6KiIoSFhbV5H6VSCaVS2en4iYiIyL51OXkJDg5GcHBwp47Nz8/HhAkTkJycjI8++ghyeceFnuTkZLi5uWHTpk2YNWsWACA9PR05OTkYOXJkV0MlIiIiB2Sxnpf8/HyMHz8e0dHReOWVV1BSUoLCwkKT3pX8/HwkJiZi7969AACVSoVFixZh2bJl2LJlCw4cOIDbb78dI0eO7NRMIyIiInJ8FlvnZcOGDcjIyEBGRgYiI00XcTPMzm5paUF6ejrq6+uNt7322muQy+WYNWsWmpqakJKSgrfffttSYRIREZGdseo6L9bAdV6IiIjsj82u80JERER0pZi8EBERkV1h8kJERER2hckLERER2RUmL0RERGRXLDZVWiqGyVPcoJGIiMh+GL63OzMJ2uGSl5qaGgDgBo1ERER2qKamBiqVqsNjHG6dF61Wi/Pnz8PHxwcymcysj11dXY2oqCjk5uZyDRkL4nm2Dp5n6+B5th6ea+uw1HkWQqCmpgYRERGX3U7I4Sovcrm81Yq+5ubr68s/DCvgebYOnmfr4Hm2Hp5r67DEeb5cxcWADbtERERkV5i8EBERkV1h8tIFSqUSTz/9NJRKpdShODSeZ+vgebYOnmfr4bm2Dls4zw7XsEtERESOjZUXIiIisitMXoiIiMiuMHkhIiIiu8LkhYiIiOwKkxciIiKyK0xeOmnVqlWIjY2Fu7s7RowYgb1790odkl175plnIJPJTC6JiYnG2xsbG7FkyRIEBgbC29sbs2bNQlFRkYQR249t27Zh+vTpiIiIgEwmw7p160xuF0JgxYoVCA8Ph4eHByZNmoQzZ86YHFNeXo558+bB19cXfn5+WLRoEWpra634Kmzf5c7zwoULW73Hp0yZYnIMz3PHVq5ciauuugo+Pj4ICQnBzJkzkZ6ebnJMZz4rcnJyMG3aNHh6eiIkJASPPfYY1Gq1NV+KzevMuR4/fnyr9/Q999xjcoy1zjWTl074+uuvsWzZMjz99NM4ePAgBg8ejJSUFBQXF0sdml3r378/CgoKjJcdO3YYb3v44Yfx448/Ys2aNfjjjz9w/vx53HjjjRJGaz/q6uowePBgrFq1qs3bX3rpJbz55pt49913sWfPHnh5eSElJQWNjY3GY+bNm4fjx49jw4YN+Omnn7Bt2zbcdddd1noJduFy5xkApkyZYvIe//LLL01u53nu2B9//IElS5Zg9+7d2LBhA1paWjB58mTU1dUZj7ncZ4VGo8G0adPQ3NyMXbt24eOPP8bq1auxYsUKKV6SzerMuQaAxYsXm7ynX3rpJeNtVj3Xgi5r+PDhYsmSJcZ/azQaERERIVauXClhVPbt6aefFoMHD27ztsrKSuHm5ibWrFljvO7kyZMCgEhNTbVShI4BgPj++++N/9ZqtSIsLEy8/PLLxusqKyuFUqkUX375pRBCiBMnTggAYt++fcZjfv31VyGTyUR+fr7VYrcnl55nIYRYsGCBmDFjRrv34XnuuuLiYgFA/PHHH0KIzn1W/PLLL0Iul4vCwkLjMe+8847w9fUVTU1N1n0BduTScy2EEOPGjRMPPvhgu/ex5rlm5eUympubceDAAUyaNMl4nVwux6RJk5CamiphZPbvzJkziIiIQHx8PObNm4ecnBwAwIEDB9DS0mJyzhMTExEdHc1zfoUyMzNRWFhocm5VKhVGjBhhPLepqanw8/PDsGHDjMdMmjQJcrkce/bssXrM9mzr1q0ICQlBnz59cO+996KsrMx4G89z11VVVQEAAgICAHTusyI1NRUDBw5EaGio8ZiUlBRUV1fj+PHjVozevlx6rg0+//xzBAUFYcCAAVi+fDnq6+uNt1nzXDvcrtLmVlpaCo1GY/IfAwBCQ0Nx6tQpiaKyfyNGjMDq1avRp08fFBQU4Nlnn8WYMWNw7NgxFBYWQqFQwM/Pz+Q+oaGhKCwslCZgB2E4f229nw23FRYWIiQkxOR2V1dXBAQE8Px3wZQpU3DjjTciLi4OZ8+exZNPPompU6ciNTUVLi4uPM9dpNVq8dBDD+Gaa67BgAEDAKBTnxWFhYVtvt8Nt1FrbZ1rALj11lsRExODiIgIHDlyBH/961+Rnp6OtWvXArDuuWbyQpKYOnWq8f8PGjQII0aMQExMDL755ht4eHhIGBmRecyZM8f4/wcOHIhBgwYhISEBW7duxcSJEyWMzD4tWbIEx44dM+mNI8to71xf3I81cOBAhIeHY+LEiTh79iwSEhKsGiOHjS4jKCgILi4urbrXi4qKEBYWJlFUjsfPzw+9e/dGRkYGwsLC0NzcjMrKSpNjeM6vnOH8dfR+DgsLa9WMrlarUV5ezvN/BeLj4xEUFISMjAwAPM9dsXTpUvz000/YsmULIiMjjdd35rMiLCyszfe74TYy1d65bsuIESMAwOQ9ba1zzeTlMhQKBZKTk7Fp0ybjdVqtFps2bcLIkSMljMyx1NbW4uzZswgPD0dycjLc3NxMznl6ejpycnJ4zq9QXFwcwsLCTM5tdXU19uzZYzy3I0eORGVlJQ4cOGA8ZvPmzdBqtcYPK+q6vLw8lJWVITw8HADPc2cIIbB06VJ8//332Lx5M+Li4kxu78xnxciRI3H06FGTRHHDhg3w9fVFv379rPNC7MDlznVb0tLSAMDkPW21c23W9l8H9dVXXwmlUilWr14tTpw4Ie666y7h5+dn0lFNXfPII4+IrVu3iszMTLFz504xadIkERQUJIqLi4UQQtxzzz0iOjpabN68Wezfv1+MHDlSjBw5UuKo7UNNTY04dOiQOHTokAAg/vWvf4lDhw6J7OxsIYQQ//znP4Wfn5/44YcfxJEjR8SMGTNEXFycaGhoMD7GlClTRFJSktizZ4/YsWOH6NWrl5g7d65UL8kmdXSea2pqxKOPPipSU1NFZmam2Lhxoxg6dKjo1auXaGxsND4Gz3PH7r33XqFSqcTWrVtFQUGB8VJfX2885nKfFWq1WgwYMEBMnjxZpKWlifXr14vg4GCxfPlyKV6Szbrcuc7IyBDPPfec2L9/v8jMzBQ//PCDiI+PF2PHjjU+hjXPNZOXTnrrrbdEdHS0UCgUYvjw4WL37t1Sh2TXbrnlFhEeHi4UCoXo0aOHuOWWW0RGRobx9oaGBnHfffcJf39/4enpKf785z+LgoICCSO2H1u2bBEAWl0WLFgghNBNl/7b3/4mQkNDhVKpFBMnThTp6ekmj1FWVibmzp0rvL29ha+vr7j99ttFTU2NBK/GdnV0nuvr68XkyZNFcHCwcHNzEzExMWLx4sWtfvDwPHesrfMLQHz00UfGYzrzWZGVlSWmTp0qPDw8RFBQkHjkkUdES0uLlV+Nbbvcuc7JyRFjx44VAQEBQqlUip49e4rHHntMVFVVmTyOtc61TB80ERERkV1gzwsRERHZFSYvREREZFeYvBAREZFdYfJCREREdoXJCxEREdkVJi9ERERkV5i8EBERkV1h8kJERER2hckLERER2RUmL0RERGRXmLwQERGRXfl/iVXV1SXJSloAAAAASUVORK5CYII=", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(f\"First instance in ArrowHead data (class {arrow_labels[0]})\")\n", + "plt.plot(arrow[0, 0])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dEZoDQSTh5IK" + }, + "source": [ + "It is possible to use a standard `sklearn` classifier for univariate, equal length\n", + "classification problems, but it is unlikely to perform as well as bespoke time series\n", + " classifiers, since `sklearn` classifiers ignore the sequence information in the variables.\n", + "\n", + "To apply `sklearn` classifiers directly, the data needs to be reshaped into a 2D\n", + "numpy array. We also offer the ability to load univariate TSC problems directly in 2D\n", + " arrays although we recommend using 3D numpy of shape `(n_channels, 1, n_timepoints)\n", + " ` for univariate collections." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "qG96PKaCh5IK", + "outputId": "07ae1abe-a9d2-4e19-f515-9ca20e017177" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "collapsed": false, - "id": "vW1usODIh5IM" - }, - "source": [ - "A list of classifiers capable of handling multivariate classification can be obtained\n", - " with this code" + "data": { + "text/plain": [ + "0.72" ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "rand_forest = RandomForestClassifier(n_estimators=100)\n", + "arrow2d = arrow.squeeze()\n", + "arrow_test, arrow_test_labels = load_arrow_head(split=\"test\", return_type=\"numpy2d\")\n", + "rand_forest.fit(arrow2d, arrow_labels)\n", + "y_pred = rand_forest.predict(arrow_test)\n", + "accuracy_score(arrow_test_labels, y_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "r2T8AclIh5IK" + }, + "source": [ + "## Time Series Classifiers in aeon\n", + "\n", + "`aeon` contains the state of the art in time series classifiers in the package\n", + "`classification`. These are grouped based on the data representation used to find\n", + "discriminatory features. We provide a separate notebook for each of type:\n", + "[convolution based](convolution_based.ipynb), [deep learning](deep_learning.ipynb), [distance based](distance_based.ipynb), [dictionary based](dictionary_based.ipynb),\n", + "[feature_based](feature_based.ipynb), [hybrid](hybrid.ipynb), [interval based](interval_based.ipynb), and [shapelet based](shapelet_based.ipynb). We also\n", + "provide some\n", + "standard classifiers not available in scikit learn in the sklearn package.\n", + "We show the simplest use cases for classifiers and demonstrate how to build bespoke\n", + "pipelines for time series classification. An accurate and relatively\n", + "fast classifier is the [ROCKET](https://link.springer.com/article/10.1007/s10618-020-00701-z) classifier. ROCKET is a convolution based algorithm\n", + "described in detail in the [convolution based](convolution_based.ipynb) notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-16T19:16:46.486243Z", + "start_time": "2024-11-16T19:15:42.973051Z" }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "-efZQXWCh5IN", - "outputId": "778d4b99-7f28-4722-bbc1-c0937d8bdfb9" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[('Arsenal', aeon.classification.convolution_based._arsenal.Arsenal),\n", - " ('CanonicalIntervalForestClassifier',\n", - " aeon.classification.interval_based._cif.CanonicalIntervalForestClassifier),\n", - " ('Catch22Classifier',\n", - " aeon.classification.feature_based._catch22.Catch22Classifier),\n", - " ('ClassifierChannelEnsemble',\n", - " aeon.classification.compose._channel_ensemble.ClassifierChannelEnsemble),\n", - " ('DisjointCNNClassifier',\n", - " aeon.classification.deep_learning._disjoint_cnn.DisjointCNNClassifier),\n", - " ('DrCIFClassifier',\n", - " aeon.classification.interval_based._drcif.DrCIFClassifier),\n", - " ('DummyClassifier', aeon.classification.dummy.DummyClassifier),\n", - " ('ElasticEnsemble',\n", - " aeon.classification.distance_based._elastic_ensemble.ElasticEnsemble),\n", - " ('EncoderClassifier',\n", - " aeon.classification.deep_learning._encoder.EncoderClassifier),\n", - " ('FCNClassifier', aeon.classification.deep_learning._fcn.FCNClassifier),\n", - " ('FreshPRINCEClassifier',\n", - " aeon.classification.feature_based._fresh_prince.FreshPRINCEClassifier),\n", - " ('HIVECOTEV2', aeon.classification.hybrid._hivecote_v2.HIVECOTEV2),\n", - " ('HydraClassifier',\n", - " aeon.classification.convolution_based._hydra.HydraClassifier),\n", - " ('InceptionTimeClassifier',\n", - " aeon.classification.deep_learning._inception_time.InceptionTimeClassifier),\n", - " ('IndividualInceptionClassifier',\n", - " aeon.classification.deep_learning._inception_time.IndividualInceptionClassifier),\n", - " ('IndividualLITEClassifier',\n", - " aeon.classification.deep_learning._lite_time.IndividualLITEClassifier),\n", - " ('IndividualOrdinalTDE',\n", - " aeon.classification.ordinal_classification._ordinal_tde.IndividualOrdinalTDE),\n", - " ('IndividualTDE', aeon.classification.dictionary_based._tde.IndividualTDE),\n", - " ('IntervalForestClassifier',\n", - " aeon.classification.interval_based._interval_forest.IntervalForestClassifier),\n", - " ('KNeighborsTimeSeriesClassifier',\n", - " aeon.classification.distance_based._time_series_neighbors.KNeighborsTimeSeriesClassifier),\n", - " ('LITETimeClassifier',\n", - " aeon.classification.deep_learning._lite_time.LITETimeClassifier),\n", - " ('LearningShapeletClassifier',\n", - " aeon.classification.shapelet_based._ls.LearningShapeletClassifier),\n", - " ('MLPClassifier', aeon.classification.deep_learning._mlp.MLPClassifier),\n", - " ('MUSE', aeon.classification.dictionary_based._muse.MUSE),\n", - " ('MiniRocketClassifier',\n", - " aeon.classification.convolution_based._minirocket.MiniRocketClassifier),\n", - " ('MultiRocketClassifier',\n", - " aeon.classification.convolution_based._multirocket.MultiRocketClassifier),\n", - " ('MultiRocketHydraClassifier',\n", - " aeon.classification.convolution_based._mr_hydra.MultiRocketHydraClassifier),\n", - " ('OrdinalTDE',\n", - " aeon.classification.ordinal_classification._ordinal_tde.OrdinalTDE),\n", - " ('QUANTClassifier',\n", - " aeon.classification.interval_based._quant.QUANTClassifier),\n", - " ('RDSTClassifier', aeon.classification.shapelet_based._rdst.RDSTClassifier),\n", - " ('REDCOMETS', aeon.classification.dictionary_based._redcomets.REDCOMETS),\n", - " ('RISTClassifier', aeon.classification.hybrid._rist.RISTClassifier),\n", - " ('RSTSF', aeon.classification.interval_based._rstsf.RSTSF),\n", - " ('RandomIntervalClassifier',\n", - " aeon.classification.interval_based._interval_pipelines.RandomIntervalClassifier),\n", - " ('RandomIntervalSpectralEnsembleClassifier',\n", - " aeon.classification.interval_based._rise.RandomIntervalSpectralEnsembleClassifier),\n", - " ('ResNetClassifier',\n", - " aeon.classification.deep_learning._resnet.ResNetClassifier),\n", - " ('RocketClassifier',\n", - " aeon.classification.convolution_based._rocket.RocketClassifier),\n", - " ('ShapeletTransformClassifier',\n", - " aeon.classification.shapelet_based._stc.ShapeletTransformClassifier),\n", - " ('SignatureClassifier',\n", - " aeon.classification.feature_based._signature_classifier.SignatureClassifier),\n", - " ('SummaryClassifier',\n", - " aeon.classification.feature_based._summary.SummaryClassifier),\n", - " ('SupervisedIntervalClassifier',\n", - " aeon.classification.interval_based._interval_pipelines.SupervisedIntervalClassifier),\n", - " ('SupervisedTimeSeriesForest',\n", - " aeon.classification.interval_based._stsf.SupervisedTimeSeriesForest),\n", - " ('TSFreshClassifier',\n", - " aeon.classification.feature_based._tsfresh.TSFreshClassifier),\n", - " ('TemporalDictionaryEnsemble',\n", - " aeon.classification.dictionary_based._tde.TemporalDictionaryEnsemble),\n", - " ('TimeCNNClassifier',\n", - " aeon.classification.deep_learning._cnn.TimeCNNClassifier),\n", - " ('TimeSeriesForestClassifier',\n", - " aeon.classification.interval_based._tsf.TimeSeriesForestClassifier)]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from aeon.utils.discovery import all_estimators\n", - "\n", - "all_estimators(\n", - " tag_filter={\"capability:multivariate\": True},\n", - " type_filter=\"classifier\",\n", - ")" - ] + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "2xIrRErYh5IL", + "outputId": "372654b5-3fae-42e8-a315-da9e33ad8e38" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "collapsed": false, - "id": "UBibXSh1h5IN" - }, - "source": [ - "An alternative for MTSC is to build a univariate classifier on each channel, then\n", - "ensemble. Channel ensembling can be easily done via ``ClassifierChannelEnsemble``\n", - "which fits classifiers independently to specified channels, then\n", - "combines predictions through a voting scheme. The example below builds a DrCIF\n", - "classifier on the first channel and a RocketClassifier on the fourth and fifth\n", - "dimensions, ignoring the second, third and sixth." + "data": { + "text/plain": [ + "0.76" ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from aeon.classification.convolution_based import RocketClassifier\n", + "\n", + "rocket = RocketClassifier(n_kernels=2000)\n", + "rocket.fit(arrow, arrow_labels)\n", + "y_pred = rocket.predict(arrow_test)\n", + "\n", + "accuracy_score(arrow_test_labels, y_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "t9Ngfjfzh5IL" + }, + "source": [ + "A slower but generally more accurate classifier for time series classification is\n", + "version 2 of the [HIVE-COTE](https://link.springer.com/article/10.1007/s10994-021-06057-9) algorithm.\n", + "(HC2) is described in the [hybrid notebook](hybrid.ipynb) notebook. HC2 is particularly\n", + "slow\n", + "on small problems like these examples. However, it can be\n", + "configured with an approximate maximum run time as follows (it may take a bit longer\n", + "than 12 seconds to run this cell, very short times are approximate since there is a\n", + "minimum amount of work the classifier needs to do):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "u0rqqET8h5IL", + "outputId": "b1347f40-c82b-4ecf-ec72-500b1f7f8a12" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "xtlozU2Hh5IN", - "outputId": "9c5478f1-0184-4afa-87e3-1526988796fe" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from aeon.classification.compose import ClassifierChannelEnsemble\n", - "from aeon.classification.interval_based import DrCIFClassifier\n", - "\n", - "cls = ClassifierChannelEnsemble(\n", - " classifiers=[\n", - " (\"DrCIF0\", DrCIFClassifier(n_estimators=5, n_intervals=2)),\n", - " (\"ROCKET3\", RocketClassifier(n_kernels=1000)),\n", - " ],\n", - " channels=[[0], [3, 4]],\n", - ")\n", - "\n", - "cls.fit(motions, motions_labels)\n", - "y_pred = cls.predict(motions_test)\n", - "\n", - "accuracy_score(motions_test_labels, y_pred)" + "data": { + "text/plain": [ + "0.8685714285714285" ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false, - "id": "-7NDHcmzh5IN" - }, - "source": [ - "## sklearn Compatibility\n", - "\n", - "`aeon` classifiers are compatible with `sklearn` model selection and\n", - "composition tools using `aeon` data formats. For example, cross-validation can\n", - "be performed using the `sklearn` `cross_val_score` and `KFold` functionality:" + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from aeon.classification.hybrid import HIVECOTEV2\n", + "\n", + "hc2 = HIVECOTEV2(time_limit_in_minutes=0.2)\n", + "hc2.fit(arrow, arrow_labels)\n", + "y_pred = hc2.predict(arrow_test)\n", + "\n", + "accuracy_score(arrow_test_labels, y_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gTQRU2rkuPvw" + }, + "source": [ + "The LITETime Classifier is an efficient deep learning-based model for time series classification. It is designed to handle both univariate and multivariate time series data effectively, offering lightweight architecture and competitive performance. For simplicity, this notebook uses 10 epochs to demonstrate the classifier's functionality. To observe the full performance of deep learning models in aeon, it’s recommended to use the library's default epochs. The reduced epochs here simplify the demonstration and reduce runtime. Deep learning approaches for time series classification, are further described in the [deep learning notebook](./deep_learning.ipynb).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-nnwMXqtSzzc", + "outputId": "5ca88c72-3d6d-4d0b-90e7-b76da94aa62f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1m6/6\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 122ms/step\n", + "\u001B[1m6/6\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 126ms/step\n", + "\u001B[1m6/6\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 132ms/step\n", + "\u001B[1m6/6\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 122ms/step\n", + "\u001B[1m6/6\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 119ms/step\n" + ] + }, + { + "data": { + "text/plain": [ + "0.3942857142857143" ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from aeon.classification.deep_learning import LITETimeClassifier\n", + "\n", + "lite_time = LITETimeClassifier(n_epochs=10, batch_size=32, random_state=42)\n", + "lite_time.fit(arrow, arrow_labels)\n", + "y_pred = lite_time.predict(arrow_test)\n", + "\n", + "accuracy_score(arrow_test_labels, y_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "3y4vwmA1h5IL" + }, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "OaBVEJmnh5IM" + }, + "source": [ + "## Multivariate Classification\n", + "To use ``sklearn`` classifiers directly on multivariate data, one option is to flatten\n", + "the data so that the 3D array `(n_cases, n_channels, n_timepoints)` becomes a 2D array\n", + "of shape `(n_cases, n_channels*n_timepoints)`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "1mfxhLaZh5IM", + "outputId": "c0a7278f-7feb-45dc-a337-e0da2bcbbf60" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Pw_ZNfJvh5IN", - "outputId": "7963c9b6-673f-4d66-95df-e7aa418e39ce" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.88888889, 0.66666667, 0.77777778, 0.77777778])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.model_selection import KFold, cross_val_score\n", - "\n", - "cross_val_score(rocket, arrow, y=arrow_labels, cv=KFold(n_splits=4))" + "data": { + "text/plain": [ + "0.925" ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "motions_test, motions_test_labels = load_basic_motions(split=\"test\")\n", + "motions2d = motions.reshape(motions.shape[0], motions.shape[1] * motions.shape[2])\n", + "motions2d_test = motions_test.reshape(\n", + " motions_test.shape[0], motions_test.shape[1] * motions_test.shape[2]\n", + ")\n", + "rand_forest.fit(motions2d, motions_labels)\n", + "y_pred = rand_forest.predict(motions2d_test)\n", + "accuracy_score(motions_test_labels, y_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "Hc2DrT2Fh5IM" + }, + "source": [ + "However, many ``aeon`` classifiers, including ROCKET and HC2, are configured to\n", + "work with multivariate input. This works exactly like univariate classification. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "yXZW8cAch5IM", + "outputId": "f3b7b3b7-8204-4e30-cca8-1f07b4d53d90" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "collapsed": false, - "id": "aJNXKkYHh5IO" - }, - "source": [ - "Parameter tuning can be done using `sklearn` `GridSearchCV`. For example, we can tune\n", - " the _k_ and distance measure for a K-NN classifier:" + "data": { + "text/plain": [ + "1.0" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "K67ps0Bnh5IO", - "outputId": "460d0d39-ae25-4cfa-ea50-02dde57654da" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.8" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.model_selection import GridSearchCV\n", - "\n", - "from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier\n", - "\n", - "knn = KNeighborsTimeSeriesClassifier()\n", - "param_grid = {\"n_neighbors\": [1, 5], \"distance\": [\"euclidean\", \"dtw\"]}\n", - "parameter_tuning_method = GridSearchCV(knn, param_grid, cv=KFold(n_splits=4))\n", - "\n", - "parameter_tuning_method.fit(arrow, arrow_labels)\n", - "y_pred = parameter_tuning_method.predict(arrow_test)\n", - "\n", - "accuracy_score(arrow_test_labels, y_pred)" + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rocket.fit(motions, motions_labels)\n", + "y_pred = rocket.predict(motions_test)\n", + "accuracy_score(motions_test_labels, y_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "vW1usODIh5IM" + }, + "source": [ + "A list of classifiers capable of handling multivariate classification can be obtained\n", + " with this code" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-efZQXWCh5IN", + "outputId": "778d4b99-7f28-4722-bbc1-c0937d8bdfb9" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('Arsenal', aeon.classification.convolution_based._arsenal.Arsenal),\n", + " ('CanonicalIntervalForestClassifier',\n", + " aeon.classification.interval_based._cif.CanonicalIntervalForestClassifier),\n", + " ('Catch22Classifier',\n", + " aeon.classification.feature_based._catch22.Catch22Classifier),\n", + " ('ClassifierChannelEnsemble',\n", + " aeon.classification.compose._channel_ensemble.ClassifierChannelEnsemble),\n", + " ('DisjointCNNClassifier',\n", + " aeon.classification.deep_learning._disjoint_cnn.DisjointCNNClassifier),\n", + " ('DrCIFClassifier',\n", + " aeon.classification.interval_based._drcif.DrCIFClassifier),\n", + " ('DummyClassifier', aeon.classification.dummy.DummyClassifier),\n", + " ('ElasticEnsemble',\n", + " aeon.classification.distance_based._elastic_ensemble.ElasticEnsemble),\n", + " ('EncoderClassifier',\n", + " aeon.classification.deep_learning._encoder.EncoderClassifier),\n", + " ('FCNClassifier', aeon.classification.deep_learning._fcn.FCNClassifier),\n", + " ('FreshPRINCEClassifier',\n", + " aeon.classification.feature_based._fresh_prince.FreshPRINCEClassifier),\n", + " ('HIVECOTEV2', aeon.classification.hybrid._hivecote_v2.HIVECOTEV2),\n", + " ('HydraClassifier',\n", + " aeon.classification.convolution_based._hydra.HydraClassifier),\n", + " ('InceptionTimeClassifier',\n", + " aeon.classification.deep_learning._inception_time.InceptionTimeClassifier),\n", + " ('IndividualInceptionClassifier',\n", + " aeon.classification.deep_learning._inception_time.IndividualInceptionClassifier),\n", + " ('IndividualLITEClassifier',\n", + " aeon.classification.deep_learning._lite_time.IndividualLITEClassifier),\n", + " ('IndividualOrdinalTDE',\n", + " aeon.classification.ordinal_classification._ordinal_tde.IndividualOrdinalTDE),\n", + " ('IndividualTDE', aeon.classification.dictionary_based._tde.IndividualTDE),\n", + " ('IntervalForestClassifier',\n", + " aeon.classification.interval_based._interval_forest.IntervalForestClassifier),\n", + " ('KNeighborsTimeSeriesClassifier',\n", + " aeon.classification.distance_based._time_series_neighbors.KNeighborsTimeSeriesClassifier),\n", + " ('LITETimeClassifier',\n", + " aeon.classification.deep_learning._lite_time.LITETimeClassifier),\n", + " ('LearningShapeletClassifier',\n", + " aeon.classification.shapelet_based._ls.LearningShapeletClassifier),\n", + " ('MLPClassifier', aeon.classification.deep_learning._mlp.MLPClassifier),\n", + " ('MUSE', aeon.classification.dictionary_based._muse.MUSE),\n", + " ('MiniRocketClassifier',\n", + " aeon.classification.convolution_based._minirocket.MiniRocketClassifier),\n", + " ('MultiRocketClassifier',\n", + " aeon.classification.convolution_based._multirocket.MultiRocketClassifier),\n", + " ('MultiRocketHydraClassifier',\n", + " aeon.classification.convolution_based._mr_hydra.MultiRocketHydraClassifier),\n", + " ('OrdinalTDE',\n", + " aeon.classification.ordinal_classification._ordinal_tde.OrdinalTDE),\n", + " ('QUANTClassifier',\n", + " aeon.classification.interval_based._quant.QUANTClassifier),\n", + " ('RDSTClassifier', aeon.classification.shapelet_based._rdst.RDSTClassifier),\n", + " ('REDCOMETS', aeon.classification.dictionary_based._redcomets.REDCOMETS),\n", + " ('RISTClassifier', aeon.classification.hybrid._rist.RISTClassifier),\n", + " ('RSTSF', aeon.classification.interval_based._rstsf.RSTSF),\n", + " ('RandomIntervalClassifier',\n", + " aeon.classification.interval_based._interval_pipelines.RandomIntervalClassifier),\n", + " ('RandomIntervalSpectralEnsembleClassifier',\n", + " aeon.classification.interval_based._rise.RandomIntervalSpectralEnsembleClassifier),\n", + " ('ResNetClassifier',\n", + " aeon.classification.deep_learning._resnet.ResNetClassifier),\n", + " ('RocketClassifier',\n", + " aeon.classification.convolution_based._rocket.RocketClassifier),\n", + " ('ShapeletTransformClassifier',\n", + " aeon.classification.shapelet_based._stc.ShapeletTransformClassifier),\n", + " ('SignatureClassifier',\n", + " aeon.classification.feature_based._signature_classifier.SignatureClassifier),\n", + " ('SummaryClassifier',\n", + " aeon.classification.feature_based._summary.SummaryClassifier),\n", + " ('SupervisedIntervalClassifier',\n", + " aeon.classification.interval_based._interval_pipelines.SupervisedIntervalClassifier),\n", + " ('SupervisedTimeSeriesForest',\n", + " aeon.classification.interval_based._stsf.SupervisedTimeSeriesForest),\n", + " ('TSFreshClassifier',\n", + " aeon.classification.feature_based._tsfresh.TSFreshClassifier),\n", + " ('TemporalDictionaryEnsemble',\n", + " aeon.classification.dictionary_based._tde.TemporalDictionaryEnsemble),\n", + " ('TimeCNNClassifier',\n", + " aeon.classification.deep_learning._cnn.TimeCNNClassifier),\n", + " ('TimeSeriesForestClassifier',\n", + " aeon.classification.interval_based._tsf.TimeSeriesForestClassifier)]" ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from aeon.utils.discovery import all_estimators\n", + "\n", + "all_estimators(\n", + " tag_filter={\"capability:multivariate\": True},\n", + " type_filter=\"classifier\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "UBibXSh1h5IN" + }, + "source": [ + "An alternative for MTSC is to build a univariate classifier on each channel, then\n", + "ensemble. Channel ensembling can be easily done via ``ClassifierChannelEnsemble``\n", + "which fits classifiers independently to specified channels, then\n", + "combines predictions through a voting scheme. The example below builds a DrCIF\n", + "classifier on the first channel and a RocketClassifier on the fourth and fifth\n", + "dimensions, ignoring the second, third and sixth." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "xtlozU2Hh5IN", + "outputId": "9c5478f1-0184-4afa-87e3-1526988796fe" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "collapsed": false, - "id": "FtiuhfARh5IO" - }, - "source": [ - "Probability calibration is possible with the `sklearn` `CalibratedClassifierCV`:" + "data": { + "text/plain": [ + "0.9" ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from aeon.classification.compose import ClassifierChannelEnsemble\n", + "from aeon.classification.interval_based import DrCIFClassifier\n", + "\n", + "cls = ClassifierChannelEnsemble(\n", + " classifiers=[\n", + " (\"DrCIF0\", DrCIFClassifier(n_estimators=5, n_intervals=2)),\n", + " (\"ROCKET3\", RocketClassifier(n_kernels=1000)),\n", + " ],\n", + " channels=[[0], [3, 4]],\n", + ")\n", + "\n", + "cls.fit(motions, motions_labels)\n", + "y_pred = cls.predict(motions_test)\n", + "\n", + "accuracy_score(motions_test_labels, y_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "-7NDHcmzh5IN" + }, + "source": [ + "## sklearn Compatibility\n", + "\n", + "`aeon` classifiers are compatible with `sklearn` model selection and\n", + "composition tools using `aeon` data formats. For example, cross-validation can\n", + "be performed using the `sklearn` `cross_val_score` and `KFold` functionality:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "Pw_ZNfJvh5IN", + "outputId": "7963c9b6-673f-4d66-95df-e7aa418e39ce" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "oyywFEuhh5IO", - "outputId": "719c1f06-7eff-429b-dd26-e7da6be20972" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.7485714285714286" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.calibration import CalibratedClassifierCV\n", - "\n", - "from aeon.classification.interval_based import DrCIFClassifier\n", - "\n", - "calibrated_drcif = CalibratedClassifierCV(\n", - " estimator=DrCIFClassifier(n_estimators=10, n_intervals=5), cv=4\n", - ")\n", - "\n", - "calibrated_drcif.fit(arrow, arrow_labels)\n", - "y_pred = calibrated_drcif.predict(arrow_test)\n", - "\n", - "accuracy_score(arrow_test_labels, y_pred)" + "data": { + "text/plain": [ + "array([0.88888889, 0.66666667, 0.77777778, 0.77777778])" ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.model_selection import KFold, cross_val_score\n", + "\n", + "cross_val_score(rocket, arrow, y=arrow_labels, cv=KFold(n_splits=4))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "aJNXKkYHh5IO" + }, + "source": [ + "Parameter tuning can be done using `sklearn` `GridSearchCV`. For example, we can tune\n", + " the _k_ and distance measure for a K-NN classifier:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "K67ps0Bnh5IO", + "outputId": "460d0d39-ae25-4cfa-ea50-02dde57654da" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "collapsed": false, - "id": "flt4wrMOh5IO" - }, - "source": [ - "### Background info and references for classifiers used here\n", - "\n", - "#### KNeighborsTimeSeriesClassifier\n", - "\n", - "One nearest neighbour (1-NN) classification with Dynamic Time Warping (DTW) is\n", - "a [distance based](distance_based.ipynb) classifier and one of the most frequently used\n", - "approaches, although it is less accurate on average than the state of the art.\n", - "\n", - "#### RocketClassifier\n", - "The RocketClassifier is a [convolution based](convolution_based.ipynb) classifier\n", - "made up of a pipeline combination of the ROCKET transformation\n", - " (transformations.panel.rocket) and the sklearn RidgeClassifierCV classifier. The RocketClassifier is configurable to use variants MiniRocket and MultiRocket. ROCKET is based on generating random convolutional kernels. A large number are generated, then a linear classifier is built on the output.\n", - "\n", - "[1] Dempster, Angus, François Petitjean, and Geoffrey I. Webb. \"Rocket: exceptionally fast and accurate time series classification using random convolutional kernels.\" Data Mining and Knowledge Discovery (2020)\n", - "[arXiv version](https://arxiv.org/abs/1910.13051)\n", - "[DAMI 2020](https://link.springer.com/article/10.1007/s10618-020-00701-z)\n", - "\n", - "#### DrCIF\n", - "The Diverse Representation Canonical Interval Forest Classifier (DrCIF) is an\n", - "[interval based](interval_based.ipynb) classifier. The algorithm takes multiple\n", - "randomised intervals from each series and extracts a range of features. These features are used to build a decision tree, which in turn are ensembled into a decision tree forest, in the style of a random forest.\n", - "\n", - "Original CIF classifier:\n", - "[2] Matthew Middlehurst and James Large and Anthony Bagnall. \"The Canonical Interval Forest (CIF) Classifier for Time Series Classification.\" IEEE International Conference on Big Data (2020)\n", - "[arXiv version](https://arxiv.org/abs/2008.09172)\n", - "[IEEE BigData (2020)](https://ieeexplore.ieee.org/abstract/document/9378424?casa_token=8g_IG5MLJZ4AAAAA:ItxW0bY4eCRwfdV9kLvf-8a8X73UFCYUGU9D19PwrHigjivLJVchxHwkM3Btn7vvlOJ_0HiLRa3LCA)\n", - "\n", - "The DrCIF adjustment was proposed in [3].\n", - "\n", - "#### HIVE-COTE 2.0 (HC2)\n", - "The HIerarchical VotE Collective of Transformation-based Ensembles is a meta ensemble\n", - " [hybrid](hybrid.ipynb) that combines classifiers built on different representations.\n", - " Version 2 combines DrCIF, TDE, an ensemble of RocketClassifiers called the Arsenal and the ShapeletTransformClassifier. It is one of the most accurate classifiers on the UCR and UEA time series archives.\n", - "\n", - "[3] Middlehurst, Matthew, James Large, Michael Flynn, Jason Lines, Aaron Bostrom, and Anthony Bagnall. \"HIVE-COTE 2.0: a new meta ensemble for time series classification.\" Machine Learning (2021)\n", - "[ML 2021](https://link.springer.com/article/10.1007/s10994-021-06057-9)\n", - "\n", - "#### LITETime Classifier\n", - "\n", - "The LITETimeClassifier, is a lightweight [deep learning model](https://github.com/aeon-toolkit/aeon/blob/main/examples/classification/deep_learning.ipynb). designed specifically for efficient and accurate time series classification (TSC). It leverages techniques like depthwise separable convolutions to minimize the number of parameters and computational overhead without compromising performance.\n", - "\n", - "[4] Ismail-Fawaz et al. LITE: Light Inception with boosTing tEchniques for Time Series Classification, IEEE International Conference on Data Science and Advanced Analytics, 2023 [LITE (pdf)](https://germain-forestier.info/publis/dsaa2023.pdf)\n", - "\n", - "[5] Ismail-Fawaz, Ali, et al. “Look Into the LITE in Deep Learning for Time Series Classification.” arXiv preprint arXiv:2409.02869 (2024).arXiv preprint arXiv:2409.02869 [arXiv preprint](https://arxiv.org/abs/2409.02869)\n" + "data": { + "text/plain": [ + "0.8" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "ms0mSnWEU11v" - }, - "outputs": [], - "source": [] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" } - ], - "metadata": { - "accelerator": "GPU", + ], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier\n", + "\n", + "knn = KNeighborsTimeSeriesClassifier()\n", + "param_grid = {\"n_neighbors\": [1, 5], \"distance\": [\"euclidean\", \"dtw\"]}\n", + "parameter_tuning_method = GridSearchCV(knn, param_grid, cv=KFold(n_splits=4))\n", + "\n", + "parameter_tuning_method.fit(arrow, arrow_labels)\n", + "y_pred = parameter_tuning_method.predict(arrow_test)\n", + "\n", + "accuracy_score(arrow_test_labels, y_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "FtiuhfARh5IO" + }, + "source": [ + "Probability calibration is possible with the `sklearn` `CalibratedClassifierCV`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "colab": { - "gpuType": "T4", - "provenance": [] - }, - "interpreter": { - "hash": "9d800c14abb2bd109b7479fe8830174a66f0a4a77373f77c2c7334932e1a4922" + "base_uri": "https://localhost:8080/" }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" + "id": "oyywFEuhh5IO", + "outputId": "719c1f06-7eff-429b-dd26-e7da6be20972" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7485714285714286" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" } + ], + "source": [ + "from sklearn.calibration import CalibratedClassifierCV\n", + "\n", + "from aeon.classification.interval_based import DrCIFClassifier\n", + "\n", + "calibrated_drcif = CalibratedClassifierCV(\n", + " estimator=DrCIFClassifier(n_estimators=10, n_intervals=5), cv=4\n", + ")\n", + "\n", + "calibrated_drcif.fit(arrow, arrow_labels)\n", + "y_pred = calibrated_drcif.predict(arrow_test)\n", + "\n", + "accuracy_score(arrow_test_labels, y_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "flt4wrMOh5IO" + }, + "source": [ + "### Background info and references for classifiers used here\n", + "\n", + "#### KNeighborsTimeSeriesClassifier\n", + "\n", + "One nearest neighbour (1-NN) classification with Dynamic Time Warping (DTW) is\n", + "a [distance based](distance_based.ipynb) classifier and one of the most frequently used\n", + "approaches, although it is less accurate on average than the state of the art.\n", + "\n", + "#### RocketClassifier\n", + "The RocketClassifier is a [convolution based](convolution_based.ipynb) classifier\n", + "made up of a pipeline combination of the ROCKET transformation\n", + " (transformations.panel.rocket) and the sklearn RidgeClassifierCV classifier. The RocketClassifier is configurable to use variants MiniRocket and MultiRocket. ROCKET is based on generating random convolutional kernels. A large number are generated, then a linear classifier is built on the output.\n", + "\n", + "[1] Dempster, Angus, François Petitjean, and Geoffrey I. Webb. \"Rocket: exceptionally fast and accurate time series classification using random convolutional kernels.\" Data Mining and Knowledge Discovery (2020)\n", + "[arXiv version](https://arxiv.org/abs/1910.13051)\n", + "[DAMI 2020](https://link.springer.com/article/10.1007/s10618-020-00701-z)\n", + "\n", + "#### DrCIF\n", + "The Diverse Representation Canonical Interval Forest Classifier (DrCIF) is an\n", + "[interval based](interval_based.ipynb) classifier. The algorithm takes multiple\n", + "randomised intervals from each series and extracts a range of features. These features are used to build a decision tree, which in turn are ensembled into a decision tree forest, in the style of a random forest.\n", + "\n", + "Original CIF classifier:\n", + "[2] Matthew Middlehurst and James Large and Anthony Bagnall. \"The Canonical Interval Forest (CIF) Classifier for Time Series Classification.\" IEEE International Conference on Big Data (2020)\n", + "[arXiv version](https://arxiv.org/abs/2008.09172)\n", + "[IEEE BigData (2020)](https://ieeexplore.ieee.org/abstract/document/9378424?casa_token=8g_IG5MLJZ4AAAAA:ItxW0bY4eCRwfdV9kLvf-8a8X73UFCYUGU9D19PwrHigjivLJVchxHwkM3Btn7vvlOJ_0HiLRa3LCA)\n", + "\n", + "The DrCIF adjustment was proposed in [3].\n", + "\n", + "#### HIVE-COTE 2.0 (HC2)\n", + "The HIerarchical VotE Collective of Transformation-based Ensembles is a meta ensemble\n", + " [hybrid](hybrid.ipynb) that combines classifiers built on different representations.\n", + " Version 2 combines DrCIF, TDE, an ensemble of RocketClassifiers called the Arsenal and the ShapeletTransformClassifier. It is one of the most accurate classifiers on the UCR and UEA time series archives.\n", + "\n", + "[3] Middlehurst, Matthew, James Large, Michael Flynn, Jason Lines, Aaron Bostrom, and Anthony Bagnall. \"HIVE-COTE 2.0: a new meta ensemble for time series classification.\" Machine Learning (2021)\n", + "[ML 2021](https://link.springer.com/article/10.1007/s10994-021-06057-9)\n", + "\n", + "#### LITETime Classifier\n", + "\n", + "The LITETimeClassifier, is a lightweight [deep learning model](https://github.com/aeon-toolkit/aeon/blob/main/examples/classification/deep_learning.ipynb). designed specifically for efficient and accurate time series classification (TSC). It leverages techniques like depthwise separable convolutions to minimize the number of parameters and computational overhead without compromising performance.\n", + "\n", + "[4] Ismail-Fawaz et al. LITE: Light Inception with boosTing tEchniques for Time Series Classification, IEEE International Conference on Data Science and Advanced Analytics, 2023 [LITE (pdf)](https://germain-forestier.info/publis/dsaa2023.pdf)\n", + "\n", + "[5] Ismail-Fawaz, Ali, et al. “Look Into the LITE in Deep Learning for Time Series Classification.” arXiv preprint arXiv:2409.02869 (2024).arXiv preprint arXiv:2409.02869 [arXiv preprint](https://arxiv.org/abs/2409.02869)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ms0mSnWEU11v" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "interpreter": { + "hash": "9d800c14abb2bd109b7479fe8830174a66f0a4a77373f77c2c7334932e1a4922" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } From 08cd4437b3334ff8d3b06ef27581fe51af93327d Mon Sep 17 00:00:00 2001 From: Balgopal Moharana <99070111+lucifer4073@users.noreply.github.com> Date: Sat, 26 Jul 2025 21:04:02 +0530 Subject: [PATCH 192/216] [ENH] Added TemporalConvolutionalNetwork in aeon/networks (#2933) * tcn network added * tcn_net pytest added * tcn_network updated with default params * tcn reshaped * tcn changed * tcn updated * doctest corrected * tcn rshaped * workflow corrected --- aeon/networks/__init__.py | 2 + aeon/networks/_tcn.py | 327 ++++++++++++++++++++++++++++++++ aeon/networks/tests/test_tcn.py | 194 +++++++++++++++++++ 3 files changed, 523 insertions(+) create mode 100644 aeon/networks/_tcn.py create mode 100644 aeon/networks/tests/test_tcn.py diff --git a/aeon/networks/__init__.py b/aeon/networks/__init__.py index d774abe102..aed37be7e7 100644 --- a/aeon/networks/__init__.py +++ b/aeon/networks/__init__.py @@ -19,6 +19,7 @@ "AEBiGRUNetwork", "DisjointCNNNetwork", "RecurrentNetwork", + "TCNNetwork", ] from aeon.networks._ae_abgru import AEAttentionBiGRUNetwork from aeon.networks._ae_bgru import AEBiGRUNetwork @@ -36,4 +37,5 @@ from aeon.networks._mlp import MLPNetwork from aeon.networks._resnet import ResNetNetwork from aeon.networks._rnn import RecurrentNetwork +from aeon.networks._tcn import TCNNetwork from aeon.networks.base import BaseDeepLearningNetwork diff --git a/aeon/networks/_tcn.py b/aeon/networks/_tcn.py new file mode 100644 index 0000000000..f88242823c --- /dev/null +++ b/aeon/networks/_tcn.py @@ -0,0 +1,327 @@ +"""Implementation of Temporal Convolutional Network (TCN).""" + +__maintainer__ = [] + +from aeon.networks.base import BaseDeepLearningNetwork + + +class TCNNetwork(BaseDeepLearningNetwork): + """Temporal Convolutional Network (TCN) for sequence modeling. + + A generic convolutional architecture for sequence modeling that combines: + - Dilated convolutions for exponentially large receptive fields + - Residual connections for training stability + + The TCN can take sequences of any length and map them to output sequences + of the same length, making it suitable for autoregressive prediction tasks. + + Parameters + ---------- + n_blocks : list of int + List specifying the number of output channels for each layer. + The length determines the depth of the network. + kernel_size : int, default=2 + Size of the convolutional kernel. Larger kernels can capture + more local context but require more parameters. + dropout : float, default=0.2 + Dropout rate applied after each convolutional layer for regularization. + + Notes + ----- + The receptive field size grows exponentially with network depth due to + dilated convolutions with dilation factors of 2^i for layer i. + + References + ---------- + .. [1] Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of + generic convolutional and recurrent networks for sequence modeling. + arXiv preprint arXiv:1803.01271. + + Examples + -------- + >>> from aeon.networks._tcn import TCNNetwork + >>> network = TCNNetwork(n_blocks=[8, 8]) + >>> input_layer, output = network.build_network(input_shape=(150, 4)) + >>> input_layer.shape, output.shape + ((None, 150, 4), (None, 4)) + """ + + _config = { + "python_dependencies": ["tensorflow"], + "python_version": "<3.13", + "structure": "encoder", + } + + def __init__( + self, + n_blocks: list = [16] * 3, + kernel_size: int = 2, + dropout: float = 0.2, + ): + """Initialize the TCN architecture. + + Parameters + ---------- + n_blocks : list of int + Number of output channels for each temporal block. + kernel_size : int, default=2 + Size of convolutional kernels. + dropout : float, default=0.2 + Dropout rate for regularization. + """ + super().__init__() + self.n_blocks = n_blocks + self.kernel_size = kernel_size + self.dropout = dropout + + def _conv1d_with_variable_padding( + self, + input_tensor, + n_filters: int, + kernel_size: int, + padding_value: int, + strides: int = 1, + dilation_rate: int = 1, + ): + """Apply 1D convolution with variable padding for causal convolutions. + + Parameters + ---------- + input_tensor : tf.Tensor + Input tensor of shape (batch_size, n_timepoints, n_channels). + n_filters : int + Number of output filters. + kernel_size : int + Size of the convolutional kernel. + padding_value : int + Amount of padding to apply. + strides : int, default=1 + Stride of the convolution. + dilation_rate : int, default=1 + Dilation rate for dilated convolutions. + + Returns + ------- + tf.Tensor + Output tensor after convolution. + """ + import tensorflow as tf + + # Apply padding in sequence dimension + padded_x = tf.keras.layers.ZeroPadding1D(padding=padding_value)(input_tensor) + + # Create and apply convolution layer + conv_layer = tf.keras.layers.Conv1D( + filters=n_filters, + kernel_size=kernel_size, + strides=strides, + dilation_rate=dilation_rate, + padding="valid", + ) + + # Apply convolution + out = conv_layer(padded_x) + + return out + + def _chomp(self, input_tensor, chomp_size: int): + """Remove padding from the end of sequences to maintain causality. + + This operation ensures that the output at time t only depends on + inputs from times 0 to t, preventing information leakage from future. + + Parameters + ---------- + input_tensor : tf.Tensor + Input tensor of shape (batch_size, channels, sequence_length). + chomp_size : int + Number of time steps to remove from the end. + + Returns + ------- + tf.Tensor + Chomped tensor with reduced sequence length. + """ + return input_tensor[:, :-chomp_size, :] + + def _temporal_block( + self, + input_tensor, + n_inputs: int, + n_filters: int, + kernel_size: int, + strides: int, + dilation_rate: int, + padding_value: int, + dropout: float = 0.2, + training: bool = None, + ): + """Create a temporal block with dilated causal convolutions. + + Each temporal block consists of: + 1. Two dilated causal convolutions + 2. ReLU activations and dropout for regularization + 3. Residual connection with optional 1x1 convolution for dimension + matching + + Parameters + ---------- + input_tensor : tf.Tensor + Input tensor of shape (batch_size, channels, sequence_length). + n_inputs : int + Number of input channels. + n_filters : int + Number of output filters. + kernel_size : int + Size of convolutional kernels. + strides : int + Stride of convolutions (typically 1). + dilation_rate : int + Dilation factor for dilated convolutions. + padding_value : int + Padding size to be chomped off. + dropout : float, default=0.2 + Dropout rate for regularization. + training : bool, optional + Whether the model is in training mode. + + Returns + ------- + tf.Tensor + Output tensor of shape (batch_size, n_filters, sequence_length). + """ + import tensorflow as tf + + # First convolution block + out = self._conv1d_with_variable_padding( + input_tensor, n_filters, kernel_size, padding_value, strides, dilation_rate + ) + out = self._chomp(out, padding_value) + out = tf.keras.layers.ReLU()(out) + out = tf.keras.layers.Dropout(dropout)(out, training=training) + + # Second convolution block + out = self._conv1d_with_variable_padding( + out, n_filters, kernel_size, padding_value, strides, dilation_rate + ) + out = self._chomp(out, padding_value) + out = tf.keras.layers.ReLU()(out) + out = tf.keras.layers.Dropout(dropout)(out, training=training) + + # Residual connection with optional dimension matching + if n_inputs != n_filters: + res = self._conv1d_with_variable_padding( + input_tensor=input_tensor, + n_filters=n_filters, + kernel_size=1, + padding_value=0, + strides=1, + dilation_rate=1, + ) + else: + res = input_tensor + + # Add residual and apply final ReLU + result = tf.keras.layers.Add()([out, res]) + return tf.keras.layers.ReLU()(result) + + def _temporal_conv_net( + self, + input_tensor, + n_inputs: int, + n_blocks: list, + kernel_size: int = 2, + dropout: float = 0.2, + training: bool = None, + ): + """Apply the complete Temporal Convolutional Network. + + Stacks multiple temporal blocks with exponentially increasing dilation + factors to achieve a large receptive field efficiently. + + Parameters + ---------- + input_tensor : tf.Tensor + Input tensor of shape (batch_size, channels, sequence_length). + n_inputs : int + Number of input channels. + n_blocks : list of int + Number of output channels for each temporal block. + kernel_size : int, default=2 + Size of convolutional kernels. + dropout : float, default=0.2 + Dropout rate for regularization. + training : bool, optional + Whether the model is in training mode. + + Returns + ------- + tf.Tensor + Output tensor after applying all temporal blocks. + """ + num_levels = len(n_blocks) + for i in range(num_levels): + dilation_rate = 2**i + in_channels = n_inputs if i == 0 else n_blocks[i - 1] + out_channels = n_blocks[i] + padding_value = (kernel_size - 1) * dilation_rate + + input_tensor = self._temporal_block( + input_tensor, + n_inputs=in_channels, + n_filters=out_channels, + kernel_size=kernel_size, + strides=1, + dilation_rate=dilation_rate, + padding_value=padding_value, + dropout=dropout, + training=training, + ) + + return input_tensor + + def build_network(self, input_shape: tuple, **kwargs) -> tuple: + """Build the complete TCN architecture. + + Constructs a series of temporal blocks with exponentially increasing + dilation factors to achieve a large receptive field efficiently. + + Parameters + ---------- + input_shape : tuple + Shape of input data (n_timepoints, n_channels). + **kwargs + Additional keyword arguments (unused). + + Returns + ------- + tuple + A tuple containing (input_layer, output_tensor) representing + the complete network architecture. + + Notes + ----- + The dilation factor for layer i is 2^i, which ensures exponential + growth of the receptive field while maintaining computational + efficiency. + """ + import tensorflow as tf + + # Create input layer + input_layer = tf.keras.layers.Input(shape=input_shape) + + # Transpose input to match the expected format (batch, n_timepoints, n_channels) + x = input_layer + n_inputs = input_shape[1] + + # Apply TCN using the private function + x = self._temporal_conv_net( + x, + n_inputs=n_inputs, + n_blocks=self.n_blocks, + kernel_size=self.kernel_size, + dropout=self.dropout, + ) + output = tf.keras.layers.Dense(input_shape[1])(x[:, :, -1]) + # output = tf.keras.layers.Dense(1)(x) + return input_layer, output diff --git a/aeon/networks/tests/test_tcn.py b/aeon/networks/tests/test_tcn.py new file mode 100644 index 0000000000..b21df0784d --- /dev/null +++ b/aeon/networks/tests/test_tcn.py @@ -0,0 +1,194 @@ +"""Tests for the TCNNetwork.""" + +import pytest + +from aeon.networks import TCNNetwork +from aeon.utils.validation._dependencies import _check_soft_dependencies + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +def test_tcn_network_basic(): + """Test basic TCN network creation and build_network functionality.""" + import tensorflow as tf + + input_shape = (100, 5) # (n_timepoints, n_channels) + n_blocks = [32, 64] + tcn_network = TCNNetwork(n_blocks=n_blocks) + + input_layer, output_layer = tcn_network.build_network(input_shape) + + # Check that layers are created correctly + assert hasattr(input_layer, "shape"), "Input layer should have a shape attribute" + assert hasattr(output_layer, "shape"), "Output layer should have a shape attribute" + assert input_layer.dtype == tf.float32 + assert output_layer.dtype == tf.float32 + + # Create a model to test the network structure + model = tf.keras.Model(inputs=input_layer, outputs=output_layer) + assert model is not None, "Model should be created successfully" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("n_blocks", [[32], [32, 64], [16, 32, 64], [64, 32, 16]]) +def test_tcn_network_different_channels(n_blocks): + """Test TCN network with different channel configurations.""" + import tensorflow as tf + + input_shape = (50, 3) # (n_timepoints, n_channels) + tcn_network = TCNNetwork(n_blocks=n_blocks) + + input_layer, output_layer = tcn_network.build_network(input_shape) + + # Create a model and verify it works + model = tf.keras.Model(inputs=input_layer, outputs=output_layer) + assert model is not None + + # Test with dummy data + import numpy as np + + dummy_input = np.random.random((8,) + input_shape) + output = model(dummy_input) + assert output is not None, "Model should produce output" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("kernel_size", [2, 3, 5]) +def test_tcn_network_kernel_sizes(kernel_size): + """Test TCN network with different kernel sizes.""" + import tensorflow as tf + + input_shape = (80, 4) # (n_timepoints, n_channels) + n_blocks = [32, 64] + + tcn_network = TCNNetwork( + n_blocks=n_blocks, + kernel_size=kernel_size, + ) + + input_layer, output_layer = tcn_network.build_network(input_shape) + + # Verify network builds successfully + model = tf.keras.Model(inputs=input_layer, outputs=output_layer) + assert model is not None + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("dropout", [0.0, 0.1, 0.3, 0.5]) +def test_tcn_network_dropout_rates(dropout): + """Test TCN network with different dropout rates.""" + import tensorflow as tf + + input_shape = (60, 2) # (n_timepoints, n_channels) + n_blocks = [16, 32] + + tcn_network = TCNNetwork(n_blocks=n_blocks, dropout=dropout) + input_layer, output_layer = tcn_network.build_network(input_shape) + + # Verify network builds successfully + model = tf.keras.Model(inputs=input_layer, outputs=output_layer) + assert model is not None + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +def test_tcn_network_output_shape(): + """Test TCN network output shapes.""" + import numpy as np + import tensorflow as tf + + input_shape = (40, 6) # (n_timepoints, n_channels) + batch_size = 16 + n_blocks = [32, 64] + + tcn_network = TCNNetwork(n_blocks=n_blocks) + input_layer, output_layer = tcn_network.build_network(input_shape) + + model = tf.keras.Model(inputs=input_layer, outputs=output_layer) + + # Create dummy input and test output shape + dummy_input = np.random.random((batch_size,) + input_shape) + output = model(dummy_input) + + # Output should have the same number of channels as input + expected_shape = (batch_size, input_shape[1]) # (batch_size, n_channels) + assert ( + output.shape == expected_shape + ), f"Expected shape {expected_shape}, got {output.shape}" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +def test_tcn_network_config(): + """Test TCN network configuration attributes.""" + tcn_network = TCNNetwork(n_blocks=[16, 32]) + + # Check _config attributes + assert "python_dependencies" in tcn_network._config + assert "tensorflow" in tcn_network._config["python_dependencies"] + assert "python_version" in tcn_network._config + assert "structure" in tcn_network._config + assert tcn_network._config["structure"] == "encoder" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +def test_tcn_network_parameter_initialization(): + """Test TCN network parameter initialization.""" + n_blocks = [32, 64, 128] + kernel_size = 3 + dropout = 0.2 + + tcn_network = TCNNetwork( + n_blocks=n_blocks, + kernel_size=kernel_size, + dropout=dropout, + ) + + # Check that parameters are set correctly + assert tcn_network.n_blocks == n_blocks + assert tcn_network.kernel_size == kernel_size + assert tcn_network.dropout == dropout + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +def test_tcn_network_single_layer(): + """Test TCN network with single temporal block.""" + import tensorflow as tf + + input_shape = (30, 2) # (n_timepoints, n_channels) + n_blocks = [16] # Single layer + + tcn_network = TCNNetwork(n_blocks=n_blocks) + input_layer, output_layer = tcn_network.build_network(input_shape) + + # Verify single layer network works + model = tf.keras.Model(inputs=input_layer, outputs=output_layer) + assert model is not None + + # Test with dummy data + import numpy as np + + dummy_input = np.random.random((4,) + input_shape) + output = model(dummy_input) + assert output.shape == (4, input_shape[1]) # (batch_size, n_channels) From 755154b260d23d99a9c681b50c9c2191926a8e82 Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Sun, 27 Jul 2025 15:34:14 +0100 Subject: [PATCH 193/216] [BUG] Improve signature docs (#2929) * signature docs * tests * typo * notebook * correct image * fix test * decalre in init * switch classifier to store SignatureTransformer * revert classifier * revert transformer * embed image rather than link * equation * equation * remove tests * maths * maths * add eq * add eq * remove example * line break --------- Co-authored-by: Antoine Guillaume --- .../collection/signature_based/_rescaling.py | 4 +- .../transformations/signature_method.ipynb | 92 +++++++++++-------- 2 files changed, 54 insertions(+), 42 deletions(-) diff --git a/aeon/transformations/collection/signature_based/_rescaling.py b/aeon/transformations/collection/signature_based/_rescaling.py index b2713a4737..99f2282cd6 100644 --- a/aeon/transformations/collection/signature_based/_rescaling.py +++ b/aeon/transformations/collection/signature_based/_rescaling.py @@ -35,9 +35,9 @@ def _rescale_path(path, depth): def _rescale_signature(signature, channels, depth): - """Rescals the output signature by multiplying the depth-d term by d!. + """Rescales the output signature by multiplying the depth-d term by d!. - Ain is that every term become ~O(1). + Aim is that every term become ~O(1). Parameters ---------- diff --git a/examples/transformations/signature_method.ipynb b/examples/transformations/signature_method.ipynb index 94a7cc14d1..a1e903e441 100644 --- a/examples/transformations/signature_method.ipynb +++ b/examples/transformations/signature_method.ipynb @@ -1,14 +1,15 @@ { "cells": [ { - "cell_type": "markdown", "metadata": {}, + "cell_type": "markdown", "source": [ - "# The Signature Method with aeon\n", + "# The Signature Method\n", "\n", "The ‘signature method’ refers to a collection of feature extraction techniques for multimodal sequential data, derived from the theory of controlled differential equations. In recent years, a large number of modifications have been suggested to the signature method so as to improve some aspect of it.\n", "\n", - "In the paper [\"A Generalised Signature Method for Time-Series\"](https://arxiv.org/abs/2006.00873) [1] the authors collated the vast majority of these modifications into a single document and ran a large hyper-parameter study over the multivariate UEA datasets to build a generic signature algorithm that is expected to work well on a wide range of datasets. We implement the best practice results from this study as the default starting values for our hyperparameters in the `SignatureClassifier` module.\n" + "In the paper [\"A Generalised Signature Method for Time-Series\"](https://arxiv.org/abs/2006.00873) [1] the authors collated the vast majority of these modifications into a single document and ran a large hyper-parameter study over the multivariate UEA datasets to build a generic signature algorithm that is expected to work well on a wide range of datasets. We implement the best practice results from \n", + "this study as the default starting values for our hyperparameters in the `SignatureClassifier` estimator.\n" ] }, { @@ -16,21 +17,29 @@ "metadata": {}, "source": [ "## The Path Signature\n", - "At the heart of the signature method is the so-called \"signature transform\".\n", "\n", - "A path $X$ of finite length in $\\textit{d}$ dimensions can be described by the mapping $X:[a, b]\\rightarrow\\mathbb{R}$ $\\!\\!^d$, or in terms of coordinates $X=(X^1_t, X^2_t, ...,X^d_t)$, where each coordinate $X^i_t$ is real-valued and parameterised by $t\\in[a,b]$.\n", + "At the heart of the signature method is the so-called *signature transform*.\n", + "\n", + "A path $X$ of finite length in $d$ dimensions can be described by the mapping\n", + "$X:[a, b] \\rightarrow \\mathbb{R}^d$, or in terms of coordinates\n", + "$X = (X^1_t, X^2_t, \\ldots, X^d_t)$, where each coordinate $X^i_t$ is\n", + "real-valued and parameterised by $t \\in [a,b]$.\n", "\n", "The **signature transform** $S$ of a path $X$ is defined as an infinite sequence of values:\n", "\n", - "\\begin{equation}\n", - " S(X)_{a, b} = (1, S(X)_{a, b}^1, S(X)_{a, b}^2, ..., S(X)_{a, b}^d, S(X)_{a,b}^{1, 1}, S(X)_{a,b}^{1, 2}, ...),\n", - " \\label{eq:path_signature}\n", - "\\end{equation}\n", + "\n", + "$$\n", + "S(X)_{a, b} = (1, S(X)_{a, b}^1, S(X)_{a, b}^2, \\ldots, S(X)_{a, b}^d, S(X)_{a,b}^{1,\n", + " 1}, S(X)_{a,b}^{1, 2}, \\ldots)\n", + "$$\n", + "\n", + "\n", "where each term is a $k$-fold iterated integral of $X$ with multi-index $i_1,...,i_k$:\n", - "\\begin{equation}\n", - " S(X)_{a, b}^{i_1,...,i_k} = \\int_{a 2:\n", - "Depths larger than 2 become more difficult to visualise graphically, however the idea is similar to that of the depth 2 case where we saw that the signature produced information on whether the increase in HR or SBP appeared to be happening first, along with some numerical quantification of how much this was happening. At higher orders the signature is doing something similar, but now with three events, rather than two. The signature picks out structural information regarding the order in which events occur." + "James Morell contributed this code as part of his \n", + "[PhD thesis](https://ora.ox.ac.uk/objects/uuid:44cb30f8-6dc8-4e0e-8347-14d40452c3e6/files/d8049g5436) \n" ] }, { @@ -64,9 +63,9 @@ "The signature is a natural tool to apply in problems related to time-series analysis. As described above it can convert multi-dimensional time-series data into static features that represent information about the sequential nature of the time-series, that can be fed through a standard machine learning model.\n", "\n", "A simplistic view of how this works is as follows:\n", - "\\begin{equation}\n", - " \\text{Model}(\\text{Signature}(\\text{Sequential data}))) = \\text{Predictions}\n", - "\\end{equation}" + "$$\n", + "\\text{Model}(\\text{Signature}(\\text{Sequential data}))) = \\text{Predictions}\n", + "$$" ] }, { @@ -100,7 +99,6 @@ }, { "cell_type": "code", - "execution_count": 6, "metadata": { "collapsed": true, "execution": { @@ -111,34 +109,43 @@ }, "jupyter": { "outputs_hidden": true + }, + "ExecuteTime": { + "end_time": "2025-07-12T13:20:25.970545Z", + "start_time": "2025-07-12T13:20:21.197682Z" } }, - "outputs": [], "source": [ "# Some additional imports we will use\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.metrics import accuracy_score\n", "\n", "from aeon.datasets import load_unit_test" - ] + ], + "outputs": [], + "execution_count": 1 }, { "cell_type": "code", - "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2021-06-25T12:41:11.529792Z", "iopub.status.busy": "2021-06-25T12:41:11.529157Z", "iopub.status.idle": "2021-06-25T12:41:11.609172Z", "shell.execute_reply": "2021-06-25T12:41:11.609562Z" + }, + "ExecuteTime": { + "end_time": "2025-07-12T13:20:26.002672Z", + "start_time": "2025-07-12T13:20:25.977454Z" } }, - "outputs": [], "source": [ "# Load an example dataset\n", "train_x, train_y = load_unit_test(split=\"train\")\n", "test_x, test_y = load_unit_test(split=\"test\")" - ] + ], + "outputs": [], + "execution_count": 2 }, { "cell_type": "markdown", @@ -146,13 +153,13 @@ "source": [ "### Overview\n", "We provide the following:\n", - "- **aeon.transformers.panel.signature_based.SignatureTransformer** - An sklearn transformer that provides the functionality to apply the signature method with some choice of variations as noted above.\n", + "- **aeon.transformers.collection.signature_based.SignatureTransformer** - An sklearn \n", + "transformer that provides the functionality to apply the signature method with some choice of variations as noted above.\n", "- **aeon.classification.feature_based.SignatureClassifier** - This provides a simple interface to append a classifier to the SignatureTransformer class." ] }, { "cell_type": "code", - "execution_count": 8, "metadata": { "collapsed": true, "execution": { @@ -163,13 +170,18 @@ }, "jupyter": { "outputs_hidden": true + }, + "ExecuteTime": { + "end_time": "2025-07-12T13:20:27.077433Z", + "start_time": "2025-07-12T13:20:26.846292Z" } }, - "outputs": [], "source": [ "from aeon.classification.feature_based import SignatureClassifier\n", "from aeon.transformations.collection.signature_based import SignatureTransformer" - ] + ], + "outputs": [], + "execution_count": 3 }, { "cell_type": "markdown", From ae57c1ba814a5afe8985bf211ab2d5028a254c3e Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Sun, 27 Jul 2025 15:34:37 +0100 Subject: [PATCH 194/216] [MNT] Workflow maintenance (#2919) * keep running doctests on fail * renames * remove 3.13.5 --- .github/actions/cpu_all_extras/action.yml | 2 +- .../utilities/{ai_spam.py => ai_pull_request.py} | 13 ++++++++----- .github/workflows/issue_assigned.yml | 2 +- .github/workflows/issue_comment_edited.yml | 2 +- .github/workflows/issue_comment_posted.yml | 2 +- .github/workflows/periodic_tests.yml | 6 +++--- .github/workflows/{ai_spam.yml => pr_labelled.yml} | 12 ++++++------ .github/workflows/pr_opened.yml | 2 +- .github/workflows/pr_pytest.yml | 4 ++-- .github/workflows/pr_typecheck.yml | 2 +- .github/workflows/release.yml | 2 +- .github/workflows/update_contributors.yml | 2 +- .github/workflows/weekly_github_maintenance.yml | 2 +- 13 files changed, 28 insertions(+), 25 deletions(-) rename .github/utilities/{ai_spam.py => ai_pull_request.py} (55%) rename .github/workflows/{ai_spam.yml => pr_labelled.yml} (80%) diff --git a/.github/actions/cpu_all_extras/action.yml b/.github/actions/cpu_all_extras/action.yml index dd772a0421..91fab623aa 100644 --- a/.github/actions/cpu_all_extras/action.yml +++ b/.github/actions/cpu_all_extras/action.yml @@ -15,7 +15,7 @@ runs: using: "composite" steps: - name: Install CPU TensorFlow - if: ${{ runner.os == 'Linux' && inputs.python_version != '3.13.5' }} + if: ${{ runner.os == 'Linux' && inputs.python_version != '3.13' }} uses: nick-fields/retry@v3 with: timeout_minutes: 30 diff --git a/.github/utilities/ai_spam.py b/.github/utilities/ai_pull_request.py similarity index 55% rename from .github/utilities/ai_spam.py rename to .github/utilities/ai_pull_request.py index 0113aaad08..d044c81b57 100644 --- a/.github/utilities/ai_spam.py +++ b/.github/utilities/ai_pull_request.py @@ -18,10 +18,13 @@ pr = repo.get_pull(pr_number) label_name = context_dict["event"]["label"]["name"] -if label_name == "AI Spam": - comment_body = ( - "This pull request has been flagged with the **AI Spam** label.\n\n" - "This PR is being closed." +if label_name == "AI pull request": + pr.create_issue_comment( + "This pull request has been flagged with the **AI pull request** label as it" + "is suspected to be comprised of mostly AI code.\n\n" + "`aeon` does not accept pull requests that are primarily generated by AI " + "tools.\n\n" + "If you believe this label has been applied in error, contact us on Slack or" + "GitHub." ) - pr.create_issue_comment(comment_body) pr.edit(state="closed") diff --git a/.github/workflows/issue_assigned.yml b/.github/workflows/issue_assigned.yml index c238c6c195..0d664771a0 100644 --- a/.github/workflows/issue_assigned.yml +++ b/.github/workflows/issue_assigned.yml @@ -1,4 +1,4 @@ -name: Issue Assigned +name: Issue assigned on: issues: diff --git a/.github/workflows/issue_comment_edited.yml b/.github/workflows/issue_comment_edited.yml index fadf0c1629..596224b049 100644 --- a/.github/workflows/issue_comment_edited.yml +++ b/.github/workflows/issue_comment_edited.yml @@ -1,4 +1,4 @@ -name: Issue Comment Edited +name: Issue comment edited on: issue_comment: diff --git a/.github/workflows/issue_comment_posted.yml b/.github/workflows/issue_comment_posted.yml index df23c18261..dc56174027 100644 --- a/.github/workflows/issue_comment_posted.yml +++ b/.github/workflows/issue_comment_posted.yml @@ -1,4 +1,4 @@ -name: Issue Comment Posted +name: Issue comment posted on: issue_comment: diff --git a/.github/workflows/periodic_tests.yml b/.github/workflows/periodic_tests.yml index a06334fa21..3a351aac90 100644 --- a/.github/workflows/periodic_tests.yml +++ b/.github/workflows/periodic_tests.yml @@ -1,4 +1,4 @@ -name: Periodic Tests +name: Periodic tests on: schedule: @@ -163,7 +163,7 @@ jobs: fail-fast: false matrix: os: [ ubuntu-24.04, macOS-14, windows-2022 ] - python-version: [ "3.10", "3.11", "3.12", "3.13.5" ] + python-version: [ "3.10", "3.11", "3.12", "3.13" ] steps: - name: Checkout @@ -239,7 +239,7 @@ jobs: run: python -m pip list - name: Run tests - run: python -m pytest -n logical --doctest-only + run: python -m pytest -n logical --doctest-only --doctest-continue-on-failure multithreaded-estimators: runs-on: ubuntu-24.04 diff --git a/.github/workflows/ai_spam.yml b/.github/workflows/pr_labelled.yml similarity index 80% rename from .github/workflows/ai_spam.yml rename to .github/workflows/pr_labelled.yml index c2d1d2751c..071074f11d 100644 --- a/.github/workflows/ai_spam.yml +++ b/.github/workflows/pr_labelled.yml @@ -1,4 +1,4 @@ -name: AI Spam Detection On PR +name: Pull request labelled on: pull_request: @@ -9,8 +9,8 @@ concurrency: cancel-in-progress: true jobs: - ai-spam-present: - if: ${{ github.event.label.name == 'AI Spam' }} + ai-pull-request: + if: ${{ github.event.label.name == 'AI pull request' }} runs-on: ubuntu-24.04 steps: @@ -26,15 +26,15 @@ jobs: with: sparse-checkout: .github/utilities - - name: Setup Python 3.11 + - name: Setup Python 3.12 uses: actions/setup-python@v5 with: - python-version: "3.11" + python-version: "3.12" - name: Install PyGithub run: pip install -Uq PyGithub - - name: Process AI Spam + - name: Process AI label id: handle_spam run: python .github/utilities/ai_spam.py env: diff --git a/.github/workflows/pr_opened.yml b/.github/workflows/pr_opened.yml index c500844d11..c4cb93ee3b 100644 --- a/.github/workflows/pr_opened.yml +++ b/.github/workflows/pr_opened.yml @@ -1,4 +1,4 @@ -name: PR Opened +name: PR opened on: pull_request_target: diff --git a/.github/workflows/pr_pytest.yml b/.github/workflows/pr_pytest.yml index fcdc665778..2ea00a8c02 100644 --- a/.github/workflows/pr_pytest.yml +++ b/.github/workflows/pr_pytest.yml @@ -55,7 +55,7 @@ jobs: fail-fast: false matrix: os: [ ubuntu-24.04, macOS-14, windows-2022 ] - python-version: [ "3.10", "3.11", "3.12", "3.13.5" ] + python-version: [ "3.10", "3.11", "3.12", "3.13" ] # skip python versions unless the PR has the 'full pytest actions' label pr-testing: - ${{ (github.event_name == 'pull_request' && !contains(github.event.pull_request.labels.*.name, 'full pytest actions')) }} @@ -128,7 +128,7 @@ jobs: run: python -m pip list - name: Run tests - run: python -m pytest -n logical --doctest-only + run: python -m pytest -n logical --doctest-only --doctest-continue-on-failure multithreaded-estimators: runs-on: ubuntu-24.04 diff --git a/.github/workflows/pr_typecheck.yml b/.github/workflows/pr_typecheck.yml index 9c93884feb..22abee9bed 100644 --- a/.github/workflows/pr_typecheck.yml +++ b/.github/workflows/pr_typecheck.yml @@ -1,4 +1,4 @@ -name: PR Typecheck +name: PR typecheck on: push: diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index c622d9dea8..17784e7dbc 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -54,7 +54,7 @@ jobs: fail-fast: false matrix: os: [ ubuntu-24.04, macOS-14, windows-2022 ] - python-version: [ "3.10", "3.11", "3.12", "3.13.5" ] + python-version: [ "3.10", "3.11", "3.12", "3.13" ] steps: - name: Checkout diff --git a/.github/workflows/update_contributors.yml b/.github/workflows/update_contributors.yml index 5b69ccb12f..641a541166 100644 --- a/.github/workflows/update_contributors.yml +++ b/.github/workflows/update_contributors.yml @@ -1,4 +1,4 @@ -name: Update Contributors +name: Update contributors on: push: diff --git a/.github/workflows/weekly_github_maintenance.yml b/.github/workflows/weekly_github_maintenance.yml index 58c6d65a2c..29daf42716 100644 --- a/.github/workflows/weekly_github_maintenance.yml +++ b/.github/workflows/weekly_github_maintenance.yml @@ -1,4 +1,4 @@ -name: Weekly GitHub Maintenance +name: Weekly GitHub maintenance on: schedule: From d9a5c7b6df23f98d0b630fa31ef4207ad521d8fa Mon Sep 17 00:00:00 2001 From: alexbanwell1 <31886108+alexbanwell1@users.noreply.github.com> Date: Mon, 28 Jul 2025 14:08:01 +0100 Subject: [PATCH 195/216] [ENH] Add Basic ARIMA model (#2860) * arima first * move utils * make functions private * Modularise SARIMA model * Add ARIMA forecaster to forecasting package * Add example to ARIMA forecaster, this also tests the forecaster is producing the expected results * Basic ARIMA model * Convert ARIMA to numba version * Adjust parameters to allow modification in fit * Update example and return native python type * Fix examples for tests * Fix Nelder-Mead Optimisation Algorithm Example * Fix Nelder-Mead Optimisation Algorithm Example #2 * Remove Nelder-Mead Example due to issues with numba caching functions * Fix return type issue * Address PR Feedback * Ignore small tolerances in floating point value in output of example * Fix kpss_test example * Fix kpss_test example #2 * Update documentation for ARIMAForecaster, change constant_term to be bool, and fix bug with it not operating on differemced data * Modify ARIMA to allow predicting multiple values by updating the state without refitting the model * Fix bug using self.d rather than self.d_ * refactor ARIMA * docstring * find forecast_ in fit * remove optional y * add iterative * typo * typo * calculate forecast_ * use differenced * example * iterative * arima tests * revert to float * switch nelder_mead version * isolate loss function * isolate loss function * remove the utils version of nelder mead * set self.c_ correctly * numba optimise * numba optimise * numba optimise * Add missing _extract_paras file * initial residuals bug and proper differencing d>1 * loss function ignores zeroed start points * fix test * revert FLOAT_CMP * refactor into forecasting * remove example --------- Co-authored-by: Tony Bagnall Co-authored-by: Alex Banwell Co-authored-by: Tony Bagnall Co-authored-by: Matthew Middlehurst --- aeon/forecasting/__init__.py | 4 +- aeon/forecasting/_arima.py | 301 +++++++++++++++++++++++++++ aeon/forecasting/_extract_paras.py | 27 +++ aeon/forecasting/_hypo_tests.py | 94 +++++++++ aeon/forecasting/_loss_functions.py | 40 ++++ aeon/forecasting/_nelder_mead.py | 130 ++++++++++++ aeon/forecasting/_seasonality.py | 101 +++++++++ aeon/forecasting/tests/test_arima.py | 113 ++++++++++ 8 files changed, 809 insertions(+), 1 deletion(-) create mode 100644 aeon/forecasting/_arima.py create mode 100644 aeon/forecasting/_extract_paras.py create mode 100644 aeon/forecasting/_hypo_tests.py create mode 100644 aeon/forecasting/_loss_functions.py create mode 100644 aeon/forecasting/_nelder_mead.py create mode 100644 aeon/forecasting/_seasonality.py create mode 100644 aeon/forecasting/tests/test_arima.py diff --git a/aeon/forecasting/__init__.py b/aeon/forecasting/__init__.py index 0b134857dd..7d7cb7e6c4 100644 --- a/aeon/forecasting/__init__.py +++ b/aeon/forecasting/__init__.py @@ -1,13 +1,15 @@ """Forecasters.""" __all__ = [ - "NaiveForecaster", "BaseForecaster", + "NaiveForecaster", "RegressionForecaster", "ETSForecaster", "TVPForecaster", + "ARIMA", ] +from aeon.forecasting._arima import ARIMA from aeon.forecasting._ets import ETSForecaster from aeon.forecasting._naive import NaiveForecaster from aeon.forecasting._regression import RegressionForecaster diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py new file mode 100644 index 0000000000..6f08af4325 --- /dev/null +++ b/aeon/forecasting/_arima.py @@ -0,0 +1,301 @@ +"""ARIMA. + +An implementation of the ARIMA forecasting algorithm. +""" + +__maintainer__ = ["alexbanwell1", "TonyBagnall"] +__all__ = ["ARIMA"] + +import numpy as np +from numba import njit + +from aeon.forecasting._extract_paras import _extract_arma_params +from aeon.forecasting._nelder_mead import nelder_mead +from aeon.forecasting.base import BaseForecaster + + +class ARIMA(BaseForecaster): + """AutoRegressive Integrated Moving Average (ARIMA) forecaster. + + ARIMA with fixed model structure and fitted parameters found with an + nelder mead optimizer to minimise the AIC. + + Parameters + ---------- + p : int, default=1, + Autoregressive (p) order of the ARIMA model + d : int, default=0, + Differencing (d) order of the ARIMA model + q : int, default=1, + Moving average (q) order of the ARIMA model + use_constant : bool = False, + Presence of a constant/intercept term in the model. + iterations : int, default = 200 + Maximum number of iterations to use in the Nelder-Mead parameter search. + + Attributes + ---------- + residuals_ : np.ndarray + Residual errors from the fitted model. + aic_ : float + Akaike Information Criterion for the fitted model. + c_ : float, default = 0 + Intercept term. + phi_ : np.ndarray + Coefficients for autoregressive terms (length p). + theta_ : np.ndarray + Coefficients for moving average terms (length q). + + References + ---------- + .. [1] R. J. Hyndman and G. Athanasopoulos, + Forecasting: Principles and Practice. OTexts, 2014. + https://otexts.com/fpp3/ + """ + + _tags = { + "capability:horizon": False, # cannot fit to a horizon other than 1 + } + + def __init__( + self, + p: int = 1, + d: int = 0, + q: int = 1, + use_constant: bool = False, + iterations: int = 200, + ): + self.p = p + self.d = d + self.q = q + self.use_constant = use_constant + self.iterations = iterations + self.phi_ = 0 + self.theta_ = 0 + self.c_ = 0 + self._series = [] + self._differenced_series = [] + self.residuals_ = [] + self.fitted_values_ = [] + self.aic_ = 0 + self._model = [] + self._parameters = [] + super().__init__(horizon=1, axis=1) + + def _fit(self, y, exog=None): + """Fit ARIMA forecaster to series y to predict one ahead using y. + + Parameters + ---------- + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead + exog : np.ndarray, default =None + Not allowed for this forecaster + + Returns + ------- + self + Fitted ARIMA. + """ + self._series = np.array(y.squeeze(), dtype=np.float64) + # Model is an array of the (c,p,q) + self._model = np.array( + (1 if self.use_constant else 0, self.p, self.q), dtype=np.int32 + ) + self._differenced_series = np.diff(self._series, n=self.d) + # Nelder Mead returns the parameters in a single array + (self._parameters, self.aic_) = nelder_mead( + 0, + np.sum(self._model[:3]), + self._differenced_series, + self._model, + max_iter=self.iterations, + ) + # + (self.aic_, self.residuals_, self.fitted_values_) = _arima_model( + self._parameters, + self._differenced_series, + self._model, + ) + formatted_params = _extract_arma_params( + self._parameters, self._model + ) # Extract + # parameters + differenced_forecast = self.fitted_values_[-1] + + if self.d == 0: + forecast_value = differenced_forecast + elif self.d == 1: + forecast_value = differenced_forecast + self._series[-1] + else: # for d > 1, iteratively undifference + forecast_value = differenced_forecast + last_vals = self._series[-self.d :] + for _ in range(self.d): + forecast_value += last_vals[-1] - last_vals[-2] + # Shift values to avoid appending to list (efficient) + last_vals = np.roll(last_vals, -1) + last_vals[-1] = forecast_value # Extract the parameter values + self.forecast_ = forecast_value + if self.use_constant: + self.c_ = formatted_params[0][0] + self.phi_ = formatted_params[1][: self.p] + self.theta_ = formatted_params[2][: self.q] + + return self + + def _predict(self, y, exog=None): + """ + Predict the next step ahead for y. + + Parameters + ---------- + y : np.ndarray, default = None + A time series to predict the value of. y can be independent of the series + seen in fit. + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + float + Prediction 1 step ahead of the last value in y. + """ + y = y.squeeze() + p, q, d = self.p, self.q, self.d + phi, theta = self.phi_, self.theta_ + c = 0.0 + if self.use_constant: + c = self.c_ + + # Apply differencing + if d > 0: + if len(y) <= d: + raise ValueError("Series too short for differencing.") + y_diff = np.diff(y, n=d) + else: + y_diff = y + + n = len(y_diff) + if n < max(p, q): + raise ValueError("Series too short for ARMA(p,q) with given order.") + + # Estimate in-sample residuals using model (fixed parameters) + residuals = np.zeros(n) + for t in range(max(p, q), n): + ar_part = np.dot(phi, y_diff[t - np.arange(1, p + 1)]) if p > 0 else 0.0 + ma_part = ( + np.dot(theta, residuals[t - np.arange(1, q + 1)]) if q > 0 else 0.0 + ) + pred = c + ar_part + ma_part + residuals[t] = y_diff[t] - pred + + # Use most recent p values of y_diff and q values of residuals to forecast t+1 + ar_forecast = np.dot(phi, y_diff[-np.arange(1, p + 1)]) if p > 0 else 0.0 + ma_forecast = np.dot(theta, residuals[-np.arange(1, q + 1)]) if q > 0 else 0.0 + + forecast_diff = c + ar_forecast + ma_forecast + + # Undifference the forecast + if d == 0: + return forecast_diff + elif d == 1: + return forecast_diff + y[-1] + else: + return forecast_diff + np.sum(y[-d:]) + + def _forecast(self, y, exog=None): + """Forecast one ahead for time series y.""" + self.fit(y, exog) + return float(self.forecast_) + + def iterative_forecast(self, y, prediction_horizon): + self.fit(y) + n = len(self._differenced_series) + p, q = self.p, self.q + phi, theta = self.phi_, self.theta_ + h = prediction_horizon + c = 0.0 + if self.use_constant: + c = self.c_ + + # Start with a copy of the original series and residuals + residuals = np.zeros(len(self.residuals_) + h) + residuals[: len(self.residuals_)] = self.residuals_ + forecast_series = np.zeros(n + h) + forecast_series[:n] = self._differenced_series + for i in range(h): + # Get most recent p values (lags) + t = n + i + ar_term = 0.0 + if p > 0: + ar_term = np.dot(phi, forecast_series[t - np.arange(1, p + 1)]) + # Get most recent q residuals (lags) + ma_term = 0.0 + if q > 0: + ma_term = np.dot(theta, residuals[t - np.arange(1, q + 1)]) + next_value = c + ar_term + ma_term + # Append prediction and a zero residual (placeholder) + forecast_series[n + i] = next_value + # Can't compute real residual during prediction, leave as zero + + # Correct differencing using forecast values + y_forecast_diff = forecast_series[n : n + h] + d = self.d + if d == 0: + return y_forecast_diff + else: # Correct undifferencing + # Start with last d values from original y + undiff = list(self._series[-d:]) + for i in range(h): + # Take the last d values and sum them + reconstructed = y_forecast_diff[i] + sum(undiff[-d:]) + undiff.append(reconstructed) + return np.array(undiff[d:]) + + +@njit(cache=True, fastmath=True) +def _aic(residuals, num_params): + """Calculate the log-likelihood of a model.""" + variance = np.mean(residuals**2) + likelihood = len(residuals) * (np.log(2 * np.pi) + np.log(variance) + 1) + return likelihood + 2 * num_params + + +# Define the ARIMA(p, d, q) likelihood function +@njit(cache=True, fastmath=True) +def _arima_model(params, data, model): + """Calculate the log-likelihood of an ARIMA model given the parameters.""" + formatted_params = _extract_arma_params(params, model) # Extract parameters + + # Initialize residuals + n = len(data) + num_predictions = n + 1 + residuals = np.zeros(num_predictions - 1) + fitted_values = np.zeros(num_predictions) + # Leave first max(p,q) residuals and fitted as zero. + for t in range(max(model[1], model[2]), num_predictions): + fitted_values[t] = _in_sample_forecast( + data, model, t, formatted_params, residuals + ) + if t != num_predictions - 1: + # Only calculate residuals for the predictions we have data for + residuals[t] = data[t] - fitted_values[t] + return _aic(residuals, len(params)), residuals, fitted_values + + +@njit(cache=True, fastmath=True) +def _in_sample_forecast(data, model, t, formatted_params, residuals): + """Efficient ARMA one-step forecast at time t for fitted model.""" + p = model[1] + q = model[2] + c = formatted_params[0][0] if model[0] else 0.0 + + ar_term = 0.0 + for j in range(min(p, t)): + ar_term += formatted_params[1, j] * data[t - j - 1] + + ma_term = 0.0 + for j in range(min(q, t)): + ma_term += formatted_params[2, j] * residuals[t - j - 1] + + return c + ar_term + ma_term diff --git a/aeon/forecasting/_extract_paras.py b/aeon/forecasting/_extract_paras.py new file mode 100644 index 0000000000..2e38a3e4dd --- /dev/null +++ b/aeon/forecasting/_extract_paras.py @@ -0,0 +1,27 @@ +"""ARIMA Utility Function.""" + +import numpy as np +from numba import njit + + +@njit(cache=True, fastmath=True) +def _extract_arma_params(params, model): + """Extract ARIMA parameters from the parameter vector.""" + n_parts = len(model) + starts = np.zeros(n_parts, dtype=np.int32) + for i in range(1, n_parts): + starts[i] = starts[i - 1] + model[i - 1] + + max_len = np.max(model) + result = np.empty((n_parts, max_len), dtype=params.dtype) + for i in range(n_parts): + for j in range(max_len): + result[i, j] = np.nan + + for i in range(n_parts): + length = model[i] + start = starts[i] + for j in range(length): + result[i, j] = params[start + j] + + return result diff --git a/aeon/forecasting/_hypo_tests.py b/aeon/forecasting/_hypo_tests.py new file mode 100644 index 0000000000..198a4472a8 --- /dev/null +++ b/aeon/forecasting/_hypo_tests.py @@ -0,0 +1,94 @@ +import numpy as np + + +def kpss_test(y, regression="c", lags=None): # Test if time series is stationary + """ + Perform the KPSS (Kwiatkowski-Phillips-Schmidt-Shin) test for stationarity. + + The KPSS test evaluates the null hypothesis that a time series is + (trend or level) stationary against the alternative of a unit root + (non-stationarity). It can test for either stationarity around a + constant (level stationarity) or arounda deterministic trend + (trend stationarity). + + Parameters + ---------- + y : array-like + Time series data to test for stationarity. + regression : str, default="c" + Indicates the null hypothesis for stationarity: + - "c" : Stationary around a constant (level stationarity) + - "ct" : Stationary around a constant and linear trend (trend stationarity) + lags : int or None, optional + Number of lags to use for the + HAC (heteroskedasticity and autocorrelation consistent) variance estimator. + If None, defaults to sqrt(n), where n is the sample size. + + Returns + ------- + kpss_stat : float + The KPSS test statistic. + stationary : bool + True if the series is judged stationary at the 5% significance level + (i.e., test statistic is below the critical value); False otherwise. + + Notes + ----- + - Uses asymptotic 5% critical values from Kwiatkowski et al. (1992): 0.463 for level + stationarity, 0.146 for trend stationarity. + - Returns True for stationary if the test statistic is below the 5% critical value. + + References + ---------- + Kwiatkowski, D., Phillips, P.C.B., Schmidt, P., & Shin, Y. (1992). + "Testing the null hypothesis of stationarity against the alternative + of a unit root." + Journal of Econometrics, 54(1–3), 159–178. + https://doi.org/10.1016/0304-4076(92)90104-Y + """ + y = np.asarray(y) + n = len(y) + + # Step 1: Fit regression model to estimate residuals + if regression == "c": # Constant + X = np.ones((n, 1)) + elif regression == "ct": # Constant + Trend + X = np.column_stack((np.ones(n), np.arange(1, n + 1))) + else: + raise ValueError("regression must be 'c' or 'ct'") + + beta = np.linalg.lstsq(X, y, rcond=None)[0] # Estimate regression coefficients + residuals = y - X @ beta # Get residuals (u_t) + + # Step 2: Compute cumulative sum of residuals (S_t) + S_t = np.cumsum(residuals) + + # Step 3: Estimate long-run variance (HAC variance) + if lags is None: + # lags = int(12 * (n / 100)**(1/4)) # Default statsmodels lag length + lags = int(np.sqrt(n)) # Default lag length + + gamma_0 = np.sum(residuals**2) / (n - X.shape[1]) # Lag-0 autocovariance + gamma = [np.sum(residuals[k:] * residuals[:-k]) / n for k in range(1, lags + 1)] + + # Bartlett weights + weights = [1 - (k / (lags + 1)) for k in range(1, lags + 1)] + + # Long-run variance + sigma_squared = gamma_0 + 2 * np.sum([w * g for w, g in zip(weights, gamma)]) + + # Step 4: Calculate the KPSS statistic + kpss_stat = np.sum(S_t**2) / (n**2 * sigma_squared) + + # 5% critical values for KPSS test + if regression == "ct": + # p. 162 Kwiatkowski et al. (1992): y_t = beta * t + r_t + e_t, + # where beta is the trend, r_t a random walk and e_t a stationary + # error term. + crit = 0.146 + else: # hypo == "c" + # special case of the model above, where beta = 0 (so the null + # hypothesis is that the data is stationary around r_0). + crit = 0.463 + + return kpss_stat, kpss_stat < crit diff --git a/aeon/forecasting/_loss_functions.py b/aeon/forecasting/_loss_functions.py new file mode 100644 index 0000000000..c88211b645 --- /dev/null +++ b/aeon/forecasting/_loss_functions.py @@ -0,0 +1,40 @@ +"""Loss functions for optimiser.""" + +import numpy as np +from numba import njit + +from aeon.forecasting._extract_paras import _extract_arma_params + +LOG_2PI = 1.8378770664093453 + + +@njit(cache=True, fastmath=True) +def _arima_fit(params, data, model): + """Calculate the AIC of an ARIMA model given the parameters.""" + formatted_params = _extract_arma_params(params, model) # Extract parameters + + # Initialize residuals + n = len(data) + residuals = np.zeros(n) + c = formatted_params[0][0] if model[0] else 0 + p = model[1] + q = model[2] + for t in range(n): + ar_term = 0.0 + max_ar = min(p, t) + for j in range(max_ar): + ar_term += formatted_params[1, j] * data[t - j - 1] + ma_term = 0.0 + max_ma = min(q, t) + for j in range(max_ma): + ma_term += formatted_params[2, j] * residuals[t - j - 1] + y_hat = c + ar_term + ma_term + residuals[t] = data[t] - y_hat + sse = 0.0 + start = max(p, q) + for i in range(start, n): + sse += residuals[i] * residuals[i] + variance = sse / (n - start) + likelihood = (n - start) * (LOG_2PI + np.log(variance) + 1.0) + k = len(params) + return likelihood + 2 * k diff --git a/aeon/forecasting/_nelder_mead.py b/aeon/forecasting/_nelder_mead.py new file mode 100644 index 0000000000..f617aa5727 --- /dev/null +++ b/aeon/forecasting/_nelder_mead.py @@ -0,0 +1,130 @@ +"""Optimisation algorithms for automatic parameter tuning.""" + +import numpy as np +from numba import njit + +from aeon.forecasting._loss_functions import _arima_fit + + +@njit(cache=True, fastmath=True) +def dispatch_loss(fn_id, params, data, model): + if fn_id == 0: + return _arima_fit(params, data, model) + else: + raise ValueError("Unknown loss function ID") + + +@njit(cache=True, fastmath=True) +def nelder_mead( + loss_id, + num_params, + data, + model, + tol=1e-6, + max_iter=500, +): + """ + Perform optimisation using the Nelder–Mead simplex algorithm. + + This function minimises a given loss (objective) function using the Nelder–Mead + algorithm, a derivative-free method that iteratively refines a simplex of candidate + solutions. The implementation supports unconstrained minimisation of functions + with a fixed number of parameters. + + Parameters + ---------- + loss_id : int + ID for loss function to optimise, used by dispatch_loss. + num_params : int + The number of parameters (dimensions) in the optimisation problem. + data : np.ndarray + The input data used by the loss function. The shape and content depend on the + specific loss function being minimised. + model : np.ndarray + The model or context in which the loss function operates. This could be any + other object that the `loss_function` requires to compute its value. + The exact type and structure of `model` should be compatible with the + `loss_function`. + tol : float, optional (default=1e-6) + Tolerance for convergence. The algorithm stops when the maximum difference + between function values at simplex vertices is less than `tol`. + max_iter : int, optional (default=500) + Maximum number of iterations to perform. + + Returns + ------- + best_params : np.ndarray, shape (`num_params`,) + The parameter vector that minimises the loss function. + best_value : float + The value of the loss function at the optimal parameter vector. + + Notes + ----- + - The initial simplex is constructed by setting each parameter to 0.5, + with one additional point per dimension at 0.6 for that dimension. + - This implementation does not support constraints or bounds on the parameters. + - The algorithm does not guarantee finding a global minimum. + + References + ---------- + .. [1] Nelder, J. A. and Mead, R. (1965). + A Simplex Method for Function Minimization. + The Computer Journal, 7(4), 308–313. + https://doi.org/10.1093/comjnl/7.4.308 + """ + points = np.full((num_params + 1, num_params), 0.5) + for i in range(num_params): + points[i + 1][i] = 0.6 + values = np.empty(len(points), dtype=np.float64) + for i in range(len(points)): + values[i] = dispatch_loss(loss_id, points[i].copy(), data, model) + for i in range(max_iter): + # Order simplex by function values + order = np.argsort(values) + points = points[order] + values = values[order] + + # Centroid of the best n points + centre_point = points[:-1].sum(axis=0) / len(points[:-1]) + + # Reflection + # centre + distance between centre and largest value + reflected_point = centre_point + (centre_point - points[-1]) + reflected_value = dispatch_loss(0, reflected_point, data, model) + # if between best and second best, use reflected value + if len(values) > 1 and values[0] <= reflected_value < values[-2]: + points[-1] = reflected_point + values[-1] = reflected_value + continue + # Expansion + # Otherwise if it is better than the best value + if reflected_value < values[0]: + expanded_point = centre_point + 2 * (reflected_point - centre_point) + expanded_value = dispatch_loss(0, expanded_point, data, model) + # if less than reflected value use expanded, otherwise go back to reflected + if expanded_value < reflected_value: + points[-1] = expanded_point + values[-1] = expanded_value + else: + points[-1] = reflected_point + values[-1] = reflected_value + continue + # Contraction + # Otherwise if reflection is worse than all current values + contracted_point = centre_point - 0.5 * (centre_point - points[-1]) + contracted_value = dispatch_loss(0, contracted_point, data, model) + # If contraction is better use that otherwise move to shrinkage + if contracted_value < values[-1]: + points[-1] = contracted_point + values[-1] = contracted_value + continue + + # Shrinkage + for i in range(1, len(points)): + points[i] = points[0] - 0.5 * (points[0] - points[i]) + values[i] = dispatch_loss(0, points[i], data, model) + + # Convergence check + if np.max(np.abs(values - values[0])) < tol: + break + return points[0], values[0] diff --git a/aeon/forecasting/_seasonality.py b/aeon/forecasting/_seasonality.py new file mode 100644 index 0000000000..356b1a40d2 --- /dev/null +++ b/aeon/forecasting/_seasonality.py @@ -0,0 +1,101 @@ +"""Seasonality Tools. + +Includes autocorrelation function (ACF) and seasonal period estimation. +""" + +import numpy as np +from numba import njit + + +@njit(cache=True, fastmath=True) +def acf(X, max_lag): + """ + Compute the sample autocorrelation function (ACF) of a time series. + + Up to a specified maximum lag. + + The autocorrelation at lag k is defined as the Pearson correlation + coefficient between the series and a lagged version of itself. + If both segments at a given lag have zero variance, the function + returns 1 for that lag. If only one segment has zero variance, + the function returns 0. + + Parameters + ---------- + X : array-like, shape (n_samples,) + The input time series data. + max_lag : int + The maximum lag (number of steps) for which to + compute the autocorrelation. + + Returns + ------- + acf_values : np.ndarray, shape (max_lag,) + The autocorrelation values for lags 1 through `max_lag`. + + Notes + ----- + The function handles cases where the lagged segments have zero + variance to avoid division by zero. + The returned values correspond to + lags 1, 2, ..., `max_lag` (not including lag 0). + """ + length = len(X) + X_t = np.zeros(max_lag, dtype=float) + for lag in range(1, max_lag + 1): + lag_length = length - lag + x1 = X[:-lag] + x2 = X[lag:] + s1 = np.sum(x1) + s2 = np.sum(x2) + m1 = s1 / lag_length + m2 = s2 / lag_length + ss1 = np.sum(x1 * x1) + ss2 = np.sum(x2 * x2) + v1 = ss1 - s1 * m1 + v2 = ss2 - s2 * m2 + v1_is_zero, v2_is_zero = v1 <= 1e-9, v2 <= 1e-9 + if v1_is_zero and v2_is_zero: # Both zero variance, + # so must be 100% correlated + X_t[lag - 1] = 1 + elif v1_is_zero or v2_is_zero: # One zero variance + # the other not + X_t[lag - 1] = 0 + else: + X_t[lag - 1] = np.sum((x1 - m1) * (x2 - m2)) / np.sqrt(v1 * v2) + return X_t + + +@njit(cache=True, fastmath=True) +def calc_seasonal_period(data): + """ + Estimate the seasonal period of a time series using autocorrelation analysis. + + This function computes the autocorrelation function (ACF) of + the input series up to lag 24. It then identifies peaks in the + ACF above the mean value, treating the first such peak + as the estimated seasonal period. If no peak is found, + a period of 1 is returned. + + Parameters + ---------- + data : array-like, shape (n_samples,) + The input time series data. + + Returns + ------- + period : int + The estimated seasonal period (lag) of the series. Returns 1 if no significant + peak is detected in the autocorrelation. + """ + lags = acf(data, 24) + lags = np.concatenate((np.array([1.0]), lags)) + peaks = [] + mean_lags = np.mean(lags) + for i in range(1, len(lags) - 1): # Skip the first (lag 0) and last elements + if lags[i] >= lags[i - 1] and lags[i] >= lags[i + 1] and lags[i] > mean_lags: + peaks.append(i) + if not peaks: + return 1 + else: + return peaks[0] diff --git a/aeon/forecasting/tests/test_arima.py b/aeon/forecasting/tests/test_arima.py new file mode 100644 index 0000000000..73d99d7b50 --- /dev/null +++ b/aeon/forecasting/tests/test_arima.py @@ -0,0 +1,113 @@ +"""Test the ARIMA forecaster.""" + +import numpy as np +import pytest + +from aeon.forecasting import ARIMA + +y = np.array( + [112, 118, 132, 129, 121, 135, 148, 148, 136, 119, 104, 118], dtype=np.float64 +) + + +def test_arima_zero_orders(): + """Test ARIMA(0,0,0) which should return the mean if constant is used.""" + model = ARIMA(p=0, d=0, q=0, use_constant=True) + model.fit(y) + forecast = model.predict(y) + assert np.isfinite(forecast) + assert abs(forecast - np.mean(y)) < 10 + + +@pytest.mark.parametrize( + "p, d, q, use_constant", + [ + (1, 0, 1, True), # basic ARIMA + (2, 1, 1, False), # no constant + (1, 2, 1, True), # higher-order differencing + ], +) +def test_arima_fit_and_predict_variants(p, d, q, use_constant): + """Test ARIMA fit and predict for various (p,d,q) and use_constant settings.""" + model = ARIMA(p=p, d=d, q=q, use_constant=use_constant) + model.fit(y) + forecast = model.forecast_ + assert isinstance(forecast, float) + assert np.isfinite(forecast) + + +def test_arima_iterative_forecast(): + """Test multi-step forecasting using iterative_forecast method.""" + model = ARIMA(p=1, d=1, q=1) + horizon = 3 + preds = model.iterative_forecast(y, prediction_horizon=horizon) + assert preds.shape == (horizon,) + assert np.all(np.isfinite(preds)) + + +@pytest.mark.parametrize( + "y_input, error_match", + [ + (np.array([1.0, 2.0]), "Series too short for differencing"), + (np.array([1.0, 2.0, 3.0]), "Series too short for ARMA"), + ], +) +def test_arima_too_short_series_errors(y_input, error_match): + """Test errors raised for too short input series.""" + model = ARIMA(p=3, d=2, q=3) + model.fit(y) + with pytest.raises(ValueError, match=error_match): + model._predict(y_input) + + +def test_forecast_attribute_set(): + """Test that calling _forecast sets the internal forecast_ attribute.""" + model = ARIMA(p=1, d=1, q=1) + forecast = model._forecast(y) + assert hasattr(model, "forecast_") + assert np.isclose(forecast, model.forecast_) + + +def test_iterative_forecast_with_d2(): + """Test iterative forecast output shape and validity with d=2.""" + model = ARIMA(p=1, d=2, q=1) + preds = model.iterative_forecast(y, prediction_horizon=5) + assert preds.shape == (5,) + assert np.all(np.isfinite(preds)) + + +@pytest.mark.parametrize( + "p, d, q, use_constant, expected_forecast", + [ + (1, 0, 1, False, 118.47506756), # precomputed from known ARIMA implementation + (2, 1, 1, False, 209.1099231455), # precomputed + (3, 0, 0, True, 137.47368045155), # precomputed + ], +) +def test_arima_fixed_paras(p, d, q, use_constant, expected_forecast): + """Test ARIMA fit/predict accuracy against known forecasts. + + expected values calculated with values fitted by Nelder-Mead: + + 1. phi = [0.99497524] theta [0.0691515] + 2. phi = [ 0.02898788 -0.4330671 ] theta [1.26699252] + 3. phi = [ 0.19202414 0.05207654 -0.07367897] theta [], constant 105.970867164 + + """ + model = ARIMA(p=p, d=d, q=q, use_constant=use_constant) + model.fit(y) + forecast = model.forecast_ + assert isinstance(forecast, float) + assert np.isfinite(forecast) + assert np.isclose(forecast, expected_forecast, atol=1e-6) + + +def test_arima_known_output(): + """Test ARIMA for fixed parameters. + + Test ARMIMA with forecast generated externally. + """ + model = ARIMA(p=1, d=0, q=1) + model.fit(y) + f = model.forecast_ + assert np.isclose(118.47506756, f) From 1e6e8c8b76b16286a897678ad98775354b1d9245 Mon Sep 17 00:00:00 2001 From: "aeon-actions-bot[bot]" <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Date: Mon, 28 Jul 2025 17:51:35 +0100 Subject: [PATCH 196/216] Automated `pre-commit` hook update (#2959) Co-authored-by: TonyBagnall <9594042+TonyBagnall@users.noreply.github.com> --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 8ae225d0b5..a29a269950 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -29,7 +29,7 @@ repos: args: [ "--create", "--python-folders", "aeon" ] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.12.4 + rev: v0.12.5 hooks: - id: ruff args: [ "--fix"] From 1b65310d1c7642e494da3b58043b8ca02910a7b7 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Mon, 28 Jul 2025 18:01:32 +0100 Subject: [PATCH 197/216] clean space for swap CI to stop sporadic failue (#2965) --- .github/workflows/periodic_tests.yml | 9 ++++++++- .github/workflows/pr_pytest.yml | 9 ++++++++- .github/workflows/release.yml | 9 ++++++++- 3 files changed, 24 insertions(+), 3 deletions(-) diff --git a/.github/workflows/periodic_tests.yml b/.github/workflows/periodic_tests.yml index 3a351aac90..844ce5b514 100644 --- a/.github/workflows/periodic_tests.yml +++ b/.github/workflows/periodic_tests.yml @@ -174,11 +174,18 @@ jobs: with: python-version: ${{ matrix.python-version }} + - name: Clear disik space + uses: jlumbroso/free-disk-space@v1.3.1 + with: + dotnet: false + large-packages: false + swap-storage: false + - name: Set swap space if: runner.os == 'Linux' uses: pierotofy/set-swap-space@v1.0 with: - swap-size-gb: 6 + swap-size-gb: 8 - name: Use numba cache to set env variables but not restore cache uses: ./.github/actions/numba_cache diff --git a/.github/workflows/pr_pytest.yml b/.github/workflows/pr_pytest.yml index 2ea00a8c02..5addb89627 100644 --- a/.github/workflows/pr_pytest.yml +++ b/.github/workflows/pr_pytest.yml @@ -72,11 +72,18 @@ jobs: with: python-version: ${{ matrix.python-version }} + - name: Clear disik space + uses: jlumbroso/free-disk-space@v1.3.1 + with: + dotnet: false + large-packages: false + swap-storage: false + - name: Set swap space if: runner.os == 'Linux' uses: pierotofy/set-swap-space@v1.0 with: - swap-size-gb: 6 + swap-size-gb: 8 - if: ${{ github.event_name != 'pull_request' || !contains(github.event.pull_request.labels.*.name, 'no numba cache') }} name: Restore numba cache diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 17784e7dbc..8c9b5c43e0 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -65,11 +65,18 @@ jobs: with: python-version: ${{ matrix.python-version }} + - name: Clear disik space + uses: jlumbroso/free-disk-space@v1.3.1 + with: + dotnet: false + large-packages: false + swap-storage: false + - name: Set swap space if: runner.os == 'Linux' uses: pierotofy/set-swap-space@v1.0 with: - swap-size-gb: 6 + swap-size-gb: 8 - uses: actions/download-artifact@v4 with: From a7c6723aab473c949446a2134c09d4054a0a1588 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 28 Jul 2025 21:18:28 +0100 Subject: [PATCH 198/216] [MNT] Bump crs-k/stale-branches in the github-actions group (#2950) --- updated-dependencies: - dependency-name: crs-k/stale-branches dependency-version: 8.2.1 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: github-actions ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/weekly_github_maintenance.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/weekly_github_maintenance.yml b/.github/workflows/weekly_github_maintenance.yml index 29daf42716..0818228bd5 100644 --- a/.github/workflows/weekly_github_maintenance.yml +++ b/.github/workflows/weekly_github_maintenance.yml @@ -123,7 +123,7 @@ jobs: private-key: ${{ secrets.PR_APP_KEY }} - name: Stale Branches - uses: crs-k/stale-branches@v8.2.0 + uses: crs-k/stale-branches@v8.2.1 with: repo-token: ${{ steps.app-token.outputs.token }} days-before-stale: 180 From d29cead62bc7f506ebcb5eb4e8621c1e7360a844 Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Tue, 29 Jul 2025 13:04:06 +0100 Subject: [PATCH 199/216] [ENH] New experimental module: imbalance in collection transformers (#2498) * first draft * [ENH] wrapper for smote and adasyn of the imbalance module in collection transformers (#2501) * smote & adasyn in aeon.transformation.imbalance * smote & adasyn in aeon.transformation.imbalance * smote & adasyn in aeon.transformation.imbalance * smote & adasyn in aeon.transformation.imbalance * make experimental * inherit from SMOTE * test equivalence to imblearn * move tests * format * import * add test parameters * Ported OHIT (#2573) * Automatic `pre-commit` fixes * docstrings * remove import * remove incorrect test parameters * docstrings * examples * examples * refactor variable name * format comments and reference * typos --------- Co-authored-by: Chuanhang Qiu <80885865+LinGinQiu@users.noreply.github.com> Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> --- .../collection/imbalance/__init__.py | 7 + .../collection/imbalance/_adasyn.py | 118 +++++++ .../collection/imbalance/_ohit.py | 288 ++++++++++++++++++ .../collection/imbalance/_smote.py | 252 +++++++++++++++ .../collection/imbalance/tests/__init__.py | 1 + .../collection/imbalance/tests/test_adasyn.py | 61 ++++ .../collection/imbalance/tests/test_ohit.py | 30 ++ .../collection/imbalance/tests/test_smote.py | 57 ++++ docs/developer_guide/deprecation.md | 1 + 9 files changed, 815 insertions(+) create mode 100644 aeon/transformations/collection/imbalance/__init__.py create mode 100644 aeon/transformations/collection/imbalance/_adasyn.py create mode 100644 aeon/transformations/collection/imbalance/_ohit.py create mode 100644 aeon/transformations/collection/imbalance/_smote.py create mode 100644 aeon/transformations/collection/imbalance/tests/__init__.py create mode 100644 aeon/transformations/collection/imbalance/tests/test_adasyn.py create mode 100644 aeon/transformations/collection/imbalance/tests/test_ohit.py create mode 100644 aeon/transformations/collection/imbalance/tests/test_smote.py diff --git a/aeon/transformations/collection/imbalance/__init__.py b/aeon/transformations/collection/imbalance/__init__.py new file mode 100644 index 0000000000..d6ee723069 --- /dev/null +++ b/aeon/transformations/collection/imbalance/__init__.py @@ -0,0 +1,7 @@ +"""Supervised transformers to rebalance colelctions of time series.""" + +__all__ = ["ADASYN", "SMOTE", "OHIT"] + +from aeon.transformations.collection.imbalance._adasyn import ADASYN +from aeon.transformations.collection.imbalance._ohit import OHIT +from aeon.transformations.collection.imbalance._smote import SMOTE diff --git a/aeon/transformations/collection/imbalance/_adasyn.py b/aeon/transformations/collection/imbalance/_adasyn.py new file mode 100644 index 0000000000..900923e45a --- /dev/null +++ b/aeon/transformations/collection/imbalance/_adasyn.py @@ -0,0 +1,118 @@ +"""ADASYN over sampling algorithm. + +See more in imblearn.over_sampling.ADASYN +original authors: +# Guillaume Lemaitre +# Fernando Nogueira +# Christos Aridas +# Dzianis Dudnik +# License: MIT +""" + +import numpy as np +from sklearn.utils import check_random_state + +from aeon.transformations.collection.imbalance._smote import SMOTE + +__maintainer__ = ["TonyBagnall"] +__all__ = ["ADASYN"] + + +class ADASYN(SMOTE): + """ + Adaptive Synthetic Sampling (ADASYN) over-sampler. + + Generates synthetic samples for the minority class based on local data + distribution. ADASYN extends SMOTE by adapting the number of synthetic samples + according to the density of the minority class: more samples are generated for + minority samples that are harder to learn (i.e., surrounded by more majority + samples). + + This implementation is adapted from imbalanced-learn's + `imblearn.over_sampling.ADASYN`. + + Parameters + ---------- + random_state : int or None, optional (default=None) + Random seed for reproducibility. + k_neighbors : int, optional (default=5) + Number of nearest neighbours used to construct synthetic samples. + + References + ---------- + .. [1] He, H., Bai, Y., Garcia, E. A., & Li, S. (2008). + ADASYN: Adaptive synthetic sampling approach for imbalanced learning. + In IEEE International Joint Conference on Neural Networks, pp. 1322-1328. + https://doi.org/10.1109/IJCNN.2008.4633969 + + Examples + -------- + >>> from aeon.transformations.collection.imbalance import ADASYN + >>> import numpy as np + >>> X = np.random.random(size=(100,1,50)) + >>> y = np.array([0] * 90 + [1] * 10) + >>> sampler = ADASYN(random_state=49) + >>> X_res, y_res = sampler.fit_transform(X, y) + """ + + def __init__(self, random_state=None, k_neighbors=5): + super().__init__(random_state=random_state, k_neighbors=k_neighbors) + + def _transform(self, X, y=None): + X = np.squeeze(X, axis=1) + random_state = check_random_state(self.random_state) + X_resampled = [X.copy()] + y_resampled = [y.copy()] + + # got the minority class label and the number needs to be generated + for class_sample, n_samples in self.sampling_strategy_.items(): + if n_samples == 0: + continue + target_class_indices = np.flatnonzero(y == class_sample) + X_class = X[target_class_indices] + + self.nn_.fit(X) + nns = self.nn_.kneighbors(X_class, return_distance=False)[:, 1:] + # The ratio is computed using a one-vs-rest manner. Using majority + # in multi-class would lead to slightly different results at the + # cost of introducing a new parameter. + n_neighbors = self.nn_.n_neighbors - 1 + ratio_nn = np.sum(y[nns] != class_sample, axis=1) / n_neighbors + if not np.sum(ratio_nn): + raise RuntimeError( + "Not any neighbours belong to the majority" + " class. This case will induce a NaN case" + " with a division by zero. ADASYN is not" + " suited for this specific dataset." + " Use SMOTE instead." + ) + ratio_nn /= np.sum(ratio_nn) + n_samples_generate = np.rint(ratio_nn * n_samples).astype(int) + # rounding may cause new amount for n_samples + n_samples = np.sum(n_samples_generate) + if not n_samples: + raise ValueError( + "No samples will be generated with the provided ratio settings." + ) + + # the nearest neighbors need to be fitted only on the current class + # to find the class NN to generate new samples + self.nn_.fit(X_class) + nns = self.nn_.kneighbors(X_class, return_distance=False)[:, 1:] + + enumerated_class_indices = np.arange(len(target_class_indices)) + rows = np.repeat(enumerated_class_indices, n_samples_generate) + cols = random_state.choice(n_neighbors, size=n_samples) + diffs = X_class[nns[rows, cols]] - X_class[rows] + steps = random_state.uniform(size=(n_samples, 1)) + X_new = X_class[rows] + steps * diffs + + X_new = X_new.astype(X.dtype) + y_new = np.full(n_samples, fill_value=class_sample, dtype=y.dtype) + X_resampled.append(X_new) + y_resampled.append(y_new) + X_resampled = np.vstack(X_resampled) + y_resampled = np.hstack(y_resampled) + + X_resampled = X_resampled[:, np.newaxis, :] + return X_resampled, y_resampled diff --git a/aeon/transformations/collection/imbalance/_ohit.py b/aeon/transformations/collection/imbalance/_ohit.py new file mode 100644 index 0000000000..6024306073 --- /dev/null +++ b/aeon/transformations/collection/imbalance/_ohit.py @@ -0,0 +1,288 @@ +"""OHIT over sampling algorithm. + +An adaptation of the oversampling method based on DRSNN clustering. + +Original authors: +# zhutuanfei +""" + +from collections import OrderedDict + +import numpy as np +from scipy.stats import multivariate_normal +from sklearn.covariance import ledoit_wolf +from sklearn.neighbors import NearestNeighbors +from sklearn.utils import check_random_state + +from aeon.transformations.collection import BaseCollectionTransformer + +__all__ = ["OHIT"] + + +class OHIT(BaseCollectionTransformer): + """ + Over-sampling based on High-density region and Iterative Thresholding (OHIT). + + OHIT generates synthetic minority class samples based on the Density-Ratio Shared + Nearest Neighbor (DRSNN) clustering algorithm. It identifies high-density regions + among the minority class using DRSNN, then produces synthetic samples within + these clusters. Covariance estimation for high-dimensional data is performed using + shrinkage techniques. + + The DRSNN procedure involves three main parameters: + - `drT`: the density ratio threshold (typically set around 1). + - `k`: the nearest neighbour parameter in shared nearest neighbour similarity. + - `kapa`: the nearest neighbour parameter in defining density ratio. + + `k` and `kapa` should be set in a complementary manner to avoid cluster merging + and dissociation. Typically, a large `k` is paired with a relatively low `kapa`. + + Parameters + ---------- + k : int or None, optional + The nearest neighbour parameter for SNN similarity. + If None, set to int(np.ceil(n ** 0.5 * 1.25)), where n is the number of + minority samples. + kapa : int or None, optional + The nearest neighbour parameter for defining the density ratio. + If None, set to int(np.ceil(n ** 0.5)), where n is the number of minority + samples. + drT : float, default=0.9 + Threshold for the density ratio in DRSNN clustering. + distance : str or callable, default='euclidean' + Distance metric to use for KNN in SNN similarity computation. + random_state : int, RandomState instance or None, default=None + Controls random number generation for reproducibility: + - If `int`, sets the random seed. + - If `RandomState` instance, uses it as the generator. + - If `None`, uses `np.random`. + + References + ---------- + .. [1] T. Zhu, C. Luo, Z. Zhang, J. Li, S. Ren, and Y. Zeng. Minority + oversampling for imbalanced time series classification. Knowledge-Based Systems, + 247:108764, 2022. + + Examples + -------- + >>> from aeon.transformations.collection.imbalance import OHIT + >>> from aeon.testing.data_generation import make_example_3d_numpy + >>> import numpy as np + >>> X = make_example_3d_numpy(n_cases=100, return_y=False, random_state=49) + >>> y = np.array([0] * 90 + [1] * 10) + >>> sampler = OHIT(random_state=49) + >>> X_res, y_res = sampler.fit_transform(X, y) + >>> y_res.shape + (180,) + """ + + _tags = { + "requires_y": True, + } + + def __init__( + self, k=None, kapa=None, drT=0.9, distance="euclidean", random_state=None + ): + self.k = k + self.kapa = kapa + self.drT = drT + self.distance = distance + self.random_state = random_state + super().__init__() + + def _fit(self, X, y=None): + + unique, counts = np.unique(y, return_counts=True) + target_stats = dict(zip(unique, counts)) + n_sample_majority = max(target_stats.values()) + class_majority = max(target_stats, key=target_stats.get) + sampling_strategy = { + key: n_sample_majority - value + for (key, value) in target_stats.items() + if key != class_majority + } + self.sampling_strategy_ = OrderedDict(sorted(sampling_strategy.items())) + + return self + + def _transform(self, X, y=None): + X = np.squeeze(X, axis=1) + X_resampled = [X.copy()] + y_resampled = [y.copy()] + + for class_sample, n_samples in self.sampling_strategy_.items(): + if n_samples == 0: + continue + target_class_indices = np.flatnonzero(y == class_sample) + if len(target_class_indices) == 1: + X_new = np.tile(X[target_class_indices], (n_samples, 1)) + y_new = np.full(n_samples, fill_value=class_sample, dtype=y.dtype) + X_resampled.append(X_new) + y_resampled.append(y_new) + continue + X_class = X[target_class_indices] + n, m = X_class.shape + # set the default value of k and kapa + if self.k is None: + self.k = int(np.ceil(n**0.5 * 1.25)) + if self.kapa is None: + self.kapa = int(np.ceil(n**0.5)) + + # Initialize NearestNeighbors for SNN similarity + self.nn_ = NearestNeighbors(metric=self.distance, n_neighbors=self.k + 1) + + clusters, cluster_label = self._cluster_minority(X_class) + Me, eigen_matrices, eigen_values = self._covStruct(X_class, clusters) + + # allocate the number of synthetic samples to be generated for each cluster + random_state = check_random_state(self.random_state) + os_ind = np.tile(np.arange(0, n), int(np.floor(n_samples / n))) + remaining = random_state.choice( + np.arange(0, n), + n_samples - n * int(np.floor(n_samples / n)), + replace=False, + ) + os_ind = np.concatenate([os_ind, remaining]) + R = 1.25 if len(clusters) > 1 else 1.1 + + # generate the structure-preserving synthetic samples for each cluster + X_new = np.zeros((n_samples, m)) + count = 0 + X_class_0 = X_class[cluster_label == 0] + if X_class_0.size != 0: + gen_0 = np.sum(np.isin(os_ind, np.where(cluster_label == 0)[0])) + idx_0 = random_state.choice(len(X_class_0), gen_0, replace=True) + X_new[count : count + gen_0, :] = X_class_0[idx_0] + count += gen_0 + for i, _ in enumerate(clusters): + gen_i = np.sum(np.isin(os_ind, np.where(cluster_label == (i + 1))[0])) + X_new[count : count + gen_i, :] = self._generate_synthetic_samples( + Me[i], eigen_matrices[i], eigen_values[i], gen_i, R + ) + count += gen_i + + assert count == n_samples + X_resampled.append(X_new) + y_new = np.full(n_samples, fill_value=class_sample, dtype=y.dtype) + y_resampled.append(y_new) + + X_resampled = np.vstack(X_resampled) + y_resampled = np.hstack(y_resampled) + X_resampled = X_resampled[:, np.newaxis, :] + return X_resampled, y_resampled + + def _cluster_minority(self, X): + """Apply DRSNN clustering on minority class samples.""" + n = X.shape[0] + k = self.k + kapa = self.kapa + drT = self.drT + + self.nn_.fit(X) + neighbors = self.nn_.kneighbors(X, return_distance=False)[:, 1:] + # construct the shared nearest neighbor similarity + strength = np.zeros((n, n)) + for i in range(n): + for j in range(i + 1, n): + shared_nn = np.intersect1d(neighbors[i, :k], neighbors[j, :k]) + strength[i, j] = strength[j, i] = np.sum( + (k + 1 - np.searchsorted(neighbors[i, :k], shared_nn)) + * (k + 1 - np.searchsorted(neighbors[j, :k], shared_nn)) + ) + + # construct the shared nearest neighbor graph + strength_nn = np.sort(strength, axis=1)[:, ::-1][:, :k] + idx_nn = np.argsort(strength, axis=1)[:, ::-1] + graph = np.zeros((n, k)) + for i in range(n): + for j in range(k): + if np.any(idx_nn[idx_nn[i, j], :k] == i): + graph[i, j] = 1 + + density = np.sum(strength_nn * graph, axis=1) + density_ratio = np.zeros(n) + for i in range(n): + non_noise = np.where(density[idx_nn[i, :kapa]] != 0)[0] + if non_noise.size == 0: + density_ratio[i] = 0 + else: + density_ratio[i] = density[i] / np.mean(density[idx_nn[i, non_noise]]) + + # identify core points + core_idx = np.where(density_ratio > drT)[0] + # find directly density-reachable samples for each core point + neighborhood = {core: set(idx_nn[core, :kapa]) for core in core_idx} + for i in core_idx: + for j in core_idx: + if np.any(idx_nn[j, :kapa] == i): + neighborhood[i].add(j) + neighborhood = {key: list(value) for key, value in neighborhood.items()} + + clusters = [] + cluster_label = np.zeros(len(neighbors), dtype=int) + cluster_id = 0 + + for i in core_idx: + if cluster_label[i] == 0: + cluster_id += 1 + seed = [i] + clusters.append(set(seed)) + while seed: + point = seed.pop(0) + idx = np.where(core_idx == point)[0] + if idx.size > 0 and cluster_label[point] == 0: + seed.extend(neighborhood[point]) + clusters[-1].update(neighborhood[point]) + cluster_label[point] = cluster_id + # no cluster has been found, the whole samples are taken as one cluster + if len(clusters) == 0: + clusters.append(list(range(n))) + cluster_label = np.ones(n, dtype=int) + return clusters, cluster_label + + def _covStruct(self, data, clusters): + """Calculate the covariance matrix of the minority samples.""" + Me, Eigen_matrices, Eigen_values = [], [], [] + for cluster in clusters: + cluster = list(cluster) + cluster_data = data[cluster] + sigma, shrinkage = ledoit_wolf(cluster_data) + me = np.mean(cluster_data, axis=0) + eigenValues, eigenVectors = np.linalg.eigh(sigma) + eigenValues = np.diag(eigenValues) + Me.append(me) + Eigen_matrices.append(eigenVectors) + Eigen_values.append(eigenValues) + return Me, Eigen_matrices, Eigen_values + + def _generate_synthetic_samples(self, Me, eigenMatrix, eigenValue, eta, R): + """Generate synthetic samples based on clustered minority samples.""" + # Initialize the output sample generator and probability arrays + n_samples = int(np.ceil(eta * R)) + SampGen = np.zeros((n_samples, len(Me))) + Prob = np.zeros(n_samples) + + # Calculate the square root of the absolute eigenvalues + DD = np.sqrt(np.abs(np.diag(eigenValue))) + DD = DD.reshape(1, -1) + + # Initialize mean and covariance for the multivariate normal distribution + Mu = np.zeros(len(Me)) + Sigma = np.eye(len(Me)) + + for cnt in range(n_samples): + # Generate a sample from the multivariate normal distribution + S = np.random.multivariate_normal(Mu, Sigma, 1) + Prob[cnt] = multivariate_normal.pdf(S, Mu, Sigma) + + # Scale the sample with the eigenvalues + S = S * DD + # Generate the final sample by applying the eigenvector matrix + x = S @ eigenMatrix.T + Me + SampGen[cnt, :] = x + + # Sort the samples based on the probability in descending order + sorted_indices = np.argsort(Prob)[::-1] + SampGen = SampGen[sorted_indices[:eta], :] + + return SampGen diff --git a/aeon/transformations/collection/imbalance/_smote.py b/aeon/transformations/collection/imbalance/_smote.py new file mode 100644 index 0000000000..cacece39e2 --- /dev/null +++ b/aeon/transformations/collection/imbalance/_smote.py @@ -0,0 +1,252 @@ +"""SMOTE over sampling algorithm. + +See more in imblearn.over_sampling.SMOTE +original authors: +# Guillaume Lemaitre +# Fernando Nogueira +# Christos Aridas +# Dzianis Dudnik +# License: MIT +""" + +from collections import OrderedDict + +import numpy as np +from sklearn.neighbors import NearestNeighbors +from sklearn.utils import check_random_state + +from aeon.transformations.collection import BaseCollectionTransformer + +__maintainer__ = ["TonyBagnall"] +__all__ = ["SMOTE"] + + +class SMOTE(BaseCollectionTransformer): + """ + Synthetic Minority Over-sampling TEchnique (SMOTE) for imbalanced datasets. + + Generates synthetic samples of the minority class to address class imbalance. + SMOTE constructs new samples by interpolating between existing minority samples + and their nearest neighbours in feature space. + + This implementation adapts the algorithm from `imblearn.over_sampling.SMOTE`. + It targets all classes except the majority, as controlled by the `sampling_strategy` + in the `_fit` method. It uses ``aeon`` distances to find the nearest neighbours. + + Parameters + ---------- + k_neighbors : int, default=5 + Number of nearest neighbours used to generate synthetic samples. A + `sklearn.neighbors.NearestNeighbors` instance is fitted for this purpose. + random_state : int, RandomState instance or None, default=None + Controls the random number generation for reproducibility: + - If `int`, sets the random seed. + - If `RandomState` instance, uses it as the generator. + - If `None`, uses `np.random`. + + See Also + -------- + ADASYN : Adaptive synthetic sampling extension to SMOTE. + + References + ---------- + .. [1] Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). + SMOTE: Synthetic minority over-sampling technique. + Journal of Artificial Intelligence Research, 16, 321–357. + https://dl.acm.org/doi/10.5555/1622407.1622416 + + Examples + -------- + >>> from aeon.transformations.collection.imbalance import SMOTE + >>> from aeon.testing.data_generation import make_example_3d_numpy + >>> import numpy as np + >>> X = make_example_3d_numpy(n_cases=100, return_y=False, random_state=49) + >>> y = np.array([0] * 90 + [1] * 10) + >>> sampler = SMOTE(random_state=49) + >>> X_res, y_res = sampler.fit_transform(X, y) + >>> y_res.shape + (180,) + """ + + _tags = { + "requires_y": True, + } + + def __init__(self, k_neighbors: int = 5, random_state=None): + self.random_state = random_state + self.k_neighbors = k_neighbors + super().__init__() + + def _fit(self, X, y=None): + # set the additional_neighbor required by SMOTE + self.nn_ = NearestNeighbors(n_neighbors=self.k_neighbors + 1) + + # generate sampling target by targeting all classes except the majority + unique, counts = np.unique(y, return_counts=True) + target_stats = dict(zip(unique, counts)) + n_sample_majority = max(target_stats.values()) + class_majority = max(target_stats, key=target_stats.get) + sampling_strategy = { + key: n_sample_majority - value + for (key, value) in target_stats.items() + if key != class_majority + } + self.sampling_strategy_ = OrderedDict(sorted(sampling_strategy.items())) + return self + + def _transform(self, X, y=None): + # remove the channel dimension to be compatible with sklearn + X = np.squeeze(X, axis=1) + X_resampled = [X.copy()] + y_resampled = [y.copy()] + + # got the minority class label and the number needs to be generated + for class_sample, n_samples in self.sampling_strategy_.items(): + if n_samples == 0: + continue + target_class_indices = np.flatnonzero(y == class_sample) + X_class = X[target_class_indices] + + self.nn_.fit(X_class) + nns = self.nn_.kneighbors(X_class, return_distance=False)[:, 1:] + X_new, y_new = self._make_samples( + X_class, y.dtype, class_sample, X_class, nns, n_samples, 1.0 + ) + X_resampled.append(X_new) + y_resampled.append(y_new) + X_resampled = np.vstack(X_resampled) + y_resampled = np.hstack(y_resampled) + X_resampled = X_resampled[:, np.newaxis, :] + return X_resampled, y_resampled + + def _make_samples( + self, X, y_dtype, y_type, nn_data, nn_num, n_samples, step_size=1.0, y=None + ): + """Make artificial samples constructed based on nearest neighbours. + + Parameters + ---------- + X : np.ndarray + Shape (n_cases, n_timepoints), time series from which the new series will + be created. + + y_dtype : dtype + The data type of the targets. + + y_type : str or int + The minority target value, just so the function can return the + target values for the synthetic variables with correct length in + a clear format. + + nn_data : ndarray of shape (n_samples_all, n_features) + Data set carrying all the neighbours to be used + + nn_num : ndarray of shape (n_samples_all, k_nearest_neighbours) + The nearest neighbours of each sample in `nn_data`. + + n_samples : int + The number of samples to generate. + + step_size : float, default=1.0 + The step size to create samples. + + y : ndarray of shape (n_samples_all,), default=None + The true target associated with `nn_data`. Used by Borderline SMOTE-2 to + weight the distances in the sample generation process. + + Returns + ------- + X_new : ndarray + Synthetically generated samples of shape (n_samples_new, n_timepoints). + + y_new : ndarray + Target values for synthetic samples of shape (n_samples_new,). + """ + random_state = check_random_state(self.random_state) + samples_indices = random_state.randint(low=0, high=nn_num.size, size=n_samples) + + # np.newaxis for backwards compatibility with random_state + steps = step_size * random_state.uniform(size=n_samples)[:, np.newaxis] + rows = np.floor_divide(samples_indices, nn_num.shape[1]) + cols = np.mod(samples_indices, nn_num.shape[1]) + + X_new = self._generate_samples(X, nn_data, nn_num, rows, cols, steps, y_type, y) + y_new = np.full(n_samples, fill_value=y_type, dtype=y_dtype) + return X_new, y_new + + def _generate_samples( + self, X, nn_data, nn_num, rows, cols, steps, y_type=None, y=None + ): + r"""Generate a synthetic sample. + + The rule for the generation is: + + .. math:: + \mathbf{s_{s}} = \mathbf{s_{i}} + \mathcal{u}(0, 1) \times + (\mathbf{s_{i}} - \mathbf{s_{nn}}) \, + + where \mathbf{s_{s}} is the new synthetic samples, \mathbf{s_{i}} is + the current sample, \mathbf{s_{nn}} is a randomly selected neighbors of + \mathbf{s_{i}} and \mathcal{u}(0, 1) is a random number between [0, 1). + + Parameters + ---------- + X : np.ndarray + Series from which the points will be created of shape (n_cases, + n_timepoints). + nn_data : ndarray of shape (n_samples_all, n_features) + Data set carrying all the neighbours to be used. + nn_num : ndarray of shape (n_samples_all, k_nearest_neighbours) + The nearest neighbours of each sample in `nn_data`. + rows : ndarray of shape (n_samples,), dtype=int + Indices pointing at feature vector in X which will be used + as a base for creating new samples. + cols : ndarray of shape (n_samples,), dtype=int + Indices pointing at which nearest neighbor of base feature vector + will be used when creating new samples. + steps : ndarray of shape (n_samples,), dtype=float + Step sizes for new samples. + y_type : str, int or None, default=None + Class label of the current target classes for which we want to generate + samples. + y : ndarray of shape (n_samples_all,), default=None + The true target associated with `nn_data`. Used by Borderline SMOTE-2 to + weight the distances in the sample generation process. + + Returns + ------- + X_new : {ndarray, sparse matrix} of shape (n_samples, n_features) + Synthetically generated samples. + """ + diffs = nn_data[nn_num[rows, cols]] - X[rows] + if y is not None: + random_state = check_random_state(self.random_state) + mask_pair_samples = y[nn_num[rows, cols]] != y_type + diffs[mask_pair_samples] *= random_state.uniform( + low=0.0, high=0.5, size=(mask_pair_samples.sum(), 1) + ) + X_new = X[rows] + steps * diffs + return X_new.astype(X.dtype) + + @classmethod + def _get_test_params(cls, parameter_set="default"): + """Return testing parameter settings for the estimator. + + Parameters + ---------- + parameter_set : str, default="default" + Name of the set of test parameters to return, for use in tests. If no + special parameters are defined for a value, will return `"default"` set. + ClassifierChannelEnsemble provides the following special sets: + - "results_comparison" - used in some classifiers to compare against + previously generated results where the default set of parameters + cannot produce suitable probability estimates + + Returns + ------- + params : dict or list of dict, default={} + Parameters to create testing instances of the class. + Each dict are parameters to construct an "interesting" test instance, i.e., + `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + """ + return {"k_neighbors": 1} diff --git a/aeon/transformations/collection/imbalance/tests/__init__.py b/aeon/transformations/collection/imbalance/tests/__init__.py new file mode 100644 index 0000000000..55831a6ec8 --- /dev/null +++ b/aeon/transformations/collection/imbalance/tests/__init__.py @@ -0,0 +1 @@ +"""Test resampling transformers.""" diff --git a/aeon/transformations/collection/imbalance/tests/test_adasyn.py b/aeon/transformations/collection/imbalance/tests/test_adasyn.py new file mode 100644 index 0000000000..0bb5c62ea6 --- /dev/null +++ b/aeon/transformations/collection/imbalance/tests/test_adasyn.py @@ -0,0 +1,61 @@ +"""Test ADASYN oversampler ported from imblearn.""" + +import numpy as np +import pytest + +from aeon.testing.data_generation import make_example_3d_numpy +from aeon.transformations.collection.imbalance import ADASYN +from aeon.utils.validation._dependencies import _check_soft_dependencies + + +def test_adasyn(): + """Test the ADASYN class. + + This function creates a 3D numpy array, applies + ADASYN using the ADASYN class, and asserts that the + transformed data has a balanced number of samples. + ADASYN is a variant of SMOTE that generates synthetic samples, + but it focuses on generating samples near the decision boundary. + Therefore, sometimes, it may generate more or less samples than SMOTE, + which is why we only check if the number of samples is nearly balanced. + """ + n_samples = 100 # Total number of labels + majority_num = 90 # number of majority class + minority_num = n_samples - majority_num # number of minority class + + X = np.random.rand(n_samples, 1, 10) + y = np.array([0] * majority_num + [1] * minority_num) + + transformer = ADASYN() + transformer.fit(X, y) + res_X, res_y = transformer.transform(X, y) + _, res_count = np.unique(res_y, return_counts=True) + + assert np.abs(len(res_X) - 2 * majority_num) < minority_num + assert np.abs(len(res_y) - 2 * majority_num) < minority_num + assert res_count[0] == majority_num + assert np.abs(res_count[0] - res_count[1]) < minority_num + + +@pytest.mark.skipif( + not _check_soft_dependencies( + "imbalanced-learn", + package_import_alias={"imbalanced-learn": "imblearn"}, + severity="none", + ), + reason="skip test if required soft dependency imbalanced-learn not available", +) +def test_equivalence_imbalance(): + """Test ported ADASYN code produces the same as imblearn version.""" + from imblearn.over_sampling import ADASYN as imbADASYN + + X, y = make_example_3d_numpy(n_cases=20, n_channels=1) + y = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) + X = X.squeeze() + s1 = imbADASYN(random_state=49) + X2, y2 = s1.fit_resample(X, y) + s2 = ADASYN(random_state=49) + X3, y3 = s2.fit_transform(X, y) + X3 = X3.squeeze() + assert np.array_equal(y2, y3) + assert np.allclose(X2, X3, atol=1e-4) diff --git a/aeon/transformations/collection/imbalance/tests/test_ohit.py b/aeon/transformations/collection/imbalance/tests/test_ohit.py new file mode 100644 index 0000000000..7162aab894 --- /dev/null +++ b/aeon/transformations/collection/imbalance/tests/test_ohit.py @@ -0,0 +1,30 @@ +"""Test function for OHIT.""" + +import numpy as np + +from aeon.transformations.collection.imbalance import OHIT + + +def test_ohit(): + """Test the OHIT class. + + This function creates a 3D numpy array, applies + OHIT using the OHIT class, and asserts that the + transformed data has a balanced number of samples. + """ + n_samples = 100 # Total number of labels + majority_num = 90 # number of majority class + minority_num = n_samples - majority_num # number of minority class + + X = np.random.rand(n_samples, 1, 10) + y = np.array([0] * majority_num + [1] * minority_num) + + transformer = OHIT() + transformer.fit(X, y) + res_X, res_y = transformer.transform(X, y) + _, res_count = np.unique(res_y, return_counts=True) + + assert len(res_X) == 2 * majority_num + assert len(res_y) == 2 * majority_num + assert res_count[0] == majority_num + assert res_count[1] == majority_num diff --git a/aeon/transformations/collection/imbalance/tests/test_smote.py b/aeon/transformations/collection/imbalance/tests/test_smote.py new file mode 100644 index 0000000000..70189633d0 --- /dev/null +++ b/aeon/transformations/collection/imbalance/tests/test_smote.py @@ -0,0 +1,57 @@ +"""Test function for SMOTE.""" + +import numpy as np +import pytest + +from aeon.testing.data_generation import make_example_3d_numpy +from aeon.transformations.collection.imbalance import SMOTE +from aeon.utils.validation._dependencies import _check_soft_dependencies + + +def test_smote(): + """Test the SMOTE class. + + This function creates a 3D numpy array, applies + SMOTE using the SMOTE class, and asserts that the + transformed data has a balanced number of samples. + """ + n_samples = 100 # Total number of labels + majority_num = 90 # number of majority class + minority_num = n_samples - majority_num # number of minority class + + X = np.random.rand(n_samples, 1, 10) + y = np.array([0] * majority_num + [1] * minority_num) + + transformer = SMOTE() + transformer.fit(X, y) + res_X, res_y = transformer.transform(X, y) + _, res_count = np.unique(res_y, return_counts=True) + + assert len(res_X) == 2 * majority_num + assert len(res_y) == 2 * majority_num + assert res_count[0] == majority_num + assert res_count[1] == majority_num + + +@pytest.mark.skipif( + not _check_soft_dependencies( + "imbalanced-learn", + package_import_alias={"imbalanced-learn": "imblearn"}, + severity="none", + ), + reason="skip test if required soft dependency imbalanced-learn not available", +) +def test_equivalence_imbalance(): + """Test ported SMOTE code produces the same as imblearn version.""" + from imblearn.over_sampling import SMOTE as imbSMOTE + + X, y = make_example_3d_numpy(n_cases=20, n_channels=1) + y = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) + X = X.squeeze() + s1 = imbSMOTE(random_state=49) + X2, y2 = s1.fit_resample(X, y) + s2 = SMOTE(random_state=49) + X3, y3 = s2.fit_transform(X, y) + X3 = X3.squeeze() + assert np.array_equal(y2, y3) + assert np.allclose(X2, X3, atol=1e-4) diff --git a/docs/developer_guide/deprecation.md b/docs/developer_guide/deprecation.md index 4b10d81cb2..04aadbab3a 100644 --- a/docs/developer_guide/deprecation.md +++ b/docs/developer_guide/deprecation.md @@ -24,6 +24,7 @@ experimental. Currently experimental modules are: - `segmentation` - `similarity_search` - `visualisation` +- `transformations.collection.imbalance` When we introduce a new module, we may classify it as experimental until the API is stable. We will try to not make drastic changes to experimental modules, but we need From bb9da38d7f08102141dd2ae1ca53b3ca5aa6f455 Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Tue, 29 Jul 2025 15:12:49 +0100 Subject: [PATCH 200/216] stop overwriting y (#2969) --- aeon/segmentation/base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/segmentation/base.py b/aeon/segmentation/base.py index 6d09d47029..ee9d28d579 100644 --- a/aeon/segmentation/base.py +++ b/aeon/segmentation/base.py @@ -118,7 +118,7 @@ def fit(self, X, y=None, axis=1): axis = self.axis X = self._preprocess_series(X, axis, True) if y is not None: - y = self._check_y(y) + self._check_y(y) self._fit(X=X, y=y) self.is_fitted = True return self From a7623de7da207c48d3fe176a494a4bea36e46b86 Mon Sep 17 00:00:00 2001 From: Antoine Guillaume Date: Wed, 30 Jul 2025 09:53:20 +0200 Subject: [PATCH 201/216] [BUG] Fix redcomets bug when using only one sample (#2952) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Fix * fix exemples * Empty commit for CI * Rework examples and add test * Try fixing example indentation error * Fix examples --------- Co-authored-by: baraline <10759117+baraline@users.noreply.github.com> Co-authored-by: Patrick Schäfer --- aeon/classification/dictionary_based/_redcomets.py | 10 +++++----- aeon/distances/mindist/_dft_sfa.py | 12 ++++++++---- aeon/distances/mindist/_sfa.py | 11 +++++++---- aeon/distances/tests/test_symbolic_mindist.py | 13 +++++++++++++ .../collection/dictionary_based/_sfa_fast.py | 3 +-- 5 files changed, 34 insertions(+), 15 deletions(-) diff --git a/aeon/classification/dictionary_based/_redcomets.py b/aeon/classification/dictionary_based/_redcomets.py index d62b09d010..88f5016ed2 100644 --- a/aeon/classification/dictionary_based/_redcomets.py +++ b/aeon/classification/dictionary_based/_redcomets.py @@ -150,7 +150,7 @@ def _fit(self, X, y): self.sfa_clfs, self.sax_transforms, self.sax_clfs, - ) = self._build_univariate_ensemble(np.squeeze(X), y) + ) = self._build_univariate_ensemble(np.squeeze(X, 1), y) else: # Multivariate if self.variant in [1, 2, 3]: # Concatenate @@ -204,7 +204,7 @@ def _build_univariate_ensemble(self, X, y): from imblearn.over_sampling import SMOTE, RandomOverSampler - X = Normalizer().fit_transform(X).squeeze() + X = Normalizer().fit_transform(X).squeeze(1) if self.variant in [1, 2, 3]: perc_length = self.perc_length / self._n_channels @@ -391,7 +391,7 @@ def _predict_proba(self, X) -> np.ndarray: Predicted probabilities using the ordering in ``classes_``. """ if X.shape[1] == 1: # Univariate - return self._predict_proba_unvivariate(np.squeeze(X)) + return self._predict_proba_unvivariate(np.squeeze(X, 1)) else: # Multivariate if self.variant in [1, 2, 3]: # Concatenate X_concat = X.reshape(*X.shape[:-2], -1) @@ -414,7 +414,7 @@ def _predict_proba_unvivariate(self, X) -> np.ndarray: 2D np.ndarray of shape (n_cases, n_classes_) Predicted probabilities using the ordering in ``classes_``. """ - X = Normalizer().fit_transform(X).squeeze() + X = Normalizer().fit_transform(X).squeeze(1) pred_mat = np.zeros((X.shape[0], self.n_classes_)) @@ -588,7 +588,7 @@ def _parallel_sax(self, sax_transforms, X): """ def _sax_wrapper(sax): - return np.squeeze(sax.fit_transform(X)) + return np.squeeze(sax.fit_transform(X), 1) sax_parallel_res = Parallel(n_jobs=self._n_jobs, backend=self.parallel_backend)( delayed(_sax_wrapper)(sax) for sax in sax_transforms diff --git a/aeon/distances/mindist/_dft_sfa.py b/aeon/distances/mindist/_dft_sfa.py index 9d8bd80794..48c6f099c2 100644 --- a/aeon/distances/mindist/_dft_sfa.py +++ b/aeon/distances/mindist/_dft_sfa.py @@ -46,8 +46,8 @@ def mindist_dft_sfa_distance( >>> import numpy as np >>> from aeon.distances import mindist_dft_sfa_distance >>> from aeon.transformations.collection.dictionary_based import SFAWhole - >>> x = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]) - >>> y = np.array([[11, 12, 13, 14, 15, 16, 17, 18, 19, 20]]) + >>> x = np.random.rand(1,1,10) # (n_cases, n_channels, n_timepoints) + >>> y = np.random.rand(1,1,10) >>> transform = SFAWhole( ... word_length=8, ... alphabet_size=8, @@ -55,11 +55,15 @@ def mindist_dft_sfa_distance( ... ) >>> x_sfa, _ = transform.fit_transform(x) >>> _, y_dft = transform.transform(y) - >>> dist = mindist_dft_sfa_distance(y_dft, x_sfa, transform.breakpoints) + >>> for i in range(x.shape[0]): + ... dist = mindist_dft_sfa_distance(y_dft[0], x_sfa[0], transform.breakpoints) """ if x_dft.ndim == 1 and y_sfa.ndim == 1: return _univariate_dft_sfa_distance(x_dft, y_sfa, breakpoints) - raise ValueError("x and y must be 1D") + raise ValueError( + f"x and y must be 1D, but got x of shape {x_dft.shape} and y of shape" + f"{y_sfa.shape}" + ) @njit(cache=True, fastmath=True) diff --git a/aeon/distances/mindist/_sfa.py b/aeon/distances/mindist/_sfa.py index 95dabd9cb6..8d6af190e0 100644 --- a/aeon/distances/mindist/_sfa.py +++ b/aeon/distances/mindist/_sfa.py @@ -46,8 +46,8 @@ def mindist_sfa_distance( >>> import numpy as np >>> from aeon.distances import mindist_sfa_distance >>> from aeon.transformations.collection.dictionary_based import SFAWhole - >>> x = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]) - >>> y = np.array([[11, 12, 13, 14, 15, 16, 17, 18, 19, 20]]) + >>> x = np.random.rand(1,1,10) # (n_cases, n_channels, n_timepoints) + >>> y = np.random.rand(1,1,10) >>> transform = SFAWhole( ... word_length=8, ... alphabet_size=8, @@ -55,11 +55,14 @@ def mindist_sfa_distance( ... ) >>> x_sfa, _ = transform.fit_transform(x) >>> y_sfa, _ = transform.transform(y) - >>> dist = mindist_sfa_distance(x_sfa, y_sfa, transform.breakpoints) + >>> for i in range(x.shape[0]): + ... dist = mindist_sfa_distance(x_sfa[i], y_sfa[i], transform.breakpoints) """ if x.ndim == 1 and y.ndim == 1: return _univariate_sfa_distance(x, y, breakpoints) - raise ValueError("x and y must be 1D") + raise ValueError( + f"x and y must be 1D, but got x of shape {x.shape} and y of shape {y.shape}" + ) @njit(cache=True, fastmath=True) diff --git a/aeon/distances/tests/test_symbolic_mindist.py b/aeon/distances/tests/test_symbolic_mindist.py index 2d81e04ddb..7cf96b1ffa 100644 --- a/aeon/distances/tests/test_symbolic_mindist.py +++ b/aeon/distances/tests/test_symbolic_mindist.py @@ -8,6 +8,7 @@ from aeon.distances.mindist._paa_sax import mindist_paa_sax_distance from aeon.distances.mindist._sax import mindist_sax_distance from aeon.distances.mindist._sfa import mindist_sfa_distance +from aeon.testing.data_generation import make_example_3d_numpy from aeon.transformations.collection.dictionary_based import SAX, SFA, SFAFast, SFAWhole @@ -49,6 +50,18 @@ def test_sax_mindist(): assert mindist_paa_sax <= ed +def test_single_sample(): + """Test the SFA Min-Distance function.""" + x, _ = make_example_3d_numpy(n_cases=1, n_channels=1, n_timepoints=10) + y = x + 10 + transform = SFAWhole(word_length=8, alphabet_size=8, norm=True) + x_sfa, _ = transform.fit_transform(x) + _, y_dft = transform.transform(y) + for i in range(len(x_sfa)): + dist = mindist_dft_sfa_distance(y_dft[i], x_sfa[i], transform.breakpoints) + assert dist == 0 + + def test_sfa_mindist(): """Test the SFA Min-Distance function.""" n_segments = 16 diff --git a/aeon/transformations/collection/dictionary_based/_sfa_fast.py b/aeon/transformations/collection/dictionary_based/_sfa_fast.py index 53e72e73c8..0e5e3f1d78 100644 --- a/aeon/transformations/collection/dictionary_based/_sfa_fast.py +++ b/aeon/transformations/collection/dictionary_based/_sfa_fast.py @@ -729,8 +729,7 @@ def transform_words(self, X): self.alphabet_size, self.breakpoints, ) - - return words.squeeze(), dfts.squeeze() + return words.squeeze(1), dfts.squeeze(1) @classmethod def _get_test_params(cls, parameter_set="default"): From 9096b3e4d32c5f73d2881b6696b89e2a5de3aede Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Wed, 30 Jul 2025 10:04:23 +0100 Subject: [PATCH 202/216] [DEP] Remove deprecated transforms (#2961) * remove depcated transforms * fixes --- aeon/transformations/collection/__init__.py | 7 - aeon/transformations/collection/_pad.py | 183 ------------------ aeon/transformations/collection/_resize.py | 105 ---------- aeon/transformations/collection/_truncate.py | 134 ------------- aeon/transformations/series/__init__.py | 12 -- aeon/transformations/series/_dft.py | 53 ----- aeon/transformations/series/_exp_smoothing.py | 64 ------ aeon/transformations/series/_gauss.py | 56 ------ .../transformations/series/_moving_average.py | 53 ----- aeon/transformations/series/_sg.py | 56 ------ aeon/transformations/series/_siv.py | 58 ------ .../series/compose/_pipeline.py | 12 +- docs/api_reference/transformations.md | 15 +- 13 files changed, 17 insertions(+), 791 deletions(-) delete mode 100644 aeon/transformations/collection/_pad.py delete mode 100644 aeon/transformations/collection/_resize.py delete mode 100644 aeon/transformations/collection/_truncate.py delete mode 100644 aeon/transformations/series/_dft.py delete mode 100644 aeon/transformations/series/_exp_smoothing.py delete mode 100644 aeon/transformations/series/_gauss.py delete mode 100644 aeon/transformations/series/_moving_average.py delete mode 100644 aeon/transformations/series/_sg.py delete mode 100644 aeon/transformations/series/_siv.py diff --git a/aeon/transformations/collection/__init__.py b/aeon/transformations/collection/__init__.py index 71abc15f08..ec0272de8d 100644 --- a/aeon/transformations/collection/__init__.py +++ b/aeon/transformations/collection/__init__.py @@ -17,10 +17,6 @@ "SlopeTransformer", "SimpleImputer", "Tabularizer", - # deprecated - "Resizer", - "Padder", - "Truncator", ] from aeon.transformations.collection._acf import AutocorrelationFunctionTransformer @@ -30,11 +26,8 @@ from aeon.transformations.collection._hog1d import HOG1DTransformer from aeon.transformations.collection._impute import SimpleImputer from aeon.transformations.collection._matrix_profile import MatrixProfile -from aeon.transformations.collection._pad import Padder from aeon.transformations.collection._periodogram import PeriodogramTransformer from aeon.transformations.collection._reduce import Tabularizer from aeon.transformations.collection._rescale import Centerer, MinMaxScaler, Normalizer -from aeon.transformations.collection._resize import Resizer from aeon.transformations.collection._slope import SlopeTransformer -from aeon.transformations.collection._truncate import Truncator from aeon.transformations.collection.base import BaseCollectionTransformer diff --git a/aeon/transformations/collection/_pad.py b/aeon/transformations/collection/_pad.py deleted file mode 100644 index cab8dd99e0..0000000000 --- a/aeon/transformations/collection/_pad.py +++ /dev/null @@ -1,183 +0,0 @@ -"""Padding transformer, pad unequal length time series to max length or fixed length.""" - -__all__ = ["Padder"] -__maintainer__ = [] - -import numpy as np -from deprecated.sphinx import deprecated - -from aeon.transformations.collection.base import BaseCollectionTransformer - - -def _get_max_length(X): - max_length = X[0].shape[1] - for x in X: - if x.shape[1] > max_length: - max_length = x.shape[1] - - return max_length - - -# TODO: Remove in v1.3.0 -@deprecated( - version="1.2.0", - reason="Padder is deprecated and will be moving to " - "transformations.collection.unequal_length with additional/reworked functionality.", - category=FutureWarning, -) -class Padder(BaseCollectionTransformer): - """Pad unequal length time series to equal, fixed length. - - Pads the input dataset to either fixed length (at least as long as the longest - series) or finds the max length series across all series and channels and - pads to that with zeroes. - - Parameters - ---------- - pad_length : int or None, default=None - length to pad the series too. if None, will find the longest sequence and use - instead. If the pad_length passed is less than the max length, it is reset to - max length. - - fill_value : Union[int, str, np.ndarray], default = 0 - Value to pad with. Can be a float or a statistic string or an numpy array for - each time series. Supported statistic strings are "mean", "median", "max", - "min". - - Examples - -------- - >>> from aeon.transformations.collection import Padder - >>> import numpy as np - >>> X = [] - >>> for i in range(10): X.append(np.random.random((4, 75 + i))) - >>> padder = Padder(pad_length=200, fill_value =42) - >>> X2 = padder.fit_transform(X) - >>> X2.shape - (10, 4, 200) - """ - - _tags = { - "X_inner_type": ["np-list", "numpy3D"], - "fit_is_empty": False, - "capability:multivariate": True, - "capability:unequal_length": True, - "removes_unequal_length": True, - } - - def __init__(self, pad_length=None, fill_value=0): - self.pad_length = pad_length - self.fill_value = fill_value - super().__init__() - - def _fit(self, X, y=None): - """Fit padding transformer to X and y. - - Calculates the max length in X unless padding length passed as an argument. - - Parameters - ---------- - X : list of [n_cases] 2D np.ndarray shape (n_channels, length_i) - where length_i can vary between time series or 3D numpy of equal length - series - y : ignored argument for interface compatibility - Additional data, e.g., labels for transformation - - Returns - ------- - self : reference to self - """ - self.fill_value_ = self.fill_value - max_length = _get_max_length(X) - if self.pad_length is None: - self.pad_length_ = max_length - else: - if self.pad_length < max_length: - self.pad_length_ = max_length - else: - self.pad_length_ = self.pad_length - - if isinstance(self.fill_value, str): - if self.fill_value == "mean": - self.fill_value_ = np.zeros((len(X), X[0].shape[0])) - for i, series in enumerate(X): - for j, channel in enumerate(series): - self.fill_value_[i][j] = np.mean(channel) - elif self.fill_value == "median": - self.fill_value_ = np.zeros((len(X), X[0].shape[0])) - for i, series in enumerate(X): - for j, channel in enumerate(series): - self.fill_value_[i][j] = np.median(channel) - elif self.fill_value == "min": - self.fill_value_ = np.zeros((len(X), X[0].shape[0])) - for i, series in enumerate(X): - for j, channel in enumerate(series): - self.fill_value_[i][j] = np.min(channel) - elif self.fill_value == "max": - self.fill_value_ = np.zeros((len(X), X[0].shape[0])) - for i, series in enumerate(X): - for j, channel in enumerate(series): - self.fill_value_[i][j] = np.max(channel) - else: - raise ValueError( - "Supported modes are mean, median, min, max. \ - Please check arguments passed." - ) - elif isinstance(self.fill_value, np.ndarray): - if not (len(self.fill_value) == len(X)): - raise ValueError( - "The length of fill_value must match the \ - length of X if a numpy array is passed as fill_value." - ) - if not self.fill_value.ndim == 2: - raise ValueError( - """The fill_value argument must be - a 2D Numpy array, containing values for - each `n_channel` for `n_cases` series.""" - ) - else: - self.fill_value_ = self.fill_value * np.ones((len(X), X[0].shape[0])) - - return self - - def _transform(self, X, y=None): - """Transform X and return a transformed version. - - Parameters - ---------- - X : list of [n_cases] 2D np.ndarray shape (n_channels, length_i) - where length_i can vary between time series or 3D numpy of equal length - series - y : ignored argument for interface compatibility - Additional data, e.g., labels for transformation - - Returns - ------- - Xt : numpy3D array (n_cases, n_channels, self.pad_length_) - padded time series from X. - """ - max_length = _get_max_length(X) - - if max_length > self.pad_length_: - raise ValueError( - "max_length of series in transform is greater than the one found in " - "fit or set in the constructor." - ) - # Calculate padding amounts - - Xt = [] - for i, series in enumerate(X): - pad_width = (0, self.pad_length_ - series.shape[1]) - temp_array = [] - for j, channel in enumerate(series): - # Pad the input array - padded_array = np.pad( - channel, - pad_width, - mode="constant", - constant_values=self.fill_value_[i][j], - ) - temp_array.append(padded_array) - Xt.append(temp_array) - Xt = np.array(Xt) - - return Xt diff --git a/aeon/transformations/collection/_resize.py b/aeon/transformations/collection/_resize.py deleted file mode 100644 index a43d0a43e8..0000000000 --- a/aeon/transformations/collection/_resize.py +++ /dev/null @@ -1,105 +0,0 @@ -"""Time series resizer.""" - -__all__ = ["Resizer"] -__maintainer__ = [] - -import numpy as np -from deprecated.sphinx import deprecated - -from aeon.transformations.collection.base import BaseCollectionTransformer - - -# TODO: Remove in v1.3.0 -@deprecated( - version="1.2.0", - reason="Resizer is deprecated and will be moving to . " - "transformations.collection.unequal_length with additional/reworked functionality.", - category=FutureWarning, -) -class Resizer(BaseCollectionTransformer): - """Time series interpolator/re-sampler. - - Transformer that resizes series using np.linspace is fitted on each channel - independently. After transformation the collection will be a numpy array shape ( - n_cases, n_channels, length). It is not capable of sensibly handling missing - values. - - Parameters - ---------- - length : integer, the length of time series to resize to. - - Examples - -------- - >>> import numpy as np - >>> from aeon.transformations.collection import Resizer - >>> # Unequal length collection of time series - >>> X_list = [] - >>> for i in range(10): X_list.append(np.random.rand(5,10+i)) - >>> # Equal length collection of time series - >>> X_array = np.random.rand(10,3,30) - >>> trans = Resizer(length = 50) - >>> X_new = trans.fit_transform(X_list) - >>> X_new.shape - (10, 5, 50) - >>> X_new = trans.fit_transform(X_array) - >>> X_new.shape - (10, 3, 50) - """ - - _tags = { - "X_inner_type": ["np-list", "numpy3D"], - "capability:multivariate": True, - "capability:unequal_length": True, - "fit_is_empty": True, - } - - def __init__(self, length): - """Initialize estimator. - - Parameters - ---------- - length : integer, the length of time series to resize to. - """ - if length <= 0 or (not isinstance(length, int)): - raise ValueError("resizing length must be integer and > 0") - - self.length = length - super().__init__() - - def _transform(self, X, y=None): - """Fit a linear function on each channel of each series, then resample. - - Parameters - ---------- - X : 3D np.ndarray of shape = (n_cases, n_channels, n_timepoints) or - list size [n_cases] of 2D nump arrays, case i has shape (n_channels, - length_i). Collection of time series to transform - y : ignored argument for interface compatibility - - Returns - ------- - 3D numpy array of shape (n_cases, n_channels, self.length) - """ - Xt = [] - for x in X: - x_new = np.zeros((x.shape[0], self.length)) - x2 = np.linspace(0, 1, x.shape[1]) - x3 = np.linspace(0, 1, self.length) - for i, row in enumerate(x): - x_new[i] = np.interp(x3, x2, row) - Xt.append(x_new) - return np.array(Xt) - - @classmethod - def _get_test_params(cls, parameter_set="default"): - """Return testing parameter settings for the estimator. - - Returns - ------- - params : dict or list of dict, default={} - Parameters to create testing instances of the class. - Each dict are parameters to construct an "interesting" test instance, i.e., - `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. - """ - params = {"length": 10} - return params diff --git a/aeon/transformations/collection/_truncate.py b/aeon/transformations/collection/_truncate.py deleted file mode 100644 index 2be0fa0583..0000000000 --- a/aeon/transformations/collection/_truncate.py +++ /dev/null @@ -1,134 +0,0 @@ -"""Truncation transformer - truncate unequal length collections.""" - -__all__ = ["Truncator"] -__maintainer__ = [] - -import numpy as np -from deprecated.sphinx import deprecated - -from aeon.transformations.collection.base import BaseCollectionTransformer - - -# TODO: Remove in v1.3.0 -@deprecated( - version="1.2.0", - reason="Truncator is deprecated and will be moving to . " - "transformations.collection.unequal_length with additional/reworked functionality.", - category=FutureWarning, -) -class Truncator(BaseCollectionTransformer): - """Truncate unequal length time series to a lower bounds. - - Truncates all series in collection between lower/upper range bounds. This - transformer assumes that all series have the same number of channels (dimensions) - and that all channels in a single series are the same length. - - Parameters - ---------- - truncated_length : int, default=None - bottom range of the values to truncate can also be used to truncate - to a specific length. - if None, will find the shortest sequence and use instead. - - Examples - -------- - >>> from aeon.transformations.collection import Truncator - >>> import numpy as np - >>> X = [] - >>> for i in range(10): X.append(np.random.random((4, 75 + i))) - >>> truncator = Truncator(truncated_length=10) - >>> X2 = truncator.fit_transform(X) - >>> X2.shape - (10, 4, 10) - - """ - - _tags = { - "X_inner_type": ["np-list", "numpy3D"], - "capability:multivariate": True, - "capability:unequal_length": True, - "removes_unequal_length": True, - } - - def __init__(self, truncated_length=None): - self.truncated_length = truncated_length - super().__init__() - - @staticmethod - def _get_min_length(X): - min_length = X[0].shape[1] - for x in X: - if x.shape[1] < min_length: - min_length = x.shape[1] - - return min_length - - def _fit(self, X, y=None): - """Fit transformer to X and y. - - Parameters - ---------- - X : list of [n_cases] 2D np.ndarray shape (n_channels, length_i) - where length_i can vary between time series or 3D numpy of equal length - series - y : ignored argument for interface compatibility - Additional data, e.g., labels for transformation - - Returns - ------- - self : reference to self - """ - # If lower is none, set to the minimum length in X - min_length = self._get_min_length(X) - if self.truncated_length is None: - self.truncated_length_ = min_length - elif min_length < self.truncated_length: - self.truncated_length_ = min_length - else: - self.truncated_length_ = self.truncated_length - - return self - - def _transform(self, X, y=None): - """Truncate X and return a transformed version. - - Parameters - ---------- - X : list of [n_cases] 2D np.ndarray shape (n_channels, length_i) - where length_i can vary between time series. - y : ignored argument for interface compatibility - - Returns - ------- - Xt : numpy3D array (n_cases, n_channels, self.truncated_length_) - truncated time series from X. - """ - min_length = self._get_min_length(X) - if min_length < self.truncated_length_: - raise ValueError( - "Error: min_length of series \ - is less than the one found when fit or set." - ) - Xt = np.array([x[:, : self.truncated_length_] for x in X]) - return Xt - - @classmethod - def _get_test_params(cls, parameter_set="default"): - """Return testing parameter settings for the estimator. - - Parameters - ---------- - parameter_set : str, default="default" - Name of the set of test parameters to return, for use in tests. If no - special parameters are defined for a value, will return `"default"` set. - - - Returns - ------- - params : dict or list of dict, default = {} - Parameters to create testing instances of the class - Each dict are parameters to construct an "interesting" test instance, i.e., - `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. - """ - params = {"truncated_length": 5} - return params diff --git a/aeon/transformations/series/__init__.py b/aeon/transformations/series/__init__.py index d97d45c7ad..55aab8ab98 100644 --- a/aeon/transformations/series/__init__.py +++ b/aeon/transformations/series/__init__.py @@ -4,21 +4,15 @@ "AutoCorrelationSeriesTransformer", "BaseSeriesTransformer", "ClaSPTransformer", - "DFTSeriesTransformer", "Dobin", - "ExpSmoothingSeriesTransformer", - "GaussSeriesTransformer", "MatrixProfileSeriesTransformer", - "MovingAverageSeriesTransformer", "LogTransformer", "PLASeriesTransformer", - "SGSeriesTransformer", "StatsModelsACF", "StatsModelsPACF", "BKFilter", "BoxCoxTransformer", "ScaledLogitSeriesTransformer", - "SIVSeriesTransformer", "PCASeriesTransformer", "WarpingSeriesTransformer", "DifferenceTransformer", @@ -32,18 +26,12 @@ from aeon.transformations.series._bkfilter import BKFilter from aeon.transformations.series._boxcox import BoxCoxTransformer from aeon.transformations.series._clasp import ClaSPTransformer -from aeon.transformations.series._dft import DFTSeriesTransformer from aeon.transformations.series._diff import DifferenceTransformer from aeon.transformations.series._dobin import Dobin -from aeon.transformations.series._exp_smoothing import ExpSmoothingSeriesTransformer -from aeon.transformations.series._gauss import GaussSeriesTransformer from aeon.transformations.series._log import LogTransformer from aeon.transformations.series._matrix_profile import MatrixProfileSeriesTransformer -from aeon.transformations.series._moving_average import MovingAverageSeriesTransformer from aeon.transformations.series._pca import PCASeriesTransformer from aeon.transformations.series._pla import PLASeriesTransformer from aeon.transformations.series._scaled_logit import ScaledLogitSeriesTransformer -from aeon.transformations.series._sg import SGSeriesTransformer -from aeon.transformations.series._siv import SIVSeriesTransformer from aeon.transformations.series._warping import WarpingSeriesTransformer from aeon.transformations.series.base import BaseSeriesTransformer diff --git a/aeon/transformations/series/_dft.py b/aeon/transformations/series/_dft.py deleted file mode 100644 index d29ff92d8f..0000000000 --- a/aeon/transformations/series/_dft.py +++ /dev/null @@ -1,53 +0,0 @@ -"""Discrete Fourier Approximation filter transformation.""" - -__maintainer__ = ["Cyril-Meyer"] -__all__ = ["DFTSeriesTransformer"] - - -from deprecated.sphinx import deprecated - -from aeon.transformations.series.smoothing import DiscreteFourierApproximation - - -# TODO: Remove in v1.3.0 -@deprecated( - version="1.2.0", - reason="DFTSeriesTransformer is deprecated and will be removed in v1.3.0. " - "Please use DiscreteFourierApproximation from " - "transformations.series.smoothing instead.", - category=FutureWarning, -) -class DFTSeriesTransformer(DiscreteFourierApproximation): - """Filter a times series using Discrete Fourier Approximation (DFT). - - Parameters - ---------- - r : float - Proportion of Fourier terms to retain [0, 1] - - sort : bool - Sort the Fourier terms by amplitude to keep most important terms - - Notes - ----- - More information of the NumPy FFT functions used - https://numpy.org/doc/stable/reference/routines.fft.html - - References - ---------- - .. [1] Cooley, J.W., & Tukey, J.W. (1965). - An algorithm for the machine calculation of complex Fourier series. - Mathematics of Computation, 19, 297-301. - - Examples - -------- - >>> import numpy as np - >>> from aeon.transformations.series._dft import DFTSeriesTransformer - >>> X = np.random.random((2, 100)) # Random series length 100 - >>> dft = DFTSeriesTransformer() - >>> X_ = dft.fit_transform(X) - >>> X_.shape - (2, 100) - """ - - pass diff --git a/aeon/transformations/series/_exp_smoothing.py b/aeon/transformations/series/_exp_smoothing.py deleted file mode 100644 index 56739e4491..0000000000 --- a/aeon/transformations/series/_exp_smoothing.py +++ /dev/null @@ -1,64 +0,0 @@ -"""Exponential smoothing transformation.""" - -__maintainer__ = ["Datadote"] -__all__ = ["ExpSmoothingSeriesTransformer"] - - -from deprecated.sphinx import deprecated - -from aeon.transformations.series.smoothing import ExponentialSmoothing - - -# TODO: Remove in v1.3.0 -@deprecated( - version="1.2.0", - reason="ExpSmoothingSeriesTransformer is deprecated and will be removed in v1.3.0. " - "Please use ExponentialSmoothing from " - "transformations.series.smoothing instead.", - category=FutureWarning, -) -class ExpSmoothingSeriesTransformer(ExponentialSmoothing): - """Filter a time series using exponential smoothing. - - - Exponential smoothing (EXP) is a generalisaton of moving average smoothing that - assigns a decaying weight to each element rather than averaging over a window. - - Assume time series T = [t_0, ..., t_j], and smoothed values S = [s_0, ..., s_j] - - Then, s_0 = t_0 and s_j = alpha * t_j + (1 - alpha) * s_j-1 - where 0 ≤ alpha ≤ 1. If window_size is given, alpha is overwritten, and set as - alpha = 2. / (window_size + 1) - - Parameters - ---------- - alpha: float, default=0.2 - decaying weight. Range [0, 1]. Overwritten by window_size if window_size exists - window_size: int or float or None, default=None - If window_size is specified, alpha is set to 2. / (window_size + 1) - - References - ---------- - Large, J., Southam, P., Bagnall, A. (2019). - Can Automated Smoothing Significantly Improve Benchmark Time Series - Classification Algorithms?. In: Pérez García, H., Sánchez González, - L., CastejónLimas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid - Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), - vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_5 - https://arxiv.org/abs/1811.00894 - - Examples - -------- - >>> import numpy as np - >>> from aeon.transformations.series._exp_smoothing import \ - ExpSmoothingSeriesTransformer - >>> X = np.array([-2, -1, 0, 1, 2]) - >>> transformer = ExpSmoothingSeriesTransformer(0.5) - >>> Xt = transformer.fit_transform(X) - >>> print(Xt) - [[-2. -1.5 -0.75 0.125 1.0625]] - >>> X = np.array([[1, 2, 3, 4], [10, 9, 8, 7]]) - >>> Xt = transformer.fit_transform(X) - >>> print(Xt) - [[ 1. 1.5 2.25 3.125] - [10. 9.5 8.75 7.875]] - """ - - pass diff --git a/aeon/transformations/series/_gauss.py b/aeon/transformations/series/_gauss.py deleted file mode 100644 index a596cf74e1..0000000000 --- a/aeon/transformations/series/_gauss.py +++ /dev/null @@ -1,56 +0,0 @@ -"""Gaussian filter transformation.""" - -__maintainer__ = ["Cyril-Meyer"] -__all__ = ["GaussSeriesTransformer"] - - -from deprecated.sphinx import deprecated - -from aeon.transformations.series.smoothing import GaussianFilter - - -# TODO: Remove in v1.3.0 -@deprecated( - version="1.2.0", - reason="GaussSeriesTransformer is deprecated and will be removed in v1.3.0. " - "Please use GaussianFilter from " - "transformations.series.smoothing instead.", - category=FutureWarning, -) -class GaussSeriesTransformer(GaussianFilter): - """Filter a times series using Gaussian filter. - - Parameters - ---------- - sigma : float, default=1 - Standard deviation for the Gaussian kernel. - - order : int, default=0 - An order of 0 corresponds to convolution with a Gaussian kernel. - A positive order corresponds to convolution with that derivative of a - Gaussian. - - - Notes - ----- - More information of the SciPy gaussian_filter1d function used - https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.gaussian_filter1d.html - - References - ---------- - .. [1] Rafael C. Gonzales and Paul Wintz. 1987. - Digital image processing. - Addison-Wesley Longman Publishing Co., Inc., USA. - - Examples - -------- - >>> import numpy as np - >>> from aeon.transformations.series._gauss import GaussSeriesTransformer - >>> X = np.random.random((2, 100)) # Random series length 100 - >>> gauss = GaussSeriesTransformer(sigma=5) - >>> X_ = gauss.fit_transform(X) - >>> X_.shape - (2, 100) - """ - - pass diff --git a/aeon/transformations/series/_moving_average.py b/aeon/transformations/series/_moving_average.py deleted file mode 100644 index 930e1a6ecb..0000000000 --- a/aeon/transformations/series/_moving_average.py +++ /dev/null @@ -1,53 +0,0 @@ -"""Moving average transformation.""" - -__maintainer__ = ["Datadote"] -__all__ = ["MovingAverageSeriesTransformer"] - - -from deprecated.sphinx import deprecated - -from aeon.transformations.series.smoothing import MovingAverage - - -# TODO: Remove in v1.3.0 -@deprecated( - version="1.2.0", - reason="MovingAverageSeriesTransformer is deprecated and will be removed in " - "v1.3.0. Please use MovingAverage from " - "transformations.series.smoothing instead.", - category=FutureWarning, -) -class MovingAverageSeriesTransformer(MovingAverage): - """Calculate the moving average of an array of numbers. - - Slides a window across the input array, and returns the averages for each window. - This implementation precomputes a cumulative sum, and then performs subtraction. - - Parameters - ---------- - window_size: int, default=5 - Number of values to average for each window - - References - ---------- - Large, J., Southam, P., Bagnall, A. (2019). - Can Automated Smoothing Significantly Improve Benchmark Time Series - Classification Algorithms?. In: Pérez García, H., Sánchez González, - L., CastejónLimas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid - Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), - vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_5 - https://arxiv.org/abs/1811.00894 - - Examples - -------- - >>> import numpy as np - >>> from aeon.transformations.series._moving_average import \ - MovingAverageSeriesTransformer - >>> X = np.array([-3, -2, -1, 0, 1, 2, 3]) - >>> transformer = MovingAverageSeriesTransformer(2) - >>> Xt = transformer.fit_transform(X) - >>> print(Xt) - [[-2.5 -1.5 -0.5 0.5 1.5 2.5]] - """ - - pass diff --git a/aeon/transformations/series/_sg.py b/aeon/transformations/series/_sg.py deleted file mode 100644 index 1f1db1761a..0000000000 --- a/aeon/transformations/series/_sg.py +++ /dev/null @@ -1,56 +0,0 @@ -"""Savitzky-Golay filter transformation.""" - -__maintainer__ = ["Cyril-Meyer"] -__all__ = ["SGSeriesTransformer"] - - -from deprecated.sphinx import deprecated - -from aeon.transformations.series.smoothing import SavitzkyGolayFilter - - -# TODO: Remove in v1.3.0 -@deprecated( - version="1.2.0", - reason="SGSeriesTransformer is deprecated and will be removed in v1.3.0. " - "Please use SavitzkyGolayFilter from " - "transformations.series.smoothing instead.", - category=FutureWarning, -) -class SGSeriesTransformer(SavitzkyGolayFilter): - """Filter a times series using Savitzky-Golay (SG). - - Parameters - ---------- - window_length : int, default=5 - The length of the filter window (i.e., the number of coefficients). - window_length must be less than or equal to the size of the input. - - polyorder : int, default=2 - The order of the polynomial used to fit the samples. - polyorder must be less than window_length. - - - Notes - ----- - More information of the SciPy savgol_filter function used - https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.savgol_filter.html - - References - ---------- - .. [1] Savitzky, A., & Golay, M. J. (1964). - Smoothing and differentiation of data by simplified least squares procedures. - Analytical chemistry, 36(8), 1627-1639. - - Examples - -------- - >>> import numpy as np - >>> from aeon.transformations.series._sg import SGSeriesTransformer - >>> X = np.random.random((2, 100)) # Random series length 100 - >>> sg = SGSeriesTransformer() - >>> X_ = sg.fit_transform(X) - >>> X_.shape - (2, 100) - """ - - pass diff --git a/aeon/transformations/series/_siv.py b/aeon/transformations/series/_siv.py deleted file mode 100644 index b34e637285..0000000000 --- a/aeon/transformations/series/_siv.py +++ /dev/null @@ -1,58 +0,0 @@ -"""Recursive Median Sieve filter transformation.""" - -__maintainer__ = ["Cyril-Meyer"] -__all__ = ["SIVSeriesTransformer"] - - -from deprecated.sphinx import deprecated - -from aeon.transformations.series.smoothing import RecursiveMedianSieve - - -# TODO: Remove in v1.3.0 -@deprecated( - version="1.2.0", - reason="SIVSeriesTransformer is deprecated and will be removed in v1.3.0. " - "Please use RecursiveMedianSieve from " - "transformations.series.smoothing instead.", - category=FutureWarning, -) -class SIVSeriesTransformer(RecursiveMedianSieve): - """Filter a times series using Recursive Median Sieve (SIV). - - Parameters - ---------- - window_length : list of int or int, default=[3, 5, 7] - The filter windows lengths (recommended increasing value). - - Notes - ----- - Use scipy.ndimage.median_filter instead of scipy.signal.medfilt : - The more general function scipy.ndimage.median_filter has a more efficient - implementation of a median filter and therefore runs much faster. - https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.medfilt.html - - References - ---------- - .. [1] Bangham J. A. (1988). - Data-sieving hydrophobicity plots. - Analytical biochemistry, 174(1), 142–145. - https://doi.org/10.1016/0003-2697(88)90528-3 - .. [2] Yli-Harja, O., Koivisto, P., Bangham, J. A., Cawley, G., - Harvey, R., & Shmulevich, I. (2001). - Simplified implementation of the recursive median sieve. - Signal Process., 81(7), 1565–1570. - https://doi.org/10.1016/S0165-1684(01)00054-8 - - Examples - -------- - >>> import numpy as np - >>> from aeon.transformations.series._siv import SIVSeriesTransformer - >>> X = np.random.random((2, 100)) # Random series length 100 - >>> siv = SIVSeriesTransformer() - >>> X_ = siv.fit_transform(X) - >>> X_.shape - (2, 100) - """ - - pass diff --git a/aeon/transformations/series/compose/_pipeline.py b/aeon/transformations/series/compose/_pipeline.py index 0f1aaef6be..24798f124c 100644 --- a/aeon/transformations/series/compose/_pipeline.py +++ b/aeon/transformations/series/compose/_pipeline.py @@ -49,12 +49,12 @@ class SeriesTransformerPipeline(BaseSeriesPipeline, BaseSeriesTransformer): Examples -------- >>> from aeon.transformations.series import LogTransformer - >>> from aeon.transformations.series import MovingAverageSeriesTransformer + >>> from aeon.transformations.series.smoothing import MovingAverage >>> from aeon.datasets import load_airline >>> from aeon.transformations.series.compose import SeriesTransformerPipeline >>> X = load_airline() >>> pipeline = SeriesTransformerPipeline( - ... [LogTransformer(), MovingAverageSeriesTransformer()] + ... [LogTransformer(), MovingAverage()] ... ) >>> pipeline.fit(X) SeriesTransformerPipeline(...) @@ -90,14 +90,12 @@ def _get_test_params(cls, parameter_set="default"): Each dict are parameters to construct an "interesting" test instance, i.e., `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. """ - from aeon.transformations.series import ( - LogTransformer, - MovingAverageSeriesTransformer, - ) + from aeon.transformations.series import LogTransformer + from aeon.transformations.series.smoothing import MovingAverage return { "transformers": [ LogTransformer(), - MovingAverageSeriesTransformer(), + MovingAverage(), ] } diff --git a/docs/api_reference/transformations.md b/docs/api_reference/transformations.md index a8cabf71c5..66d2e58d10 100644 --- a/docs/api_reference/transformations.md +++ b/docs/api_reference/transformations.md @@ -26,12 +26,9 @@ All transformers in `aeon` can be listed using the `aeon.registry MatrixProfile MinMaxScaler Normalizer - Padder PeriodogramTransformer - Resizer SlopeTransformer SimpleImputer - Truncator Tabularizer ``` @@ -173,6 +170,18 @@ All transformers in `aeon` can be listed using the `aeon.registry SignatureTransformer ``` +### Unequal length + +```{eval-rst} +.. currentmodule:: aeon.transformations.collection.unequal_length + :toctree: auto_generated/ + :template: class.rst + + Padder + Resizer + Truncator +``` + ## Series transforms ```{eval-rst} From 50568eb5c7c37b8c22fc34008f3f7b87510fd6a6 Mon Sep 17 00:00:00 2001 From: chrisholder Date: Wed, 30 Jul 2025 16:32:49 +0100 Subject: [PATCH 203/216] updated dtw gi --- aeon/distances/elastic/_dtw_gi.py | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/aeon/distances/elastic/_dtw_gi.py b/aeon/distances/elastic/_dtw_gi.py index f757eaa202..48ad4f5896 100644 --- a/aeon/distances/elastic/_dtw_gi.py +++ b/aeon/distances/elastic/_dtw_gi.py @@ -5,10 +5,11 @@ from typing import Optional, Union import numpy as np -from numba import njit +from numba import njit, prange from numba.typed import List as NumbaList from aeon.distances.elastic._dtw import dtw_alignment_path, dtw_cost_matrix +from aeon.utils._threading import threaded from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list from aeon.utils.validation.collection import _is_numpy_list_multivariate @@ -323,6 +324,7 @@ def dtw_gi_cost_matrix( return dtw_cost_matrix(xnew, y_trans, window, itakura_max_slope) +@threaded def dtw_gi_pairwise_distance( X: Union[np.ndarray, list[np.ndarray]], y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, @@ -332,6 +334,7 @@ def dtw_gi_pairwise_distance( init_p: np.ndarray = None, max_iter: int = 20, use_bias: bool = False, + n_jobs: int = 1, ) -> np.ndarray: r"""Compute the DTW_GI pairwise distance between a set of time series. @@ -373,6 +376,10 @@ def dtw_gi_pairwise_distance( Maximum number of iterations for the iterative optimization. use_bias : bool, default=False If True, the feature space map is affine (with a bias term). + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Returns ------- @@ -452,7 +459,7 @@ def _dtw_gi_from_multiple_to_multiple_distance( m_cases = len(y) distances = np.zeros((n_cases, m_cases)) - for i in range(n_cases): + for i in prange(n_cases): for j in range(m_cases): x1, y1 = x[i], y[j] distances[i, j] = dtw_gi_distance( @@ -474,7 +481,7 @@ def _dtw_gi_pairwise_distance( n_cases = len(X) distances = np.zeros((n_cases, n_cases)) - for i in range(n_cases): + for i in prange(n_cases): for j in range(i + 1, n_cases): x1, x2 = X[i], X[j] distances[i, j] = dtw_gi_distance( From b1094ee862a58fbba2dab1afb7c39225aef39743 Mon Sep 17 00:00:00 2001 From: chrisholder Date: Wed, 30 Jul 2025 19:04:14 +0100 Subject: [PATCH 204/216] debug --- aeon/utils/_threading.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/aeon/utils/_threading.py b/aeon/utils/_threading.py index adf32df0a6..91b393e621 100644 --- a/aeon/utils/_threading.py +++ b/aeon/utils/_threading.py @@ -57,6 +57,13 @@ def wrapper(*args: Any, **kwargs: Any) -> Any: result = func(*args, **kwargs) return result finally: + if original_thread_count is None: + original_thread_count = 1 + if not isinstance(original_thread_count, int): + raise TypeError( + f"Expected original_thread_count to be an int, got type: " + f"{type(original_thread_count)} value: {original_thread_count}" + ) set_num_threads(original_thread_count) return wrapper From f757c0ff0adf7e18ef4b915ac4386dcb4969012f Mon Sep 17 00:00:00 2001 From: chrisholder Date: Wed, 30 Jul 2025 19:52:51 +0100 Subject: [PATCH 205/216] debug --- aeon/utils/_threading.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/aeon/utils/_threading.py b/aeon/utils/_threading.py index 91b393e621..98fdafb630 100644 --- a/aeon/utils/_threading.py +++ b/aeon/utils/_threading.py @@ -57,13 +57,14 @@ def wrapper(*args: Any, **kwargs: Any) -> Any: result = func(*args, **kwargs) return result finally: - if original_thread_count is None: - original_thread_count = 1 - if not isinstance(original_thread_count, int): - raise TypeError( - f"Expected original_thread_count to be an int, got type: " - f"{type(original_thread_count)} value: {original_thread_count}" + try: + set_num_threads(original_thread_count) + except Exception: + raise ValueError( + f"Failed to restore original thread count: {original_thread_count} " + f"type {type(original_thread_count)} \n\n " + f"Other debug info: {inspect.getsource(func)} \n\n Args: {args} " + f"\n\n Kwargs: {kwargs}" ) - set_num_threads(original_thread_count) return wrapper From 828d0bf562e7341467c12037ae49e1a37e854f1c Mon Sep 17 00:00:00 2001 From: chrisholder Date: Wed, 30 Jul 2025 20:34:44 +0100 Subject: [PATCH 206/216] debug --- aeon/utils/_threading.py | 19 ++++++++++++++++++- 1 file changed, 18 insertions(+), 1 deletion(-) diff --git a/aeon/utils/_threading.py b/aeon/utils/_threading.py index 98fdafb630..9a706554d3 100644 --- a/aeon/utils/_threading.py +++ b/aeon/utils/_threading.py @@ -22,13 +22,28 @@ def threaded(func: Callable) -> Callable: def wrapper(*args: Any, **kwargs: Any) -> Any: numba_env_threads = os.environ.get("NUMBA_NUM_THREADS") + first = False + second = False + third = False + first_info = "" + second_info = "" + third_info = "" + if numba_env_threads is not None and numba_env_threads.isdigit(): + first = True + first_info = f"NUMBA_NUM_THREADS={numba_env_threads}" original_thread_count = int(numba_env_threads) else: active_count = threading.active_count() if isinstance(active_count, int): + second = True + second_info = f"threading.active_count()={active_count}" original_thread_count = threading.active_count() else: + third = True + third_info = ( + f"threading.active_count()={active_count} type {type(active_count)}" + ) original_thread_count = 1 n_jobs = None @@ -63,8 +78,10 @@ def wrapper(*args: Any, **kwargs: Any) -> Any: raise ValueError( f"Failed to restore original thread count: {original_thread_count} " f"type {type(original_thread_count)} \n\n " - f"Other debug info: {inspect.getsource(func)} \n\n Args: {args} " f"\n\n Kwargs: {kwargs}" + f"\n\n First: {first} Second: {second} Third: {third}" + f"\n\n First info: {first_info} \n\n Second info: {second_info} " + f"\n\n Third info: {third_info}" ) return wrapper From 280a9b1253210376638769364848a7aa07b4cd83 Mon Sep 17 00:00:00 2001 From: chrisholder Date: Wed, 30 Jul 2025 21:03:20 +0100 Subject: [PATCH 207/216] debug --- aeon/utils/_threading.py | 47 ++++++++++++++++------------------------ 1 file changed, 19 insertions(+), 28 deletions(-) diff --git a/aeon/utils/_threading.py b/aeon/utils/_threading.py index 9a706554d3..5743796b67 100644 --- a/aeon/utils/_threading.py +++ b/aeon/utils/_threading.py @@ -1,7 +1,6 @@ import functools import inspect import os -import threading from typing import Any, Callable from numba import set_num_threads @@ -18,33 +17,28 @@ def threaded(func: Callable) -> Callable: The decorated function is expected to have a 'n_jobs' parameter. """ + def num_threads_default(): + try: + sched_getaffinity = os.sched_getaffinity + except AttributeError: + pass + else: + return max(1, len(sched_getaffinity(0))) + + cpu_count = os.cpu_count() + if cpu_count is not None: + return max(1, cpu_count) + + return 1 + @functools.wraps(func) def wrapper(*args: Any, **kwargs: Any) -> Any: - numba_env_threads = os.environ.get("NUMBA_NUM_THREADS") - - first = False - second = False - third = False - first_info = "" - second_info = "" - third_info = "" - - if numba_env_threads is not None and numba_env_threads.isdigit(): - first = True - first_info = f"NUMBA_NUM_THREADS={numba_env_threads}" - original_thread_count = int(numba_env_threads) + NUMBA_ENV_THREADS = os.environ.get("NUMBA_NUM_THREADS") + + if NUMBA_ENV_THREADS is not None and NUMBA_ENV_THREADS.isdigit(): + original_thread_count = int(NUMBA_ENV_THREADS) else: - active_count = threading.active_count() - if isinstance(active_count, int): - second = True - second_info = f"threading.active_count()={active_count}" - original_thread_count = threading.active_count() - else: - third = True - third_info = ( - f"threading.active_count()={active_count} type {type(active_count)}" - ) - original_thread_count = 1 + original_thread_count = num_threads_default() n_jobs = None if "n_jobs" in kwargs: @@ -79,9 +73,6 @@ def wrapper(*args: Any, **kwargs: Any) -> Any: f"Failed to restore original thread count: {original_thread_count} " f"type {type(original_thread_count)} \n\n " f"\n\n Kwargs: {kwargs}" - f"\n\n First: {first} Second: {second} Third: {third}" - f"\n\n First info: {first_info} \n\n Second info: {second_info} " - f"\n\n Third info: {third_info}" ) return wrapper From 3efcf61ffc511fe7429417c9592eca060ddfe972 Mon Sep 17 00:00:00 2001 From: chrisholder Date: Wed, 30 Jul 2025 22:32:26 +0100 Subject: [PATCH 208/216] fix --- aeon/utils/_threading.py | 39 ++++++++------------ aeon/utils/tests/test_threading_decorator.py | 13 ++----- 2 files changed, 19 insertions(+), 33 deletions(-) diff --git a/aeon/utils/_threading.py b/aeon/utils/_threading.py index 5743796b67..a2487e282d 100644 --- a/aeon/utils/_threading.py +++ b/aeon/utils/_threading.py @@ -8,6 +8,21 @@ from aeon.utils.validation import check_n_jobs +def num_threads_default(): + try: + sched_getaffinity = os.sched_getaffinity + except AttributeError: + pass + else: + return max(1, len(sched_getaffinity(0))) + + cpu_count = os.cpu_count() + if cpu_count is not None: + return max(1, cpu_count) + + return 1 + + def threaded(func: Callable) -> Callable: """Set thread count based on n_jobs parameter and restore it afterward. @@ -17,20 +32,6 @@ def threaded(func: Callable) -> Callable: The decorated function is expected to have a 'n_jobs' parameter. """ - def num_threads_default(): - try: - sched_getaffinity = os.sched_getaffinity - except AttributeError: - pass - else: - return max(1, len(sched_getaffinity(0))) - - cpu_count = os.cpu_count() - if cpu_count is not None: - return max(1, cpu_count) - - return 1 - @functools.wraps(func) def wrapper(*args: Any, **kwargs: Any) -> Any: NUMBA_ENV_THREADS = os.environ.get("NUMBA_NUM_THREADS") @@ -56,7 +57,6 @@ def wrapper(*args: Any, **kwargs: Any) -> Any: n_jobs = default if default is not inspect.Parameter.empty else None if n_jobs is None and args and hasattr(args[0], "n_jobs"): - # This gets n_jobs if it belongs to a object (i.e. self.n_jobs) n_jobs = args[0].n_jobs adjusted_n_jobs = check_n_jobs(n_jobs) @@ -66,13 +66,6 @@ def wrapper(*args: Any, **kwargs: Any) -> Any: result = func(*args, **kwargs) return result finally: - try: - set_num_threads(original_thread_count) - except Exception: - raise ValueError( - f"Failed to restore original thread count: {original_thread_count} " - f"type {type(original_thread_count)} \n\n " - f"\n\n Kwargs: {kwargs}" - ) + set_num_threads(original_thread_count) return wrapper diff --git a/aeon/utils/tests/test_threading_decorator.py b/aeon/utils/tests/test_threading_decorator.py index 76c0d80503..aac58b7c83 100644 --- a/aeon/utils/tests/test_threading_decorator.py +++ b/aeon/utils/tests/test_threading_decorator.py @@ -67,21 +67,14 @@ def sample_func(n_jobs=None): def test_fallback_to_threading_count(clean_env): - """ - Test the fallback mechanism to the system's active thread count. - - When the NUMBA_NUM_THREADS environment variable is not set or is invalid, - the decorator should use the system's active thread count as the baseline. - This ensures proper thread management even when no explicit configuration is - provided. - """ + """Test the fallback mechanism to CPU count/affinity.""" check_jobs_mock = MagicMock(side_effect=lambda x: x if x is not None else 1) set_threads_mock = MagicMock() - thread_count_mock = MagicMock(return_value=3) + # Mock the new fallback mechanism with patch("aeon.utils._threading.check_n_jobs", check_jobs_mock): with patch("aeon.utils._threading.set_num_threads", set_threads_mock): - with patch("threading.active_count", thread_count_mock): + with patch("aeon.utils._threading.num_threads_default", return_value=3): @threaded def sample_func(n_jobs=None): From 0fce8626e64a59ca8c11a87147ca8b0899a72a1d Mon Sep 17 00:00:00 2001 From: chrisholder Date: Mon, 4 Aug 2025 15:20:05 +0100 Subject: [PATCH 209/216] refactored regressor --- .../distance_based/_time_series_neighbors.py | 125 +++++++++++------- 1 file changed, 79 insertions(+), 46 deletions(-) diff --git a/aeon/regression/distance_based/_time_series_neighbors.py b/aeon/regression/distance_based/_time_series_neighbors.py index e5e04cdaff..83217742d4 100644 --- a/aeon/regression/distance_based/_time_series_neighbors.py +++ b/aeon/regression/distance_based/_time_series_neighbors.py @@ -5,6 +5,7 @@ It can also be used with callables, or aeon (pairwise transformer) estimators. """ +import numbers from typing import Optional __maintainer__ = [] @@ -14,8 +15,9 @@ import numpy as np -from aeon.distances import get_distance_function +from aeon.distances import pairwise_distance from aeon.regression.base import BaseRegressor +from aeon.utils._threading import threaded from aeon.utils.validation import check_n_jobs WEIGHTS_SUPPORTED = ["uniform", "distance"] @@ -47,15 +49,10 @@ class KNeighborsTimeSeriesRegressor(BaseRegressor): n_timepoints)`` as input and returns a float. distance_params : dict, default = None Dictionary for metric parameters for the case that distance is a str. - n_jobs : int, default = 1 - The number of parallel jobs to run for neighbors search. - ``None`` means 1 unless in a :obj:``joblib.parallel_backend`` context. - ``-1`` means using all processors. - parallel_backend : str, ParallelBackendBase instance or None, default=None - Specify the parallelisation backend implementation in joblib, if None - a ‘prefer’ value of “threads” is used by default. Valid options are - “loky”, “multiprocessing”, “threading” or a custom backend. - See the joblib Parallel documentation for more details. + n_jobs : int, default=1 + The number of jobs to run in parallel. If -1, then the number of jobs is set + to the number of CPU cores. If 1, then the function is executed in a single + thread. If greater than 1, then the function is executed in parallel. Examples -------- @@ -84,13 +81,11 @@ def __init__( n_neighbors: int = 1, weights: Union[str, Callable] = "uniform", n_jobs: int = 1, - parallel_backend: str = None, ) -> None: self.distance = distance self.distance_params = distance_params self.n_neighbors = n_neighbors self.n_jobs = n_jobs - self.parallel_backend = parallel_backend self._distance_params = distance_params if self._distance_params is None: @@ -118,7 +113,6 @@ def _fit(self, X, y): y : array-like, shape = (n_cases) The output value. """ - self.metric_ = get_distance_function(method=self.distance) self.X_ = X self.y_ = y self._n_jobs = check_n_jobs(self.n_jobs) @@ -142,54 +136,93 @@ def _predict(self, X): """ preds = np.empty(len(X)) for i in range(len(X)): - idx, weights = self.kneighbors(X[i]) - preds[i] = np.average(self.y_[idx], weights=weights) + neigh_dist, neigh_ind = self.kneighbors(X[i : i + 1]) + neigh_dist = neigh_dist[0] + neigh_ind = neigh_ind[0] + + if self.weights == "distance": + # Using epsilon ~= 0 to avoid division by zero + weights = 1 / (neigh_dist + np.finfo(float).eps) + elif self.weights == "uniform": + weights = np.repeat(1.0, len(neigh_ind)) + else: + raise Exception(f"Invalid kNN weights: {self.weights}") + + preds[i] = np.average(self.y_[neigh_ind], weights=weights) return preds - def _kneighbors(self, X): - """ - Find the K-neighbors of a point. + @threaded + def kneighbors(self, X=None, n_neighbors=None, return_distance=True): + """Find the K-neighbors of a point. - Returns indices and weights of each point. + Returns indices of and distances to the neighbors of each point. Parameters ---------- - X : np.ndarray - A single time series instance if shape = (n_channels, n_timepoints) + X : 3D np.ndarray of shape = (n_cases, n_channels, n_timepoints) or list of + shape [n_cases] of 2D arrays shape (n_channels,n_timepoints_i) + The query point or points. + If not provided, neighbors of each indexed point are returned. + In this case, the query point is not considered its own neighbor. + n_neighbors : int, default=None + Number of neighbors required for each sample. The default is the value + passed to the constructor. + return_distance : bool, default=True + Whether or not to return the distances. Returns ------- - ind : array + neigh_dist : ndarray of shape (n_queries, n_neighbors) + Array representing the distances to points, only present if + return_distance=True. + neigh_ind : ndarray of shape (n_queries, n_neighbors) Indices of the nearest points in the population matrix. - ws : array - Array representing the weights of each neighbor. """ - distances = np.array( - [ - self.metric_(X, self.X_[j], **self._distance_params) - for j in range(len(self.X_)) - ] - ) + self._check_is_fitted() + if n_neighbors is None: + n_neighbors = self.n_neighbors + elif n_neighbors <= 0: + raise ValueError(f"Expected n_neighbors > 0. Got {n_neighbors}") + elif not isinstance(n_neighbors, numbers.Integral): + raise TypeError( + f"n_neighbors does not take {type(n_neighbors)} value, " + "enter integer value" + ) - # Find indices of k nearest neighbors using partitioning: - # [0..k-1], [k], [k+1..n-1] - # They might not be ordered within themselves, - # but it is not necessary and partitioning is - # O(n) while sorting is O(nlogn) - closest_idx = np.argpartition(distances, self.n_neighbors) - closest_idx = closest_idx[: self.n_neighbors] - - if self.weights == "distance": - ws = distances[closest_idx] - # Using epsilon ~= 0 to avoid division by zero - ws = 1 / (ws + np.finfo(float).eps) - elif self.weights == "uniform": - ws = np.repeat(1.0, self.n_neighbors) + query_is_train = X is None + if query_is_train: + X = self.X_ + n_neighbors += 1 else: - raise Exception(f"Invalid kNN weights: {self.weights}") + X = self._preprocess_collection(X, store_metadata=False) + self._check_shape(X) + + distances = pairwise_distance( + X, + self.X_ if not query_is_train else None, + method=self.distance, + n_jobs=self.n_jobs, + **self._distance_params, + ) + + sample_range = np.arange(distances.shape[0])[:, None] + neigh_ind = np.argpartition(distances, n_neighbors - 1, axis=1) + neigh_ind = neigh_ind[:, :n_neighbors] + neigh_ind = neigh_ind[ + sample_range, np.argsort(distances[sample_range, neigh_ind]) + ] + + if query_is_train: + neigh_ind = neigh_ind[:, 1:] + + if return_distance: + if query_is_train: + neigh_dist = distances[sample_range, neigh_ind] + return neigh_dist, neigh_ind + return distances[sample_range, neigh_ind], neigh_ind - return closest_idx, ws + return neigh_ind @classmethod def _get_test_params( From 4ea3b48ef2ab4db64cdf302267b80a57a16bb26f Mon Sep 17 00:00:00 2001 From: chrisholder Date: Thu, 7 Aug 2025 16:07:36 +0100 Subject: [PATCH 210/216] removed file --- .../distance_based/_time_series_neighbors.py | 2 +- .../distance_based/_time_series_neighbors.py | 2 +- aeon/utils/_threading.py | 71 ------------------- 3 files changed, 2 insertions(+), 73 deletions(-) delete mode 100644 aeon/utils/_threading.py diff --git a/aeon/classification/distance_based/_time_series_neighbors.py b/aeon/classification/distance_based/_time_series_neighbors.py index ac19960a4c..ade120d8ed 100644 --- a/aeon/classification/distance_based/_time_series_neighbors.py +++ b/aeon/classification/distance_based/_time_series_neighbors.py @@ -17,7 +17,7 @@ from aeon.classification.base import BaseClassifier from aeon.distances import pairwise_distance -from aeon.utils._threading import threaded +from aeon.utils.numba._threading import threaded from aeon.utils.validation import check_n_jobs WEIGHTS_SUPPORTED = ["uniform", "distance"] diff --git a/aeon/regression/distance_based/_time_series_neighbors.py b/aeon/regression/distance_based/_time_series_neighbors.py index 83217742d4..6765216772 100644 --- a/aeon/regression/distance_based/_time_series_neighbors.py +++ b/aeon/regression/distance_based/_time_series_neighbors.py @@ -17,7 +17,7 @@ from aeon.distances import pairwise_distance from aeon.regression.base import BaseRegressor -from aeon.utils._threading import threaded +from aeon.utils.numba._threading import threaded from aeon.utils.validation import check_n_jobs WEIGHTS_SUPPORTED = ["uniform", "distance"] diff --git a/aeon/utils/_threading.py b/aeon/utils/_threading.py deleted file mode 100644 index a2487e282d..0000000000 --- a/aeon/utils/_threading.py +++ /dev/null @@ -1,71 +0,0 @@ -import functools -import inspect -import os -from typing import Any, Callable - -from numba import set_num_threads - -from aeon.utils.validation import check_n_jobs - - -def num_threads_default(): - try: - sched_getaffinity = os.sched_getaffinity - except AttributeError: - pass - else: - return max(1, len(sched_getaffinity(0))) - - cpu_count = os.cpu_count() - if cpu_count is not None: - return max(1, cpu_count) - - return 1 - - -def threaded(func: Callable) -> Callable: - """Set thread count based on n_jobs parameter and restore it afterward. - - A decorator that sets the number of threads based on the n_jobs parameter - passed to the function, and restores the original thread count afterward. - - The decorated function is expected to have a 'n_jobs' parameter. - """ - - @functools.wraps(func) - def wrapper(*args: Any, **kwargs: Any) -> Any: - NUMBA_ENV_THREADS = os.environ.get("NUMBA_NUM_THREADS") - - if NUMBA_ENV_THREADS is not None and NUMBA_ENV_THREADS.isdigit(): - original_thread_count = int(NUMBA_ENV_THREADS) - else: - original_thread_count = num_threads_default() - - n_jobs = None - if "n_jobs" in kwargs: - n_jobs = kwargs["n_jobs"] - else: - sig = inspect.signature(func) - param_names = list(sig.parameters.keys()) - - if "n_jobs" in param_names: - n_jobs_index = param_names.index("n_jobs") - if n_jobs_index < len(args): - n_jobs = args[n_jobs_index] - else: - default = sig.parameters["n_jobs"].default - n_jobs = default if default is not inspect.Parameter.empty else None - - if n_jobs is None and args and hasattr(args[0], "n_jobs"): - n_jobs = args[0].n_jobs - - adjusted_n_jobs = check_n_jobs(n_jobs) - set_num_threads(adjusted_n_jobs) - - try: - result = func(*args, **kwargs) - return result - finally: - set_num_threads(original_thread_count) - - return wrapper From e18a7305301648e45c9ed532ad61ed739cd1ba87 Mon Sep 17 00:00:00 2001 From: chrisholder Date: Thu, 7 Aug 2025 16:13:20 +0100 Subject: [PATCH 211/216] added test for knn regressor kneighbors --- .../tests/test_time_series_neighbors.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/aeon/regression/distance_based/tests/test_time_series_neighbors.py b/aeon/regression/distance_based/tests/test_time_series_neighbors.py index 01c3326ad7..d93e8b53a0 100644 --- a/aeon/regression/distance_based/tests/test_time_series_neighbors.py +++ b/aeon/regression/distance_based/tests/test_time_series_neighbors.py @@ -1,5 +1,6 @@ """Tests for KNeighborsTimeSeriesRegressor.""" +import numpy as np import pytest from numpy.testing import assert_almost_equal from sklearn.metrics import mean_squared_error @@ -63,3 +64,15 @@ def test_knn_bounding_matrix(distance_key): mse = mean_squared_error(y_test, y_pred) assert_almost_equal(mse, expected_mse_window[distance_key]) + + +@pytest.mark.parametrize("distance_key", distance_functions) +def test_knn_kneighbors(distance_key): + """Test knn kneighbors.""" + knn = KNeighborsTimeSeriesRegressor(distance=distance_key, n_neighbors=3) + knn.fit(X_train, y_train) + dists, ind = knn.kneighbors(X_test, n_neighbors=3) + assert isinstance(dists, np.ndarray) + assert isinstance(ind, np.ndarray) + assert dists.shape == (X_test.shape[0], 3) + assert ind.shape == (X_test.shape[0], 3) From 28e9f725ea709c50d3f3be3b6147e7356f324c5e Mon Sep 17 00:00:00 2001 From: chrisholder Date: Thu, 7 Aug 2025 20:26:51 +0100 Subject: [PATCH 212/216] fixed sklearn compatiability tests --- .../distance_based/_time_series_neighbors.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/aeon/classification/distance_based/_time_series_neighbors.py b/aeon/classification/distance_based/_time_series_neighbors.py index ade120d8ed..26d88ee316 100644 --- a/aeon/classification/distance_based/_time_series_neighbors.py +++ b/aeon/classification/distance_based/_time_series_neighbors.py @@ -146,8 +146,18 @@ def _predict_proba(self, X): """ preds = np.zeros((len(X), len(self.classes_))) for i in range(len(X)): - weights, idx = self.kneighbors(X[i]) - for id, w in zip(idx, weights): + neigh_dist, neigh_ind = self.kneighbors(X[i : i + 1]) + neigh_dist = neigh_dist[0] + neigh_ind = neigh_ind[0] + + if self.weights == "distance": + weights = 1 / (neigh_dist + np.finfo(float).eps) + elif self.weights == "uniform": + weights = np.repeat(1.0, len(neigh_ind)) + else: + raise Exception(f"Invalid kNN weights: {self.weights}") + + for id, w in zip(neigh_ind, weights): predicted_class = self.y_[id] preds[i, predicted_class] += w From 0c17ff865db7c2489264c38fd741deed887d7dc9 Mon Sep 17 00:00:00 2001 From: chrisholder Date: Fri, 8 Aug 2025 12:23:29 +0100 Subject: [PATCH 213/216] cleaned up and commented kneighbors function --- .../distance_based/_time_series_neighbors.py | 55 ++++++++++++------- .../distance_based/_time_series_neighbors.py | 55 ++++++++++++------- 2 files changed, 72 insertions(+), 38 deletions(-) diff --git a/aeon/classification/distance_based/_time_series_neighbors.py b/aeon/classification/distance_based/_time_series_neighbors.py index 26d88ee316..068f32621d 100644 --- a/aeon/classification/distance_based/_time_series_neighbors.py +++ b/aeon/classification/distance_based/_time_series_neighbors.py @@ -215,48 +215,65 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True): Indices of the nearest points in the population matrix. """ self._check_is_fitted() + if n_neighbors is None: n_neighbors = self.n_neighbors - elif n_neighbors <= 0: - raise ValueError(f"Expected n_neighbors > 0. Got {n_neighbors}") elif not isinstance(n_neighbors, numbers.Integral): raise TypeError( - f"n_neighbors does not take {type(n_neighbors)} value, " - "enter integer value" + f"n_neighbors does not take {type(n_neighbors)} value, enter integer " + f"value" ) + elif n_neighbors <= 0: + raise ValueError(f"Expected n_neighbors > 0. Got {n_neighbors}") query_is_train = X is None if query_is_train: X = self.X_ - n_neighbors += 1 else: X = self._preprocess_collection(X, store_metadata=False) self._check_shape(X) + n_samples_fit = self.X_.shape[0] + if query_is_train: + if not (n_neighbors < n_samples_fit): + raise ValueError( + "Expected n_neighbors < n_samples_fit, but " + f"n_neighbors = {n_neighbors}, n_samples_fit = {n_samples_fit}, " + f"n_samples = {X.shape[0]}" + ) + else: + if not (n_neighbors <= n_samples_fit): + raise ValueError( + "Expected n_neighbors <= n_samples_fit, but " + f"n_neighbors = {n_neighbors}, n_samples_fit = {n_samples_fit}, " + f"n_samples = {X.shape[0]}" + ) + distances = pairwise_distance( X, - self.X_ if not query_is_train else None, + None if query_is_train else self.X_, method=self.distance, n_jobs=self.n_jobs, **self._distance_params, ) - sample_range = np.arange(distances.shape[0])[:, None] - neigh_ind = np.argpartition(distances, n_neighbors - 1, axis=1) - neigh_ind = neigh_ind[:, :n_neighbors] - neigh_ind = neigh_ind[ - sample_range, np.argsort(distances[sample_range, neigh_ind]) - ] - + # If querying the training set, exclude self by setting diag to +inf if query_is_train: - neigh_ind = neigh_ind[:, 1:] + np.fill_diagonal(distances, np.inf) + + k = n_neighbors + # 1) partial select smallest k + idx_part = np.argpartition(distances, kth=k - 1, axis=1)[:, :k] + # 2) sort those k by (distance, index) + row_idx = np.arange(distances.shape[0])[:, None] + part_d = distances[row_idx, idx_part] + # argsort by distance, then by index for ties (lexsort uses last key as primary) + order = np.lexsort((idx_part, part_d), axis=1) + neigh_ind = idx_part[row_idx, order] if return_distance: - if query_is_train: - neigh_dist = distances[sample_range, neigh_ind] - return neigh_dist, neigh_ind - return distances[sample_range, neigh_ind], neigh_ind - + neigh_dist = distances[row_idx, neigh_ind] + return neigh_dist, neigh_ind return neigh_ind @classmethod diff --git a/aeon/regression/distance_based/_time_series_neighbors.py b/aeon/regression/distance_based/_time_series_neighbors.py index 6765216772..4d5da84ec6 100644 --- a/aeon/regression/distance_based/_time_series_neighbors.py +++ b/aeon/regression/distance_based/_time_series_neighbors.py @@ -180,48 +180,65 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True): Indices of the nearest points in the population matrix. """ self._check_is_fitted() + if n_neighbors is None: n_neighbors = self.n_neighbors - elif n_neighbors <= 0: - raise ValueError(f"Expected n_neighbors > 0. Got {n_neighbors}") elif not isinstance(n_neighbors, numbers.Integral): raise TypeError( - f"n_neighbors does not take {type(n_neighbors)} value, " - "enter integer value" + f"n_neighbors does not take {type(n_neighbors)} value, enter integer " + f"value" ) + elif n_neighbors <= 0: + raise ValueError(f"Expected n_neighbors > 0. Got {n_neighbors}") query_is_train = X is None if query_is_train: X = self.X_ - n_neighbors += 1 else: X = self._preprocess_collection(X, store_metadata=False) self._check_shape(X) + n_samples_fit = self.X_.shape[0] + if query_is_train: + if not (n_neighbors < n_samples_fit): + raise ValueError( + "Expected n_neighbors < n_samples_fit, but " + f"n_neighbors = {n_neighbors}, n_samples_fit = {n_samples_fit}, " + f"n_samples = {X.shape[0]}" + ) + else: + if not (n_neighbors <= n_samples_fit): + raise ValueError( + "Expected n_neighbors <= n_samples_fit, but " + f"n_neighbors = {n_neighbors}, n_samples_fit = {n_samples_fit}, " + f"n_samples = {X.shape[0]}" + ) + distances = pairwise_distance( X, - self.X_ if not query_is_train else None, + None if query_is_train else self.X_, method=self.distance, n_jobs=self.n_jobs, **self._distance_params, ) - sample_range = np.arange(distances.shape[0])[:, None] - neigh_ind = np.argpartition(distances, n_neighbors - 1, axis=1) - neigh_ind = neigh_ind[:, :n_neighbors] - neigh_ind = neigh_ind[ - sample_range, np.argsort(distances[sample_range, neigh_ind]) - ] - + # If querying the training set, exclude self by setting diag to +inf if query_is_train: - neigh_ind = neigh_ind[:, 1:] + np.fill_diagonal(distances, np.inf) + + k = n_neighbors + # 1) partial select smallest k + idx_part = np.argpartition(distances, kth=k - 1, axis=1)[:, :k] + # 2) sort those k by (distance, index) + row_idx = np.arange(distances.shape[0])[:, None] + part_d = distances[row_idx, idx_part] + # argsort by distance, then by index for ties (lexsort uses last key as primary) + order = np.lexsort((idx_part, part_d), axis=1) + neigh_ind = idx_part[row_idx, order] if return_distance: - if query_is_train: - neigh_dist = distances[sample_range, neigh_ind] - return neigh_dist, neigh_ind - return distances[sample_range, neigh_ind], neigh_ind - + neigh_dist = distances[row_idx, neigh_ind] + return neigh_dist, neigh_ind return neigh_ind @classmethod From 0e1c1e96c03c883817c69e4bec3ccad2669015cd Mon Sep 17 00:00:00 2001 From: chrisholder Date: Fri, 8 Aug 2025 13:12:22 +0100 Subject: [PATCH 214/216] fixed when list passed --- .../classification/distance_based/_time_series_neighbors.py | 6 +++--- aeon/regression/distance_based/_time_series_neighbors.py | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/aeon/classification/distance_based/_time_series_neighbors.py b/aeon/classification/distance_based/_time_series_neighbors.py index 068f32621d..1a61b5de59 100644 --- a/aeon/classification/distance_based/_time_series_neighbors.py +++ b/aeon/classification/distance_based/_time_series_neighbors.py @@ -233,20 +233,20 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True): X = self._preprocess_collection(X, store_metadata=False) self._check_shape(X) - n_samples_fit = self.X_.shape[0] + n_samples_fit = len(self.X_) if query_is_train: if not (n_neighbors < n_samples_fit): raise ValueError( "Expected n_neighbors < n_samples_fit, but " f"n_neighbors = {n_neighbors}, n_samples_fit = {n_samples_fit}, " - f"n_samples = {X.shape[0]}" + f"n_samples = {len(X)}" ) else: if not (n_neighbors <= n_samples_fit): raise ValueError( "Expected n_neighbors <= n_samples_fit, but " f"n_neighbors = {n_neighbors}, n_samples_fit = {n_samples_fit}, " - f"n_samples = {X.shape[0]}" + f"n_samples = {len(X)}" ) distances = pairwise_distance( diff --git a/aeon/regression/distance_based/_time_series_neighbors.py b/aeon/regression/distance_based/_time_series_neighbors.py index 4d5da84ec6..2f63da156c 100644 --- a/aeon/regression/distance_based/_time_series_neighbors.py +++ b/aeon/regression/distance_based/_time_series_neighbors.py @@ -198,20 +198,20 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True): X = self._preprocess_collection(X, store_metadata=False) self._check_shape(X) - n_samples_fit = self.X_.shape[0] + n_samples_fit = len(self.X_) if query_is_train: if not (n_neighbors < n_samples_fit): raise ValueError( "Expected n_neighbors < n_samples_fit, but " f"n_neighbors = {n_neighbors}, n_samples_fit = {n_samples_fit}, " - f"n_samples = {X.shape[0]}" + f"n_samples = {len(X)}" ) else: if not (n_neighbors <= n_samples_fit): raise ValueError( "Expected n_neighbors <= n_samples_fit, but " f"n_neighbors = {n_neighbors}, n_samples_fit = {n_samples_fit}, " - f"n_samples = {X.shape[0]}" + f"n_samples = {len(X)}" ) distances = pairwise_distance( From ce499555973035e781cf0ea8cf44d3fd3e71749a Mon Sep 17 00:00:00 2001 From: chrisholder Date: Sun, 10 Aug 2025 14:05:07 +0100 Subject: [PATCH 215/216] added public and private methods for kneigbors and additionally expanded testing with comments --- .../distance_based/_time_series_neighbors.py | 37 +++++++++++++- .../tests/test_time_series_neighbors.py | 38 +++++++++++++- .../distance_based/_time_series_neighbors.py | 37 +++++++++++++- .../tests/test_time_series_neighbors.py | 50 ++++++++++++++++++- 4 files changed, 158 insertions(+), 4 deletions(-) diff --git a/aeon/classification/distance_based/_time_series_neighbors.py b/aeon/classification/distance_based/_time_series_neighbors.py index 1a61b5de59..f7d720aaeb 100644 --- a/aeon/classification/distance_based/_time_series_neighbors.py +++ b/aeon/classification/distance_based/_time_series_neighbors.py @@ -187,7 +187,6 @@ def _predict(self, X): indexes = self.kneighbors(X, return_distance=False)[:, 0] return self.classes_[self.y_[indexes]] - @threaded def kneighbors(self, X=None, n_neighbors=None, return_distance=True): """Find the K-neighbors of a point. @@ -216,6 +215,7 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True): """ self._check_is_fitted() + # Input validation if n_neighbors is None: n_neighbors = self.n_neighbors elif not isinstance(n_neighbors, numbers.Integral): @@ -226,6 +226,12 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True): elif n_neighbors <= 0: raise ValueError(f"Expected n_neighbors > 0. Got {n_neighbors}") + if not isinstance(return_distance, bool): + raise TypeError( + f"return_distance must be a boolean, got {type(return_distance)}" + ) + + # Preprocess X if provided query_is_train = X is None if query_is_train: X = self.X_ @@ -233,6 +239,7 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True): X = self._preprocess_collection(X, store_metadata=False) self._check_shape(X) + # Validate n_neighbors against data size n_samples_fit = len(self.X_) if query_is_train: if not (n_neighbors < n_samples_fit): @@ -249,6 +256,34 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True): f"n_samples = {len(X)}" ) + return self._kneighbors(X, n_neighbors, return_distance, query_is_train) + + @threaded + def _kneighbors(self, X, n_neighbors, return_distance, query_is_train): + """Find the K-neighbors of a point. + + Returns indices of and distances to the neighbors of each point. + + Parameters + ---------- + X : 3D np.ndarray of shape = (n_cases, n_channels, n_timepoints) or list of + shape [n_cases] of 2D arrays shape (n_channels,n_timepoints_i) + The query point or points. + n_neighbors : int + Number of neighbors required for each sample. + return_distance : bool + Whether or not to return the distances. + query_is_train : bool + Whether the query points are from the training set. + + Returns + ------- + neigh_dist : ndarray of shape (n_queries, n_neighbors) + Array representing the distances to points, only present if + return_distance=True. + neigh_ind : ndarray of shape (n_queries, n_neighbors) + Indices of the nearest points in the population matrix. + """ distances = pairwise_distance( X, None if query_is_train else self.X_, diff --git a/aeon/classification/distance_based/tests/test_time_series_neighbors.py b/aeon/classification/distance_based/tests/test_time_series_neighbors.py index e68cad0019..b87d9553a8 100644 --- a/aeon/classification/distance_based/tests/test_time_series_neighbors.py +++ b/aeon/classification/distance_based/tests/test_time_series_neighbors.py @@ -79,17 +79,33 @@ def test_knn_bounding_matrix(distance_key): @pytest.mark.parametrize("distance_key", distance_functions) def test_knn_kneighbors(distance_key): - """Test knn kneighbors.""" + """Test knn kneighbors with comprehensive validation.""" X_train, y_train = load_unit_test(split="train") X_test, y_test = load_unit_test(split="test") knn = KNeighborsTimeSeriesClassifier(distance=distance_key) knn.fit(X_train, y_train) + + # Test basic kneighbors functionality dists, ind = knn.kneighbors(X_test, n_neighbors=3) assert isinstance(dists, np.ndarray) assert isinstance(ind, np.ndarray) assert dists.shape == (X_test.shape[0], 3) assert ind.shape == (X_test.shape[0], 3) + + # Test that distances are non-negative + assert np.all(dists >= 0) + + # Test that indices are within valid range + assert np.all(ind >= 0) + assert np.all(ind < len(X_train)) + + # Test that distances are sorted (closest first) + assert np.all(dists[:, 0] <= dists[:, 1]) + assert np.all(dists[:, 1] <= dists[:, 2]) + + # Test using kneighbors results to make predictions manually + # This validates that the kneighbors method returns correct neighbor indices indexes = ind[:, 0] classes, y = np.unique(y_train, return_inverse=True) pred = classes[y[indexes]] @@ -98,3 +114,23 @@ def test_knn_kneighbors(distance_key): if pred[j] == y_test[j]: correct = correct + 1 assert correct == expected_correct_window[distance_key] + + # Test kneighbors with different n_neighbors values + dists_2, ind_2 = knn.kneighbors(X_test, n_neighbors=2) + assert dists_2.shape == (X_test.shape[0], 2) + assert ind_2.shape == (X_test.shape[0], 2) + + # Test kneighbors without returning distances + ind_only = knn.kneighbors(X_test, n_neighbors=3, return_distance=False) + assert isinstance(ind_only, np.ndarray) + assert ind_only.shape == (X_test.shape[0], 3) + # Should return same indices as when return_distance=True + np.testing.assert_array_equal(ind_only, ind) + + # Test kneighbors on training data (should exclude self) + train_dists, train_ind = knn.kneighbors(n_neighbors=2) + assert train_dists.shape == (len(X_train), 2) + assert train_ind.shape == (len(X_train), 2) + # Each point should not be its own neighbor (diagonal should be excluded) + for i in range(len(X_train)): + assert i not in train_ind[i] diff --git a/aeon/regression/distance_based/_time_series_neighbors.py b/aeon/regression/distance_based/_time_series_neighbors.py index 2f63da156c..df81fb731c 100644 --- a/aeon/regression/distance_based/_time_series_neighbors.py +++ b/aeon/regression/distance_based/_time_series_neighbors.py @@ -152,7 +152,6 @@ def _predict(self, X): return preds - @threaded def kneighbors(self, X=None, n_neighbors=None, return_distance=True): """Find the K-neighbors of a point. @@ -181,6 +180,7 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True): """ self._check_is_fitted() + # Input validation if n_neighbors is None: n_neighbors = self.n_neighbors elif not isinstance(n_neighbors, numbers.Integral): @@ -191,6 +191,12 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True): elif n_neighbors <= 0: raise ValueError(f"Expected n_neighbors > 0. Got {n_neighbors}") + if not isinstance(return_distance, bool): + raise TypeError( + f"return_distance must be a boolean, got {type(return_distance)}" + ) + + # Preprocess X if provided query_is_train = X is None if query_is_train: X = self.X_ @@ -198,6 +204,7 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True): X = self._preprocess_collection(X, store_metadata=False) self._check_shape(X) + # Validate n_neighbors against data size n_samples_fit = len(self.X_) if query_is_train: if not (n_neighbors < n_samples_fit): @@ -214,6 +221,34 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True): f"n_samples = {len(X)}" ) + return self._kneighbors(X, n_neighbors, return_distance, query_is_train) + + @threaded + def _kneighbors(self, X, n_neighbors, return_distance, query_is_train): + """Find the K-neighbors of a point. + + Returns indices of and distances to the neighbors of each point. + + Parameters + ---------- + X : 3D np.ndarray of shape = (n_cases, n_channels, n_timepoints) or list of + shape [n_cases] of 2D arrays shape (n_channels,n_timepoints_i) + The query point or points. + n_neighbors : int + Number of neighbors required for each sample. + return_distance : bool + Whether or not to return the distances. + query_is_train : bool + Whether the query points are from the training set. + + Returns + ------- + neigh_dist : ndarray of shape (n_queries, n_neighbors) + Array representing the distances to points, only present if + return_distance=True. + neigh_ind : ndarray of shape (n_queries, n_neighbors) + Indices of the nearest points in the population matrix. + """ distances = pairwise_distance( X, None if query_is_train else self.X_, diff --git a/aeon/regression/distance_based/tests/test_time_series_neighbors.py b/aeon/regression/distance_based/tests/test_time_series_neighbors.py index d93e8b53a0..131073653e 100644 --- a/aeon/regression/distance_based/tests/test_time_series_neighbors.py +++ b/aeon/regression/distance_based/tests/test_time_series_neighbors.py @@ -68,11 +68,59 @@ def test_knn_bounding_matrix(distance_key): @pytest.mark.parametrize("distance_key", distance_functions) def test_knn_kneighbors(distance_key): - """Test knn kneighbors.""" + """Test knn kneighbors with comprehensive validation.""" knn = KNeighborsTimeSeriesRegressor(distance=distance_key, n_neighbors=3) knn.fit(X_train, y_train) + + # Test basic kneighbors functionality dists, ind = knn.kneighbors(X_test, n_neighbors=3) assert isinstance(dists, np.ndarray) assert isinstance(ind, np.ndarray) assert dists.shape == (X_test.shape[0], 3) assert ind.shape == (X_test.shape[0], 3) + + # Test that distances are non-negative + assert np.all(dists >= 0) + + # Test that indices are within valid range + assert np.all(ind >= 0) + assert np.all(ind < len(X_train)) + + # Test that distances are sorted (closest first) + assert np.all(dists[:, 0] <= dists[:, 1]) + assert np.all(dists[:, 1] <= dists[:, 2]) + + # Test using kneighbors results to make predictions manually + # This validates that the kneighbors method returns correct neighbor indices + manual_preds = np.empty(len(X_test)) + for i in range(len(X_test)): + # Get the first neighbor (closest) for each test point + neighbor_idx = ind[i, 0] + manual_preds[i] = y_train[neighbor_idx] + + # Calculate MSE using manual predictions + manual_mse = mean_squared_error(y_test, manual_preds) + + # The manual MSE should be close to the expected MSE for n_neighbors=1 + # We use a tolerance since we're only using the first neighbor + assert_almost_equal(manual_mse, expected_mse[distance_key], decimal=1) + + # Test kneighbors with different n_neighbors values + dists_2, ind_2 = knn.kneighbors(X_test, n_neighbors=2) + assert dists_2.shape == (X_test.shape[0], 2) + assert ind_2.shape == (X_test.shape[0], 2) + + # Test kneighbors without returning distances + ind_only = knn.kneighbors(X_test, n_neighbors=3, return_distance=False) + assert isinstance(ind_only, np.ndarray) + assert ind_only.shape == (X_test.shape[0], 3) + # Should return same indices as when return_distance=True + np.testing.assert_array_equal(ind_only, ind) + + # Test kneighbors on training data (should exclude self) + train_dists, train_ind = knn.kneighbors(n_neighbors=2) + assert train_dists.shape == (len(X_train), 2) + assert train_ind.shape == (len(X_train), 2) + # Each point should not be its own neighbor (diagonal should be excluded) + for i in range(len(X_train)): + assert i not in train_ind[i] From 30577c307e25c61718331fa6b42384681395938d Mon Sep 17 00:00:00 2001 From: chrisholder Date: Mon, 11 Aug 2025 15:55:49 +0100 Subject: [PATCH 216/216] removed threaded decorator and swapped to private method --- .../distance_based/_time_series_neighbors.py | 7 ++++--- aeon/regression/distance_based/_time_series_neighbors.py | 9 ++++++--- 2 files changed, 10 insertions(+), 6 deletions(-) diff --git a/aeon/classification/distance_based/_time_series_neighbors.py b/aeon/classification/distance_based/_time_series_neighbors.py index f7d720aaeb..8431837a1d 100644 --- a/aeon/classification/distance_based/_time_series_neighbors.py +++ b/aeon/classification/distance_based/_time_series_neighbors.py @@ -17,7 +17,6 @@ from aeon.classification.base import BaseClassifier from aeon.distances import pairwise_distance -from aeon.utils.numba._threading import threaded from aeon.utils.validation import check_n_jobs WEIGHTS_SUPPORTED = ["uniform", "distance"] @@ -184,7 +183,10 @@ def _predict(self, X): """ self._check_is_fitted() - indexes = self.kneighbors(X, return_distance=False)[:, 0] + neigh_ind = self._kneighbors( + X, n_neighbors=1, return_distance=False, query_is_train=False + ) + indexes = neigh_ind[:, 0] return self.classes_[self.y_[indexes]] def kneighbors(self, X=None, n_neighbors=None, return_distance=True): @@ -258,7 +260,6 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True): return self._kneighbors(X, n_neighbors, return_distance, query_is_train) - @threaded def _kneighbors(self, X, n_neighbors, return_distance, query_is_train): """Find the K-neighbors of a point. diff --git a/aeon/regression/distance_based/_time_series_neighbors.py b/aeon/regression/distance_based/_time_series_neighbors.py index df81fb731c..5208a09b38 100644 --- a/aeon/regression/distance_based/_time_series_neighbors.py +++ b/aeon/regression/distance_based/_time_series_neighbors.py @@ -17,7 +17,6 @@ from aeon.distances import pairwise_distance from aeon.regression.base import BaseRegressor -from aeon.utils.numba._threading import threaded from aeon.utils.validation import check_n_jobs WEIGHTS_SUPPORTED = ["uniform", "distance"] @@ -136,7 +135,12 @@ def _predict(self, X): """ preds = np.empty(len(X)) for i in range(len(X)): - neigh_dist, neigh_ind = self.kneighbors(X[i : i + 1]) + neigh_dist, neigh_ind = self._kneighbors( + X[i : i + 1], + n_neighbors=self.n_neighbors, + return_distance=True, + query_is_train=False, + ) neigh_dist = neigh_dist[0] neigh_ind = neigh_ind[0] @@ -223,7 +227,6 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True): return self._kneighbors(X, n_neighbors, return_distance, query_is_train) - @threaded def _kneighbors(self, X, n_neighbors, return_distance, query_is_train): """Find the K-neighbors of a point.