[ENH] Set outlier_norm default to True for Catch22 estimators

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,

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,

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,

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,

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,