Feat/progress

doc/progress.rst

@@ -9,6 +9,9 @@ Changelog
 next
 ~~~~~~
 
+ * ADD #1335: Improve MinIO support.
+    * Add progress bar for downloading MinIO files. Enable it with setting `show_progress` to true on either `openml.config` or the configuration file.
+    * When using `download_all_files`, files are only downloaded if they do not yet exist in the cache.
  * MAINT #1340: Add Numpy 2.0 support. Update tests to work with scikit-learn <= 1.5.
  * ADD #1342: Add HTTP header to requests to indicate they are from openml-python.
 

examples/20_basic/simple_datasets_tutorial.py

@@ -50,6 +50,15 @@
 X, y, categorical_indicator, attribute_names = dataset.get_data(
     dataset_format="dataframe", target=dataset.default_target_attribute
 )
+
+############################################################################
+# Tip: you can get a progress bar for dataset downloads, simply set it in
+# the configuration. Either in code or in the configuration file
+# (see also the introduction tutorial)
+
+openml.config.show_progress = True
+
+
 ############################################################################
 # Visualize the dataset
 # =====================

openml/_api_calls.py

@@ -1,6 +1,7 @@
 # License: BSD 3-Clause
 from __future__ import annotations
 
+import contextlib
 import hashlib
 import logging
 import math
@@ -26,6 +27,7 @@
     OpenMLServerException,
     OpenMLServerNoResult,
 )
+from .utils import ProgressBar
 
 _HEADERS = {"user-agent": f"openml-python/{__version__}"}
 
@@ -161,12 +163,12 @@ def _download_minio_file(
     proxy_client = ProxyManager(proxy) if proxy else None
 
     client = minio.Minio(endpoint=parsed_url.netloc, secure=False, http_client=proxy_client)
-
     try:
         client.fget_object(
             bucket_name=bucket,
             object_name=object_name,
             file_path=str(destination),
+            progress=ProgressBar() if config.show_progress else None,
             request_headers=_HEADERS,
         )
         if destination.is_file() and destination.suffix == ".zip":
@@ -206,11 +208,12 @@ def _download_minio_bucket(source: str, destination: str | Path) -> None:
         if file_object.object_name is None:
             raise ValueError("Object name is None.")
 
-        _download_minio_file(
-            source=source.rsplit("/", 1)[0] + "/" + file_object.object_name.rsplit("/", 1)[1],
-            destination=Path(destination, file_object.object_name.rsplit("/", 1)[1]),
-            exists_ok=True,
-        )
+        with contextlib.suppress(FileExistsError):  # Simply use cached version instead
+            _download_minio_file(
+                source=source.rsplit("/", 1)[0] + "/" + file_object.object_name.rsplit("/", 1)[1],
+                destination=Path(destination, file_object.object_name.rsplit("/", 1)[1]),
+                exists_ok=False,
+            )
 
 
 def _download_text_file(

openml/config.py

@@ -28,6 +28,7 @@ class _Config(TypedDict):
     avoid_duplicate_runs: bool
     retry_policy: Literal["human", "robot"]
     connection_n_retries: int
+    show_progress: bool
 
 
 def _create_log_handlers(create_file_handler: bool = True) -> None:  # noqa: FBT001, FBT002
@@ -111,6 +112,7 @@ def set_file_log_level(file_output_level: int) -> None:
     "avoid_duplicate_runs": True,
     "retry_policy": "human",
     "connection_n_retries": 5,
+    "show_progress": False,
 }
 
 # Default values are actually added here in the _setup() function which is
@@ -131,6 +133,7 @@ def get_server_base_url() -> str:
 
 
 apikey: str = _defaults["apikey"]
+show_progress: bool = _defaults["show_progress"]
 # The current cache directory (without the server name)
 _root_cache_directory = Path(_defaults["cachedir"])
 avoid_duplicate_runs = _defaults["avoid_duplicate_runs"]
@@ -238,6 +241,7 @@ def _setup(config: _Config | None = None) -> None:
     global server  # noqa: PLW0603
     global _root_cache_directory  # noqa: PLW0603
     global avoid_duplicate_runs  # noqa: PLW0603
+    global show_progress  # noqa: PLW0603
 
     config_file = determine_config_file_path()
     config_dir = config_file.parent
@@ -255,6 +259,7 @@ def _setup(config: _Config | None = None) -> None:
     avoid_duplicate_runs = config["avoid_duplicate_runs"]
     apikey = config["apikey"]
     server = config["server"]
+    show_progress = config["show_progress"]
     short_cache_dir = Path(config["cachedir"])
     n_retries = int(config["connection_n_retries"])
 
@@ -328,11 +333,11 @@ def _parse_config(config_file: str | Path) -> _Config:
         logger.info("Error opening file %s: %s", config_file, e.args[0])
     config_file_.seek(0)
     config.read_file(config_file_)
-    if isinstance(config["FAKE_SECTION"]["avoid_duplicate_runs"], str):
-        config["FAKE_SECTION"]["avoid_duplicate_runs"] = config["FAKE_SECTION"].getboolean(
-            "avoid_duplicate_runs"
-        )  # type: ignore
-    return dict(config.items("FAKE_SECTION"))  # type: ignore
+    configuration = dict(config.items("FAKE_SECTION"))
+    for boolean_field in ["avoid_duplicate_runs", "show_progress"]:
+        if isinstance(config["FAKE_SECTION"][boolean_field], str):
+            configuration[boolean_field] = config["FAKE_SECTION"].getboolean(boolean_field)  # type: ignore
+    return configuration  # type: ignore
 
 
 def get_config_as_dict() -> _Config:
@@ -343,6 +348,7 @@ def get_config_as_dict() -> _Config:
         "avoid_duplicate_runs": avoid_duplicate_runs,
         "connection_n_retries": connection_n_retries,
         "retry_policy": retry_policy,
+        "show_progress": show_progress,
     }
 
 

openml/datasets/functions.py

@@ -1262,10 +1262,9 @@ def _get_dataset_parquet(
     if old_file_path.is_file():
         old_file_path.rename(output_file_path)
 
-    # For this release, we want to be able to force a new download even if the
-    # parquet file is already present when ``download_all_files`` is set.
-    # For now, it would be the only way for the user to fetch the additional
-    # files in the bucket (no function exists on an OpenMLDataset to do this).
+    # The call below skips files already on disk, so avoids downloading the parquet file twice.
+    # To force the old behavior of always downloading everything, use `force_refresh_cache`
+    # of `get_dataset`
     if download_all_files:
         openml._api_calls._download_minio_bucket(source=url, destination=cache_directory)
 

openml/utils.py

@@ -12,6 +12,8 @@
 import numpy as np
 import pandas as pd
 import xmltodict
+from minio.helpers import ProgressType
+from tqdm import tqdm
 
 import openml
 import openml._api_calls
@@ -471,3 +473,39 @@ def _create_lockfiles_dir() -> Path:
     with contextlib.suppress(OSError):
         path.mkdir(exist_ok=True, parents=True)
     return path
+
+
+class ProgressBar(ProgressType):
+    """Progressbar for MinIO function's `progress` parameter."""
+
+    def __init__(self) -> None:
+        self._object_name = ""
+        self._progress_bar: tqdm | None = None
+
+    def set_meta(self, object_name: str, total_length: int) -> None:
+        """Initializes the progress bar.
+
+        Parameters
+        ----------
+        object_name: str
+          Not used.
+
+        total_length: int
+          File size of the object in bytes.
+        """
+        self._object_name = object_name
+        self._progress_bar = tqdm(total=total_length, unit_scale=True, unit="B")
+
+    def update(self, length: int) -> None:
+        """Updates the progress bar.
+
+        Parameters
+        ----------
+        length: int
+          Number of bytes downloaded since last `update` call.
+        """
+        if not self._progress_bar:
+            raise RuntimeError("Call `set_meta` before calling `update`.")
+        self._progress_bar.update(length)
+        if self._progress_bar.total <= self._progress_bar.n:
+            self._progress_bar.close()

pyproject.toml

@@ -19,6 +19,7 @@ dependencies = [
   "numpy>=1.6.2",
   "minio",
   "pyarrow",
+  "tqdm",  # For MinIO download progress bars
   "packaging",
 ]
 requires-python = ">=3.8"

tests/test_openml/test_api_calls.py

@@ -1,11 +1,16 @@
 from __future__ import annotations
 
 import unittest.mock
+from pathlib import Path
+from typing import NamedTuple, Iterable, Iterator
+from unittest import mock
 
+import minio
 import pytest
 
 import openml
 import openml.testing
+from openml._api_calls import _download_minio_bucket
 
 
 class TestConfig(openml.testing.TestBase):
@@ -30,3 +35,39 @@ def test_retry_on_database_error(self, Session_class_mock, _):
             openml._api_calls._send_request("get", "/abc", {})
 
         assert Session_class_mock.return_value.__enter__.return_value.get.call_count == 20
+
+class FakeObject(NamedTuple):
+    object_name: str
+
+class FakeMinio:
+    def __init__(self, objects: Iterable[FakeObject] | None = None):
+        self._objects = objects or []
+
+    def list_objects(self, *args, **kwargs) -> Iterator[FakeObject]:
+        yield from self._objects
+
+    def fget_object(self, object_name: str, file_path: str, *args, **kwargs) -> None:
+        if object_name in [obj.object_name for obj in self._objects]:
+            Path(file_path).write_text("foo")
+            return
+        raise FileNotFoundError
+
+
+@mock.patch.object(minio, "Minio")
+def test_download_all_files_observes_cache(mock_minio, tmp_path: Path) -> None:
+    some_prefix, some_filename = "some/prefix", "dataset.arff"
+    some_object_path = f"{some_prefix}/{some_filename}"
+    some_url = f"https://not.real.com/bucket/{some_object_path}"
+    mock_minio.return_value = FakeMinio(
+        objects=[
+            FakeObject(some_object_path),
+        ],
+    )
+
+    _download_minio_bucket(source=some_url, destination=tmp_path)
+    time_created = (tmp_path / "dataset.arff").stat().st_ctime
+
+    _download_minio_bucket(source=some_url, destination=tmp_path)
+    time_modified = (tmp_path / some_filename).stat().st_mtime
+
+    assert time_created == time_modified

tests/test_openml/test_config.py

@@ -133,3 +133,13 @@ def test_configuration_file_not_overwritten_on_load():
 
     assert config_file_content == new_file_content
     assert "abcd" == read_config["apikey"]
+
+def test_configuration_loads_booleans(tmp_path):
+    config_file_content = "avoid_duplicate_runs=true\nshow_progress=false"
+    with (tmp_path/"config").open("w") as config_file:
+        config_file.write(config_file_content)
+        read_config = openml.config._parse_config(tmp_path)
+
+    # Explicit test to avoid truthy/falsy modes of other types
+    assert True == read_config["avoid_duplicate_runs"]
+    assert False == read_config["show_progress"]