132 lines
4.3 KiB
Python
132 lines
4.3 KiB
Python
"""Test loaders for common functionality."""
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import inspect
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import os
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import pytest
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import numpy as np
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import sklearn.datasets
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def is_pillow_installed():
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try:
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import PIL # noqa
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return True
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except ImportError:
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return False
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FETCH_PYTEST_MARKERS = {
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"return_X_y": {
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"fetch_20newsgroups": pytest.mark.xfail(
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reason="X is a list and does not have a shape argument"
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),
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"fetch_openml": pytest.mark.xfail(
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reason="fetch_opeml requires a dataset name or id"
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),
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"fetch_lfw_people": pytest.mark.skipif(
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not is_pillow_installed(),
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reason="pillow is not installed"
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)
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},
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"as_frame": {
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"fetch_openml": pytest.mark.xfail(
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reason="fetch_opeml requires a dataset name or id"
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),
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}
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}
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def check_pandas_dependency_message(fetch_func):
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try:
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import pandas # noqa
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pytest.skip("This test requires pandas to not be installed")
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except ImportError:
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# Check that pandas is imported lazily and that an informative error
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# message is raised when pandas is missing:
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name = fetch_func.__name__
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expected_msg = f'{name} with as_frame=True requires pandas'
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with pytest.raises(ImportError, match=expected_msg):
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fetch_func(as_frame=True)
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def check_return_X_y(bunch, dataset_func):
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X_y_tuple = dataset_func(return_X_y=True)
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assert isinstance(X_y_tuple, tuple)
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assert X_y_tuple[0].shape == bunch.data.shape
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assert X_y_tuple[1].shape == bunch.target.shape
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def check_as_frame(bunch, dataset_func,
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expected_data_dtype=None, expected_target_dtype=None):
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pd = pytest.importorskip('pandas')
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frame_bunch = dataset_func(as_frame=True)
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assert hasattr(frame_bunch, 'frame')
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assert isinstance(frame_bunch.frame, pd.DataFrame)
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assert isinstance(frame_bunch.data, pd.DataFrame)
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assert frame_bunch.data.shape == bunch.data.shape
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if frame_bunch.target.ndim > 1:
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assert isinstance(frame_bunch.target, pd.DataFrame)
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else:
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assert isinstance(frame_bunch.target, pd.Series)
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assert frame_bunch.target.shape[0] == bunch.target.shape[0]
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if expected_data_dtype is not None:
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assert np.all(frame_bunch.data.dtypes == expected_data_dtype)
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if expected_target_dtype is not None:
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assert np.all(frame_bunch.target.dtypes == expected_target_dtype)
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# Test for return_X_y and as_frame=True
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frame_X, frame_y = dataset_func(as_frame=True, return_X_y=True)
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assert isinstance(frame_X, pd.DataFrame)
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if frame_y.ndim > 1:
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assert isinstance(frame_X, pd.DataFrame)
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else:
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assert isinstance(frame_y, pd.Series)
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def _skip_network_tests():
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return os.environ.get('SKLEARN_SKIP_NETWORK_TESTS', '1') == '1'
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def _generate_func_supporting_param(param, dataset_type=("load", "fetch")):
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markers_fetch = FETCH_PYTEST_MARKERS.get(param, {})
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for name, obj in inspect.getmembers(sklearn.datasets):
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if not inspect.isfunction(obj):
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continue
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is_dataset_type = any([name.startswith(t) for t in dataset_type])
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is_support_param = param in inspect.signature(obj).parameters
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if is_dataset_type and is_support_param:
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# check if we should skip if we don't have network support
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marks = [pytest.mark.skipif(
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condition=name.startswith("fetch") and _skip_network_tests(),
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reason="Skip because fetcher requires internet network",
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)]
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if name in markers_fetch:
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marks.append(markers_fetch[name])
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yield pytest.param(name, obj, marks=marks)
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@pytest.mark.parametrize(
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"name, dataset_func", _generate_func_supporting_param("return_X_y")
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)
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def test_common_check_return_X_y(name, dataset_func):
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bunch = dataset_func()
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check_return_X_y(bunch, dataset_func)
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@pytest.mark.parametrize(
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"name, dataset_func", _generate_func_supporting_param("as_frame")
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)
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def test_common_check_as_frame(name, dataset_func):
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bunch = dataset_func()
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check_as_frame(bunch, dataset_func)
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@pytest.mark.parametrize(
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"name, dataset_func", _generate_func_supporting_param("as_frame")
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)
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def test_common_check_pandas_dependency(name, dataset_func):
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check_pandas_dependency_message(dataset_func)
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