621 lines
19 KiB
Python
621 lines
19 KiB
Python
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"""
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General tests for all estimators in sklearn.
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"""
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# Authors: Andreas Mueller <amueller@ais.uni-bonn.de>
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# Gael Varoquaux gael.varoquaux@normalesup.org
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# License: BSD 3 clause
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import os
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import warnings
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import sys
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import re
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import pkgutil
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from inspect import isgenerator, signature
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from itertools import product, chain
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from functools import partial
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import pytest
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import numpy as np
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from sklearn.cluster import (
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AffinityPropagation,
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Birch,
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MeanShift,
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OPTICS,
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SpectralClustering,
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)
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from sklearn.datasets import make_blobs
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from sklearn.manifold import Isomap, TSNE, LocallyLinearEmbedding
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from sklearn.neighbors import (
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LocalOutlierFactor,
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KNeighborsClassifier,
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KNeighborsRegressor,
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RadiusNeighborsClassifier,
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RadiusNeighborsRegressor,
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)
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from sklearn.preprocessing import FunctionTransformer
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from sklearn.semi_supervised import LabelPropagation, LabelSpreading
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from sklearn.utils import all_estimators
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from sklearn.utils._testing import ignore_warnings
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from sklearn.exceptions import ConvergenceWarning
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from sklearn.exceptions import FitFailedWarning
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from sklearn.utils.estimator_checks import check_estimator
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import sklearn
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# make it possible to discover experimental estimators when calling `all_estimators`
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from sklearn.experimental import enable_iterative_imputer # noqa
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from sklearn.experimental import enable_halving_search_cv # noqa
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from sklearn.decomposition import PCA
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from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
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from sklearn.linear_model._base import LinearClassifierMixin
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from sklearn.linear_model import LogisticRegression
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from sklearn.linear_model import Ridge
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from sklearn.model_selection import GridSearchCV
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from sklearn.model_selection import RandomizedSearchCV
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from sklearn.model_selection import HalvingGridSearchCV
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from sklearn.model_selection import HalvingRandomSearchCV
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from sklearn.pipeline import make_pipeline
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from sklearn.utils import IS_PYPY
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from sklearn.utils._tags import _DEFAULT_TAGS, _safe_tags
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from sklearn.utils._testing import (
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SkipTest,
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set_random_state,
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)
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from sklearn.utils.estimator_checks import (
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_construct_instance,
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_set_checking_parameters,
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_get_check_estimator_ids,
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check_class_weight_balanced_linear_classifier,
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parametrize_with_checks,
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check_dataframe_column_names_consistency,
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check_n_features_in_after_fitting,
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check_param_validation,
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check_transformer_get_feature_names_out,
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check_transformer_get_feature_names_out_pandas,
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check_set_output_transform,
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check_set_output_transform_pandas,
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check_global_ouptut_transform_pandas,
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check_get_feature_names_out_error,
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)
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def test_all_estimator_no_base_class():
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# test that all_estimators doesn't find abstract classes.
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for name, Estimator in all_estimators():
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msg = (
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"Base estimators such as {0} should not be included in all_estimators"
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).format(name)
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assert not name.lower().startswith("base"), msg
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def _sample_func(x, y=1):
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pass
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@pytest.mark.parametrize(
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"val, expected",
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[
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(partial(_sample_func, y=1), "_sample_func(y=1)"),
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(_sample_func, "_sample_func"),
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(partial(_sample_func, "world"), "_sample_func"),
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(LogisticRegression(C=2.0), "LogisticRegression(C=2.0)"),
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(
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LogisticRegression(
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random_state=1,
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solver="newton-cg",
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class_weight="balanced",
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warm_start=True,
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),
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"LogisticRegression(class_weight='balanced',random_state=1,"
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"solver='newton-cg',warm_start=True)",
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),
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],
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)
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def test_get_check_estimator_ids(val, expected):
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assert _get_check_estimator_ids(val) == expected
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def _tested_estimators(type_filter=None):
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for name, Estimator in all_estimators(type_filter=type_filter):
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try:
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estimator = _construct_instance(Estimator)
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except SkipTest:
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continue
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yield estimator
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@parametrize_with_checks(list(_tested_estimators()))
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def test_estimators(estimator, check, request):
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# Common tests for estimator instances
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with ignore_warnings(category=(FutureWarning, ConvergenceWarning, UserWarning)):
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_set_checking_parameters(estimator)
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check(estimator)
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def test_check_estimator_generate_only():
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all_instance_gen_checks = check_estimator(LogisticRegression(), generate_only=True)
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assert isgenerator(all_instance_gen_checks)
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def test_configure():
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# Smoke test `python setup.py config` command run at the root of the
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# scikit-learn source tree.
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# This test requires Cython which is not necessarily there when running
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# the tests of an installed version of scikit-learn or when scikit-learn
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# is installed in editable mode by pip build isolation enabled.
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pytest.importorskip("Cython")
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cwd = os.getcwd()
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setup_path = os.path.abspath(os.path.join(sklearn.__path__[0], ".."))
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setup_filename = os.path.join(setup_path, "setup.py")
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if not os.path.exists(setup_filename):
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pytest.skip("setup.py not available")
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try:
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os.chdir(setup_path)
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old_argv = sys.argv
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sys.argv = ["setup.py", "config"]
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with warnings.catch_warnings():
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# The configuration spits out warnings when not finding
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# Blas/Atlas development headers
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warnings.simplefilter("ignore", UserWarning)
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with open("setup.py") as f:
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exec(f.read(), dict(__name__="__main__"))
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finally:
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sys.argv = old_argv
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os.chdir(cwd)
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def _tested_linear_classifiers():
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classifiers = all_estimators(type_filter="classifier")
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with warnings.catch_warnings(record=True):
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for name, clazz in classifiers:
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required_parameters = getattr(clazz, "_required_parameters", [])
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if len(required_parameters):
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# FIXME
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continue
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if "class_weight" in clazz().get_params().keys() and issubclass(
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clazz, LinearClassifierMixin
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):
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yield name, clazz
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@pytest.mark.parametrize("name, Classifier", _tested_linear_classifiers())
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def test_class_weight_balanced_linear_classifiers(name, Classifier):
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check_class_weight_balanced_linear_classifier(name, Classifier)
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@ignore_warnings
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def test_import_all_consistency():
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# Smoke test to check that any name in a __all__ list is actually defined
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# in the namespace of the module or package.
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pkgs = pkgutil.walk_packages(
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path=sklearn.__path__, prefix="sklearn.", onerror=lambda _: None
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)
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submods = [modname for _, modname, _ in pkgs]
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for modname in submods + ["sklearn"]:
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if ".tests." in modname:
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continue
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if IS_PYPY and (
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"_svmlight_format_io" in modname
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or "feature_extraction._hashing_fast" in modname
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):
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continue
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package = __import__(modname, fromlist="dummy")
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for name in getattr(package, "__all__", ()):
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assert hasattr(package, name), "Module '{0}' has no attribute '{1}'".format(
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modname, name
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)
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def test_root_import_all_completeness():
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EXCEPTIONS = ("utils", "tests", "base", "setup", "conftest")
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for _, modname, _ in pkgutil.walk_packages(
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path=sklearn.__path__, onerror=lambda _: None
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):
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if "." in modname or modname.startswith("_") or modname in EXCEPTIONS:
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continue
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assert modname in sklearn.__all__
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def test_all_tests_are_importable():
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# Ensure that for each contentful subpackage, there is a test directory
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# within it that is also a subpackage (i.e. a directory with __init__.py)
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HAS_TESTS_EXCEPTIONS = re.compile(
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r"""(?x)
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\.externals(\.|$)|
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\.tests(\.|$)|
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\._
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"""
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)
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resource_modules = {
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"sklearn.datasets.data",
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"sklearn.datasets.descr",
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"sklearn.datasets.images",
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}
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lookup = {
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name: ispkg
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for _, name, ispkg in pkgutil.walk_packages(sklearn.__path__, prefix="sklearn.")
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}
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missing_tests = [
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name
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for name, ispkg in lookup.items()
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if ispkg
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and name not in resource_modules
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and not HAS_TESTS_EXCEPTIONS.search(name)
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and name + ".tests" not in lookup
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]
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assert missing_tests == [], (
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"{0} do not have `tests` subpackages. "
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"Perhaps they require "
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"__init__.py or an add_subpackage directive "
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"in the parent "
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"setup.py".format(missing_tests)
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)
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def test_class_support_removed():
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# Make sure passing classes to check_estimator or parametrize_with_checks
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# raises an error
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msg = "Passing a class was deprecated.* isn't supported anymore"
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with pytest.raises(TypeError, match=msg):
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check_estimator(LogisticRegression)
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with pytest.raises(TypeError, match=msg):
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parametrize_with_checks([LogisticRegression])
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def _generate_search_cv_instances():
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for SearchCV, (Estimator, param_grid) in product(
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[
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GridSearchCV,
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HalvingGridSearchCV,
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RandomizedSearchCV,
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HalvingGridSearchCV,
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],
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[
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(Ridge, {"alpha": [0.1, 1.0]}),
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(LogisticRegression, {"C": [0.1, 1.0]}),
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],
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):
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init_params = signature(SearchCV).parameters
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extra_params = (
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{"min_resources": "smallest"} if "min_resources" in init_params else {}
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)
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search_cv = SearchCV(Estimator(), param_grid, cv=2, **extra_params)
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set_random_state(search_cv)
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yield search_cv
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for SearchCV, (Estimator, param_grid) in product(
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[
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GridSearchCV,
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HalvingGridSearchCV,
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RandomizedSearchCV,
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HalvingRandomSearchCV,
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],
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[
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(Ridge, {"ridge__alpha": [0.1, 1.0]}),
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(LogisticRegression, {"logisticregression__C": [0.1, 1.0]}),
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],
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):
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init_params = signature(SearchCV).parameters
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extra_params = (
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{"min_resources": "smallest"} if "min_resources" in init_params else {}
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)
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search_cv = SearchCV(
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make_pipeline(PCA(), Estimator()), param_grid, cv=2, **extra_params
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).set_params(error_score="raise")
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set_random_state(search_cv)
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yield search_cv
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@parametrize_with_checks(list(_generate_search_cv_instances()))
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def test_search_cv(estimator, check, request):
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# Common tests for SearchCV instances
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# We have a separate test because those meta-estimators can accept a
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# wide range of base estimators (classifiers, regressors, pipelines)
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with ignore_warnings(
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category=(
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FutureWarning,
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ConvergenceWarning,
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UserWarning,
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FitFailedWarning,
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)
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):
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check(estimator)
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@pytest.mark.parametrize(
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"estimator", _tested_estimators(), ids=_get_check_estimator_ids
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)
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def test_valid_tag_types(estimator):
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"""Check that estimator tags are valid."""
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tags = _safe_tags(estimator)
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for name, tag in tags.items():
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correct_tags = type(_DEFAULT_TAGS[name])
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if name == "_xfail_checks":
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# _xfail_checks can be a dictionary
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correct_tags = (correct_tags, dict)
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assert isinstance(tag, correct_tags)
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@pytest.mark.parametrize(
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"estimator", _tested_estimators(), ids=_get_check_estimator_ids
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)
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def test_check_n_features_in_after_fitting(estimator):
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_set_checking_parameters(estimator)
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check_n_features_in_after_fitting(estimator.__class__.__name__, estimator)
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def _estimators_that_predict_in_fit():
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for estimator in _tested_estimators():
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est_params = set(estimator.get_params())
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if "oob_score" in est_params:
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yield estimator.set_params(oob_score=True, bootstrap=True)
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elif "early_stopping" in est_params:
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est = estimator.set_params(early_stopping=True, n_iter_no_change=1)
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if est.__class__.__name__ in {"MLPClassifier", "MLPRegressor"}:
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|
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# TODO: FIX MLP to not check validation set during MLP
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yield pytest.param(
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est, marks=pytest.mark.xfail(msg="MLP still validates in fit")
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)
|
||
|
|
else:
|
||
|
|
yield est
|
||
|
|
elif "n_iter_no_change" in est_params:
|
||
|
|
yield estimator.set_params(n_iter_no_change=1)
|
||
|
|
|
||
|
|
|
||
|
|
# NOTE: When running `check_dataframe_column_names_consistency` on a meta-estimator that
|
||
|
|
# delegates validation to a base estimator, the check is testing that the base estimator
|
||
|
|
# is checking for column name consistency.
|
||
|
|
column_name_estimators = list(
|
||
|
|
chain(
|
||
|
|
_tested_estimators(),
|
||
|
|
[make_pipeline(LogisticRegression(C=1))],
|
||
|
|
list(_generate_search_cv_instances()),
|
||
|
|
_estimators_that_predict_in_fit(),
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"estimator", column_name_estimators, ids=_get_check_estimator_ids
|
||
|
|
)
|
||
|
|
def test_pandas_column_name_consistency(estimator):
|
||
|
|
_set_checking_parameters(estimator)
|
||
|
|
with ignore_warnings(category=(FutureWarning)):
|
||
|
|
with warnings.catch_warnings(record=True) as record:
|
||
|
|
check_dataframe_column_names_consistency(
|
||
|
|
estimator.__class__.__name__, estimator
|
||
|
|
)
|
||
|
|
for warning in record:
|
||
|
|
assert "was fitted without feature names" not in str(warning.message)
|
||
|
|
|
||
|
|
|
||
|
|
# TODO: As more modules support get_feature_names_out they should be removed
|
||
|
|
# from this list to be tested
|
||
|
|
GET_FEATURES_OUT_MODULES_TO_IGNORE = [
|
||
|
|
"ensemble",
|
||
|
|
"kernel_approximation",
|
||
|
|
]
|
||
|
|
|
||
|
|
|
||
|
|
def _include_in_get_feature_names_out_check(transformer):
|
||
|
|
if hasattr(transformer, "get_feature_names_out"):
|
||
|
|
return True
|
||
|
|
module = transformer.__module__.split(".")[1]
|
||
|
|
return module not in GET_FEATURES_OUT_MODULES_TO_IGNORE
|
||
|
|
|
||
|
|
|
||
|
|
GET_FEATURES_OUT_ESTIMATORS = [
|
||
|
|
est
|
||
|
|
for est in _tested_estimators("transformer")
|
||
|
|
if _include_in_get_feature_names_out_check(est)
|
||
|
|
]
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"transformer", GET_FEATURES_OUT_ESTIMATORS, ids=_get_check_estimator_ids
|
||
|
|
)
|
||
|
|
def test_transformers_get_feature_names_out(transformer):
|
||
|
|
_set_checking_parameters(transformer)
|
||
|
|
|
||
|
|
with ignore_warnings(category=(FutureWarning)):
|
||
|
|
check_transformer_get_feature_names_out(
|
||
|
|
transformer.__class__.__name__, transformer
|
||
|
|
)
|
||
|
|
check_transformer_get_feature_names_out_pandas(
|
||
|
|
transformer.__class__.__name__, transformer
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
ESTIMATORS_WITH_GET_FEATURE_NAMES_OUT = [
|
||
|
|
est for est in _tested_estimators() if hasattr(est, "get_feature_names_out")
|
||
|
|
]
|
||
|
|
|
||
|
|
WHITELISTED_FAILING_ESTIMATORS = [
|
||
|
|
"AdditiveChi2Sampler",
|
||
|
|
"Binarizer",
|
||
|
|
"DictVectorizer",
|
||
|
|
"GaussianRandomProjection",
|
||
|
|
"GenericUnivariateSelect",
|
||
|
|
"IterativeImputer",
|
||
|
|
"IsotonicRegression",
|
||
|
|
"KBinsDiscretizer",
|
||
|
|
"KNNImputer",
|
||
|
|
"MaxAbsScaler",
|
||
|
|
"MinMaxScaler",
|
||
|
|
"MissingIndicator",
|
||
|
|
"Normalizer",
|
||
|
|
"OrdinalEncoder",
|
||
|
|
"PowerTransformer",
|
||
|
|
"QuantileTransformer",
|
||
|
|
"RFE",
|
||
|
|
"RFECV",
|
||
|
|
"RobustScaler",
|
||
|
|
"SelectFdr",
|
||
|
|
"SelectFpr",
|
||
|
|
"SelectFromModel",
|
||
|
|
"SelectFwe",
|
||
|
|
"SelectKBest",
|
||
|
|
"SelectPercentile",
|
||
|
|
"SequentialFeatureSelector",
|
||
|
|
"SimpleImputer",
|
||
|
|
"SparseRandomProjection",
|
||
|
|
"SplineTransformer",
|
||
|
|
"StackingClassifier",
|
||
|
|
"StackingRegressor",
|
||
|
|
"StandardScaler",
|
||
|
|
"TfidfTransformer",
|
||
|
|
"VarianceThreshold",
|
||
|
|
"VotingClassifier",
|
||
|
|
"VotingRegressor",
|
||
|
|
]
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"estimator", ESTIMATORS_WITH_GET_FEATURE_NAMES_OUT, ids=_get_check_estimator_ids
|
||
|
|
)
|
||
|
|
def test_estimators_get_feature_names_out_error(estimator):
|
||
|
|
estimator_name = estimator.__class__.__name__
|
||
|
|
if estimator_name in WHITELISTED_FAILING_ESTIMATORS:
|
||
|
|
return pytest.xfail(
|
||
|
|
reason=f"{estimator_name} is not failing with a consistent NotFittedError"
|
||
|
|
)
|
||
|
|
_set_checking_parameters(estimator)
|
||
|
|
check_get_feature_names_out_error(estimator_name, estimator)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"Estimator",
|
||
|
|
[est for name, est in all_estimators()],
|
||
|
|
)
|
||
|
|
def test_estimators_do_not_raise_errors_in_init_or_set_params(Estimator):
|
||
|
|
"""Check that init or set_param does not raise errors."""
|
||
|
|
params = signature(Estimator).parameters
|
||
|
|
|
||
|
|
smoke_test_values = [-1, 3.0, "helloworld", np.array([1.0, 4.0]), [1], {}, []]
|
||
|
|
for value in smoke_test_values:
|
||
|
|
new_params = {key: value for key in params}
|
||
|
|
|
||
|
|
# Does not raise
|
||
|
|
est = Estimator(**new_params)
|
||
|
|
|
||
|
|
# Also do does not raise
|
||
|
|
est.set_params(**new_params)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"estimator", _tested_estimators(), ids=_get_check_estimator_ids
|
||
|
|
)
|
||
|
|
def test_check_param_validation(estimator):
|
||
|
|
name = estimator.__class__.__name__
|
||
|
|
_set_checking_parameters(estimator)
|
||
|
|
check_param_validation(name, estimator)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"Estimator",
|
||
|
|
[
|
||
|
|
AffinityPropagation,
|
||
|
|
Birch,
|
||
|
|
MeanShift,
|
||
|
|
KNeighborsClassifier,
|
||
|
|
KNeighborsRegressor,
|
||
|
|
RadiusNeighborsClassifier,
|
||
|
|
RadiusNeighborsRegressor,
|
||
|
|
LabelPropagation,
|
||
|
|
LabelSpreading,
|
||
|
|
OPTICS,
|
||
|
|
SpectralClustering,
|
||
|
|
LocalOutlierFactor,
|
||
|
|
LocallyLinearEmbedding,
|
||
|
|
Isomap,
|
||
|
|
TSNE,
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_f_contiguous_array_estimator(Estimator):
|
||
|
|
# Non-regression test for:
|
||
|
|
# https://github.com/scikit-learn/scikit-learn/issues/23988
|
||
|
|
# https://github.com/scikit-learn/scikit-learn/issues/24013
|
||
|
|
|
||
|
|
X, _ = make_blobs(n_samples=80, n_features=4, random_state=0)
|
||
|
|
X = np.asfortranarray(X)
|
||
|
|
y = np.round(X[:, 0])
|
||
|
|
|
||
|
|
est = Estimator()
|
||
|
|
est.fit(X, y)
|
||
|
|
|
||
|
|
if hasattr(est, "transform"):
|
||
|
|
est.transform(X)
|
||
|
|
|
||
|
|
if hasattr(est, "predict"):
|
||
|
|
est.predict(X)
|
||
|
|
|
||
|
|
|
||
|
|
SET_OUTPUT_ESTIMATORS = list(
|
||
|
|
chain(
|
||
|
|
_tested_estimators("transformer"),
|
||
|
|
[
|
||
|
|
make_pipeline(StandardScaler(), MinMaxScaler()),
|
||
|
|
OneHotEncoder(sparse_output=False),
|
||
|
|
FunctionTransformer(feature_names_out="one-to-one"),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"estimator", SET_OUTPUT_ESTIMATORS, ids=_get_check_estimator_ids
|
||
|
|
)
|
||
|
|
def test_set_output_transform(estimator):
|
||
|
|
name = estimator.__class__.__name__
|
||
|
|
if not hasattr(estimator, "set_output"):
|
||
|
|
pytest.skip(
|
||
|
|
f"Skipping check_set_output_transform for {name}: Does not support"
|
||
|
|
" set_output API"
|
||
|
|
)
|
||
|
|
_set_checking_parameters(estimator)
|
||
|
|
with ignore_warnings(category=(FutureWarning)):
|
||
|
|
check_set_output_transform(estimator.__class__.__name__, estimator)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"estimator", SET_OUTPUT_ESTIMATORS, ids=_get_check_estimator_ids
|
||
|
|
)
|
||
|
|
def test_set_output_transform_pandas(estimator):
|
||
|
|
name = estimator.__class__.__name__
|
||
|
|
if not hasattr(estimator, "set_output"):
|
||
|
|
pytest.skip(
|
||
|
|
f"Skipping check_set_output_transform_pandas for {name}: Does not support"
|
||
|
|
" set_output API yet"
|
||
|
|
)
|
||
|
|
_set_checking_parameters(estimator)
|
||
|
|
with ignore_warnings(category=(FutureWarning)):
|
||
|
|
check_set_output_transform_pandas(estimator.__class__.__name__, estimator)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"estimator", SET_OUTPUT_ESTIMATORS, ids=_get_check_estimator_ids
|
||
|
|
)
|
||
|
|
def test_global_output_transform_pandas(estimator):
|
||
|
|
name = estimator.__class__.__name__
|
||
|
|
if not hasattr(estimator, "set_output"):
|
||
|
|
pytest.skip(
|
||
|
|
f"Skipping check_global_ouptut_transform_pandas for {name}: Does not"
|
||
|
|
" support set_output API yet"
|
||
|
|
)
|
||
|
|
_set_checking_parameters(estimator)
|
||
|
|
with ignore_warnings(category=(FutureWarning)):
|
||
|
|
check_global_ouptut_transform_pandas(estimator.__class__.__name__, estimator)
|