MAINT replace enable_slep006 fixture by @config_context(enable_metadata_routing=True) (scikit-learn#30038)

Author: ogrisel

sklearn/compose/tests/test_column_transformer.py

@@ -13,6 +13,7 @@
 from numpy.testing import assert_allclose
 from scipy import sparse
 
+from sklearn import config_context
 from sklearn.base import BaseEstimator, TransformerMixin
 from sklearn.compose import (
     ColumnTransformer,
@@ -2685,8 +2686,8 @@ def test_routing_passed_metadata_not_supported(method):
         getattr(trs, method)([[1]], sample_weight=[1], prop="a")
 
 
-@pytest.mark.usefixtures("enable_slep006")
 @pytest.mark.parametrize("method", ["transform", "fit_transform", "fit"])
+@config_context(enable_metadata_routing=True)
 def test_metadata_routing_for_column_transformer(method):
     """Test that metadata is routed correctly for column transformer."""
     X = np.array([[0, 1, 2], [2, 4, 6]]).T
@@ -2722,7 +2723,7 @@ def test_metadata_routing_for_column_transformer(method):
         )
 
 
-@pytest.mark.usefixtures("enable_slep006")
+@config_context(enable_metadata_routing=True)
 def test_metadata_routing_no_fit_transform():
     """Test metadata routing when the sub-estimator doesn't implement
     ``fit_transform``."""
@@ -2757,8 +2758,8 @@ def transform(self, X, sample_weight=None, metadata=None):
     trs.fit_transform(X, y, sample_weight=sample_weight, metadata=metadata)
 
 
-@pytest.mark.usefixtures("enable_slep006")
 @pytest.mark.parametrize("method", ["transform", "fit_transform", "fit"])
+@config_context(enable_metadata_routing=True)
 def test_metadata_routing_error_for_column_transformer(method):
     """Test that the right error is raised when metadata is not requested."""
     X = np.array([[0, 1, 2], [2, 4, 6]]).T
@@ -2778,15 +2779,15 @@ def test_metadata_routing_error_for_column_transformer(method):
             getattr(trs, method)(X, y, sample_weight=sample_weight, metadata=metadata)
 
 
-@pytest.mark.usefixtures("enable_slep006")
+@config_context(enable_metadata_routing=True)
 def test_get_metadata_routing_works_without_fit():
     # Regression test for https://github.com/scikit-learn/scikit-learn/issues/28186
     # Make sure ct.get_metadata_routing() works w/o having called fit.
     ct = ColumnTransformer([("trans", ConsumingTransformer(), [0])])
     ct.get_metadata_routing()
 
 
-@pytest.mark.usefixtures("enable_slep006")
+@config_context(enable_metadata_routing=True)
 def test_remainder_request_always_present():
     # Test that remainder request is always present.
     ct = ColumnTransformer(
@@ -2799,7 +2800,7 @@ def test_remainder_request_always_present():
     assert router.consumes("fit", ["metadata"]) == set(["metadata"])
 
 
-@pytest.mark.usefixtures("enable_slep006")
+@config_context(enable_metadata_routing=True)
 def test_unused_transformer_request_present():
     # Test that the request of a transformer is always present even when not
     # used due to no selected columns.

sklearn/conftest.py

@@ -15,7 +15,7 @@
 from _pytest.doctest import DoctestItem
 from threadpoolctl import threadpool_limits
 
-from sklearn import config_context, set_config
+from sklearn import set_config
 from sklearn._min_dependencies import PYTEST_MIN_VERSION
 from sklearn.datasets import (
     fetch_20newsgroups,
@@ -46,13 +46,6 @@
 scipy_datasets_require_network = sp_version >= parse_version("1.10")
 
 
-@pytest.fixture
-def enable_slep006():
-    """Enable SLEP006 for all tests."""
-    with config_context(enable_metadata_routing=True):
-        yield
-
-
 def raccoon_face_or_skip():
     # SciPy >= 1.10 requires network to access to get data
     if scipy_datasets_require_network:

sklearn/covariance/tests/test_graphical_lasso.py

@@ -8,7 +8,7 @@
 from numpy.testing import assert_allclose
 from scipy import linalg
 
-from sklearn import datasets
+from sklearn import config_context, datasets
 from sklearn.covariance import (
     GraphicalLasso,
     GraphicalLassoCV,
@@ -263,7 +263,7 @@ def test_graphical_lasso_cv_scores():
     )
 
 
-@pytest.mark.usefixtures("enable_slep006")
+@config_context(enable_metadata_routing=True)
 def test_graphical_lasso_cv_scores_with_routing(global_random_seed):
     """Check that `GraphicalLassoCV` internally dispatches metadata to
     the splitter.

sklearn/ensemble/tests/test_stacking.py

@@ -11,6 +11,7 @@
 from numpy.testing import assert_array_equal
 from scipy import sparse
 
+from sklearn import config_context
 from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin, clone
 from sklearn.datasets import (
     load_breast_cancer,
@@ -920,21 +921,20 @@ def test_routing_passed_metadata_not_supported(Estimator, Child):
         )
 
 
-@pytest.mark.usefixtures("enable_slep006")
 @pytest.mark.parametrize(
     "Estimator, Child",
     [
         (StackingClassifier, ConsumingClassifier),
         (StackingRegressor, ConsumingRegressor),
     ],
 )
+@config_context(enable_metadata_routing=True)
 def test_get_metadata_routing_without_fit(Estimator, Child):
     # Test that metadata_routing() doesn't raise when called before fit.
     est = Estimator([("sub_est", Child())])
     est.get_metadata_routing()
 
 
-@pytest.mark.usefixtures("enable_slep006")
 @pytest.mark.parametrize(
     "Estimator, Child",
     [
@@ -945,6 +945,7 @@ def test_get_metadata_routing_without_fit(Estimator, Child):
 @pytest.mark.parametrize(
     "prop, prop_value", [("sample_weight", np.ones(X_iris.shape[0])), ("metadata", "a")]
 )
+@config_context(enable_metadata_routing=True)
 def test_metadata_routing_for_stacking_estimators(Estimator, Child, prop, prop_value):
     """Test that metadata is routed correctly for Stacking*."""
 
@@ -991,14 +992,14 @@ def test_metadata_routing_for_stacking_estimators(Estimator, Child, prop, prop_v
     )
 
 
-@pytest.mark.usefixtures("enable_slep006")
 @pytest.mark.parametrize(
     "Estimator, Child",
     [
         (StackingClassifier, ConsumingClassifier),
         (StackingRegressor, ConsumingRegressor),
     ],
 )
+@config_context(enable_metadata_routing=True)
 def test_metadata_routing_error_for_stacking_estimators(Estimator, Child):
     """Test that the right error is raised when metadata is not requested."""
     sample_weight, metadata = np.ones(X_iris.shape[0]), "a"

sklearn/ensemble/tests/test_voting.py

@@ -5,7 +5,7 @@
 import numpy as np
 import pytest
 
-from sklearn import datasets
+from sklearn import config_context, datasets
 from sklearn.base import BaseEstimator, ClassifierMixin, clone
 from sklearn.datasets import make_multilabel_classification
 from sklearn.dummy import DummyRegressor
@@ -606,8 +606,7 @@ def test_voting_verbose(estimator, capsys):
         r"\[Voting\].*\(1 of 2\) Processing lr, total=.*\n"
         r"\[Voting\].*\(2 of 2\) Processing rf, total=.*\n$"
     )
-
-    estimator.fit(X, y)
+    clone(estimator).fit(X, y)
     assert re.match(pattern, capsys.readouterr()[0])
 
 
@@ -712,23 +711,23 @@ def test_routing_passed_metadata_not_supported(Estimator, Child):
         Estimator(["clf", Child()]).fit(X, y, sample_weight=[1, 1, 1], metadata="a")
 
 
-@pytest.mark.usefixtures("enable_slep006")
 @pytest.mark.parametrize(
     "Estimator, Child",
     [(VotingClassifier, ConsumingClassifier), (VotingRegressor, ConsumingRegressor)],
 )
+@config_context(enable_metadata_routing=True)
 def test_get_metadata_routing_without_fit(Estimator, Child):
     # Test that metadata_routing() doesn't raise when called before fit.
     est = Estimator([("sub_est", Child())])
     est.get_metadata_routing()
 
 
-@pytest.mark.usefixtures("enable_slep006")
 @pytest.mark.parametrize(
     "Estimator, Child",
     [(VotingClassifier, ConsumingClassifier), (VotingRegressor, ConsumingRegressor)],
 )
 @pytest.mark.parametrize("prop", ["sample_weight", "metadata"])
+@config_context(enable_metadata_routing=True)
 def test_metadata_routing_for_voting_estimators(Estimator, Child, prop):
     """Test that metadata is routed correctly for Voting*."""
     X = np.array([[0, 1], [2, 2], [4, 6]])
@@ -762,11 +761,11 @@ def test_metadata_routing_for_voting_estimators(Estimator, Child, prop):
             check_recorded_metadata(obj=sub_est, method="fit", parent="fit", **kwargs)
 
 
-@pytest.mark.usefixtures("enable_slep006")
 @pytest.mark.parametrize(
     "Estimator, Child",
     [(VotingClassifier, ConsumingClassifier), (VotingRegressor, ConsumingRegressor)],
 )
+@config_context(enable_metadata_routing=True)
 def test_metadata_routing_error_for_voting_estimators(Estimator, Child):
     """Test that the right error is raised when metadata is not requested."""
     X = np.array([[0, 1], [2, 2], [4, 6]])

sklearn/linear_model/tests/test_coordinate_descent.py

@@ -9,7 +9,7 @@
 import pytest
 from scipy import interpolate, sparse
 
-from sklearn.base import clone, is_classifier
+from sklearn.base import clone, config_context, is_classifier
 from sklearn.datasets import load_diabetes, make_regression
 from sklearn.exceptions import ConvergenceWarning
 from sklearn.linear_model import (
@@ -1638,11 +1638,11 @@ def test_cv_estimators_reject_params_with_no_routing_enabled(EstimatorCV):
         estimator.fit(X, y, groups=groups)
 
 
-@pytest.mark.usefixtures("enable_slep006")
 @pytest.mark.parametrize(
     "MultiTaskEstimatorCV",
     [MultiTaskElasticNetCV, MultiTaskLassoCV],
 )
+@config_context(enable_metadata_routing=True)
 def test_multitask_cv_estimators_with_sample_weight(MultiTaskEstimatorCV):
     """Check that for :class:`MultiTaskElasticNetCV` and
     class:`MultiTaskLassoCV` if `sample_weight` is passed and the

sklearn/linear_model/tests/test_logistic.py

@@ -2163,7 +2163,7 @@ def test_liblinear_not_stuck():
         clf.fit(X_prep, y)
 
 
-@pytest.mark.usefixtures("enable_slep006")
+@config_context(enable_metadata_routing=True)
 def test_lr_cv_scores_differ_when_sample_weight_is_requested():
     """Test that `sample_weight` is correctly passed to the scorer in
     `LogisticRegressionCV.fit` and `LogisticRegressionCV.score` by

sklearn/linear_model/tests/test_ridge.py

@@ -2360,23 +2360,23 @@ def custom_multioutput_scorer(estimator, X, y):
 # ======================
 
 
-@pytest.mark.usefixtures("enable_slep006")
 @pytest.mark.parametrize("metaestimator", [RidgeCV, RidgeClassifierCV])
+@config_context(enable_metadata_routing=True)
 def test_metadata_routing_with_default_scoring(metaestimator):
     """Test that `RidgeCV` or `RidgeClassifierCV` with default `scoring`
     argument (`None`), don't enter into `RecursionError` when metadata is routed.
     """
     metaestimator().get_metadata_routing()
 
 
-@pytest.mark.usefixtures("enable_slep006")
 @pytest.mark.parametrize(
     "metaestimator, make_dataset",
     [
         (RidgeCV(), make_regression),
         (RidgeClassifierCV(), make_classification),
     ],
 )
+@config_context(enable_metadata_routing=True)
 def test_set_score_request_with_default_scoring(metaestimator, make_dataset):
     """Test that `set_score_request` is set within `RidgeCV.fit()` and
     `RidgeClassifierCV.fit()` when using the default scoring and no

sklearn/metrics/tests/test_score_objects.py

@@ -1211,8 +1211,8 @@ def test_scorer_set_score_request_raises(name):
         scorer.set_score_request()
 
 
-@pytest.mark.usefixtures("enable_slep006")
 @pytest.mark.parametrize("name", get_scorer_names(), ids=get_scorer_names())
+@config_context(enable_metadata_routing=True)
 def test_scorer_metadata_request(name):
     """Testing metadata requests for scorers.
 
@@ -1262,7 +1262,7 @@ def test_scorer_metadata_request(name):
     assert list(routed_params.scorer.score.keys()) == ["sample_weight"]
 
 
-@pytest.mark.usefixtures("enable_slep006")
+@config_context(enable_metadata_routing=True)
 def test_metadata_kwarg_conflict():
     """This test makes sure the right warning is raised if the user passes
     some metadata both as a constructor to make_scorer, and during __call__.
@@ -1285,7 +1285,7 @@ def test_metadata_kwarg_conflict():
         scorer(lr, X, y, labels=lr.classes_)
 
 
-@pytest.mark.usefixtures("enable_slep006")
+@config_context(enable_metadata_routing=True)
 def test_PassthroughScorer_set_score_request():
     """Test that _PassthroughScorer.set_score_request adds the correct metadata request
     on itself and doesn't change its estimator's routing."""
@@ -1320,7 +1320,7 @@ def test_PassthroughScorer_set_score_request_raises_without_routing_enabled():
         scorer.set_score_request(sample_weight="my_weights")
 
 
-@pytest.mark.usefixtures("enable_slep006")
+@config_context(enable_metadata_routing=True)
 def test_multimetric_scoring_metadata_routing():
     # Test that _MultimetricScorer properly routes metadata.
     def score1(y_true, y_pred):

sklearn/model_selection/tests/test_classification_threshold.py

@@ -1,6 +1,7 @@
 import numpy as np
 import pytest
 
+from sklearn import config_context
 from sklearn.base import BaseEstimator, ClassifierMixin, clone
 from sklearn.datasets import (
     load_breast_cancer,
@@ -101,7 +102,7 @@ def test_fit_and_score_over_thresholds_prefit():
     assert_allclose(scores, [0.5, 1.0])
 
 
-@pytest.mark.usefixtures("enable_slep006")
+@config_context(enable_metadata_routing=True)
 def test_fit_and_score_over_thresholds_sample_weight():
     """Check that we dispatch the sample-weight to fit and score the classifier."""
     X, y = load_iris(return_X_y=True)
@@ -150,8 +151,8 @@ def test_fit_and_score_over_thresholds_sample_weight():
     assert_allclose(scores_repeated, scores)
 
 
-@pytest.mark.usefixtures("enable_slep006")
 @pytest.mark.parametrize("fit_params_type", ["list", "array"])
+@config_context(enable_metadata_routing=True)
 def test_fit_and_score_over_thresholds_fit_params(fit_params_type):
     """Check that we pass `fit_params` to the classifier when calling `fit`."""
     X, y = make_classification(n_samples=100, random_state=0)
@@ -344,8 +345,8 @@ def test_tuned_threshold_classifier_with_string_targets(response_method, metric)
     assert_array_equal(np.unique(y_pred), np.sort(classes))
 
 
-@pytest.mark.usefixtures("enable_slep006")
 @pytest.mark.parametrize("with_sample_weight", [True, False])
+@config_context(enable_metadata_routing=True)
 def test_tuned_threshold_classifier_refit(with_sample_weight, global_random_seed):
     """Check the behaviour of the `refit` parameter."""
     rng = np.random.RandomState(global_random_seed)
@@ -396,8 +397,8 @@ def test_tuned_threshold_classifier_refit(with_sample_weight, global_random_seed
     assert_allclose(model.estimator_.coef_, estimator.coef_)
 
 
-@pytest.mark.usefixtures("enable_slep006")
 @pytest.mark.parametrize("fit_params_type", ["list", "array"])
+@config_context(enable_metadata_routing=True)
 def test_tuned_threshold_classifier_fit_params(fit_params_type):
     """Check that we pass `fit_params` to the classifier when calling `fit`."""
     X, y = make_classification(n_samples=100, random_state=0)
@@ -412,7 +413,7 @@ def test_tuned_threshold_classifier_fit_params(fit_params_type):
     model.fit(X, y, **fit_params)
 
 
-@pytest.mark.usefixtures("enable_slep006")
+@config_context(enable_metadata_routing=True)
 def test_tuned_threshold_classifier_cv_zeros_sample_weights_equivalence():
     """Check that passing removing some sample from the dataset `X` is
     equivalent to passing a `sample_weight` with a factor 0."""
@@ -579,7 +580,7 @@ def test_fixed_threshold_classifier(response_method, threshold, pos_label):
         )
 
 
-@pytest.mark.usefixtures("enable_slep006")
+@config_context(enable_metadata_routing=True)
 def test_fixed_threshold_classifier_metadata_routing():
     """Check that everything works with metadata routing."""
     X, y = make_classification(random_state=0)