8000 TST Adds numpydoc tests for functions (#21245) · samronsin/scikit-learn@be40443 · GitHub
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TST Adds numpydoc tests for functions (scikit-learn#21245)
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‎maint_tools/test_docstrings.py

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Original file line numberDiff line numberDiff line change
@@ -1,9 +1,13 @@
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import re
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from inspect import signature
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import pkgutil
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import inspect
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import importlib
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from typing import Optional
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58
import pytest
69
from sklearn.utils import all_estimators
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import sklearn
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numpydoc_validation = pytest.importorskip("numpydoc.validate")
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@@ -30,6 +34,237 @@
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"TransformedTargetRegressor",
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]
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FUNCTION_DOCSTRING_IGNORE_LIST = [
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"sklearn._config.config_context",
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"sklearn._config.get_config",
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"sklearn.base.clone",
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"sklearn.cluster._affinity_propagation.affinity_propagation",
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"sklearn.cluster._agglomerative.linkage_tree",
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"sklearn.cluster._kmeans.k_means",
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"sklearn.cluster._kmeans.kmeans_plusplus",
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"sklearn.cluster._mean_shift.estimate_bandwidth",
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"sklearn.cluster._mean_shift.get_bin_seeds",
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"sklearn.cluster._mean_shift.mean_shift",
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"sklearn.cluster._optics.cluster_optics_dbscan",
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"sklearn.cluster._optics.cluster_optics_xi",
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"sklearn.cluster._optics.compute_optics_graph",
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"sklearn.cluster._spectral.spectral_clustering",
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"sklearn.compose._column_transformer.make_column_transformer",
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"sklearn.covariance._empirical_covariance.empirical_covariance",
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"sklearn.covariance._empirical_covariance.log_likelihood",
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"sklearn.covariance._graph_lasso.graphical_lasso",
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"sklearn.covariance._robust_covariance.fast_mcd",
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"sklearn.covariance._shrunk_covariance.ledoit_wolf",
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"sklearn.covariance._shrunk_covariance.ledoit_wolf_shrinkage",
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"sklearn.covariance._shrunk_covariance.shrunk_covariance",
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"sklearn.datasets._base.get_data_home",
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"sklearn.datasets._base.load_boston",
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"sklearn.datasets._base.load_breast_cancer",
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"sklearn.datasets._base.load_diabetes",
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"sklearn.datasets._base.load_digits",
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"sklearn.datasets._base.load_files",
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"sklearn.datasets._base.load_iris",
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"sklearn.datasets._base.load_linnerud",
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"sklearn.datasets._base.load_sample_image",
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"sklearn.datasets._base.load_wine",
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"sklearn.datasets._california_housing.fetch_california_housing",
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"sklearn.datasets._covtype.fetch_covtype",
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"sklearn.datasets._kddcup99.fetch_kddcup99",
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"sklearn.datasets._lfw.fetch_lfw_pairs",
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"sklearn.datasets._lfw.fetch_lfw_people",
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"sklearn.datasets._olivetti_faces.fetch_olivetti_faces",
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"sklearn.datasets._openml.fetch_openml",
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"sklearn.datasets._rcv1.fetch_rcv1",
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"sklearn.datasets._samples_generator.make_biclusters",
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"sklearn.datasets._samples_generator.make_blobs",
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"sklearn.datasets._samples_generator.make_checkerboard",
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"sklearn.datasets._samples_generator.make_classification",
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"sklearn.datasets._samples_generator.make_gaussian_quantiles",
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"sklearn.datasets._samples_generator.make_hastie_10_2",
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"sklearn.datasets._samples_generator.make_multilabel_classification",
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"sklearn.datasets._samples_generator.make_regression",
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"sklearn.datasets._samples_generator.make_sparse_coded_signal",
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"sklearn.datasets._samples_generator.make_sparse_spd_matrix",
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"sklearn.datasets._samples_generator.make_spd_matrix",
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"sklearn.datasets._species_distributions.fetch_species_distributions",
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"sklearn.datasets._svmlight_format_io.dump_svmlight_file",
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"sklearn.datasets._svmlight_format_io.load_svmlight_file",
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"sklearn.datasets._svmlight_format_io.load_svmlight_files",
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"sklearn.datasets._twenty_newsgroups.fetch_20newsgroups",
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"sklearn.decomposition._dict_learning.dict_learning",
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"sklearn.decomposition._dict_learning.dict_learning_online",
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"sklearn.decomposition._dict_learning.sparse_encode",
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"sklearn.decomposition._fastica.fastica",
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"sklearn.decomposition._nmf.non_negative_factorization",
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"sklearn.externals._packaging.version.parse",
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"sklearn.feature_extraction.image.extract_patches_2d",
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"sklearn.feature_extraction.image.grid_to_graph",
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"sklearn.feature_extraction.image.img_to_graph",
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"sklearn.feature_extraction.text.strip_accents_ascii",
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"sklearn.feature_extraction.text.strip_accents_unicode",
105+< B41A div class="diff-text-inner"> "sklearn.feature_extraction.text.strip_tags",
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"sklearn.feature_selection._univariate_selection.chi2",
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"sklearn.feature_selection._univariate_selection.f_oneway",
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"sklearn.feature_selection._univariate_selection.r_regression",
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"sklearn.inspection._partial_dependence.partial_dependence",
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"sklearn.inspection._plot.partial_dependence.plot_partial_dependence",
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"sklearn.isotonic.isotonic_regression",
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"sklearn.linear_model._least_angle.lars_path",
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"sklearn.linear_model._least_angle.lars_path_gram",
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"sklearn.linear_model._omp.orthogonal_mp",
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"sklearn.linear_model._omp.orthogonal_mp_gram",
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"sklearn.linear_model._ridge.ridge_regression",
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"sklearn.manifold._locally_linear.locally_linear_embedding",
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"sklearn.manifold._t_sne.trustworthiness",
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"sklearn.metrics._classification.accuracy_score",
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"sklearn.metrics._classification.balanced_accuracy_score",
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"sklearn.metrics._classification.brier_score_loss",
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"sklearn.metrics._classification.classification_report",
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"sklearn.metrics._classification.cohen_kappa_score",
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"sklearn.metrics._classification.confusion_matrix",
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"sklearn.metrics._classification.f1_score",
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"sklearn.metrics._classification.fbeta_score",
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"sklearn.metrics._classification.hamming_loss",
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"sklearn.metrics._classification.hinge_loss",
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"sklearn.metrics._classification.jaccard_score",
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"sklearn.metrics._classification.log_loss",
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"sklearn.metrics._classification.precision_recall_fscore_support",
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"sklearn.metrics._classification.precision_score",
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"sklearn.metrics._classification.recall_score",
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"sklearn.metrics._classification.zero_one_loss",
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"sklearn.metrics._plot.confusion_matrix.plot_confusion_matrix",
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"sklearn.metrics._plot.det_curve.plot_det_curve",
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"sklearn.metrics._plot.precision_recall_curve.plot_precision_recall_curve",
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"sklearn.metrics._plot.roc_curve.plot_roc_curve",
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"sklearn.metrics._ranking.auc",
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"sklearn.metrics._ranking.average_precision_score",
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"sklearn.metrics._ranking.coverage_error",
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"sklearn.metrics._ranking.dcg_score",
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"sklearn.metrics._ranking.label_ranking_average_precision_score",
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"sklearn.metrics._ranking.label_ranking_loss",
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"sklearn.metrics._ranking.ndcg_score",
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"sklearn.metrics._ranking.precision_recall_curve",
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"sklearn.metrics._ranking.roc_auc_score",
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"sklearn.metrics._ranking.roc_curve",
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"sklearn.metrics._ranking.top_k_accuracy_score",
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"sklearn.metrics._regression.max_error",
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"sklearn.metrics._regression.mean_absolute_error",
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"sklearn.metrics._regression.mean_pinball_loss",
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"sklearn.metrics._scorer.make_scorer",
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"sklearn.metrics.cluster._bicluster.consensus_score",
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"sklearn.metrics.cluster._supervised.adjusted_mutual_info_score",
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"sklearn.metrics.cluster._supervised.adjusted_rand_score",
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"sklearn.metrics.cluster._supervised.completeness_score",
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"sklearn.metrics.cluster._supervised.entropy",
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"sklearn.metrics.cluster._supervised.fowlkes_mallows_score",
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"sklearn.metrics.cluster._supervised.homogeneity_completeness_v_measure",
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"sklearn.metrics.cluster._supervised.homogeneity_score",
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"sklearn.metrics.cluster._supervised.mutual_info_score",
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"sklearn.metrics.cluster._supervised.normalized_mutual_info_score",
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"sklearn.metrics.cluster._supervised.pair_confusion_matrix",
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"sklearn.metrics.cluster._supervised.rand_score",
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"sklearn.metrics.cluster._supervised.v_measure_score",
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"sklearn.metrics.cluster._unsupervised.davies_bouldin_score",
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"sklearn.metrics.cluster._unsupervised.silhouette_samples",
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"sklearn.metrics.cluster._unsupervised.silhouette_score",
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"sklearn.metrics.pairwise.additive_chi2_kernel",
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"sklearn.metrics.pairwise.check_paired_arrays",
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"sklearn.metrics.pairwise.check_pairwise_arrays",
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"sklearn.metrics.pairwise.chi2_kernel",
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"sklearn.metrics.pairwise.cosine_distances",
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"sklearn.metrics.pairwise.cosine_similarity",
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"sklearn.metrics.pairwise.distance_metrics",
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"sklearn.metrics.pairwise.euclidean_distances",
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"sklearn.metrics.pairwise.haversine_distances",
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"sklearn.metrics.pairwise.kernel_metrics",
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"sklearn.metrics.pairwise.laplacian_kernel",
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"sklearn.metrics.pairwise.linear_kernel",
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"sklearn.metrics.pairwise.manhattan_distances",
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"sklearn.metrics.pairwise.nan_euclidean_distances",
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"sklearn.metrics.pairwise.paired_cosine_distances",
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"sklearn.metrics.pairwise.paired_distances",
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"sklearn.metrics.pairwise.paired_euclidean_distances",
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"sklearn.metrics.pairwise.paired_manhattan_distances",
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"sklearn.metrics.pairwise.pairwise_distances_argmin",
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"sklearn.metrics.pairwise.pairwise_distances_argmin_min",
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"sklearn.metrics.pairwise.pairwise_distances_chunked",
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"sklearn.metrics.pairwise.pairwise_kernels",
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"sklearn.metrics.pairwise.polynomial_kernel",
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"sklearn.metrics.pairwise.rbf_kernel",
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"sklearn.metrics.pairwise.sigmoid_kernel",
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"sklearn.model_selection._split.check_cv",
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"sklearn.model_selection._split.train_test_split",
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"sklearn.model_selection._validation.cross_val_predict",
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"sklearn.model_selection._validation.cross_val_score",
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"sklearn.model_selection._validation.cross_validate",
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"sklearn.model_selection._validation.learning_curve",
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"sklearn.model_selection._validation.permutation_test_score",
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"sklearn.model_selection._validation.validation_curve",
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"sklearn.neighbors._graph.kneighbors_graph",
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"sklearn.neighbors._graph.radius_neighbors_graph",
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"sklearn.pipeline.make_union",
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"sklearn.preprocessing._data.binarize",
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"sklearn.preprocessing._data.maxabs_scale",
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"sklearn.preprocessing._data.normalize",
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"sklearn.preprocessing._data.power_transform",
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"sklearn.preprocessing._data.quantile_transform",
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"sklearn.preprocessing._data.robust_scale",
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"sklearn.preprocessing._data.scale",
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"sklearn.preprocessing._label.label_binarize",
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"sklearn.random_projection.johnson_lindenstrauss_min_dim",
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"sklearn.svm._bounds.l1_min_c",
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"sklearn.tree._export.plot_tree",
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"sklearn.utils.axis0_safe_slice",
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"sklearn.utils.check_pandas_support",
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"sklearn.utils.extmath.cartesian",
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"sklearn.utils.extmath.density",
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"sklearn.utils.extmath.fast_logdet",
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"sklearn.utils.extmath.randomized_range_finder",
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"sklearn.utils.extmath.randomized_svd",
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"sklearn.utils.extmath.safe_sparse_dot",
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"sklearn.utils.extmath.squared_norm",
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"sklearn.utils.extmath.stable_cumsum",
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"sklearn.utils.extmath.svd_flip",
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"sklearn.utils.extmath.weighted_mode",
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"sklearn.utils.fixes.delayed",
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"sklearn.utils.fixes.linspace",
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"sklearn.utils.gen_batches",
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"sklearn.utils.gen_even_slices",
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"sklearn.utils.get_chunk_n_rows",
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"sklearn.utils.graph.graph_shortest_path",
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"sklearn.utils.graph.single_source_shortest_path_length",
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"sklearn.utils.is_scalar_nan",
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"sklearn.utils.metaestimators.available_if",
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"sklearn.utils.metaestimators.if_delegate_has_method",
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"sklearn.utils.multiclass.check_classification_targets",
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"sklearn.utils.multiclass.class_distribution",
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"sklearn.utils.multiclass.type_of_target",
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"sklearn.utils.multiclass.unique_labels",
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"sklearn.utils.resample",
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"sklearn.utils.safe_mask",
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"sklearn.utils.safe_sqr",
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"sklearn.utils.shuffle",
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"sklearn.utils.sparsefuncs.count_nonzero",
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"sklearn.utils.sparsefuncs.csc_median_axis_0",
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"sklearn.utils.sparsefuncs.incr_mean_variance_axis",
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"sklearn.utils.sparsefuncs.inplace_swap_column",
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"sklearn.utils.sparsefuncs.inplace_swap_row",
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"sklearn.utils.sparsefuncs.inplace_swap_row_csc",
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"sklearn.utils.sparsefuncs.inplace_swap_row_csr",
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"sklearn.utils.sparsefuncs.mean_variance_axis",
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"sklearn.utils.sparsefuncs.min_max_axis",
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"sklearn.utils.tosequence",
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"sklearn.utils.validation.as_float_array",
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"sklearn.utils.validation.assert_all_finite",
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"sklearn.utils.validation.check_is_fitted",
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"sklearn.utils.validation.check_memory",
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"sklearn.utils.validation.check_random_state",
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"sklearn.utils.validation.column_or_1d",
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"sklearn.utils.validation.has_fit_parameter",
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"sklearn.utils.validation.indexable",
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]
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FUNCTION_DOCSTRING_IGNORE_LIST = set(FUNCTION_DOCSTRING_IGNORE_LIST)
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def get_all_methods():
35270
estimators = all_estimators()
@@ -50,6 +285,51 @@ def get_all_methods():
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yield Estimator, method
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def _is_checked_function(item):
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if not inspect.isfunction(item):
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return False
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if item.__name__.startswith("_"):
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return False
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mod = item.__module__
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if not mod.startswith("sklearn.") or mod.endswith("estimator_checks"):
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return False
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return True
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def get_all_functions_names():
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"""Get all public functions define in the sklearn module"""
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modules_to_ignore = {
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"tests",
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"externals",
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"setup",
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"conftest",
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"experimental",
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"estimator_checks",
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}
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all_functions_names = set()
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for module_finder, module_name, ispkg in pkgutil.walk_packages(
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path=sklearn.__path__, prefix="sklearn."
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):
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module_parts = module_name.split(".")
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if (
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any(part in modules_to_ignore for part in module_parts)
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or "._" in module_name
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):
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continue
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module = importlib.import_module(module_name)
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functions = inspect.getmembers(module, _is_checked_function)
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for name, func in functions:
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full_name = f"{func.__module__}.{func.__name__}"
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all_functions_names.add(full_name)
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return sorted(all_functions_names)
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def filter_errors(errors, method, Estimator=None):
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"""
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Ignore some errors based on the method type.
@@ -144,6 +424,24 @@ def repr_errors(res, estimator=None, method: Optional[str] = None) -> str:
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return msg
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@pytest.mark.parametrize("function_name", get_all_functions_names())
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def test_function_docstring(function_name, request):
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"""Check function docstrings using numpydoc."""
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if function_name in FUNCTION_DOCSTRING_IGNORE_LIST:
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request.applymarker(
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pytest.mark.xfail(run=False, reason="TODO pass numpydoc validation")
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)
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res = numpydoc_validation.validate(function_name)
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res["errors"] = list(filter_errors(res["errors"], method="function"))
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if res["errors"]:
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msg = repr_errors(res, method=f"Tested function: {function_name}")
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raise ValueError(msg)
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@pytest.mark.parametrize("Estimator, method", get_all_methods())
148446
def test_docstring(Estimator, method, request):
149447
base_import_path = Estimator.__module__

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