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sklearn/tests/test_public_functions.py
405 строк
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Lucy Liu
FEA Add metric_at_thresholds (#32732)
07 мар 2026, 08:58
Не верифицирован
07 мар 2026, 08:58
3acced3
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from importlib import import_module from inspect import signature from numbers import Integral, Real import pytest from sklearn.utils._param_validation import ( Interval, InvalidParameterError, generate_invalid_param_val, generate_valid_param, make_constraint, ) def _get_func_info(func_module): module_name, func_name = func_module.rsplit(".", 1) module = import_module(module_name) func = getattr(module, func_name) func_sig = signature(func) func_params = [ p.name for p in func_sig.parameters.values() if p.kind not in (p.VAR_POSITIONAL, p.VAR_KEYWORD) ] # The parameters `*args` and `**kwargs` are ignored since we cannot generate # constraints. required_params = [ p.name for p in func_sig.parameters.values() if p.default is p.empty and p.kind not in (p.VAR_POSITIONAL, p.VAR_KEYWORD) ] return func, func_name, func_params, required_params def _check_function_param_validation( func, func_name, func_params, required_params, parameter_constraints ): """Check that an informative error is raised when the value of a parameter does not have an appropriate type or value. """ # generate valid values for the required parameters valid_required_params = {} for param_name in required_params: if parameter_constraints[param_name] == "no_validation": valid_required_params[param_name] = 1 else: valid_required_params[param_name] = generate_valid_param( make_constraint(parameter_constraints[param_name][0]) ) # check that there is a constraint for each parameter if func_params: validation_params = parameter_constraints.keys() unexpected_params = set(validation_params) - set(func_params) missing_params = set(func_params) - set(validation_params) err_msg = ( "Mismatch between _parameter_constraints and the parameters of" f" {func_name}.\nConsider the unexpected parameters {unexpected_params} and" f" expected but missing parameters {missing_params}\n" ) assert set(validation_params) == set(func_params), err_msg # this object does not have a valid type for sure for all params param_with_bad_type = type("BadType", (), {})() for param_name in func_params: constraints = parameter_constraints[param_name] if constraints == "no_validation": # This parameter is not validated continue # Mixing an interval of reals and an interval of integers must be avoided. if any( isinstance(constraint, Interval) and constraint.type == Integral for constraint in constraints ) and any( isinstance(constraint, Interval) and constraint.type == Real for constraint in constraints ): raise ValueError( f"The constraint for parameter {param_name} of {func_name} can't have a" " mix of intervals of Integral and Real types. Use the type" " RealNotInt instead of Real." ) match = ( rf"The '{param_name}' parameter of {func_name} must be .* Got .* instead." ) err_msg = ( f"{func_name} does not raise an informative error message when the " f"parameter {param_name} does not have a valid type. If any Python type " "is valid, the constraint should be 'no_validation'." ) # First, check that the error is raised if param doesn't match any valid type. with pytest.raises(InvalidParameterError, match=match): func(**{**valid_required_params, param_name: param_with_bad_type}) pytest.fail(err_msg) # Then, for constraints that are more than a type constraint, check that the # error is raised if param does match a valid type but does not match any valid # value for this type. constraints = [make_constraint(constraint) for constraint in constraints] for constraint in constraints: try: bad_value = generate_invalid_param_val(constraint) except NotImplementedError: continue err_msg = ( f"{func_name} does not raise an informative error message when the " f"parameter {param_name} does not have a valid value.\n" "Constraints should be disjoint. For instance " "[StrOptions({'a_string'}), str] is not an acceptable set of " "constraint because generating an invalid string for the first " "constraint will always produce a valid string for the second " "constraint." ) with pytest.raises(InvalidParameterError, match=match): func(**{**valid_required_params, param_name: bad_value}) pytest.fail(err_msg) PARAM_VALIDATION_FUNCTION_LIST = [ "sklearn.calibration.calibration_curve", "sklearn.cluster.cluster_optics_dbscan", "sklearn.cluster.compute_optics_graph", "sklearn.cluster.estimate_bandwidth", "sklearn.cluster.kmeans_plusplus", "sklearn.cluster.cluster_optics_xi", "sklearn.cluster.ward_tree", "sklearn.covariance.empirical_covariance", "sklearn.covariance.ledoit_wolf_shrinkage", "sklearn.covariance.log_likelihood", "sklearn.covariance.shrunk_covariance", "sklearn.datasets.clear_data_home", "sklearn.datasets.dump_svmlight_file", "sklearn.datasets.fetch_20newsgroups", "sklearn.datasets.fetch_20newsgroups_vectorized", "sklearn.datasets.fetch_california_housing", "sklearn.datasets.fetch_covtype", "sklearn.datasets.fetch_kddcup99", "sklearn.datasets.fetch_lfw_pairs", "sklearn.datasets.fetch_lfw_people", "sklearn.datasets.fetch_olivetti_faces", "sklearn.datasets.fetch_rcv1", "sklearn.datasets.fetch_openml", "sklearn.datasets.fetch_species_distributions", "sklearn.datasets.get_data_home", "sklearn.datasets.load_breast_cancer", "sklearn.datasets.load_diabetes", "sklearn.datasets.load_digits", "sklearn.datasets.load_files", "sklearn.datasets.load_iris", "sklearn.datasets.load_linnerud", "sklearn.datasets.load_sample_image", "sklearn.datasets.load_svmlight_file", "sklearn.datasets.load_svmlight_files", "sklearn.datasets.load_wine", "sklearn.datasets.make_biclusters", "sklearn.datasets.make_blobs", "sklearn.datasets.make_checkerboard", "sklearn.datasets.make_circles", "sklearn.datasets.make_classification", "sklearn.datasets.make_friedman1", "sklearn.datasets.make_friedman2", "sklearn.datasets.make_friedman3", "sklearn.datasets.make_gaussian_quantiles", "sklearn.datasets.make_hastie_10_2", "sklearn.datasets.make_low_rank_matrix", "sklearn.datasets.make_moons", "sklearn.datasets.make_multilabel_classification", "sklearn.datasets.make_regression", "sklearn.datasets.make_s_curve", "sklearn.datasets.make_sparse_coded_signal", "sklearn.datasets.make_sparse_spd_matrix", "sklearn.datasets.make_sparse_uncorrelated", "sklearn.datasets.make_spd_matrix", "sklearn.datasets.make_swiss_roll", "sklearn.decomposition.sparse_encode", "sklearn.feature_extraction.grid_to_graph", "sklearn.feature_extraction.img_to_graph", "sklearn.feature_extraction.image.extract_patches_2d", "sklearn.feature_extraction.image.reconstruct_from_patches_2d", "sklearn.feature_selection.chi2", "sklearn.feature_selection.f_classif", "sklearn.feature_selection.f_regression", "sklearn.feature_selection.mutual_info_classif", "sklearn.feature_selection.mutual_info_regression", "sklearn.feature_selection.r_regression", "sklearn.inspection.partial_dependence", "sklearn.inspection.permutation_importance", "sklearn.isotonic.check_increasing", "sklearn.isotonic.isotonic_regression", "sklearn.linear_model.enet_path", "sklearn.linear_model.lars_path", "sklearn.linear_model.lars_path_gram", "sklearn.linear_model.lasso_path", "sklearn.linear_model.orthogonal_mp", "sklearn.linear_model.orthogonal_mp_gram", "sklearn.linear_model.ridge_regression", "sklearn.manifold.locally_linear_embedding", "sklearn.manifold.smacof", "sklearn.manifold.spectral_embedding", "sklearn.manifold.trustworthiness", "sklearn.metrics.accuracy_score", "sklearn.metrics.auc", "sklearn.metrics.average_precision_score", "sklearn.metrics.balanced_accuracy_score", "sklearn.metrics.brier_score_loss", "sklearn.metrics.calinski_harabasz_score", "sklearn.metrics.check_scoring", "sklearn.metrics.completeness_score", "sklearn.metrics.class_likelihood_ratios", "sklearn.metrics.classification_report", "sklearn.metrics.cluster.adjusted_mutual_info_score", "sklearn.metrics.cluster.contingency_matrix", "sklearn.metrics.cluster.fowlkes_mallows_score", "sklearn.metrics.cluster.homogeneity_completeness_v_measure", "sklearn.metrics.cluster.normalized_mutual_info_score", "sklearn.metrics.cluster.silhouette_samples", "sklearn.metrics.cluster.silhouette_score", "sklearn.metrics.cohen_kappa_score", "sklearn.metrics.confusion_matrix", "sklearn.metrics.confusion_matrix_at_thresholds", "sklearn.metrics.consensus_score", "sklearn.metrics.coverage_error", "sklearn.metrics.d2_absolute_error_score", "sklearn.metrics.d2_brier_score", "sklearn.metrics.d2_log_loss_score", "sklearn.metrics.d2_pinball_score", "sklearn.metrics.d2_tweedie_score", "sklearn.metrics.davies_bouldin_score", "sklearn.metrics.dcg_score", "sklearn.metrics.det_curve", "sklearn.metrics.explained_variance_score", "sklearn.metrics.f1_score", "sklearn.metrics.fbeta_score", "sklearn.metrics.get_scorer", "sklearn.metrics.hamming_loss", "sklearn.metrics.hinge_loss", "sklearn.metrics.homogeneity_score", "sklearn.metrics.jaccard_score", "sklearn.metrics.label_ranking_average_precision_score", "sklearn.metrics.label_ranking_loss", "sklearn.metrics.log_loss", "sklearn.metrics.make_scorer", "sklearn.metrics.matthews_corrcoef", "sklearn.metrics.max_error", "sklearn.metrics.mean_absolute_error", "sklearn.metrics.mean_absolute_percentage_error", "sklearn.metrics.mean_gamma_deviance", "sklearn.metrics.mean_pinball_loss", "sklearn.metrics.mean_poisson_deviance", "sklearn.metrics.mean_squared_error", "sklearn.metrics.mean_squared_log_error", "sklearn.metrics.mean_tweedie_deviance", "sklearn.metrics.median_absolute_error", "sklearn.metrics.metric_at_thresholds", "sklearn.metrics.multilabel_confusion_matrix", "sklearn.metrics.mutual_info_score", "sklearn.metrics.ndcg_score", "sklearn.metrics.pair_confusion_matrix", "sklearn.metrics.adjusted_rand_score", "sklearn.metrics.pairwise.additive_chi2_kernel", "sklearn.metrics.pairwise.chi2_kernel", "sklearn.metrics.pairwise.cosine_distances", "sklearn.metrics.pairwise.cosine_similarity", "sklearn.metrics.pairwise.euclidean_distances", "sklearn.metrics.pairwise.haversine_distances", "sklearn.metrics.pairwise.laplacian_kernel", "sklearn.metrics.pairwise.linear_kernel", "sklearn.metrics.pairwise.manhattan_distances", "sklearn.metrics.pairwise.nan_euclidean_distances", "sklearn.metrics.pairwise.paired_cosine_distances", "sklearn.metrics.pairwise.paired_distances", "sklearn.metrics.pairwise.paired_euclidean_distances", "sklearn.metrics.pairwise.paired_manhattan_distances", "sklearn.metrics.pairwise.pairwise_distances_argmin_min", "sklearn.metrics.pairwise.pairwise_kernels", "sklearn.metrics.pairwise.polynomial_kernel", "sklearn.metrics.pairwise.rbf_kernel", "sklearn.metrics.pairwise.sigmoid_kernel", "sklearn.metrics.pairwise_distances", "sklearn.metrics.pairwise_distances_argmin", "sklearn.metrics.pairwise_distances_chunked", "sklearn.metrics.precision_recall_curve", "sklearn.metrics.precision_recall_fscore_support", "sklearn.metrics.precision_score", "sklearn.metrics.r2_score", "sklearn.metrics.rand_score", "sklearn.metrics.recall_score", "sklearn.metrics.roc_auc_score", "sklearn.metrics.roc_curve", "sklearn.metrics.root_mean_squared_error", "sklearn.metrics.root_mean_squared_log_error", "sklearn.metrics.top_k_accuracy_score", "sklearn.metrics.v_measure_score", "sklearn.metrics.zero_one_loss", "sklearn.model_selection.cross_val_predict", "sklearn.model_selection.cross_val_score", "sklearn.model_selection.cross_validate", "sklearn.model_selection.learning_curve", "sklearn.model_selection.permutation_test_score", "sklearn.model_selection.train_test_split", "sklearn.model_selection.validation_curve", "sklearn.neighbors.kneighbors_graph", "sklearn.neighbors.radius_neighbors_graph", "sklearn.neighbors.sort_graph_by_row_values", "sklearn.preprocessing.add_dummy_feature", "sklearn.preprocessing.binarize", "sklearn.preprocessing.label_binarize", "sklearn.preprocessing.normalize", "sklearn.preprocessing.scale", "sklearn.random_projection.johnson_lindenstrauss_min_dim", "sklearn.svm.l1_min_c", "sklearn.tree.export_graphviz", "sklearn.tree.export_text", "sklearn.tree.plot_tree", "sklearn.utils.gen_batches", "sklearn.utils.gen_even_slices", "sklearn.utils.resample", "sklearn.utils.safe_mask", "sklearn.utils.extmath.randomized_svd", "sklearn.utils.class_weight.compute_class_weight", "sklearn.utils.class_weight.compute_sample_weight", "sklearn.utils.graph.single_source_shortest_path_length", ] @pytest.mark.parametrize("func_module", PARAM_VALIDATION_FUNCTION_LIST) def test_function_param_validation(func_module): """Check param validation for public functions that are not wrappers around estimators. """ func, func_name, func_params, required_params = _get_func_info(func_module) parameter_constraints = getattr(func, "_skl_parameter_constraints") _check_function_param_validation( func, func_name, func_params, required_params, parameter_constraints ) PARAM_VALIDATION_CLASS_WRAPPER_LIST = [ ("sklearn.cluster.affinity_propagation", "sklearn.cluster.AffinityPropagation"), ("sklearn.cluster.dbscan", "sklearn.cluster.DBSCAN"), ("sklearn.cluster.k_means", "sklearn.cluster.KMeans"), ("sklearn.cluster.mean_shift", "sklearn.cluster.MeanShift"), ("sklearn.cluster.spectral_clustering", "sklearn.cluster.SpectralClustering"), ("sklearn.covariance.graphical_lasso", "sklearn.covariance.GraphicalLasso"), ("sklearn.covariance.ledoit_wolf", "sklearn.covariance.LedoitWolf"), ("sklearn.covariance.oas", "sklearn.covariance.OAS"), ("sklearn.decomposition.dict_learning", "sklearn.decomposition.DictionaryLearning"), ( "sklearn.decomposition.dict_learning_online", "sklearn.decomposition.MiniBatchDictionaryLearning", ), ("sklearn.decomposition.fastica", "sklearn.decomposition.FastICA"), ("sklearn.decomposition.non_negative_factorization", "sklearn.decomposition.NMF"), ("sklearn.preprocessing.maxabs_scale", "sklearn.preprocessing.MaxAbsScaler"), ("sklearn.preprocessing.minmax_scale", "sklearn.preprocessing.MinMaxScaler"), ("sklearn.preprocessing.power_transform", "sklearn.preprocessing.PowerTransformer"), ( "sklearn.preprocessing.quantile_transform", "sklearn.preprocessing.QuantileTransformer", ), ("sklearn.preprocessing.robust_scale", "sklearn.preprocessing.RobustScaler"), ] @pytest.mark.parametrize( "func_module, class_module", PARAM_VALIDATION_CLASS_WRAPPER_LIST ) def test_class_wrapper_param_validation(func_module, class_module): """Check param validation for public functions that are wrappers around estimators. """ func, func_name, func_params, required_params = _get_func_info(func_module) module_name, class_name = class_module.rsplit(".", 1) module = import_module(module_name) klass = getattr(module, class_name) parameter_constraints_func = getattr(func, "_skl_parameter_constraints") parameter_constraints_class = getattr(klass, "_parameter_constraints") parameter_constraints = { **parameter_constraints_class, **parameter_constraints_func, } parameter_constraints = { k: v for k, v in parameter_constraints.items() if k in func_params } _check_function_param_validation( func, func_name, func_params, required_params, parameter_constraints )