The assert_docstring_consistency function allows you to check the consistency between docstring parameters/attributes/returns of objects.

In scikit-learn there are often classes that share a parent (e.g., AdaBoostClassifier, AdaBoostRegressor) or related functions (e.g, f1_score, fbeta_score). In these cases, some parameters are often shared/common and we would like to check that the docstring type and description matches.

The assert_docstring_consistency function allows you to include/exclude specific parameters/attibutes/returns. In some cases only part of the description should match between objects. In this case you can use descr_regex_pattern to pass a regular expression to be matched to all descriptions. Please read the docstring of this function carefully.

Guide on how to contribute to this issue:

1. Pick an item below and comment the item you are working on so others know it has been taken.
   • NOT all items listed require a test to be added. If you find that the item you selected does not require a test, this is still a valuable contribution, please comment the reason why and we can tick it off the list.
2. Determine common parameters/attributes/returns between the objects.
   • If the description does not match but should, decide on the best wording and amend all objects to match. If only part of the description should match, consider using descr_regex_pattern.
3. Write a new test.
   • The test should live in sklearn/tests/test_docstring_parameters_consistency.py (cf. TST move test for parameters consistency checks #30853)
   • Add @skip_if_no_numpydoc to the top of the test (these tests can only be run if numpydoc is installed)

See #29831 for an example. This PR adds a test for the stacking estimators StackingClassifier and StackingRegressor.

Classes that share a common parent:

• BaseWeightBoosting: ['AdaBoostClassifier', 'AdaBoostRegressor']
• BaseBagging: ['BaggingClassifier', 'BaggingRegressor', 'IsolationForest']
• BaseMixture: ['BayesianGaussianMixture', 'GaussianMixture']
• _BaseDiscreteNB: ['BernoulliNB', 'CategoricalNB', 'ComplementNB', 'MultinomialNB']
• _BaseKMeans: ['BisectingKMeans', 'KMeans', 'MiniBatchKMeans']
• _PLS: ['CCA', 'PLSCanonical', 'PLSRegression']
• _BaseChain: ['ClassifierChain', 'RegressorChain']
• _VectorizerMixin: ['CountVectorizer', 'HashingVectorizer', 'TfidfVectorizer']
• BaseDecisionTree: ['DecisionTreeClassifier', 'DecisionTreeRegressor', 'ExtraTreeClassifier', 'ExtraTreeRegressor']
• _BaseSparseCoding: ['DictionaryLearning', 'MiniBatchDictionaryLearning', 'SparseCoder']
• LinearModelCV: ['ElasticNetCV', 'LassoCV', 'MultiTaskElasticNetCV', 'MultiTaskLassoCV']
• OutlierMixin: ['EllipticEnvelope', 'IsolationForest', 'LocalOutlierFactor', 'OneClassSVM', 'SGDOneClassSVM']
• EmpiricalCovariance: ['EllipticEnvelope', 'GraphicalLasso', 'GraphicalLassoCV', 'LedoitWolf', 'MinCovDet', 'OAS', 'ShrunkCovariance']
• ForestClassifier: ['ExtraTreesClassifier', 'RandomForestClassifier']
• `BaseForest: ['ExtraTreesClassifier', 'ExtraTreesRegressor', 'RandomForestClassifier', 'RandomForestRegressor', 'RandomTreesEmbedding']
• ForestRegressor: ['ExtraTreesRegressor', 'RandomForestRegressor']
• BaseThresholdClassifier: ['FixedThresholdClassifier', 'TunedThresholdClassifierCV']
• _GeneralizedLinearRegressor: ['GammaRegressor', 'PoissonRegressor', 'TweedieRegressor']
• BaseRandomProjection: ['GaussianRandomProjection', 'SparseRandomProjection']
• _BaseFilter: ['GenericUnivariateSelect', 'SelectFdr', 'SelectFpr', 'SelectFwe', 'SelectKBest', 'SelectPercentile']
• BaseGradientBoosting: ['GradientBoostingClassifier', 'GradientBoostingRegressor']
• BaseGraphicalLasso: ['GraphicalLasso', 'GraphicalLassoCV']
• BaseSearchCV: ['GridSearchCV', 'RandomizedSearchCV']
• BaseHistGradientBoosting: ['HistGradientBoostingClassifier', 'HistGradientBoostingRegressor']
• _BasePCA: ['IncrementalPCA', 'PCA']
• _BaseImputer: ['KNNImputer', 'SimpleImputer']
• KNeighborsMixin: ['KNeighborsClassifier', 'KNeighborsRegressor', 'KNeighborsTransformer', 'LocalOutlierFactor', 'NearestNeighbors']
• NeighborsBase: ['KNeighborsClassifier', 'KNeighborsRegressor', 'KNeighborsTransformer', 'LocalOutlierFactor', 'NearestNeighbors', 'RadiusNeighborsClassifier', 'RadiusNeighborsRegressor', 'RadiusNeighborsTransformer']
• BaseLabelPropagation: ['LabelPropagation', 'LabelSpreading']
• Lars: ['LarsCV', 'LassoLars', 'LassoLarsCV', 'LassoLarsIC']
• ElasticNet: ['Lasso', 'MultiTaskElasticNet', 'MultiTaskLasso']
• BaseMultilayerPerceptron: ['MLPClassifier', 'MLPRegressor']
• _BaseNMF: ['MiniBatchNMF', 'NMF']
• _BaseSparsePCA: ['MiniBatchSparsePCA', 'SparsePCA']
• _MultiOutputEstimator: ['MultiOutputClassifier', 'MultiOutputRegressor']
• Lasso: ['MultiTaskElasticNet', 'MultiTaskLasso']
• RadiusNeighborsMixin: ['NearestNeighbors', 'RadiusNeighborsClassifier', 'RadiusNeighborsRegressor', 'RadiusNeighborsTransformer']
• BaseSVC: ['NuSVC', 'SVC']
• BaseLibSVM: ['NuSVC', 'NuSVR', 'OneClassSVM', 'SVC', 'SVR']
• _BaseEncoder: ['OneHotEncoder', 'OrdinalEncoder', 'TargetEncoder']
• BaseSGDClassifier: ['PassiveAggressiveClassifier', 'Perceptron', 'SGDClassifier']
• BaseSGD: ['PassiveAggressiveClassifier', 'PassiveAggressiveRegressor', 'Perceptron', 'SGDClassifier', 'SGDOneClassSVM', 'SGDRegressor']
• BaseSGDRegressor: ['PassiveAggressiveRegressor', 'SGDRegressor']
• _BaseRidge: ['Ridge', 'RidgeClassifier']
• _BaseRidgeCV: ['RidgeCV', 'RidgeClassifierCV']
• _RidgeClassifierMixin: ['RidgeClassifier', 'RidgeClassifierCV']
• BaseSpectral: ['SpectralBiclustering', 'SpectralCoclustering']
• BiclusterMixin: ['SpectralBiclustering', 'SpectralCoclustering']
• _BaseStacking: ['StackingClassifier', 'StackingRegressor']
• _BaseHeterogeneousEnsemble: ['StackingClassifier', 'StackingRegressor', 'VotingClassifier', 'VotingRegressor']
• _BaseVoting: ['VotingClassifier', 'VotingRegressor']

Functions, from the same module, that share parameters.

Module: sklearn.utils

• Functions: compute_class_weight, compute_sample_weight / Shared parameters: class_weight
• Functions: resample, shuffle / Shared parameters: random_state

Module: sklearn.utils.class_weight

• Functions: compute_class_weight, compute_sample_weight / Shared parameters: class_weight, y

Module: sklearn.utils.extmath

• Functions: randomized_range_finder, randomized_svd / Shared parameters: n_iter, power_iteration_normalizer, random_state

Module: sklearn.utils.validation

• Functions: as_float_array, check_X_y, check_array / Shared parameters: copy, force_all_finite, ensure_all_finite
• Functions: check_X_y, check_array / Shared parameters: accept_sparse, accept_large_sparse, order, force_writeable, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features

Module: sklearn.metrics

• Functions: adjusted_mutual_info_score, adjusted_rand_score, completeness_score, fowlkes_mallows_score, homogeneity_completeness_v_measure, homogeneity_score, mutual_info_score, normalized_mutual_info_score, pair_confusion_matrix, rand_score, v_measure_score / Shared parameters: labels_true, labels_pred

Module: sklearn.metrics.pairwise

• Functions: pairwise_distances_argmin, pairwise_distances_argmin_min / Shared parameters: axis, metric_kwargs
• Functions: pairwise_distances, pairwise_distances_chunked, pairwise_kernels / Shared parameters: n_jobs
• Functions: check_pairwise_arrays, pairwise_distances / Shared parameters: force_all_finite, ensure_all_finite
• Functions: chi2_kernel, laplacian_kernel, polynomial_kernel, rbf_kernel, sigmoid_kernel / Shared parameters: gamma

Module: sklearn.cluster

• Functions: affinity_propagation, estimate_bandwidth, k_means, kmeans_plusplus, spectral_clustering / Shared parameters: random_state
• Functions: cluster_optics_dbscan, cluster_optics_xi / Shared parameters: reachability, ordering
• Functions: compute_optics_graph, dbscan / Shared parameters: metric, p, metric_params, leaf_size
• Functions: linkage_tree, ward_tree / Shared parameters: connectivity, return_distance

Module: sklearn.datasets

• Functions: dump_svmlight_file, load_svmlight_file, load_svmlight_files / Shared parameters: zero_based, query_id, multilabel
• Functions: fetch_20newsgroups, fetch_20newsgroups_vectorized, fetch_california_housing, fetch_covtype, fetch_file, fetch_kddcup99, fetch_lfw_pairs, fetch_lfw_people, fetch_olivetti_faces, fetch_openml, fetch_rcv1, fetch_species_distributions / Shared parameters: n_retries, delay
• Functions: fetch_20newsgroups_vectorized, fetch_california_housing, fetch_covtype, fetch_kddcup99, fetch_openml, load_breast_cancer, load_diabetes, load_digits, load_iris, load_linnerud, load_wine / Shared parameters: as_frame
• Functions: make_biclusters, make_checkerboard / Shared parameters: shape, n_clusters, minval, maxval
• Functions: make_low_rank_matrix, make_regression / Shared parameters: effective_rank, tail_strength

Module: sklearn.decomposition

• Functions: dict_learning, dict_learning_online, fastica, non_negative_factorization / Shared parameters: X, max_iter, n_components, random_state
• Functions: dict_learning, dict_learning_online, sparse_encode / Shared parameters: alpha, n_jobs
• Functions: dict_learning, dict_learning_online / Shared parameters: method, dict_init, callback, positive_dict, positive_code, method_max_iter

Module: sklearn.feature_extraction

• Functions: grid_to_graph, img_to_graph / Shared parameters: mask, return_as, dtype, mask, return_as

Module: sklearn.linear_model

• Functions: lars_path, lars_path_gram, orthogonal_mp_gram / Shared parameters: Gram, copy_Gram
• Functions: lars_path, lars_path_gram / Shared parameters: alpha_min, method
• Functions: lars_path, lars_path_gram, ridge_regression / Shared parameters: max_iter
• Functions: lars_path, lars_path_gram, orthogonal_mp, orthogonal_mp_gram / Shared parameters: return_path
• Functions: orthogonal_mp, orthogonal_mp_gram / Shared parameters: n_nonzero_coefs, tol

Module: sklearn.neighbors

• Functions: kneighbors_graph, radius_neighbors_graph / Shared parameters: X, mode, metric, p, metric_params, include_self, n_jobs

Module: sklearn.tree

• Functions: export_graphviz, export_text, plot_tree / Shared parameters: decision_tree, max_depth, feature_names, class_names
• Functions: export_graphviz, plot_tree / Shared parameters: label, filled, impurity, node_ids, proportion, rounded, precision

Module: sklearn.feature_selection

• Functions: f_regression, r_regression / Shared parameters: center, force_finite
• Functions: mutual_info_classif, mutual_info_regression / Shared parameters: discrete_features, n_neighbors, copy, random_state, n_jobs