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1.8.0
doc/api_reference.py
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Success Moses
FEA add `confusion_matrix_at_thresholds` (#30134)
05 ноя 2025, 16:44
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05 ноя 2025, 16:44
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"""Configuration for the API reference documentation.""" def _get_guide(*refs, is_developer=False): """Get the rst to refer to user/developer guide. `refs` is several references that can be used in the :ref:`...` directive. """ if len(refs) == 1: ref_desc = f":ref:`{refs[0]}` section" elif len(refs) == 2: ref_desc = f":ref:`{refs[0]}` and :ref:`{refs[1]}` sections" else: ref_desc = ", ".join(f":ref:`{ref}`" for ref in refs[:-1]) ref_desc += f", and :ref:`{refs[-1]}` sections" guide_name = "Developer" if is_developer else "User" return f"**{guide_name} guide.** See the {ref_desc} for further details." def _get_submodule(module_name, submodule_name): """Get the submodule docstring and automatically add the hook. `module_name` is e.g. `sklearn.feature_extraction`, and `submodule_name` is e.g. `image`, so we get the docstring and hook for `sklearn.feature_extraction.image` submodule. `module_name` is used to reset the current module because autosummary automatically changes the current module. """ lines = [ f".. automodule:: {module_name}.{submodule_name}", f".. currentmodule:: {module_name}", ] return "\n\n".join(lines) """ CONFIGURING API_REFERENCE ========================= API_REFERENCE maps each module name to a dictionary that consists of the following components: short_summary (required) The text to be printed on the index page; it has nothing to do the API reference page of each module. description (required, `None` if not needed) The additional description for the module to be placed under the module docstring, before the sections start. sections (required) A list of sections, each of which consists of: - title (required, `None` if not needed): the section title, commonly it should not be `None` except for the first section of a module, - description (optional): the optional additional description for the section, - autosummary (required): an autosummary block, assuming current module is the current module name. Essentially, the rendered page would look like the following: |---------------------------------------------------------------------------------| | {{ module_name }} | | ================= | | {{ module_docstring }} | | {{ description }} | | | | {{ section_title_1 }} <-------------- Optional if one wants the first | | --------------------- section to directly follow | | {{ section_description_1 }} without a second-level heading. | | {{ section_autosummary_1 }} | | | | {{ section_title_2 }} | | --------------------- | | {{ section_description_2 }} | | {{ section_autosummary_2 }} | | | | More sections... | |---------------------------------------------------------------------------------| Hooks will be automatically generated for each module and each section. For a module, e.g., `sklearn.feature_extraction`, the hook would be `feature_extraction_ref`; for a section, e.g., "From text" under `sklearn.feature_extraction`, the hook would be `feature_extraction_ref-from-text`. However, note that a better way is to refer using the :mod: directive, e.g., :mod:`sklearn.feature_extraction` for the module and :mod:`sklearn.feature_extraction.text` for the section. Only in case that a section is not a particular submodule does the hook become useful, e.g., the "Loaders" section under `sklearn.datasets`. """ API_REFERENCE = { "sklearn": { "short_summary": "Settings and information tools.", "description": None, "sections": [ { "title": None, "autosummary": [ "config_context", "get_config", "set_config", "show_versions", ], }, ], }, "sklearn.base": { "short_summary": "Base classes and utility functions.", "description": None, "sections": [ { "title": None, "autosummary": [ "BaseEstimator", "BiclusterMixin", "ClassNamePrefixFeaturesOutMixin", "ClassifierMixin", "ClusterMixin", "DensityMixin", "MetaEstimatorMixin", "OneToOneFeatureMixin", "OutlierMixin", "RegressorMixin", "TransformerMixin", "clone", "is_classifier", "is_clusterer", "is_regressor", "is_outlier_detector", ], } ], }, "sklearn.calibration": { "short_summary": "Probability calibration.", "description": _get_guide("calibration"), "sections": [ { "title": None, "autosummary": ["CalibratedClassifierCV", "calibration_curve"], }, { "title": "Visualization", "autosummary": ["CalibrationDisplay"], }, ], }, "sklearn.cluster": { "short_summary": "Clustering.", "description": _get_guide("clustering", "biclustering"), "sections": [ { "title": None, "autosummary": [ "AffinityPropagation", "AgglomerativeClustering", "Birch", "BisectingKMeans", "DBSCAN", "FeatureAgglomeration", "HDBSCAN", "KMeans", "MeanShift", "MiniBatchKMeans", "OPTICS", "SpectralBiclustering", "SpectralClustering", "SpectralCoclustering", "affinity_propagation", "cluster_optics_dbscan", "cluster_optics_xi", "compute_optics_graph", "dbscan", "estimate_bandwidth", "k_means", "kmeans_plusplus", "mean_shift", "spectral_clustering", "ward_tree", ], }, ], }, "sklearn.compose": { "short_summary": "Composite estimators.", "description": _get_guide("combining_estimators"), "sections": [ { "title": None, "autosummary": [ "ColumnTransformer", "TransformedTargetRegressor", "make_column_selector", "make_column_transformer", ], }, ], }, "sklearn.covariance": { "short_summary": "Covariance estimation.", "description": _get_guide("covariance"), "sections": [ { "title": None, "autosummary": [ "EllipticEnvelope", "EmpiricalCovariance", "GraphicalLasso", "GraphicalLassoCV", "LedoitWolf", "MinCovDet", "OAS", "ShrunkCovariance", "empirical_covariance", "graphical_lasso", "ledoit_wolf", "ledoit_wolf_shrinkage", "oas", "shrunk_covariance", ], }, ], }, "sklearn.cross_decomposition": { "short_summary": "Cross decomposition.", "description": _get_guide("cross_decomposition"), "sections": [ { "title": None, "autosummary": ["CCA", "PLSCanonical", "PLSRegression", "PLSSVD"], }, ], }, "sklearn.datasets": { "short_summary": "Datasets.", "description": _get_guide("datasets"), "sections": [ { "title": "Loaders", "autosummary": [ "clear_data_home", "dump_svmlight_file", "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", "get_data_home", "load_breast_cancer", "load_diabetes", "load_digits", "load_files", "load_iris", "load_linnerud", "load_sample_image", "load_sample_images", "load_svmlight_file", "load_svmlight_files", "load_wine", ], }, { "title": "Sample generators", "autosummary": [ "make_biclusters", "make_blobs", "make_checkerboard", "make_circles", "make_classification", "make_friedman1", "make_friedman2", "make_friedman3", "make_gaussian_quantiles", "make_hastie_10_2", "make_low_rank_matrix", "make_moons", "make_multilabel_classification", "make_regression", "make_s_curve", "make_sparse_coded_signal", "make_sparse_spd_matrix", "make_sparse_uncorrelated", "make_spd_matrix", "make_swiss_roll", ], }, ], }, "sklearn.decomposition": { "short_summary": "Matrix decomposition.", "description": _get_guide("decompositions"), "sections": [ { "title": None, "autosummary": [ "DictionaryLearning", "FactorAnalysis", "FastICA", "IncrementalPCA", "KernelPCA", "LatentDirichletAllocation", "MiniBatchDictionaryLearning", "MiniBatchNMF", "MiniBatchSparsePCA", "NMF", "PCA", "SparseCoder", "SparsePCA", "TruncatedSVD", "dict_learning", "dict_learning_online", "fastica", "non_negative_factorization", "sparse_encode", ], }, ], }, "sklearn.discriminant_analysis": { "short_summary": "Discriminant analysis.", "description": _get_guide("lda_qda"), "sections": [ { "title": None, "autosummary": [ "LinearDiscriminantAnalysis", "QuadraticDiscriminantAnalysis", ], }, ], }, "sklearn.dummy": { "short_summary": "Dummy estimators.", "description": _get_guide("model_evaluation"), "sections": [ { "title": None, "autosummary": ["DummyClassifier", "DummyRegressor"], }, ], }, "sklearn.ensemble": { "short_summary": "Ensemble methods.", "description": _get_guide("ensemble"), "sections": [ { "title": None, "autosummary": [ "AdaBoostClassifier", "AdaBoostRegressor", "BaggingClassifier", "BaggingRegressor", "ExtraTreesClassifier", "ExtraTreesRegressor", "GradientBoostingClassifier", "GradientBoostingRegressor", "HistGradientBoostingClassifier", "HistGradientBoostingRegressor", "IsolationForest", "RandomForestClassifier", "RandomForestRegressor", "RandomTreesEmbedding", "StackingClassifier", "StackingRegressor", "VotingClassifier", "VotingRegressor", ], }, ], }, "sklearn.exceptions": { "short_summary": "Exceptions and warnings.", "description": None, "sections": [ { "title": None, "autosummary": [ "ConvergenceWarning", "DataConversionWarning", "DataDimensionalityWarning", "EfficiencyWarning", "FitFailedWarning", "InconsistentVersionWarning", "NotFittedError", "UndefinedMetricWarning", "EstimatorCheckFailedWarning", ], }, ], }, "sklearn.experimental": { "short_summary": "Experimental tools.", "description": None, "sections": [ { "title": None, "autosummary": ["enable_halving_search_cv", "enable_iterative_imputer"], }, ], }, "sklearn.feature_extraction": { "short_summary": "Feature extraction.", "description": _get_guide("feature_extraction"), "sections": [ { "title": None, "autosummary": ["DictVectorizer", "FeatureHasher"], }, { "title": "From images", "description": _get_submodule("sklearn.feature_extraction", "image"), "autosummary": [ "image.PatchExtractor", "image.extract_patches_2d", "image.grid_to_graph", "image.img_to_graph", "image.reconstruct_from_patches_2d", ], }, { "title": "From text", "description": _get_submodule("sklearn.feature_extraction", "text"), "autosummary": [ "text.CountVectorizer", "text.HashingVectorizer", "text.TfidfTransformer", "text.TfidfVectorizer", ], }, ], }, "sklearn.feature_selection": { "short_summary": "Feature selection.", "description": _get_guide("feature_selection"), "sections": [ { "title": None, "autosummary": [ "GenericUnivariateSelect", "RFE", "RFECV", "SelectFdr", "SelectFpr", "SelectFromModel", "SelectFwe", "SelectKBest", "SelectPercentile", "SelectorMixin", "SequentialFeatureSelector", "VarianceThreshold", "chi2", "f_classif", "f_regression", "mutual_info_classif", "mutual_info_regression", "r_regression", ], }, ], }, "sklearn.frozen": { "short_summary": "Frozen estimators.", "description": None, "sections": [ { "title": None, "autosummary": ["FrozenEstimator"], }, ], }, "sklearn.gaussian_process": { "short_summary": "Gaussian processes.", "description": _get_guide("gaussian_process"), "sections": [ { "title": None, "autosummary": [ "GaussianProcessClassifier", "GaussianProcessRegressor", ], }, { "title": "Kernels", "description": _get_submodule("sklearn.gaussian_process", "kernels"), "autosummary": [ "kernels.CompoundKernel", "kernels.ConstantKernel", "kernels.DotProduct", "kernels.ExpSineSquared", "kernels.Exponentiation", "kernels.Hyperparameter", "kernels.Kernel", "kernels.Matern", "kernels.PairwiseKernel", "kernels.Product", "kernels.RBF", "kernels.RationalQuadratic", "kernels.Sum", "kernels.WhiteKernel", ], }, ], }, "sklearn.impute": { "short_summary": "Imputation.", "description": _get_guide("impute"), "sections": [ { "title": None, "autosummary": [ "IterativeImputer", "KNNImputer", "MissingIndicator", "SimpleImputer", ], }, ], }, "sklearn.inspection": { "short_summary": "Inspection.", "description": _get_guide("inspection"), "sections": [ { "title": None, "autosummary": ["partial_dependence", "permutation_importance"], }, { "title": "Plotting", "autosummary": ["DecisionBoundaryDisplay", "PartialDependenceDisplay"], }, ], }, "sklearn.isotonic": { "short_summary": "Isotonic regression.", "description": _get_guide("isotonic"), "sections": [ { "title": None, "autosummary": [ "IsotonicRegression", "check_increasing", "isotonic_regression", ], }, ], }, "sklearn.kernel_approximation": { "short_summary": "Kernel approximation.", "description": _get_guide("kernel_approximation"), "sections": [ { "title": None, "autosummary": [ "AdditiveChi2Sampler", "Nystroem", "PolynomialCountSketch", "RBFSampler", "SkewedChi2Sampler", ], }, ], }, "sklearn.kernel_ridge": { "short_summary": "Kernel ridge regression.", "description": _get_guide("kernel_ridge"), "sections": [ { "title": None, "autosummary": ["KernelRidge"], }, ], }, "sklearn.linear_model": { "short_summary": "Generalized linear models.", "description": ( _get_guide("linear_model") + "\n\nThe following subsections are only rough guidelines: the same " "estimator can fall into multiple categories, depending on its parameters." ), "sections": [ { "title": "Linear classifiers", "autosummary": [ "LogisticRegression", "LogisticRegressionCV", "PassiveAggressiveClassifier", # TODO(1.10): remove "Perceptron", "RidgeClassifier", "RidgeClassifierCV", "SGDClassifier", "SGDOneClassSVM", ], }, { "title": "Classical linear regressors", "autosummary": ["LinearRegression", "Ridge", "RidgeCV", "SGDRegressor"], }, { "title": "Regressors with variable selection", "description": ( "The following estimators have built-in variable selection fitting " "procedures, but any estimator using a L1 or elastic-net penalty " "also performs variable selection: typically " ":class:`~linear_model.SGDRegressor` or " ":class:`~sklearn.linear_model.SGDClassifier` with an appropriate " "penalty." ), "autosummary": [ "ElasticNet", "ElasticNetCV", "Lars", "LarsCV", "Lasso", "LassoCV", "LassoLars", "LassoLarsCV", "LassoLarsIC", "OrthogonalMatchingPursuit", "OrthogonalMatchingPursuitCV", ], }, { "title": "Bayesian regressors", "autosummary": ["ARDRegression", "BayesianRidge"], }, { "title": "Multi-task linear regressors with variable selection", "description": ( "These estimators fit multiple regression problems (or tasks)" " jointly, while inducing sparse coefficients. While the inferred" " coefficients may differ between the tasks, they are constrained" " to agree on the features that are selected (non-zero" " coefficients)." ), "autosummary": [ "MultiTaskElasticNet", "MultiTaskElasticNetCV", "MultiTaskLasso", "MultiTaskLassoCV", ], }, { "title": "Outlier-robust regressors", "description": ( "Any estimator using the Huber loss would also be robust to " "outliers, e.g., :class:`~linear_model.SGDRegressor` with " "``loss='huber'``." ), "autosummary": [ "HuberRegressor", "QuantileRegressor", "RANSACRegressor", "TheilSenRegressor", ], }, { "title": "Generalized linear models (GLM) for regression", "description": ( "These models allow for response variables to have error " "distributions other than a normal distribution." ), "autosummary": [ "GammaRegressor", "PoissonRegressor", "TweedieRegressor", ], }, { "title": "Miscellaneous", "autosummary": [ "PassiveAggressiveRegressor", # TODO(1.10): remove "enet_path", "lars_path", "lars_path_gram", "lasso_path", "orthogonal_mp", "orthogonal_mp_gram", "ridge_regression", ], }, ], }, "sklearn.manifold": { "short_summary": "Manifold learning.", "description": _get_guide("manifold"), "sections": [ { "title": None, "autosummary": [ "ClassicalMDS", "Isomap", "LocallyLinearEmbedding", "MDS", "SpectralEmbedding", "TSNE", "locally_linear_embedding", "smacof", "spectral_embedding", "trustworthiness", ], }, ], }, "sklearn.metrics": { "short_summary": "Metrics.", "description": _get_guide("model_evaluation", "metrics"), "sections": [ { "title": "Model selection interface", "description": _get_guide("scoring_parameter"), "autosummary": [ "check_scoring", "get_scorer", "get_scorer_names", "make_scorer", ], }, { "title": "Classification metrics", "description": _get_guide("classification_metrics"), "autosummary": [ "accuracy_score", "auc", "average_precision_score", "balanced_accuracy_score", "brier_score_loss", "class_likelihood_ratios", "classification_report", "cohen_kappa_score", "confusion_matrix", "confusion_matrix_at_thresholds", "d2_brier_score", "d2_log_loss_score", "dcg_score", "det_curve", "f1_score", "fbeta_score", "hamming_loss", "hinge_loss", "jaccard_score", "log_loss", "matthews_corrcoef", "multilabel_confusion_matrix", "ndcg_score", "precision_recall_curve", "precision_recall_fscore_support", "precision_score", "recall_score", "roc_auc_score", "roc_curve", "top_k_accuracy_score", "zero_one_loss", ], }, { "title": "Regression metrics", "description": _get_guide("regression_metrics"), "autosummary": [ "d2_absolute_error_score", "d2_pinball_score", "d2_tweedie_score", "explained_variance_score", "max_error", "mean_absolute_error", "mean_absolute_percentage_error", "mean_gamma_deviance", "mean_pinball_loss", "mean_poisson_deviance", "mean_squared_error", "mean_squared_log_error", "mean_tweedie_deviance", "median_absolute_error", "r2_score", "root_mean_squared_error", "root_mean_squared_log_error", ], }, { "title": "Multilabel ranking metrics", "description": _get_guide("multilabel_ranking_metrics"), "autosummary": [ "coverage_error", "label_ranking_average_precision_score", "label_ranking_loss", ], }, { "title": "Clustering metrics", "description": ( _get_submodule("sklearn.metrics", "cluster") + "\n\n" + _get_guide("clustering_evaluation") ), "autosummary": [ "adjusted_mutual_info_score", "adjusted_rand_score", "calinski_harabasz_score", "cluster.contingency_matrix", "cluster.pair_confusion_matrix", "completeness_score", "davies_bouldin_score", "fowlkes_mallows_score", "homogeneity_completeness_v_measure", "homogeneity_score", "mutual_info_score", "normalized_mutual_info_score", "rand_score", "silhouette_samples", "silhouette_score", "v_measure_score", ], }, { "title": "Biclustering metrics", "description": _get_guide("biclustering_evaluation"), "autosummary": ["consensus_score"], }, { "title": "Distance metrics", "autosummary": ["DistanceMetric"], }, { "title": "Pairwise metrics", "description": ( _get_submodule("sklearn.metrics", "pairwise") + "\n\n" + _get_guide("metrics") ), "autosummary": [ "pairwise.additive_chi2_kernel", "pairwise.chi2_kernel", "pairwise.cosine_distances", "pairwise.cosine_similarity", "pairwise.distance_metrics", "pairwise.euclidean_distances", "pairwise.haversine_distances", "pairwise.kernel_metrics", "pairwise.laplacian_kernel", "pairwise.linear_kernel", "pairwise.manhattan_distances", "pairwise.nan_euclidean_distances", "pairwise.paired_cosine_distances", "pairwise.paired_distances", "pairwise.paired_euclidean_distances", "pairwise.paired_manhattan_distances", "pairwise.pairwise_kernels", "pairwise.polynomial_kernel", "pairwise.rbf_kernel", "pairwise.sigmoid_kernel", "pairwise_distances", "pairwise_distances_argmin", "pairwise_distances_argmin_min", "pairwise_distances_chunked", ], }, { "title": "Plotting", "description": _get_guide("visualizations"), "autosummary": [ "ConfusionMatrixDisplay", "DetCurveDisplay", "PrecisionRecallDisplay", "PredictionErrorDisplay", "RocCurveDisplay", ], }, ], }, "sklearn.mixture": { "short_summary": "Gaussian mixture models.", "description": _get_guide("mixture"), "sections": [ { "title": None, "autosummary": ["BayesianGaussianMixture", "GaussianMixture"], }, ], }, "sklearn.model_selection": { "short_summary": "Model selection.", "description": _get_guide("cross_validation", "grid_search", "learning_curve"), "sections": [ { "title": "Splitters", "autosummary": [ "GroupKFold", "GroupShuffleSplit", "KFold", "LeaveOneGroupOut", "LeaveOneOut", "LeavePGroupsOut", "LeavePOut", "PredefinedSplit", "RepeatedKFold", "RepeatedStratifiedKFold", "ShuffleSplit", "StratifiedGroupKFold", "StratifiedKFold", "StratifiedShuffleSplit", "TimeSeriesSplit", "check_cv", "train_test_split", ], }, { "title": "Hyper-parameter optimizers", "autosummary": [ "GridSearchCV", "HalvingGridSearchCV", "HalvingRandomSearchCV", "ParameterGrid", "ParameterSampler", "RandomizedSearchCV", ], }, { "title": "Post-fit model tuning", "autosummary": [ "FixedThresholdClassifier", "TunedThresholdClassifierCV", ], }, { "title": "Model validation", "autosummary": [ "cross_val_predict", "cross_val_score", "cross_validate", "learning_curve", "permutation_test_score", "validation_curve", ], }, { "title": "Visualization", "autosummary": ["LearningCurveDisplay", "ValidationCurveDisplay"], }, ], }, "sklearn.multiclass": { "short_summary": "Multiclass classification.", "description": _get_guide("multiclass_classification"), "sections": [ { "title": None, "autosummary": [ "OneVsOneClassifier", "OneVsRestClassifier", "OutputCodeClassifier", ], }, ], }, "sklearn.multioutput": { "short_summary": "Multioutput regression and classification.", "description": _get_guide( "multilabel_classification", "multiclass_multioutput_classification", "multioutput_regression", ), "sections": [ { "title": None, "autosummary": [ "ClassifierChain", "MultiOutputClassifier", "MultiOutputRegressor", "RegressorChain", ], }, ], }, "sklearn.naive_bayes": { "short_summary": "Naive Bayes.", "description": _get_guide("naive_bayes"), "sections": [ { "title": None, "autosummary": [ "BernoulliNB", "CategoricalNB", "ComplementNB", "GaussianNB", "MultinomialNB", ], }, ], }, "sklearn.neighbors": { "short_summary": "Nearest neighbors.", "description": _get_guide("neighbors"), "sections": [ { "title": None, "autosummary": [ "BallTree", "KDTree", "KNeighborsClassifier", "KNeighborsRegressor", "KNeighborsTransformer", "KernelDensity", "LocalOutlierFactor", "NearestCentroid", "NearestNeighbors", "NeighborhoodComponentsAnalysis", "RadiusNeighborsClassifier", "RadiusNeighborsRegressor", "RadiusNeighborsTransformer", "kneighbors_graph", "radius_neighbors_graph", "sort_graph_by_row_values", ], }, ], }, "sklearn.neural_network": { "short_summary": "Neural network models.", "description": _get_guide( "neural_networks_supervised", "neural_networks_unsupervised" ), "sections": [ { "title": None, "autosummary": ["BernoulliRBM", "MLPClassifier", "MLPRegressor"], }, ], }, "sklearn.pipeline": { "short_summary": "Pipeline.", "description": _get_guide("combining_estimators"), "sections": [ { "title": None, "autosummary": [ "FeatureUnion", "Pipeline", "make_pipeline", "make_union", ], }, ], }, "sklearn.preprocessing": { "short_summary": "Preprocessing and normalization.", "description": _get_guide("preprocessing"), "sections": [ { "title": None, "autosummary": [ "Binarizer", "FunctionTransformer", "KBinsDiscretizer", "KernelCenterer", "LabelBinarizer", "LabelEncoder", "MaxAbsScaler", "MinMaxScaler", "MultiLabelBinarizer", "Normalizer", "OneHotEncoder", "OrdinalEncoder", "PolynomialFeatures", "PowerTransformer", "QuantileTransformer", "RobustScaler", "SplineTransformer", "StandardScaler", "TargetEncoder", "add_dummy_feature", "binarize", "label_binarize", "maxabs_scale", "minmax_scale", "normalize", "power_transform", "quantile_transform", "robust_scale", "scale", ], }, ], }, "sklearn.random_projection": { "short_summary": "Random projection.", "description": _get_guide("random_projection"), "sections": [ { "title": None, "autosummary": [ "GaussianRandomProjection", "SparseRandomProjection", "johnson_lindenstrauss_min_dim", ], }, ], }, "sklearn.semi_supervised": { "short_summary": "Semi-supervised learning.", "description": _get_guide("semi_supervised"), "sections": [ { "title": None, "autosummary": [ "LabelPropagation", "LabelSpreading", "SelfTrainingClassifier", ], }, ], }, "sklearn.svm": { "short_summary": "Support vector machines.", "description": _get_guide("svm"), "sections": [ { "title": None, "autosummary": [ "LinearSVC", "LinearSVR", "NuSVC", "NuSVR", "OneClassSVM", "SVC", "SVR", "l1_min_c", ], }, ], }, "sklearn.tree": { "short_summary": "Decision trees.", "description": _get_guide("tree"), "sections": [ { "title": None, "autosummary": [ "DecisionTreeClassifier", "DecisionTreeRegressor", "ExtraTreeClassifier", "ExtraTreeRegressor", ], }, { "title": "Exporting", "autosummary": ["export_graphviz", "export_text"], }, { "title": "Plotting", "autosummary": ["plot_tree"], }, ], }, "sklearn.utils": { "short_summary": "Utilities.", "description": _get_guide("developers-utils", is_developer=True), "sections": [ { "title": None, "autosummary": [ "Bunch", "_safe_indexing", "as_float_array", "assert_all_finite", "deprecated", "estimator_html_repr", "gen_batches", "gen_even_slices", "indexable", "murmurhash3_32", "resample", "safe_mask", "safe_sqr", "shuffle", "Tags", "InputTags", "TargetTags", "ClassifierTags", "RegressorTags", "TransformerTags", "get_tags", ], }, { "title": "Input and parameter validation", "description": _get_submodule("sklearn.utils", "validation"), "autosummary": [ "check_X_y", "check_array", "check_consistent_length", "check_random_state", "check_scalar", "validation.check_is_fitted", "validation.check_memory", "validation.check_symmetric", "validation.column_or_1d", "validation.has_fit_parameter", "validation.validate_data", ], }, { "title": "Meta-estimators", "description": _get_submodule("sklearn.utils", "metaestimators"), "autosummary": ["metaestimators.available_if"], }, { "title": "Weight handling based on class labels", "description": _get_submodule("sklearn.utils", "class_weight"), "autosummary": [ "class_weight.compute_class_weight", "class_weight.compute_sample_weight", ], }, { "title": "Dealing with multiclass target in classifiers", "description": _get_submodule("sklearn.utils", "multiclass"), "autosummary": [ "multiclass.is_multilabel", "multiclass.type_of_target", "multiclass.unique_labels", ], }, { "title": "Optimal mathematical operations", "description": _get_submodule("sklearn.utils", "extmath"), "autosummary": [ "extmath.density", "extmath.fast_logdet", "extmath.randomized_range_finder", "extmath.randomized_svd", "extmath.safe_sparse_dot", "extmath.weighted_mode", ], }, { "title": "Working with sparse matrices and arrays", "description": _get_submodule("sklearn.utils", "sparsefuncs"), "autosummary": [ "sparsefuncs.incr_mean_variance_axis", "sparsefuncs.inplace_column_scale", "sparsefuncs.inplace_csr_column_scale", "sparsefuncs.inplace_row_scale", "sparsefuncs.inplace_swap_column", "sparsefuncs.inplace_swap_row", "sparsefuncs.mean_variance_axis", ], }, { "title": None, "description": _get_submodule("sklearn.utils", "sparsefuncs_fast"), "autosummary": [ "sparsefuncs_fast.inplace_csr_row_normalize_l1", "sparsefuncs_fast.inplace_csr_row_normalize_l2", ], }, { "title": "Working with graphs", "description": _get_submodule("sklearn.utils", "graph"), "autosummary": ["graph.single_source_shortest_path_length"], }, { "title": "Random sampling", "description": _get_submodule("sklearn.utils", "random"), "autosummary": ["random.sample_without_replacement"], }, { "title": "Auxiliary functions that operate on arrays", "description": _get_submodule("sklearn.utils", "arrayfuncs"), "autosummary": ["arrayfuncs.min_pos"], }, { "title": "Metadata routing", "description": ( _get_submodule("sklearn.utils", "metadata_routing") + "\n\n" + _get_guide("metadata_routing") ), "autosummary": [ "metadata_routing.MetadataRequest", "metadata_routing.MetadataRouter", "metadata_routing.MethodMapping", "metadata_routing.get_routing_for_object", "metadata_routing.process_routing", ], }, { "title": "Discovering scikit-learn objects", "description": _get_submodule("sklearn.utils", "discovery"), "autosummary": [ "discovery.all_displays", "discovery.all_estimators", "discovery.all_functions", ], }, { "title": "API compatibility checkers", "description": _get_submodule("sklearn.utils", "estimator_checks"), "autosummary": [ "estimator_checks.check_estimator", "estimator_checks.parametrize_with_checks", "estimator_checks.estimator_checks_generator", ], }, { "title": "Parallel computing", "description": _get_submodule("sklearn.utils", "parallel"), "autosummary": [ "parallel.Parallel", "parallel.delayed", ], }, ], }, } """ CONFIGURING DEPRECATED_API_REFERENCE ==================================== DEPRECATED_API_REFERENCE maps each deprecation target version to a corresponding autosummary block. It will be placed at the bottom of the API index page under the "Recently deprecated" section. Essentially, the rendered section would look like the following: |------------------------------------------| | To be removed in {{ version_1 }} | | -------------------------------- | | {{ autosummary_1 }} | | | | To be removed in {{ version_2 }} | | -------------------------------- | | {{ autosummary_2 }} | | | | More versions... | |------------------------------------------| Note that the autosummary here assumes that the current module is `sklearn`, i.e., if `sklearn.utils.Memory` is deprecated, one should put `utils.Memory` in the "entries" slot of the autosummary block. Example: DEPRECATED_API_REFERENCE = { "0.24": [ "model_selection.fit_grid_point", "utils.safe_indexing", ], } """ DEPRECATED_API_REFERENCE = {} # type: ignore[var-annotated]