diff --git a/.gitignore b/.gitignore index 770f0b84f074a..2bbb3923b74f7 100644 --- a/.gitignore +++ b/.gitignore @@ -13,6 +13,7 @@ sklearn/**/*.html dist/ MANIFEST +doc/api/ doc/sg_execution_times.rst doc/_build/ doc/auto_examples/ diff --git a/build_tools/circle/build_doc.sh b/build_tools/circle/build_doc.sh index 35fee3ae50b65..0b0ca94116a9a 100755 --- a/build_tools/circle/build_doc.sh +++ b/build_tools/circle/build_doc.sh @@ -244,7 +244,7 @@ then ( echo '

General: Home | API Reference | Examples

' + echo '

General: Home | API Reference | Examples

' echo 'Sphinx Warnings in affected files' diff --git a/doc/Makefile b/doc/Makefile index 44f02585f6205..336c0d8f50eb9 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -50,6 +50,7 @@ clean: -rm -rf auto_examples/ -rm -rf generated/* -rm -rf modules/generated/ + -rm -rf api/ # Default to SPHINX_NUMJOBS=1 for full documentation build. Using # SPHINX_NUMJOBS!=1 may actually slow down the build, or cause weird issues in diff --git a/doc/conf.py b/doc/conf.py index c0846cb9ae29e..d9ce3d795f9f2 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -284,6 +284,7 @@ # redirects dictionary maps from old links to new links redirects = { "documentation": "index", + "modules/classes": "api/index", "auto_examples/feature_selection/plot_permutation_test_for_classification": ( "auto_examples/model_selection/plot_permutation_tests_for_classification" ), @@ -602,7 +603,7 @@ def filter_search_index(app, exception): f.write(searchindex_text) -def generate_min_dependency_table(app): +def generate_min_dependency_table(): """Generate min dependency table for docs.""" from sklearn._min_dependencies import dependent_packages @@ -652,7 +653,7 @@ def generate_min_dependency_table(app): f.write(output) -def generate_min_dependency_substitutions(app): +def generate_min_dependency_substitutions(): """Generate min dependency substitutions for docs.""" from sklearn._min_dependencies import dependent_packages @@ -669,6 +670,60 @@ def generate_min_dependency_substitutions(app): f.write(output) +def generate_api_reference(): + from sklearn._api_reference import ( + API_REFERENCE, + DEPRECATED_API_REFERENCE, + get_api_reference_rst, + get_deprecated_api_reference_rst, + ) + + # Create the directory if it does not already exist + api_dir = Path(".") / "api" + api_dir.mkdir(exist_ok=True) + + # Write API reference for each module + for module in API_REFERENCE: + with (api_dir / f"{module}.rst").open("w") as f: + f.write(get_api_reference_rst(module)) + + # Write the API reference index page + with (api_dir / "index.rst").open("w") as f: + f.write(".. _api_ref:\n\n") + f.write("=============\n") + f.write("API Reference\n") + f.write("=============\n\n") + f.write( + "This is the class and function reference of scikit-learn. Please refer to " + "the :ref:`full user guide ` for further details, as the raw " + "specifications of classes and functions may not be enough to give full " + "guidelines on their uses. For reference on concepts repeated across the" + "API, see :ref:`glossary`.\n\n" + ) + + # Define the toctree + f.write(".. toctree::\n") + f.write(" :maxdepth: 2\n") + f.write(" :hidden:\n\n") + sorted_module_names = sorted(API_REFERENCE) + for module_name in sorted_module_names: + f.write(f" {module_name}\n") + f.write("\n") + + # Write the module table + f.write(".. list-table::\n\n") + for module_name in sorted_module_names: + f.write(f" * - :mod:`{module_name}`\n") + f.write(f" - {API_REFERENCE[module_name]['short_summary']}\n\n") + + # Write deprecated API + if DEPRECATED_API_REFERENCE: + f.write("Recently deprecated\n") + f.write("===================\n\n") + for ver in sorted(DEPRECATED_API_REFERENCE, key=parse, reverse=True): + f.write(get_deprecated_api_reference_rst(ver)) + + # Config for sphinx_issues # we use the issues path for PRs since the issues URL will forward @@ -684,8 +739,6 @@ def setup(app): # do not run the examples when using linkcheck by using a small priority # (default priority is 500 and sphinx-gallery using builder-inited event too) app.connect("builder-inited", disable_plot_gallery_for_linkcheck, priority=50) - app.connect("builder-inited", generate_min_dependency_table) - app.connect("builder-inited", generate_min_dependency_substitutions) # to hide/show the prompt in code examples: app.connect("build-finished", make_carousel_thumbs) @@ -822,3 +875,9 @@ def setup(app): linkcheck_request_headers = { "https://github.com/": {"Authorization": f"token {github_token}"}, } + + +# Write the files in advance to avoid any potential conflict with other extensions +generate_min_dependency_table() +generate_min_dependency_substitutions() +generate_api_reference() diff --git a/doc/contents.rst b/doc/contents.rst index a28634621d558..b0d6deeaf8837 100644 --- a/doc/contents.rst +++ b/doc/contents.rst @@ -20,5 +20,5 @@ Table Of Contents user_guide glossary auto_examples/index - modules/classes + api/index developers/index diff --git a/doc/make.bat b/doc/make.bat index b7e269a6a7836..abe216331701a 100644 --- a/doc/make.bat +++ b/doc/make.bat @@ -31,6 +31,9 @@ if "%1" == "help" ( if "%1" == "clean" ( for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i del /q /s %BUILDDIR%\* + if exist api\ ( + rmdir /q /s api + ) goto end ) diff --git a/doc/modules/classes.rst b/doc/modules/classes.rst deleted file mode 100644 index 55336389f93d5..0000000000000 --- a/doc/modules/classes.rst +++ /dev/null @@ -1,1904 +0,0 @@ -.. _api_ref: - -============= -API Reference -============= - -This is the class and function reference of scikit-learn. Please refer to -the :ref:`full user guide ` for further details, as the class and -function raw specifications may not be enough to give full guidelines on their -uses. -For reference on concepts repeated across the API, see :ref:`glossary`. - -:mod:`sklearn`: Settings and information tools -============================================== - -.. automodule:: sklearn - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - config_context - get_config - set_config - show_versions - -:mod:`sklearn.base`: Base classes and utility functions -======================================================= - -.. automodule:: sklearn.base - :no-members: - :no-inherited-members: - -Base classes ------------- -.. currentmodule:: sklearn - -.. autosummary:: - :nosignatures: - :toctree: generated/ - :template: class.rst - - base.BaseEstimator - base.BiclusterMixin - base.ClassifierMixin - base.ClusterMixin - base.DensityMixin - base.RegressorMixin - base.TransformerMixin - base.MetaEstimatorMixin - base.OneToOneFeatureMixin - base.OutlierMixin - base.ClassNamePrefixFeaturesOutMixin - feature_selection.SelectorMixin - -Functions ---------- -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - base.clone - base.is_classifier - base.is_regressor - -.. _calibration_ref: - -:mod:`sklearn.calibration`: Probability Calibration -=================================================== - -.. automodule:: sklearn.calibration - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`calibration` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - calibration.CalibratedClassifierCV - - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - calibration.calibration_curve - -.. _cluster_ref: - -:mod:`sklearn.cluster`: Clustering -================================== - -.. automodule:: sklearn.cluster - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`clustering` and :ref:`biclustering` sections for -further details. - -Classes -------- -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - cluster.AffinityPropagation - cluster.AgglomerativeClustering - cluster.Birch - cluster.DBSCAN - cluster.HDBSCAN - cluster.FeatureAgglomeration - cluster.KMeans - cluster.BisectingKMeans - cluster.MiniBatchKMeans - cluster.MeanShift - cluster.OPTICS - cluster.SpectralClustering - cluster.SpectralBiclustering - cluster.SpectralCoclustering - -Functions ---------- -.. autosummary:: - :toctree: generated/ - :template: function.rst - - cluster.affinity_propagation - cluster.cluster_optics_dbscan - cluster.cluster_optics_xi - cluster.compute_optics_graph - cluster.dbscan - cluster.estimate_bandwidth - cluster.k_means - cluster.kmeans_plusplus - cluster.mean_shift - cluster.spectral_clustering - cluster.ward_tree - -.. _compose_ref: - -:mod:`sklearn.compose`: Composite Estimators -============================================ - -.. automodule:: sklearn.compose - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`combining_estimators` section for further -details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - compose.ColumnTransformer - compose.TransformedTargetRegressor - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - compose.make_column_transformer - compose.make_column_selector - -.. _covariance_ref: - -:mod:`sklearn.covariance`: Covariance Estimators -================================================ - -.. automodule:: sklearn.covariance - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`covariance` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - covariance.EmpiricalCovariance - covariance.EllipticEnvelope - covariance.GraphicalLasso - covariance.GraphicalLassoCV - covariance.LedoitWolf - covariance.MinCovDet - covariance.OAS - covariance.ShrunkCovariance - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - covariance.empirical_covariance - covariance.graphical_lasso - covariance.ledoit_wolf - covariance.ledoit_wolf_shrinkage - covariance.oas - covariance.shrunk_covariance - -.. _cross_decomposition_ref: - -:mod:`sklearn.cross_decomposition`: Cross decomposition -======================================================= - -.. automodule:: sklearn.cross_decomposition - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`cross_decomposition` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - cross_decomposition.CCA - cross_decomposition.PLSCanonical - cross_decomposition.PLSRegression - cross_decomposition.PLSSVD - -.. _datasets_ref: - -:mod:`sklearn.datasets`: Datasets -================================= - -.. automodule:: sklearn.datasets - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`datasets` section for further details. - -Loaders -------- - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - datasets.clear_data_home - datasets.dump_svmlight_file - datasets.fetch_20newsgroups - datasets.fetch_20newsgroups_vectorized - datasets.fetch_california_housing - datasets.fetch_covtype - datasets.fetch_kddcup99 - datasets.fetch_lfw_pairs - datasets.fetch_lfw_people - datasets.fetch_olivetti_faces - datasets.fetch_openml - datasets.fetch_rcv1 - datasets.fetch_species_distributions - datasets.get_data_home - datasets.load_breast_cancer - datasets.load_diabetes - datasets.load_digits - datasets.load_files - datasets.load_iris - datasets.load_linnerud - datasets.load_sample_image - datasets.load_sample_images - datasets.load_svmlight_file - datasets.load_svmlight_files - datasets.load_wine - -Samples generator ------------------ - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - datasets.make_biclusters - datasets.make_blobs - datasets.make_checkerboard - datasets.make_circles - datasets.make_classification - datasets.make_friedman1 - datasets.make_friedman2 - datasets.make_friedman3 - datasets.make_gaussian_quantiles - datasets.make_hastie_10_2 - datasets.make_low_rank_matrix - datasets.make_moons - datasets.make_multilabel_classification - datasets.make_regression - datasets.make_s_curve - datasets.make_sparse_coded_signal - datasets.make_sparse_spd_matrix - datasets.make_sparse_uncorrelated - datasets.make_spd_matrix - datasets.make_swiss_roll - - -.. _decomposition_ref: - -:mod:`sklearn.decomposition`: Matrix Decomposition -================================================== - -.. automodule:: sklearn.decomposition - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`decompositions` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - decomposition.DictionaryLearning - decomposition.FactorAnalysis - decomposition.FastICA - decomposition.IncrementalPCA - decomposition.KernelPCA - decomposition.LatentDirichletAllocation - decomposition.MiniBatchDictionaryLearning - decomposition.MiniBatchSparsePCA - decomposition.NMF - decomposition.MiniBatchNMF - decomposition.PCA - decomposition.SparsePCA - decomposition.SparseCoder - decomposition.TruncatedSVD - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - decomposition.dict_learning - decomposition.dict_learning_online - decomposition.fastica - decomposition.non_negative_factorization - decomposition.sparse_encode - -.. _lda_ref: - -:mod:`sklearn.discriminant_analysis`: Discriminant Analysis -=========================================================== - -.. automodule:: sklearn.discriminant_analysis - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`lda_qda` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - discriminant_analysis.LinearDiscriminantAnalysis - discriminant_analysis.QuadraticDiscriminantAnalysis - -.. _dummy_ref: - -:mod:`sklearn.dummy`: Dummy estimators -====================================== - -.. automodule:: sklearn.dummy - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`model_evaluation` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - dummy.DummyClassifier - dummy.DummyRegressor - -.. autosummary:: - :toctree: generated/ - :template: function.rst - -.. _ensemble_ref: - -:mod:`sklearn.ensemble`: Ensemble Methods -========================================= - -.. automodule:: sklearn.ensemble - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`ensemble` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - ensemble.AdaBoostClassifier - ensemble.AdaBoostRegressor - ensemble.BaggingClassifier - ensemble.BaggingRegressor - ensemble.ExtraTreesClassifier - ensemble.ExtraTreesRegressor - ensemble.GradientBoostingClassifier - ensemble.GradientBoostingRegressor - ensemble.IsolationForest - ensemble.RandomForestClassifier - ensemble.RandomForestRegressor - ensemble.RandomTreesEmbedding - ensemble.StackingClassifier - ensemble.StackingRegressor - ensemble.VotingClassifier - ensemble.VotingRegressor - ensemble.HistGradientBoostingRegressor - ensemble.HistGradientBoostingClassifier - - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - -.. _exceptions_ref: - -:mod:`sklearn.exceptions`: Exceptions and warnings -================================================== - -.. automodule:: sklearn.exceptions - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - exceptions.ConvergenceWarning - exceptions.DataConversionWarning - exceptions.DataDimensionalityWarning - exceptions.EfficiencyWarning - exceptions.FitFailedWarning - exceptions.InconsistentVersionWarning - exceptions.NotFittedError - exceptions.UndefinedMetricWarning - - -:mod:`sklearn.experimental`: Experimental -========================================= - -.. automodule:: sklearn.experimental - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - - experimental.enable_iterative_imputer - experimental.enable_halving_search_cv - - -.. _feature_extraction_ref: - -:mod:`sklearn.feature_extraction`: Feature Extraction -===================================================== - -.. automodule:: sklearn.feature_extraction - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`feature_extraction` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - feature_extraction.DictVectorizer - feature_extraction.FeatureHasher - -From images ------------ - -.. automodule:: sklearn.feature_extraction.image - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - feature_extraction.image.extract_patches_2d - feature_extraction.image.grid_to_graph - feature_extraction.image.img_to_graph - feature_extraction.image.reconstruct_from_patches_2d - - :template: class.rst - - feature_extraction.image.PatchExtractor - -.. _text_feature_extraction_ref: - -From text ---------- - -.. automodule:: sklearn.feature_extraction.text - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - feature_extraction.text.CountVectorizer - feature_extraction.text.HashingVectorizer - feature_extraction.text.TfidfTransformer - feature_extraction.text.TfidfVectorizer - - -.. _feature_selection_ref: - -:mod:`sklearn.feature_selection`: Feature Selection -=================================================== - -.. automodule:: sklearn.feature_selection - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`feature_selection` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - feature_selection.GenericUnivariateSelect - feature_selection.SelectPercentile - feature_selection.SelectKBest - feature_selection.SelectFpr - feature_selection.SelectFdr - feature_selection.SelectFromModel - feature_selection.SelectFwe - feature_selection.SequentialFeatureSelector - feature_selection.RFE - feature_selection.RFECV - feature_selection.VarianceThreshold - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - feature_selection.chi2 - feature_selection.f_classif - feature_selection.f_regression - feature_selection.r_regression - feature_selection.mutual_info_classif - feature_selection.mutual_info_regression - - -.. _gaussian_process_ref: - -:mod:`sklearn.gaussian_process`: Gaussian Processes -=================================================== - -.. automodule:: sklearn.gaussian_process - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`gaussian_process` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - gaussian_process.GaussianProcessClassifier - gaussian_process.GaussianProcessRegressor - -Kernels -------- - -.. automodule:: sklearn.gaussian_process.kernels - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class_with_call.rst - - gaussian_process.kernels.CompoundKernel - gaussian_process.kernels.ConstantKernel - gaussian_process.kernels.DotProduct - gaussian_process.kernels.ExpSineSquared - gaussian_process.kernels.Exponentiation - gaussian_process.kernels.Hyperparameter - gaussian_process.kernels.Kernel - gaussian_process.kernels.Matern - gaussian_process.kernels.PairwiseKernel - gaussian_process.kernels.Product - gaussian_process.kernels.RBF - gaussian_process.kernels.RationalQuadratic - gaussian_process.kernels.Sum - gaussian_process.kernels.WhiteKernel - - -.. _impute_ref: - -:mod:`sklearn.impute`: Impute -============================= - -.. automodule:: sklearn.impute - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`Impute` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - impute.SimpleImputer - impute.IterativeImputer - impute.MissingIndicator - impute.KNNImputer - - -.. _inspection_ref: - -:mod:`sklearn.inspection`: Inspection -===================================== - -.. automodule:: sklearn.inspection - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - inspection.partial_dependence - inspection.permutation_importance - -Plotting --------- - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: display_only_from_estimator.rst - - inspection.DecisionBoundaryDisplay - inspection.PartialDependenceDisplay - -.. _isotonic_ref: - -:mod:`sklearn.isotonic`: Isotonic regression -============================================ - -.. automodule:: sklearn.isotonic - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`isotonic` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - isotonic.IsotonicRegression - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - isotonic.check_increasing - isotonic.isotonic_regression - - -.. _kernel_approximation_ref: - -:mod:`sklearn.kernel_approximation`: Kernel Approximation -========================================================= - -.. automodule:: sklearn.kernel_approximation - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`kernel_approximation` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - kernel_approximation.AdditiveChi2Sampler - kernel_approximation.Nystroem - kernel_approximation.PolynomialCountSketch - kernel_approximation.RBFSampler - kernel_approximation.SkewedChi2Sampler - -.. _kernel_ridge_ref: - -:mod:`sklearn.kernel_ridge`: Kernel Ridge Regression -==================================================== - -.. automodule:: sklearn.kernel_ridge - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`kernel_ridge` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - kernel_ridge.KernelRidge - -.. _linear_model_ref: - -:mod:`sklearn.linear_model`: Linear Models -========================================== - -.. automodule:: sklearn.linear_model - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`linear_model` section for further details. - -The following subsections are only rough guidelines: the same estimator can -fall into multiple categories, depending on its parameters. - -.. currentmodule:: sklearn - -Linear classifiers ------------------- -.. autosummary:: - :toctree: generated/ - :template: class.rst - - linear_model.LogisticRegression - linear_model.LogisticRegressionCV - linear_model.PassiveAggressiveClassifier - linear_model.Perceptron - linear_model.RidgeClassifier - linear_model.RidgeClassifierCV - linear_model.SGDClassifier - linear_model.SGDOneClassSVM - -Classical linear regressors ---------------------------- - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - linear_model.LinearRegression - linear_model.Ridge - linear_model.RidgeCV - linear_model.SGDRegressor - -Regressors with variable selection ----------------------------------- - -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:: - :toctree: generated/ - :template: class.rst - - linear_model.ElasticNet - linear_model.ElasticNetCV - linear_model.Lars - linear_model.LarsCV - linear_model.Lasso - linear_model.LassoCV - linear_model.LassoLars - linear_model.LassoLarsCV - linear_model.LassoLarsIC - linear_model.OrthogonalMatchingPursuit - linear_model.OrthogonalMatchingPursuitCV - -Bayesian regressors -------------------- - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - linear_model.ARDRegression - linear_model.BayesianRidge - -Multi-task linear regressors with variable selection ----------------------------------------------------- - -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:: - :toctree: generated/ - :template: class.rst - - linear_model.MultiTaskElasticNet - linear_model.MultiTaskElasticNetCV - linear_model.MultiTaskLasso - linear_model.MultiTaskLassoCV - -Outlier-robust regressors -------------------------- - -Any estimator using the Huber loss would also be robust to outliers, e.g. -:class:`~linear_model.SGDRegressor` with ``loss='huber'``. - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - linear_model.HuberRegressor - linear_model.QuantileRegressor - linear_model.RANSACRegressor - linear_model.TheilSenRegressor - -Generalized linear models (GLM) for regression ----------------------------------------------- - -These models allow for response variables to have error distributions other -than a normal distribution: - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - linear_model.PoissonRegressor - linear_model.TweedieRegressor - linear_model.GammaRegressor - - -Miscellaneous -------------- - -.. autosummary:: - :toctree: generated/ - :template: classes.rst - - linear_model.PassiveAggressiveRegressor - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - linear_model.enet_path - linear_model.lars_path - linear_model.lars_path_gram - linear_model.lasso_path - linear_model.orthogonal_mp - linear_model.orthogonal_mp_gram - linear_model.ridge_regression - - -.. _manifold_ref: - -:mod:`sklearn.manifold`: Manifold Learning -========================================== - -.. automodule:: sklearn.manifold - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`manifold` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated - :template: class.rst - - manifold.Isomap - manifold.LocallyLinearEmbedding - manifold.MDS - manifold.SpectralEmbedding - manifold.TSNE - -.. autosummary:: - :toctree: generated - :template: function.rst - - manifold.locally_linear_embedding - manifold.smacof - manifold.spectral_embedding - manifold.trustworthiness - - -.. _metrics_ref: - -:mod:`sklearn.metrics`: Metrics -=============================== - -See the :ref:`model_evaluation` section and the :ref:`metrics` section of the -user guide for further details. - -.. automodule:: sklearn.metrics - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -Model Selection Interface -------------------------- -See the :ref:`scoring_parameter` section of the user guide for further -details. - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - metrics.check_scoring - metrics.get_scorer - metrics.get_scorer_names - metrics.make_scorer - -Classification metrics ----------------------- - -See the :ref:`classification_metrics` section of the user guide for further -details. - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - metrics.accuracy_score - metrics.auc - metrics.average_precision_score - metrics.balanced_accuracy_score - metrics.brier_score_loss - metrics.class_likelihood_ratios - metrics.classification_report - metrics.cohen_kappa_score - metrics.confusion_matrix - metrics.dcg_score - metrics.det_curve - metrics.f1_score - metrics.fbeta_score - metrics.hamming_loss - metrics.hinge_loss - metrics.jaccard_score - metrics.log_loss - metrics.matthews_corrcoef - metrics.multilabel_confusion_matrix - metrics.ndcg_score - metrics.precision_recall_curve - metrics.precision_recall_fscore_support - metrics.precision_score - metrics.recall_score - metrics.roc_auc_score - metrics.roc_curve - metrics.top_k_accuracy_score - metrics.zero_one_loss - -Regression metrics ------------------- - -See the :ref:`regression_metrics` section of the user guide for further -details. - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - metrics.explained_variance_score - metrics.max_error - metrics.mean_absolute_error - metrics.mean_squared_error - metrics.mean_squared_log_error - metrics.median_absolute_error - metrics.mean_absolute_percentage_error - metrics.r2_score - metrics.root_mean_squared_log_error - metrics.root_mean_squared_error - metrics.mean_poisson_deviance - metrics.mean_gamma_deviance - metrics.mean_tweedie_deviance - metrics.d2_tweedie_score - metrics.mean_pinball_loss - metrics.d2_pinball_score - metrics.d2_absolute_error_score - -Multilabel ranking metrics --------------------------- -See the :ref:`multilabel_ranking_metrics` section of the user guide for further -details. - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - metrics.coverage_error - metrics.label_ranking_average_precision_score - metrics.label_ranking_loss - - -Clustering metrics ------------------- - -See the :ref:`clustering_evaluation` section of the user guide for further -details. - -.. automodule:: sklearn.metrics.cluster - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - metrics.adjusted_mutual_info_score - metrics.adjusted_rand_score - metrics.calinski_harabasz_score - metrics.davies_bouldin_score - metrics.completeness_score - metrics.cluster.contingency_matrix - metrics.cluster.pair_confusion_matrix - metrics.fowlkes_mallows_score - metrics.homogeneity_completeness_v_measure - metrics.homogeneity_score - metrics.mutual_info_score - metrics.normalized_mutual_info_score - metrics.rand_score - metrics.silhouette_score - metrics.silhouette_samples - metrics.v_measure_score - -Biclustering metrics --------------------- - -See the :ref:`biclustering_evaluation` section of the user guide for -further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - metrics.consensus_score - -Distance metrics ----------------- - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - metrics.DistanceMetric - -Pairwise metrics ----------------- - -See the :ref:`metrics` section of the user guide for further details. - -.. automodule:: sklearn.metrics.pairwise - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - metrics.pairwise.additive_chi2_kernel - metrics.pairwise.chi2_kernel - metrics.pairwise.cosine_similarity - metrics.pairwise.cosine_distances - metrics.pairwise.distance_metrics - metrics.pairwise.euclidean_distances - metrics.pairwise.haversine_distances - metrics.pairwise.kernel_metrics - metrics.pairwise.laplacian_kernel - metrics.pairwise.linear_kernel - metrics.pairwise.manhattan_distances - metrics.pairwise.nan_euclidean_distances - metrics.pairwise.pairwise_kernels - metrics.pairwise.polynomial_kernel - metrics.pairwise.rbf_kernel - metrics.pairwise.sigmoid_kernel - metrics.pairwise.paired_euclidean_distances - metrics.pairwise.paired_manhattan_distances - metrics.pairwise.paired_cosine_distances - metrics.pairwise.paired_distances - metrics.pairwise_distances - metrics.pairwise_distances_argmin - metrics.pairwise_distances_argmin_min - metrics.pairwise_distances_chunked - - -Plotting --------- - -See the :ref:`visualizations` section of the user guide for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: display_all_class_methods.rst - - metrics.ConfusionMatrixDisplay - metrics.DetCurveDisplay - metrics.PrecisionRecallDisplay - metrics.PredictionErrorDisplay - metrics.RocCurveDisplay - calibration.CalibrationDisplay - -.. _mixture_ref: - -:mod:`sklearn.mixture`: Gaussian Mixture Models -=============================================== - -.. automodule:: sklearn.mixture - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`mixture` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - mixture.BayesianGaussianMixture - mixture.GaussianMixture - -.. _modelselection_ref: - -:mod:`sklearn.model_selection`: Model Selection -=============================================== - -.. automodule:: sklearn.model_selection - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`cross_validation`, :ref:`grid_search` and -:ref:`learning_curve` sections for further details. - -Splitter Classes ----------------- - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - model_selection.GroupKFold - model_selection.GroupShuffleSplit - model_selection.KFold - model_selection.LeaveOneGroupOut - model_selection.LeavePGroupsOut - model_selection.LeaveOneOut - model_selection.LeavePOut - model_selection.PredefinedSplit - model_selection.RepeatedKFold - model_selection.RepeatedStratifiedKFold - model_selection.ShuffleSplit - model_selection.StratifiedKFold - model_selection.StratifiedShuffleSplit - model_selection.StratifiedGroupKFold - model_selection.TimeSeriesSplit - -Splitter Functions ------------------- - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - model_selection.check_cv - model_selection.train_test_split - -.. _hyper_parameter_optimizers: - -Hyper-parameter optimizers --------------------------- - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - model_selection.GridSearchCV - model_selection.HalvingGridSearchCV - model_selection.ParameterGrid - model_selection.ParameterSampler - model_selection.RandomizedSearchCV - model_selection.HalvingRandomSearchCV - - -Model validation ----------------- - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - model_selection.cross_validate - model_selection.cross_val_predict - model_selection.cross_val_score - model_selection.learning_curve - model_selection.permutation_test_score - model_selection.validation_curve - -Visualization -------------- - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: display_only_from_estimator.rst - - model_selection.LearningCurveDisplay - model_selection.ValidationCurveDisplay - -.. _multiclass_ref: - -:mod:`sklearn.multiclass`: Multiclass classification -==================================================== - -.. automodule:: sklearn.multiclass - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`multiclass_classification` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - multiclass.OneVsRestClassifier - multiclass.OneVsOneClassifier - multiclass.OutputCodeClassifier - -.. _multioutput_ref: - -:mod:`sklearn.multioutput`: Multioutput regression and classification -===================================================================== - -.. automodule:: sklearn.multioutput - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`multilabel_classification`, -:ref:`multiclass_multioutput_classification`, and -:ref:`multioutput_regression` sections for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated - :template: class.rst - - multioutput.ClassifierChain - multioutput.MultiOutputRegressor - multioutput.MultiOutputClassifier - multioutput.RegressorChain - -.. _naive_bayes_ref: - -:mod:`sklearn.naive_bayes`: Naive Bayes -======================================= - -.. automodule:: sklearn.naive_bayes - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`naive_bayes` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - naive_bayes.BernoulliNB - naive_bayes.CategoricalNB - naive_bayes.ComplementNB - naive_bayes.GaussianNB - naive_bayes.MultinomialNB - - -.. _neighbors_ref: - -:mod:`sklearn.neighbors`: Nearest Neighbors -=========================================== - -.. automodule:: sklearn.neighbors - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`neighbors` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - neighbors.BallTree - neighbors.KDTree - neighbors.KernelDensity - neighbors.KNeighborsClassifier - neighbors.KNeighborsRegressor - neighbors.KNeighborsTransformer - neighbors.LocalOutlierFactor - neighbors.RadiusNeighborsClassifier - neighbors.RadiusNeighborsRegressor - neighbors.RadiusNeighborsTransformer - neighbors.NearestCentroid - neighbors.NearestNeighbors - neighbors.NeighborhoodComponentsAnalysis - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - neighbors.kneighbors_graph - neighbors.radius_neighbors_graph - neighbors.sort_graph_by_row_values - -.. _neural_network_ref: - -:mod:`sklearn.neural_network`: Neural network models -==================================================== - -.. automodule:: sklearn.neural_network - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`neural_networks_supervised` and :ref:`neural_networks_unsupervised` sections for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - neural_network.BernoulliRBM - neural_network.MLPClassifier - neural_network.MLPRegressor - -.. _pipeline_ref: - -:mod:`sklearn.pipeline`: Pipeline -================================= - -.. automodule:: sklearn.pipeline - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`combining_estimators` section for further -details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - pipeline.FeatureUnion - pipeline.Pipeline - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - pipeline.make_pipeline - pipeline.make_union - -.. _preprocessing_ref: - -:mod:`sklearn.preprocessing`: Preprocessing and Normalization -============================================================= - -.. automodule:: sklearn.preprocessing - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`preprocessing` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - preprocessing.Binarizer - preprocessing.FunctionTransformer - preprocessing.KBinsDiscretizer - preprocessing.KernelCenterer - preprocessing.LabelBinarizer - preprocessing.LabelEncoder - preprocessing.MultiLabelBinarizer - preprocessing.MaxAbsScaler - preprocessing.MinMaxScaler - preprocessing.Normalizer - preprocessing.OneHotEncoder - preprocessing.OrdinalEncoder - preprocessing.PolynomialFeatures - preprocessing.PowerTransformer - preprocessing.QuantileTransformer - preprocessing.RobustScaler - preprocessing.SplineTransformer - preprocessing.StandardScaler - preprocessing.TargetEncoder - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - preprocessing.add_dummy_feature - preprocessing.binarize - preprocessing.label_binarize - preprocessing.maxabs_scale - preprocessing.minmax_scale - preprocessing.normalize - preprocessing.quantile_transform - preprocessing.robust_scale - preprocessing.scale - preprocessing.power_transform - - -.. _random_projection_ref: - -:mod:`sklearn.random_projection`: Random projection -=================================================== - -.. automodule:: sklearn.random_projection - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`random_projection` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - random_projection.GaussianRandomProjection - random_projection.SparseRandomProjection - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - random_projection.johnson_lindenstrauss_min_dim - - -.. _semi_supervised_ref: - -:mod:`sklearn.semi_supervised`: Semi-Supervised Learning -======================================================== - -.. automodule:: sklearn.semi_supervised - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`semi_supervised` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - semi_supervised.LabelPropagation - semi_supervised.LabelSpreading - semi_supervised.SelfTrainingClassifier - - -.. _svm_ref: - -:mod:`sklearn.svm`: Support Vector Machines -=========================================== - -.. automodule:: sklearn.svm - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`svm` section for further details. - -Estimators ----------- - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - svm.LinearSVC - svm.LinearSVR - svm.NuSVC - svm.NuSVR - svm.OneClassSVM - svm.SVC - svm.SVR - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - svm.l1_min_c - -.. _tree_ref: - -:mod:`sklearn.tree`: Decision Trees -=================================== - -.. automodule:: sklearn.tree - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`tree` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - tree.DecisionTreeClassifier - tree.DecisionTreeRegressor - tree.ExtraTreeClassifier - tree.ExtraTreeRegressor - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - tree.export_graphviz - tree.export_text - -Plotting --------- - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - tree.plot_tree - -.. _utils_ref: - -:mod:`sklearn.utils`: Utilities -=============================== - -.. automodule:: sklearn.utils - :no-members: - :no-inherited-members: - -**Developer guide:** See the :ref:`developers-utils` page for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - utils.Bunch - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.as_float_array - utils.assert_all_finite - utils.deprecated - utils.estimator_html_repr - utils.gen_batches - utils.gen_even_slices - utils.indexable - utils.murmurhash3_32 - utils.resample - utils._safe_indexing - utils.safe_mask - utils.safe_sqr - utils.shuffle - -Input and parameter validation ------------------------------- - -.. automodule:: sklearn.utils.validation - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.check_X_y - utils.check_array - utils.check_scalar - utils.check_consistent_length - utils.check_random_state - utils.validation.check_is_fitted - utils.validation.check_memory - utils.validation.check_symmetric - utils.validation.column_or_1d - utils.validation.has_fit_parameter - -Utilities used in meta-estimators ---------------------------------- - -.. automodule:: sklearn.utils.metaestimators - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.metaestimators.available_if - -Utilities to handle weights based on class labels -------------------------------------------------- - -.. automodule:: sklearn.utils.class_weight - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.class_weight.compute_class_weight - utils.class_weight.compute_sample_weight - -Utilities to deal with multiclass target in classifiers -------------------------------------------------------- - -.. automodule:: sklearn.utils.multiclass - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.multiclass.type_of_target - utils.multiclass.is_multilabel - utils.multiclass.unique_labels - -Utilities for optimal mathematical operations ---------------------------------------------- - -.. automodule:: sklearn.utils.extmath - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.extmath.safe_sparse_dot - utils.extmath.randomized_range_finder - utils.extmath.randomized_svd - utils.extmath.fast_logdet - utils.extmath.density - utils.extmath.weighted_mode - -Utilities to work with sparse matrices and arrays -------------------------------------------------- - -.. automodule:: sklearn.utils.sparsefuncs - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.sparsefuncs.incr_mean_variance_axis - utils.sparsefuncs.inplace_column_scale - utils.sparsefuncs.inplace_row_scale - utils.sparsefuncs.inplace_swap_row - utils.sparsefuncs.inplace_swap_column - utils.sparsefuncs.mean_variance_axis - utils.sparsefuncs.inplace_csr_column_scale - -.. automodule:: sklearn.utils.sparsefuncs_fast - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.sparsefuncs_fast.inplace_csr_row_normalize_l1 - utils.sparsefuncs_fast.inplace_csr_row_normalize_l2 - -Utilities to work with graphs ------------------------------ - -.. automodule:: sklearn.utils.graph - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.graph.single_source_shortest_path_length - -Utilities for random sampling ------------------------------ - -.. automodule:: sklearn.utils.random - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.random.sample_without_replacement - - -Utilities to operate on arrays ------------------------------- - -.. automodule:: sklearn.utils.arrayfuncs - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.arrayfuncs.min_pos - -Metadata routing ----------------- - -.. automodule:: sklearn.utils.metadata_routing - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.metadata_routing.get_routing_for_object - utils.metadata_routing.process_routing - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - utils.metadata_routing.MetadataRouter - utils.metadata_routing.MetadataRequest - utils.metadata_routing.MethodMapping - -Scikit-learn object discovery ------------------------------ - -.. automodule:: sklearn.utils.discovery - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.discovery.all_estimators - utils.discovery.all_displays - utils.discovery.all_functions - -Scikit-learn compatibility checker ----------------------------------- - -.. automodule:: sklearn.utils.estimator_checks - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.estimator_checks.check_estimator - utils.estimator_checks.parametrize_with_checks - -Utilities for parallel computing --------------------------------- - -.. automodule:: sklearn.utils.parallel - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.parallel.delayed - utils.parallel_backend - utils.register_parallel_backend - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - utils.parallel.Parallel - - -Recently deprecated -=================== diff --git a/doc/modules/feature_extraction.rst b/doc/modules/feature_extraction.rst index 7ac538a89849b..ffe8ea9d98e61 100644 --- a/doc/modules/feature_extraction.rst +++ b/doc/modules/feature_extraction.rst @@ -310,7 +310,7 @@ counting in a single class:: This model has many parameters, however the default values are quite reasonable (please see the :ref:`reference documentation -` for the details):: +` for the details):: >>> vectorizer = CountVectorizer() >>> vectorizer @@ -492,7 +492,7 @@ class:: TfidfTransformer(smooth_idf=False) Again please see the :ref:`reference documentation -` for the details on all the parameters. +` for the details on all the parameters. |details-start| **Numeric example of a tf-idf matrix** diff --git a/doc/templates/deprecated_class.rst b/doc/templates/deprecated_class.rst deleted file mode 100644 index 5c31936f6fc36..0000000000000 --- a/doc/templates/deprecated_class.rst +++ /dev/null @@ -1,28 +0,0 @@ -.. - The empty line below should not be removed. It is added such that the `rst_prolog` - is added before the :mod: directive. Otherwise, the rendering will show as a - paragraph instead of a header. - -:mod:`{{module}}`.{{objname}} -{{ underline }}============== - -.. meta:: - :robots: noindex - -.. warning:: - **DEPRECATED** - - -.. currentmodule:: {{ module }} - -.. autoclass:: {{ objname }} - - {% block methods %} - .. automethod:: __init__ - {% endblock %} - -.. include:: {{module}}.{{objname}}.examples - -.. raw:: html - -
diff --git a/doc/templates/deprecated_class_with_call.rst b/doc/templates/deprecated_class_with_call.rst deleted file mode 100644 index 072a31112be50..0000000000000 --- a/doc/templates/deprecated_class_with_call.rst +++ /dev/null @@ -1,29 +0,0 @@ -.. - The empty line below should not be removed. It is added such that the `rst_prolog` - is added before the :mod: directive. Otherwise, the rendering will show as a - paragraph instead of a header. - -:mod:`{{module}}`.{{objname}} -{{ underline }}=============== - -.. meta:: - :robots: noindex - -.. warning:: - **DEPRECATED** - - -.. currentmodule:: {{ module }} - -.. autoclass:: {{ objname }} - - {% block methods %} - .. automethod:: __init__ - .. automethod:: __call__ - {% endblock %} - -.. include:: {{module}}.{{objname}}.examples - -.. raw:: html - -
diff --git a/doc/templates/deprecated_class_without_init.rst b/doc/templates/deprecated_class_without_init.rst deleted file mode 100644 index a26afbead5451..0000000000000 --- a/doc/templates/deprecated_class_without_init.rst +++ /dev/null @@ -1,24 +0,0 @@ -.. - The empty line below should not be removed. It is added such that the `rst_prolog` - is added before the :mod: directive. Otherwise, the rendering will show as a - paragraph instead of a header. - -:mod:`{{module}}`.{{objname}} -{{ underline }}============== - -.. meta:: - :robots: noindex - -.. warning:: - **DEPRECATED** - - -.. currentmodule:: {{ module }} - -.. autoclass:: {{ objname }} - -.. include:: {{module}}.{{objname}}.examples - -.. raw:: html - -
diff --git a/doc/templates/deprecated_function.rst b/doc/templates/deprecated_function.rst deleted file mode 100644 index ead5abec27076..0000000000000 --- a/doc/templates/deprecated_function.rst +++ /dev/null @@ -1,24 +0,0 @@ -.. - The empty line below should not be removed. It is added such that the `rst_prolog` - is added before the :mod: directive. Otherwise, the rendering will show as a - paragraph instead of a header. - -:mod:`{{module}}`.{{objname}} -{{ underline }}==================== - -.. meta:: - :robots: noindex - -.. warning:: - **DEPRECATED** - - -.. currentmodule:: {{ module }} - -.. autofunction:: {{ objname }} - -.. include:: {{module}}.{{objname}}.examples - -.. raw:: html - -
diff --git a/doc/templates/generate_deprecated.sh b/doc/templates/generate_deprecated.sh deleted file mode 100755 index a7301fb5dc419..0000000000000 --- a/doc/templates/generate_deprecated.sh +++ /dev/null @@ -1,8 +0,0 @@ -#!/bin/bash -for f in [^d]*; do (head -n2 < $f; echo ' -.. meta:: - :robots: noindex - -.. warning:: - **DEPRECATED** -'; tail -n+3 $f) > deprecated_$f; done diff --git a/doc/themes/scikit-learn-modern/nav.html b/doc/themes/scikit-learn-modern/nav.html index 14d82e2e46e95..2ff3dadb15c6b 100644 --- a/doc/themes/scikit-learn-modern/nav.html +++ b/doc/themes/scikit-learn-modern/nav.html @@ -65,7 +65,7 @@ User Guide
  • - API + API
  • Examples diff --git a/sklearn/_api_reference.py b/sklearn/_api_reference.py new file mode 100644 index 0000000000000..fbfcf7d95887e --- /dev/null +++ b/sklearn/_api_reference.py @@ -0,0 +1,1945 @@ +"""Configuration for the API reference documentation. + +CONFIGURING AN AUTOSUMMARY BLOCK +================================ + +An autosummary block is configured as a list of dictionaries, each containing "template" +that maps to a template name available under `doc/templates/` or `None` if no custom +template is needed, and "entries" that maps to a list of functions/classes/etc. that +uses the corresponding template. For instance, we have the following translation: + +|---------------------------------------------| |--------------------------------------| +| [ | | | +| { | | .. autosummary:: | +| "template": "class", | | :toctree: ../modules/generated/ | +| "entries": [ | | :template: class.rst | +| "ColumnTransformer", | | | +| "TransformedTargetRegressor", | | ColumnTransformer | +| ], | | TransformedTargetRegressor | +| }, | | | +| { | | :template: function.rst | +| "template": "function", | | | +| "entries": [ | | make_column_selector | +| "make_column_transformer", | | make_column_transformer | +| "make_column_selector", | | | +| ], | | | +| }, | | | +| ], | | | +|---------------------------------------------| |--------------------------------------| + +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`. + +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. +""" + +from io import StringIO + + +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. Note + that the generated rst does not include any leading or trailing newlines. + """ + 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. + + Note that the generated string does not include any leading or trailing newlines, + so one must manually take care of that to ensure valid rst syntax. + """ + lines = [ + f".. automodule:: {module_name}.{submodule_name}", + " :no-members:", + " :no-inherited-members:\n", + f".. currentmodule:: {module_name}", + ] + return "\n\n" + "\n".join(lines) + "\n\n" + + +API_REFERENCE = { + "sklearn": { + "short_summary": "Settings and information tools.", + "description": None, + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "function", + "entries": [ + "config_context", + "get_config", + "set_config", + "show_versions", + ], + }, + ], + }, + ], + }, + "sklearn.base": { + "short_summary": "Base classes and utility functions.", + "description": None, + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": [ + "BaseEstimator", + "BiclusterMixin", + "ClassifierMixin", + "ClusterMixin", + "DensityMixin", + "RegressorMixin", + "TransformerMixin", + "MetaEstimatorMixin", + "OneToOneFeatureMixin", + "OutlierMixin", + "ClassNamePrefixFeaturesOutMixin", + ], + }, + { + "template": "function", + "entries": [ + "clone", + "is_classifier", + "is_regressor", + ], + }, + ], + } + ], + }, + "sklearn.calibration": { + "short_summary": "Probability calibration.", + "description": _get_guide("calibration"), + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": ["CalibratedClassifierCV"], + }, + { + "template": "function", + "entries": ["calibration_curve"], + }, + ], + }, + { + "title": "Visualization", + "autosummary": [ + { + "template": "display_all_class_methods", + "entries": ["CalibrationDisplay"], + }, + ], + }, + ], + }, + "sklearn.cluster": { + "short_summary": "Clustering.", + "description": _get_guide("clustering", "biclustering"), + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": [ + "AffinityPropagation", + "AgglomerativeClustering", + "Birch", + "DBSCAN", + "HDBSCAN", + "FeatureAgglomeration", + "KMeans", + "BisectingKMeans", + "MiniBatchKMeans", + "MeanShift", + "OPTICS", + "SpectralClustering", + "SpectralBiclustering", + "SpectralCoclustering", + ], + }, + { + "template": "function", + "entries": [ + "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": [ + { + "template": "class", + "entries": [ + "ColumnTransformer", + "TransformedTargetRegressor", + ], + }, + { + "template": "function", + "entries": [ + "make_column_transformer", + "make_column_selector", + ], + }, + ], + }, + ], + }, + "sklearn.covariance": { + "short_summary": "Covariance estimation.", + "description": _get_guide("covariance"), + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": [ + "EmpiricalCovariance", + "EllipticEnvelope", + "GraphicalLasso", + "GraphicalLassoCV", + "LedoitWolf", + "MinCovDet", + "OAS", + "ShrunkCovariance", + ], + }, + { + "template": "function", + "entries": [ + "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": [ + { + "template": "class", + "entries": [ + "CCA", + "PLSCanonical", + "PLSRegression", + "PLSSVD", + ], + }, + ], + }, + ], + }, + "sklearn.datasets": { + "short_summary": "Datasets.", + "description": _get_guide("datasets"), + "sections": [ + { + "title": "Loaders", + "autosummary": [ + { + "template": "function", + "entries": [ + "clear_data_home", + "dump_svmlight_file", + "fetch_20newsgroups", + "fetch_20newsgroups_vectorized", + "fetch_california_housing", + "fetch_covtype", + "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": [ + { + "template": "function", + "entries": [ + "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": [ + { + "template": "class", + "entries": [ + "DictionaryLearning", + "FactorAnalysis", + "FastICA", + "IncrementalPCA", + "KernelPCA", + "LatentDirichletAllocation", + "MiniBatchDictionaryLearning", + "MiniBatchSparsePCA", + "NMF", + "MiniBatchNMF", + "PCA", + "SparsePCA", + "SparseCoder", + "TruncatedSVD", + ], + }, + { + "template": "function", + "entries": [ + "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": [ + { + "template": "class", + "entries": [ + "LinearDiscriminantAnalysis", + "QuadraticDiscriminantAnalysis", + ], + }, + ], + }, + ], + }, + "sklearn.dummy": { + "short_summary": "Dummy estimators.", + "description": _get_guide("model_evaluation"), + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": [ + "DummyClassifier", + "DummyRegressor", + ], + }, + ], + }, + ], + }, + "sklearn.ensemble": { + "short_summary": "Ensemble methods.", + "description": _get_guide("ensemble"), + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": [ + "AdaBoostClassifier", + "AdaBoostRegressor", + "BaggingClassifier", + "BaggingRegressor", + "ExtraTreesClassifier", + "ExtraTreesRegressor", + "GradientBoostingClassifier", + "GradientBoostingRegressor", + "IsolationForest", + "RandomForestClassifier", + "RandomForestRegressor", + "RandomTreesEmbedding", + "StackingClassifier", + "StackingRegressor", + "VotingClassifier", + "VotingRegressor", + "HistGradientBoostingRegressor", + "HistGradientBoostingClassifier", + ], + }, + ], + }, + ], + }, + "sklearn.exceptions": { + "short_summary": "Exceptions and warnings.", + "description": None, + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": [ + "DataConversionWarning", + "ConvergenceWarning", + "DataDimensionalityWarning", + "EfficiencyWarning", + "FitFailedWarning", + "InconsistentVersionWarning", + "NotFittedError", + "UndefinedMetricWarning", + ], + }, + ], + }, + ], + }, + "sklearn.experimental": { + "short_summary": "Experimental tools.", + "description": None, + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": None, + "entries": [ + "enable_iterative_imputer", + "enable_halving_search_cv", + ], + }, + ], + }, + ], + }, + "sklearn.feature_extraction": { + "short_summary": "Feature extraction.", + "description": _get_guide("feature_extraction"), + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": [ + "DictVectorizer", + "FeatureHasher", + ], + }, + ], + }, + { + "title": "From images", + "description": _get_submodule("sklearn.feature_extraction", "image"), + "autosummary": [ + { + "template": "class", + "entries": ["image.PatchExtractor"], + }, + { + "template": "function", + "entries": [ + "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": [ + { + "template": "class", + "entries": [ + "text.CountVectorizer", + "text.HashingVectorizer", + "text.TfidfTransformer", + "text.TfidfVectorizer", + ], + }, + ], + }, + ], + }, + "sklearn.feature_selection": { + "short_summary": "Feature selection.", + "description": _get_guide("feature_selection"), + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": [ + "GenericUnivariateSelect", + "SelectPercentile", + "SelectKBest", + "SelectFpr", + "SelectFdr", + "SelectFromModel", + "SelectFwe", + "SequentialFeatureSelector", + "RFE", + "RFECV", + "VarianceThreshold", + "SelectorMixin", + ], + }, + { + "template": "function", + "entries": [ + "chi2", + "f_classif", + "f_regression", + "r_regression", + "mutual_info_classif", + "mutual_info_regression", + ], + }, + ], + }, + ], + }, + "sklearn.gaussian_process": { + "short_summary": "Gaussian processes.", + "description": _get_guide("gaussian_process"), + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": [ + "GaussianProcessClassifier", + "GaussianProcessRegressor", + ], + }, + ], + }, + { + "title": "Kernels", + "description": _get_submodule("sklearn.gaussian_process", "kernels"), + "autosummary": [ + { + "template": "class_with_call", + "entries": [ + "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": [ + { + "template": "class", + "entries": [ + "SimpleImputer", + "IterativeImputer", + "MissingIndicator", + "KNNImputer", + ], + }, + ], + }, + ], + }, + "sklearn.inspection": { + "short_summary": "Inspection.", + "description": _get_guide("inspection"), + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "function", + "entries": [ + "partial_dependence", + "permutation_importance", + ], + }, + ], + }, + { + "title": "Plotting", + "autosummary": [ + { + "template": "display_only_from_estimator", + "entries": [ + "DecisionBoundaryDisplay", + "PartialDependenceDisplay", + ], + }, + ], + }, + ], + }, + "sklearn.isotonic": { + "short_summary": "Isotonic regression.", + "description": _get_guide("isotonic"), + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": ["IsotonicRegression"], + }, + { + "template": "function", + "entries": [ + "check_increasing", + "isotonic_regression", + ], + }, + ], + }, + ], + }, + "sklearn.kernel_approximation": { + "short_summary": "Isotonic regression.", + "description": _get_guide("kernel_approximation"), + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": [ + "AdditiveChi2Sampler", + "Nystroem", + "PolynomialCountSketch", + "RBFSampler", + "SkewedChi2Sampler", + ], + }, + ], + }, + ], + }, + "sklearn.kernel_ridge": { + "short_summary": "Kernel ridge regression.", + "description": _get_guide("kernel_ridge"), + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": ["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": [ + { + "template": "class", + "entries": [ + "LogisticRegression", + "LogisticRegressionCV", + "PassiveAggressiveClassifier", + "Perceptron", + "RidgeClassifier", + "RidgeClassifierCV", + "SGDClassifier", + "SGDOneClassSVM", + ], + }, + ], + }, + { + "title": "Classical linear regressors", + "autosummary": [ + { + "template": "class", + "entries": [ + "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": [ + { + "template": "class", + "entries": [ + "ElasticNet", + "ElasticNetCV", + "Lars", + "LarsCV", + "Lasso", + "LassoCV", + "LassoLars", + "LassoLarsCV", + "LassoLarsIC", + "OrthogonalMatchingPursuit", + "OrthogonalMatchingPursuitCV", + ], + }, + ], + }, + { + "title": "Bayesian regressors", + "autosummary": [ + { + "template": "class", + "entries": [ + "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": [ + { + "template": "class", + "entries": [ + "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": [ + { + "template": "class", + "entries": [ + "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": [ + { + "template": "class", + "entries": [ + "PoissonRegressor", + "TweedieRegressor", + "GammaRegressor", + ], + }, + ], + }, + { + "title": "Miscellaneous", + "autosummary": [ + { + "template": "class", + "entries": ["PassiveAggressiveRegressor"], + }, + { + "template": "function", + "entries": [ + "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": [ + { + "template": "class", + "entries": [ + "Isomap", + "LocallyLinearEmbedding", + "MDS", + "SpectralEmbedding", + "TSNE", + ], + }, + { + "template": "function", + "entries": [ + "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": [ + { + "template": "function", + "entries": [ + "check_scoring", + "get_scorer", + "get_scorer_names", + "make_scorer", + ], + }, + ], + }, + { + "title": "Classification metrics", + "description": _get_guide("classification_metrics"), + "autosummary": [ + { + "template": "function", + "entries": [ + "accuracy_score", + "auc", + "average_precision_score", + "balanced_accuracy_score", + "brier_score_loss", + "class_likelihood_ratios", + "classification_report", + "cohen_kappa_score", + "confusion_matrix", + "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": [ + { + "template": "function", + "entries": [ + "explained_variance_score", + "max_error", + "mean_absolute_error", + "mean_squared_error", + "mean_squared_log_error", + "median_absolute_error", + "mean_absolute_percentage_error", + "r2_score", + "root_mean_squared_log_error", + "root_mean_squared_error", + "mean_poisson_deviance", + "mean_gamma_deviance", + "mean_tweedie_deviance", + "d2_tweedie_score", + "mean_pinball_loss", + "d2_pinball_score", + "d2_absolute_error_score", + ], + }, + ], + }, + { + "title": "Multilabel ranking metrics", + "description": _get_guide("multilabel_ranking_metrics"), + "autosummary": [ + { + "template": "function", + "entries": [ + "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": [ + { + "template": "function", + "entries": [ + "adjusted_mutual_info_score", + "adjusted_rand_score", + "calinski_harabasz_score", + "davies_bouldin_score", + "completeness_score", + "cluster.contingency_matrix", + "cluster.pair_confusion_matrix", + "fowlkes_mallows_score", + "homogeneity_completeness_v_measure", + "homogeneity_score", + "mutual_info_score", + "normalized_mutual_info_score", + "rand_score", + "silhouette_score", + "silhouette_samples", + "v_measure_score", + ], + }, + ], + }, + { + "title": "Biclustering metrics", + "description": _get_guide("biclustering_evaluation"), + "autosummary": [ + { + "template": "function", + "entries": ["consensus_score"], + }, + ], + }, + { + "title": "Distance metrics", + "autosummary": [ + { + "template": "class", + "entries": ["DistanceMetric"], + }, + ], + }, + { + "title": "Pairwise metrics", + "description": ( + _get_submodule("sklearn.metrics", "pairwise") + + "\n\n" + + _get_guide("metrics") + ), + "autosummary": [ + { + "template": "function", + "entries": [ + "pairwise.additive_chi2_kernel", + "pairwise.chi2_kernel", + "pairwise.cosine_similarity", + "pairwise.cosine_distances", + "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.pairwise_kernels", + "pairwise.polynomial_kernel", + "pairwise.rbf_kernel", + "pairwise.sigmoid_kernel", + "pairwise.paired_euclidean_distances", + "pairwise.paired_manhattan_distances", + "pairwise.paired_cosine_distances", + "pairwise.paired_distances", + "pairwise_distances", + "pairwise_distances_argmin", + "pairwise_distances_argmin_min", + "pairwise_distances_chunked", + ], + }, + ], + }, + { + "title": "Plotting", + "description": _get_guide("visualizations"), + "autosummary": [ + { + "template": "display_all_class_methods", + "entries": [ + "ConfusionMatrixDisplay", + "DetCurveDisplay", + "PrecisionRecallDisplay", + "PredictionErrorDisplay", + "RocCurveDisplay", + ], + }, + ], + }, + ], + }, + "sklearn.mixture": { + "short_summary": "Gaussian mixture models.", + "description": _get_guide("mixture"), + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": [ + "BayesianGaussianMixture", + "GaussianMixture", + ], + }, + ], + }, + ], + }, + "sklearn.model_selection": { + "short_summary": "Model selection.", + "description": _get_guide("cross_validation", "grid_search", "learning_curve"), + "sections": [ + { + "title": "Splitters", + "autosummary": [ + { + "template": "class", + "entries": [ + "GroupKFold", + "GroupShuffleSplit", + "KFold", + "LeaveOneGroupOut", + "LeavePGroupsOut", + "LeaveOneOut", + "LeavePOut", + "PredefinedSplit", + "RepeatedKFold", + "RepeatedStratifiedKFold", + "ShuffleSplit", + "StratifiedKFold", + "StratifiedShuffleSplit", + "StratifiedGroupKFold", + "TimeSeriesSplit", + ], + }, + { + "template": "function", + "entries": [ + "check_cv", + "train_test_split", + ], + }, + ], + }, + { + "title": "Hyper-parameter optimizers", + "autosummary": [ + { + "template": "class", + "entries": [ + "GridSearchCV", + "HalvingGridSearchCV", + "ParameterGrid", + "ParameterSampler", + "RandomizedSearchCV", + "HalvingRandomSearchCV", + ], + }, + ], + }, + { + "title": "Model validation", + "autosummary": [ + { + "template": "function", + "entries": [ + "cross_validate", + "cross_val_predict", + "cross_val_score", + "learning_curve", + "permutation_test_score", + "validation_curve", + ], + }, + ], + }, + { + "title": "Visualization", + "autosummary": [ + { + "template": "display_only_from_estimator", + "entries": [ + "LearningCurveDisplay", + "ValidationCurveDisplay", + ], + }, + ], + }, + ], + }, + "sklearn.multiclass": { + "short_summary": "Multiclass classification.", + "description": _get_guide("multiclass_classification"), + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": [ + "OneVsRestClassifier", + "OneVsOneClassifier", + "OutputCodeClassifier", + ], + }, + ], + }, + ], + }, + "sklearn.multioutput": { + "short_summary": "Multioutput regression and classification.", + "description": _get_guide( + "multilabel_classification", + "multiclass_multioutput_classification", + "multioutput_regression", + ), + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": [ + "ClassifierChain", + "MultiOutputRegressor", + "MultiOutputClassifier", + "RegressorChain", + ], + }, + ], + }, + ], + }, + "sklearn.naive_bayes": { + "short_summary": "Naive Bayes.", + "description": _get_guide("naive_bayes"), + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": [ + "BernoulliNB", + "CategoricalNB", + "ComplementNB", + "GaussianNB", + "MultinomialNB", + ], + }, + ], + }, + ], + }, + "sklearn.neighbors": { + "short_summary": "Nearest neighbors.", + "description": _get_guide("neighbors"), + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": [ + "BallTree", + "KDTree", + "KernelDensity", + "KNeighborsClassifier", + "KNeighborsRegressor", + "KNeighborsTransformer", + "LocalOutlierFactor", + "RadiusNeighborsClassifier", + "RadiusNeighborsRegressor", + "RadiusNeighborsTransformer", + "NearestCentroid", + "NearestNeighbors", + "NeighborhoodComponentsAnalysis", + ], + }, + { + "template": "function", + "entries": [ + "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": [ + { + "template": "class", + "entries": [ + "BernoulliRBM", + "MLPClassifier", + "MLPRegressor", + ], + }, + ], + }, + ], + }, + "sklearn.pipeline": { + "short_summary": "Pipeline.", + "description": _get_guide("combining_estimators"), + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": [ + "FeatureUnion", + "Pipeline", + ], + }, + { + "template": "function", + "entries": [ + "make_pipeline", + "make_union", + ], + }, + ], + }, + ], + }, + "sklearn.preprocessing": { + "short_summary": "Preprocessing and normalization.", + "description": _get_guide("preprocessing"), + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": [ + "Binarizer", + "FunctionTransformer", + "KBinsDiscretizer", + "KernelCenterer", + "LabelBinarizer", + "LabelEncoder", + "MultiLabelBinarizer", + "MaxAbsScaler", + "MinMaxScaler", + "Normalizer", + "OneHotEncoder", + "OrdinalEncoder", + "PolynomialFeatures", + "PowerTransformer", + "QuantileTransformer", + "RobustScaler", + "SplineTransformer", + "StandardScaler", + "TargetEncoder", + ], + }, + { + "template": "function", + "entries": [ + "add_dummy_feature", + "binarize", + "label_binarize", + "maxabs_scale", + "minmax_scale", + "normalize", + "quantile_transform", + "robust_scale", + "scale", + "power_transform", + ], + }, + ], + }, + ], + }, + "sklearn.random_projection": { + "short_summary": "Random projection.", + "description": _get_guide("random_projection"), + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": [ + "GaussianRandomProjection", + "SparseRandomProjection", + ], + }, + { + "template": "function", + "entries": ["johnson_lindenstrauss_min_dim"], + }, + ], + }, + ], + }, + "sklearn.semi_supervised": { + "short_summary": "Semi-supervised learning.", + "description": _get_guide("semi_supervised"), + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": [ + "LabelPropagation", + "LabelSpreading", + "SelfTrainingClassifier", + ], + }, + ], + }, + ], + }, + "sklearn.svm": { + "short_summary": "Support vector machines.", + "description": _get_guide("svm"), + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": [ + "LinearSVC", + "LinearSVR", + "NuSVC", + "NuSVR", + "OneClassSVM", + "SVC", + "SVR", + ], + }, + { + "template": "function", + "entries": ["l1_min_c"], + }, + ], + }, + ], + }, + "sklearn.tree": { + "short_summary": "Decision trees.", + "description": _get_guide("tree"), + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": [ + "DecisionTreeClassifier", + "DecisionTreeRegressor", + "ExtraTreeClassifier", + "ExtraTreeRegressor", + ], + }, + ], + }, + { + "title": "Exporting", + "autosummary": [ + { + "template": "function", + "entries": [ + "export_graphviz", + "export_text", + ], + }, + ], + }, + { + "title": "Plotting", + "autosummary": [ + { + "template": "function", + "entries": ["plot_tree"], + }, + ], + }, + ], + }, + "sklearn.utils": { + "short_summary": "Utilities.", + "description": _get_guide("developers-utils", is_developer=True), + "sections": [ + { + "title": None, + "autosummary": [ + { + "template": "class", + "entries": ["Bunch"], + }, + { + "template": "function", + "entries": [ + "as_float_array", + "assert_all_finite", + "deprecated", + "estimator_html_repr", + "gen_batches", + "gen_even_slices", + "indexable", + "murmurhash3_32", + "resample", + "_safe_indexing", + "safe_mask", + "safe_sqr", + "shuffle", + ], + }, + ], + }, + { + "title": "Input and parameter validation", + "description": _get_submodule("sklearn.utils", "validation"), + "autosummary": [ + { + "template": "function", + "entries": [ + "check_X_y", + "check_array", + "check_scalar", + "check_consistent_length", + "check_random_state", + "validation.check_is_fitted", + "validation.check_memory", + "validation.check_symmetric", + "validation.column_or_1d", + "validation.has_fit_parameter", + ], + }, + ], + }, + { + "title": "Meta-estimators", + "description": _get_submodule("sklearn.utils", "metaestimators"), + "autosummary": [ + { + "template": "function", + "entries": ["metaestimators.available_if"], + }, + ], + }, + { + "title": "Weight handling based on class labels", + "description": _get_submodule("sklearn.utils", "class_weight"), + "autosummary": [ + { + "template": "function", + "entries": [ + "class_weight.compute_class_weight", + "class_weight.compute_sample_weight", + ], + }, + ], + }, + { + "title": "Dealing with multiclass target in classifiers", + "description": _get_submodule("sklearn.utils", "multiclass"), + "autosummary": [ + { + "template": "function", + "entries": [ + "multiclass.type_of_target", + "multiclass.is_multilabel", + "multiclass.unique_labels", + ], + }, + ], + }, + { + "title": "Optimal mathematical operations", + "description": _get_submodule("sklearn.utils", "extmath"), + "autosummary": [ + { + "template": "function", + "entries": [ + "extmath.safe_sparse_dot", + "extmath.randomized_range_finder", + "extmath.randomized_svd", + "extmath.fast_logdet", + "extmath.density", + "extmath.weighted_mode", + ], + }, + ], + }, + { + "title": "Working with sparse matrices and arrays", + "description": _get_submodule("sklearn.utils", "sparsefuncs"), + "autosummary": [ + { + "template": "function", + "entries": [ + "sparsefuncs.incr_mean_variance_axis", + "sparsefuncs.inplace_column_scale", + "sparsefuncs.inplace_row_scale", + "sparsefuncs.inplace_swap_row", + "sparsefuncs.inplace_swap_column", + "sparsefuncs.mean_variance_axis", + "sparsefuncs.inplace_csr_column_scale", + ], + }, + ], + }, + { + "title": None, + "description": _get_submodule("sklearn.utils", "sparsefuncs_fast"), + "autosummary": [ + { + "template": "function", + "entries": [ + "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": [ + { + "template": "function", + "entries": ["graph.single_source_shortest_path_length"], + }, + ], + }, + { + "title": "Random sampling", + "description": _get_submodule("sklearn.utils", "random"), + "autosummary": [ + { + "template": "function", + "entries": ["random.sample_without_replacement"], + }, + ], + }, + { + "title": "Auxiliary functions that operate on arrays", + "description": _get_submodule("sklearn.utils", "arrayfuncs"), + "autosummary": [ + { + "template": "function", + "entries": ["arrayfuncs.min_pos"], + }, + ], + }, + { + "title": "Metadata routing", + "description": ( + _get_submodule("sklearn.utils", "metadata_routing") + + "\n\n" + + _get_guide("metadata_routing") + ), + "autosummary": [ + { + "template": "class", + "entries": [ + "metadata_routing.MetadataRouter", + "metadata_routing.MetadataRequest", + "metadata_routing.MethodMapping", + ], + }, + { + "template": "function", + "entries": [ + "metadata_routing.get_routing_for_object", + "metadata_routing.process_routing", + ], + }, + ], + }, + { + "title": "Discovering scikit-learn objects", + "description": _get_submodule("sklearn.utils", "discovery"), + "autosummary": [ + { + "template": "function", + "entries": [ + "discovery.all_estimators", + "discovery.all_displays", + "discovery.all_functions", + ], + }, + ], + }, + { + "title": "API compatibility checkers", + "description": _get_submodule("sklearn.utils", "estimator_checks"), + "autosummary": [ + { + "template": "function", + "entries": [ + "estimator_checks.check_estimator", + "estimator_checks.parametrize_with_checks", + ], + }, + ], + }, + { + "title": "Parallel computing", + "description": _get_submodule("sklearn.utils", "parallel"), + "autosummary": [ + { + "template": "class", + "entries": ["parallel.Parallel"], + }, + { + "template": "function", + "entries": [ + "parallel.delayed", + "parallel_backend", + "register_parallel_backend", + ], + }, + ], + }, + ], + }, +} + + +DEPRECATED_API_REFERENCE = {} # type: ignore + + +def _write_autosummary_rst(autosummary, f): + """Write the autosummary rst to a text stream.""" + f.write(".. autosummary::\n") + f.write(" :toctree: ../modules/generated/\n") + for autosummary_item in autosummary: + if autosummary_item["template"] is not None: + f.write(f" :template: {autosummary_item['template']}.rst\n") + f.write("\n") + # Sort the entries in alphabetical order + for entry in sorted(autosummary_item["entries"]): + f.write(f" {entry}\n") + f.write("\n") + + +def get_api_reference_rst(module_name): + """Get the API reference rst for a module.""" + output = StringIO() + + module_info = API_REFERENCE[module_name] + if module_name == "sklearn": + subname = None + else: + assert module_name.startswith("sklearn.") + subname = module_name[8:] + + # Print the cross-reference hook + if subname is not None: + output.write(f".. _{subname}_ref:\n\n") + + # Print the top-level heading + output.write(f"{module_name}\n") + output.write("=" * len(module_name) + "\n\n") + + # Print the module docstring + output.write(f".. automodule:: {module_name}\n") + output.write(" :no-members:\n") + output.write(" :no-inherited-members:\n\n") + + # Print the additional description if it exists + if module_info["description"] is not None: + output.write(module_info["description"] + "\n\n") + + for section in module_info["sections"]: + # Print the cross-reference hook + section_title = section["title"] + if section_title is not None and subname is not None: + section_refname = section_title.lower().replace(" ", "-") + output.write(f".. _{subname}_ref-{section_refname}:\n\n") + + # Print the title if it exists + if section_title is not None: + output.write(section_title + "\n") + output.write("-" * len(section_title) + "\n\n") + + # Print the additional description if it exists + section_description = section.get("description", None) + if section_description is not None: + output.write(section_description + "\n\n") + + # Print the autosummary + _write_autosummary_rst(section["autosummary"], output) + + return output.getvalue() + + +def get_deprecated_api_reference_rst(version): + """Print the deprecated API reference for a version.""" + output = StringIO() + + output.write(f"To be removed in {version}\n") + output.write("-" * (len(version) + 17) + "\n\n") + + # Print the autosummary + _write_autosummary_rst(DEPRECATED_API_REFERENCE[version], output) + + return output.getvalue() diff --git a/sklearn/base.py b/sklearn/base.py index e73ae4c8a180e..436c7aa1146f2 100644 --- a/sklearn/base.py +++ b/sklearn/base.py @@ -1,4 +1,7 @@ -"""Base classes for all estimators.""" +""" +The :mod:`sklearn.base` module includes base classes for all estimators and various +utility functions. +""" # Author: Gael Varoquaux # License: BSD 3 clause diff --git a/sklearn/calibration.py b/sklearn/calibration.py index e13f705b08c08..94f3778a2a3df 100644 --- a/sklearn/calibration.py +++ b/sklearn/calibration.py @@ -1,4 +1,7 @@ -"""Calibration of predicted probabilities.""" +""" +The :mod:`sklearn.calibration` module includes methods for calibrating predicted +probabilities. +""" # Author: Alexandre Gramfort # Balazs Kegl diff --git a/sklearn/compose/__init__.py b/sklearn/compose/__init__.py index 7b137cdf9e07f..88073d1a9d244 100644 --- a/sklearn/compose/__init__.py +++ b/sklearn/compose/__init__.py @@ -1,8 +1,8 @@ -"""Meta-estimators for building composite models with transformers - -In addition to its current contents, this module will eventually be home to -refurbished versions of Pipeline and FeatureUnion. - +""" +The :mod:`sklearn.compose` module includes meta-estimators for building composite +models with transformers. In addition to its current contents, this module will +eventually be home to refurbished versions of :class:`~sklearn.pipeline.Pipeline` and +:class:`~sklearn.pipeline.FeatureUnion`. """ from ._column_transformer import ( diff --git a/sklearn/cross_decomposition/__init__.py b/sklearn/cross_decomposition/__init__.py index 47b78783caf9c..5c24c802ca6e6 100644 --- a/sklearn/cross_decomposition/__init__.py +++ b/sklearn/cross_decomposition/__init__.py @@ -1,3 +1,8 @@ +""" +The :mod:`sklearn.cross_decomposition` module includes algorithms for cross +decomposition techniques. +""" + from ._pls import CCA, PLSSVD, PLSCanonical, PLSRegression __all__ = ["PLSCanonical", "PLSRegression", "PLSSVD", "CCA"] diff --git a/sklearn/discriminant_analysis.py b/sklearn/discriminant_analysis.py index 46cb96ddd2886..613c899de48e6 100644 --- a/sklearn/discriminant_analysis.py +++ b/sklearn/discriminant_analysis.py @@ -1,5 +1,6 @@ """ -Linear Discriminant Analysis and Quadratic Discriminant Analysis +The :mod:`sklearn.discriminant_analysis` module includes linear discriminant analysis +and quadratic discriminant analysis. """ # Authors: Clemens Brunner diff --git a/sklearn/dummy.py b/sklearn/dummy.py index 17812fe1b3d05..97a358ad1b449 100644 --- a/sklearn/dummy.py +++ b/sklearn/dummy.py @@ -1,3 +1,8 @@ +""" +The :mod:`sklearn.dummy` module includes dummy estimators that implement simple rules +of thumb. +""" + # Author: Mathieu Blondel # Arnaud Joly # Maheshakya Wijewardena diff --git a/sklearn/experimental/__init__.py b/sklearn/experimental/__init__.py index 0effaf5b05fa0..3b38fe326520b 100644 --- a/sklearn/experimental/__init__.py +++ b/sklearn/experimental/__init__.py @@ -2,6 +2,8 @@ The :mod:`sklearn.experimental` module provides importable modules that enable the use of experimental features or estimators. -The features and estimators that are experimental aren't subject to -deprecation cycles. Use them at your own risks! +.. warning:: + + The features and estimators that are experimental aren't subject to + deprecation cycles. Use them at your own risks! """ diff --git a/sklearn/gaussian_process/kernels.py b/sklearn/gaussian_process/kernels.py index 3b995c48b1f71..7e8e827fd1256 100644 --- a/sklearn/gaussian_process/kernels.py +++ b/sklearn/gaussian_process/kernels.py @@ -1,5 +1,5 @@ """ -The :mod:`sklearn.gaussian_process.kernels` module implements a set of kernels that +The :mod:`sklearn.gaussian_process.kernels` submodule implements a set of kernels that can be combined by operators and used in Gaussian processes. """ diff --git a/sklearn/impute/__init__.py b/sklearn/impute/__init__.py index e305bc2a657dc..7c346f9b63eb3 100644 --- a/sklearn/impute/__init__.py +++ b/sklearn/impute/__init__.py @@ -1,4 +1,7 @@ -"""Transformers for missing value imputation""" +""" +The :mod:`sklearn.impute` module includes transformers for missing value imputation. +""" + import typing from ._base import MissingIndicator, SimpleImputer diff --git a/sklearn/isotonic.py b/sklearn/isotonic.py index 04456b1763791..3377d3791375e 100644 --- a/sklearn/isotonic.py +++ b/sklearn/isotonic.py @@ -1,3 +1,8 @@ +""" +The :mod:`sklearn.isotonic` module implements isotonic regression for obtaining +monotonic fit to data. +""" + # Authors: Fabian Pedregosa # Alexandre Gramfort # Nelle Varoquaux diff --git a/sklearn/kernel_ridge.py b/sklearn/kernel_ridge.py index 23890f3a68cd7..03781917d9eb3 100644 --- a/sklearn/kernel_ridge.py +++ b/sklearn/kernel_ridge.py @@ -1,4 +1,4 @@ -"""Module :mod:`sklearn.kernel_ridge` implements kernel ridge regression.""" +"""The :mod:`sklearn.kernel_ridge` module implements kernel ridge regression.""" # Authors: Mathieu Blondel # Jan Hendrik Metzen diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py index 740c08fe7a2f6..1004eae0dff3f 100644 --- a/sklearn/metrics/pairwise.py +++ b/sklearn/metrics/pairwise.py @@ -1,3 +1,8 @@ +""" +The :mod:`sklearn.metrics.pairwise` submodule implements metrics for pairwise +distances and affinity of sets of samples. +""" + # Authors: Alexandre Gramfort # Mathieu Blondel # Robert Layton diff --git a/sklearn/model_selection/__init__.py b/sklearn/model_selection/__init__.py index d7d316d95ada4..eeefe5098c621 100644 --- a/sklearn/model_selection/__init__.py +++ b/sklearn/model_selection/__init__.py @@ -1,3 +1,8 @@ +""" +The :mod:`sklearn.model_selection` module includes tools for model selection, such as +cross validation, hyper-parameter tuning, and dataset splitting. +""" + import typing from ._plot import LearningCurveDisplay, ValidationCurveDisplay diff --git a/sklearn/multiclass.py b/sklearn/multiclass.py index 07b19115f0912..76b1aa914c1e3 100644 --- a/sklearn/multiclass.py +++ b/sklearn/multiclass.py @@ -1,11 +1,9 @@ """ -Multiclass classification strategies -==================================== +The :mod:`sklearn.multiclass` module implements multiclass learning algorithms: -This module implements multiclass learning algorithms: - - one-vs-the-rest / one-vs-all - - one-vs-one - - error correcting output codes +- one-vs-the-rest / one-vs-all +- one-vs-one +- error correcting output codes The estimators provided in this module are meta-estimators: they require a base estimator to be provided in their constructor. For example, it is possible to diff --git a/sklearn/multioutput.py b/sklearn/multioutput.py index 1e1c2b646ca82..cd71bbdab24c4 100644 --- a/sklearn/multioutput.py +++ b/sklearn/multioutput.py @@ -1,5 +1,6 @@ """ -This module implements multioutput regression and classification. +The :mod:`sklearn.multioutput` module implements multioutput regression and +classification. The estimators provided in this module are meta-estimators: they require a base estimator to be provided in their constructor. The meta-estimator diff --git a/sklearn/neural_network/__init__.py b/sklearn/neural_network/__init__.py index 0b321b605de0b..40f9abf0496ae 100644 --- a/sklearn/neural_network/__init__.py +++ b/sklearn/neural_network/__init__.py @@ -1,6 +1,9 @@ """ The :mod:`sklearn.neural_network` module includes models based on neural networks. + +**User guide.** See the :ref:`neural_networks_supervised` and +:ref:`neural_networks_unsupervised` sections for further details. """ # License: BSD 3 clause diff --git a/sklearn/random_projection.py b/sklearn/random_projection.py index c8c0193ac9b0b..37bc9a5900aa9 100644 --- a/sklearn/random_projection.py +++ b/sklearn/random_projection.py @@ -1,11 +1,13 @@ -"""Random Projection transformers. +""" +The :mod:`sklearn.random_projection` modules implements random projection +transformers. -Random Projections are a simple and computationally efficient way to +Random projections are a simple and computationally efficient way to reduce the dimensionality of the data by trading a controlled amount of accuracy (as additional variance) for faster processing times and smaller model sizes. -The dimensions and distribution of Random Projections matrices are +The dimensions and distribution of random projections matrices are controlled so as to preserve the pairwise distances between any two samples of the dataset. @@ -20,8 +22,8 @@ much lower dimension in such a way that distances between the points are nearly preserved. The map used for the embedding is at least Lipschitz, and can even be taken to be an orthogonal projection. - """ + # Authors: Olivier Grisel , # Arnaud Joly # License: BSD 3 clause diff --git a/sklearn/utils/arrayfuncs.pyx b/sklearn/utils/arrayfuncs.pyx index 94b221460884c..f200548cfb9a6 100644 --- a/sklearn/utils/arrayfuncs.pyx +++ b/sklearn/utils/arrayfuncs.pyx @@ -1,5 +1,5 @@ """ -The :mod:`sklearn.utils.arrayfuncs` module includes a small collection of auxiliary +The :mod:`sklearn.utils.arrayfuncs` submodule includes a small collection of auxiliary functions that operate on arrays. """ diff --git a/sklearn/utils/class_weight.py b/sklearn/utils/class_weight.py index 55802f780ed41..dd7f381b3e265 100644 --- a/sklearn/utils/class_weight.py +++ b/sklearn/utils/class_weight.py @@ -1,5 +1,5 @@ """ -The :mod:`sklearn.utils.class_weight` module includes utilities for handling +The :mod:`sklearn.utils.class_weight` submodule includes utilities for handling weights based on class labels. """ diff --git a/sklearn/utils/discovery.py b/sklearn/utils/discovery.py index c1fdca3beafb2..b4c4224a7dcca 100644 --- a/sklearn/utils/discovery.py +++ b/sklearn/utils/discovery.py @@ -1,6 +1,6 @@ """ -The :mod:`sklearn.utils.discovery` module includes utilities to discover -objects (i.e. estimators, displays, functions) from the `sklearn` package. +The :mod:`sklearn.utils.discovery` submodule includes utilities to discover +scikit-learn objects (i.e., estimators, displays, and functions). """ import inspect diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py index 564bd6928a5a6..1f36ee3e3a46b 100644 --- a/sklearn/utils/estimator_checks.py +++ b/sklearn/utils/estimator_checks.py @@ -1,5 +1,5 @@ """ -The :mod:`sklearn.utils.estimator_checks` module includes various utilities to +The :mod:`sklearn.utils.estimator_checks` submodule includes various utilities to check the compatibility of estimators with the scikit-learn API. """ diff --git a/sklearn/utils/extmath.py b/sklearn/utils/extmath.py index faf3c94ad5483..66bd0fadf91bc 100644 --- a/sklearn/utils/extmath.py +++ b/sklearn/utils/extmath.py @@ -1,5 +1,5 @@ """ -The :mod:`sklearn.utils.extmath` module includes utilities to perform +The :mod:`sklearn.utils.extmath` submodule includes utilities to perform optimal mathematical operations in scikit-learn that are not available in SciPy. """ # Authors: Gael Varoquaux diff --git a/sklearn/utils/graph.py b/sklearn/utils/graph.py index 06b2e152101a9..a2614be626083 100644 --- a/sklearn/utils/graph.py +++ b/sklearn/utils/graph.py @@ -1,5 +1,5 @@ """ -The :mod:`sklearn.utils.graph` module includes graph utilities and algorithms. +The :mod:`sklearn.utils.graph` submodule includes graph utilities and algorithms. """ # Authors: Aric Hagberg diff --git a/sklearn/utils/metadata_routing.py b/sklearn/utils/metadata_routing.py index bb98d2f08b93e..6ebc6a2e0b197 100644 --- a/sklearn/utils/metadata_routing.py +++ b/sklearn/utils/metadata_routing.py @@ -1,5 +1,5 @@ """ -The :mod:`sklearn.utils.metadata_routing` module includes utilities to route +The :mod:`sklearn.utils.metadata_routing` submodule includes utilities to route metadata within scikit-learn estimators. """ diff --git a/sklearn/utils/metaestimators.py b/sklearn/utils/metaestimators.py index 639e000dd77a7..fe25edc05af59 100644 --- a/sklearn/utils/metaestimators.py +++ b/sklearn/utils/metaestimators.py @@ -1,5 +1,6 @@ """ -The :mod:`sklearn.utils.metaestimators` module includes utilities for meta-estimators. +The :mod:`sklearn.utils.metaestimators` submodule includes utilities for +meta-estimators. """ # Author: Joel Nothman diff --git a/sklearn/utils/multiclass.py b/sklearn/utils/multiclass.py index a4b23427e5b70..2a09f78a0f1b1 100644 --- a/sklearn/utils/multiclass.py +++ b/sklearn/utils/multiclass.py @@ -1,5 +1,5 @@ """ -The :mod:`sklearn.utils.multiclass` module includes utilities to handle +The :mod:`sklearn.utils.multiclass` submodule includes utilities to handle multiclass/multioutput target in classifiers. """ diff --git a/sklearn/utils/parallel.py b/sklearn/utils/parallel.py index d0dc2ec2be030..63f5de4a31dd6 100644 --- a/sklearn/utils/parallel.py +++ b/sklearn/utils/parallel.py @@ -1,5 +1,6 @@ """ -The :mod:`sklearn.utils.parallel` customizes `joblib` tools for scikit-learn usage. +The :mod:`sklearn.utils.parallel` submodule customizes the :mod:`joblib` tools for +scikit-learn usage. """ import functools diff --git a/sklearn/utils/random.py b/sklearn/utils/random.py index 1dfe8d83a94b3..5ff3fca3dfa31 100644 --- a/sklearn/utils/random.py +++ b/sklearn/utils/random.py @@ -1,5 +1,5 @@ """ -The mod:`sklearn.utils.random` module includes utilities for random sampling. +The :mod:`sklearn.utils.random` submodule includes utilities for random sampling. """ # Author: Hamzeh Alsalhi diff --git a/sklearn/utils/sparsefuncs.py b/sklearn/utils/sparsefuncs.py index a46e9e4d9ed93..7e5241f425e28 100644 --- a/sklearn/utils/sparsefuncs.py +++ b/sklearn/utils/sparsefuncs.py @@ -1,5 +1,5 @@ """ -The :mod:`sklearn.utils.sparsefuncs` module includes a collection of utilities to +The :mod:`sklearn.utils.sparsefuncs` submodule includes a collection of utilities to work with sparse matrices and arrays. """ diff --git a/sklearn/utils/sparsefuncs_fast.pyx b/sklearn/utils/sparsefuncs_fast.pyx index 00c8fc9542e18..99bfa867cb2fb 100644 --- a/sklearn/utils/sparsefuncs_fast.pyx +++ b/sklearn/utils/sparsefuncs_fast.pyx @@ -1,6 +1,6 @@ """ -The :mod:`sklearn.utils.sparsefuncs_fast` module includes a collection of utilities to -work with sparse matrices and arrays written in Cython. +The :mod:`sklearn.utils.sparsefuncs_fast` submodule includes a collection of utilities +to work with sparse matrices and arrays written in Cython. """ # Authors: Mathieu Blondel diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py index bb9feb7fef289..a1e510aaa62d6 100644 --- a/sklearn/utils/validation.py +++ b/sklearn/utils/validation.py @@ -1,5 +1,5 @@ """ -The :mod:`sklearn.utils.validation` module includes functions to validate +The :mod:`sklearn.utils.validation` submodule includes functions to validate input and parameters within scikit-learn estimators. """