1335 lines
47 KiB
Python
1335 lines
47 KiB
Python
|
"""Configuration for the API reference documentation."""
|
||
|
|
|
||
|
|
|
||
|
|
def _get_guide(*refs, is_developer=False):
|
||
|
|
"""Get the rst to refer to user/developer guide.
|
||
|
|
|
||
|
|
`refs` is several references that can be used in the :ref:`...` directive.
|
||
|
|
"""
|
||
|
|
if len(refs) == 1:
|
||
|
|
ref_desc = f":ref:`{refs[0]}` section"
|
||
|
|
elif len(refs) == 2:
|
||
|
|
ref_desc = f":ref:`{refs[0]}` and :ref:`{refs[1]}` sections"
|
||
|
|
else:
|
||
|
|
ref_desc = ", ".join(f":ref:`{ref}`" for ref in refs[:-1])
|
||
|
|
ref_desc += f", and :ref:`{refs[-1]}` sections"
|
||
|
|
|
||
|
|
guide_name = "Developer" if is_developer else "User"
|
||
|
|
return f"**{guide_name} guide.** See the {ref_desc} for further details."
|
||
|
|
|
||
|
|
|
||
|
|
def _get_submodule(module_name, submodule_name):
|
||
|
|
"""Get the submodule docstring and automatically add the hook.
|
||
|
|
|
||
|
|
`module_name` is e.g. `sklearn.feature_extraction`, and `submodule_name` is e.g.
|
||
|
|
`image`, so we get the docstring and hook for `sklearn.feature_extraction.image`
|
||
|
|
submodule. `module_name` is used to reset the current module because autosummary
|
||
|
|
automatically changes the current module.
|
||
|
|
"""
|
||
|
|
lines = [
|
||
|
|
f".. automodule:: {module_name}.{submodule_name}",
|
||
|
|
f".. currentmodule:: {module_name}",
|
||
|
|
]
|
||
|
|
return "\n\n".join(lines)
|
||
|
|
|
||
|
|
|
||
|
|
"""
|
||
|
|
CONFIGURING API_REFERENCE
|
||
|
|
=========================
|
||
|
|
|
||
|
|
API_REFERENCE maps each module name to a dictionary that consists of the following
|
||
|
|
components:
|
||
|
|
|
||
|
|
short_summary (required)
|
||
|
|
The text to be printed on the index page; it has nothing to do the API reference
|
||
|
|
page of each module.
|
||
|
|
description (required, `None` if not needed)
|
||
|
|
The additional description for the module to be placed under the module
|
||
|
|
docstring, before the sections start.
|
||
|
|
sections (required)
|
||
|
|
A list of sections, each of which consists of:
|
||
|
|
- title (required, `None` if not needed): the section title, commonly it should
|
||
|
|
not be `None` except for the first section of a module,
|
||
|
|
- description (optional): the optional additional description for the section,
|
||
|
|
- autosummary (required): an autosummary block, assuming current module is the
|
||
|
|
current module name.
|
||
|
|
|
||
|
|
Essentially, the rendered page would look like the following:
|
||
|
|
|
||
|
|
|---------------------------------------------------------------------------------|
|
||
|
|
| {{ module_name }} |
|
||
|
|
| ================= |
|
||
|
|
| {{ module_docstring }} |
|
||
|
|
| {{ description }} |
|
||
|
|
| |
|
||
|
|
| {{ section_title_1 }} <-------------- Optional if one wants the first |
|
||
|
|
| --------------------- section to directly follow |
|
||
|
|
| {{ section_description_1 }} without a second-level heading. |
|
||
|
|
| {{ section_autosummary_1 }} |
|
||
|
|
| |
|
||
|
|
| {{ section_title_2 }} |
|
||
|
|
| --------------------- |
|
||
|
|
| {{ section_description_2 }} |
|
||
|
|
| {{ section_autosummary_2 }} |
|
||
|
|
| |
|
||
|
|
| More sections... |
|
||
|
|
|---------------------------------------------------------------------------------|
|
||
|
|
|
||
|
|
Hooks will be automatically generated for each module and each section. For a module,
|
||
|
|
e.g., `sklearn.feature_extraction`, the hook would be `feature_extraction_ref`; for a
|
||
|
|
section, e.g., "From text" under `sklearn.feature_extraction`, the hook would be
|
||
|
|
`feature_extraction_ref-from-text`. However, note that a better way is to refer using
|
||
|
|
the :mod: directive, e.g., :mod:`sklearn.feature_extraction` for the module and
|
||
|
|
:mod:`sklearn.feature_extraction.text` for the section. Only in case that a section
|
||
|
|
is not a particular submodule does the hook become useful, e.g., the "Loaders" section
|
||
|
|
under `sklearn.datasets`.
|
||
|
|
"""
|
||
|
|
|
||
|
|
API_REFERENCE = {
|
||
|
|
"sklearn": {
|
||
|
|
"short_summary": "Settings and information tools.",
|
||
|
|
"description": None,
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"config_context",
|
||
|
|
"get_config",
|
||
|
|
"set_config",
|
||
|
|
"show_versions",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.base": {
|
||
|
|
"short_summary": "Base classes and utility functions.",
|
||
|
|
"description": None,
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"BaseEstimator",
|
||
|
|
"BiclusterMixin",
|
||
|
|
"ClassNamePrefixFeaturesOutMixin",
|
||
|
|
"ClassifierMixin",
|
||
|
|
"ClusterMixin",
|
||
|
|
"DensityMixin",
|
||
|
|
"MetaEstimatorMixin",
|
||
|
|
"OneToOneFeatureMixin",
|
||
|
|
"OutlierMixin",
|
||
|
|
"RegressorMixin",
|
||
|
|
"TransformerMixin",
|
||
|
|
"clone",
|
||
|
|
"is_classifier",
|
||
|
|
"is_regressor",
|
||
|
|
],
|
||
|
|
}
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.calibration": {
|
||
|
|
"short_summary": "Probability calibration.",
|
||
|
|
"description": _get_guide("calibration"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": ["CalibratedClassifierCV", "calibration_curve"],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Visualization",
|
||
|
|
"autosummary": ["CalibrationDisplay"],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.cluster": {
|
||
|
|
"short_summary": "Clustering.",
|
||
|
|
"description": _get_guide("clustering", "biclustering"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"AffinityPropagation",
|
||
|
|
"AgglomerativeClustering",
|
||
|
|
"Birch",
|
||
|
|
"BisectingKMeans",
|
||
|
|
"DBSCAN",
|
||
|
|
"FeatureAgglomeration",
|
||
|
|
"HDBSCAN",
|
||
|
|
"KMeans",
|
||
|
|
"MeanShift",
|
||
|
|
"MiniBatchKMeans",
|
||
|
|
"OPTICS",
|
||
|
|
"SpectralBiclustering",
|
||
|
|
"SpectralClustering",
|
||
|
|
"SpectralCoclustering",
|
||
|
|
"affinity_propagation",
|
||
|
|
"cluster_optics_dbscan",
|
||
|
|
"cluster_optics_xi",
|
||
|
|
"compute_optics_graph",
|
||
|
|
"dbscan",
|
||
|
|
"estimate_bandwidth",
|
||
|
|
"k_means",
|
||
|
|
"kmeans_plusplus",
|
||
|
|
"mean_shift",
|
||
|
|
"spectral_clustering",
|
||
|
|
"ward_tree",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.compose": {
|
||
|
|
"short_summary": "Composite estimators.",
|
||
|
|
"description": _get_guide("combining_estimators"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"ColumnTransformer",
|
||
|
|
"TransformedTargetRegressor",
|
||
|
|
"make_column_selector",
|
||
|
|
"make_column_transformer",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.covariance": {
|
||
|
|
"short_summary": "Covariance estimation.",
|
||
|
|
"description": _get_guide("covariance"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"EllipticEnvelope",
|
||
|
|
"EmpiricalCovariance",
|
||
|
|
"GraphicalLasso",
|
||
|
|
"GraphicalLassoCV",
|
||
|
|
"LedoitWolf",
|
||
|
|
"MinCovDet",
|
||
|
|
"OAS",
|
||
|
|
"ShrunkCovariance",
|
||
|
|
"empirical_covariance",
|
||
|
|
"graphical_lasso",
|
||
|
|
"ledoit_wolf",
|
||
|
|
"ledoit_wolf_shrinkage",
|
||
|
|
"oas",
|
||
|
|
"shrunk_covariance",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.cross_decomposition": {
|
||
|
|
"short_summary": "Cross decomposition.",
|
||
|
|
"description": _get_guide("cross_decomposition"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": ["CCA", "PLSCanonical", "PLSRegression", "PLSSVD"],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.datasets": {
|
||
|
|
"short_summary": "Datasets.",
|
||
|
|
"description": _get_guide("datasets"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": "Loaders",
|
||
|
|
"autosummary": [
|
||
|
|
"clear_data_home",
|
||
|
|
"dump_svmlight_file",
|
||
|
|
"fetch_20newsgroups",
|
||
|
|
"fetch_20newsgroups_vectorized",
|
||
|
|
"fetch_california_housing",
|
||
|
|
"fetch_covtype",
|
||
|
|
"fetch_kddcup99",
|
||
|
|
"fetch_lfw_pairs",
|
||
|
|
"fetch_lfw_people",
|
||
|
|
"fetch_olivetti_faces",
|
||
|
|
"fetch_openml",
|
||
|
|
"fetch_rcv1",
|
||
|
|
"fetch_species_distributions",
|
||
|
|
"get_data_home",
|
||
|
|
"load_breast_cancer",
|
||
|
|
"load_diabetes",
|
||
|
|
"load_digits",
|
||
|
|
"load_files",
|
||
|
|
"load_iris",
|
||
|
|
"load_linnerud",
|
||
|
|
"load_sample_image",
|
||
|
|
"load_sample_images",
|
||
|
|
"load_svmlight_file",
|
||
|
|
"load_svmlight_files",
|
||
|
|
"load_wine",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Sample generators",
|
||
|
|
"autosummary": [
|
||
|
|
"make_biclusters",
|
||
|
|
"make_blobs",
|
||
|
|
"make_checkerboard",
|
||
|
|
"make_circles",
|
||
|
|
"make_classification",
|
||
|
|
"make_friedman1",
|
||
|
|
"make_friedman2",
|
||
|
|
"make_friedman3",
|
||
|
|
"make_gaussian_quantiles",
|
||
|
|
"make_hastie_10_2",
|
||
|
|
"make_low_rank_matrix",
|
||
|
|
"make_moons",
|
||
|
|
"make_multilabel_classification",
|
||
|
|
"make_regression",
|
||
|
|
"make_s_curve",
|
||
|
|
"make_sparse_coded_signal",
|
||
|
|
"make_sparse_spd_matrix",
|
||
|
|
"make_sparse_uncorrelated",
|
||
|
|
"make_spd_matrix",
|
||
|
|
"make_swiss_roll",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.decomposition": {
|
||
|
|
"short_summary": "Matrix decomposition.",
|
||
|
|
"description": _get_guide("decompositions"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"DictionaryLearning",
|
||
|
|
"FactorAnalysis",
|
||
|
|
"FastICA",
|
||
|
|
"IncrementalPCA",
|
||
|
|
"KernelPCA",
|
||
|
|
"LatentDirichletAllocation",
|
||
|
|
"MiniBatchDictionaryLearning",
|
||
|
|
"MiniBatchNMF",
|
||
|
|
"MiniBatchSparsePCA",
|
||
|
|
"NMF",
|
||
|
|
"PCA",
|
||
|
|
"SparseCoder",
|
||
|
|
"SparsePCA",
|
||
|
|
"TruncatedSVD",
|
||
|
|
"dict_learning",
|
||
|
|
"dict_learning_online",
|
||
|
|
"fastica",
|
||
|
|
"non_negative_factorization",
|
||
|
|
"sparse_encode",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.discriminant_analysis": {
|
||
|
|
"short_summary": "Discriminant analysis.",
|
||
|
|
"description": _get_guide("lda_qda"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"LinearDiscriminantAnalysis",
|
||
|
|
"QuadraticDiscriminantAnalysis",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.dummy": {
|
||
|
|
"short_summary": "Dummy estimators.",
|
||
|
|
"description": _get_guide("model_evaluation"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": ["DummyClassifier", "DummyRegressor"],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.ensemble": {
|
||
|
|
"short_summary": "Ensemble methods.",
|
||
|
|
"description": _get_guide("ensemble"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"AdaBoostClassifier",
|
||
|
|
"AdaBoostRegressor",
|
||
|
|
"BaggingClassifier",
|
||
|
|
"BaggingRegressor",
|
||
|
|
"ExtraTreesClassifier",
|
||
|
|
"ExtraTreesRegressor",
|
||
|
|
"GradientBoostingClassifier",
|
||
|
|
"GradientBoostingRegressor",
|
||
|
|
"HistGradientBoostingClassifier",
|
||
|
|
"HistGradientBoostingRegressor",
|
||
|
|
"IsolationForest",
|
||
|
|
"RandomForestClassifier",
|
||
|
|
"RandomForestRegressor",
|
||
|
|
"RandomTreesEmbedding",
|
||
|
|
"StackingClassifier",
|
||
|
|
"StackingRegressor",
|
||
|
|
"VotingClassifier",
|
||
|
|
"VotingRegressor",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.exceptions": {
|
||
|
|
"short_summary": "Exceptions and warnings.",
|
||
|
|
"description": None,
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"ConvergenceWarning",
|
||
|
|
"DataConversionWarning",
|
||
|
|
"DataDimensionalityWarning",
|
||
|
|
"EfficiencyWarning",
|
||
|
|
"FitFailedWarning",
|
||
|
|
"InconsistentVersionWarning",
|
||
|
|
"NotFittedError",
|
||
|
|
"UndefinedMetricWarning",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.experimental": {
|
||
|
|
"short_summary": "Experimental tools.",
|
||
|
|
"description": None,
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": ["enable_halving_search_cv", "enable_iterative_imputer"],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.feature_extraction": {
|
||
|
|
"short_summary": "Feature extraction.",
|
||
|
|
"description": _get_guide("feature_extraction"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": ["DictVectorizer", "FeatureHasher"],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "From images",
|
||
|
|
"description": _get_submodule("sklearn.feature_extraction", "image"),
|
||
|
|
"autosummary": [
|
||
|
|
"image.PatchExtractor",
|
||
|
|
"image.extract_patches_2d",
|
||
|
|
"image.grid_to_graph",
|
||
|
|
"image.img_to_graph",
|
||
|
|
"image.reconstruct_from_patches_2d",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "From text",
|
||
|
|
"description": _get_submodule("sklearn.feature_extraction", "text"),
|
||
|
|
"autosummary": [
|
||
|
|
"text.CountVectorizer",
|
||
|
|
"text.HashingVectorizer",
|
||
|
|
"text.TfidfTransformer",
|
||
|
|
"text.TfidfVectorizer",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.feature_selection": {
|
||
|
|
"short_summary": "Feature selection.",
|
||
|
|
"description": _get_guide("feature_selection"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"GenericUnivariateSelect",
|
||
|
|
"RFE",
|
||
|
|
"RFECV",
|
||
|
|
"SelectFdr",
|
||
|
|
"SelectFpr",
|
||
|
|
"SelectFromModel",
|
||
|
|
"SelectFwe",
|
||
|
|
"SelectKBest",
|
||
|
|
"SelectPercentile",
|
||
|
|
"SelectorMixin",
|
||
|
|
"SequentialFeatureSelector",
|
||
|
|
"VarianceThreshold",
|
||
|
|
"chi2",
|
||
|
|
"f_classif",
|
||
|
|
"f_regression",
|
||
|
|
"mutual_info_classif",
|
||
|
|
"mutual_info_regression",
|
||
|
|
"r_regression",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.gaussian_process": {
|
||
|
|
"short_summary": "Gaussian processes.",
|
||
|
|
"description": _get_guide("gaussian_process"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"GaussianProcessClassifier",
|
||
|
|
"GaussianProcessRegressor",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Kernels",
|
||
|
|
"description": _get_submodule("sklearn.gaussian_process", "kernels"),
|
||
|
|
"autosummary": [
|
||
|
|
"kernels.CompoundKernel",
|
||
|
|
"kernels.ConstantKernel",
|
||
|
|
"kernels.DotProduct",
|
||
|
|
"kernels.ExpSineSquared",
|
||
|
|
"kernels.Exponentiation",
|
||
|
|
"kernels.Hyperparameter",
|
||
|
|
"kernels.Kernel",
|
||
|
|
"kernels.Matern",
|
||
|
|
"kernels.PairwiseKernel",
|
||
|
|
"kernels.Product",
|
||
|
|
"kernels.RBF",
|
||
|
|
"kernels.RationalQuadratic",
|
||
|
|
"kernels.Sum",
|
||
|
|
"kernels.WhiteKernel",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.impute": {
|
||
|
|
"short_summary": "Imputation.",
|
||
|
|
"description": _get_guide("impute"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"IterativeImputer",
|
||
|
|
"KNNImputer",
|
||
|
|
"MissingIndicator",
|
||
|
|
"SimpleImputer",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.inspection": {
|
||
|
|
"short_summary": "Inspection.",
|
||
|
|
"description": _get_guide("inspection"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": ["partial_dependence", "permutation_importance"],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Plotting",
|
||
|
|
"autosummary": ["DecisionBoundaryDisplay", "PartialDependenceDisplay"],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.isotonic": {
|
||
|
|
"short_summary": "Isotonic regression.",
|
||
|
|
"description": _get_guide("isotonic"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"IsotonicRegression",
|
||
|
|
"check_increasing",
|
||
|
|
"isotonic_regression",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.kernel_approximation": {
|
||
|
|
"short_summary": "Isotonic regression.",
|
||
|
|
"description": _get_guide("kernel_approximation"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"AdditiveChi2Sampler",
|
||
|
|
"Nystroem",
|
||
|
|
"PolynomialCountSketch",
|
||
|
|
"RBFSampler",
|
||
|
|
"SkewedChi2Sampler",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.kernel_ridge": {
|
||
|
|
"short_summary": "Kernel ridge regression.",
|
||
|
|
"description": _get_guide("kernel_ridge"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": ["KernelRidge"],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.linear_model": {
|
||
|
|
"short_summary": "Generalized linear models.",
|
||
|
|
"description": (
|
||
|
|
_get_guide("linear_model")
|
||
|
|
+ "\n\nThe following subsections are only rough guidelines: the same "
|
||
|
|
"estimator can fall into multiple categories, depending on its parameters."
|
||
|
|
),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": "Linear classifiers",
|
||
|
|
"autosummary": [
|
||
|
|
"LogisticRegression",
|
||
|
|
"LogisticRegressionCV",
|
||
|
|
"PassiveAggressiveClassifier",
|
||
|
|
"Perceptron",
|
||
|
|
"RidgeClassifier",
|
||
|
|
"RidgeClassifierCV",
|
||
|
|
"SGDClassifier",
|
||
|
|
"SGDOneClassSVM",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Classical linear regressors",
|
||
|
|
"autosummary": ["LinearRegression", "Ridge", "RidgeCV", "SGDRegressor"],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Regressors with variable selection",
|
||
|
|
"description": (
|
||
|
|
"The following estimators have built-in variable selection fitting "
|
||
|
|
"procedures, but any estimator using a L1 or elastic-net penalty "
|
||
|
|
"also performs variable selection: typically "
|
||
|
|
":class:`~linear_model.SGDRegressor` or "
|
||
|
|
":class:`~sklearn.linear_model.SGDClassifier` with an appropriate "
|
||
|
|
"penalty."
|
||
|
|
),
|
||
|
|
"autosummary": [
|
||
|
|
"ElasticNet",
|
||
|
|
"ElasticNetCV",
|
||
|
|
"Lars",
|
||
|
|
"LarsCV",
|
||
|
|
"Lasso",
|
||
|
|
"LassoCV",
|
||
|
|
"LassoLars",
|
||
|
|
"LassoLarsCV",
|
||
|
|
"LassoLarsIC",
|
||
|
|
"OrthogonalMatchingPursuit",
|
||
|
|
"OrthogonalMatchingPursuitCV",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Bayesian regressors",
|
||
|
|
"autosummary": ["ARDRegression", "BayesianRidge"],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Multi-task linear regressors with variable selection",
|
||
|
|
"description": (
|
||
|
|
"These estimators fit multiple regression problems (or tasks)"
|
||
|
|
" jointly, while inducing sparse coefficients. While the inferred"
|
||
|
|
" coefficients may differ between the tasks, they are constrained"
|
||
|
|
" to agree on the features that are selected (non-zero"
|
||
|
|
" coefficients)."
|
||
|
|
),
|
||
|
|
"autosummary": [
|
||
|
|
"MultiTaskElasticNet",
|
||
|
|
"MultiTaskElasticNetCV",
|
||
|
|
"MultiTaskLasso",
|
||
|
|
"MultiTaskLassoCV",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Outlier-robust regressors",
|
||
|
|
"description": (
|
||
|
|
"Any estimator using the Huber loss would also be robust to "
|
||
|
|
"outliers, e.g., :class:`~linear_model.SGDRegressor` with "
|
||
|
|
"``loss='huber'``."
|
||
|
|
),
|
||
|
|
"autosummary": [
|
||
|
|
"HuberRegressor",
|
||
|
|
"QuantileRegressor",
|
||
|
|
"RANSACRegressor",
|
||
|
|
"TheilSenRegressor",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Generalized linear models (GLM) for regression",
|
||
|
|
"description": (
|
||
|
|
"These models allow for response variables to have error "
|
||
|
|
"distributions other than a normal distribution."
|
||
|
|
),
|
||
|
|
"autosummary": [
|
||
|
|
"GammaRegressor",
|
||
|
|
"PoissonRegressor",
|
||
|
|
"TweedieRegressor",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Miscellaneous",
|
||
|
|
"autosummary": [
|
||
|
|
"PassiveAggressiveRegressor",
|
||
|
|
"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": [
|
||
|
|
"Isomap",
|
||
|
|
"LocallyLinearEmbedding",
|
||
|
|
"MDS",
|
||
|
|
"SpectralEmbedding",
|
||
|
|
"TSNE",
|
||
|
|
"locally_linear_embedding",
|
||
|
|
"smacof",
|
||
|
|
"spectral_embedding",
|
||
|
|
"trustworthiness",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.metrics": {
|
||
|
|
"short_summary": "Metrics.",
|
||
|
|
"description": _get_guide("model_evaluation", "metrics"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": "Model selection interface",
|
||
|
|
"description": _get_guide("scoring_parameter"),
|
||
|
|
"autosummary": [
|
||
|
|
"check_scoring",
|
||
|
|
"get_scorer",
|
||
|
|
"get_scorer_names",
|
||
|
|
"make_scorer",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Classification metrics",
|
||
|
|
"description": _get_guide("classification_metrics"),
|
||
|
|
"autosummary": [
|
||
|
|
"accuracy_score",
|
||
|
|
"auc",
|
||
|
|
"average_precision_score",
|
||
|
|
"balanced_accuracy_score",
|
||
|
|
"brier_score_loss",
|
||
|
|
"class_likelihood_ratios",
|
||
|
|
"classification_report",
|
||
|
|
"cohen_kappa_score",
|
||
|
|
"confusion_matrix",
|
||
|
|
"d2_log_loss_score",
|
||
|
|
"dcg_score",
|
||
|
|
"det_curve",
|
||
|
|
"f1_score",
|
||
|
|
"fbeta_score",
|
||
|
|
"hamming_loss",
|
||
|
|
"hinge_loss",
|
||
|
|
"jaccard_score",
|
||
|
|
"log_loss",
|
||
|
|
"matthews_corrcoef",
|
||
|
|
"multilabel_confusion_matrix",
|
||
|
|
"ndcg_score",
|
||
|
|
"precision_recall_curve",
|
||
|
|
"precision_recall_fscore_support",
|
||
|
|
"precision_score",
|
||
|
|
"recall_score",
|
||
|
|
"roc_auc_score",
|
||
|
|
"roc_curve",
|
||
|
|
"top_k_accuracy_score",
|
||
|
|
"zero_one_loss",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Regression metrics",
|
||
|
|
"description": _get_guide("regression_metrics"),
|
||
|
|
"autosummary": [
|
||
|
|
"d2_absolute_error_score",
|
||
|
|
"d2_pinball_score",
|
||
|
|
"d2_tweedie_score",
|
||
|
|
"explained_variance_score",
|
||
|
|
"max_error",
|
||
|
|
"mean_absolute_error",
|
||
|
|
"mean_absolute_percentage_error",
|
||
|
|
"mean_gamma_deviance",
|
||
|
|
"mean_pinball_loss",
|
||
|
|
"mean_poisson_deviance",
|
||
|
|
"mean_squared_error",
|
||
|
|
"mean_squared_log_error",
|
||
|
|
"mean_tweedie_deviance",
|
||
|
|
"median_absolute_error",
|
||
|
|
"r2_score",
|
||
|
|
"root_mean_squared_error",
|
||
|
|
"root_mean_squared_log_error",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Multilabel ranking metrics",
|
||
|
|
"description": _get_guide("multilabel_ranking_metrics"),
|
||
|
|
"autosummary": [
|
||
|
|
"coverage_error",
|
||
|
|
"label_ranking_average_precision_score",
|
||
|
|
"label_ranking_loss",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Clustering metrics",
|
||
|
|
"description": (
|
||
|
|
_get_submodule("sklearn.metrics", "cluster")
|
||
|
|
+ "\n\n"
|
||
|
|
+ _get_guide("clustering_evaluation")
|
||
|
|
),
|
||
|
|
"autosummary": [
|
||
|
|
"adjusted_mutual_info_score",
|
||
|
|
"adjusted_rand_score",
|
||
|
|
"calinski_harabasz_score",
|
||
|
|
"cluster.contingency_matrix",
|
||
|
|
"cluster.pair_confusion_matrix",
|
||
|
|
"completeness_score",
|
||
|
|
"davies_bouldin_score",
|
||
|
|
"fowlkes_mallows_score",
|
||
|
|
"homogeneity_completeness_v_measure",
|
||
|
|
"homogeneity_score",
|
||
|
|
"mutual_info_score",
|
||
|
|
"normalized_mutual_info_score",
|
||
|
|
"rand_score",
|
||
|
|
"silhouette_samples",
|
||
|
|
"silhouette_score",
|
||
|
|
"v_measure_score",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Biclustering metrics",
|
||
|
|
"description": _get_guide("biclustering_evaluation"),
|
||
|
|
"autosummary": ["consensus_score"],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Distance metrics",
|
||
|
|
"autosummary": ["DistanceMetric"],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Pairwise metrics",
|
||
|
|
"description": (
|
||
|
|
_get_submodule("sklearn.metrics", "pairwise")
|
||
|
|
+ "\n\n"
|
||
|
|
+ _get_guide("metrics")
|
||
|
|
),
|
||
|
|
"autosummary": [
|
||
|
|
"pairwise.additive_chi2_kernel",
|
||
|
|
"pairwise.chi2_kernel",
|
||
|
|
"pairwise.cosine_distances",
|
||
|
|
"pairwise.cosine_similarity",
|
||
|
|
"pairwise.distance_metrics",
|
||
|
|
"pairwise.euclidean_distances",
|
||
|
|
"pairwise.haversine_distances",
|
||
|
|
"pairwise.kernel_metrics",
|
||
|
|
"pairwise.laplacian_kernel",
|
||
|
|
"pairwise.linear_kernel",
|
||
|
|
"pairwise.manhattan_distances",
|
||
|
|
"pairwise.nan_euclidean_distances",
|
||
|
|
"pairwise.paired_cosine_distances",
|
||
|
|
"pairwise.paired_distances",
|
||
|
|
"pairwise.paired_euclidean_distances",
|
||
|
|
"pairwise.paired_manhattan_distances",
|
||
|
|
"pairwise.pairwise_kernels",
|
||
|
|
"pairwise.polynomial_kernel",
|
||
|
|
"pairwise.rbf_kernel",
|
||
|
|
"pairwise.sigmoid_kernel",
|
||
|
|
"pairwise_distances",
|
||
|
|
"pairwise_distances_argmin",
|
||
|
|
"pairwise_distances_argmin_min",
|
||
|
|
"pairwise_distances_chunked",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Plotting",
|
||
|
|
"description": _get_guide("visualizations"),
|
||
|
|
"autosummary": [
|
||
|
|
"ConfusionMatrixDisplay",
|
||
|
|
"DetCurveDisplay",
|
||
|
|
"PrecisionRecallDisplay",
|
||
|
|
"PredictionErrorDisplay",
|
||
|
|
"RocCurveDisplay",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.mixture": {
|
||
|
|
"short_summary": "Gaussian mixture models.",
|
||
|
|
"description": _get_guide("mixture"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": ["BayesianGaussianMixture", "GaussianMixture"],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.model_selection": {
|
||
|
|
"short_summary": "Model selection.",
|
||
|
|
"description": _get_guide("cross_validation", "grid_search", "learning_curve"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": "Splitters",
|
||
|
|
"autosummary": [
|
||
|
|
"GroupKFold",
|
||
|
|
"GroupShuffleSplit",
|
||
|
|
"KFold",
|
||
|
|
"LeaveOneGroupOut",
|
||
|
|
"LeaveOneOut",
|
||
|
|
"LeavePGroupsOut",
|
||
|
|
"LeavePOut",
|
||
|
|
"PredefinedSplit",
|
||
|
|
"RepeatedKFold",
|
||
|
|
"RepeatedStratifiedKFold",
|
||
|
|
"ShuffleSplit",
|
||
|
|
"StratifiedGroupKFold",
|
||
|
|
"StratifiedKFold",
|
||
|
|
"StratifiedShuffleSplit",
|
||
|
|
"TimeSeriesSplit",
|
||
|
|
"check_cv",
|
||
|
|
"train_test_split",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Hyper-parameter optimizers",
|
||
|
|
"autosummary": [
|
||
|
|
"GridSearchCV",
|
||
|
|
"HalvingGridSearchCV",
|
||
|
|
"HalvingRandomSearchCV",
|
||
|
|
"ParameterGrid",
|
||
|
|
"ParameterSampler",
|
||
|
|
"RandomizedSearchCV",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Post-fit model tuning",
|
||
|
|
"autosummary": [
|
||
|
|
"FixedThresholdClassifier",
|
||
|
|
"TunedThresholdClassifierCV",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Model validation",
|
||
|
|
"autosummary": [
|
||
|
|
"cross_val_predict",
|
||
|
|
"cross_val_score",
|
||
|
|
"cross_validate",
|
||
|
|
"learning_curve",
|
||
|
|
"permutation_test_score",
|
||
|
|
"validation_curve",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Visualization",
|
||
|
|
"autosummary": ["LearningCurveDisplay", "ValidationCurveDisplay"],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.multiclass": {
|
||
|
|
"short_summary": "Multiclass classification.",
|
||
|
|
"description": _get_guide("multiclass_classification"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"OneVsOneClassifier",
|
||
|
|
"OneVsRestClassifier",
|
||
|
|
"OutputCodeClassifier",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.multioutput": {
|
||
|
|
"short_summary": "Multioutput regression and classification.",
|
||
|
|
"description": _get_guide(
|
||
|
|
"multilabel_classification",
|
||
|
|
"multiclass_multioutput_classification",
|
||
|
|
"multioutput_regression",
|
||
|
|
),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"ClassifierChain",
|
||
|
|
"MultiOutputClassifier",
|
||
|
|
"MultiOutputRegressor",
|
||
|
|
"RegressorChain",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.naive_bayes": {
|
||
|
|
"short_summary": "Naive Bayes.",
|
||
|
|
"description": _get_guide("naive_bayes"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"BernoulliNB",
|
||
|
|
"CategoricalNB",
|
||
|
|
"ComplementNB",
|
||
|
|
"GaussianNB",
|
||
|
|
"MultinomialNB",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.neighbors": {
|
||
|
|
"short_summary": "Nearest neighbors.",
|
||
|
|
"description": _get_guide("neighbors"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"BallTree",
|
||
|
|
"KDTree",
|
||
|
|
"KNeighborsClassifier",
|
||
|
|
"KNeighborsRegressor",
|
||
|
|
"KNeighborsTransformer",
|
||
|
|
"KernelDensity",
|
||
|
|
"LocalOutlierFactor",
|
||
|
|
"NearestCentroid",
|
||
|
|
"NearestNeighbors",
|
||
|
|
"NeighborhoodComponentsAnalysis",
|
||
|
|
"RadiusNeighborsClassifier",
|
||
|
|
"RadiusNeighborsRegressor",
|
||
|
|
"RadiusNeighborsTransformer",
|
||
|
|
"kneighbors_graph",
|
||
|
|
"radius_neighbors_graph",
|
||
|
|
"sort_graph_by_row_values",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.neural_network": {
|
||
|
|
"short_summary": "Neural network models.",
|
||
|
|
"description": _get_guide(
|
||
|
|
"neural_networks_supervised", "neural_networks_unsupervised"
|
||
|
|
),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": ["BernoulliRBM", "MLPClassifier", "MLPRegressor"],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.pipeline": {
|
||
|
|
"short_summary": "Pipeline.",
|
||
|
|
"description": _get_guide("combining_estimators"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"FeatureUnion",
|
||
|
|
"Pipeline",
|
||
|
|
"make_pipeline",
|
||
|
|
"make_union",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.preprocessing": {
|
||
|
|
"short_summary": "Preprocessing and normalization.",
|
||
|
|
"description": _get_guide("preprocessing"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"Binarizer",
|
||
|
|
"FunctionTransformer",
|
||
|
|
"KBinsDiscretizer",
|
||
|
|
"KernelCenterer",
|
||
|
|
"LabelBinarizer",
|
||
|
|
"LabelEncoder",
|
||
|
|
"MaxAbsScaler",
|
||
|
|
"MinMaxScaler",
|
||
|
|
"MultiLabelBinarizer",
|
||
|
|
"Normalizer",
|
||
|
|
"OneHotEncoder",
|
||
|
|
"OrdinalEncoder",
|
||
|
|
"PolynomialFeatures",
|
||
|
|
"PowerTransformer",
|
||
|
|
"QuantileTransformer",
|
||
|
|
"RobustScaler",
|
||
|
|
"SplineTransformer",
|
||
|
|
"StandardScaler",
|
||
|
|
"TargetEncoder",
|
||
|
|
"add_dummy_feature",
|
||
|
|
"binarize",
|
||
|
|
"label_binarize",
|
||
|
|
"maxabs_scale",
|
||
|
|
"minmax_scale",
|
||
|
|
"normalize",
|
||
|
|
"power_transform",
|
||
|
|
"quantile_transform",
|
||
|
|
"robust_scale",
|
||
|
|
"scale",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.random_projection": {
|
||
|
|
"short_summary": "Random projection.",
|
||
|
|
"description": _get_guide("random_projection"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"GaussianRandomProjection",
|
||
|
|
"SparseRandomProjection",
|
||
|
|
"johnson_lindenstrauss_min_dim",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.semi_supervised": {
|
||
|
|
"short_summary": "Semi-supervised learning.",
|
||
|
|
"description": _get_guide("semi_supervised"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"LabelPropagation",
|
||
|
|
"LabelSpreading",
|
||
|
|
"SelfTrainingClassifier",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.svm": {
|
||
|
|
"short_summary": "Support vector machines.",
|
||
|
|
"description": _get_guide("svm"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"LinearSVC",
|
||
|
|
"LinearSVR",
|
||
|
|
"NuSVC",
|
||
|
|
"NuSVR",
|
||
|
|
"OneClassSVM",
|
||
|
|
"SVC",
|
||
|
|
"SVR",
|
||
|
|
"l1_min_c",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.tree": {
|
||
|
|
"short_summary": "Decision trees.",
|
||
|
|
"description": _get_guide("tree"),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"DecisionTreeClassifier",
|
||
|
|
"DecisionTreeRegressor",
|
||
|
|
"ExtraTreeClassifier",
|
||
|
|
"ExtraTreeRegressor",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Exporting",
|
||
|
|
"autosummary": ["export_graphviz", "export_text"],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Plotting",
|
||
|
|
"autosummary": ["plot_tree"],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
"sklearn.utils": {
|
||
|
|
"short_summary": "Utilities.",
|
||
|
|
"description": _get_guide("developers-utils", is_developer=True),
|
||
|
|
"sections": [
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"autosummary": [
|
||
|
|
"Bunch",
|
||
|
|
"_safe_indexing",
|
||
|
|
"as_float_array",
|
||
|
|
"assert_all_finite",
|
||
|
|
"deprecated",
|
||
|
|
"estimator_html_repr",
|
||
|
|
"gen_batches",
|
||
|
|
"gen_even_slices",
|
||
|
|
"indexable",
|
||
|
|
"murmurhash3_32",
|
||
|
|
"resample",
|
||
|
|
"safe_mask",
|
||
|
|
"safe_sqr",
|
||
|
|
"shuffle",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Input and parameter validation",
|
||
|
|
"description": _get_submodule("sklearn.utils", "validation"),
|
||
|
|
"autosummary": [
|
||
|
|
"check_X_y",
|
||
|
|
"check_array",
|
||
|
|
"check_consistent_length",
|
||
|
|
"check_random_state",
|
||
|
|
"check_scalar",
|
||
|
|
"validation.check_is_fitted",
|
||
|
|
"validation.check_memory",
|
||
|
|
"validation.check_symmetric",
|
||
|
|
"validation.column_or_1d",
|
||
|
|
"validation.has_fit_parameter",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Meta-estimators",
|
||
|
|
"description": _get_submodule("sklearn.utils", "metaestimators"),
|
||
|
|
"autosummary": ["metaestimators.available_if"],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Weight handling based on class labels",
|
||
|
|
"description": _get_submodule("sklearn.utils", "class_weight"),
|
||
|
|
"autosummary": [
|
||
|
|
"class_weight.compute_class_weight",
|
||
|
|
"class_weight.compute_sample_weight",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Dealing with multiclass target in classifiers",
|
||
|
|
"description": _get_submodule("sklearn.utils", "multiclass"),
|
||
|
|
"autosummary": [
|
||
|
|
"multiclass.is_multilabel",
|
||
|
|
"multiclass.type_of_target",
|
||
|
|
"multiclass.unique_labels",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Optimal mathematical operations",
|
||
|
|
"description": _get_submodule("sklearn.utils", "extmath"),
|
||
|
|
"autosummary": [
|
||
|
|
"extmath.density",
|
||
|
|
"extmath.fast_logdet",
|
||
|
|
"extmath.randomized_range_finder",
|
||
|
|
"extmath.randomized_svd",
|
||
|
|
"extmath.safe_sparse_dot",
|
||
|
|
"extmath.weighted_mode",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Working with sparse matrices and arrays",
|
||
|
|
"description": _get_submodule("sklearn.utils", "sparsefuncs"),
|
||
|
|
"autosummary": [
|
||
|
|
"sparsefuncs.incr_mean_variance_axis",
|
||
|
|
"sparsefuncs.inplace_column_scale",
|
||
|
|
"sparsefuncs.inplace_csr_column_scale",
|
||
|
|
"sparsefuncs.inplace_row_scale",
|
||
|
|
"sparsefuncs.inplace_swap_column",
|
||
|
|
"sparsefuncs.inplace_swap_row",
|
||
|
|
"sparsefuncs.mean_variance_axis",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": None,
|
||
|
|
"description": _get_submodule("sklearn.utils", "sparsefuncs_fast"),
|
||
|
|
"autosummary": [
|
||
|
|
"sparsefuncs_fast.inplace_csr_row_normalize_l1",
|
||
|
|
"sparsefuncs_fast.inplace_csr_row_normalize_l2",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Working with graphs",
|
||
|
|
"description": _get_submodule("sklearn.utils", "graph"),
|
||
|
|
"autosummary": ["graph.single_source_shortest_path_length"],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Random sampling",
|
||
|
|
"description": _get_submodule("sklearn.utils", "random"),
|
||
|
|
"autosummary": ["random.sample_without_replacement"],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Auxiliary functions that operate on arrays",
|
||
|
|
"description": _get_submodule("sklearn.utils", "arrayfuncs"),
|
||
|
|
"autosummary": ["arrayfuncs.min_pos"],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Metadata routing",
|
||
|
|
"description": (
|
||
|
|
_get_submodule("sklearn.utils", "metadata_routing")
|
||
|
|
+ "\n\n"
|
||
|
|
+ _get_guide("metadata_routing")
|
||
|
|
),
|
||
|
|
"autosummary": [
|
||
|
|
"metadata_routing.MetadataRequest",
|
||
|
|
"metadata_routing.MetadataRouter",
|
||
|
|
"metadata_routing.MethodMapping",
|
||
|
|
"metadata_routing.get_routing_for_object",
|
||
|
|
"metadata_routing.process_routing",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Discovering scikit-learn objects",
|
||
|
|
"description": _get_submodule("sklearn.utils", "discovery"),
|
||
|
|
"autosummary": [
|
||
|
|
"discovery.all_displays",
|
||
|
|
"discovery.all_estimators",
|
||
|
|
"discovery.all_functions",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "API compatibility checkers",
|
||
|
|
"description": _get_submodule("sklearn.utils", "estimator_checks"),
|
||
|
|
"autosummary": [
|
||
|
|
"estimator_checks.check_estimator",
|
||
|
|
"estimator_checks.parametrize_with_checks",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"title": "Parallel computing",
|
||
|
|
"description": _get_submodule("sklearn.utils", "parallel"),
|
||
|
|
"autosummary": [
|
||
|
|
"parallel.Parallel",
|
||
|
|
"parallel.delayed",
|
||
|
|
],
|
||
|
|
},
|
||
|
|
],
|
||
|
|
},
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
"""
|
||
|
|
CONFIGURING DEPRECATED_API_REFERENCE
|
||
|
|
====================================
|
||
|
|
|
||
|
|
DEPRECATED_API_REFERENCE maps each deprecation target version to a corresponding
|
||
|
|
autosummary block. It will be placed at the bottom of the API index page under the
|
||
|
|
"Recently deprecated" section. Essentially, the rendered section would look like the
|
||
|
|
following:
|
||
|
|
|
||
|
|
|------------------------------------------|
|
||
|
|
| To be removed in {{ version_1 }} |
|
||
|
|
| -------------------------------- |
|
||
|
|
| {{ autosummary_1 }} |
|
||
|
|
| |
|
||
|
|
| To be removed in {{ version_2 }} |
|
||
|
|
| -------------------------------- |
|
||
|
|
| {{ autosummary_2 }} |
|
||
|
|
| |
|
||
|
|
| More versions... |
|
||
|
|
|------------------------------------------|
|
||
|
|
|
||
|
|
Note that the autosummary here assumes that the current module is `sklearn`, i.e., if
|
||
|
|
`sklearn.utils.Memory` is deprecated, one should put `utils.Memory` in the "entries"
|
||
|
|
slot of the autosummary block.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
|
||
|
|
DEPRECATED_API_REFERENCE = {
|
||
|
|
"0.24": [
|
||
|
|
"model_selection.fit_grid_point",
|
||
|
|
"utils.safe_indexing",
|
||
|
|
],
|
||
|
|
}
|
||
|
|
"""
|
||
|
|
|
||
|
|
DEPRECATED_API_REFERENCE = {
|
||
|
|
"1.7": [
|
||
|
|
"utils.parallel_backend",
|
||
|
|
"utils.register_parallel_backend",
|
||
|
|
]
|
||
|
|
} # type: ignore
|