sklearn.utils #

Various utilities to help with development.

Developer guide. See theUtilities for Developers section for further details.

`Bunch`	Container object exposing keys as attributes.
`_safe_indexing`	Return rows, items or columns of X using indices.
`as_float_array`	Convert an array-like to an array of floats.
`assert_all_finite`	Throw a ValueError if X contains NaN or infinity.
`deprecated`	Decorator to mark a function or class as deprecated.
`estimator_html_repr`	Build a HTML representation of an estimator.
`gen_batches`	Generator to create slices containing`batch_size` elements from 0 to`n`.
`gen_even_slices`	Generator to create`n_packs` evenly spaced slices going up to`n`.
`indexable`	Make arrays indexable for cross-validation.
`murmurhash3_32`	Compute the 32bit murmurhash3 of key at seed.
`resample`	Resample arrays or sparse matrices in a consistent way.
`safe_mask`	Return a mask which is safe to use on X.
`safe_sqr`	Element wise squaring of array-likes and sparse matrices.
`shuffle`	Shuffle arrays or sparse matrices in a consistent way.
`Tags`	Tags for the estimator.
`InputTags`	Tags for the input data.
`TargetTags`	Tags for the target data.
`ClassifierTags`	Tags for the classifier.
`RegressorTags`	Tags for the regressor.
`TransformerTags`	Tags for the transformer.
`get_tags`	Get estimator tags.

Input and parameter validation#

Functions to validate input and parameters within scikit-learn estimators.

`check_X_y`	Input validation for standard estimators.
`check_array`	Input validation on an array, list, sparse matrix or similar.
`check_consistent_length`	Check that all arrays have consistent first dimensions.
`check_random_state`	Turn seed into a np.random.RandomState instance.
`check_scalar`	Validate scalar parameters type and value.
`validation.check_is_fitted`	Perform is_fitted validation for estimator.
`validation.check_memory`	Check that`memory` is joblib.Memory-like.
`validation.check_symmetric`	Make sure that array is 2D, square and symmetric.
`validation.column_or_1d`	Ravel column or 1d numpy array, else raises an error.
`validation.has_fit_parameter`	Check whether the estimator's fit method supports the given parameter.
`validation.validate_data`	Validate input data and set or check feature names and counts of the input.

Meta-estimators#

Utilities for meta-estimators.

metaestimators.available_if

An attribute that is available only if check returns a truthy value.

Weight handling based on class labels#

Utilities for handling weights based on class labels.

`class_weight.compute_class_weight`	Estimate class weights for unbalanced datasets.
`class_weight.compute_sample_weight`	Estimate sample weights by class for unbalanced datasets.

Dealing with multiclass target in classifiers#

Utilities to handle multiclass/multioutput target in classifiers.

`multiclass.is_multilabel`	Check if`y` is in a multilabel format.
`multiclass.type_of_target`	Determine the type of data indicated by the target.
`multiclass.unique_labels`	Extract an ordered array of unique labels.

Optimal mathematical operations#

Utilities to perform optimal mathematical operations in scikit-learn.

`extmath.density`	Compute density of a sparse vector.
`extmath.fast_logdet`	Compute logarithm of determinant of a square matrix.
`extmath.randomized_range_finder`	Compute an orthonormal matrix whose range approximates the range of A.
`extmath.randomized_svd`	Compute a truncated randomized SVD.
`extmath.safe_sparse_dot`	Dot product that handle the sparse matrix case correctly.
`extmath.weighted_mode`	Return an array of the weighted modal (most common) value in the passed array.

Working with sparse matrices and arrays#

A collection of utilities to work with sparse matrices and arrays.

`sparsefuncs.incr_mean_variance_axis`	Compute incremental mean and variance along an axis on a CSR or CSC matrix.
`sparsefuncs.inplace_column_scale`	Inplace column scaling of a CSC/CSR matrix.
`sparsefuncs.inplace_csr_column_scale`	Inplace column scaling of a CSR matrix.
`sparsefuncs.inplace_row_scale`	Inplace row scaling of a CSR or CSC matrix.
`sparsefuncs.inplace_swap_column`	Swap two columns of a CSC/CSR matrix in-place.
`sparsefuncs.inplace_swap_row`	Swap two rows of a CSC/CSR matrix in-place.
`sparsefuncs.mean_variance_axis`	Compute mean and variance along an axis on a CSR or CSC matrix.

Utilities to work with sparse matrices and arrays written in Cython.

`sparsefuncs_fast.inplace_csr_row_normalize_l1`	Normalize inplace the rows of a CSR matrix or array by their L1 norm.
`sparsefuncs_fast.inplace_csr_row_normalize_l2`	Normalize inplace the rows of a CSR matrix or array by their L2 norm.

Working with graphs#

Graph utilities and algorithms.

graph.single_source_shortest_path_length

Return the length of the shortest path from source to all reachable nodes.

Random sampling#

Utilities for random sampling.

random.sample_without_replacement

Sample integers without replacement.

Auxiliary functions that operate on arrays#

A small collection of auxiliary functions that operate on arrays.

arrayfuncs.min_pos

Find the minimum value of an array over positive values.

Metadata routing#

Utilities to route metadata within scikit-learn estimators.

User guide. See theMetadata Routing section for further details.

`metadata_routing.MetadataRequest`	Contains the metadata request info of a consumer.
`metadata_routing.MetadataRouter`	Coordinates metadata routing for arouter object.
`metadata_routing.MethodMapping`	Stores the mapping between caller and callee methods for arouter.
`metadata_routing.get_routing_for_object`	Get a`Metadata{Router,Request}` instance from the given object.
`metadata_routing.process_routing`	Validate and route metadata.

Discovering scikit-learn objects#

Utilities to discover scikit-learn objects.

`discovery.all_displays`	Get a list of all displays from`sklearn`.
`discovery.all_estimators`	Get a list of all estimators from`sklearn`.
`discovery.all_functions`	Get a list of all functions from`sklearn`.

API compatibility checkers#

Various utilities to check the compatibility of estimators with scikit-learn API.

`estimator_checks.check_estimator`	Check if estimator adheres to scikit-learn conventions.
`estimator_checks.parametrize_with_checks`	Pytest specific decorator for parametrizing estimator checks.
`estimator_checks.estimator_checks_generator`	Iteratively yield all check callables for an estimator.

Parallel computing#

Customizations ofjoblib andthreadpoolctl tools for scikit-learnusage.

`parallel.Parallel`	Tweak of`joblib.Parallel` that propagates the scikit-learn configuration.
`parallel.delayed`	Decorator used to capture the arguments of a function.

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sklearn.utils#