sklearn.utils

Various utilities to help with development.

Developer guide. See the Utilities 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 the Metadata Routing section for further details.

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

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 of joblib and threadpoolctl tools for scikit-learn usage.

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