sklearn.utils.resample(*arrays, replace=True, n_samples=None, random_state=None, stratify=None)[source]#

Resample arrays or sparse matrices in a consistent way.

The default strategy implements one step of the bootstrapping procedure.

*arrayssequence of array-like of shape (n_samples,) or (n_samples, n_outputs)

Indexable data-structures can be arrays, lists, dataframes or scipy sparse matrices with consistent first dimension.

replacebool, default=True

Implements resampling with replacement. If False, this will implement (sliced) random permutations.

n_samplesint, default=None

Number of samples to generate. If left to None this is automatically set to the first dimension of the arrays. If replace is False it should not be larger than the length of arrays.

random_stateint, RandomState instance or None, default=None

Determines random number generation for shuffling the data. Pass an int for reproducible results across multiple function calls. See Glossary.

stratify{array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_outputs), default=None

If not None, data is split in a stratified fashion, using this as the class labels.

resampled_arrayssequence of array-like of shape (n_samples,) or (n_samples, n_outputs)

Sequence of resampled copies of the collections. The original arrays are not impacted.

See also


Shuffle arrays or sparse matrices in a consistent way.


It is possible to mix sparse and dense arrays in the same run:

>>> import numpy as np
>>> X = np.array([[1., 0.], [2., 1.], [0., 0.]])
>>> y = np.array([0, 1, 2])

>>> from scipy.sparse import coo_matrix
>>> X_sparse = coo_matrix(X)

>>> from sklearn.utils import resample
>>> X, X_sparse, y = resample(X, X_sparse, y, random_state=0)
>>> X
array([[1., 0.],
       [2., 1.],
       [1., 0.]])

>>> X_sparse
<3x2 sparse matrix of type '<... 'numpy.float64'>'
    with 4 stored elements in Compressed Sparse Row format>

>>> X_sparse.toarray()
array([[1., 0.],
       [2., 1.],
       [1., 0.]])

>>> y
array([0, 1, 0])

>>> resample(y, n_samples=2, random_state=0)
array([0, 1])

Example using stratification:

>>> y = [0, 0, 1, 1, 1, 1, 1, 1, 1]
>>> resample(y, n_samples=5, replace=False, stratify=y,
...          random_state=0)
[1, 1, 1, 0, 1]