`sklearn.utils`.resample¶

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.

Parameters

*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.
stratifyarray-like 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.

Returns

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

Examples

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]

sklearn.utils.resample¶

`sklearn.utils`.resample¶