sklearn.utils.resample(*arrays, **options)[source]

Resample arrays or sparse matrices in a consistent way

The default strategy implements one step of the bootstrapping procedure.

*arrayssequence of indexable data-structures

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

resampled_arrayssequence of indexable data-structures

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

Other Parameters
replaceboolean, True by default

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

n_samplesint, None by default

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, optional (default=None)

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

stratifyarray-like or None (default=None)

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


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

>>> 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]