sklearn.utils.resample

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.

Parameters:

*arrays : sequence of indexable data-structures Indexable data-structures can be arrays, lists, dataframes or scipy sparse matrices with consistent first dimension. replace : boolean, True by default Implements resampling with replacement. If False, this will implement (sliced) random permutations. n_samples : int, 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_state : int, RandomState instance or None, optional (default=None) The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Returns:

resampled_arrays : sequence of indexable data-structures Sequence of resampled views of the collections. The original arrays are not impacted.

See also

sklearn.utils.shuffle

Examples

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])