sklearn.utils.resample¶
- sklearn.utils.resample(*arrays, **options)¶
Resample arrays or sparse matrices in a consistent way
The default strategy implements one step of the bootstrapping procedure.
Parameters : `*arrays` : sequence of arrays or scipy.sparse matrices with same shape[0]
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.
random_state : int or RandomState instance
Control the shuffling for reproducible behavior.
Returns : Sequence of resampled views of the collections. The original arrays are :
not impacted. :
Examples
It is possible to mix sparse and dense arrays in the same run:
>>> X = [[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])