sklearn.utils.shuffle

sklearn.utils.shuffle(*arrays, **options)

Shuffle arrays or sparse matrices in a consistent way

This is a convenience alias to resample(*arrays, replace=False) to do random permutations of the collections.

Parameters:

`*arrays` : sequence of arrays or scipy.sparse matrices with same shape[0]

random_state : int or RandomState instance

Control the shuffling for reproducible behavior.

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.

Returns:

Sequence of shuffled views of the collections. The original arrays are :

not impacted. :

See also

sklearn.utils.resample

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 shuffle
>>> X, X_sparse, y = shuffle(X, X_sparse, y, random_state=0)
>>> X
array([[ 0.,  0.],
       [ 2.,  1.],
       [ 1.,  0.]])

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

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

>>> y
array([2, 1, 0])

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

Examples using sklearn.utils.shuffle

Model Complexity Influence

Color Quantization using K-Means

Empirical evaluation of the impact of k-means initialization

Gradient Boosting regression