resample#

sklearn.utils.resample(*arrays,replace=True,n_samples=None,random_state=None,stratify=None,sample_weight=None)[source]#

Resample arrays or sparse matrices in a consistent way.

The default strategy implements one step of the bootstrappingprocedure.

Parameters:

*arrayssequence of array-like of shape (n_samples,) or (n_samples, n_outputs)

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

replacebool, default=True

Implements resampling with replacement. It must be set to Truewhenever sampling with non-uniform weights: a few data points with very largeweights are expected to be sampled several times with probability to preservethe distribution induced by the weights. If False, this will implement(sliced) random permutations.

n_samplesint, default=None

Number of samples to generate. If left to None this isautomatically set to the first dimension of the arrays.If replace is False it should not be larger than the length ofarrays.

random_stateint, RandomState instance or None, default=None

Determines random number generation for shufflingthe data.Pass an int for reproducible results across multiple function calls.SeeGlossary.

stratify{array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_outputs), default=None

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

sample_weightarray-like of shape (n_samples,), default=None

Contains weight values to be associated with each sample. Values arenormalized to sum to one and interpreted as probability for samplingeach data point.

Added in version 1.7.

Returns:

resampled_arrayssequence of array-like of shape (n_samples,) or (n_samples, n_outputs)

Sequence of resampled copies of the collections. The original arraysare not impacted.

See also

shuffle

Shuffle arrays or sparse matrices in a consistent way.

Examples

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

>>>importnumpyasnp>>>X=np.array([[1.,0.],[2.,1.],[0.,0.]])>>>y=np.array([0,1,2])>>>fromscipy.sparseimportcoo_matrix>>>X_sparse=coo_matrix(X)>>>fromsklearn.utilsimportresample>>>X,X_sparse,y=resample(X,X_sparse,y,random_state=0)>>>Xarray([[1., 0.],       [2., 1.],       [1., 0.]])>>>X_sparse<Compressed Sparse Row sparse matrix of dtype 'float64'    with 4 stored elements and shape (3, 2)>>>>X_sparse.toarray()array([[1., 0.],       [2., 1.],       [1., 0.]])>>>yarray([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]