sklearn.cross_validation.train_test_split

sklearn.cross_validation.train_test_split(*arrays, **options)[source]

Split arrays or matrices into random train and test subsets

Quick utility that wraps input validation and next(iter(ShuffleSplit(n_samples))) and application to input data into a single call for splitting (and optionally subsampling) data in a oneliner.

Read more in the User Guide.

Parameters:

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

Python lists or tuples occurring in arrays are converted to 1D numpy arrays.

test_size : float, int, or None (default is None)

If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples. If None, the value is automatically set to the complement of the train size. If train size is also None, test size is set to 0.25.

train_size : float, int, or None (default is None)

If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train samples. If None, the value is automatically set to the complement of the test size.

random_state : int or RandomState

Pseudo-random number generator state used for random sampling.

stratify : array-like or None (default is None)

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

splitting : list of arrays, length=2 * len(arrays)

List containing train-test split of input array.

Examples

>>> import numpy as np
>>> from sklearn.cross_validation import train_test_split
>>> X, y = np.arange(10).reshape((5, 2)), range(5)
>>> X
array([[0, 1],
       [2, 3],
       [4, 5],
       [6, 7],
       [8, 9]])
>>> list(y)
[0, 1, 2, 3, 4]

>>> X_train, X_test, y_train, y_test = train_test_split(
...     X, y, test_size=0.33, random_state=42)
...
>>> X_train
array([[4, 5],
       [0, 1],
       [6, 7]])
>>> y_train
[2, 0, 3]
>>> X_test
array([[2, 3],
       [8, 9]])
>>> y_test
[1, 4]