sklearn.model_selection.StratifiedShuffleSplit

class sklearn.model_selection.StratifiedShuffleSplit(n_splits=10, test_size='default', train_size=None, random_state=None)[source]

Stratified ShuffleSplit cross-validator

Provides train/test indices to split data in train/test sets.

This cross-validation object is a merge of StratifiedKFold and ShuffleSplit, which returns stratified randomized folds. The folds are made by preserving the percentage of samples for each class.

Note: like the ShuffleSplit strategy, stratified random splits do not guarantee that all folds will be different, although this is still very likely for sizeable datasets.

Read more in the User Guide.

Parameters:
n_splits : int, default 10

Number of re-shuffling & splitting iterations.

test_size : float, int, None, optional

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 set to the complement of the train size. By default, the value is set to 0.1. The default will change in version 0.21. It will remain 0.1 only if train_size is unspecified, otherwise it will complement the specified train_size.

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, RandomState instance or None, optional (default=None)

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.

Examples

>>> from sklearn.model_selection import StratifiedShuffleSplit
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 0, 1, 1, 1])
>>> sss = StratifiedShuffleSplit(n_splits=5, test_size=0.5, random_state=0)
>>> sss.get_n_splits(X, y)
5
>>> print(sss)       # doctest: +ELLIPSIS
StratifiedShuffleSplit(n_splits=5, random_state=0, ...)
>>> for train_index, test_index in sss.split(X, y):
...    print("TRAIN:", train_index, "TEST:", test_index)
...    X_train, X_test = X[train_index], X[test_index]
...    y_train, y_test = y[train_index], y[test_index]
TRAIN: [5 2 3] TEST: [4 1 0]
TRAIN: [5 1 4] TEST: [0 2 3]
TRAIN: [5 0 2] TEST: [4 3 1]
TRAIN: [4 1 0] TEST: [2 3 5]
TRAIN: [0 5 1] TEST: [3 4 2]

Methods

get_n_splits([X, y, groups]) Returns the number of splitting iterations in the cross-validator
split(X, y[, groups]) Generate indices to split data into training and test set.
__init__(n_splits=10, test_size='default', train_size=None, random_state=None)[source]

Initialize self. See help(type(self)) for accurate signature.

get_n_splits(X=None, y=None, groups=None)[source]

Returns the number of splitting iterations in the cross-validator

Parameters:
X : object

Always ignored, exists for compatibility.

y : object

Always ignored, exists for compatibility.

groups : object

Always ignored, exists for compatibility.
Returns:
n_splits : int

Returns the number of splitting iterations in the cross-validator.
split(X, y, groups=None)[source]

Generate indices to split data into training and test set.

Parameters:
X : array-like, shape (n_samples, n_features)

Training data, where n_samples is the number of samples and n_features is the number of features.

Note that providing y is sufficient to generate the splits and hence np.zeros(n_samples) may be used as a placeholder for X instead of actual training data.

y : array-like, shape (n_samples,)

The target variable for supervised learning problems. Stratification is done based on the y labels.

groups : object

Always ignored, exists for compatibility.
Yields:
train : ndarray

The training set indices for that split.

test : ndarray

The testing set indices for that split.

Notes

Randomized CV splitters may return different results for each call of split. You can make the results identical by setting random_state to an integer.

Examples using sklearn.model_selection.StratifiedShuffleSplit