package sklearn

Shuffle-Group(s)-Out cross-validation iterator

Provides randomized train/test indices to split data according to a third-party provided group. This group information can be used to encode arbitrary domain specific stratifications of the samples as integers.

For instance the groups could be the year of collection of the samples and thus allow for cross-validation against time-based splits.

The difference between LeavePGroupsOut and GroupShuffleSplit is that the former generates splits using all subsets of size ``p`` unique groups, whereas GroupShuffleSplit generates a user-determined number of random test splits, each with a user-determined fraction of unique groups.

For example, a less computationally intensive alternative to ``LeavePGroupsOut(p=10)`` would be ``GroupShuffleSplit(test_size=10, n_splits=100)``.

Note: The parameters ``test_size`` and ``train_size`` refer to groups, and not to samples, as in ShuffleSplit.

Parameters ---------- n_splits : int, default=5 Number of re-shuffling & splitting iterations.

test_size : float, int, default=0.2 If float, should be between 0.0 and 1.0 and represent the proportion of groups to include in the test split (rounded up). If int, represents the absolute number of test groups. If None, the value is set to the complement of the train size. The default will change in version 0.21. It will remain 0.2 only if ``train_size`` is unspecified, otherwise it will complement the specified ``train_size``.

train_size : float or int, default=None If float, should be between 0.0 and 1.0 and represent the proportion of the groups to include in the train split. If int, represents the absolute number of train groups. If None, the value is automatically set to the complement of the test size.

random_state : int or RandomState instance, default=None Controls the randomness of the training and testing indices produced. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`.

Examples -------- >>> import numpy as np >>> from sklearn.model_selection import GroupShuffleSplit >>> X = np.ones(shape=(8, 2)) >>> y = np.ones(shape=(8, 1)) >>> groups = np.array(1, 1, 2, 2, 2, 3, 3, 3) >>> print(groups.shape) (8,) >>> gss = GroupShuffleSplit(n_splits=2, train_size=.7, random_state=42) >>> gss.get_n_splits() 2 >>> for train_idx, test_idx in gss.split(X, y, groups): ... print('TRAIN:', train_idx, 'TEST:', test_idx) TRAIN: 2 3 4 5 6 7 TEST: 0 1 TRAIN: 0 1 5 6 7 TEST: 2 3 4

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.

Generate indices to split data into training and test set.

Parameters ---------- X : array-like of shape (n_samples, n_features) Training data, where n_samples is the number of samples and n_features is the number of features.

y : array-like of shape (n_samples,), default=None The target variable for supervised learning problems.

groups : array-like of shape (n_samples,) Group labels for the samples used while splitting the dataset into train/test set.

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.

Print the object to a human-readable representation.

Print the object to a human-readable representation.

Pretty-print the object to a formatter.