model_selection.LeavePGroupsOut()

Leave P Group(s) Out cross-validator

Provides 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 LeaveOneGroupOut is that the former builds the test sets with all the samples assigned to p different values of the groups while the latter uses samples all assigned the same groups.

Read more in the User Guide.

Examples

>>> from sklearn.model_selection import LeavePGroupsOut
>>> X = np.array([[1, 2], [3, 4], [5, 6]])
>>> y = np.array([1, 2, 1])
>>> groups = np.array([1, 2, 3])
>>> lpgo = LeavePGroupsOut(n_groups=2)
>>> lpgo.get_n_splits(X, y, groups)
3
>>> print(lpgo)
LeavePGroupsOut(n_groups=2)
>>> for train_index, test_index in lpgo.split(X, y, groups):
...    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]
...    print(X_train, X_test, y_train, y_test)
TRAIN: [2] TEST: [0 1]
[[5 6]] [[1 2]
 [3 4]] [1] [1 2]
TRAIN: [1] TEST: [0 2]
[[3 4]] [[1 2]
 [5 6]] [2] [1 1]
TRAIN: [0] TEST: [1 2]
[[1 2]] [[3 4]
 [5 6]] [1] [2 1]

Methods

__init__(n_groups) [source]

get_n_splits(X, y, groups) [source]

Returns the number of splitting iterations in the cross-validator

Parameters:	X : object Always ignored, exists for compatibility. np.zeros(n_samples) may be used as a placeholder. y : object Always ignored, exists for compatibility. np.zeros(n_samples) may be used as a placeholder. groups : array-like, with shape (n_samples,), optional Group labels for the samples used while splitting the dataset into train/test set.
Returns:	n_splits : int Returns the number of splitting iterations in the cross-validator.

split(X, y=None, 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. y : array-like, of length n_samples The target variable for supervised learning problems. groups : array-like, with shape (n_samples,), optional Group labels for the samples used while splitting the dataset into train/test set.
Returns:	train : ndarray The training set indices for that split. test : ndarray The testing set indices for that split.