sklearn.model_selection.LeavePGroupsOut

class sklearn.model_selection.LeavePGroupsOut(n_groups)[source]

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.

Parameters:
n_groups : int

Number of groups (p) to leave out in the test split.

See also

GroupKFold
K-fold iterator variant with non-overlapping groups.

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
>>> lpgo.get_n_splits(groups=groups)  # 'groups' is always required
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

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

Returns the number of splitting iterations in the cross-validator

Parameters:
X : object, optional

Always ignored, exists for compatibility.

y : object, optional

Always ignored, exists for compatibility.

groups : array-like, with shape (n_samples,), optional

Group labels for the samples used while splitting the dataset into train/test set. This ‘groups’ parameter must always be specified to calculate the number of splits, though the other parameters can be omitted.

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.

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.