sklearn.model_selection.LeavePLabelOut

class sklearn.model_selection.LeavePLabelOut(n_labels)

Leave P Labels Out cross-validator

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

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

The difference between LeavePLabelOut and LeaveOneLabelOut is that the former builds the test sets with all the samples assigned to p different values of the labels while the latter uses samples all assigned the same labels.

Read more in the User Guide.

Parameters:

n_labels : int

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

See also

LabelKFold

K-fold iterator variant with non-overlapping labels.

Examples

>>> from sklearn.model_selection import LeavePLabelOut
>>> X = np.array([[1, 2], [3, 4], [5, 6]])
>>> y = np.array([1, 2, 1])
>>> labels = np.array([1, 2, 3])
>>> lpl = LeavePLabelOut(n_labels=2)
>>> lpl.get_n_splits(X, y, labels)
3
>>> print(lpl)
LeavePLabelOut(n_labels=2)
>>> for train_index, test_index in lpl.split(X, y, labels):
...    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, labels)	Returns the number of splitting iterations in the cross-validator
split(X[, y, labels])	Generate indices to split data into training and test set.

__init__(n_labels)

get_n_splits(X, y, labels)

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.

labels : 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, labels=None)

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, shape (n_samples,)

The target variable for supervised learning problems.

labels : 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.