sklearn.model_selection.LabelKFold

class sklearn.model_selection.LabelKFold(n_folds=3)

K-fold iterator variant with non-overlapping labels.

The same label will not appear in two different folds (the number of distinct labels has to be at least equal to the number of folds).

The folds are approximately balanced in the sense that the number of distinct labels is approximately the same in each fold.

Parameters:

n_folds : int, default=3

Number of folds. Must be at least 2.

See also

LeaveOneLabelOut

For splitting the data according to explicit domain-specific stratification of the dataset.

Examples

>>> from sklearn.model_selection import LabelKFold
>>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
>>> y = np.array([1, 2, 3, 4])
>>> labels = np.array([0, 0, 2, 2])
>>> label_kfold = LabelKFold(n_folds=2)
>>> label_kfold.get_n_splits(X, y, labels)
2
>>> print(label_kfold)
LabelKFold(n_folds=2)
>>> for train_index, test_index in label_kfold.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: [0 1] TEST: [2 3]
[[1 2]
 [3 4]] [[5 6]
 [7 8]] [1 2] [3 4]
TRAIN: [2 3] TEST: [0 1]
[[5 6]
 [7 8]] [[1 2]
 [3 4]] [3 4] [1 2]

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_folds=3)

get_n_splits(X=None, y=None, labels=None)

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 : object

Always ignored, exists for compatibility.

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,), optional

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.