sklearn.model_selection.LeaveOneLabelOut

class sklearn.model_selection.LeaveOneLabelOut

Leave One Label 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.

Read more in the User Guide.

Examples

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

x.__init__(...) initializes x; see help(type(x)) for signature

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.