sklearn.model_selection.LeaveOneGroupOut

class sklearn.model_selection.LeaveOneGroupOut

Leave One Group 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.

Read more in the User Guide.

Examples

>>> import numpy as np
>>> from sklearn.model_selection import LeaveOneGroupOut
>>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
>>> y = np.array([1, 2, 1, 2])
>>> groups = np.array([1, 1, 2, 2])
>>> logo = LeaveOneGroupOut()
>>> logo.get_n_splits(X, y, groups)
2
>>> logo.get_n_splits(groups=groups)  # 'groups' is always required
2
>>> print(logo)
LeaveOneGroupOut()
>>> for train_index, test_index in logo.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 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(self[, X, y, groups])	Returns the number of splitting iterations in the cross-validator
split(self, X[, y, groups])	Generate indices to split data into training and test set.

__init__(self, /, *args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

get_n_splits(self, X=None, y=None, groups=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.

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

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(self, X, y=None, groups=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, of length n_samples, optional

The target variable for supervised learning problems.

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

Group labels for the samples used while splitting the dataset into train/test set.

Yields:

train : ndarray

The training set indices for that split.

test : ndarray

The testing set indices for that split.