sklearn.model_selection.LeaveOneGroupOut

class sklearn.model_selection.LeaveOneGroupOut[source]

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)[source]

Returns the number of splitting iterations in the cross-validator

Parameters
Xobject

Always ignored, exists for compatibility.

yobject

Always ignored, exists for compatibility.

groupsarray-like of 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_splitsint

Returns the number of splitting iterations in the cross-validator.

split(self, X, y=None, groups=None)[source]

Generate indices to split data into training and test set.

Parameters
Xarray-like of shape (n_samples, n_features)

Training data, where n_samples is the number of samples and n_features is the number of features.

yarray-like of shape (n_samples,), default=None

The target variable for supervised learning problems.

groupsarray-like of shape (n_samples,)

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

Yields
trainndarray

The training set indices for that split.

testndarray

The testing set indices for that split.