sklearn.model_selection.LeaveOneGroupOut

class sklearn.model_selection.LeaveOneGroupOut

Leave One Group Out cross-validator

Provides train/test indices to split data such that each training set is comprised of all samples except ones belonging to one specific group. Arbitrary domain specific group information is provided an array integers that encodes the group of each sample.

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.

See also

GroupKFold

K-fold iterator variant with non-overlapping groups.

Notes

Splits are ordered according to the index of the group left out. The first split has testing set consisting of the group whose index in groups is lowest, and so on.

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 i, (train_index, test_index) in enumerate(logo.split(X, y, groups)):
...     print(f"Fold {i}:")
...     print(f"  Train: index={train_index}, group={groups[train_index]}")
...     print(f"  Test:  index={test_index}, group={groups[test_index]}")
Fold 0:
  Train: index=[2 3], group=[2 2]
  Test:  index=[0 1], group=[1 1]
Fold 1:
  Train: index=[0 1], group=[1 1]
  Test:  index=[2 3], group=[2 2]

Methods

get_n_splits([X, y, groups])	Returns the number of splitting iterations in the cross-validator
split(X[, y, groups])	Generate indices to split data into training and test set.

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

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(X, y=None, groups=None)

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.