LeaveOneGroupOut#

class sklearn.model_selection.LeaveOneGroupOut[source]#

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 as an array of 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]
get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_n_splits(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.

set_split_request(*, groups: bool | None | str = '$UNCHANGED$') → LeaveOneGroupOut[source]#

Request metadata passed to the split method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to split if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to split.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
groupsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for groups parameter in split.

Returns:
selfobject

The updated object.

split(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.