LeavePGroupsOut#

class sklearn.model_selection.LeavePGroupsOut(n_groups)[source]#

Leave P Group(s) 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.

The difference between LeavePGroupsOut and LeaveOneGroupOut is that the former builds the test sets with all the samples assigned to `p` different values of the groups while the latter uses samples all assigned the same groups.

Read more in the User Guide.

Parameters:
n_groupsint

Number of groups (`p`) to leave out in the test split.

`GroupKFold`

K-fold iterator variant with non-overlapping groups.

Examples

```>>> import numpy as np
>>> from sklearn.model_selection import LeavePGroupsOut
>>> X = np.array([[1, 2], [3, 4], [5, 6]])
>>> y = np.array([1, 2, 1])
>>> groups = np.array([1, 2, 3])
>>> lpgo = LeavePGroupsOut(n_groups=2)
>>> lpgo.get_n_splits(X, y, groups)
3
>>> lpgo.get_n_splits(groups=groups)  # 'groups' is always required
3
>>> print(lpgo)
LeavePGroupsOut(n_groups=2)
>>> for i, (train_index, test_index) in enumerate(lpgo.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], group=[3]
Test:  index=[0 1], group=[1 2]
Fold 1:
Train: index=[1], group=[2]
Test:  index=[0 2], group=[1 3]
Fold 2:
Train: index=[0], group=[1]
Test:  index=[1 2], group=[2 3]
```

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

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: = '\$UNCHANGED\$') [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.

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.