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.
Number of groups (p) to leave out in the test split.
See also
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.
A MetadataRequest encapsulating routing information.
Returns the number of splitting iterations in the cross-validator.
Always ignored, exists for compatibility.
Always ignored, exists for compatibility.
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 the number of splitting iterations in the cross-validator.
Configure whether metadata should be requested to be passed to the split method.
Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with
enable_metadata_routing=True(seesklearn.set_config). Please check the User Guide on how the routing mechanism works.The options for each parameter are:
True: metadata is requested, and passed tosplitif provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it tosplit.
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.
Metadata routing for groups parameter in split.
The updated object.
Generate indices to split data into training and test set.
Training data, where n_samples is the number of samples and n_features is the number of features.
The target variable for supervised learning problems.
Group labels for the samples used while splitting the dataset into train/test set.
The training set indices for that split.
The testing set indices for that split.
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