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 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.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4