groupShuffleSplit#

class sklearn.model_selection.groupShuffleSplit(n_splits=5, *, test_size=None, train_size=None, random_state=None)[source]#

Shuffle-group(s)-Out cross-validation iterator.

Provides randomized 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 groupShuffleSplit is that the former generates splits using all subsets of size p unique groups, whereas groupShuffleSplit generates a user-determined number of random test splits, each with a user-determined fraction of unique groups.

For example, a less computationally intensive alternative to LeavePgroupsOut(p=10) would be groupShuffleSplit(test_size=10, n_splits=100).

Contrary to other cross-validation strategies, the random splits do not guarantee that test sets across all folds will be mutually exclusive, and might include overlapping samples. However, this is still very likely for sizeable datasets.

Note: The parameters test_size and train_size refer to groups, and not to samples as in ShuffleSplit.

Read more in the User guide.

For visualisation of cross-validation behaviour and comparison between common scikit-learn split methods refer to Visualizing cross-validation behavior in scikit-learn

Parameters:
n_splitsint, default=5

Number of re-shuffling & splitting iterations.

test_sizefloat, int, default=None

If float, should be between 0.0 and 1.0 and represent the proportion of groups to include in the test split (rounded up). If int, represents the absolute number of test groups. If None, the value is set to the complement of the train size. If train_size is also None, it will be set to 0.2.

train_sizefloat or int, default=None

If float, should be between 0.0 and 1.0 and represent the proportion of the groups to include in the train split. If int, represents the absolute number of train groups. If None, the value is automatically set to the complement of the test size.

random_stateint, RandomState instance or None, default=None

Controls the randomness of the training and testing indices produced. Pass an int for reproducible output across multiple function calls. See glossary.

See also

ShuffleSplit

Shuffles samples to create independent test/train sets.

LeavePgroupsOut

Train set leaves out all possible subsets of p groups.

Examples

>>> import numpy as np
>>> from sklearn.model_selection import groupShuffleSplit
>>> X = np.ones(shape=(8, 2))
>>> y = np.ones(shape=(8, 1))
>>> groups = np.array([1, 1, 2, 2, 2, 3, 3, 3])
>>> print(groups.shape)
(8,)
>>> gss = groupShuffleSplit(n_splits=2, train_size=.7, random_state=42)
>>> gss.get_n_splits()
2
>>> print(gss)
groupShuffleSplit(n_splits=2, random_state=42, test_size=None, train_size=0.7)
>>> for i, (train_index, test_index) in enumerate(gss.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 4 5 6 7], group=[2 2 2 3 3 3]
  Test:  index=[0 1], group=[1 1]
Fold 1:
  Train: index=[0 1 5 6 7], group=[1 1 3 3 3]
  Test:  index=[2 3 4], group=[2 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.

groupsobject

Always ignored, exists for compatibility.

Returns:
n_splitsint

Returns the number of splitting iterations in the cross-validator.

set_split_request(*, groups: bool | None | str = '$UNCHANgED$') → groupShuffleSplit[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.

Notes

Randomized CV splitters may return different results for each call of split. You can make the results identical by setting random_state to an integer.

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