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# `sklearn.model_selection`.GroupShuffleSplit

class sklearn.model_selection.GroupShuffleSplit(n_splits=5, *, test_size=None, train_size=None, random_state=None)

  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)`.

  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_splits int, default=5

      Number of re-shuffling & splitting iterations.

    test_size float, int, default=0.2

      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. The default will change in version 0.21. It will remain 0.2 only if `train_size` is unspecified, otherwise it will complement the specified `train_size`.

    train_size float 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_state int, 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]

  Methods

  `get_metadata_routing`() | Get metadata routing of this object.
  `get_n_splits`([X, y, groups]) | Returns the number of splitting iterations in the cross-validator
  `set_split_request`(*[, groups]) | Request metadata passed to the `split` method.
  `split`(X[, y, groups]) | Generate indices to split data into training and test set.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_n_splits(X=None, y=None, groups=None)

    Returns the number of splitting iterations in the cross-validator

    Parameters:
      X object

        Always ignored, exists for compatibility.

      y object

        Always ignored, exists for compatibility.

      groups object

        Always ignored, exists for compatibility.

    Returns:
      n_splits int

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

  set_split_request(*, groups: bool | None | str = '$UNCHANGED$') → GroupShuffleSplit

    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.

    New 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:
      groups str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `groups` parameter in `split`.

    Returns:
      self object

        The updated object.

  split(X, y=None, groups=None)

    Generate indices to split data into training and test set.

    Parameters:
      X array-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.

      y array-like of shape (n_samples,), default=None

        The target variable for supervised learning problems.

      groups array-like of shape (n_samples,)

        Group labels for the samples used while splitting the dataset into train/test set.

    Yields:
      train ndarray

        The training set indices for that split.

      test ndarray

        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.

## Examples using `sklearn.model_selection.GroupShuffleSplit`

Visualizing cross-validation behavior in scikit-learn

Visualizing cross-validation behavior in scikit-learn