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scikit-learn 1.3.2
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# `sklearn.model_selection`.LeaveOneGroupOut

class sklearn.model_selection.LeaveOneGroupOut

  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 an array 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]

  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 array-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_splits int

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

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

    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.