sklearn.model_selection
.LeavePGroupsOut¶

class
sklearn.model_selection.
LeavePGroupsOut
(n_groups)[source]¶ Leave P Group(s) Out crossvalidator
Provides train/test indices to split data according to a thirdparty 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 crossvalidation against timebased 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.
Parameters:  n_groups : int
Number of groups (
p
) to leave out in the test split.
See also
GroupKFold
 Kfold iterator variant with nonoverlapping 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 train_index, test_index in lpgo.split(X, y, groups): ... print("TRAIN:", train_index, "TEST:", test_index) ... X_train, X_test = X[train_index], X[test_index] ... y_train, y_test = y[train_index], y[test_index] ... print(X_train, X_test, y_train, y_test) TRAIN: [2] TEST: [0 1] [[5 6]] [[1 2] [3 4]] [1] [1 2] TRAIN: [1] TEST: [0 2] [[3 4]] [[1 2] [5 6]] [2] [1 1] TRAIN: [0] TEST: [1 2] [[1 2]] [[3 4] [5 6]] [1] [2 1]
Methods
get_n_splits
(self[, X, y, groups])Returns the number of splitting iterations in the crossvalidator split
(self, X[, y, groups])Generate indices to split data into training and test set. 
get_n_splits
(self, X=None, y=None, groups=None)[source]¶ Returns the number of splitting iterations in the crossvalidator
Parameters:  X : object
Always ignored, exists for compatibility.
 y : object
Always ignored, exists for compatibility.
 groups : arraylike, with 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 crossvalidator.

split
(self, X, y=None, groups=None)[source]¶ Generate indices to split data into training and test set.
Parameters:  X : arraylike, shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
 y : arraylike, of length n_samples, optional
The target variable for supervised learning problems.
 groups : arraylike, with 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.