sklearn.model_selection
.GroupShuffleSplit¶

class
sklearn.model_selection.
GroupShuffleSplit
(n_splits=5, test_size=0.2, train_size=None, random_state=None)[source]¶ ShuffleGroup(s)Out crossvalidation iterator
Provides randomized 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 GroupShuffleSplit is that the former generates splits using all subsets of size
p
unique groups, whereas GroupShuffleSplit generates a userdetermined number of random test splits, each with a userdetermined fraction of unique groups.For example, a less computationally intensive alternative to
LeavePGroupsOut(p=10)
would beGroupShuffleSplit(test_size=10, n_splits=100)
.Note: The parameters
test_size
andtrain_size
refer to groups, and not to samples, as in ShuffleSplit.Parameters: n_splits : int (default 5)
Number of reshuffling & splitting iterations.
test_size : float (default 0.2), int, or None
If float, should be between 0.0 and 1.0 and represent the proportion of the groups to include in the test split. If int, represents the absolute number of test groups. If None, the value is automatically set to the complement of the train size.
train_size : float, int, or None (default is 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 or RandomState
Pseudorandom number generator state used for random sampling.
Methods
get_n_splits
([X, y, groups])Returns the number of splitting iterations in the crossvalidator split
(X[, y, groups])Generate indices to split data into training and test set. 
get_n_splits
(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 : object
Always ignored, exists for compatibility.
Returns: n_splits : int
Returns the number of splitting iterations in the crossvalidator.

split
(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, shape (n_samples,)
The target variable for supervised learning problems.
groups : arraylike, with shape (n_samples,), optional
Group labels for the samples used while splitting the dataset into train/test set.
Returns: train : ndarray
The training set indices for that split.
test : ndarray
The testing set indices for that split.
