sklearn.model_selection
.GroupShuffleSplit¶

class
sklearn.model_selection.
GroupShuffleSplit
(n_splits=5, test_size=None, 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_splitsint (default 5)
Number of reshuffling & splitting iterations.
 test_sizefloat, int, None, optional (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. By default, the value is set to 0.2. The default will change in version 0.21. It will remain 0.2 only if
train_size
is unspecified, otherwise it will complement the specifiedtrain_size
. train_sizefloat, 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_stateint, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by
np.random
.
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 >>> for train_idx, test_idx in gss.split(X, y, groups): ... print("TRAIN:", train_idx, "TEST:", test_idx) TRAIN: [2 3 4 5 6 7] TEST: [0 1] TRAIN: [0 1 5 6 7] TEST: [2 3 4]
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.

__init__
(self, n_splits=5, test_size=None, train_size=None, random_state=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.

get_n_splits
(self, X=None, y=None, groups=None)[source]¶ Returns the number of splitting iterations in the crossvalidator
 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 crossvalidator.

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