`sklearn.model_selection`.StratifiedGroupKFold¶

class sklearn.model_selection.StratifiedGroupKFold(n_splits=5, shuffle=False, random_state=None)[source]¶

Stratified K-Folds iterator variant with non-overlapping groups.

This cross-validation object is a variation of StratifiedKFold attempts to return stratified folds with non-overlapping groups. The folds are made by preserving the percentage of samples for each class.

The same group will not appear in two different folds (the number of distinct groups has to be at least equal to the number of folds).

The difference between GroupKFold and StratifiedGroupKFold is that the former attempts to create balanced folds such that the number of distinct groups is approximately the same in each fold, whereas StratifiedGroupKFold attempts to create folds which preserve the percentage of samples for each class as much as possible given the constraint of non-overlapping groups between splits.

See also

StratifiedKFold: Takes class information into account to build folds which retain class distributions (for binary or multiclass classification tasks).
GroupKFold: K-fold iterator variant with non-overlapping groups.

Notes

The implementation is designed to:

Mimic the behavior of StratifiedKFold as much as possible for trivial groups (e.g. when each group contains only one sample).
Be invariant to class label: relabelling y = ["Happy", "Sad"] to y = [1, 0] should not change the indices generated.
Stratify based on samples as much as possible while keeping non-overlapping groups constraint. That means that in some cases when there is a small number of groups containing a large number of samples the stratification will not be possible and the behavior will be close to GroupKFold.

Examples

>>> import numpy as np
>>> from sklearn.model_selection import StratifiedGroupKFold
>>> X = np.ones((17, 2))
>>> y = np.array([0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0])
>>> groups = np.array([1, 1, 2, 2, 3, 3, 3, 4, 5, 5, 5, 5, 6, 6, 7, 8, 8])
>>> cv = StratifiedGroupKFold(n_splits=3)
>>> for train_idxs, test_idxs in cv.split(X, y, groups):
...     print("TRAIN:", groups[train_idxs])
...     print("      ", y[train_idxs])
...     print(" TEST:", groups[test_idxs])
...     print("      ", y[test_idxs])
TRAIN: [1 1 2 2 4 5 5 5 5 8 8]
       [0 0 1 1 1 0 0 0 0 0 0]
 TEST: [3 3 3 6 6 7]
       [1 1 1 0 0 0]
TRAIN: [3 3 3 4 5 5 5 5 6 6 7]
       [1 1 1 1 0 0 0 0 0 0 0]
 TEST: [1 1 2 2 8 8]
       [0 0 1 1 0 0]
TRAIN: [1 1 2 2 3 3 3 6 6 7 8 8]
       [0 0 1 1 1 1 1 0 0 0 0 0]
 TEST: [4 5 5 5 5]
       [1 0 0 0 0]

Methods

`get_n_splits`([X, y, groups])	Returns the number of splitting iterations in the cross-validator
`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 cross-validator

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 cross-validator.

split(X, y=None, groups=None)[source]¶

Generate indices to split data into training and test set.

Parameters

Xarray-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.
yarray-like of shape (n_samples,), default=None: The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,), default=None: 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.

Examples using `sklearn.model_selection.StratifiedGroupKFold`¶

sklearn.model_selection.StratifiedGroupKFold¶

Examples using sklearn.model_selection.StratifiedGroupKFold¶

`sklearn.model_selection`.StratifiedGroupKFold¶

Examples using `sklearn.model_selection.StratifiedGroupKFold`¶