StratifiedShuffleSplit#

class sklearn.model_selection.StratifiedShuffleSplit(n_splits=10, *, test_size=None, train_size=None, random_state=None)[source]#

Stratified ShuffleSplit cross-validator.

Provides train/test indices to split data in train/test sets.

This cross-validation object is a merge of StratifiedKFold and ShuffleSplit, which returns stratified randomized folds. The folds are made by preserving the percentage of samples for each class.

Note: like the ShuffleSplit strategy, stratified random splits do not guarantee that test sets across all folds will be mutually exclusive, and might include overlapping samples. However, this is still very likely for sizeable datasets.

Read more in the User Guide.

For visualisation of cross-validation behaviour and comparison between common scikit-learn split methods refer to Visualizing cross-validation behavior in scikit-learn

Parameters:

n_splitsint, default=10: Number of re-shuffling & splitting iterations.
test_sizefloat or int, default=None: If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples. If None, the value is set to the complement of the train size. If train_size is also None, it will be set to 0.1.
train_sizefloat or int, default=None: If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train samples. If None, the value is automatically set to the complement of the test size.
random_stateint, RandomState instance or None, default=None: Controls the randomness of the training and testing indices produced. Pass an int for reproducible output across multiple function calls. See Glossary.

Examples

>>> import numpy as np
>>> from sklearn.model_selection import StratifiedShuffleSplit
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 0, 1, 1, 1])
>>> sss = StratifiedShuffleSplit(n_splits=5, test_size=0.5, random_state=0)
>>> sss.get_n_splits(X, y)
5
>>> print(sss)
StratifiedShuffleSplit(n_splits=5, random_state=0, ...)
>>> for i, (train_index, test_index) in enumerate(sss.split(X, y)):
...     print(f"Fold {i}:")
...     print(f"  Train: index={train_index}")
...     print(f"  Test:  index={test_index}")
Fold 0:
  Train: index=[5 2 3]
  Test:  index=[4 1 0]
Fold 1:
  Train: index=[5 1 4]
  Test:  index=[0 2 3]
Fold 2:
  Train: index=[5 0 2]
  Test:  index=[4 3 1]
Fold 3:
  Train: index=[4 1 0]
  Test:  index=[2 3 5]
Fold 4:
  Train: index=[0 5 1]
  Test:  index=[3 4 2]

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

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, 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.

Note that providing y is sufficient to generate the splits and hence np.zeros(n_samples) may be used as a placeholder for X instead of actual training data.

yarray-like of shape (n_samples,) or (n_samples, n_labels)

The target variable for supervised learning problems. Stratification is done based on the y labels.

groupsobject

Always ignored, exists for compatibility.

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.

Gallery examples#

Visualizing cross-validation behavior in scikit-learn

RBF SVM parameters