Predefined split cross-validator.
Provides train/test indices to split data into train/test sets using a predefined scheme specified by the user with the test_fold parameter.
Read more in the User Guide.
Added in version 0.16.
The entry test_fold[i] represents the index of the test set that sample i belongs to. It is possible to exclude sample i from any test set (i.e. include sample i in every training set) by setting test_fold[i] equal to -1.
Examples
>>> import numpy as np
>>> from sklearn.model_selection import PredefinedSplit
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> test_fold = [0, 1, -1, 1]
>>> ps = PredefinedSplit(test_fold)
>>> ps.get_n_splits()
2
>>> print(ps)
PredefinedSplit(test_fold=array([ 0, 1, -1, 1]))
>>> for i, (train_index, test_index) in enumerate(ps.split()):
... print(f"Fold {i}:")
... print(f" Train: index={train_index}")
... print(f" Test: index={test_index}")
Fold 0:
Train: index=[1 2 3]
Test: index=[0]
Fold 1:
Train: index=[0 2]
Test: index=[1 3]
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
A MetadataRequest encapsulating routing information.
Returns the number of splitting iterations in the cross-validator.
Always ignored, exists for compatibility.
Always ignored, exists for compatibility.
Always ignored, exists for compatibility.
Returns the number of splitting iterations in the cross-validator.
Generate indices to split data into training and test set.
Always ignored, exists for compatibility.
Always ignored, exists for compatibility.
Always ignored, exists for compatibility.
The training set indices for that split.
The testing set indices for that split.
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