sklearn.model_selection.PredefinedSplit

class sklearn.model_selection.PredefinedSplit(test_fold)

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.

New in version 0.16.

Parameters:

test_foldarray-like of shape (n_samples,)

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]

Methods

get_metadata_routing()	Get metadata routing of this object.
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_metadata_routing()

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)

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=None, y=None, groups=None)

Generate indices to split data into training and test set.

Parameters:

Xobject

Always ignored, exists for compatibility.

yobject

Always ignored, exists for compatibility.

groupsobject

Always ignored, exists for compatibility.

Yields:

trainndarray

The training set indices for that split.

testndarray

The testing set indices for that split.