PredefinedSplit (original) (raw)

class sklearn.model_selection.PredefinedSplit(test_fold)[source]#

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.

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]

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=None, y=None, groups=None)[source]#

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.