PredefinedSplit#
- 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 samplei
belongs to. It is possible to exclude samplei
from any test set (i.e. include samplei
in every training set) by settingtest_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.