skada.model_selection.StratifiedDomainShuffleSplit

class skada.model_selection.StratifiedDomainShuffleSplit(n_splits=10, *, test_size=None, train_size=None, random_state=None)[source]

Stratified-Domain-Shuffle-Split cross-validator.

This cross-validation object returns stratified randomized folds. The folds are made by preserving the percentage of samples for each class and for each sample domain.

Parameters:
n_splitsint, default=10

Number of folds. Must be at least 2.

Examples

>>> import numpy as np
>>> from skada.model_selection import StratifiedDomainShuffleSplit
>>> X = np.ones((10, 2))
>>> y = np.array([-1, 0, 1, -1, 0, 1, -1, 0, 1, -1])
>>> sample_domain = np.array([-2, 1, 1, -2, 1, 1, -2, 1, 1, -2])
>>> da_shufflesplit = StratifiedDomainShuffleSplit(n_splits=2,
... random_state=0, test_size=0.5)
>>> da_shufflesplit.get_n_splits(X, y, sample_domain)
2
>>> print(da_shufflesplit)
StratifiedDomainShuffleSplit(n_splits=2, random_state=0,
    test_size=0.5, train_size=None)
>>> for i, (train_index, test_index) in enumerate(
...    da_shufflesplit.split(X, y, sample_domain)
... ):
...     print(f"Fold {i}:")
...     print(f"  Train: index={train_index}, "
...     f'''group={[[b.item(), a.item()]
...     for a, b in zip(y[train_index], sample_domain[train_index])
...     ]}''')
...     print(f"  Test: index={test_index}, "
...     f'''group={[[b.item(), a.item()]
...     for a, b in zip(y[test_index], sample_domain[test_index])
...     ]}''')
Fold 0:
    Train: index=[0 6 1 8 2], group=[[-2, -1], [-2, -1], [1, 0], [1, 1], [1, 1]]
    Test:  index=[4 9 7 5 3], group=[[1, 0], [-2, -1], [1, 0], [1, 1], [-2, -1]]
Fold 1:
    Train: index=[1 2 8 0 3], group=[[1, 0], [1, 1], [1, 1], [-2, -1], [-2, -1]]
    Test:  index=[7 5 9 4 6], group=[[1, 0], [1, 1], [-2, -1], [1, 0], [-2, -1]]
set_split_request(*, sample_domain: bool | None | str = '$UNCHANGED$') → StratifiedDomainShuffleSplit

Configure whether metadata should be requested to be passed to the split method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to split if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to split.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:
sample_domainstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_domain parameter in split.

Returns:
selfobject

The updated object.

split(X, y, sample_domain=None)[source]

XXX: Docstring here

Examples using skada.model_selection.StratifiedDomainShuffleSplit

Visualizing cross-validation behavior in skada

Visualizing cross-validation behavior in skada