summit.tests.test_multiview_classifiers.test_additions package

Submodules

summit.tests.test_multiview_classifiers.test_additions.test_diversity_utils module

class FakeDataset(views, labels)

Bases: object

get_nb_class(sample_indices)
get_v(view_index, sample_indices)
class FakeDivCoupleClf(rs, classifier_names=None, classifiers_config=None, monoview_estimators=None)

Bases: CoupleDiversityFusionClassifier

diversity_measure(a, b, c)
set_fit_request(*, train_indices: bool | None | str = '$UNCHANGED$', view_indices: bool | None | str = '$UNCHANGED$') → FakeDivCoupleClf

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit 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 fit.

  • 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.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

  • train_indices (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for train_indices parameter in fit.

  • view_indices (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for view_indices parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_predict_request(*, sample_indices: bool | None | str = '$UNCHANGED$', view_indices: bool | None | str = '$UNCHANGED$') → FakeDivCoupleClf

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to predict 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 predict.

  • 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.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

  • sample_indices (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_indices parameter in predict.

  • view_indices (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for view_indices parameter in predict.

Returns:

self – The updated object.

Return type:

object

class FakeDivGlobalClf(rs, classifier_names=None, classifiers_config=None, monoview_estimators=None)

Bases: GlobalDiversityFusionClassifier

diversity_measure(a, b, c)
set_fit_request(*, train_indices: bool | None | str = '$UNCHANGED$', view_indices: bool | None | str = '$UNCHANGED$') → FakeDivGlobalClf

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit 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 fit.

  • 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.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

  • train_indices (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for train_indices parameter in fit.

  • view_indices (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for view_indices parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_predict_request(*, sample_indices: bool | None | str = '$UNCHANGED$', view_indices: bool | None | str = '$UNCHANGED$') → FakeDivGlobalClf

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to predict 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 predict.

  • 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.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

  • sample_indices (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_indices parameter in predict.

  • view_indices (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for view_indices parameter in predict.

Returns:

self – The updated object.

Return type:

object

class Test_DiversityFusion(methodName='runTest')

Bases: TestCase

classmethod setUpClass()

Hook method for setting up class fixture before running tests in the class.

test_simple_couple()
test_simple_global()

summit.tests.test_multiview_classifiers.test_additions.test_jumbo_fusion_utils module

class FakeDataset(views, labels)

Bases: object

get_nb_class(sample_indices)
get_v(view_index, sample_indices)

Module contents