ertk.sklearn.utils.OneVsRestClassifier

class ertk.sklearn.utils.OneVsRestClassifier(estimator, *, n_jobs=None, verbose=0)

Bases: OneVsRestClassifier

__init__(estimator, *, n_jobs=None, verbose=0)

Methods

fit(X, y, **kwargs)

Fit underlying estimators.

set_partial_fit_request(*[, classes])

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

set_score_request(*[, sample_weight])

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

Inherited Methods

__init__(estimator, *[, n_jobs, verbose])

decision_function(X)

Decision function for the OneVsRestClassifier.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

partial_fit(X, y[, classes])

Partially fit underlying estimators.

predict(X)

Predict multi-class targets using underlying estimators.

predict_proba(X)

Probability estimates.

score(X, y[, sample_weight])

Return accuracy on provided data and labels.

set_params(**params)

Set the parameters of this estimator.

Attributes

multilabel_

Whether this is a multilabel classifier.

n_classes_

Number of classes.
fit(X, y, **kwargs)

Fit underlying estimators.

Parameters:
X{array-like, sparse matrix} of shape (n_samples, n_features)

Data.

y{array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)

Multi-class targets. An indicator matrix turns on multilabel classification.

**fit_paramsdict

Parameters passed to the estimator.fit method of each sub-estimator.

New in version 1.4: Only available if enable_metadata_routing=True. See Metadata Routing User Guide for more details.

Returns:
selfobject

Instance of fitted estimator.

set_partial_fit_request(*, classes: bool | None | str = '$UNCHANGED$') → OneVsRestClassifier

Configure whether metadata should be requested to be passed to the partial_fit 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 partial_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 partial_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.

New in version 1.3.

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

Metadata routing for classes parameter in partial_fit.

Returns:
selfobject

The updated object.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → OneVsRestClassifier

Configure whether metadata should be requested to be passed to the score 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 score 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 score.

  • 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.

New in version 1.3.

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

Metadata routing for sample_weight parameter in score.

Returns:
selfobject

The updated object.