quantificationlib.estimators.ensembles module

Estimators for an Ensemble of Quantifiers

class EnsembleOfClassifiers(base_estimator=None, n_jobs=None, verbose=0)[source]

Bases: BaseEstimator, ClassifierMixin

Ensemble of Classifiers

This kind of objects train the set of classifiers for an ensemble of quantifiers

Parameters:

  • base_estimator (estimator object (default=None)) – An estimator object implementing fit and one of predict or predict_proba. It is the base estimator used to learn the set of classifiers

  • n_jobs (int or None, optional (default=None)) – The number of jobs to use for the computation. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors

  • verbose (int, optional, (default=0)) – The verbosity level. The default value, zero, means silent mode

base_estimator

The base estimator used to build ensemble

Type:

estimator object

n_jobs

The number of jobs to use for the computation.

Type:

int or None,

verbose

The verbosity level. The default value, zero, means silent mode

Type:

int

classes_

Class labels

Type:

ndarray, shape (n_classes, )

n_estimators_

Number of estimators

Type:

int,

estimators_

List of estimators

Type:

ndarray, shape(n_ensembles, )

References

Pérez-Gállego, P., Quevedo, J. R., & del Coz, J. J. (2017). Using ensembles for problems with characterizable changes in data distribution: A case study on quantification. Information Fusion, 34, 87-100.

Pérez-Gállego, P., Castano, A., Quevedo, J. R., & del Coz, J. J. (2019). Dynamic ensemble selection for quantification tasks. Information Fusion, 45, 1-15.

fit(X, y, indexes)[source]

Fits the set of estimators for the training set (X, y) using the bags contained in indexes

Parameters:

  • X ((sparse) array-like, shape (n_examples, n_features)) – Data

  • y ((sparse) array-like, shape (n_examples, )) – True classes

  • indexes (array-like, shape (n_ensembles, bag_size)) – i-th row contains the indexes of the examples in (X, y) that must be used to train i-th estimator

Raises:

ValueError – When base_estimator is None

predict(X)[source]

Predict the class for each testing example applying each estimator

Parameters:

X ((sparse) array-like, shape (n_examples, n_features)) – Data

Returns:

preds – The predicted class for each example with each estimator

Return type:

array-like, shape(n_examples, n_estimators)

Raises:

NotFittedError – When the estimators are not fitted yet

predict_proba(X)[source]

Predict the class probabilities for each example `

Parameters:

X ((sparse) array-like, shape (n_examples, n_features)) – Data

Returns:

preds – The posterior probability for each example and for each class with each estimator

Return type:

array-like, shape(n_examples, n_estimators)

Raises:

NotFittedError – When the estimators are not fitted yet

set_fit_request(*, indexes='$UNCHANGED$')

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.

New 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:

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

  • self (EnsembleOfClassifiers) –

Returns:

self – The updated object.

Return type:

object

set_score_request(*, sample_weight='$UNCHANGED$')

Request metadata passed to the score 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 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.

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_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

  • self (EnsembleOfClassifiers) –

Returns:

self – The updated object.

Return type:

object