quantificationlib.decomposition.multiclass module

Generic quantifiers based on multiclass decompositions

class OneVsRestQuantifier(base_quantifier, estimator_train=None, estimator_test=None, verbose=0)[source]

Bases: UsingClassifiers

Implements a One Vs Rest Multiclass Quantifier given any base quantifier

Trains one quantifier per class that predicts the prevalence on such class. The aggregation strategy just normalizes these prevalences to sum 1

The class works both with quantifiers that require classifiers or not. In the former case, the estimator used for the training distribution and the testing distribution must be a OneVsRestQuantifier

Parameters:

  • base_quantifier (quantifier object) – The base quantifier used to build the One versus Rest decomposition. Any quantifier can be used

  • estimator_train (estimator object, optional, (default=None)) – An estimator object implementing fit and one of predict or predict_proba. It is used to classify the examples of the training set and to obtain their distribution when the base quantifier is an instance of the class UsingClassifiers. Notice that some quantifiers of this kind, namely CC and PCC, do not require an estimator for the training distribution

  • estimator_test (estimator object, optional, (default=None)) – An estimator object implementing fit and one of predict or predict_proba. It is used to classify the examples of the testing bag and to obtain their distribution when the base quantifier is an instance of the class UsingClassifiers. For some experiments both estimators could be the same

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

base_quantifier

The base quantifier used to build the One versus Rest decomposition

Type:

quantifier object

estimator_train

Estimator used to classify the examples of the training set

Type:

estimator

estimator_test

Estimator used to classify the examples of the testing bag

Type:

estimator

needs_predictions_train

True if the base quantifier needs to estimate the training distribution

Type:

bool, (default=True)

probabilistic_predictions

Not used

Type:

bool

predictions_train_

Predictions of the examples in the training set

Type:

ndarray, shape (n_examples, n_classes) (probabilistic)

predictions_test_

Predictions of the examples in the testing bag

Type:

ndarray, shape (n_examples, n_classes) (probabilistic)

quantifiers_

List of quantifiers, one for each class

Type:

ndarray, shape (n_classes, )

classes_

Class labels

Type:

ndarray, shape (n_classes, )

y_ext_

Repmat of true labels of the training set. When CV_estimator is used with averaged_predictions=False, predictions_train_ will have a larger dimension (factor=n_repetitions * n_folds of the underlying CV_estimator) than y. In other cases, y_ext_ == y. y_ext_ must be used in fit/predict methods whenever the true labels of the training set are needed, instead of y

Type:

ndarray, shape(len(predictions_train_), )

verbose

The verbosity level

Type:

int

fit(X, y, predictions_train=None)[source]

Fits all the quanfifiers of a OneVsRest decomposition

First, the method fits the estimators (estimator_train and estimator_test) (if needed) using the fit method of its superclass

Then, it creates (using deepcopy) the set on quantifiers_, one per class, and fit them

Parameters:

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

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

  • predictions_train (ndarray, optional, shape (n_examples, n_classes) (probs)) – Predictions of the examples in the training set

Raises:

ValueError – When estimator_train or estimator_test are not instances of OneVsRestClassifier

predict(X, predictions_test=None)[source]

Aggregates the prevalences of the quantifiers_ to compute the final prediction

Just one aggregation strategy is implemented. It normalizes the prevalences given by each quantifier to sum 1

Parameters:

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

  • predictions_test (ndarray, shape (n_examples, n_classes) (default=None)) – Predictions for the testing bag

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

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:

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

  • self (OneVsRestQuantifier) –

Returns:

self – The updated object.

Return type:

object

set_predict_request(*, predictions_test='$UNCHANGED$')

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.

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:

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

  • self (OneVsRestQuantifier) –

Returns:

self – The updated object.

Return type:

object