fango

examples/custom_quantifier.py

@@ -2,7 +2,7 @@ import quapy as qp
 from quapy.data import LabelledCollection
 from quapy.method.base import BinaryQuantifier
 from quapy.model_selection import GridSearchQ
-from quapy.method.aggregative import AggregativeProbabilisticQuantifier
+from quapy.method.aggregative import AggregativeSoftQuantifier
 from quapy.protocol import APP
 import numpy as np
 from sklearn.linear_model import LogisticRegression
@@ -15,7 +15,7 @@ from sklearn.linear_model import LogisticRegression
 # internal hyperparameter (let say, alpha) which is the decision threshold. Let's also assume the quantifier
 # is binary, for simplicity.
 
-class MyQuantifier(AggregativeProbabilisticQuantifier, BinaryQuantifier):
+class MyQuantifier(AggregativeSoftQuantifier, BinaryQuantifier):
     def __init__(self, classifier, alpha=0.5):
         self.alpha = alpha
         # aggregative quantifiers have an internal self.classifier attribute

quapy/classification/calibration.py

@@ -24,7 +24,8 @@ class RecalibratedProbabilisticClassifier:
 class RecalibratedProbabilisticClassifierBase(BaseEstimator, RecalibratedProbabilisticClassifier):
     """
     Applies a (re)calibration method from `abstention.calibration`, as defined in
-    `Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_:
+    `Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_.
+
 
     :param classifier: a scikit-learn probabilistic classifier
     :param calibrator: the calibration object (an instance of abstention.calibration.CalibratorFactory)

quapy/method/aggregative.py

@@ -2,6 +2,7 @@ from abc import ABC, abstractmethod
 from copy import deepcopy
 from typing import Callable, Union
 import numpy as np
+from abstention.calibration import NoBiasVectorScaling, TempScaling, VectorScaling
 from scipy import optimize
 from sklearn.base import BaseEstimator
 from sklearn.calibration import CalibratedClassifierCV
@@ -46,7 +47,7 @@ class AggregativeQuantifier(BaseQuantifier, ABC):
         :return: self
         """
         classif_predictions = self.classifier_fit_predict(data, fit_classifier)
-        self.aggregation_fit(classif_predictions)
+        self.aggregation_fit(classif_predictions, data)
         return self
 
     def classifier_fit_predict(self, data: LabelledCollection, fit_classifier=True, predict_on=None):
@@ -66,7 +67,6 @@ class AggregativeQuantifier(BaseQuantifier, ABC):
         """
         assert isinstance(fit_classifier, bool), 'unexpected type for "fit_classifier", must be boolean'
 
-        print(type(self))
         self._check_classifier(adapt_if_necessary=(self._classifier_method() == 'predict_proba'))
 
         if predict_on is None:
@@ -80,7 +80,7 @@ class AggregativeQuantifier(BaseQuantifier, ABC):
                     raise ValueError(f'proportion {predict_on=} out of range, must be in (0,1)')
                 train, val = data.split_stratified(train_prop=(1 - predict_on))
                 self.classifier.fit(*train.Xy)
-                predictions = (self.classify(val.X), val.y)
+                predictions = LabelledCollection(self.classify(val.X), val.y, classes=data.classes_)
             else:
                 raise ValueError(f'wrong type for predict_on: since fit_classifier=False, '
                                  f'the set on which predictions have to be issued must be '
@@ -89,15 +89,17 @@ class AggregativeQuantifier(BaseQuantifier, ABC):
         elif isinstance(predict_on, LabelledCollection):
             if fit_classifier:
                 self.classifier.fit(*data.Xy)
-            predictions = (self.classify(predict_on.X), predict_on.y)
+            predictions = LabelledCollection(self.classify(predict_on.X), predict_on.y, classes=predict_on.classes_)
 
         elif isinstance(predict_on, int):
             if fit_classifier:
-                if not predict_on > 1:
+                if predict_on <= 1:
                     raise ValueError(f'invalid value {predict_on} in fit. '
                                      f'Specify a integer >1 for kFCV estimation.')
+                else:
                     predictions = cross_val_predict(
-                        classifier, *data.Xy, cv=predict_on, n_jobs=self.n_jobs, method=self._classifier_method())
+                        self.classifier, *data.Xy, cv=predict_on, n_jobs=self.n_jobs, method=self._classifier_method())
+                    predictions = LabelledCollection(predictions, data.y, classes=data.classes_)
                     self.classifier.fit(*data.Xy)
             else:
                 raise ValueError(f'wrong type for predict_on: since fit_classifier=False, '
@@ -113,12 +115,13 @@ class AggregativeQuantifier(BaseQuantifier, ABC):
         return predictions
 
     @abstractmethod
-    def aggregation_fit(self, classif_predictions: LabelledCollection):
+    def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
         """
         Trains the aggregation function.
 
-        :param classif_predictions: typically an `ndarray` containing the label predictions, but could be a
-            tuple containing any information needed for fitting the aggregation function
+        :param classif_predictions: a LabelledCollection containing the label predictions issued
+            by the classifier
+        :param data: a :class:`quapy.data.base.LabelledCollection` consisting of the training data
         """
         ...
 
@@ -140,23 +143,36 @@ class AggregativeQuantifier(BaseQuantifier, ABC):
         """
         self.classifier_ = classifier
 
+    @abstractmethod
     def classify(self, instances):
         """
         Provides the label predictions for the given instances. The predictions should respect the format expected by
-        :meth:`aggregate`, i.e., posterior probabilities for probabilistic quantifiers, or crisp predictions for
+        :meth:`aggregate`, e.g., posterior probabilities for probabilistic quantifiers, or crisp predictions for
         non-probabilistic quantifiers
 
-        :param instances: array-like
+        :param instances: array-like of shape `(n_instances, n_features,)`
         :return: np.ndarray of shape `(n_instances,)` with label predictions
         """
-        return self.classifier.predict(instances)
+        ...
 
+    @abstractmethod
     def _classifier_method(self):
-        print('using predict')
-        return 'predict'
+        """
+        Name of the method that must be used for issuing label predictions.
 
+        :return: string
+        """
+        ...
+
+    @abstractmethod
     def _check_classifier(self, adapt_if_necessary=False):
-        assert hasattr(self.classifier, self._classifier_method())
+        """
+        Guarantees that the underlying classifier implements the method required for issuing predictions, i.e.,
+        the method indicated by the :meth:`_classifier_method`
+
+        :param adapt_if_necessary: if True, the method will try to comply with the required specifications
+        """
+        ...
 
     def quantify(self, instances):
         """
@@ -190,22 +206,77 @@ class AggregativeQuantifier(BaseQuantifier, ABC):
         return self.classifier.classes_
 
 
-class AggregativeProbabilisticQuantifier(AggregativeQuantifier, ABC):
+class AggregativeCrispQuantifier(AggregativeQuantifier, ABC):
     """
-    Abstract class for quantification methods that base their estimations on the aggregation of posterior probabilities
-    as returned by a probabilistic classifier. Aggregative Probabilistic Quantifiers thus extend Aggregative
-    Quantifiers by implementing a _posterior_probabilities_ method returning values in [0,1] -- the posterior
-    probabilities.
+    Abstract class for quantification methods that base their estimations on the aggregation of crips decisions
+    as returned by a hard classifier. Aggregative crisp quantifiers thus extend Aggregative
+    Quantifiers by implementing specifications about crisp predictions.
     """
 
     def classify(self, instances):
+        """
+        Provides the label (crisp) predictions for the given instances.
+
+        :param instances: array-like of shape `(n_instances, n_dimensions,)`
+        :return: np.ndarray of shape `(n_instances,)` with label predictions
+        """
+        return self.classifier.predict(instances)
+
+    def _classifier_method(self):
+        """
+        Name of the method that must be used for issuing label predictions.
+
+        :return: the string "predict", i.e., the standard method name for scikit-learn hard predictions
+        """
+        print('using predict')
+        return 'predict'
+
+    def _check_classifier(self, adapt_if_necessary=False):
+        """
+        Guarantees that the underlying classifier implements the method indicated by the :meth:`_classifier_method`
+
+        :param adapt_if_necessary: unused, added for compatibility
+        """
+        assert hasattr(self.classifier, self._classifier_method()), \
+            f"the method does not implement the required {self._classifier_method()} method"
+
+
+class AggregativeSoftQuantifier(AggregativeQuantifier, ABC):
+    """
+    Abstract class for quantification methods that base their estimations on the aggregation of posterior
+    probabilities as returned by a probabilistic classifier.
+    Aggregative soft quantifiers thus extend Aggregative Quantifiers by implementing specifications
+    about soft predictions.
+    """
+
+    def classify(self, instances):
+        """
+        Provides the posterior probabilities for the given instances.
+
+        :param instances: array-like of shape `(n_instances, n_dimensions,)`
+        :return: np.ndarray of shape `(n_instances, n_classes,)` with posterior probabilities
+        """
         return self.classifier.predict_proba(instances)
 
     def _classifier_method(self):
+        """
+        Name of the method that must be used for issuing label predictions.
+
+        :return: the string "predict_proba", i.e., the standard method name for scikit-learn soft predictions
+        """
         print('using predict_proba')
         return 'predict_proba'
 
     def _check_classifier(self, adapt_if_necessary=False):
+        """
+        Guarantees that the underlying classifier implements the method indicated by the :meth:`_classifier_method`.
+        In case it does not, the classifier is calibrated (by means of the Platt's calibration method implemented by
+        scikit-learn in CalibratedClassifierCV, with cv=5). This calibration is only allowed if `adapt_if_necessary`
+        is set to True. If otherwise (i.e., the classifier is not probabilistic, and `adapt_if_necessary` is set
+        to False), an exception will be raised.
+
+        :param adapt_if_necessary: a hard classifier is turned into a soft classifier if `adapt_if_necessary==True`
+        """
         if not hasattr(self.classifier, self._classifier_method()):
             if adapt_if_necessary:
                 print(f'warning: The learner {self.classifier.__class__.__name__} does not seem to be '
@@ -217,9 +288,42 @@ class AggregativeProbabilisticQuantifier(AggregativeQuantifier, ABC):
                                      f'fit_classifier is set to False')
 
 
+
+class CorrectionbasedAggregativeQuantifier(AggregativeQuantifier):
+    """
+    Abstract class for quantification methods that carry out an adjustment (or correction) that requires,
+    at training time, the predictions to be issued in validation mode, i.e., on a set of held-out data that
+    is not the training set. There are three ways in which this distinction can be made, depending on how
+    the internal parameter `val_split` is specified, namely, (i) a float in (0, 1) indicating the proportion
+    of training instances that should be devoted to validate, or (ii) an integer indicating the
+    number of folds to consider in a k-fold cross-validation mode, or (iii) the specific set of data to
+    use for validation.
+    """
+
+    @property
+    def val_split(self):
+        return self.val_split_
+
+    @val_split.setter
+    def val_split(self, val_split):
+        if isinstance(val_split, LabelledCollection):
+            print('warning: setting val_split with a LabelledCollection will be inefficient in'
+                  'model selection. Rather pass the LabelledCollection at fit time')
+        self.val_split_ = val_split
+
+    def fit(self, data: LabelledCollection, fit_classifier=True, predict_on=None):
+        print('method from CorrectionbasedAggregativeQuantifier')
+        if predict_on is None:
+            predict_on = self.val_split
+        classif_predictions = self.classifier_fit_predict(data, fit_classifier, predict_on)
+        self.aggregation_fit(classif_predictions, data)
+        return self
+
+
+
 # Methods
 # ------------------------------------
-class CC(AggregativeQuantifier):
+class CC(AggregativeCrispQuantifier):
     """
     The most basic Quantification method. One that simply classifies all instances and counts how many have been
     attributed to each of the classes in order to compute class prevalence estimates.
@@ -230,7 +334,7 @@ class CC(AggregativeQuantifier):
     def __init__(self, classifier: BaseEstimator):
         self.classifier = classifier
 
-    def aggregation_fit(self, classif_predictions: LabelledCollection):
+    def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
         """
         Nothing to do here!
 
@@ -248,19 +352,21 @@ class CC(AggregativeQuantifier):
         return F.prevalence_from_labels(classif_predictions, self.classes_)
 
 
-class ACC(AggregativeQuantifier):
+class ACC(AggregativeCrispQuantifier, CorrectionbasedAggregativeQuantifier):
     """
     `Adjusted Classify & Count <https://link.springer.com/article/10.1007/s10618-008-0097-y>`_,
     the "adjusted" variant of :class:`CC`, that corrects the predictions of CC
     according to the `misclassification rates`.
 
     :param classifier: a sklearn's Estimator that generates a classifier
-    :param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the
-        misclassification rates are to be estimated.
-        This parameter can be indicated as a real value (between 0 and 1, default 0.4), representing a proportion of
-        validation data, or as an integer, indicating that the misclassification rates should be estimated via
-        `k`-fold cross validation (this integer stands for the number of folds `k`), or as a
-        :class:`quapy.data.base.LabelledCollection` (the split itself).
+    :param val_split: specifies the data used for generating classifier predictions. This specification
+        can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to
+        be extracted from the training set (default 0.4); or as an integer, indicating that the predictions
+        are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value
+        for `k`); or as a collection defining the specific set of data to use for validation.
+        Alternatively, this set can be specified at fit time by indicating the exact set of data
+        on which the predictions are to be generated.
+    :param n_jobs: number of parallel workers
     """
 
     def __init__(self, classifier: BaseEstimator, val_split=0.4, n_jobs=None):
@@ -268,7 +374,7 @@ class ACC(AggregativeQuantifier):
         self.val_split = val_split
         self.n_jobs = qp._get_njobs(n_jobs)
 
-    def aggregation_fit(self, classif_predictions: LabelledCollection):
+    def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
         """
         Estimates the misclassification rates.
 
@@ -292,9 +398,6 @@ class ACC(AggregativeQuantifier):
                 conf[:, i] /= class_counts[i]
         return conf
 
-    def classify(self, data):
-        return self.cc.classify(data)
-
     def aggregate(self, classif_predictions):
         prevs_estim = self.cc.aggregate(classif_predictions)
         return ACC.solve_adjustment(self.Pte_cond_estim_, prevs_estim)
@@ -321,7 +424,7 @@ class ACC(AggregativeQuantifier):
         return adjusted_prevs
 
 
-class PCC(AggregativeProbabilisticQuantifier):
+class PCC(AggregativeSoftQuantifier):
     """
     `Probabilistic Classify & Count <https://ieeexplore.ieee.org/abstract/document/5694031>`_,
     the probabilistic variant of CC that relies on the posterior probabilities returned by a probabilistic classifier.
@@ -332,7 +435,7 @@ class PCC(AggregativeProbabilisticQuantifier):
     def __init__(self, classifier: BaseEstimator):
         self.classifier = classifier
 
-    def aggregation_fit(self, classif_predictions: LabelledCollection):
+    def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
         """
         Nothing to do here!
 
@@ -344,18 +447,18 @@ class PCC(AggregativeProbabilisticQuantifier):
         return F.prevalence_from_probabilities(classif_posteriors, binarize=False)
 
 
-class PACC(AggregativeProbabilisticQuantifier):
+class PACC(AggregativeSoftQuantifier, CorrectionbasedAggregativeQuantifier):
     """
     `Probabilistic Adjusted Classify & Count <https://ieeexplore.ieee.org/abstract/document/5694031>`_,
     the probabilistic variant of ACC that relies on the posterior probabilities returned by a probabilistic classifier.
 
     :param classifier: a sklearn's Estimator that generates a classifier
-    :param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the
-        misclassification rates are to be estimated.
-        This parameter can be indicated as a real value (between 0 and 1, default 0.4), representing a proportion of
-        validation data, or as an integer, indicating that the misclassification rates should be estimated via
-        `k`-fold cross validation (this integer stands for the number of folds `k`), or as a
-        :class:`quapy.data.base.LabelledCollection` (the split itself).
+    :param val_split: specifies the data used for generating classifier predictions. This specification
+        can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to
+        be extracted from the training set (default 0.4); or as an integer, indicating that the predictions
+        are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value
+        for `k`). Alternatively, this set can be specified at fit time by indicating the exact set of data
+        on which the predictions are to be generated.
     :param n_jobs: number of parallel workers
     """
 
@@ -364,16 +467,20 @@ class PACC(AggregativeProbabilisticQuantifier):
         self.val_split = val_split
         self.n_jobs = qp._get_njobs(n_jobs)
 
-    def aggregation_fit(self, classif_predictions: LabelledCollection):
+    def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
         """
         Estimates the misclassification rates
 
-        :param classif_predictions: classifier predictions with true labels
+        :param classif_predictions: classifier soft predictions with true labels
         """
-        true_labels, posteriors = classif_predictions
+        posteriors, true_labels = classif_predictions.Xy
         self.pcc = PCC(self.classifier)
         self.Pte_cond_estim_ = self.getPteCondEstim(self.classifier.classes_, true_labels, posteriors)
 
+    def aggregate(self, classif_posteriors):
+        prevs_estim = self.pcc.aggregate(classif_posteriors)
+        return ACC.solve_adjustment(self.Pte_cond_estim_, prevs_estim)
+
     @classmethod
     def getPteCondEstim(cls, classes, y, y_):
         # estimate the matrix with entry (i,j) being the estimate of P(yi|yj), that is, the probability that a
@@ -387,15 +494,8 @@ class PACC(AggregativeProbabilisticQuantifier):
 
         return confusion.T
 
-    def aggregate(self, classif_posteriors):
-        prevs_estim = self.pcc.aggregate(classif_posteriors)
-        return ACC.solve_adjustment(self.Pte_cond_estim_, prevs_estim)
 
-    def classify(self, data):
-        return self.pcc.classify(data)
-
-
-class EMQ(AggregativeProbabilisticQuantifier):
+class EMQ(AggregativeSoftQuantifier):
     """
     `Expectation Maximization for Quantification <https://ieeexplore.ieee.org/abstract/document/6789744>`_ (EMQ),
     aka `Saerens-Latinne-Decaestecker` (SLD) algorithm.
@@ -404,74 +504,30 @@ class EMQ(AggregativeProbabilisticQuantifier):
     maximum-likelihood estimation, in a mutually recursive way, until convergence.
 
     :param classifier: a sklearn's Estimator that generates a classifier
-    :param exact_train_prev: set to True (default) for using, as the initial observation, the true training prevalence;
-        or set to False for computing the training prevalence as an estimate, akin to PCC, i.e., as the expected
-        value of the posterior probabilities of the training instances as suggested in
-        `Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_:
-    :param recalib: a string indicating the method of recalibration. Available choices include "nbvs" (No-Bias Vector
-        Scaling), "bcts" (Bias-Corrected Temperature Scaling), "ts" (Temperature Scaling), and "vs" (Vector Scaling).
-        The default value is None, indicating no recalibration.
     """
 
     MAX_ITER = 1000
     EPSILON = 1e-4
 
-    def __init__(self, classifier: BaseEstimator, exact_train_prev=True, recalib=None):
+    def __init__(self, classifier: BaseEstimator):
         self.classifier = classifier
-        self.non_calibrated = classifier
-        self.exact_train_prev = exact_train_prev
-        self.recalib = recalib
 
-    def classifier_fit_predict(self, data: LabelledCollection, fit_classifier=True):
-        self.classifier, true_labels, posteriors, classes, class_count = cross_generate_predictions(
-            data, self.classifier, val_split, probabilistic=True, fit_classifier=fit_classifier, n_jobs=self.n_jobs
-        )
-
-        return (true_labels, posteriors)
-
-        if self.recalib is not None:
-            if self.recalib == 'nbvs':
-                self.classifier = NBVSCalibration(self.non_calibrated)
-            elif self.recalib == 'bcts':
-                self.classifier = BCTSCalibration(self.non_calibrated)
-            elif self.recalib == 'ts':
-                self.classifier = TSCalibration(self.non_calibrated)
-            elif self.recalib == 'vs':
-                self.classifier = VSCalibration(self.non_calibrated)
-            elif self.recalib == 'platt':
-                self.classifier = CalibratedClassifierCV(self.classifier, ensemble=False)
-            else:
-                raise ValueError('invalid param argument for recalibration method; available ones are '
-                                 '"nbvs", "bcts", "ts", and "vs".')
-            self.recalib = None
-        else:
-            self.classifier = self.non_calibrated
-        self.classifier, _ = _training_helper(self.classifier, data, fit_classifier, ensure_probabilistic=True)
-        if self.exact_train_prev:
-            self.train_prevalence = F.prevalence_from_labels(data.labels, self.classes_)
-        else:
-            self.train_prevalence = qp.model_selection.cross_val_predict(
-                quantifier=PCC(deepcopy(self.classifier)),
-                data=data,
-                nfolds=3,
-                random_state=0
-            )
-        return None
-
-    def aggregation_fit(self, classif_predictions: np.ndarray):
-        """
-        Nothing to do here!
-
-        :param classif_predictions: this is actually None
-        """
-        pass
+    def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
+        self.train_prevalence = data.prevalence()
 
     def aggregate(self, classif_posteriors, epsilon=EPSILON):
         priors, posteriors = self.EM(self.train_prevalence, classif_posteriors, epsilon)
         return priors
 
     def predict_proba(self, instances, epsilon=EPSILON):
-        classif_posteriors = self.classifier.predict_proba(instances)
+        """
+        Returns the posterior probabilities updated by the EM algorithm.
+
+        :param instances: np.ndarray of shape `(n_instances, n_dimensions)`
+        :param epsilon: error tolerance
+        :return: np.ndarray of shape `(n_instances, n_classes)`
+        """
+        classif_posteriors = self.classify(instances)
         priors, posteriors = self.EM(self.train_prevalence, classif_posteriors, epsilon)
         return posteriors
 
@@ -514,7 +570,94 @@ class EMQ(AggregativeProbabilisticQuantifier):
         return qs, ps
 
 
-class HDy(AggregativeProbabilisticQuantifier, BinaryQuantifier):
+class EMQrecalib(AggregativeSoftQuantifier, CorrectionbasedAggregativeQuantifier):
+    """
+    `Expectation Maximization for Quantification <https://ieeexplore.ieee.org/abstract/document/6789744>`_ (EMQ),
+    aka `Saerens-Latinne-Decaestecker` (SLD) algorithm, with the heuristics proposed by
+    `Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_.
+
+    These heuristics consist of using, as the training prevalence, an estimate of it obtained via k-fold cross
+    validation (instead of the true training prevalence), and to recalibrate the posterior probabilities of
+    the classifier.
+
+    :param classifier: a sklearn's Estimator that generates a classifier
+    :param val_split: specifies the data used for generating classifier predictions. This specification
+        can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to
+        be extracted from the training set (default 0.4); or as an integer, indicating that the predictions
+        are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value
+        for `k`); or as a collection defining the specific set of data to use for validation.
+        Alternatively, this set can be specified at fit time by indicating the exact set of data
+        on which the predictions are to be generated.
+    :param exact_train_prev: set to True (default) for using, as the initial observation, the true training prevalence;
+        or set to False for computing the training prevalence as an estimate of it, i.e., as the expected
+        value of the posterior probabilities of the training instances
+    :param recalib: a string indicating the method of recalibration.
+        Available choices include "nbvs" (No-Bias Vector Scaling), "bcts" (Bias-Corrected Temperature Scaling,
+        default), "ts" (Temperature Scaling), and "vs" (Vector Scaling).
+    :param n_jobs: number of parallel workers
+    """
+
+    MAX_ITER = 1000
+    EPSILON = 1e-4
+
+    def __init__(self, classifier: BaseEstimator, val_split=5, exact_train_prev=False, recalib='bcts', n_jobs=None):
+        self.classifier = classifier
+        self.val_split = val_split
+        self.exact_train_prev = exact_train_prev
+        self.recalib = recalib
+        self.n_jobs = n_jobs
+
+    def classify(self, instances):
+        """
+        Provides the posterior probabilities for the given instances. If the classifier is
+        recalibrated, then these posteriors will be recalibrated accordingly.
+
+        :param instances: array-like of shape `(n_instances, n_dimensions,)`
+        :return: np.ndarray of shape `(n_instances, n_classes,)` with posterior probabilities
+        """
+        posteriors = self.classifier.predict_proba(instances)
+        if hasattr(self, 'calibration_function') and self.calibration_function is not None:
+            posteriors = self.calibration_function(posteriors)
+        return posteriors
+
+    def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
+        if self.recalib is not None:
+            P, y = classif_predictions.Xy
+            if self.recalib == 'nbvs':
+                calibrator = NoBiasVectorScaling()
+            elif self.recalib == 'bcts':
+                calibrator = TempScaling(bias_positions='all')
+            elif self.recalib == 'ts':
+                calibrator = TempScaling()
+            elif self.recalib == 'vs':
+                calibrator = VectorScaling()
+            else:
+                raise ValueError('invalid param argument for recalibration method; available ones are '
+                                 '"nbvs", "bcts", "ts", and "vs".')
+
+            self.calibration_function = calibrator(P, np.eye(data.n_classes)[y], posterior_supplied=True)
+
+        if self.exact_train_prev:
+            self.train_prevalence = F.prevalence_from_labels(data.labels, self.classes_)
+        else:
+            if self.recalib is not None:
+                train_posteriors = self.classify(data.X)
+            else:
+                train_posteriors = classif_predictions.X
+
+            self.train_prevalence = np.mean(train_posteriors, axis=0)
+
+    def aggregate(self, classif_posteriors, epsilon=EPSILON):
+        priors, posteriors = EMQ.EM(self.train_prevalence, classif_posteriors, epsilon)
+        return priors
+
+    def predict_proba(self, instances, epsilon=EPSILON):
+        classif_posteriors = self.classify(instances)
+        priors, posteriors = EMQ.EM(self.train_prevalence, classif_posteriors, epsilon)
+        return posteriors
+
+
+class HDy(AggregativeSoftQuantifier, BinaryQuantifier, CorrectionbasedAggregativeQuantifier):
     """
     `Hellinger Distance y <https://www.sciencedirect.com/science/article/pii/S0020025512004069>`_ (HDy).
     HDy is a probabilistic method for training binary quantifiers, that models quantification as the problem of
@@ -533,7 +676,7 @@ class HDy(AggregativeProbabilisticQuantifier, BinaryQuantifier):
         self.classifier = classifier
         self.val_split = val_split
 
-    def fit(self, data: LabelledCollection, fit_classifier=True, val_split: Union[float, LabelledCollection] = None):
+    def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
         """
         Trains a HDy quantifier.
 
@@ -544,22 +687,23 @@ class HDy(AggregativeProbabilisticQuantifier, BinaryQuantifier):
          :class:`quapy.data.base.LabelledCollection` indicating the validation set itself
         :return: self
         """
-        if val_split is None:
-            val_split = self.val_split
 
         self._check_binary(data, self.__class__.__name__)
-        self.classifier, validation = _training_helper(
-            self.classifier, data, fit_classifier, ensure_probabilistic=True, val_split=val_split)
-        Px = self.classify(validation.instances)[:, 1]  # takes only the P(y=+1|x)
-        self.Pxy1 = Px[validation.labels == self.classifier.classes_[1]]
-        self.Pxy0 = Px[validation.labels == self.classifier.classes_[0]]
+        P, y = classif_predictions.Xy
+        Px = P[:, 1]  # takes only the P(y=+1|x)
+        self.Pxy1 = Px[y == self.classifier.classes_[1]]
+        self.Pxy0 = Px[y == self.classifier.classes_[0]]
+
         # pre-compute the histogram for positive and negative examples
         self.bins = np.linspace(10, 110, 11, dtype=int)  # [10, 20, 30, ..., 100, 110]
+
         def hist(P, bins):
             h = np.histogram(P, bins=bins, range=(0, 1), density=True)[0]
             return h / h.sum()
+
         self.Pxy1_density = {bins: hist(self.Pxy1, bins) for bins in self.bins}
         self.Pxy0_density = {bins: hist(self.Pxy0, bins) for bins in self.bins}
+
         return self
 
     def aggregate(self, classif_posteriors):
@@ -583,7 +727,7 @@ class HDy(AggregativeProbabilisticQuantifier, BinaryQuantifier):
             # at small steps (modern implementations resort to an optimization procedure,
             # see class DistributionMatching)
             prev_selected, min_dist = None, None
-            for prev in F.prevalence_linspace(n_prevalences=100, repeats=1, smooth_limits_epsilon=0.0):
+            for prev in F.prevalence_linspace(n_prevalences=101, repeats=1, smooth_limits_epsilon=0.0):
                 Px_train = prev * Pxy1_density + (1 - prev) * Pxy0_density
                 hdy = F.HellingerDistance(Px_train, Px_test)
                 if prev_selected is None or hdy < min_dist:
@@ -594,7 +738,7 @@ class HDy(AggregativeProbabilisticQuantifier, BinaryQuantifier):
         return np.asarray([1 - class1_prev, class1_prev])
 
 
-class DyS(AggregativeProbabilisticQuantifier, BinaryQuantifier):
+class DyS(AggregativeSoftQuantifier, BinaryQuantifier):
     """
     `DyS framework <https://ojs.aaai.org/index.php/AAAI/article/view/4376>`_ (DyS).
     DyS is a generalization of HDy method, using a Ternary Search in order to find the prevalence that
@@ -661,7 +805,7 @@ class DyS(AggregativeProbabilisticQuantifier, BinaryQuantifier):
         return np.asarray([1 - class1_prev, class1_prev])
 
 
-class SMM(AggregativeProbabilisticQuantifier, BinaryQuantifier):
+class SMM(AggregativeSoftQuantifier, BinaryQuantifier):
     """
     `SMM method <https://ieeexplore.ieee.org/document/9260028>`_ (SMM).
     SMM is a simplification of matching distribution methods where the representation of the examples
@@ -700,7 +844,7 @@ class SMM(AggregativeProbabilisticQuantifier, BinaryQuantifier):
         return np.asarray([1 - class1_prev, class1_prev])
 
 
-class DMy(AggregativeProbabilisticQuantifier):
+class DMy(AggregativeSoftQuantifier, CorrectionbasedAggregativeQuantifier):
     """
     Generic Distribution Matching quantifier for binary or multiclass quantification based on the space of posterior
     probabilities. This implementation takes the number of bins, the divergence, and the possibility to work on CDF
@@ -736,7 +880,7 @@ class DMy(AggregativeProbabilisticQuantifier):
         from quapy.method.meta import MedianEstimator
 
         hdy = DMy(classifier=classifier, val_split=val_split, search='linear_search', divergence='HD')
-        hdy = MedianEstimator(hdy, param_grid={'nbins': np.linspace(10, 110, 11).astype(int)}, n_jobs=n_jobs)
+        hdy = AggregativeMedianEstimator(hdy, param_grid={'nbins': np.linspace(10, 110, 11).astype(int)}, n_jobs=n_jobs)
         return hdy
 
     def __get_distributions(self, posteriors):
@@ -755,7 +899,7 @@ class DMy(AggregativeProbabilisticQuantifier):
             distributions = np.cumsum(distributions, axis=1)
         return distributions
 
-    def classifier_fit_predict(self, data: LabelledCollection, fit_classifier=True, val_split: Union[float, LabelledCollection] = None):
+    def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
         """
         Trains the classifier (if requested) and generates the validation distributions out of the training data.
         The validation distributions have shape `(n, ch, nbins)`, with `n` the number of classes, `ch` the number of
@@ -771,21 +915,13 @@ class DMy(AggregativeProbabilisticQuantifier):
          indicating the validation set itself, or an int indicating the number k of folds to be used in kFCV
          to estimate the parameters
         """
-        if val_split is None:
-            val_split = self.val_split
-
-        self.classifier, true_labels, posteriors, classes, class_count = cross_generate_predictions(
-            data, self.classifier, val_split, probabilistic=True, fit_classifier=fit_classifier, n_jobs=self.n_jobs
-        )
-
-        return (true_labels, posteriors)
-
-    def aggregation_fit(self, classif_predictions):
-        true_labels, posteriors = classif_predictions
+        posteriors, true_labels = classif_predictions.Xy
         n_classes = len(self.classifier.classes_)
 
-        self.validation_distribution = np.asarray(
-            [self.__get_distributions(posteriors[true_labels == cat]) for cat in range(n_classes)]
+        self.validation_distribution = qp.util.parallel(
+            func=self.__get_distributions,
+            args=[posteriors[true_labels==cat] for cat in range(n_classes)],
+            n_jobs=self.n_jobs
         )
 
     def aggregate(self, posteriors: np.ndarray):
@@ -1252,7 +1388,7 @@ class OneVsAllAggregative(OneVsAllGeneric, AggregativeQuantifier):
         """
 
         classif_predictions = self._parallel(self._delayed_binary_classification, instances)
-        if isinstance(self.binary_quantifier, AggregativeProbabilisticQuantifier):
+        if isinstance(self.binary_quantifier, AggregativeSoftQuantifier):
             return np.swapaxes(classif_predictions, 0, 1)
         else:
             return classif_predictions.T
@@ -1269,6 +1405,130 @@ class OneVsAllAggregative(OneVsAllGeneric, AggregativeQuantifier):
         return self.dict_binary_quantifiers[c].aggregate(classif_predictions[:, c])[1]
 
 
+class AggregativeMedianEstimator(BinaryQuantifier):
+    """
+    This method is a meta-quantifier that returns, as the estimated class prevalence values, the median of the
+    estimation returned by differently (hyper)parameterized base quantifiers.
+    The median of unit-vectors is only guaranteed to be a unit-vector for n=2 dimensions,
+    i.e., in cases of binary quantification.
+
+    :param base_quantifier: the base, binary quantifier
+    :param random_state: a seed to be set before fitting any base quantifier (default None)
+    :param param_grid: the grid or parameters towards which the median will be computed
+    :param n_jobs: number of parllel workes
+    """
+    def __init__(self, base_quantifier: AggregativeQuantifier, param_grid: dict, random_state=None, n_jobs=None):
+        self.base_quantifier = base_quantifier
+        self.param_grid = param_grid
+        self.random_state = random_state
+        self.n_jobs = qp._get_njobs(n_jobs)
+
+    def get_params(self, deep=True):
+        return self.base_quantifier.get_params(deep)
+
+    def set_params(self, **params):
+        self.base_quantifier.set_params(**params)
+
+    def _delayed_fit(self, args):
+        with qp.util.temp_seed(self.random_state):
+            params, training = args
+            model = deepcopy(self.base_quantifier)
+            model.set_params(**params)
+            model.fit(training)
+            return model
+
+    def _delayed_fit_classifier(self, args):
+        with qp.util.temp_seed(self.random_state):
+            print('enter job')
+            cls_params, training, kwargs = args
+            model = deepcopy(self.base_quantifier)
+            model.set_params(**cls_params)
+            predictions = model.classifier_fit_predict(training, **kwargs)
+            print('exit job')
+            return (model, predictions)
+
+    def _delayed_fit_aggregation(self, args):
+        with qp.util.temp_seed(self.random_state):
+            print('\tenter job')
+            ((model, predictions), q_params), training = args
+            model = deepcopy(model)
+            print('fitaggr', model, predictions, len(predictions), print(self.training))
+            model.set_params(**q_params)
+            model.aggregation_fit(predictions, training)
+            print('\texit job')
+            return model
+
+
+    def fit(self, training: LabelledCollection, **kwargs):
+        import itertools
+
+        self._check_binary(training, self.__class__.__name__)
+
+        if isinstance(self.base_quantifier, AggregativeQuantifier):
+            cls_configs, q_configs = qp.model_selection.group_params(self.param_grid)
+
+            if len(cls_configs) > 1:
+                models_preds = qp.util.parallel(
+                    self._delayed_fit_classifier,
+                    ((params, training, kwargs) for params in cls_configs),
+                    seed=qp.environ.get('_R_SEED', None),
+                    n_jobs=self.n_jobs,
+                    asarray=False
+                )
+            else:
+                print('only 1')
+                model = self.base_quantifier
+                model.set_params(**cls_configs[0])
+                predictions = model.classifier_fit_predict(training, **kwargs)
+                models_preds = [(model, predictions)]
+
+            self.training = training
+
+            self.models = []
+            print('WITHOUT PARALLEL JOBS')
+            for ((model, predictions), q_params) in itertools.product(models_preds, q_configs):
+                print('\tenter job')
+                model = deepcopy(model)
+                print('fitaggr', model, predictions, len(predictions), print(self.training))
+                model.set_params(**q_params)
+                model.aggregation_fit(predictions, training)
+                self.models.append(model)
+                print('\texit job')
+
+
+            # self.models = qp.util.parallel(
+            #     self._delayed_fit_aggregation,
+            #     ((setup, training) for setup in itertools.product(models_preds, q_configs)),
+            #     seed=qp.environ.get('_R_SEED', None),
+            #     n_jobs=self.n_jobs,
+            #     asarray=False
+            # )
+        else:
+            configs = qp.model_selection.expand_grid(self.param_grid)
+            self.models = qp.util.parallel(
+                self._delayed_fit,
+                ((params, training) for params in configs),
+                seed=qp.environ.get('_R_SEED', None),
+                n_jobs=self.n_jobs,
+                asarray=False
+            )
+        return self
+
+    def _delayed_predict(self, args):
+        model, instances = args
+        return model.quantify(instances)
+
+    def quantify(self, instances):
+        prev_preds = qp.util.parallel(
+            self._delayed_predict,
+            ((model, instances) for model in self.models),
+            seed=qp.environ.get('_R_SEED', None),
+            n_jobs=self.n_jobs,
+            asarray=False
+        )
+        prev_preds = np.asarray(prev_preds)
+        return np.median(prev_preds, axis=0)
+
 #---------------------------------------------------------------
 # aliases
 #---------------------------------------------------------------

quapy/method/meta.py

@@ -12,7 +12,7 @@ from quapy import functional as F
 from quapy.data import LabelledCollection
 from quapy.model_selection import GridSearchQ
 from quapy.method.base import BaseQuantifier, BinaryQuantifier
-from quapy.method.aggregative import CC, ACC, PACC, HDy, EMQ
+from quapy.method.aggregative import CC, ACC, PACC, HDy, EMQ, AggregativeQuantifier
 
 try:
     from . import neural
@@ -26,6 +26,65 @@ else:
     QuaNet = "QuaNet is not available due to missing torch package"
 
 
+class MedianEstimator2(BinaryQuantifier):
+    """
+    This method is a meta-quantifier that returns, as the estimated class prevalence values, the median of the
+    estimation returned by differently (hyper)parameterized base quantifiers.
+    The median of unit-vectors is only guaranteed to be a unit-vector for n=2 dimensions,
+    i.e., in cases of binary quantification.
+
+    :param base_quantifier: the base, binary quantifier
+    :param random_state: a seed to be set before fitting any base quantifier (default None)
+    :param param_grid: the grid or parameters towards which the median will be computed
+    :param n_jobs: number of parllel workes
+    """
+    def __init__(self, base_quantifier: BinaryQuantifier, param_grid: dict, random_state=None, n_jobs=None):
+        self.base_quantifier = base_quantifier
+        self.param_grid = param_grid
+        self.random_state = random_state
+        self.n_jobs = qp._get_njobs(n_jobs)
+
+    def get_params(self, deep=True):
+        return self.base_quantifier.get_params(deep)
+
+    def set_params(self, **params):
+        self.base_quantifier.set_params(**params)
+
+    def _delayed_fit(self, args):
+        with qp.util.temp_seed(self.random_state):
+            params, training = args
+            model = deepcopy(self.base_quantifier)
+            model.set_params(**params)
+            model.fit(training)
+            return model
+
+    def fit(self, training: LabelledCollection):
+        self._check_binary(training, self.__class__.__name__)
+
+        configs = qp.model_selection.expand_grid(self.param_grid)
+        self.models = qp.util.parallel(
+            self._delayed_fit,
+            ((params, training) for params in configs),
+            seed=qp.environ.get('_R_SEED', None),
+            n_jobs=self.n_jobs
+        )
+        return self
+
+    def _delayed_predict(self, args):
+        model, instances = args
+        return model.quantify(instances)
+
+    def quantify(self, instances):
+        prev_preds = qp.util.parallel(
+            self._delayed_predict,
+            ((model, instances) for model in self.models),
+            seed=qp.environ.get('_R_SEED', None),
+            n_jobs=self.n_jobs
+        )
+        prev_preds = np.asarray(prev_preds)
+        return np.median(prev_preds, axis=0)
+
+
 class MedianEstimator(BinaryQuantifier):
     """
     This method is a meta-quantifier that returns, as the estimated class prevalence values, the median of the
@@ -58,17 +117,64 @@ class MedianEstimator(BinaryQuantifier):
             model.fit(training)
             return model
 
+    def _delayed_fit_classifier(self, args):
+        with qp.util.temp_seed(self.random_state):
+            print('enter job')
+            cls_params, training = args
+            model = deepcopy(self.base_quantifier)
+            model.set_params(**cls_params)
+            predictions = model.classifier_fit_predict(training, predict_on=model.val_split)
+            print('exit job')
+            return (model, predictions)
+
+    def _delayed_fit_aggregation(self, args):
+        with qp.util.temp_seed(self.random_state):
+            print('\tenter job')
+            ((model, predictions), q_params), training = args
+            model = deepcopy(model)
+            model.set_params(**q_params)
+            model.aggregation_fit(predictions, training)
+            print('\texit job')
+            return model
+
+
     def fit(self, training: LabelledCollection):
         self._check_binary(training, self.__class__.__name__)
-        params_keys = list(self.param_grid.keys())
-        params_values = list(self.param_grid.values())
-        hyper = [dict({k: val[i] for i, k in enumerate(params_keys)}) for val in itertools.product(*params_values)]
-        self.models = qp.util.parallel(
-            self._delayed_fit,
-            ((params, training) for params in hyper),
-            seed=qp.environ.get('_R_SEED', None),
-            n_jobs=self.n_jobs
-        )
+
+        if isinstance(self.base_quantifier, AggregativeQuantifier):
+            cls_configs, q_configs = qp.model_selection.group_params(self.param_grid)
+
+            if len(cls_configs) > 1:
+                models_preds = qp.util.parallel(
+                    self._delayed_fit_classifier,
+                    ((params, training) for params in cls_configs),
+                    seed=qp.environ.get('_R_SEED', None),
+                    n_jobs=self.n_jobs,
+                    asarray=False
+                )
+            else:
+                print('only 1')
+                model = self.base_quantifier
+                model.set_params(**cls_configs[0])
+                predictions = model.classifier_fit_predict(training, predict_on=model.val_split)
+                models_preds = [(model, predictions)]
+
+            self.models = qp.util.parallel(
+                self._delayed_fit_aggregation,
+                ((setup, training) for setup in itertools.product(models_preds, q_configs)),
+                seed=qp.environ.get('_R_SEED', None),
+                n_jobs=self.n_jobs,
+                asarray=False
+            )
+        else:
+            configs = qp.model_selection.expand_grid(self.param_grid)
+            self.models = qp.util.parallel(
+                self._delayed_fit,
+                ((params, training) for params in configs),
+                seed=qp.environ.get('_R_SEED', None),
+                n_jobs=self.n_jobs,
+                asarray=False
+            )
         return self
 
     def _delayed_predict(self, args):
@@ -80,13 +186,13 @@ class MedianEstimator(BinaryQuantifier):
             self._delayed_predict,
             ((model, instances) for model in self.models),
             seed=qp.environ.get('_R_SEED', None),
-            n_jobs=self.n_jobs
+            n_jobs=self.n_jobs,
+            asarray=False
         )
         prev_preds = np.asarray(prev_preds)
         return np.median(prev_preds, axis=0)
 
 
-
 class Ensemble(BaseQuantifier):
     VALID_POLICIES = {'ave', 'ptr', 'ds'} | qp.error.QUANTIFICATION_ERROR_NAMES
 

quapy/method/neural.py

@@ -194,7 +194,7 @@ class QuaNetTrainer(BaseQuantifier):
         label_predictions = np.argmax(posteriors, axis=-1)
         prevs_estim = []
         for quantifier in self.quantifiers.values():
-            predictions = posteriors if isinstance(quantifier, AggregativeProbabilisticQuantifier) else label_predictions
+            predictions = posteriors if isinstance(quantifier, AggregativeSoftQuantifier) else label_predictions
             prevs_estim.extend(quantifier.aggregate(predictions))
 
         # there is no real need for adding static estims like the TPR or FPR from training since those are constant

quapy/model_selection.py

@@ -76,8 +76,6 @@ class GridSearchQ(BaseQuantifier):
         :param training: the training set on which to optimize the hyperparameters
         :return: self
         """
-        params_keys = list(self.param_grid.keys())
-        params_values = list(self.param_grid.values())
 
         protocol = self.protocol
 
@@ -86,12 +84,13 @@ class GridSearchQ(BaseQuantifier):
 
         tinit = time()
 
-        hyper = [dict({k: val[i] for i, k in enumerate(params_keys)}) for val in itertools.product(*params_values)]
+        configs = expand_grid(self.param_grid)
+
         self._sout(f'starting model selection with {self.n_jobs =}')
-        #pass a seed to parallel so it is set in clild processes
+        #pass a seed to parallel so it is set in child processes
         scores = qp.util.parallel(
             self._delayed_eval,
-            ((params, training) for params in hyper),
+            ((params, training) for params in configs),
             seed=qp.environ.get('_R_SEED', None),
             n_jobs=self.n_jobs
         )
@@ -204,8 +203,6 @@ class GridSearchQ(BaseQuantifier):
         raise ValueError('best_model called before fit')
 
 
-
-
 def cross_val_predict(quantifier: BaseQuantifier, data: LabelledCollection, nfolds=3, random_state=0):
     """
     Akin to `scikit-learn's cross_val_predict <https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_predict.html>`_
@@ -229,3 +226,43 @@ def cross_val_predict(quantifier: BaseQuantifier, data: LabelledCollection, nfol
     return total_prev
 
 
+def expand_grid(param_grid: dict):
+    """
+    Expands a param_grid dictionary as a list of configurations.
+    Example:
+
+    >>> combinations = expand_grid({'A': [1, 10, 100], 'B': [True, False]})
+    >>> print(combinations)
+    >>> [{'A': 1, 'B': True}, {'A': 1, 'B': False}, {'A': 10, 'B': True}, {'A': 10, 'B': False}, {'A': 100, 'B': True}, {'A': 100, 'B': False}]
+
+    :param param_grid: dictionary with keys representing hyper-parameter names, and values representing the range
+        to explore for that hyper-parameter
+    :return: a list of configurations, i.e., combinations of hyper-parameter assignments in the grid.
+    """
+    params_keys = list(param_grid.keys())
+    params_values = list(param_grid.values())
+    configs = [{k: combs[i] for i, k in enumerate(params_keys)} for combs in itertools.product(*params_values)]
+    return configs
+
+
+def group_params(param_grid: dict):
+    """
+    Partitions a param_grid dictionary as two lists of configurations, one for the classifier-specific
+    hyper-parameters, and another for que quantifier-specific hyper-parameters
+
+    :param param_grid: dictionary with keys representing hyper-parameter names, and values representing the range
+        to explore for that hyper-parameter
+    :return: two expanded grids of configurations, one for the classifier, another for the quantifier
+    """
+    classifier_params, quantifier_params = {}, {}
+    for key, values in param_grid.items():
+        if key.startswith('classifier__') or key == 'val_split':
+            classifier_params[key] = values
+        else:
+            quantifier_params[key] = values
+
+    classifier_configs = expand_grid(classifier_params)
+    quantifier_configs = expand_grid(quantifier_params)
+
+    return classifier_configs, quantifier_configs
+

quapy/tests/test_hierarchy.py

@@ -22,9 +22,9 @@ class HierarchyTestCase(unittest.TestCase):
     def test_probabilistic(self):
         lr = LogisticRegression()
         for m in [CC(lr), ACC(lr)]:
-            self.assertEqual(isinstance(m, AggregativeProbabilisticQuantifier), False)
+            self.assertEqual(isinstance(m, AggregativeSoftQuantifier), False)
         for m in [PCC(lr), PACC(lr)]:
-            self.assertEqual(isinstance(m, AggregativeProbabilisticQuantifier), True)
+            self.assertEqual(isinstance(m, AggregativeSoftQuantifier), True)
 
 
 if __name__ == '__main__':

quapy/util.py

@@ -38,7 +38,7 @@ def map_parallel(func, args, n_jobs):
     return list(itertools.chain.from_iterable(results))
 
 
-def parallel(func, args, n_jobs, seed=None):
+def parallel(func, args, n_jobs, seed=None, asarray=True):
     """
     A wrapper of multiprocessing:
 
@@ -58,9 +58,12 @@ def parallel(func, args, n_jobs, seed=None):
                 stack.enter_context(qp.util.temp_seed(seed))
             return func(*args)
     
-    return Parallel(n_jobs=n_jobs)(
+    out = Parallel(n_jobs=n_jobs)(
         delayed(func_dec)(qp.environ, None if seed is None else seed+i, args_i) for i, args_i in enumerate(args)
     )
+    if asarray:
+        out = np.asarray(out)
+    return out
 
 
 @contextlib.contextmanager