small fixes in kdey (now should work with string labels) and EMQ (in case some training prior prob was 0, it broke)

quapy/method/_kdey.py

@@ -52,7 +52,7 @@ class KDEBase:
         """
         return np.exp(kde.score_samples(X))
 
-    def get_mixture_components(self, X, y, n_classes, bandwidth):
+    def get_mixture_components(self, X, y, classes, bandwidth):
         """
         Returns an array containing the mixture components, i.e., the KDE functions for each class.
 
@@ -62,7 +62,7 @@ class KDEBase:
         :param bandwidth: float, the bandwidth of the kernel
         :return: a list of KernelDensity objects, each fitted with the corresponding class-specific covariates
         """
-        return [self.get_kde_function(X[y == cat], bandwidth) for cat in range(n_classes)]
+        return [self.get_kde_function(X[y == cat], bandwidth) for cat in classes]
 
 
 
@@ -114,7 +114,7 @@ class KDEyML(AggregativeSoftQuantifier, KDEBase):
         self.random_state=random_state
 
     def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
-        self.mix_densities = self.get_mixture_components(*classif_predictions.Xy, data.n_classes, self.bandwidth)
+        self.mix_densities = self.get_mixture_components(*classif_predictions.Xy, data.classes_, self.bandwidth)
         return self
 
     def aggregate(self, posteriors: np.ndarray):
@@ -196,7 +196,7 @@ class KDEyHD(AggregativeSoftQuantifier, KDEBase):
         self.montecarlo_trials = montecarlo_trials
 
     def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
-        self.mix_densities = self.get_mixture_components(*classif_predictions.Xy, data.n_classes, self.bandwidth)
+        self.mix_densities = self.get_mixture_components(*classif_predictions.Xy, data.classes_, self.bandwidth)
 
         N = self.montecarlo_trials
         rs = self.random_state

quapy/method/aggregative.py

@@ -640,6 +640,8 @@ class EMQ(AggregativeSoftQuantifier):
                 raise ValueError('invalid param argument for recalibration method; available ones are '
                                  '"nbvs", "bcts", "ts", and "vs".')
 
+            if not np.issubdtype(y.dtype, np.number):
+                y = np.searchsorted(data.classes_, y)
             self.calibration_function = calibrator(P, np.eye(data.n_classes)[y], posterior_supplied=True)
 
         if self.exact_train_prev:
@@ -681,6 +683,11 @@ class EMQ(AggregativeSoftQuantifier):
         """
         Px = posterior_probabilities
         Ptr = np.copy(tr_prev)
+
+        if np.product(Ptr) == 0:  # some entry is 0; we should smooth the values to avoid 0 division
+            Ptr += epsilon
+            Ptr /= Ptr.sum()
+
         qs = np.copy(Ptr)  # qs (the running estimate) is initialized as the training prevalence
 
         s, converged = 0, False