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

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Alejandro Moreo Fernandez 2024-02-23 16:29:53 +01:00
parent 9542eaee61
commit 320b3eac38
2 changed files with 11 additions and 4 deletions
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8
quapy/method/_kdey.py
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@ -52,7 +52,7 @@ class KDEBase:
""" """
return np.exp(kde.score_samples(X)) 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. 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 :param bandwidth: float, the bandwidth of the kernel
:return: a list of KernelDensity objects, each fitted with the corresponding class-specific covariates :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 self.random_state=random_state
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection): 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 return self
def aggregate(self, posteriors: np.ndarray): def aggregate(self, posteriors: np.ndarray):
@ -196,7 +196,7 @@ class KDEyHD(AggregativeSoftQuantifier, KDEBase):
self.montecarlo_trials = montecarlo_trials self.montecarlo_trials = montecarlo_trials
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection): 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 N = self.montecarlo_trials
rs = self.random_state rs = self.random_state

7
quapy/method/aggregative.py
View File

@ -640,6 +640,8 @@ class EMQ(AggregativeSoftQuantifier):
raise ValueError('invalid param argument for recalibration method; available ones are ' raise ValueError('invalid param argument for recalibration method; available ones are '
'"nbvs", "bcts", "ts", and "vs".') '"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) self.calibration_function = calibrator(P, np.eye(data.n_classes)[y], posterior_supplied=True)
if self.exact_train_prev: if self.exact_train_prev:
@ -681,6 +683,11 @@ class EMQ(AggregativeSoftQuantifier):
""" """
Px = posterior_probabilities Px = posterior_probabilities
Ptr = np.copy(tr_prev) 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 qs = np.copy(Ptr) # qs (the running estimate) is initialized as the training prevalence
s, converged = 0, False s, converged = 0, False