quick test kdex

distribution_matching/method/kdex.py

@@ -0,0 +1,50 @@
+from quapy.method.base import BaseQuantifier
+import numpy as np
+from distribution_matching.method.kdey import KDEBase
+
+import quapy as qp
+from quapy.data import LabelledCollection
+import quapy.functional as F
+from sklearn.preprocessing import StandardScaler
+
+
+class KDExML(BaseQuantifier, KDEBase):
+
+    def __init__(self, bandwidth=0.1, standardize=True):
+        self._check_bandwidth(bandwidth)
+        self.bandwidth = bandwidth
+        self.standardize = standardize
+
+    def fit(self, data: LabelledCollection):
+        X, y = data.Xy
+        if self.standardize:
+            self.scaler = StandardScaler()
+            X = self.scaler.fit_transform(X)
+
+        self.mix_densities = self.get_mixture_components(X, y, data.n_classes, self.bandwidth)
+        return self
+
+    def quantify(self, X):
+        """
+        Searches for the mixture model parameter (the sought prevalence values) that maximizes the likelihood
+        of the data (i.e., that minimizes the negative log-likelihood)
+
+        :param X: instances in the sample 
+        :return: a vector of class prevalence estimates
+        """
+        epsilon = 1e-10
+        n_classes = len(self.mix_densities)
+        if self.standardize:
+            X = self.scaler.transform(X)
+        test_densities = [self.pdf(kde_i, X) for kde_i in self.mix_densities]
+
+        def neg_loglikelihood(prev):
+            test_mixture_likelihood = sum(prev_i * dens_i for prev_i, dens_i in zip (prev, test_densities))
+            test_loglikelihood = np.log(test_mixture_likelihood + epsilon)
+            return  -np.sum(test_loglikelihood)
+
+        return F.optim_minimize(neg_loglikelihood, n_classes)
+
+
+
+