1002cfbcc9f8182404fb058f959d625de2eabbfc,deslib/dcs/rank.py,Rank,estimate_competence,#Rank#Any#Any#,87

Before Change

                     The competence level estimated for each base classifier
        
        dists, idx_neighbors = self._get_region_competence(query)
        competences = np.zeros(self.n_classifiers)

        for clf_index in range(self.n_classifiers):

            // Check if the dynamic frienemy pruning (DFP) should be used used
            if self.DFP_mask[clf_index]:
                // count the number of correctly classified samples in the
                // neighborhood.
                for counter, index in enumerate(idx_neighbors):
                    if self.processed_dsel[index][clf_index]:
                        continue
                    else:
                        competences[clf_index] = counter
                        break

        return competences

After Change

                     The competence level estimated for each base classifier
        
        _, idx_neighbors = self._get_region_competence(query)
        idx_neighbors = np.atleast_2d(idx_neighbors)
        results_neighbors = self.processed_dsel[idx_neighbors, :]

        // Get the shape of the vector in order to know the number of samples, base classifiers and neighbors considered.
        shape = results_neighbors.shape

        // add an row with zero for the case where the base classifier correctly classifies the whole neighborhood.
        // That way the search will always find a zero after comparing to self.K + 1
        addition = np.zeros((shape[0], shape[2]))
        results_neighbors = np.insert(results_neighbors, shape[1], addition, axis=1)
        competences = np.argmax(results_neighbors == 0, axis=1)

        // competences = np.zeros(self.n_classifiers)
        //
        // for clf_index in range(self.n_classifiers):

Instances