BKS_query = predictions
// Use the BKS to filter the competence region
selected_idx = []
for sample_index in idx_neighbors:
T = (self.BKS_dsel[sample_index][:] == BKS_query)
S = sum(T) / self.n_classifiers
if S > self.similarity_threshold:
selected_idx.append(sample_index)
// Use the whole neighborhood if no sample is selected to form the region of competence
if len(selected_idx) == 0:
selected_idx = idx_neighbors
// Estimate the classifier competence for the filtered region of competence
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]:
clf_competence = [self.processed_dsel[sample_idx][clf_index] for sample_idx in selected_idx]competences[clf_index] = np.mean(np.array(clf_competence))
return competences
After Change
_, idx_neighbors = self._get_region_competence(query)
idx_neighbors = np.atleast_2d(idx_neighbors)
// Use the pre-compute decisions to transform the query to the BKS space
BKS_query = predictions
T = (self.BKS_dsel[idx_neighbors] == BKS_query.reshape(BKS_query.shape[0], -1, BKS_query.shape[1]))
S = np.sum(T, axis=2) / self.n_classifiers
// get a mask with the neighbors that will be considered for the competence estimation for all samples.
boolean_mask = (S > self.similarity_threshold)
boolean_mask[~np.any(boolean_mask, axis=1), :] = True
// Expanding this mask to the third axis (n_classifiers) since it is the same for each classifier.
boolean_mask = np.repeat(np.expand_dims(boolean_mask, axis=2), self.n_classifiers, axis=2)
// Use the masked array mean to take into account the removed neighbors
processed_pred = np.ma.MaskedArray(self.processed_dsel[idx_neighbors, :], mask=~boolean_mask)
competences = np.ma.mean(processed_pred, axis=1)
return competences
