Added compute_voter_weight and update_info_containers

multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py

@@ -88,26 +88,14 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
                 self.break_cause = " epsilon was too small."
                 break
 
-            if self.use_r:
-                self.q = 0.5*math.log((1+r)/(1-r))
-            else:
-                self.q = math.log((1 - epsilon) / epsilon)
-            self.weights_.append(self.q)
+            self.compute_voter_weight(r, epsilon)
 
             # Update the distribution on the examples.
-            self._update_example_weights(formatted_y)
-            self.example_weights_.append(self.example_weights)
+            self.update_example_weights(formatted_y)
 
             # Update the "previous vote" to prepare for the next iteration
-            self.previous_vote = np.matmul(self.classification_matrix[:, self.chosen_columns_],
-                                           np.array(self.weights_).reshape((k + 2, 1))).reshape((m, 1))
-            self.previous_votes.append(self.previous_vote)
+            self.update_info_containers(formatted_y, r, k)
 
-            self.previous_margins.append(np.multiply(formatted_y, self.previous_vote))
-            self.train_metrics.append(self.plotted_metric.score(formatted_y, np.sign(self.previous_vote)))
-            # self.bounds.append(np.prod(np.sqrt(1-4*np.square(0.5-np.array(self.epsilons)))))
-
-            self.bounds.append(self.bounds[-1]*math.sqrt(1-r**2))
 
         self.nb_opposed_voters = self.check_opposed_voters()
         self.estimators_generator.estimators_ = self.estimators_generator.estimators_[self.chosen_columns_]
@@ -134,6 +122,27 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
         self.predict_time = end - start
         return signs_array
 
+    def update_info_containers(self, y, r, k):
+        self.example_weights_.append(self.example_weights)
+        self.previous_vote = np.matmul(
+            self.classification_matrix[:, self.chosen_columns_],
+            np.array(self.weights_).reshape((k + 2, 1))).reshape((self.n_total_examples, 1))
+        self.previous_votes.append(self.previous_vote)
+
+        self.previous_margins.append(
+            np.multiply(y, self.previous_vote))
+        self.train_metrics.append(
+            self.plotted_metric.score(y, np.sign(self.previous_vote)))
+        self.bounds.append(self.bounds[-1] * math.sqrt(1 - r ** 2))
+        # self.bounds.append(np.prod(np.sqrt(1-4*np.square(0.5-np.array(self.epsilons)))))
+
+    def compute_voter_weight(self, r, epsilon):
+        if self.use_r:
+            self.q = 0.5 * math.log((1 + r) / (1 - r))
+        else:
+            self.q = math.log((1 - epsilon) / epsilon)
+        self.weights_.append(self.q)
+
     def compute_voter_perf(self, formatted_y):
         epsilon = self._compute_epsilon(formatted_y)
         self.epsilons.append(epsilon)
@@ -186,7 +195,7 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
         self.weights_.append(self.q)
 
         # Update the distribution on the examples.
-        self._update_example_weights(y)
+        self.update_example_weights(y)
         self.example_weights_.append(self.example_weights)
 
         self.previous_margins.append(
@@ -241,7 +250,7 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
         r = np.average(ones_matrix, weights=self.example_weights, axis=0)
         return r
 
-    def _update_example_weights(self, y):
+    def update_example_weights(self, y):
         """Old fashioned exaple weights update uses the whole majority vote, the other way uses only the last voter."""
         new_weights = self.example_weights.reshape((self.n_total_examples, 1))*np.exp(-self.q*np.multiply(y,self.new_voter))
         self.example_weights = new_weights/np.sum(new_weights)