Added choice between eps and r

multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py

@@ -64,7 +64,7 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
         for k in range(min(n-1, self.n_max_iterations-1 if self.n_max_iterations is not None else np.inf)):
 
             # Print dynamically the step and the error of the current classifier
-            print("{}/{}, eps :{}".format(k+2, self.n_max_iterations, self.voter_perfs[-1]), end="\r")
+            print("Resp. bound : {}, {}/{}, eps :{}".format(self.respected_bound, k+2, self.n_max_iterations, self.voter_perfs[-1]), end="\r")
 
             sol, new_voter_index = self.choose_new_voter(y_kernel_matrix, formatted_y)
 
@@ -125,12 +125,17 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
 
         self.previous_margins.append(
             np.multiply(y, self.previous_vote))
-        self.train_metrics.append(
-            self.plotted_metric.score(y, np.sign(self.previous_vote)))
+        train_metric = self.plotted_metric.score(y, np.sign(self.previous_vote))
         if self.use_r:
-            self.bounds.append(self.bounds[-1] * math.sqrt(1 - voter_perf ** 2))
+            bound = self.bounds[-1] * math.sqrt(1 - voter_perf ** 2)
         else:
-            self.bounds.append(np.prod(np.sqrt(1-4*np.square(0.5-np.array(self.voter_perfs)))))
+            bound = np.prod(np.sqrt(1-4*np.square(0.5-np.array(self.voter_perfs))))
+
+        if train_metric > bound:
+            self.respected_bound = False
+
+        self.train_metrics.append(train_metric)
+        self.bounds.append(bound)
 
     def compute_voter_weight(self, voter_perf):
         """used to compute the voter's weight according to the specified method (edge or error) """
@@ -207,10 +212,19 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
 
         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(math.sqrt(1 - r ** 2))
+        train_metric =self.plotted_metric.score(y, np.sign(self.previous_vote))
+        if self.use_r:
+            bound = math.sqrt(1 - r ** 2)
+        else:
+            bound = np.prod(np.sqrt(1-4*np.square(0.5-np.array(epsilon))))
+
+        if train_metric > bound:
+            self.respected_bound = False
+
+        self.train_metrics.append(train_metric)
+
+        self.bounds.append(bound)
 
     def format_X_y(self, X, y):
         """Formats the data  : X -the examples- and y -the labels- to be used properly by the algorithm """
@@ -246,6 +260,7 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
         self.bounds = []
         self.previous_votes = []
         self.previous_margins = []
+        self.respected_bound = True
 
     def _compute_epsilon(self,y):
         """Updating the error variable, the old fashioned way uses the whole majority vote to update the error"""
@@ -434,6 +449,8 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
         args_dict = dict((arg_name, str(self.__dict__[arg_name])) for arg_name in self.printed_args_name_list)
         interpretString += "\n \n With arguments : \n"+u'\u2022 '+ ("\n"+u'\u2022 ').join(['%s: \t%s' % (key, value)
                                                                                            for (key, value) in args_dict.items()])
+        if not self.respected_bound:
+            interpretString += "\n\n The bound was not respected"
 
         return interpretString