Before tests

multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py

@@ -72,7 +72,6 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
                                        "twice_the_same",
                                        "c_bound_choice", "random_start",
                                        "n_stumps", "use_r", "c_bound_sol"]
-        self.matrix_compute = False
 
     def set_params(self, **params):
         self.self_complemented = params["self_complemented"]
@@ -242,7 +241,6 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
         """THis initialization corressponds to the first round of boosting with equal weights for each examples and the voter chosen by it's margin."""
         self.example_weights = self._initialize_alphas(m).reshape((m, 1))
 
-        # self.previous_margins.append(np.multiply(y, y))
         self.example_weights_.append(self.example_weights)
         if self.random_start:
             first_voter_index = self.random_state.choice(
@@ -369,24 +367,6 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
         pseudo_h_values[self.chosen_columns_] = ma.masked
         return np.argmax(pseudo_h_values), [0]
 
-    def _is_not_too_wrong(self, hypothese, y):
-        """Check if the weighted margin is better than random"""
-        if self.c_bound_sol:
-            return np.sum(hypothese) > 0
-        else:
-            print(np.average(hypothese.reshape(y.shape), weights=self.example_weights))
-            quit()
-            weighted_margin = np.average(hypothese.reshape(y.shape), weights=self.example_weights)#ondes matrix, axis=0
-            return weighted_margin > 0
-
-    def get_possible(self, y_kernel_matrix, y):
-        """Get all the indices of the hypothesis that are good enough to be chosen"""
-        possibleIndices = []
-        for hypIndex, hypothese in enumerate(np.transpose(y_kernel_matrix)):
-            if self._is_not_too_wrong(hypothese, y):
-                possibleIndices.append(hypIndex)
-        return np.array(possibleIndices)
-
     def _find_new_voter(self, y_kernel_matrix, y):
         """Here, we solve the two_voters_mincq_problem for each potential new voter,
         and select the one that has the smallest minimum"""

multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CGreed.py

@@ -12,7 +12,7 @@ class CGreed(ColumnGenerationClassifierQar, BaseMonoviewClassifier):
             twice_the_same=True,
             c_bound_choice=True,
             random_start=False,
-            n_stumps_per_attribute=1,
+            n_stumps_per_attribute=10,
             use_r=True,
             c_bound_sol=True
             )