diff --git a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/CQBoostUtils.py b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/CQBoostUtils.py
index fd402cdafb5b1d6b9485929cdc8726a0d7623b6a..716ebf8c10a7523b21f9bf96a7fa25c959240b54 100644
--- a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/CQBoostUtils.py
+++ b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/CQBoostUtils.py
@@ -59,10 +59,8 @@ class ColumnGenerationClassifier(BaseEstimator, ClassifierMixin, BaseBoost):
         w= None
         self.collected_weight_vectors_ = {}
         self.collected_dual_constraint_violations_ = {}
-        print("Begin")
 
         for k in range(min(n, self.n_max_iterations if self.n_max_iterations is not None else np.inf)):
-            beg=time.time()
             # Find worst weak hypothesis given alpha.
             h_values = ma.array(np.squeeze(np.array((alpha).T.dot(y_kernel_matrix).T)), fill_value=-np.inf)
             h_values[self.chosen_columns_] = ma.masked
@@ -89,8 +87,6 @@ class ColumnGenerationClassifier(BaseEstimator, ClassifierMixin, BaseBoost):
             self.train_metrics.append(self.plotted_metric.score(y, signs_array))
             self.gammas.append(accuracy_score(y, signs_array))
             self.bounds.append(math.exp(-2 * np.sum(np.square(np.array(self.gammas)))))
-            end=time.time()
-            print(beg-end)
 
         self.nb_opposed_voters = self.check_opposed_voters()
         self.compute_weights_(w)
diff --git a/multiview_platform/MonoMultiViewClassifiers/Monoview/MonoviewUtils.py b/multiview_platform/MonoMultiViewClassifiers/Monoview/MonoviewUtils.py
index 64ecb3ffe0d82a7b6fc8d760fcb705ef56b4aeff..3367e44303812381424339357a4b9f298a36d057 100644
--- a/multiview_platform/MonoMultiViewClassifiers/Monoview/MonoviewUtils.py
+++ b/multiview_platform/MonoMultiViewClassifiers/Monoview/MonoviewUtils.py
@@ -28,6 +28,7 @@ def randomizedSearch(X_train, y_train, randomState, outputFileName, classifierMo
         scorer = metricModule.get_scorer(**metricKWARGS)
         nb_possible_combinations = compute_possible_combinations(params_dict)
         min_list = np.array([min(nb_possible_combination, nIter) for nb_possible_combination in nb_possible_combinations])
+        print(nbCores)
         randomSearch = RandomizedSearchCV(estimator, n_iter=int(np.sum(min_list)), param_distributions=params_dict, refit=True,
                                           n_jobs=nbCores, scoring=scorer, cv=KFolds, random_state=randomState)
         detector = randomSearch.fit(X_train, y_train)