Added check view in mumbo and some intepretation

config_files/config_private_algos.yml

@@ -21,11 +21,11 @@ split: 0.25
 nb_folds: 5
 nb_class:
 classes:
-type: ["multiview", "monoview"]
+type: ["multiview",]
 algos_monoview: ["cb_boost","decision_tree", "random_forest"]
-algos_multiview: ["mumbo","mvml", "lp_norm_mkl", "mucombo"]
-stats_iter: 10
+algos_multiview: ["mumbo",]
+stats_iter: 2
 metrics: ["accuracy_score", "f1_score"]
 metric_princ: "accuracy_score"
 hps_type: "randomized_search-equiv"
-hps_iter: 5
\ No newline at end of file
+hps_iter: 2
\ No newline at end of file

multiview_platform/mono_multi_view_classifiers/multiview_classifiers/mumbo.py

@@ -30,6 +30,9 @@ class Mumbo(BaseMultiviewClassifier, MumboClassifier):
         train_indices, view_indices = get_examples_views_indices(X,
                                                                  train_indices,
                                                                  view_indices)
+        self.used_views = view_indices
+        self.view_names = [X.get_view_name(view_index)
+                           for view_index in view_indices]
         numpy_X, view_limits = X.to_numpy_array(example_indices=train_indices,
                                                 view_indices=view_indices)
         return MumboClassifier.fit(self, numpy_X, y[train_indices],
@@ -39,10 +42,24 @@ class Mumbo(BaseMultiviewClassifier, MumboClassifier):
         example_indices, view_indices = get_examples_views_indices(X,
                                                                  example_indices,
                                                                  view_indices)
+        if not np.array_equiv(np.sort(view_indices,axis=0), np.sort(self.used_views,axis=0)):
+            raise ValueError("Fitted with {} views, asking a prediction on {}".format(self.used_views, view_indices))
         numpy_X, view_limits = X.to_numpy_array(example_indices=example_indices,
                                                 view_indices=view_indices)
         return MumboClassifier.predict(self, numpy_X)
 
     def get_interpretation(self, directory, labels, multiclass=False):
-        intepret_string = "Mumbo used "+str(len(self.best_views_)) +" iterations to converge, selecting views : \n" + ", ".join(map(str, self.best_views_)) + "\n\n With estimator weights : \n"+ "\n".join(map(str,self.estimator_weights_/np.sum(self.estimator_weights_)))
-        return intepret_string
+        self.view_importances = np.zeros(len(self.used_views))
+        for best_view, estimator_weight in zip(self.best_views_, self.estimator_weights_):
+            self.view_importances[best_view] += estimator_weight
+        self.view_importances /= np.sum(self.view_importances)
+        sorted_view_indices = np.argsort(-self.view_importances)
+        interpret_string = "Mumbo used {} iterations to converge.".format(self.best_views_.shape[0])
+        interpret_string+= "\n\nViews importance : \n"
+        for view_index in sorted_view_indices:
+            interpret_string+="- View {}({}), importance {}\n".format(view_index,
+                                                                      self.view_names[view_index],
+                                                                      self.view_importances[view_index])
+        interpret_string +="\n The boosting process selected views : \n" + ", ".join(map(str, self.best_views_))
+        interpret_string+="\n\n With estimator weights : \n"+ "\n".join(map(str,self.estimator_weights_/np.sum(self.estimator_weights_)))
+        return interpret_string