Propagated on private algos check views

config_files/config_private_algos.yml

@@ -17,13 +17,13 @@ track_tracebacks: False
 
 # All the classification-realted configuration options
 multiclass_method: "oneVersusOne"
-split: 0.25
+split: 0.75
 nb_folds: 5
 nb_class:
 classes:
 type: ["multiview",]
-algos_monoview: ["cb_boost","decision_tree", "random_forest"]
-algos_multiview: ["mumbo",]
+algos_monoview: ["decision_tree", "random_forest"]
+algos_multiview: ["mumbo","mvml"]
 stats_iter: 2
 metrics: ["accuracy_score", "f1_score"]
 metric_princ: "accuracy_score"

multiview_platform/mono_multi_view_classifiers/multiview_classifiers/mucombo.py

@@ -26,6 +26,7 @@ class MuCumbo(BaseMultiviewClassifier, MuCumboClassifier):
         train_indices, view_indices = get_examples_views_indices(X,
                                                                  train_indices,
                                                                  view_indices)
+        self.used_views = view_indices
         numpy_X, view_limits = X.to_numpy_array(example_indices=train_indices,
                                                 view_indices=view_indices)
         return MuCumboClassifier.fit(self, numpy_X, y[train_indices],
@@ -35,6 +36,7 @@ class MuCumbo(BaseMultiviewClassifier, MuCumboClassifier):
         example_indices, view_indices = get_examples_views_indices(X,
                                                                  example_indices,
                                                                  view_indices)
+        self._check_views(view_indices)
         numpy_X, view_limits = X.to_numpy_array(example_indices=example_indices,
                                                 view_indices=view_indices)
         return MuCumboClassifier.predict(self, numpy_X)

multiview_platform/mono_multi_view_classifiers/multiview_classifiers/mumbo.py

@@ -42,8 +42,7 @@ 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))
+        self._check_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)