None error in config file

config_files/config_test.yml

 # The base configuration of the benchmark
 Base :
   log: True
-  name: ["control_vs_malade"]
+  name: ["plausible"]
   label: "_"
   type: ".hdf5"
   views: ["300nm", "350nm"]
@@ -19,12 +19,12 @@ Base :
 Classification:
   multiclass_method: "oneVersusOne"
   split: 0.4
-  nb_folds: 5
+  nb_folds: 2
   nb_class: 2
   classes:
-  type: ["monoview",]
+  type: ["multiview",]
   algos_monoview: ["decision_tree"]
-  algos_multiview: ["all"]
+  algos_multiview: ["weighted_linear_early_fusion"]
   stats_iter: 2
   metrics: ["accuracy_score", "f1_score"]
   metric_princ: "f1_score"
@@ -123,7 +123,7 @@ gradient_boosting:
 ######################################
 
 weighted_linear_early_fusion:
-  view_weights: [None]
+  view_weights: [null]
   monoview_classifier_name: ["decision_tree"]
   monoview_classifier_config:
     decision_tree:
@@ -200,7 +200,7 @@ weighted_linear_late_fusion:
       splitter: ["best"]
 
 mumbo:
-  base_estimator: [None]
+  base_estimator: [null]
   n_estimators: [10]
   best_view_mode: ["edge"]
 

multiview_platform/mono_multi_view_classifiers/multiview/exec_multiview.py

@@ -264,7 +264,6 @@ def exec_multiview(directory, dataset_var, name, classification_indices, k_folds
 
     logging.debug("Start:\t Optimizing hyperparameters")
     if hyper_param_search != "None":
-        print(metrics)
         classifier_config = hyper_parameter_search.search_best_settings(
             dataset_var, labels, classifier_module, classifier_name,
             metrics[0], learning_indices, k_folds, random_state,

multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_early_fusion.py

@@ -31,7 +31,7 @@ class WeightedLinearEarlyFusion(BaseMultiviewClassifier, BaseFusionClassifier):
     def __init__(self, random_state=None, view_weights=None,
                  monoview_classifier_name="decision_tree",
                  monoview_classifier_config={}):
-
+        print(type(view_weights), view_weights)
         super(WeightedLinearEarlyFusion, self).__init__(random_state=random_state)
         self.view_weights = view_weights
         self.monoview_classifier_name = monoview_classifier_name
@@ -84,10 +84,12 @@ class WeightedLinearEarlyFusion(BaseMultiviewClassifier, BaseFusionClassifier):
         example_indices, self.view_indices = get_examples_views_indices(dataset,
                                                                         example_indices,
                                                                         view_indices)
+        print(type(self.view_weights))
         if self.view_weights is None:
             self.view_weights = np.ones(len(self.view_indices), dtype=float)
         else:
             self.view_weights = np.array(self.view_weights)
+        print(self.view_weights)
         self.view_weights /= float(np.sum(self.view_weights))
 
         X = self.hdf5_to_monoview(dataset, example_indices)