Tests passing

multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_late_fusion.py

@@ -7,11 +7,11 @@ classifier_class_name = "WeightedLinearLateFusion"
 
 
 class WeightedLinearLateFusion(LateFusionClassifier):
-    def __init__(self, random_state, classifier_names=None,
+    def __init__(self, random_state, classifiers_names=None,
                  classifier_configs=None, weights=None, nb_cores=1):
         self.need_probas=True
         super(WeightedLinearLateFusion, self).__init__(random_state=random_state,
-                                      classifier_names=classifier_names,
+                                      classifiers_names=classifiers_names,
                                       classifier_configs=classifier_configs,
                                       nb_cores=nb_cores,weights=weights)
 

multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py

@@ -183,7 +183,11 @@ class MultiviewCompatibleRandomizedSearchCV(RandomizedSearchCV):
         folds = list(self.cv.split(self.available_indices, y[self.available_indices]))
         if self.equivalent_draws:
             self.n_iter = self.n_iter*X.nb_view
-        candidate_params = list(self._get_param_iterator())
+        # Fix to allow sklearn > 0.19
+        from sklearn.model_selection import ParameterSampler
+        candidate_params = list(
+            ParameterSampler(self.param_distributions, self.n_iter,
+                             random_state=self.random_state))
         base_estimator = clone(self.estimator)
         results = {}
         self.cv_results_ = dict(("param_"+param_name, []) for param_name in candidate_params[0].keys())

multiview_platform/tests/test_ResultAnalysis.py

@@ -210,7 +210,7 @@ class Test_format_previous_results(unittest.TestCase):
         biclass_results["01"]["example_errors"][1]["mv"] = mv_error_data_2
 
         # Running the function
-        metric_analysis, error_analysis, feature_importances, feature_stds = result_analysis.format_previous_results(biclass_results)
+        metric_analysis, error_analysis, feature_importances, feature_stds,labels = result_analysis.format_previous_results(biclass_results)
         mean_df = pd.DataFrame(data=np.mean(np.array([metrics_1_data,
                                                       metrics_2_data]),
                                             axis=0),