Feature_importance for multiview only possible

"

summit/multiview_platform/result_analysis/feature_importances.py

@@ -30,14 +30,8 @@ def get_feature_importances(result, feature_ids=None, view_names=None,):
                 feature_importances[classifier_result.view_name] = pd.DataFrame(
                     index=feature_ids[classifier_result.view_index])
             if hasattr(classifier_result.clf, 'feature_importances_'):
-                print(classifier_result.classifier_name, classifier_result.view_name)
-
                 feature_importances[classifier_result.view_name][
                     classifier_result.classifier_name] = classifier_result.clf.feature_importances_
-                print(classifier_result.clf.feature_importances_.shape,
-                      feature_importances[classifier_result.view_name][
-                          classifier_result.classifier_name].shape)
-
             else:
                 feature_importances[classifier_result.view_name][
                     classifier_result.classifier_name] = np.zeros(
@@ -62,7 +56,7 @@ def get_feature_importances(result, feature_ids=None, view_names=None,):
 
 def publish_feature_importances(feature_importances, directory, database_name,
                                 feature_stds=None, metric_scores=None, test=False):  # pragma: no cover
-    importance_dfs = []
+    importance_dfs = [pd.DataFrame()]
     std_dfs = []
     if not os.path.exists(os.path.join(directory, "feature_importances")):
         os.mkdir(os.path.join(directory, "feature_importances"))
@@ -90,13 +84,17 @@ def publish_feature_importances(feature_importances, directory, database_name,
             #                                    columns=feature_std.columns,
             #                                    data=np.zeros((1, len(
             #                                        feature_std.columns)))))
+    if "mv" in feature_importances:
+        importance_dfs.append(feature_importances["mv"].loc[(feature_importances["mv"] != 0).any(axis=1), :])
     if len(importance_dfs)>0:
         feature_importances_df = pd.concat(importance_dfs)
         feature_importances_df = feature_importances_df/feature_importances_df.sum(axis=0)
-
-        feature_std_df = pd.concat(std_dfs)
+        if len(std_dfs)>0:
+            feature_std_df = pd.concat(std_dfs)
+        else:
+            feature_std_df = pd.DataFrame()
         if "mv" in feature_importances:
-            feature_importances_df = pd.concat([feature_importances_df,feature_importances["mv"].loc[(feature_importances["mv"] != 0).any(axis=1), :]], axis=1).fillna(0)
+            # feature_importances_df = pd.concat([feature_importances_df,feature_importances["mv"].loc[(feature_importances["mv"] != 0).any(axis=1), :]], axis=1).fillna(0)
             if feature_stds is not None:
                 feature_std_df = pd.concat([feature_std_df, feature_stds["mv"]], axis=1,).fillna(0)
             else: