Fix feature_importances issue due to mismatch between classifier name and...

Fix feature_importances issue due to mismatch between classifier name and registered name in metrics_scores

summit/multiview_platform/result_analysis/feature_importances.py

@@ -44,19 +44,22 @@ def get_feature_importances(result, feature_ids=None, view_names=None,):
                                  v_feature_id]
                 feature_importances["mv"] = pd.DataFrame(index=feat_ids)
             if hasattr(classifier_result.clf, 'feature_importances_'):
-                feature_importances["mv"][classifier_result.classifier_name] = classifier_result.clf.feature_importances_
+                feature_importances["mv"][classifier_result.get_classifier_name()] = classifier_result.clf.feature_importances_
+            else:
+                # HACK: Assigning a default features importances values to classifier that hasn't feature_importances_
+                #  attribute (eg: Linear Late Fusion)
+                feature_importances["mv"][classifier_result.get_classifier_name()] = np.zeros(len(feature_importances["mv"].index))
     return feature_importances
 
 
 def publish_feature_importances(feature_importances, directory, database_name,
                                 feature_stds=None, metric_scores=None):  # pragma: no cover
+    # TODO: Manage the case with NAN values
     importance_dfs = []
     std_dfs = []
     if not os.path.exists(os.path.join(directory, "feature_importances")):
         os.mkdir(os.path.join(directory, "feature_importances"))
     for view_name, feature_importance in feature_importances.items():
-        if view_name!="mv":
-
         if feature_stds is not None:
             feature_std = feature_stds[view_name]
         else:
@@ -65,33 +68,22 @@ def publish_feature_importances(feature_importances, directory, database_name,
                                        columns=feature_importance.columns)
         feature_std = feature_std.loc[feature_importance.index]
 
+        if view_name == "mv":
+            importance_dfs.append(feature_importance)
+            std_dfs.append(feature_std)
+        else:
+            importance_dfs.append(feature_importance.set_index(
+                pd.Index([view_name + "-" + ind for ind in list(feature_importance.index)])))
 
-            importance_dfs.append(feature_importance.set_index(pd.Index([view_name+"-"+ind for ind in list(feature_importance.index)])))
-            # importance_dfs.append(pd.DataFrame(index=[view_name+"-br"],
-            #                                    columns=feature_importance.columns,
-            #                                    data=np.zeros((1, len(
-            #                                        feature_importance.columns)))))
             std_dfs.append(feature_std.set_index(pd.Index([view_name + "-" + ind
                                                            for ind
                                                            in list(feature_std.index)])))
-            # std_dfs.append(pd.DataFrame(index=[view_name + "-br"],
-            #                                    columns=feature_std.columns,
-            #                                    data=np.zeros((1, len(
-            #                                        feature_std.columns)))))
+
     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 "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)
-            if feature_stds is not None:
-                feature_std_df = pd.concat([feature_std_df, feature_stds["mv"]], axis=1,).fillna(0)
-            else:
-                fake = pd.DataFrame(data=np.zeros((feature_importances_df.shape[0], feature_importances["mv"].shape[1])),
-                                               index=feature_importances_df.index,
-                                               columns=feature_importances["mv"].columns).fillna(0)
-                feature_std_df = pd.concat([feature_std_df, fake], axis=1,).fillna(0)
         plot_feature_importances(os.path.join(directory, "feature_importances",
                                               database_name), feature_importances_df, feature_std_df)
         if metric_scores is not None: