Added feature relevance

summit/multiview_platform/result_analysis/execution.py

@@ -84,7 +84,7 @@ def analyze_iterations(results, benchmark_argument_dictionaries, stats_iter,
         sample_errors = get_sample_errors(labels, result)
         feature_importances = get_feature_importances(result,
                                                       feature_ids=feature_ids,
-                                                      view_names=view_names)
+                                                      view_names=view_names,)
         durations = get_duration(result)
         directory = arguments["directory"]
 
@@ -98,7 +98,7 @@ def analyze_iterations(results, benchmark_argument_dictionaries, stats_iter,
         publish_sample_errors(sample_errors, directory, database_name,
                               labels_names, sample_ids, labels)
         publish_feature_importances(feature_importances, directory,
-                                    database_name)
+                                    database_name, metric_scores=metrics_scores)
         plot_durations(durations, directory, database_name)
 
         iter_results["metrics_scores"][iter_index] = metrics_scores

summit/multiview_platform/result_analysis/feature_importances.py

@@ -7,7 +7,7 @@ import plotly
 from ..monoview.monoview_utils import MonoviewResult
 
 
-def get_feature_importances(result, feature_ids=None, view_names=None):
+def get_feature_importances(result, feature_ids=None, view_names=None,):
     r"""Extracts the feature importance from the monoview results and stores
     them in a dictionnary :
     feature_importance[view_name] is a pandas.DataFrame of size n_feature*n_clf
@@ -49,7 +49,7 @@ def get_feature_importances(result, feature_ids=None, view_names=None):
 
 
 def publish_feature_importances(feature_importances, directory, database_name,
-                                feature_stds=None):  # pragma: no cover
+                                feature_stds=None, metric_scores=None):  # pragma: no cover
     importance_dfs = []
     std_dfs = []
     if not os.path.exists(os.path.join(directory, "feature_importances")):
@@ -94,6 +94,9 @@ def publish_feature_importances(feature_importances, directory, database_name,
                 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:
+            plot_feature_relevance(os.path.join(directory, "feature_importances",
+                                     database_name), feature_importances_df, feature_std_df, metric_scores)
 
 
 def plot_feature_importances(file_name, feature_importance,
@@ -125,3 +128,18 @@ def plot_feature_importances(file_name, feature_importance,
     plotly.offline.plot(fig, filename=file_name + ".html", auto_open=False)
 
     del fig
+    
+
+def plot_feature_relevance(file_name, feature_importance,
+                             feature_std, metric_scores): # pragma: no cover
+    for metric, score_df in metric_scores.items():
+        if metric.endswith("*"):
+            for score in score_df.columns:
+                if len(score.split("-"))>1:
+                    algo, view = score.split("-")
+                    feature_importance[algo].loc[[ind for ind in feature_importance.index if ind.startswith(view)]]*=score_df[score]['test']
+                else:
+                    feature_importance[score] *= score_df[score]['test']
+    file_name+="_relevance"
+    plot_feature_importances(file_name, feature_importance,
+                             feature_std)