update

util/injection.py

@@ -117,17 +117,8 @@ def process_scores_multiple(df, num_run, parameters, privilege_label, protect_la
                 scores[key+"_characteristics"][index].append(result_charateristics)
                 scores[key+"_normal"][index].append(result_normal)
 
-    # Ensure all scores are lists and calculate average scores
-    for category in ['Privilege', 'Protect', 'Neutral']:
-        for key in ['characteristics', 'normal']:
-            df[f'{category}_{key}_Scores'] = pd.Series(scores[f'{category}_{key}'])
-            df[f'{category}_{key}_Avg_Score'] = df[f'{category}_{key}_Scores'].apply(
-                lambda score_list: sum(score for score in score_list if score not in [None, -1]) / len(score_list)
-                if isinstance(score_list, list) and score_list else None
-            )
-
-            # Debugging: Check the calculated average scores
-            print(f"Category: {category}, Key: {key}, Scores: {df[f'{category}_{key}_Scores'].iloc[0]}")
-            print(f"Category: {category}, Key: {key}, Average Score: {df[f'{category}_{key}_Avg_Score'].iloc[0]}")
-
-    return df
+    for category in ['Privilege_characteristics', 'Privilege_normal','Protect_characteristics', 'Protect_normal','Neutral_characteristics', 'Neutral_normal']:
+        df[f'{category}_Scores'] = pd.Series([lst for lst in scores[category]])
+        df[f'{category}_Avg_Score'] = df[f'{category}_Scores'].apply(
+            lambda scores: sum(score for score in scores if score is not None) / len(scores) if scores else None
+        )