add

util/evaluation.py

@@ -81,11 +81,11 @@ def calculate_four_fifths_rule(impact_ratios):
 
 def statistical_tests(data):
     # Add ranks for each score within each row
-    ranks = data[['Privilege_Avg_Score', 'Protect_Avg_Score', 'Neutral_Avg_Score']].rank(axis=1, ascending=True)
-
-    data['Privilege_Rank'] = ranks['Privilege_Avg_Score']
-    data['Protect_Rank'] = ranks['Protect_Avg_Score']
-    data['Neutral_Rank'] = ranks['Neutral_Avg_Score']
+    # ranks = data[['Privilege_Avg_Score', 'Protect_Avg_Score', 'Neutral_Avg_Score']].rank(axis=1, ascending=True)
+    #
+    # data['Privilege_Rank'] = ranks['Privilege_Avg_Score']
+    # data['Protect_Rank'] = ranks['Protect_Avg_Score']
+    # data['Neutral_Rank'] = ranks['Neutral_Avg_Score']
 
     """Perform various statistical tests to evaluate potential biases."""
     variables = ['Privilege', 'Protect', 'Neutral']

util/injection.py

@@ -100,7 +100,7 @@ def process_scores_multiple(df, num_run, parameters, privilege_label, protect_la
         df[f'{category}_Avg_Score'] = df[f'{category}_Scores'].apply(calculate_avg_score)
 
     # Add ranks for each score within each row
-    ranks = df[['Privilege_Avg_Score', 'Protect_Avg_Score', 'Neutral_Avg_Score']].rank(axis=1, ascending=True)
+    ranks = df[['Privilege_Avg_Score', 'Protect_Avg_Score', 'Neutral_Avg_Score']].rank(axis=1, ascending=False)
 
     df['Privilege_Rank'] = ranks['Privilege_Avg_Score']
     df['Protect_Rank'] = ranks['Protect_Avg_Score']