Update src/populate.py

src/populate.py

@@ -97,45 +97,14 @@ def create_intervention_averaged_df(df: pd.DataFrame) -> pd.DataFrame:
 
 
 def get_leaderboard_df_mib_causalgraph(results_path: str) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
-    # print(f"results_path is {results_path}, requests_path is {requests_path}")
-
-    detailed_df, aggregated_df, intervention_averaged_df = get_raw_eval_results_mib_causalgraph(results_path)
 
-    # all_data_json = [v.to_dict() for v in raw_detailed_df]
-    # detailed_df = pd.DataFrame.from_records(all_data_json)
 
-    # all_data_json = [v.to_dict() for v in raw_aggregated_df]
-    # aggregated_df = pd.DataFrame.from_records(all_data_json)
-
-    # all_data_json = [v.to_dict() for v in raw_intervention_averaged_df]
-    # intervention_averaged_df = pd.DataFrame.from_records(all_data_json)
-    
-    # # Rename columns to match schema
-    # column_mapping = {}
-    # for col in detailed_df.columns:
-    #     if col in ['eval_name', 'Method']:
-    #         continue
-    #     # Ensure consistent casing for the column names
-    #     new_col = col.replace('Qwen2ForCausalLM', 'qwen2forcausallm') \
-    #                 .replace('Gemma2ForCausalLM', 'gemma2forcausallm') \
-    #                 .replace('LlamaForCausalLM', 'llamaforcausallm')
-    #     column_mapping[col] = new_col
-    
-    # detailed_df = detailed_df.rename(columns=column_mapping)
-    
-    # # Create aggregated df
-    # aggregated_df = aggregate_methods(detailed_df)
-    
-    # # Create intervention-averaged df
-    # intervention_averaged_df = create_intervention_averaged_df(aggregated_df)
-    
-    # print("Transformed columns:", detailed_df.columns.tolist())
+    aggregated_df, intervention_averaged_df = get_raw_eval_results_mib_causalgraph(results_path)
 
-    print(f"Columns in detailed_df: {detailed_df.columns.tolist()}")
     print(f"Columns in aggregated_df: {aggregated_df.columns.tolist()}")
     print(f"Columns in intervention_averaged_df: {intervention_averaged_df.columns.tolist()}")
     
-    return detailed_df, aggregated_df, intervention_averaged_df
+    return aggregated_df, intervention_averaged_df
 
 
 def get_evaluation_queue_df(save_path: str, cols: list, track: str) -> list[pd.DataFrame]: