Update src/saving_utils.py

src/saving_utils.py

@@ -56,13 +56,13 @@ def save_similarity_output(output_dict, method_name, leaderboard_path="/home/use
         # Calculate averages if all three aspects (MF, BP, CC) are present
         if len(correlation_values) == 3:
             averages[f"{dataset}_Ave_correlation"] = sum(correlation_values) / 3
-            similarity_df.at[similarity_df['Method'] == method_name, f"{dataset}_Ave_correlation"] = averages[f"{dataset}_Ave_correlation"]
-            leaderboard_df.at[leaderboard_df['Method'] == method_name, f"sim_{dataset}_Ave_correlation"] = averages[f"{dataset}_Ave_correlation"]
+            similarity_df.loc[similarity_df['Method'] == method_name, f"{dataset}_Ave_correlation"] = averages[f"{dataset}_Ave_correlation"]
+            leaderboard_df.loc[leaderboard_df['Method'] == method_name, f"sim_{dataset}_Ave_correlation"] = averages[f"{dataset}_Ave_correlation"]
 
         if len(pvalue_values) == 3:
             averages[f"{dataset}_Ave_pvalue"] = sum(pvalue_values) / 3
-            similarity_df.at[similarity_df['Method'] == method_name, f"{dataset}_Ave_pvalue"] = averages[f"{dataset}_Ave_pvalue"]
-            leaderboard_df.at[leaderboard_df['Method'] == method_name, f"sim_{dataset}_Ave_pvalue"] = averages[f"{dataset}_Ave_pvalue"]
+            similarity_df.loc[similarity_df['Method'] == method_name, f"{dataset}_Ave_pvalue"] = averages[f"{dataset}_Ave_pvalue"]
+            leaderboard_df.loc[leaderboard_df['Method'] == method_name, f"sim_{dataset}_Ave_pvalue"] = averages[f"{dataset}_Ave_pvalue"]
 
     # Save the updated DataFrames back to CSV
     leaderboard_df.to_csv(leaderboard_path, index=False)