Update src/saving_utils.py

src/saving_utils.py

@@ -41,15 +41,17 @@ def save_similarity_output(output_dict, method_name, leaderboard_path="/home/use
             
             # Process correlation if present
             if correlation_key in output_dict:
-                correlation_values.append(output_dict[correlation_key])
-                similarity_df.at[similarity_df['Method'] == method_name, f"{dataset}_{aspect}_correlation"] = output_dict[correlation_key]
-                leaderboard_df.at[leaderboard_df['Method'] == method_name, f"sim_{dataset}_{aspect}_correlation"] = output_dict[correlation_key]
+                correlation = output_dict[correlation_key].item()
+                correlation_values.append(correlation)
+                similarity_df.at[similarity_df['Method'] == method_name, f"{dataset}_{aspect}_correlation"] = correlation
+                leaderboard_df.at[leaderboard_df['Method'] == method_name, f"sim_{dataset}_{aspect}_correlation"] = correlation
             
             # Process pvalue if present
             if pvalue_key in output_dict:
-                pvalue_values.append(output_dict[pvalue_key])
-                similarity_df.at[similarity_df['Method'] == method_name, f"{dataset}_{aspect}_pvalue"] = output_dict[pvalue_key]
-                leaderboard_df.at[leaderboard_df['Method'] == method_name, f"sim_{dataset}_{aspect}_pvalue"] = output_dict[pvalue_key]
+                pvalue = output_dict[pvalue_key].item()
+                pvalue_values.append(pvalue)
+                similarity_df.at[similarity_df['Method'] == method_name, f"{dataset}_{aspect}_pvalue"] = pvalue
+                leaderboard_df.at[leaderboard_df['Method'] == method_name, f"sim_{dataset}_{aspect}_pvalue"] = pvalue
 
         # Calculate averages if all three aspects (MF, BP, CC) are present
         if len(correlation_values) == 3: