Update src/populate.py

src/populate.py

@@ -17,20 +17,20 @@ def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchm
     # df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
     # df = df.sort_values(by=[AutoEvalColumn.task5.name], ascending=True)
 
-    df[AutoEvalColumn.task3.name] = pd.Series(
+    df[AutoEvalColumn.task2.name] = pd.Series(
         np.stack(
-            np.array(df[AutoEvalColumn.task3.name].values)
+            np.array(df[AutoEvalColumn.task2.name].values)
         ).squeeze()
     )
-    df[AutoEvalColumn.task4.name] = pd.Series(
+    df[AutoEvalColumn.task3.name] = pd.Series(
         np.stack(
-            np.array(df[AutoEvalColumn.task4.name].values)
+            np.array(df[AutoEvalColumn.task3.name].values)
         ).squeeze()
     )
 
     
-    mos_rank = df[AutoEvalColumn.task3.name].rank(method="max", numeric_only=True, ascending=True)
-    bitrate_rank = df[AutoEvalColumn.task4.name].rank(method="min", numeric_only=True, ascending=True)
+    mos_rank = df[AutoEvalColumn.task2.name].rank(method="max", numeric_only=True, ascending=True)
+    bitrate_rank = df[AutoEvalColumn.task3.name].rank(method="min", numeric_only=True, ascending=True)
     df["Ranking"] = pd.Series((mos_rank + bitrate_rank)/2)
     df = df.sort_values(by=["Ranking", AutoEvalColumn.task2.name], ascending=True)
     df["Rank"] = df.groupby("Precision").cumcount() + 1