[update] overall score

app.py

@@ -64,11 +64,26 @@ def add_new_eval(
     submission_repo.push_to_hub()
     return 0
 
-def get_final_score(df):
-    # 分数计算公式
+def get_normalized_df(df):
+    # final_score = df.drop('name', axis=1).sum(axis=1)
+    # df.insert(1, 'Overall Score', final_score)
+    normalize_df = df.copy()
+    for column in normalize_df.columns[1:]:
+        min_val = NORMALIZE_DIC[column]['Min']
+        max_val = NORMALIZE_DIC[column]['Max']
+        normalize_df[column] = (normalize_df[column] - min_val) / (max_val - min_val)
+    return normalize_df
+
+def calculate_selected_score(df, selected_columns):
+    selected_score = df[selected_columns].sum(axis=1)
+    return selected_score
+
+def get_final_score(df, selected_columns):
+    normalize_df = get_normalized_df(df)
     final_score = df.drop('name', axis=1).sum(axis=1)
-    # 将总分列放在第二列
-    df.insert(1, 'Final Score', final_score)
+    df.insert(1, 'Overall Score', final_score)
+    selected_score = calculate_selected_score(normalize_df, selected_columns)
+    df.insert(1, 'Selected Score', selected_score)
     return df
 
 def get_baseline_df():
@@ -76,8 +91,8 @@ def get_baseline_df():
     submission_repo.git_pull()
     df = pd.read_csv(CSV_DIR)
     df = get_final_score(df)
-    # calculate the final score
-    df = df.sort_values(by="Final Score", ascending=False)
+    # calculate the Overall Score
+    df = df.sort_values(by="Overall Score", ascending=False)
     present_columns = MODEL_INFO + checkbox_group.value
     df = df[present_columns]
     return df
@@ -87,7 +102,7 @@ def get_all_df():
     submission_repo.git_pull()
     df = pd.read_csv(CSV_DIR)
     df = get_final_score(df)
-    df = df.sort_values(by="Final Score", ascending=False)
+    df = df.sort_values(by="Selected Score", ascending=False)
     return df
 
 def on_filter_model_size_method_change(selected_columns):
@@ -138,7 +153,7 @@ with block:
             # selection for column part:
             checkbox_group = gr.CheckboxGroup(
                 choices=TASK_INFO,
-                value=AVG_INFO,
+                value=DEFAULT_INFO,
                 label="Evaluation Dimension",
                 interactive=True,
             )