fix: handle division by zero in percentage calculations (#550)

* fix: Handle division by zero in percentage calculations

* lint

rdagent/app/cli.py

@@ -1,7 +1,7 @@
 """
 CLI entrance for all rdagent application.
 
-This will 
+This will
 - make rdagent a nice entry and
 - autoamtically load dotenv
 """

rdagent/components/coder/data_science/model/eval.py

@@ -1,6 +1,6 @@
 """
 Beyond previous tests
-- 
+-
 """
 
 import json

rdagent/components/coder/data_science/raw_data_loader/__init__.py

@@ -3,8 +3,8 @@
 Loop should not large change exclude
 - Action Choice[current data loader & spec]
 - other should share
-    - Propose[choice] => Task[Choice] => CoSTEER => 
-        - 
+    - Propose[choice] => Task[Choice] => CoSTEER =>
+        -
 
 Extra feature:
 - cache

rdagent/core/proposal.py

@@ -1,6 +1,4 @@
-"""
-
-"""
+""" """
 
 from __future__ import annotations
 

rdagent/log/ui/dsapp.py

@@ -270,10 +270,10 @@ def all_summarize_win():
         base_df.loc[k, "Competition"] = v["competition"]
         base_df.loc[k, "Total Loops"] = loop_num
         base_df.loc[k, "Made Submission"] = (
-            f"{v['made_submission_num']} ({round(v['made_submission_num'] / loop_num * 100, 2)}%)"
+            f"{v['made_submission_num']} ({round(v['made_submission_num'] / loop_num * 100, 2) if loop_num != 0 else 0}%)"
         )
         base_df.loc[k, "Successful Final Decision"] = (
-            f"{v['success_loop_num']} ({round(v['success_loop_num'] / loop_num * 100, 2)}%)"
+            f"{v['success_loop_num']} ({round(v['success_loop_num'] / loop_num * 100, 2) if loop_num != 0 else 0}%)"
         )
         base_df.loc[k, "Medal"] = v["medal"]
     st.dataframe(base_df)

rdagent/scenarios/kaggle/experiment/templates/new-york-city-taxi-fare-prediction/model/model_linear.py

@@ -1,7 +1,7 @@
 """
 Motivation of the model:
 The Linear Regression model is chosen for its simplicity and interpretability. It is a good starting point for regression tasks
-and provides a baseline to compare more complex models against. Linear Regression assumes a linear relationship between the 
+and provides a baseline to compare more complex models against. Linear Regression assumes a linear relationship between the
 features and the target variable, which can be a reasonable assumption for many problems.
 """