Extract factors from financial reports loop finished. (#90)

- Extract factors from financial reports loop finished.

- Fix some small bugs.

rdagent/components/coder/factor_coder/CoSTEER/evaluators.py

@@ -334,6 +334,13 @@ class FactorValueEvaluator(FactorEvaluator):
     ) -> Tuple:
         conclusions = []
 
+        # Initialize result variables
+        single_column_result = None
+        same_index_result = None
+        output_format_result = None
+        equal_value_ratio_result = 0
+        high_correlation_result = False
+
         # Check if both dataframe has only one columns
         feedback_str, _ = FactorSingleColumnEvaluator(self.scen).evaluate(implementation, gt_implementation)
         conclusions.append(feedback_str)
@@ -373,9 +380,6 @@ class FactorValueEvaluator(FactorEvaluator):
                 high_correlation_result = False
                 feedback_str = "The source dataframe and the ground truth dataframe have different index. Give up comparing the values and correlation because it's useless"
             conclusions.append(feedback_str)
-        else:
-            equal_value_ratio_result = 0
-            high_correlation_result = False
 
         # Combine all conclusions into a single string
         conclusion_str = "\n".join(conclusions)

rdagent/components/coder/factor_coder/CoSTEER/evolving_strategy.py

@@ -68,9 +68,9 @@ class MultiProcessEvolvingStrategy(EvolvingStrategy):
         # if the number of factors to be implemented is larger than the limit, we need to select some of them
         if FACTOR_IMPLEMENT_SETTINGS.select_ratio < 1:
             # if the number of loops is equal to the select_loop, we need to select some of them
-            implementation_factors_per_round = int(
-                FACTOR_IMPLEMENT_SETTINGS.select_ratio * len(to_be_finished_task_index)
-            )
+            implementation_factors_per_round = round(FACTOR_IMPLEMENT_SETTINGS.select_ratio * len(to_be_finished_task_index) + 0.5)  # ceilling
+            implementation_factors_per_round = min(implementation_factors_per_round, len(to_be_finished_task_index))  # but not exceed the total number of tasks
+
             if FACTOR_IMPLEMENT_SETTINGS.select_method == "random":
                 to_be_finished_task_index = RandomSelect(
                     to_be_finished_task_index,