refactor: remove unused code and update documentation in CoSTEER module (#720)

* refactor: Remove unused code and update documentation in CoSTEER module

* fix mypy error

* fix black error

---------

Co-authored-by: Young <afe.young@gmail.com>

rdagent/components/coder/CoSTEER/config.py

@@ -33,8 +33,6 @@ class CoSTEERSettings(ExtendedBaseSettings):
     new_knowledge_base_path: Union[str, None] = None
     """Path to the new knowledge base"""
 
-    select_threshold: int = 10
-
     max_seconds: int = 10**6
 
 

rdagent/components/coder/CoSTEER/evaluators.py

@@ -162,7 +162,14 @@ class CoSTEERMultiFeedback(Feedback):
     def __iter__(self):
         return iter(self.feedback_list)
 
-    def __bool__(self):
+    def finished(self) -> bool:
+        """
+        In some implementations, tasks may fail multiple times, leading agents to skip the implementation.
+        This results in None feedback. However, we want to accept the correct parts and ignore None feedback.
+        """
+        return all(feedback.final_decision for feedback in self.feedback_list if feedback is not None)
+
+    def __bool__(self) -> bool:
         return all(feedback.final_decision for feedback in self.feedback_list)
 
 

rdagent/components/coder/CoSTEER/evolvable_subjects.py

@@ -14,7 +14,6 @@ class EvolvingItem(Experiment, EvolvableSubjects):
         sub_gt_implementations: list[FBWorkspace] = None,
     ):
         Experiment.__init__(self, sub_tasks=sub_tasks)
-        self.corresponding_selection: list = None
         if sub_gt_implementations is not None and len(
             sub_gt_implementations,
         ) != len(self.sub_tasks):

rdagent/components/coder/CoSTEER/evolving_strategy.py

@@ -12,7 +12,6 @@ from rdagent.components.coder.CoSTEER.evolvable_subjects import EvolvingItem
 from rdagent.components.coder.CoSTEER.knowledge_management import (
     CoSTEERQueriedKnowledge,
 )
-from rdagent.components.coder.CoSTEER.scheduler import random_select
 from rdagent.core.conf import RD_AGENT_SETTINGS
 from rdagent.core.evolving_framework import EvolvingStrategy, EvoStep, QueriedKnowledge
 from rdagent.core.experiment import FBWorkspace, Task
@@ -59,17 +58,6 @@ class MultiProcessEvolvingStrategy(EvolvingStrategy):
         """
         raise NotImplementedError
 
-    def select_one_round_tasks(
-        self,
-        to_be_finished_task_index: list,
-        evo: EvolvingItem,
-        selected_num: int,
-        queried_knowledge: CoSTEERQueriedKnowledge,
-        scen: Scenario,
-    ) -> list:
-        """Since scheduler is not essential, we implement a simple random selection here."""
-        return random_select(to_be_finished_task_index, evo, selected_num, queried_knowledge, scen)
-
     @abstractmethod
     def assign_code_list_to_evo(self, code_list: list[dict], evo: EvolvingItem) -> None:
         """
@@ -92,10 +80,12 @@ class MultiProcessEvolvingStrategy(EvolvingStrategy):
         **kwargs,
     ) -> EvolvingItem:
         # 1.找出需要evolve的task
-        to_be_finished_task_index = []
+        to_be_finished_task_index: list[int] = []
         for index, target_task in enumerate(evo.sub_tasks):
             target_task_desc = target_task.get_task_information()
             if target_task_desc in queried_knowledge.success_task_to_knowledge_dict:
+                # NOTE: very weird logic:
+                # it depends on the knowledge to set the already finished task
                 evo.sub_workspace_list[index] = queried_knowledge.success_task_to_knowledge_dict[
                     target_task_desc
                 ].implementation
@@ -105,19 +95,11 @@ class MultiProcessEvolvingStrategy(EvolvingStrategy):
             ):
                 to_be_finished_task_index.append(index)
 
-        # 2. 选择selection方法
-        # if the number of factors to be implemented is larger than the limit, we need to select some of them
-
-        if self.settings.select_threshold < len(to_be_finished_task_index):
-            # Select a fixed number of factors if the total exceeds the threshold
-            to_be_finished_task_index = self.select_one_round_tasks(
-                to_be_finished_task_index, evo, self.settings.select_threshold, queried_knowledge, self.scen
-            )
-
         last_feedback = None
         if len(evolving_trace) > 0:
             last_feedback = evolving_trace[-1].feedback
             assert isinstance(last_feedback, CoSTEERMultiFeedback)
+
         result = multiprocessing_wrapper(
             [
                 (
@@ -138,6 +120,5 @@ class MultiProcessEvolvingStrategy(EvolvingStrategy):
             code_list[target_index] = result[index]
 
         evo = self.assign_code_list_to_evo(code_list, evo)
-        evo.corresponding_selection = to_be_finished_task_index
 
         return evo

rdagent/components/coder/CoSTEER/scheduler.py

@@ -1,25 +0,0 @@
-import random
-
-from rdagent.components.coder.CoSTEER.evolvable_subjects import EvolvingItem
-from rdagent.components.coder.CoSTEER.knowledge_management import (
-    CoSTEERQueriedKnowledge,
-)
-from rdagent.core.scenario import Scenario
-from rdagent.log import rdagent_logger as logger
-
-
-def random_select(
-    to_be_finished_task_index: list,
-    evo: EvolvingItem,
-    selected_num: int,
-    queried_knowledge: CoSTEERQueriedKnowledge,
-    scen: Scenario,
-):
-
-    to_be_finished_task_index = random.sample(
-        to_be_finished_task_index,
-        selected_num,
-    )
-
-    logger.info(f"The random selection is: {to_be_finished_task_index}")
-    return to_be_finished_task_index

rdagent/components/coder/factor_coder/evaluators.py

@@ -15,7 +15,6 @@ from rdagent.core.evolving_framework import QueriedKnowledge
 from rdagent.core.experiment import Workspace
 
 FactorSingleFeedback = CoSTEERSingleFeedbackDeprecated
-FactorMultiFeedback = CoSTEERMultiFeedback
 
 
 class FactorEvaluatorForCoder(CoSTEEREvaluator):