Several update on the repo (see desc) (#76)

* ignore result csv file

* fix app scripts

* rename taskgenerator to developer and generate to develop

* fix a config bug in coder

* fix a small bug in factor coder evaluators

* remove a single logger in factor coder evaluators

* fix a small bug in model coder main.py

* rename Implementation to Workspace

* move the prepare the inject_code into FBWorkspace to align all the behavior

* fix a small bug in model feedback

* remove debug lines for multi processing and simplify evaluators multi proc

* add a copy function to workspace to freeze the workspace && add config prefix to speed up debugging

* make hypothesisgen a abc class

* use Qlib***Experiment

* fix a small bug

* rename Imp to Ws

* rename sub_implementations to sub_workspace_list

* fix a bug in feedback not presented as content in prompts

* move proposal pys to proposal folder

* reformat the folder

* align factor and model qlib workspace and use template to handle the workspace

* add a filter to evoagent to filter out false evo

* align multi_proc_n into RDAGENT seeting

* handle when runner gets empty experiment

* fix logger merge remaining problems

* fix black and isort automatically

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

@@ -11,7 +11,7 @@ from rdagent.components.coder.model_coder.CoSTEER.evolvable_subjects import (
 from rdagent.components.coder.model_coder.CoSTEER.knowledge_management import (
     ModelQueriedKnowledge,
 )
-from rdagent.components.coder.model_coder.model import ModelImplementation, ModelTask
+from rdagent.components.coder.model_coder.model import ModelFBWorkspace, ModelTask
 from rdagent.core.conf import RD_AGENT_SETTINGS
 from rdagent.core.evolving_framework import EvolvingStrategy
 from rdagent.core.prompts import Prompts
@@ -26,7 +26,7 @@ class ModelCoderEvolvingStrategy(EvolvingStrategy):
         self,
         target_task: ModelTask,
         queried_knowledge: ModelQueriedKnowledge = None,
-    ) -> ModelImplementation:
+    ) -> str:
         model_information_str = target_task.get_task_information()
 
         if queried_knowledge is not None and model_information_str in queried_knowledge.success_task_to_knowledge_dict:
@@ -86,19 +86,15 @@ class ModelCoderEvolvingStrategy(EvolvingStrategy):
                     queried_similar_successful_knowledge_to_render = queried_similar_successful_knowledge_to_render[1:]
 
             code = json.loads(
-                APIBackend(use_chat_cache=False).build_messages_and_create_chat_completion(
+                APIBackend(
+                    use_chat_cache=MODEL_IMPL_SETTINGS.coder_use_cache
+                ).build_messages_and_create_chat_completion(
                     user_prompt=user_prompt,
                     system_prompt=system_prompt,
                     json_mode=True,
                 ),
             )["code"]
-            model_implementation = ModelImplementation(
-                target_task,
-            )
-            model_implementation.prepare()
-            model_implementation.inject_code(**{"model.py": code})
-
-            return model_implementation
+            return code
 
     def evolve(
         self,
@@ -107,14 +103,12 @@ class ModelCoderEvolvingStrategy(EvolvingStrategy):
         queried_knowledge: ModelQueriedKnowledge | None = None,
         **kwargs,
     ) -> ModelEvolvingItem:
-        new_evo = deepcopy(evo)
-
         # 1.找出需要evolve的model
         to_be_finished_task_index = []
-        for index, target_model_task in enumerate(new_evo.sub_tasks):
+        for index, target_model_task in enumerate(evo.sub_tasks):
             target_model_task_desc = target_model_task.get_task_information()
             if target_model_task_desc in queried_knowledge.success_task_to_knowledge_dict:
-                new_evo.sub_implementations[index] = queried_knowledge.success_task_to_knowledge_dict[
+                evo.sub_workspace_list[index] = queried_knowledge.success_task_to_knowledge_dict[
                     target_model_task_desc
                 ].implementation
             elif (
@@ -125,20 +119,17 @@ class ModelCoderEvolvingStrategy(EvolvingStrategy):
 
         result = multiprocessing_wrapper(
             [
-                (self.implement_one_model, (new_evo.sub_tasks[target_index], queried_knowledge))
+                (self.implement_one_model, (evo.sub_tasks[target_index], queried_knowledge))
                 for target_index in to_be_finished_task_index
             ],
-            n=MODEL_IMPL_SETTINGS.evo_multi_proc_n,
+            n=RD_AGENT_SETTINGS.multi_proc_n,
         )
 
         for index, target_index in enumerate(to_be_finished_task_index):
-            new_evo.sub_implementations[target_index] = result[index]
+            if evo.sub_workspace_list[target_index] is None:
+                evo.sub_workspace_list[target_index] = ModelFBWorkspace(target_task=evo.sub_tasks[target_index])
+            evo.sub_workspace_list[target_index].inject_code(**{"model.py": result[index]})
 
-        # for target_index in to_be_finished_task_index:
-        #     new_evo.sub_implementations[target_index] = self.implement_one_model(
-        #         new_evo.sub_tasks[target_index], queried_knowledge
-        #     )
+        evo.corresponding_selection = to_be_finished_task_index
 
-        new_evo.corresponding_selection = to_be_finished_task_index
-
-        return new_evo
+        return evo