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/core/evolving_agent.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Any
+from typing import Any, List
 
 from tqdm import tqdm
 
@@ -14,14 +14,18 @@ class EvoAgent(ABC):
 
     @abstractmethod
     def multistep_evolve(self, evo: EvolvableSubjects, eva: Evaluator | Feedback, **kwargs: Any) -> EvolvableSubjects:
-        pass
+        ...
+
+    @abstractmethod
+    def filter_evolvable_subjects_by_feedback(self, evo: EvolvableSubjects, feedback: Feedback) -> EvolvableSubjects:
+        ...
 
 
 class RAGEvoAgent(EvoAgent):
     def __init__(self, max_loop, evolving_strategy, rag) -> None:
         super().__init__(max_loop, evolving_strategy)
         self.rag = rag
-        self.evolving_trace = []
+        self.evolving_trace: List[EvoStep] = []
 
     def multistep_evolve(
         self,
@@ -31,6 +35,7 @@ class RAGEvoAgent(EvoAgent):
         with_knowledge: bool = False,
         with_feedback: bool = True,
         knowledge_self_gen: bool = False,
+        filter_final_evo: bool = False,
     ) -> EvolvableSubjects:
         for _ in tqdm(range(self.max_loop), "Implementing"):
             # 1. knowledge self-evolving
@@ -60,5 +65,6 @@ class RAGEvoAgent(EvoAgent):
 
             # 6. update trace
             self.evolving_trace.append(es)
-
+        if with_feedback and filter_final_evo:
+            evo = self.filter_evolvable_subjects_by_feedback(evo, self.evolving_trace[-1].feedback)
         return evo