mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-28 07:57:44 +00:00
Xu Yang
d5a6a08210
* 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
71 lines
2.4 KiB
Python
71 lines
2.4 KiB
Python
from abc import ABC, abstractmethod
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from typing import Any, List
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from tqdm import tqdm
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from rdagent.core.evaluation import Evaluator
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from rdagent.core.evolving_framework import EvolvableSubjects, EvoStep, Feedback
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class EvoAgent(ABC):
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def __init__(self, max_loop, evolving_strategy) -> None:
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self.max_loop = max_loop
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self.evolving_strategy = evolving_strategy
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@abstractmethod
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def multistep_evolve(self, evo: EvolvableSubjects, eva: Evaluator | Feedback, **kwargs: Any) -> EvolvableSubjects:
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...
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@abstractmethod
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def filter_evolvable_subjects_by_feedback(self, evo: EvolvableSubjects, feedback: Feedback) -> EvolvableSubjects:
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...
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class RAGEvoAgent(EvoAgent):
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def __init__(self, max_loop, evolving_strategy, rag) -> None:
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super().__init__(max_loop, evolving_strategy)
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self.rag = rag
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self.evolving_trace: List[EvoStep] = []
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def multistep_evolve(
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self,
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evo: EvolvableSubjects,
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eva: Evaluator | Feedback,
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*,
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with_knowledge: bool = False,
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with_feedback: bool = True,
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knowledge_self_gen: bool = False,
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filter_final_evo: bool = False,
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) -> EvolvableSubjects:
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for _ in tqdm(range(self.max_loop), "Implementing"):
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# 1. knowledge self-evolving
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if knowledge_self_gen and self.rag is not None:
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self.rag.generate_knowledge(self.evolving_trace)
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# 2. RAG
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queried_knowledge = None
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if with_knowledge and self.rag is not None:
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# TODO: Putting the evolving trace in here doesn't actually work
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queried_knowledge = self.rag.query(evo, self.evolving_trace)
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# 3. evolve
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evo = self.evolving_strategy.evolve(
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evo=evo,
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evolving_trace=self.evolving_trace,
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queried_knowledge=queried_knowledge,
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)
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# 4. Pack evolve results
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es = EvoStep(evo, queried_knowledge)
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# 5. Evaluation
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if with_feedback:
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es.feedback = (
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eva if isinstance(eva, Feedback) else eva.evaluate(evo, queried_knowledge=queried_knowledge)
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)
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# 6. update trace
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self.evolving_trace.append(es)
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if with_feedback and filter_final_evo:
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evo = self.filter_evolvable_subjects_by_feedback(evo, self.evolving_trace[-1].feedback)
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return evo
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