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feat: base data science scenario UI (#525)

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* base data science ui

* fix bug

* fix mle grade

* not cache when mle prepare

* fix

* fix grade sample

* fix a small bug

* fix

* cache mle score

* fix

* add gen_mle_score script

* update for mle score

* simple debug show

* small change

* summary folder

* add evo loop tag

* add loop id

* add comment

* fix CI

* add enable_cache for docker conf

* CI

* use setting data path

* fix ui bug

---------

Co-authored-by: yuanteli <1957922024@qq.com>
This commit is contained in:
XianBW
2025-01-23 16:12:22 +08:00
committed by GitHub
parent e5b1912bfd
commit eb597924b2
8 changed files with 565 additions and 70 deletions
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rdagent/core/evolving_agent.py
+41 -41
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@@ -58,51 +58,51 @@ class RAGEvoAgent(EvoAgent):
eva: Evaluator | Feedback,
filter_final_evo: bool = False,
) -> EvolvableSubjects:
for _ in tqdm(range(self.max_loop), "Implementing"):
# with logger.tag(f"evo_loop_{evo_loop_id}"):
# 1. knowledge self-evolving
if self.knowledge_self_gen and self.rag is not None:
self.rag.generate_knowledge(self.evolving_trace)
# 2. RAG
queried_knowledge = None
if self.with_knowledge and self.rag is not None:
# TODO: Putting the evolving trace in here doesn't actually work
queried_knowledge = self.rag.query(evo, self.evolving_trace)
for evo_loop_id in tqdm(range(self.max_loop), "Implementing"):
with logger.tag(f"evo_loop_{evo_loop_id}"):
# 1. knowledge self-evolving
if self.knowledge_self_gen and self.rag is not None:
self.rag.generate_knowledge(self.evolving_trace)
# 2. RAG
queried_knowledge = None
if self.with_knowledge and self.rag is not None:
# TODO: Putting the evolving trace in here doesn't actually work
queried_knowledge = self.rag.query(evo, self.evolving_trace)
# 3. evolve
evo = self.evolving_strategy.evolve(
evo=evo,
evolving_trace=self.evolving_trace,
queried_knowledge=queried_knowledge,
)
# TODO: Due to design issues, we have chosen to ignore this mypy error.
logger.log_object(evo.sub_workspace_list, tag="evolving code") # type: ignore[attr-defined]
for sw in evo.sub_workspace_list: # type: ignore[attr-defined]
logger.info(f"evolving code workspace: {sw}")
# 4. Pack evolve results
es = EvoStep(evo, queried_knowledge)
# 5. Evaluation
if self.with_feedback:
es.feedback = (
# TODO: Due to the irregular design of rdagent.core.evaluation.Evaluator,
# it fails mypy's test here, so we'll ignore this error for now.
eva
if isinstance(eva, Feedback)
else eva.evaluate(evo, queried_knowledge=queried_knowledge) # type: ignore[arg-type, call-arg]
# 3. evolve
evo = self.evolving_strategy.evolve(
evo=evo,
evolving_trace=self.evolving_trace,
queried_knowledge=queried_knowledge,
)
logger.log_object(es.feedback, tag="evolving feedback")
# TODO: Due to design issues, we have chosen to ignore this mypy error.
logger.log_object(evo.sub_workspace_list, tag="evolving code") # type: ignore[attr-defined]
for sw in evo.sub_workspace_list: # type: ignore[attr-defined]
logger.info(f"evolving code workspace: {sw}")
# 6. update trace
self.evolving_trace.append(es)
# 4. Pack evolve results
es = EvoStep(evo, queried_knowledge)
# 7. check if all tasks are completed
if self.with_feedback:
all_completed = all(es.feedback) if isinstance(es.feedback, list) else es.feedback
if all_completed:
logger.info("All tasks in evolving subject have been completed.")
break
# 5. Evaluation
if self.with_feedback:
es.feedback = (
# TODO: Due to the irregular design of rdagent.core.evaluation.Evaluator,
# it fails mypy's test here, so we'll ignore this error for now.
eva
if isinstance(eva, Feedback)
else eva.evaluate(evo, queried_knowledge=queried_knowledge) # type: ignore[arg-type, call-arg]
)
logger.log_object(es.feedback, tag="evolving feedback")
# 6. update trace
self.evolving_trace.append(es)
# 7. check if all tasks are completed
if self.with_feedback:
all_completed = all(es.feedback) if isinstance(es.feedback, list) else es.feedback
if all_completed:
logger.info("All tasks in evolving subject have been completed.")
break
if self.with_feedback and filter_final_evo:
evo = self.filter_evolvable_subjects_by_feedback(evo, self.evolving_trace[-1].feedback)