mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-28 16:07:46 +00:00
you-n-g
35e3156018
* refactor: Replace API call with build_cls_from_json_with_retry function * fix lint error * fix lint errors * lint * trigger
79 lines
3.1 KiB
Python
79 lines
3.1 KiB
Python
import json
|
|
from pathlib import Path
|
|
|
|
from rdagent.app.data_science.conf import DS_RD_SETTING
|
|
from rdagent.components.coder.CoSTEER.evaluators import (
|
|
CoSTEEREvaluator,
|
|
CoSTEERSingleFeedback,
|
|
)
|
|
from rdagent.core.evolving_framework import QueriedKnowledge
|
|
from rdagent.core.experiment import FBWorkspace, Task
|
|
from rdagent.oai.llm_utils import APIBackend
|
|
from rdagent.utils.agent.tpl import T
|
|
from rdagent.utils.agent.workflow import build_cls_from_json_with_retry
|
|
from rdagent.utils.env import DockerEnv, DSDockerConf
|
|
from rdagent.utils.fmt import shrink_text
|
|
|
|
DIRNAME = Path(__file__).absolute().resolve().parent
|
|
|
|
FeatureEvalFeedback = CoSTEERSingleFeedback
|
|
|
|
|
|
class FeatureCoSTEEREvaluator(CoSTEEREvaluator):
|
|
|
|
def evaluate(
|
|
self,
|
|
target_task: Task,
|
|
implementation: FBWorkspace,
|
|
gt_implementation: FBWorkspace,
|
|
queried_knowledge: QueriedKnowledge = None,
|
|
**kwargs,
|
|
) -> FeatureEvalFeedback:
|
|
|
|
target_task_information = target_task.get_task_information()
|
|
if (
|
|
queried_knowledge is not None
|
|
and target_task_information in queried_knowledge.success_task_to_knowledge_dict
|
|
):
|
|
return queried_knowledge.success_task_to_knowledge_dict[target_task_information].feedback
|
|
elif queried_knowledge is not None and target_task_information in queried_knowledge.failed_task_info_set:
|
|
return FeatureEvalFeedback(
|
|
execution="This task has failed too many times, skip implementation.",
|
|
return_checking="This task has failed too many times, skip implementation.",
|
|
code="This task has failed too many times, skip implementation.",
|
|
final_decision=False,
|
|
)
|
|
|
|
ds_docker_conf = DSDockerConf()
|
|
# TODO: we should /= 20 for the timeout period on debug component
|
|
ds_docker_conf.extra_volumes = {
|
|
f"{DS_RD_SETTING.local_data_path}/sample/{self.scen.competition}": "/kaggle/input"
|
|
}
|
|
de = DockerEnv(conf=ds_docker_conf)
|
|
|
|
# TODO: do we need to clean the generated temporary content?
|
|
fname = "feature_test.py"
|
|
test_code = (DIRNAME / "eval_tests" / "feature_test.txt").read_text()
|
|
implementation.inject_files(**{fname: test_code})
|
|
|
|
stdout = implementation.execute(env=de, entry=f"python {fname}")
|
|
|
|
if "main.py" in implementation.file_dict:
|
|
workflow_stdout = implementation.execute(env=de, entry="python main.py")
|
|
else:
|
|
workflow_stdout = None
|
|
|
|
system_prompt = T(".prompts:feature_eval.system").r(
|
|
task_desc=target_task.get_task_information(),
|
|
test_code=test_code,
|
|
code=implementation.file_dict["feature.py"],
|
|
workflow_stdout=workflow_stdout,
|
|
workflow_code=implementation.all_codes,
|
|
)
|
|
user_prompt = T(".prompts:feature_eval.user").r(
|
|
stdout=shrink_text(stdout),
|
|
workflow_stdout=workflow_stdout,
|
|
)
|
|
|
|
return build_cls_from_json_with_retry(FeatureEvalFeedback, system_prompt=system_prompt, user_prompt=user_prompt)
|