mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-28 16:07:46 +00:00
Tim
6f9843a7df
* custom data * fix: simplify competition check and log local description file * no sample data * feat: add test evaluation module with error handling support * fix: update eval path to use eval_sub_dir and add valid_check TODO * refactor: add MLETestEval check to conditionally run grading steps * avoid blank stdout * valid in testeval * rename test.csv to avoid conflict * Support Disabling sample submission * refactoring * fix: remove DS_KAGGLE_DATA and update prompt instructions * add try for grade * ignore submission * fix: remove tee from eval command and warn about pipeline exit code detection * optional to use raw description * support old data * add execution result to stdout * add metric to raw description * custom data explain * add debug_path * rst update --------- Co-authored-by: Young <afe.young@gmail.com>
96 lines
3.7 KiB
Python
96 lines
3.7 KiB
Python
import json
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import re
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from pathlib import Path
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from jinja2 import Environment, StrictUndefined
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from rdagent.app.data_science.conf import DS_RD_SETTING
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from rdagent.components.coder.CoSTEER.evaluators import (
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CoSTEEREvaluator,
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CoSTEERSingleFeedback,
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)
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from rdagent.components.coder.data_science.conf import get_ds_env
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from rdagent.components.coder.data_science.utils import remove_eda_part
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from rdagent.core.evolving_framework import QueriedKnowledge
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from rdagent.core.experiment import FBWorkspace, Task
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from rdagent.utils.agent.tpl import T
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from rdagent.utils.agent.workflow import build_cls_from_json_with_retry
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DIRNAME = Path(__file__).absolute().resolve().parent
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EnsembleEvalFeedback = CoSTEERSingleFeedback
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class EnsembleCoSTEEREvaluator(CoSTEEREvaluator):
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def evaluate(
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self,
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target_task: Task,
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implementation: FBWorkspace,
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gt_implementation: FBWorkspace,
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queried_knowledge: QueriedKnowledge = None,
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**kwargs,
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) -> EnsembleEvalFeedback:
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target_task_information = target_task.get_task_information()
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metric_name = self.scen.metric_name
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if (
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queried_knowledge is not None
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and target_task_information in queried_knowledge.success_task_to_knowledge_dict
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):
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return queried_knowledge.success_task_to_knowledge_dict[target_task_information].feedback
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elif queried_knowledge is not None and target_task_information in queried_knowledge.failed_task_info_set:
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return EnsembleEvalFeedback(
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execution="This task has failed too many times, skip implementation.",
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code="This task has failed too many times, skip implementation.",
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return_checking="This task has failed too many times, skip implementation.",
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final_decision=False,
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)
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env = get_ds_env(extra_volumes={self.scen.debug_path: T("scenarios.data_science.share:scen.input_path").r()})
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fname = "test/ensemble_test.txt"
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test_code = (DIRNAME / "eval_tests" / "ensemble_test.txt").read_text()
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test_code = (
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Environment(undefined=StrictUndefined)
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.from_string(test_code)
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.render(
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model_names=[
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fn[:-3] for fn in implementation.file_dict.keys() if fn.startswith("model_") and "test" not in fn
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],
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metric_name=metric_name,
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)
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)
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implementation.inject_files(**{fname: test_code})
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stdout, ret_code = implementation.execute_ret_code(env=env, entry=f"python {fname}")
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stdout += f"\nNOTE: the above scripts run with return code {ret_code}"
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if "main.py" in implementation.file_dict and ret_code == 0:
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workflow_stdout = implementation.execute(env=env, entry="python main.py")
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workflow_stdout = remove_eda_part(workflow_stdout)
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else:
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workflow_stdout = None
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system_prompt = T(".prompts:ensemble_eval.system").r(
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task_desc=target_task_information,
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test_code=test_code,
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metric_name=metric_name,
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code=implementation.file_dict["ensemble.py"],
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workflow_stdout=workflow_stdout,
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workflow_code=implementation.all_codes,
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)
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user_prompt = T(".prompts:ensemble_eval.user").r(
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stdout=stdout,
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workflow_stdout=workflow_stdout,
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)
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efb = build_cls_from_json_with_retry(
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EnsembleEvalFeedback,
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system_prompt=system_prompt,
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user_prompt=user_prompt,
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init_kwargs_update_func=EnsembleEvalFeedback.val_and_update_init_dict,
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)
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efb.final_decision = efb.final_decision and ret_code == 0
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return efb
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