mirror of
https://github.com/NicolasBohn/NexQuant.git
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you-n-g
20778a9b87
* use conda to run kaggle and mlebench code * refactor: Simplify environment configuration and execution logic * add setting to use local env in ds * refine dockerfile * fix: Move MLEBDockerEnv initialization inside conditionals & fix condaenv * refactor: reformat code for better readability and consistency * feat: add conda env to all envs. * fix: fix bugs when run loop * refactor: Simplify DockerEnv configuration in mle_summary.py * fix image bug * style: reformat code for better readability and consistency * change commit * feat: Add entrypoint script for sing_docker scenario in rdagent * refactor: add Any type hints and comments for clarity in env.py * feat: Create log directory if it doesn't exist in entrypoint script * feat: Add debug mode and list root directory in entrypoint script * fix: Remove specific branch checkout in Dockerfile for RD-Agent * fix: Add competition argument to loop.py script execution * fix: Correct directory navigation and dependency installation in entrypoint.sh * fix: Correct user ownership assignment in entrypoint script * refactor: Comment out redundant log copying to RD_OUTPUT_DIR * fix: Unset LOG_TRACE_PATH to prevent log contamination in entrypoint.sh --------- Co-authored-by: Xu Yang <peteryang@vip.qq.com>
76 lines
3.0 KiB
Python
76 lines
3.0 KiB
Python
import json
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import re
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from pathlib import Path
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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.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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from rdagent.utils.fmt import shrink_text
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DIRNAME = Path(__file__).absolute().resolve().parent
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FeatureEvalFeedback = CoSTEERSingleFeedback
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class FeatureCoSTEEREvaluator(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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) -> FeatureEvalFeedback:
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target_task_information = target_task.get_task_information()
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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 FeatureEvalFeedback(
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execution="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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code="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()
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env.conf.extra_volumes = {f"{DS_RD_SETTING.local_data_path}/sample/{self.scen.competition}": "/kaggle/input"}
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# TODO: do we need to clean the generated temporary content?
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fname = "test/feature_test.py"
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test_code = (DIRNAME / "eval_tests" / "feature_test.txt").read_text()
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implementation.inject_files(**{fname: test_code})
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stdout = implementation.execute(env=env, entry=f"python {fname}")
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if "main.py" in implementation.file_dict:
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workflow_stdout = implementation.execute(env=env, entry="python main.py")
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workflow_stdout = re.sub(r"=== Start of EDA part ===(.*)=== End of EDA part ===", "", workflow_stdout)
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else:
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workflow_stdout = None
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system_prompt = T(".prompts:feature_eval.system").r(
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task_desc=target_task.get_task_information(),
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test_code=test_code,
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code=implementation.file_dict["feature.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:feature_eval.user").r(
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stdout=shrink_text(stdout),
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workflow_stdout=workflow_stdout,
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)
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return build_cls_from_json_with_retry(FeatureEvalFeedback, system_prompt=system_prompt, user_prompt=user_prompt)
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