chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# tess successfully running.
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# (GPT) if it aligns with the spec & rationality of the spec.
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import json
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import re
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from dataclasses import dataclass
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from pathlib import Path
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import pandas as pd
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from rdagent.app.data_science.conf import DS_RD_SETTING
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from rdagent.components.agent.context7 import Agent as DocAgent
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from rdagent.components.coder.CoSTEER import CoSTEERMultiFeedback
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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.CoSTEER.knowledge_management import (
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CoSTEERQueriedKnowledgeV2,
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)
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from rdagent.components.coder.data_science.conf import get_clear_ws_cmd, get_ds_env
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from rdagent.components.coder.data_science.share.notebook import NotebookConverter
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from rdagent.components.coder.data_science.utils import remove_eda_part
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from rdagent.core.experiment import FBWorkspace, Task
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from rdagent.log import rdagent_logger as logger
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from rdagent.scenarios.data_science.test_eval import get_test_eval
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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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@dataclass
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class DSCoderFeedback(CoSTEERSingleFeedback):
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"""
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Feedback for Data Science CoSTEER evaluation.
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This feedback is used to evaluate the code and execution of the Data Science CoSTEER task.
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"""
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requires_documentation_search: bool | None = None # Keep None means the feature is disabled
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error_message: str | None = None
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@staticmethod
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def val_and_update_init_dict(data: dict) -> dict:
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# First call parent class validation method to handle base fields
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data = CoSTEERSingleFeedback.val_and_update_init_dict(data)
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# Validate new fields
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if "requires_documentation_search" in data:
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if isinstance(data["requires_documentation_search"], str):
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if data["requires_documentation_search"] == "false" or data["requires_documentation_search"] == "False":
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data["requires_documentation_search"] = False
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elif data["requires_documentation_search"] == "true" or data["requires_documentation_search"] == "True":
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data["requires_documentation_search"] = True
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else:
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raise ValueError(
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f"'requires_documentation_search' string value must be 'true', 'True', 'false', or 'False', not '{data['requires_documentation_search']}'"
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)
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elif data["requires_documentation_search"] is not None and not isinstance(
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data["requires_documentation_search"], bool
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):
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raise ValueError(
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f"'requires_documentation_search' must be a boolean, string, or None, not {type(data['requires_documentation_search'])}"
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)
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if "error_message" in data:
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if data["error_message"] is not None and not isinstance(data["error_message"], str):
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raise ValueError(f"'error_message' must be a string or None, not {type(data['error_message'])}")
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return data
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def __str__(self) -> str:
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base_str = super().__str__()
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if self.requires_documentation_search is not None:
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base_str += f"-------------------Documentation Search Required------------------\n{self.requires_documentation_search}\n"
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if self.error_message is not None:
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# Check if error_message contains Context7 documentation results
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if "### API Documentation Reference:" in self.error_message:
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base_str += f"-------------------Error Analysis & Documentation Search Results ------------------\n{self.error_message}\n"
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else:
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base_str += f"-------------------Error Message------------------\n{self.error_message}\n"
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return base_str
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@classmethod
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def merge(cls, feedback_li: list[CoSTEERSingleFeedback]) -> "DSCoderFeedback":
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# Call parent class merge method to handle base fields
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merged_fb = super().merge(feedback_li)
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# Convert to DSCoderFeedback type if needed
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if not isinstance(merged_fb, DSCoderFeedback):
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merged_fb = DSCoderFeedback(
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execution=merged_fb.execution,
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return_checking=merged_fb.return_checking,
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code=merged_fb.code,
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final_decision=merged_fb.final_decision,
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)
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# Merge error_message fields
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error_messages = [
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fb.error_message for fb in feedback_li if isinstance(fb, DSCoderFeedback) and fb.error_message is not None
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]
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if error_messages:
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merged_fb.error_message = "\n\n".join(error_messages)
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# Merge requires_documentation_search fields (True if any is True)
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requires_search = [
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fb.requires_documentation_search
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for fb in feedback_li
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if isinstance(fb, DSCoderFeedback) and fb.requires_documentation_search is not None
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]
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if requires_search:
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merged_fb.requires_documentation_search = any(requires_search)
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return merged_fb
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PipelineSingleFeedback = DSCoderFeedback # Only for compatible
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PipelineMultiFeedback = CoSTEERMultiFeedback
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class PipelineCoSTEEREvaluator(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: CoSTEERQueriedKnowledgeV2 = None,
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**kwargs,
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) -> PipelineSingleFeedback:
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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 PipelineSingleFeedback(
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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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error_message="This task has failed too many times, skip implementation.",
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requires_documentation_search=None,
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final_decision=False,
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)
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env = get_ds_env(
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extra_volumes={self.scen.debug_path: T("scenarios.data_science.share:scen.input_path").r()},
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running_timeout_period=self.scen.real_debug_timeout(),
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)
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stdout = ""
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implementation.execute(env=env, entry=get_clear_ws_cmd())
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if DS_RD_SETTING.sample_data_by_LLM:
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# Because coder runs on full data, we need to run debug mode in advance to save time
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result = implementation.run(
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env=env, entry=f"strace -e trace=file -f -o trace.log python -m coverage run main.py --debug"
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)
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else:
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result = implementation.run(
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env=env, entry=f"strace -e trace=file -f -o trace.log python -m coverage run main.py"
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)
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result_stdout = result.stdout
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nb_conversion_ret_code = 0
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nb_conversion_check_text = ""
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if DS_RD_SETTING.enable_notebook_conversion:
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notebook_converter = NotebookConverter()
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code = implementation.file_dict["main.py"]
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error_msg = notebook_converter.validate_code_format(code)
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if error_msg is not None:
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nb_conversion_check_text = error_msg
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nb_conversion_ret_code = 1
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else:
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notebook_converter.convert(
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task=target_task,
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code=code,
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stdout=result_stdout,
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outfile=implementation.workspace_path / "main.ipynb",
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use_debug_flag=DS_RD_SETTING.sample_data_by_LLM,
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)
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sample_submission_check = True
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test_eval = get_test_eval()
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if (sample_submission_file_name := test_eval.get_sample_submission_name(self.scen.competition)) is not None:
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# check whether code ever opens the sample submission file
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if (implementation.workspace_path / "trace.log").exists():
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opened_trace_lines = [
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line
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for line in (implementation.workspace_path / "trace.log").read_text().splitlines()
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if "openat" in line and sample_submission_file_name in line
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]
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if len(opened_trace_lines) > 0:
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stdout += f"Code opened the sample submission file '{sample_submission_file_name}' during execution.\n Reject the implementation!\n"
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sample_submission_check = False
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result_stdout = remove_eda_part(result_stdout)
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if result.exit_code != 0:
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stdout += f"Code failed to run. Please check the stdout:\n Following the stdout of the debug mode run:\n{result_stdout.strip()}\n"
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else:
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stdout += f"Code ran successfully.\n Following the stdout of the debug mode run:\n{result_stdout.strip()}\n"
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if DS_RD_SETTING.sample_data_by_LLM:
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debug_time, full_estimated_time = None, None
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if match := re.search(r"debug_time:\s*(\d+(?:.\d+)?)", result_stdout, re.DOTALL):
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debug_time = float(match.group(1))
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if match := re.search(r"estimated_time:\s*(\d+(?:.\d+)?)", result_stdout, re.DOTALL):
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full_estimated_time = float(match.group(1))
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if debug_time is not None and full_estimated_time is not None:
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stdout += f"Debug mode ran in {debug_time:.2f} seconds, estimated full run time is {full_estimated_time:.2f} seconds. The estimated time is {full_estimated_time / env.conf.running_timeout_period * 100:.2f}% the debug time."
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else:
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stdout += "Debug mode did not provide debug_time or estimated_time, it's a buggy implementation.\n"
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score_fp = implementation.workspace_path / "scores.csv"
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score_ret_code = 0
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score_check_text = ""
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if not score_fp.exists():
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score_check_text = "[Error] Metrics file (scores.csv) is not generated!"
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score_ret_code = 1
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else:
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try:
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score_df = pd.read_csv(score_fp, index_col=0)
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model_set_in_scores = set(score_df.index)
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# Check model names (index)
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if not score_df.index.is_unique:
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score_check_text += "\n[Error] The file 'scores.csv' contains duplicate model names."
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score_ret_code = 1
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if "ensemble" not in model_set_in_scores:
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score_check_text += "\n[Error] The file 'scores.csv' doesn't contain the ensemble model."
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score_ret_code = 1
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if score_ret_code != 0:
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score_check_text += f"The dataframe in file 'scores.csv' is:\n{score_df}"
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# Check metric name (columns) - case insensitive
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if [col.lower() for col in score_df.columns.tolist()] != [self.scen.metric_name.lower()]:
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score_check_text += f"\n[Error] The scores dataframe does not contain the correct column names.\nCorrect columns is: ['{self.scen.metric_name}']\nBut got: {score_df.columns.tolist()}"
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score_ret_code = 1
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# Check if scores contain NaN (values)
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if score_df.isnull().values.any():
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nan_locations = score_df[score_df.isnull().any(axis=1)]
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score_check_text += f"\n[Error] The scores dataframe contains NaN values at the following locations:\n{nan_locations}"
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score_ret_code = 1
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except Exception as e:
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score_check_text += f"\n[Error] in checking the scores.csv file: {e}\nscores.csv's content:\n-----\n{score_fp.read_text()}\n-----"
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score_ret_code = 1
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test_eval = get_test_eval()
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if DS_RD_SETTING.sample_data_by_LLM and test_eval.enabled(self.scen.competition):
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submission_check_out, submission_ret_code = test_eval.valid(self.scen.competition, implementation)
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stdout += f"\n### Submission check:\n{submission_check_out}\nIf Submission check returns a 'Submission is valid' or similar message, despite some warning messages, you should still consider the submission as valid and give a positive final decision. "
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elif not test_eval.is_sub_enabled(self.scen.competition):
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submission_ret_code = 0
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else:
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# Check submission file
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base_check_code = T(".eval_tests.submission_format_test", ftype="txt").r()
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implementation.inject_files(**{"test/submission_format_test.py": base_check_code})
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# stdout += "----Submission Check 1-----\n"
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submission_result = implementation.run(env=env, entry="python test/submission_format_test.py")
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submission_check_out = submission_result.stdout
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submission_ret_code = submission_result.exit_code
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stdout += "\n" + submission_check_out
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if not isinstance(implementation, FBWorkspace):
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eda_output = None
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else:
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eda_output = implementation.file_dict.get("EDA.md", None)
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# extract enable_mcp_documentation_search from data science configuration
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enable_mcp_documentation_search = DS_RD_SETTING.enable_mcp_documentation_search
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queried_similar_successful_knowledge = (
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queried_knowledge.task_to_similar_task_successful_knowledge[target_task.get_task_information()]
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if queried_knowledge is not None
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else []
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)
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system_prompt = T(".prompts:pipeline_eval.system").r(
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is_sub_enabled=test_eval.is_sub_enabled(self.scen.competition),
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debug_mode=DS_RD_SETTING.sample_data_by_LLM,
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enable_mcp_documentation_search=enable_mcp_documentation_search,
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mle_check=DS_RD_SETTING.sample_data_by_LLM,
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queried_similar_successful_knowledge=queried_similar_successful_knowledge,
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)
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user_prompt = T(".prompts:pipeline_eval.user").r(
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scenario=self.scen.get_scenario_all_desc(eda_output=eda_output),
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task_desc=target_task.get_task_information(),
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stdout=stdout.strip(),
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spec=T("scenarios.data_science.share:component_spec.Pipeline").r(
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metric_name=self.scen.metric_name,
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enable_notebook_conversion=DS_RD_SETTING.enable_notebook_conversion,
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),
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code=implementation.file_dict["main.py"],
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)
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wfb = build_cls_from_json_with_retry(
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PipelineSingleFeedback,
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system_prompt=system_prompt,
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user_prompt=user_prompt,
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init_kwargs_update_func=PipelineSingleFeedback.val_and_update_init_dict,
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)
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# judge whether we should perform documentation search
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do_documentation_search = enable_mcp_documentation_search and wfb.requires_documentation_search
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if do_documentation_search:
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# Use MCPAgent for clean, user-friendly interface
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try:
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# Create agent targeting Context7 service - model config comes from mcp_config.json
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doc_agent = DocAgent()
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# Synchronous query - perfect for evaluation context
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if wfb.error_message: # Type safety check
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context7_result = doc_agent.query(query=wfb.error_message)
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if context7_result:
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logger.info("Context7: Documentation search completed successfully")
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wfb.error_message += f"\n\n### API Documentation Reference:\nThe following API documentation was retrieved based on the error. This provides factual information about API changes or parameter specifications only:\n\n{context7_result}"
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else:
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logger.warning("Context7: Documentation search failed or no results found")
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else:
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logger.warning("Context7: No error message to search for")
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# TODO: confirm what exception will be raised when timeout
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# except concurrent.futures.TimeoutError:
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# logger.error("Context7: Query timed out after 180 seconds")
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except Exception as e:
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error_msg = str(e) if str(e) else type(e).__name__
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logger.error(f"Context7: Query failed - {error_msg}")
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if score_ret_code != 0 and wfb.final_decision is True:
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wfb.final_decision = False
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wfb.return_checking += "\n" + score_check_text
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if submission_ret_code != 0 and wfb.final_decision is True:
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wfb.final_decision = False
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wfb.return_checking += "\nSubmission file check failed."
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if sample_submission_check is False and wfb.final_decision is True:
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wfb.final_decision = False
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wfb.return_checking += (
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"\nSample submission file check failed. Code should not open the sample submission file."
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)
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if nb_conversion_ret_code != 0 and wfb.final_decision is True:
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wfb.final_decision = False
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wfb.return_checking += "\n" + nb_conversion_check_text
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return wfb
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