mirror of
https://github.com/NicolasBohn/NexQuant.git
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you-n-g
96 lines
3.8 KiB
Python
96 lines
3.8 KiB
Python
"""
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Beyond previous tests
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-
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"""
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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.core.evolving_framework import QueriedKnowledge
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from rdagent.core.exception import CoderError
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from rdagent.core.experiment import FBWorkspace, Task
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from rdagent.oai.llm_utils import APIBackend
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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.env import DockerEnv, DSDockerConf
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DIRNAME = Path(__file__).absolute().resolve().parent
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ModelSingleFeedback = CoSTEERSingleFeedback
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# Below are unit tests for testing the specification of the implemented model ------------------
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class ModelGeneralCaseSpecEvaluator(CoSTEEREvaluator):
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"""
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Motivation case:
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- Simplest case, we already split the data into train_data, valid_data, and test_data. We require the model to learn (optionally validate on valid data), and infer on test data.
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Test workflow:
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- Build train, valid, and test data to run it, and test the output (e.g., shape, etc.)
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"""
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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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) -> ModelSingleFeedback:
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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 ModelSingleFeedback(
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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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ds_docker_conf = DSDockerConf()
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ds_docker_conf.extra_volumes = {
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f"{DS_RD_SETTING.local_data_path}/sample/{self.scen.competition}": "/kaggle/input"
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}
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de = DockerEnv(conf=ds_docker_conf)
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fname = "model_test.py"
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test_code = (
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(DIRNAME / "eval_tests" / "model_test.txt").read_text().replace("model01", target_task.name)
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) # only check the model changed this time
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implementation.inject_files(**{fname: test_code})
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stdout = implementation.execute(env=de, entry=f"python {fname}")
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if stdout is None:
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raise CoderError(
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"The execution output contains too many progress bars and results in the LLM's token size exceeding the limit."
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)
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if "main.py" in implementation.file_dict:
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workflow_stdout = implementation.execute(env=de, entry="python main.py")
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else:
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workflow_stdout = None
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system_prompt = T(".prompts:model_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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scenario=self.scen.get_scenario_all_desc(),
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spec=implementation.file_dict["spec/model.md"],
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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:model_eval.user").r(
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stdout=stdout,
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code=implementation.file_dict[f"{target_task.name}.py"],
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workflow_stdout=workflow_stdout,
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)
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return build_cls_from_json_with_retry(ModelSingleFeedback, system_prompt=system_prompt, user_prompt=user_prompt)
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