chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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from collections import defaultdict
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from pathlib import Path
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from typing import Dict, List, Tuple, Union
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import pandas as pd
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from tqdm import tqdm
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from rdagent.components.coder.factor_coder.config import FACTOR_COSTEER_SETTINGS
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from rdagent.components.coder.factor_coder.eva_utils import (
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FactorCorrelationEvaluator,
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FactorEqualValueRatioEvaluator,
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FactorEvaluator,
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FactorIndexEvaluator,
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FactorRowCountEvaluator,
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FactorSingleColumnEvaluator,
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)
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from rdagent.components.coder.factor_coder.factor import FactorFBWorkspace
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from rdagent.core.conf import RD_AGENT_SETTINGS
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from rdagent.core.developer import Developer
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from rdagent.core.exception import CoderError
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from rdagent.core.experiment import Experiment, Task, Workspace
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from rdagent.core.scenario import Scenario
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from rdagent.core.utils import multiprocessing_wrapper
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EVAL_RES = Dict[
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str,
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List[Tuple[FactorEvaluator, Union[object, CoderError]]],
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]
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class TestCase:
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def __init__(
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self,
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target_task: Task,
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ground_truth: Workspace,
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):
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self.target_task = target_task
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self.ground_truth = ground_truth
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class TestCases:
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def __init__(self, test_case_l: list[TestCase] = []):
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# self.test_case_l = [TestCase(task, gt) for task, gt in zip(target_task, ground_truth)]
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self.test_case_l = test_case_l
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def __getitem__(self, item):
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return self.test_case_l[item]
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def __len__(self):
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return len(self.test_case_l)
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def get_exp(self):
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return Experiment([case.target_task for case in self.test_case_l])
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@property
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def target_task(self):
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return [case.target_task for case in self.test_case_l]
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@property
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def ground_truth(self):
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return [case.ground_truth for case in self.test_case_l]
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class BaseEval:
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"""
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The benchmark benchmark evaluation.
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"""
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def __init__(
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self,
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evaluator_l: List[FactorEvaluator],
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test_cases: TestCases,
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generate_method: Developer,
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catch_eval_except: bool = True,
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):
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"""Parameters
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----------
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test_cases : TestCases
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cases to be evaluated, ground truth are included in the test cases.
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evaluator_l : List[FactorEvaluator]
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A list of evaluators to evaluate the generated code.
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catch_eval_except : bool
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If we want to debug the evaluators, we recommend to set the this parameter to True.
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"""
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self.evaluator_l = evaluator_l
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self.test_cases = test_cases
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self.generate_method = generate_method
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self.catch_eval_except = catch_eval_except
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def load_cases_to_eval(
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self,
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path: Union[Path, str],
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**kwargs,
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) -> List[Workspace]:
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path = Path(path)
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fi_l = []
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for tc in self.test_cases:
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try:
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fi = FactorFBWorkspace.from_folder(tc.task, path, **kwargs)
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fi_l.append(fi)
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except FileNotFoundError:
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print("Fail to load test case for factor: ", tc.task.factor_name)
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return fi_l
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def eval_case(
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self,
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case_gt: Workspace,
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case_gen: Workspace,
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) -> List[Union[Tuple[FactorEvaluator, object], Exception]]:
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"""Parameters
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----------
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case_gt : FactorImplementation
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case_gen : FactorImplementation
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Returns
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-------
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List[Union[Tuple[FactorEvaluator, object],Exception]]
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for each item
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If the evaluation run successfully, return the evaluate results. Otherwise, return the exception.
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"""
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eval_res = []
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for ev in self.evaluator_l:
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try:
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case_gen.raise_exception = True
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eval_res.append((ev, ev.evaluate(implementation=case_gen, gt_implementation=case_gt)))
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# if the corr ev is successfully evaluated and achieve the best performance, then break
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except CoderError as e:
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return e
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except Exception as e:
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# exception when evaluation
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if self.catch_eval_except:
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eval_res.append((ev, e))
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else:
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raise e
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return eval_res
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class FactorImplementEval(BaseEval):
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def __init__(
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self,
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test_cases: TestCases,
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method: Developer,
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*args,
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scen: Scenario,
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test_round: int = 10,
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**kwargs,
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):
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online_evaluator_l = [
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FactorSingleColumnEvaluator(scen),
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FactorRowCountEvaluator(scen),
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FactorIndexEvaluator(scen),
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FactorEqualValueRatioEvaluator(scen),
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FactorCorrelationEvaluator(hard_check=False, scen=scen),
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]
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super().__init__(online_evaluator_l, test_cases, method, *args, **kwargs)
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self.test_round = test_round
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def develop(self):
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gen_factor_l_all_rounds = []
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for _ in tqdm(range(self.test_round), desc="Rounds of Eval"):
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print("\n========================================================")
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print(f"Eval {_}-th times...")
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print("========================================================\n")
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try:
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gen_factor_l = self.generate_method.develop(self.test_cases.get_exp())
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except KeyboardInterrupt:
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# TODO: Why still need to save result after KeyboardInterrupt?
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print("Manually interrupted the evaluation. Saving existing results")
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break
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if len(gen_factor_l.sub_workspace_list) != len(self.test_cases.ground_truth):
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raise ValueError(
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"The number of cases to eval should be equal to the number of test cases.",
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)
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gen_factor_l_all_rounds.extend(gen_factor_l.sub_workspace_list)
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return gen_factor_l_all_rounds
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def eval(self, gen_factor_l_all_rounds):
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test_cases_all_rounds = []
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res = defaultdict(list)
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for _ in range(self.test_round):
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test_cases_all_rounds.extend(self.test_cases.ground_truth)
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eval_res_list = multiprocessing_wrapper(
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[
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(self.eval_case, (gt_case, gen_factor))
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for gt_case, gen_factor in zip(test_cases_all_rounds, gen_factor_l_all_rounds)
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],
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n=RD_AGENT_SETTINGS.multi_proc_n,
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)
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for gt_case, eval_res, gen_factor in tqdm(zip(test_cases_all_rounds, eval_res_list, gen_factor_l_all_rounds)):
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res[gt_case.target_task.factor_name].append((gen_factor, eval_res))
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return res
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@staticmethod
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def summarize_res(res: EVAL_RES) -> pd.DataFrame:
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# None: indicate that it raises exception and get no results
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sum_res = {}
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for factor_name, runs in res.items():
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for fi, err_or_res_l in runs:
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# NOTE: str(fi) may not be unique!! Because the workspace can be skipped when hitting the cache.
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uniq_key = f"{str(fi)},{id(fi)}"
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key = (factor_name, uniq_key)
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val = {}
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if isinstance(err_or_res_l, Exception):
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val["run factor error"] = str(err_or_res_l.__class__)
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else:
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val["run factor error"] = None
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for ev_obj, err_or_res in err_or_res_l:
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if isinstance(err_or_res, Exception):
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val[str(ev_obj)] = None
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else:
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feedback, metric = err_or_res
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val[str(ev_obj)] = metric
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sum_res[key] = val
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return pd.DataFrame(sum_res)
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