# -*- coding: utf-8 -*-
"""
author: zengbin93
email: zeng_bin8888@163.com
create_dt: 2024/3/21 13:56
describe: optuna 工具函数
"""
import hashlib
import optuna
import inspect
import pandas as pd
[docs]def optuna_study(objective, direction="maximize", n_trials=100, **kwargs):
"""使用optuna进行参数优化"""
objective_code = inspect.getsource(objective)
study_name = hashlib.md5(f"{objective_code}_{direction}".encode("utf-8")).hexdigest().upper()[:12]
study = optuna.create_study(direction=direction, study_name=study_name)
timeout = kwargs.pop("timeout", None)
n_jobs = kwargs.pop("n_jobs", 1)
study.optimize(objective, n_trials=n_trials, timeout=timeout, n_jobs=n_jobs, **kwargs)
return study
[docs]def optuna_good_params(study: optuna.Study, keep=0.2) -> pd.DataFrame:
"""获取optuna优化结果中的最优参数
:param study: optuna.study.Study
:param keep: float, 保留最优参数的比例, 默认0.2
如果keep小于0，则按比例保留；如果keep大于0，则保留keep个参数组
:return: pd.DataFrame, 最优参数组列表
"""
assert keep > 0, "keep必须大于0"
params = []
for trail in study.trials:
if trail.state != optuna.trial.TrialState.COMPLETE:
continue
if trail.value is None:
continue
p = {"params": trail.params, "objective": trail.value}
params.append(p)
n = int(len(params) * keep) if keep < 1 else int(keep)
reverse = study.direction == 2
params = sorted(params, key=lambda x: x['objective'], reverse=reverse)
dfp = pd.DataFrame(params[:n])
dfp = dfp.drop_duplicates(subset=['params'], keep='first').reset_index(drop=True)
return dfp