md_bayesian_er_ditto/hpo/ditto_hpo.py

import sys
sys.path.append('/root/hjt/md_bayesian_er_ditto/')

import json
import time
from colorama import init, Fore
from ConfigSpace import Categorical, Configuration, ConfigurationSpace, Integer, Float
from ConfigSpace.conditions import InCondition, EqualsCondition, AndConjunction
from ConfigSpace.read_and_write import json as csj
from smac import Scenario, BlackBoxFacade

from ml_er.ditto_er import matching
from setting import hpo_output_dir


class Optimization:
    @property
    def configspace(self) -> ConfigurationSpace:
        cs = ConfigurationSpace(seed=0)

        # task
        # run_id
        batch_size = Categorical('batch_size', [32, 64], default=64)
        max_len = Categorical('max_len', [64, 128, 256], default=256)
        # lr 3e-5
        # n_epochs 20
        # fine_tuning
        # save_model
        # logdir
        lm = Categorical('language_model', ['distilbert', 'roberta', 'bert-base-uncased', 'xlnet-base-cased'], default='distilbert')
        fp16 = Categorical('half_precision_float', [True, False])
        da = Categorical('data_augmentation', ['del', 'swap', 'drop_col', 'append_col', 'all'])
        # alpha_aug
        # dk
        summarize = Categorical('summarize', [True, False])
        # size

        cs.add_hyperparameters([batch_size, max_len, lm, fp16, da, summarize])
        return cs

    # todo train函数
    def train(self, hpo_config: Configuration, seed: int = 0, ) -> float:
        indicators = matching(hpo_config)
        return 1 - indicators['performance']


def ml_er_hpo():
    optimization = Optimization()
    cs = optimization.configspace
    str_configspace = csj.write(cs)
    dict_configspace = json.loads(str_configspace)
    # 将超参数空间保存本地
    with open(hpo_output_dir + "/configspace.json", "w") as f:
        json.dump(dict_configspace, f, indent=4)

    scenario = Scenario(
        cs,
        crash_cost=1.0,
        deterministic=True,
        n_trials=16,
        n_workers=1
    )

    initial_design = BlackBoxFacade.get_initial_design(scenario, n_configs=5)

    smac = BlackBoxFacade(
        scenario,
        optimization.train,
        initial_design=initial_design,
        overwrite=True,  # If the run exists, we overwrite it; alternatively, we can continue from last state
    )

    incumbent = smac.optimize()
    incumbent_cost = smac.validate(incumbent)
    default = cs.get_default_configuration()
    default_cost = smac.validate(default)
    print(Fore.BLUE + f"Default Cost: {default_cost}")
    print(Fore.BLUE + f"Incumbent Cost: {incumbent_cost}")

    if incumbent_cost > default_cost:
        incumbent = default
        print(Fore.RED + f'Updated Incumbent Cost: {default_cost}')

    print(Fore.BLUE + f"Optimized Configuration:{incumbent.values()}")

    with open(hpo_output_dir + "/incumbent.json", "w") as f:
        json.dump(dict(incumbent), f, indent=4)
    return incumbent


if __name__ == '__main__':
    init(autoreset=True)
    print(Fore.CYAN + f'Start Time: {time.time()}')
    ml_er_hpo()