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import argparse
import optuna
import warnings
from kurobako import solver
from kurobako.solver.optuna import OptunaSolverFactory
warnings.filterwarnings(
"ignore",
category=optuna.exceptions.ExperimentalWarning,
module="optuna.samplers._cmaes",
)
parser = argparse.ArgumentParser()
parser.add_argument(
"sampler",
choices=["cmaes", "sep-cmaes", "ipop-cmaes", "ipop-sep-cmaes", "pycma", "ws-cmaes"],
)
parser.add_argument(
"--loglevel", choices=["debug", "info", "warning", "error"], default="warning"
)
parser.add_argument("--warm-starting-trials", type=int, default=0)
args = parser.parse_args()
if args.loglevel == "debug":
optuna.logging.set_verbosity(optuna.logging.DEBUG)
elif args.loglevel == "info":
optuna.logging.set_verbosity(optuna.logging.INFO)
elif args.loglevel == "warning":
optuna.logging.set_verbosity(optuna.logging.WARNING)
elif args.loglevel == "error":
optuna.logging.set_verbosity(optuna.logging.ERROR)
def create_cmaes_study(seed):
sampler = optuna.samplers.CmaEsSampler(seed=seed, warn_independent_sampling=True)
return optuna.create_study(sampler=sampler, pruner=optuna.pruners.NopPruner())
def create_sep_cmaes_study(seed):
sampler = optuna.samplers.CmaEsSampler(
seed=seed, warn_independent_sampling=True, use_separable_cma=True
)
return optuna.create_study(sampler=sampler, pruner=optuna.pruners.NopPruner())
def create_ipop_cmaes_study(seed):
sampler = optuna.samplers.CmaEsSampler(
seed=seed,
warn_independent_sampling=True,
restart_strategy="ipop",
inc_popsize=2,
)
return optuna.create_study(sampler=sampler, pruner=optuna.pruners.NopPruner())
def create_ipop_sep_cmaes_study(seed):
sampler = optuna.samplers.CmaEsSampler(
seed=seed,
warn_independent_sampling=True,
restart_strategy="ipop",
inc_popsize=2,
use_separable_cma=True,
)
return optuna.create_study(sampler=sampler, pruner=optuna.pruners.NopPruner())
def create_pycma_study(seed):
sampler = optuna.integration.PyCmaSampler(
seed=seed,
warn_independent_sampling=True,
)
return optuna.create_study(sampler=sampler, pruner=optuna.pruners.NopPruner())
class WarmStartingCmaEsSampler(optuna.samplers.BaseSampler):
def __init__(self, seed, warm_starting_trials: int) -> None:
self._seed = seed
self._warm_starting = True
self._warm_starting_trials = warm_starting_trials
self._sampler = optuna.samplers.RandomSampler(seed=seed)
self._source_trials = []
def infer_relative_search_space(self, study, trial):
return self._sampler.infer_relative_search_space(study, trial)
def sample_relative(
self,
study,
trial,
search_space,
):
return self._sampler.sample_relative(study, trial, search_space)
def sample_independent(self, study, trial, param_name, param_distribution):
return self._sampler.sample_independent(
study, trial, param_name, param_distribution
)
def after_trial(
self,
study,
trial,
state,
values,
):
if not self._warm_starting:
return self._sampler.after_trial(study, trial, state, values)
if len(self._source_trials) < self._warm_starting_trials:
assert state == optuna.trial.TrialState.PRUNED
self._source_trials.append(
optuna.create_trial(
params=trial.params,
distributions=trial.distributions,
values=values,
)
)
if len(self._source_trials) == self._warm_starting_trials:
self._sampler = optuna.samplers.CmaEsSampler(
seed=self._seed + 1, source_trials=self._source_trials or None
)
self._warm_starting = False
else:
return self._sampler.after_trial(study, trial, state, values)
def create_warm_start_study(seed):
sampler = WarmStartingCmaEsSampler(seed, args.warm_starting_trials)
return optuna.create_study(sampler=sampler, pruner=optuna.pruners.NopPruner())
if __name__ == "__main__":
if args.sampler == "cmaes":
factory = OptunaSolverFactory(create_cmaes_study)
elif args.sampler == "sep-cmaes":
factory = OptunaSolverFactory(create_sep_cmaes_study)
elif args.sampler == "ipop-cmaes":
factory = OptunaSolverFactory(create_ipop_cmaes_study)
elif args.sampler == "ipop-sep-cmaes":
factory = OptunaSolverFactory(create_ipop_sep_cmaes_study)
elif args.sampler == "pycma":
factory = OptunaSolverFactory(create_pycma_study)
elif args.sampler == "ws-cmaes":
factory = OptunaSolverFactory(
create_warm_start_study, warm_starting_trials=args.warm_starting_trials
)
else:
raise ValueError("unsupported sampler")
runner = solver.SolverRunner(factory)
runner.run()
|
