from types import TracebackType # NOQA
from typing import Any # NOQA
from typing import Callable # NOQA
from typing import Dict # NOQA
from typing import Optional # NOQA
from typing import Type # NOQA
try:
import chainermn
from chainermn.communicators.communicator_base import CommunicatorBase # NOQA
_available = True
except ImportError:
_available = False
STORAGE_MODES = ['new', 'common']
PRUNER_INIT_FUNCS = [lambda: pruners.MedianPruner(), lambda: pruners.SuccessiveHalvingPruner()]
CACHE_MODES = [True, False]
def setup_module():
# type: () -> None
StorageSupplier.setup_common_tempfile()
def teardown_module():
# type: () -> None
StorageSupplier.teardown_common_tempfile()
from optuna.testing.sampler import DeterministicRelativeSampler
from optuna.testing.storage import StorageSupplier
from optuna.trial import Trial
from optuna.trial import TrialState
try:
import chainermn
from chainermn.communicators.communicator_base import CommunicatorBase # NOQA
_available = True
except ImportError:
_available = False
STORAGE_MODES = ["sqlite"]
PRUNER_INIT_FUNCS = [
lambda: pruners.MedianPruner(), lambda: pruners.SuccessiveHalvingPruner()
]
class Func(object):
def __init__(self) -> None:
self.suggested_values: Dict[int, Dict[str, Any]] = {}
def __call__(self, trial: ChainerMNTrial, comm: CommunicatorBase) -> float:
x = trial.suggest_uniform("x", -10, 10)
y = trial.suggest_loguniform("y", 20, 30)
z = trial.suggest_categorical("z", (-1.0, 1.0))
self.suggested_values[trial.number] = {}
'n_embed':100,
'n_categ':48,
'n_area':8
}
def objective(trial:Trial):
if args_cl.study != '':# 過去の best_param 使う場合
model = CbLossClassifier(**model_params)
else:
model = get_model(trial,model_params)
trainer,reporter = get_trainer_and_reporter(
trial=trial,
model=model,
iter_train=iter_train,
iter_test=iter_test,
batch_converter=batch_converter_area,
args=args_cl,
device=0,
best_params=best_params
)
trainer.run()
(epoch,best_loss) = get_bestresult(reporter.log,args_cl.pruning_key)
return best_loss
study = create_study(study_name=args_cl.optuna_output,
storage=f'sqlite:///{args_cl.optuna_output}/optuna_{args_cl.desc}.db',
load_if_exists=True,
pruner=pruners.SuccessiveHalvingPruner())
study.optimize(objective, n_trials=args_cl.n_trials)
