Source code for

from typing import Callable
from typing import Optional
from typing import Sequence
from typing import Union

import optuna
from optuna.pruners import BasePruner
from optuna.samplers import BaseSampler
from optuna.storages import BaseStorage
from import Study
from import StudyDirection
from optuna.trial import Trial
from typing_extensions import Literal

from import AbstractRunner
from import LocalRunner

OptunaDirection = Literal["minimize", "maximize"]

[docs]class Optuna: """Class for encapsulate work with Optuna.""" def __init__( self, direction: Union[OptunaDirection, StudyDirection], study_name: Optional[str] = None, sampler: Optional[BaseSampler] = None, storage: Optional[BaseStorage] = None, pruner: Optional[BasePruner] = None, directions: Optional[Sequence[Union[OptunaDirection, StudyDirection]]] = None, load_if_exists: bool = True, ): """Init wrapper for Optuna. Parameters ---------- direction: optuna direction study_name: name of study sampler: optuna sampler to use storage: storage to use pruner: optuna pruner directions: directions to optimize in case of multi-objective optimization load_if_exists: load study from storage if it exists or raise exception if it doesn't """ self._study = optuna.create_study( storage=storage, study_name=study_name, direction=direction, sampler=sampler, load_if_exists=load_if_exists, pruner=pruner, directions=directions, )
[docs] def tune( self, objective: Callable[[Trial], Union[float, Sequence[float]]], n_trials: Optional[int] = None, timeout: Optional[int] = None, runner: Optional[AbstractRunner] = None, **kwargs, ): """Call optuna ``optimize`` for chosen Runner. Parameters ---------- objective: objective function to optimize in optuna style n_trials: number of trials to run. N.B. in case of parallel runner, this is number of trials per worker timeout: timeout for optimization. N.B. in case of parallel runner, this is timeout per worker kwargs: additional arguments to pass to :py:meth:`` """ if runner is None: runner = LocalRunner() _ = runner(, objective, n_trials=n_trials, timeout=timeout, **kwargs)
@property def study(self) -> Study: """Get optuna study.""" return self._study