Optuna¶
- class Optuna(direction: Union[Literal['minimize', 'maximize'], optuna.study._study_direction.StudyDirection], study_name: Optional[str] = None, sampler: Optional[optuna.samplers._base.BaseSampler] = None, storage: Optional[optuna.storages._base.BaseStorage] = None, pruner: Optional[optuna.pruners._base.BasePruner] = None, directions: Optional[Sequence[Union[Literal['minimize', 'maximize'], optuna.study._study_direction.StudyDirection]]] = None, load_if_exists: bool = True)[source]¶
Bases:
object
Class for encapsulate work with Optuna.
Init wrapper for Optuna.
- Parameters
direction (Union[Literal['minimize', 'maximize'], optuna.study._study_direction.StudyDirection]) – optuna direction
study_name (Optional[str]) – name of study
sampler (Optional[optuna.samplers._base.BaseSampler]) – optuna sampler to use
storage (Optional[optuna.storages._base.BaseStorage]) – storage to use
pruner (Optional[optuna.pruners._base.BasePruner]) – optuna pruner
directions (Optional[Sequence[Union[Literal['minimize', 'maximize'], optuna.study._study_direction.StudyDirection]]]) – directions to optimize in case of multi-objective optimization
load_if_exists (bool) – load study from storage if it exists or raise exception if it doesn’t
- Inherited-members
Methods
tune
(objective[, n_trials, timeout, runner])Call optuna
optimize
for chosen Runner.Attributes
Get optuna study.
- tune(objective: Callable[[optuna.trial._trial.Trial], Union[float, Sequence[float]]], n_trials: Optional[int] = None, timeout: Optional[int] = None, runner: Optional[etna.auto.runner.base.AbstractRunner] = None, **kwargs)[source]¶
Call optuna
optimize
for chosen Runner.- Parameters
objective (Callable[[optuna.trial._trial.Trial], Union[float, Sequence[float]]]) – objective function to optimize in optuna style
n_trials (Optional[int]) – number of trials to run. N.B. in case of parallel runner, this is number of trials per worker
timeout (Optional[int]) – timeout for optimization. N.B. in case of parallel runner, this is timeout per worker
kwargs – additional arguments to pass to
optuna.study.Study.optimize()
runner (Optional[etna.auto.runner.base.AbstractRunner]) –
- property study: optuna.study.study.Study¶
Get optuna study.