optuna.load_study¶

optuna.load_study(study_name: str, storage: Union[str, optuna.storages._base.BaseStorage], sampler: Optional[samplers.BaseSampler] = None, pruner: Optional[optuna.pruners._base.BasePruner] = None) → optuna.study.Study [source]¶

Load the existing Study that has the specified name.

Example

import optuna


def objective(trial):
    x = trial.suggest_float("x", 0, 10)
    return x ** 2


study = optuna.create_study(storage="sqlite:///example.db", study_name="my_study")
study.optimize(objective, n_trials=3)

loaded_study = optuna.load_study(study_name="my_study", storage="sqlite:///example.db")
assert len(loaded_study.trials) == len(study.trials)

Parameters

study_name – Study’s name. Each study has a unique name as an identifier.
storage – Database URL such as sqlite:///example.db. Please see also the documentation of create_study() for further details.
sampler – A sampler object that implements background algorithm for value suggestion. If None is specified, TPESampler is used as the default. See also samplers.
pruner – A pruner object that decides early stopping of unpromising trials. If None is specified, MedianPruner is used as the default. See also pruners.