optuna.study.load_study(*, study_name, storage, sampler=None, pruner=None)[source]

Load the existing Study that has the specified name.


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)
  • study_name (Optional[str]) – Study’s name. Each study has a unique name as an identifier. If None, checks whether the storage contains a single study, and if so loads that study. study_name is required if there are multiple studies in the storage.

  • storage (Union[str, BaseStorage]) – Database URL such as sqlite:///example.db. Please see also the documentation of create_study() for further details.

  • sampler (Optional[BaseSampler]) – A sampler object that implements background algorithm for value suggestion. If None is specified, TPESampler is used as the default. See also samplers.

  • pruner (Optional[BasePruner]) – A pruner object that decides early stopping of unpromising trials. If None is specified, MedianPruner is used as the default. See also pruners.

Return type