Optuna example that optimizes a simple quadratic function in parallel using joblib with arbitrary arguments to the objective function.

quadratic_joblib_arbitrary_arguments_simple.py

"""
Optuna example that optimizes a simple quadratic function in parallel using `joblib` allowing
arbitrary arguments to the objective function.

Run the example as follows.

    $ python quadratic_joblib_simple.py

If you need to rerun the example and thus delete previous studies, you can use the Optuna CLI.

    $ optuna delete-study --study-name joblib-quadratic --storage "sqlite:///example.db"

See also
https://optuna.readthedocs.io/en/latest/faq.html#how-to-define-objective-functions-that-have-own-arguments

"""

from joblib import Parallel, delayed
import optuna


def print_study(study):
    print('Number of finished trials: ', len(study.trials))
    print('Best trial:')
    trial = study.best_trial
    print('  Value: ', trial.value)
    print('  Params: ')
    for key, value in trial.params.items():
        print('    {}: {}'.format(key, value))


def optimize(n_trials, min_x, max_x):
    study = optuna.load_study(study_name='joblib-quadratic', storage='sqlite:///example.db')
    # You can either use a lambda (as shown here) or define a class that holds the arguments and
    # implements `__call__`.
    study.optimize(lambda trial: objective(trial, min_x, max_x), n_trials=n_trials)


# An objective function does not only take the trial, but also additional arguments.
def objective(trial, min_x, max_x):
    x = trial.suggest_uniform('x', min_x, max_x)
    return (x - 2) ** 2


if __name__ == '__main__':
    study = optuna.create_study(study_name='joblib-quadratic', storage='sqlite:///example.db')

    # `Study.optimize` arguments.
    n_trials = 10

    # Arbitrary arguments to the objective function.
    min_x = -100
    max_x = 100

    # `joblib` arguments.
    n_iterables = 3

    r = Parallel(n_jobs=-1)(
        [delayed(optimize)(n_trials, min_x, max_x) for _ in range(n_iterables)])

    assert len(study.trials) == n_trials * n_iterables
    print_study(study)