Optuna example that optimizes a quadratic function in parallel using joblib.

quadratic_joblib_simple.py

"""
Optuna example that optimizes a simple quadratic function in parallel using joblib.

In this example, we optimize a simple quadratic function. We also demonstrate how to continue an
optimization and to use timeouts.

We have the following way to execute this example:

    $ python quadratic_joblib_simple.py

"""

from joblib import Parallel, delayed
import optuna


# Define a simple 2-dimensional objective function whose minimum value is -1 when (x, y) = (0, -1).
def objective(trial):
    x = trial.suggest_uniform('x', -100, 100)
    y = trial.suggest_categorical('y', [-1, 0, 1])
    return x**2 + y


def optimize(n_trials):
    study = optuna.load_study(study_name='joblib-quadratic', storage='sqlite:///example.db')
    study.optimize(objective, n_trials=n_trials)


if __name__ == '__main__':
    # Let us minimize the objective function above.
    print('Running 10 trials...')
    study = optuna.create_study(study_name='joblib-quadratic', storage='sqlite:///example.db')

    r = Parallel(n_jobs=-1)([delayed(optimize)(10) for _ in range(10)])

    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))