optuna_v2_blog_hpi.py

import optuna

def objective(trial):
    x = trial.suggest_int("x", 0, 2)
    y = trial.suggest_float("y", -1.0, 1.0)
    z = trial.suggest_float("z", 0.0, 1.5)
    return x ** 2 + y ** 3 - z ** 4

study = optuna.create_study(sampler=optuna.samplers.RandomSampler())
study.optimize(objective, n_trials=100)

# Compute importances.
evaluator = optuna.importance.FanovaImportanceEvaluator()
importances = optuna.importance.get_param_importances(study, evaluator=evaluator)
for param_name, importance in importances.items():
    # x: 0.5604609292882394
    # z: 0.4170708373363104
    # y: 0.022468233375450276
    print("{}: {}".format(param_name, importance))

# Plot importances.
fig = optuna.visualization.plot_param_importances(study, evaluator=evaluator)
fig.show()