def objective(trial: optuna.multi_objective.trial.MultiObjectiveTrial) -> List[float]: p0 = trial.suggest_float("p0", -10, 10) p1 = trial.suggest_uniform("p1", 3, 5) p2 = trial.suggest_loguniform("p2", 0.00001, 0.1) p3 = trial.suggest_discrete_uniform("p3", 100, 200, q=5) p4 = trial.suggest_int("p4", -20, -15) p5 = trial.suggest_categorical("p5", [7, 1, 100]) p6 = trial.suggest_float("p6", -10, 10, step=1.0) return [p0 + p1 + p2, p3 + p4 + p5 + p6]
def objective(trial: optuna.multi_objective.trial.MultiObjectiveTrial) -> Tuple[float, float]: p0 = trial.suggest_float("p0", -10, 10) p1 = trial.suggest_uniform("p1", 3, 5) p2 = trial.suggest_loguniform("p2", 0.00001, 0.1) p3 = trial.suggest_discrete_uniform("p3", 100, 200, q=5) p4 = trial.suggest_int("p4", -20, -15) p5 = cast(int, trial.suggest_categorical("p5", [7, 1, 100])) p6 = trial.suggest_float("p6", -10, 10, step=1.0) p7 = trial.suggest_int("p7", 1, 7, log=True) return ( p0 + p1 + p2, p3 + p4 + p5 + p6 + p7, )