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)
p7 = trial.suggest_int("p7", 1, 7, log=True)
return [p0 + p1 + p2, p3 + p4 + p5 + p6 + p7]
def objective(trial: optuna.multi_objective.trial.MultiObjectiveTrial) -> Tuple[float, float]:
p0 = trial.suggest_float("p0", -10, 10)
p1 = trial.suggest_float("p1", 3, 5)
p2 = trial.suggest_float("p2", 0.00001, 0.1, log=True)
p3 = trial.suggest_float("p3", 100, 200, step=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,
)