def objective(trial: optuna.multi_objective.trial.MultiObjectiveTrial) -> List[float]: if trial.number == 0: assert trial.suggest_uniform("x", 0, 100) == 2 elif trial.number == 1: assert trial.suggest_uniform("x", 0, 100) == 3 return [0, 0]
def objective( trial: optuna.multi_objective.trial.MultiObjectiveTrial ) -> List[float]: return [ trial.suggest_uniform("v{}".format(i), 0, 5) for i in range(n_objectives) ]
def objective(trial: optuna.multi_objective.trial.MultiObjectiveTrial) -> List[float]: x = trial.suggest_uniform("x", 0, 10) assert set(trial.params.keys()) == {"x"} assert set(trial.distributions.keys()) == {"x"} assert isinstance(trial.distributions["x"], optuna.distributions.UniformDistribution) return [x, x, x]
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, )