import matplotlib.pyplot as plt
import optuna

N_TRIAL = 50
N_BATCH = 10

multivariate = True
constant_liar = True
sampler = optuna.samplers.TPESampler(
    seed=42,
    multivariate=multivariate,
    constant_liar=constant_liar,
)
study = optuna.create_study(sampler=sampler)

for i in range(0, N_TRIAL, N_BATCH):
    trials = []
    for j in range(N_BATCH):
        trials.append(study.ask())
    X = [trial.suggest_float("x", -10, 10) for trial in trials]
    Y = [trial.suggest_float("y", -10, 10) for trial in trials]
    for j in range(N_BATCH):
        study.tell(trials[j], X[j] ** 2 + Y[j] ** 2)

    # Skip first random sampling.
    if i > 0:
        plt.plot(X, Y, ".")
plt.xlim(-10, 10)
plt.ylim(-10, 10)
plt.savefig(f"{multivariate}-{constant_liar}.png")
plt.clf()

import optuna

def objective(trial: optuna.Trial) -> float:
    x = trial.suggest_float("x", -100, 100)
    y = trial.suggest_int("y", -100, 100)
    return x**2 + y**2

sampler = optuna.samplers.TPESampler(seed=42, multivariate=True, constant_liar=True)
study = optuna.create_study(sampler=sampler, storage="sqlite:///tmp.db")
study.optimize(objective, n_trials=1000)