Plotting bandit ucb behavior

Show the evolution of the current min and the selected input with bandit ucb.

Out:

best input : 0.00, best output : -2.00

import numpy as np
import matplotlib.pyplot as plt
from scipy import minimum

from fluentopt import Bandit
from fluentopt.bandit import ucb_minimize

np.random.seed(42)


def sampler(rng):
    return rng.uniform(-1, 1)


def feval(x):
    return (x ** 2 - 2)

opt = Bandit(sampler=sampler, score=ucb_minimize)
n_iter = 100
for _ in range(n_iter):
    x = opt.suggest()
    y = feval(x)
    opt.update(x=x, y=y)

idx = np.argmin(opt.output_history_)
best_input = opt.input_history_[idx]
best_output = opt.output_history_[idx]
print('best input : {:.2f}, best output : {:.2f}'.format(best_input, best_output))
iters = np.arange(len(opt.output_history_))
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))

ax1.plot(iters, minimum.accumulate(opt.output_history_))
ax1.set_xlabel('iteration')
ax1.set_ylabel('current $min_x({x**2-2})$')

ax2.plot(iters, opt.input_history_)
ax2.set_xlabel('iteration')
ax2.set_ylabel('selected input')

plt.show()

Total running time of the script: ( 0 minutes 2.127 seconds)