main.py

#! usr/bin/env python
# -*- coding : utf-8 -*-

from skopt import gp_maximize
import numpy as np
from skopt.plots import plot_convergence
np.random.seed(123)

#%matplotlib inline
import matplotlib.pyplot as plt


noise_level = 0.1

def f(x, noise_level=noise_level):
    return np.sin(5 * x[0]) * (1 - np.tanh(x[0] ** 2)) + np.random.randn() * noise_level
# Plot f(x) + contours
x = np.linspace(-2, 2, 400).reshape(-1, 1)
fx = [f(x_i, noise_level=0.0) for x_i in x]
plt.plot(x, fx, "r--", label="True (unknown)")
plt.fill(np.concatenate([x, x[::-1]]),
         np.concatenate(([fx_i - 1.9600 * noise_level for fx_i in fx],
                         [fx_i + 1.9600 * noise_level for fx_i in fx[::-1]])),
         alpha=.2, fc="r", ec="None")
#plt.legend()
#plt.grid()
#plt.show()

res = gp_maximize(f,                  # the function to minimize
                  [(-2.0, 2.0)],      # the bounds on each dimension of x
                  acq_func="EI",      # the acquisition function
                  n_calls=15,         # the number of evaluations of f
                  n_random_starts=5,  # the number of random initialization points
                  noise=0.1**2,       # the noise level (optional)
                  random_state=123)   # the random seed

#print(res)
#plot_convergence(res);
plt.rcParams["figure.figsize"] = (6, 4)

# Plot f(x) + contours
x = np.linspace(-2, 2, 400).reshape(-1, 1)
x_gp = res.space.transform(x.tolist())

fx = [f(x_i, noise_level=0.0) for x_i in x]
plt.plot(x, fx, "r--", label="True (unknown)")
plt.fill(np.concatenate([x, x[::-1]]),
         np.concatenate(([fx_i - 1.9600 * noise_level for fx_i in fx], 
                         [fx_i + 1.9600 * noise_level for fx_i in fx[::-1]])),
         alpha=.2, fc="r", ec="None")

# Plot GP(x) + contours
gp = res.models[-1]
y_pred, sigma = gp.predict(x_gp, return_std=True)

plt.plot(x, y_pred, "g--", label=r"$\mu_{GP}(x)$")
plt.fill(np.concatenate([x, x[::-1]]),
         np.concatenate([y_pred - 1.9600 * sigma, 
                         (y_pred + 1.9600 * sigma)[::-1]]),
         alpha=.2, fc="g", ec="None")

# Plot sampled points
plt.plot(res.x_iters, 
         res.func_vals, 
         "r.", markersize=15, label="Observations")

plt.title(r"$x^* = %.4f, f(x^*) = %.4f$" % (res.x[0], res.fun))
plt.legend(loc="best", prop={'size': 8}, numpoints=1)
plt.grid()

plt.show()