def callback(X_next, Y_next, i):
global X_sample, Y_sample
# Plot samples, surrogate function, noise-free objective and next sampling location
#plt.subplot(n_iter, 2, 2 * i + 1)
plt.figure()
plot_approximation(gpr,
X,
Y,
X_sample,
Y_sample,
X_next,
show_legend=i == 0)
plt.title(f'Iteration {i+1}')
if save_figures: save_fig('bayes-opt-surrogate-{}.pdf'.format(i + 1))
plt.show()
plt.figure()
#plt.subplot(n_iter, 2, 2 * i + 2)
plot_acquisition(X,
expected_improvement(X, X_sample, Y_sample, gpr),
X_next,
show_legend=i == 0)
if save_figures: save_fig('bayes-opt-acquisition-{}.pdf'.format(i + 1))
plt.show()
# Add sample to previous samples
X_sample = np.append(X_sample, np.atleast_2d(X_next), axis=0)
Y_sample = np.append(Y_sample, np.atleast_2d(Y_next), axis=0)
def EI(X, X_sample, Y_sample, surrogate):
X = np.atleast_2d(X)
return expected_improvement(X,
X_sample,
Y_sample,
surrogate,
improvement_thresh=0.01,
trust_incumbent=True)