Bopt = bo_optimizer.optimize(test_function, n_iter=nb_iter_bo)
print(Bopt)
if display_figures:
# Plot acquisition function
fig = plt.figure(figsize=(10, 10))
ax = Axes3D(fig)
bo_plot_acquisition_sphere(ax, acq_fct, xs=bo_optimizer.acquisition.data[0], opt_x=Bopt.x, true_opt_x=true_min,
elev=10, azim=30, n_elems=100)
ax.set_title('Acquisition function', fontsize=50)
plt.show()
# Plot GP
fig = plt.figure(figsize=(10, 10))
ax = Axes3D(fig)
bo_plot_gp_sphere(ax, model, xs=bo_optimizer.acquisition.data[0], opt_x=Bopt.x, true_opt_x=true_min,
true_opt_y=true_opt_val, max_colors=max_colors, elev=10, azim=30, n_elems=100)
ax.set_title('GP mean', fontsize=50)
plt.show()
# Plot GP projected on planes
fig = plt.figure(figsize=(20, 10))
bo_plot_gp_sphere_planar(fig, model, var_fact=2., xs=bo_optimizer.acquisition.data[0],
ys=bo_optimizer.acquisition.data[1], opt_x=Bopt.x, opt_y=test_function(Bopt.x),
true_opt_x=true_min, true_opt_y=true_opt_val, max_colors=max_colors, n_elems=100)
plt.title('GP mean and variance', fontsize=50)
plt.show()
if display_figures:
# Plot observations on the sphere
# 3D figure
fig = plt.figure(figsize=(5, 5))
opt_x=best_x[-1][None],
true_opt_x=true_min,
elev=10,
azim=30,
n_elems=100)
ax.set_title('Acquisition function', fontsize=20)
plt.show()
# Plot GP
fig = plt.figure(figsize=(5, 5))
ax = Axes3D(fig)
bo_plot_gp_sphere(ax,
model,
xs=x_eval,
opt_x=best_x[-1][None],
true_opt_x=true_min,
true_opt_y=true_opt_val,
max_colors=max_colors,
elev=10,
azim=30,
n_elems=100)
ax.set_title('GP mean', fontsize=20)
plt.show()
# Plot GP projected on planes
fig = plt.figure(figsize=(10, 5))
bo_plot_gp_sphere_planar(fig,
model,
var_fact=2.,
xs=x_eval,
ys=y_eval,
opt_x=best_x[-1][None],
