ManifoldAnchorPointsGenerator(manifold=sphere_manifold))
# ### Bayesian optimization
# Define the Bayesian optimization
# An optimizer for the acquisition function can additionally be specified with the "optimizer" parameter
bo_optimizer = gpflowopt.bo.BayesianOptimizer(domain=domain, acquisition=acq_fct, optimizer=acq_fct_opt,
scaling=False, verbose=True)
# Run the Bayessian optimization
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),
print("Iteration " + str(iteration) + "\t Best f " +
str(new_best_f.item()))
# To numpy
x_eval = x_data.numpy()
y_eval = y_data.numpy()[:, None]
if disp_fig:
# Plot acquisition function
fig = plt.figure(figsize=(5, 5))
ax = Axes3D(fig)
bo_plot_acquisition_sphere(ax,
acq_fct,
xs=x_eval,
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,