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],