Nf=3500, loss_function=logLoss, ) # ei = EI(model, X_upper=X_upper, X_lower=X_lower, par=0.3) # pi = PI(model, X_upper= X_upper, X_lower=X_lower, par =0.3) for acquisition_fkt in [entropy_mc]: bo = BayesianOptimization( acquisition_fkt=acquisition_fkt, model=model, maximize_fkt=maximize_fkt, X_lower=X_lower, X_upper=X_upper, dims=dims, objective_fkt=objective_funktion, save_dir=None, ) next_x = bo.choose_next(initial_X, initial_Y) print model.m Visualization( bo, next_x, X=initial_X, Y=initial_Y, show_acq_method=False, show_obj_method=True, show_model_method=True, resolution=1000, dest_folder="./test_output", )
kernel = GPy.kern.RBF(input_dim=dims) kernel = GPy.kern.Matern52(input_dim=dims) maximize_fkt = grid_search model = GPyModel(kernel, optimize=True, noise_variance=1e-4, num_restarts=10) # entropy = Entropy(model, X_upper= X_upper, X_lower=X_lower, sampling_acquisition= LogEI, Nb=10, Np=600, loss_function = logLoss) entropy_mc = EntropyMC(model, X_upper=X_upper, X_lower=X_lower, compute_incumbent=compute_incumbent, sampling_acquisition=LogEI, Nb=10, Np=300, Nf=3500, loss_function=logLoss) #ei = EI(model, X_upper=X_upper, X_lower=X_lower, par=0.3) # pi = PI(model, X_upper= X_upper, X_lower=X_lower, par =0.3) for acquisition_fkt in [entropy_mc]: bo = BayesianOptimization(acquisition_fkt=acquisition_fkt, model=model, maximize_fkt=maximize_fkt, X_lower=X_lower, X_upper=X_upper, dims=dims, objective_fkt=objective_funktion, save_dir=None) next_x = bo.choose_next(initial_X, initial_Y) print model.m Visualization(bo, next_x, X=initial_X, Y=initial_Y, show_acq_method=False, show_obj_method=True, show_model_method=True, resolution=1000, dest_folder="./test_output")