#optimal value
#[-0.53699102]
# --- Acquisition optimizer
acq_opt = GPyOpt.optimization.AcquisitionOptimizer(optimizer='CMA',
inner_optimizer='lbfgs2',
space=space)
# --- Acquisition function
#acquisition = uEI_noiseless(model, space, optimizer=acq_opt, utility=U)
acquisition = uPI(model, space, optimizer=acq_opt, utility=U)
# --- Evaluator
evaluator = GPyOpt.core.evaluators.Sequential(acquisition)
# --- Run CBO algorithm
max_iter = 50
for i in range(1):
filename = './experiments/test5_PI_h_noiseless_' + str(i) + '.txt'
print(filename)
bo_model = cbo.CBO(model,
space,
objective,
acquisition,
evaluator,
initial_design,
expectation_utility=expectation_U)
bo_model.run_optimization(max_iter=max_iter,
parallel=False,
plot=False,
results_file=filename)
best_val_found = np.inf
for x0 in starting_points:
res = scipy.optimize.fmin_l_bfgs_b(func, x0, approx_grad=True, bounds=bounds)
if best_val_found > res[1]:
best_val_found = res[1]
x_opt = res[0]
print('optimum')
print(x_opt)
print('h(optimum)')
print(h(x_opt))
print('optimal value')
print(-best_val_found)
# --- Acquisition optimizer
acq_opt = GPyOpt.optimization.AcquisitionOptimizer(optimizer='lbfgs2', inner_optimizer='lbfgs2', space=space)
# --- Acquisition function
acquisition = uEI_noiseless(model, space, optimizer=acq_opt, utility=U)
# --- Evaluator
evaluator = GPyOpt.core.evaluators.Sequential(acquisition)
# --- Run CBO algorithm
max_iter = 50
for i in range(1):
filename = './experiments/test4_EIh_noiseless_' + str(i) + '.txt'
bo_model = cbo.CBO(model, space, objective, acquisition, evaluator, initial_design)
bo_model.run_optimization(max_iter=max_iter, parallel=False, plot=False, results_file=filename)
