Y = r.multivariate_normal(mean, cov[j % 2])
Y = np.reshape(Y, (6**d, 1))
aux_model.append(
GPy.models.GPRegression(grid,
Y,
kernel[j % 2],
noise_var=1e-10))
def f(X):
X = np.atleast_2d(X)
fX = np.empty((m, X.shape[0]))
for j in range(m):
fX[j, :] = aux_model[j].posterior_mean(X)[:, 0]
return fX
attributes = Attributes(f, as_list=False, output_dim=m)
# Initial design
initial_design = GPyOpt.experiment_design.initial_design(
'random', space, 2 * (d + 1), experiment_number)
# True underlying utility
true_underlying_utility_parameter = utility.sample_parameter_from_prior(
1, experiment_number)
print('True underlying utility parameter: {}'.format(
true_underlying_utility_parameter))
def true_underlying_utility_func(y):
return utility_func(y, true_underlying_utility_parameter)
# Run full optimization loop
np.sum(np.cos(2 * np.pi * (X[:, 1:] - 0.5)), axis=1))
return gX
def f1(X):
X = np.atleast_2d(X)
f1X = np.empty((X.shape[0], 1))
f1X[:, 0] = 0.5 * X[:, 0] * (1 + g(X)[:, 0])
return -f1X
def f2(X):
X = np.atleast_2d(X)
f2X = np.empty((X.shape[0], 1))
f2X[:, 0] = 0.5 * (1 - X[:, 0]) * (1 + g(X)[:, 0])
return -f2X
attributes = Attributes([f1, f2], as_list=True, output_dim=m)
# Utility function
def utility_func(y, parameter):
return np.dot(parameter, y)
def utility_gradient(y, parameter):
return parameter
def prior_sample_generator(n_samples=1, seed=None):
if seed is None:
samples = np.random.dirichlet(np.ones((m, )), n_samples)
else:
random_state = np.random.RandomState(seed)
samples = random_state.dirichlet(np.ones((m, )), n_samples)
return samples
# --- Sampling policy
sampling_policy_name = 'Random'
if sampling_policy_name is 'uTS':
sampling_policy = uTS(model, space, optimizer='CMA', utility=utility)
elif sampling_policy_name is 'Random':
sampling_policy = Random(model, space)
# Attributes
def f(X):
X = np.atleast_2d(X)
fX = -np.sin(10 * X[:, 0]) - np.square(X[:, 1])
fX = np.atleast_2d(fX)
return fX
attributes = Attributes(f, noise_var=[0.01], as_list=False, output_dim=m)
# BO model
max_iter = 100
experiment_name = 'test_sinequad'
if len(sys.argv) > 1:
experiment_number = int(sys.argv[1])
filename = [experiment_name, sampling_policy_name, str(experiment_number)]
# Initial design
initial_design = GPyOpt.experiment_design.initial_design('random', space, 2 * (d + 1), experiment_number)
# Run full optimization loop
bopl = BOPL(model, space, attributes, sampling_policy, utility, initial_design, dynamic_utility_parameter_distribution=dynamic_utility_parameter_distribution)
bopl.run_optimization(max_iter=max_iter, filename=filename, report_evaluated_designs_only=False,
utility_distribution_update_interval=1, compute_true_underlying_optimal_value=False,
