else:
dynamic_utility_parameter_distribution = False
sampling_policy_name = 'uEI_prior'
elif sampling_policy_name is 'uTS':
model = MultiOutputGP(output_dim=m,
exact_feval=[True] * m,
fixed_hyps=False) # Model (Multi-output GP)
sampling_policy = uTS(model, space, optimizer='CMA', utility=utility)
if learn_preferences:
dynamic_utility_parameter_distribution = True
else:
dynamic_utility_parameter_distribution = False
sampling_policy_name = 'uTS_prior'
elif sampling_policy_name is 'Random':
model = BasicModel(output_dim=m)
sampling_policy = Random(model=None, space=space)
dynamic_utility_parameter_distribution = False
elif sampling_policy_name is 'ParEGO':
model = BasicModel(output_dim=m)
sampling_policy = ParEGO(model, space, utility)
dynamic_utility_parameter_distribution = False
# BO model
max_iter = 150
experiment_name = 'test_dtlz1a'
if len(sys.argv) > 1:
experiment_number = int(sys.argv[1])
filename = [
experiment_name, sampling_policy_name,
str(experiment_number)
]
# --- Sampling policy
sampling_policy_name = 'Random'
if sampling_policy_name is 'uEI':
# Acquisition optimizer
acquisition_optimizer = U_AcquisitionOptimizer(space=space, model=model, utility=utility, optimizer='lbfgs', inner_optimizer='lbfgs')
acquisition = uEI_affine(model, space, optimizer=acquisition_optimizer, utility=utility)
evaluator = GPyOpt.core.evaluators.Sequential(acquisition)
sampling_policy = AcquisitionFunction(model, space, acquisition, evaluator)
elif sampling_policy_name is 'TS':
sampling_policy = TS(model, optimization_space, optimizer='CMA', scenario_distribution=scenario_distribution,
utility=utility, expectation_utility=expectation_utility)
acquisition = None
elif sampling_policy_name is 'Random':
sampling_policy = Random(model, space)
# BO model
max_iter = 100
experiment_name = 'test_portfolio1'
if len(sys.argv) > 1:
experiment_number = str(sys.argv[1])
filename = [experiment_name, sampling_policy_name, experiment_number]
# True underlying utility
#true_underlying_utility_parameter = utility.sample_parameter()
beta = 0.75
true_underlying_utility_parameter = np.atleast_1d([beta**j for j in range(m)])
print(true_underlying_utility_parameter)
def true_underlying_utility_func(y):