elif args.optimizer == 'local':
acq_optimizer_type = 'local_random'
else:
raise ValueError('Unknown optimizer %s' % args.optimizer)
if acq_type in ['mesmoc', 'mesmoc2']:
surrogate_type = None
acq_optimizer_type = None
rep = args.rep
start_id = args.start_id
plot_mode = args.plot_mode
if acq_type == 'ehvic':
mth = 'openbox'
else:
mth = 'openbox-%s' % acq_type
problem = get_problem(problem_str)
if initial_runs == 0:
initial_runs = 2 * (problem.dim + 1)
cs = problem.get_configspace(optimizer='smac')
time_limit_per_trial = 600
task_id = '%s_%s_%s' % (mth, acq_type, problem_str)
def evaluate(mth, run_i, seed):
print(mth, run_i, seed, '===== start =====', flush=True)
def objective_function(config):
res = problem.evaluate_config(config)
res['config'] = config
res['objs'] = np.asarray(res['objs']).tolist()
res['constraints'] = np.asarray(res['constraints']).tolist()
else:
hv_diffs, pf, config_list, perf_list, time_list = save_item
if len(hv_diffs) / times != max_runs:
print('Error len: ', file, len(hv_diffs))
continue
result.append(hv_diffs)
print('last hv_diff =', hv_diffs[-1])
if pf is not None:
print(mth, 'pareto num =', len(pf))
print('result rep =', len(result), mth)
result = np.array(result)
if problem_str in ['bnh', 'srn', 'constr']: # fix max hv
import sys
sys.path.insert(0, '.')
from moc_benchmark_function import get_problem
max_hv = get_problem(problem_str).new_max_hv
result += max_hv
print('===== fix max_hv =', max_hv, problem_str)
if (result < 0).any() and (result > 0).any():
raise ValueError
if log_phv:
if (result == 0.0).any():
print('===== WARNING: zero value in result. set to 1e-10 to do log.')
result[result == 0.0] = 1e-10
result = log_func(result) # log
mean_res = np.mean(result, axis=0)
std_res = np.std(result, axis=0)
# plot
x = (np.arange(len(mean_res)) + 1) / times
p, = plt.plot(x, mean_res, label=get_mth_legend(mth), color=color_dict[mth])