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
return res
bo = SMBO(objective_function, cs,
num_objs=problem.num_objs,
num_constraints=0,
surrogate_type=surrogate_type, # default: gp
acq_type=acq_type, # default: ehvi
acq_optimizer_type=acq_optimizer_type, # default: random_scipy
initial_runs=initial_runs, # default: 2 * (problem.dim + 1)
init_strategy=init_strategy, # default: sobol
max_runs=max_runs,
ref_point=problem.ref_point,
time_limit_per_trial=time_limit_per_trial, task_id=task_id, random_state=seed)
# bo.run()
hv_diffs = []
config_list = []
perf_list = []
time_list = []
global_start_time = time.time()
for i in range(max_runs):
config, trial_state, objs, trial_info = bo.iterate()
global_time = time.time() - global_start_time
print(seed, i, objs, config, trial_state, trial_info, 'time=', global_time)
hv = Hypervolume(problem.ref_point).compute(bo.get_history().get_pareto_front())
hv_diff = problem.max_hv - hv
print(seed, i, 'hypervolume =', hv)
print(seed, i, 'hv diff =', hv_diff)
hv_diffs.append(hv_diff)
config_list.append(config)
perf_list.append(objs)
time_list.append(global_time)
pf = np.asarray(bo.get_history().get_pareto_front())
# plot for debugging
if plot_mode == 1:
Y_init = None
plot_pf(problem, problem_str, mth, pf, Y_init)
return hv_diffs, pf, config_list, perf_list, time_list
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
sys.path.insert(0, os.getcwd())
from litebo.optimizer.generic_smbo import SMBO
from litebo.benchmark.objective_functions.synthetic import BraninCurrin
prob = BraninCurrin()
bo = SMBO(prob.evaluate,
prob.config_space,
advisor_type='mcadvisor',
task_id='mcehvi',
num_objs=prob.num_objs,
num_constraints=prob.num_constraints,
acq_type='mcehvi',
ref_point=prob.ref_point,
max_runs=100,
random_state=2)
bo.run()
hvs = bo.get_history().hv_data
log_hv_diff = np.log10(prob.max_hv - np.asarray(hvs))
pf = np.asarray(bo.get_history().get_pareto_front())
plt.scatter(pf[:, 0], pf[:, 1])
# plt.plot(log_hv_diff)
# plt.show()
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
sys.path.insert(0, os.getcwd())
from litebo.optimizer.generic_smbo import SMBO
from litebo.benchmark.objective_functions.synthetic import BraninCurrin
prob = BraninCurrin(constrained=True)
bo = SMBO(prob.evaluate,
prob.config_space,
advisor_type='mcadvisor',
task_id='mccmo',
num_objs=prob.num_objs,
num_constraints=prob.num_constraints,
acq_type='mcparegoc',
ref_point=prob.ref_point,
max_runs=100,
random_state=2)
bo.run()
hvs = bo.get_history().hv_data
log_hv_diff = np.log10(prob.max_hv - np.asarray(hvs))
plt.plot(log_hv_diff)
plt.show()
time_limit_per_trial=time_limit_per_trial,
task_id='mo',
random_state=seed)
bo.config_advisor.optimizer.random_chooser.prob = rand_prob # set rand_prob, default 0
bo.config_advisor.acquisition_function.sample_num = sample_num # set sample_num
bo.config_advisor.acquisition_function.random_state = seed # set random_state
bo.config_advisor.optimizer.num_mc = 10000 # MESMO optimizer only
bo.config_advisor.optimizer.num_opt = 10 # MESMO optimizer only
print(mth, '===== start =====')
# bo.run()
hv_diffs = []
for i in range(max_runs):
config, trial_state, objs, trial_info = bo.iterate()
print(i, objs, config)
hv = Hypervolume(referencePoint).compute(
bo.get_history().get_pareto_front())
print(i, 'hypervolume =', hv)
hv_diff = real_hv - hv
hv_diffs.append(hv_diff)
print(i, 'hv diff =', hv_diff)
# Print result
pf = np.asarray(bo.get_history().get_pareto_front())
print(mth, 'pareto num:', pf.shape[0])
print('real hv =', real_hv)
print('hv_diffs:', hv_diffs)
# Evaluate the random search.
bo_r = SMBO(multi_objective_func,
cs,
num_objs=num_objs,
time_limit_per_trial=time_limit_per_trial,
task_id='mo',
random_state=seed)
bo.config_advisor.optimizer.random_chooser.prob = rand_prob # set rand_prob, default 0
bo.config_advisor.acquisition_function.sample_num = sample_num # set sample_num
bo.config_advisor.acquisition_function.random_state = seed # set random_state
bo.config_advisor.optimizer.num_mc = opt_num_mc # MESMO optimizer only
bo.config_advisor.optimizer.num_opt = opt_num_opt # MESMO optimizer only
print(seed, mth, '===== start =====')
# bo.run()
hv_diffs = []
for i in range(max_runs):
config, trial_state, objs, trial_info = bo.iterate()
print(seed, i, objs, config)
hv = Hypervolume(referencePoint).compute(
bo.get_history().get_pareto_front())
print(seed, i, 'hypervolume =', hv)
hv_diff = real_hv - hv
hv_diffs.append(hv_diff)
print(seed, i, 'hv diff =', hv_diff)
# Save result
pf = np.asarray(bo.get_history().get_pareto_front())
data = bo.get_history().data
print(seed, mth, 'pareto num:', pf.shape[0])
print(seed, 'real hv =', real_hv)
print(seed, 'hv_diffs:', hv_diffs)
timestamp = time.strftime('%Y-%m-%d-%H-%M-%S',
time.localtime(time.time()))
dir_path = 'logs/mo_benchmark_%s_%d/%s-%d/' % (
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()
return res
bo = SMBO(
objective_function,
cs,
num_objs=problem.num_objs,
num_constraints=problem.num_constraints,
surrogate_type=surrogate_type, # default: gp
acq_type=acq_type, # default: ehvic
acq_optimizer_type=acq_optimizer_type, # default: random_scipy
initial_runs=initial_runs, # default: 2 * (problem.dim + 1)
init_strategy=init_strategy, # default: sobol
max_runs=max_runs,
ref_point=problem.ref_point,
time_limit_per_trial=time_limit_per_trial,
task_id=task_id,
random_state=seed)
# bo.run()
hv_diffs = []
config_list = []
perf_list = []
time_list = []
global_start_time = time.time()
for i in range(max_runs):
config, trial_state, origin_objs, trial_info = bo.iterate()
global_time = time.time() - global_start_time
constraints = [
bo.config_advisor.constraint_perfs[i][-1]
for i in range(problem.num_constraints)
]
if any(c > 0 for c in constraints):
objs = [9999999.0] * problem.num_objs
else:
objs = origin_objs
print(seed, i, origin_objs, objs, constraints, config, trial_state,
trial_info, 'time=', global_time)
assert len(bo.config_advisor.constraint_perfs[0]
) == i + 1 # make sure no repeat or failed config
config_list.append(config)
perf_list.append(objs)
time_list.append(global_time)
hv = Hypervolume(problem.ref_point).compute(perf_list)
hv_diff = problem.max_hv - hv
hv_diffs.append(hv_diff)
print(seed, i, 'hypervolume =', hv)
print(seed, i, 'hv diff =', hv_diff)
pf = np.asarray(bo.get_history().get_pareto_front())
# plot for debugging
if plot_mode == 1:
Y_init = None
plot_pf(problem, problem_str, mth, pf, Y_init)
return hv_diffs, pf, config_list, perf_list, time_list
from litebo.optimizer.generic_smbo import SMBO
from litebo.benchmark.objective_functions.synthetic import Ackley
num_inputs = 10
acq_optimizer_type = 'random_scipy'
seed = 1
prob = Ackley(dim=num_inputs, constrained=False)
initial_runs = 2 * (num_inputs + 1)
max_runs = 250
bo = SMBO(prob.evaluate,
prob.config_space,
task_id='turbo',
advisor_type='mcadvisor',
num_objs=prob.num_objs,
num_constraints=prob.num_constraints,
acq_type='mcei',
acq_optimizer_type=acq_optimizer_type,
use_trust_region=True,
surrogate_type='gp',
max_runs=max_runs,
initial_runs=initial_runs,
init_strategy='latin_hypercube',
random_state=seed)
bo.run()
values = list(bo.get_history().data.values())
plt.plot(values)
plt.show()