def __init__(self, objective_function, *args, **kwargs):
super().__init__(*args, **kwargs)
self.objective_function = objective_function
self.online_acq_optimizer = InterleavedLocalAndRandomSearch( # replace offline search
self.acquisition_function,
self.config_space,
rng=self.rng,
max_steps=None,
n_steps_plateau_walk=10,
n_sls_iterations=10,
rand_prob=0.0,
)
self.configuration_list_copy = self.configuration_list.copy()
def __init__(self,
objective_function,
config_space,
time_limit_per_trial=180,
max_runs=200,
model_type='gp_mcmc',
logging_dir='./logs',
initial_configurations=None,
initial_runs=3,
task_id=None,
rng=None):
super().__init__(config_space, task_id, output_dir=logging_dir)
self.logger = super()._get_logger(self.__class__.__name__)
if rng is None:
run_id, rng = get_rng()
self.rng = rng
self.seed = rng.randint(MAXINT)
self.init_num = initial_runs
self.max_iterations = max_runs
self.iteration_id = 0
self.sls_max_steps = None
self.n_sls_iterations = 5
self.sls_n_steps_plateau_walk = 10
self.time_limit_per_trial = time_limit_per_trial
self.default_obj_value = MAXINT
self.configurations = list()
self.failed_configurations = list()
self.perfs = list()
# Initialize the basic component in BO.
self.config_space.seed(rng.randint(MAXINT))
self.objective_function = objective_function
self.model = build_model(model_type=model_type,
config_space=config_space,
rng=self.rng)
self.acquisition_function = EI(self.model)
self.optimizer = InterleavedLocalAndRandomSearch(
acquisition_function=self.acquisition_function,
config_space=self.config_space,
rng=np.random.RandomState(seed=self.seed),
max_steps=self.sls_max_steps,
n_steps_plateau_walk=self.sls_n_steps_plateau_walk,
n_sls_iterations=self.n_sls_iterations)
self._random_search = RandomSearch(self.acquisition_function,
self.config_space, rng)
self.random_configuration_chooser = ChooserProb(prob=0.25, rng=rng)
class SMBO_SEARCH_SPACE_Enlarge_ONLINE(SMBO_SEARCH_SPACE_Enlarge):
def __init__(self,
objective_function,
*args, **kwargs):
super().__init__(*args, **kwargs)
self.objective_function = objective_function
self.online_acq_optimizer = InterleavedLocalAndRandomSearch( # replace offline search
self.acquisition_function,
self.config_space,
rng=self.rng,
max_steps=None,
n_steps_plateau_walk=10,
n_sls_iterations=10,
rand_prob=0.0,
)
self.configuration_list_copy = self.configuration_list.copy()
def evaluate(self, config):
try:
perf = self.target_hpo_measurements.get(config)
if perf is None:
print('[Evaluation] on real objective!')
perf = self.objective_function(config)
else:
print('[Evaluation] get cache.')
except Exception:
print(traceback.format_exc())
perf = MAXINT
return perf
def update_configuration_list(self):
"""
start offline search based on online searched samples
"""
start_time = time.time()
challengers = self.online_acq_optimizer.maximize(
runhistory=self.history_container,
num_points=5000,
)
self.configuration_list = challengers.challengers + self.configuration_list_copy
print('optimizing online acq func took', time.time() - start_time)
def check_space(self, X_candidate):
print("Reduced: %.2f/%.2f, Rate: %.2f" % (
len(X_candidate), len(self.configuration_list), len(X_candidate) / len(self.configuration_list)))
if len(X_candidate) != len(self.configuration_list):
self.reduce_cnt += 1
print("Reduced space is applied for %d iterations!" % self.reduce_cnt)
class SMBO(BasePipeline):
def __init__(self,
objective_function,
config_space,
time_limit_per_trial=180,
max_runs=200,
model_type='gp_mcmc',
logging_dir='./logs',
initial_configurations=None,
initial_runs=3,
task_id=None,
rng=None):
super().__init__(config_space, task_id, output_dir=logging_dir)
self.logger = super()._get_logger(self.__class__.__name__)
if rng is None:
run_id, rng = get_rng()
self.rng = rng
self.seed = rng.randint(MAXINT)
self.init_num = initial_runs
self.max_iterations = max_runs
self.iteration_id = 0
self.sls_max_steps = None
self.n_sls_iterations = 5
self.sls_n_steps_plateau_walk = 10
self.time_limit_per_trial = time_limit_per_trial
self.default_obj_value = MAXINT
self.configurations = list()
self.failed_configurations = list()
self.perfs = list()
# Initialize the basic component in BO.
self.config_space.seed(rng.randint(MAXINT))
self.objective_function = objective_function
self.model = build_model(model_type=model_type,
config_space=config_space,
rng=self.rng)
self.acquisition_function = EI(self.model)
self.optimizer = InterleavedLocalAndRandomSearch(
acquisition_function=self.acquisition_function,
config_space=self.config_space,
rng=np.random.RandomState(seed=self.seed),
max_steps=self.sls_max_steps,
n_steps_plateau_walk=self.sls_n_steps_plateau_walk,
n_sls_iterations=self.n_sls_iterations)
self._random_search = RandomSearch(self.acquisition_function,
self.config_space, rng)
self.random_configuration_chooser = ChooserProb(prob=0.25, rng=rng)
def run(self):
while self.iteration_id < self.max_iterations:
self.iterate()
def iterate(self):
if len(self.configurations) == 0:
X = np.array([])
else:
X = convert_configurations_to_array(self.configurations)
Y = np.array(self.perfs, dtype=np.float64)
config = self.choose_next(X, Y)
trial_state = SUCCESS
trial_info = None
if config not in (self.configurations + self.failed_configurations):
# Evaluate this configuration.
try:
with time_limit(self.time_limit_per_trial):
perf = self.objective_function(config)
except Exception as e:
traceback.print_exc(file=sys.stdout)
perf = MAXINT
trial_info = str(e)
trial_state = FAILDED if not isinstance(
e, TimeoutException) else TIMEOUT
self.logger.error(trial_info)
if trial_state == SUCCESS and perf < MAXINT:
if len(self.configurations) == 0:
self.default_obj_value = perf
self.configurations.append(config)
self.perfs.append(perf)
self.history_container.add(config, perf)
else:
self.failed_configurations.append(config)
else:
self.logger.debug(
'This configuration has been evaluated! Skip it.')
if config in self.configurations:
config_idx = self.configurations.index(config)
trial_state, perf = SUCCESS, self.perfs[config_idx]
else:
trial_state, perf = FAILDED, MAXINT
self.iteration_id += 1
self.logger.info(
'Iteration-%d, objective improvement: %.4f' %
(self.iteration_id, max(0, self.default_obj_value - perf)))
return config, trial_state, perf, trial_info
def choose_next(self, X: np.ndarray, Y: np.ndarray):
_config_num = X.shape[0]
if _config_num < self.init_num:
default_config = self.config_space.get_default_configuration()
if default_config not in (self.configurations +
self.failed_configurations):
return default_config
else:
return self._random_search.maximize(
runhistory=self.history_container, num_points=5000)[0]
if self.random_configuration_chooser.check(self.iteration_id):
return self.config_space.sample_configuration()
else:
self.model.train(X, Y)
incumbent_value = self.history_container.get_incumbents()[0][1]
self.acquisition_function.update(model=self.model,
eta=incumbent_value,
num_data=len(
self.history_container.data))
challengers = self.optimizer.maximize(
runhistory=self.history_container,
num_points=5000,
random_configuration_chooser=self.random_configuration_chooser)
return challengers.challengers[0]