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 __init__(
self,
acquisition_function: AbstractAcquisitionFunction,
config_space: ConfigurationSpace,
rng: Union[bool, np.random.RandomState] = None,
max_steps: Optional[int] = None,
n_steps_plateau_walk: int = 10,
n_sls_iterations: int = 10,
rand_prob=0.1,
):
super().__init__(acquisition_function, config_space, rng)
self.random_search = RandomSearch(
acquisition_function=acquisition_function,
config_space=config_space,
rng=rng)
self.local_search = LocalSearch(
acquisition_function=acquisition_function,
config_space=config_space,
rng=rng,
max_steps=max_steps,
n_steps_plateau_walk=n_steps_plateau_walk)
self.n_sls_iterations = n_sls_iterations
self.random_chooser = ChooserProb(prob=rand_prob, rng=rng)
def __init__(self,
target_hpo_data: Dict,
config_space: ConfigurationSpace,
surrogate_model: BaseFacade,
acq_func: str = 'ei',
source_hpo_data=None,
enable_init_design=False,
num_src_hpo_trial=50,
surrogate_type='rf',
max_runs=200,
logging_dir='./logs',
initial_runs=3,
task_id=None,
random_seed=None):
super().__init__(config_space, task_id, output_dir=logging_dir)
self.logger = super()._get_logger(self.__class__.__name__)
if random_seed is None:
_, rng = get_rng()
random_seed = rng.randint(MAXINT)
self.random_seed = random_seed
self.rng = np.random.RandomState(self.random_seed)
self.source_hpo_data = source_hpo_data
self.num_src_hpo_trial = num_src_hpo_trial
self.surrogate_type = surrogate_type
self.acq_func = acq_func
self.max_iterations = max_runs
self.iteration_id = 0
self.default_obj_value = MAXINT
self.target_hpo_measurements = target_hpo_data
self.configuration_list = list(self.target_hpo_measurements.keys())
print('Target problem space: %d configurations' %
len(self.configuration_list))
self.configurations = list()
self.failed_configurations = list()
self.perfs = list()
self.test_perfs = list()
if enable_init_design:
self.initial_configurations = self.initial_design(initial_runs)
else:
self.initial_configurations = None
if self.initial_configurations is None:
self.init_num = initial_runs
else:
self.init_num = len(self.initial_configurations)
# Initialize the basic component in BO.
self.config_space.seed(self.random_seed)
np.random.seed(self.random_seed)
self.model = surrogate_model
assert self.model.method_id == 'rgpe'
self.acquisition_function = EI(self.model)
self.space_classifier = None
self.build_classifier()
self.random_configuration_chooser = ChooserProb(
prob=0.1, rng=np.random.RandomState(self.random_seed))
# Set the parameter in metric. TODO: add test perf
ys = np.array(list(self.target_hpo_measurements.values()))
if ys.ndim == 2:
assert ys.shape[1] == 2
val_ys = ys[:, 0]
else:
val_ys = ys
self.ys = val_ys
self.y_max, self.y_min = np.max(self.ys), np.min(self.ys)
class SMBO_SEARCH_SPACE_TRANSFER(BasePipeline):
def __init__(self,
target_hpo_data: Dict,
config_space: ConfigurationSpace,
surrogate_model: BaseFacade,
acq_func: str = 'ei',
source_hpo_data=None,
enable_init_design=False,
num_src_hpo_trial=50,
surrogate_type='rf',
max_runs=200,
logging_dir='./logs',
initial_runs=3,
task_id=None,
random_seed=None):
super().__init__(config_space, task_id, output_dir=logging_dir)
self.logger = super()._get_logger(self.__class__.__name__)
if random_seed is None:
_, rng = get_rng()
random_seed = rng.randint(MAXINT)
self.random_seed = random_seed
self.rng = np.random.RandomState(self.random_seed)
self.source_hpo_data = source_hpo_data
self.num_src_hpo_trial = num_src_hpo_trial
self.surrogate_type = surrogate_type
self.acq_func = acq_func
self.max_iterations = max_runs
self.iteration_id = 0
self.default_obj_value = MAXINT
self.target_hpo_measurements = target_hpo_data
self.configuration_list = list(self.target_hpo_measurements.keys())
print('Target problem space: %d configurations' %
len(self.configuration_list))
self.configurations = list()
self.failed_configurations = list()
self.perfs = list()
self.test_perfs = list()
if enable_init_design:
self.initial_configurations = self.initial_design(initial_runs)
else:
self.initial_configurations = None
if self.initial_configurations is None:
self.init_num = initial_runs
else:
self.init_num = len(self.initial_configurations)
# Initialize the basic component in BO.
self.config_space.seed(self.random_seed)
np.random.seed(self.random_seed)
self.model = surrogate_model
assert self.model.method_id == 'rgpe'
self.acquisition_function = EI(self.model)
self.space_classifier = None
self.build_classifier()
self.random_configuration_chooser = ChooserProb(
prob=0.1, rng=np.random.RandomState(self.random_seed))
# Set the parameter in metric. TODO: add test perf
ys = np.array(list(self.target_hpo_measurements.values()))
if ys.ndim == 2:
assert ys.shape[1] == 2
val_ys = ys[:, 0]
else:
val_ys = ys
self.ys = val_ys
self.y_max, self.y_min = np.max(self.ys), np.min(self.ys)
def get_adtm(self):
y_inc = self.get_inc_y()
assert self.y_max != self.y_min
return (y_inc - self.y_min) / (self.y_max - self.y_min)
def get_inc_y(self):
y_inc = np.min(self.perfs)
return y_inc
def run(self):
while self.iteration_id < self.max_iterations:
self.iterate()
def sort_configs_by_score(self):
from tlbo.facade.obtl_es import ES
surrogate = ES(self.config_space,
self.source_hpo_data,
self.configuration_list,
self.random_seed,
surrogate_type=self.surrogate_type,
num_src_hpo_trial=self.num_src_hpo_trial)
X = convert_configurations_to_array(self.configuration_list)
scores, _ = surrogate.predict(X)
sorted_items = sorted(list(zip(self.configuration_list, scores)),
key=lambda x: x[1])
return [item[0] for item in sorted_items]
def initial_design(self, n_init=3):
initial_configs = list()
configs_ = self.sort_configs_by_score()[:25]
from sklearn.cluster import KMeans
X = convert_configurations_to_array(configs_)
kmeans = KMeans(n_clusters=n_init, random_state=0).fit(X)
labels = kmeans.predict(X)
label_set = set()
for idx, _config in enumerate(configs_):
if labels[idx] not in label_set:
label_set.add(labels[idx])
initial_configs.append(_config)
return initial_configs
def evaluate(self, config):
value = self.target_hpo_measurements[config]
try:
perf, test_perf = value
except Exception:
perf = value
test_perf = -1
return perf, test_perf
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)
# start_time = time.time()
config = self.choose_next(X, Y)
# print('In %d-th iter, config selection took %.3fs' % (self.iteration_id, time.time() - start_time))
trial_state = SUCCESS
trial_info = None
if config not in (self.configurations + self.failed_configurations):
# Evaluate this configuration.
perf, test_perf = self.evaluate(config)
if perf == MAXINT:
trial_info = 'failed configuration evaluation.'
trial_state = FAILDED
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.test_perfs.append(test_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 sample_random_config(self, config_set=None, config_num=1):
configs = list()
sample_cnt = 0
configurations = self.configuration_list if config_set is None else config_set
while len(configs) < config_num:
sample_cnt += 1
_idx = self.rng.randint(len(configurations))
config = configurations[_idx]
if config not in (self.configurations +
self.failed_configurations + configs):
configs.append(config)
sample_cnt = 0
else:
sample_cnt += 1
if sample_cnt >= 200:
configs.append(config)
sample_cnt = 0
return configs
def choose_next(self, X: np.ndarray, Y: np.ndarray):
"""
Step 1. sample a batch of random configs.
Step 2. identify and preserve the configs in the good regions (union)
Step 3. calculate their acquisition functions and choose the config with the largest value.
Parameters
----------
X
Y
Returns
-------
the config to evaluate next.
"""
_config_num = X.shape[0]
if _config_num < self.init_num:
if self.initial_configurations is None:
default_config = self.config_space.get_default_configuration()
if default_config not in self.configuration_list:
default_config = self.configuration_list[0]
if default_config not in (self.configurations +
self.failed_configurations):
config = default_config
else:
config = self.sample_random_config()[0]
return config
else:
print('This is a config for warm-start!')
return self.initial_configurations[_config_num]
start_time = time.time()
self.model.train(X, Y)
print('Training surrogate model took %.3f' %
(time.time() - start_time))
y_, _, _ = zero_mean_unit_var_normalization(Y)
incumbent_value = np.min(y_)
if self.acq_func == 'ei':
self.acquisition_function.update(model=self.model,
eta=incumbent_value,
num_data=len(
self.history_container.data))
else:
raise ValueError('invalid acquisition function ~ %s.' %
self.acq_func)
# Do task selection.
weights = self.model.w[:-1]
assert len(weights) == len(self.space_classifier)
task_indexes = np.argsort(weights)[-5:]
task_indexes = [idx_ for idx_ in task_indexes if weights[idx_] > 0.]
self.update_configuration_list() # for online benchmark
X_ALL = convert_configurations_to_array(self.configuration_list)
y_pred = list()
for _idx, _clf in enumerate(self.space_classifier):
if _idx in task_indexes:
y_pred.append(_clf.predict(X_ALL))
X_candidate = list()
if len(y_pred) > 0:
# Count the #intersection.
pred_mat = np.array(y_pred)
_cnt = 0
for _col in range(pred_mat.shape[1]):
if (pred_mat[:, _col] == 1).all():
_cnt += 1
print('The intersection of candidate space is %d.' % _cnt)
y_pred = np.max(y_pred, axis=0)
for _idx, _flag in enumerate(y_pred):
if _flag == 1:
X_candidate.append(self.configuration_list[_idx])
print('After space transfer, the candidate space size is %d.' %
len(X_candidate))
if len(X_candidate) == 0:
# Deal with the space with no candidates.
if len(self.configurations) <= 20:
X_candidate = self.configuration_list
else:
X_candidate = self.choose_config_target_space()
else:
X_candidate = self.choose_config_target_space()
assert len(X_candidate) > 0
if self.random_configuration_chooser.check(self.iteration_id):
config = self.sample_random_config(config_set=X_candidate)[0]
if len(self.model.target_weight) == 0:
self.model.target_weight.append(0.)
else:
self.model.target_weight.append(self.model.target_weight[-1])
return config
acq_optimizer = OfflineSearch(X_candidate,
self.acquisition_function,
self.config_space,
rng=np.random.RandomState(
self.random_seed))
start_time = time.time()
sorted_configs = acq_optimizer.maximize(
runhistory=self.history_container, num_points=5000)
print('Optimizing Acq. func took %.3f' % (time.time() - start_time))
for _config in sorted_configs:
if _config not in (self.configurations +
self.failed_configurations):
return _config
excluded_set = list()
candidate_set = set(X_candidate)
for _config in self.configuration_list:
if _config not in candidate_set and _config not in (
self.configurations + self.failed_configurations):
excluded_set.append(_config)
if len(excluded_set) == 0:
excluded_set = self.configuration_list
return self.sample_random_config(config_set=excluded_set)[0]
def build_classifier(self):
# Train the binary classifier.
print('start to train binary classifier.')
start_time = time.time()
self.space_classifier = list()
normalize = 'standardize'
for hpo_evaluation_data in self.source_hpo_data:
print('.', end='')
_X, _y = list(), list()
for _config, _config_perf in hpo_evaluation_data.items():
_X.append(_config)
_y.append(_config_perf)
X = convert_configurations_to_array(_X)
y = np.array(_y, dtype=np.float64)
X = X[:self.num_src_hpo_trial]
y = y[:self.num_src_hpo_trial]
if normalize == 'standardize':
if (y == y[0]).all():
y[0] += 1e-4
y, _, _ = zero_mean_unit_var_normalization(y)
elif normalize == 'scale':
if (y == y[0]).all():
y[0] += 1e-4
y, _, _ = zero_one_normalization(y)
y = 2 * y - 1.
else:
raise ValueError('Invalid parameter in norm.')
percentile = np.percentile(y, 30)
space_label = np.array(np.array(y) < percentile)
if (np.array(y) == percentile).all():
raise ValueError('assertion violation: the same eval values!')
if (space_label[0] == space_label).all():
space_label = np.array(np.array(y) < np.mean(y))
print('Label treatment triggers!')
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
clf = make_pipeline(StandardScaler(), SVC(gamma='auto'))
clf.fit(X, space_label)
self.space_classifier.append(clf)
print('Building base classifier took %.3fs.' %
(time.time() - start_time))
def choose_config_target_space(self):
X_ALL = convert_configurations_to_array(self.configuration_list)
X_ = convert_configurations_to_array(self.configurations)
y_ = np.array(self.perfs, dtype=np.float64)
percentile = np.percentile(y_, 30)
label_ = np.array(y_ < percentile)
if (y_ == percentile).all():
raise ValueError('assertion violation: the same eval values!')
if (label_[0] == label_).all():
label_ = np.array(y_ < np.mean(y_))
print('Label treatment triggers!')
clf = make_pipeline(StandardScaler(), SVC(gamma='auto'))
clf.fit(X_, label_)
pred_ = clf.predict(X_ALL)
X_candidate = list()
for _i, _config in enumerate(self.configuration_list):
if pred_[_i] == 1:
X_candidate.append(_config)
if len(X_candidate) == 0:
print('Warning: feasible set is empty!')
X_candidate = self.configuration_list
return X_candidate
def update_configuration_list(self):
return
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]
def __init__(self,
target_hpo_data: Dict,
config_space: ConfigurationSpace,
surrogate_model: BaseFacade,
acq_func: str = 'ei',
mode='best',
model='gp',
source_hpo_data=None,
enable_init_design=False,
num_src_hpo_trial=50,
surrogate_type='rf',
max_runs=200,
logging_dir='./logs',
initial_runs=3,
task_id=None,
random_seed=None):
super().__init__(config_space, task_id, output_dir=logging_dir)
self.logger = super()._get_logger(self.__class__.__name__)
if random_seed is None:
_, rng = get_rng()
random_seed = rng.randint(MAXINT)
self.random_seed = random_seed
self.rng = np.random.RandomState(self.random_seed)
self.source_hpo_data = source_hpo_data
self.num_src_hpo_trial = num_src_hpo_trial
self.surrogate_type = surrogate_type
self.acq_func = acq_func
self.mode = mode
self.clf_type = model
print(mode, model)
self.max_iterations = max_runs
self.iteration_id = 0
self.default_obj_value = MAXINT
self.target_hpo_measurements = target_hpo_data
self.configuration_list = list(self.target_hpo_measurements.keys())
print('Target problem space: %d configurations' %
len(self.configuration_list))
self.configurations = list()
self.failed_configurations = list()
self.perfs = list()
self.test_perfs = list()
self.reduce_cnt = 0
if enable_init_design:
self.initial_configurations = self.initial_design(initial_runs)
else:
self.initial_configurations = None
if self.initial_configurations is None:
self.init_num = initial_runs
else:
self.init_num = len(self.initial_configurations)
# Initialize the basic component in BO.
self.config_space.seed(self.random_seed)
np.random.seed(self.random_seed)
self.model = surrogate_model
self.acquisition_function = EI(self.model)
self.space_classifier = None
self.random_configuration_chooser = ChooserProb(
prob=0.1, rng=np.random.RandomState(self.random_seed))
# self.prop_eta = 0.9
# self.prop_init = 0.3
# Set the parameter in metric. TODO: add test perf
ys = np.array(list(self.target_hpo_measurements.values()))
if ys.ndim == 2:
assert ys.shape[1] == 2
val_ys = ys[:, 0]
else:
val_ys = ys
self.ys = val_ys
self.y_max, self.y_min = np.max(self.ys), np.min(self.ys)
self.reduce_cnt = 0
self.p_min = 5
self.p_max = 50
self.use_correct_rate = False
if self.mode in ['box', 'ellipsoid']:
continuous_types = (UniformFloatHyperparameter,
UniformIntegerHyperparameter)
self.continuous_mask = [
isinstance(hp, continuous_types)
for hp in self.config_space.get_hyperparameters()
]
self.calculate_box_area()
self.space_threshold = 0.04
class SMBO_OFFLINE(BasePipeline):
def __init__(self,
target_hpo_data: Dict,
config_space: ConfigurationSpace,
surrogate_model: BaseFacade,
acq_func: str = 'ei',
source_hpo_data=None,
enable_init_design=False,
num_src_hpo_trial=50,
surrogate_type='rf',
max_runs=200,
logging_dir='./logs',
initial_runs=3,
task_id=None,
random_seed=None):
super().__init__(config_space, task_id, output_dir=logging_dir)
self.logger = super()._get_logger(self.__class__.__name__)
if random_seed is None:
_, rng = get_rng()
random_seed = rng.randint(MAXINT)
self.random_seed = random_seed
self.rng = np.random.RandomState(self.random_seed)
self.source_hpo_data = source_hpo_data
self.num_src_hpo_trial = num_src_hpo_trial
self.surrogate_type = surrogate_type
self.acq_func = acq_func
self.max_iterations = max_runs
self.iteration_id = 0
self.default_obj_value = MAXINT
self.target_hpo_measurements = target_hpo_data
self.configuration_list = list(self.target_hpo_measurements.keys())
print('Target problem space: %d configurations' %
len(self.configuration_list))
self.configurations = list()
self.failed_configurations = list()
self.perfs = list()
self.test_perfs = list()
if enable_init_design:
self.initial_configurations = self.initial_design(initial_runs)
else:
self.initial_configurations = None
if self.initial_configurations is None:
self.init_num = initial_runs
else:
self.init_num = len(self.initial_configurations)
# Initialize the basic component in BO.
self.config_space.seed(self.random_seed)
np.random.seed(self.random_seed)
self.model = surrogate_model
if self.acq_func == 'ei':
self.acquisition_function = EI(self.model)
elif self.acq_func == 'taf':
self.acquisition_function = TAQ_EI(self.model.target_surrogate,
self.model.source_surrogates)
self.acquisition_function.update(source_etas=self.model.eta_list)
else:
raise ValueError('invalid acquisition function ~ %s.' %
self.acq_func)
self.acq_optimizer = OfflineSearch(self.configuration_list,
self.acquisition_function,
config_space,
rng=np.random.RandomState(
self.random_seed))
self.random_configuration_chooser = ChooserProb(
prob=0.001, rng=np.random.RandomState(self.random_seed))
# Set the parameter in metric. TODO: add test perf
ys = np.array(list(self.target_hpo_measurements.values()))
if ys.ndim == 2:
assert ys.shape[1] == 2
val_ys = ys[:, 0]
else:
val_ys = ys
self.ys = val_ys
self.y_max, self.y_min = np.max(self.ys), np.min(self.ys)
def get_adtm(self):
y_inc = self.get_inc_y()
assert self.y_max != self.y_min
return (y_inc - self.y_min) / (self.y_max - self.y_min)
def get_inc_y(self):
y_inc = np.min(self.perfs)
return y_inc
def run(self):
while self.iteration_id < self.max_iterations:
self.iterate()
def sort_configs_by_score(self):
from tlbo.facade.obtl_es import ES
surrogate = ES(self.config_space,
self.source_hpo_data,
self.configuration_list,
self.random_seed,
surrogate_type=self.surrogate_type,
num_src_hpo_trial=self.num_src_hpo_trial)
X = convert_configurations_to_array(self.configuration_list)
scores, _ = surrogate.predict(X)
sorted_items = sorted(list(zip(self.configuration_list, scores)),
key=lambda x: x[1])
return [item[0] for item in sorted_items]
def initial_design(self, n_init=3):
initial_configs = list()
configs_ = self.sort_configs_by_score()[:25]
from sklearn.cluster import KMeans
X = convert_configurations_to_array(configs_)
kmeans = KMeans(n_clusters=n_init, random_state=0).fit(X)
labels = kmeans.predict(X)
label_set = set()
for idx, _config in enumerate(configs_):
if labels[idx] not in label_set:
label_set.add(labels[idx])
initial_configs.append(_config)
return initial_configs
def evaluate(self, config):
value = self.target_hpo_measurements[config]
try:
perf, test_perf = value
except Exception:
perf = value
test_perf = -1
return perf, test_perf
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)
# start_time = time.time()
config = self.choose_next(X, Y)
# print('In %d-th iter, config selection took %.3fs' % (self.iteration_id, time.time() - start_time))
trial_state = SUCCESS
trial_info = None
if config not in (self.configurations + self.failed_configurations):
# Evaluate this configuration.
perf, test_perf = self.evaluate(config)
if perf == MAXINT:
trial_info = 'failed configuration evaluation.'
trial_state = FAILDED
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.test_perfs.append(test_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 sample_random_config(self, config_num=1):
configs = list()
sample_cnt = 0
while len(configs) < config_num:
sample_cnt += 1
_idx = self.rng.randint(len(self.configuration_list))
config = self.configuration_list[_idx]
if config not in (self.configurations +
self.failed_configurations + configs):
configs.append(config)
sample_cnt = 0
else:
sample_cnt += 1
if sample_cnt >= 200:
configs.append(config)
sample_cnt = 0
return configs
def choose_next(self, X: np.ndarray, Y: np.ndarray):
_config_num = X.shape[0]
if _config_num < self.init_num:
if self.initial_configurations is None:
default_config = self.config_space.get_default_configuration()
if default_config not in self.configuration_list:
default_config = self.configuration_list[0]
if default_config not in (self.configurations +
self.failed_configurations):
config = default_config
else:
config = self.sample_random_config()[0]
return config
else:
print('This is a config for warm-start!')
return self.initial_configurations[_config_num]
if self.random_configuration_chooser.check(self.iteration_id):
config = self.sample_random_config()[0]
if len(self.model.target_weight) == 0:
self.model.target_weight.append(0.)
else:
self.model.target_weight.append(self.model.target_weight[-1])
return config
else:
start_time = time.time()
self.model.train(X, Y)
print('Training surrogate model took %.3f' %
(time.time() - start_time))
incumbent_value = self.history_container.get_incumbents()[0][1]
if self.model.method_id in ['tst', 'tstm']:
y_, _, _ = zero_one_normalization(Y)
incumbent_value = np.min(y_)
else:
y_, _, _ = zero_mean_unit_var_normalization(Y)
incumbent_value = np.min(y_)
if self.acq_func == 'ei':
self.acquisition_function.update(
model=self.model,
eta=incumbent_value,
num_data=len(self.history_container.data))
elif self.acq_func == 'taf':
self.acquisition_function.update_target_model(
self.model.target_surrogate,
incumbent_value,
num_data=len(self.history_container.data),
model_weights=self.model.w)
else:
raise ValueError('invalid acquisition function ~ %s.' %
self.acq_func)
start_time = time.time()
sorted_configs = self.acq_optimizer.maximize(
runhistory=self.history_container,
num_points=5000,
)
print('optimizing acq func took', time.time() - start_time)
for _config in sorted_configs:
if _config not in (self.configurations +
self.failed_configurations):
return _config
raise ValueError('The configuration in the SET (%d) is over' %
len(self.configuration_list))
def load_topk_configs(self,
src_meta_features,
tar_meta_feature,
k=5,
trial_num=50):
dataset_array = self.scale_fit_meta_features(src_meta_features)
tar_dataset_array = self.scale_transform_meta_features(
tar_meta_feature)
dis_list = list()
for src_feature in dataset_array:
dis_list.append(
np.sqrt(np.sum(np.square(src_feature - tar_dataset_array))))
sorted_dis_idx = np.argsort(dis_list)
topk_id = sorted_dis_idx[:k]
topk_configs = list()
for id in topk_id:
configs_id = list(self.source_hpo_data[id].items())[:trial_num]
sorted_configs = sorted(configs_id, key=lambda x: x[1])
topk_configs.append(sorted_configs[0][0])
return topk_configs
def scale_fit_meta_features(self, meta_features):
from sklearn.preprocessing import MinMaxScaler, Imputer
meta_features = np.array(meta_features)
self.meta_feature_imputer = Imputer(missing_values='NaN',
strategy='mean',
axis=0)
self.meta_feature_imputer.fit(meta_features)
meta_features = self.meta_feature_imputer.transform(meta_features)
self.meta_feature_scaler = MinMaxScaler()
self.meta_feature_scaler.fit(meta_features)
return self.meta_feature_scaler.transform(meta_features)
def scale_transform_meta_features(self, meta_feature):
_meta_features = np.array([meta_feature])
_meta_feature = self.meta_feature_imputer.transform(_meta_features)
_meta_feature = self.meta_feature_scaler.transform(_meta_feature)
return np.clip(_meta_feature, 0, 1)