def __init__(self,
config_space: ConfigurationSpace,
source_hpo_data: List,
seed: int,
history_dataset_features: List = None,
num_src_hpo_trial: int = 50,
surrogate_type='rf'):
self.method_id = None
self.config_space = config_space
self.random_seed = seed
self.num_src_hpo_trial = num_src_hpo_trial
self.source_hpo_data = source_hpo_data
self.source_surrogates = None
self.target_surrogate = None
self.history_dataset_features = history_dataset_features
# The number of source problems.
if source_hpo_data is not None:
self.K = len(source_hpo_data)
if history_dataset_features is not None:
assert len(history_dataset_features) == self.K
self.surrogate_type = surrogate_type
self.types, self.bounds = get_types(config_space)
self.instance_features = None
self.var_threshold = VERY_SMALL_NUMBER
self.w = None
self.eta_list = list()
# meta features.
self.meta_feature_scaler = None
self.meta_feature_imputer = None
self.target_weight = list()
self.logger = get_logger(self.__class__.__name__)
def __init__(self, task_id):
self.task_id = task_id
self.data = collections.OrderedDict()
self.config_counter = 0
self.incumbent_value = MAXINT
self.incumbents = list()
self.logger = get_logger(self.__class__.__name__)
def __init__(self, task_id, ref_point=None):
self.task_id = task_id
self.data = collections.OrderedDict()
self.config_counter = 0
self.pareto = collections.OrderedDict()
self.num_objs = None
self.mo_incumbent_value = None
self.mo_incumbents = None
self.ref_point = ref_point
self.hv_data = list()
self.logger = get_logger(self.__class__.__name__)
def __init__(self,
config_space,
initial_trials=10,
initial_configurations=None,
init_strategy='random_explore_first',
history_bo_data=None,
optimization_strategy='bo',
surrogate_type='prf',
output_dir='logs',
task_id=None,
rng=None):
# Create output (logging) directory.
# Init logging module.
# Random seed generator.
self.init_strategy = init_strategy
self.output_dir = output_dir
if rng is None:
run_id, rng = get_rng()
self.rng = rng
self.logger = get_logger(self.__class__.__name__)
# Basic components in Advisor.
self.optimization_strategy = optimization_strategy
self.default_obj_value = MAXINT
self.configurations = list()
self.failed_configurations = list()
self.perfs = list()
self.scale_perc = 5
self.perc = None
self.min_y = None
self.max_y = None
# Init the basic ingredients in Bayesian optimization.
self.history_bo_data = history_bo_data
self.surrogate_type = surrogate_type
self.init_num = initial_trials
self.config_space = config_space
self.config_space.seed(rng.randint(MAXINT))
if initial_configurations is not None and len(
initial_configurations) > 0:
self.initial_configurations = initial_configurations
self.init_num = len(initial_configurations)
else:
self.initial_configurations = self.create_initial_design(
self.init_strategy)
self.init_num = len(self.initial_configurations)
self.history_container = HistoryContainer(task_id)
self.surrogate_model = None
self.acquisition_function = None
self.optimizer = None
self.setup_bo_basics()
def _get_logger(self, name):
logger_name = 'lite-bo-%s' % name
setup_logger(os.path.join(self.output_dir,
'%s.log' % str(logger_name)))
return get_logger(logger_name)
def _get_logger(self, name):
logger_name = 'Lite-BO-%s' % name
self.logger_name = os.path.join(self.output_dir,
'%s.log' % str(logger_name))
setup_logger(self.logger_name)
return get_logger(logger_name)
def __init__(self,
config_space,
task_info,
initial_trials=10,
initial_configurations=None,
init_strategy='random_explore_first',
history_bo_data=None,
optimization_strategy='bo',
surrogate_type=None,
acq_type=None,
acq_optimizer_type='local_random',
ref_point=None,
use_trust_region=False,
output_dir='logs',
task_id=None,
random_state=None):
# Create output (logging) directory.
# Init logging module.
# Random seed generator.
self.task_info = task_info
self.num_objs = task_info['num_objs']
self.num_constraints = task_info['num_constraints']
self.init_strategy = init_strategy
self.output_dir = output_dir
self.rng = np.random.RandomState(random_state)
self.logger = get_logger(self.__class__.__name__)
history_folder = os.path.join(self.output_dir, 'bo_history')
if not os.path.exists(history_folder):
os.makedirs(history_folder)
self.history_file = os.path.join(history_folder,
'bo_history_%s.json' % task_id)
# Basic components in Advisor.
self.optimization_strategy = optimization_strategy
self.default_obj_value = [MAXINT] * self.num_objs
self.configurations = list()
self.failed_configurations = list()
self.perfs = list()
self.scale_perc = 5
self.perc = None
self.min_y = None
self.max_y = None
if self.num_constraints > 0:
self.constraint_perfs = [
list() for _ in range(self.num_constraints)
]
# Init the basic ingredients in Bayesian optimization.
self.history_bo_data = history_bo_data
self.surrogate_type = surrogate_type
self.constraint_surrogate_type = None
self.acq_type = acq_type
self.acq_optimizer_type = acq_optimizer_type
self.init_num = initial_trials
self.config_space = config_space
self.config_space.seed(self.rng.randint(MAXINT))
self.ref_point = ref_point
if initial_configurations is not None and len(
initial_configurations) > 0:
self.initial_configurations = initial_configurations
self.init_num = len(initial_configurations)
else:
self.initial_configurations = self.create_initial_design(
self.init_strategy)
self.init_num = len(self.initial_configurations)
if use_trust_region:
self.history_container = MultiStartHistoryContainer(
task_id, self.num_objs, ref_point)
elif self.num_objs == 1:
self.history_container = HistoryContainer(task_id)
else: # multi-objectives
self.history_container = MOHistoryContainer(task_id, ref_point)
self.surrogate_model = None
self.constraint_models = None
self.acquisition_function = None
self.optimizer = None
self.check_setup()
self.setup_bo_basics()