def __init__(
self,
objective_func,
restart_needed=False,
need_lc=False,
method_name='default_method_name',
log_directory='logs',
data_directory='data',
time_limit_per_trial=600,
runtime_limit=None,
max_queue_len=300,
ip='',
port=13579,
authkey=b'abc',
):
self.log_directory = log_directory
if not os.path.exists(self.log_directory):
os.makedirs(self.log_directory)
self.data_directory = data_directory
if not os.path.exists(self.data_directory):
os.makedirs(self.data_directory)
self.logger = self._get_logger(method_name)
self.objective_func = objective_func
self.trial_statistics = list()
self.recorder = list()
self.global_start_time = time.time()
self.runtime_limit = None
self._history = {
"time_elapsed": list(),
"performance": list(),
"best_trial_id": list(),
"configuration": list()
}
self.global_incumbent = 1e10
self.global_incumbent_configuration = None
self.global_trial_counter = 0
self.restart_needed = restart_needed
self.record_lc = need_lc
self.method_name = method_name
# evaluation metrics
self.stage_id = 1
self.stage_history = {'stage_id': list(), 'performance': list()}
self.grid_search_perf = list()
if self.method_name is None:
raise ValueError('Method name must be specified! NOT NONE.')
self.time_limit_per_trial = time_limit_per_trial
self.runtime_limit = runtime_limit
assert self.runtime_limit is not None
max_queue_len = max(300, max_queue_len)
self.master_messager = MasterMessager(ip, port, authkey, max_queue_len,
max_queue_len)
def __init__(
self,
objective_function,
config_space,
eval_type='holdout',
parallel_strategy='async',
batch_size=4,
batch_strategy='median_imputation',
num_constraints=0,
num_objs=1,
sample_strategy: str = 'bo',
runtime_limit=600,
time_limit_per_trial=180,
surrogate_type=None,
acq_type=None,
acq_optimizer_type='local_random',
initial_runs=3,
init_strategy='random_explore_first',
initial_configurations=None,
ref_point=None,
history_bo_data: List[OrderedDict] = None,
logging_dir='logs',
task_id='default',
random_state=1,
ip="",
port=13579,
authkey=b'abc',
):
self.task_info = {
'num_constraints': num_constraints,
'num_objs': num_objs
}
self.FAILED_PERF = [MAXINT] * num_objs
super().__init__(objective_function,
config_space,
task_id=task_id,
output_dir=logging_dir,
random_state=random_state,
initial_runs=initial_runs,
max_runs=int(1e10),
runtime_limit=runtime_limit,
sample_strategy=sample_strategy,
time_limit_per_trial=time_limit_per_trial,
history_bo_data=history_bo_data)
if parallel_strategy == 'sync':
self.config_advisor = SyncBatchAdvisor(
config_space,
self.task_info,
batch_size=batch_size,
batch_strategy=batch_strategy,
initial_trials=initial_runs,
initial_configurations=initial_configurations,
init_strategy=init_strategy,
history_bo_data=history_bo_data,
optimization_strategy=sample_strategy,
surrogate_type=surrogate_type,
acq_type=acq_type,
acq_optimizer_type=acq_optimizer_type,
ref_point=ref_point,
task_id=task_id,
output_dir=logging_dir,
random_state=random_state)
elif parallel_strategy == 'async':
self.config_advisor = AsyncBatchAdvisor(
config_space,
self.task_info,
batch_size=batch_size,
batch_strategy=batch_strategy,
initial_trials=initial_runs,
initial_configurations=initial_configurations,
init_strategy=init_strategy,
history_bo_data=history_bo_data,
optimization_strategy=sample_strategy,
surrogate_type=surrogate_type,
acq_type=acq_type,
acq_optimizer_type=acq_optimizer_type,
ref_point=ref_point,
task_id=task_id,
output_dir=logging_dir,
random_state=random_state)
else:
raise ValueError('Invalid parallel strategy - %s.' %
parallel_strategy)
self.eval_type = eval_type
self.parallel_strategy = parallel_strategy
self.batch_size = batch_size
max_queue_len = max(100, 3 * batch_size)
self.master_messager = MasterMessager(ip, port, authkey, max_queue_len,
max_queue_len)
self.start_time = time.time()
self.configs = list()
self.perfs = list()
self.incumbent_perf = float("-INF")
self.incumbent_config = self.config_space.get_default_configuration()
self.eval_dict = dict()
self.workers = dict()
def __init__(
self,
objective_function,
config_space,
parallel_strategy='async',
batch_size=4,
batch_strategy='default',
num_constraints=0,
num_objs=1,
sample_strategy: str = 'bo',
max_runs=200,
time_limit_per_trial=180,
surrogate_type='auto',
acq_type='auto',
acq_optimizer_type='auto',
initial_runs=3,
init_strategy='random_explore_first',
initial_configurations=None,
ref_point=None,
history_bo_data: List[OrderedDict] = None,
logging_dir='logs',
task_id='default_task_id',
random_state=None,
advisor_kwargs: dict = None,
ip="",
port=13579,
authkey=b'abc',
):
if task_id is None:
raise ValueError(
'Task id is not SPECIFIED. Please input task id first.')
self.num_objs = num_objs
self.num_constraints = num_constraints
self.FAILED_PERF = [MAXINT] * num_objs
super().__init__(objective_function,
config_space,
task_id=task_id,
output_dir=logging_dir,
random_state=random_state,
initial_runs=initial_runs,
max_runs=max_runs,
sample_strategy=sample_strategy,
time_limit_per_trial=time_limit_per_trial,
history_bo_data=history_bo_data)
self.parallel_strategy = parallel_strategy
self.batch_size = batch_size
max_queue_len = max(100, 3 * batch_size)
self.master_messager = MasterMessager(ip, port, authkey, max_queue_len,
max_queue_len)
advisor_kwargs = advisor_kwargs or {}
if parallel_strategy == 'sync':
self.config_advisor = SyncBatchAdvisor(
config_space,
num_objs=num_objs,
num_constraints=num_constraints,
batch_size=batch_size,
batch_strategy=batch_strategy,
initial_trials=initial_runs,
initial_configurations=initial_configurations,
init_strategy=init_strategy,
history_bo_data=history_bo_data,
optimization_strategy=sample_strategy,
surrogate_type=surrogate_type,
acq_type=acq_type,
acq_optimizer_type=acq_optimizer_type,
ref_point=ref_point,
task_id=task_id,
output_dir=logging_dir,
random_state=random_state,
**advisor_kwargs)
elif parallel_strategy == 'async':
self.config_advisor = AsyncBatchAdvisor(
config_space,
num_objs=num_objs,
num_constraints=num_constraints,
batch_size=batch_size,
batch_strategy=batch_strategy,
initial_trials=initial_runs,
initial_configurations=initial_configurations,
init_strategy=init_strategy,
history_bo_data=history_bo_data,
optimization_strategy=sample_strategy,
surrogate_type=surrogate_type,
acq_type=acq_type,
acq_optimizer_type=acq_optimizer_type,
ref_point=ref_point,
task_id=task_id,
output_dir=logging_dir,
random_state=random_state,
**advisor_kwargs)
else:
raise ValueError('Invalid parallel strategy - %s.' %
parallel_strategy)