def setup_bo_basics(self):
"""
Prepare the basic BO components.
Returns
-------
An optimizer object.
"""
if self.num_objs == 1 or self.acq_type == 'parego':
self.surrogate_model = build_surrogate(
func_str=self.surrogate_type,
config_space=self.config_space,
rng=self.rng,
history_hpo_data=self.history_bo_data)
else: # multi-objectives
self.surrogate_model = [
build_surrogate(func_str=self.surrogate_type,
config_space=self.config_space,
rng=self.rng,
history_hpo_data=self.history_bo_data)
for _ in range(self.num_objs)
]
if self.num_constraints > 0:
self.constraint_models = [
build_surrogate(func_str=self.constraint_surrogate_type,
config_space=self.config_space,
rng=self.rng)
for _ in range(self.num_constraints)
]
if self.acq_type in ['mesmo', 'mesmoc', 'mesmoc2', 'usemo']:
types, bounds = get_types(self.config_space)
self.acquisition_function = build_acq_func(
func_str=self.acq_type,
model=self.surrogate_model,
constraint_models=self.constraint_models,
types=types,
bounds=bounds)
else:
self.acquisition_function = build_acq_func(
func_str=self.acq_type,
model=self.surrogate_model,
constraint_models=self.constraint_models,
ref_point=self.ref_point)
if self.acq_type == 'usemo':
self.acq_optimizer_type = 'usemo_optimizer'
elif self.acq_type.startswith('mesmo'):
self.acq_optimizer_type = 'mesmo_optimizer'
self.optimizer = build_optimizer(func_str=self.acq_optimizer_type,
acq_func=self.acquisition_function,
config_space=self.config_space,
rng=self.rng)
def setup_bo_basics(self):
if self.num_objs == 1:
self.surrogate_model = build_surrogate(func_str=self.surrogate_type,
config_space=self.config_space,
rng=self.rng,
history_hpo_data=self.history_bo_data)
else: # multi-objectives
self.surrogate_model = [build_surrogate(func_str=self.surrogate_type,
config_space=self.config_space,
rng=self.rng,
history_hpo_data=self.history_bo_data)
for _ in range(self.num_objs)]
if self.num_constraints > 0:
self.constraint_models = [build_surrogate(func_str=self.constraint_surrogate_type,
config_space=self.config_space,
rng=self.rng) for _ in range(self.num_constraints)]
self.acquisition_function = build_acq_func(func_str=self.acq_type, model=self.surrogate_model,
constraint_models=self.constraint_models,
mc_times=self.mc_times, ref_point=self.ref_point)
self.optimizer = build_optimizer(func_str=self.acq_optimizer_type,
acq_func=self.acquisition_function,
config_space=self.config_space,
rng=self.rng)
if self.use_trust_region:
types, bounds = get_types(self.config_space)
cont_dim = np.sum(types == 0)
self.turbo_state = TurboState(cont_dim)
def setup_bo_basics(self, acq_type='ei', acq_optimizer_type='local_random'):
self.surrogate_model = build_surrogate(func_str=self.surrogate_type,
config_space=self.config_space,
rng=self.rng,
history_hpo_data=self.history_bo_data)
self.acquisition_function = build_acq_func(func_str=acq_type, model=self.surrogate_model)
self.optimizer = build_optimizer(func_str=acq_optimizer_type,
acq_func=self.acquisition_function,
config_space=self.config_space,
rng=self.rng)
def __init__(self,
model: List[AbstractModel],
types: List[int],
bounds: List[Tuple[float, float]],
acq_type='ei',
**kwargs):
"""Constructor
Parameters
----------
model : List[AbstractEPM]
A list of surrogate that implements at least
- predict_marginalized_over_instances(X)
types : List[int]
Specifies the number of categorical values of an input dimension where
the i-th entry corresponds to the i-th input dimension. Let's say we
have 2 dimension where the first dimension consists of 3 different
categorical choices and the second dimension is continuous than we
have to pass [3, 0]. Note that we count starting from 0.
bounds : List[Tuple[float, float]]
Bounds of input dimensions: (lower, upper) for continuous dims; (n_cat, np.nan) for categorical dims
# todo cat dims
acq_type:
todo
"""
super(USeMO, self).__init__(model)
self.long_name = 'Uncertainty-Aware Search'
self.types = np.asarray(types)
self.bounds = np.asarray(bounds)
from openbox.core.base import build_acq_func
self.single_acq = [
build_acq_func(func_str=acq_type, model=m) for m in model
]
self.uncertainty_acq = [Uncertainty(model=m) for m in model]
self.X = None
self.Y = None
self.X_dim = None
self.Y_dim = None
self.eta = None
self.num_data = None
self.uncertainties = None
self.candidates = None
self.check_types_bounds()
def __init__(self,
model: List[AbstractModel],
config_space,
random_state=1,
acq_type='ei',
**kwargs):
"""Constructor
Parameters
----------
model : List[AbstractEPM]
A list of surrogate that implements at least
- predict_marginalized_over_instances(X)
config_space : openbox.space.Space
Configuration space
random_state : int
Random seed for RNG.
acq_type:
Type of base acquisition function.
"""
super(USeMO, self).__init__(model)
self.long_name = 'Uncertainty-Aware Search'
self.rng = np.random.RandomState(random_state)
random.seed(self.rng.randint(MAXINT))
self.config_space = config_space
from openbox.core.base import build_acq_func
self.single_acq = [
build_acq_func(func_str=acq_type, model=m) for m in model
]
self.uncertainty_acq = [Uncertainty(model=m) for m in model]
self.X = None
self.Y = None
self.X_dim = None
self.Y_dim = None
self.eta = None
self.num_data = None
self.uncertainties = None
self.candidates = None