def get_botorch_objective(
model: Model,
objective_weights: Tensor,
use_scalarized_objective: bool = True,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
objective_thresholds: Optional[Tensor] = None,
X_observed: Optional[Tensor] = None,
) -> AcquisitionObjective:
"""Constructs a BoTorch `AcquisitionObjective` object.
Args:
model: A BoTorch Model
objective_weights: The objective is to maximize a weighted sum of
the columns of f(x). These are the weights.
use_scalarized_objective: A boolean parameter that defaults to True,
specifying whether ScalarizedObjective should be used.
NOTE: when using outcome_constraints, use_scalarized_objective
will be ignored.
outcome_constraints: A tuple of (A, b). For k outcome constraints
and m outputs at f(x), A is (k x m) and b is (k x 1) such that
A f(x) <= b. (Not used by single task models)
objective_thresholds: A tensor containing thresholds forming a reference point
from which to calculate pareto frontier hypervolume. Points that do not
dominate the objective_thresholds contribute nothing to hypervolume.
X_observed: Observed points that are feasible and appear in the
objective or the constraints. None if there are no such points.
Returns:
A BoTorch `AcquisitionObjective` object. It will be one of:
`ScalarizedObjective`, `LinearMCOObjective`, `ConstrainedMCObjective`.
"""
if objective_thresholds is not None:
nonzero_idcs = torch.nonzero(objective_weights).view(-1)
objective_weights = objective_weights[nonzero_idcs]
return WeightedMCMultiOutputObjective(weights=objective_weights,
outcomes=nonzero_idcs.tolist())
if X_observed is None:
raise UnsupportedError(
"X_observed is required to construct a BoTorch Objective.")
if outcome_constraints:
if use_scalarized_objective:
logger.warning(
"Currently cannot use ScalarizedObjective when there are outcome "
"constraints. Ignoring (default) kwarg `use_scalarized_objective`"
"= True. Creating ConstrainedMCObjective.")
obj_tf = get_objective_weights_transform(objective_weights)
def objective(samples: Tensor, X: Optional[Tensor] = None) -> Tensor:
return obj_tf(samples)
con_tfs = get_outcome_constraint_transforms(outcome_constraints)
inf_cost = get_infeasible_cost(X=X_observed,
model=model,
objective=obj_tf)
return ConstrainedMCObjective(objective=objective,
constraints=con_tfs or [],
infeasible_cost=inf_cost)
elif use_scalarized_objective:
return ScalarizedObjective(weights=objective_weights)
return LinearMCObjective(weights=objective_weights)
def get_NEI(
model: Model,
objective_weights: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
X_observed: Optional[Tensor] = None,
X_pending: Optional[Tensor] = None,
**kwargs: Any,
) -> AcquisitionFunction:
r"""Instantiates a qNoisyExpectedImprovement acquisition function.
Args:
objective_weights: The objective is to maximize a weighted sum of
the columns of f(x). These are the weights.
outcome_constraints: A tuple of (A, b). For k outcome constraints
and m outputs at f(x), A is (k x m) and b is (k x 1) such that
A f(x) <= b. (Not used by single task models)
X_observed: A tensor containing points observed for all objective
outcomes and outcomes that appear in the outcome constraints (if
there are any).
X_pending: A tensor containing points whose evaluation is pending (i.e.
that have been submitted for evaluation) present for all objective
outcomes and outcomes that appear in the outcome constraints (if
there are any).
mc_samples: The number of MC samples to use (default: 512).
qmc: If True, use qMC instead of MC (default: True).
prune_baseline: If True, prune the baseline points for NEI (default: True).
Returns:
qNoisyExpectedImprovement: The instantiated acquisition function.
"""
if X_observed is None:
raise ValueError("There are no feasible observed points.")
# Parse random_scalarization params
objective_weights = _extract_random_scalarization_settings(
objective_weights, outcome_constraints, **kwargs)
# construct Objective module
if outcome_constraints is None:
objective = LinearMCObjective(weights=objective_weights)
else:
obj_tf = get_objective_weights_transform(objective_weights)
con_tfs = get_outcome_constraint_transforms(outcome_constraints)
X_observed = torch.as_tensor(X_observed)
inf_cost = get_infeasible_cost(X=X_observed,
model=model,
objective=obj_tf)
objective = ConstrainedMCObjective(objective=obj_tf,
constraints=con_tfs or [],
infeasible_cost=inf_cost)
return get_acquisition_function(
acquisition_function_name="qNEI",
model=model,
objective=objective,
X_observed=X_observed,
X_pending=X_pending,
prune_baseline=kwargs.get("prune_baseline", True),
mc_samples=kwargs.get("mc_samples", 512),
qmc=kwargs.get("qmc", True),
seed=torch.randint(1, 10000, (1, )).item(),
)
return Z[..., 0]
# def constraint_callable(Z):
# return Z[..., 1]
def constraint_callable_list(num_constraints, num_objs=1):
return [lambda Z: Z[..., i + num_objs] for i in range(num_constraints)]
# define a feasibility-weighted objective for optimization
constrained_obj = ConstrainedMCObjective(
objective=obj_callable,
constraints=constraint_callable_list(problem.num_constraints, 1),
infeasible_cost=
MIN_OBJ_UPPER_BOUND, # CAUTION if objective to be minimized can return positive value!!!
# (or objective to be maximized can return nagetive value)
)
def optimize_acqf_and_get_observation(acq_func, obj_func, time_list,
global_start_time):
"""Optimizes the acquisition function, and returns a new candidate and observation."""
# optimize
candidates, _ = optimize_acqf(
acq_function=acq_func,
bounds=standard_bounds,
q=1,
num_restarts=10,
raw_samples=512, # used for intialization heuristic