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_botorch_objective_and_transform(
model: Model,
objective_weights: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
objective_thresholds: Optional[Tensor] = None,
X_observed: Optional[Tensor] = None,
) -> Tuple[Optional[MCAcquisitionObjective], Optional[PosteriorTransform]]:
"""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.
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 two-tuple containing (optioally) an `MCAcquisitionObjective` and
(optionally) a `PosteriorTransform`.
"""
if objective_thresholds is not None:
# we are doing multi-objective optimization
nonzero_idcs = torch.nonzero(objective_weights).view(-1)
objective_weights = objective_weights[nonzero_idcs]
objective = WeightedMCMultiOutputObjective(
weights=objective_weights, outcomes=nonzero_idcs.tolist())
return objective, None
if X_observed is None:
raise UnsupportedError(
"X_observed is required to construct a BoTorch objective.")
if outcome_constraints:
# If there are outcome constraints, we use MC Acquistion functions
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)
objective = ConstrainedMCObjective(objective=objective,
constraints=con_tfs or [],
infeasible_cost=inf_cost)
return objective, None
# Case of linear weights - use ScalarizedPosteriorTransform
transform = ScalarizedPosteriorTransform(weights=objective_weights)
return None, transform
def get_botorch_objective(
model: Model,
objective_weights: Tensor,
use_scalarized_objective: bool = True,
outcome_constraints: Optional[Tuple[Tensor, 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)
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 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)
con_tfs = get_outcome_constraint_transforms(outcome_constraints)
inf_cost = get_infeasible_cost(X=X_observed,
model=model,
objective=obj_tf)
return ConstrainedMCObjective(objective=obj_tf,
constraints=con_tfs or [],
infeasible_cost=inf_cost)
if use_scalarized_objective:
return ScalarizedObjective(weights=objective_weights)
return LinearMCObjective(weights=objective_weights)
def _get_objective(
model: Model,
objective_weights: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
X_observed: Optional[Tensor] = None,
) -> AcquisitionObjective:
if outcome_constraints is None:
objective = ScalarizedObjective(weights=objective_weights)
else:
X_observed = torch.as_tensor(X_observed)
obj_tf = get_objective_weights_transform(objective_weights)
con_tfs = get_outcome_constraint_transforms(outcome_constraints)
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 objective
def get_botorch_objective(
model: Model,
objective_weights: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
X_observed: Optional[Tensor] = None,
) -> AcquisitionObjective:
"""Constructs a BoTorch `Objective`."""
if X_observed is None:
raise UnsupportedError(
"X_observed is required to construct a BoTorch Objective.")
if outcome_constraints is None:
objective = ScalarizedObjective(weights=objective_weights)
else:
obj_tf = get_objective_weights_transform(objective_weights)
con_tfs = get_outcome_constraint_transforms(outcome_constraints)
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 objective
