def gen(
self,
n: int,
bounds: List[Tuple[float, float]],
objective_weights: Tensor, # objective_directions
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
objective_thresholds: Optional[Tensor] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
pending_observations: Optional[List[Tensor]] = None,
model_gen_options: Optional[TConfig] = None,
rounding_func: Optional[Callable[[Tensor], Tensor]] = None,
target_fidelities: Optional[Dict[int, float]] = None,
) -> Tuple[Tensor, Tensor, TGenMetadata,
Optional[List[TCandidateMetadata]]]:
options = model_gen_options or {}
acf_options = options.get("acquisition_function_kwargs", {})
optimizer_options = options.get("optimizer_kwargs", {})
if target_fidelities:
raise NotImplementedError(
"target_fidelities not implemented for base BotorchModel")
X_pending, X_observed = _get_X_pending_and_observed(
Xs=self.Xs,
pending_observations=pending_observations,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
bounds=bounds,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
)
model = self.model
# subset model only to the outcomes we need for the optimization
if options.get(Keys.SUBSET_MODEL, True):
model, objective_weights, outcome_constraints, Ys = subset_model(
model=model, # pyre-ignore [6]
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
Ys=self.Ys,
)
else:
Ys = self.Ys
bounds_ = torch.tensor(bounds, dtype=self.dtype, device=self.device)
bounds_ = bounds_.transpose(0, 1)
botorch_rounding_func = get_rounding_func(rounding_func)
if acf_options.get("random_scalarization", False) or acf_options.get(
"chebyshev_scalarization", False):
# If using a list of acquisition functions, the algorithm to generate
# that list is configured by acquisition_function_kwargs.
objective_weights_list = [
randomize_objective_weights(objective_weights, **acf_options)
for _ in range(n)
]
acquisition_function_list = [
self.acqf_constructor( # pyre-ignore: [28]
model=model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
X_pending=X_pending,
Ys=Ys, # Required for chebyshev scalarization calculations.
**acf_options,
) for objective_weights in objective_weights_list
]
acquisition_function_list = [
checked_cast(AcquisitionFunction, acq_function)
for acq_function in acquisition_function_list
]
# Multiple acquisition functions require a sequential optimizer
# always use scipy_optimizer_list.
# TODO(jej): Allow any optimizer.
candidates, expected_acquisition_value = scipy_optimizer_list(
acq_function_list=acquisition_function_list,
bounds=bounds_,
inequality_constraints=_to_inequality_constraints(
linear_constraints=linear_constraints),
fixed_features=fixed_features,
rounding_func=botorch_rounding_func,
**optimizer_options,
)
else:
acquisition_function = self.acqf_constructor( # pyre-ignore: [28]
model=model,
objective_weights=objective_weights,
objective_thresholds=objective_thresholds,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
X_pending=X_pending,
Ys=self.Ys, # Required for qEHVI calculations.
**acf_options,
)
acquisition_function = checked_cast(AcquisitionFunction,
acquisition_function)
# pyre-ignore: [28]
candidates, expected_acquisition_value = self.acqf_optimizer(
acq_function=checked_cast(AcquisitionFunction,
acquisition_function),
bounds=bounds_,
n=n,
inequality_constraints=_to_inequality_constraints(
linear_constraints=linear_constraints),
fixed_features=fixed_features,
rounding_func=botorch_rounding_func,
**optimizer_options,
)
return (
candidates.detach().cpu(),
torch.ones(n, dtype=self.dtype),
{
"expected_acquisition_value":
expected_acquisition_value.tolist()
},
None,
)
def gen(
self,
n: int,
bounds: List[Tuple[float, float]],
objective_weights: Tensor, # objective_directions
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
objective_thresholds: Optional[Tensor] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
pending_observations: Optional[List[Tensor]] = None,
model_gen_options: Optional[TConfig] = None,
rounding_func: Optional[Callable[[Tensor], Tensor]] = None,
target_fidelities: Optional[Dict[int, float]] = None,
) -> Tuple[Tensor, Tensor, TGenMetadata,
Optional[List[TCandidateMetadata]]]:
options = model_gen_options or {}
acf_options = options.get("acquisition_function_kwargs", {})
optimizer_options = options.get("optimizer_kwargs", {})
if target_fidelities:
raise NotImplementedError(
"target_fidelities not implemented for base BotorchModel")
if (objective_thresholds is not None and
objective_weights.shape[0] != objective_thresholds.shape[0]):
raise AxError(
"Objective weights and thresholds most both contain an element for"
" each modeled metric.")
X_pending, X_observed = _get_X_pending_and_observed(
Xs=self.Xs,
pending_observations=pending_observations,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
bounds=bounds,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
)
model = not_none(self.model)
full_objective_thresholds = objective_thresholds
full_objective_weights = objective_weights
full_outcome_constraints = outcome_constraints
# subset model only to the outcomes we need for the optimization
if options.get(Keys.SUBSET_MODEL, True):
full_objective_weights
subset_model_results = subset_model(
model=model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
objective_thresholds=objective_thresholds,
)
model = subset_model_results.model
objective_weights = subset_model_results.objective_weights
outcome_constraints = subset_model_results.outcome_constraints
objective_thresholds = subset_model_results.objective_thresholds
idcs = subset_model_results.indices
else:
idcs = None
if objective_thresholds is None:
full_objective_thresholds = infer_objective_thresholds(
model=model,
X_observed=not_none(X_observed),
objective_weights=full_objective_weights,
outcome_constraints=full_outcome_constraints,
subset_idcs=idcs,
)
# subset the objective thresholds
objective_thresholds = (full_objective_thresholds
if idcs is None else
full_objective_thresholds[idcs].clone())
bounds_ = torch.tensor(bounds, dtype=self.dtype, device=self.device)
bounds_ = bounds_.transpose(0, 1)
botorch_rounding_func = get_rounding_func(rounding_func)
if acf_options.get("random_scalarization", False) or acf_options.get(
"chebyshev_scalarization", False):
# If using a list of acquisition functions, the algorithm to generate
# that list is configured by acquisition_function_kwargs.
objective_weights_list = [
randomize_objective_weights(objective_weights, **acf_options)
for _ in range(n)
]
acquisition_function_list = [
self.acqf_constructor( # pyre-ignore: [28]
model=model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
X_pending=X_pending,
**acf_options,
) for objective_weights in objective_weights_list
]
acquisition_function_list = [
checked_cast(AcquisitionFunction, acq_function)
for acq_function in acquisition_function_list
]
# Multiple acquisition functions require a sequential optimizer
# always use scipy_optimizer_list.
# TODO(jej): Allow any optimizer.
candidates, expected_acquisition_value = scipy_optimizer_list(
acq_function_list=acquisition_function_list,
bounds=bounds_,
inequality_constraints=_to_inequality_constraints(
linear_constraints=linear_constraints),
fixed_features=fixed_features,
rounding_func=botorch_rounding_func,
**optimizer_options,
)
else:
acquisition_function = self.acqf_constructor( # pyre-ignore: [28]
model=model,
objective_weights=objective_weights,
objective_thresholds=objective_thresholds,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
X_pending=X_pending,
**acf_options,
)
acquisition_function = checked_cast(AcquisitionFunction,
acquisition_function)
# pyre-ignore: [28]
candidates, expected_acquisition_value = self.acqf_optimizer(
acq_function=checked_cast(AcquisitionFunction,
acquisition_function),
bounds=bounds_,
n=n,
inequality_constraints=_to_inequality_constraints(
linear_constraints=linear_constraints),
fixed_features=fixed_features,
rounding_func=botorch_rounding_func,
**optimizer_options,
)
gen_metadata = {
"expected_acquisition_value": expected_acquisition_value.tolist(),
"objective_thresholds": not_none(full_objective_thresholds).cpu(),
}
return (
candidates.detach().cpu(),
torch.ones(n, dtype=self.dtype),
gen_metadata,
None,
)