def gen(
self,
n: int,
bounds: List[Tuple[float, float]],
objective_weights: Tensor,
objective_thresholds: Optional[Tensor] = None,
outcome_constraints: Optional[Tuple[Tensor, 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]]]:
if self._search_space_digest is None:
raise RuntimeError("Must `fit` the model before calling `gen`.")
acq_options, opt_options = construct_acquisition_and_optimizer_options(
acqf_options=self.acquisition_options,
model_gen_options=model_gen_options)
# update bounds / target fidelities
new_ssd_args = {
**dataclasses.asdict(self._search_space_digest),
"bounds": bounds,
"target_fidelities": target_fidelities or {},
}
search_space_digest = SearchSpaceDigest(**new_ssd_args)
acqf = self._instantiate_acquisition(
search_space_digest=search_space_digest,
objective_weights=objective_weights,
objective_thresholds=objective_thresholds,
outcome_constraints=outcome_constraints,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
pending_observations=pending_observations,
acq_options=acq_options,
)
botorch_rounding_func = get_rounding_func(rounding_func)
candidates, expected_acquisition_value = acqf.optimize(
n=n,
search_space_digest=search_space_digest,
inequality_constraints=_to_inequality_constraints(
linear_constraints=linear_constraints),
fixed_features=fixed_features,
rounding_func=botorch_rounding_func,
optimizer_options=checked_cast(dict, opt_options),
)
gen_metadata: TGenMetadata = {
Keys.EXPECTED_ACQF_VAL: expected_acquisition_value.tolist()
}
if objective_weights.nonzero().numel() > 1:
gen_metadata["objective_thresholds"] = acqf.objective_thresholds
gen_metadata["objective_weights"] = acqf.objective_weights
return (
candidates.detach().cpu(),
torch.ones(n, dtype=self.surrogate.dtype),
gen_metadata,
None,
)
def gen(
self,
n: int,
bounds: List[Tuple[float, float]],
objective_weights: Tensor,
objective_thresholds: Optional[Tensor] = None,
outcome_constraints: Optional[Tuple[Tensor, 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]]]:
acq_options, opt_options = construct_acquisition_and_optimizer_options(
acqf_options=self.acquisition_options,
model_gen_options=model_gen_options)
acqf = self._instantiate_acquisition(
bounds=bounds,
objective_weights=objective_weights,
objective_thresholds=objective_thresholds,
outcome_constraints=outcome_constraints,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
pending_observations=pending_observations,
target_fidelities=target_fidelities,
acq_options=acq_options,
)
botorch_rounding_func = get_rounding_func(rounding_func)
candidates, expected_acquisition_value = acqf.optimize(
bounds=self._bounds_as_tensor(bounds=bounds),
n=n,
inequality_constraints=_to_inequality_constraints(
linear_constraints=linear_constraints),
fixed_features=fixed_features,
rounding_func=botorch_rounding_func,
optimizer_options=checked_cast(dict, opt_options),
)
return (
candidates.detach().cpu(),
torch.ones(n, dtype=self.surrogate.dtype),
{
Keys.EXPECTED_ACQF_VAL: expected_acquisition_value.tolist()
},
None,
)
def test_construct_acquisition_and_optimizer_options(self):
# Two dicts for `Acquisition` should be concatenated
acqf_options = {Keys.NUM_FANTASIES: 64}
acquisition_function_kwargs = {Keys.CURRENT_VALUE: torch.tensor([1.0])}
optimizer_kwargs = {Keys.NUM_RESTARTS: 40, Keys.RAW_SAMPLES: 1024}
model_gen_options = {
Keys.ACQF_KWARGS: acquisition_function_kwargs,
Keys.OPTIMIZER_KWARGS: optimizer_kwargs,
}
(
final_acq_options,
final_opt_options,
) = construct_acquisition_and_optimizer_options(
acqf_options=acqf_options, model_gen_options=model_gen_options
)
self.assertEqual(
final_acq_options,
{Keys.NUM_FANTASIES: 64, Keys.CURRENT_VALUE: torch.tensor([1.0])},
)
self.assertEqual(final_opt_options, optimizer_kwargs)
