def _filter_X_observed(
Xs: List[Tensor],
objective_weights: Tensor,
bounds: List[Tuple[float, float]],
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
) -> Optional[Tensor]:
r"""Filter input points to those appearing in objective or constraints.
Args:
Xs: The input tensors of a model.
objective_weights: The objective is to maximize a weighted sum of
the columns of f(x). These are the weights.
bounds: A list of (lower, upper) tuples for each column of X.
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)
linear_constraints: A tuple of (A, b). For k linear constraints on
d-dimensional x, A is (k x d) and b is (k x 1) such that
A x <= b. (Not used by single task models)
fixed_features: A map {feature_index: value} for features that
should be fixed to a particular value during generation.
Returns:
Tensor: All points that are feasible and appear in the objective or
the constraints. None if there are no such points.
"""
# Get points observed for all objective and constraint outcomes
X_obs = get_observed(
Xs=Xs,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
)
# Filter to those that satisfy constraints.
X_obs = filter_constraints_and_fixed_features(
X=X_obs,
bounds=bounds,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
)
if len(X_obs) > 0:
return torch.as_tensor(X_obs) # please the linter
def best_point(
self,
bounds: List[Tuple[float, float]],
objective_weights: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
model_gen_options: Optional[TConfig] = None,
target_fidelities: Optional[Dict[int, float]] = None,
) -> Optional[Tensor]:
"""
Identify the current best point, satisfying the constraints in the same
format as to gen.
Return None if no such point can be identified.
Args:
bounds: A list of (lower, upper) tuples for each column of X.
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.
linear_constraints: A tuple of (A, b). For k linear constraints on
d-dimensional x, A is (k x d) and b is (k x 1) such that
A x <= b.
fixed_features: A map {feature_index: value} for features that
should be fixed to a particular value in the best point.
model_gen_options: A config dictionary that can contain
model-specific options.
target_fidelities: A map {feature_index: value} of fidelity feature
column indices to their respective target fidelities. Used for
multi-fidelity optimization.
Returns:
d-tensor of the best point.
"""
options = model_gen_options or {}
fixed_features = fixed_features or {}
acf_options = options.get("acquisition_function_kwargs", {})
optimizer_options = options.get("optimizer_kwargs", {})
X_observed = get_observed(
Xs=self.Xs,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
)
acq_function, non_fixed_idcs = self._get_best_point_acqf(
mc_samples=acf_options.get("mc_samples", 512),
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
seed_inner=acf_options.get("seed_inner", None),
fixed_features=fixed_features,
target_fidelities=target_fidelities,
qmc=acf_options.get("qmc", True),
)
inequality_constraints = _to_inequality_constraints(linear_constraints)
# TODO: update optimizers to handle inequality_constraints
# (including transforming constraints b/c of fixed features)
if inequality_constraints is not None:
raise UnsupportedError("Inequality constraints are not supported!")
return_best_only = optimizer_options.get("return_best_only", True)
bounds_ = torch.tensor(bounds, dtype=self.dtype, device=self.device)
bounds_ = bounds_.transpose(-1, -2)
if non_fixed_idcs is not None:
bounds_ = bounds_[..., non_fixed_idcs]
candidates, _ = optimize_acqf(
acq_function=acq_function,
bounds=bounds_,
q=1,
num_restarts=optimizer_options.get("num_restarts", 60),
raw_samples=optimizer_options.get("raw_samples", 1024),
inequality_constraints=inequality_constraints,
fixed_features=None, # handled inside the acquisition function
options={
"batch_limit": optimizer_options.get("batch_limit", 8),
"maxiter": optimizer_options.get("maxiter", 200),
"nonnegative": optimizer_options.get("nonnegative", False),
"method": "L-BFGS-B",
},
return_best_only=return_best_only,
)
rec_point = candidates.detach().cpu()
if isinstance(acq_function, FixedFeatureAcquisitionFunction):
rec_point = acq_function._construct_X_full(rec_point)
if return_best_only:
rec_point = rec_point.view(-1)
return rec_point
