def get_EHVI(
model: Model,
objective_weights: Tensor,
objective_thresholds: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
X_observed: Optional[Tensor] = None,
X_pending: Optional[Tensor] = None,
**kwargs: Any,
) -> AcquisitionFunction:
r"""Instantiates a qExpectedHyperVolumeImprovement acquisition function.
Args:
model: The underlying model which the acqusition function uses
to estimate acquisition values of candidates.
objective_weights: The objective is to maximize a weighted sum of
the columns of f(x). These are the weights.
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.
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).
Returns:
qExpectedHypervolumeImprovement: The instantiated acquisition function.
"""
if X_observed is None:
raise ValueError("There are no feasible observed points.")
# construct Objective module
(
objective,
objective_thresholds,
) = get_weighted_mc_objective_and_objective_thresholds(
objective_weights=objective_weights,
objective_thresholds=objective_thresholds)
with torch.no_grad():
Y = model.posterior(X_observed).mean
# For EHVI acquisition functions we pass the constraint transform directly.
if outcome_constraints is None:
cons_tfs = None
else:
cons_tfs = get_outcome_constraint_transforms(outcome_constraints)
num_objectives = objective_thresholds.shape[0]
return get_acquisition_function(
acquisition_function_name="qEHVI",
model=model,
# TODO (jej): Fix pyre error below by restructuring class hierarchy.
# pyre-fixme[6]: Expected `botorch.acquisition.objective.
# MCAcquisitionObjective` for 3rd parameter `objective` to call
# `get_acquisition_function` but got `IdentityMCMultiOutputObjective`.
objective=objective,
X_observed=X_observed,
X_pending=X_pending,
constraints=cons_tfs,
mc_samples=kwargs.get("mc_samples", DEFAULT_EHVI_MC_SAMPLES),
qmc=kwargs.get("qmc", True),
alpha=kwargs.get(
"alpha",
get_default_partitioning_alpha(num_objectives=num_objectives)),
seed=torch.randint(1, 10000, (1, )).item(),
ref_point=objective_thresholds.tolist(),
Y=Y,
)
def get_NEHVI(
model: Model,
objective_weights: Tensor,
objective_thresholds: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
X_observed: Optional[Tensor] = None,
X_pending: Optional[Tensor] = None,
**kwargs: Any,
) -> AcquisitionFunction:
r"""Instantiates a qNoisyExpectedHyperVolumeImprovement acquisition function.
Args:
model: The underlying model which the acqusition function uses
to estimate acquisition values of candidates.
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).
chebyshev_scalarization: Use augmented Chebyshev scalarization.
Returns:
qNoisyExpectedHyperVolumeImprovement: The instantiated acquisition function.
"""
if X_observed is None:
raise ValueError("There are no feasible observed points.")
# construct Objective module
(
objective,
objective_thresholds,
) = get_weighted_mc_objective_and_objective_thresholds(
objective_weights=objective_weights,
objective_thresholds=objective_thresholds)
# For EHVI acquisition functions we pass the constraint transform directly.
if outcome_constraints is None:
cons_tfs = None
else:
cons_tfs = get_outcome_constraint_transforms(outcome_constraints)
num_objectives = objective_thresholds.shape[0]
return get_acquisition_function(
acquisition_function_name="qNEHVI",
model=model,
objective=objective, # pyre-ignore [6]
X_observed=X_observed,
X_pending=X_pending,
constraints=cons_tfs,
prune_baseline=kwargs.get("prune_baseline", True),
mc_samples=kwargs.get("mc_samples", DEFAULT_EHVI_MC_SAMPLES),
alpha=kwargs.get(
"alpha",
get_default_partitioning_alpha(num_objectives=num_objectives)),
qmc=kwargs.get("qmc", True),
# pyre-fixme[6]: Expected `Optional[int]` for 11th param but got
# `Union[float, int]`.
seed=torch.randint(1, 10000, (1, )).item(),
ref_point=objective_thresholds.tolist(),
marginalize_dim=kwargs.get("marginalize_dim"),
match_right_most_batch_dim=kwargs.get("match_right_most_batch_dim",
False),
cache_root=kwargs.get("cache_root", True),
)
def pareto_frontier_evaluator(
model: TorchModel,
objective_weights: Tensor,
objective_thresholds: Optional[Tensor] = None,
X: Optional[Tensor] = None,
Y: Optional[Tensor] = None,
Yvar: Optional[Tensor] = None,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
) -> Tuple[Tensor, Tensor, Tensor]:
"""Return outcomes predicted to lie on a pareto frontier.
Given a model and a points to evaluate use the model to predict which points
lie on the pareto frontier.
Args:
model: Model used to predict outcomes.
objective_weights: A `m` tensor of values indicating the weight to put
on different outcomes. For pareto frontiers only the sign matters.
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: A `n x d` tensor of features to evaluate.
Y: A `n x m` tensor of outcomes to use instead of predictions.
Yvar: A `n x m x m` tensor of input covariances (NaN if unobserved).
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.
Returns:
3-element tuple containing
- A `j x m` tensor of outcome on the pareto frontier. j is the number
of frontier points.
- A `j x m x m` tensor of predictive covariances.
cov[j, m1, m2] is Cov[m1@j, m2@j].
- A `j` tensor of the index of each frontier point in the input Y.
"""
if X is not None:
Y, Yvar = model.predict(X)
elif Y is None or Yvar is None:
raise ValueError(
"Requires `X` to predict or both `Y` and `Yvar` to select a subset of "
"points on the pareto frontier.")
# Apply objective_weights to outcomes and objective_thresholds.
# If objective_thresholds is not None use a dummy tensor of zeros.
(
obj,
weighted_objective_thresholds,
) = get_weighted_mc_objective_and_objective_thresholds(
objective_weights=objective_weights,
objective_thresholds=(objective_thresholds
if objective_thresholds is not None else
torch.zeros(objective_weights.shape)),
)
Y_obj = obj(Y)
indx_frontier = torch.arange(Y.shape[0], dtype=torch.long, device=Y.device)
# Filter Y, Yvar, Y_obj to items that dominate all objective thresholds
if objective_thresholds is not None:
objective_thresholds_mask = (Y_obj >=
weighted_objective_thresholds).all(dim=1)
Y = Y[objective_thresholds_mask]
Yvar = Yvar[objective_thresholds_mask]
Y_obj = Y_obj[objective_thresholds_mask]
indx_frontier = indx_frontier[objective_thresholds_mask]
# Get feasible points that do not violate outcome_constraints
if outcome_constraints is not None:
cons_tfs = get_outcome_constraint_transforms(outcome_constraints)
# pyre-ignore [16]
feas = torch.stack([c(Y) <= 0 for c in cons_tfs], dim=-1).all(dim=-1)
Y = Y[feas]
Yvar = Yvar[feas]
Y_obj = Y_obj[feas]
indx_frontier = indx_frontier[feas]
if Y.shape[0] == 0:
# if there are no feasible points that are better than the reference point
# return empty tensors
return Y, Yvar, indx_frontier
# calculate pareto front with only objective outcomes:
frontier_mask = is_non_dominated(Y_obj)
# Apply masks
Y_frontier = Y[frontier_mask]
Yvar_frontier = Yvar[frontier_mask]
indx_frontier = indx_frontier[frontier_mask]
return Y_frontier, Yvar_frontier, indx_frontier
def infer_objective_thresholds(
model: Model,
objective_weights: Tensor, # objective_directions
bounds: Optional[List[Tuple[float, float]]] = None,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
subset_idcs: Optional[Tensor] = None,
Xs: Optional[List[Tensor]] = None,
X_observed: Optional[Tensor] = None,
) -> Tensor:
"""Infer objective thresholds.
This method uses the model-estimated Pareto frontier over the in-sample points
to infer absolute (not relativized) objective thresholds.
This uses a heuristic that sets the objective threshold to be a scaled nadir
point, where the nadir point is scaled back based on the range of each
objective across the current in-sample Pareto frontier.
See `botorch.utils.multi_objective.hypervolume.infer_reference_point` for
details on the heuristic.
Args:
model: A fitted botorch Model.
objective_weights: The objective is to maximize a weighted sum of
the columns of f(x). These are the weights. These should not
be subsetted.
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. These should not be subsetted.
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 during generation.
subset_idcs: The indices of the outcomes that are modeled by the
provided model. If subset_idcs not None, this method infers
whether the model is subsetted.
Xs: A list of m (k_i x d) feature tensors X. Number of rows k_i can
vary from i=1,...,m.
X_observed: A `n x d`-dim tensor of in-sample points to use for
determining the current in-sample Pareto frontier.
Returns:
A `m`-dim tensor of objective thresholds, where the objective
threshold is `nan` if the outcome is not an objective.
"""
if X_observed is None:
if bounds is None:
raise ValueError("bounds is required if X_observed is None.")
elif Xs is None:
raise ValueError("Xs is required if X_observed is None.")
_, X_observed = _get_X_pending_and_observed(
Xs=Xs,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
bounds=bounds,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
)
num_outcomes = objective_weights.shape[0]
if subset_idcs is None:
# check if only a subset of outcomes are modeled
nonzero = objective_weights != 0
if outcome_constraints is not None:
A, _ = outcome_constraints
nonzero = nonzero | torch.any(A != 0, dim=0)
expected_subset_idcs = nonzero.nonzero().view(-1)
if model.num_outputs > expected_subset_idcs.numel():
# subset the model so that we only compute the posterior
# over the relevant outcomes
subset_model_results = subset_model(
model=model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
)
model = subset_model_results.model
objective_weights = subset_model_results.objective_weights
outcome_constraints = subset_model_results.outcome_constraints
subset_idcs = subset_model_results.indices
else:
# model is already subsetted.
subset_idcs = expected_subset_idcs
# subset objective weights and outcome constraints
objective_weights = objective_weights[subset_idcs]
if outcome_constraints is not None:
outcome_constraints = (
outcome_constraints[0][:, subset_idcs],
outcome_constraints[1],
)
else:
objective_weights = objective_weights[subset_idcs]
if outcome_constraints is not None:
outcome_constraints = (
outcome_constraints[0][:, subset_idcs],
outcome_constraints[1],
)
with torch.no_grad():
pred = not_none(model).posterior(not_none(X_observed)).mean
if outcome_constraints is not None:
cons_tfs = get_outcome_constraint_transforms(outcome_constraints)
# pyre-ignore [16]
feas = torch.stack([c(pred) <= 0 for c in cons_tfs],
dim=-1).all(dim=-1)
pred = pred[feas]
if pred.shape[0] == 0:
raise AxError("There are no feasible observed points.")
obj_mask = objective_weights.nonzero().view(-1)
obj_weights_subset = objective_weights[obj_mask]
obj = pred[..., obj_mask] * obj_weights_subset
pareto_obj = obj[is_non_dominated(obj)]
objective_thresholds = infer_reference_point(
pareto_Y=pareto_obj,
scale=0.1,
)
# multiply by objective weights to return objective thresholds in the
# unweighted space
objective_thresholds = objective_thresholds * obj_weights_subset
full_objective_thresholds = torch.full(
(num_outcomes, ),
float("nan"),
dtype=objective_weights.dtype,
device=objective_weights.device,
)
obj_idcs = subset_idcs[obj_mask]
full_objective_thresholds[obj_idcs] = objective_thresholds.clone()
return full_objective_thresholds
