def testTorchModelPredict(self):
torch_model = TorchModel()
with self.assertRaises(NotImplementedError):
torch_model.predict(np.array([0]))
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
