def test_pareto_frontier_evaluator_raw(self):
Y, cov = pareto_frontier_evaluator(
model=self.model,
objective_weights=self.objective_weights,
objective_thresholds=self.objective_thresholds,
Y=self.Y,
Yvar=self.Yvar,
)
pred = self.Y[2:4]
self.assertTrue(torch.allclose(Y, pred), f"{Y} does not match {pred}")
# Omit objective_thresholds
Y, cov = pareto_frontier_evaluator(
model=self.model,
objective_weights=self.objective_weights,
Y=self.Y,
Yvar=self.Yvar,
)
pred = self.Y[2:]
self.assertTrue(torch.allclose(Y, pred), f"{Y} does not match {pred}")
# Change objective_weights so goal is to minimize b
Y, cov = pareto_frontier_evaluator(
model=self.model,
objective_weights=torch.tensor([1.0, -1.0]),
objective_thresholds=self.objective_thresholds,
Y=self.Y,
Yvar=self.Yvar,
)
pred = self.Y[[0, 4]]
self.assertTrue(torch.allclose(Y, pred),
f"actual {Y} does not match pred {pred}")
def test_pareto_frontier_raise_error_when_missing_data(self):
with self.assertRaises(ValueError):
pareto_frontier_evaluator(
model=self.model,
objective_thresholds=self.objective_thresholds,
objective_weights=self.objective_weights,
Yvar=self.Yvar,
)
def test_pareto_frontier_evaluator_raw(self):
Yvar = torch.diag_embed(self.Yvar)
Y, cov, indx = pareto_frontier_evaluator(
model=self.model,
objective_weights=self.objective_weights,
objective_thresholds=self.objective_thresholds,
Y=self.Y,
Yvar=Yvar,
)
pred = self.Y[2:4]
self.assertTrue(torch.allclose(Y, pred), f"{Y} does not match {pred}")
expected_cov = Yvar[2:4]
self.assertTrue(torch.allclose(expected_cov, cov))
self.assertTrue(torch.equal(torch.arange(2, 4), indx))
# Omit objective_thresholds
Y, cov, indx = pareto_frontier_evaluator(
model=self.model,
objective_weights=self.objective_weights,
Y=self.Y,
Yvar=Yvar,
)
pred = self.Y[2:]
self.assertTrue(torch.allclose(Y, pred), f"{Y} does not match {pred}")
expected_cov = Yvar[2:]
self.assertTrue(torch.allclose(expected_cov, cov))
self.assertTrue(torch.equal(torch.arange(2, 5), indx))
# Change objective_weights so goal is to minimize b
Y, cov, indx = pareto_frontier_evaluator(
model=self.model,
objective_weights=torch.tensor([1.0, -1.0]),
objective_thresholds=self.objective_thresholds,
Y=self.Y,
Yvar=Yvar,
)
pred = self.Y[[0, 4]]
self.assertTrue(torch.allclose(Y, pred),
f"actual {Y} does not match pred {pred}")
expected_cov = Yvar[[0, 4]]
self.assertTrue(torch.allclose(expected_cov, cov))
# test no points better than reference point
Y, cov, indx = pareto_frontier_evaluator(
model=self.model,
objective_weights=self.objective_weights,
objective_thresholds=torch.full_like(self.objective_thresholds,
100.0),
Y=self.Y,
Yvar=Yvar,
)
self.assertTrue(torch.equal(Y, self.Y[:0]))
self.assertTrue(torch.equal(cov, torch.zeros(0, 3, 3)))
self.assertTrue(torch.equal(torch.tensor([], dtype=torch.long), indx))
def test_pareto_frontier_evaluator_predict(self):
Y, cov = pareto_frontier_evaluator(
model=self.model,
objective_weights=self.objective_weights,
objective_thresholds=self.objective_thresholds,
X=self.X,
)
pred = self.Y[2:4]
self.assertTrue(torch.allclose(Y, pred),
f"actual {Y} does not match pred {pred}")
def test_pareto_frontier_evaluator_with_outcome_constraints(self):
Y, cov = pareto_frontier_evaluator(
model=self.model,
objective_weights=self.objective_weights,
objective_thresholds=self.objective_thresholds,
Y=self.Y,
Yvar=self.Yvar,
outcome_constraints=self.outcome_constraints,
)
pred = self.Y[2, :]
self.assertTrue(torch.allclose(Y, pred),
f"actual {Y} does not match pred {pred}")
