def test_MaxValueEntropySearch_MultiFidelity(self):
model = MaxValueEntropySearch()
model.fit(
Xs=self.Xs,
Ys=self.Ys,
Yvars=self.Yvars,
bounds=self.bounds,
task_features=[],
feature_names=self.feature_names,
metric_names=self.metric_names,
fidelity_features=[-1],
)
# Check best point selection within bounds (some numerical tolerance)
xbest = model.best_point(
bounds=self.bounds,
objective_weights=self.objective_weights,
target_fidelities={2: 5.0},
)
lb = torch.tensor([b[0] for b in self.bounds]) - 1e-5
ub = torch.tensor([b[1] for b in self.bounds]) + 1e-5
self.assertTrue(torch.all(xbest <= ub))
self.assertTrue(torch.all(xbest >= lb))
# check error when no target fidelities are specified
with self.assertRaises(RuntimeError):
model.best_point(bounds=self.bounds,
objective_weights=self.objective_weights)
# check error when target fidelity and fixed features have the same key
with self.assertRaises(RuntimeError):
model.best_point(
bounds=self.bounds,
objective_weights=self.objective_weights,
target_fidelities={2: 1.0},
fixed_features={2: 1.0},
)
# check generation
n = 1
new_X_dummy = torch.rand(1, n, 3, dtype=self.dtype, device=self.device)
with mock.patch(self.optimize_acqf, side_effect=[
(new_X_dummy, None)
]) as mock_optimize_acqf:
Xgen, wgen, _, __ = model.gen(
n=n,
bounds=self.bounds,
objective_weights=self.objective_weights,
outcome_constraints=None,
linear_constraints=None,
model_gen_options={
"acquisition_function_kwargs": self.acq_options,
"optimizer_kwargs": self.optimizer_options,
},
target_fidelities={2: 1.0},
)
self.assertTrue(torch.equal(Xgen, new_X_dummy.cpu()))
self.assertTrue(torch.equal(wgen, torch.ones(n, dtype=self.dtype)))
mock_optimize_acqf.assert_called()
def test_MaxValueEntropySearch(self):
model = MaxValueEntropySearch()
model.fit(
Xs=self.Xs,
Ys=self.Ys,
Yvars=self.Yvars,
bounds=self.bounds,
feature_names=self.feature_names,
metric_names=self.metric_names,
task_features=[],
fidelity_features=[],
)
# test model.gen()
new_X_dummy = torch.rand(1, 1, 3, dtype=self.dtype, device=self.device)
with mock.patch(self.optimize_acqf) as mock_optimize_acqf:
mock_optimize_acqf.side_effect = [(new_X_dummy, None)]
Xgen, wgen, _ = model.gen(
n=1,
bounds=self.bounds,
objective_weights=self.objective_weights,
outcome_constraints=None,
linear_constraints=None,
model_gen_options={
"acquisition_function_kwargs": self.acq_options,
"optimizer_kwargs": self.optimizer_options,
},
)
self.assertTrue(torch.equal(Xgen, new_X_dummy.cpu()))
self.assertTrue(torch.equal(wgen, torch.ones(1, dtype=self.dtype)))
mock_optimize_acqf.assert_called_once()
# test model.best_point()
best_point_dummy = torch.rand(1,
3,
dtype=self.dtype,
device=self.device)
with mock.patch(self.optimize_acqf) as mock_optimize_acqf:
mock_optimize_acqf.side_effect = [(best_point_dummy, None)]
rec_point = model.best_point(
bounds=self.bounds,
objective_weights=self.objective_weights,
outcome_constraints=None,
linear_constraints=None,
model_gen_options={
"acquisition_function_kwargs": self.acq_options,
"optimizer_kwargs": self.optimizer_options,
},
)
self.assertTrue(
torch.equal(rec_point,
best_point_dummy.cpu().view(-1)))
mock_optimize_acqf.assert_called_once()
# test error message in case of constraints
linear_constraints = (
torch.tensor([[0.0, 0.0, 0.0], [0.0, 1.0, 0.0]]),
torch.tensor([[0.5], [1.0]]),
)
with self.assertRaises(UnsupportedError):
Xgen, wgen, _ = model.gen(
n=1,
bounds=self.bounds,
objective_weights=self.objective_weights,
linear_constraints=linear_constraints,
)
# test error message in case of >1 objective weights
objective_weights = torch.tensor([1.0, 1.0],
dtype=self.dtype,
device=self.device)
with self.assertRaises(UnsupportedError):
Xgen, wgen, _ = model.gen(n=1,
bounds=self.bounds,
objective_weights=objective_weights)
# test error message in best_point()
with self.assertRaises(UnsupportedError):
Xgen = model.best_point(
bounds=self.bounds,
objective_weights=self.objective_weights,
linear_constraints=linear_constraints,
)
with self.assertRaises(RuntimeError):
Xgen = model.best_point(
bounds=self.bounds,
objective_weights=self.objective_weights,
target_fidelities={2: 1.0},
)
def test_MaxValueEntropySearch(self):
model = MaxValueEntropySearch()
model.fit(
Xs=self.Xs,
Ys=self.Ys,
Yvars=self.Yvars,
search_space_digest=SearchSpaceDigest(
feature_names=self.feature_names,
bounds=self.bounds,
),
metric_names=self.metric_names,
)
# test model.gen()
new_X_dummy = torch.rand(1, 1, 3, dtype=self.dtype, device=self.device)
with mock.patch(self.optimize_acqf) as mock_optimize_acqf:
mock_optimize_acqf.side_effect = [(new_X_dummy, None)]
Xgen, wgen, _, __ = model.gen(
n=1,
bounds=self.bounds,
objective_weights=self.objective_weights,
outcome_constraints=None,
linear_constraints=None,
model_gen_options={
"acquisition_function_kwargs": self.acq_options,
"optimizer_kwargs": self.optimizer_options,
},
)
self.assertTrue(torch.equal(Xgen, new_X_dummy.cpu()))
self.assertTrue(torch.equal(wgen, torch.ones(1, dtype=self.dtype)))
mock_optimize_acqf.assert_called_once()
# Check best point selection within bounds (some numerical tolerance)
xbest = model.best_point(
bounds=self.bounds,
objective_weights=self.objective_weights,
outcome_constraints=None,
linear_constraints=None,
model_gen_options={
"acquisition_function_kwargs": self.acq_options,
"optimizer_kwargs": self.optimizer_options,
},
)
lb = torch.tensor([b[0] for b in self.bounds]) - 1e-5
ub = torch.tensor([b[1] for b in self.bounds]) + 1e-5
self.assertTrue(torch.all(xbest <= ub))
self.assertTrue(torch.all(xbest >= lb))
# test error message in case of constraints
linear_constraints = (
torch.tensor([[0.0, 0.0, 0.0], [0.0, 1.0, 0.0]]),
torch.tensor([[0.5], [1.0]]),
)
with self.assertRaises(UnsupportedError):
Xgen, wgen, _, __ = model.gen(
n=1,
bounds=self.bounds,
objective_weights=self.objective_weights,
linear_constraints=linear_constraints,
)
# test error message in case of >1 objective weights
objective_weights = torch.tensor(
[1.0, 1.0], dtype=self.dtype, device=self.device
)
with self.assertRaises(UnsupportedError):
Xgen, wgen, _, __ = model.gen(
n=1, bounds=self.bounds, objective_weights=objective_weights
)
# test error message in best_point()
with self.assertRaises(UnsupportedError):
Xgen = model.best_point(
bounds=self.bounds,
objective_weights=self.objective_weights,
linear_constraints=linear_constraints,
)
with self.assertRaises(RuntimeError):
Xgen = model.best_point(
bounds=self.bounds,
objective_weights=self.objective_weights,
target_fidelities={2: 1.0},
)
# test input warping
self.assertFalse(model.use_input_warping)
model = MaxValueEntropySearch(use_input_warping=True)
model.fit(
Xs=self.Xs,
Ys=self.Ys,
Yvars=self.Yvars,
search_space_digest=SearchSpaceDigest(
feature_names=self.feature_names,
bounds=self.bounds,
),
metric_names=self.metric_names,
)
self.assertTrue(model.use_input_warping)
self.assertTrue(hasattr(model.model, "input_transform"))
self.assertIsInstance(model.model.input_transform, Warp)
# test loocv pseudo likelihood
self.assertFalse(model.use_loocv_pseudo_likelihood)
model = MaxValueEntropySearch(use_loocv_pseudo_likelihood=True)
model.fit(
Xs=self.Xs,
Ys=self.Ys,
Yvars=self.Yvars,
search_space_digest=SearchSpaceDigest(
feature_names=self.feature_names,
bounds=self.bounds,
),
metric_names=self.metric_names,
)
self.assertTrue(model.use_loocv_pseudo_likelihood)