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add mixed optimization for list optimization #1342

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add mixed optimization for list optimization #1342

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08d5400
add mixed optimization for list optimization
jduerholt Aug 5, 2022
bb05701
Merge branch 'main' into acqf_list
jduerholt Nov 28, 2022
79e4140
add tests
jduerholt Nov 28, 2022
63068ed
change api behavior when providing both fixed_features and fixed_feat…
jduerholt Nov 28, 2022
b5c1620
Merge branch 'main' into acqf_list
saitcakmak Dec 1, 2022
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50 changes: 36 additions & 14 deletions botorch/optim/optimize.py
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Original file line number Diff line number Diff line change
Expand Up @@ -470,6 +470,7 @@ def optimize_acqf_list(
inequality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
equality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
fixed_features: Optional[Dict[int, float]] = None,
fixed_features_list: Optional[List[Dict[int, float]]] = None,
post_processing_func: Optional[Callable[[Tensor], Tensor]] = None,
) -> Tuple[Tensor, Tensor]:
r"""Generate a list of candidates from a list of acquisition functions.
Expand All @@ -495,6 +496,9 @@ def optimize_acqf_list(
`\sum_i (X[indices[i]] * coefficients[i]) = rhs`
fixed_features: A map `{feature_index: value}` for features that
should be fixed to a particular value during generation.
fixed_features_list: A list of maps `{feature_index: value}`. The i-th
item represents the fixed_feature for the i-th optimization. If
`fixed_features_list` is provided, `optimize_acqf_mixed` is invoked.
post_processing_func: A function that post-processes an optimization
result appropriately (i.e., according to `round-trip`
transformations).
Expand All @@ -507,6 +511,10 @@ def optimize_acqf_list(
index `i` is the acquisition value conditional on having observed
all candidates except candidate `i`.
"""
if fixed_features and fixed_features_list:
raise ValueError(
"Èither `fixed_feature` or `fixed_features_list` can be provided, not both."
)
if not acq_function_list:
raise ValueError("acq_function_list must be non-empty.")
candidate_list, acq_value_list = [], []
Expand All @@ -519,20 +527,34 @@ def optimize_acqf_list(
if base_X_pending is not None
else candidates
)
candidate, acq_value = optimize_acqf(
acq_function=acq_function,
bounds=bounds,
q=1,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options or {},
inequality_constraints=inequality_constraints,
equality_constraints=equality_constraints,
fixed_features=fixed_features,
post_processing_func=post_processing_func,
return_best_only=True,
sequential=False,
)
if fixed_features_list:
candidate, acq_value = optimize_acqf_mixed(
acq_function=acq_function,
bounds=bounds,
q=1,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options or {},
inequality_constraints=inequality_constraints,
equality_constraints=equality_constraints,
fixed_features_list=fixed_features_list,
post_processing_func=post_processing_func,
)
else:
candidate, acq_value = optimize_acqf(
acq_function=acq_function,
bounds=bounds,
q=1,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options or {},
inequality_constraints=inequality_constraints,
equality_constraints=equality_constraints,
fixed_features=fixed_features,
post_processing_func=post_processing_func,
return_best_only=True,
sequential=False,
)
candidate_list.append(candidate)
acq_value_list.append(acq_value)
candidates = torch.cat(candidate_list, dim=-2)
Expand Down
221 changes: 131 additions & 90 deletions test/optim/test_optimize.py
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Original file line number Diff line number Diff line change
Expand Up @@ -908,7 +908,8 @@ def test_optimize_acqf_cyclic(self, mock_optimize_acqf):

class TestOptimizeAcqfList(BotorchTestCase):
@mock.patch("botorch.optim.optimize.optimize_acqf") # noqa: C901
def test_optimize_acqf_list(self, mock_optimize_acqf):
@mock.patch("botorch.optim.optimize.optimize_acqf_mixed")
def test_optimize_acqf_list(self, mock_optimize_acqf, mock_optimize_acqf_mixed):
num_restarts = 2
raw_samples = 10
options = {}
Expand All @@ -921,97 +922,123 @@ def test_optimize_acqf_list(self, mock_optimize_acqf):
mock_acq_function_1 = MockAcquisitionFunction()
mock_acq_function_2 = MockAcquisitionFunction()
mock_acq_function_list = [mock_acq_function_1, mock_acq_function_2]
for num_acqf, dtype in itertools.product([1, 2], (torch.float, torch.double)):
for m in mock_acq_function_list:
# clear previous X_pending
m.set_X_pending(None)
tkwargs["dtype"] = dtype
inequality_constraints[0] = [
t.to(**tkwargs) for t in inequality_constraints[0]
]
mock_optimize_acqf.reset_mock()
bounds = bounds.to(**tkwargs)
candidate_rvs = []
acq_val_rvs = []
gcs_return_vals = [
(torch.rand(1, 3, **tkwargs), torch.rand(1, **tkwargs))
for _ in range(num_acqf)
]
for rv in gcs_return_vals:
candidate_rvs.append(rv[0])
acq_val_rvs.append(rv[1])
side_effect = list(zip(candidate_rvs, acq_val_rvs))
mock_optimize_acqf.side_effect = side_effect
orig_candidates = candidate_rvs[0].clone()
# Wrap the set_X_pending method for checking that call arguments
with mock.patch.object(
MockAcquisitionFunction,
"set_X_pending",
wraps=mock_acq_function_1.set_X_pending,
) as mock_set_X_pending_1, mock.patch.object(
MockAcquisitionFunction,
"set_X_pending",
wraps=mock_acq_function_2.set_X_pending,
) as mock_set_X_pending_2:
candidates, acq_values = optimize_acqf_list(
acq_function_list=mock_acq_function_list[:num_acqf],
bounds=bounds,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options,
inequality_constraints=inequality_constraints,
post_processing_func=rounding_func,
)
# check that X_pending is set correctly in sequential optimization
if num_acqf > 1:
x_pending_call_args_list = mock_set_X_pending_2.call_args_list
idxr = torch.ones(num_acqf, dtype=torch.bool, device=self.device)
for i in range(len(x_pending_call_args_list) - 1):
idxr[i] = 0
self.assertTrue(
torch.equal(
x_pending_call_args_list[i][0][0], orig_candidates[idxr]
)
)
idxr[i] = 1
orig_candidates[i] = candidate_rvs[i + 1]
else:
mock_set_X_pending_1.assert_not_called()
# check final candidates
expected_candidates = (
torch.cat(candidate_rvs[-num_acqf:], dim=0)
if num_acqf > 1
else candidate_rvs[0]
)
self.assertTrue(torch.equal(candidates, expected_candidates))
# check call arguments for optimize_acqf
call_args_list = mock_optimize_acqf.call_args_list
expected_call_args = {
"acq_function": None,
"bounds": bounds,
"q": 1,
"num_restarts": num_restarts,
"raw_samples": raw_samples,
"options": options,
"inequality_constraints": inequality_constraints,
"equality_constraints": None,
"fixed_features": None,
"post_processing_func": rounding_func,
"batch_initial_conditions": None,
"return_best_only": True,
"sequential": False,
}
for i in range(len(call_args_list)):
expected_call_args["acq_function"] = mock_acq_function_list[i]
for k, v in call_args_list[i][1].items():
if torch.is_tensor(v):
self.assertTrue(torch.equal(expected_call_args[k], v))
elif k == "acq_function":
self.assertIsInstance(
mock_acq_function_list[i], MockAcquisitionFunction
fixed_features_list = [None, [{0: 0.5}]]
for ffl in fixed_features_list:
for num_acqf, dtype in itertools.product(
[1, 2], (torch.float, torch.double)
):
for m in mock_acq_function_list:
# clear previous X_pending
m.set_X_pending(None)
tkwargs["dtype"] = dtype
inequality_constraints[0] = [
t.to(**tkwargs) for t in inequality_constraints[0]
]
mock_optimize_acqf.reset_mock()
mock_optimize_acqf_mixed.reset_mock()
bounds = bounds.to(**tkwargs)
candidate_rvs = []
acq_val_rvs = []
gcs_return_vals = [
(torch.rand(1, 3, **tkwargs), torch.rand(1, **tkwargs))
for _ in range(num_acqf)
]
for rv in gcs_return_vals:
candidate_rvs.append(rv[0])
acq_val_rvs.append(rv[1])
side_effect = list(zip(candidate_rvs, acq_val_rvs))
mock_optimize_acqf.side_effect = side_effect
mock_optimize_acqf_mixed.side_effect = side_effect
orig_candidates = candidate_rvs[0].clone()
# Wrap the set_X_pending method for checking that call arguments
with mock.patch.object(
MockAcquisitionFunction,
"set_X_pending",
wraps=mock_acq_function_1.set_X_pending,
) as mock_set_X_pending_1, mock.patch.object(
MockAcquisitionFunction,
"set_X_pending",
wraps=mock_acq_function_2.set_X_pending,
) as mock_set_X_pending_2:
candidates, _ = optimize_acqf_list(
acq_function_list=mock_acq_function_list[:num_acqf],
bounds=bounds,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options,
inequality_constraints=inequality_constraints,
post_processing_func=rounding_func,
fixed_features_list=ffl,
)
# check that X_pending is set correctly in sequential optimization
if num_acqf > 1:
x_pending_call_args_list = mock_set_X_pending_2.call_args_list
idxr = torch.ones(
num_acqf, dtype=torch.bool, device=self.device
)
for i in range(len(x_pending_call_args_list) - 1):
idxr[i] = 0
self.assertTrue(
torch.equal(
x_pending_call_args_list[i][0][0],
orig_candidates[idxr],
)
)
idxr[i] = 1
orig_candidates[i] = candidate_rvs[i + 1]
else:
self.assertEqual(expected_call_args[k], v)
mock_set_X_pending_1.assert_not_called()
# check final candidates
expected_candidates = (
torch.cat(candidate_rvs[-num_acqf:], dim=0)
if num_acqf > 1
else candidate_rvs[0]
)
self.assertTrue(torch.equal(candidates, expected_candidates))
# check call arguments for optimize_acqf
if ffl is None:
call_args_list = mock_optimize_acqf.call_args_list
expected_call_args = {
"acq_function": None,
"bounds": bounds,
"q": 1,
"num_restarts": num_restarts,
"raw_samples": raw_samples,
"options": options,
"inequality_constraints": inequality_constraints,
"equality_constraints": None,
"fixed_features": None,
"post_processing_func": rounding_func,
"batch_initial_conditions": None,
"return_best_only": True,
"sequential": False,
}
else:
call_args_list = mock_optimize_acqf_mixed.call_args_list
expected_call_args = {
"acq_function": None,
"bounds": bounds,
"q": 1,
"num_restarts": num_restarts,
"raw_samples": raw_samples,
"options": options,
"inequality_constraints": inequality_constraints,
"equality_constraints": None,
"post_processing_func": rounding_func,
"batch_initial_conditions": None,
"fixed_features_list": ffl,
}
for i in range(len(call_args_list)):
expected_call_args["acq_function"] = mock_acq_function_list[i]
for k, v in call_args_list[i][1].items():
if torch.is_tensor(v):
self.assertTrue(torch.equal(expected_call_args[k], v))
elif k == "acq_function":
self.assertIsInstance(
mock_acq_function_list[i], MockAcquisitionFunction
)
else:
self.assertEqual(expected_call_args[k], v)

def test_optimize_acqf_list_empty_list(self):
with self.assertRaises(ValueError):
Expand All @@ -1022,6 +1049,20 @@ def test_optimize_acqf_list_empty_list(self):
raw_samples=10,
)

def test_optimize_acqf_list_fixed_features(self):
with self.assertRaises(ValueError):
optimize_acqf_list(
acq_function_list=[
MockAcquisitionFunction(),
MockAcquisitionFunction(),
],
bounds=torch.stack([torch.zeros(3), 4 * torch.ones(3)]),
num_restarts=2,
raw_samples=10,
fixed_features_list=[{0: 0.5}],
fixed_features={0: 0.5},
)


class TestOptimizeAcqfMixed(BotorchTestCase):
@mock.patch("botorch.optim.optimize.optimize_acqf") # noqa: C901
Expand Down