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Make raw_samples optional. #801

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Make raw_samples optional. #801

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27 changes: 18 additions & 9 deletions botorch/optim/optimize.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,7 @@ def optimize_acqf(
bounds: Tensor,
q: int,
num_restarts: int,
raw_samples: int,
raw_samples: Optional[int] = None,
options: Optional[Dict[str, Union[bool, float, int, str]]] = None,
inequality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
equality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
Expand All @@ -52,7 +52,8 @@ def optimize_acqf(
q: The number of candidates.
num_restarts: The number of starting points for multistart acquisition
function optimization.
raw_samples: The number of samples for initialization.
raw_samples: The number of samples for initialization. This is required
if `batch_initial_conditions` is not specified.
options: Options for candidate generation.
inequality constraints: A list of tuples (indices, coefficients, rhs),
with each tuple encoding an inequality constraint of the form
Expand Down Expand Up @@ -99,7 +100,7 @@ def optimize_acqf(
if sequential and q > 1:
if not return_best_only:
raise NotImplementedError(
"return_best_only=False only supported for joint optimization"
"`return_best_only=False` only supported for joint optimization."
)
if isinstance(acq_function, OneShotAcquisitionFunction):
raise NotImplementedError(
Expand Down Expand Up @@ -152,6 +153,11 @@ def optimize_acqf(
return X, acq_value

if batch_initial_conditions is None:
if raw_samples is None:
raise ValueError(
"Must specify `raw_samples` when `batch_initial_conditions` is `None`."
)

ic_gen = (
gen_one_shot_kg_initial_conditions
if isinstance(acq_function, qKnowledgeGradient)
Expand Down Expand Up @@ -214,7 +220,7 @@ def optimize_acqf_cyclic(
bounds: Tensor,
q: int,
num_restarts: int,
raw_samples: int,
raw_samples: Optional[int] = None,
options: Optional[Dict[str, Union[bool, float, int, str]]] = None,
inequality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
equality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
Expand All @@ -231,7 +237,8 @@ def optimize_acqf_cyclic(
q: The number of candidates.
num_restarts: Number of starting points for multistart acquisition
function optimization.
raw_samples: Number of samples for initialization.
raw_samples: Number of samples for initialization. This is required
if `batch_initial_conditions` is not specified.
options: Options for candidate generation.
inequality constraints: A list of tuples (indices, coefficients, rhs),
with each tuple encoding an inequality constraint of the form
Expand Down Expand Up @@ -325,7 +332,7 @@ def optimize_acqf_list(
acq_function_list: List[AcquisitionFunction],
bounds: Tensor,
num_restarts: int,
raw_samples: int,
raw_samples: Optional[int] = None,
options: Optional[Dict[str, Union[bool, float, int, str]]] = None,
inequality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
equality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
Expand All @@ -342,7 +349,8 @@ def optimize_acqf_list(
bounds: A `2 x d` tensor of lower and upper bounds for each column of `X`.
num_restarts: Number of starting points for multistart acquisition
function optimization.
raw_samples: Number of samples for initialization.
raw_samples: Number of samples for initialization. This is required
if `batch_initial_conditions` is not specified.
options: Options for candidate generation.
inequality constraints: A list of tuples (indices, coefficients, rhs),
with each tuple encoding an inequality constraint of the form
Expand Down Expand Up @@ -401,8 +409,8 @@ def optimize_acqf_mixed(
bounds: Tensor,
q: int,
num_restarts: int,
raw_samples: int,
fixed_features_list: List[Dict[int, float]],
raw_samples: Optional[int] = None,
options: Optional[Dict[str, Union[bool, float, int, str]]] = None,
inequality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
equality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
Expand All @@ -419,7 +427,8 @@ def optimize_acqf_mixed(
q: The number of candidates.
num_restarts: Number of starting points for multistart acquisition
function optimization.
raw_samples: Number of samples for initialization.
raw_samples: Number of samples for initialization. This is required
if `batch_initial_conditions` is not specified.
fixed_features_list: A list of maps `{feature_index: value}`. The i-th
item represents the fixed_feature for the i-th optimization.
options: Options for candidate generation.
Expand Down
10 changes: 10 additions & 0 deletions test/optim/test_optimize.py
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Original file line number Diff line number Diff line change
Expand Up @@ -160,6 +160,16 @@ def test_optimize_acqf_joint(
)
self.assertTrue(torch.equal(acq_vals, mock_acq_values[0]))

# verify ValueError
with self.assertRaisesRegex(ValueError, "Must specify"):
optimize_acqf(
acq_function=MockAcquisitionFunction(),
bounds=bounds,
q=q,
num_restarts=num_restarts,
options=options,
)

@mock.patch("botorch.optim.optimize.gen_batch_initial_conditions")
@mock.patch("botorch.optim.optimize.gen_candidates_scipy")
def test_optimize_acqf_sequential(
Expand Down