def test_q_simple_regret(self):
for dtype in (torch.float, torch.double):
# the event shape is `b x q x t` = 1 x 1 x 1
samples = torch.zeros(1, 1, 1, device=self.device, dtype=dtype)
mm = MockModel(MockPosterior(samples=samples))
# X is `q x d` = 1 x 1. X is a dummy and unused b/c of mocking
X = torch.zeros(1, 1, device=self.device, dtype=dtype)
# basic test
sampler = IIDNormalSampler(num_samples=2)
acqf = qSimpleRegret(model=mm, sampler=sampler)
res = acqf(X)
self.assertEqual(res.item(), 0.0)
# basic test, no resample
sampler = IIDNormalSampler(num_samples=2, seed=12345)
acqf = qSimpleRegret(model=mm, sampler=sampler)
res = acqf(X)
self.assertEqual(res.item(), 0.0)
self.assertEqual(acqf.sampler.base_samples.shape, torch.Size([2, 1, 1, 1]))
bs = acqf.sampler.base_samples.clone()
res = acqf(X)
self.assertTrue(torch.equal(acqf.sampler.base_samples, bs))
# basic test, qmc, no resample
sampler = SobolQMCNormalSampler(num_samples=2)
acqf = qSimpleRegret(model=mm, sampler=sampler)
res = acqf(X)
self.assertEqual(res.item(), 0.0)
self.assertEqual(acqf.sampler.base_samples.shape, torch.Size([2, 1, 1, 1]))
bs = acqf.sampler.base_samples.clone()
acqf(X)
self.assertTrue(torch.equal(acqf.sampler.base_samples, bs))
# basic test, qmc, resample
sampler = SobolQMCNormalSampler(num_samples=2, resample=True)
acqf = qSimpleRegret(model=mm, sampler=sampler)
res = acqf(X)
self.assertEqual(res.item(), 0.0)
self.assertEqual(acqf.sampler.base_samples.shape, torch.Size([2, 1, 1, 1]))
bs = acqf.sampler.base_samples.clone()
acqf(X)
self.assertFalse(torch.equal(acqf.sampler.base_samples, bs))
# basic test for X_pending and warning
acqf.set_X_pending()
self.assertIsNone(acqf.X_pending)
acqf.set_X_pending(None)
self.assertIsNone(acqf.X_pending)
acqf.set_X_pending(X)
self.assertEqual(acqf.X_pending, X)
res = acqf(X)
X2 = torch.zeros(
1, 1, 1, device=self.device, dtype=dtype, requires_grad=True
)
with warnings.catch_warnings(record=True) as ws, settings.debug(True):
acqf.set_X_pending(X2)
self.assertEqual(acqf.X_pending, X2)
self.assertEqual(len(ws), 1)
self.assertTrue(issubclass(ws[-1].category, BotorchWarning))
def _get_value_function(
model: Model,
objective: Optional[Union[MCAcquisitionObjective, ScalarizedObjective]] = None,
sampler: Optional[MCSampler] = None,
project: Optional[Callable[[Tensor], Tensor]] = None,
valfunc_cls: Optional[Type[AcquisitionFunction]] = None,
valfunc_argfac: Optional[Callable[[Model, Dict[str, Any]]]] = None,
) -> AcquisitionFunction:
r"""Construct value function (i.e. inner acquisition function)."""
if valfunc_cls is not None:
common_kwargs: Dict[str, Any] = {"model": model, "objective": objective}
if issubclass(valfunc_cls, MCAcquisitionFunction):
common_kwargs["sampler"] = sampler
kwargs = valfunc_argfac(model=model) if valfunc_argfac is not None else {}
base_value_function = valfunc_cls(**common_kwargs, **kwargs)
else:
if isinstance(objective, MCAcquisitionObjective):
base_value_function = qSimpleRegret(
model=model, sampler=sampler, objective=objective
)
else:
base_value_function = PosteriorMean(model=model, objective=objective)
if project is None:
return base_value_function
else:
return ProjectedAcquisitionFunction(
base_value_function=base_value_function,
project=project,
)
def test_q_simple_regret(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
# the event shape is `b x q x t` = 1 x 1 x 1
samples = torch.zeros(1, 1, 1, device=device, dtype=dtype)
mm = MockModel(MockPosterior(samples=samples))
# X is `q x d` = 1 x 1. X is a dummy and unused b/c of mocking
X = torch.zeros(1, 1, device=device, dtype=dtype)
# basic test
sampler = IIDNormalSampler(num_samples=2)
acqf = qSimpleRegret(model=mm, sampler=sampler)
res = acqf(X)
self.assertEqual(res.item(), 0.0)
# basic test, no resample
sampler = IIDNormalSampler(num_samples=2, seed=12345)
acqf = qSimpleRegret(model=mm, sampler=sampler)
res = acqf(X)
self.assertEqual(res.item(), 0.0)
self.assertEqual(acqf.sampler.base_samples.shape,
torch.Size([2, 1, 1, 1]))
bs = acqf.sampler.base_samples.clone()
res = acqf(X)
self.assertTrue(torch.equal(acqf.sampler.base_samples, bs))
# basic test, qmc, no resample
sampler = SobolQMCNormalSampler(num_samples=2)
acqf = qSimpleRegret(model=mm, sampler=sampler)
res = acqf(X)
self.assertEqual(res.item(), 0.0)
self.assertEqual(acqf.sampler.base_samples.shape,
torch.Size([2, 1, 1, 1]))
bs = acqf.sampler.base_samples.clone()
acqf(X)
self.assertTrue(torch.equal(acqf.sampler.base_samples, bs))
# basic test, qmc, resample
sampler = SobolQMCNormalSampler(num_samples=2, resample=True)
acqf = qSimpleRegret(model=mm, sampler=sampler)
res = acqf(X)
self.assertEqual(res.item(), 0.0)
self.assertEqual(acqf.sampler.base_samples.shape,
torch.Size([2, 1, 1, 1]))
bs = acqf.sampler.base_samples.clone()
acqf(X)
self.assertFalse(torch.equal(acqf.sampler.base_samples, bs))
def get_PosteriorMean(
model: Model,
objective_weights: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
X_observed: Optional[Tensor] = None,
X_pending: Optional[Tensor] = None,
**kwargs: Any,
) -> AcquisitionFunction:
r"""Instantiates a PosteriorMean acquisition function.
Note: If no OutcomeConstraints given, return an analytic acquisition
function. This requires {optimizer_kwargs: {joint_optimization: True}} or an
optimizer that does not assume pending point support.
Args:
objective_weights: The objective is to maximize a weighted sum of
the columns of f(x). These are the weights.
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. (Not used by single task models)
X_observed: A tensor containing points observed for all objective
outcomes and outcomes that appear in the outcome constraints (if
there are any).
X_pending: A tensor containing points whose evaluation is pending (i.e.
that have been submitted for evaluation) present for all objective
outcomes and outcomes that appear in the outcome constraints (if
there are any).
Returns:
PosteriorMean: The instantiated acquisition function.
"""
if X_observed is None:
raise ValueError("There are no feasible observed points.")
# construct Objective module
if kwargs.get("chebyshev_scalarization", False):
obj_tf = get_chebyshev_scalarization(
weights=objective_weights,
Y=torch.stack(kwargs.get("Ys")).transpose(0, 1).squeeze(-1),
)
else:
obj_tf = get_objective_weights_transform(objective_weights)
def obj_fn(samples: Tensor, X: Optional[Tensor] = None) -> Tensor:
return obj_tf(samples)
if outcome_constraints is None:
objective = GenericMCObjective(objective=obj_fn)
else:
con_tfs = get_outcome_constraint_transforms(outcome_constraints)
inf_cost = get_infeasible_cost(X=X_observed,
model=model,
objective=obj_fn)
objective = ConstrainedMCObjective(objective=obj_fn,
constraints=con_tfs or [],
infeasible_cost=inf_cost)
# Use qSimpleRegret, not analytic posterior, to handle arbitrary objective fns.
acq_func = qSimpleRegret(model, objective=objective)
return acq_func
def test_q_simple_regret(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
# the event shape is `b x q x t` = 1 x 1 x 1
samples = torch.zeros(1, 1, 1, device=device, dtype=dtype)
mm = MockModel(MockPosterior(samples=samples))
# X is `q x d` = 1 x 1. X is a dummy and unused b/c of mocking
X = torch.zeros(1, 1, device=device, dtype=dtype)
# basic test
sampler = IIDNormalSampler(num_samples=2)
acqf = qSimpleRegret(model=mm, sampler=sampler)
res = acqf(X)
self.assertEqual(res.item(), 0.0)
# basic test, no resample
sampler = IIDNormalSampler(num_samples=2, seed=12345)
acqf = qSimpleRegret(model=mm, sampler=sampler)
res = acqf(X)
self.assertEqual(res.item(), 0.0)
self.assertEqual(acqf.sampler.base_samples.shape, torch.Size([2, 1, 1, 1]))
bs = acqf.sampler.base_samples.clone()
res = acqf(X)
self.assertTrue(torch.equal(acqf.sampler.base_samples, bs))
# basic test, qmc, no resample
sampler = SobolQMCNormalSampler(num_samples=2)
acqf = qSimpleRegret(model=mm, sampler=sampler)
res = acqf(X)
self.assertEqual(res.item(), 0.0)
self.assertEqual(acqf.sampler.base_samples.shape, torch.Size([2, 1, 1, 1]))
bs = acqf.sampler.base_samples.clone()
acqf(X)
self.assertTrue(torch.equal(acqf.sampler.base_samples, bs))
# basic test, qmc, resample
sampler = SobolQMCNormalSampler(num_samples=2, resample=True)
acqf = qSimpleRegret(model=mm, sampler=sampler)
res = acqf(X)
self.assertEqual(res.item(), 0.0)
self.assertEqual(acqf.sampler.base_samples.shape, torch.Size([2, 1, 1, 1]))
bs = acqf.sampler.base_samples.clone()
acqf(X)
self.assertFalse(torch.equal(acqf.sampler.base_samples, bs))
def _get_value_function(
model: Model,
objective: Optional[Union[MCAcquisitionObjective, ScalarizedObjective]] = None,
sampler: Optional[MCSampler] = None,
) -> AcquisitionFunction:
r"""Construct value function (i.e. inner acquisition function)."""
if isinstance(objective, MCAcquisitionObjective):
return qSimpleRegret(model=model, sampler=sampler, objective=objective)
else:
return PosteriorMean(model=model, objective=objective)
def test_acquisition_functions(self):
tkwargs = {"device": self.device, "dtype": torch.double}
train_X, train_Y, train_Yvar, model = self._get_data_and_model(
infer_noise=True, **tkwargs
)
fit_fully_bayesian_model_nuts(
model, warmup_steps=8, num_samples=5, thinning=2, disable_progbar=True
)
sampler = IIDNormalSampler(num_samples=2)
acquisition_functions = [
ExpectedImprovement(model=model, best_f=train_Y.max()),
ProbabilityOfImprovement(model=model, best_f=train_Y.max()),
PosteriorMean(model=model),
UpperConfidenceBound(model=model, beta=4),
qExpectedImprovement(model=model, best_f=train_Y.max(), sampler=sampler),
qNoisyExpectedImprovement(model=model, X_baseline=train_X, sampler=sampler),
qProbabilityOfImprovement(
model=model, best_f=train_Y.max(), sampler=sampler
),
qSimpleRegret(model=model, sampler=sampler),
qUpperConfidenceBound(model=model, beta=4, sampler=sampler),
qNoisyExpectedHypervolumeImprovement(
model=ModelListGP(model, model),
X_baseline=train_X,
ref_point=torch.zeros(2, **tkwargs),
sampler=sampler,
),
qExpectedHypervolumeImprovement(
model=ModelListGP(model, model),
ref_point=torch.zeros(2, **tkwargs),
sampler=sampler,
partitioning=NondominatedPartitioning(
ref_point=torch.zeros(2, **tkwargs), Y=train_Y.repeat([1, 2])
),
),
]
for acqf in acquisition_functions:
for batch_shape in [[5], [6, 5, 2]]:
test_X = torch.rand(*batch_shape, 1, 4, **tkwargs)
self.assertEqual(acqf(test_X).shape, torch.Size(batch_shape))
def _get_value_function(
model: Model,
objective: Optional[Union[MCAcquisitionObjective,
ScalarizedObjective]] = None,
sampler: Optional[MCSampler] = None,
project: Optional[Callable[[Tensor], Tensor]] = None,
) -> AcquisitionFunction:
r"""Construct value function (i.e. inner acquisition function)."""
if isinstance(objective, MCAcquisitionObjective):
base_value_function = qSimpleRegret(model=model,
sampler=sampler,
objective=objective)
else:
base_value_function = PosteriorMean(model=model, objective=objective)
if project is None:
return base_value_function
else:
return ProjectedAcquisitionFunction(
base_value_function=base_value_function,
project=project,
)
def get_acquisition_function(
acquisition_function_name: str,
model: Model,
objective: MCAcquisitionObjective,
X_observed: Tensor,
X_pending: Optional[Tensor] = None,
mc_samples: int = 500,
qmc: bool = True,
seed: Optional[int] = None,
**kwargs,
) -> monte_carlo.MCAcquisitionFunction:
r"""Convenience function for initializing botorch acquisition functions.
Args:
acquisition_function_name: Name of the acquisition function.
model: A fitted model.
objective: A MCAcquisitionObjective.
X_observed: A `m1 x d`-dim Tensor of `m1` design points that have
already been observed.
X_pending: A `m2 x d`-dim Tensor of `m2` design points whose evaluation
is pending.
mc_samples: The number of samples to use for (q)MC evaluation of the
acquisition function.
qmc: If True, use quasi-Monte-Carlo sampling (instead of iid).
seed: If provided, perform deterministic optimization (i.e. the
function to optimize is fixed and not stochastic).
Returns:
The requested acquisition function.
Example:
>>> model = SingleTaskGP(train_X, train_Y)
>>> obj = LinearMCObjective(weights=torch.tensor([1.0, 2.0]))
>>> acqf = get_acquisition_function("qEI", model, obj, train_X)
"""
# initialize the sampler
if qmc:
sampler = SobolQMCNormalSampler(num_samples=mc_samples, seed=seed)
else:
sampler = IIDNormalSampler(num_samples=mc_samples, seed=seed)
# instantiate and return the requested acquisition function
if acquisition_function_name == "qEI":
best_f = objective(model.posterior(X_observed).mean).max().item()
return monte_carlo.qExpectedImprovement(
model=model,
best_f=best_f,
sampler=sampler,
objective=objective,
X_pending=X_pending,
)
elif acquisition_function_name == "qPI":
best_f = objective(model.posterior(X_observed).mean).max().item()
return monte_carlo.qProbabilityOfImprovement(
model=model,
best_f=best_f,
sampler=sampler,
objective=objective,
X_pending=X_pending,
tau=kwargs.get("tau", 1e-3),
)
elif acquisition_function_name == "qNEI":
return monte_carlo.qNoisyExpectedImprovement(
model=model,
X_baseline=X_observed,
sampler=sampler,
objective=objective,
X_pending=X_pending,
prune_baseline=kwargs.get("prune_baseline", False),
)
elif acquisition_function_name == "qSR":
return monte_carlo.qSimpleRegret(model=model,
sampler=sampler,
objective=objective,
X_pending=X_pending)
elif acquisition_function_name == "qUCB":
if "beta" not in kwargs:
raise ValueError("`beta` must be specified in kwargs for qUCB.")
return monte_carlo.qUpperConfidenceBound(
model=model,
beta=kwargs["beta"],
sampler=sampler,
objective=objective,
X_pending=X_pending,
)
raise NotImplementedError(
f"Unknown acquisition function {acquisition_function_name}")
def _get_value_function(model, objective=None, sampler=None):
r"""Construct value function (i.e. inner acquisition function)."""
if isinstance(objective, MCAcquisitionObjective):
return qSimpleRegret(model=model, sampler=sampler, objective=objective)
else:
return PosteriorMean(model=model, objective=objective)
def get_acquisition_function(
acquisition_function_name: str,
model: Model,
objective: MCAcquisitionObjective,
X_observed: Tensor,
X_pending: Optional[Tensor] = None,
constraints: Optional[List[Callable[[Tensor], Tensor]]] = None,
mc_samples: int = 500,
qmc: bool = True,
seed: Optional[int] = None,
**kwargs,
) -> monte_carlo.MCAcquisitionFunction:
r"""Convenience function for initializing botorch acquisition functions.
Args:
acquisition_function_name: Name of the acquisition function.
model: A fitted model.
objective: A MCAcquisitionObjective.
X_observed: A `m1 x d`-dim Tensor of `m1` design points that have
already been observed.
X_pending: A `m2 x d`-dim Tensor of `m2` design points whose evaluation
is pending.
constraints: A list of callables, each mapping a Tensor of dimension
`sample_shape x batch-shape x q x m` to a Tensor of dimension
`sample_shape x batch-shape x q`, where negative values imply
feasibility. Used when constraint_transforms are not passed
as part of the objective.
mc_samples: The number of samples to use for (q)MC evaluation of the
acquisition function.
qmc: If True, use quasi-Monte-Carlo sampling (instead of iid).
seed: If provided, perform deterministic optimization (i.e. the
function to optimize is fixed and not stochastic).
Returns:
The requested acquisition function.
Example:
>>> model = SingleTaskGP(train_X, train_Y)
>>> obj = LinearMCObjective(weights=torch.tensor([1.0, 2.0]))
>>> acqf = get_acquisition_function("qEI", model, obj, train_X)
"""
# initialize the sampler
if qmc:
sampler = SobolQMCNormalSampler(num_samples=mc_samples, seed=seed)
else:
sampler = IIDNormalSampler(num_samples=mc_samples, seed=seed)
# instantiate and return the requested acquisition function
if acquisition_function_name == "qEI":
best_f = objective(model.posterior(X_observed).mean).max().item()
return monte_carlo.qExpectedImprovement(
model=model,
best_f=best_f,
sampler=sampler,
objective=objective,
X_pending=X_pending,
)
elif acquisition_function_name == "qPI":
best_f = objective(model.posterior(X_observed).mean).max().item()
return monte_carlo.qProbabilityOfImprovement(
model=model,
best_f=best_f,
sampler=sampler,
objective=objective,
X_pending=X_pending,
tau=kwargs.get("tau", 1e-3),
)
elif acquisition_function_name == "qNEI":
return monte_carlo.qNoisyExpectedImprovement(
model=model,
X_baseline=X_observed,
sampler=sampler,
objective=objective,
X_pending=X_pending,
prune_baseline=kwargs.get("prune_baseline", False),
)
elif acquisition_function_name == "qSR":
return monte_carlo.qSimpleRegret(model=model,
sampler=sampler,
objective=objective,
X_pending=X_pending)
elif acquisition_function_name == "qUCB":
if "beta" not in kwargs:
raise ValueError("`beta` must be specified in kwargs for qUCB.")
return monte_carlo.qUpperConfidenceBound(
model=model,
beta=kwargs["beta"],
sampler=sampler,
objective=objective,
X_pending=X_pending,
)
elif acquisition_function_name == "qEHVI":
# pyre-fixme [16]: `Model` has no attribute `train_targets`
try:
ref_point = kwargs["ref_point"]
except KeyError:
raise ValueError(
"`ref_point` must be specified in kwargs for qEHVI")
try:
Y = kwargs["Y"]
except KeyError:
raise ValueError("`Y` must be specified in kwargs for qEHVI")
# get feasible points
if constraints is not None:
feas = torch.stack([c(Y) <= 0 for c in constraints],
dim=-1).all(dim=-1)
Y = Y[feas]
obj = objective(Y)
partitioning = NondominatedPartitioning(
ref_point=torch.as_tensor(ref_point,
dtype=Y.dtype,
device=Y.device),
Y=obj,
alpha=kwargs.get("alpha", 0.0),
)
return moo_monte_carlo.qExpectedHypervolumeImprovement(
model=model,
ref_point=ref_point,
partitioning=partitioning,
sampler=sampler,
objective=objective,
constraints=constraints,
X_pending=X_pending,
)
raise NotImplementedError(
f"Unknown acquisition function {acquisition_function_name}")
def _get_best_point_acqf(
self,
X_observed: Tensor,
objective_weights: Tensor,
mc_samples: int = 512,
fixed_features: Optional[Dict[int, float]] = None,
target_fidelities: Optional[Dict[int, float]] = None,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
seed_inner: Optional[int] = None,
qmc: bool = True,
) -> Tuple[AcquisitionFunction, Optional[List[int]]]:
model = self.model
fixed_features = fixed_features or {}
target_fidelities = target_fidelities or {}
objective = _get_objective(
model=model, # pyre-ignore [6]
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
)
if isinstance(objective, ScalarizedObjective):
acq_function = PosteriorMean(
model=model,
objective=objective # pyre-ignore: [6]
)
elif isinstance(objective, MCAcquisitionObjective):
if qmc:
sampler = SobolQMCNormalSampler(num_samples=mc_samples,
seed=seed_inner)
else:
sampler = IIDNormalSampler(num_samples=mc_samples,
seed=seed_inner)
acq_function = qSimpleRegret(
model=model,
sampler=sampler,
objective=objective # pyre-ignore [6]
)
else:
raise UnsupportedError(
f"Unknown objective type: {objective.__class__}" # pragma: nocover
)
if self.fidelity_features:
# we need to optimize at the target fidelities
if any(f in self.fidelity_features for f in fixed_features):
raise RuntimeError(
"Fixed features cannot also be fidelity features")
elif not set(self.fidelity_features) == set(target_fidelities):
raise RuntimeError(
"Must provide a target fidelity for every fidelity feature"
)
# make sure to not modify fixed_features in-place
fixed_features = {**fixed_features, **target_fidelities}
elif target_fidelities:
raise RuntimeError(
"Must specify fidelity_features in fit() when using target fidelities"
)
if fixed_features:
acq_function = FixedFeatureAcquisitionFunction(
acq_function=acq_function,
d=X_observed.size(-1),
columns=list(fixed_features.keys()),
values=list(fixed_features.values()),
)
non_fixed_idcs = [
i for i in range(self.Xs[0].size(-1))
if i not in fixed_features
]
else:
non_fixed_idcs = None
return acq_function, non_fixed_idcs
def test_q_simple_regret_batch(self):
# the event shape is `b x q x t` = 2 x 2 x 1
for dtype in (torch.float, torch.double):
samples = torch.zeros(2, 2, 1, device=self.device, dtype=dtype)
samples[0, 0, 0] = 1.0
mm = MockModel(MockPosterior(samples=samples))
# X is a dummy and unused b/c of mocking
X = torch.zeros(1, 1, 1, device=self.device, dtype=dtype)
# test batch mode
sampler = IIDNormalSampler(num_samples=2)
acqf = qSimpleRegret(model=mm, sampler=sampler)
res = acqf(X)
self.assertEqual(res[0].item(), 1.0)
self.assertEqual(res[1].item(), 0.0)
# test batch mode, no resample
sampler = IIDNormalSampler(num_samples=2, seed=12345)
acqf = qSimpleRegret(model=mm, sampler=sampler)
res = acqf(X) # 1-dim batch
self.assertEqual(res[0].item(), 1.0)
self.assertEqual(res[1].item(), 0.0)
self.assertEqual(acqf.sampler.base_samples.shape, torch.Size([2, 1, 2, 1]))
bs = acqf.sampler.base_samples.clone()
acqf(X)
self.assertTrue(torch.equal(acqf.sampler.base_samples, bs))
res = acqf(X.expand(2, 1, 1)) # 2-dim batch
self.assertEqual(res[0].item(), 1.0)
self.assertEqual(res[1].item(), 0.0)
# the base samples should have the batch dim collapsed
self.assertEqual(acqf.sampler.base_samples.shape, torch.Size([2, 1, 2, 1]))
bs = acqf.sampler.base_samples.clone()
acqf(X.expand(2, 1, 1))
self.assertTrue(torch.equal(acqf.sampler.base_samples, bs))
# test batch mode, qmc, no resample
sampler = SobolQMCNormalSampler(num_samples=2)
acqf = qSimpleRegret(model=mm, sampler=sampler)
res = acqf(X)
self.assertEqual(res[0].item(), 1.0)
self.assertEqual(res[1].item(), 0.0)
self.assertEqual(acqf.sampler.base_samples.shape, torch.Size([2, 1, 2, 1]))
bs = acqf.sampler.base_samples.clone()
acqf(X)
self.assertTrue(torch.equal(acqf.sampler.base_samples, bs))
# test batch mode, qmc, resample
sampler = SobolQMCNormalSampler(num_samples=2, resample=True)
acqf = qSimpleRegret(model=mm, sampler=sampler)
res = acqf(X) # 1-dim batch
self.assertEqual(res[0].item(), 1.0)
self.assertEqual(res[1].item(), 0.0)
self.assertEqual(acqf.sampler.base_samples.shape, torch.Size([2, 1, 2, 1]))
bs = acqf.sampler.base_samples.clone()
acqf(X)
self.assertFalse(torch.equal(acqf.sampler.base_samples, bs))
res = acqf(X.expand(2, 1, 1)) # 2-dim batch
self.assertEqual(res[0].item(), 1.0)
self.assertEqual(res[1].item(), 0.0)
# the base samples should have the batch dim collapsed
self.assertEqual(acqf.sampler.base_samples.shape, torch.Size([2, 1, 2, 1]))
bs = acqf.sampler.base_samples.clone()
acqf(X.expand(2, 1, 1))
self.assertFalse(torch.equal(acqf.sampler.base_samples, bs))
def get_out_of_sample_best_point_acqf(
model: Model,
Xs: List[Tensor],
X_observed: Tensor,
objective_weights: Tensor,
mc_samples: int = 512,
fixed_features: Optional[Dict[int, float]] = None,
fidelity_features: Optional[List[int]] = None,
target_fidelities: Optional[Dict[int, float]] = None,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
seed_inner: Optional[int] = None,
qmc: bool = True,
**kwargs: Any,
) -> Tuple[AcquisitionFunction, Optional[List[int]]]:
"""Picks an appropriate acquisition function to find the best
out-of-sample (not observed) point using the given surrogate model.
NOTE: Typically the appropriate function is the posterior mean,
but can differ to account for fidelities etc.
"""
model = model
# subset model only to the outcomes we need for the optimization
if kwargs.get("subset_model", True):
model, objective_weights, outcome_constraints = subset_model(
model=model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
)
fixed_features = fixed_features or {}
target_fidelities = target_fidelities or {}
objective = get_botorch_objective(
model=model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
)
if isinstance(objective, ScalarizedObjective):
acq_function = PosteriorMean(model=model, objective=objective)
elif isinstance(objective, MCAcquisitionObjective):
if qmc:
sampler = SobolQMCNormalSampler(num_samples=mc_samples,
seed=seed_inner)
else:
sampler = IIDNormalSampler(num_samples=mc_samples, seed=seed_inner)
acq_function = qSimpleRegret(model=model,
sampler=sampler,
objective=objective)
else:
raise UnsupportedError(
f"Unknown objective type: {objective.__class__}" # pragma: nocover
)
if fidelity_features:
# we need to optimize at the target fidelities
if any(f in fidelity_features for f in fixed_features):
raise RuntimeError(
"Fixed features cannot also be fidelity features")
elif set(fidelity_features) != set(target_fidelities):
raise RuntimeError(
"Must provide a target fidelity for every fidelity feature")
# make sure to not modify fixed_features in-place
fixed_features = {**fixed_features, **target_fidelities}
elif target_fidelities:
raise RuntimeError(
"Must specify fidelity_features in fit() when using target fidelities"
)
if fixed_features:
acq_function = FixedFeatureAcquisitionFunction(
acq_function=acq_function,
d=X_observed.size(-1),
columns=list(fixed_features.keys()),
values=list(fixed_features.values()),
)
non_fixed_idcs = [
i for i in range(Xs[0].size(-1)) if i not in fixed_features
]
else:
non_fixed_idcs = None
return acq_function, non_fixed_idcs
def optimize_objective(
model: Model,
bounds: Tensor,
q: int,
objective: Optional[MCAcquisitionObjective] = None,
posterior_transform: Optional[PosteriorTransform] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
target_fidelities: Optional[Dict[int, float]] = None,
qmc: bool = True,
mc_samples: int = 512,
seed_inner: Optional[int] = None,
optimizer_options: Dict[str, Any] = None,
post_processing_func: Optional[Callable[[Tensor], Tensor]] = None,
batch_initial_conditions: Optional[Tensor] = None,
sequential: bool = False,
**ignore,
) -> Tuple[Tensor, Tensor]:
r"""Optimize an objective under the given model.
Args:
model: The model to be used in the objective.
bounds: A `2 x d` tensor of lower and upper bounds for each column of `X`.
q: The cardinality of input sets on which the objective is to be evaluated.
objective: The objective to optimize.
posterior_transform: The posterior transform to be used in the
acquisition function.
linear_constraints: A tuple of (A, b). Given `k` linear constraints on a
`d`-dimensional space, `A` is `k x d` and `b` is `k x 1` such that
`A x <= b`. (Not used by single task models).
fixed_features: A dictionary of feature assignments `{feature_index: value}` to
hold fixed during generation.
target_fidelities: A dictionary mapping input feature indices to fidelity
values. Defaults to `{-1: 1.0}`.
qmc: Toggle for enabling (qmc=1) or disabling (qmc=0) use of Quasi Monte Carlo.
mc_samples: Integer number of samples used to estimate Monte Carlo objectives.
seed_inner: Integer seed used to initialize the sampler passed to MCObjective.
optimizer_options: Table used to lookup keyword arguments for the optimizer.
post_processing_func: A function that post-processes an optimization
result appropriately (i.e. according to `round-trip` transformations).
batch_initial_conditions: A Tensor of initial values for the optimizer.
sequential: If False, uses joint optimization, otherwise uses sequential
optimization.
Returns:
A tuple containing the best input locations and corresponding objective values.
"""
if optimizer_options is None:
optimizer_options = {}
if objective is not None:
sampler_cls = SobolQMCNormalSampler if qmc else IIDNormalSampler
acq_function = qSimpleRegret(
model=model,
objective=objective,
posterior_transform=posterior_transform,
sampler=sampler_cls(num_samples=mc_samples, seed=seed_inner),
)
else:
acq_function = PosteriorMean(model=model,
posterior_transform=posterior_transform)
if fixed_features:
acq_function = FixedFeatureAcquisitionFunction(
acq_function=acq_function,
d=bounds.shape[-1],
columns=list(fixed_features.keys()),
values=list(fixed_features.values()),
)
free_feature_dims = list(range(len(bounds)) - fixed_features.keys())
free_feature_bounds = bounds[:, free_feature_dims] # (2, d' <= d)
else:
free_feature_bounds = bounds
if linear_constraints is None:
inequality_constraints = None
else:
A, b = linear_constraints
inequality_constraints = []
k, d = A.shape
for i in range(k):
indicies = A[i, :].nonzero(as_tuple=False).squeeze()
coefficients = -A[i, indicies]
rhs = -b[i, 0]
inequality_constraints.append((indicies, coefficients, rhs))
return optimize_acqf(
acq_function=acq_function,
bounds=free_feature_bounds,
q=q,
num_restarts=optimizer_options.get("num_restarts", 60),
raw_samples=optimizer_options.get("raw_samples", 1024),
options={
"batch_limit": optimizer_options.get("batch_limit", 8),
"maxiter": optimizer_options.get("maxiter", 200),
"nonnegative": optimizer_options.get("nonnegative", False),
"method": optimizer_options.get("method", "L-BFGS-B"),
},
inequality_constraints=inequality_constraints,
fixed_features=None, # handled inside the acquisition function
post_processing_func=post_processing_func,
batch_initial_conditions=batch_initial_conditions,
return_best_only=True,
sequential=sequential,
)
def test_q_simple_regret_batch(self, cuda=False):
# the event shape is `b x q x t` = 2 x 2 x 1
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
samples = torch.zeros(2, 2, 1, device=device, dtype=dtype)
samples[0, 0, 0] = 1.0
mm = MockModel(MockPosterior(samples=samples))
# X is a dummy and unused b/c of mocking
X = torch.zeros(1, 1, 1, device=device, dtype=dtype)
# test batch mode
sampler = IIDNormalSampler(num_samples=2)
acqf = qSimpleRegret(model=mm, sampler=sampler)
res = acqf(X)
self.assertEqual(res[0].item(), 1.0)
self.assertEqual(res[1].item(), 0.0)
# test batch mode, no resample
sampler = IIDNormalSampler(num_samples=2, seed=12345)
acqf = qSimpleRegret(model=mm, sampler=sampler)
res = acqf(X) # 1-dim batch
self.assertEqual(res[0].item(), 1.0)
self.assertEqual(res[1].item(), 0.0)
self.assertEqual(acqf.sampler.base_samples.shape, torch.Size([2, 1, 2, 1]))
bs = acqf.sampler.base_samples.clone()
acqf(X)
self.assertTrue(torch.equal(acqf.sampler.base_samples, bs))
res = acqf(X.expand(2, 1, 1)) # 2-dim batch
self.assertEqual(res[0].item(), 1.0)
self.assertEqual(res[1].item(), 0.0)
# the base samples should have the batch dim collapsed
self.assertEqual(acqf.sampler.base_samples.shape, torch.Size([2, 1, 2, 1]))
bs = acqf.sampler.base_samples.clone()
acqf(X.expand(2, 1, 1))
self.assertTrue(torch.equal(acqf.sampler.base_samples, bs))
# test batch mode, qmc, no resample
sampler = SobolQMCNormalSampler(num_samples=2)
acqf = qSimpleRegret(model=mm, sampler=sampler)
res = acqf(X)
self.assertEqual(res[0].item(), 1.0)
self.assertEqual(res[1].item(), 0.0)
self.assertEqual(acqf.sampler.base_samples.shape, torch.Size([2, 1, 2, 1]))
bs = acqf.sampler.base_samples.clone()
acqf(X)
self.assertTrue(torch.equal(acqf.sampler.base_samples, bs))
# test batch mode, qmc, resample
sampler = SobolQMCNormalSampler(num_samples=2, resample=True)
acqf = qSimpleRegret(model=mm, sampler=sampler)
res = acqf(X) # 1-dim batch
self.assertEqual(res[0].item(), 1.0)
self.assertEqual(res[1].item(), 0.0)
self.assertEqual(acqf.sampler.base_samples.shape, torch.Size([2, 1, 2, 1]))
bs = acqf.sampler.base_samples.clone()
acqf(X)
self.assertFalse(torch.equal(acqf.sampler.base_samples, bs))
res = acqf(X.expand(2, 1, 1)) # 2-dim batch
self.assertEqual(res[0].item(), 1.0)
self.assertEqual(res[1].item(), 0.0)
# the base samples should have the batch dim collapsed
self.assertEqual(acqf.sampler.base_samples.shape, torch.Size([2, 1, 2, 1]))
bs = acqf.sampler.base_samples.clone()
acqf(X.expand(2, 1, 1))
self.assertFalse(torch.equal(acqf.sampler.base_samples, bs))
