def alebo_acqf_optimizer(
acq_function: AcquisitionFunction,
bounds: Tensor,
n: int,
inequality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]],
fixed_features: Optional[Dict[int, float]],
rounding_func: Optional[Callable[[Tensor], Tensor]],
raw_samples: int,
num_restarts: int,
B: Tensor,
) -> Tuple[Tensor, Tensor]:
"""
Optimize the acquisition function for ALEBO.
We are optimizing over a polytope within the subspace, and so begin each
random restart of the acquisition function optimization with points that
lie within that polytope.
"""
candidate_list, acq_value_list = [], []
candidates = torch.tensor([], device=B.device, dtype=B.dtype)
base_X_pending = acq_function.X_pending # pyre-ignore
for i in range(n):
# Generate initial points for optimization inside embedding
m_init = ALEBOInitializer(B.cpu().numpy(), nsamp=10 * raw_samples)
Xrnd_npy, _ = m_init.gen(n=raw_samples,
bounds=[(-1.0, 1.0)] * B.shape[1])
Xrnd = torch.tensor(Xrnd_npy, dtype=B.dtype,
device=B.device).unsqueeze(1)
Yrnd = torch.matmul(Xrnd, B.t()) # Project down to the embedding
with gpytorch.settings.max_cholesky_size(2000):
with torch.no_grad():
alpha = acq_function(Yrnd)
Yinit = initialize_q_batch_nonneg(X=Yrnd, Y=alpha, n=num_restarts)
# Optimize the acquisition function, separately for each random restart.
candidate, acq_value = optimize_acqf(
acq_function=acq_function,
bounds=[None, None], # pyre-ignore
q=1,
num_restarts=num_restarts,
raw_samples=0,
options={
"method": "SLSQP",
"batch_limit": 1
},
inequality_constraints=inequality_constraints,
batch_initial_conditions=Yinit,
sequential=False,
)
candidate_list.append(candidate)
acq_value_list.append(acq_value)
candidates = torch.cat(candidate_list, dim=-2)
acq_function.set_X_pending(
torch.cat([base_X_pending, candidates], dim=-2
) if base_X_pending is not None else candidates)
logger.info(f"Generated sequential candidate {i+1} of {n}")
acq_function.set_X_pending(base_X_pending)
return candidates, torch.stack(acq_value_list)
def alebo_acqf_optimizer(
acq_function: AcquisitionFunction,
bounds: Tensor,
n: int,
inequality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]],
fixed_features: Optional[Dict[int, float]],
rounding_func: Optional[Callable[[Tensor], Tensor]],
raw_samples: int,
num_restarts: int,
B: Tensor,
) -> Tuple[Tensor, Tensor]:
"""
Optimize the acquisition function for ALEBO.
We are optimizing over a polytope within the subspace, and so begin each
random restart of the acquisition function optimization with points that
lie within that polytope.
"""
assert n == 1 # Handle batch later
# Generate initial points for optimization inside embedding
m_init = ALEBOInitializer(B.cpu().numpy(), nsamp=10 * raw_samples)
Xrnd_npy, _ = m_init.gen(n=raw_samples, bounds=[(-1.0, 1.0)] * B.shape[1])
Xrnd = torch.tensor(Xrnd_npy, dtype=B.dtype, device=B.device).unsqueeze(1)
Yrnd = torch.matmul(Xrnd, B.t()) # Project down to the embedding
with gpytorch.settings.max_cholesky_size(2000):
with torch.no_grad():
alpha = acq_function(Yrnd)
Yinit = initialize_q_batch_nonneg(X=Yrnd, Y=alpha, n=num_restarts)
# Optimize the acquisition function, separately for each random restart.
Xopt = optimize_acqf(
acq_function=acq_function,
bounds=[None, None], # pyre-ignore
q=n,
num_restarts=num_restarts,
raw_samples=0,
options={
"method": "SLSQP",
"batch_limit": 1
},
inequality_constraints=inequality_constraints,
batch_initial_conditions=Yinit,
sequential=False,
)
return Xopt