def gen_batch_initial_conditions(acq_function,
bounds,
q,
num_restarts,
raw_samples,
options=None):
r"""[Copy of original botorch function]
Generate a batch of initial conditions for random-restart optimziation.
Args:
acq_function: The acquisition function to be optimized.
bounds: A `2 x d` tensor of lower and upper bounds for each column of `X`.
q: The number of candidates to consider.
num_restarts: The number of starting points for multistart acquisition
function optimization.
raw_samples: The number of raw samples to consider in the initialization
heuristic.
options: Options for initial condition generation. For valid options see
`initialize_q_batch` and `initialize_q_batch_nonneg`. If `options`
contains a `nonnegative=True` entry, then `acq_function` is
assumed to be non-negative (useful when using custom acquisition
functions).
Returns:
A `num_restarts x q x d` tensor of initial conditions.
Example:
>>> qEI = qExpectedImprovement(model, best_f=0.2)
>>> bounds = torch.tensor([[0.], [1.]])
>>> Xinit = gen_batch_initial_conditions(
>>> qEI, bounds, q=3, num_restarts=25, raw_samples=500
>>> )
"""
options = options or {}
seed: Optional[int] = options.get("seed")
batch_limit: Optional[int] = options.get("batch_limit")
batch_initial_arms: Tensor
factor, max_factor = 1, 5
init_kwargs = {}
device = bounds.device
bounds = bounds.cpu()
if "eta" in options:
init_kwargs["eta"] = options.get("eta")
if options.get("nonnegative") or is_nonnegative(acq_function):
init_func = initialize_q_batch_nonneg
if "alpha" in options:
init_kwargs["alpha"] = options.get("alpha")
else:
init_func = initialize_q_batch
q = 1 if q is None else q
# the dimension the samples are drawn from
dim = bounds.shape[-1] * q
if dim > SobolEngine.MAXDIM and settings.debug.on():
warnings.warn(
f"Sample dimension q*d={dim} exceeding Sobol max dimension "
f"({SobolEngine.MAXDIM}). Using iid samples instead.",
SamplingWarning,
)
while factor < max_factor:
with warnings.catch_warnings(record=True) as ws:
n = raw_samples * factor
if dim <= SobolEngine.MAXDIM:
X_rnd = draw_sobol_samples(bounds=bounds, n=n, q=q, seed=seed)
else:
with manual_seed(seed):
# load on cpu
X_rnd_nlzd = torch.rand(n * dim, dtype=bounds.dtype).view(
n, q, bounds.shape[-1])
X_rnd = bounds[0] + (bounds[1] - bounds[0]) * X_rnd_nlzd
with torch.no_grad():
if batch_limit is None:
batch_limit = X_rnd.shape[0]
Y_rnd_list = []
start_idx = 0
while start_idx < X_rnd.shape[0]:
end_idx = min(start_idx + batch_limit, X_rnd.shape[0])
Y_rnd_curr = acq_function(
X_rnd[start_idx:end_idx].to(device=device)).cpu()
Y_rnd_list.append(Y_rnd_curr)
start_idx += batch_limit
Y_rnd = torch.cat(Y_rnd_list)
batch_initial_conditions = init_func(
X=X_rnd, Y=Y_rnd, n=num_restarts,
**init_kwargs).to(device=device)
if not any(
issubclass(w.category, BadInitialCandidatesWarning)
for w in ws):
return batch_initial_conditions
if factor < max_factor:
factor += 1
if seed is not None:
seed += 1 # make sure to sample different X_rnd
warnings.warn(
"Unable to find non-zero acquisition function values - initial conditions "
"are being selected randomly.",
BadInitialCandidatesWarning,
)
return batch_initial_conditions
def gen_batch_initial_conditions(
acq_function: AcquisitionFunction,
bounds: Tensor,
q: int,
num_restarts: int,
raw_samples: int,
fixed_features: Optional[Dict[int, float]] = None,
options: Optional[Dict[str, Union[bool, float, int]]] = None,
inequality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
equality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
) -> Tensor:
r"""Generate a batch of initial conditions for random-restart optimziation.
TODO: Support t-batches of initial conditions.
Args:
acq_function: The acquisition function to be optimized.
bounds: A `2 x d` tensor of lower and upper bounds for each column of `X`.
q: The number of candidates to consider.
num_restarts: The number of starting points for multistart acquisition
function optimization.
raw_samples: The number of raw samples to consider in the initialization
heuristic. Note: if `sample_around_best` is True (the default is False),
then `2 * raw_samples` samples are used.
fixed_features: A map `{feature_index: value}` for features that
should be fixed to a particular value during generation.
options: Options for initial condition generation. For valid options see
`initialize_q_batch` and `initialize_q_batch_nonneg`. If `options`
contains a `nonnegative=True` entry, then `acq_function` is
assumed to be non-negative (useful when using custom acquisition
functions). In addition, an "init_batch_limit" option can be passed
to specify the batch limit for the initialization. This is useful
for avoiding memory limits when computing the batch posterior over
raw samples.
inequality constraints: A list of tuples (indices, coefficients, rhs),
with each tuple encoding an inequality constraint of the form
`\sum_i (X[indices[i]] * coefficients[i]) >= rhs`.
equality constraints: A list of tuples (indices, coefficients, rhs),
with each tuple encoding an inequality constraint of the form
`\sum_i (X[indices[i]] * coefficients[i]) = rhs`.
Returns:
A `num_restarts x q x d` tensor of initial conditions.
Example:
>>> qEI = qExpectedImprovement(model, best_f=0.2)
>>> bounds = torch.tensor([[0.], [1.]])
>>> Xinit = gen_batch_initial_conditions(
>>> qEI, bounds, q=3, num_restarts=25, raw_samples=500
>>> )
"""
options = options or {}
seed: Optional[int] = options.get("seed")
batch_limit: Optional[int] = options.get(
"init_batch_limit", options.get("batch_limit")
)
batch_initial_arms: Tensor
factor, max_factor = 1, 5
init_kwargs = {}
device = bounds.device
bounds_cpu = bounds.cpu()
if "eta" in options:
init_kwargs["eta"] = options.get("eta")
if options.get("nonnegative") or is_nonnegative(acq_function):
init_func = initialize_q_batch_nonneg
if "alpha" in options:
init_kwargs["alpha"] = options.get("alpha")
else:
init_func = initialize_q_batch
q = 1 if q is None else q
# the dimension the samples are drawn from
effective_dim = bounds.shape[-1] * q
if effective_dim > SobolEngine.MAXDIM and settings.debug.on():
warnings.warn(
f"Sample dimension q*d={effective_dim} exceeding Sobol max dimension "
f"({SobolEngine.MAXDIM}). Using iid samples instead.",
SamplingWarning,
)
while factor < max_factor:
with warnings.catch_warnings(record=True) as ws:
n = raw_samples * factor
if inequality_constraints is None and equality_constraints is None:
if effective_dim <= SobolEngine.MAXDIM:
X_rnd = draw_sobol_samples(bounds=bounds_cpu, n=n, q=q, seed=seed)
else:
with manual_seed(seed):
# load on cpu
X_rnd_nlzd = torch.rand(
n, q, bounds_cpu.shape[-1], dtype=bounds.dtype
)
X_rnd = bounds_cpu[0] + (bounds_cpu[1] - bounds_cpu[0]) * X_rnd_nlzd
else:
X_rnd = (
get_polytope_samples(
n=n * q,
bounds=bounds,
inequality_constraints=inequality_constraints,
equality_constraints=equality_constraints,
seed=seed,
n_burnin=options.get("n_burnin", 10000),
thinning=options.get("thinning", 32),
)
.view(n, q, -1)
.cpu()
)
# sample points around best
if options.get("sample_around_best", False):
X_best_rnd = sample_points_around_best(
acq_function=acq_function,
n_discrete_points=n * q,
sigma=options.get("sample_around_best_sigma", 1e-3),
bounds=bounds,
subset_sigma=options.get("sample_around_best_subset_sigma", 1e-1),
prob_perturb=options.get("sample_around_best_prob_perturb"),
)
if X_best_rnd is not None:
X_rnd = torch.cat(
[
X_rnd,
X_best_rnd.view(n, q, bounds.shape[-1]).cpu(),
],
dim=0,
)
X_rnd = fix_features(X_rnd, fixed_features=fixed_features)
with torch.no_grad():
if batch_limit is None:
batch_limit = X_rnd.shape[0]
Y_rnd_list = []
start_idx = 0
while start_idx < X_rnd.shape[0]:
end_idx = min(start_idx + batch_limit, X_rnd.shape[0])
Y_rnd_curr = acq_function(
X_rnd[start_idx:end_idx].to(device=device)
).cpu()
Y_rnd_list.append(Y_rnd_curr)
start_idx += batch_limit
Y_rnd = torch.cat(Y_rnd_list)
batch_initial_conditions = init_func(
X=X_rnd, Y=Y_rnd, n=num_restarts, **init_kwargs
).to(device=device)
if not any(issubclass(w.category, BadInitialCandidatesWarning) for w in ws):
return batch_initial_conditions
if factor < max_factor:
factor += 1
if seed is not None:
seed += 1 # make sure to sample different X_rnd
warnings.warn(
"Unable to find non-zero acquisition function values - initial conditions "
"are being selected randomly.",
BadInitialCandidatesWarning,
)
return batch_initial_conditions
def gen_batch_initial_conditions(
acq_function: AcquisitionFunction,
bounds: Tensor,
q: int,
num_restarts: int,
raw_samples: int,
options: Optional[Dict[str, Union[bool, float, int]]] = None,
post_processing_init: Optional[Callable[[Tensor], Tensor]] = None,
) -> Tensor:
"""
This function generates a batch of initial conditions for random-restart optimization
Parameters
----------
:param acq_function: the acquisition function to be optimized.
:param bounds: a `2 x d` tensor of lower and upper bounds for each column of `X`
:param q: number of candidates
:param num_restarts: number of starting points for multistart acquisition function optimization
:param raw_samples: number of samples for initialization
Optional parameters
-------------------
:param options: options for candidate generation
:param post_processing_init: A function that post processes the generated initial samples
(e.g. so that they fulfill some constraints).
Returns
-------
:return: a `num_restarts x q x d` tensor of initial conditions
"""
options = options or {}
seed: Optional[int] = options.get("seed") # pyre-ignore
batch_limit: Optional[int] = options.get("batch_limit") # pyre-ignore
batch_initial_arms: Tensor
factor, max_factor = 1, 5
init_kwargs = {}
if "eta" in options:
init_kwargs["eta"] = options.get("eta")
if options.get("nonnegative") or is_nonnegative(acq_function):
init_func = initialize_q_batch_nonneg
if "alpha" in options:
init_kwargs["alpha"] = options.get("alpha")
else:
init_func = initialize_q_batch
while factor < max_factor:
with warnings.catch_warnings(record=True) as ws:
X_rnd = draw_sobol_samples(
bounds=bounds,
n=raw_samples * factor,
q=1 if q is None else q,
seed=seed,
)
# Constraints the samples
if post_processing_init is not None:
X_rnd = post_processing_init(X_rnd)
with torch.no_grad():
if batch_limit is None:
batch_limit = X_rnd.shape[0]
Y_rnd_list = []
start_idx = 0
while start_idx < X_rnd.shape[0]:
end_idx = min(start_idx + batch_limit, X_rnd.shape[0])
Y_rnd_curr = acq_function(X_rnd[start_idx:end_idx])
Y_rnd_list.append(Y_rnd_curr)
start_idx += batch_limit
Y_rnd = torch.cat(Y_rnd_list).to(X_rnd)
batch_initial_conditions = init_func(X=X_rnd,
Y=Y_rnd,
n=num_restarts,
**init_kwargs)
if not any(
issubclass(w.category, BadInitialCandidatesWarning)
for w in ws):
return batch_initial_conditions
if factor < max_factor:
factor += 1
warnings.warn(
"Unable to find non-zero acquisition function values - initial conditions "
"are being selected randomly.",
BadInitialCandidatesWarning,
)
return batch_initial_conditions
def gen_batch_initial_conditions_manifold(
acq_function: AcquisitionFunction,
manifold: Manifold,
bounds: Tensor,
q: int,
num_restarts: int,
raw_samples: int,
sample_type: torch.dtype = torch.float64,
options: Optional[Dict[str, Union[bool, float, int]]] = None,
post_processing_manifold: Optional[Callable[[Tensor], Tensor]] = None,
) -> Tensor:
"""
This function generates a batch of initial conditions for random-restart optimization
Parameters
----------
:param acq_function: the acquisition function to be optimized.
:param manifold: the manifold in the optimization takes place (pymanopt manifold)
:param bounds: a `2 x d` tensor of lower and upper bounds for each column of `X`
:param q: number of candidates
:param num_restarts: number of starting points for multistart acquisition function optimization
:param raw_samples: number of samples for initialization
:param sample_type: type of the generated samples for initialization
Optional parameters
-------------------
:param options: options for candidate generation
:param post_processing_manifold: a function that post-process the data on the manifold after the optimization.
Typically, this can be used to transform matrices (required for matrix-manifold optimization) to vectors
(required by the GP).
Returns
-------
:return: a `num_restarts x q x d` tensor of initial conditions
"""
options = options or {}
seed: Optional[int] = options.get("seed") # pyre-ignore
batch_limit: Optional[int] = options.get("batch_limit") # pyre-ignore
batch_initial_arms: Tensor
factor, max_factor = 1, 5
init_kwargs = {}
if "eta" in options:
init_kwargs["eta"] = options.get("eta")
if options.get("nonnegative") or is_nonnegative(acq_function):
init_func = initialize_q_batch_nonneg
if "alpha" in options:
init_kwargs["alpha"] = options.get("alpha")
else:
init_func = initialize_q_batch
if q is None:
q = 1
while factor < max_factor:
with warnings.catch_warnings(record=True) as ws:
# Generate random points on the manifold
points = [torch.from_numpy(manifold.rand())[None, None] for i in range(raw_samples * factor * q)]
# Final tensor of random points
X_rnd = torch.cat(points).to(sample_type)
# If necessary post-process the points
if post_processing_manifold is not None:
X_rnd = post_processing_manifold(X_rnd)
with torch.no_grad():
if batch_limit is None:
batch_limit = X_rnd.shape[0]
Y_rnd_list = []
start_idx = 0
while start_idx < X_rnd.shape[0]:
end_idx = min(start_idx + batch_limit, X_rnd.shape[0])
Y_rnd_curr = acq_function(X_rnd[start_idx:end_idx])
Y_rnd_list.append(Y_rnd_curr)
start_idx += batch_limit
Y_rnd = torch.cat(Y_rnd_list).to(X_rnd)
batch_initial_conditions = init_func(X=X_rnd, Y=Y_rnd, n=num_restarts, **init_kwargs)
if not any(issubclass(w.category, BadInitialCandidatesWarning) for w in ws):
return batch_initial_conditions
if factor < max_factor:
factor += 1
warnings.warn("Unable to find non-zero acquisition function values - initial conditions are being selected "
"randomly.", BadInitialCandidatesWarning,)
return batch_initial_conditions