def gen(
self,
n: int,
bounds: List[Tuple[float, float]],
objective_weights: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
pending_observations: Optional[List[Tensor]] = None,
model_gen_options: Optional[TConfig] = None,
rounding_func: Optional[Callable[[Tensor], Tensor]] = None,
target_fidelities: Optional[Dict[int, float]] = None,
) -> Tuple[Tensor, Tensor, TGenMetadata]:
options = model_gen_options or {}
acf_options = options.get("acquisition_function_kwargs", {})
optimizer_options = options.get("optimizer_kwargs", {})
if target_fidelities:
raise NotImplementedError(
"target_fidelities not implemented for base BotorchModel")
X_pending, X_observed = _get_X_pending_and_observed(
Xs=self.Xs,
pending_observations=pending_observations,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
bounds=bounds,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
)
acquisition_function = self.acqf_constructor( # pyre-ignore: [28]
model=self.model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
X_pending=X_pending,
**acf_options,
)
bounds_ = torch.tensor(bounds, dtype=self.dtype, device=self.device)
bounds_ = bounds_.transpose(0, 1)
if linear_constraints is not None:
A, b = linear_constraints
inequality_constraints = []
k, d = A.shape
for i in range(k):
indicies = A[i, :].nonzero().view(-1)
coefficients = -A[i, indicies]
rhs = -b[i, 0]
inequality_constraints.append((indicies, coefficients, rhs))
else:
inequality_constraints = None
botorch_rounding_func = get_rounding_func(rounding_func)
# pyre-ignore: [28]
candidates, expected_acquisition_value = self.acqf_optimizer(
acq_function=checked_cast(AcquisitionFunction,
acquisition_function),
bounds=bounds_,
n=n,
inequality_constraints=inequality_constraints,
fixed_features=fixed_features,
rounding_func=botorch_rounding_func,
**optimizer_options,
)
return (
candidates.detach().cpu(),
torch.ones(n, dtype=self.dtype),
{
"expected_acquisition_value":
expected_acquisition_value.tolist()
},
)
def gen(
self,
n: int,
bounds: List,
objective_weights: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
pending_observations: Optional[List[Tensor]] = None,
model_gen_options: Optional[TConfig] = None,
rounding_func: Optional[Callable[[Tensor], Tensor]] = None,
target_fidelities: Optional[Dict[int, float]] = None,
) -> Tuple[Tensor, Tensor, TGenMetadata,
Optional[List[TCandidateMetadata]]]:
r"""Generate new candidates.
Args:
n: Number of candidates to generate.
bounds: A list of (lower, upper) tuples for each column of X.
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.
linear_constraints: A tuple of (A, b). For k linear constraints on
d-dimensional x, A is (k x d) and b is (k x 1) such that
A x <= b.
fixed_features: A map {feature_index: value} for features that
should be fixed to a particular value during generation.
pending_observations: A list of m (k_i x d) feature tensors X
for m outcomes and k_i pending observations for outcome i.
model_gen_options: A config dictionary that can contain
model-specific options.
rounding_func: A function that rounds an optimization result
appropriately (i.e., according to `round-trip` transformations).
target_fidelities: A map {feature_index: value} of fidelity feature
column indices to their respective target fidelities. Used for
multi-fidelity optimization.
Returns:
3-element tuple containing
- (n x d) tensor of generated points.
- n-tensor of weights for each point.
- Dictionary of model-specific metadata for the given
generation candidates.
"""
options = model_gen_options or {}
acf_options = options.get("acquisition_function_kwargs", {})
optimizer_options = options.get("optimizer_kwargs", {})
X_pending, X_observed = _get_X_pending_and_observed(
Xs=self.Xs,
pending_observations=pending_observations,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
bounds=bounds,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
)
# subset model only to the outcomes we need for the optimization
model = not_none(self.model)
if options.get("subset_model", True):
subset_model_results = subset_model(
model=model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
)
model = subset_model_results.model
objective_weights = subset_model_results.objective_weights
outcome_constraints = subset_model_results.outcome_constraints
objective = get_botorch_objective(
model=model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
)
inequality_constraints = _to_inequality_constraints(linear_constraints)
# TODO: update optimizers to handle inequality_constraints
if inequality_constraints is not None:
raise UnsupportedError(
"Inequality constraints are not yet supported for KnowledgeGradient!"
)
# extract a few options
n_fantasies = acf_options.get("num_fantasies", 64)
qmc = acf_options.get("qmc", True)
seed_inner = acf_options.get("seed_inner", None)
num_restarts = optimizer_options.get("num_restarts", 40)
raw_samples = optimizer_options.get("raw_samples", 1024)
# get current value
current_value = self._get_current_value(
model=model,
bounds=bounds,
X_observed=not_none(X_observed),
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
linear_constraints=linear_constraints,
seed_inner=seed_inner,
fixed_features=fixed_features,
model_gen_options=model_gen_options,
target_fidelities=target_fidelities,
qmc=qmc,
)
bounds_ = torch.tensor(bounds, dtype=self.dtype, device=self.device)
bounds_ = bounds_.transpose(0, 1)
# get acquisition function
acq_function = _instantiate_KG(
model=model,
objective=objective,
qmc=qmc,
n_fantasies=n_fantasies,
num_trace_observations=options.get("num_trace_observations", 0),
mc_samples=acf_options.get("mc_samples", 256),
seed_inner=seed_inner,
seed_outer=acf_options.get("seed_outer", None),
X_pending=X_pending,
target_fidelities=target_fidelities,
fidelity_weights=options.get("fidelity_weights"),
current_value=current_value,
cost_intercept=self.cost_intercept,
)
# optimize and get new points
new_x = _optimize_and_get_candidates(
acq_function=acq_function,
bounds_=bounds_,
n=n,
num_restarts=num_restarts,
raw_samples=raw_samples,
optimizer_options=optimizer_options,
rounding_func=rounding_func,
inequality_constraints=inequality_constraints,
fixed_features=fixed_features,
)
return new_x, torch.ones(n, dtype=self.dtype), {}, None
def gen(
self,
n: int,
bounds: List[Tuple[float, float]],
objective_weights: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
pending_observations: Optional[List[Tensor]] = None,
model_gen_options: Optional[TConfig] = None,
rounding_func: Optional[Callable[[Tensor], Tensor]] = None,
target_fidelities: Optional[Dict[int, float]] = None,
) -> Tuple[Tensor, Tensor, TGenMetadata,
Optional[List[TCandidateMetadata]]]:
options = model_gen_options or {}
acf_options = options.get(Keys.ACQF_KWARGS, {})
optimizer_options = options.get(Keys.OPTIMIZER_KWARGS, {})
if target_fidelities:
raise NotImplementedError(
"target_fidelities not implemented for base BotorchModel")
X_pending, X_observed = _get_X_pending_and_observed(
Xs=self.Xs,
pending_observations=pending_observations,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
bounds=bounds,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
)
model = self.model
# subset model only to the outcomes we need for the optimization
if options.get(Keys.SUBSET_MODEL, True):
model, objective_weights, outcome_constraints = subset_model(
model=model, # pyre-ignore [6]
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
)
bounds_ = torch.tensor(bounds, dtype=self.dtype, device=self.device)
bounds_ = bounds_.transpose(0, 1)
botorch_rounding_func = get_rounding_func(rounding_func)
if acf_options.get("random_scalarization", False) or acf_options.get(
"chebyshev_scalarization", False):
# TODO (jej): Move into MultiObjectiveBotorch
# If using a list of acquisition functions, the algorithm to generate
# that list is configured by acquisition_function_kwargs.
objective_weights_list = [
randomize_objective_weights(objective_weights, **acf_options)
for _ in range(n)
]
acquisition_function_list = [
self.acqf_constructor( # pyre-ignore: [28]
model=model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
X_pending=X_pending,
Ys=self.
Ys, # Required for chebyshev scalarization calculations.
**acf_options,
) for objective_weights in objective_weights_list
]
acquisition_function_list = [
checked_cast(AcquisitionFunction, acq_function)
for acq_function in acquisition_function_list
]
# Multiple acquisition functions require a sequential optimizer
# always use scipy_optimizer_list.
# TODO(jej): Allow any optimizer.
candidates, expected_acquisition_value = scipy_optimizer_list(
acq_function_list=acquisition_function_list,
bounds=bounds_,
inequality_constraints=_to_inequality_constraints(
linear_constraints=linear_constraints),
fixed_features=fixed_features,
rounding_func=botorch_rounding_func,
**optimizer_options,
)
else:
acquisition_function = self.acqf_constructor( # pyre-ignore: [28]
model=model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
X_pending=X_pending,
**acf_options,
)
acquisition_function = checked_cast(AcquisitionFunction,
acquisition_function)
# pyre-ignore: [28]
candidates, expected_acquisition_value = self.acqf_optimizer(
acq_function=checked_cast(AcquisitionFunction,
acquisition_function),
bounds=bounds_,
n=n,
inequality_constraints=_to_inequality_constraints(
linear_constraints=linear_constraints),
fixed_features=fixed_features,
rounding_func=botorch_rounding_func,
**optimizer_options,
)
return (
candidates.detach().cpu(),
torch.ones(n, dtype=self.dtype),
{
"expected_acquisition_value":
expected_acquisition_value.tolist()
},
None,
)
def __init__(
self,
surrogate: Surrogate,
bounds: List[Tuple[float, float]],
objective_weights: Tensor,
botorch_acqf_class: Optional[Type[AcquisitionFunction]] = None,
options: Optional[Dict[str, Any]] = None,
pending_observations: Optional[List[Tensor]] = None,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
target_fidelities: Optional[Dict[int, float]] = None,
) -> None:
if not botorch_acqf_class and not self.default_botorch_acqf_class:
raise ValueError(
f"Acquisition class {self.__class__} does not specify a default "
"BoTorch `AcquisitionFunction`, so `botorch_acqf_class` "
"argument must be specified.")
botorch_acqf_class = not_none(botorch_acqf_class
or self.default_botorch_acqf_class)
self.surrogate = surrogate
self.options = options or {}
X_pending, X_observed = _get_X_pending_and_observed(
Xs=[self.surrogate.training_data.X],
pending_observations=pending_observations,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
bounds=bounds,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
)
# Subset model only to the outcomes we need for the optimization.
if self.options.get(Keys.SUBSET_MODEL, True):
model, objective_weights, outcome_constraints, _ = subset_model(
self.surrogate.model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
)
else:
model = self.surrogate.model
objective = get_botorch_objective(
model=model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
use_scalarized_objective=issubclass(botorch_acqf_class,
AnalyticAcquisitionFunction),
)
# NOTE: Computing model dependencies might be handled entirely on
# BoTorch side.
model_deps = self.compute_model_dependencies(
surrogate=surrogate,
bounds=bounds,
objective_weights=objective_weights,
pending_observations=pending_observations,
outcome_constraints=outcome_constraints,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
target_fidelities=target_fidelities,
options=self.options,
)
data_deps = self.compute_data_dependencies(
training_data=self.surrogate.training_data)
# pyre-ignore[28]: Some kwargs are not expected in base `Model`
# but are expected in its subclasses.
self.acqf = botorch_acqf_class(
model=model,
objective=objective,
X_pending=X_pending,
X_baseline=X_observed,
**self.options,
**model_deps,
**data_deps,
)
def gen(
self,
n: int,
bounds: List,
objective_weights: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
pending_observations: Optional[List[Tensor]] = None,
model_gen_options: Optional[TConfig] = None,
rounding_func: Optional[Callable[[Tensor], Tensor]] = None,
target_fidelities: Optional[Dict[int, float]] = None,
) -> Tuple[Tensor, Tensor, TGenMetadata]:
"""
Generate new candidates.
Args:
n: Number of candidates to generate.
bounds: A list of (lower, upper) tuples for each column of X.
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.
linear_constraints: A tuple of (A, b). For k linear constraints on
d-dimensional x, A is (k x d) and b is (k x 1) such that
A x <= b.
fixed_features: A map {feature_index: value} for features that
should be fixed to a particular value during generation.
pending_observations: A list of m (k_i x d) feature tensors X
for m outcomes and k_i pending observations for outcome i.
model_gen_options: A config dictionary that can contain
model-specific options.
rounding_func: A function that rounds an optimization result
appropriately (i.e., according to `round-trip` transformations).
target_fidelities: A map {feature_index: value} of fidelity feature
column indices to their respective target fidelities. Used for
multi-fidelity optimization.
Returns:
3-element tuple containing
- (n x d) tensor of generated points.
- n-tensor of weights for each point.
- Dictionary of model-specific metadata for the given
generation candidates.
"""
options = model_gen_options or {}
acf_options = options.get("acquisition_function_kwargs", {})
optimizer_options = options.get("optimizer_kwargs", {})
X_pending, X_observed = _get_X_pending_and_observed(
Xs=self.Xs,
pending_observations=pending_observations,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
bounds=bounds,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
)
model = self.model
# subset model only to the outcomes we need for the optimization
if options.get("subset_model", True):
model, objective_weights, outcome_constraints = subset_model(
model=model, # pyre-ignore [6]
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
)
objective = _get_objective(
model=model, # pyre-ignore [6]
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
)
# get the acquisition function
n_fantasies = acf_options.get("num_fantasies", 64)
qmc = acf_options.get("qmc", True)
seed_inner = acf_options.get("seed_inner", None)
num_restarts = optimizer_options.get("num_restarts", 40)
raw_samples = optimizer_options.get("raw_samples", 1024)
inequality_constraints = _to_inequality_constraints(linear_constraints)
# TODO: update optimizers to handle inequality_constraints
if inequality_constraints is not None:
raise UnsupportedError(
"Inequality constraints are not yet supported for KnowledgeGradient!"
)
# get current value
best_point_acqf, non_fixed_idcs = self._get_best_point_acqf(
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
X_observed=X_observed, # pyre-ignore: [6]
seed_inner=seed_inner,
fixed_features=fixed_features,
target_fidelities=target_fidelities,
qmc=qmc,
)
# solution from previous iteration
recommended_point = self.best_point(
bounds=bounds,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
model_gen_options=model_gen_options,
target_fidelities=target_fidelities,
)
recommended_point = recommended_point.detach().unsqueeze(
0) # pyre-ignore: [16]
# Extract acquisition value (TODO: Make this less painful and repetitive)
if non_fixed_idcs is not None:
recommended_point = recommended_point[..., non_fixed_idcs]
current_value = best_point_acqf(recommended_point).max()
acq_function = _instantiate_KG(
model=model, # pyre-ignore [6]
objective=objective,
qmc=qmc,
n_fantasies=n_fantasies,
num_trace_observations=options.get("num_trace_observations", 0),
mc_samples=acf_options.get("mc_samples", 256),
seed_inner=seed_inner,
seed_outer=acf_options.get("seed_outer", None),
X_pending=X_pending,
target_fidelities=target_fidelities,
fidelity_weights=options.get("fidelity_weights"),
current_value=current_value,
cost_intercept=self.cost_intercept,
)
# optimize and get new points
bounds_ = torch.tensor(bounds, dtype=self.dtype, device=self.device)
bounds_ = bounds_.transpose(0, 1)
batch_initial_conditions = gen_one_shot_kg_initial_conditions(
acq_function=acq_function,
bounds=bounds_,
q=n,
num_restarts=num_restarts,
raw_samples=raw_samples,
options={
"frac_random": optimizer_options.get("frac_random", 0.1),
"num_inner_restarts": num_restarts,
"raw_inner_samples": raw_samples,
},
)
botorch_rounding_func = get_rounding_func(rounding_func)
candidates, _ = optimize_acqf(
acq_function=acq_function,
bounds=bounds_,
q=n,
inequality_constraints=inequality_constraints,
fixed_features=fixed_features,
post_processing_func=botorch_rounding_func,
num_restarts=num_restarts,
raw_samples=raw_samples,
options={
"batch_limit": optimizer_options.get("batch_limit", 8),
"maxiter": optimizer_options.get("maxiter", 200),
"method": "L-BFGS-B",
"nonnegative": optimizer_options.get("nonnegative", False),
},
batch_initial_conditions=batch_initial_conditions,
)
new_x = candidates.detach().cpu()
return new_x, torch.ones(n, dtype=self.dtype), {}
def gen(
self,
n: int,
bounds: List[Tuple[float, float]],
objective_weights: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
pending_observations: Optional[List[Tensor]] = None,
model_gen_options: Optional[TConfig] = None,
rounding_func: Optional[Callable[[Tensor], Tensor]] = None,
) -> Tuple[Tensor, Tensor]:
"""Generate new candidates.
An initialized acquisition function can be passed in as
model_gen_options["acquisition_function"].
Args:
n: Number of candidates to generate.
bounds: A list of (lower, upper) tuples for each column of X.
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)
linear_constraints: A tuple of (A, b). For k linear constraints on
d-dimensional x, A is (k x d) and b is (k x 1) such that
A x <= b.
fixed_features: A map {feature_index: value} for features that
should be fixed to a particular value during generation.
pending_observations: A list of m (k_i x d) feature tensors X
for m outcomes and k_i pending observations for outcome i.
model_gen_options: A config dictionary that can contain
model-specific options.
rounding_func: A function that rounds an optimization result
appropriately (i.e., according to `round-trip` transformations).
Returns:
Tensor: `n x d`-dim Tensor of generated points.
Tensor: `n`-dim Tensor of weights for each point.
"""
options = model_gen_options or {}
acf_options = options.get("acqiusition_function_kwargs", {})
optimizer_options = options.get("optimizer_kwargs", {})
X_pending, X_observed = _get_X_pending_and_observed(
Xs=self.Xs,
pending_observations=pending_observations,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
bounds=bounds,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
)
acquisition_function = self.acqf_constructor( # pyre-ignore: [28]
model=self.model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
X_pending=X_pending,
**acf_options,
)
bounds_ = torch.tensor(bounds, dtype=self.dtype, device=self.device)
bounds_ = bounds_.transpose(0, 1)
if linear_constraints is not None:
A, b = linear_constraints
inequality_constraints = []
k, d = A.shape
for i in range(k):
indicies = A[i, :].nonzero().squeeze()
coefficients = -A[i, indicies]
rhs = -b[i, 0]
inequality_constraints.append((indicies, coefficients, rhs))
else:
inequality_constraints = None
candidates = self.acqf_optimizer( # pyre-ignore: [28]
acq_function=checked_cast(AcquisitionFunction,
acquisition_function),
bounds=bounds_,
n=n,
inequality_constraints=inequality_constraints,
fixed_features=fixed_features,
rounding_func=rounding_func,
**optimizer_options,
)
return candidates.detach().cpu(), torch.ones(n, dtype=self.dtype)
def gen(
self,
n: int,
bounds: List[Tuple[float, float]],
objective_weights: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
pending_observations: Optional[List[Tensor]] = None,
model_gen_options: Optional[TConfig] = None,
rounding_func: Optional[Callable[[Tensor], Tensor]] = None,
target_fidelities: Optional[Dict[int, float]] = None,
) -> Tuple[Tensor, Tensor, TGenMetadata, Optional[List[TCandidateMetadata]]]:
options = model_gen_options or {}
acf_options = options.get(Keys.ACQF_KWARGS, {})
optimizer_options = options.get(Keys.OPTIMIZER_KWARGS, {})
if target_fidelities:
raise NotImplementedError(
"target_fidelities not implemented for base BotorchModel"
)
X_pending, X_observed = _get_X_pending_and_observed(
Xs=self.Xs,
pending_observations=pending_observations,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
bounds=bounds,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
)
model = self.model
# subset model only to the outcomes we need for the optimization
if options.get(Keys.SUBSET_MODEL, True):
model, objective_weights, outcome_constraints = subset_model(
model=model, # pyre-ignore [6]
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
)
bounds_ = torch.tensor(bounds, dtype=self.dtype, device=self.device)
bounds_ = bounds_.transpose(0, 1)
acquisition_function = self.acqf_constructor( # pyre-ignore: [28]
model=model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
X_pending=X_pending,
**acf_options,
)
botorch_rounding_func = get_rounding_func(rounding_func)
# pyre-ignore: [28]
candidates, expected_acquisition_value = self.acqf_optimizer(
acq_function=checked_cast(AcquisitionFunction, acquisition_function),
bounds=bounds_,
n=n,
inequality_constraints=_to_inequality_constraints(
linear_constraints=linear_constraints
),
fixed_features=fixed_features,
rounding_func=botorch_rounding_func,
**optimizer_options,
)
return (
candidates.detach().cpu(),
torch.ones(n, dtype=self.dtype),
{"expected_acquisition_value": expected_acquisition_value.tolist()},
None,
)
def gen(
self,
n: int,
bounds: List[Tuple[float, float]],
objective_weights: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
pending_observations: Optional[List[Tensor]] = None,
model_gen_options: Optional[TConfig] = None,
rounding_func: Optional[Callable[[Tensor], Tensor]] = None,
target_fidelities: Optional[Dict[int, float]] = None,
) -> Tuple[Tensor, Tensor, TGenMetadata,
Optional[List[TCandidateMetadata]]]:
options = model_gen_options or {}
acf_options = options.get(Keys.ACQF_KWARGS, {})
optimizer_options = options.get(Keys.OPTIMIZER_KWARGS, {})
if target_fidelities:
raise NotImplementedError(
"target_fidelities not implemented for base BotorchModel")
X_pending, X_observed = _get_X_pending_and_observed(
Xs=self.Xs,
pending_observations=pending_observations,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
bounds=bounds,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
)
model = self.model
# subset model only to the outcomes we need for the optimization 357
if options.get(Keys.SUBSET_MODEL, True):
subset_model_results = subset_model(
model=model, # pyre-ignore [6]
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
)
model = subset_model_results.model
objective_weights = subset_model_results.objective_weights
outcome_constraints = subset_model_results.outcome_constraints
bounds_ = torch.tensor(bounds, dtype=self.dtype, device=self.device)
bounds_ = bounds_.transpose(0, 1)
botorch_rounding_func = get_rounding_func(rounding_func)
# The following logic is to work around the limitation of PyTorch's Sobol
# sampler to <1111 dimensions.
# TODO: Remove once https://github.com/pytorch/pytorch/issues/41489 is resolved.
from botorch.exceptions.errors import UnsupportedError
def make_and_optimize_acqf(
override_qmc: bool = False) -> Tuple[Tensor, Tensor]:
add_kwargs = {"qmc": False} if override_qmc else {}
acquisition_function = self.acqf_constructor( # pyre-ignore: [28]
model=model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
X_pending=X_pending,
**acf_options,
**add_kwargs,
)
acquisition_function = checked_cast(AcquisitionFunction,
acquisition_function)
# pyre-ignore: [28]
candidates, expected_acquisition_value = self.acqf_optimizer(
acq_function=checked_cast(AcquisitionFunction,
acquisition_function),
bounds=bounds_,
n=n,
inequality_constraints=_to_inequality_constraints(
linear_constraints=linear_constraints),
fixed_features=fixed_features,
rounding_func=botorch_rounding_func,
**optimizer_options,
)
return candidates, expected_acquisition_value
try:
candidates, expected_acquisition_value = make_and_optimize_acqf()
except UnsupportedError as e:
if "SobolQMCSampler only supports dimensions q * o <= 1111" in str(
e):
# dimension too large for Sobol, let's use IID
candidates, expected_acquisition_value = make_and_optimize_acqf(
override_qmc=True)
else:
raise e
return (
candidates.detach().cpu(),
torch.ones(n, dtype=self.dtype),
{
"expected_acquisition_value":
expected_acquisition_value.tolist()
},
None,
)
def gen(
self,
n: int,
bounds: List,
objective_weights: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
pending_observations: Optional[List[Tensor]] = None,
model_gen_options: Optional[TConfig] = None,
rounding_func: Optional[Callable[[Tensor], Tensor]] = None,
target_fidelities: Optional[Dict[int, float]] = None,
) -> Tuple[Tensor, Tensor, TGenMetadata, List[TCandidateMetadata]]:
if linear_constraints is not None or outcome_constraints is not None:
raise UnsupportedError(
"Constraints are not yet supported by max-value entropy search!"
)
if len(objective_weights) > 1:
raise UnsupportedError(
"Models with multiple outcomes are not yet supported by MES!"
)
options = model_gen_options or {}
acf_options = options.get("acquisition_function_kwargs", {})
optimizer_options = options.get("optimizer_kwargs", {})
X_pending, X_observed = _get_X_pending_and_observed(
Xs=self.Xs,
pending_observations=pending_observations,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
bounds=bounds,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
)
model = self.model
# subset model only to the outcomes we need for the optimization
if options.get("subset_model", True):
model, objective_weights, outcome_constraints, _ = subset_model(
model=model, # pyre-ignore [6]
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
)
# get the acquisition function
num_fantasies = acf_options.get("num_fantasies", 16)
num_mv_samples = acf_options.get("num_mv_samples", 10)
num_y_samples = acf_options.get("num_y_samples", 128)
candidate_size = acf_options.get("candidate_size", 1000)
num_restarts = optimizer_options.get("num_restarts", 40)
raw_samples = optimizer_options.get("raw_samples", 1024)
# generate the discrete points in the design space to sample max values
bounds_ = torch.tensor(bounds, dtype=self.dtype, device=self.device)
bounds_ = bounds_.transpose(0, 1)
candidate_set = torch.rand(candidate_size, bounds_.size(1))
candidate_set = bounds_[0] + (bounds_[1] - bounds_[0]) * candidate_set
acq_function = _instantiate_MES(
model=model, # pyre-ignore [6]
candidate_set=candidate_set,
num_fantasies=num_fantasies,
num_trace_observations=options.get("num_trace_observations", 0),
num_mv_samples=num_mv_samples,
num_y_samples=num_y_samples,
X_pending=X_pending,
maximize=True if objective_weights[0] == 1 else False,
target_fidelities=target_fidelities,
fidelity_weights=options.get("fidelity_weights"),
cost_intercept=self.cost_intercept,
)
# optimize and get new points
botorch_rounding_func = get_rounding_func(rounding_func)
candidates, _ = optimize_acqf(
acq_function=acq_function,
bounds=bounds_,
q=n,
inequality_constraints=None,
fixed_features=fixed_features,
post_processing_func=botorch_rounding_func,
num_restarts=num_restarts,
raw_samples=raw_samples,
options={
"batch_limit": optimizer_options.get("batch_limit", 8),
"maxiter": optimizer_options.get("maxiter", 200),
"method": "L-BFGS-B",
"nonnegative": optimizer_options.get("nonnegative", False),
},
sequential=True,
)
new_x = candidates.detach().cpu()
# pyre-fixme[7]: Expected `Tuple[Tensor, Tensor, Dict[str, typing.Any],
# List[Optional[Dict[str, typing.Any]]]]` but got `Tuple[Tensor, typing.Any,
# Dict[str, typing.Any], None]`.
return new_x, torch.ones(n, dtype=self.dtype), {}, None
def __init__(
self,
surrogate: Surrogate,
search_space_digest: SearchSpaceDigest,
objective_weights: Tensor,
botorch_acqf_class: Type[AcquisitionFunction],
options: Optional[Dict[str, Any]] = None,
pending_observations: Optional[List[Tensor]] = None,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
objective_thresholds: Optional[Tensor] = None,
) -> None:
self.surrogate = surrogate
self.options = options or {}
X_pending, X_observed = _get_X_pending_and_observed(
Xs=self.surrogate.training_data.Xs,
objective_weights=objective_weights,
bounds=search_space_digest.bounds,
pending_observations=pending_observations,
outcome_constraints=outcome_constraints,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
)
# Store objective thresholds for all outcomes (including non-objectives).
self._objective_thresholds = objective_thresholds
self._full_objective_weights = objective_weights
full_outcome_constraints = outcome_constraints
# Subset model only to the outcomes we need for the optimization.
if self.options.get(Keys.SUBSET_MODEL, True):
subset_model_results = subset_model(
model=self.surrogate.model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
objective_thresholds=objective_thresholds,
)
model = subset_model_results.model
objective_weights = subset_model_results.objective_weights
outcome_constraints = subset_model_results.outcome_constraints
objective_thresholds = subset_model_results.objective_thresholds
subset_idcs = subset_model_results.indices
else:
model = self.surrogate.model
subset_idcs = None
# If objective weights suggest multiple objectives but objective
# thresholds are not specified, infer them using the model that
# has already been subset to avoid re-subsetting it within
# `inter_objective_thresholds`.
if (objective_weights.nonzero().numel() > 1
and self._objective_thresholds is None):
self._objective_thresholds = infer_objective_thresholds(
model=model,
objective_weights=self._full_objective_weights,
outcome_constraints=full_outcome_constraints,
X_observed=X_observed,
subset_idcs=subset_idcs,
)
objective_thresholds = (
not_none(self._objective_thresholds)[subset_idcs]
if subset_idcs is not None else self._objective_thresholds)
objective, posterior_transform = self.get_botorch_objective_and_transform(
botorch_acqf_class=botorch_acqf_class,
model=model,
objective_weights=objective_weights,
objective_thresholds=objective_thresholds,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
)
model_deps = self.compute_model_dependencies(
surrogate=surrogate,
search_space_digest=search_space_digest,
objective_weights=objective_weights,
pending_observations=pending_observations,
outcome_constraints=outcome_constraints,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
options=self.options,
)
input_constructor_kwargs = {
"X_baseline": X_observed,
"X_pending": X_pending,
"objective_thresholds": objective_thresholds,
"outcome_constraints": outcome_constraints,
**model_deps,
**self.options,
}
input_constructor = get_acqf_input_constructor(botorch_acqf_class)
acqf_inputs = input_constructor(
model=model,
training_data=self.surrogate.training_data,
objective=objective,
posterior_transform=posterior_transform,
**input_constructor_kwargs,
)
self.acqf = botorch_acqf_class(**acqf_inputs) # pyre-ignore [45]
def infer_objective_thresholds(
model: Model,
objective_weights: Tensor, # objective_directions
bounds: Optional[List[Tuple[float, float]]] = None,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
subset_idcs: Optional[Tensor] = None,
Xs: Optional[List[Tensor]] = None,
X_observed: Optional[Tensor] = None,
) -> Tensor:
"""Infer objective thresholds.
This method uses the model-estimated Pareto frontier over the in-sample points
to infer absolute (not relativized) objective thresholds.
This uses a heuristic that sets the objective threshold to be a scaled nadir
point, where the nadir point is scaled back based on the range of each
objective across the current in-sample Pareto frontier.
See `botorch.utils.multi_objective.hypervolume.infer_reference_point` for
details on the heuristic.
Args:
model: A fitted botorch Model.
objective_weights: The objective is to maximize a weighted sum of
the columns of f(x). These are the weights. These should not
be subsetted.
bounds: A list of (lower, upper) tuples for each column of X.
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. These should not be subsetted.
linear_constraints: A tuple of (A, b). For k linear constraints on
d-dimensional x, A is (k x d) and b is (k x 1) such that
A x <= b.
fixed_features: A map {feature_index: value} for features that
should be fixed to a particular value during generation.
subset_idcs: The indices of the outcomes that are modeled by the
provided model. If subset_idcs not None, this method infers
whether the model is subsetted.
Xs: A list of m (k_i x d) feature tensors X. Number of rows k_i can
vary from i=1,...,m.
X_observed: A `n x d`-dim tensor of in-sample points to use for
determining the current in-sample Pareto frontier.
Returns:
A `m`-dim tensor of objective thresholds, where the objective
threshold is `nan` if the outcome is not an objective.
"""
if X_observed is None:
if bounds is None:
raise ValueError("bounds is required if X_observed is None.")
elif Xs is None:
raise ValueError("Xs is required if X_observed is None.")
_, X_observed = _get_X_pending_and_observed(
Xs=Xs,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
bounds=bounds,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
)
num_outcomes = objective_weights.shape[0]
if subset_idcs is None:
# check if only a subset of outcomes are modeled
nonzero = objective_weights != 0
if outcome_constraints is not None:
A, _ = outcome_constraints
nonzero = nonzero | torch.any(A != 0, dim=0)
expected_subset_idcs = nonzero.nonzero().view(-1) # pyre-ignore [16]
if model.num_outputs > expected_subset_idcs.numel():
# subset the model so that we only compute the posterior
# over the relevant outcomes
subset_model_results = subset_model(
model=model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
)
model = subset_model_results.model
objective_weights = subset_model_results.objective_weights
outcome_constraints = subset_model_results.outcome_constraints
subset_idcs = subset_model_results.indices
else:
# model is already subsetted.
subset_idcs = expected_subset_idcs
# subset objective weights and outcome constraints
objective_weights = objective_weights[subset_idcs]
if outcome_constraints is not None:
outcome_constraints = (
outcome_constraints[0][:, subset_idcs],
outcome_constraints[1],
)
else:
objective_weights = objective_weights[subset_idcs]
if outcome_constraints is not None:
outcome_constraints = (
outcome_constraints[0][:, subset_idcs],
outcome_constraints[1],
)
with torch.no_grad():
pred = not_none(model).posterior(not_none(X_observed)).mean
if outcome_constraints is not None:
cons_tfs = get_outcome_constraint_transforms(outcome_constraints)
# pyre-ignore [16]
feas = torch.stack([c(pred) <= 0 for c in cons_tfs],
dim=-1).all(dim=-1)
pred = pred[feas]
if pred.shape[0] == 0:
raise AxError("There are no feasible observed points.")
obj_mask = objective_weights.nonzero().view(-1)
obj_weights_subset = objective_weights[obj_mask]
obj = pred[..., obj_mask] * obj_weights_subset
pareto_obj = obj[is_non_dominated(obj)]
objective_thresholds = infer_reference_point(
pareto_Y=pareto_obj,
scale=0.1,
)
# multiply by objective weights to return objective thresholds in the
# unweighted space
objective_thresholds = objective_thresholds * obj_weights_subset
full_objective_thresholds = torch.full(
(num_outcomes, ),
float("nan"),
dtype=objective_weights.dtype,
device=objective_weights.device,
)
full_objective_thresholds[subset_idcs] = objective_thresholds.clone()
return full_objective_thresholds
def __init__(
self,
surrogate: Surrogate,
bounds: List[Tuple[float, float]],
objective_weights: Tensor,
botorch_acqf_class: Optional[Type[AcquisitionFunction]] = None,
options: Optional[Dict[str, Any]] = None,
pending_observations: Optional[List[Tensor]] = None,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
target_fidelities: Optional[Dict[int, float]] = None,
) -> None:
if not botorch_acqf_class and not self.default_botorch_acqf_class:
raise ValueError(
f"Acquisition class {self.__class__} does not specify a default "
"BoTorch `AcquisitionFunction`, so `botorch_acqf_class` "
"argument must be specified."
)
self._botorch_acqf_class = not_none(
botorch_acqf_class or self.default_botorch_acqf_class
)
self.surrogate = surrogate
self.options = options or {}
trd = self._extract_training_data(surrogate=surrogate)
Xs = (
# Assumes 1-D objective_weights, which should be safe.
[trd.X for o in range(objective_weights.shape[0])]
if isinstance(trd, TrainingData)
else [i.X for i in trd.values()]
)
X_pending, X_observed = _get_X_pending_and_observed(
Xs=Xs,
pending_observations=pending_observations,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
bounds=bounds,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
)
# Subset model only to the outcomes we need for the optimization.
if self.options.get(Keys.SUBSET_MODEL, True):
model, objective_weights, outcome_constraints, _ = subset_model(
self.surrogate.model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
)
else:
model = self.surrogate.model
objective = self._get_botorch_objective(
model=model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
)
model_deps = self.compute_model_dependencies(
surrogate=surrogate,
bounds=bounds,
objective_weights=objective_weights,
pending_observations=pending_observations,
outcome_constraints=outcome_constraints,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
target_fidelities=target_fidelities,
options=self.options,
)
X_baseline = X_observed
overriden_X_baseline = model_deps.get(Keys.X_BASELINE)
if overriden_X_baseline is not None:
X_baseline = overriden_X_baseline
model_deps.pop(Keys.X_BASELINE)
self.acqf = self._botorch_acqf_class( # pyre-ignore[28]: Some kwargs are
# not expected in base `AcquisitionFunction` but are expected in
# its subclasses.
model=model,
objective=objective,
X_pending=X_pending,
X_baseline=X_baseline,
**self.options,
**model_deps,
)
def gen(
self,
n: int,
bounds: List[Tuple[float, float]],
objective_weights: Tensor, # objective_directions
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
objective_thresholds: Optional[Tensor] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
pending_observations: Optional[List[Tensor]] = None,
model_gen_options: Optional[TConfig] = None,
rounding_func: Optional[Callable[[Tensor], Tensor]] = None,
target_fidelities: Optional[Dict[int, float]] = None,
) -> Tuple[Tensor, Tensor, TGenMetadata, Optional[List[TCandidateMetadata]]]:
options = model_gen_options or {}
acf_options = options.get("acquisition_function_kwargs", {})
optimizer_options = options.get("optimizer_kwargs", {})
if target_fidelities:
raise NotImplementedError(
"target_fidelities not implemented for base BotorchModel"
)
if (
objective_thresholds is not None
and objective_weights.shape[0] != objective_thresholds.shape[0]
):
raise AxError(
"Objective weights and thresholds most both contain an element for"
" each modeled metric."
)
X_pending, X_observed = _get_X_pending_and_observed(
Xs=self.Xs,
pending_observations=pending_observations,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
bounds=bounds,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
)
model = not_none(self.model)
full_objective_thresholds = objective_thresholds
full_objective_weights = objective_weights
full_outcome_constraints = outcome_constraints
# subset model only to the outcomes we need for the optimization
if options.get(Keys.SUBSET_MODEL, True):
subset_model_results = subset_model(
model=model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
objective_thresholds=objective_thresholds,
)
model = subset_model_results.model
objective_weights = subset_model_results.objective_weights
outcome_constraints = subset_model_results.outcome_constraints
objective_thresholds = subset_model_results.objective_thresholds
idcs = subset_model_results.indices
else:
idcs = None
if objective_thresholds is None:
full_objective_thresholds = infer_objective_thresholds(
model=model,
X_observed=not_none(X_observed),
objective_weights=full_objective_weights,
outcome_constraints=full_outcome_constraints,
subset_idcs=idcs,
)
# subset the objective thresholds
objective_thresholds = (
full_objective_thresholds
if idcs is None
else full_objective_thresholds[idcs].clone()
)
bounds_ = torch.tensor(bounds, dtype=self.dtype, device=self.device)
bounds_ = bounds_.transpose(0, 1)
botorch_rounding_func = get_rounding_func(rounding_func)
if acf_options.get("random_scalarization", False) or acf_options.get(
"chebyshev_scalarization", False
):
# If using a list of acquisition functions, the algorithm to generate
# that list is configured by acquisition_function_kwargs.
objective_weights_list = [
randomize_objective_weights(objective_weights, **acf_options)
for _ in range(n)
]
acquisition_function_list = [
self.acqf_constructor( # pyre-ignore: [28]
model=model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
X_pending=X_pending,
**acf_options,
)
for objective_weights in objective_weights_list
]
acquisition_function_list = [
checked_cast(AcquisitionFunction, acq_function)
for acq_function in acquisition_function_list
]
# Multiple acquisition functions require a sequential optimizer
# always use scipy_optimizer_list.
# TODO(jej): Allow any optimizer.
candidates, expected_acquisition_value = scipy_optimizer_list(
acq_function_list=acquisition_function_list,
bounds=bounds_,
inequality_constraints=_to_inequality_constraints(
linear_constraints=linear_constraints
),
fixed_features=fixed_features,
rounding_func=botorch_rounding_func,
**optimizer_options,
)
else:
acquisition_function = self.acqf_constructor( # pyre-ignore: [28]
model=model,
objective_weights=objective_weights,
objective_thresholds=objective_thresholds,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
X_pending=X_pending,
**acf_options,
)
acquisition_function = checked_cast(
AcquisitionFunction, acquisition_function
)
# pyre-ignore: [28]
candidates, expected_acquisition_value = self.acqf_optimizer(
acq_function=checked_cast(AcquisitionFunction, acquisition_function),
bounds=bounds_,
n=n,
inequality_constraints=_to_inequality_constraints(
linear_constraints=linear_constraints
),
fixed_features=fixed_features,
rounding_func=botorch_rounding_func,
**optimizer_options,
)
gen_metadata = {
"expected_acquisition_value": expected_acquisition_value.tolist(),
"objective_thresholds": not_none(full_objective_thresholds).cpu(),
"objective_weights": full_objective_weights.cpu(),
}
return (
candidates.detach().cpu(),
torch.ones(n, dtype=self.dtype),
gen_metadata,
None,
)
