Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

qLogNEI #1937

Closed
wants to merge 1 commit into from
Closed

qLogNEI #1937

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 2 additions & 0 deletions botorch/acquisition/__init__.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -37,6 +37,7 @@
from botorch.acquisition.logei import (
LogImprovementMCAcquisitionFunction,
qLogExpectedImprovement,
qLogNoisyExpectedImprovement,
)
from botorch.acquisition.max_value_entropy_search import (
MaxValueBase,
Expand Down Expand Up @@ -96,6 +97,7 @@
"qExpectedImprovement",
"LogImprovementMCAcquisitionFunction",
"qLogExpectedImprovement",
"qLogNoisyExpectedImprovement",
"qKnowledgeGradient",
"MaxValueBase",
"qMultiFidelityKnowledgeGradient",
Expand Down
152 changes: 149 additions & 3 deletions botorch/acquisition/input_constructors.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -47,7 +47,12 @@
qKnowledgeGradient,
qMultiFidelityKnowledgeGradient,
)
from botorch.acquisition.logei import qLogExpectedImprovement
from botorch.acquisition.logei import (
qLogExpectedImprovement,
qLogNoisyExpectedImprovement,
TAU_MAX,
TAU_RELU,
)
from botorch.acquisition.max_value_entropy_search import (
qMaxValueEntropy,
qMultiFidelityMaxValueEntropy,
Expand Down Expand Up @@ -450,7 +455,7 @@ def construct_inputs_qSimpleRegret(
)


@acqf_input_constructor(qExpectedImprovement, qLogExpectedImprovement)
@acqf_input_constructor(qExpectedImprovement)
def construct_inputs_qEI(
model: Model,
training_data: MaybeDict[SupervisedDataset],
Expand Down Expand Up @@ -508,6 +513,72 @@ def construct_inputs_qEI(
return {**base_inputs, "best_f": best_f, "constraints": constraints, "eta": eta}


@acqf_input_constructor(qLogExpectedImprovement)
def construct_inputs_qLogEI(
model: Model,
training_data: MaybeDict[SupervisedDataset],
objective: Optional[MCAcquisitionObjective] = None,
posterior_transform: Optional[PosteriorTransform] = None,
X_pending: Optional[Tensor] = None,
sampler: Optional[MCSampler] = None,
best_f: Optional[Union[float, Tensor]] = None,
constraints: Optional[List[Callable[[Tensor], Tensor]]] = None,
eta: Union[Tensor, float] = 1e-3,
fat: bool = True,
tau_max: float = TAU_MAX,
tau_relu: float = TAU_RELU,
**ignored: Any,
) -> Dict[str, Any]:
r"""Construct kwargs for the `qExpectedImprovement` constructor.
Args:
model: The model to be used in the acquisition function.
training_data: Dataset(s) used to train the model.
objective: The objective to be used in the acquisition function.
posterior_transform: The posterior transform to be used in the
acquisition function.
X_pending: A `m x d`-dim Tensor of `m` design points that have been
submitted for function evaluation but have not yet been evaluated.
Concatenated into X upon forward call.
sampler: The sampler used to draw base samples. If omitted, uses
the acquisition functions's default sampler.
best_f: Threshold above (or below) which improvement is defined.
constraints: A list of constraint callables which map a Tensor of posterior
samples of dimension `sample_shape x batch-shape x q x m`-dim to a
`sample_shape x batch-shape x q`-dim Tensor. The associated constraints
are considered satisfied if the output is less than zero.
eta: Temperature parameter(s) governing the smoothness of the sigmoid
approximation to the constraint indicators. For more details, on this
parameter, see the docs of `compute_smoothed_feasibility_indicator`.
fat: Toggles the logarithmic / linear asymptotic behavior of the smooth
approximation to the ReLU.
tau_max: Temperature parameter controlling the sharpness of the smooth
approximations to max.
tau_relu: Temperature parameter controlling the sharpness of the smooth
approximations to ReLU.
ignored: Not used.
Returns:
A dict mapping kwarg names of the constructor to values.
"""
return {
**construct_inputs_qEI(
model=model,
training_data=training_data,
objective=objective,
posterior_transform=posterior_transform,
X_pending=X_pending,
sampler=sampler,
best_f=best_f,
constraints=constraints,
eta=eta,
),
"fat": fat,
"tau_max": tau_max,
"tau_relu": tau_relu,
}


@acqf_input_constructor(qNoisyExpectedImprovement)
def construct_inputs_qNEI(
model: Model,
Expand Down Expand Up @@ -570,7 +641,6 @@ def construct_inputs_qNEI(
assert_shared=True,
first_only=True,
)

return {
**base_inputs,
"X_baseline": X_baseline,
Expand All @@ -581,6 +651,82 @@ def construct_inputs_qNEI(
}


@acqf_input_constructor(qLogNoisyExpectedImprovement)
def construct_inputs_qLogNEI(
model: Model,
training_data: MaybeDict[SupervisedDataset],
objective: Optional[MCAcquisitionObjective] = None,
posterior_transform: Optional[PosteriorTransform] = None,
X_pending: Optional[Tensor] = None,
sampler: Optional[MCSampler] = None,
X_baseline: Optional[Tensor] = None,
prune_baseline: Optional[bool] = True,
cache_root: Optional[bool] = True,
constraints: Optional[List[Callable[[Tensor], Tensor]]] = None,
eta: Union[Tensor, float] = 1e-3,
fat: bool = True,
tau_max: float = TAU_MAX,
tau_relu: float = TAU_RELU,
**ignored: Any,
):
r"""Construct kwargs for the `qNoisyExpectedImprovement` constructor.
Args:
model: The model to be used in the acquisition function.
training_data: Dataset(s) used to train the model.
objective: The objective to be used in the acquisition function.
posterior_transform: The posterior transform to be used in the
acquisition function.
X_pending: A `m x d`-dim Tensor of `m` design points that have been
submitted for function evaluation but have not yet been evaluated.
Concatenated into X upon forward call.
sampler: The sampler used to draw base samples. If omitted, uses
the acquisition functions's default sampler.
X_baseline: A `batch_shape x r x d`-dim Tensor of `r` design points
that have already been observed. These points are considered as
the potential best design point. If omitted, checks that all
training_data have the same input features and take the first `X`.
prune_baseline: If True, remove points in `X_baseline` that are
highly unlikely to be the best point. This can significantly
improve performance and is generally recommended.
constraints: A list of constraint callables which map a Tensor of posterior
samples of dimension `sample_shape x batch-shape x q x m`-dim to a
`sample_shape x batch-shape x q`-dim Tensor. The associated constraints
are considered satisfied if the output is less than zero.
eta: Temperature parameter(s) governing the smoothness of the sigmoid
approximation to the constraint indicators. For more details, on this
parameter, see the docs of `compute_smoothed_feasibility_indicator`.
fat: Toggles the logarithmic / linear asymptotic behavior of the smooth
approximation to the ReLU.
tau_max: Temperature parameter controlling the sharpness of the smooth
approximations to max.
tau_relu: Temperature parameter controlling the sharpness of the smooth
approximations to ReLU.
ignored: Not used.
Returns:
A dict mapping kwarg names of the constructor to values.
"""
return {
**construct_inputs_qNEI(
model=model,
training_data=training_data,
objective=objective,
posterior_transform=posterior_transform,
X_pending=X_pending,
sampler=sampler,
X_baseline=X_baseline,
prune_baseline=prune_baseline,
cache_root=cache_root,
constraint=constraints,
eta=eta,
),
"fat": fat,
"tau_max": tau_max,
"tau_relu": tau_relu,
}


@acqf_input_constructor(qProbabilityOfImprovement)
def construct_inputs_qPI(
model: Model,
Expand Down
Loading