# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Monte-Carlo variants of the LogEI family of improvements-based acquisition functions,
see [Ament2023logei]_ for details.
References
.. [Ament2023logei]
S. Ament, S. Daulton, D. Eriksson, M. Balandat, and E. Bakshy.
Unexpected Improvements to Expected Improvement for Bayesian Optimization. Advances
in Neural Information Processing Systems 36, 2023.
"""
from __future__ import annotations
from copy import deepcopy
from functools import partial
from typing import Callable, List, Optional, Tuple, TypeVar, Union
import torch
from botorch.acquisition.cached_cholesky import CachedCholeskyMCSamplerMixin
from botorch.acquisition.monte_carlo import SampleReducingMCAcquisitionFunction
from botorch.acquisition.objective import (
ConstrainedMCObjective,
MCAcquisitionObjective,
PosteriorTransform,
)
from botorch.acquisition.utils import (
compute_best_feasible_objective,
prune_inferior_points,
)
from botorch.exceptions.errors import BotorchError
from botorch.models.model import Model
from botorch.sampling.base import MCSampler
from botorch.utils.safe_math import (
fatmax,
log_fatplus,
log_softplus,
logmeanexp,
smooth_amax,
)
from botorch.utils.transforms import match_batch_shape
from torch import Tensor
"""
NOTE: On the default temperature parameters:
tau_relu: It is generally important to set `tau_relu` to be very small, in particular,
smaller than the expected improvement value. Otherwise, the optimization can stagnate.
By setting `tau_relu=1e-6` by default, stagnation is exceedingly unlikely to occur due
to the smooth ReLU approximation for practical applications of BO.
IDEA: We could consider shrinking `tau_relu` with the progression of the optimization.
tau_max: This is only relevant for the batch (`q > 1`) case, and `tau_max=1e-2` is
sufficient to get a good approximation to the maximum improvement in the batch of
candidates. If `fat=False`, the smooth approximation to the maximum can saturate
numerically. It is therefore recommended to use `fat=True` when optimizing batches
of `q > 1` points.
"""
TAU_RELU = 1e-6
TAU_MAX = 1e-2
FloatOrTensor = TypeVar("FloatOrTensor", float, Tensor)
[docs]
class LogImprovementMCAcquisitionFunction(SampleReducingMCAcquisitionFunction):
r"""
Abstract base class for Monte-Carlo-based batch LogEI acquisition functions.
"""
_log: bool = True
def __init__(
self,
model: Model,
sampler: Optional[MCSampler] = None,
objective: Optional[MCAcquisitionObjective] = None,
posterior_transform: Optional[PosteriorTransform] = None,
X_pending: Optional[Tensor] = None,
constraints: Optional[List[Callable[[Tensor], Tensor]]] = None,
eta: Union[Tensor, float] = 1e-3,
fat: bool = True,
tau_max: float = TAU_MAX,
) -> None:
r"""
Args:
model: A fitted model.
sampler: The sampler used to draw base samples. If not given,
a sampler is generated using `get_sampler`.
NOTE: For posteriors that do not support base samples,
a sampler compatible with intended use case must be provided.
See `ForkedRNGSampler` and `StochasticSampler` as examples.
objective: The MCAcquisitionObjective under which the samples are
evaluated. Defaults to `IdentityMCObjective()`.
posterior_transform: A PosteriorTransform (optional).
X_pending: A `batch_shape, m x d`-dim Tensor of `m` design points
that have points that have been submitted for function evaluation
but have not yet been evaluated.
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 satisfied if `constraint(samples) < 0`.
eta: Temperature parameter(s) governing the smoothness of the sigmoid
approximation to the constraint indicators. See the docs of
`compute_(log_)constraint_indicator` for more details on this parameter.
fat: Toggles the logarithmic / linear asymptotic behavior of the smooth
approximation to the ReLU.
tau_max: Temperature parameter controlling the sharpness of the
approximation to the `max` operator over the `q` candidate points.
"""
if isinstance(objective, ConstrainedMCObjective):
raise BotorchError(
"Log-Improvement should not be used with `ConstrainedMCObjective`."
"Please pass the `constraints` directly to the constructor of the "
"acquisition function."
)
q_reduction = partial(fatmax if fat else smooth_amax, tau=tau_max)
super().__init__(
model=model,
sampler=sampler,
objective=objective,
posterior_transform=posterior_transform,
X_pending=X_pending,
sample_reduction=logmeanexp,
q_reduction=q_reduction,
constraints=constraints,
eta=eta,
fat=fat,
)
self.tau_max = tau_max
[docs]
class qLogExpectedImprovement(LogImprovementMCAcquisitionFunction):
r"""MC-based batch Log Expected Improvement.
This computes qLogEI by
(1) sampling the joint posterior over q points,
(2) evaluating the smoothed log improvement over the current best for each sample,
(3) smoothly maximizing over q, and
(4) averaging over the samples in log space.
See [Ament2023logei]_ for details. Formally,
`qLogEI(X) ~ log(qEI(X)) = log(E(max(max Y - best_f, 0)))`.
where `Y ~ f(X)`, and `X = (x_1,...,x_q)`, .
Example:
>>> model = SingleTaskGP(train_X, train_Y)
>>> best_f = train_Y.max()[0]
>>> sampler = SobolQMCNormalSampler(1024)
>>> qLogEI = qLogExpectedImprovement(model, best_f, sampler)
>>> qei = qLogEI(test_X)
"""
def __init__(
self,
model: Model,
best_f: Union[float, Tensor],
sampler: Optional[MCSampler] = None,
objective: Optional[MCAcquisitionObjective] = None,
posterior_transform: Optional[PosteriorTransform] = None,
X_pending: Optional[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,
) -> None:
r"""q-Log Expected Improvement.
Args:
model: A fitted model.
best_f: The best objective value observed so far (assumed noiseless). Can be
a scalar, or a `batch_shape`-dim tensor. In case of a batched model, the
tensor can specify different values for each element of the batch.
sampler: The sampler used to draw base samples. See `MCAcquisitionFunction`
more details.
objective: The MCAcquisitionObjective under which the samples are evaluated.
Defaults to `IdentityMCObjective()`.
posterior_transform: A PosteriorTransform (optional).
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. Copied and set to have no
gradient.
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 satisfied if `constraint(samples) < 0`.
eta: Temperature parameter(s) governing the smoothness of the sigmoid
approximation to the constraint indicators. See the docs of
`compute_(log_)smoothed_constraint_indicator` for details.
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.
"""
super().__init__(
model=model,
sampler=sampler,
objective=objective,
posterior_transform=posterior_transform,
X_pending=X_pending,
constraints=constraints,
eta=eta,
tau_max=check_tau(tau_max, name="tau_max"),
fat=fat,
)
self.register_buffer("best_f", torch.as_tensor(best_f, dtype=float))
self.tau_relu = check_tau(tau_relu, name="tau_relu")
def _sample_forward(self, obj: Tensor) -> Tensor:
r"""Evaluate qLogExpectedImprovement on the candidate set `X`.
Args:
obj: `mc_shape x batch_shape x q`-dim Tensor of MC objective values.
Returns:
A `mc_shape x batch_shape x q`-dim Tensor of expected improvement values.
"""
li = _log_improvement(
Y=obj,
best_f=self.best_f,
tau=self.tau_relu,
fat=self._fat,
)
return li
[docs]
class qLogNoisyExpectedImprovement(
LogImprovementMCAcquisitionFunction, CachedCholeskyMCSamplerMixin
):
r"""MC-based batch Log Noisy Expected Improvement.
This function does not assume a `best_f` is known (which would require
noiseless observations). Instead, it uses samples from the joint posterior
over the `q` test points and previously observed points. A smooth approximation
to the canonical improvement over previously observed points is computed
for each sample and the logarithm of the average is returned.
See [Ament2023logei]_ for details. Formally,
`qLogNEI(X) ~ log(qNEI(X)) = Log E(max(max Y - max Y_baseline, 0))`,
where `(Y, Y_baseline) ~ f((X, X_baseline)), X = (x_1,...,x_q)`.
Example:
>>> model = SingleTaskGP(train_X, train_Y)
>>> sampler = SobolQMCNormalSampler(1024)
>>> qLogNEI = qLogNoisyExpectedImprovement(model, train_X, sampler)
>>> acqval = qLogNEI(test_X)
"""
def __init__(
self,
model: Model,
X_baseline: Tensor,
sampler: Optional[MCSampler] = None,
objective: Optional[MCAcquisitionObjective] = None,
posterior_transform: Optional[PosteriorTransform] = None,
X_pending: Optional[Tensor] = None,
constraints: Optional[List[Callable[[Tensor], Tensor]]] = None,
eta: Union[Tensor, float] = 1e-3,
fat: bool = True,
prune_baseline: bool = False,
cache_root: bool = True,
tau_max: float = TAU_MAX,
tau_relu: float = TAU_RELU,
marginalize_dim: Optional[int] = None,
) -> None:
r"""q-Noisy Expected Improvement.
Args:
model: A fitted model.
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.
sampler: The sampler used to draw base samples. See `MCAcquisitionFunction`
more details.
objective: The MCAcquisitionObjective under which the samples are
evaluated. Defaults to `IdentityMCObjective()`.
posterior_transform: A PosteriorTransform (optional).
X_pending: A `batch_shape x m x d`-dim Tensor of `m` design points
that have points that have been submitted for function evaluation
but have not yet been evaluated. Concatenated into `X` upon
forward call. Copied and set to have no gradient.
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 satisfied if `constraint(samples) < 0`.
eta: Temperature parameter(s) governing the smoothness of the sigmoid
approximation to the constraint indicators. See the docs of
`compute_(log_)smoothed_constraint_indicator` for details.
fat: Toggles the logarithmic / linear asymptotic behavior of the smooth
approximation to the ReLU.
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. In order to
customize pruning parameters, instead manually call
`botorch.acquisition.utils.prune_inferior_points` on `X_baseline`
before instantiating the acquisition function.
cache_root: A boolean indicating whether to cache the root
decomposition over `X_baseline` and use low-rank updates.
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.
marginalize_dim: The dimension to marginalize over.
TODO: similar to qNEHVI, when we are using sequential greedy candidate
selection, we could incorporate pending points X_baseline and compute
the incremental q(Log)NEI from the new point. This would greatly increase
efficiency for large batches.
"""
# TODO: separate out baseline variables initialization and other functions
# in qNEI to avoid duplication of both code and work at runtime.
super().__init__(
model=model,
sampler=sampler,
objective=objective,
posterior_transform=posterior_transform,
X_pending=X_pending,
constraints=constraints,
eta=eta,
fat=fat,
tau_max=tau_max,
)
self.tau_relu = tau_relu
self._init_baseline(
model=model,
X_baseline=X_baseline,
sampler=sampler,
objective=objective,
posterior_transform=posterior_transform,
prune_baseline=prune_baseline,
cache_root=cache_root,
marginalize_dim=marginalize_dim,
)
def _sample_forward(self, obj: Tensor) -> Tensor:
r"""Evaluate qLogNoisyExpectedImprovement per sample on the candidate set `X`.
Args:
obj: `mc_shape x batch_shape x q`-dim Tensor of MC objective values.
Returns:
A `sample_shape x batch_shape x q`-dim Tensor of log noisy expected smoothed
improvement values.
"""
return _log_improvement(
Y=obj,
best_f=self.compute_best_f(obj),
tau=self.tau_relu,
fat=self._fat,
)
def _init_baseline(
self,
model: Model,
X_baseline: Tensor,
sampler: Optional[MCSampler] = None,
objective: Optional[MCAcquisitionObjective] = None,
posterior_transform: Optional[PosteriorTransform] = None,
prune_baseline: bool = False,
cache_root: bool = True,
marginalize_dim: Optional[int] = None,
) -> None:
CachedCholeskyMCSamplerMixin.__init__(
self, model=model, cache_root=cache_root, sampler=sampler
)
if prune_baseline:
X_baseline = prune_inferior_points(
model=model,
X=X_baseline,
objective=objective,
posterior_transform=posterior_transform,
marginalize_dim=marginalize_dim,
constraints=self._constraints,
)
self.register_buffer("X_baseline", X_baseline)
# registering buffers for _get_samples_and_objectives in the next `if` block
self.register_buffer("baseline_samples", None)
self.register_buffer("baseline_obj", None)
if self._cache_root:
self.q_in = -1
# set baseline samples
with torch.no_grad(): # this is _get_samples_and_objectives(X_baseline)
posterior = self.model.posterior(
X_baseline, posterior_transform=self.posterior_transform
)
# Note: The root decomposition is cached in two different places. It
# may be confusing to have two different caches, but this is not
# trivial to change since each is needed for a different reason:
# - LinearOperator caching to `posterior.mvn` allows for reuse within
# this function, which may be helpful if the same root decomposition
# is produced by the calls to `self.base_sampler` and
# `self._cache_root_decomposition`.
# - self._baseline_L allows a root decomposition to be persisted outside
# this method.
self.baseline_samples = self.get_posterior_samples(posterior)
self.baseline_obj = self.objective(self.baseline_samples, X=X_baseline)
# We make a copy here because we will write an attribute `base_samples`
# to `self.base_sampler.base_samples`, and we don't want to mutate
# `self.sampler`.
self.base_sampler = deepcopy(self.sampler)
self.register_buffer(
"_baseline_best_f",
self._compute_best_feasible_objective(
samples=self.baseline_samples, obj=self.baseline_obj
),
)
self._baseline_L = self._compute_root_decomposition(posterior=posterior)
[docs]
def compute_best_f(self, obj: Tensor) -> Tensor:
"""Computes the best (feasible) noisy objective value.
Args:
obj: `sample_shape x batch_shape x q`-dim Tensor of objectives in forward.
Returns:
A `sample_shape x batch_shape`-dim Tensor of best feasible objectives.
"""
if self._cache_root:
val = self._baseline_best_f
else:
val = self._compute_best_feasible_objective(
samples=self.baseline_samples, obj=self.baseline_obj
)
# ensuring shape, dtype, device compatibility with obj
n_sample_dims = len(self.sample_shape)
view_shape = torch.Size(
[
*val.shape[:n_sample_dims], # sample dimensions
*(1,) * (obj.ndim - val.ndim - 1), # pad to match obj without `q`-dim
*val.shape[n_sample_dims:], # the rest
]
)
return val.view(view_shape).to(obj) # obj.shape[:-1], i.e. without `q`-dim`
def _get_samples_and_objectives(self, X: Tensor) -> Tuple[Tensor, Tensor]:
r"""Compute samples at new points, using the cached root decomposition.
Args:
X: A `batch_shape x q x d`-dim tensor of inputs.
Returns:
A two-tuple `(samples, obj)`, where `samples` is a tensor of posterior
samples with shape `sample_shape x batch_shape x q x m`, and `obj` is a
tensor of MC objective values with shape `sample_shape x batch_shape x q`.
"""
n_baseline, q = self.X_baseline.shape[-2], X.shape[-2]
X_full = torch.cat([match_batch_shape(self.X_baseline, X), X], dim=-2)
# TODO: Implement more efficient way to compute posterior over both training and
# test points in GPyTorch (https://github.com/cornellius-gp/gpytorch/issues/567)
posterior = self.model.posterior(
X_full, posterior_transform=self.posterior_transform
)
if not self._cache_root:
samples_full = super().get_posterior_samples(posterior)
obj_full = self.objective(samples_full, X=X_full)
# assigning baseline buffers so `best_f` can be computed in _sample_forward
self.baseline_samples, samples = samples_full.split([n_baseline, q], dim=-2)
self.baseline_obj, obj = obj_full.split([n_baseline, q], dim=-1)
return samples, obj
# handle one-to-many input transforms
n_plus_q = X_full.shape[-2]
n_w = posterior._extended_shape()[-2] // n_plus_q
q_in = q * n_w
self._set_sampler(q_in=q_in, posterior=posterior)
samples = self._get_f_X_samples(posterior=posterior, q_in=q_in)
obj = self.objective(samples, X=X_full[..., -q:, :])
return samples, obj
def _compute_best_feasible_objective(self, samples: Tensor, obj: Tensor) -> Tensor:
r"""Computes best feasible objective value from samples.
Args:
samples: `sample_shape x batch_shape x q x m`-dim posterior samples.
obj: A `sample_shape x batch_shape x q`-dim Tensor of MC objective values.
Returns:
A `sample_shape x batch_shape`-dim Tensor of best feasible objectives.
"""
return compute_best_feasible_objective(
samples=samples,
obj=obj,
constraints=self._constraints,
model=self.model,
objective=self.objective,
posterior_transform=self.posterior_transform,
X_baseline=self.X_baseline,
)
"""
###################################### utils ##########################################
"""
def _log_improvement(
Y: Tensor,
best_f: Tensor,
tau: Union[float, Tensor],
fat: bool,
) -> Tensor:
"""Computes the logarithm of the softplus-smoothed improvement, i.e.
`log_softplus(Y - best_f, beta=(1 / tau))`.
Note that softplus is an approximation to the regular ReLU objective whose maximum
pointwise approximation error is linear with respect to tau as tau goes to zero.
Args:
obj: `mc_samples x batch_shape x q`-dim Tensor of output samples.
best_f: Best previously observed objective value(s), broadcastable with
`mc_samples x batch_shape`-dim Tensor, i.e. `obj`'s dims without `q`.
tau: Temperature parameter for smooth approximation of ReLU.
as `tau -> 0`, maximum pointwise approximation error is linear w.r.t. `tau`.
fat: Toggles the logarithmic / linear asymptotic behavior of the
smooth approximation to ReLU.
Returns:
A `mc_samples x batch_shape x q`-dim Tensor of improvement values.
"""
log_soft_clamp = log_fatplus if fat else log_softplus
Z = Y - best_f.unsqueeze(-1).to(Y)
return log_soft_clamp(Z, tau=tau) # ~ ((Y - best_f) / Y_std).clamp(0)
[docs]
def check_tau(tau: FloatOrTensor, name: str) -> FloatOrTensor:
"""Checks the validity of the tau arguments of the functions below, and returns
`tau` if it is valid."""
if isinstance(tau, Tensor) and tau.numel() != 1:
raise ValueError(name + f" is not a scalar: {tau.numel() = }.")
if not (tau > 0):
raise ValueError(name + f" is non-positive: {tau = }.")
return tau
