def test_match_batch_shape(self):
X = torch.rand(3, 2)
Y = torch.rand(1, 3, 2)
X_tf = match_batch_shape(X, Y)
self.assertTrue(torch.equal(X_tf, X.unsqueeze(0)))
X = torch.rand(1, 3, 2)
Y = torch.rand(2, 3, 2)
X_tf = match_batch_shape(X, Y)
self.assertTrue(torch.equal(X_tf, X.repeat(2, 1, 1)))
X = torch.rand(2, 3, 2)
Y = torch.rand(1, 3, 2)
with self.assertRaises(RuntimeError):
match_batch_shape(X, Y)
def test_match_batch_shape(self):
X = torch.rand(3, 2)
Y = torch.rand(1, 3, 2)
X_tf = match_batch_shape(X, Y)
self.assertTrue(torch.equal(X_tf, X.unsqueeze(0)))
X = torch.rand(1, 3, 2)
Y = torch.rand(2, 3, 2)
X_tf = match_batch_shape(X, Y)
self.assertTrue(torch.equal(X_tf, X.repeat(2, 1, 1)))
X = torch.rand(2, 3, 2)
Y = torch.rand(1, 3, 2)
with self.assertRaises(RuntimeError):
match_batch_shape(X, Y)
def forward(self, X: Tensor) -> Tensor:
r"""Evaluate qNoisyExpectedImprovement on the candidate set `X`.
Args:
X: A `batch_shape x q x d`-dim Tensor of t-batches with `q` `d`-dim design
points each.
Returns:
A `batch_shape'`-dim Tensor of Noisy Expected Improvement values at the
given design points `X`, where `batch_shape'` is the broadcasted batch shape
of model and input `X`.
"""
q = 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 self._cache_root:
diffs = self._forward_cached(posterior=posterior,
X_full=X_full,
q=q)
else:
samples = self.sampler(posterior)
obj = self.objective(samples, X=X_full)
diffs = obj[..., -q:].max(dim=-1).values - obj[..., :-q].max(
dim=-1).values
return diffs.clamp_min(0).mean(dim=0)
def _sample_max_values(self):
r"""Sample max values for MC approximation of the expectation in MES"""
with torch.no_grad():
# Append X_pending to candidate set
if self.X_pending is None:
X_pending = torch.tensor(
[], dtype=self.candidate_set.dtype, device=self.candidate_set.device
)
else:
X_pending = self.X_pending
X_pending = match_batch_shape(X_pending, self.candidate_set)
candidate_set = torch.cat([self.candidate_set, X_pending], dim=0)
# project the candidate_set to the highest fidelity,
# which is needed for the multi-fidelity MES
try:
candidate_set = self.project(candidate_set)
except AttributeError:
pass
# sample max values
if self.use_gumbel:
self.posterior_max_values = _sample_max_value_Gumbel(
self.model, candidate_set, self.num_mv_samples, self.maximize
)
else:
self.posterior_max_values = _sample_max_value_Thompson(
self.model, candidate_set, self.num_mv_samples, self.maximize
)
def get_multi_step_tree_input_representation(self,
X: Tensor) -> List[Tensor]:
r"""Get the multi-step tree representation of X.
Args:
X: A `batch_shape x q' x d`-dim Tensor with `q'` design points for each
batch, where `q' = q_0 + f_1 q_1 + f_2 f_1 q_2 + ...`. Here `q_i`
is the number of candidates jointly considered in look-ahead step
`i`, and `f_i` is respective number of fantasies.
Returns:
A list `[X_j, ..., X_k]` of tensors, where `X_i` has shape
`f_i x .... x f_1 x batch_shape x q_i x d`.
"""
batch_shape, shapes, sizes = self.get_split_shapes(X=X)
# Each X_i in Xsplit has shape batch_shape x qtilde x d with
# qtilde = f_i * ... * f_1 * q_i
Xsplit = torch.split(X, sizes, dim=-2)
# now reshape (need to permute batch_shape and qtilde dimensions for i > 0)
perm = [-2] + list(range(len(batch_shape))) + [-1]
X0 = Xsplit[0].reshape(shapes[0])
Xother = [
X.permute(*perm).reshape(shape)
for X, shape in zip(Xsplit[1:], shapes[1:])
]
# concatenate in pending points
if self.X_pending is not None:
X0 = torch.cat([X0, match_batch_shape(self.X_pending, X0)], dim=-2)
return [X0] + Xother
def __init__(
self,
model: Model,
candidate_set: Tensor,
num_fantasies: int = 16,
num_mv_samples: int = 10,
num_y_samples: int = 128,
use_gumbel: bool = True,
maximize: bool = True,
X_pending: Optional[Tensor] = None,
train_inputs: Tensor = None,
**kwargs: Any,
) -> None:
r"""Single-outcome max-value entropy search acquisition function.
Args:
model: A fitted single-outcome model.
candidate_set: A `n x d` Tensor including `n` candidate points to
discretize the design space. Max values are sampled from the
(joint) model posterior over these points.
num_fantasies: Number of fantasies to generate. The higher this
number the more accurate the model (at the expense of model
complexity, wall time and memory). Ignored if `X_pending` is `None`.
num_mv_samples: Number of max value samples.
num_y_samples: Number of posterior samples at specific design point `X`.
use_gumbel: If True, use Gumbel approximation to sample the max values.
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.
maximize: If True, consider the problem a maximization problem.
train_inputs: A `n_train x d` Tensor that the model has been fitted on,
optional if model is an exact GP model.
"""
sampler = SobolQMCNormalSampler(num_y_samples)
super().__init__(model=model, sampler=sampler)
# Batch GP models (e.g. fantasized models) are not currently supported
if train_inputs is None:
train_inputs = self.model.train_inputs[0]
if train_inputs.ndim > 2:
raise NotImplementedError(
"Batch GP models (e.g. fantasized models) "
"are not yet supported by qMaxValueEntropy")
self._init_model = model # only used for the `fantasize()` in `set_X_pending()`
train_inputs = match_batch_shape(train_inputs, candidate_set)
self.candidate_set = torch.cat([candidate_set, train_inputs], dim=0)
self.fantasies_sampler = SobolQMCNormalSampler(num_fantasies)
self.num_fantasies = num_fantasies
self.use_gumbel = use_gumbel
self.num_mv_samples = num_mv_samples
self.maximize = maximize
self.weight = 1.0 if maximize else -1.0
# If we put the `self._sample_max_values()` to `set_X_pending()`,
# it will throw errors when the initial `super().__init__()` is called,
# since some members required by `_sample_max_values()` are not yet initialized.
if X_pending is None:
self._sample_max_values()
else:
self.set_X_pending(X_pending)
def forward(self, X: Tensor) -> Tensor:
r"""Evaluate qKnowledgeGradient on the candidate set `X`.
Args:
X: A `b x (q + num_fantasies) x d` Tensor with `b` t-batches of
`q + num_fantasies` design points each. We split this X tensor
into two parts in the `q` dimension (`dim=-2`). The first `q`
are the q-batch of design points and the last num_fantasies are
the current solutions of the inner optimization problem.
`X_fantasies = X[..., -num_fantasies:, :]`
`X_fantasies.shape = b x num_fantasies x d`
`X_actual = X[..., :-num_fantasies, :]`
`X_actual.shape = b x q x d`
Returns:
A Tensor of shape `b`. For t-batch b, the q-KG value of the design
`X_actual[b]` is averaged across the fantasy models, where
`X_fantasies[b, i]` is chosen as the final selection for the
`i`-th fantasy model.
NOTE: If `current_value` is not provided, then this is not the
true KG value of `X_actual[b]`, and `X_fantasies[b, : ]` must be
maximized at fixed `X_actual[b]`.
"""
X_actual, X_fantasies = _split_fantasy_points(X=X,
n_f=self.num_fantasies)
# We only concatenate X_pending into the X part after splitting
if self.X_pending is not None:
X_actual = torch.cat(
[X_actual,
match_batch_shape(self.X_pending, X_actual)],
dim=-2)
# construct the fantasy model of shape `num_fantasies x b`
fantasy_model = self.model.fantasize(X=X_actual,
sampler=self.sampler,
observation_noise=True)
# get the value function
value_function = _get_value_function(
model=fantasy_model,
objective=self.objective,
posterior_transform=self.posterior_transform,
sampler=self.inner_sampler,
)
# make sure to propagate gradients to the fantasy model train inputs
with settings.propagate_grads(True):
values = value_function(X=X_fantasies) # num_fantasies x b
if self.current_value is not None:
values = values - self.current_value
# return average over the fantasy samples
return values.mean(dim=0)
def test_match_batch_shape_multi_dim(self):
X = torch.rand(1, 3, 2)
Y = torch.rand(5, 4, 3, 2)
X_tf = match_batch_shape(X, Y)
self.assertTrue(torch.equal(X_tf, X.expand(5, 4, 3, 2)))
X = torch.rand(4, 3, 2)
Y = torch.rand(5, 4, 3, 2)
X_tf = match_batch_shape(X, Y)
self.assertTrue(torch.equal(X_tf, X.repeat(5, 1, 1, 1)))
X = torch.rand(2, 1, 3, 2)
Y = torch.rand(2, 4, 3, 2)
X_tf = match_batch_shape(X, Y)
self.assertTrue(torch.equal(X_tf, X.repeat(1, 4, 1, 1)))
X = torch.rand(4, 2, 3, 2)
Y = torch.rand(4, 3, 3, 2)
with self.assertRaises(RuntimeError):
match_batch_shape(X, Y)
def test_match_batch_shape_multi_dim(self):
X = torch.rand(1, 3, 2)
Y = torch.rand(5, 4, 3, 2)
X_tf = match_batch_shape(X, Y)
self.assertTrue(torch.equal(X_tf, X.expand(5, 4, 3, 2)))
X = torch.rand(4, 3, 2)
Y = torch.rand(5, 4, 3, 2)
X_tf = match_batch_shape(X, Y)
self.assertTrue(torch.equal(X_tf, X.repeat(5, 1, 1, 1)))
X = torch.rand(2, 1, 3, 2)
Y = torch.rand(2, 4, 3, 2)
X_tf = match_batch_shape(X, Y)
self.assertTrue(torch.equal(X_tf, X.repeat(1, 4, 1, 1)))
X = torch.rand(4, 2, 3, 2)
Y = torch.rand(4, 3, 3, 2)
with self.assertRaises(RuntimeError):
match_batch_shape(X, Y)
def __init__(
self,
model: Model,
candidate_set: Tensor,
num_mv_samples: int = 10,
posterior_transform: Optional[PosteriorTransform] = None,
use_gumbel: bool = True,
maximize: bool = True,
X_pending: Optional[Tensor] = None,
train_inputs: Optional[Tensor] = None,
) -> None:
r"""Single-outcome MES-like acquisition functions based on discrete MV sampling.
Args:
model: A fitted single-outcome model.
candidate_set: A `n x d` Tensor including `n` candidate points to
discretize the design space. Max values are sampled from the
(joint) model posterior over these points.
num_mv_samples: Number of max value samples.
posterior_transform: A PosteriorTransform. If using a multi-output model,
a PosteriorTransform that transforms the multi-output posterior into a
single-output posterior is required.
use_gumbel: If True, use Gumbel approximation to sample the max values.
maximize: If True, consider the problem a maximization problem.
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.
train_inputs: A `n_train x d` Tensor that the model has been fitted on.
Not required if the model is an instance of a GPyTorch ExactGP model.
"""
self.use_gumbel = use_gumbel
if train_inputs is None and hasattr(model, "train_inputs"):
train_inputs = model.train_inputs[0]
if train_inputs is not None:
if train_inputs.ndim > 2:
raise NotImplementedError(
"Batch GP models (e.g. fantasized models) "
"are not yet supported by `MaxValueBase`"
)
train_inputs = match_batch_shape(train_inputs, candidate_set)
candidate_set = torch.cat([candidate_set, train_inputs], dim=0)
self.candidate_set = candidate_set
super().__init__(
model=model,
num_mv_samples=num_mv_samples,
posterior_transform=posterior_transform,
maximize=maximize,
X_pending=X_pending,
)
def forward(self, X: Tensor) -> Tensor:
X_full = torch.cat([match_batch_shape(self.X_baseline, X), X], dim=-2)
# Note: it is important to compute the full posterior over `(X_baseline, X)`
# to ensure that we properly sample `f(X)` from the joint distribution `
# `f(X_baseline, X) ~ P(f | D)` given that we can already fixed the sampled
# function values for `f(X_baseline)`
# TODO: improve efficiency by not recomputing baseline-baseline
# covariance matrix
posterior = self.model.posterior(X_full)
q = X.shape[-2]
self._set_sampler(q=q, posterior=posterior)
samples = self._get_f_X_samples(posterior=posterior, q=q)
# add previous nehvi from pending points
return self._compute_qehvi(samples=samples) + self._prev_nehvi
def forward(self, X: Tensor) -> Tensor:
X_full = torch.cat([match_batch_shape(self.X_baseline, X), X], dim=-2)
# Note: it is important to compute the full posterior over `(X_baseline, X)`
# to ensure that we properly sample `f(X)` from the joint distribution `
# `f(X_baseline, X) ~ P(f | D)` given that we can already fixed the sampled
# function values for `f(X_baseline)`.
# TODO: improve efficiency by not recomputing baseline-baseline
# covariance matrix.
posterior = self.model.posterior(X_full)
# Account for possible one-to-many transform.
n_w = posterior.event_shape[X_full.dim() - 2] // X_full.shape[-2]
q_in = X.shape[-2] * n_w
self._set_sampler(q_in=q_in, posterior=posterior)
samples = self._get_f_X_samples(posterior=posterior, q_in=q_in)
# Add previous nehvi from pending points.
return self._compute_qehvi(samples=samples, X=X) + self._prev_nehvi
def _sample_max_values(self,
num_samples: int,
X_pending: Optional[Tensor] = None) -> Tensor:
r"""Draw samples from the posterior over maximum values on a discrete set.
These samples are used to compute Monte Carlo approximations of expecations
over the posterior over the function maximum.
Args:
num_samples: The number of samples to draw.
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.
Returns:
A `num_samples x num_fantasies` Tensor of posterior max value samples
(`num_fantasies=1` for non-fantasized models).
"""
if self.use_gumbel:
sample_max_values = _sample_max_value_Gumbel
else:
sample_max_values = _sample_max_value_Thompson
candidate_set = self.candidate_set
with torch.no_grad():
if X_pending is not None:
# Append X_pending to candidate set
X_pending = match_batch_shape(X_pending, self.candidate_set)
candidate_set = torch.cat([self.candidate_set, X_pending],
dim=0)
# project the candidate_set to the highest fidelity,
# which is needed for the multi-fidelity MES
try:
candidate_set = self.project(candidate_set)
except AttributeError:
pass
self.posterior_max_values = sample_max_values(
model=self.model,
candidate_set=candidate_set,
num_samples=self.num_mv_samples,
maximize=self.maximize,
)
def forward(self, X: Tensor) -> Tensor:
r"""Evaluate qNoisyExpectedImprovement on the candidate set `X`.
Args:
X: A `batch_shape x q x d`-dim Tensor of t-batches with `q` `d`-dim design
points each.
Returns:
A `batch_shape'`-dim Tensor of Noisy Expected Improvement values at the
given design points `X`, where `batch_shape'` is the broadcasted batch shape
of model and input `X`.
"""
q = X.shape[-2]
X_full = torch.cat([X, match_batch_shape(self.X_baseline, X)], dim=-2)
# TODO (T41248036): Implement more efficient way to compute posterior
# over both training and test points in GPyTorch
posterior = self.model.posterior(X_full)
samples = self.sampler(posterior)
obj = self.objective(samples)
diffs = obj[:, :, :q].max(dim=-1)[0] - obj[:, :, q:].max(dim=-1)[0]
return diffs.clamp_min(0).mean(dim=0)
def forward(self, X: Tensor) -> Tensor:
r"""Evaluate analytical EUBO on the candidate set X.
Args:
X: A `batch_shape x q x d`-dim Tensor, where `q = 2` if `previous_winner`
is not `None`, and `q = 1` otherwise.
Returns:
The acquisition value for each batch as a tensor of shape `batch_shape`.
"""
if not ((X.shape[-2] == 2) or ((X.shape[-2] == 1) and
(self.previous_winner is not None))):
raise UnsupportedError(
f"{self.__class__.__name__} only support q=2 or q=1"
"with a previous winner specified")
Y = X if self.outcome_model is None else self.outcome_model(X)
if self.previous_winner is not None:
Y = torch.cat([Y, match_batch_shape(self.previous_winner, Y)],
dim=-2)
# Calling forward directly instead of posterior here to
# obtain the full covariance matrix
pref_posterior = self.model(Y)
pref_mean = pref_posterior.mean
pref_cov = pref_posterior.covariance_matrix
delta = pref_mean[..., 0] - pref_mean[..., 1]
sigma = torch.sqrt(pref_cov[..., 0, 0] + pref_cov[..., 1, 1] -
pref_cov[..., 0, 1] - pref_cov[..., 1, 0])
u = delta / sigma
ucdf = self.std_norm.cdf(u)
updf = torch.exp(self.std_norm.log_prob(u))
acqf_val = sigma * (updf + u * ucdf)
if self.previous_winner is None:
acqf_val = acqf_val + pref_mean[..., 1]
return acqf_val
def forward(self, X: Tensor) -> Tensor:
r"""Evaluate qMultiFidelityKnowledgeGradient on the candidate set `X`.
Args:
X: A `b x (q + num_fantasies) x d` Tensor with `b` t-batches of
`q + num_fantasies` design points each. We split this X tensor
into two parts in the `q` dimension (`dim=-2`). The first `q`
are the q-batch of design points and the last num_fantasies are
the current solutions of the inner optimization problem.
`X_fantasies = X[..., -num_fantasies:, :]`
`X_fantasies.shape = b x num_fantasies x d`
`X_actual = X[..., :-num_fantasies, :]`
`X_actual.shape = b x q x d`
In addition, `X` may be augmented with fidelity parameteres as
part of thee `d`-dimension. Projecting fidelities to the target
fidelity is handled by `project`.
Returns:
A Tensor of shape `b`. For t-batch b, the q-KG value of the design
`X_actual[b]` is averaged across the fantasy models, where
`X_fantasies[b, i]` is chosen as the final selection for the
`i`-th fantasy model.
NOTE: If `current_value` is not provided, then this is not the
true KG value of `X_actual[b]`, and `X_fantasies[b, : ]` must be
maximized at fixed `X_actual[b]`.
"""
X_actual, X_fantasies = _split_fantasy_points(X=X, n_f=self.num_fantasies)
# We only concatenate X_pending into the X part after splitting
if self.X_pending is not None:
X_eval = torch.cat(
[X_actual, match_batch_shape(self.X_pending, X_actual)], dim=-2
)
else:
X_eval = X_actual
# construct the fantasy model of shape `num_fantasies x b`
# expand X (to potentially add trace observations)
fantasy_model = self.model.fantasize(
X=self.expand(X_eval), sampler=self.sampler, observation_noise=True
)
# get the value function
value_function = _get_value_function(
model=fantasy_model, objective=self.objective, sampler=self.inner_sampler
)
# make sure to propagate gradients to the fantasy model train inputs
# project the fantasy points
with settings.propagate_grads(True):
values = value_function(X=self.project(X_fantasies)) # num_fantasies x b
if self.current_value is not None:
values = values - self.current_value
if self.cost_aware_utility is not None:
values = self.cost_aware_utility(
X=X_actual, deltas=values, sampler=self.cost_sampler
)
# return average over the fantasy samples
return values.mean(dim=0)
