def posterior(self,
X: Tensor,
observation_noise: Union[bool, Tensor] = False,
**kwargs: Any) -> GPyTorchPosterior:
r"""Computes the posterior over model outputs at the provided points.
Args:
X: A `(batch_shape) x q x d`-dim Tensor, where `d` is the dimension
of the feature space and `q` is the number of points considered
jointly.
observation_noise: If True, add the observation noise from the
likelihood to the posterior. If a Tensor, use it directly as the
observation noise (must be of shape `(batch_shape) x q`).
Returns:
A `GPyTorchPosterior` object, representing a batch of `b` joint
distributions over `q` points. Includes observation noise if
specified.
"""
self.eval() # make sure model is in eval mode
with gpt_posterior_settings():
mvn = self(X)
if observation_noise is not False:
if torch.is_tensor(observation_noise):
# TODO: Make sure observation noise is transformed correctly
self._validate_tensor_args(X=X, Y=observation_noise)
if observation_noise.size(-1) == 1:
observation_noise = observation_noise.squeeze(-1)
mvn = self.likelihood(mvn, X, noise=observation_noise)
else:
mvn = self.likelihood(mvn, X)
posterior = GPyTorchPosterior(mvn=mvn)
if hasattr(self, "outcome_transform"):
posterior = self.outcome_transform.untransform_posterior(posterior)
return posterior
def posterior(
self,
X: Tensor,
output_indices: Optional[List[int]] = None,
observation_noise: Union[bool, Tensor] = False,
**kwargs: Any,
) -> GPyTorchPosterior:
r"""Computes the posterior over model outputs at the provided points.
Args:
X: A `q x d` or `batch_shape x q x d` (batch mode) tensor, where `d` is the
dimension of the feature space (not including task indices) and
`q` is the number of points considered jointly.
output_indices: A list of indices, corresponding to the outputs over
which to compute the posterior (if the model is multi-output).
Can be used to speed up computation if only a subset of the
model's outputs are required for optimization. If omitted,
computes the posterior over all model outputs.
observation_noise: If True, add observation noise from the respective
likelihoods. If a Tensor, specifies the observation noise levels
to add.
Returns:
A `GPyTorchPosterior` object, representing `batch_shape` joint
distributions over `q` points and the outputs selected by
`output_indices`. Includes measurement noise if
`observation_noise` is specified.
"""
if output_indices is None:
output_indices = self._output_tasks
if any(i not in self._output_tasks for i in output_indices):
raise ValueError("Too many output indices")
cls_name = self.__class__.__name__
if hasattr(self, "outcome_transform"):
raise NotImplementedError(
f"Outcome transforms currently not supported by {cls_name}")
# construct evaluation X
X_full = _make_X_full(X=X,
output_indices=output_indices,
tf=self._task_feature)
self.eval() # make sure model is in eval mode
with gpt_posterior_settings():
mvn = self(X_full)
if observation_noise is not False:
raise NotImplementedError(
f"Specifying observation noise is not yet supported by {cls_name}"
)
# If single-output, return the posterior of a single-output model
if len(output_indices) == 1:
return GPyTorchPosterior(mvn=mvn)
# Otherwise, make a MultitaskMultivariateNormal out of this
mtmvn = MultitaskMultivariateNormal(
mean=mvn.mean.view(*X.shape[:-1], len(output_indices)),
covariance_matrix=mvn.lazy_covariance_matrix,
interleaved=False,
)
return GPyTorchPosterior(mvn=mtmvn)
def test_gpt_posterior_settings(self):
for propagate_grads in (False, True):
with settings.propagate_grads(propagate_grads):
with gpt_posterior_settings():
self.assertTrue(gpt_settings.debug.off())
self.assertTrue(gpt_settings.fast_pred_var.on())
if settings.propagate_grads.off():
self.assertTrue(gpt_settings.detach_test_caches.on())
else:
self.assertTrue(gpt_settings.detach_test_caches.off())
def posterior(
self,
X: Tensor,
output_indices: Optional[List[int]] = None,
observation_noise: Union[bool, Tensor] = False,
**kwargs: Any,
) -> GPyTorchPosterior:
r"""Computes the posterior over model outputs at the provided points.
Args:
X: A `b x q x d`-dim Tensor, where `d` is the dimension of the
feature space, `q` is the number of points considered jointly,
and `b` is the batch dimension.
output_indices: A list of indices, corresponding to the outputs over
which to compute the posterior (if the model is multi-output).
Can be used to speed up computation if only a subset of the
model's outputs are required for optimization. If omitted,
computes the posterior over all model outputs.
observation_noise: If True, add the observation noise from the
respective likelihoods to the posterior. If a Tensor of shape
`(batch_shape) x q x m`, use it directly as the observation
noise (with `observation_noise[...,i]` added to the posterior
of the `i`-th model).
Returns:
A `GPyTorchPosterior` object, representing `batch_shape` joint
distributions over `q` points and the outputs selected by
`output_indices` each. Includes measurement noise if
`observation_noise` is specified.
"""
self.eval() # make sure model is in eval mode
mvn_gen: Iterator
with gpt_posterior_settings():
# only compute what's necessary
if output_indices is not None:
mvns = [self.forward_i(i, X) for i in output_indices]
if observation_noise is not False:
if torch.is_tensor(observation_noise):
lh_kwargs = [{
"noise": observation_noise[..., i]
} for i, lh in enumerate(self.likelihood.likelihoods)]
else:
lh_kwargs = [
{
"noise": lh.noise.mean().expand(X.shape[:-1])
} if isinstance(
lh, FixedNoiseGaussianLikelihood) else {}
for lh in self.likelihood.likelihoods
]
mvns = [
self.likelihood_i(i, mvn, X,
**lkws) for i, mvn, lkws in zip(
output_indices, mvns, lh_kwargs)
]
mvn_gen = zip(output_indices, mvns)
else:
mvns = self(*[X for _ in range(self.num_outputs)])
if observation_noise is not False:
if torch.is_tensor(observation_noise):
mvns = self.likelihood(*[(mvn, X) for mvn in mvns],
noise=observation_noise)
else:
mvns = self.likelihood(*[(mvn, X) for mvn in mvns])
mvn_gen = enumerate(mvns)
# apply output transforms of individual models if present
mvns = []
for i, mvn in mvn_gen:
try:
oct = self.models[i].outcome_transform
tf_mvn = oct.untransform_posterior(GPyTorchPosterior(mvn)).mvn
except AttributeError:
tf_mvn = mvn
mvns.append(tf_mvn)
# return result as a GPyTorchPosteriors
if len(mvns) == 1:
return GPyTorchPosterior(mvn=mvns[0])
else:
return GPyTorchPosterior(
mvn=MultitaskMultivariateNormal.from_independent_mvns(
mvns=mvns))
def posterior(
self,
X: Tensor,
output_indices: Optional[List[int]] = None,
observation_noise: Union[bool, Tensor] = False,
**kwargs: Any,
) -> GPyTorchPosterior:
r"""Computes the posterior over model outputs at the provided points.
Args:
X: A `(batch_shape) x q x d`-dim Tensor, where `d` is the dimension
of the feature space and `q` is the number of points considered
jointly.
output_indices: A list of indices, corresponding to the outputs over
which to compute the posterior (if the model is multi-output).
Can be used to speed up computation if only a subset of the
model's outputs are required for optimization. If omitted,
computes the posterior over all model outputs.
observation_noise: If True, add the observation noise from the
likelihood to the posterior. If a Tensor, use it directly as the
observation noise (must be of shape `(batch_shape) x q x m`).
Returns:
A `GPyTorchPosterior` object, representing `batch_shape` joint
distributions over `q` points and the outputs selected by
`output_indices` each. Includes observation noise if specified.
"""
self.eval() # make sure model is in eval mode
with gpt_posterior_settings():
# insert a dimension for the output dimension
if self._num_outputs > 1:
X, output_dim_idx = add_output_dim(
X=X, original_batch_shape=self._input_batch_shape)
mvn = self(X)
if observation_noise is not False:
if torch.is_tensor(observation_noise):
# TODO: Validate noise shape
# make observation_noise `batch_shape x q x n`
obs_noise = observation_noise.transpose(-1, -2)
mvn = self.likelihood(mvn, X, noise=obs_noise)
elif isinstance(self.likelihood, FixedNoiseGaussianLikelihood):
# Use the mean of the previous noise values (TODO: be smarter here).
noise = self.likelihood.noise.mean().expand(X.shape[:-1])
mvn = self.likelihood(mvn, X, noise=noise)
else:
mvn = self.likelihood(mvn, X)
if self._num_outputs > 1:
mean_x = mvn.mean
covar_x = mvn.covariance_matrix
output_indices = output_indices or range(self._num_outputs)
mvns = [
MultivariateNormal(
mean_x.select(dim=output_dim_idx, index=t),
lazify(covar_x.select(dim=output_dim_idx, index=t)),
) for t in output_indices
]
mvn = MultitaskMultivariateNormal.from_independent_mvns(
mvns=mvns)
posterior = GPyTorchPosterior(mvn=mvn)
if hasattr(self, "outcome_transform"):
posterior = self.outcome_transform.untransform_posterior(posterior)
return posterior