def __init__(self, train_X: Tensor, train_Y: Tensor, train_Yvar: Tensor) -> None:
r"""A single-task exact GP model using a heteroskedastic noise model.
Args:
train_X: A `n x d` or `batch_shape x n x d` (batch mode) tensor of training
features.
train_Y: A `n x (o)` or `batch_shape x n x (o)` (batch mode) tensor of
training observations.
train_Yvar: A `batch_shape x n x (o)` or `batch_shape x n x (o)`
(batch mode) tensor of observed measurement noise..
Example:
>>> train_X = torch.rand(20, 2)
>>> train_Y = torch.sin(train_X[:, 0]]) + torch.cos(train_X[:, 1])
>>> se = torch.norm(train_X - 0.5, dim=-1)
>>> train_Yvar = 0.1 + se * torch.rand_like(train_Y)
>>> model = HeteroskedasticSingleTaskGP(train_X, train_Y, train_Yvar)
"""
self._set_dimensions(train_X=train_X, train_Y=train_Y)
train_Y_log_var = torch.log(train_Yvar)
noise_likelihood = GaussianLikelihood(
noise_prior=SmoothedBoxPrior(-3, 5, 0.5, transform=torch.log),
batch_shape=self._aug_batch_shape,
noise_constraint=GreaterThan(MIN_INFERRED_NOISE_LEVEL, transform=None),
)
noise_model = SingleTaskGP(
train_X=train_X, train_Y=train_Y_log_var, likelihood=noise_likelihood
)
likelihood = _GaussianLikelihoodBase(HeteroskedasticNoise(noise_model))
super().__init__(train_X=train_X, train_Y=train_Y, likelihood=likelihood)
self.to(train_X)
def __init__(self, train_X: Tensor, train_Y: Tensor, train_Yvar: Tensor) -> None:
r"""A single-task exact GP model using a heteroskedastic noise model.
Args:
train_X: A `n x d` or `batch_shape x n x d` (batch mode) tensor of training
features.
train_Y: A `n x m` or `batch_shape x n x m` (batch mode) tensor of
training observations.
train_Yvar: A `batch_shape x n x m` or `batch_shape x n x m`
(batch mode) tensor of observed measurement noise.
Example:
>>> train_X = torch.rand(20, 2)
>>> train_Y = torch.sin(train_X).sum(dim=1, keepdim=True)
>>> se = torch.norm(train_X, dim=1, keepdim=True)
>>> train_Yvar = 0.1 + se * torch.rand_like(train_Y)
>>> model = HeteroskedasticSingleTaskGP(train_X, train_Y, train_Yvar)
"""
validate_input_scaling(train_X=train_X, train_Y=train_Y, train_Yvar=train_Yvar)
self._validate_tensor_args(X=train_X, Y=train_Y, Yvar=train_Yvar)
self._set_dimensions(train_X=train_X, train_Y=train_Y)
noise_likelihood = GaussianLikelihood(
noise_prior=SmoothedBoxPrior(-3, 5, 0.5, transform=torch.log),
batch_shape=self._aug_batch_shape,
noise_constraint=GreaterThan(
MIN_INFERRED_NOISE_LEVEL, transform=None, initial_value=1.0
),
)
noise_model = SingleTaskGP(
train_X=train_X, train_Y=train_Yvar.log(), likelihood=noise_likelihood
)
likelihood = _GaussianLikelihoodBase(HeteroskedasticNoise(noise_model))
super().__init__(train_X=train_X, train_Y=train_Y, likelihood=likelihood)
self.to(train_X)
def __init__(
self,
train_X: Tensor,
train_Y: Tensor,
train_Yvar: Tensor,
outcome_transform: Optional[OutcomeTransform] = None,
) -> None:
r"""A single-task exact GP model using a heteroskedastic noise model.
Args:
train_X: A `batch_shape x n x d` tensor of training features.
train_Y: A `batch_shape x n x m` tensor of training observations.
train_Yvar: A `batch_shape x n x m` tensor of observed measurement
noise.
outcome_transform: An outcome transform that is applied to the
training data during instantiation and to the posterior during
inference (that is, the `Posterior` obtained by calling
`.posterior` on the model will be on the original scale).
Note that the noise model internally log-transforms the
variances, which will happen after this transform is applied.
Example:
>>> train_X = torch.rand(20, 2)
>>> train_Y = torch.sin(train_X).sum(dim=1, keepdim=True)
>>> se = torch.norm(train_X, dim=1, keepdim=True)
>>> train_Yvar = 0.1 + se * torch.rand_like(train_Y)
>>> model = HeteroskedasticSingleTaskGP(train_X, train_Y, train_Yvar)
"""
if outcome_transform is not None:
train_Y, train_Yvar = outcome_transform(train_Y, train_Yvar)
validate_input_scaling(train_X=train_X,
train_Y=train_Y,
train_Yvar=train_Yvar)
self._validate_tensor_args(X=train_X, Y=train_Y, Yvar=train_Yvar)
self._set_dimensions(train_X=train_X, train_Y=train_Y)
noise_likelihood = GaussianLikelihood(
noise_prior=SmoothedBoxPrior(-3, 5, 0.5, transform=torch.log),
batch_shape=self._aug_batch_shape,
noise_constraint=GreaterThan(MIN_INFERRED_NOISE_LEVEL,
transform=None,
initial_value=1.0),
)
noise_model = SingleTaskGP(
train_X=train_X,
train_Y=train_Yvar,
likelihood=noise_likelihood,
outcome_transform=Log(),
)
likelihood = _GaussianLikelihoodBase(HeteroskedasticNoise(noise_model))
super().__init__(train_X=train_X,
train_Y=train_Y,
likelihood=likelihood)
self.register_added_loss_term("noise_added_loss")
self.update_added_loss_term("noise_added_loss",
NoiseModelAddedLossTerm(noise_model))
if outcome_transform is not None:
self.outcome_transform = outcome_transform
self.to(train_X)