botorch.models¶

Base Model API¶

Abstract base module for all BoTorch models.

Contains Model, the abstract base class for all BoTorch models, and ModelList, a container for a list of Models.

class botorch.models.model.Model[source]¶

Bases: Module, ABC

Abstract base class for BoTorch models.

Model cannot be used directly; it only defines an API for other BoTorch models.

Parameters:

• _has_transformed_inputs – A boolean denoting whether train_inputs are currently stored as transformed or not.

• _original_train_inputs – A Tensor storing the original train inputs for use in _revert_to_original_inputs. Note that this is necessary since transform / untransform cycle introduces numerical errors which lead to upstream errors during training.

Initializes internal Module state, shared by both nn.Module and ScriptModule.

abstract posterior(X, output_indices=None, observation_noise=False, posterior_transform=None, **kwargs)[source]¶

Computes the posterior over model outputs at the provided points.

Note: The input transforms should be applied here using

self.transform_inputs(X) after the self.eval() call and before any model.forward or model.likelihood calls.

Parameters:

• X (Tensor) – 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 (Optional[List[int]]) – 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 (bool) – If True, add observation noise to the posterior.

• posterior_transform (Optional[Callable[[Posterior], Posterior]]) – An optional PosteriorTransform.

• kwargs (Any) –

Returns:

A Posterior object, representing a batch of b joint distributions over q points and m outputs each.

Return type:

Posterior

property batch_shape: Size¶

The batch shape of the model.

This is a batch shape from an I/O perspective, independent of the internal representation of the model (as e.g. in BatchedMultiOutputGPyTorchModel). For a model with m outputs, a test_batch_shape x q x d-shaped input X to the posterior method returns a Posterior object over an output of shape broadcast(test_batch_shape, model.batch_shape) x q x m.

property num_outputs: int¶

The number of outputs of the model.

subset_output(idcs)[source]¶

Subset the model along the output dimension.

Parameters:

idcs (List[int]) – The output indices to subset the model to.

Returns:

A Model object of the same type and with the same parameters as the current model, subset to the specified output indices.

Return type:

Model

condition_on_observations(X, Y, **kwargs)[source]¶

Condition the model on new observations.

Parameters:

• X (Tensor) – A batch_shape x n’ x d-dim Tensor, where d is the dimension of the feature space, n’ is the number of points per batch, and batch_shape is the batch shape (must be compatible with the batch shape of the model).

• Y (Tensor) – A batch_shape’ x n’ x m-dim Tensor, where m is the number of model outputs, n’ is the number of points per batch, and batch_shape’ is the batch shape of the observations. batch_shape’ must be broadcastable to batch_shape using standard broadcasting semantics. If Y has fewer batch dimensions than X, it is assumed that the missing batch dimensions are the same for all Y.

• kwargs (Any) –

Returns:

A Model object of the same type, representing the original model conditioned on the new observations (X, Y) (and possibly noise observations passed in via kwargs).

Return type:

Model

fantasize(X, sampler, observation_noise=True, **kwargs)[source]¶

Construct a fantasy model.

Constructs a fantasy model in the following fashion: (1) compute the model posterior at X (including observation noise if observation_noise=True). (2) sample from this posterior (using sampler) to generate “fake” observations. (3) condition the model on the new fake observations.

Parameters:

• X (Tensor) – A batch_shape x n’ x d-dim Tensor, where d is the dimension of the feature space, n’ is the number of points per batch, and batch_shape is the batch shape (must be compatible with the batch shape of the model).

• sampler (MCSampler) – The sampler used for sampling from the posterior at X.

• observation_noise (bool) – If True, include observation noise.

• kwargs (Any) –

Returns:

The constructed fantasy model.

Return type:

Model

classmethod construct_inputs(training_data, **kwargs)[source]¶

Construct Model keyword arguments from a dict of BotorchDataset.

Parameters:

• training_data (Union[BotorchDataset, Dict[Hashable, BotorchDataset]]) –

• kwargs (Any) –

Return type:

Dict[str, Any]

transform_inputs(X, input_transform=None)[source]¶

Transform inputs.

Parameters:

• X (Tensor) – A tensor of inputs

• input_transform (Optional[Module]) – A Module that performs the input transformation.

Returns:

A tensor of transformed inputs

Return type:

Tensor

eval()[source]¶

Puts the model in eval mode and sets the transformed inputs.

Return type:

Model

train(mode=True)[source]¶

Puts the model in train mode and reverts to the original inputs.

Parameters:

mode (bool) – A boolean denoting whether to put in train or eval mode. If False, model is put in eval mode.

Return type:

Model

class botorch.models.model.ModelList(*models)[source]¶

Bases: Model

A multi-output Model represented by a list of independent models.

All BoTorch models are acceptable as inputs. The cost of this flexibility is that ModelList does not support all methods that may be implemented by its component models. One use case for ModelList is combining a regression model and a deterministic model in one multi-output container model, e.g. for cost-aware or multi-objective optimization where one of the outcomes is a deterministic function of the inputs.

Parameters:

*models (Model) – A variable number of models.

Example

>>> m_1 = SingleTaskGP(train_X, train_Y)
>>> m_2 = GenericDeterministicModel(lambda x: x.sum(dim=-1))
>>> m_12 = ModelList(m_1, m_2)
>>> m_12.predict(test_X)

posterior(X, output_indices=None, observation_noise=False, posterior_transform=None, **kwargs)[source]¶

Computes the posterior over model outputs at the provided points.

Note: The input transforms should be applied here using

self.transform_inputs(X) after the self.eval() call and before any model.forward or model.likelihood calls.

Parameters:

• X (Tensor) – 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 (Optional[List[int]]) – 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 (bool) – If True, add observation noise to the posterior.

• posterior_transform (Optional[Callable[[Posterior], Posterior]]) – An optional PosteriorTransform.

• kwargs (Any) –

Returns:

A Posterior object, representing a batch of b joint distributions over q points and m outputs each.

Return type:

Posterior

property batch_shape: Size¶

The batch shape of the model.

This is a batch shape from an I/O perspective, independent of the internal representation of the model (as e.g. in BatchedMultiOutputGPyTorchModel). For a model with m outputs, a test_batch_shape x q x d-shaped input X to the posterior method returns a Posterior object over an output of shape broadcast(test_batch_shape, model.batch_shape) x q x m.

property num_outputs: int¶

The number of outputs of the model.

Equal to the sum of the number of outputs of the individual models in the ModelList.

subset_output(idcs)[source]¶

Subset the model along the output dimension.

Parameters:

idcs (List[int]) – The output indices to subset the model to. Relative to the overall number of outputs of the model.

Returns:

A Model (either a ModelList or one of the submodels) with the outputs subset to the indices in idcs.

Return type:

Model

Internally, this drops (if single-output) or subsets (if multi-output) the constitutent models and returns them as a ModelList. If the result is a single (possibly subset) model from the list, returns this model (instead of forming a degenerate singe-model ModelList). For instance, if m = ModelList(m1, m2) with m1 a two-output model and m2 a single-output model, then m.subset_output([1]) ` will return the model `m1 subset to its second output.

transform_inputs(X)[source]¶

Individually transform the inputs for each model.

Parameters:

X (Tensor) – A tensor of inputs.

Returns:

A list of tensors of transformed inputs.

Return type:

List[Tensor]

load_state_dict(state_dict, strict=True)[source]¶

Initialize the fully Bayesian models before loading the state dict.

Parameters:

• state_dict (Mapping[str, Any]) –

• strict (bool) –

Return type:

None

GPyTorch Model API¶

Abstract model class for all GPyTorch-based botorch models.

To implement your own, simply inherit from both the provided classes and a GPyTorch Model class such as an ExactGP.

Deterministic Model API¶

Deterministic Models: Simple wrappers that allow the usage of deterministic mappings via the BoTorch Model and Posterior APIs.

Deterministic models are useful for expressing known input-output relationships within the BoTorch Model API. This is useful e.g. for multi-objective optimization with known objective functions (e.g. the number of parameters of a Neural Network in the context of Neural Architecture Search is usually a known function of the architecture configuration), or to encode cost functions for cost-aware acquisition utilities. Cost-aware optimization is desirable when evaluations have a cost that is heterogeneous, either in the inputs X or in a particular fidelity parameter that directly encodes the fidelity of the observation. GenericDeterministicModel supports arbitrary deterministic functions, while AffineFidelityCostModel is a particular cost model for multi-fidelity optimization. Other use cases of deterministic models include representing approximate GP sample paths, e.g. random Fourier features obtained with get_gp_samples, which allows them to be substituted in acquisition functions or in other places where a Model is expected.

class botorch.models.deterministic.GenericDeterministicModel(f, num_outputs=1)[source]¶

Bases: DeterministicModel

A generic deterministic model constructed from a callable.

Example

>>> f = lambda x: x.sum(dim=-1, keep_dims=True)
>>> model = GenericDeterministicModel(f)

Parameters:

• f (Callable[[Tensor], Tensor]) – A callable mapping a batch_shape x n x d-dim input tensor X to a batch_shape x n x m-dimensional output tensor (the outcome dimension m must be explicit, even if m=1).

• num_outputs (int) – The number of outputs m.

subset_output(idcs)[source]¶

Subset the model along the output dimension.

Parameters:

idcs (List[int]) – The output indices to subset the model to.

Returns:

The current model, subset to the specified output indices.

Return type:

GenericDeterministicModel

forward(X)[source]¶

Compute the (deterministic) model output at X.

Parameters:

X (Tensor) – A batch_shape x n x d-dim input tensor X.

Returns:

A batch_shape x n x m-dimensional output tensor.

Return type:

Tensor

class botorch.models.deterministic.AffineDeterministicModel(a, b=0.01)[source]¶

Bases: DeterministicModel

An affine deterministic model.

Affine deterministic model from weights and offset terms.

A simple model of the form

y[…, m] = b[m] + sum_{i=1}^d a[i, m] * X[…, i]

Parameters:

• a (Tensor) – A d x m-dim tensor of linear weights, where m is the number of outputs (must be explicit if m=1)

• b (Union[Tensor, float]) – The affine (offset) term. Either a float (for single-output models or if the offset is shared), or a m-dim tensor (with different offset values for for the m different outputs).

subset_output(idcs)[source]¶

Subset the model along the output dimension.

Parameters:

idcs (List[int]) – The output indices to subset the model to.

Returns:

The current model, subset to the specified output indices.

Return type:

AffineDeterministicModel

forward(X)[source]¶

Compute the (deterministic) model output at X.

Parameters:

X (Tensor) – A batch_shape x n x d-dim input tensor X.

Returns:

A batch_shape x n x m-dimensional output tensor (the outcome dimension m must be explicit if m=1).

Return type:

Tensor

class botorch.models.deterministic.PosteriorMeanModel(model)[source]¶

Bases: DeterministicModel

A deterministic model that always returns the posterior mean.

Parameters:

model (Model) – The base model.

forward(X)[source]¶

Compute the (deterministic) model output at X.

Parameters:

X (Tensor) – A batch_shape x n x d-dim input tensor X.

Returns:

A batch_shape x n x m-dimensional output tensor (the outcome dimension m must be explicit if m=1).

Return type:

Tensor

class botorch.models.deterministic.FixedSingleSampleModel(model, w=None)[source]¶

Bases: DeterministicModel

A deterministic model defined by a single sample w.

Given a base model f and a fixed sample w, the model always outputs

y = f_mean(x) + f_stddev(x) * w

We assume the outcomes are uncorrelated here.

Parameters:

• model (Model) – The base model.

• w (Optional[Tensor]) – A 1-d tensor with length model.num_outputs. If None, draw it from a standard normal distribution.

forward(X)[source]¶

Compute the (deterministic) model output at X.

Parameters:

X (Tensor) – A batch_shape x n x d-dim input tensor X.

Returns:

A batch_shape x n x m-dimensional output tensor (the outcome dimension m must be explicit if m=1).

Return type:

Tensor

Cost Models (for cost-aware optimization)¶

Cost models to be used with multi-fidelity optimization.

Cost are useful for defining known cost functions when the cost of an evaluation is heterogeneous in fidelity. For a full worked example, see the tutorial on continuous multi-fidelity Bayesian Optimization.

class botorch.models.cost.AffineFidelityCostModel(fidelity_weights=None, fixed_cost=0.01)[source]¶

Bases: DeterministicModel

Deterministic, affine cost model operating on fidelity parameters.

For each (q-batch) element of a candidate set X, this module computes a cost of the form

cost = fixed_cost + sum_j weights[j] * X[fidelity_dims[j]]

For a full worked example, see the tutorial on continuous multi-fidelity Bayesian Optimization.

Example

>>> from botorch.models import AffineFidelityCostModel
>>> from botorch.acquisition.cost_aware import InverseCostWeightedUtility
>>> cost_model = AffineFidelityCostModel(
>>>    fidelity_weights={6: 1.0}, fixed_cost=5.0
>>> )
>>> cost_aware_utility = InverseCostWeightedUtility(cost_model=cost_model)

Parameters:

• fidelity_weights (Optional[Dict[int, float]]) – A dictionary mapping a subset of columns of X (the fidelity parameters) to its associated weight in the affine cost expression. If omitted, assumes that the last column of X is the fidelity parameter with a weight of 1.0.

• fixed_cost (float) – The fixed cost of running a single candidate point (i.e. an element of a q-batch).

forward(X)[source]¶

Evaluate the cost on a candidate set X.

Computes a cost of the form

cost = fixed_cost + sum_j weights[j] * X[fidelity_dims[j]]

for each element of the q-batch

Parameters:

X (Tensor) – A batch_shape x q x d’-dim tensor of candidate points.

Returns:

A batch_shape x q x 1-dim tensor of costs.

Return type:

Tensor

GP Regression Models¶

Gaussian Process Regression models based on GPyTorch models.

These models are often a good starting point and are further documented in the tutorials.

SingleTaskGP, FixedNoiseGP, and HeteroskedasticSingleTaskGP are all single-task exact GP models, differing in how they treat noise. They use relatively strong priors on the Kernel hyperparameters, which work best when covariates are normalized to the unit cube and outcomes are standardized (zero mean, unit variance).

These models all work in batch mode (each batch having its own hyperparameters). When the training observations include multiple outputs, these models use batching to model outputs independently.

These models all support multiple outputs. However, as single-task models, SingleTaskGP, FixedNoiseGP, and HeteroskedasticSingleTaskGP should be used only when the outputs are independent and all use the same training data. If outputs are independent and outputs have different training data, use the ModelListGP. When modeling correlations between outputs, use a multi-task model like MultiTaskGP.

class botorch.models.gp_regression.SingleTaskGP(train_X, train_Y, likelihood=None, covar_module=None, mean_module=None, outcome_transform=None, input_transform=None)[source]¶

Bases: BatchedMultiOutputGPyTorchModel, ExactGP

A single-task exact GP model.

A single-task exact GP using relatively strong priors on the Kernel hyperparameters, which work best when covariates are normalized to the unit cube and outcomes are standardized (zero mean, unit variance).

This model works in batch mode (each batch having its own hyperparameters). When the training observations include multiple outputs, this model will use batching to model outputs independently.

Use this model when you have independent output(s) and all outputs use the same training data. If outputs are independent and outputs have different training data, use the ModelListGP. When modeling correlations between outputs, use the MultiTaskGP.

Example

>>> train_X = torch.rand(20, 2)
>>> train_Y = torch.sin(train_X).sum(dim=1, keepdim=True)
>>> model = SingleTaskGP(train_X, train_Y)

Parameters:

• train_X (Tensor) – A batch_shape x n x d tensor of training features.

• train_Y (Tensor) – A batch_shape x n x m tensor of training observations.

• likelihood (Optional[Likelihood]) – A likelihood. If omitted, use a standard GaussianLikelihood with inferred noise level.

• covar_module (Optional[Module]) – The module computing the covariance (Kernel) matrix. If omitted, use a MaternKernel.

• mean_module (Optional[Mean]) – The mean function to be used. If omitted, use a ConstantMean.

• outcome_transform (Optional[OutcomeTransform]) – 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).

• input_transform (Optional[InputTransform]) – An input transform that is applied in the model’s forward pass.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Parameters:

x (Tensor) –

Return type:

MultivariateNormal

class botorch.models.gp_regression.FixedNoiseGP(train_X, train_Y, train_Yvar, covar_module=None, mean_module=None, outcome_transform=None, input_transform=None)[source]¶

Bases: BatchedMultiOutputGPyTorchModel, ExactGP

A single-task exact GP model using fixed noise levels.

A single-task exact GP that uses fixed observation noise levels, differing from SingleTaskGP only in that noise levels are provided rather than inferred. This model also uses relatively strong priors on the Kernel hyperparameters, which work best when covariates are normalized to the unit cube and outcomes are standardized (zero mean, unit variance).

This model works in batch mode (each batch having its own hyperparameters).

An example of a case in which noise levels are known is online experimentation, where noise can be measured using the variability of different observations from the same arm, or provided by outside software. Another use case is simulation optimization, where the evaluation can provide variance estimates, perhaps from bootstrapping. In any case, these noise levels must be provided to FixedNoiseGP as train_Yvar.

FixedNoiseGP is also commonly used when the observations are known to be noise-free. Noise-free observations can be modeled using arbitrarily small noise values, such as train_Yvar=torch.full_like(train_Y, 1e-6).

FixedNoiseGP cannot predict noise levels out of sample. If this is needed, use HeteroskedasticSingleTaskGP, which will create another model for the observation noise.

Example

>>> train_X = torch.rand(20, 2)
>>> train_Y = torch.sin(train_X).sum(dim=1, keepdim=True)
>>> train_Yvar = torch.full_like(train_Y, 0.2)
>>> model = FixedNoiseGP(train_X, train_Y, train_Yvar)

Parameters:

• train_X (Tensor) – A batch_shape x n x d tensor of training features.

• train_Y (Tensor) – A batch_shape x n x m tensor of training observations.

• train_Yvar (Tensor) – A batch_shape x n x m tensor of observed measurement noise.

• covar_module (Optional[Module]) – The module computing the covariance (Kernel) matrix. If omitted, use a MaternKernel.

• mean_module (Optional[Mean]) – The mean function to be used. If omitted, use a ConstantMean.

• outcome_transform (Optional[OutcomeTransform]) – 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).

• input_transform (Optional[InputTransform]) – An input transfrom that is applied in the model’s forward pass.

fantasize(X, sampler, observation_noise=True, **kwargs)[source]¶

Construct a fantasy model.

Constructs a fantasy model in the following fashion: (1) compute the model posterior at X (if observation_noise=True, this includes observation noise taken as the mean across the observation noise in the training data. If observation_noise is a Tensor, use it directly as the observation noise to add). (2) sample from this posterior (using sampler) to generate “fake” observations. (3) condition the model on the new fake observations.

Parameters:

• X (Tensor) – A batch_shape x n’ x d-dim Tensor, where d is the dimension of the feature space, n’ is the number of points per batch, and batch_shape is the batch shape (must be compatible with the batch shape of the model).

• sampler (MCSampler) – The sampler used for sampling from the posterior at X.

• observation_noise (Union[bool, Tensor]) – If True, include the mean across the observation noise in the training data as observation noise in the posterior from which the samples are drawn. If a Tensor, use it directly as the specified measurement noise.

• kwargs (Any) –

Returns:

The constructed fantasy model.

Return type:

FixedNoiseGP

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Parameters:

x (Tensor) –

Return type:

MultivariateNormal

subset_output(idcs)[source]¶

Subset the model along the output dimension.

Parameters:

idcs (List[int]) – The output indices to subset the model to.

Returns:

The current model, subset to the specified output indices.

Return type:

BatchedMultiOutputGPyTorchModel

class botorch.models.gp_regression.HeteroskedasticSingleTaskGP(train_X, train_Y, train_Yvar, outcome_transform=None, input_transform=None)[source]¶

Bases: SingleTaskGP

A single-task exact GP model using a heteroskedastic noise model.

This model differs from SingleTaskGP in that noise levels are provided rather than inferred, and differs from FixedNoiseGP in that it can predict noise levels out of sample, because it internally wraps another GP (a SingleTaskGP) to model the observation noise. Noise levels must be provided to HeteroskedasticSingleTaskGP as train_Yvar.

Examples of cases in which noise levels are known include online experimentation and simulation optimization.

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)

Parameters:

• train_X (Tensor) – A batch_shape x n x d tensor of training features.

• train_Y (Tensor) – A batch_shape x n x m tensor of training observations.

• train_Yvar (Tensor) – A batch_shape x n x m tensor of observed measurement noise.

• outcome_transform (Optional[OutcomeTransform]) – 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.

• input_transform (Optional[InputTransform]) – An input transfrom that is applied in the model’s forward pass.

condition_on_observations(X, Y, **kwargs)[source]¶

Condition the model on new observations.

Parameters:

• X (Tensor) – A batch_shape x n’ x d-dim Tensor, where d is the dimension of the feature space, m is the number of points per batch, and batch_shape is the batch shape (must be compatible with the batch shape of the model).

• Y (Tensor) – A batch_shape’ x n’ x m-dim Tensor, where m is the number of model outputs, n’ is the number of points per batch, and batch_shape’ is the batch shape of the observations. batch_shape’ must be broadcastable to batch_shape using standard broadcasting semantics. If Y has fewer batch dimensions than X, its is assumed that the missing batch dimensions are the same for all Y.

• kwargs (Any) –

Returns:

A BatchedMultiOutputGPyTorchModel object of the same type with n + n’ training examples, representing the original model conditioned on the new observations (X, Y) (and possibly noise observations passed in via kwargs).

Return type:

HeteroskedasticSingleTaskGP

Example

>>> train_X = torch.rand(20, 2)
>>> train_Y = torch.cat(
>>>     [torch.sin(train_X[:, 0]), torch.cos(train_X[:, 1])], -1
>>> )
>>> model = SingleTaskGP(train_X, train_Y)
>>> new_X = torch.rand(5, 2)
>>> new_Y = torch.cat([torch.sin(new_X[:, 0]), torch.cos(new_X[:, 1])], -1)
>>> model = model.condition_on_observations(X=new_X, Y=new_Y)

subset_output(idcs)[source]¶

Subset the model along the output dimension.

Parameters:

idcs (List[int]) – The output indices to subset the model to.

Returns:

The current model, subset to the specified output indices.

Return type:

HeteroskedasticSingleTaskGP

Multi-Fidelity GP Regression Models¶

Multi-Fidelity Gaussian Process Regression models based on GPyTorch models.

For more on Multi-Fidelity BO, see the tutorial.

A common use case of multi-fidelity regression modeling is optimizing a “high-fidelity” function that is expensive to simulate when you have access to one or more cheaper “lower-fidelity” versions that are not fully accurate but are correlated with the high-fidelity function. The multi-fidelity model models both the low- and high-fidelity functions together, including the correlation between them, which can help you predict and optimize the high-fidelity function without having to do too many expensive high-fidelity evaluations.

class botorch.models.gp_regression_fidelity.SingleTaskMultiFidelityGP(train_X, train_Y, iteration_fidelity=None, data_fidelity=None, linear_truncated=True, nu=2.5, likelihood=None, outcome_transform=None, input_transform=None)[source]¶

Bases: SingleTaskGP

A single task multi-fidelity GP model.

A SingleTaskGP model using a DownsamplingKernel for the data fidelity parameter (if present) and an ExponentialDecayKernel for the iteration fidelity parameter (if present).

This kernel is described in [Wu2019mf].

Example

>>> train_X = torch.rand(20, 4)
>>> train_Y = train_X.pow(2).sum(dim=-1, keepdim=True)
>>> model = SingleTaskMultiFidelityGP(train_X, train_Y, data_fidelity=3)

Parameters:

• train_X (Tensor) – A batch_shape x n x (d + s) tensor of training features, where s is the dimension of the fidelity parameters (either one or two).

• train_Y (Tensor) – A batch_shape x n x m tensor of training observations.

• iteration_fidelity (Optional[int]) – The column index for the training iteration fidelity parameter (optional).

• data_fidelity (Optional[int]) – The column index for the downsampling fidelity parameter (optional).

• linear_truncated (bool) – If True, use a LinearTruncatedFidelityKernel instead of the default kernel.

• nu (float) – The smoothness parameter for the Matern kernel: either 1/2, 3/2, or 5/2. Only used when linear_truncated=True.

• likelihood (Optional[Likelihood]) – A likelihood. If omitted, use a standard GaussianLikelihood with inferred noise level.

• outcome_transform (Optional[OutcomeTransform]) – 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).

• input_transform (Optional[InputTransform]) – An input transform that is applied in the model’s forward pass.

classmethod construct_inputs(training_data, fidelity_features, **kwargs)[source]¶

Construct Model keyword arguments from a dict of SupervisedDataset.

Parameters:

• training_data (SupervisedDataset) – Dictionary of SupervisedDataset.

• fidelity_features (List[int]) – Index of fidelity parameter as input columns.

Return type:

Dict[str, Any]

class botorch.models.gp_regression_fidelity.FixedNoiseMultiFidelityGP(train_X, train_Y, train_Yvar, iteration_fidelity=None, data_fidelity=None, linear_truncated=True, nu=2.5, outcome_transform=None, input_transform=None)[source]¶

Bases: FixedNoiseGP

A single task multi-fidelity GP model using fixed noise levels.

A FixedNoiseGP model analogue to SingleTaskMultiFidelityGP, using a DownsamplingKernel for the data fidelity parameter (if present) and an ExponentialDecayKernel for the iteration fidelity parameter (if present).

This kernel is described in [Wu2019mf].

Example

>>> train_X = torch.rand(20, 4)
>>> train_Y = train_X.pow(2).sum(dim=-1, keepdim=True)
>>> train_Yvar = torch.full_like(train_Y) * 0.01
>>> model = FixedNoiseMultiFidelityGP(
>>>     train_X,
>>>     train_Y,
>>>     train_Yvar,
>>>     data_fidelity=3,
>>> )

Parameters:

• train_X (Tensor) – A batch_shape x n x (d + s) tensor of training features, where s is the dimension of the fidelity parameters (either one or two).

• train_Y (Tensor) – A batch_shape x n x m tensor of training observations.

• train_Yvar (Tensor) – A batch_shape x n x m tensor of observed measurement noise.

• iteration_fidelity (Optional[int]) – The column index for the training iteration fidelity parameter (optional).

• data_fidelity (Optional[int]) – The column index for the downsampling fidelity parameter (optional).

• linear_truncated (bool) – If True, use a LinearTruncatedFidelityKernel instead of the default kernel.

• nu (float) – The smoothness parameter for the Matern kernel: either 1/2, 3/2, or 5/2. Only used when linear_truncated=True.

• outcome_transform (Optional[OutcomeTransform]) – 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).

• input_transform (Optional[InputTransform]) – An input transform that is applied in the model’s forward pass.

classmethod construct_inputs(training_data, fidelity_features, **kwargs)[source]¶

Construct Model keyword arguments from a dict of SupervisedDataset.

Parameters:

• training_data (SupervisedDataset) – Dictionary of SupervisedDataset.

• fidelity_features (List[int]) – Column indices of fidelity features.

Return type:

Dict[str, Any]

GP Regression Models for Mixed Parameter Spaces¶

class botorch.models.gp_regression_mixed.MixedSingleTaskGP(train_X, train_Y, cat_dims, cont_kernel_factory=None, likelihood=None, outcome_transform=None, input_transform=None)[source]¶

Bases: SingleTaskGP

A single-task exact GP model for mixed search spaces.

This model is similar to SingleTaskGP, but supports mixed search spaces, which combine discrete and continuous features, as well as solely discrete spaces. It uses a kernel that combines a CategoricalKernel (based on Hamming distances) and a regular kernel into a kernel of the form

K((x1, c1), (x2, c2)) =

K_cont_1(x1, x2) + K_cat_1(c1, c2) + K_cont_2(x1, x2) * K_cat_2(c1, c2)

where xi and ci are the continuous and categorical features of the input, respectively. The suffix _i indicates that we fit different lengthscales for the kernels in the sum and product terms.

Since this model does not provide gradients for the categorical features, optimization of the acquisition function will need to be performed in a mixed fashion, i.e., treating the categorical features properly as discrete optimization variables. We recommend using optimize_acqf_mixed.

Example

>>> train_X = torch.cat(
        [torch.rand(20, 2), torch.randint(3, (20, 1))], dim=-1)
    )
>>> train_Y = (
        torch.sin(train_X[..., :-1]).sum(dim=1, keepdim=True)
        + train_X[..., -1:]
    )
>>> model = MixedSingleTaskGP(train_X, train_Y, cat_dims=[-1])

A single-task exact GP model supporting categorical parameters.

Parameters:

• train_X (Tensor) – A batch_shape x n x d tensor of training features.

• train_Y (Tensor) – A batch_shape x n x m tensor of training observations.

• cat_dims (List[int]) – A list of indices corresponding to the columns of the input X that should be considered categorical features.

• cont_kernel_factory (Optional[Callable[[int, List[int]], Kernel]]) – A method that accepts ard_num_dims and active_dims arguments and returns an instantiated GPyTorch Kernel object to be used as the ase kernel for the continuous dimensions. If omitted, this model uses a Matern-2.5 kernel as the kernel for the ordinal parameters.

• likelihood (Optional[Likelihood]) – A likelihood. If omitted, use a standard GaussianLikelihood with inferred noise level.

• outcome_transform (Optional[OutcomeTransform]) – 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).

• input_transform (Optional[InputTransform]) – An input transform that is applied in the model’s forward pass. Only input transforms are allowed which do not transform the categorical dimensions. This can be achieved by using the indices argument when constructing the transform.

classmethod construct_inputs(training_data, categorical_features, likelihood=None, **kwargs)[source]¶

Construct Model keyword arguments from a dict of BotorchDataset.

Parameters:

• training_data (SupervisedDataset) – A SupervisedDataset containing the training data.

• categorical_features (List[int]) – Column indices of categorical features.

• likelihood (Optional[Likelihood]) – Optional likelihood used to constuct the model.

• kwargs (Any) –

Return type:

Dict[str, Any]

Model List GP Regression Models¶

Model List GP Regression models.

class botorch.models.model_list_gp_regression.ModelListGP(*gp_models)[source]¶

Bases: IndependentModelList, ModelListGPyTorchModel

A multi-output GP model with independent GPs for the outputs.

This model supports different-shaped training inputs for each of its sub-models. It can be used with any number of single-output GPyTorchModels and the models can be of different types. Use this model when you have independent outputs with different training data. When modeling correlations between outputs, use MultiTaskGP.

Internally, this model is just a list of individual models, but it implements the same input/output interface as all other BoTorch models. This makes it very flexible and convenient to work with. The sequential evaluation comes at a performance cost though - if you are using a block design (i.e. the same number of training example for each output, and a similar model structure, you should consider using a batched GP model instead, such as SingleTaskGP with batched inputs).

Parameters:

*gp_models (GPyTorchModel) – A number of single-output GPyTorchModels. If models have input/output transforms, these are honored individually for each model.

Example

>>> model1 = SingleTaskGP(train_X1, train_Y1)
>>> model2 = SingleTaskGP(train_X2, train_Y2)
>>> model = ModelListGP(model1, model2)

condition_on_observations(X, Y, **kwargs)[source]¶

Condition the model on new observations.

Parameters:

• X (List[Tensor]) – A m-list of batch_shape x n’ x d-dim Tensors, where d is the dimension of the feature space, n’ is the number of points per batch, and batch_shape is the batch shape (must be compatible with the batch shape of the model).

• Y (Tensor) – A batch_shape’ x n’ x m-dim Tensor, where m is the number of model outputs, n’ is the number of points per batch, and batch_shape’ is the batch shape of the observations. batch_shape’ must be broadcastable to batch_shape using standard broadcasting semantics. If Y has fewer batch dimensions than X, its is assumed that the missing batch dimensions are the same for all Y.

• kwargs (Any) – Keyword arguments passed to IndependentModelList.get_fantasy_model.

Returns:

A ModelListGP representing the original model conditioned on the new observations (X, Y) (and possibly noise observations passed in via kwargs). Here the i-th model has n_i + n’ training examples, where the n’ training examples have been added and all test-time caches have been updated.

Return type:

ModelListGP

subset_output(idcs)[source]¶

Subset the model along the output dimension.

Parameters:

idcs (List[int]) – The output indices to subset the model to.

Returns:

The current model, subset to the specified output indices.

Return type:

ModelListGP

Multitask GP Models¶

Multi-Task GP models.

References

class botorch.models.multitask.MultiTaskGP(train_X, train_Y, task_feature, covar_module=None, task_covar_prior=None, output_tasks=None, rank=None, input_transform=None, outcome_transform=None)[source]¶

Bases: ExactGP, MultiTaskGPyTorchModel

Multi-Task GP model using an ICM kernel, inferring observation noise.

Multi-task exact GP that uses a simple ICM kernel. Can be single-output or multi-output. This model uses relatively strong priors on the base Kernel hyperparameters, which work best when covariates are normalized to the unit cube and outcomes are standardized (zero mean, unit variance).

This model infers the noise level. WARNING: It currently does not support different noise levels for the different tasks. If you have known observation noise, please use FixedNoiseMultiTaskGP instead.

Multi-Task GP model using an ICM kernel, inferring observation noise.

Parameters:

• train_X (Tensor) – A n x (d + 1) or b x n x (d + 1) (batch mode) tensor of training data. One of the columns should contain the task features (see task_feature argument).

• train_Y (Tensor) – A n x 1 or b x n x 1 (batch mode) tensor of training observations.

• task_feature (int) – The index of the task feature (-d <= task_feature <= d).

• output_tasks (Optional[List[int]]) – A list of task indices for which to compute model outputs for. If omitted, return outputs for all task indices.

• rank (Optional[int]) – The rank to be used for the index kernel. If omitted, use a full rank (i.e. number of tasks) kernel.

• task_covar_prior (Optional[Prior]) – A Prior on the task covariance matrix. Must operate on p.s.d. matrices. A common prior for this is the LKJ prior.

• input_transform (Optional[InputTransform]) – An input transform that is applied in the model’s forward pass.

• outcome_transform (Optional[OutcomeTransform]) – 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).

• covar_module (Optional[Module]) –

Example

>>> X1, X2 = torch.rand(10, 2), torch.rand(20, 2)
>>> i1, i2 = torch.zeros(10, 1), torch.ones(20, 1)
>>> train_X = torch.cat([
>>>     torch.cat([X1, i1], -1), torch.cat([X2, i2], -1),
>>> ])
>>> train_Y = torch.cat(f1(X1), f2(X2)).unsqueeze(-1)
>>> model = MultiTaskGP(train_X, train_Y, task_feature=-1)

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Parameters:

x (Tensor) –

Return type:

MultivariateNormal

classmethod get_all_tasks(train_X, task_feature, output_tasks=None)[source]¶

Parameters:

• train_X (Tensor) –

• task_feature (int) –

• output_tasks (Optional[List[int]]) –

Return type:

Tuple[List[int], int, int]

classmethod construct_inputs(training_data, task_feature, task_covar_prior=None, prior_config=None, rank=None, **kwargs)[source]¶

Construct Model keyword arguments from dictionary of SupervisedDataset.

Parameters:

• training_data (Dict[str, SupervisedDataset]) – Dictionary of SupervisedDataset.

• task_feature (int) – Column index of embedded task indicator features. For details, see parse_training_data.

• task_covar_prior (Optional[Prior]) – A GPyTorch Prior object to use as prior on the cross-task covariance matrix,

• prior_config (Optional[dict]) – Configuration for inter-task covariance prior. Should only be used if task_covar_prior is not passed directly. Must contain use_LKJ_prior indicator and should contain float value eta.

• rank (Optional[int]) – The rank of the cross-task covariance matrix.

Return type:

Dict[str, Any]

class botorch.models.multitask.FixedNoiseMultiTaskGP(train_X, train_Y, train_Yvar, task_feature, covar_module=None, task_covar_prior=None, output_tasks=None, rank=None, input_transform=None, outcome_transform=None)[source]¶

Bases: MultiTaskGP

Multi-Task GP model using an ICM kernel, with known observation noise.

This is the fixed-noise version of MultiTaskGP -– that is, FixedNoiseMultiTaskGP is to MultiTaskGP as FixedNoiseGP is to SingleTaskGP. It can be single-output or multi-output. This model uses relatively strong priors on the base Kernel hyperparameters, which work best when covariates are normalized to the unit cube and outcomes are standardized (zero mean, unit variance).

This model requires observation noise data (specified in train_Yvar).

Parameters:

• train_X (Tensor) – A n x (d + 1) or b x n x (d + 1) (batch mode) tensor of training data. One of the columns should contain the task features (see task_feature argument).

• train_Y (Tensor) – A n x 1 or b x n x 1 (batch mode) tensor of training observations.

• train_Yvar (Tensor) – A n or b x n (batch mode) tensor of observation noise standard errors.

• task_feature (int) – The index of the task feature (-d <= task_feature <= d).

• task_covar_prior (Optional[Prior]) – A Prior on the task covariance matrix. Must operate on p.s.d. matrices. A common prior for this is the LKJ prior.

• output_tasks (Optional[List[int]]) – A list of task indices for which to compute model outputs for. If omitted, return outputs for all task indices.

• rank (Optional[int]) – The rank to be used for the index kernel. If omitted, use a full rank (i.e. number of tasks) kernel.

• input_transform (Optional[InputTransform]) – An input transform that is applied in the model’s forward pass.

• outcome_transform (Optional[OutcomeTransform]) – 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).

• covar_module (Optional[Module]) –

Example

>>> X1, X2 = torch.rand(10, 2), torch.rand(20, 2)
>>> i1, i2 = torch.zeros(10, 1), torch.ones(20, 1)
>>> train_X = torch.cat([
>>>     torch.cat([X1, i1], -1), torch.cat([X2, i2], -1),
>>> ], dim=0)
>>> train_Y = torch.cat(f1(X1), f2(X2))
>>> train_Yvar = 0.1 + 0.1 * torch.rand_like(train_Y)
>>> model = FixedNoiseMultiTaskGP(train_X, train_Y, train_Yvar, -1)

class botorch.models.multitask.KroneckerMultiTaskGP(train_X, train_Y, likelihood=None, data_covar_module=None, task_covar_prior=None, rank=None, input_transform=None, outcome_transform=None, **kwargs)[source]¶

Bases: ExactGP, GPyTorchModel

Multi-task GP with Kronecker structure, using an ICM kernel.

This model assumes the “block design” case, i.e., it requires that all tasks are observed at all data points.

For posterior sampling, this model uses Matheron’s rule [Doucet2010sampl] to compute the posterior over all tasks as in [Maddox2021bohdo] by exploiting Kronecker structure.

When a multi-fidelity model has Kronecker structure, this means there is one covariance kernel over the fidelity features (call it K_f) and another over the rest of the input parameters (call it K_i), and the resulting covariance across inputs and fidelities is given by the Kronecker product of the two covariance matrices. This is equivalent to saying the covariance between two input and feature pairs is given by

K((parameter_1, fidelity_1), (parameter_2, fidelity_2))

= K_f(fidelity_1, fidelity_2) * K_i(parameter_1, parameter_2).

Then the covariance matrix of n_i parameters and n_f fidelities can be codified as a Kronecker product of an n_i x n_i matrix and an n_f x n_f matrix, which is far more parsimonious than specifying the whole (n_i * n_f) x (n_i * n_f) covariance matrix.

Example

>>> train_X = torch.rand(10, 2)
>>> train_Y = torch.cat([f_1(X), f_2(X)], dim=-1)
>>> model = KroneckerMultiTaskGP(train_X, train_Y)

Parameters:

• train_X (Tensor) – A batch_shape x n x d tensor of training features.

• train_Y (Tensor) – A batch_shape x n x m tensor of training observations.

• likelihood (Optional[MultitaskGaussianLikelihood]) – A MultitaskGaussianLikelihood. If omitted, uses a MultitaskGaussianLikelihood with a GammaPrior(1.1, 0.05) noise prior.

• data_covar_module (Optional[Module]) – The module computing the covariance (Kernel) matrix in data space. If omitted, use a MaternKernel.

• task_covar_prior (Optional[Prior]) – A Prior on the task covariance matrix. Must operate on p.s.d. matrices. A common prior for this is the LKJ prior. If omitted, uses LKJCovariancePrior with eta parameter as specified in the keyword arguments (if not specified, use eta=1.5).

• rank (Optional[int]) – The rank of the ICM kernel. If omitted, use a full rank kernel.

• kwargs (Any) – Additional arguments to override default settings of priors, including: - eta: The eta parameter on the default LKJ task_covar_prior. A value of 1.0 is uninformative, values <1.0 favor stronger correlations (in magnitude), correlations vanish as eta -> inf. - sd_prior: A scalar prior over nonnegative numbers, which is used for the default LKJCovariancePrior task_covar_prior. - likelihood_rank: The rank of the task covariance matrix to fit. Defaults to 0 (which corresponds to a diagonal covariance matrix).

• input_transform (Optional[InputTransform]) –

• outcome_transform (Optional[OutcomeTransform]) –

forward(X)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Parameters:

X (Tensor) –

Return type:

MultitaskMultivariateNormal

property train_full_covar¶

property predictive_mean_cache¶

posterior(X, output_indices=None, observation_noise=False, posterior_transform=None, **kwargs)[source]¶

Computes the posterior over model outputs at the provided points.

Parameters:

• X (Tensor) – 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 (Union[bool, Tensor]) – 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).

• posterior_transform (Optional[PosteriorTransform]) – An optional PosteriorTransform.

• output_indices (Optional[List[int]]) –

• kwargs (Any) –

Returns:

A GPyTorchPosterior object, representing a batch of b joint distributions over q points. Includes observation noise if specified.

Return type:

MultitaskGPPosterior

train(val=True, *args, **kwargs)[source]¶

Puts the model in train mode and reverts to the original inputs.

Parameters:

mode – A boolean denoting whether to put in train or eval mode. If False, model is put in eval mode.

Higher Order GP Models¶

References

class botorch.models.higher_order_gp.FlattenedStandardize(output_shape, batch_shape=None, min_stdv=1e-08)[source]¶

Bases: Standardize

Standardize outcomes in a structured multi-output settings by reshaping the batched output dimensions to be a vector. Specifically, an output dimension of [a x b x c] will be squeezed to be a vector of [a * b * c].

Parameters:

• output_shape (torch.Size) – A n x output_shape-dim tensor of training targets.

• batch_shape (torch.Size) – The batch_shape of the training targets.

• min_stddv – The minimum standard deviation for which to perform standardization (if lower, only de-mean the data).

• min_stdv (float) –

forward(Y, Yvar=None)[source]¶

Standardize outcomes.

If the module is in train mode, this updates the module state (i.e. the mean/std normalizing constants). If the module is in eval mode, simply applies the normalization using the module state.

Parameters:

• Y (Tensor) – A batch_shape x n x m-dim tensor of training targets.

• Yvar (Optional[Tensor]) – A batch_shape x n x m-dim tensor of observation noises associated with the training targets (if applicable).

Returns:

• The transformed outcome observations.

• The transformed observation noise (if applicable).

Return type:

A two-tuple with the transformed outcomes

untransform(Y, Yvar=None)[source]¶

Un-standardize outcomes.

Parameters:

• Y (Tensor) – A batch_shape x n x m-dim tensor of standardized targets.

• Yvar (Optional[Tensor]) – A batch_shape x n x m-dim tensor of standardized observation noises associated with the targets (if applicable).

Returns:

• The un-standardized outcome observations.

• The un-standardized observation noise (if applicable).

Return type:

A two-tuple with the un-standardized outcomes

untransform_posterior(posterior)[source]¶

Un-standardize the posterior.

Parameters:

posterior (HigherOrderGPPosterior) – A posterior in the standardized space.

Returns:

The un-standardized posterior. If the input posterior is a MVN, the transformed posterior is again an MVN.

Return type:

TransformedPosterior

training: bool¶

class botorch.models.higher_order_gp.HigherOrderGP(train_X, train_Y, likelihood=None, covar_modules=None, num_latent_dims=None, learn_latent_pars=True, latent_init='default', outcome_transform=None, input_transform=None)[source]¶

Bases: BatchedMultiOutputGPyTorchModel, ExactGP

A model for high-dimensional output regression.

As described in [Zhe2019hogp]. “Higher-order” means that the predictions are matrices (tensors) with at least two dimensions, such as images or grids of images, or measurements taken from a region of at least two dimensions. The posterior uses Matheron’s rule [Doucet2010sampl] as described in [Maddox2021bohdo].

HigherOrderGP differs from a “vector” multi-output model in that it uses Kronecker algebra to obtain parsimonious covariance matrices for these outputs (see KroneckerMultiTaskGP for more information). For example, imagine a 10 x 20 x 30 grid of images. If we were to vectorize the resulting 6,000 data points in order to use them in a non-higher-order GP, they would have a 6,000 x 6,000 covariance matrix, with 36 million entries. The Kronecker structure allows representing this as a product of 10x10, 20x20, and 30x30 covariance matrices, with only 1,400 entries.

Parameters:

• train_X (Tensor) – A batch_shape x n x d-dim tensor of training inputs.

• train_Y (Tensor) – A batch_shape x n x output_shape-dim tensor of training targets.

• likelihood (Optional[Likelihood]) – Gaussian likelihood for the model.

• covar_modules (Optional[List[Kernel]]) – List of kernels for each output structure.

• num_latent_dims (Optional[List[int]]) – Sizes for the latent dimensions.

• learn_latent_pars (bool) – If true, learn the latent parameters.

• latent_init (str) – [default or gp] how to initialize the latent parameters.

• outcome_transform (Optional[OutcomeTransform]) –

• input_transform (Optional[InputTransform]) –

forward(X)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Parameters:

X (Tensor) –

Return type:

MultivariateNormal

get_fantasy_model(inputs, targets, **kwargs)[source]¶

Returns a new GP model that incorporates the specified inputs and targets as new training data.

Using this method is more efficient than updating with set_train_data when the number of inputs is relatively small, because any computed test-time caches will be updated in linear time rather than computed from scratch.

Note

If targets is a batch (e.g. b x m), then the GP returned from this method will be a batch mode GP. If inputs is of the same (or lesser) dimension as targets, then it is assumed that the fantasy points are the same for each target batch.

Parameters:

• inputs (torch.Tensor) – (b1 x … x bk x m x d or f x b1 x … x bk x m x d) Locations of fantasy observations.

• targets (torch.Tensor) – (b1 x … x bk x m or f x b1 x … x bk x m) Labels of fantasy observations.

Returns:

An ExactGP model with n + m training examples, where the m fantasy examples have been added and all test-time caches have been updated.

Return type:

ExactGP

condition_on_observations(X, Y, **kwargs)[source]¶

Condition the model on new observations.

Parameters:

• X (Tensor) – A batch_shape x n’ x d-dim Tensor, where d is the dimension of the feature space, m is the number of points per batch, and batch_shape is the batch shape (must be compatible with the batch shape of the model).

• Y (Tensor) – A batch_shape’ x n’ x m_d-dim Tensor, where m_d is the shaping of the model outputs, n’ is the number of points per batch, and batch_shape’ is the batch shape of the observations. batch_shape’ must be broadcastable to batch_shape using standard broadcasting semantics. If Y has fewer batch dimensions than X, its is assumed that the missing batch dimensions are the same for all Y.

• kwargs (Any) –

Returns:

A BatchedMultiOutputGPyTorchModel object of the same type with n + n’ training examples, representing the original model conditioned on the new observations (X, Y) (and possibly noise observations passed in via kwargs).

Return type:

HigherOrderGP

posterior(X, output_indices=None, observation_noise=False, posterior_transform=None, **kwargs)[source]¶

Computes the posterior over model outputs at the provided points.

Parameters:

• X (Tensor) – 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 (Optional[List[int]]) – 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 (Union[bool, Tensor]) – 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).

• posterior_transform (Optional[PosteriorTransform]) – An optional PosteriorTransform.

• kwargs (Any) –

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.

Return type:

GPyTorchPosterior

make_posterior_variances(joint_covariance_matrix)[source]¶

Computes the posterior variances given the data points X. As currently implemented, it computes another forwards call with the stacked data to get out the joint covariance across all data points.

Parameters:

joint_covariance_matrix (LinearOperator) –

Return type:

Tensor

Pairwise GP Models¶

Preference Learning with Gaussian Process

class botorch.models.pairwise_gp.PairwiseGP(datapoints, comparisons, likelihood=None, covar_module=None, input_transform=None, **kwargs)[source]¶

Bases: Model, GP

Probit GP for preference learning with Laplace approximation

A probit-likelihood GP that learns via pairwise comparison data, using a Laplace approximation of the posterior of the estimated utility values. By default it uses a scaled RBF kernel.

Implementation is based on [Chu2005preference]. Also see [Brochu2010tutorial] for additional reference.

Note that in [Chu2005preference] the likelihood of a pairwise comparison is \(\left(\frac{f(x_1) - f(x_2)}{\sqrt{2}\sigma}\right)\), i.e. a scale is used in the denominator. To maintain consistency with usage of kernels elsewhere in BoTorch, we instead do not include \(\sigma\) in the code (implicitly setting it to 1) and use ScaleKernel to scale the function.

In the example below, the user/decision maker has stated that they prefer the first item over the second item and the third item over the second item, generating comparisons [0, 1] and [2, 1].

Example

>>> from botorch.models import PairwiseGP
>>> import torch
>>> datapoints = torch.Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
>>> comparisons = torch.Tensor([[0, 1], [2, 1]])
>>> model = PairwiseGP(datapoints, comparisons)

Parameters:

• datapoints (Tensor) – A batch_shape x n x d tensor of training features.

• comparisons (Tensor) – A batch_shape x m x 2 training comparisons; comparisons[i] is a noisy indicator suggesting the utility value of comparisons[i, 0]-th is greater than comparisons[i, 1]-th.

• likelihood (Optional[PairwiseLikelihood]) – A PairwiseLikelihood.

• covar_module (Optional[Module]) – Covariance module.

• input_transform (Optional[InputTransform]) – An input transform that is applied in the model’s forward pass.

property num_outputs: int¶

The number of outputs of the model.

property batch_shape: Size¶

The batch shape of the model.

This is a batch shape from an I/O perspective, independent of the internal representation of the model (as e.g. in BatchedMultiOutputGPyTorchModel). For a model with m outputs, a test_batch_shape x q x d-shaped input X to the posterior method returns a Posterior object over an output of shape broadcast(test_batch_shape, model.batch_shape) x q x m.

set_train_data(datapoints=None, comparisons=None, strict=False, update_model=True)[source]¶

Set datapoints and comparisons and update model properties if needed

Parameters:

• datapoints (Optional[Tensor]) – A batch_shape x n x d dimension tensor X. If there are input transformations, assume the datapoints are not transformed

• comparisons (Optional[Tensor]) – A tensor of size batch_shape x m x 2. (i, j) means f_i is preferred over f_j.

• strict (bool) – strict argument as in gpytorch.models.exact_gp for compatibility when using fit_gpytorch_model with input_transform.

• update_model (bool) – True if we want to refit the model (see _update) after re-setting the data.

Return type:

None

load_state_dict(state_dict, strict=False)[source]¶

Removes data related buffers from the state_dict and calls super().load_state_dict with strict=False.

Parameters:

• state_dict (Dict[str, Tensor]) – The state dict.

• strict (bool) – Boolean specifying whether or not given and instance-bound state_dicts should have identical keys. Only implemented for strict=False since buffers will filters out when calling _load_from_state_dict.

Returns:

A named tuple _IncompatibleKeys, containing the missing_keys and unexpected_keys.

Return type:

_IncompatibleKeys

forward(datapoints)[source]¶

Calculate a posterior or prior prediction.

During training mode, forward implemented solely for gradient-based hyperparam opt. Essentially what it does is to re-calculate the utility f using its analytical form at f_map so that we are able to obtain gradients of the hyperparameters.

Parameters:

datapoints (Tensor) – A batch_shape x n x d Tensor, should be the same as self.datapoints during training

Returns:

1. Posterior centered at MAP points for training data (training mode)

2. Prior predictions (prior mode)

3. Predictive posterior (eval mode)

Return type:

A MultivariateNormal object, being one of the followings

posterior(X, output_indices=None, observation_noise=False, posterior_transform=None, **kwargs)[source]¶

Computes the posterior over model outputs at the provided points.

Parameters:

• X (Tensor) – 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 (Optional[List[int]]) – As defined in parent Model class, not used for this model.

• observation_noise (bool) – Ignored (since noise is not identifiable from scale in probit models).

• posterior_transform (Optional[PosteriorTransform]) – An optional PosteriorTransform.

• kwargs (Any) –

Returns:

A Posterior object, representing joint

distributions over q points.

Return type:

Posterior

condition_on_observations(X, Y, **kwargs)[source]¶

Condition the model on new observations.

Note that unlike other BoTorch models, PairwiseGP requires Y to be pairwise comparisons

Parameters:

• X (Tensor) – A batch_shape x n x d dimension tensor X

• Y (Tensor) – A tensor of size batch_shape x m x 2. (i, j) means f_i is preferred over f_j

• kwargs (Any) –

Returns:

A (deepcopied) Model object of the same type, representing the original model conditioned on the new observations (X, Y).

Return type:

Model

class botorch.models.pairwise_gp.PairwiseLaplaceMarginalLogLikelihood(likelihood, model)[source]¶

Bases: MarginalLogLikelihood

Laplace-approximated marginal log likelihood/evidence for PairwiseGP

See (12) from [Chu2005preference].

Parameters:

• likelihood – Used as in args to GPyTorch MarginalLogLikelihood

• model (GP) – Used as in args to GPyTorch MarginalLogLikelihood

forward(post, comp)[source]¶

Calculate approximated log evidence, i.e., log(P(D|theta))

Parameters:

• post (Posterior) – training posterior distribution from self.model

• comp (Tensor) – Comparisons pairs, see PairwiseGP.__init__ for more details

Returns:

The approximated evidence, i.e., the marginal log likelihood

Return type:

Tensor

training: bool¶

Contextual GP Models with Aggregate Rewards¶

class botorch.models.contextual.SACGP(train_X, train_Y, train_Yvar, decomposition)[source]¶

Bases: FixedNoiseGP

A GP using a Structural Additive Contextual(SAC) kernel.

Parameters:

• train_X (Tensor) – (n x d) X training data.

• train_Y (Tensor) – (n x 1) Y training data.

• train_Yvar (Tensor) – (n x 1) Noise variances of each training Y.

• decomposition (Dict[str, List[int]]) – Keys are context names. Values are the indexes of parameters belong to the context. The parameter indexes are in the same order across contexts.

class botorch.models.contextual.LCEAGP(train_X, train_Y, train_Yvar, decomposition, train_embedding=True, cat_feature_dict=None, embs_feature_dict=None, embs_dim_list=None, context_weight_dict=None)[source]¶

Bases: FixedNoiseGP

A GP using a Latent Context Embedding Additive (LCE-A) Kernel.

Note that the model does not support batch training. Input training data sets should have dim = 2.

Parameters:

• train_X (Tensor) – (n x d) X training data.

• train_Y (Tensor) – (n x 1) Y training data.

• train_Yvar (Tensor) – (n x 1) Noise variance of Y.

• decomposition (Dict[str, List[int]]) – Keys are context names. Values are the indexes of parameters belong to the context. The parameter indexes are in the same order across contexts.

• cat_feature_dict (Optional[Dict]) – Keys are context names and values are list of categorical features i.e. {“context_name” : [cat_0, …, cat_k]}, where k is the number of categorical variables. If None, we use context names in the decomposition as the only categorical feature, i.e., k = 1.

• embs_feature_dict (Optional[Dict]) – Pre-trained continuous embedding features of each context.

• embs_dim_list (Optional[List[int]]) – Embedding dimension for each categorical variable. The length equals the number of categorical features k. If None, the embedding dimension is set to 1 for each categorical variable.

• context_weight_dict (Optional[Dict]) – Known population weights of each context.

• train_embedding (bool) –

Contextual GP Models with Context Rewards¶

class botorch.models.contextual_multioutput.LCEMGP(train_X, train_Y, task_feature, context_cat_feature=None, context_emb_feature=None, embs_dim_list=None, output_tasks=None, input_transform=None, outcome_transform=None)[source]¶

Bases: MultiTaskGP

The Multi-Task GP with the latent context embedding multioutput (LCE-M) kernel.

Parameters:

• train_X (Tensor) – (n x d) X training data.

• train_Y (Tensor) – (n x 1) Y training data.

• task_feature (int) – Column index of train_X to get context indices.

• context_cat_feature (Optional[Tensor]) – (n_contexts x k) one-hot encoded context features. Rows are ordered by context indices, where k is the number of categorical variables. If None, task indices will be used and k = 1.

• context_emb_feature (Optional[Tensor]) – (n_contexts x m) pre-given continuous embedding features. Rows are ordered by context indices.

• embs_dim_list (Optional[List[int]]) – Embedding dimension for each categorical variable. The length equals k. If None, the embedding dimension is set to 1 for each categorical variable.

• output_tasks (Optional[List[int]]) – A list of task indices for which to compute model outputs for. If omitted, return outputs for all task indices.

• input_transform (Optional[InputTransform]) –

• outcome_transform (Optional[OutcomeTransform]) –

task_covar_matrix(task_idcs)[source]¶

compute covariance matrix of a list of given context

Parameters:

task_idcs (Tensor) – (n x 1) or (b x n x 1) task indices tensor

Return type:

Tensor

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Parameters:

x (Tensor) –

Return type:

MultivariateNormal

class botorch.models.contextual_multioutput.FixedNoiseLCEMGP(train_X, train_Y, train_Yvar, task_feature, context_cat_feature=None, context_emb_feature=None, embs_dim_list=None, output_tasks=None)[source]¶

Bases: LCEMGP

The Multi-Task GP the latent context embedding multioutput (LCE-M) kernel, with known observation noise.

Parameters:

• train_X (Tensor) – (n x d) X training data.

• train_Y (Tensor) – (n x 1) Y training data.

• train_Yvar (Tensor) – (n x 1) Noise variances of each training Y.

• task_feature (int) – Column index of train_X to get context indices.

• context_cat_feature (Optional[Tensor]) – (n_contexts x k) one-hot encoded context features. Rows are ordered by context indices, where k is the number of categorical variables. If None, task indices will be used and k = 1.

• context_emb_feature (Optional[Tensor]) – (n_contexts x m) pre-given continuous embedding features. Rows are ordered by context indices.

• embs_dim_list (Optional[List[int]]) – Embedding dimension for each categorical variable. The length equals to k. If None, the embedding dimension is set to 1 for each categorical variable.

• output_tasks (Optional[List[int]]) – A list of task indices for which to compute model outputs for. If omitted, return outputs for all task indices.

Variational GP Models¶

References

class botorch.models.approximate_gp.ApproximateGPyTorchModel(model=None, likelihood=None, num_outputs=1, *args, **kwargs)[source]¶

Bases: GPyTorchModel

Botorch wrapper class for various (variational) approximate GP models in GPyTorch.

This can either include stochastic variational GPs (SVGPs) or variational implementations of weight space approximate GPs.

Parameters:

• model (Optional[ApproximateGP]) – Instance of gpytorch.approximate GP models. If omitted, constructs a _SingleTaskVariationalGP.

• likelihood (Optional[Likelihood]) – Instance of a GPyTorch likelihood. If omitted, uses a either a GaussianLikelihood (if num_outputs=1) or a MultitaskGaussianLikelihood`(if `num_outputs>1).

• num_outputs (int) – Number of outputs expected for the GP model.

• args – Optional positional arguments passed to the _SingleTaskVariationalGP constructor if no model is provided.

• kwargs – Optional keyword arguments passed to the _SingleTaskVariationalGP constructor if no model is provided.

property num_outputs¶

The number of outputs of the model.

posterior(X, output_indices=None, observation_noise=False, *args, **kwargs)[source]¶

Computes the posterior over model outputs at the provided points.

Parameters:

• 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).

• posterior_transform – An optional PosteriorTransform.

Returns:

A GPyTorchPosterior object, representing a batch of b joint distributions over q points. Includes observation noise if specified.

Return type:

GPyTorchPosterior

forward(X, *args, **kwargs)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Return type:

MultivariateNormal

fantasize(X, sampler=<class 'botorch.sampling.samplers.MCSampler'>, observation_noise=True, *args, **kwargs)[source]¶

Construct a fantasy model.

Constructs a fantasy model in the following fashion: (1) compute the model posterior at X (including observation noise if observation_noise=True). (2) sample from this posterior (using sampler) to generate “fake” observations. (3) condition the model on the new fake observations.

Parameters:

• X – A batch_shape x n’ x d-dim Tensor, where d is the dimension of the feature space, n’ is the number of points per batch, and batch_shape is the batch shape (must be compatible with the batch shape of the model).

• sampler – The sampler used for sampling from the posterior at X.

• observation_noise – If True, include observation noise.

Returns:

The constructed fantasy model.

class botorch.models.approximate_gp.SingleTaskVariationalGP(train_X, train_Y=None, likelihood=None, num_outputs=1, learn_inducing_points=True, covar_module=None, mean_module=None, variational_distribution=None, variational_strategy=<class 'gpytorch.variational.variational_strategy.VariationalStrategy'>, inducing_points=None, outcome_transform=None, input_transform=None)[source]¶

Bases: ApproximateGPyTorchModel

A single-task variational GP model following [hensman2013svgp] with pivoted Cholesky initialization following [chen2018dpp] and [burt2020svgp].

A single-task variational GP using relatively strong priors on the Kernel hyperparameters, which work best when covariates are normalized to the unit cube and outcomes are standardized (zero mean, unit variance).

This model works in batch mode (each batch having its own hyperparameters). When the training observations include multiple outputs, this model will use batching to model outputs independently. However, batches of multi-output models are not supported at this time, if you need to use those, please use a ModelListGP.

Use this model if you have a lot of data or if your responses are non-Gaussian.

To train this model, you should use gpytorch.mlls.VariationalELBO and not the exact marginal log likelihood.

Example

>>> import torch
>>> from botorch.models import SingleTaskVariationalGP
>>> from gpytorch.mlls import VariationalELBO
>>>
>>> train_X = torch.rand(20, 2)
>>> model = SingleTaskVariationalGP(train_X)
>>> mll = VariationalELBO(
>>>     model.likelihood, model.model, num_data=train_X.shape[-2]
>>> )

Parameters:

• train_X (Tensor) – Training inputs (due to the ability of the SVGP to sub-sample this does not have to be all of the training inputs).

• train_Y (Optional[Tensor]) – Training targets (optional).

• likelihood (Optional[Likelihood]) – Instance of a GPyTorch likelihood. If omitted, uses a either a GaussianLikelihood (if num_outputs=1) or a MultitaskGaussianLikelihood`(if `num_outputs>1).

• num_outputs (int) – Number of output responses per input (default: 1).

• covar_module (Optional[Kernel]) – Kernel function. If omitted, uses a MaternKernel.

• mean_module (Optional[Mean]) – Mean of GP model. If omitted, uses a ConstantMean.

• variational_distribution (Optional[_VariationalDistribution]) – Type of variational distribution to use (default: CholeskyVariationalDistribution), the properties of the variational distribution will encourage scalability or ease of optimization.

• variational_strategy (Type[_VariationalStrategy]) – Type of variational strategy to use (default: VariationalStrategy). The default setting uses “whitening” of the variational distribution to make training easier.

• inducing_points (Optional[Union[Tensor, int]]) – The number or specific locations of the inducing points.

• learn_inducing_points (bool) –

• outcome_transform (Optional[OutcomeTransform]) –

• input_transform (Optional[InputTransform]) –

init_inducing_points(inputs)[source]¶

Reinitialize the inducing point locations in-place with the current kernel applied to inputs. The variational distribution and variational strategy caches are reset.

Parameters:

inputs (Tensor) – (*batch_shape, n, d)-dim input data tensor.

Returns:

(*batch_shape, m, d)-dim tensor of selected inducing point locations.

Return type:

Tensor

Fully Bayesian GP Models¶

Gaussian Process Regression models with fully Bayesian inference.

Fully Bayesian models use Bayesian inference over model hyperparameters, such as lengthscales and noise variance, learning a posterior distribution for the hyperparameters using the No-U-Turn-Sampler (NUTS). This is followed by sampling a small set of hyperparameters (often ~16) from the posterior that we will use for model predictions and for computing acquisition function values. By contrast, our “standard” models (e.g. SingleTaskGP) learn only a single best value for each hyperparameter using MAP. The fully Bayesian method generally results in a better and more well-calibrated model, but is more computationally intensive. For a full description, see [Eriksson2021saasbo].

We use a lightweight PyTorch implementation of a Matern-5/2 kernel as there are some performance issues with running NUTS on top of standard GPyTorch models. The resulting hyperparameter samples are loaded into a batched GPyTorch model after fitting.

References:

botorch.models.fully_bayesian.matern52_kernel(X, lengthscale)[source]¶

Matern-5/2 kernel.

Parameters:

• X (Tensor) –

• lengthscale (Tensor) –

Return type:

Tensor

botorch.models.fully_bayesian.compute_dists(X, lengthscale)[source]¶

Compute kernel distances.

Parameters:

• X (Tensor) –

• lengthscale (Tensor) –

Return type:

Tensor

botorch.models.fully_bayesian.reshape_and_detach(target, new_value)[source]¶

Detach and reshape new_value to match target.

Parameters:

• target (Tensor) –

• new_value (Tensor) –

Return type:

None

class botorch.models.fully_bayesian.SaasPyroModel[source]¶

Bases: PyroModel

Implementation of the sparse axis-aligned subspace priors (SAAS) model.

The SAAS model uses sparsity-inducing priors to identify the most important parameters. This model is suitable for high-dimensional BO with potentially hundreds of tunable parameters. See [Eriksson2021saasbo] for more details.

SaasPyroModel is not a standard BoTorch model; instead, it is used as an input to SaasFullyBayesianSingleTaskGP. It is used as a default keyword argument, and end users are not likely to need to instantiate or modify a SaasPyroModel unless they want to customize its attributes (such as covar_module).

set_inputs(train_X, train_Y, train_Yvar=None)[source]¶

Set the training data.

Parameters:

• train_X (Tensor) – Training inputs (n x d)

• train_Y (Tensor) – Training targets (n x 1)

• train_Yvar (Optional[Tensor]) – Observed noise variance (n x 1). Inferred if None.

sample()[source]¶

Sample from the SAAS model.

This samples the mean, noise variance, outputscale, and lengthscales according to the SAAS prior.

Return type:

None

sample_outputscale(concentration=2.0, rate=0.15, **tkwargs)[source]¶

Sample the outputscale.

Parameters:

• concentration (float) –

• rate (float) –

• tkwargs (Any) –

Return type:

Tensor

sample_mean(**tkwargs)[source]¶

Sample the mean constant.

Parameters:

tkwargs (Any) –

Return type:

Tensor

sample_noise(**tkwargs)[source]¶

Sample the noise variance.

Parameters:

tkwargs (Any) –

Return type:

Tensor

sample_lengthscale(dim, alpha=0.1, **tkwargs)[source]¶

Sample the lengthscale.

Parameters:

• dim (int) –

• alpha (float) –

• tkwargs (Any) –

Return type:

Tensor

postprocess_mcmc_samples(mcmc_samples)[source]¶

Post-process the MCMC samples.

This computes the true lengthscales and removes the inverse lengthscales and tausq (global shrinkage).

Parameters:

mcmc_samples (Dict[str, Tensor]) –

Return type:

Dict[str, Tensor]

load_mcmc_samples(mcmc_samples)[source]¶

Load the MCMC samples into the mean_module, covar_module, and likelihood.

Parameters:

mcmc_samples (Dict[str, Tensor]) –

Return type:

Tuple[Mean, Kernel, Likelihood]

class botorch.models.fully_bayesian.SaasFullyBayesianSingleTaskGP(train_X, train_Y, train_Yvar=None, outcome_transform=None, input_transform=None, pyro_model=None)[source]¶

Bases: SingleTaskGP

A fully Bayesian single-task GP model with the SAAS prior.

This model assumes that the inputs have been normalized to [0, 1]^d and that the output has been standardized to have zero mean and unit variance. You can either normalize and standardize the data before constructing the model or use an input_transform and outcome_transform. The SAAS model [Eriksson2021saasbo] with a Matern-5/2 kernel is used by default.

You are expected to use fit_fully_bayesian_model_nuts to fit this model as it isn’t compatible with fit_gpytorch_model.

Example

>>> saas_gp = SaasFullyBayesianSingleTaskGP(train_X, train_Y)
>>> fit_fully_bayesian_model_nuts(saas_gp)
>>> posterior = saas_gp.posterior(test_X)

Initialize the fully Bayesian single-task GP model.

Parameters:

• train_X (Tensor) – Training inputs (n x d)

• train_Y (Tensor) – Training targets (n x 1)

• train_Yvar (Optional[Tensor]) – Observed noise variance (n x 1). Inferred if None.

• outcome_transform (Optional[OutcomeTransform]) – 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).

• input_transform (Optional[InputTransform]) – An input transform that is applied in the model’s forward pass.

• pyro_model (Optional[PyroModel]) – Optional PyroModel, defaults to SaasPyroModel.

property median_lengthscale: Tensor¶

Median lengthscales across the MCMC samples.

property num_mcmc_samples: int¶

Number of MCMC samples in the model.

property batch_shape: Size¶

Batch shape of the model, equal to the number of MCMC samples. Note that SaasFullyBayesianSingleTaskGP does not support batching over input data at this point.

fantasize(X, sampler, observation_noise=True, **kwargs)[source]¶

Construct a fantasy model.

Constructs a fantasy model in the following fashion: (1) compute the model posterior at X (including observation noise if observation_noise=True). (2) sample from this posterior (using sampler) to generate “fake” observations. (3) condition the model on the new fake observations.

Parameters:

• X (Tensor) – A batch_shape x n’ x d-dim Tensor, where d is the dimension of the feature space, n’ is the number of points per batch, and batch_shape is the batch shape (must be compatible with the batch shape of the model).

• sampler (MCSampler) – The sampler used for sampling from the posterior at X.

• observation_noise (Union[bool, Tensor]) – If True, include observation noise.

• kwargs (Any) –

Returns:

The constructed fantasy model.

Return type:

FixedNoiseGP

train(mode=True)[source]¶

Puts the model in train mode.

Parameters:

mode (bool) –

Return type:

None

load_mcmc_samples(mcmc_samples)[source]¶

Load the MCMC hyperparameter samples into the model.

This method will be called by fit_fully_bayesian_model_nuts when the model has been fitted in order to create a batched SingleTaskGP model.

Parameters:

mcmc_samples (Dict[str, Tensor]) –

Return type:

None

load_state_dict(state_dict, strict=True)[source]¶

Custom logic for loading the state dict.

The standard approach of calling load_state_dict currently doesn’t play well with the SaasFullyBayesianSingleTaskGP since the mean_module, covar_module and likelihood aren’t initialized until the model has been fitted. The reason for this is that we don’t know the number of MCMC samples until NUTS is called. Given the state dict, we can initialize a new model with some dummy samples and then load the state dict into this model. This currently only works for a SaasPyroModel and supporting more Pyro models likely requires moving the model construction logic into the Pyro model itself.

Parameters:

• state_dict (Mapping[str, Any]) –

• strict (bool) –

forward(X)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Parameters:

X (Tensor) –

Return type:

MultivariateNormal

posterior(X, output_indices=None, observation_noise=False, posterior_transform=None, **kwargs)[source]¶

Computes the posterior over model outputs at the provided points.

Parameters:

• X (Tensor) – 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 (Optional[List[int]]) – 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 (bool) – 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).

• posterior_transform (Optional[PosteriorTransform]) – An optional PosteriorTransform.

• kwargs (Any) –

Returns:

A FullyBayesianPosterior object. Includes observation noise if specified.

Return type:

FullyBayesianPosterior

Fully Bayesian Multitask GP Models¶

Multi-task Gaussian Process Regression models with fully Bayesian inference.

class botorch.models.fully_bayesian_multitask.MultitaskSaasPyroModel[source]¶

Bases: SaasPyroModel

Implementation of the multi-task sparse axis-aligned subspace priors (SAAS) model.

The multi-task model uses an ICM kernel. The data kernel is same as the single task SAAS model in order to handle high-dimensional parameter spaces. The task kernel is a Matern-5/2 kernel using learned task embeddings as the input.

set_inputs(train_X, train_Y, train_Yvar, task_feature, task_rank=None)[source]¶

Set the training data.

Parameters:

• train_X (Tensor) – Training inputs (n x (d + 1))

• train_Y (Tensor) – Training targets (n x 1)

• train_Yvar (Tensor) – Observed noise variance (n x 1).

• task_feature (int) – The index of the task feature (-d <= task_feature <= d).

• task_rank (Optional[int]) – The num of learned task embeddings to be used in the task kernel. If omitted, set it to be 1.

sample()[source]¶

Sample from the SAAS model.

This samples the mean, noise variance, outputscale, and lengthscales according to the SAAS prior.

Return type:

None

sample_latent_features(**tkwargs)[source]¶

Parameters:

tkwargs (Any) –

sample_task_lengthscale(concentration=6.0, rate=3.0, **tkwargs)[source]¶

Parameters:

• concentration (float) –

• rate (float) –

• tkwargs (Any) –

load_mcmc_samples(mcmc_samples)[source]¶

Load the MCMC samples into the mean_module, covar_module, and likelihood.

Parameters:

mcmc_samples (Dict[str, Tensor]) –

Return type:

Tuple[Mean, Kernel, Likelihood, Kernel, Parameter]