gpflow.models.svgp

Classes

gpflow.models.svgp.SVGP_deprecated

class gpflow.models.svgp.SVGP_deprecated(kernel, likelihood, inducing_variable, *, mean_function=None, num_latent_gps=1, q_diag=False, q_mu=None, q_sqrt=None, whiten=True, num_data=None)

Bases: GPModel, ExternalDataTrainingLossMixin

This is the Sparse Variational GP (SVGP).

The key reference is Hensman et al. [HMG15].

Parameters:

• kernel (Kernel) –

• likelihood (Likelihood) –

• inducing_variable (Union[InducingVariables, Tensor, ndarray[Any, Any]]) –

• mean_function (Optional[MeanFunction]) –

• num_latent_gps (int) –

• q_diag (bool) –

• q_mu (Optional[Tensor]) –

• q_sqrt (Optional[Tensor]) –

• whiten (bool) –

• num_data (Optional[Tensor]) –

elbo(data)

This gives a variational bound (the evidence lower bound or ELBO) on the log marginal likelihood of the model.

Return type:

Tensor

Returns:

• return has shape [].

Parameters:

data (Tuple[Union[ndarray[Any, Any], Tensor, Variable, Parameter], Union[ndarray[Any, Any], Tensor, Variable, Parameter]]) –

maximum_log_likelihood_objective(data)

Objective for maximum likelihood estimation. Should be maximized. E.g. log-marginal likelihood (hyperparameter likelihood) for GPR, or lower bound to the log-marginal likelihood (ELBO) for sparse and variational GPs.

Return type:

Tensor

Returns:

• return has shape [].

Parameters:

data (Tuple[Union[ndarray[Any, Any], Tensor, Variable, Parameter], Union[ndarray[Any, Any], Tensor, Variable, Parameter]]) –

gpflow.models.svgp.SVGP_with_posterior

class gpflow.models.svgp.SVGP_with_posterior(kernel, likelihood, inducing_variable, *, mean_function=None, num_latent_gps=1, q_diag=False, q_mu=None, q_sqrt=None, whiten=True, num_data=None)

Bases: SVGP_deprecated

This is the Sparse Variational GP (SVGP).

The key reference is Hensman et al. [HMG15].

This class provides a posterior() method that enables caching for faster subsequent predictions.

Parameters:

• kernel (Kernel) –

• likelihood (Likelihood) –

• inducing_variable (Union[InducingVariables, Tensor, ndarray[Any, Any]]) –

• mean_function (Optional[MeanFunction]) –

• num_latent_gps (int) –

• q_diag (bool) –

• q_mu (Optional[Tensor]) –

• q_sqrt (Optional[Tensor]) –

• whiten (bool) –

• num_data (Optional[Tensor]) –

posterior(precompute_cache=PrecomputeCacheType.TENSOR)

Create the Posterior object which contains precomputed matrices for faster prediction.

precompute_cache has three settings:

• PrecomputeCacheType.TENSOR (or “tensor”): Precomputes the cached quantities and stores them as tensors (which allows differentiating through the prediction). This is the default.

• PrecomputeCacheType.VARIABLE (or “variable”): Precomputes the cached quantities and stores them as variables, which allows for updating their values without changing the compute graph (relevant for AOT compilation).

• PrecomputeCacheType.NOCACHE (or “nocache” or None): Avoids immediate cache computation. This is useful for avoiding extraneous computations when you only want to call the posterior’s fused_predict_f method.

Parameters:

precompute_cache (PrecomputeCacheType) –

Return type:

BasePosterior

predict_f(Xnew, full_cov=False, full_output_cov=False)

For backwards compatibility, SVGP’s predict_f uses the fused (no-cache) computation, which is more efficient during training.

For faster (cached) prediction, predict directly from the posterior object, i.e.,:

model.posterior().predict_f(Xnew, …)

Parameters:

• Xnew (Union[ndarray[Any, Any], Tensor, Variable, Parameter]) –

  • Xnew has shape [batch…, N, D].

• full_cov (bool) –

• full_output_cov (bool) –

Return type:

Tuple[Tensor, Tensor]

Returns:

• return[0] has shape [batch…, N, P].

• return[1] has shape [batch…, N, P, N, P] if full_cov and full_output_cov.

• return[1] has shape [batch…, N, P, P] if (not full_cov) and full_output_cov.

• return[1] has shape [batch…, N, P] if (not full_cov) and (not full_output_cov).

• return[1] has shape [batch…, P, N, N] if full_cov and (not full_output_cov).