gpflow.models.svgp¶

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)[source]¶

Bases: gpflow.models.model.GPModel, gpflow.models.training_mixins.ExternalDataTrainingLossMixin

This is the Sparse Variational GP (SVGP). The key reference is

@inproceedings{hensman2014scalable,
  title={Scalable Variational Gaussian Process Classification},
  author={Hensman, James and Matthews, Alexander G. de G. and Ghahramani, Zoubin},
  booktitle={Proceedings of AISTATS},
  year={2015}
}

Attributes

name: Returns the name of this module as passed or determined in the ctor.
name_scope: Returns a tf.name_scope instance for this class.
non_trainable_variables: Sequence of non-trainable variables owned by this module and its submodules.
parameters
submodules: Sequence of all sub-modules.
trainable_parameters
trainable_variables: Sequence of trainable variables owned by this module and its submodules.
variables: Sequence of variables owned by this module and its submodules.

Methods

`calc_num_latent_gps`(kernel, likelihood, ...)	Calculates the number of latent GPs required given the number of outputs output_dim and the type of likelihood and kernel.
`calc_num_latent_gps_from_data`(data, kernel, ...)	Calculates the number of latent GPs required based on the data as well as the type of kernel and likelihood.
`elbo`(data)	This gives a variational bound (the evidence lower bound or ELBO) on the log marginal likelihood of the model.
`log_posterior_density`(args, *kwargs)	This may be the posterior with respect to the hyperparameters (e.g.
`log_prior_density`()	Sum of the log prior probability densities of all (constrained) variables in this model.
`maximum_log_likelihood_objective`(data)	Objective for maximum likelihood estimation.
`predict_f_samples`(Xnew[, num_samples, ...])	Produce samples from the posterior latent function(s) at the input points.
`predict_log_density`(data[, full_cov, ...])	Compute the log density of the data at the new data points.
`predict_y`(Xnew[, full_cov, full_output_cov])	Compute the mean and variance of the held-out data at the input points.
`training_loss`(data)	Returns the training loss for this model.
`training_loss_closure`(data, *[, compile])	Returns a closure that computes the training loss, which by default is wrapped in tf.function().
`with_name_scope`(method)	Decorator to automatically enter the module name scope.

predict_f
prior_kl

Parameters

num_latent_gps (int) –
q_diag (bool) –
whiten (bool) –

elbo(data)[source]¶

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

Parameters: data (Tuple[Union[ndarray, Tensor, Variable, Parameter], Union[ndarray, Tensor, Variable, Parameter]]) –
Return type: Tensor

maximum_log_likelihood_objective(data)[source]¶

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.

Parameters: data (Tuple[Union[ndarray, Tensor, Variable, Parameter], Union[ndarray, Tensor, Variable, Parameter]]) –
Return type: Tensor

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)[source]¶

Bases: gpflow.models.svgp.SVGP_deprecated

This is the Sparse Variational GP (SVGP). The key reference is

@inproceedings{hensman2014scalable,
  title={Scalable Variational Gaussian Process Classification},
  author={Hensman, James and Matthews, Alexander G. de G. and Ghahramani, Zoubin},
  booktitle={Proceedings of AISTATS},
  year={2015}
}

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

Attributes

name: Returns the name of this module as passed or determined in the ctor.
name_scope: Returns a tf.name_scope instance for this class.
non_trainable_variables: Sequence of non-trainable variables owned by this module and its submodules.
parameters
submodules: Sequence of all sub-modules.
trainable_parameters
trainable_variables: Sequence of trainable variables owned by this module and its submodules.
variables: Sequence of variables owned by this module and its submodules.

Methods

`calc_num_latent_gps`(kernel, likelihood, ...)	Calculates the number of latent GPs required given the number of outputs output_dim and the type of likelihood and kernel.
`calc_num_latent_gps_from_data`(data, kernel, ...)	Calculates the number of latent GPs required based on the data as well as the type of kernel and likelihood.
`elbo`(data)	This gives a variational bound (the evidence lower bound or ELBO) on the log marginal likelihood of the model.
`log_posterior_density`(args, *kwargs)	This may be the posterior with respect to the hyperparameters (e.g.
`log_prior_density`()	Sum of the log prior probability densities of all (constrained) variables in this model.
`maximum_log_likelihood_objective`(data)	Objective for maximum likelihood estimation.
`posterior`([precompute_cache])	Create the Posterior object which contains precomputed matrices for faster prediction.
`predict_f`(Xnew[, full_cov, full_output_cov])	For backwards compatibility, SVGP's predict_f uses the fused (no-cache) computation, which is more efficient during training.
`predict_f_samples`(Xnew[, num_samples, ...])	Produce samples from the posterior latent function(s) at the input points.
`predict_log_density`(data[, full_cov, ...])	Compute the log density of the data at the new data points.
`predict_y`(Xnew[, full_cov, full_output_cov])	Compute the mean and variance of the held-out data at the input points.
`training_loss`(data)	Returns the training loss for this model.
`training_loss_closure`(data, *[, compile])	Returns a closure that computes the training loss, which by default is wrapped in tf.function().
`with_name_scope`(method)	Decorator to automatically enter the module name scope.

prior_kl

Parameters

num_latent_gps (int) –
q_diag (bool) –
whiten (bool) –

posterior(precompute_cache=PrecomputeCacheType.TENSOR)[source]¶

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.

predict_f(Xnew, full_cov=False, full_output_cov=False)[source]¶

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, Tensor, Variable, Parameter]) –
Return type: Tuple[Tensor, Tensor]