mgplvm.models.svgp module

class mgplvm.models.svgp.Svgp(kernel, n, m, n_samples, z, likelihood, whiten=True, tied_samples=True)

Bases: mgplvm.models.svgp.SvgpBase

g0_parameters()

g1_parameters()

property msg

name = 'Svgp'

property prms: Tuple[torch.Tensor, torch.Tensor, torch.Tensor]

Return type

Tuple[Tensor, Tensor, Tensor]

training: bool

class mgplvm.models.svgp.SvgpBase(kernel, n, m, n_samples, n_inducing, likelihood, q_mu=None, q_sqrt=None, whiten=True, tied_samples=True)

Bases: mgplvm.models.gp_base.GpBase

elbo(y, x, sample_idxs=None, m=None)

Parameters

yTensor

data tensor with dimensions (n_samples x n x m)

xTensor (single kernel) or Tensor list (product kernels)

input tensor(s) with dimensions (n_mc x n_samples x d x m)

mOptional int

used to scale the svgp likelihood. If not provided, self.m is used which is provided at initialization. This parameter is useful if we subsample data but want to weight the prior as if it was the full dataset. We use this e.g. in crossvalidation

Returns

lik, prior_klTuple[torch.Tensor, torch.Tensor]

lik has dimensions (n_mc x n) prior_kl has dimensions (n)

Notes

Implementation largely follows derivation of the ELBO presented in here.

Return type

Tuple[Tensor, Tensor]

predict(x, full_cov, sample_idxs=None)

Parameters

xTensor (single kernel) or Tensor list (product kernels)

test input tensor(s) with dimensions (n_b x n_samples x d x m)

full_covbool

returns full covariance if true otherwise returns the diagonal

Returns

muTensor

mean of predictive density at test inputs [ s ]

vTensor

variance/covariance of predictive density at test inputs [ s ] if full_cov is true returns full covariance, otherwise returns diagonal variance

Return type

Tuple[Tensor, Tensor]

prior_kl(sample_idxs=None)

sample(query, n_mc=1000, square=False, noise=True)

Parameters

queryTensor (single kernel)

test input tensor with dimensions (n_samples x d x m)

n_mcint

numper of samples to return

squarebool

determines whether to square the output

noisebool

determines whether we also sample explicitly from the noise model or simply return samples of the mean

Returns

y_sampsTensor

samples from the model (n_mc x n_samples x d x m)

training: bool