loadest_gp.models.gpytorch

loadest_gp.models.gpytorch#

Classes

ExactGPModel(train_x, train_y, likelihood)

LoadestGPMarginalGPyTorch([model_config])

Gaussian Process implementation of the LOAD ESTimation (LOADEST) model
class loadest_gp.models.gpytorch.ExactGPModel(train_x, train_y, likelihood)#
forward(x)#

Define 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.

class loadest_gp.models.gpytorch.LoadestGPMarginalGPyTorch(model_config: ModelConfig = ModelConfig(transform='log'))#

Gaussian Process implementation of the LOAD ESTimation (LOADEST) model

This model currrently uses the marginal likelihood implementation, which is fast but does not account for censored data. Censored data require a slower latent variable implementation.

build_model(X, y) → ExactGP#

Build marginal likelihood version of LoadestGP

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