pyzag.stochastic

Tools for converting deterministc models implemented in pytorch to stochastic models

class pyzag.stochastic.MapNormal(cov: float, loc_suffix: str = '_loc', scale_suffix: str = '_scale')

Bases: object

A map between a deterministic torch parameter and a two-scale normal distribution

Parameters:

cov – coefficient of variation used to define the scale priors

Keyword Arguments:

  • sep (str) – seperator character in names

  • loc_suffix – suffix to add to parameter name to give the upper-level distribution for the scale

  • scale_suffix – suffix to add to the parameter name to give the lower-level distribution for the scale

class pyzag.stochastic.HierarchicalStatisticalModel(base: Module, parameter_mapper: MapNormal, noise_prior: Tensor, update_mask: bool = False)

Bases: PyroModule

Converts a torch model over to being a Pyro-based hierarchical statistical model

Parameters:

  • base (torch.nn.Module) – base torch module

  • parameter_mapper (MapParameter) – mapper class describing how to convert from Parameter to Distribution

  • noise_prior (float) – scale prior for white noise

Keyword Arguments:

update_mask (bool) – if True, update the mask to remove samples that are not valid

training: bool
forward(*args: Tensor, results: Tensor | None = None, weights: Tensor | None = None, **kwargs) → Tensor

Call the base forward with the appropriate args.

Parameters:

*args – arguments forwarded to the underlying model. At least one must be a tensor so the batch shape can be inferred.

Keyword Arguments:

  • results (torch.tensor or None) – results to condition on.

  • weights (torch.tensor or None) – weights on the results; defaults to ones.