beanmachine.ppl.inference.vi package

Submodules

• beanmachine.ppl.inference.vi.autoguide module
• beanmachine.ppl.inference.vi.discrepancy module
• beanmachine.ppl.inference.vi.gradient_estimator module
• beanmachine.ppl.inference.vi.variational_infer module
• beanmachine.ppl.inference.vi.variational_world module

Module contents

class beanmachine.ppl.inference.vi.ADVI(queries: Iterable[beanmachine.ppl.model.rv_identifier.RVIdentifier], observations: Dict[beanmachine.ppl.model.rv_identifier.RVIdentifier, torch.Tensor], **kwargs)

Bases: beanmachine.ppl.inference.vi.autoguide.AutoGuideVI

Automatic Differentiation Variational Inference (ADVI).

ADVI automates construction of guides by initializing variational distributions as Gaussians and possibly bijecting them so the supports match.

See https://arxiv.org/abs/1506.03431.

static get_guide(query, distrib)

class beanmachine.ppl.inference.vi.MAP(queries: Iterable[beanmachine.ppl.model.rv_identifier.RVIdentifier], observations: Dict[beanmachine.ppl.model.rv_identifier.RVIdentifier, torch.Tensor], **kwargs)

Bases: beanmachine.ppl.inference.vi.autoguide.AutoGuideVI

Maximum A Posteriori (MAP) Inference.

Uses Delta distributions to perform a point estimate of the posterior mode.

static get_guide(query, distrib)

class beanmachine.ppl.inference.vi.VariationalInfer(queries_to_guides: Dict[beanmachine.ppl.model.rv_identifier.RVIdentifier, beanmachine.ppl.model.rv_identifier.RVIdentifier], observations: Dict[beanmachine.ppl.model.rv_identifier.RVIdentifier, torch.Tensor], optimizer: Callable[[torch.Tensor], torch.optim.optimizer.Optimizer] = <function VariationalInfer.<lambda>>, device: torch.device = device(type='cpu'))

Bases: object

infer(num_steps: int, num_samples: int = 1, discrepancy_fn=<function kl_reverse>, mc_approx=<function monte_carlo_approximate_reparam>, step_callback: Optional[Callable[[int, torch.Tensor, beanmachine.ppl.inference.vi.variational_infer.VariationalInfer], None]] = None, subsample_factor: float = 1) → beanmachine.ppl.inference.vi.variational_world.VariationalWorld

Perform variatonal inference.

Parameters

• num_steps – number of optimizer steps

• num_samples – number of samples per Monte-Carlo gradient estimate of E[f(logp - logq)]

• discrepancy_fn – discrepancy function f, use kl_reverse to minimize negative ELBO

• mc_approx – Monte-Carlo gradient estimator to use

• step_callback – callback function invoked each optimizer step

• subsample_factor – subsampling factor used for subsampling, helps scale the observations to avoid overshrinking towards the prior

Returns

A world with variational guide distributions initialized with optimized parameters

Return type

VariationalWorld

initialize_world() → beanmachine.ppl.inference.vi.variational_world.VariationalWorld

Initializes a VariationalWorld using samples from guide distributions evaluated at the current parameter values.

Returns

a World where guide samples and distributions have replaced their corresponding queries

Return type

VariationalWorld

step(num_samples: int = 1, discrepancy_fn=<function kl_reverse>, mc_approx=<function monte_carlo_approximate_reparam>, subsample_factor: float = 1) → torch.Tensor

Perform one step of variatonal inference.

Parameters

• num_samples – number of samples per Monte-Carlo gradient estimate of E[f(logp - logq)]

• discrepancy_fn – discrepancy function f, use kl_reverse to minimize negative ELBO

• mc_approx – Monte-Carlo gradient estimator to use

• subsample_factor – subsampling factor used for subsampling, helps scale the observations to avoid overshrinking towards the prior

Returns

the loss value (before the step)

Return type

torch.Tensor