beanmachine.ppl.inference.bmg_inference module

An inference engine which uses Bean Machine Graph to make inferences on Bean Machine models.

class beanmachine.ppl.inference.bmg_inference.BMGInference

Bases: object

Interface to Bean Machine Graph (BMG) Inference, an experimental framework for high-performance implementations of inference algorithms.

Internally, BMGInference consists of a compiler and C++ runtime implementations of various inference algorithms. Currently, only Newtonian Monte Carlo (NMC) inference is supported, and is the algorithm used by default.

Please note that this is a highly experimental implementation under active development, and that the subset of Bean Machine model is limited. Limitations include that the runtime graph should be static (meaning, it does not change during inference), and that the types of primitive distributions supported is currently limited.

infer(queries: List[beanmachine.ppl.model.rv_identifier.RVIdentifier], observations: Dict[beanmachine.ppl.model.rv_identifier.RVIdentifier, torch.Tensor], num_samples: int, num_chains: int = 4, num_adaptive_samples: int = 0, inference_type: beanmachine.graph.InferenceType = <InferenceType.NMC: 3>, skip_optimizations: Set[str] = {'beta_bernoulli_conjugate_fixer', 'beta_binomial_conjugate_fixer', 'normal_normal_conjugate_fixer'}) → beanmachine.ppl.inference.monte_carlo_samples.MonteCarloSamples

Perform inference by (runtime) compilation of Python source code associated with its parameters, constructing a BMG graph, and then calling the BMG implementation of a particular inference method on this graph.

Parameters

  • queries – queried random variables

  • observations – observations dict

  • num_samples – number of samples in each chain

  • num_chains – number of chains generated

  • num_adaptive_samples – number of burn in samples to discard

  • inference_type – inference method, currently only NMC is supported

  • skip_optimizations – list of optimization to disable in this call

Returns

The requested samples

Return type

MonteCarloSamples

to_bm_python(queries: List[beanmachine.ppl.model.rv_identifier.RVIdentifier], observations: Dict[beanmachine.ppl.model.rv_identifier.RVIdentifier, torch.Tensor]) → str

Produce a string containing a BM Python program from the graph.

to_cpp(queries: List[beanmachine.ppl.model.rv_identifier.RVIdentifier], observations: Dict[beanmachine.ppl.model.rv_identifier.RVIdentifier, torch.Tensor]) → str

Produce a string containing a C++ program fragment which produces the graph deduced from the model.

to_dot(queries: List[beanmachine.ppl.model.rv_identifier.RVIdentifier], observations: Dict[beanmachine.ppl.model.rv_identifier.RVIdentifier, torch.Tensor], after_transform: bool = True, label_edges: bool = False, skip_optimizations: Set[str] = {'beta_bernoulli_conjugate_fixer', 'beta_binomial_conjugate_fixer', 'normal_normal_conjugate_fixer'}) → str

Produce a string containing a program in the GraphViz DOT language representing the graph deduced from the model.

to_graph(queries: List[beanmachine.ppl.model.rv_identifier.RVIdentifier], observations: Dict[beanmachine.ppl.model.rv_identifier.RVIdentifier, torch.Tensor]) → Tuple[beanmachine.graph.Graph, Dict[beanmachine.ppl.model.rv_identifier.RVIdentifier, int]]

Produce a BMG graph and a map from queried RVIdentifiers to the corresponding indices of the inference results.

to_graphviz(queries: List[beanmachine.ppl.model.rv_identifier.RVIdentifier], observations: Dict[beanmachine.ppl.model.rv_identifier.RVIdentifier, torch.Tensor], after_transform: bool = True, label_edges: bool = False, skip_optimizations: Set[str] = {'beta_bernoulli_conjugate_fixer', 'beta_binomial_conjugate_fixer', 'normal_normal_conjugate_fixer'}) → graphviz.sources.Source

Small wrapper to generate an actual graphviz object

to_python(queries: List[beanmachine.ppl.model.rv_identifier.RVIdentifier], observations: Dict[beanmachine.ppl.model.rv_identifier.RVIdentifier, torch.Tensor]) → str

Produce a string containing a Python program fragment which produces the graph deduced from the model.