https://github.com/pymc-devs/symbolic-pymc

Tools for the symbolic manipulation of PyMC models, Theano, and TensorFlow graphs.
https://github.com/pymc-devs/symbolic-pymc

bayesian minikanren probabilistic-programming pymc relational-programming symbolic-computation symbolic-math tensorflow theano

Last synced: about 1 month ago
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Tools for the symbolic manipulation of PyMC models, Theano, and TensorFlow graphs.

• Host: GitHub
• URL: https://github.com/pymc-devs/symbolic-pymc
• Owner: pymc-devs
• License: other
• Created: 2019-02-16T18:35:05.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2024-03-20T15:48:21.000Z (over 1 year ago)
• Last Synced: 2025-08-05T13:38:48.574Z (2 months ago)
• Topics: bayesian, minikanren, probabilistic-programming, pymc, relational-programming, symbolic-computation, symbolic-math, tensorflow, theano
• Language: Python
• Homepage: https://pymc-devs.github.io/symbolic-pymc
• Size: 3.79 MB
• Stars: 62
• Watchers: 22
• Forks: 8
• Open Issues: 20
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Symbolic PyMC

[![Build Status](https://travis-ci.org/pymc-devs/symbolic-pymc.svg?branch=master)](https://travis-ci.org/pymc-devs/symbolic-pymc) [![Coverage Status](https://coveralls.io/repos/github/pymc-devs/symbolic-pymc/badge.svg?branch=master)](https://coveralls.io/github/pymc-devs/symbolic-pymc?branch=master)

[Symbolic PyMC](https://pymc-devs.github.io/symbolic-pymc) provides tools for the symbolic manipulation of [PyMC](https://github.com/pymc-devs) models and their underlying computational graphs in [Theano](https://github.com/Theano/Theano) and [TensorFlow](https://github.com/tensorflow/tensorflow).  It enables graph manipulations in the relational DSL [miniKanren](http://minikanren.org/)—via the [`miniKanren`](https://github.com/pythological/kanren) package—by way of meta classes and S-expression forms of a graph.

This work stems from a series of articles starting [here](https://brandonwillard.github.io/a-role-for-symbolic-computation-in-the-general-estimation-of-statistical-models.html).  Documentation and examples for Symbolic PyMC are available [here](https://pymc-devs.github.io/symbolic-pymc).

*This package is currently in alpha, so expect large-scale changes at any time!*

## Installation

The package name is `symbolic_pymc` and it can be installed with `pip` directly from GitHub

```shell

$ pip install git+https://github.com/pymc-devs/symbolic-pymc

```

or after cloning the repo (and then installing with `pip`).

## Features

### General

* Support for [Theano](https://github.com/Theano/Theano) and [TensorFlow](https://github.com/tensorflow/tensorflow) graphs

  - [Unification and reification](https://github.com/pythological/unification) for all components of a graph

  - A more robust Theano `Op` for representing random variables

  - Conversion of PyMC3 models into sample-able Theano graphs representing all random variable inter-dependencies

  - A Theano LaTeX pretty printer that displays shape information and distributions in mathematical notation

  - Simple text-based TensorFlow graph print-outs

* Full [miniKanren](http://minikanren.org/) integration for relational graph/model manipulation.

  - Perform simple and robust "search and replace" over arbitrary graphs (e.g. Python builtin collections, AST, tensor algebra graphs, etc.)

  - Create and compose relations with explicit high-level statistical/mathematical meaning and functionality, such as "`X` is a normal scale mixture with mixing distribution `Y`", and automatically "solve" for components (i.e. `X` and `Y`) that satisfy a relation

  - Apply non-trivial conditions—as relations—to produce sophisticated graph manipulations (e.g. search for normal scale mixtures and scale a term in the mixing distribution)

  - Integrate standard Python operations into relations (e.g. use a symbolic math library to compute an inverse-Laplace transform to determine if a distribution is a scale mixture—and find its mixing distribution)

* Convert graphs to an S-expression-like tuple-based form and perform manipulations at the syntax level

* Pre-built example relations for graph traversal, fixed-points, symbolic closed-form posteriors, and standard statistical model reformulations