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README.md

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[![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)
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[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 [`kanren`](https://github.com/logpy/logpy) package—by way of meta classes and S-expression forms of a graph.
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[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.
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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).
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*This package is currently in alpha, so expect large-scale changes at any time!*
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## Installation
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The package name is `symbolic_pymc` and it can be installed with `pip` directly from GitHub
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```shell
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$ pip install git+https://github.com/pymc-devs/symbolic-pymc
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```
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or after cloning the repo (and then installing with `pip`).
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## Features
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### General
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* Support for [Theano](https://github.com/Theano/Theano) and [TensorFlow](https://github.com/tensorflow/tensorflow) graphs
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- [Unification and reification](https://github.com/mrocklin/unification) for all components of a graph
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- [Unification and reification](https://github.com/pythological/unification) for all components of a graph
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- A more robust Theano `Op` for representing random variables
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- Conversion of PyMC3 models into sample-able Theano graphs representing all random variable inter-dependencies
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- A Theano LaTeX pretty printer that displays shape information and distributions in mathematical notation
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- 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)
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* Convert graphs to an S-expression-like tuple-based form and perform manipulations at the syntax level
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* Pre-built example relations for graph traversal, fixed-points, symbolic closed-form posteriors, and standard statistical model reformulations
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## Installation
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The package name is `symbolic_pymc` and it can be installed with `pip` directly from GitHub
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```shell
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$ pip install git+https://github.com/pymc-devs/symbolic-pymc
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```
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or after cloning the repo (and then installing with `pip`).

docs/source/symbolic-pymc-tour.org

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As mentioned at the end of the last section, Symbolic PyMC uses a Python
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implementation of the embedded domain-specific language miniKanren--provided by
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the src_python[:eval never]{kanren} package--to orchestrate more sophisticated uses of
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unification and reification. For a quick intro, see [[https://github.com/logpy/logpy/blob/master/doc/basic.md][the basic introduction]]
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unification and reification. For a quick intro, see [[https://github.com/pythological/kanren/blob/master/doc/basic.md][the basic introduction]]
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provided by the src_python[:eval never]{kanren} package. We'll cover most of the same
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basic material here, but not all.
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docs/source/symbolic-pymc-tour.rst

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As mentioned at the end of the last section, Symbolic PyMC uses a Python
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implementation of the embedded domain-specific language miniKanren--provided by
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the \ ``kanren``\ package--to orchestrate more sophisticated uses of
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unification and reification. For a quick intro, see `the basic introduction <https://github.com/logpy/logpy/blob/master/doc/basic.md>`_
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unification and reification. For a quick intro, see `the basic introduction <https://github.com/pythological/kanren/blob/master/doc/basic.md>`_
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provided by the \ ``kanren``\ package. We'll cover most of the same
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basic material here, but not all.
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docs/source/tensorflow-radon-example.org

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Python. This DSL provides primitive *goals* that can be composed and eventually
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evaluated by the src_python[:eval never]{run} function. We refer the reader
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to any one of the many great introductions to miniKanren available at [[http://minikanren.org]],
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or, for the specific Python package used here: [[https://github.com/logpy/logpy/blob/master/doc/basic.md][this simple introduction]].
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or, for the specific Python package used here: [[https://github.com/pythological/kanren/blob/master/doc/basic.md][this simple introduction]].
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For the matter at hand, we need to create goals that implement the substitution
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described above. The first step is to understand the exact TF graphs involved,

docs/source/tensorflow-radon-example.rst

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Python. This DSL provides primitive **goals** that can be composed and eventually
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evaluated by the \ ``run``\ function. We refer the reader
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to any one of the many great introductions to miniKanren available at `http://minikanren.org <http://minikanren.org>`_,
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or, for the specific Python package used here: `this simple introduction <https://github.com/logpy/logpy/blob/master/doc/basic.md>`_.
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or, for the specific Python package used here: `this simple introduction <https://github.com/pythological/kanren/blob/master/doc/basic.md>`_.
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For the matter at hand, we need to create goals that implement the substitution
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described above. The first step is to understand the exact TF graphs involved,

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