Applied AI Internal Project

Daft PGM Demo

Autumn 2016

Barebones repo to demonstrate usage of daft-pgm, a small package for drawing plate notation diagrams purely in Python. The package is developed by Dan Foreman-Mackey, and there's lots of info and some demos at the project website http://daft-pgm.org

As per the project: Daft is a Python package that uses matplotlib to render pixel-perfect probabilistic graphical models for publication in a journal or on the internet. With a short Python script and an intuitive model building syntax you can design directed (Bayesian Networks, directed acyclic graphs) and undirected (Markov random fields) models and save them in any formats that matplotlib supports (including PDF, PNG, EPS and SVG).

This repo accompanies a larger project called pymc3_vs_pystan by Jonathan Sedar of Applied AI Ltd, which was written primarily for presentation at the PyData London 2016 Conference.

---

Development

Install Continuum Anaconda Python 3.5 64bit for your OS

Main site: https://www.continuum.io/downloads

e.g. for MacOSX: http://repo.continuum.io/archive/Anaconda3-4.0.0-MacOSX-x86_64.pkg

Git clone the repo to your workspace.

e.g. in Mac OSX terminal:

    $> git clone https://github.com/appliedailtd/appliedai_daftpgmdemo.git
    $> cd appliedai_daftpgmdemo

To work on Python code, setup a virtual environment for Python libraries

1. Using create a new virtualenv, install packages from env YAML file:

    $> conda env create --file condaenv_appliedai_daftpgmdemo.yml
    $> source activate appliedai_daftpgmdemo
   

2. Launch Jupyter Notebook server

    (appliedai_daftpgmdemo)$> jupyter notebook
   

---

General Notes:

AOB

---

Applied AI Ltd © 2016 applied.ai