python + lea: revisiting the medical tests example

README.md

A script that replicates all examples in my blog post on revisiting the Bayesian medical tests example-- using Python and Lea to do some probabilistic programming: Medical test/Lea post

Run all the examples

    $ python lea_ex02.py

Or,

    $ chmod u+x lea_ex02.py
    $ ./lea_ex02.py

That's it, give it a try!

lea_ex02.py

#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2015 Christopher C. Strelioff <chris.strelioff@gmail.com>
#
# Distributed under terms of the MIT license.
 
"""
Revisiting the Bayesian medical tests example, a second example of probabilistic programming using Lea.

Blog post: http://chrisstrelioff.ws/sandbox/2015/05/27/revisiting_the_medical_tests_example_with_python_and_lea.html
"""
from __future__ import division, print_function
from lea import Lea

# define cancer dist
cancer = Lea.fromValFreqs(('yes', 1),
                          ('no',  99))

print('\nCancer Distribution',
      'P(C)',
      cancer.asPct(),
      sep='\n')

# prob for mamm given cancer == yes
mamm_g_cancer = Lea.fromValFreqs(('pos', 80),
                                 ('neg', 20))

print('\nProb for mammogram given cancer',
      'P(M|C=yes)',
      mamm_g_cancer.asPct(),
      sep='\n')

# prob for mamm given cancer == no
mamm_g_no_cancer = Lea.fromValFreqs(('pos', 96),
                                    ('neg', 1000-96))

print('\nProb for mammogram given NO cancer',
      'P(M|C=no)',
      mamm_g_no_cancer.asPct(),
      sep='\n')

# conditional probability table
mammograms = Lea.buildCPT((cancer == 'yes', mamm_g_cancer),
                          (cancer == 'no', mamm_g_no_cancer))

print('\nMammograms',
      'P(M)',
      mammograms.asPct(),
      sep='\n')

# get joint probs for all events
joint_probs = Lea.cprod(mammograms, cancer)

print('\nJoint Probabilities',
      'P(M, C)',
      joint_probs.asPct(),
      sep='\n')

# prob cancer GIVEN mammogram==pos
print('\nThe Answer',
      'P(C|M=pos)',
      cancer.given(mammograms == 'pos').asPct(),
      sep='\n')

# prob cancer GIVEN mammogram==neg
print('\nExtra Info',
      'P(C|M=neg)',
      cancer.given(mammograms == 'neg').asPct(),
      sep='\n')