# Probabilistic programming with Python and Lea

In this post I will cover installation of a probabilistic programming package for Python called Lea and provide some simple examples of using the package to do calculations with joint, conditional and marginal distributions. These examples follow the by-hand calculations done in my previous Joint, conditional and marginal probabilities post. Lea is really interesting to me because it makes probabilistic programming very easy– think reasoning with distributions and Bayesian networks instead of MCMC calculations. In this post I’ll start with basic calculations to demonstrate usage, but I’ll move onto classic Bayesian and Bayesian network examples in future posts. Also, be sure to check out the Lea Python tutorials for other great examples.

## Installing Lea

Okay, let’s get started with the installation. To do this we’ll use pip, and optionally virtualenv, for the install. If you do not have these tools setup, I have posts that cover this in detail for Ubuntu 14.04:

Assuming these tools are available you can install in one of the following ways:

1. as a user

$pip install --user lea  2. global install $ sudo pip install lea


3. in a virtual environment

$mkvirtualenv lea_env (lea_env)$ pip install lea


To check the install, whichever way you chose to do it, you should be able to do:

\$ pip show lea
---
Name: lea
Version: 2.1.1
Location: /home/cstrelioff/.local/lib/python2.7/site-packages
Requires:


and get something like the above. From the output we can see the Version 2.1.1 is installed, the current version at this time. From the Location output, we can also see that I’ve installed Lea as a user. You should also able to start Python and import the package without errors:

>>> import lea


Okay, that’s it, Lea is installed and we’re ready to do the examples. As I mentioned above, the Lea Python tutorials are also very nicely done, so you should check those out for many more examples.

## Examples

All the examples to follow are available as a gist: my lea gist, or you can follow along at your terminal. To start we do imports:

from __future__ import division, print_function
from lea import Lea


These statements import Lea as well as some utility functions that let Python 2.7 behave more like Python 3.x with respect to division and print.

Next, let’s review the two scenarios from the Joint, conditional and marginal probabilities post. The goal will be to use Lea to replicate the calculations done there. So, to the scenarios:

• scenario 1: A coin is tossed, resulting in a heads: $$C=H$$, or tails: $$C=T$$, with equal probability. Next, a six sided die is tossed, resulting in $$D=1, D=2, \ldots$$ with equal probability.
• scenario 2: In scenario two, a coin is again tossed. As in scenario 1, the probabilities of $$C=T$$ and $$C=H$$ are equal. However, if a $$C=T$$ a four-sided die is tossed and if $$C=H$$ a six-sided die is tossed.

To review, scenario 1 is designed to have the coin toss and die roll be independent: a six-sided die is always thrown, whether the coin resulted in an H or T. Scenario 2 is designed to have the coin toss and die roll be dependent: whether a six-sided or four-sided die is tossed depends on the outcome of the coin-toss.

To implement these scenarios, we’ll start by defining distributions for the coin, four-sided die and six-sided die. First, the coin:

# define coin
coin = Lea.fromValFreqs(('H', 1),
('T', 1))

print('Coin Distribution',
coin,
sep='\n')

Coin Distribution
H : 1/2
T : 1/2


next, the six-sided die:

# define six-sided die
die6 = Lea.fromValFreqs(('1', 1),
('2', 1),
('3', 1),
('4', 1),
('5', 1),
('6', 1))

print('Six-sided Die Distribution',
die6,
sep='\n')

Six-sided Die Distribution
1 : 1/6
2 : 1/6
3 : 1/6
4 : 1/6
5 : 1/6
6 : 1/6


and, finally, the four-sided die:

# define four-side die
die4 = Lea.fromValFreqs(('1', 1),
('2', 1),
('3', 1),
('4', 1))

print('Four-sided Die Distribution',
die4,
sep='\n')

Four-sided Die Distribution
1 : 1/4
2 : 1/4
3 : 1/4
4 : 1/4


Next we define the scenarios in Lea using conditional probability tables and the building blocks defined above. For the first scenario we have:

# construct Scenario 1
scenario1 = Lea.buildCPT((coin == 'H', die6),
(coin == 'T', die6))

print('Scenario 1',
scenario1,
sep='\n')

Scenario 1
1 : 1/6
2 : 1/6
3 : 1/6
4 : 1/6
5 : 1/6
6 : 1/6


and for the second scenario we change to die4 if a T is thrown:

# construct Scenario 2
scenario2 = Lea.buildCPT((coin == 'H', die6),
(coin == 'T', die4))

print('Scenario 2',
scenario2,
sep='\n')

Scenario 2
1 : 5/24
2 : 5/24
3 : 5/24
4 : 5/24
5 : 2/24
6 : 2/24


In each case Lea provides the marginal probabilities for the value obtained from the die roll. To get a better sense of the two scenarios we can also have Lea provide the joint probabilities for all outcomes, both coin toss and die roll, using the Cartesian product:

# get joint probs for all events
# -- scenario 1
joint_prob1 = Lea.cprod(coin, scenario1)

print('Scenario 1',
'* Joint Probabilities',
joint_prob1,
sep='\n')

Scenario 1
* Joint Probabilities
('H', '1') : 1/12
('H', '2') : 1/12
('H', '3') : 1/12
('H', '4') : 1/12
('H', '5') : 1/12
('H', '6') : 1/12
('T', '1') : 1/12
('T', '2') : 1/12
('T', '3') : 1/12
('T', '4') : 1/12
('T', '5') : 1/12
('T', '6') : 1/12


and, for scenario 2:

# get joint probs for all events
# -- scenario 2
joint_prob2 = Lea.cprod(coin, scenario2)

print('Scenario 2',
'* Joint Probabilities',
joint_prob2,
sep='\n')

Scenario 2
* Joint Probabilities
('H', '1') : 2/24
('H', '2') : 2/24
('H', '3') : 2/24
('H', '4') : 2/24
('H', '5') : 2/24
('H', '6') : 2/24
('T', '1') : 3/24
('T', '2') : 3/24
('T', '3') : 3/24
('T', '4') : 3/24


These should be compared with the Joint Probability Tables that I constructed in my Joint, conditional and marginal probabilities post– exactly the same output and super simple to obtain with Lea.

Let’s finish up by calculating the some conditional probabilities. In this case, what are the probabilities of an ‘H’ or ‘T’ given that we have a ‘6’ from the die? Using Lea, this is simple:

# prob coin given D=6, scenario 1
print("Scenario 1 -> P(C|D=6)",
coin.given(scenario1 == '6'),
sep='\n')

Scenario 1 -> P(C|D=6)
H : 1/2
T : 1/2


whereas for scenario 2 we get:

# prob coin given D=6, scenario 2
print("Scenario 2 -> P(C|D=6)",
coin.given(scenario2 == '6'),
sep='\n')

Scenario 2 -> P(C|D=6)
H : 1


The results are very different for the two scenarios by construction. Does the difference make sense? Calculate things out by-hand if they don’t and then reflect on how easy Lea makes things!

What if we’d seen a ‘4’ instead? For scenario 1

# prob coin given D=4, scenario 1
print("Scenario 1 -> P(C|D=4)",
coin.given(scenario1 == '4'),
sep='\n')

Scenario 1 -> P(C|D=4)
H : 1/2
T : 1/2


and for scenario 2:

# prob coin given D=4, scenario 2
print("Scenario 2 -> P(C|D=4)",
coin.given(scenario2 == '4'),
sep='\n')

Scenario 2 -> P(C|D=4)
H : 2/5
T : 3/5


In this example the difference between scenarios 1 and 2 is more subtle, but it’s still there. Again, make sure the difference makes sense.

## Summing Up

Lea is a great tool for probabilistic programming and thinking in Python. I’ll definitely be posting more examples with a goal of looking at Bayesian (aka Belief) networks using Lea. As always corrections, comments and questions are welcome below.