thomas-core 0.1.1

Thomas, a library for working with Bayesian Networks.

Thomas

Very simple (almost naive ;-) bayesian network implementation.

Contains examples (thomas.core.examples) from the book "Probabilistic Graphical Models: Principles and Techniques" from Koller and Friedman (PGM Stanford) and from the lecture by Adnan Darwiche on YouTube: * 6a. Inference by Variable Elimination I (Chapter 6). * 6b. Inference by Variable Elimination II (Chapter 6).

Installation

Regular installation

To install the latest version from PyPI:

    pip install thomas-core

Install from PyyPI TEST:

    pip install -i https://test.pypi.org/simple/ --extra-index-url=https://pypi.python.org/simple thomas-core
    pip install -i https://test.pypi.org/simple/ --extra-index-url=https://pypi.python.org/simple thomas-jupyter-widget

To install the latest version from source:

    pip install git+https://github.com/mellesies/thomas-core

Development installation

To do a development install:

    git clone https://github.com/mellesies/thomas-core
    cd thomas-core
    pip install -e .

Additional packages

If you're using JupyterLab, I'd recommend also installing the Widget that can display Bayesian Networks.

Docker

A Docker image is available should you just want to try things out.The following command will start a JupyterLab server, listening on http://localhost:8888.

    docker run --rm -it -p 8888:8888 mellesies/thomas-core

Usage

To get started with querying a network, try the following:

from thomas.core import examples

# Load an example network
Gs = examples.get_student_network()

# This should output the prior probability of random variable 'S' (SAT score).
print(Gs.P('S'))
print()

# Expected output:
# P(S)
# S
# s0    0.725
# s1    0.275
# dtype: float64

# Query for the conditional probability of S given the student is intelligent.
print(Gs.P('S|I=i1'))

# Expected output:
# P(S)
# S
# s0    0.2
# s1    0.8
# dtype: float64

Alternatively, you can have a go at the example notebooks through Binder:

• notebooks/1. Factors.ipynb
• notebooks/2. Bags of factors.ipynb
• notebooks/3. Conditional probability tables.ipynb
• notebooks/4. Bayesian Networks.ipynb