pybbn 2.0.0

Learning and Inference in Bayesian Belief Networks

PyBBN

PyBBN is Python library for Bayesian Belief Networks (BBNs) exact inference using the junction tree algorithm or Probability Propagation in Trees of Clusters. The implementation is taken directly from C. Huang and A. Darwiche, “Inference in Belief Networks: A Procedural Guide,” in International Journal of Approximate Reasoning, vol. 15, pp. 225–263, 1999. Additionally, there is the ability to generate singly- and multi-connected graphs, which is taken from JS Ide and FG Cozman, “Random Generation of Bayesian Network,” in Advances in Artificial Intelligence, Lecture Notes in Computer Science, vol 2507. There is also the option to generate sample data from your BBN. This synthetic data may be summarized to generate your posterior marginal probabilities and work as a form of approximate inference.

Power Up, Next Level

If you like py-bbn, please inquire about our next-generation products below! [e-mail]

• turing_bbn is a C++17 implementation of py-bbn; take your causal and probabilistic inferences to the next computing level!

• pyspark-bbn is a is a scalable, massively parallel processing MPP framework for learning structures and parameters of Bayesian Belief Networks BBNs using Apache Spark.

Exact Inference Usage

Below is an example code to create a Bayesian Belief Network, transform it into a join tree, and then set observation evidence. The last line prints the marginal probabilities for each node.

from pybbn.graph.dag import Bbn
from pybbn.graph.edge import Edge, EdgeType
from pybbn.graph.jointree import EvidenceBuilder
from pybbn.graph.node import BbnNode
from pybbn.graph.variable import Variable
from pybbn.pptc.inferencecontroller import InferenceController

# create the nodes
a = BbnNode(Variable(0, 'a', ['on', 'off']), [0.5, 0.5])
b = BbnNode(Variable(1, 'b', ['on', 'off']), [0.5, 0.5, 0.4, 0.6])
c = BbnNode(Variable(2, 'c', ['on', 'off']), [0.7, 0.3, 0.2, 0.8])
d = BbnNode(Variable(3, 'd', ['on', 'off']), [0.9, 0.1, 0.5, 0.5])
e = BbnNode(Variable(4, 'e', ['on', 'off']), [0.3, 0.7, 0.6, 0.4])
f = BbnNode(Variable(5, 'f', ['on', 'off']), [0.01, 0.99, 0.01, 0.99, 0.01, 0.99, 0.99, 0.01])
g = BbnNode(Variable(6, 'g', ['on', 'off']), [0.8, 0.2, 0.1, 0.9])
h = BbnNode(Variable(7, 'h', ['on', 'off']), [0.05, 0.95, 0.95, 0.05, 0.95, 0.05, 0.95, 0.05])

# create the network structure
bbn = Bbn() \
    .add_node(a) \
    .add_node(b) \
    .add_node(c) \
    .add_node(d) \
    .add_node(e) \
    .add_node(f) \
    .add_node(g) \
    .add_node(h) \
    .add_edge(Edge(a, b, EdgeType.DIRECTED)) \
    .add_edge(Edge(a, c, EdgeType.DIRECTED)) \
    .add_edge(Edge(b, d, EdgeType.DIRECTED)) \
    .add_edge(Edge(c, e, EdgeType.DIRECTED)) \
    .add_edge(Edge(d, f, EdgeType.DIRECTED)) \
    .add_edge(Edge(e, f, EdgeType.DIRECTED)) \
    .add_edge(Edge(c, g, EdgeType.DIRECTED)) \
    .add_edge(Edge(e, h, EdgeType.DIRECTED)) \
    .add_edge(Edge(g, h, EdgeType.DIRECTED))

# convert the BBN to a join tree
join_tree = InferenceController.apply(bbn)

# insert an observation evidence
ev = EvidenceBuilder() \
    .with_node(join_tree.get_bbn_node_by_name('a')) \
    .with_evidence('on', 1.0) \
    .build()
join_tree.set_observation(ev)

# print the marginal probabilities
for node in join_tree.get_bbn_nodes():
    potential = join_tree.get_bbn_potential(node)
    print(node)
    print(potential)

Building

To build, you will need Python 3.7. Managing environments through Anaconda is highly recommended to be able to build this project (though not absolutely required if you know what you are doing). Assuming you have installed Anaconda, you may create an environment as follows (make sure you cd into the root of this project’s location).

conda env create -f environment.yml
conda activate pybbn37
python -m ipykernel install --user --name pybbn37 --display-name "pybbn37"

Then you may build the project as follows. (Note that in Python 3.6 you will get some warnings).

make build

To build the documents, go into the docs sub-directory and type in the following.

make html

Installing

Use pip to install the package as it has been published to PyPi.

pip install pybbn

Other Python Bayesian Belief Network Inference Libraries

Here is a list of other Python libraries for inference in Bayesian Belief Networks.

• BayesPy

• pomegranate

• pgmpy

• libpgm

• bayesnetinference

I found other packages in PyPI too.

Citation

@misc{vang_2017,
title={PyBBN},
url={https://github.com/vangj/py-bbn/},
journal={GitHub},
author={Vang, Jee},
year={2017},
month={Jan}}

Copyright Stuff

Copyright 2017 Jee Vang

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.