Learning and Inference in Bayesian Belief Networks

## Project description

## 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. PyBBN also has approximate inference algorithm using Gibbs sampling for linear Gaussian BBN models. The exact inference algorithm is for BBNs that have all variables that are discrete, while the approximate inference algorithm is for BBNs that have all variables that are continuous (and assume to take a multivariate Gaussian distribution). 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.

## 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)

## Approximate Inference Usage

Below is an example to create a linear Gaussian BBN and perform inference.

import numpy as np from pybbn.lg.graph import Dag, Parameters, Bbn # create the directed acylic graph dag = Dag() dag.add_node(0) dag.add_node(1) dag.add_edge(0, 1) # create parameters means = np.array([0, 25]) cov = np.array([ [1.09, 1.95], [1.95, 4.52] ]) params = Parameters(means, cov) # create the bayesian belief network bbn = Bbn(dag, params) # do the inference M, C = bbn.do_inference() print(M) # set the evidence on node 0 to a value of 1 bbn.set_evidence(0, 1) M, C = bbn.do_inference() print(M) bbn.clear_evidences() # set the evidence on node 1 to a value of 20 bbn.set_evidence(1, 20) M, C = bbn.do_inference() print(M) bbn.clear_evidences()

## Building

To build, you will need Python 2.7 or 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).

For Python 2.7.

```
conda env create -f environment-py27.yml
conda activate pybbn27
python -m ipykernel install --user --name pybbn27 --display-name "pybbn27"
```

For Python 3.7.

```
conda env create -f environment-py37.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

## Other Python Bayesian Belief Network Inference Libraries

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

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.

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