bayes-nets-sample 1.5

A class to represent a Bayes Network. And apply all the different sampling strategies to the calculate a given query.

Bayes Nets and Sampling

About:

Developed code from scratch to compute an input probability query on a given Bayes net on discrete random variables using Prior sampling, Rejection Sampling, Likelihood weighting and Gibbs sampling.
I had also written helper function to generate samples from any input univariate discrete distribution and then use it in your Bayes Net sampling code.

BayesNets Class:

I implemented a BayesNets Class to do all the required task and computations.
Basically I created a single class to do all the calculation, extractions of details from the file as well as implement all the sampling methods and sample generation.

Install the Python Package

pip install bayes-nets-sample

Import the Python Package

from bayes import bayesNets

To see the Sample .txt file

from bayes.sample_input_txt import SampleInputTXT
SampleInputTXT().generate_sample()

To Get more information about the BayesNets Class as well as its methods:

run the following command:

help(BayesNets)

Lets Start By Creating an object of BayesNets and extract the information from the file:

bayesNet = BayesNets(filepath = "example_bayesnet.txt")

Let's Print The Extracted Information:

print(bayesNet)

Let's See how a single sample is generated:

bayesNet.generateSample()

Now Lets Calculate the Query result using Prior Sampling Method:

With verbose = True

bayesNet.doPriorSampling(TOTAL_SAMPLE = 10000, verbose = True)

Now Lets Calculate the Query result using Prior Sampling Method:

With verbose = False

• Just to see the final result.

bayesNet.doPriorSampling(TOTAL_SAMPLE = 10000, verbose = False)

Now Lets Calculate the Query result using Rejection Sampling Method:

With verbose = True

bayesNet.doRejectionSampling(TOTAL_SAMPLE_REQUIRED = 10000, verbose = True)

Now Lets Calculate the Query result using Rejection Sampling Method:

With verbose = False

• Just to see the final result.

bayesNet.doRejectionSampling(TOTAL_SAMPLE_REQUIRED = 10000, verbose = False)

Let's See how a single weighted sample is generated:

bayesNet.generateWeightedSample(verbose = True)

Now Lets Calculate the Query result using Likelihood Weighting Method:

With verbose = True

bayesNet.doLikelihoodWeighting(TOTAL_SAMPLE = 10000, verbose = True)

Now Lets Calculate the Query result using Likelihood Weighting Method:

With verbose = False

• Just to see the final result.

bayesNet.doLikelihoodWeighting(TOTAL_SAMPLE = 10000, verbose = False)

Sample File

5, B, E, A, J, M
B, +b, -b
E, +e, -e
A, +a, -a
J, +j, -j
M, +m, -m
B |
+b, 0.001
-b, 0.999
E |
+e, 0.002
-e, 0.998
A | B,E
+b, +e, +a, 0.95
+b, +e, -a, 0.05
+b, -e, +a, 0.94
+b, -e, -a, 0.06
-b, +e, +a, 0.29
-b, +e, -a, 0.71
-b, -e, +a, 0.001
-b, -e, -a, 0.999
J | A
+a, +j, 0.9
+a, -j, 0.1
-a, +j, 0.05
-a, -j, 0.95
M | A
+a, +m, 0.7
+a, -m, 0.3
-a, +m, 0.01
-a, -m, 0.99
Query: P( B=+b| J=+j)