LGNpy 0.0.5

Linear Gaussian Networks - Inference, Parameter Learning and Representation

LGNpy

Representation, Learning and Inference for Linear Gaussian Bayesian Networks

LGNs are Bayesian Networks where all the nodes have continuous data. Gaussians are surprisingly good approximation for many real world continuous distributions. This package helps in modelling the network, learning parameters through data and running inference with evidence(s)

Installation

$ pip install lgnpy

or clone the repository.

$ pip install https://github.com/ostwalprasad/lgnpy

Getting Started

1. Create Network

import pandas as pd
imoprt numpy as np
from lgnpy import LinearGaussian

lg = LinearGaussian()
lg.set_edges_from([('A', 'D'), ('B', 'D'), ('D', 'E'), ('C', 'E')])

2 Create Data and assign to it to network.

​ Create synthetic data for network using pandas and bind network with the data. There's no need to separately calculate means and covariance matrix.

np.random.seed(42)
n=100
data = pd.DataFrame(columns=['A','B','C','D','E'])
data['A'] = np.random.normal(5,2,n)
data['B'] = np.random.normal(10,2,n)
data['D'] = 2*data['A'] + 3*data['B'] + np.random.normal(0,2,n)
data['C'] = np.random.normal(-5,2,n)
data['E'] = 3*data['C'] + 3*data['D'] + np.rando	m.normal(0,2,n)

lg.set_data(data)

3. Set Evidence(s)

Evidence are optional and can be set before running inference.

 lg.set_evidences({'A':5,'B':10})

4. Run Inference

For each node, CPT (Conditional Probability Distribution) is defined as::

where, it's parameters are calculated using conditional distribution of parent(s) and nodes:

run_inference() returns inferred means and variances of each nodes.

lg.run_inference(debug=False)

Additional Functions:

lg.plot_distributions(save=False)

lg.network_summary()

lg.draw_network(filename='sample_network',open=True)

References:

Probabilistic Graphical Models - Principles and Techniques , Daphne Koller, Chapter 7.2

Gaussian Bayesian Networks, Sargur Srihari

License

MIT License Copyright (c) 2020, Prasad Ostwal