Bayesian Networks (BNs) are probabilistic, graphical models for representing complex dependency structures. They have many applications in science and engineering. Their particularly powerful variant – Non-Parametric BNs – are implemented as a Matlab toolbox and an open-access scriptable code, in the form of a Python-based package.
This repository contains:
- PyBanshee a Python-based open source of the MATLAB toolbox.
The supporting SoftwareX paper for PyBanhsee, PyBanshee version (1.0): A Python implementation of the MATLAB toolbox BANSHEE for Non-Parametric Bayesian Networks with updated features, can be found at https://doi.org/10.1016/j.softx.2022.101279
These codes are an update of the original version supporting SoftwareX paper: https://doi.org/10.1016/j.softx.2020.100588. The latest version of MATLAB BANSHEE (v1.3) supprting SoftwareX papper can be found at https://doi.org/10.1016/j.softx.2023.101479
The packages allows for quantifying the BN, validating the underlying assumptions of the model, visualizing the network and its corresponding rank correlation matrix, sampling and finally making inference with a BN based on existing or new evidence. MATLAB BANSHEE v1.3 and Py_BANSHEE have the same features.
PyBanshee is a Python-based open source of the MATLAB toolbox BANSHEE.
Use the package manager pip to install last stable version of the package.
pip install py-bansheeHowever, if you are looking for the latest updates, consider installation directly from sources.
git clone https://github.com/mike-mendoza/py_banshee.git
cd py_banshee
python setup.py install
Features:
- py_banshee.rankcorr.bn_rankcorr --> Create a Bayesian Network rank correlation matrix
- py_banshee.bn_plot.bn_visualize --> Visualize the structure of a defined Bayesian Network
- py_banshee.copula_test.cvm_statistic --> Goodness-of-fit test for copulas
- py_banshee.d_cal.gaussian_distance --> Measure the distance between Gaussian densities
- py_banshee.sample_bn.generate_samples --> Make samples using the defined Bayesian Network
- py_banshee.sample_bn.sample_base_conditioning --> Make samples based conditioning on intervals
- py_banshee.prediction.inference --> Make predictions using a non-parametric Bayesian Network
- py_banshee.prediction.conditional_margins_hist --> Visualize the un-conditional and conditional marginal histograms
from py_banshee.rankcorr import bn_rankcorr
from py_banshee.bn_plot import bn_visualize
from py_banshee.prediction import inference,conditional_margins_hist
#Defining the variables of the BN
names = ['V1','V2','V3'] #names of the variables (nodes)
N = len(names) #number of nodes
#parametric distributions of the nodes
distributions = ['norm','genextreme','norm']
parameters = [[100,23],[-0.15,130,50],[500,100]]
#Defining the structure of the BN
ParentCell = [None]*N
ParentCell[0] = []
ParentCell[1] = [0]
ParentCell[2] = [0,1]
#Defining the rank correlation matrix
RankCorr = [None]*N
RankCorr[0] = []
RankCorr[1] = [.1]
RankCorr[2] = [.41,-.25]
#Conditional rank correlation matrix
R = bn_rankcorr(ParentCell,RankCorr,var_names=names,is_data=False, plot=True)
#Plot of the Bayesian Network
bn_visualize(ParentCell,R,names,fig_name='BN_TEST')
# Inference
condition_nodes = [0] #conditionalized variables (node V1)
condition_values = [181] #conditionalized value (node V1)
F = inference(Nodes = condition_nodes,
Values = condition_values,
R=R,
DATA=[],
SampleSize=100000,
empirical_data=False,
distributions=distributions,
parameters=parameters,
Output='full')
#Conditional and un-conditional histograms
conditional_margins_hist(F,[],names,condition_nodes,
empirical_data = False,
distributions=distributions,
parameters=parameters)Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.