Project summary:

The code in this repository is used in the Bachelor Thesis of Rutger Mauritz

File descriptions:

Runpannel:

• From this file both the performance of the DAE and PIBN model are tested on several synthetic data sets
• The data-sets are loaded from the \Datasets directory

DataQuality:

• This file contains quality measures that are used for evaluation of the model performance
• Those quality measures are a.o. used in Runpannel

DataPyAgrum:

• Used for creating synthetic data sets via a Bayesian Network
• Contains an example of how a Bayesian Network can be constructed such that data can be sampled from it
• Used in Runpannel

DataConversion:

• Contains two methods:
  1. dataConversion: is used to add noise to the ground truth data and converts the synthetic data to one-hot encoding like structures
  2. noise: is used to deliberately add noise to the already corrupted training data. this is a part of the regularization of the DAE
• Used in AE_denoising

BN_sup:

• Implementation of the supervised PIBN model. It loads a pre-defined Bayesian Network (.bif file) and propagates the probabilistic data as virtual evidence through this BN, after which it extracts the posterior probabilities

AE_denoising:

• Implementation of the Denoising Autoencoder Model. This trained model is then applied to unseen corrupted test-data after which the resulting output is return, together with the training history

Backend:

• Contains a method jsd that is a Keras implementation of the Jensen Shannon Divergence
• Used in AE_denoising for training

Directories

\Archive:

• Contains some extra files that are not necessary for the main process anymore.

\Datasets:

• Contains the data sets that are used in runpannelto evaluate the model performances.
• Each of these data sets contains a description of its underling Bayesian Network (BN) model and comes with a .csv file containing the data, a .png file for a visualization of the BN network and a .bif file that can be used in PyAgrum.

\Results:

• A directory for storing the results of model evaluations.

\Images:

• Contains images that are used in the Bachelor Thesis

Needed packages:

• Numpy
• Pandas
• Keras
• PyAgrum: https://agrum.gitlab.io/pages/pyagrum.html
• Sklearn

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