Code of the methods proposed in the PhD thesis "Learning Tractable Bayesian Networks"
This software has been developed as a Python 2.7.15 package and includes some functionalities in Cython and C++11 (version 5.4.0). Consequently, it is needed a Python environment and internet connectivity to download additional package dependencies. Python software can be downloaded from https://www.python.org/downloads/.
We provide the steps for a clean installation in Ubuntu 16.04. This software has not been tried under Windows.
The package also uses the following dependencies.
| Library | Version | License |
|---|---|---|
| pandas | 0.23 | BSD 3 |
| numpy | 1.14.3 | BSD |
| Cython | 0.28.2 | Apache |
| cloudpickle | 0.5.3 | BSD 3 |
| scikit-learn | 0.20.2 | New BSD |
| matplotlib | 1.5.1 | Matplotlib |
| rpy2 | 2.8.2 | GPLv2+ |
| pathlib | 1.0.1 | MIT |
They can be installed through the following sentence: sudo pip install "Library" where Library must be replaced by the library to be installed.
Open the folder where you have saved TSEM project files (e.g., "~/Downloads/TSEM") and compile Cython files running the following commands in the command console:
python2.7 setup_dt.py build_ext --inplace
python2.7 setup_tw.py build_ext --inplace
python2.7 setup_et.py build_ext --inplace
python2.7 setup_cplus.py build_ext --inplace
python2.7 setup_cplus_data.py build_ext --inplace
python2.7 setup_gs.py build_ext --inplace
python2.7 setup_etc.py build_ext --inplace
File "example_learn_et.py" provides a demo that shows how to learn a bounded treewidth Baysian network (Chapter 3). File "example_tsem.py" provides a demo that shows how to use the code to learn Bayesian networks in the presence of missing values (Chapter 4). File "example_mbcs.py" shows examples of how to learn an MBC in a generative and discriminative way (Chapters 5 and 6). File "example_epilepsy.py" shows an example of how to train an MBC using the aproach used in the paper "Patient Specific Prediction of Temporal Lobe Epilepsy Surgical Outcomes".