PyExplainer 
PyExplainer is a local rule-based model-agnostic technique for generating explanations (i.e., why a commit is predicted as defective) of Just-In-Time (JIT) defect prediction defect models.
Through a case study of two open-source software projects, we find that our PyExplainer produces (1) synthetic neighbous that are 41%-45% more similar to an instance to be explained; (2) 18%-38% more accurate local models; and (3) explanations that are 69%-98% more unique and 17%-54% more consistent with the actual characteristics of defect-introducing commits in the future than LIME (a state-of-the-art model-agnostic technique).
This work is published at the International Conference on Automated Software Engineering 2021 (ASE2021): "PyExplainer: Explaining the Predictions ofJust-In-Time Defect Models". Preprint
@inproceedings{PyExplainer,
author = {Pornprasit, Chanathip and Tantithamthavorn, Chakkrit and Jiarpakdee, Jirayus and Fu, Micheal and Thongtanunam, Patanamon},
title = {PyExplainer: Explaining the Predictions ofJust-In-Time Defect Models},
booktitle = {Proceedings of th International Conference on Automated Software Engineering (ASE)},
year = {2021},
numpages = {12},
}
Quick Start
You can try our PyExplainer directly without installation at this online JupyterNotebook (just open and run TUTORIAL.ipynb). The tutorial video below demonstrates how to use our PyExplainer in this JupyterNotebook.
In the JupyterNotebook:
- Run cells from step 1 to step 3 to create an interactive visualization in jupyter notebook cell (like the example above). You can change the input feature values of ML model at slide bar.
- Run the cells in appendix section if you would like to get more detail about variable used to build PyExplainer
See the instructions below how to install our PyExplainer Python Package.
Table of Contents
Installation
Dependencies
- python >= "3.6"
- scikit-learn >= "0.24.2"
- numpy >= "1.20.1"
- scipy >= "1.6.1"
- ipywidgets >= "7.6.3"
- ipython >= "7.21.0"
- pandas >= "1.2.5"
- statsmodels >= "0.12.2"
Install PyExplainer Python Package
Installing pyexplainer is easily done using pip, simply run the following command. This will also install the necessary dependencies.
pip install pyexplainer
See this PyExplainer python package documentation for how to install our PyExplainer from source and its dependencies.
If you are with conda, run the command below in your conda environment
conda install -c conda-forge pyexplainer
Contributions
We welcome and recognize all contributions. You can see a list of current contributors in the contributors tab.
Please click here for more information about making a contribution to this project.
Documentation
The official documentation is hosted on Read the Docs
License
MIT License, click here for more information.
Credits
This package was created with Cookiecutter and the UBC-MDS/cookiecutter-ubc-mds project template, modified from the pyOpenSci/cookiecutter-pyopensci project template and the audreyr/cookiecutter-pypackage.
71 Dec 19, 2022
28 Aug 22, 2022
2 Jan 09, 2022
3 Jul 23, 2022
764 Dec 31, 2022
14 Jul 13, 2022
9 Jul 05, 2022
1 Dec 19, 2021
33 Dec 02, 2022
8 Jan 08, 2023
202 Jan 04, 2023
4 Sep 13, 2022
2 Dec 14, 2022
7 Nov 03, 2022
0 Nov 12, 2021
12 Oct 25, 2021
21 Dec 22, 2022
34 Nov 12, 2022
60 Jan 04, 2023
19 Dec 17, 2022