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EXPLAN: Explaining Black-box Classifiers using Adaptive Neighborhood Generation

Home Page: https://ieeexplore.ieee.org/document/9206710

License: GNU General Public License v3.0

Python 1.54% JavaScript 13.42% CSS 0.01% HTML 0.01% TeX 0.01% Jupyter Notebook 85.02% Less 0.01%
xai interpretability machinelearning blackbox-models local-explanation rule-based-explanations

explan's Introduction

EXPLAN

This repository contains the implementation source code of the following paper:

EXPLAN: Explaining Black-box Classifiers using Adaptive Neighborhood Generation

BibTeX:

@inproceedings{rasouli2020explan,
               title={EXPLAN: Explaining Black-box Classifiers using Adaptive Neighborhood Generation},
               author={Rasouli, Peyman and Yu, Ingrid Chieh},
               booktitle={2020 International Joint Conference on Neural Networks (IJCNN)},
               pages={1--9},
               year={2020},
               organization={IEEE}
}

Setup

1- Clone the repository using HTTP/SSH:

git clone https://github.com/peymanrasouli/EXPLAN

2- Create a conda virtual environment:

conda create -n EXPLAN python=3.6

3- Activate the conda environment:

conda activate EXPLAN

4- Standing in EXPLAN directory, install the requirements:

pip install -r requirements.txt

5- Run initial setup:

python setup.py

6- Install TBB library required by YaDT:

# Ubuntu/Debian
sudo apt-get update
sudo apt-get install libtbb2 

# CentOS
sudo yum update
sudo yum install tbb

Reproducing the results

1- To test EXPLAN on a single instance run:

python test_explan.py

2- To reproduce the fidelity and coverage results run:

python fidelity_coverage_experiments.py

3- To reproduce the stability results run:

python stability_experiments.py

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