Meaningful Locality

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

Meaningful Data Sampling for a Faithful Local Explanation Method

BibTeX:

@inproceedings{rasouli2019meaningful,
               title={Meaningful Data Sampling for a Faithful Local Explanation Method},
               author={Rasouli, Peyman and Yu, Ingrid Chieh},
               booktitle={International Conference on Intelligent Data Engineering and Automated Learning},
               pages={28--38},
               year={2019},
               organization={Springer}
}

Setup

1- Clone the repository using HTTP/SSH:

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

2- Create a conda virtual environment:

conda create -n meaningful_locality python=3.6

3- Activate the conda environment:

conda activate meaningful_locality

4- Standing in meaningful_locality directory, install the requirements:

pip install -r requirements.txt

Reproducing the results

To reproduce the results of meaningful_locality method on LIME with:

1- Linear Regression as interpretable model run:

python test_lime_lr.py

2- Decision Tree as interpretable model run:

python test_lime_dt.py