Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability
Experiments
Setup Environment
The following python environment and packages are used to conduct the experiments:
- python==3.6
- Boruta==0.1.5
- numpy==1.16.1
- pandas==0.24.2
- scikit-learn==0.20.2
- scipy==1.2.1
These packages can be installed by executing the following command: pip3.6 install -r requirements.txt
Datasets
To conduct the experiments we have used the following three healthcare datasets from UCI repository:
Breast cancer dataset comes along with scikit-learn package, therefore, there is no need to download this dataset. The rest of the datasets are already downloaded and available in "data" folder.
Algorithms
The following classifiers and algorithms are used in this study:
- Random Forest
- Neural Networks
- Linear Regression
- Logistic Regression
- K-Nearest Neighbours
- K-Means Clustering
- Agglomerative Hierarchical Clustering
Execute Code
Run the following files to reproduce the results. The results of LIME are not deterministic and it may produce different results.
Experiments on Breast Cancer Dataset:
- python3.6 experiments_bc_nn.py
- python3.6 experiments_bc_rf.py
Experiments on Indian Liver Patient Dataset:
- python3.6 experiments_ildp_nn.py
- python3.6 experiments_ildp_rf.py
Experiments on Hepatitis Dataset:
- python3.6 experiments_hp_nn.py
- python3.6 experiments_hp_rf.py
For the quality of the explanations:
- python3.6 experiments_bc_lgr_fidelity_v2p0-mc-v2.py
- python3.6 evaluate_quality_v0.py
Results
The results will be saved inside "results" directory in pdf and csv format. The quality of the explanation is shown in the image below:
Citation
Please consider citing our work if you use this code for your research.
Initial Results
@InProceedings{zafar2019dlime,
author = {Muhammad Rehman Zafar and Naimul Mefraz Khan},
title = {DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems},
booktitle = {In proceeding of ACM SIGKDD Workshop on Explainable AI/ML (XAI) for Accountability, Fairness, and Transparency},
year = {2019},
publisher = {ACM},
address = {Anchorage, Alaska}
}
Extended Version
@article{zafar2021deterministic,
title={Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability},
author={Zafar, Muhammad Rehman and Khan, Naimul},
journal={Machine Learning and Knowledge Extraction},
volume={3},
number={3},
pages={525--541},
year={2021},
publisher={Multidisciplinary Digital Publishing Institute}
}