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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: Quality of Explanations

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}
}