DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer Aided Diagnostic Systems

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

Dataset

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 download and available inside the "data" folder in csv format.

Algorithms

The following classifiers and algorithms are used in this study:

Random Forest
Neural Networks
Linear Regression
K-Nearest Neighbours
Agglomerative Hierarchical Clustering

In the experiments, 80% data is used for training and the remaining 20% data is used for testing. Further, the Random Forest, Neural Networks and KNN classifiers are trained with the following parameters:

RandomForestClassifier(n_estimators=10, random_state=0, max_depth=5, max_features=5)

MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1)

NearestNeighbors(n_neighbors=1, algorithm='ball_tree')

AgglomerativeClustering(n_clusters=2, affinity='euclidean', compute_full_tree='auto', connectivity=None, linkage='ward', memory=None)

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

Results

The results will be saved inside "results" directory in pdf and csv format.

Citation

Please consider citing our work if you use this code for your research.

@InProceedings{rehman2019dlime,
  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}
}

wabc1994/dlime_experiments