DisCERN: Discovering Counterfactual Explanations using Relevance Features from Neighbourhoods
Project description
DisCERN-XAI
DisCERN: Discovering Counterfactual Explanations using Relevance Features from Neighbourhoods
Installing DisCERN
DisCERN supports Python 3+. The stable version of DisCERN is available on PyPI:
pip install discern-xai
To install the dev version of DisCERN and its dependencies, clone this repo and run pip install
from the top-most folder of the repo:
pip install -e .
DisCERN requires the following packages:
numpy
pandas
lime
shap
scikit-learn
Compatible Libraries
Attribution Explainer | scikit-learn | TensorFlow/Keras | PyTorch |
---|---|---|---|
LIME | ✓ | ✓ | N/A |
SHAP | ✓ shap.TreeExplainer | ✓ shap.DeepExplainer | N/A |
Integrated Gradients | ✗ | ✓ | N/A |
Getting Started with DisCERN
Binary Classification example on the Adult Income dataset using RandomForest and Keras Deep Neural Net classifiers are here
Multi-class Classification example on the Cancer risk dataset using RandomForest and Keras Deep Neural Net classifiers are here
Citing
Please cite it follows:
-
Wiratunga, N., Wijekoon, A., Nkisi-Orji, I., Martin, K., Palihawadana, C., & Corsar, D. (2021, November). Discern: discovering counterfactual explanations using relevance features from neighbourhoods. In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 1466-1473). IEEE.
-
Wijekoon, A., Wiratunga, N., Nkisi-Orji, I., Palihawadana, C., Corsar, D., & Martin, K. (2022, August). How Close Is Too Close? The Role of Feature Attributions in Discovering Counterfactual Explanations. In Case-Based Reasoning Research and Development: 30th International Conference, ICCBR 2022, Nancy, France, September 12–15, 2022, Proceedings (pp. 33-47). Cham: Springer International Publishing.
Bibtex:
@misc{wiratunga2021discerndiscovering,
title={DisCERN:Discovering Counterfactual Explanations using Relevance Features from Neighbourhoods},
author={Nirmalie Wiratunga and Anjana Wijekoon and Ikechukwu Nkisi-Orji and Kyle Martin and Chamath Palihawadana and David Corsar},
year={2021},
eprint={2109.05800},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
@inproceedings{wijekoon2022close,
title={How Close Is Too Close? The Role of Feature Attributions in Discovering Counterfactual Explanations},
author={Wijekoon, Anjana and Wiratunga, Nirmalie and Nkisi-Orji, Ikechukwu and Palihawadana, Chamath and Corsar, David and Martin, Kyle},
booktitle={Case-Based Reasoning Research and Development: 30th International Conference, ICCBR 2022, Nancy, France, September 12--15, 2022, Proceedings},
pages={33--47},
year={2022},
organization={Springer}
}
This research is funded by the iSee project which received funding from EPSRC under the grant number EP/V061755/1. iSee is part of the CHIST-ERA pathfinder programme for European coordinated research on future and emerging information and communication technologies.
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