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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

Getting Started with DisCERN

Binary Classification example using the Adult Income dataset and RandomForest classifier is in tests/test_adult_income.py

Multi-class Classification example using the Cancer risk dataset and RandomForest classifier is in tests/test_cancer_risk.py

Citing

Please cite it as follows:

Nirmalie Wiratunga and Anjana Wijekoon and Ikechukwu Nkisi-Orji and Kyle Martin and Chamath Palihawadana and David Corsar (2021). DisCERN:Discovering Counterfactual Explanations using Relevance Features from Neighbourhoods. ArXiv, vol. abs/2109.05800

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}

}





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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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