Metadata-Version: 2.1
Name: discern-xai
Version: 0.0.2
Summary: DisCERN: Discovering Counterfactual Explanations using Relevance Features from Neighbourhoods
Home-page: https://github.com/RGU-Computing/discern-xai
Author: Anjana Wijekoon
Author-email: a.wijekoon1@rgu.ac.uk
License: UNKNOWN
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](https://pypi.org/project/discern-xai/):
        
            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:<br>
        `numpy`<br>
        `pandas`<br>
        `lime`<br>
        `shap`<br>
        `scikit-learn`
        
        
        ### Getting Started with DisCERN
        
        Examples using the Adult Income dataset is in the test folder. 
        
        ### 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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        <img align="left" src="isee.png" alt="drawing" height="50"/>
        <img align="right" src="chistera.png" alt="drawing" height="50"/><br><br><br>
        <center>This research is funded by the <a href="https://isee4xai.com">iSee project</a> which received funding from EPSRC under the grant number EP/V061755/1. iSee is part of the <a href="https://www.chistera.eu/">CHIST-ERA pathfinder programme</a> for European coordinated research on future and emerging information and communication technologies.</center>
        
        
        
        
Keywords: machine-learning explanation interpretability counterfactual
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Description-Content-Type: text/markdown
