This repository contains the Python implementation of the Feature Relevance Intervals method (FRI)[1,2].

Check out our online documentation here. There you can find a quick start guide and more background information. You can also run the guide directly in Colab.

The library needs various dependencies which should automatically be installed. We highly recommend the Anaconda Python distribution to provide all dependencies. The library was written with Python 3 in mind and due to the foreseeable ending of Python 2 support, backwards compatibility is not planned.

If you just want to use the stable version from PyPI use

$ pip install fri

To install the module in development clone the repo and execute:

$ python setup.py install

To test if the library was installed correctly you can use the pytest command to run all included tests.

$ pip install pytest

then run in the root directory:

$ pytest

Examples and API descriptions can be found here.

[1] Göpfert C, Pfannschmidt L, Hammer B. Feature Relevance Bounds for Linear Classification. In: Proceedings of the ESANN. 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning; https://pub.uni-bielefeld.de/publication/2908201

[2] Göpfert C, Pfannschmidt L, Göpfert JP, Hammer B. Interpretation of Linear Classifiers by Means of Feature Relevance Bounds. Neurocomputing. https://pub.uni-bielefeld.de/publication/2915273

[3] Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl, Peter Tino, Barbara Hammer: Feature Relevance Bounds for Ordinal Regression . Proceedings of the ESANN. 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning; Accepted. https://pub.uni-bielefeld.de/record/2933893