Metadata-Version: 2.1
Name: fri
Version: 4.0.0
Summary: Feature relevance interval method
Home-page: https://github.com/lpfann/fri
Author: Lukas Pfannschmidt
Author-email: lpfannschmidt@techfak.uni-bielefeld.de
License: MIT
Description: # Feature relevance intervals
        
        [![Build Status](https://travis-ci.org/lpfann/fri.svg?branch=master)](https://travis-ci.org/lpfann/fri)
        [![Coverage Status](https://coveralls.io/repos/github/lpfann/fri/badge.svg)](https://coveralls.io/github/lpfann/fri)
        [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1188749.svg)](https://doi.org/10.5281/zenodo.1188749)
        [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lpfann/fri/blob/master/doc/source/notebooks/Guide.ipynb)
        
        This repository contains the Python implementation of the Feature Relevance Intervals method (FRI)[1,2].
        
        Check out our online documentation [here](https://lpfann.github.io/fri/).
        There you can find a quick start guide and more background information.
        You can also run the guide directly [in Colab](https://colab.research.google.com/github/lpfann/fri/blob/master/doc/source/notebooks/Guide.ipynb).
        
        ![Example output of method for biomedical dataset](doc/source/examples/example_plot.png?raw=true)
        
        ## Installation
        The library needs various dependencies which should automatically be installed.
        We highly recommend the [Anaconda](https://www.anaconda.com/download) 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
        ```shell
        $ pip install fri
        ```
        
        To install the module in __development__ clone the repo and execute:
        ```shell
        $ python setup.py install
        ```
        
        ## Testing
        To test if the library was installed correctly you can use the `pytest` command to run all included tests.
        
        ```shell
        $ pip install pytest
        ```
        then run in the root directory:
        ```shell
        $ pytest
        ```
        
        ## Usage
        Examples and API descriptions can be found [here](https://lpfann.github.io/fri/).
        
        
        
        ## References  
        >[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; Accepted.
        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. Accepted.
        https://pub.uni-bielefeld.de/publication/2915273
        
Keywords: feature selection relevance bounds machine learning bioinformatics
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
Provides-Extra: tests
Provides-Extra: docs
