Neyman-Pearson Based Feature Selection (NPFS) post-hoc test

Python implementation of NPFS. It is still under development and is not considered stable. Contact gregory.ditzler@gmail.com.

There are types of feature subset selection problems that require that the size of the subset be specied prior to running the selection algorithm. NPFS works with the decisions of a base subset selection algorithm to determine an appropriate number of features to select given an initial starting point. NPFS uses the FEAST feature selection toolbox; however, the approach is not limited to using the this toolbox.

Make sure that if you are loading the data from a file and converting the data to a numpy array that you set order="F". This is very important.

Module Installation

  cd src/
  python setup.py build
  sudo python setup.py install 

Citing NPFS

• Gregory Ditzler, Robi Polikar, Gail Rosen, "A Bootstrap Based Neyman–Pearson Test for Identifying Variable Importance," 2014, In press. (link)

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