Python bindings to the FEAST Feature Selection Toolbox..
PyFeast is a interface for the FEAST feature selection toolbox, which was originally written in C with a interface to Matlab.
Because Python is also commonly used in computational science, writing bindings to enable researchers to utilize these feature selection algorithms in Python was only natural.
At Drexel University's EESI Lab, we are using PyFeast to create a feature selection tool for the Department of Energy's upcoming KBase platform. We are also integrating a tool that utilizes PyFeast as a script for Qiime users: Qiime Fizzy Branch
In order to use the feast module, you will need the following dependencies
python ./setup.py build sudo python ./setup.py install
See test/test.py for an example with uniform data and an image
data set. The image data set was collected from the digits example in
the Scikits-Learn 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.
We have documentation for each of the functions available here
FEAST - The Feature Selection Toolbox
- Fizzy - A KBase Service for Feature Selection
- Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection