PermutationImportance 1.0.3

Important variables determined through permutation selection

PermutationImportance

Provides an efficient method to compute variable importance through the permutation of input variables. Uses multithreading and supports both Windows and Unix systems and Python 2 and 3.

This repository provides the stand-alone functionality of a permutation-based method of computing variable importance in an arbitrary model. Importance for a given variable is computed in accordance with Lakshmanan et al. (2015)'s paper[1]. Variables which, when their values are permuted, cause the worst resulting score are considered most important. This implementation provides the functionality for an arbitrary method for computing the ``worst'' score and for using an arbitrary scoring metric. The most common case of this is to chose the variable which most negatively impacts the accuracy of the model.

Functionality is provided not only for returning the most important variable (along with the raw scores for each variable) but also for returning the sequential importance of variables. To do this, the most important variable is determined and then it is left permuted while the next most important variable is determined. In the extreme case, this is continued until the sequential ordering of all variables is determined, but this can be terminated at an earlier level by choice.

¹Lakshmanan, V., C. Karstens, J. Krause, K. Elmore, A. Ryzhkov, and S. Berkseth, 2015: Which Polarimetric Variables Are Important for Weather/No-Weather Discrimination?. J. Atmos. Oceanic Technol., 32, 1209–1223, https://doi.org/10.1175/JTECH-D-13-00205.1

Setup

PermutationImportance is now available on pip, so you can simply install with pip install PermutationImportance and import the desired method with

from permutation_importance.variable_importance import permutation_selection_importance