This repository contains a simple Julia implementation of the feature relevance bounds method.
It exists a much more complete python version here. This is mostly a proof of concept and is missing features such as cross validation for hyper parameteres, regression and ordinal regression models and advanced statistics for feature classification.
A runnable example is included in the example notebook.
include("src/Fri.jl")┌ Info: Precompiling JuMP [4076af6c-e467-56ae-b986-b466b2749572]
└ @ Base loading.jl:1273
┌ Info: Precompiling ECOS [e2685f51-7e38-5353-a97d-a921fd2c8199]
└ @ Base loading.jl:1273
Main.Fri
We generate dataset with 200 samples, 5 strongly relevant features, 4 weakly relevant features and 10 noise features (irrelevant).
X,y = Main.Fri.dataset.generate(200,d_rel=5,d_irrel=10,d_weak=4);relev_bounds = Main.Fri.relevance_bounds(X,y)17×2 Array{Float64,2}:
2.55727 2.55727
2.01039 2.01039
2.32116 2.32116
2.09897 2.09897
2.35608 2.35608
-3.85029e-12 2.22748
-3.8504e-12 2.22748
0.0948441 0.0948441
0.100589 0.100589
0.156153 0.156153
0.0760626 0.0760626
0.11897 0.11897
0.19504 0.19504
0.09535 0.09535
0.0669823 0.0669823
0.22617 0.22617
0.0860516 0.0860516
relev_bounds[1,1]2.5572730462733446
relev_bounds[1,2]2.557273051045558