fanova ======= [![fanova](https://img.shields.io/crates/v/fanova.svg)](https://crates.io/crates/fanova) [![Documentation](https://docs.rs/fanova/badge.svg)](https://docs.rs/fanova) [![Actions Status](https://github.com/sile/fanova/workflows/CI/badge.svg)](https://github.com/sile/fanova/actions) [![Coverage Status](https://coveralls.io/repos/github/sile/fanova/badge.svg?branch=master)](https://coveralls.io/github/sile/fanova?branch=master) [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE) A Rust [fANOVA] (functional analysis of variance) implementation. fANOVA provides a way to calculate feature importance. Examples -------- ```rust use fanova::{FanovaOptions, RandomForestOptions}; use rand::{Rng, SeedableRng}; let mut feature1 = Vec::new(); let mut feature2 = Vec::new(); let mut feature3 = Vec::new(); let mut target = Vec::new(); let mut rng = rand::rngs::StdRng::seed_from_u64(0); for _ in 0..100 { let f1 = rng.gen(); let f2 = rng.gen(); let f3 = rng.gen(); let t = f1 + f2 * 2.0 + f3 * 3.0; feature1.push(f1); feature2.push(f2); feature3.push(f3); target.push(t); } let mut fanova = FanovaOptions::new() .random_forest(RandomForestOptions::new().seed(0)) .fit(vec![&feature1, &feature2, &feature3], &target).unwrap(); let importances = (0..3) .map(|i| fanova.quantify_importance(&[i]).mean) .collect::>(); assert_eq!( importances, vec![0.02744461966313835, 0.22991883769286145, 0.6288784011550144] ); ``` References ---------- - [An Efficient Approach for Assessing Hyperparameter Importance][fANOVA] [fANOVA]: http://proceedings.mlr.press/v32/hutter14.html