Files
smartcore/src/metrics/r2.rs

83 lines
2.4 KiB
Rust

//! Coefficient of Determination (R2)
//!
//! Coefficient of determination, denoted R2 is the proportion of the variance in the dependent variable that can be explained be explanatory (independent) variable(s).
//!
//! \\[R^2(y, \hat{y}) = 1 - \frac{\sum_{i=1}^{n}(y_i - \hat{y_i})^2}{\sum_{i=1}^{n}(y_i - \bar{y})^2} \\]
//!
//! where \\(\hat{y}\\) are predictions, \\(y\\) are true target values, \\(\bar{y}\\) is the mean of the observed data
//!
//! Example:
//!
//! ```
//! use smartcore::metrics::mean_absolute_error::MeanAbsoluteError;
//! let y_pred: Vec<f64> = vec![3., -0.5, 2., 7.];
//! let y_true: Vec<f64> = vec![2.5, 0.0, 2., 8.];
//!
//! let mse: f64 = MeanAbsoluteError {}.get_score(&y_pred, &y_true);
//! ```
//!
//! <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
//! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
use serde::{Deserialize, Serialize};
use crate::linalg::BaseVector;
use crate::math::num::RealNumber;
/// Coefficient of Determination (R2)
#[derive(Serialize, Deserialize, Debug)]
pub struct R2 {}
impl R2 {
/// Computes R2 score
/// * `y_true` - Ground truth (correct) target values.
/// * `y_pred` - Estimated target values.
pub fn get_score<T: RealNumber, V: BaseVector<T>>(&self, y_true: &V, y_pred: &V) -> T {
if y_true.len() != y_pred.len() {
panic!(
"The vector sizes don't match: {} != {}",
y_true.len(),
y_pred.len()
);
}
let n = y_true.len();
let mut mean = T::zero();
for i in 0..n {
mean += y_true.get(i);
}
mean /= T::from_usize(n).unwrap();
let mut ss_tot = T::zero();
let mut ss_res = T::zero();
for i in 0..n {
let y_i = y_true.get(i);
let f_i = y_pred.get(i);
ss_tot += (y_i - mean).square();
ss_res += (y_i - f_i).square();
}
T::one() - (ss_res / ss_tot)
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn r2() {
let y_true: Vec<f64> = vec![3., -0.5, 2., 7.];
let y_pred: Vec<f64> = vec![2.5, 0.0, 2., 8.];
let score1: f64 = R2 {}.get_score(&y_true, &y_pred);
let score2: f64 = R2 {}.get_score(&y_true, &y_true);
assert!((score1 - 0.948608137).abs() < 1e-8);
assert!((score2 - 1.0).abs() < 1e-8);
}
}