100 lines
3.0 KiB
Rust
100 lines
3.0 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;
|
|
//! use smartcore::metrics::Metrics;
|
|
//! 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::new().get_score( &y_true, &y_pred);
|
|
//! ```
|
|
//!
|
|
//! <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 std::marker::PhantomData;
|
|
|
|
#[cfg(feature = "serde")]
|
|
use serde::{Deserialize, Serialize};
|
|
|
|
use crate::linalg::basic::arrays::ArrayView1;
|
|
use crate::numbers::basenum::Number;
|
|
|
|
use crate::metrics::Metrics;
|
|
|
|
/// Coefficient of Determination (R2)
|
|
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
|
|
#[derive(Debug)]
|
|
pub struct R2<T> {
|
|
_phantom: PhantomData<T>,
|
|
}
|
|
|
|
impl<T: Number> Metrics<T> for R2<T> {
|
|
/// create a typed object to call R2 functions
|
|
fn new() -> Self {
|
|
Self {
|
|
_phantom: PhantomData,
|
|
}
|
|
}
|
|
fn new_with(_parameter: f64) -> Self {
|
|
Self {
|
|
_phantom: PhantomData,
|
|
}
|
|
}
|
|
/// Computes R2 score
|
|
/// * `y_true` - Ground truth (correct) target values.
|
|
/// * `y_pred` - Estimated target values.
|
|
fn get_score(&self, y_true: &dyn ArrayView1<T>, y_pred: &dyn ArrayView1<T>) -> f64 {
|
|
if y_true.shape() != y_pred.shape() {
|
|
panic!(
|
|
"The vector sizes don't match: {} != {}",
|
|
y_true.shape(),
|
|
y_pred.shape()
|
|
);
|
|
}
|
|
|
|
let n = y_true.shape();
|
|
|
|
let mean: f64 = y_true.mean_by();
|
|
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 - T::from(mean).unwrap()) * (y_i - T::from(mean).unwrap());
|
|
ss_res += (y_i - f_i) * (y_i - f_i);
|
|
}
|
|
|
|
(T::one() - ss_res / ss_tot).to_f64().unwrap()
|
|
}
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
|
|
#[cfg_attr(
|
|
all(target_arch = "wasm32", not(target_os = "wasi")),
|
|
wasm_bindgen_test::wasm_bindgen_test
|
|
)]
|
|
#[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::new().get_score(&y_true, &y_pred);
|
|
let score2: f64 = R2::new().get_score(&y_true, &y_true);
|
|
|
|
assert!((score1 - 0.948608137).abs() < 1e-8);
|
|
assert!((score2 - 1.0).abs() < 1e-8);
|
|
}
|
|
}
|