Files
smartcore/src/metrics/accuracy.rs
Lorenzo (Mec-iS) d298709040 cargo clippy
2022-11-03 13:44:27 +00:00

118 lines
3.4 KiB
Rust

//! # Accuracy score
//!
//! Calculates accuracy of predictions \\(\hat{y}\\) when compared to true labels \\(y\\)
//!
//! \\[ accuracy(y, \hat{y}) = \frac{1}{n_{samples}} \sum_{i=1}^{n_{samples}} 1(y_i = \hat{y_i}) \\]
//!
//! Example:
//!
//! ```
//! use smartcore::metrics::accuracy::Accuracy;
//! use smartcore::metrics::Metrics;
//! let y_pred: Vec<f64> = vec![0., 2., 1., 3.];
//! let y_true: Vec<f64> = vec![0., 1., 2., 3.];
//!
//! let score: f64 = Accuracy::new().get_score( &y_true, &y_pred);
//! ```
//! With integers:
//! ```
//! use smartcore::metrics::accuracy::Accuracy;
//! use smartcore::metrics::Metrics;
//! let y_pred: Vec<i64> = vec![0, 2, 1, 3];
//! let y_true: Vec<i64> = vec![0, 1, 2, 3];
//!
//! let score: f64 = Accuracy::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>
#[cfg(feature = "serde")]
use serde::{Deserialize, Serialize};
use crate::linalg::basic::arrays::ArrayView1;
use crate::numbers::basenum::Number;
use std::marker::PhantomData;
use crate::metrics::Metrics;
/// Accuracy metric.
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug)]
pub struct Accuracy<T> {
_phantom: PhantomData<T>,
}
impl<T: Number> Metrics<T> for Accuracy<T> {
/// create a typed object to call Accuracy functions
fn new() -> Self {
Self {
_phantom: PhantomData,
}
}
fn new_with(_parameter: f64) -> Self {
Self {
_phantom: PhantomData,
}
}
/// Function that calculated accuracy score.
/// * `y_true` - cround truth (correct) labels
/// * `y_pred` - predicted labels, as returned by a classifier.
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 mut positive: i32 = 0;
for i in 0..n {
if *y_true.get(i) == *y_pred.get(i) {
positive += 1;
}
}
positive as f64 / n as f64
}
}
#[cfg(test)]
mod tests {
use super::*;
#[cfg_attr(
all(target_arch = "wasm32", not(target_os = "wasi")),
wasm_bindgen_test::wasm_bindgen_test
)]
#[test]
fn accuracy_float() {
let y_pred: Vec<f64> = vec![0., 2., 1., 3.];
let y_true: Vec<f64> = vec![0., 1., 2., 3.];
let score1: f64 = Accuracy::<f64>::new().get_score(&y_true, &y_pred);
let score2: f64 = Accuracy::<f64>::new().get_score(&y_true, &y_true);
assert!((score1 - 0.5).abs() < 1e-8);
assert!((score2 - 1.0).abs() < 1e-8);
}
#[cfg_attr(
all(target_arch = "wasm32", not(target_os = "wasi")),
wasm_bindgen_test::wasm_bindgen_test
)]
#[test]
fn accuracy_int() {
let y_pred: Vec<i32> = vec![0, 2, 1, 3];
let y_true: Vec<i32> = vec![0, 1, 2, 3];
let score1: f64 = Accuracy::<i32>::new().get_score(&y_true, &y_pred);
let score2: f64 = Accuracy::<i32>::new().get_score(&y_true, &y_true);
assert_eq!(score1, 0.5);
assert_eq!(score2, 1.0);
}
}