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use serde::{Deserialize, Serialize};
use crate::linalg::BaseVector;
use crate::math::num::RealNumber;
#[derive(Serialize, Deserialize, Debug)]
pub struct Precision {}
impl Precision {
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 mut tp = 0;
let mut p = 0;
let n = y_true.len();
for i in 0..n {
if y_true.get(i) != T::zero() && y_true.get(i) != T::one() {
panic!(
"Precision can only be applied to binary classification: {}",
y_true.get(i)
);
}
if y_pred.get(i) != T::zero() && y_pred.get(i) != T::one() {
panic!(
"Precision can only be applied to binary classification: {}",
y_pred.get(i)
);
}
if y_pred.get(i) == T::one() {
p += 1;
if y_true.get(i) == T::one() {
tp += 1;
}
}
}
T::from_i64(tp).unwrap() / T::from_i64(p).unwrap()
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn precision() {
let y_true: Vec<f64> = vec![0., 1., 1., 0.];
let y_pred: Vec<f64> = vec![0., 0., 1., 1.];
let score1: f64 = Precision {}.get_score(&y_pred, &y_true);
let score2: f64 = Precision {}.get_score(&y_pred, &y_pred);
assert!((score1 - 0.5).abs() < 1e-8);
assert!((score2 - 1.0).abs() < 1e-8);
}
}