smartcore 0.3.0

Machine Learning in Rust.
Documentation 
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//! # Precision score
//!
//! How many predicted items are relevant?
//!
//! \\[precision = \frac{tp}{tp + fp}\\]
//!
//! where tp (true positive) - correct result, fp (false positive) - unexpected result
//!
//! Example:
//!
//! ```
//! use smartcore::metrics::precision::Precision;
//! use smartcore::metrics::Metrics;
//! let y_pred: Vec<f64> = vec![0., 1., 1., 0.];
//! let y_true: Vec<f64> = vec![0., 0., 1., 1.];
//!
//! let score: f64 = Precision::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::collections::HashSet;
use std::marker::PhantomData;

#[cfg(feature = "serde")]
use serde::{Deserialize, Serialize};

use crate::linalg::basic::arrays::ArrayView1;
use crate::numbers::realnum::RealNumber;

use crate::metrics::Metrics;

/// Precision metric.
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug)]
pub struct Precision<T> {
    _phantom: PhantomData<T>,
}

impl<T: RealNumber> Metrics<T> for Precision<T> {
    /// create a typed object to call Precision functions
    fn new() -> Self {
        Self {
            _phantom: PhantomData,
        }
    }
    fn new_with(_parameter: f64) -> Self {
        Self {
            _phantom: PhantomData,
        }
    }
    /// Calculated precision score
    /// * `y_true` - ground 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 mut classes = HashSet::new();
        for i in 0..y_true.shape() {
            classes.insert(y_true.get(i).to_f64_bits());
        }
        let classes = classes.len();

        let mut tp = 0;
        let mut fp = 0;
        for i in 0..y_true.shape() {
            if y_pred.get(i) == y_true.get(i) {
                if classes == 2 {
                    if *y_true.get(i) == T::one() {
                        tp += 1;
                    }
                } else {
                    tp += 1;
                }
            } else if classes == 2 {
                if *y_true.get(i) == T::one() {
                    fp += 1;
                }
            } else {
                fp += 1;
            }
        }

        tp as f64 / (tp as f64 + fp 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 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::new().get_score(&y_true, &y_pred);
        let score2: f64 = Precision::new().get_score(&y_pred, &y_pred);

        assert!((score1 - 0.5).abs() < 1e-8);
        assert!((score2 - 1.0).abs() < 1e-8);

        let y_true: Vec<f64> = vec![0., 1., 1., 0., 1., 0.];
        let y_pred: Vec<f64> = vec![0., 0., 1., 1., 1., 1.];

        let score3: f64 = Precision::new().get_score(&y_true, &y_pred);
        assert!((score3 - 0.6666666666).abs() < 1e-8);
    }

    #[cfg_attr(
        all(target_arch = "wasm32", not(target_os = "wasi")),
        wasm_bindgen_test::wasm_bindgen_test
    )]
    #[test]
    fn precision_multiclass() {
        let y_true: Vec<f64> = vec![0., 0., 0., 1., 1., 1., 2., 2., 2.];
        let y_pred: Vec<f64> = vec![0., 1., 2., 0., 1., 2., 0., 1., 2.];

        let score1: f64 = Precision::new().get_score(&y_true, &y_pred);
        let score2: f64 = Precision::new().get_score(&y_pred, &y_pred);

        assert!((score1 - 0.333333333).abs() < 1e-8);
        assert!((score2 - 1.0).abs() < 1e-8);
    }
}