rust_metrics 0.1.7

Incremental evaluation metrics for various machine learning pipelines.
Documentation 
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use crate::core::{Metric, MetricError};

use super::stat_scores::BinaryStatScores;

/// 2×2 confusion matrix for binary classification.
///
/// Returns a matrix in the
/// `[[TP, FP], [FN, TN]]` layout.
///
/// ```
/// use rust_metrics::{BinaryConfusionMatrix, Metric};
///
/// let target = [1_usize, 1, 0, 0];
/// let preds = [0.35, 0.85, 0.48, 0.01];
///
/// let mut bcm = BinaryConfusionMatrix::default();
/// bcm.update((&preds, &target)).unwrap();
/// assert_eq!(bcm.compute().unwrap(), [[1, 0], [1, 2]]);
/// ```
#[derive(Debug, Clone, Default)]
pub struct BinaryConfusionMatrix {
    pub stat_scores: BinaryStatScores,
}

impl BinaryConfusionMatrix {
    pub fn new(threshold: f64) -> Self {
        let stat_scores = BinaryStatScores::new(threshold);
        Self { stat_scores }
    }
}

impl Metric<(&[f64], &[usize])> for BinaryConfusionMatrix {
    type Output = [[usize; 2]; 2];

    fn update(&mut self, (predictions, targets): (&[f64], &[usize])) -> Result<(), MetricError> {
        self.stat_scores.update((predictions, targets))?;
        Ok(())
    }
    fn reset(&mut self) {
        self.stat_scores.reset();
    }
    fn compute(&self) -> Option<Self::Output> {
        if self.stat_scores.total == 0 {
            return None;
        }
        let confusion_matrix = [
            [
                self.stat_scores.true_positive,
                self.stat_scores.false_positive,
            ],
            [
                self.stat_scores.false_negative,
                self.stat_scores.true_negative,
            ],
        ];
        Some(confusion_matrix)
    }
}