datarust 0.4.0

Scikit-Learn Preprocessing in Rust
Documentation 
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//! Integration tests for `linear_model::LogisticRegression` and
//! `metrics::classification`.

use datarust::linear_model::LogisticRegression;
use datarust::metrics::classification::{
    accuracy_score, confusion_matrix, f1_score, log_loss, precision_score, recall_score,
};
use datarust::traits::Regressor;
use datarust::Matrix;

fn approx(a: f64, b: f64, tol: f64) -> bool {
    (a - b).abs() < tol
}

/// Linearly separable: y = 1 when x0 > 0, no sample at the boundary.
fn separable() -> (Matrix, Vec<f64>) {
    let rows: Vec<Vec<f64>> = (-5..=5)
        .map(|i| vec![i as f64 * 0.5])
        .filter(|r| r[0].abs() > 0.01)
        .collect();
    let x = Matrix::new(rows.clone()).unwrap();
    let y: Vec<f64> = rows
        .iter()
        .map(|r| if r[0] > 0.0 { 1.0 } else { 0.0 })
        .collect();
    (x, y)
}

#[test]
fn classifies_separable_data_perfectly() {
    let (x, y) = separable();
    let mut model = LogisticRegression::new().with_max_iter(100);
    model.fit(&x, &y).unwrap();
    let classes = model.predict_class(&x).unwrap();
    assert_eq!(classes, y);
}

#[test]
fn score_is_one_on_separable_data() {
    let (x, y) = separable();
    let mut model = LogisticRegression::new();
    model.fit(&x, &y).unwrap();
    let acc = model.score(&x, &y).unwrap();
    assert!(approx(acc, 1.0, 1e-9));
}

#[test]
fn predict_returns_probabilities() {
    let (x, y) = separable();
    let mut model = LogisticRegression::new();
    model.fit(&x, &y).unwrap();
    let probs = model.predict(&x).unwrap();
    for &p in &probs {
        assert!((0.0..=1.0).contains(&p));
    }
    // Negative samples should have p < 0.5, positive p > 0.5.
    for (&p, &t) in probs.iter().zip(y.iter()) {
        if t == 0.0 {
            assert!(p < 0.5);
        } else {
            assert!(p > 0.5);
        }
    }
}

#[test]
fn predict_class_thresholds_at_half() {
    let (x, y) = separable();
    let mut model = LogisticRegression::new();
    model.fit(&x, &y).unwrap();
    let probs = model.predict(&x).unwrap();
    let classes = model.predict_class(&x).unwrap();
    for (&p, &c) in probs.iter().zip(classes.iter()) {
        let expected = if p >= 0.5 { 1.0 } else { 0.0 };
        assert_eq!(c, expected);
    }
}

#[test]
fn n_iter_positive_and_bounded() {
    let (x, y) = separable();
    let mut model = LogisticRegression::new().with_max_iter(50);
    model.fit(&x, &y).unwrap();
    assert!(model.n_iter() > 0);
    assert!(model.n_iter() <= 50);
}

#[test]
fn non_binary_labels_rejected() {
    let x = Matrix::new(vec![vec![1.0], vec![2.0], vec![3.0]]).unwrap();
    let mut model = LogisticRegression::new();
    let err = model.fit(&x, &[0.0, 1.0, 2.0]).unwrap_err();
    assert!(matches!(err, datarust::DatarustError::InvalidInput(_)));
}

#[test]
fn predict_before_fit_errors() {
    let model = LogisticRegression::new();
    let x = Matrix::new(vec![vec![1.0]]).unwrap();
    assert!(matches!(
        model.predict(&x).unwrap_err(),
        datarust::DatarustError::NotFitted(_)
    ));
}

#[test]
fn predict_shape_mismatch_errors() {
    let (x, y) = separable();
    let mut model = LogisticRegression::new();
    model.fit(&x, &y).unwrap();
    let bad = Matrix::new(vec![vec![1.0, 2.0]]).unwrap();
    assert!(matches!(
        model.predict(&bad).unwrap_err(),
        datarust::DatarustError::ShapeMismatch { .. }
    ));
}

#[test]
fn is_fitted_flag() {
    let (x, y) = separable();
    let mut model = LogisticRegression::new();
    assert!(!model.is_fitted());
    model.fit(&x, &y).unwrap();
    assert!(model.is_fitted());
}

#[test]
fn fit_intercept_false_still_classifies() {
    let (x, y) = separable();
    let mut model = LogisticRegression::new()
        .with_fit_intercept(false)
        .with_max_iter(100);
    model.fit(&x, &y).unwrap();
    assert!(model.intercept().abs() < 1e-12);
    let classes = model.predict_class(&x).unwrap();
    assert_eq!(classes, y);
}

#[test]
fn metrics_known_values() {
    let y_true = vec![0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0];
    let y_pred = vec![0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0];
    // cm = [[2,1],[1,3]], accuracy=5/7, precision=3/4, recall=3/4, f1=0.75
    assert!((accuracy_score(&y_true, &y_pred).unwrap() - 5.0 / 7.0).abs() < 1e-12);
    assert!((precision_score(&y_true, &y_pred).unwrap() - 0.75).abs() < 1e-12);
    assert!((recall_score(&y_true, &y_pred).unwrap() - 0.75).abs() < 1e-12);
    assert!((f1_score(&y_true, &y_pred).unwrap() - 0.75).abs() < 1e-12);
    assert_eq!(
        confusion_matrix(&y_true, &y_pred).unwrap(),
        [[2, 1], [1, 3]]
    );
}

#[test]
fn log_loss_known_value() {
    let y_true = vec![0.0, 0.0, 1.0, 1.0];
    let p = vec![0.1, 0.2, 0.8, 0.9];
    let ll = log_loss(&y_true, &p, 1e-15).unwrap();
    let expected = -(0.9_f64.ln() + 0.8_f64.ln() + 0.8_f64.ln() + 0.9_f64.ln()) / 4.0;
    assert!((ll - expected).abs() < 1e-12);
}

#[test]
fn perfect_classifier_metrics() {
    let y = vec![0.0, 1.0, 0.0, 1.0];
    assert!((accuracy_score(&y, &y).unwrap() - 1.0).abs() < 1e-12);
    assert!((precision_score(&y, &y).unwrap() - 1.0).abs() < 1e-12);
    assert!((recall_score(&y, &y).unwrap() - 1.0).abs() < 1e-12);
    assert!((f1_score(&y, &y).unwrap() - 1.0).abs() < 1e-12);
}

#[test]
fn metrics_length_mismatch_errors() {
    let err = accuracy_score(&[1.0, 0.0], &[1.0]).unwrap_err();
    assert!(matches!(err, datarust::DatarustError::ShapeMismatch { .. }));
}

#[test]
fn metrics_empty_errors() {
    let err = accuracy_score(&[], &[]).unwrap_err();
    assert!(matches!(err, datarust::DatarustError::EmptyInput(_)));
}

#[test]
fn logistic_implements_regressor_trait() {
    fn predict_via_trait<R: Regressor>(m: &R, x: &Matrix) -> usize {
        m.predict(x).map(|v| v.len()).unwrap_or(0)
    }
    let (x, y) = separable();
    let mut model = LogisticRegression::new();
    model.fit(&x, &y).unwrap();
    assert_eq!(predict_via_trait(&model, &x), x.nrows());
}