aprender-core 0.29.2

// =========================================================================
// FALSIFY-KNN: knn tests (part of linear-models-v1.yaml / classification suite)
//
// Five-Whys (PMAT-354):
//   Why 1: aprender had no inline FALSIFY-KNN-* tests
//   Why 2: KNN tests only in tests/contracts/, not near implementation
//   Why 3: no mapping to inline test names
//   Why 4: aprender predates the inline FALSIFY convention
//   Why 5: KNN was "obviously correct" (nearest neighbor lookup)
//
// References:
//   - Cover & Hart (1967) "Nearest Neighbor Pattern Classification"
// =========================================================================

use super::*;
use crate::primitives::Matrix;

/// FALSIFY-KNN-001: Predictions in training label set
#[test]
fn falsify_knn_001_predictions_in_label_range() {
    let x = Matrix::from_vec(
        6,
        2,
        vec![0.0, 0.0, 0.5, 0.5, 1.0, 0.0, 5.0, 5.0, 5.5, 5.5, 6.0, 5.0],
    )
    .expect("valid");
    let y = vec![0_usize, 0, 0, 1, 1, 1];

    let mut knn = KNearestNeighbors::new(3);
    knn.fit(&x, &y).expect("fit");

    let preds = knn.predict(&x).expect("predict");
    for (i, &p) in preds.iter().enumerate() {
        assert!(
            p <= 1,
            "FALSIFIED KNN-001: prediction[{i}] = {p}, not in {{0, 1}}"
        );
    }
}

/// FALSIFY-KNN-002: Prediction count matches input count
#[test]
fn falsify_knn_002_prediction_count() {
    let x = Matrix::from_vec(
        6,
        2,
        vec![0.0, 0.0, 0.5, 0.5, 1.0, 0.0, 5.0, 5.0, 5.5, 5.5, 6.0, 5.0],
    )
    .expect("valid");
    let y = vec![0_usize, 0, 0, 1, 1, 1];

    let mut knn = KNearestNeighbors::new(3);
    knn.fit(&x, &y).expect("fit");

    let x_test = Matrix::from_vec(3, 2, vec![0.2, 0.2, 3.0, 3.0, 5.8, 5.8]).expect("valid");
    let preds = knn.predict(&x_test).expect("predict");
    assert_eq!(
        preds.len(),
        3,
        "FALSIFIED KNN-002: {} predictions for 3 inputs",
        preds.len()
    );
}

/// FALSIFY-KNN-003: Well-separated clusters classified correctly
#[test]
fn falsify_knn_003_separable_data() {
    let x = Matrix::from_vec(
        6,
        2,
        vec![
            0.0, 0.0, 0.1, 0.1, 0.2, 0.2, 100.0, 100.0, 100.1, 100.1, 100.2, 100.2,
        ],
    )
    .expect("valid");
    let y = vec![0_usize, 0, 0, 1, 1, 1];

    let mut knn = KNearestNeighbors::new(3);
    knn.fit(&x, &y).expect("fit");

    let preds = knn.predict(&x).expect("predict");
    assert_eq!(
        preds, y,
        "FALSIFIED KNN-003: KNN cannot classify well-separated clusters"
    );
}

/// FALSIFY-KNN-004: Deterministic predictions
#[test]
fn falsify_knn_004_deterministic() {
    let x = Matrix::from_vec(4, 2, vec![0.0, 0.0, 1.0, 1.0, 5.0, 5.0, 6.0, 6.0]).expect("valid");
    let y = vec![0_usize, 0, 1, 1];

    let mut knn = KNearestNeighbors::new(1);
    knn.fit(&x, &y).expect("fit");

    let p1 = knn.predict(&x).expect("predict 1");
    let p2 = knn.predict(&x).expect("predict 2");
    assert_eq!(
        p1, p2,
        "FALSIFIED KNN-004: predictions differ on same input"
    );
}

mod knn_proptest_falsify {
    use super::*;
    use proptest::prelude::*;

    // FALSIFY-KNN-002-prop: Prediction count matches input count
    proptest! {
        #![proptest_config(ProptestConfig::with_cases(15))]

        #[test]
        fn falsify_knn_002_prop_prediction_count(
            n_train in 6..=12usize,
            n_test in 3..=8usize,
            seed in 0..200u32,
        ) {
            // Construct two well-separated clusters for training
            let mut x_data = Vec::with_capacity(n_train * 2);
            let mut y_data = Vec::with_capacity(n_train);
            let half = n_train / 2;
            for i in 0..half {
                let offset = (seed as f32 + i as f32) * 0.01;
                x_data.push(0.0 + offset);
                x_data.push(0.0 + offset);
                y_data.push(0_usize);
            }
            for i in 0..(n_train - half) {
                let offset = (seed as f32 + i as f32) * 0.01;
                x_data.push(10.0 + offset);
                x_data.push(10.0 + offset);
                y_data.push(1_usize);
            }
            let x = Matrix::from_vec(n_train, 2, x_data).expect("valid");

            let mut knn = KNearestNeighbors::new(3.min(half));
            knn.fit(&x, &y_data).expect("fit");

            let x_test_data: Vec<f32> = (0..n_test * 2)
                .map(|i| ((i as f32 + seed as f32) * 0.37).sin() * 5.0 + 5.0)
                .collect();
            let x_test = Matrix::from_vec(n_test, 2, x_test_data).expect("valid");

            let preds = knn.predict(&x_test).expect("predict");
            prop_assert_eq!(
                preds.len(),
                n_test,
                "FALSIFIED KNN-002-prop: {} predictions for {} inputs",
                preds.len(), n_test
            );
        }
    }

    // FALSIFY-KNN-004-prop: Deterministic predictions for random data
    proptest! {
        #![proptest_config(ProptestConfig::with_cases(15))]

        #[test]
        fn falsify_knn_004_prop_deterministic(
            seed in 0..200u32,
        ) {
            let x = Matrix::from_vec(
                6, 2,
                vec![0.0, 0.0, 0.5, 0.5, 1.0, 0.0,
                     5.0, 5.0, 5.5, 5.5, 6.0, 5.0],
            ).expect("valid");
            let y = vec![0_usize, 0, 0, 1, 1, 1];

            let k = ((seed % 3) + 1) as usize;
            let mut knn = KNearestNeighbors::new(k);
            knn.fit(&x, &y).expect("fit");

            let p1 = knn.predict(&x).expect("predict 1");
            let p2 = knn.predict(&x).expect("predict 2");
            prop_assert_eq!(
                p1, p2,
                "FALSIFIED KNN-004-prop: predictions differ (k={})",
                k
            );
        }
    }
}