aprender-core 0.31.2

// =========================================================================
// FALSIFY-HC: Agglomerative (hierarchical) clustering contract (aprender cluster)
//
// Five-Whys (PMAT-354):
//   Why 1: aprender had no inline FALSIFY-HC-* tests for AgglomerativeClustering
//   Why 2: agglomerative tests exist but lack contract-mapped FALSIFY naming
//   Why 3: no YAML contract for hierarchical clustering yet
//   Why 4: aprender predates the inline FALSIFY convention
//   Why 5: Hierarchical clustering was "obviously correct" (textbook algorithm)
//
// References:
//   - Ward (1963) "Hierarchical grouping to optimize an objective function"
// =========================================================================

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

/// FALSIFY-HC-001: Labels length matches sample count
#[test]
fn falsify_hc_001_labels_length() {
    let data = Matrix::from_vec(
        6,
        2,
        vec![
            0.0, 0.0, 0.1, 0.1, 0.2, 0.0, 10.0, 10.0, 10.1, 10.1, 10.0, 10.2,
        ],
    )
    .expect("valid matrix");

    let mut hc = AgglomerativeClustering::new(2, Linkage::Average);
    hc.fit(&data).expect("fit succeeds");

    let labels = hc.labels();
    assert_eq!(
        labels.len(),
        6,
        "FALSIFIED HC-001: labels len={}, expected 6",
        labels.len()
    );
}

/// FALSIFY-HC-002: Number of distinct labels equals n_clusters
#[test]
fn falsify_hc_002_n_clusters_correct() {
    let data = Matrix::from_vec(
        6,
        2,
        vec![
            0.0, 0.0, 0.1, 0.1, 0.2, 0.0, 10.0, 10.0, 10.1, 10.1, 10.0, 10.2,
        ],
    )
    .expect("valid matrix");

    let mut hc = AgglomerativeClustering::new(2, Linkage::Ward);
    hc.fit(&data).expect("fit succeeds");

    let labels = hc.labels();
    let mut unique: Vec<usize> = labels.clone();
    unique.sort_unstable();
    unique.dedup();

    assert_eq!(
        unique.len(),
        2,
        "FALSIFIED HC-002: unique labels={}, expected 2",
        unique.len()
    );
}

/// FALSIFY-HC-003: Two well-separated clusters get distinct labels
#[test]
fn falsify_hc_003_distinct_clusters() {
    let data = Matrix::from_vec(
        6,
        2,
        vec![
            0.0, 0.0, 0.1, 0.0, 0.0, 0.1, 100.0, 100.0, 100.1, 100.0, 100.0, 100.1,
        ],
    )
    .expect("valid matrix");

    let mut hc = AgglomerativeClustering::new(2, Linkage::Single);
    hc.fit(&data).expect("fit succeeds");

    let labels = hc.labels();
    // Cluster A points should share a label
    assert_eq!(
        labels[0], labels[1],
        "FALSIFIED HC-003: cluster A inconsistent"
    );
    assert_eq!(
        labels[1], labels[2],
        "FALSIFIED HC-003: cluster A inconsistent"
    );
    // Different clusters
    assert_ne!(
        labels[0], labels[3],
        "FALSIFIED HC-003: clusters have same label={}",
        labels[0]
    );
}

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

    // FALSIFY-HC-001-prop: Labels length matches sample count for random sizes
    proptest! {
        #![proptest_config(ProptestConfig::with_cases(15))]

        #[test]
        fn falsify_hc_001_prop_labels_length(
            n in 4..=15usize,
            seed in 0..500u32,
        ) {
            let data: Vec<f32> = (0..n * 2)
                .map(|i| ((i as f32 + seed as f32) * 0.37).sin() * 10.0)
                .collect();
            let matrix = Matrix::from_vec(n, 2, data).expect("valid");
            let mut hc = AgglomerativeClustering::new(2, Linkage::Average);
            hc.fit(&matrix).expect("fit");

            prop_assert_eq!(
                hc.labels().len(),
                n,
                "FALSIFIED HC-001-prop: labels len {} != {}",
                hc.labels().len(), n
            );
        }
    }

    // FALSIFY-HC-002-prop: Number of distinct labels equals n_clusters
    proptest! {
        #![proptest_config(ProptestConfig::with_cases(15))]

        #[test]
        fn falsify_hc_002_prop_n_clusters(
            n in 6..=15usize,
            seed in 0..500u32,
        ) {
            let data: Vec<f32> = (0..n * 2)
                .map(|i| ((i as f32 + seed as f32) * 0.37).sin() * 10.0)
                .collect();
            let matrix = Matrix::from_vec(n, 2, data).expect("valid");
            let mut hc = AgglomerativeClustering::new(2, Linkage::Ward);
            hc.fit(&matrix).expect("fit");

            let labels = hc.labels();
            let mut unique: Vec<usize> = labels.clone();
            unique.sort_unstable();
            unique.dedup();

            prop_assert_eq!(
                unique.len(),
                2,
                "FALSIFIED HC-002-prop: unique labels {} != 2",
                unique.len()
            );
        }
    }
}