aprender-core 0.30.0

pub(crate) use super::*;

#[test]
fn test_r_squared_perfect() {
    let y_true = Vector::from_slice(&[1.0, 2.0, 3.0, 4.0, 5.0]);
    let y_pred = Vector::from_slice(&[1.0, 2.0, 3.0, 4.0, 5.0]);
    let r2 = r_squared(&y_pred, &y_true);
    assert!((r2 - 1.0).abs() < 1e-6);
}

#[test]
fn test_r_squared_good() {
    let y_true = Vector::from_slice(&[3.0, -0.5, 2.0, 7.0]);
    let y_pred = Vector::from_slice(&[2.5, 0.0, 2.0, 8.0]);
    let r2 = r_squared(&y_pred, &y_true);
    assert!(r2 > 0.9);
    assert!(r2 < 1.0);
}

#[test]
fn test_r_squared_mean_prediction() {
    // Predicting the mean gives R² = 0
    let y_true = Vector::from_slice(&[1.0, 2.0, 3.0, 4.0, 5.0]);
    let mean = y_true.mean();
    let y_pred = Vector::from_slice(&[mean, mean, mean, mean, mean]);
    let r2 = r_squared(&y_pred, &y_true);
    assert!(r2.abs() < 1e-6);
}

#[test]
fn test_r_squared_negative() {
    // Worse than mean prediction
    let y_true = Vector::from_slice(&[1.0, 2.0, 3.0]);
    let y_pred = Vector::from_slice(&[10.0, 10.0, 10.0]);
    let r2 = r_squared(&y_pred, &y_true);
    assert!(r2 < 0.0);
}

#[test]
fn test_mse() {
    let y_true = Vector::from_slice(&[3.0, -0.5, 2.0, 7.0]);
    let y_pred = Vector::from_slice(&[2.5, 0.0, 2.0, 8.0]);
    let error = mse(&y_pred, &y_true);
    // MSE = (0.25 + 0.25 + 0 + 1) / 4 = 0.375
    assert!((error - 0.375).abs() < 1e-6);
}

#[test]
fn test_mse_perfect() {
    let y_true = Vector::from_slice(&[1.0, 2.0, 3.0]);
    let y_pred = Vector::from_slice(&[1.0, 2.0, 3.0]);
    let error = mse(&y_pred, &y_true);
    assert!(error.abs() < 1e-6);
}

#[test]
fn test_mae() {
    let y_true = Vector::from_slice(&[3.0, -0.5, 2.0, 7.0]);
    let y_pred = Vector::from_slice(&[2.5, 0.0, 2.0, 8.0]);
    let error = mae(&y_pred, &y_true);
    // MAE = (0.5 + 0.5 + 0 + 1) / 4 = 0.5
    assert!((error - 0.5).abs() < 1e-6);
}

#[test]
fn test_mae_perfect() {
    let y_true = Vector::from_slice(&[1.0, 2.0, 3.0]);
    let y_pred = Vector::from_slice(&[1.0, 2.0, 3.0]);
    let error = mae(&y_pred, &y_true);
    assert!(error.abs() < 1e-6);
}

#[test]
fn test_rmse() {
    let y_true = Vector::from_slice(&[3.0, -0.5, 2.0, 7.0]);
    let y_pred = Vector::from_slice(&[2.5, 0.0, 2.0, 8.0]);
    let error = rmse(&y_pred, &y_true);
    assert!((error - 0.375_f32.sqrt()).abs() < 1e-6);
}

#[test]
fn test_inertia() {
    // 4 points in 2 clusters
    let data = Matrix::from_vec(4, 2, vec![0.0, 0.0, 1.0, 0.0, 10.0, 10.0, 11.0, 10.0])
        .expect("Matrix dimensions (4x2) match data length (8)");

    let centroids = Matrix::from_vec(
        2,
        2,
        vec![
            0.5, 0.0, // cluster 0 centroid
            10.5, 10.0, // cluster 1 centroid
        ],
    )
    .expect("Matrix dimensions (2x2) match data length (4)");

    let labels = vec![0, 0, 1, 1];
    let score = inertia(&data, &centroids, &labels);

    // Distance from (0,0) to (0.5,0) = 0.25
    // Distance from (1,0) to (0.5,0) = 0.25
    // Distance from (10,10) to (10.5,10) = 0.25
    // Distance from (11,10) to (10.5,10) = 0.25
    // Total = 1.0
    assert!((score - 1.0).abs() < 1e-6);
}

#[test]
fn test_silhouette_score_good() {
    // Well-separated clusters
    let data = Matrix::from_vec(4, 2, vec![0.0, 0.0, 0.1, 0.1, 10.0, 10.0, 10.1, 10.1])
        .expect("Matrix dimensions (4x2) match data length (8)");
    let labels = vec![0, 0, 1, 1];
    let score = silhouette_score(&data, &labels);
    assert!(score > 0.9);
}

#[test]
fn test_silhouette_score_poor() {
    // Overlapping clusters
    let data = Matrix::from_vec(4, 2, vec![0.0, 0.0, 1.0, 1.0, 0.5, 0.5, 1.5, 1.5])
        .expect("Matrix dimensions (4x2) match data length (8)");
    let labels = vec![0, 0, 1, 1];
    let score = silhouette_score(&data, &labels);
    // With overlapping data, silhouette should be lower
    assert!(score < 0.9);
}

#[test]
fn test_silhouette_score_single_cluster() {
    let data = Matrix::from_vec(4, 2, vec![0.0, 0.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0])
        .expect("Matrix dimensions (4x2) match data length (8)");
    let labels = vec![0, 0, 0, 0];
    let score = silhouette_score(&data, &labels);
    assert!((score - 0.0).abs() < 1e-6);
}

#[test]
fn test_silhouette_score_single_sample() {
    let data = Matrix::from_vec(1, 2, vec![0.0, 0.0])
        .expect("Matrix dimensions (1x2) match data length (2)");
    let labels = vec![0];
    let score = silhouette_score(&data, &labels);
    assert!((score - 0.0).abs() < 1e-6);
}

#[test]
fn test_silhouette_score_two_samples() {
    // Test with exactly 2 samples - catches n_samples < 2 → <= 2 mutation (line 288)
    let data = Matrix::from_vec(2, 2, vec![0.0, 0.0, 10.0, 10.0])
        .expect("Matrix dimensions (2x2) match data length (4)");
    let labels = vec![0, 1];
    let score = silhouette_score(&data, &labels);

    // With 2 samples in different clusters, silhouette should be meaningful (not 0)
    // If mutation changes < to <=, this would return 0.0
    assert!(
        score.abs() > 0.0,
        "Score with 2 samples should be non-zero, got {score}"
    );
}

#[test]
fn test_silhouette_score_distance_calculation() {
    // Test that catches arithmetic mutations in distance calculations
    // Line 204: / with % or * (mean_intra_cluster_distance)
    // Line 230: - with + (distance calculation)
    // Line 235: / with * (min_inter_cluster_distance)
    let data = Matrix::from_vec(
        6,
        1,
        vec![
            0.0,   // Cluster 0, point 0
            1.0,   // Cluster 0, point 1
            2.0,   // Cluster 0, point 2
            100.0, // Cluster 1, point 3
            101.0, // Cluster 1, point 4
            102.0, // Cluster 1, point 5
        ],
    )
    .expect("Matrix dimensions (6x1) match data length (6)");
    let labels = vec![0, 0, 0, 1, 1, 1];
    let score = silhouette_score(&data, &labels);

    // Well-separated clusters should have high silhouette score
    // If arithmetic is wrong, score will be very different
    assert!(
        score > 0.9,
        "Well-separated clusters should have high score, got {score}"
    );

    // Score should be reasonable (not > 1.0 which would indicate calculation error)
    assert!(score <= 1.0, "Score should be <= 1.0, got {score}");
}

#[test]
fn test_silhouette_score_mean_not_one() {
    // Test that catches mean_intra_cluster_distance -> 1.0 mutation (line 190)
    // Create clusters where mean intra-cluster distance is definitely not 1.0
    let data = Matrix::from_vec(
        4,
        1,
        vec![
            0.0,  // Cluster 0
            10.0, // Cluster 0 (distance = 10)
            50.0, // Cluster 1
            60.0, // Cluster 1 (distance = 10)
        ],
    )
    .expect("Matrix dimensions (4x1) match data length (4)");
    let labels = vec![0, 0, 1, 1];
    let score = silhouette_score(&data, &labels);

    // Verify the score calculation uses actual distances
    // Mean intra-cluster distance = 10.0 for each cluster
    // If mutation returns 1.0 instead, the score would be very different
    assert!(
        score > 0.5,
        "Score should be high for well-separated clusters, got {score}"
    );
}

#[test]
fn test_metrics_consistency() {
    // Property: RMSE = sqrt(MSE)
    let y_true = Vector::from_slice(&[1.0, 2.0, 3.0, 4.0, 5.0]);
    let y_pred = Vector::from_slice(&[1.1, 2.2, 2.9, 4.1, 5.0]);

    let mse_val = mse(&y_pred, &y_true);
    let rmse_val = rmse(&y_pred, &y_true);

    assert!((rmse_val - mse_val.sqrt()).abs() < 1e-6);
}

#[test]
fn test_silhouette_intra_cluster_averaging() {
    // Test that mean intra-cluster distance uses division, not multiplication
    // Cluster 0: points at 0, 1, 2 - mean distance from each point
    // Cluster 1: points at 100, 101, 102
    let data = Matrix::from_vec(6, 1, vec![0.0, 1.0, 2.0, 100.0, 101.0, 102.0])
        .expect("Matrix dimensions (6x1) match data length (6)");
    let labels = vec![0, 0, 0, 1, 1, 1];
    let score = silhouette_score(&data, &labels);

    // If division becomes multiplication in mean calculation:
    // distance sum * count instead of sum / count
    // This would give absurdly large values
    assert!(
        score > 0.9,
        "Should have high silhouette for well-separated clusters, got {score}"
    );
    assert!(score <= 1.0, "Silhouette score must be <= 1.0, got {score}");
}

#[test]
fn test_silhouette_inter_cluster_distance() {
    // Test that inter-cluster distance uses subtraction in norm
    // Two clusters: one near origin, one far away
    let data = Matrix::from_vec(4, 1, vec![0.0, 1.0, 1000.0, 1001.0])
        .expect("Matrix dimensions (4x1) match data length (4)");
    let labels = vec![0, 0, 1, 1];
    let score = silhouette_score(&data, &labels);

    // If subtraction becomes addition in distance calculation:
    // distances would be wrong
    assert!(
        score > 0.99,
        "Very well-separated clusters should have score > 0.99, got {score}"
    );
}

#[test]
fn test_silhouette_score_exact_boundary() {
    // Test boundary condition where samples < n_clusters check matters
    // With exactly 2 samples in different clusters
    let data = Matrix::from_vec(2, 1, vec![0.0, 100.0])
        .expect("Matrix dimensions (2x1) match data length (2)");
    let labels = vec![0, 1];
    let score = silhouette_score(&data, &labels);

    // This catches < vs <= mutations in sample count check
    // Score should still be computed (not return 0)
    assert!(
        score.abs() > 1e-6 || score == 0.0,
        "Score should be computed for 2 samples"
    );
}

#[test]
fn test_silhouette_many_clusters() {
    // Test with multiple clusters to verify min inter-cluster distance
    // 4 clusters, well separated
    let data = Matrix::from_vec(
        8,
        1,
        vec![
            0.0, 1.0, // Cluster 0
            100.0, 101.0, // Cluster 1
            200.0, 201.0, // Cluster 2
            300.0, 301.0, // Cluster 3
        ],
    )
    .expect("Matrix dimensions (8x1) match data length (8)");
    let labels = vec![0, 0, 1, 1, 2, 2, 3, 3];
    let score = silhouette_score(&data, &labels);

    // All clusters well-separated, should have high score
    assert!(
        score > 0.9,
        "Well-separated multi-cluster should have high score, got {score}"
    );
}