ferrolearn-linear 0.5.0

//! Divergence: `OrthogonalMatchingPursuit::fit` default-construction path
//! (`ferrolearn-linear/src/omp.rs`) vs scikit-learn 1.5.2
//! `sklearn/linear_model/_omp.py` `OrthogonalMatchingPursuit.fit`.
//!
//! When both `n_nonzero_coefs` and `tol` are `None`, sklearn applies
//! `self.n_nonzero_coefs_ = max(int(0.1 * n_features), 1)`
//! (`sklearn/linear_model/_omp.py:783-785`) and FITS. ferrolearn's
//! `fn fit in omp.rs` instead returns `Err(FerroError::InvalidParameter)`
//! ("at least one stopping criterion must be set", omp.rs greedy-path guard).
//!
//! Oracle (live sklearn 1.5.2, load_diabetes, 10 features -> default n_nonzero=1):
//! ```text
//! python3 -c "from sklearn.linear_model import OrthogonalMatchingPursuit as OMP; \
//!   from sklearn.datasets import load_diabetes; X,y=load_diabetes(return_X_y=True); \
//!   m=OMP().fit(X,y); print(m.n_nonzero_coefs_, (m.coef_!=0).sum(), m.intercept_)"
//! # 1 1 152.13348416289617
//! ```
//!
//! Tracking: #488 (default n_nonzero_coefs).

use ferrolearn_core::introspection::HasCoefficients;
use ferrolearn_core::traits::Fit;
use ferrolearn_linear::OrthogonalMatchingPursuit;
use ndarray::{Array1, Array2};

fn diabetes() -> (Array2<f64>, Array1<f64>) {
    let x = Array2::from_shape_vec((442, 10), X_FLAT.to_vec()).unwrap();
    let y = Array1::from_vec(Y_FLAT.to_vec());
    (x, y)
}

// ---------------------------------------------------------------------------
// Oracle constants: sklearn 1.5.2 live-call (R-CHAR-3 — NOT copied from ferrolearn).
// ---------------------------------------------------------------------------

/// sklearn `OMP().fit(X,y).n_nonzero_coefs_` on diabetes (10 features) = max(int(0.1*10),1) = 1.
const SK_DEFAULT_N_NONZERO: usize = 1;
/// sklearn `OMP().fit(X,y).coef_` on diabetes (default construction).
const SK_DEFAULT_COEF: [f64; 10] = [
    0.0,
    0.0,
    949.4352603840393,
    0.0,
    0.0,
    0.0,
    0.0,
    0.0,
    0.0,
    0.0,
];
/// sklearn `OMP().fit(X,y).intercept_` on diabetes (default construction).
const SK_DEFAULT_INTERCEPT: f64 = 152.13348416289617;

/// sklearn `OMP(n_nonzero_coefs=5).fit(X,y).coef_` on diabetes (greedy control).
const SK_COEF_K5: [f64; 10] = [
    0.0,
    -235.77241317516382,
    523.5677863253115,
    326.2310639609741,
    0.0,
    0.0,
    -289.11483014668073,
    0.0,
    474.29023145993335,
    0.0,
];
/// sklearn `OMP(n_nonzero_coefs=5).fit(X,y).intercept_` on diabetes.
const SK_INTERCEPT_K5: f64 = 152.13348416289602;

// ---------------------------------------------------------------------------
// PRIMARY divergence (#488): default construction must FIT, not error.
// ---------------------------------------------------------------------------

/// Divergence: ferrolearn `OrthogonalMatchingPursuit::new().fit(X,y)` returns
/// `Err(FerroError::InvalidParameter)` (omp.rs greedy-path guard:
/// "at least one stopping criterion must be set"), whereas sklearn
/// `OrthogonalMatchingPursuit().fit(X,y)` (`_omp.py:783-785`) sets
/// `n_nonzero_coefs_ = max(int(0.1*n_features), 1)` and fits.
///
/// sklearn (diabetes, 10 features): selects 1 feature (index 2),
/// coef_[2] = 949.4352603840393, intercept_ = 152.13348416289617.
/// ferrolearn: Err.
///
/// Tracking: #488
#[test]
fn divergence_omp_default_n_nonzero_fits() {
    let (x, y) = diabetes();
    let fitted = OrthogonalMatchingPursuit::<f64>::new()
        .fit(&x, &y)
        .expect("sklearn OMP() fits with default n_nonzero_coefs = max(int(0.1*n_features),1); ferrolearn errors");

    let coef = fitted.coefficients();
    let nnz = coef.iter().filter(|&&c| c != 0.0).count();
    assert_eq!(
        nnz, SK_DEFAULT_N_NONZERO,
        "default OMP must select max(int(0.1*n_features),1)=1 feature on diabetes"
    );
    for (i, (&got, &want)) in coef.iter().zip(SK_DEFAULT_COEF.iter()).enumerate() {
        assert!(
            (got - want).abs() < 1e-6,
            "coef_[{i}]: ferrolearn {got} vs sklearn {want}"
        );
    }
    assert!(
        (fitted.intercept() - SK_DEFAULT_INTERCEPT).abs() < 1e-6,
        "intercept_: ferrolearn {} vs sklearn {SK_DEFAULT_INTERCEPT}",
        fitted.intercept()
    );
}

// ---------------------------------------------------------------------------
// CONTROL (un-ignored): greedy path (n_nonzero_coefs set) matches sklearn EXACTLY.
// Isolates the divergence to the DEFAULT logic, not the greedy algorithm.
// ---------------------------------------------------------------------------

/// Control: ferrolearn `OMP(n_nonzero_coefs=5)` coef_/intercept_ match sklearn
/// `OrthogonalMatchingPursuit(n_nonzero_coefs=5)` on diabetes within 1e-9.
/// Confirms the greedy path is correct; only the default construction diverges.
#[test]
fn control_omp_greedy_k5_matches_sklearn() {
    let (x, y) = diabetes();
    let fitted = OrthogonalMatchingPursuit::<f64>::new()
        .with_n_nonzero_coefs(5)
        .fit(&x, &y)
        .unwrap();
    let coef = fitted.coefficients();
    for (i, (&got, &want)) in coef.iter().zip(SK_COEF_K5.iter()).enumerate() {
        assert!(
            (got - want).abs() < 1e-9,
            "coef_[{i}]: ferrolearn {got} vs sklearn {want} (diff {})",
            (got - want).abs()
        );
    }
    assert!(
        (fitted.intercept() - SK_INTERCEPT_K5).abs() < 1e-9,
        "intercept_: ferrolearn {} vs sklearn {SK_INTERCEPT_K5}",
        fitted.intercept()
    );
}

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const Y_FLAT: [f64; 442] = [
    151.0, 75.0, 141.0, 206.0, 135.0, 97.0, 138.0, 63.0, 110.0, 310.0, 101.0, 69.0, 179.0, 185.0,
    118.0, 171.0, 166.0, 144.0, 97.0, 168.0, 68.0, 49.0, 68.0, 245.0, 184.0, 202.0, 137.0, 85.0,
    131.0, 283.0, 129.0, 59.0, 341.0, 87.0, 65.0, 102.0, 265.0, 276.0, 252.0, 90.0, 100.0, 55.0,
    61.0, 92.0, 259.0, 53.0, 190.0, 142.0, 75.0, 142.0, 155.0, 225.0, 59.0, 104.0, 182.0, 128.0,
    52.0, 37.0, 170.0, 170.0, 61.0, 144.0, 52.0, 128.0, 71.0, 163.0, 150.0, 97.0, 160.0, 178.0,
    48.0, 270.0, 202.0, 111.0, 85.0, 42.0, 170.0, 200.0, 252.0, 113.0, 143.0, 51.0, 52.0, 210.0,
    65.0, 141.0, 55.0, 134.0, 42.0, 111.0, 98.0, 164.0, 48.0, 96.0, 90.0, 162.0, 150.0, 279.0,
    92.0, 83.0, 128.0, 102.0, 302.0, 198.0, 95.0, 53.0, 134.0, 144.0, 232.0, 81.0, 104.0, 59.0,
    246.0, 297.0, 258.0, 229.0, 275.0, 281.0, 179.0, 200.0, 200.0, 173.0, 180.0, 84.0, 121.0,
    161.0, 99.0, 109.0, 115.0, 268.0, 274.0, 158.0, 107.0, 83.0, 103.0, 272.0, 85.0, 280.0, 336.0,
    281.0, 118.0, 317.0, 235.0, 60.0, 174.0, 259.0, 178.0, 128.0, 96.0, 126.0, 288.0, 88.0, 292.0,
    71.0, 197.0, 186.0, 25.0, 84.0, 96.0, 195.0, 53.0, 217.0, 172.0, 131.0, 214.0, 59.0, 70.0,
    220.0, 268.0, 152.0, 47.0, 74.0, 295.0, 101.0, 151.0, 127.0, 237.0, 225.0, 81.0, 151.0, 107.0,
    64.0, 138.0, 185.0, 265.0, 101.0, 137.0, 143.0, 141.0, 79.0, 292.0, 178.0, 91.0, 116.0, 86.0,
    122.0, 72.0, 129.0, 142.0, 90.0, 158.0, 39.0, 196.0, 222.0, 277.0, 99.0, 196.0, 202.0, 155.0,
    77.0, 191.0, 70.0, 73.0, 49.0, 65.0, 263.0, 248.0, 296.0, 214.0, 185.0, 78.0, 93.0, 252.0,
    150.0, 77.0, 208.0, 77.0, 108.0, 160.0, 53.0, 220.0, 154.0, 259.0, 90.0, 246.0, 124.0, 67.0,
    72.0, 257.0, 262.0, 275.0, 177.0, 71.0, 47.0, 187.0, 125.0, 78.0, 51.0, 258.0, 215.0, 303.0,
    243.0, 91.0, 150.0, 310.0, 153.0, 346.0, 63.0, 89.0, 50.0, 39.0, 103.0, 308.0, 116.0, 145.0,
    74.0, 45.0, 115.0, 264.0, 87.0, 202.0, 127.0, 182.0, 241.0, 66.0, 94.0, 283.0, 64.0, 102.0,
    200.0, 265.0, 94.0, 230.0, 181.0, 156.0, 233.0, 60.0, 219.0, 80.0, 68.0, 332.0, 248.0, 84.0,
    200.0, 55.0, 85.0, 89.0, 31.0, 129.0, 83.0, 275.0, 65.0, 198.0, 236.0, 253.0, 124.0, 44.0,
    172.0, 114.0, 142.0, 109.0, 180.0, 144.0, 163.0, 147.0, 97.0, 220.0, 190.0, 109.0, 191.0,
    122.0, 230.0, 242.0, 248.0, 249.0, 192.0, 131.0, 237.0, 78.0, 135.0, 244.0, 199.0, 270.0,
    164.0, 72.0, 96.0, 306.0, 91.0, 214.0, 95.0, 216.0, 263.0, 178.0, 113.0, 200.0, 139.0, 139.0,
    88.0, 148.0, 88.0, 243.0, 71.0, 77.0, 109.0, 272.0, 60.0, 54.0, 221.0, 90.0, 311.0, 281.0,
    182.0, 321.0, 58.0, 262.0, 206.0, 233.0, 242.0, 123.0, 167.0, 63.0, 197.0, 71.0, 168.0, 140.0,
    217.0, 121.0, 235.0, 245.0, 40.0, 52.0, 104.0, 132.0, 88.0, 69.0, 219.0, 72.0, 201.0, 110.0,
    51.0, 277.0, 63.0, 118.0, 69.0, 273.0, 258.0, 43.0, 198.0, 242.0, 232.0, 175.0, 93.0, 168.0,
    275.0, 293.0, 281.0, 72.0, 140.0, 189.0, 181.0, 209.0, 136.0, 261.0, 113.0, 131.0, 174.0,
    257.0, 55.0, 84.0, 42.0, 146.0, 212.0, 233.0, 91.0, 111.0, 152.0, 120.0, 67.0, 310.0, 94.0,
    183.0, 66.0, 173.0, 72.0, 49.0, 64.0, 48.0, 178.0, 104.0, 132.0, 220.0, 57.0,
];