Function score_model

crates/openquant/src/feature_importance.rs:272–340

(
    model: &C,
    x_test: &[Vec<f64>],
    y_test: &[f64],
    sample_weight: Option<&[f64]>,
    scoring: Scoring,
)

}

fn score_model<C: SimpleClassifier>(
    model: &C,
    x_test: &[Vec<f64>],
    y_test: &[f64],
    sample_weight: Option<&[f64]>,
    scoring: Scoring,
) -> f64 {
    match scoring {
        Scoring::Accuracy => {
            let pred = model.predict(x_test);
            let mut num = 0.0;
            let mut den = 0.0;
            for i in 0..y_test.len() {
                let w = sample_weight.map(|sw| sw[i]).unwrap_or(1.0);
                den += w;
                if (pred[i] - y_test[i]).abs() < 1e-12 {
                    num += w;
                }
            }
            if den > 0.0 {
                num / den
            } else {
                0.0
            }
        }
        Scoring::NegLogLoss => {
            let probs = model.predict_proba(x_test);
            let mut loss = 0.0;
            let mut den = 0.0;
            let eps = 1e-15;
            for i in 0..y_test.len() {
                let w = sample_weight.map(|sw| sw[i]).unwrap_or(1.0);
                let p = probs[i].clamp(eps, 1.0 - eps);
                loss += w * (-(y_test[i] * p.ln() + (1.0 - y_test[i]) * (1.0 - p).ln()));
                den += w;
            }
            if den > 0.0 {
                -(loss / den)
            } else {
                0.0
            }
        }
        Scoring::F1 => {
            let pred = model.predict(x_test);
            let mut tp = 0.0;
            let mut fp = 0.0;
            let mut fnn = 0.0;
            for i in 0..y_test.len() {
                let w = sample_weight.map(|sw| sw[i]).unwrap_or(1.0);
                let p_pos = pred[i] > 0.5;
                let y_pos = y_test[i] > 0.5;
                if p_pos && y_pos {
                    tp += w;
                } else if p_pos && !y_pos {
                    fp += w;
                } else if !p_pos && y_pos {
                    fnn += w;
                }