Function search_over_params

crates/openquant/src/hyperparameter_tuning.rs:320–358

(
    build_classifier: F,
    param_sets: Vec<ParamSet>,
    data: SearchData<'_>,
    n_splits: usize,
    pct_embargo: f64,
    scoring: SearchScoring,
)

}

fn search_over_params<C, F>(
    build_classifier: F,
    param_sets: Vec<ParamSet>,
    data: SearchData<'_>,
    n_splits: usize,
    pct_embargo: f64,
    scoring: SearchScoring,
) -> Result<SearchResult, String>
where
    C: SimpleClassifier,
    F: Fn(&ParamSet) -> C,
{
    validate_search_data(&data, n_splits)?;
    let cv = PurgedKFold::new(n_splits, data.samples_info_sets.to_vec(), pct_embargo)?;
    let splits = cv.split(data.x.len())?;

    let mut trials = Vec::with_capacity(param_sets.len());
    for params in param_sets {
        let fold_scores = evaluate_params(
            &build_classifier,
            &params,
            &splits,
            data.x,
            data.y,
            data.sample_weight,
            scoring,
        )?;
        let mean_score = fold_scores.iter().sum::<f64>() / fold_scores.len() as f64;
        trials.push(SearchTrial { params, fold_scores, mean_score });
    }

    let best = trials
        .iter()
        .max_by(|a, b| a.mean_score.partial_cmp(&b.mean_score).unwrap_or(std::cmp::Ordering::Equal))
        .cloned()
        .ok_or_else(|| "no trials produced".to_string())?;

    Ok(SearchResult { best_params: best.params, best_score: best.mean_score, trials })
}

fn validate_search_data(data: &SearchData<'_>, n_splits: usize) -> Result<(), String> {
    if data.x.is_empty() {