Metrics

Model evaluation. Mirrors sklearn.metrics. Live in datarust::metrics.

Regression metrics

In datarust::metrics::regression. Each takes y_true and y_pred as &[f64].

#![allow(unused)]
fn main() {
use datarust::metrics::regression::*;

let mse  = mean_squared_error(&y_true, &y_pred, true)?;   // squared=true → MSE
let rmse = mean_squared_error(&y_true, &y_pred, false)?;  // squared=false → RMSE
let mae  = mean_absolute_error(&y_true, &y_pred)?;
let r2   = r2_score(&y_true, &y_pred)?;
let me   = max_error(&y_true, &y_pred)?;
let ev   = explained_variance_score(&y_true, &y_pred)?;
}

Classification metrics

In datarust::metrics::classification. Hard-label metrics accept arbitrary non-negative integer labels such as {2, 5, 9} and compact them internally. Fractional, negative, or non-finite labels are rejected. The compatibility precision, recall, and F1 functions use macro averaging; the *_with variants make the averaging strategy explicit.

#![allow(unused)]
fn main() {
use datarust::metrics::classification::*;

// Hard-label metrics (work for binary and multiclass):
let acc  = accuracy_score(&y_true, &y_pred)?;
let prec = precision_score(&y_true, &y_pred)?;     // macro-avg for multiclass
let rec  = recall_score(&y_true, &y_pred)?;
let f1   = f1_score(&y_true, &y_pred)?;
let cm   = confusion_matrix(&y_true, &y_pred)?;    // compact Vec<Vec<usize>>, n×n
let labeled = confusion_matrix_labeled(&y_true, &y_pred)?;
// labeled.labels[i] identifies row and column i

let weighted_f1 = f1_score_with(&y_true, &y_pred, Average::Weighted)?;
let positive_f1 = f1_score_with(
    &y_true,
    &y_pred,
    Average::Binary { positive_label: 5.0 },
)?;
let per_class = classification_report(&y_true, &y_pred)?;
let ll   = log_loss(&y_true, &y_proba, 1e-15)?;     // {0, 1}; P(label = 1)

// Ranking metrics for {0, 1}; scores refer to label 1:
let auc  = roc_auc_score(&y_true, &y_score)?;        // ROC-AUC (Mann–Whitney U)
let ap   = average_precision_score(&y_true, &y_score)?; // average precision

// For another binary label space, name the positive class explicitly:
let ll_custom = log_loss_with_positive_label(&y_true, &y_proba, 5.0, 1e-15)?;
let auc_custom = roc_auc_score_with_positive_label(&y_true, &y_score, 5.0)?;
let ap_custom = average_precision_score_with_positive_label(&y_true, &y_score, 5.0)?;

// Agreement & correlation (binary + multiclass):
let kap  = cohen_kappa_score(&y_true, &y_pred)?;     // chance-corrected agreement
let mcc  = matthews_corrcoef(&y_true, &y_pred)?;     // Matthews correlation
}

precision_score_with_options, recall_score_with_options, and f1_score_with_options additionally accept ZeroDivision::Zero (the default) or ZeroDivision::Error for undefined class metrics.

Clustering metrics

In datarust::cluster::metrics. Evaluate clustering quality without ground-truth labels.

#![allow(unused)]
fn main() {
use datarust::cluster::metrics::silhouette_score;

let s = silhouette_score(&x, &labels)?;  // [-1, 1], higher is better
}

Cluster IDs are compacted internally rather than used as vector indices. At least two clusters and fewer clusters than samples are required, and a sample in a singleton cluster receives coefficient zero.

Estimator .score() shorthand

Every estimator has a built-in score method:

• Regression models (LinearRegression, Ridge, Lasso) → R².
• LogisticRegression → accuracy.

#![allow(unused)]
fn main() {
let r2 = ridge.score(&x, &y)?;       // R²
let acc = logistic.score(&x, &y)?;   // accuracy
}