Examples

Runnable examples from the examples/ directory. Each can be run with cargo run --example <name>.

Basic preprocessing

cargo run --example basic_preprocessing

Standardize, scale, and encode a small mixed-type dataset. Demonstrates StandardScaler, MinMaxScaler, OneHotEncoder, and the Transformer trait.

Pipeline workflow

cargo run --example pipeline_workflow

Build a 3-step pipeline (StandardScaler → MinMaxScaler → RobustScaler), fit and transform, then inspect individual steps via the runtime ergonomics API.

Target encoding

cargo run --example target_encoding

Use TargetEncoder with smoothed mean encoding on a high-cardinality categorical feature, including the TargetTransformer trait and fit_with_target.

Regression workflow

cargo run --example regression_workflow

End-to-end regression on synthetic house-price data: generate data, train_test_split, fit a StandardScaler on train only (data-leakage prevention), train a Ridge, score test R², then run 5-fold cross_val_score. Shows the full preprocess → train → evaluate → cross-validate loop on a single realistic dataset.

Classification workflow

cargo run --example classification_workflow

Customer-churn binary classification with mixed numeric + categorical features. A ColumnTransformer applies StandardScaler to numeric columns and OneHotEncoder to the categorical column, feeding a LogisticRegression. Reports the full metric suite (accuracy, precision, recall, f1, confusion_matrix, log_loss), compares decision thresholds (0.3 / 0.5 / 0.7) to show the precision–recall trade-off, and finishes with stratified 5-fold cross-validation driven manually (StratifiedKFold::split, since cross_val_score only accepts KFold).

Regularization comparison

cargo run --example regularization_comparison

Compares Ridge (L2) and Lasso (L1) across multiple alpha values on an 8-feature dataset where only 3 features carry signal, one feature is collinear, and the rest are pure noise. Ridge shrinks all coefficients but zeros none; Lasso’s soft-thresholding drives the noise features to exactly zero (automatic feature selection). The collinear feature would make LinearRegression singular — both regularized solvers handle it.

Model persistence

cargo run --example model_persistence --features serde

Production-style model persistence. Trains a SupervisedPipeline<Ridge> (StandardScaler → PCA → Ridge), writes it to a JSON file with save_json, reloads it with load_json, and confirms the restored model is still is_fitted() and produces bit-identical predictions without refitting. Requires the serde feature.

KMeans clustering

cargo run --example kmeans_clustering

Unsupervised clustering on synthetic 2-D point data. Generates three well-separated blobs, fits a KMeans with k-means++ initialization, inspects the learned centroids against the true blob centers, predicts cluster assignments for new points, compares k-means++ vs random initialization by inertia, and (with --features serde) round-trips the fitted model through JSON. Demonstrates the Clusterer trait.

Benchmark comparison

cargo run --release --example bench_compare_rust

The deterministic benchmark harness that produces the numbers on the Performance page. Uses a xorshift64 PRNG (seed 42) so Rust and Python generate identical data. Pass a repetition count as an argument:

cargo run --release --features matrixmultiply --example bench_compare_rust 15

Using embedded datasets

The datasets feature provides classic toy datasets (Iris, Breast Cancer, Wine, Diabetes) as const arrays — no file I/O needed. See the Datasets guide for the full API.

#![allow(unused)]
fn main() {
use datarust::datasets::iris;
use datarust::linear_model::LogisticRegression;
use datarust::traits::Predictor;

let data = iris::load();
let x = data.features();
let y = data.targets().to_vec();

let mut model = LogisticRegression::new().with_max_iter(200);
model.fit(&x, &y)?;
println!("Iris accuracy: {:.1}%", model.score(&x, &y)? * 100.0);
}