Quick Start

Get from cargo add to a fitted, transformed pipeline in five minutes.

1. Add the dependency

[dependencies]
datarust = "0.6"

Or with the most useful feature flags:

[dependencies]
datarust = { version = "0.6", features = ["serde", "rayon"] }

See Installation for what each feature does.

2. Create a matrix

All numeric data flows through Matrix — a row-major dense matrix backed by a single contiguous Vec<f64>:

#![allow(unused)]
fn main() {
use datarust::Matrix;

let x = Matrix::new(vec![
    vec![1.0, 10.0],
    vec![2.0, 20.0],
    vec![3.0, 30.0],
    vec![4.0, 40.0],
])?;
}

3. Fit and transform

Every numeric transformer implements the Transformer trait: call fit to learn parameters, then transform to apply them.

#![allow(unused)]
fn main() {
use datarust::scaler::StandardScaler;
use datarust::traits::Transformer;

let mut scaler = StandardScaler::new();
let standardized = scaler.fit_transform(&x)?;  // fit + transform in one call

// mean=0, variance=1 per column
}

4. Train a model

Regression and classification models implement Predictor: call fit(X, y) then predict(X).

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

let y = vec![3.0, 5.0, 7.0, 9.0]; // y = 2x + 1 (for feature 0)
let features = x.select_columns(&[0])?; // pick one feature
let mut model = LinearRegression::new();
model.fit(&features, &y)?;
let pred = model.predict(&features)?;
}

5. Evaluate

#![allow(unused)]
fn main() {
use datarust::metrics::regression::r2_score;

let r2 = r2_score(&y, &pred)?;
println!("R² = {r2:.4}"); // ≈ 1.0 for a clean linear signal
}

6. Split and cross-validate

#![allow(unused)]
fn main() {
use datarust::model_selection::{train_test_split, cross_val_score, KFold};
use datarust::metrics::regression::r2_score;

let (x_tr, x_te, y_tr, y_te) = train_test_split(&x, &y)?;

let cv = KFold::new().with_n_splits(3);
let scores = cross_val_score(&LinearRegression::new(), &x, &y, &cv, r2_score)?;
// scores.len() == 3, one R² per fold
}

Where to go next

• All transformers at a glance: Module Guide
• Mixing numeric + categorical columns: Compose
• Why datarust is fast: Performance