aprender

Next Generation Machine Learning, Statistics and Deep Learning in PURE Rust

Overview

Aprender is a lightweight, pure Rust machine learning library designed for efficiency and ease of use. Built with EXTREME TDD methodology, it provides reliable implementations of core ML algorithms with comprehensive test coverage.

Features

Core Primitives

• Vector - 1D numerical array with statistical operations (mean, sum, dot, norm, variance)
  • Powered by trueno v0.4.0 for SIMD acceleration
• Matrix - 2D numerical array with linear algebra (matmul, transpose, Cholesky decomposition)
  • SIMD-optimized operations via trueno backend
• DataFrame - Named column container for ML data preparation workflows

Machine Learning Models

• LinearRegression - Ordinary Least Squares via normal equations
• KMeans - K-means++ initialization with Lloyd's algorithm
• DecisionTreeClassifier - GINI-based decision tree with configurable max depth
• RandomForestClassifier - Bootstrap aggregating ensemble with majority voting

Model Selection & Evaluation

• train_test_split - Random train/test splitting with reproducible seeds
• KFold - K-fold cross-validator with optional shuffling
• cross_validate - Automated cross-validation with statistics (mean, std, min, max)

Model Persistence

• Serialization - Save/load models to disk (serde + bincode)
• Works with all models: LinearRegression, KMeans, DecisionTree, RandomForest

Metrics

• Regression: r_squared, mse, rmse, mae
• Clustering: silhouette_score, inertia

Installation

Add to your Cargo.toml:

[dependencies]
aprender = "0.4.0"

Quick Start

Linear Regression

use aprender::prelude::*;

fn main() {
    // Features: [sqft, bedrooms]
    let x = Matrix::from_vec(5, 2, vec![
        1500.0, 3.0,
        2000.0, 4.0,
        1200.0, 2.0,
        1800.0, 3.0,
        2500.0, 5.0,
    ]).unwrap();

    // Target: price
    let y = Vector::from_slice(&[250.0, 350.0, 180.0, 280.0, 450.0]);

    let mut model = LinearRegression::new();
    model.fit(&x, &y).expect("Failed to fit");

    let predictions = model.predict(&x);
    let r2 = model.score(&x, &y);

    println!("R² Score: {:.4}", r2);
}

K-Means Clustering

use aprender::prelude::*;

fn main() {
    let x = Matrix::from_vec(6, 2, vec![
        1.0, 2.0,
        1.5, 1.8,
        5.0, 8.0,
        6.0, 8.0,
        1.0, 0.6,
        9.0, 11.0,
    ]).unwrap();

    let mut kmeans = KMeans::new(2)
        .with_max_iter(100)
        .with_random_state(42);

    kmeans.fit(&x).expect("Failed to fit");

    let labels = kmeans.predict(&x);
    let score = silhouette_score(&x, &labels);

    println!("Silhouette Score: {:.4}", score);
}

Random Forest Classification

use aprender::prelude::*;
use aprender::tree::RandomForestClassifier;

fn main() {
    // Iris dataset (simplified)
    let x = Matrix::from_vec(12, 2, vec![
        1.4, 0.2, 1.3, 0.2, 1.5, 0.2, 1.7, 0.4,  // Setosa
        4.7, 1.4, 4.5, 1.5, 4.9, 1.5, 4.6, 1.3,  // Versicolor
        6.0, 2.5, 5.9, 2.1, 6.1, 2.3, 5.8, 2.2,  // Virginica
    ]).unwrap();
    let y = vec![0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2];

    let mut rf = RandomForestClassifier::new(20)
        .with_max_depth(5)
        .with_random_state(42);

    rf.fit(&x, &y).expect("Failed to fit");

    let predictions = rf.predict(&x);
    let accuracy = rf.score(&x, &y);

    println!("Accuracy: {:.1}%", accuracy * 100.0);
}

Cross-Validation

use aprender::prelude::*;
use aprender::model_selection::{cross_validate, KFold};

fn main() {
    let x = Matrix::from_vec(100, 1, (0..100).map(|i| i as f32).collect()).unwrap();
    let y = Vector::from_vec((0..100).map(|i| 2.0 * i as f32 + 1.0).collect());

    let model = LinearRegression::new();
    let kfold = KFold::new(10).with_random_state(42);

    let results = cross_validate(&model, &x, &y, &kfold).unwrap();

    println!("Mean R²: {:.4} ± {:.4}", results.mean(), results.std());
    println!("Min/Max: {:.4} / {:.4}", results.min(), results.max());
}

Examples

Run the included examples:

cargo run --example boston_housing       # Linear regression demo
cargo run --example iris_clustering      # K-Means clustering demo
cargo run --example dataframe_basics     # DataFrame operations demo
cargo run --example decision_tree_iris   # Decision Tree classifier demo
cargo run --example random_forest_iris   # Random Forest ensemble demo
cargo run --example cross_validation     # Cross-validation demo
cargo run --example model_persistence    # Model save/load demo

Quality Metrics

• TDG Score: 93.3/100 (A grade)
• Total Tests: 184 passing
• Property Tests: 22 (proptest)
• Doc Tests: 16
• Coverage: ~97%
• Max Cyclomatic Complexity: ≤10
• Clippy Warnings: 0

Documentation

• EXTREME TDD Book: https://paiml.github.io/aprender/
• API Reference: Run cargo doc --open or visit docs.rs/aprender

Roadmap

See ROADMAP.md for planned features and version roadmap.

License

MIT License - see LICENSE for details.

Contributing

Contributions welcome! Please ensure:

• All tests pass: cargo test --all
• No clippy warnings: cargo clippy --all-targets
• Code is formatted: cargo fmt