CatBoost Rust Bindings

Rust bindings for CatBoost, a gradient boosting library for machine learning. This crate provides a safe and ergonomic Rust interface to CatBoost's C API.

Features

• Cross-platform: Works on Linux, macOS, and Windows
• Self-contained: Downloads CatBoost binaries at runtime - no system dependencies required
• Version control: Specify different CatBoost versions via environment variable
• Safe Rust API: Memory-safe wrapper around CatBoost's C API
• Multiple feature types: Support for numeric, categorical, text, and embedding features
• GPU support: Optional GPU acceleration (requires gpu feature)

Installation

Add this to your Cargo.toml:

[dependencies]
catboost-rust = "0.2.0"

For GPU support:

[dependencies]
catboost-rust = { version = "0.2.0", features = ["gpu"] }

Quick Start

use catboost_rust::{Model, ObjectsOrderFeatures};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Load a trained CatBoost model
    let model = Model::load("path/to/model.cbm")?;
    
    // Make predictions with numeric features
    let features = ObjectsOrderFeatures::new()
        .with_float_features(&[
            &[1.0, 2.0, 3.0, 4.0, 5.0],
            &[2.0, 3.0, 4.0, 5.0, 6.0],
        ]);
    
    let predictions = model.predict(features)?;
    println!("Predictions: {:?}", predictions);
    
    Ok(())
}

Usage Examples

Basic Usage

use catboost_rust::{Model, ObjectsOrderFeatures};

// Load model from file
let model = Model::load("model.cbm")?;

// Simple numeric features prediction
let features = ObjectsOrderFeatures::new()
    .with_float_features(&[&[1.0, 2.0, 3.0, 4.0, 5.0]]);

let predictions = model.predict(features)?;

Categorical Features

use catboost_rust::{Model, ObjectsOrderFeatures};

let model = Model::load("model.cbm")?;

// Mixed numeric and categorical features
let features = ObjectsOrderFeatures::new()
    .with_float_features(&[&[1.0, 2.0, 3.0]])
    .with_cat_features(&[&["A", "B", "C"]]);

let predictions = model.predict(features)?;

Text Features

use catboost_rust::{Model, ObjectsOrderFeatures};
use std::ffi::CString;

let model = Model::load("model.cbm")?;

let text_features = vec![
    CString::new("This is a sample text").unwrap(),
    CString::new("Another text sample").unwrap(),
];

let features = ObjectsOrderFeatures::new()
    .with_float_features(&[&[1.0, 2.0]])
    .with_text_features(&[&text_features]);

let predictions = model.predict(features)?;

Embedding Features

use catboost_rust::{Model, ObjectsOrderFeatures};

let model = Model::load("model.cbm")?;

let embeddings = vec![
    vec![0.1, 0.2, 0.3, 0.4], // First embedding
    vec![0.5, 0.6, 0.7, 0.8], // Second embedding
];

let features = ObjectsOrderFeatures::new()
    .with_float_features(&[&[1.0, 2.0]])
    .with_embedding_features(&[&embeddings]);

let predictions = model.predict(features)?;

Configuration

CatBoost Version

You can specify which version of CatBoost to use by setting the CATBOOST_VERSION environment variable:

export CATBOOST_VERSION=1.2.8
cargo build

The default version is 1.2.8.

GPU Support

To enable GPU acceleration, compile with the gpu feature:

cargo build --features gpu

Then enable GPU evaluation in your code:

let model = Model::load("model.cbm")?;
model.enable_gpu_evaluation()?;

Model Information

You can inspect model properties:

let model = Model::load("model.cbm")?;

println!("Float features: {}", model.get_float_features_count());
println!("Categorical features: {}", model.get_cat_features_count());
println!("Text features: {}", model.get_text_features_count());
println!("Embedding features: {}", model.get_embedding_features_count());
println!("Trees: {}", model.get_tree_count());
println!("Dimensions: {}", model.get_dimensions_count());

Error Handling

The crate provides comprehensive error handling:

use catboost_rust::{Model, CatBoostError, CatBoostResult};

fn load_and_predict() -> CatBoostResult<Vec<f64>> {
    let model = Model::load("model.cbm")?;
    
    let features = ObjectsOrderFeatures::new()
        .with_float_features(&[&[1.0, 2.0, 3.0]]);
    
    model.predict(features)
}

match load_and_predict() {
    Ok(predictions) => println!("Success: {:?}", predictions),
    Err(CatBoostError { description }) => println!("Error: {}", description),
}

Platform Support

This crate automatically downloads the appropriate CatBoost binary for your platform:

• Linux: x86_64, aarch64
• macOS: Universal binary (x86_64 + arm64)
• Windows: x86_64

Building from Source

The crate downloads CatBoost binaries at runtime, so no system dependencies are required. However, if you want to build from source, you can set the CATBOOST_BUILD_FROM_SOURCE environment variable.

License

This project is licensed under the Apache License, Version 2.0. See the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Examples

See the examples/ directory for more detailed usage examples:

• basic_usage.rs - Simple prediction examples
• advanced_usage.rs - Advanced features and model inspection

Run examples with:

cargo run --example basic_usage
cargo run --example advanced_usage