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.
- 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
gpufeature)
Add this to your Cargo.toml:
[dependencies]
catboost-rust = "0.2.0"For GPU support:
[dependencies]
catboost-rust = { version = "0.2.0", features = ["gpu"] }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(())
}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)?;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)?;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)?;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)?;You can specify which version of CatBoost to use by setting the CATBOOST_VERSION environment variable:
export CATBOOST_VERSION=1.2.8
cargo buildThe default version is 1.2.8.
To enable GPU acceleration, compile with the gpu feature:
cargo build --features gpuThen enable GPU evaluation in your code:
let model = Model::load("model.cbm")?;
model.enable_gpu_evaluation()?;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());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),
}This crate automatically downloads the appropriate CatBoost binary for your platform:
- Linux: x86_64, aarch64
- macOS: Universal binary (x86_64 + arm64)
- Windows: x86_64
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.
This project is licensed under the Apache License, Version 2.0. See the LICENSE file for details.
Contributions are welcome! Please feel free to submit a Pull Request.
See the examples/ directory for more detailed usage examples:
basic_usage.rs- Simple prediction examplesadvanced_usage.rs- Advanced features and model inspection
Run examples with:
cargo run --example basic_usage
cargo run --example advanced_usage
