GitHub - Mottl/lightgbm3-rs: Rust bindings for LightGBM (original) (raw)

lightgbm3 — Rust bindings for LightGBM

Crates.io Docs.rs build

lightgbm3 is based on lightgbm crate (which is unsupported by now), but it is not back-compatible with it.

Installation

Since lightgbm3 compiles LightGBM from source, you also need to install development libraries:

for Linux:

apt install -y cmake clang libclang-dev libc++-dev gcc-multilib

for Mac:

brew install cmake
brew install libomp # only required if you compile with "openmp" feature

for Windows

  1. Install CMake and VS Build Tools.
  2. Install LLVM and set LIBCLANG_PATH environment variable (i.e. C:\Program Files\LLVM\bin)

Please see below for details.

Usage

Training:

use lightgbm3::{Dataset, Booster}; use serde_json::json;

let features = vec![vec![1.0, 0.1, 0.2], vec![0.7, 0.4, 0.5], vec![0.9, 0.8, 0.5], vec![0.2, 0.2, 0.8], vec![0.1, 0.7, 1.0]]; let labels = vec![0.0, 0.0, 0.0, 1.0, 1.0]; let dataset = Dataset::from_vec_of_vec(features, labels, true).unwrap(); let params = json!{ { "num_iterations": 10, "objective": "binary", "metric": "auc", } }; let bst = Booster::train(dataset, &params).unwrap(); bst.save_file("path/to/model.lgb").unwrap();

Inference:

use lightgbm3::{Dataset, Booster};

let bst = Booster::from_file("path/to/model.lgb").unwrap(); let features = vec![1.0, 2.0, -5.0]; let n_features = features.len(); let y_pred = bst.predict_with_params(&features, n_features as i32, true, "num_threads=1").unwrap()[0];

Look in the ./examples/ folder for more details:

Features

lightgbm3 supports the following features:

Benchmarks

Add --features=openmp, --features=gpu and --features=cuda appropriately.

Development

git clone --recursive https://github.com/Mottl/lightgbm3-rs.git

Thanks

Great respect to vaaaaanquish for the LightGBM Rust package, which unfortunately no longer supported.

Much reference was made to implementation and documentation. Thanks.