lightgbm-rs

LightGBM Rust binding

Fork from https://github.com/vaaaaanquish/lightgbm-rs

LightGBM version: V4.3.0

Require

You need an environment that can build LightGBM.

# linux
apt install -y cmake libclang-dev libc++-dev gcc-multilib

# OS X
brew install cmake libomp

On Windows

1. Install CMake and VS Build Tools.
2. Install LLVM and set an environment variable LIBCLANG_PATH to PATH_TO_LLVM_BINARY (example: C:\Program Files\LLVM\bin)

Please see below for details.

• LightGBM Installation-Guide

Usage

Add dependency:

lightgbm = { git = "https://github.com/magicalne/lightgbm-rs.git", branch = "main", features = ["dataframe"] }

Example LightGBM train.

extern crate serde_json;
use lightgbm::{Dataset, Booster};
use serde_json::json;

let data = vec![vec![1.0, 0.1, 0.2, 0.1],
               vec![0.7, 0.4, 0.5, 0.1],
               vec![0.9, 0.8, 0.5, 0.1],
               vec![0.2, 0.2, 0.8, 0.7],
               vec![0.1, 0.7, 1.0, 0.9]];
let label = vec![0.0, 0.0, 0.0, 1.0, 1.0];
let dataset = Dataset::from_mat(data, label).unwrap();
let params = json!{
   {
        "num_iterations": 3,
        "objective": "binary",
        "metric": "auc"
    }
};
let bst = Booster::train(dataset, &params).unwrap();

Please see the ./examples for details.

example	link
binary classification	link
multiclass classification	link
regression	link

Develop

git clone --recursive https://github.com/magicalne/lightgbm-rs

docker build -t lgbmrs .
docker run -it -v $PWD:/app lgbmrs bash

# cargo build

Thanks

Much reference was made to implementation and documentation. Thanks.

• microsoft/LightGBM
• davechallis/rust-xgboost