LightGBM:基于决策树算法的快速，分布式，高性能梯度提升（GBT，GBDT，GBRT，GBM或MART）框架，用于排名，分...

# LightGBM R-package [](https://cran.r-project.org/package=lightgbm) [](https://cran.r-project.org/package=lightgbm) [](https://lightgbm.readthedocs.io/en/latest/R/reference/) <img src="man/figures/logo.svg" align="right" alt="" width="175" /> ### Contents * [Installation](#installation) - [Installing the CRAN Package](#installing-the-cran-package) - [Installing from Source with CMake](#install) - [Installing a GPU-enabled Build](#installing-a-gpu-enabled-build) - [Installing Precompiled Binaries](#installing-precompiled-binaries) - [Installing from a Pre-compiled lib_lightgbm](#lib_lightgbm) * [Examples](#examples) * [Testing](#testing) * [Preparing a CRAN Package](#preparing-a-cran-package) * [External Repositories](#external-unofficial-repositories) * [Known Issues](#known-issues) Installation ------------ For the easiest installation, go to ["Installing the CRAN package"](#installing-the-cran-package). If you experience any issues with that, try ["Installing from Source with CMake"](#install). This can produce a more efficient version of the library on Windows systems with Visual Studio. To build a GPU-enabled version of the package, follow the steps in ["Installing a GPU-enabled Build"](#installing-a-gpu-enabled-build). If any of the above options do not work for you or do not meet your needs, please let the maintainers know by [opening an issue](https://github.com/microsoft/LightGBM/issues). When your package installation is done, you can check quickly if your LightGBM R-package is working by running the following: ```r library(lightgbm) data(agaricus.train, package='lightgbm') train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) model <- lgb.cv( params = list( objective = "regression" , metric = "l2" ) , data = dtrain ) ``` ### Installing the CRAN package `{lightgbm}` is [available on CRAN](https://cran.r-project.org/package=lightgbm), and can be installed with the following R code. ```r install.packages("lightgbm", repos = "https://cran.r-project.org") ``` This is the easiest way to install `{lightgbm}`. It does not require `CMake` or `Visual Studio`, and should work well on many different operating systems and compilers. Each CRAN package is also available on [LightGBM releases](https://github.com/microsoft/LightGBM/releases), with a name like `lightgbm-{VERSION}-r-cran.tar.gz`. #### Custom Installation (Linux, Mac) The steps above should work on most systems, but users with highly-customized environments might want to change how R builds packages from source. To change the compiler used when installing the CRAN package, you can create a file `~/.R/Makevars` which overrides `CC` (`C` compiler) and `CXX` (`C++` compiler). For example, to use `gcc` instead of `clang` on Mac, you could use something like the following: ```make # ~/.R/Makevars CC=gcc-8 CXX=g++-8 CXX11=g++-8 ``` ### Installing from Source with CMake <a name="install"></a> You need to install git and [CMake](https://cmake.org/) first. Note: this method is only supported on 64-bit systems. If you need to run LightGBM on 32-bit Windows (i386), follow the instructions in ["Installing the CRAN Package"](#installing-the-cran-package). #### Windows Preparation NOTE: Windows users may need to run with administrator rights (either R or the command prompt, depending on the way you are installing this package). Installing a 64-bit version of [Rtools](https://cran.r-project.org/bin/windows/Rtools/) is mandatory. After installing `Rtools` and `CMake`, be sure the following paths are added to the environment variable `PATH`. These may have been automatically added when installing other software. * `Rtools` - If you have `Rtools` 3.x, example: - `C:\Rtools\mingw_64\bin` - If you have `Rtools` 4.0, example: - `C:\rtools40\mingw64\bin` - `C:\rtools40\usr\bin` * `CMake` - example: `C:\Program Files\CMake\bin` * `R` - example: `C:\Program Files\R\R-3.6.1\bin` NOTE: Two `Rtools` paths are required from `Rtools` 4.0 onwards because paths and the list of included software was changed in `Rtools` 4.0. #### Windows Toolchain Options A "toolchain" refers to the collection of software used to build the library. The R package can be built with three different toolchains. **Warning for Windows users**: it is recommended to use *Visual Studio* for its better multi-threading efficiency in Windows for many core systems. For very simple systems (dual core computers or worse), MinGW64 is recommended for maximum performance. If you do not know what to choose, it is recommended to use [Visual Studio](https://visualstudio.microsoft.com/downloads/), the default compiler. **Do not try using MinGW in Windows on many core systems. It may result in 10x slower results than Visual Studio.** **Visual Studio (default)** By default, the package will be built with [Visual Studio Build Tools](https://visualstudio.microsoft.com/downloads/). **MinGW (R 3.x)** If you are using R 3.x and installation fails with Visual Studio, `LightGBM` will fall back to using [MinGW](http://mingw-w64.org/doku.php) bundled with `Rtools`. If you want to force `LightGBM` to use MinGW (for any R version), pass `--use-mingw` to the installation script. ```shell Rscript build_r.R --use-mingw ``` **MSYS2 (R 4.x)** If you are using R 4.x and installation fails with Visual Studio, `LightGBM` will fall back to using [MSYS2](https://www.msys2.org/). This should work with the tools already bundled in `Rtools` 4.0. If you want to force `LightGBM` to use MSYS2 (for any R version), pass `--use-msys2` to the installation script. ```shell Rscript build_r.R --use-msys2 ``` #### Mac OS Preparation You can perform installation either with **Apple Clang** or **gcc**. In case you prefer **Apple Clang**, you should install **OpenMP** (details for installation can be found in [Installation Guide](https://github.com/microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#apple-clang)) first and **CMake** version 3.16 or higher is required. In case you prefer **gcc**, you need to install it (details for installation can be found in [Installation Guide](https://github.com/microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#gcc)) and set some environment variables to tell R to use `gcc` and `g++`. If you install these from Homebrew, your versions of `g++` and `gcc` are most likely in `/usr/local/bin`, as shown below. ``` # replace 8 with version of gcc installed on your machine export CXX=/usr/local/bin/g++-8 CC=/usr/local/bin/gcc-8 ``` #### Install with CMake After following the "preparation" steps above for your operating system, build and install the R-package with the following commands: ```sh git clone --recursive https://github.com/microsoft/LightGBM cd LightGBM Rscript build_r.R ``` The `build_r.R` script builds the package in a temporary directory called `lightgbm_r`. It will destroy and recreate that directory each time you run the script. That script supports the following command-line options: - `--skip-install`: Build the package tarball, but do not install it. - `--use-gpu`: Build a GPU-enabled version of the library. - `--use-mingw`: Force the use of MinGW toolchain, regardless of R version. - `--use-msys2`: Force the use of MSYS2 toolchain, regardless of R version. Note: for the build with Visual Studio/VS Build Tools in Windows, you should use the Windows CMD or PowerShell. ### Installing a GPU-enabled Build You will need to install Boost and OpenCL first: details for installation can be found in [Installation-Guide](https://github.com/microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#build-gpu-version). After installing these other libraries, follow the steps in ["Inst