GPBoost R Package

This is the R package implementation of the GPBoost library. See https://github.com/fabsig/GPBoost for more information on the modeling background and the software implementation.

Table of Contents

• Examples
• Installation
  • Installation from CRAN
  • Installation from source
• Testing

Examples

• Detailed R examples:
  • GPBoost and LaGaBoost algorithms for Gaussian data (“regression”) and non-Gaussian data (“classification”, etc.) combining tree-boosting with Gaussian process and random effects models
  • Parameter tuning using deterministic or random grid search
  • Generalized linear Gaussian process and mixed effects models
• Blog posts on how to
  • Combine tree-boosting with Gaussian processes for spatial data
  • GPBoost for generalized linear mixed effects models (GLMMs)
• Demo on how GPBoost can be used in R and Python

This is also a short example:

# Combine tree-boosting and grouped random effects model
library(gpboost)
data(GPBoost_data, package = "gpboost")
gp_model <- GPModel(group_data = group_data)
bst <- gpboost(data = X, label = y, gp_model = gp_model,
               nrounds = 10, objective = "regression_l2")
summary(gp_model)
pred <- predict(bst, data = X_test, group_data_pred = group_data_test)
pred$response_mean

Installation

Installation from CRAN

The gpboost package is available on CRAN and can be installed as follows:

install.packages("gpboost", repos = "https://cran.r-project.org")

Installation from source

It is much easier to install the package from CRAN. However, the package can also be build from source as described in the following. In short, the main steps for installation are the following ones:

• Install git
• Install CMake
• Install Rtools (for Windows only). Choose the option ‘add rtools to system PATH’.
• Make sure that you have an appropriate C++ compiler (see below for more details). E.g. for Windows, simply download the free Visual Studio Community Edition and do not forget to select ‘Desktop development with C++’ when installing it
• Install the GPBoost package from the command line using:

git clone --recursive https://github.com/fabsig/GPBoost
cd GPBoost
Rscript build_r.R

Below is a more complete installation guide.

Preparation

You need to install git and CMake first. Note that 32-bit R/Rtools is not supported for custom installation.

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 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.x, example (NOTE: two paths are required):
    • C:\rtools40\mingw64\bin
    • C:\rtools40\usr\bin
    • For instance, when installing in R with install.packages(), these paths can be added locally in R as follows prior to installation:

    Sys.setenv(PATH=paste0(Sys.getenv("PATH"),";C:\\Rtools\\mingw_64\\bin\\;C:\\rtools40\\usr\\bin\\"))

• CMake
  • example: C:\Program Files\CMake\bin
• R
  • example: C:\Program Files\R\R-3.6.1\bin

The default compiler is Visual Studio (or VS Build Tools) in Windows, with an automatic fallback to MingGW64 (i.e. it is enough to only have Rtools and CMake). To force the usage of MinGW64, you can add the --use-mingw (for R 3.x) or --use-msys2 (for R 4.x) flags (see below).

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 the Installation Guide) first and CMake version 3.12 or higher is required. Only Apple Clang version 8.1 or higher is supported.
• In case you prefer gcc, you need to install it (details for installation can be found in the Installation Guide) 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

Build and install the R package with the following commands:

git clone --recursive https://github.com/fabsig/GPBoost
cd GPBoost
Rscript build_r.R

The build_r.R script builds the package in a temporary directory called gpboost_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.

Testing

There is currently no integration service set up that automatically runs unit tests. However, any contribution needs to pass all unit tests in the R-package/tests/testthat directory. These tests can be run using the run_tests_coverage_R_package.R file. In any case, make sure that you run the full set of tests by speciying the following environment variable

Sys.setenv(GPBOOST_ALL_TESTS = "GPBOOST_ALL_TESTS")

before runing the tests in the R-package/tests/testthat directory.