* [ci] test 32-bit R in CI

* add R 3.6 Windows CRAN job

* add tests
This commit is contained in:
James Lamb 2020-11-23 17:38:24 +00:00 коммит произвёл GitHub
Родитель 5285974449
Коммит c5d9d2436b
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Идентификатор ключа GPG: 4AEE18F83AFDEB23
3 изменённых файлов: 13 добавлений и 3 удалений

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@ -94,7 +94,7 @@ Download-File-With-Retries -url "https://github.com/microsoft/LightGBM/releases/
# Install R
Write-Output "Installing R"
Start-Process -FilePath R-win.exe -NoNewWindow -Wait -ArgumentList "/VERYSILENT /DIR=$env:R_LIB_PATH/R /COMPONENTS=main,x64" ; Check-Output $?
Start-Process -FilePath R-win.exe -NoNewWindow -Wait -ArgumentList "/VERYSILENT /DIR=$env:R_LIB_PATH/R /COMPONENTS=main,x64,i386" ; Check-Output $?
Write-Output "Done installing R"
Write-Output "Installing Rtools"

6
.github/workflows/r_package.yml поставляемый
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@ -94,6 +94,12 @@ jobs:
###############
# CRAN builds #
###############
- os: windows-latest
task: r-package
compiler: MINGW
toolchain: MINGW
r_version: 3.6
build_type: cran
- os: windows-latest
task: r-package
compiler: MINGW

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@ -4,6 +4,7 @@ context("Learning to rank")
TOLERANCE <- 1e-06
ON_SOLARIS <- Sys.info()["sysname"] == "SunOS"
ON_32_BIT_WINDOWS <- .Platform$OS.type == "windows" && .Machine$sizeof.pointer != 8L
test_that("learning-to-rank with lgb.train() works as expected", {
set.seed(708L)
@ -48,14 +49,17 @@ test_that("learning-to-rank with lgb.train() works as expected", {
}
expect_identical(sapply(eval_results, function(x) {x$name}), eval_names)
expect_equal(eval_results[[1L]][["value"]], 0.775)
if (!ON_SOLARIS) {
if (!(ON_SOLARIS || ON_32_BIT_WINDOWS)) {
expect_true(abs(eval_results[[2L]][["value"]] - 0.745986) < TOLERANCE)
expect_true(abs(eval_results[[3L]][["value"]] - 0.7351959) < TOLERANCE)
}
})
test_that("learning-to-rank with lgb.cv() works as expected", {
testthat::skip_if(ON_SOLARIS, message = "Skipping on Solaris")
testthat::skip_if(
ON_SOLARIS || ON_32_BIT_WINDOWS
, message = "Skipping on Solaris and 32-bit Windows"
)
set.seed(708L)
data(agaricus.train, package = "lightgbm")
# just keep a few features,to generate an model with imperfect fit