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Add Github Action for R. (#5911)

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* Fix lintr errors.
This commit is contained in:
Jiaming Yuan
2020-07-20 19:23:36 +08:00
committed by GitHub
parent b3d2e7644a
commit 8b1afce316
33 changed files with 589 additions and 544 deletions
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20
R-package/tests/testthat/test_custom_objective.R
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@@ -4,8 +4,8 @@ require(xgboost)
set.seed(1994)
data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')
data(agaricus.train, package = 'xgboost')
data(agaricus.test, package = 'xgboost')
dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
watchlist <- list(eval = dtest, train = dtrain)
@@ -24,8 +24,8 @@ evalerror <- function(preds, dtrain) {
return(list(metric = "error", value = err))
}
param <- list(max_depth=2, eta=1, nthread = 2,
objective=logregobj, eval_metric=evalerror)
param <- list(max_depth = 2, eta = 1, nthread = 2,
objective = logregobj, eval_metric = evalerror)
num_round <- 2
test_that("custom objective works", {
@@ -37,7 +37,7 @@ test_that("custom objective works", {
})
test_that("custom objective in CV works", {
cv <- xgb.cv(param, dtrain, num_round, nfold=10, verbose=FALSE)
cv <- xgb.cv(param, dtrain, num_round, nfold = 10, verbose = FALSE)
expect_false(is.null(cv$evaluation_log))
expect_equal(dim(cv$evaluation_log), c(2, 5))
expect_lt(cv$evaluation_log[num_round, test_error_mean], 0.03)
@@ -54,14 +54,14 @@ test_that("custom objective using DMatrix attr works", {
hess <- preds * (1 - preds)
return(list(grad = grad, hess = hess))
}
param$objective = logregobjattr
param$objective <- logregobjattr
bst <- xgb.train(param, dtrain, num_round, watchlist)
expect_equal(class(bst), "xgb.Booster")
})
test_that("custom objective with multi-class works", {
data = as.matrix(iris[, -5])
label = as.numeric(iris$Species) - 1
data <- as.matrix(iris[, -5])
label <- as.numeric(iris$Species) - 1
dtrain <- xgb.DMatrix(data = data, label = label)
nclasses <- 3
@@ -72,6 +72,6 @@ test_that("custom objective with multi-class works", {
hess <- rnorm(dim(as.matrix(preds))[1])
return (list(grad = grad, hess = hess))
}
param$objective = fake_softprob
bst <- xgb.train(param, dtrain, 1, num_class=nclasses)
param$objective <- fake_softprob
bst <- xgb.train(param, dtrain, 1, num_class = nclasses)
})