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[R] Replace xgboost() with xgb.train() in most tests and examples (#9941)

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david-cortes 2024-01-02 14:20:01 +01:00 committed by GitHub
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27 changed files with 156 additions and 150 deletions
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5
R-package/R/utils.R
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@ -383,8 +383,9 @@ NULL
#'
#' @examples
#' data(agaricus.train, package='xgboost')
#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
#' eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
#' bst <- xgb.train(data = xgb.DMatrix(agaricus.train$data, label = agaricus.train$label),
#' max_depth = 2, eta = 1, nthread = 2, nrounds = 2,
#' objective = "binary:logistic")
#'
#' # Save as a stand-alone file; load it with xgb.load()
#' xgb.save(bst, 'xgb.model')

15
R-package/R/xgb.Booster.R
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@ -272,9 +272,8 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
#' train <- agaricus.train
#' test <- agaricus.test
#'
#' bst <- xgboost(
#' data = train$data,
#' label = train$label,
#' bst <- xgb.train(
#' data = xgb.DMatrix(train$data, label = train$label),
#' max_depth = 2,
#' eta = 0.5,
#' nthread = nthread,
@ -316,9 +315,8 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
#'
#' set.seed(11)
#'
#' bst <- xgboost(
#' data = as.matrix(iris[, -5]),
#' label = lb,
#' bst <- xgb.train(
#' data = xgb.DMatrix(as.matrix(iris[, -5]), label = lb),
#' max_depth = 4,
#' eta = 0.5,
#' nthread = 2,
@ -341,9 +339,8 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
#' # compare with predictions from softmax:
#' set.seed(11)
#'
#' bst <- xgboost(
#' data = as.matrix(iris[, -5]),
#' label = lb,
#' bst <- xgb.train(
#' data = xgb.DMatrix(as.matrix(iris[, -5]), label = lb),
#' max_depth = 4,
#' eta = 0.5,
#' nthread = 2,

6
R-package/R/xgb.load.R
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@ -29,8 +29,10 @@
#'
#' train <- agaricus.train
#' test <- agaricus.test
#' bst <- xgboost(
#' data = train$data, label = train$label, max_depth = 2, eta = 1,
#' bst <- xgb.train(
#' data = xgb.DMatrix(train$data, label = train$label),
#' max_depth = 2,
#' eta = 1,
#' nthread = nthread,
#' nrounds = 2,
#' objective = "binary:logistic"

6
R-package/R/xgb.save.R
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@ -32,8 +32,10 @@
#'
#' train <- agaricus.train
#' test <- agaricus.test
#' bst <- xgboost(
#' data = train$data, label = train$label, max_depth = 2, eta = 1,
#' bst <- xgb.train(
#' data = xgb.DMatrix(train$data, label = train$label),
#' max_depth = 2,
#' eta = 1,
#' nthread = nthread,
#' nrounds = 2,
#' objective = "binary:logistic"

2
R-package/R/xgb.save.raw.R
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@ -23,7 +23,7 @@
#'
#' train <- agaricus.train
#' test <- agaricus.test
#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
#' bst <- xgb.train(data = xgb.DMatrix(train$data, label = train$label), max_depth = 2,
#' eta = 1, nthread = nthread, nrounds = 2,objective = "binary:logistic")
#'
#' raw <- xgb.save.raw(bst)

2
R-package/R/xgb.serialize.R
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@ -9,7 +9,7 @@
#' data(agaricus.test, package='xgboost')
#' train <- agaricus.train
#' test <- agaricus.test
#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
#' bst <- xgb.train(data = xgb.DMatrix(train$data, label = train$label), max_depth = 2,
#' eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
#' raw <- xgb.serialize(bst)
#' bst <- xgb.unserialize(raw)

2
R-package/demo/create_sparse_matrix.R
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@ -81,7 +81,7 @@ output_vector <- df[, Y := 0][Improved == "Marked", Y := 1][, Y]
# Following is the same process as other demo
cat("Learning...\n")
bst <- xgboost(data = sparse_matrix, label = output_vector, max_depth = 9,
bst <- xgb.train(data = xgb.DMatrix(sparse_matrix, label = output_vector), max_depth = 9,
eta = 1, nthread = 2, nrounds = 10, objective = "binary:logistic")
importance <- xgb.importance(feature_names = colnames(sparse_matrix), model = bst)

6
R-package/demo/interaction_constraints.R
View File

@ -74,7 +74,7 @@ cols2ids <- function(object, col_names) {
interaction_list_fid <- cols2ids(interaction_list, colnames(train))
# Fit model with interaction constraints
bst <- xgboost(data = train, label = y, max_depth = 4,
bst <- xgb.train(data = xgb.DMatrix(train, label = y), max_depth = 4,
eta = 0.1, nthread = 2, nrounds = 1000,
interaction_constraints = interaction_list_fid)
@ -83,14 +83,14 @@ bst_interactions <- treeInteractions(bst_tree, 4)
# interactions constrained to combinations of V1*V2 and V3*V4*V5
# Fit model without interaction constraints
bst2 <- xgboost(data = train, label = y, max_depth = 4,
bst2 <- xgb.train(data = xgb.DMatrix(train, label = y), max_depth = 4,
eta = 0.1, nthread = 2, nrounds = 1000)
bst2_tree <- xgb.model.dt.tree(colnames(train), bst2)
bst2_interactions <- treeInteractions(bst2_tree, 4) # much more interactions
# Fit model with both interaction and monotonicity constraints
bst3 <- xgboost(data = train, label = y, max_depth = 4,
bst3 <- xgb.train(data = xgb.DMatrix(train, label = y), max_depth = 4,
eta = 0.1, nthread = 2, nrounds = 1000,
interaction_constraints = interaction_list_fid,
monotone_constraints = c(-1, 0, 0, 0, 0, 0, 0, 0, 0, 0))

2
R-package/demo/poisson_regression.R
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@ -1,6 +1,6 @@
data(mtcars)
head(mtcars)
bst <- xgboost(data = as.matrix(mtcars[, -11]), label = mtcars[, 11],
bst <- xgb.train(data = xgb.DMatrix(as.matrix(mtcars[, -11]), label = mtcars[, 11]),
objective = 'count:poisson', nrounds = 5)
pred <- predict(bst, as.matrix(mtcars[, -11]))
sqrt(mean((pred - mtcars[, 11]) ^ 2))

5
R-package/man/a-compatibility-note-for-saveRDS-save.Rd
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@ -33,8 +33,9 @@ For more details and explanation about model persistence and archival, consult t
}
\examples{
data(agaricus.train, package='xgboost')
bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
bst <- xgb.train(data = xgb.DMatrix(agaricus.train$data, label = agaricus.train$label),
max_depth = 2, eta = 1, nthread = 2, nrounds = 2,
objective = "binary:logistic")
# Save as a stand-alone file; load it with xgb.load()
xgb.save(bst, 'xgb.model')

15
R-package/man/predict.xgb.Booster.Rd
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@ -136,9 +136,8 @@ data.table::setDTthreads(nthread)
train <- agaricus.train
test <- agaricus.test
bst <- xgboost(
data = train$data,
label = train$label,
bst <- xgb.train(
data = xgb.DMatrix(train$data, label = train$label),
max_depth = 2,
eta = 0.5,
nthread = nthread,
@ -180,9 +179,8 @@ num_class <- 3
set.seed(11)
bst <- xgboost(
data = as.matrix(iris[, -5]),
label = lb,
bst <- xgb.train(
data = xgb.DMatrix(as.matrix(iris[, -5]), label = lb),
max_depth = 4,
eta = 0.5,
nthread = 2,
@ -205,9 +203,8 @@ sum(pred_labels != lb) / length(lb)
# compare with predictions from softmax:
set.seed(11)
bst <- xgboost(
data = as.matrix(iris[, -5]),
label = lb,
bst <- xgb.train(
data = xgb.DMatrix(as.matrix(iris[, -5]), label = lb),
max_depth = 4,
eta = 0.5,
nthread = 2,

6
R-package/man/xgb.load.Rd
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@ -34,8 +34,10 @@ data.table::setDTthreads(nthread)
train <- agaricus.train
test <- agaricus.test
bst <- xgboost(
data = train$data, label = train$label, max_depth = 2, eta = 1,
bst <- xgb.train(
data = xgb.DMatrix(train$data, label = train$label),
max_depth = 2,
eta = 1,
nthread = nthread,
nrounds = 2,
objective = "binary:logistic"

6
R-package/man/xgb.save.Rd
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@ -38,8 +38,10 @@ data.table::setDTthreads(nthread)
train <- agaricus.train
test <- agaricus.test
bst <- xgboost(
data = train$data, label = train$label, max_depth = 2, eta = 1,
bst <- xgb.train(
data = xgb.DMatrix(train$data, label = train$label),
max_depth = 2,
eta = 1,
nthread = nthread,
nrounds = 2,
objective = "binary:logistic"

2
R-package/man/xgb.save.raw.Rd
View File

@ -32,7 +32,7 @@ data.table::setDTthreads(nthread)
train <- agaricus.train
test <- agaricus.test
bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
bst <- xgb.train(data = xgb.DMatrix(train$data, label = train$label), max_depth = 2,
eta = 1, nthread = nthread, nrounds = 2,objective = "binary:logistic")
raw <- xgb.save.raw(bst)

2
R-package/man/xgb.serialize.Rd
View File

@ -21,7 +21,7 @@ data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')
train <- agaricus.train
test <- agaricus.test
bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
bst <- xgb.train(data = xgb.DMatrix(train$data, label = train$label), max_depth = 2,
eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
raw <- xgb.serialize(bst)
bst <- xgb.unserialize(raw)

84
R-package/tests/testthat/test_basic.R
View File

@ -16,10 +16,11 @@ n_threads <- 1
test_that("train and predict binary classification", {
nrounds <- 2
expect_output(
bst <- xgboost(
data = train$data, label = train$label, max_depth = 2,
bst <- xgb.train(
data = xgb.DMatrix(train$data, label = train$label), max_depth = 2,
eta = 1, nthread = n_threads, nrounds = nrounds,
objective = "binary:logistic", eval_metric = "error"
objective = "binary:logistic", eval_metric = "error",
watchlist = list(train = xgb.DMatrix(train$data, label = train$label))
),
"train-error"
)
@ -104,9 +105,8 @@ test_that("dart prediction works", {
rnorm(100)
set.seed(1994)
booster_by_xgboost <- xgboost(
data = d,
label = y,
booster_by_xgboost <- xgb.train(
data = xgb.DMatrix(d, label = y),
max_depth = 2,
booster = "dart",
rate_drop = 0.5,
@ -151,10 +151,11 @@ test_that("train and predict softprob", {
lb <- as.numeric(iris$Species) - 1
set.seed(11)
expect_output(
bst <- xgboost(
data = as.matrix(iris[, -5]), label = lb,
bst <- xgb.train(
data = xgb.DMatrix(as.matrix(iris[, -5]), label = lb),
max_depth = 3, eta = 0.5, nthread = n_threads, nrounds = 5,
objective = "multi:softprob", num_class = 3, eval_metric = "merror"
objective = "multi:softprob", num_class = 3, eval_metric = "merror",
watchlist = list(train = xgb.DMatrix(as.matrix(iris[, -5]), label = lb))
),
"train-merror"
)
@ -201,10 +202,11 @@ test_that("train and predict softmax", {
lb <- as.numeric(iris$Species) - 1
set.seed(11)
expect_output(
bst <- xgboost(
data = as.matrix(iris[, -5]), label = lb,
bst <- xgb.train(
data = xgb.DMatrix(as.matrix(iris[, -5]), label = lb),
max_depth = 3, eta = 0.5, nthread = n_threads, nrounds = 5,
objective = "multi:softmax", num_class = 3, eval_metric = "merror"
objective = "multi:softmax", num_class = 3, eval_metric = "merror",
watchlist = list(train = xgb.DMatrix(as.matrix(iris[, -5]), label = lb))
),
"train-merror"
)
@ -222,11 +224,12 @@ test_that("train and predict RF", {
set.seed(11)
lb <- train$label
# single iteration
bst <- xgboost(
data = train$data, label = lb, max_depth = 5,
bst <- xgb.train(
data = xgb.DMatrix(train$data, label = lb), max_depth = 5,
nthread = n_threads,
nrounds = 1, objective = "binary:logistic", eval_metric = "error",
num_parallel_tree = 20, subsample = 0.6, colsample_bytree = 0.1
num_parallel_tree = 20, subsample = 0.6, colsample_bytree = 0.1,
watchlist = list(train = xgb.DMatrix(train$data, label = lb))
)
expect_equal(bst$niter, 1)
expect_equal(xgb.ntree(bst), 20)
@ -248,12 +251,13 @@ test_that("train and predict RF with softprob", {
lb <- as.numeric(iris$Species) - 1
nrounds <- 15
set.seed(11)
bst <- xgboost(
data = as.matrix(iris[, -5]), label = lb,
bst <- xgb.train(
data = xgb.DMatrix(as.matrix(iris[, -5]), label = lb),
max_depth = 3, eta = 0.9, nthread = n_threads, nrounds = nrounds,
objective = "multi:softprob", eval_metric = "merror",
num_class = 3, verbose = 0,
num_parallel_tree = 4, subsample = 0.5, colsample_bytree = 0.5
num_parallel_tree = 4, subsample = 0.5, colsample_bytree = 0.5,
watchlist = list(train = xgb.DMatrix(as.matrix(iris[, -5]), label = lb))
)
expect_equal(bst$niter, 15)
expect_equal(xgb.ntree(bst), 15 * 3 * 4)
@ -271,10 +275,11 @@ test_that("train and predict RF with softprob", {
test_that("use of multiple eval metrics works", {
expect_output(
bst <- xgboost(
data = train$data, label = train$label, max_depth = 2,
bst <- xgb.train(
data = xgb.DMatrix(train$data, label = train$label), max_depth = 2,
eta = 1, nthread = n_threads, nrounds = 2, objective = "binary:logistic",
eval_metric = "error", eval_metric = "auc", eval_metric = "logloss"
eval_metric = "error", eval_metric = "auc", eval_metric = "logloss",
watchlist = list(train = xgb.DMatrix(train$data, label = train$label))
),
"train-error.*train-auc.*train-logloss"
)
@ -282,10 +287,11 @@ test_that("use of multiple eval metrics works", {
expect_equal(dim(bst$evaluation_log), c(2, 4))
expect_equal(colnames(bst$evaluation_log), c("iter", "train_error", "train_auc", "train_logloss"))
expect_output(
bst2 <- xgboost(
data = train$data, label = train$label, max_depth = 2,
bst2 <- xgb.train(
data = xgb.DMatrix(train$data, label = train$label), max_depth = 2,
eta = 1, nthread = n_threads, nrounds = 2, objective = "binary:logistic",
eval_metric = list("error", "auc", "logloss")
eval_metric = list("error", "auc", "logloss"),
watchlist = list(train = xgb.DMatrix(train$data, label = train$label))
),
"train-error.*train-auc.*train-logloss"
)
@ -361,7 +367,7 @@ test_that("xgb.cv works", {
expect_is(cv, "xgb.cv.synchronous")
expect_false(is.null(cv$evaluation_log))
expect_lt(cv$evaluation_log[, min(test_error_mean)], 0.03)
expect_lt(cv$evaluation_log[, min(test_error_std)], 0.008)
expect_lt(cv$evaluation_log[, min(test_error_std)], 0.0085)
expect_equal(cv$niter, 2)
expect_false(is.null(cv$folds) && is.list(cv$folds))
expect_length(cv$folds, 5)
@ -391,8 +397,8 @@ test_that("xgb.cv works with stratified folds", {
test_that("train and predict with non-strict classes", {
# standard dense matrix input
train_dense <- as.matrix(train$data)
bst <- xgboost(
data = train_dense, label = train$label, max_depth = 2,
bst <- xgb.train(
data = xgb.DMatrix(train_dense, label = train$label), max_depth = 2,
eta = 1, nthread = n_threads, nrounds = 2, objective = "binary:logistic",
verbose = 0
)
@ -402,8 +408,8 @@ test_that("train and predict with non-strict classes", {
class(train_dense) <- "shmatrix"
expect_true(is.matrix(train_dense))
expect_error(
bst <- xgboost(
data = train_dense, label = train$label, max_depth = 2,
bst <- xgb.train(
data = xgb.DMatrix(train_dense, label = train$label), max_depth = 2,
eta = 1, nthread = n_threads, nrounds = 2, objective = "binary:logistic",
verbose = 0
),
@ -416,8 +422,8 @@ test_that("train and predict with non-strict classes", {
class(train_dense) <- c("pphmatrix", "shmatrix")
expect_true(is.matrix(train_dense))
expect_error(
bst <- xgboost(
data = train_dense, label = train$label, max_depth = 2,
bst <- xgb.train(
data = xgb.DMatrix(train_dense, label = train$label), max_depth = 2,
eta = 1, nthread = n_threads, nrounds = 2, objective = "binary:logistic",
verbose = 0
),
@ -480,8 +486,8 @@ test_that("colsample_bytree works", {
})
test_that("Configuration works", {
bst <- xgboost(
data = train$data, label = train$label, max_depth = 2,
bst <- xgb.train(
data = xgb.DMatrix(train$data, label = train$label), max_depth = 2,
eta = 1, nthread = n_threads, nrounds = 2, objective = "binary:logistic",
eval_metric = "error", eval_metric = "auc", eval_metric = "logloss"
)
@ -521,8 +527,8 @@ test_that("strict_shape works", {
y <- as.numeric(iris$Species) - 1
X <- as.matrix(iris[, -5])
bst <- xgboost(
data = X, label = y,
bst <- xgb.train(
data = xgb.DMatrix(X, label = y),
max_depth = 2, nrounds = n_rounds, nthread = n_threads,
objective = "multi:softprob", num_class = 3, eval_metric = "merror"
)
@ -536,8 +542,8 @@ test_that("strict_shape works", {
X <- agaricus.train$data
y <- agaricus.train$label
bst <- xgboost(
data = X, label = y, max_depth = 2, nthread = n_threads,
bst <- xgb.train(
data = xgb.DMatrix(X, label = y), max_depth = 2, nthread = n_threads,
nrounds = n_rounds, objective = "binary:logistic",
eval_metric = "error", eval_metric = "auc", eval_metric = "logloss"
)
@ -555,8 +561,8 @@ test_that("'predict' accepts CSR data", {
x_csc <- as(X[1L, , drop = FALSE], "CsparseMatrix")
x_csr <- as(x_csc, "RsparseMatrix")
x_spv <- as(x_csc, "sparseVector")
bst <- xgboost(
data = X, label = y, objective = "binary:logistic",
bst <- xgb.train(
data = xgb.DMatrix(X, label = y), objective = "binary:logistic",
nrounds = 5L, verbose = FALSE, nthread = n_threads,
)
p_csc <- predict(bst, x_csc)

8
R-package/tests/testthat/test_callbacks.R
View File

@ -265,14 +265,14 @@ test_that("early stopping works with titanic", {
dtx <- model.matrix(~ 0 + ., data = titanic[, c("Pclass", "Sex")])
dty <- titanic$Survived
xgboost::xgboost(
data = dtx,
label = dty,
xgboost::xgb.train(
data = xgb.DMatrix(dtx, label = dty),
objective = "binary:logistic",
eval_metric = "auc",
nrounds = 100,
early_stopping_rounds = 3,
nthread = n_threads
nthread = n_threads,
watchlist = list(train = xgb.DMatrix(dtx, label = dty))
)
expect_true(TRUE) # should not crash

2
R-package/tests/testthat/test_gc_safety.R
View File

@ -6,7 +6,7 @@ test_that("train and prediction when gctorture is on", {
train <- agaricus.train
test <- agaricus.test
gctorture(TRUE)
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
bst <- xgb.train(data = xgb.DMatrix(train$data, label = train$label), max.depth = 2,
eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
pred <- predict(bst, test$data)
gctorture(FALSE)

23
R-package/tests/testthat/test_helpers.R
View File

@ -25,12 +25,12 @@ if (isTRUE(VCD_AVAILABLE)) {
label <- df[, ifelse(Improved == "Marked", 1, 0)]
# binary
bst.Tree <- xgboost(data = sparse_matrix, label = label, max_depth = 9,
bst.Tree <- xgb.train(data = xgb.DMatrix(sparse_matrix, label = label), max_depth = 9,
eta = 1, nthread = 2, nrounds = nrounds, verbose = 0,
objective = "binary:logistic", booster = "gbtree",
base_score = 0.5)
bst.GLM <- xgboost(data = sparse_matrix, label = label,
bst.GLM <- xgb.train(data = xgb.DMatrix(sparse_matrix, label = label),
eta = 1, nthread = 1, nrounds = nrounds, verbose = 0,
objective = "binary:logistic", booster = "gblinear",
base_score = 0.5)
@ -41,11 +41,11 @@ if (isTRUE(VCD_AVAILABLE)) {
# multiclass
mlabel <- as.numeric(iris$Species) - 1
nclass <- 3
mbst.Tree <- xgboost(data = as.matrix(iris[, -5]), label = mlabel, verbose = 0,
mbst.Tree <- xgb.train(data = xgb.DMatrix(as.matrix(iris[, -5]), label = mlabel), verbose = 0,
max_depth = 3, eta = 0.5, nthread = 2, nrounds = nrounds,
objective = "multi:softprob", num_class = nclass, base_score = 0)
mbst.GLM <- xgboost(data = as.matrix(iris[, -5]), label = mlabel, verbose = 0,
mbst.GLM <- xgb.train(data = xgb.DMatrix(as.matrix(iris[, -5]), label = mlabel), verbose = 0,
booster = "gblinear", eta = 0.1, nthread = 1, nrounds = nrounds,
objective = "multi:softprob", num_class = nclass, base_score = 0)
@ -71,7 +71,8 @@ test_that("xgb.dump works for gblinear", {
expect_length(xgb.dump(bst.GLM), 14)
# also make sure that it works properly for a sparse model where some coefficients
# are 0 from setting large L1 regularization:
bst.GLM.sp <- xgboost(data = sparse_matrix, label = label, eta = 1, nthread = 2, nrounds = 1,
bst.GLM.sp <- xgb.train(data = xgb.DMatrix(sparse_matrix, label = label), eta = 1,
nthread = 2, nrounds = 1,
alpha = 2, objective = "binary:logistic", booster = "gblinear")
d.sp <- xgb.dump(bst.GLM.sp)
expect_length(d.sp, 14)
@ -168,7 +169,7 @@ test_that("SHAPs sum to predictions, with or without DART", {
nrounds <- 30
for (booster in list("gbtree", "dart")) {
fit <- xgboost(
fit <- xgb.train(
params = c(
list(
nthread = 2,
@ -177,8 +178,7 @@ test_that("SHAPs sum to predictions, with or without DART", {
eval_metric = "rmse"),
if (booster == "dart")
list(rate_drop = .01, one_drop = TRUE)),
data = d,
label = y,
data = xgb.DMatrix(d, label = y),
nrounds = nrounds)
pr <- function(...) {
@ -360,9 +360,8 @@ test_that("xgb.importance works with and without feature names", {
expect_equal(importance_from_dump(), importance, tolerance = 1e-6)
## decision stump
m <- xgboost::xgboost(
data = as.matrix(data.frame(x = c(0, 1))),
label = c(1, 2),
m <- xgboost::xgb.train(
data = xgb.DMatrix(as.matrix(data.frame(x = c(0, 1))), label = c(1, 2)),
nrounds = 1,
base_score = 0.5,
nthread = 2
@ -393,7 +392,7 @@ test_that("xgb.importance works with GLM model", {
test_that("xgb.model.dt.tree and xgb.importance work with a single split model", {
.skip_if_vcd_not_available()
bst1 <- xgboost(data = sparse_matrix, label = label, max_depth = 1,
bst1 <- xgb.train(data = xgb.DMatrix(sparse_matrix, label = label), max_depth = 1,
eta = 1, nthread = 2, nrounds = 1, verbose = 0,
objective = "binary:logistic")
expect_error(dt <- xgb.model.dt.tree(model = bst1), regexp = NA) # no error

2
R-package/tests/testthat/test_interaction_constraints.R
View File

@ -13,7 +13,7 @@ train <- matrix(c(x1, x2, x3), ncol = 3)
test_that("interaction constraints for regression", {
# Fit a model that only allows interaction between x1 and x2
bst <- xgboost(data = train, label = y, max_depth = 3,
bst <- xgb.train(data = xgb.DMatrix(train, label = y), max_depth = 3,
eta = 0.1, nthread = 2, nrounds = 100, verbose = 0,
interaction_constraints = list(c(0, 1)))

10
R-package/tests/testthat/test_interactions.R
View File

@ -98,15 +98,14 @@ test_that("SHAP contribution values are not NAN", {
ivs <- c("x1", "x2")
fit <- xgboost(
fit <- xgb.train(
verbose = 0,
params = list(
objective = "reg:squarederror",
eval_metric = "rmse",
nthread = n_threads
),
data = as.matrix(subset(d, fold == 2)[, ivs]),
label = subset(d, fold == 2)$y,
data = xgb.DMatrix(as.matrix(subset(d, fold == 2)[, ivs]), label = subset(d, fold == 2)$y),
nrounds = 3
)
@ -169,9 +168,8 @@ test_that("multiclass feature interactions work", {
test_that("SHAP single sample works", {
train <- agaricus.train
test <- agaricus.test
booster <- xgboost(
data = train$data,
label = train$label,
booster <- xgb.train(
data = xgb.DMatrix(train$data, label = train$label),
max_depth = 2,
nrounds = 4,
objective = "binary:logistic",

4
R-package/tests/testthat/test_io.R
View File

@ -7,8 +7,8 @@ test <- agaricus.test
test_that("load/save raw works", {
nrounds <- 8
booster <- xgboost(
data = train$data, label = train$label,
booster <- xgb.train(
data = xgb.DMatrix(train$data, label = train$label),
nrounds = nrounds, objective = "binary:logistic",
nthread = 2
)

2
R-package/tests/testthat/test_monotone.R
View File

@ -7,7 +7,7 @@ train <- matrix(x, ncol = 1)
test_that("monotone constraints for regression", {
bst <- xgboost(data = train, label = y, max_depth = 2,
bst <- xgb.train(data = xgb.DMatrix(train, label = y), max_depth = 2,
eta = 0.1, nthread = 2, nrounds = 100, verbose = 0,
monotone_constraints = -1)

2
R-package/tests/testthat/test_parameter_exposure.R
View File

@ -10,7 +10,7 @@ dtest <- xgb.DMatrix(
agaricus.test$data, label = agaricus.test$label, nthread = 2
)
bst <- xgboost(data = dtrain,
bst <- xgb.train(data = dtrain,
max_depth = 2,
eta = 1,
nrounds = 10,

4
R-package/tests/testthat/test_poisson_regression.R
View File

@ -4,8 +4,8 @@ set.seed(1994)
test_that("Poisson regression works", {
data(mtcars)
bst <- xgboost(
data = as.matrix(mtcars[, -11]), label = mtcars[, 11],
bst <- xgb.train(
data = xgb.DMatrix(as.matrix(mtcars[, -11]), label = mtcars[, 11]),
objective = 'count:poisson', nrounds = 10, verbose = 0, nthread = 2
)
expect_equal(class(bst), "xgb.Booster")

2
R-package/tests/testthat/test_unicode.R
View File

@ -8,7 +8,7 @@ set.seed(1994)
test_that("Can save and load models with Unicode paths", {
nrounds <- 2
bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
bst <- xgb.train(data = xgb.DMatrix(train$data, label = train$label), max_depth = 2,
eta = 1, nthread = 2, nrounds = nrounds, objective = "binary:logistic",
eval_metric = "error")
tmpdir <- tempdir()

5
R-package/vignettes/xgboostfromJSON.Rmd
View File

@ -52,9 +52,8 @@ labels <- c(1, 1, 1,
data <- data.frame(dates = dates, labels = labels)
bst <- xgboost(
data = as.matrix(data$dates),
label = labels,
bst <- xgb.train(
data = xgb.DMatrix(as.matrix(data$dates), label = labels),
nthread = 2,
nrounds = 1,
objective = "binary:logistic",