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Use hist as the default tree method. (#9320)

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This commit is contained in:
Jiaming Yuan
2023-06-27 23:04:24 +08:00
committed by GitHub
parent bc267dd729
commit f4798718c7
10 changed files with 138 additions and 178 deletions
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43
R-package/tests/testthat/test_basic.R
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@@ -85,9 +85,18 @@ test_that("dart prediction works", {
rnorm(100)
set.seed(1994)
booster_by_xgboost <- xgboost(data = d, label = y, max_depth = 2, booster = "dart",
rate_drop = 0.5, one_drop = TRUE,
eta = 1, nthread = 2, nrounds = nrounds, objective = "reg:squarederror")
booster_by_xgboost <- xgboost(
data = d,
label = y,
max_depth = 2,
booster = "dart",
rate_drop = 0.5,
one_drop = TRUE,
eta = 1,
nthread = 2,
nrounds = nrounds,
objective = "reg:squarederror"
)
pred_by_xgboost_0 <- predict(booster_by_xgboost, newdata = d, ntreelimit = 0)
pred_by_xgboost_1 <- predict(booster_by_xgboost, newdata = d, ntreelimit = nrounds)
expect_true(all(matrix(pred_by_xgboost_0, byrow = TRUE) == matrix(pred_by_xgboost_1, byrow = TRUE)))
@@ -97,19 +106,19 @@ test_that("dart prediction works", {
set.seed(1994)
dtrain <- xgb.DMatrix(data = d, info = list(label = y))
booster_by_train <- xgb.train(params = list(
booster = "dart",
max_depth = 2,
eta = 1,
rate_drop = 0.5,
one_drop = TRUE,
nthread = 1,
tree_method = "exact",
objective = "reg:squarederror"
),
data = dtrain,
nrounds = nrounds
)
booster_by_train <- xgb.train(
params = list(
booster = "dart",
max_depth = 2,
eta = 1,
rate_drop = 0.5,
one_drop = TRUE,
nthread = 1,
objective = "reg:squarederror"
),
data = dtrain,
nrounds = nrounds
)
pred_by_train_0 <- predict(booster_by_train, newdata = dtrain, ntreelimit = 0)
pred_by_train_1 <- predict(booster_by_train, newdata = dtrain, ntreelimit = nrounds)
pred_by_train_2 <- predict(booster_by_train, newdata = dtrain, training = TRUE)
@@ -399,7 +408,7 @@ test_that("colsample_bytree works", {
xgb.importance(model = bst)
# If colsample_bytree works properly, a variety of features should be used
# in the 100 trees
expect_gte(nrow(xgb.importance(model = bst)), 30)
expect_gte(nrow(xgb.importance(model = bst)), 28)
})
test_that("Configuration works", {

5
R-package/tests/testthat/test_update.R
View File

@@ -13,7 +13,10 @@ test_that("updating the model works", {
watchlist <- list(train = dtrain, test = dtest)
# no-subsampling
p1 <- list(objective = "binary:logistic", max_depth = 2, eta = 0.05, nthread = 2)
p1 <- list(
objective = "binary:logistic", max_depth = 2, eta = 0.05, nthread = 2,
updater = "grow_colmaker,prune"
)
set.seed(11)
bst1 <- xgb.train(p1, dtrain, nrounds = 10, watchlist, verbose = 0)
tr1 <- xgb.model.dt.tree(model = bst1)