Nothing
# TODO: this particular model relies on a setup and teardown infrastructure
# since saving the model object from xgboost in R results in a handle
# (pointer) to an internal xgboost model that is invalid
test_that("xgb.Booster + linear solver + predict() works", {
skip_on_cran()
skip_if_not_installed("xgboost")
suppressPackageStartupMessages(library(xgboost))
# Load data
data(agaricus.train)
data(agaricus.test)
bst <- xgboost(data = agaricus.train$data,
label = agaricus.train$label,
eta = 1,
nthread = 2,
nrounds = 2,
eval_metric = "logloss",
objective = "binary:logistic",
verbose = 0)
x <- axe_call(bst)
expect_equal(x$call, rlang::expr(dummy_call()))
x <- axe_env(bst)
expect_lt(lobstr::obj_size(x), lobstr::obj_size(bst))
x <- butcher(bst)
expect_equal(xgb.importance(model = x),
xgb.importance(model = bst))
expect_equal(predict(x, agaricus.test$data),
predict(bst, agaricus.test$data))
expect_equal(xgb.dump(x, with_stats = TRUE),
xgb.dump(bst, with_stats = TRUE))
})
test_that("xgb.Booster + tree-learning algo + predict() works", {
skip_on_cran()
skip_if_not_installed("xgboost")
suppressPackageStartupMessages(library(xgboost))
# Load data
data(agaricus.train)
data(agaricus.test)
dtrain <- xgb.DMatrix(data = agaricus.train$data,
label = agaricus.train$label)
bst <- xgb.train(data = dtrain,
booster = "gblinear",
nthread = 2,
nrounds = 2,
eval_metric = "logloss",
objective = "binary:logistic",
print_every_n = 10000L)
x <- axe_call(bst)
expect_equal(x$call, rlang::expr(dummy_call()))
x <- axe_env(bst)
expect_lt(lobstr::obj_size(x), lobstr::obj_size(bst))
x <- butcher(bst)
expect_equal(xgb.importance(model = x),
xgb.importance(model = bst))
expect_equal(predict(x, agaricus.test$data),
predict(bst, agaricus.test$data))
expect_equal(xgb.dump(x, with_stats = TRUE),
xgb.dump(bst, with_stats = TRUE))
})
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