context("regr_xgboost")
test_that("regr_xgboost", {
requirePackagesOrSkip("xgboost", default.method = "load")
parset.list = list(
list(),
list(nrounds = 20)
)
old.predicts.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
if (is.null(parset$verbose)) parset$verbose = 0L
if (is.null(parset$nrounds)) parset$nrounds = 1L
if (is.null(parset$objective)) parset$objective = "reg:linear"
pars = list(data = data.matrix(regr.num.train[, -regr.num.class.col]), label = as.numeric(regr.num.train[, regr.num.class.col]))
pars = c(pars, parset)
set.seed(getOption("mlr.debug.seed"))
model = do.call(xgboost::xgboost, pars)
#model = xgboost::xgboost(data = data.matrix(regr.num.train[,-regr.num.class.col]), verbose = 0L,
#label = as.numeric(regr.num.train[,regr.num.class.col]),
#nrounds = 20, objective = "reg:linear", missing = NULL)
old.predicts.list[[i]] = predict(model, data.matrix(regr.num.test[, -regr.num.class.col]))
}
#set.seed(getOption("mlr.debug.seed"))
testSimpleParsets("regr.xgboost", regr.num.df, regr.num.target, regr.num.train.inds,
old.predicts.list, parset.list)
})
test_that("xgboost works with different 'missing' arg vals", {
lrn = makeLearner("regr.xgboost", missing = NA_real_)
lrn = makeLearner("regr.xgboost", missing = NA)
lrn = makeLearner("regr.xgboost", missing = NULL)
})
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.