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test_that("regr_km", {
requirePackagesOrSkip("DiceKriging", default.method = "load")
parset.list = list(
list(),
# list(covtype="gauss"),
list(covtype = "matern5_2")
)
dd = regr.num.df[1:50, ]
old.predicts.list = list()
des1 = dd[1:25, setdiff(colnames(dd), regr.num.target)]
des2 = dd[26:50, setdiff(colnames(dd), regr.num.target)]
y = dd[1:25, regr.num.target]
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
pars = list(~1, design = des1, response = y)
pars = c(pars, parset)
set.seed(getOption("mlr.debug.seed"))
capture.output({
m = do.call(DiceKriging::km, pars)
})
old.predicts.list[[i]] = DiceKriging::predict(m, newdata = des2,
type = "SK")$mean
}
testSimpleParsets("regr.km", dd, regr.num.target, 1:25, old.predicts.list,
parset.list)
## Test that nugget.stability has an effect.
ps = makeNumericParamSet(len = 1, lower = 0, upper = 1)
set.seed(123)
rs = generateRandomDesign(n = 100, ps)
rs$y = apply(rs, 1, function(x) (x - 0.5)^2)
tsk = makeRegrTask(data = rs, target = "y")
lrn = makeLearner("regr.km")
# expect_error(train(lrn, tsk), "leading minor of order")
lrn = setHyperPars(lrn, nugget.stability = 10^-8)
m = train(lrn, tsk)
expect_class(m$learner.model, "km")
})
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