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test_that("regr_btgpllm", {
skip("not runnable in parallel")
# due to https://github.com/cran/tgp/blob/689168f5e43941e2808c36bc43603329641028db/R/tgp.postprocess.R#L75 # nocov
requirePackagesOrSkip("tgp", default.method = "load")
parset.list = list(
list(meanfn = "linear", bprior = "bflat", corr = "expsep")
)
df = regr.df[, 2:5]
col.types = vcapply(df, function(x) class(x))
factor.ind = (col.types == "factor")
df.num = df[, !factor.ind, drop = FALSE]
n.num = ncol(df.num)
df.factor = df[, factor.ind, drop = FALSE]
df.factor = createDummyFeatures(df.factor, method = "reference")
df = cbind(df.num, df.factor)
inds = 1:10
train = df[inds, ]
test = df[-inds, ]
y = regr.df[inds, regr.target]
old.predicts.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
pars = list(X = train, Z = y, verb = 0, basemax = n.num, pred.n = FALSE)
pars = c(pars, parset)
m = do.call(tgp::btgpllm, pars)
old.predicts.list[[i]] = predict(m, XX = test, pred.n = FALSE)$ZZ.km
}
testSimpleParsets("regr.btgpllm", regr.df[, c(2:5, 14)], regr.target, inds,
old.predicts.list, parset.list)
})
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