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test_that("regr_h2odeeplearning", {
skip("h2o problematic")
skip_on_ci()
requirePackages("h2o", default.method = "load")
h2o::h2o.init()
parset.list = list(
list(),
list(distribution = "gaussian"),
list(distribution = "quantile", quantile_alpha = 0.2),
list(distribution = "tweedie", tweedie_power = 1.2)
)
# h20deeplearning needs seed in function call to be reproducible
debug.seed = getOption("mlr.debug.seed")
parset.list = lapply(parset.list, function(x) {
c(x, seed = debug.seed,
reproducible = TRUE)
})
old.predicts.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
parset = c(parset, list(x = colnames(regr.train[, -regr.class.col]),
y = regr.target,
training_frame = h2o::as.h2o(regr.train)))
set.seed(getOption("mlr.debug.seed"))
m = do.call(h2o::h2o.deeplearning, parset)
p = predict(m, newdata = h2o::as.h2o(regr.test))
old.predicts.list[[i]] = as.data.frame(p)[, 1L]
}
testSimpleParsets("regr.h2o.deeplearning", regr.df, regr.target,
regr.train.inds, old.predicts.list, parset.list)
})
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