context("classif_dbnDNN")
test_that("classif_dbnDNN", {
requirePackagesOrSkip("deepnet", default.method = "load")
parset.list1 = list(
list(output = "softmax"),
list(output = "softmax", hidden = 7)
)
parset.list2 = list(
list(),
list(hidden = 7)
)
old.predicts.list = list()
for (i in seq_along(parset.list1)) {
parset = parset.list1[[i]]
set.seed(getOption("mlr.debug.seed"))
capture.output({
# neuralnet is not dealing with formula with `.` well
x = data.matrix(binaryclass.train[, -ncol(binaryclass.train)])
y = binaryclass.train[, ncol(binaryclass.train)]
dict = sort(unique(y))
onehot = matrix(0, length(y), length(dict))
for (j in seq_along(dict)) {
ind = which(y == dict[j])
onehot[ind, j] = 1
}
pars = list(x = x, y = onehot)
pars = c(pars, parset)
m = do.call(deepnet::dbn.dnn.train, pars)
p = deepnet::nn.predict(m, data.matrix(binaryclass.test[, -ncol(binaryclass.test)]))
colnames(p) = binaryclass.class.levs
old.predicts.list[[i]] = as.factor(colnames(p)[max.col(p)])
})
}
testSimpleParsets("classif.dbnDNN", binaryclass.df, binaryclass.target,
binaryclass.train.inds, old.predicts.list, parset.list2)
})
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