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test_that("classif_saeDNN", {
requirePackagesOrSkip("deepnet", default.method = "load")
# test with empty empty paramset
# 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 (i in seq_along(dict)) {
ind = which(y == dict[i])
onehot[ind, i] = 1
}
m = suppressMessages(deepnet::sae.dnn.train(x = x, y = onehot, output = "softmax"))
p = deepnet::nn.predict(
m,
data.matrix(binaryclass.test[, -ncol(binaryclass.test)]))
colnames(p) = binaryclass.class.levs
p = as.factor(colnames(p)[max.col(p)])
testSimple("classif.saeDNN", binaryclass.df, binaryclass.target,
binaryclass.train.inds, p,
parset = list())
# test with params passed
capture.output({
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 (i in seq_along(dict)) {
ind = which(y == dict[i])
onehot[ind, i] = 1
}
m = suppressMessages(deepnet::sae.dnn.train(x = x, y = onehot, hidden = 7, output = "softmax"))
p = deepnet::nn.predict(
m,
data.matrix(binaryclass.test[, -ncol(binaryclass.test)]))
colnames(p) = binaryclass.class.levs
p = as.factor(colnames(p)[max.col(p)])
})
testSimple("classif.saeDNN", binaryclass.df, binaryclass.target,
binaryclass.train.inds, p,
parset = list(hidden = 7))
})
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