1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
|
test_that("classif_nnTrain", {
requirePackagesOrSkip("deepnet", default.method = "load")
# test with empty paramset
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 (i in seq_along(dict)) {
ind = which(y == dict[i])
onehot[ind, i] = 1
}
m = deepnet::nn.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.nnTrain", binaryclass.df, binaryclass.target,
binaryclass.train.inds, p,
parset = list())
# test with params passed
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 (i in seq_along(dict)) {
ind = which(y == dict[i])
onehot[ind, i] = 1
}
m = deepnet::nn.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.nnTrain", binaryclass.df, binaryclass.target,
binaryclass.train.inds, p,
parset = list(hidden = 7))
lrn = makeLearner("classif.nnTrain", max.number.of.layers = 2, hidden = 1:3)
m = getLearnerModel(train(lrn, binaryclass.task))
expect_equal(m$hidden, 1:2)
})
|
