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
57
58
59
60
61
62
63
64
|
test_that("classif_adaboostm1", {
requirePackagesOrSkip("RWeka", default.method = "load")
parset.list = list(
list(),
list(W = list(RWeka::J48, M = 30)),
list(P = 100),
list(I = 10, S = 1),
list(I = 5, Q = FALSE)
)
old.predicts.list = list()
old.probs.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
set.seed(getOption("mlr.debug.seed"))
ctrl = do.call(RWeka::Weka_control, parset)
m = RWeka::AdaBoostM1(formula = binaryclass.formula,
data = binaryclass.train, control = ctrl)
set.seed(getOption("mlr.debug.seed"))
p = predict(m, newdata = binaryclass.test, type = "probability")
old.probs.list[[i]] = p[, 1]
old.predicts.list[[i]] = as.factor(binaryclass.class.levs[ifelse(p[, 2] > 0.5, 2, 1)])
}
testSimpleParsets("classif.adaboostm1", binaryclass.df, binaryclass.target,
binaryclass.train.inds, old.predicts.list, parset.list)
testProbParsets("classif.adaboostm1", binaryclass.df, binaryclass.target,
binaryclass.train.inds, old.probs.list, parset.list)
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
set.seed(getOption("mlr.debug.seed"))
ctrl = do.call(RWeka::Weka_control, parset)
m = RWeka::AdaBoostM1(formula = multiclass.formula, data = multiclass.train,
control = ctrl)
set.seed(getOption("mlr.debug.seed"))
p = predict(m, newdata = multiclass.test, type = "class")
set.seed(getOption("mlr.debug.seed"))
p2 = predict(m, newdata = multiclass.test, type = "prob")
old.predicts.list[[i]] = p
old.probs.list[[i]] = p2
}
testSimpleParsets("classif.adaboostm1", multiclass.df, multiclass.target,
multiclass.train.inds, old.predicts.list, parset.list)
testProbParsets("classif.adaboostm1", multiclass.df, multiclass.target,
multiclass.train.inds, old.probs.list, parset.list)
tt = function(formula, data, subset, ...) {
RWeka::AdaBoostM1(formula, data = data[subset, ],
control = RWeka::Weka_control(...))
}
tp = function(model, newdata) predict(model, newdata, type = "class")
testCVParsets("classif.adaboostm1", multiclass.df, multiclass.target, #
tune.train = tt, tune.predict = tp, parset.list = parset.list)
})
|
