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
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
|
test_that("SupervisedTask", {
ct1 = multiclass.task
expect_equal(getTaskTargetNames(ct1), "Species")
expect_equal(getTaskTargets(ct1), multiclass.df[, multiclass.target])
ct = binaryclass.task
td = getTaskDesc(ct)
pn = c(td$positive, td$negative)
expect_true(setequal(getTaskClassLevels(ct), sort(pn)))
ct2 = subsetTask(ct, subset = 1:150)
expect_equal(getTaskDesc(ct)$positive, getTaskDesc(ct2)$positive)
ct2 = subsetTask(ct, subset = 1:150, features = colnames(binaryclass.df)[1:2])
expect_equal(getTaskSize(ct2), 150)
expect_equal(sum(getTaskNFeats(ct2)), 2)
ct2 = subsetTask(ct, subset = 1:150, features = colnames(binaryclass.df)[1:2])
expect_equal(getTaskSize(ct2), 150)
expect_equal(sum(getTaskNFeats(ct2)), 2)
# wrong data
expect_error(makeClassifTask(data = 44, target = "y"), "'data.frame'")
# wrong target type
expect_error(makeClassifTask(data = regr.df, target = regr.target), "'factor'")
expect_error(makeRegrTask(data = multiclass.df, target = multiclass.target), "'numeric'")
# wrong vars
expect_error(subsetTask(multiclass.task, features = c("Sepal.Length", "x", "y")))
# check missing accessors
df = multiclass.df
df[1, 1:3] = NA
df[2, 1:3] = NA
ct = makeClassifTask(data = df, target = multiclass.target)
expect_true(getTaskDesc(ct)$has.missings)
# check that blocking is still there after subsetting
ct1 = makeClassifTask(data = multiclass.df, target = multiclass.target,
blocking = as.factor(seq_len(nrow(multiclass.df))))
expect_true(getTaskDesc(ct1)$has.blocking)
ct2 = subsetTask(ct1)
expect_true(getTaskDesc(ct2)$has.blocking)
})
test_that("SupervisedTask dropping of levels works", {
d = iris
levs1 = levels(iris$Species)
levs2 = c(levs1, "foo")
levels(d$Species) = levs2
task = makeRegrTask(data = d, target = colnames(iris)[1], fixup.data = "quiet")
e = getTaskData(task)
expect_true(setequal(levels(e$Species), levs1))
expect_warning({
task = makeRegrTask(data = d, target = colnames(iris)[1], fixup.data = "warn")
},
"Empty factor levels")
e = getTaskData(task)
expect_true(setequal(levels(e$Species), levs1))
expect_warning(makeMultilabelTask("multilabel", multilabel.df[1:10, ], target = c("y1", "y2"), fixup.data = "warn"),
"Empty factor levels")
expect_warning(makeMultilabelTask("multilabel", multilabel.df[1:10, ], target = c("y1", "y2"), fixup.data = "quiet"), NA)
})
test_that("SupervisedTask does not drop positive class", {
data = iris[1:100, ]
expect_warning({
task = makeClassifTask(data = data, target = "Species")
}, "empty factor levels")
td = getTaskDesc(task)
expect_true(setequal(c(td$positive, td$negative), unique(data$Species)))
})
test_that("Task $type and $task.desc$type agree", {
check = list(
classif = binaryclass.task,
multilabel = multilabel.task,
regr = regr.task,
surv = surv.task,
costsens = costsens.task)
for (type in names(check)) {
expect_identical(type, check[[type]]$type)
expect_identical(type, getTaskDesc(check[[type]])$type)
}
})
|
