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
|
test_that("filterFeatures_fselectorrcpp", {
a = c(1, 2, 5.3, 6, -2, 4, 8.3, 9.2, 10.1) # numeric vector
b = c("one", "two", "three") # character vector
c = c(TRUE, TRUE, TRUE, FALSE, TRUE, FALSE) # logical vector
d = c(1L, 3L, 5L, 7L, 9L, 17L)
f = rep(c("c1", "c2"), 9)
df = data.frame(a = a, b = b, c = c, d = d, f = f)
df = convertDataFrameCols(df, logicals.as.factor = TRUE, chars.as.factor = TRUE)
task = makeClassifTask(data = df, target = "f")
candidates = as.character(listFilterMethods()$id)
candidates = candidates[startsWith(candidates, "FSelectorRcpp")]
for (candidate in candidates) {
fv = generateFilterValuesData(task, method = candidate, nselect = 2L)
expect_class(fv, "FilterValues")
expect_data_frame(fv$data, nrow = getTaskNFeats(task))
expect_set_equal(fv$data$name, getTaskFeatureNames(task))
expect_numeric(fv$data$value, any.missing = FALSE, finite = TRUE)
}
lrn = makeLearner("classif.rpart")
lrn = makeFilterWrapper(learner = lrn,
fw.method = "FSelectorRcpp_information.gain", fw.perc = 0.1)
res = resample(learner = lrn, task = binaryclass.task, resampling = cv3,
measures = list(mmce, timetrain), extract = getFilteredFeatures,
show.info = FALSE)
expect_length(res$extract[[1L]], 6)
})
|
