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# this is a long test suite that is used to test the validity of ALL filters
context("filterFeatures")
cat("filters")
test_that("filterFeatures 1", {
# FSelector not avail
skip_on_os("windows")
# Loop through all filters
# univariate.model.score, permutation.importance and auc are handled extra test below
# 'univariate', 'randomForest_importance' and 'rfsrc_var.select' are deprecated
filter.list = listFilterMethods(desc = FALSE, tasks = TRUE, features = FALSE)
filter.list.classif = as.character(filter.list$id)[filter.list$task.classif]
filter.list.classif = setdiff(filter.list.classif, c(
"univariate.model.score", "permutation.importance", "auc",
"univariate", "randomForest_importance", "randomForestSRC_var.select"))
for (filter in filter.list.classif) {
filterFeatures(task = multiclass.task, method = filter, perc = 0.5)
}
filter.list.regr = as.character(filter.list$id)[!filter.list$task.classif &
filter.list$task.regr]
for (filter in filter.list.regr) {
filterFeatures(task = regr.num.task, method = filter, perc = 0.5)
}
fv = generateFilterValuesData(task = multiclass.task,
method = "univariate.model.score", perc = -1.5,
perf.learner = makeLearner("classif.rpart"), measures = mmce)
expect_class(fv, classes = "FilterValues")
expect_numeric(fv$data$value, any.missing = FALSE, all.missing = FALSE,
len = getTaskNFeats(multiclass.task))
})
test_that("filterFeatures - permutation imp", {
# extra test of the permutation.importance filter
fv = generateFilterValuesData(task = multiclass.task,
method = "permutation.importance",
imp.learner = makeLearner("classif.rpart"),
measure = acc,
contrast = function(x, y) abs(x - y),
aggregation = median,
nmc = 2L)
expect_class(fv, classes = "FilterValues")
expect_numeric(fv$data$value, any.missing = FALSE, all.missing = FALSE,
len = getTaskNFeats(multiclass.task))
})
test_that("filterFeatures: auc", {
# extra test for auc filter (two class dataset)
toy.data = data.frame(
Class = factor(c(1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L)),
V1 = 1:8,
V2 = 8:1,
V3 = c(5L, 1L, 6L, 7L, 2L, 8L, 3L, 4L),
V4 = c(1L, 5L, 2L, 3L, 6L, 4L, 7L, 8L),
V5 = c(1L, 2L, 3L, 5L, 4L, 7L, 6L, 8L))
toy.task = makeClassifTask("toy.task", data = toy.data, target = "Class",
positive = 1L)
fv = generateFilterValuesData(toy.task, method = "auc")
expect_class(fv, classes = "FilterValues")
expect_numeric(fv$data$value, any.missing = FALSE, all.missing = FALSE,
len = getTaskNFeats(toy.task))
expect_equal(fv$data$value, c(0.25, 0.25, 0.5, 0.5, 0.125))
})
test_that("randomForestSRC_var.select filter handles user choices correctly", {
expect_silent(
suppressWarnings(generateFilterValuesData(task = multiclass.task,
method = "randomForestSRC_var.select",
more.args = list("randomForestSRC_var.select" = c(method = "vh",
conservative = "high", fast = TRUE))))
)
# method = "vh.imp" is not supported
expect_error(
fv = suppressWarnings(generateFilterValuesData(task = multiclass.task,
method = "randomForestSRC_var.select",
more.args = list("randomForestSRC_var.select" = c(method = "vh.imp"))))
)
})
test_that("Custom threshold function for filtering works correctly", {
biggest_gap = function(values, diff) {
gap_size = 0
gap_location = 0
for (i in (diff + 1):length(values)) {
gap = values[[i - diff]] - values[[i]]
if (gap > gap_size) {
gap_size = gap
gap_location = i - 1
}
}
return(gap_location)
}
ftask = filterFeatures(task = multiclass.task,
method = "variance",
fun = biggest_gap,
fun.args = list("diff" = 1)
)
feats = getTaskFeatureNames(ftask)
expect_equal(feats, c("Petal.Length"))
})
test_that("randomForestSRC_var.select minimal depth filter returns NA for features below the threshold", {
dat = generateFilterValuesData(task = multiclass.task,
method = "randomForestSRC_var.select",
nselect = 5,
more.args = list(method = "md", nrep = 5))
expect_equal(all(is.na(dat$data$value[dat$data$name %in% c("Sepal.Length", "Sepal.Width")])), TRUE)
expect_equal(all(is.na(dat$data$value[dat$data$name %in% c("Petal.Length", "Petal.Width")])), FALSE)
})
test_that("ensemble filters subset the task correctly", {
# expectation for all filters was checked manually just right after the
# internal aggregation (in filterFeatures.R)
task.filtered = filterFeatures(bh.task,
method = "E-mean",
abs = 5,
base.methods = c("univariate.model.score", "praznik_CMIM"))
expect_equal(getTaskFeatureNames(task.filtered), c("indus", "nox", "rm",
"ptratio", "lstat"))
task.filtered = filterFeatures(bh.task,
method = "E-min",
abs = 5,
base.methods = c("univariate.model.score", "praznik_CMIM"))
expect_equal(getTaskFeatureNames(task.filtered), c("nox", "rm", "tax",
"ptratio", "lstat"))
task.filtered = filterFeatures(bh.task,
method = "E-max",
abs = 5,
base.methods = c("univariate.model.score", "praznik_CMIM"))
expect_equal(getTaskFeatureNames(task.filtered), c("indus", "nox", "rm",
"ptratio", "lstat"))
task.filtered = filterFeatures(bh.task,
method = "E-median",
abs = 5,
base.methods = c("univariate.model.score", "praznik_CMIM"))
expect_equal(getTaskFeatureNames(task.filtered), c("indus", "nox", "rm",
"ptratio", "lstat"))
task.filtered = filterFeatures(bh.task,
method = "E-Borda",
abs = 5,
base.methods = c("univariate.model.score", "praznik_CMIM"))
expect_equal(getTaskFeatureNames(task.filtered), c("indus", "nox", "rm",
"ptratio", "lstat"))
})
test_that("Thresholding works with ensemble filters", {
foo = filterFeatures(iris.task, method = "E-min",
base.methods = c("FSelectorRcpp_gain.ratio",
"FSelectorRcpp_information.gain"),
thresh = 2)
expect_equal(getTaskNFeats(foo), 3)
})
test_that("Ensemble filters can deal with non-unique base methods", {
lda = makeLearner(cl = "classif.lda", id = "lda_class", predict.type = "response")
ff = filterFeatures(multiclass.task,
method = "E-mean",
base.methods = list("filter1" = "univariate.model.score", "filter2" = "univariate.model.score"),
abs = 2,
more.args = list("filter1" = list(perf.learner = lda), "filter2" = list(perf.learner = lda))
)
expect_equal(getTaskFeatureNames(ff), c("Petal.Length", "Petal.Width"))
})
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