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test_that("benchmark", {
set.seed(getOption("mlr.debug.seed"))
task.names = c("binary", "multiclass")
tasks = list(binaryclass.task, multiclass.task)
learner.names = c("classif.lda", "classif.rpart")
learners = lapply(learner.names, makeLearner)
rin = makeResampleDesc("CV", iters = 2L)
res = benchmark(
learners = makeLearner("classif.lda", predict.type = "prob"),
task = binaryclass.task, resamplings = rin)
preds = getBMRPredictions(res, as.df = FALSE)
expect_true(is.list(preds))
expect_true(setequal(names(preds), "binary"))
preds1 = preds[[1L]]
expect_true(is.list(preds1))
expect_true(setequal(names(preds1), "classif.lda"))
preds11 = preds1[[1L]]
expect_s3_class(preds11, "Prediction")
preds = getBMRPredictions(res, as.df = TRUE)
expect_s3_class(preds, "data.frame")
expect_equal(nrow(preds), getTaskSize(binaryclass.task))
expect_equal(ncol(preds), 9)
expect_equal(unique(preds$iter), 1:2)
res = benchmark(learners = learners, task = tasks, resampling = rin)
expect_true("BenchmarkResult" %in% class(res))
df = as.data.frame(res)
expect_true(is.data.frame(df))
expect_equal(dim(df), c(rin$iters * length(task.names) *
length(learner.names), 4L))
expect_true(setequal(df$task.id, task.names))
expect_true(setequal(df$learner.id, learner.names))
expect_true(is.numeric(df$mmce))
expect_equal(getBMRTaskIds(res), task.names)
expect_equal(getBMRLearnerIds(res), learner.names)
expect_equal(getBMRMeasures(res), list(mmce))
expect_equal(getBMRMeasureIds(res), "mmce")
preds = getBMRPredictions(res, as.df = FALSE)
expect_true(is.list(preds))
expect_true(setequal(names(preds), task.names))
preds1 = preds[[1L]]
expect_true(is.list(preds1))
expect_true(setequal(names(preds1), learner.names))
preds11 = preds1[[1L]]
expect_s3_class(preds11, "Prediction")
preds = getBMRPredictions(res, as.df = TRUE)
expect_s3_class(preds, "data.frame")
expect_equal(nrow(preds), 2 * (getTaskSize(multiclass.task) +
getTaskSize(binaryclass.task)))
p = getBMRPerformances(res, as.df = TRUE)
expect_s3_class(p, "data.frame")
expect_equal(nrow(p), length(task.names) * length(learner.names) * rin$iters)
a = getBMRAggrPerformances(res, as.df = TRUE)
expect_s3_class(a, "data.frame")
expect_equal(nrow(a), length(task.names) * length(learner.names))
# make it more complex
ps = makeParamSet(makeDiscreteLearnerParam("cp", values = c(0.01, 0.1)))
learner.names = c(
"classif.lda", "classif.rpart", "classif.lda.featsel",
"classif.rpart.tuned", "classif.lda.filtered")
learners = list(
makeLearner("classif.lda"),
makeLearner("classif.rpart"))
learners = c(learners, list(
makeFeatSelWrapper(learners[[1L]],
resampling = rin,
control = makeFeatSelControlRandom(maxit = 3)),
makeTuneWrapper(learners[[2L]],
resampling = rin, par.set = ps,
control = makeTuneControlGrid()),
makeFilterWrapper(learners[[1L]], fw.perc = 0.5)
))
resamplings = list(rin, makeResampleDesc("Bootstrap", iters = 2L))
measures = list(mmce, acc)
res = benchmark(
learners = learners, tasks = tasks, resamplings = resamplings,
measures = measures, keep.extract = TRUE)
expect_true("BenchmarkResult" %in% class(res))
df = as.data.frame(res)
expect_true(is.data.frame(df))
expect_equal(dim(df), c(rin$iters * length(task.names) *
length(learner.names), 5L))
expect_true(setequal(df$task.id, task.names))
expect_true(setequal(df$learner.id, learner.names))
expect_true(is.numeric(df$mmce))
expect_true(is.numeric(df$acc))
expect_equal(getBMRTaskIds(res), task.names)
expect_equal(getBMRLearnerIds(res), learner.names)
preds = getBMRPredictions(res, as.df = FALSE)
expect_true(is.list(preds))
expect_true(setequal(names(preds), task.names))
preds1 = preds[[1L]]
expect_true(is.list(preds1))
expect_true(setequal(names(preds1), learner.names))
preds11 = preds1[[1L]]
expect_s3_class(preds11, "Prediction")
preds = getBMRPredictions(res, as.df = TRUE)
expect_s3_class(preds, "data.frame")
p = getBMRPerformances(res, as.df = TRUE)
expect_s3_class(p, "data.frame")
expect_equal(nrow(p), length(task.names) * length(learner.names) * rin$iters)
a = getBMRAggrPerformances(res, as.df = TRUE)
expect_s3_class(a, "data.frame")
expect_equal(nrow(a), length(task.names) * length(learner.names))
tr = getBMRTuneResults(res, as.df = FALSE)
expect_list(tr)
expect_equal(length(tr), 2)
expect_true(setequal(names(tr), task.names))
tr1 = tr[[task.names[1L]]]
expect_true(is.list(tr1))
expect_true(setequal(names(tr1), learner.names))
tr11 = tr1[[paste0(learner.names[2L], ".tuned")]]
expect_equal(length(tr11), 2)
tr111 = tr11[[1L]]
expect_s3_class(tr111, "TuneResult")
trd = getBMRTuneResults(res, as.df = TRUE)
expect_s3_class(trd, "data.frame")
expect_equal(ncol(trd), 5)
expect_equal(nrow(trd), 4)
expect_equal(levels(as.factor(trd$task.id)), task.names)
expect_equal(levels(as.factor(trd$learner.id)), "classif.rpart.tuned")
expect_equal(unique(trd$iter), 1:2)
tf = getBMRFeatSelResults(res, as.df = FALSE)
expect_list(tf)
expect_equal(length(tf), 2)
expect_true(setequal(names(tf), task.names))
tf1 = tf[[task.names[1L]]]
expect_true(is.list(tf1))
expect_true(setequal(names(tf1), learner.names))
tf11 = tf1[[paste0(learner.names[1L], ".featsel")]]
expect_equal(length(tf11), 2)
tf111 = tf11[[1L]]
expect_s3_class(tf111, "FeatSelResult")
tfd = getBMRFeatSelResults(res, as.df = TRUE)
expect_s3_class(tfd, "data.frame")
expect_equal(ncol(tfd), 4)
expect_gt(nrow(tfd), 10)
expect_equal(levels(as.factor(tfd$task.id)), task.names)
expect_equal(levels(as.factor(tfd$learner.id)), "classif.lda.featsel")
expect_equal(unique(tfd$iter), 1:2)
tff = getBMRFilteredFeatures(res, as.df = FALSE)
expect_list(tff)
expect_equal(length(tff), 2)
expect_true(setequal(names(tff), task.names))
tff1 = tff[[task.names[1L]]]
expect_true(is.list(tff1))
expect_true(setequal(names(tff1), learner.names))
tff11 = tff1[[paste0(learner.names[1L], ".filtered")]]
expect_equal(length(tff11), 2)
tff111 = tff11[[1L]]
expect_type(tff111, "character")
tffd = getBMRFilteredFeatures(res, as.df = TRUE)
expect_s3_class(tffd, "data.frame")
expect_equal(ncol(tffd), 4)
expect_equal(nrow(tffd), 64)
expect_equal(levels(as.factor(tffd$task.id)), task.names)
expect_equal(levels(as.factor(tffd$learner.id)), "classif.lda.filtered")
expect_equal(unique(tffd$iter), 1:2)
f = function(tmp, cl) {
expect_true(is.list(tmp))
expect_true(setequal(names(tmp), task.names))
tmp = tmp[[1L]]
expect_equal(length(tmp), length(learners))
tmp = Filter(Negate(is.null), tmp)
expect_equal(length(tmp), 1L)
tmp = tmp[[1L]]
expect_true(inherits(tmp[[1L]], cl))
}
f(getBMRFeatSelResults(res), "FeatSelResult")
f(getBMRTuneResults(res), "TuneResult")
f(getBMRFilteredFeatures(res), "character")
})
test_that("keep.preds and models are passed down to resample()", {
task.names = "binary"
tasks = list(binaryclass.task)
learner.names = "classif.lda"
learners = lapply(learner.names, makeLearner)
rin = makeResampleDesc("CV", iters = 2L)
res = benchmark(
learners = makeLearner("classif.lda", predict.type = "prob"),
task = binaryclass.task, resampling = rin, keep.pred = TRUE, models = TRUE)
x = res$results$binary$classif.lda
expect_s3_class(x, "ResampleResult")
expect_list(x$models, types = "WrappedModel")
expect_s3_class(x$pred, "ResamplePrediction")
## test getter function for models
models = getBMRModels(res)
expect_true(is.list(models))
expect_true(setequal(names(models), "binary"))
models1 = models[[1L]]
expect_true(is.list(models1))
expect_true(setequal(names(models1), "classif.lda"))
models11 = models1[[1L]]
expect_true(is.list(models11))
expect_equal(length(models11), 2L)
models111 = models11[[1L]]
expect_s3_class(models111, "WrappedModel")
res = benchmark(
learners = makeLearner("classif.lda", predict.type = "prob"),
task = binaryclass.task, resampling = rin, keep.pred = FALSE,
models = FALSE)
x = res$results$binary$classif.lda
models11 = getBMRModels(res)[[1L]][[1L]]
expect_s3_class(x, "ResampleResult")
expect_null(x$pred)
expect_null(models11)
})
test_that("benchmark work with learner string", {
# we had a bug here, check that learner(s) are created from string
b = benchmark("classif.rpart", iris.task, hout)
expect_class(b, "BenchmarkResult")
b = benchmark(c("classif.rpart", "classif.lda"), iris.task, hout)
expect_class(b, "BenchmarkResult")
})
test_that("drop option works for BenchmarkResults_operators", {
# setup bmrs for checking
task.names = c("binary", "multiclass")
tasks = list(binaryclass.task, multiclass.task)
learner.names = c("classif.lda", "classif.rpart")
learners = lapply(learner.names, makeLearner)
two_two = benchmark(learners = learners, task = tasks, resampling = hout)
two_one = benchmark(
learners = learners[[1L]], task = tasks,
resampling = hout)
one_two = benchmark(
learners = learners, task = tasks[[1L]],
resampling = hout)
one_one = benchmark(
learners = learners[[1L]], task = tasks[[1L]],
resampling = hout)
# check behavior extensively
res = getBMRPredictions(two_two, drop = TRUE)
expect_true(all(names(res) == task.names))
res = getBMRPredictions(two_one, drop = TRUE)
expect_true(all(names(res) == task.names))
res = getBMRPredictions(one_two, drop = TRUE)
expect_true(all(names(res) == learner.names))
res = getBMRPredictions(one_one, drop = TRUE)
expect_class(res, "Prediction")
# check all other functions that use 'drop' briefly
testDropOption = function(bmr, fun, new.names, ...) {
extra.args = list(...)
res = do.call(fun, c(list(bmr, drop = TRUE), extra.args))
expect_true(all(names(res) == new.names))
}
funs_to_test = c(
getBMRPerformances, getBMRTuneResults,
getBMRFeatSelResults, getBMRFilteredFeatures, getBMRModels)
lapply(funs_to_test,
FUN = testDropOption, bmr = one_two,
new.names = learner.names)
# getBMROptResults needs separate checking since it needs extra argument
testDropOption(one_two, getBMROptResults,
new.names = learner.names,
wrapper.class = "cl")
})
test_that("benchmark works with ensemble filters", {
lrn = makeLearner("classif.ksvm")
lrn = makeFilterWrapper(lrn,
fw.method = "E-Borda",
fw.base.methods = c("anova.test", "variance"))
par.set = makeParamSet(
makeNumericParam("C",
lower = -2, upper = 2,
trafo = function(x) 2^x),
makeNumericParam("sigma",
lower = -2, upper = 2,
trafo = function(x) 2^x),
makeNumericParam("fw.perc", lower = 0, upper = 1)
)
task.names = c("binary", "multiclass")
tasks = list(binaryclass.task, multiclass.task)
rin = makeResampleDesc("CV", iters = 2L)
tune.ctrl = makeTuneControlRandom(maxit = 3)
tune_wrapper_svm = makeTuneWrapper(lrn,
resampling = rin, par.set = par.set,
control = tune.ctrl, show.info = FALSE,
measures = list(acc))
expect_class(benchmark(
learners = tune_wrapper_svm, task = tasks,
resampling = rin, measures = list(acc)), "BenchmarkResult")
})
test_that("benchmark handles failure models correctly", {
# RWeka not avail
skip_on_cran()
skip_on_os("windows")
# Define task
task = binaryclass.task
# Define filter parameter set
filter.ps = makeParamSet(makeIntegerParam("fw.abs",
lower = 1,
upper = getTaskNFeats(task)))
# Define tuning control
ctrl = makeTuneControlRandom(maxit = 10L)
# Define resampling strategies
inner = mlr::makeResampleDesc("CV", stratify = FALSE, iters = 2L)
outer = mlr::makeResampleDesc("CV", stratify = FALSE, iters = 2L)
# Define learner
quiet_learner = makeLearner("classif.__mlrmocklearners__3",
config = list("on.learner.error" = "quiet"))
quiet_learner = makeFilterWrapper(quiet_learner,
fw.method = "FSelector_chi.squared")
quiet_learner = makeTuneWrapper(quiet_learner,
resampling = inner,
control = ctrl,
par.set = filter.ps, show.info = TRUE)
stop.learner = makeLearner("classif.__mlrmocklearners__3",
config = list("on.learner.error" = "stop"))
stop.learner = makeFilterWrapper(stop.learner,
fw.method = "FSelector_chi.squared")
stop.learner = makeTuneWrapper(stop.learner,
resampling = inner,
control = ctrl,
par.set = filter.ps, show.info = TRUE)
warn_learner = makeLearner("classif.__mlrmocklearners__3",
config = list("on.learner.error" = "warn"))
warn_learner = makeFilterWrapper(warn_learner,
fw.method = "FSelector_chi.squared")
warn_learner = makeTuneWrapper(warn_learner,
resampling = inner,
control = ctrl,
par.set = filter.ps, show.info = TRUE)
# Tests
# Expect benchmark failing
suppressMessages(
expect_error(benchmark(
learners = stop.learner, tasks = task,
resamplings = outer,
keep.pred = FALSE, models = FALSE, show.info = TRUE))
)
# Expect benchmark warning
suppressWarnings(suppressMessages(
expect_warning(benchmark(
learners = warn_learner, tasks = task,
resamplings = outer,
keep.pred = FALSE, models = FALSE, show.info = TRUE))
))
# Expect benchmark messages
suppressMessages(
expect_message({
bmr = benchmark(
learners = quiet_learner, tasks = task,
resamplings = outer, keep.pred = FALSE, models = FALSE, show.info = TRUE)
})
)
aggr_perf = getBMRAggrPerformances(bmr = bmr)
# Check result
expect_class(x = bmr, classes = "BenchmarkResult")
expect_true(object = is.na(aggr_perf[[1]][[1]]))
})
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