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test_that("parallel resampling", {
skip_on_os("mac")
skip_on_ci()
skip_on_cran()
doit = function(mode, level) {
lrn = makeLearner("classif.rpart")
rdesc = makeResampleDesc("CV", iters = 2L)
on.exit(parallelStop())
parallelStart(mode = mode, cpus = 2L, level = level, show.info = FALSE)
r = resample(lrn, multiclass.task, rdesc)
expect_true(!is.na(r$aggr[1]))
}
if (Sys.info()["sysname"] != "Windows") {
doit("mpi", as.character(NA))
doit("mpi", "mlr.resample")
doit("mpi", "mlr.tuneParams")
}
})
test_that("parallel tuning", {
skip_on_os("mac")
skip_on_ci()
skip_on_cran()
doit = function(mode, level) {
lrn = makeLearner("classif.rpart")
rdesc = makeResampleDesc("CV", iters = 2L)
ps = makeParamSet(makeDiscreteParam("cp", values = c(0.01, 0.05)))
ctrl = makeTuneControlGrid()
on.exit(parallelStop())
parallelStart(mode = mode, cpus = 2L, level = level, show.info = FALSE)
res = tuneParams(lrn, multiclass.task, rdesc, par.set = ps, control = ctrl)
expect_true(!is.na(res$y))
}
if (Sys.info()["sysname"] != "Windows") {
doit("mpi", as.character(NA))
doit("mpi", "mlr.resample")
doit("mpi", "mlr.tuneParams")
}
})
test_that("parallel featsel", {
skip_on_os("mac")
skip_on_ci()
skip_on_cran()
doit = function(mode, level) {
lrn = makeLearner("classif.rpart")
rdesc = makeResampleDesc("CV", iters = 2L)
ctrl = makeFeatSelControlRandom(maxit = 2L)
on.exit(parallelStop())
parallelStart(mode = mode, cpus = 2L, level = level, show.info = FALSE)
res = selectFeatures(lrn, multiclass.task, rdesc, control = ctrl)
expect_true(!is.na(res$y))
}
if (Sys.info()["sysname"] != "Windows") {
doit("mpi", as.character(NA))
doit("mpi", "mlr.resample")
doit("mpi", "mlr.tuneParams")
}
})
test_that("parallel exporting of options works", {
skip_on_os("mac")
skip_on_ci()
skip_on_cran()
doit = function(mode, level) {
data = iris
data[, 1] = 1 # this is going to crash lda
task = makeClassifTask(data = data, target = "Species")
lrn = makeLearner("classif.lda")
rdesc = makeResampleDesc("CV", iters = 3)
configureMlr(on.learner.error = "warn")
on.exit(configureMlr(on.learner.error = "stop"))
parallelStart(mode = mode, cpus = 2L, level = level, show.info = FALSE)
on.exit(parallelStop())
# if the option is not exported, we cannot pass the next line without error
# on slave
r = resample(lrn, task, rdesc)
}
doit("socket", as.character(NA))
# make sure
configureMlr(on.learner.error = "stop")
})
test_that("parallel partial dependence", {
skip_on_os("mac")
skip_on_ci()
skip_on_cran()
doit = function(mode) {
lrn = makeLearner("regr.rpart")
fit = train(lrn, regr.task)
on.exit(parallelStop())
parallelStart(mode = mode, cpus = 2L, show.info = FALSE)
pd = generatePartialDependenceData(fit, regr.task, "lstat")
expect_true(ncol(pd$data) == 2L)
}
if (Sys.info()["sysname"] != "Windows") {
doit("mpi")
}
})
test_that("parallel ensembles", {
skip_on_os("mac")
skip_on_ci()
skip_on_cran()
doit = function(mode, level) {
on.exit(parallelStop())
parallelStart(mode = mode, cpus = 2L, show.info = FALSE)
## bagging wrapper
lrn = makeBaggingWrapper(makeLearner("regr.rpart"), bw.iters = 2L)
fit = train(lrn, regr.task)
models = getLearnerModel(fit, more.unwrap = TRUE)
expect_equal(length(models), 2L)
expect_equal(class(models[[1]]), "rpart")
p = predict(fit, regr.task)
## multiclass wrapper
lrn = makeMulticlassWrapper(makeLearner("classif.rpart"))
fit = train(lrn, multiclass.task)
models = getLearnerModel(fit)
expect_equal(length(models), length(getTaskClassLevels(multiclass.task)))
levs = do.call("rbind", extractSubList(models, "factor.levels"))
expect_equal(unique(levs[, 1]), "-1")
expect_equal(unique(levs[, 2]), "1")
p = predict(fit, multiclass.task)
## overbagging wrapper
lrn = makeOverBaggingWrapper(makeLearner("classif.rpart"), 2L)
fit = train(lrn, binaryclass.task)
models = getLearnerModel(fit)
expect_equal(length(models), 2L)
p = predict(fit, binaryclass.task) ## calls predictHomogeneousEnsemble
## costsensregrwrapper
lrn = makeCostSensRegrWrapper(makeLearner("regr.rpart"))
fit = train(lrn, costsens.task)
models = getLearnerModel(fit)
expect_equal(length(models), ncol(getTaskCosts(costsens.task)))
p = predict(fit, costsens.task)
## MultilabelBinaryRelevanceWrapper
lrn = makeMultilabelBinaryRelevanceWrapper("classif.rpart")
lrn = setPredictType(lrn, "prob")
fit = train(lrn, multilabel.task)
p = predict(fit, multilabel.task)
}
## CostSensWeightedPairsWrapper
if (Sys.info()["sysname"] != "Windows") {
doit("mpi", "mlr.ensemble")
}
})
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