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test_that("tune", {
requirePackagesOrSkip("e1071", default.method = "load")
cp = c(0.05, 0.9)
minsplit = 1:2
ps1 = makeParamSet(
makeDiscreteParam("cp", values = cp),
makeDiscreteParam("minsplit", values = minsplit)
)
ctrl = makeTuneControlGrid()
folds = 3
tr = e1071::tune.rpart(
formula = multiclass.formula, data = multiclass.df,
cp = cp, minsplit = minsplit,
tunecontrol = e1071::tune.control(sampling = "cross", cross = folds))
lrn = makeLearner("classif.rpart")
cv.instance = e1071CVToMlrCV(tr)
m1 = setAggregation(mmce, test.mean)
m2 = setAggregation(mmce, test.sd)
tr2 = tuneParams(lrn, multiclass.task, cv.instance,
par.set = ps1,
control = ctrl, measures = list(m1, m2))
pp = as.data.frame(tr2$opt.path)
for (i in seq_len(nrow(tr$performances))) {
cp = tr$performances[i, "cp"]
ms = tr$performances[i, "minsplit"]
j = which(pp$cp == cp & pp$minsplit == ms)
expect_equal(tr$performances[i, "error"], pp[j, "mmce.test.mean"])
expect_equal(tr$performances[i, "dispersion"], pp[j, "mmce.test.sd"])
}
# test printing
expect_output(print(ctrl), "Imputation value: <worst>")
ctrl$impute.val = 10
expect_output(print(ctrl), "Imputation value: 10")
expect_output(print(tr2), "mmce.test.mean=")
# check multiple measures and binary thresholding
rdesc = makeResampleDesc("Holdout")
ms = c("acc", "mmce", "timefit")
lrn2 = makeLearner("classif.rpart", predict.type = "prob")
ctrl = makeTuneControlGrid(tune.threshold = TRUE, tune.threshold.args = list(nsub = 2L))
tr2 = tuneParams(lrn2, binaryclass.task, rdesc,
par.set = ps1, control = ctrl,
measures = getDefaultMeasure(binaryclass.task))
expect_true(is.numeric(as.data.frame(tr2$opt.path)$threshold))
expect_true(isScalarNumeric(tr2$threshold))
# check multiclass thresholding
ctrl = makeTuneControlGrid(
tune.threshold = TRUE,
tune.threshold.args = list(control = list(maxit = 2)))
tr3 = tuneParams(lrn2, multiclass.task, rdesc, par.set = ps1, control = ctrl)
op.df = as.data.frame(tr3$opt.path)
op.df = op.df[, grepl("threshold_", colnames(op.df))]
expect_true(all(sapply(op.df, is.numeric)))
expect_true(is.numeric(tr3$threshold) && length(tr3$threshold) == 3L && !any(is.na(tr3$threshold)))
expect_error(tuneParams(lrn, multiclass.task, cv.instance,
par.set = makeParamSet(), control = ctrl,
measures = getDefaultMeasure(multiclass.task)))
})
test_that("tuning works with infeasible pars", {
# i am not sure if we want that behavior always but currently we impute Inf
# when we eval outside of constraints and there was a bug in that code so we
# test now
ps = makeParamSet(
makeDiscreteParam("cp", values = c(0.05, 2))
)
lrn = makeLearner("classif.rpart")
rdesc = makeResampleDesc("Holdout", split = 0.2)
ctrl = makeTuneControlGrid()
z = tuneParams(lrn, multiclass.task, rdesc,
par.set = ps, control = ctrl,
measures = getDefaultMeasure(multiclass.task))
d = as.data.frame(z$opt.path)
expect_true(is.finite(d[1L, "mmce.test.mean"]))
expect_true(is.na(d[1L, "error.message"]))
expect_true(is.na(d[2L, "mmce.test.mean"]))
expect_true(!is.na(d[2L, "error.message"]))
})
test_that("tuning works with errors", {
configureMlr(on.learner.error = "quiet")
ps = makeParamSet(
makeDiscreteParam("alpha", values = c(1, 0))
)
lrn = makeLearner("classif.__mlrmocklearners__2")
rdesc = makeResampleDesc("Holdout")
ctrl = makeTuneControlGrid()
z = tuneParams(lrn, multiclass.task, rdesc,
par.set = ps, control = ctrl,
measures = getDefaultMeasure(multiclass.task))
d = as.data.frame(z$opt.path)
expect_true(is.finite(d[1L, "mmce.test.mean"]))
expect_true(is.na(d[1L, "error.message"]))
expect_true(is.na(d[2L, "mmce.test.mean"]))
expect_true(grep("foo", d[2L, "error.message"]) == 1L)
configureMlr(on.learner.error = "stop")
})
# see bug in issue 219
test_that("tuning works with tuneThreshold and multiple measures", {
lrn = makeLearner("classif.rpart", predict.type = "prob")
rdesc = makeResampleDesc("Holdout")
ctrl = makeTuneControlRandom(tune.threshold = TRUE, maxit = 2L)
ps = makeParamSet(
makeNumericParam("cp", lower = 0.1, upper = 0.2)
)
res = tuneParams(lrn, binaryclass.task,
resampling = rdesc,
measures = list(mmce, auc), par.set = ps, control = ctrl)
expect_true(is.numeric(res$y) && length(res$y) == 2L && !any(is.na(res$y)))
# also check with infeasible stuff
ps = makeParamSet(
makeDiscreteParam("cp", values = c(0.1, -1))
)
ctrl = makeTuneControlGrid(tune.threshold = TRUE)
res = tuneParams(lrn, sonar.task,
resampling = rdesc, measures = list(mmce, auc),
par.set = ps, control = ctrl)
expect_true(is.numeric(res$y) && length(res$y) == 2L && !any(is.na(res$y)))
})
test_that("tuning allows usage of budget", {
lrn = makeLearner("classif.rpart", predict.type = "prob")
rdesc = makeResampleDesc("Holdout")
ctrl = makeTuneControlCMAES(budget = 18, lambda = 6, maxit = 3)
ps = makeParamSet(
makeNumericParam("cp", lower = 0.1, upper = 0.2),
makeIntegerParam("minsplit", lower = 1, upper = 10)
)
res = tuneParams(lrn, binaryclass.task, resampling = rdesc, par.set = ps, control = ctrl)
expect_true(is.numeric(res$y) && (length(res$y) == 1L) && !any(is.na(res$y)))
# also check with infeasible stuff
ps = makeParamSet(
makeDiscreteParam("cp", values = c(0.1, -1))
)
ctrl = makeTuneControlGrid(tune.threshold = TRUE, budget = 2L)
res = tuneParams(lrn, sonar.task,
resampling = rdesc, measures = list(mmce, auc),
par.set = ps, control = ctrl)
expect_true(is.numeric(res$y) && length(res$y) == 2L && !any(is.na(res$y)))
lrn = makeLearner("classif.rpart", predict.type = "prob")
rdesc = makeResampleDesc("Holdout")
ctrl = makeTuneControlRandom(tune.threshold = TRUE, maxit = NULL, budget = 3L)
ps = makeParamSet(
makeNumericParam("cp", lower = 0.1, upper = 0.2)
)
res = tuneParams(lrn, binaryclass.task,
resampling = rdesc, measures = list(mmce, auc),
par.set = ps, control = ctrl)
expect_true(is.numeric(res$y) && length(res$y) == 2L && !any(is.na(res$y)))
expect_identical(getOptPathLength(res$opt.path), 3L)
})
test_that("Learner defined with expression in param requires, see #369 and PH #52", {
ps = makeParamSet(
makeDiscreteLearnerParam(id = "a", values = c("x", "y")),
makeNumericLearnerParam(id = "b", lower = 0.0, upper = 1.0, requires = expression(a == "x"))
)
rdesc = makeResampleDesc("Holdout")
ctrl = makeTuneControlRandom()
res = tuneParams("classif.__mlrmocklearners__5", binaryclass.task, resampling = rdesc, par.set = ps, control = ctrl)
expect_class(res, "TuneResult")
})
test_that("tuning does not break with small discrete values, see bug in #1115", {
ctrl = makeTuneControlGrid()
ps = makeParamSet(
makeDiscreteParam("cp", values = c(1e-8, 1e-9))
)
# this next line created an exception in the bug
res = tuneParams("classif.rpart", multiclass.task, hout, par.set = ps, control = ctrl)
expect_class(res, "TuneResult")
})
test_that("tuning works with large param.sets", {
lrn = makeLearner("classif.__mlrmocklearners__5")
ctrl = makeTuneControlRandom(maxit = 3)
# create long list of learner params
ps.length = 200
long.learner.params = do.call(base::c, lapply(seq_len(ps.length), function(x) {
makeParamSet(makeIntegerLearnerParam(paste0("some.parameter", x), 1, 10))
}))
lrn$par.set = c(lrn$par.set, long.learner.params)
suppressMessages({
res = tuneParams(lrn, pid.task, cv5, par.set = long.learner.params, control = ctrl, show.info = TRUE)
})
expect_class(res, "TuneResult")
})
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