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test_that("tuneParamsMultiCrit", {
requirePackagesOrSkip("emoa", default.method = "load")
lrn = makeLearner("classif.rpart")
rdesc = makeResampleDesc("Holdout")
ps = makeParamSet(
makeIntegerParam("minsplit", lower = 1, upper = 50)
)
ctrl = makeTuneMultiCritControlRandom(maxit = 2)
expect_error(tuneParamsMultiCrit(lrn, binaryclass.task, rdesc,
par.set = ps, measures = list(mmce), control = ctrl),
".* Must have length >= 2, but has length 1.")
mycheck = function(res, k) {
expect_output(print(res), "Points on front")
expect_true(is.integer(res$ind))
expect_true(is.list(res$x))
expect_true(is.matrix(res$y))
expect_equal(getOptPathLength(res$opt.path), k)
}
# random search
ctrl = makeTuneMultiCritControlRandom(maxit = 2)
res = tuneParamsMultiCrit(lrn, binaryclass.task, rdesc, par.set = ps,
measures = list(tpr, fpr), control = ctrl)
mycheck(res, 2)
# and check plotting
print(plotTuneMultiCritResult(res, path = TRUE))
print(plotTuneMultiCritResult(res, path = FALSE))
# grid search
ctrl = makeTuneMultiCritControlGrid(resolution = 2L)
res = tuneParamsMultiCrit(lrn, binaryclass.task, rdesc, par.set = ps,
measures = list(tpr, fpr), control = ctrl)
mycheck(res, 2)
# nsga2
ctrl = makeTuneMultiCritControlNSGA2(popsize = 4L, generations = 1L)
res = tuneParamsMultiCrit(lrn, binaryclass.task, rdesc, par.set = ps,
measures = list(tpr, fpr), control = ctrl)
mycheck(res, 8L)
# MBO
ctrl = makeTuneMultiCritControlMBO(2L, budget = 4L * length(ps$pars) + 1L)
# suppressed warnings: "generateDesign could only produce 50 points instead of
# 1000!"
res = suppressWarnings(tuneParamsMultiCrit(lrn, binaryclass.task, rdesc, par.set = ps,
measures = list(tpr, fpr), control = ctrl))
mycheck(res, 4L * length(ps$pars) + 1L)
# MBO with mbo.control
# Size of init design is 4 * length(ps$pars) by default of mlrMBO
mbo.control = mlrMBO::makeMBOControl(n.objectives = 2L)
mbo.control = mlrMBO::setMBOControlInfill(mbo.control,
crit = mlrMBO::makeMBOInfillCritDIB())
mbo.control = mlrMBO::setMBOControlMultiObj(mbo.control)
mbo.control = mlrMBO::setMBOControlTermination(mbo.control, iters = 1)
ctrl = makeTuneMultiCritControlMBO(mbo.control = mbo.control)
# suppressed warnings: "generateDesign could only produce 50 points instead of
# 1000!"
res = suppressWarnings(tuneParamsMultiCrit(lrn, binaryclass.task, rdesc,
par.set = ps, measures = list(tpr, fpr), control = ctrl))
mycheck(res, 4L * length(ps$pars) + 1L)
# MBO with dependent param set
lrn = makeLearner("classif.ksvm")
ps = makeParamSet(makeDiscreteParam("kernel", c("polydot", "rbfdot")),
makeNumericParam("sigma", lower = -12, upper = 12,
trafo = function(x) 2^x, requires = quote(kernel == "rbfdot")))
ctrl = makeTuneMultiCritControlMBO(2L, budget = 4L * length(ps$pars) + 1L)
res = tuneParamsMultiCrit(lrn, binaryclass.task, rdesc, par.set = ps,
measures = list(tpr, fpr), control = ctrl)
mycheck(res, 4L * length(ps$pars) + 1L)
})
test_that("tuneParamsMultiCrit works with low number of evals and dependencies", {
requirePackagesOrSkip("emoa", default.method = "load")
# we had a bug here triggered thru code in PH
ps = makeParamSet(
makeNumericParam("C", lower = -12, upper = 12, trafo = function(x) 2^x),
makeDiscreteParam("kernel", values = c("vanilladot", "polydot", "rbfdot")),
makeNumericParam("sigma", lower = -12, upper = 12, trafo = function(x) 2^x,
requires = quote(kernel == "rbfdot")),
makeIntegerParam("degree", lower = 2L, upper = 5L,
requires = quote(kernel == "polydot"))
)
ctrl = makeTuneMultiCritControlRandom(maxit = 1L)
rdesc = makeResampleDesc("Holdout")
expect_silent(tuneParamsMultiCrit("classif.ksvm", sonar.task, rdesc,
par.set = ps, measures = list(tpr, fpr), control = ctrl))
})
# FIXME: I am not sure how we can check wich value is imputed for the optimizer?
test_that("y imputing works", {
requirePackagesOrSkip("emoa", default.method = "load")
configureMlr(on.learner.error = "quiet")
lrn = makeLearner("classif.__mlrmocklearners__2")
rdesc = makeResampleDesc("Holdout")
ps = makeParamSet(
makeNumericParam("alpha", lower = 0, upper = 1)
)
ctrl = makeTuneMultiCritControlNSGA2(popsize = 4L, generations = 1L)
res = tuneParamsMultiCrit(lrn, binaryclass.task, rdesc, par.set = ps,
measures = list(tpr, fpr), control = ctrl)
ctrl = makeTuneMultiCritControlNSGA2(impute.val = c(100, 100), popsize = 4L,
generations = 1L)
expect_silent(tuneParamsMultiCrit(lrn, binaryclass.task, rdesc, par.set = ps,
measures = list(tpr, fpr), control = ctrl))
configureMlr(on.learner.error = "stop")
})
test_that("tuneParamsMultiCrit with budget", {
requirePackagesOrSkip("emoa", default.method = "load")
lrn = makeLearner("classif.rpart")
rdesc = makeResampleDesc("Holdout")
ps = makeParamSet(
makeNumericParam("cp", lower = 0.001, upper = 1),
makeIntegerParam("minsplit", lower = 1, upper = 50)
)
mycheck = function(ctrl, expected.budget) {
if ("TuneMultiCritControlGrid" %in% class(ctrl)) {
if (!is.null(ctrl$budget)) {
expect_equal(ctrl$budget, expected.budget)
}
} else {
expect_equal(ctrl$budget, expected.budget)
}
res = tuneParamsMultiCrit(lrn, binaryclass.task, rdesc, par.set = ps,
measures = list(tpr, fpr), control = ctrl)
expect_equal(getOptPathLength(res$opt.path), expected.budget)
}
# random search
ctrl = makeTuneMultiCritControlRandom(maxit = 3L)
mycheck(ctrl, ctrl$extra.args$maxit)
ctrl = makeTuneMultiCritControlRandom(maxit = 3L, budget = 3L)
mycheck(ctrl, ctrl$extra.args$maxit)
expect_error(makeTuneMultiCritControlRandom(maxit = 3L, budget = 5L),
"The parameters .* differ.")
# grid search
ctrl = makeTuneMultiCritControlGrid(resolution = 3)
mycheck(ctrl, ctrl$extra.args$resolution^2)
ctrl = makeTuneMultiCritControlGrid(resolution = 3, budget = 9L)
mycheck(ctrl, ctrl$extra.args$resolution^2)
ctrl = makeTuneMultiCritControlGrid(resolution = 3, budget = 10L)
expect_error(tuneParamsMultiCrit(lrn, binaryclass.task, rdesc, par.set = ps,
measures = list(tpr, fpr), control = ctrl),
".* does not fit to the size of the grid .*")
# nsga2
ctrl = makeTuneMultiCritControlNSGA2(popsize = 4L, generations = 1L)
mycheck(ctrl, ctrl$extra.args$popsize * (ctrl$extra.args$generations + 1))
expect_error(makeTuneMultiCritControlNSGA2(popsize = 4L, generations = 2L,
budget = 8L),
".* contradicts the product of .*")
expect_error(makeTuneMultiCritControlNSGA2(generations = 4L, budget = 12L),
".* contradicts the product of .*")
ctrl = makeTuneMultiCritControlNSGA2(popsize = 4L, budget = 12L)
expect_equal(ctrl$extra.args$generations, 2L)
mycheck(ctrl, 12L)
})
test_that("plotTuneMultiCritResult works with pretty.names", {
requirePackagesOrSkip("emoa", default.method = "load")
lrn = makeLearner("classif.rpart")
ps = makeParamSet(
makeDiscreteParam("minsplit", values = c(5, 10))
)
ctrl.grid = makeTuneMultiCritControlGrid()
opt.multi.crit = tuneParamsMultiCrit(lrn, multiclass.task, hout,
list(mmce, acc), par.set = ps, control = ctrl.grid)
expect_silent(plotTuneMultiCritResult(opt.multi.crit))
expect_silent(plotTuneMultiCritResult(opt.multi.crit, pretty.names = FALSE))
})
test_that("tuneParamsMultiCrit with resample.fun", {
requirePackagesOrSkip("emoa", default.method = "load")
lrn = makeLearner("classif.rpart")
rdesc = makeResampleDesc("Holdout")
ps = makeParamSet(
makeIntegerParam("minsplit", lower = 1, upper = 50)
)
# random search
ctrl = makeTuneMultiCritControlRandom(maxit = 2)
res = tuneParamsMultiCrit(lrn, binaryclass.task, rdesc, par.set = ps,
measures = list(tpr, fpr), control = ctrl, resample.fun = constant05Resample)
expect_true(all(getOptPathY(res$opt.path) == 0.5))
# grid search
ctrl = makeTuneMultiCritControlGrid(resolution = 2L)
res = tuneParamsMultiCrit(lrn, binaryclass.task, rdesc, par.set = ps,
measures = list(tpr, fpr), control = ctrl, resample.fun = constant05Resample)
expect_true(all(getOptPathY(res$opt.path) == 0.5))
# nsga2
ctrl = makeTuneMultiCritControlNSGA2(popsize = 4L, generations = 1L)
res = tuneParamsMultiCrit(lrn, binaryclass.task, rdesc, par.set = ps,
measures = list(tpr, fpr), control = ctrl, resample.fun = constant05Resample)
expect_true(all(getOptPathY(res$opt.path) == 0.5))
# MBO
ctrl = makeTuneMultiCritControlMBO(2L, budget = 4L * length(ps$pars) + 1L,
learner = "regr.lm")
# suppressed warnings: "generateDesign could only produce 50 points instead of
# 1000!"
res = suppressWarnings(tuneParamsMultiCrit(lrn, binaryclass.task, rdesc,
par.set = ps, measures = list(tpr, fpr), control = ctrl,
resample.fun = constant05Resample))
expect_true(all(getOptPathY(res$opt.path) == 0.5))
})
test_that("check n.objectives for MBO multi crit", {
requirePackagesOrSkip("emoa", default.method = "load")
lrn = makeLearner("classif.rpart")
rdesc = makeResampleDesc("Holdout")
ps = makeParamSet(
makeIntegerParam("minsplit", lower = 1, upper = 50)
)
expect_error(makeTuneMultiCritControlMBO(1L),
".* >= 2")
expect_error(makeTuneMultiCritControlMBO(1.5),
".* Must be of type 'single integerish value', not 'double'.")
ctrl = makeTuneMultiCritControlMBO(2L)
expect_error(tuneParamsMultiCrit(lrn, binaryclass.task, rdesc,
measures = list(mmce),
par.set = ps, control = ctrl),
".* Must have length >= 2, but has length 1.")
expect_error(tuneParamsMultiCrit(lrn, binaryclass.task, rdesc,
measures = list(mmce, tpr, fpr),
par.set = ps, control = ctrl),
".* Must have length 2, but has length 3.")
})
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