context("Mlr Tuning Wrapper")
# Check if wrapped mlr tuning object equals mlr.tuning.example
test_that("MlrTuningWrapper Test", {
set.seed(2017)
# Create mlr benchmark
num.ps = ParamHelpers::makeParamSet(
ParamHelpers::makeNumericParam(
"C",
lower = -10,
upper = 10,
trafo = function(x)
10 ^ x
),
ParamHelpers::makeNumericParam(
"sigma",
lower = -10,
upper = 10,
trafo = function(x)
10 ^ x
)
)
ctrl = mlr::makeTuneControlRandom(maxit = 100L)
rdesc = mlr::makeResampleDesc("CV", iters = 3L)
res = mlr::tuneParams(
"classif.ksvm",
task = mlr::iris.task,
resampling = rdesc,
par.set = num.ps,
control = ctrl,
measures = list(mlr::acc, mlr::setAggregation(mlr::acc, test.sd)),
show.info = FALSE
)
dt = useMlrTuningWrapper(res)
# Second tune control strategy
ctrl = mlr::makeTuneControlGrid(resolution = 15L)
res = mlr::tuneParams(
"classif.ksvm",
task = mlr::iris.task,
resampling = rdesc,
par.set = num.ps,
control = ctrl,
measures = list(mlr::acc, mlr::setAggregation(mlr::acc, test.sd)),
show.info = FALSE
)
dt = rbind(dt, useMlrTuningWrapper(res))
# Identical?
expect_equal(dt, mlr.tuning.example)
})
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