makeModelMultiplexer(base.learners)

set.seed(123)

library(BBmisc)
bls = list(
  makeLearner("classif.ksvm"),
  makeLearner("classif.randomForest")
)
lrn = makeModelMultiplexer(bls)
# simple way to contruct param set for tuning
# parameter names are prefixed automatically and the 'requires'
# element is set, too, to make all paramaters subordinate to 'selected.learner'
ps = makeModelMultiplexerParamSet(lrn,
  makeNumericParam("sigma", lower = -10, upper = 10, trafo = function(x) 2^x),
  makeIntegerParam("ntree", lower = 1L, upper = 500L)
)
print(ps)
rdesc = makeResampleDesc("CV", iters = 2L)
# to save some time we use random search. but you probably want something like this:
# ctrl = makeTuneControlIrace(maxExperiments = 500L)
ctrl = makeTuneControlRandom(maxit = 10L)
res = tuneParams(lrn, iris.task, rdesc, par.set = ps, control = ctrl)
print(res)

df = as.data.frame(res$opt.path)
print(head(df[, -ncol(df)]))

# more unique and reliable way to construct the param set
ps = makeModelMultiplexerParamSet(lrn,
  classif.ksvm = makeParamSet(
    makeNumericParam("sigma", lower = -10, upper = 10, trafo = function(x) 2^x)
  ),
  classif.randomForest = makeParamSet(
    makeIntegerParam("ntree", lower = 1L, upper = 500L)
  )
)

# this is how you would construct the param set manually, works too
ps = makeParamSet(
  makeDiscreteParam("selected.learner", values = extractSubList(bls, "id")),
  makeNumericParam("classif.ksvm.sigma", lower = -10, upper = 10, trafo = function(x) 2^x,
    requires = quote(selected.learner == "classif.ksvm")),
  makeIntegerParam("classif.randomForest.ntree", lower = 1L, upper = 500L,
    requires = quote(selected.learner == "classif.randomForst"))
)

# all three ps-objects are exactly the same internally.