R/ModelMultiplexer.R
Combines multiple base learners by dispatching on the hyperparameter “selected.learner” to a specific model class. This allows to tune not only the model class (SVM, random forest, etc) but also their hyperparameters in one go. Combine this with [tuneParams] and [makeTuneControlIrace] for a very powerful approach, see example below.
The parameter set is the union of all (unique) base learners. In order to avoid name clashes all parameter names are prefixed with the base learner id, i.e. “[learner.id].[parameter.name]”.
The predict.type of the Multiplexer is inherited from the predict.type of the base learners.
The getter [getLearnerProperties] returns the properties of the selected base learner.
makeModelMultiplexer(base.learners)
| base.learners | ([list` of [Learner]) |
|---|
([ModelMultiplexer]). A [Learner] specialized as `ModelMultiplexer`.
Note that logging output during tuning is somewhat shortened to make it more readable. I.e., the artificial prefix before parameter names is suppressed.
Other multiplexer: makeModelMultiplexerParamSet
Other tune: TuneControl,
getNestedTuneResultsOptPathDf,
getNestedTuneResultsX,
getResamplingIndices,
getTuneResult,
makeModelMultiplexerParamSet,
makeTuneControlCMAES,
makeTuneControlDesign,
makeTuneControlGenSA,
makeTuneControlGrid,
makeTuneControlIrace,
makeTuneControlMBO,
makeTuneControlRandom,
makeTuneWrapper, tuneParams,
tuneThreshold
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)#> Type len Def Constr #> selected.learner discrete - - classif.ksvm,classif.randomForest #> classif.ksvm.sigma numeric - - -10 to 10 #> classif.randomForest.ntree integer - - 1 to 500 #> Req Tunable Trafo #> selected.learner - TRUE - #> classif.ksvm.sigma Y TRUE Y #> classif.randomForest.ntree Y TRUE -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)#> Tune result: #> Op. pars: selected.learner=classif.rand...; classif.randomForest.ntree=452 #> mmce.test.mean=0.0266667print(head(as.data.frame(res$opt.path)))#> selected.learner classif.ksvm.sigma classif.randomForest.ntree #> 1 classif.ksvm -0.7057245 NA #> 2 classif.randomForest NA 368 #> 3 classif.ksvm -0.9047674 NA #> 4 classif.ksvm 6.3016299 NA #> 5 classif.ksvm -3.7577442 NA #> 6 classif.randomForest NA 452 #> mmce.test.mean dob eol error.message exec.time #> 1 0.04666667 1 NA <NA> 0.044 #> 2 0.02666667 2 NA <NA> 0.083 #> 3 0.04666667 3 NA <NA> 0.034 #> 4 0.67333333 4 NA <NA> 0.043 #> 5 0.05333333 5 NA <NA> 0.051 #> 6 0.02666667 6 NA <NA> 0.092# 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.