R/tuningFunction.R

In urmanml/easyMLR: Easy Machine Learning - end to end ML worklflow in simple english language

#' hyperparameter tuning function
tuningFunction<-function(lrn,task)
{

  #set 3 fold cross validation
  rdesc <- makeResampleDesc("CV",iters = 3L)
  #1) Search for hyperparameters
  param_set<-getParamSet(lrn)
  #2) Make param set
  #option a)uncomment this for manually setting the parameters in the paramSet function

  gs <- paramSet(lrn)
  #class(gs)

  ##option b: using mlrhyperopt
  #gs<- mlrHyperopt::generateParConfig(lrn)$par.set

  ##option c: using CaretParamset
  #gs<-getCaretParamSet(lrn$short.name,task = task)$par.set


  gs
  #3) Specify to do a grid search/vs a random search
  #gscontrol <- makeTuneControlGrid(resolution = 1)
  gscontrol<-makeTuneControlRandom(maxit = 2,tune.threshold = logical(1))
  #4) hypertune the parameters
  stune <- tuneParams(learner = lrn, resampling = rdesc, task = task, par.set = gs, control = gscontrol, measures = c(tpr,f1))

  #5) using hyperparameters for modeling
  lrn <- setHyperPars(lrn, par.vals = stune$x)
  return(lrn)
}