context("classif_C50")

test_that("classif_C50", {
  
requirePackages("C50", default.method = "load")
  parset.list = list(
    list(),
    list(control = C50::C5.0Control(seed = getOption("mlr.debug.seed"))),
    list(trials = 5L, control = C50::C5.0Control(minCases = 10L,
      seed = getOption("mlr.debug.seed"))),
    list(rules = TRUE, control = C50::C5.0Control(bands = 100L,
      seed = getOption("mlr.debug.seed"))),
    list(control = C50::C5.0Control(CF = 0.5, sample = 0.444,
      seed = getOption("mlr.debug.seed")))
  )
  
  old.predicts.list = list()
  old.probs.list = list()
  
  for (i in 1:length(parset.list)) {
    parset = parset.list[[i]]
    pars = list(x = multiclass.train[, names(multiclass.train) %nin% multiclass.target], 
      y = multiclass.train[, multiclass.target])
    pars = c(pars, parset)
    set.seed(getOption("mlr.debug.seed"))
    m = do.call(C50::C5.0, pars)
    set.seed(getOption("mlr.debug.seed"))
    p = predict(m, newdata = multiclass.test, type = "class")
    set.seed(getOption("mlr.debug.seed"))
    p2 = predict(m, newdata = multiclass.test, type = "prob")
    old.predicts.list[[i]] = p
    old.probs.list[[i]] = p2
  }
  
  parset.list = list(
    list(),
    list(seed = getOption("mlr.debug.seed")),
    list(trials = 5L, minCases = 10L, seed = getOption("mlr.debug.seed")),
    list(rules = TRUE, bands = 100L, seed = getOption("mlr.debug.seed")),
    list(CF = 0.5, sample = 0.444, seed = getOption("mlr.debug.seed"))
  )
  
  testSimpleParsets("classif.C50", multiclass.df, multiclass.target,
                    multiclass.train.inds, old.predicts.list, parset.list)
  testProbParsets("classif.C50", multiclass.df, multiclass.target,
                  multiclass.train.inds, old.probs.list, parset.list)
})

