tests/testthat/test_classif_PART.R

context("classif_PART")

test_that("classif_PART", {
  requirePackagesOrSkip("RWeka", default.method = "load")

  parset.list = list(
    list(),
    list(M = 10),
    list(M = 5, C = 0.4),
    list(M = 5, R = TRUE)
  )

  old.predicts.list = list()
  old.probs.list = list()

  for (i in seq_along(parset.list)) {
    parset = parset.list[[i]]
    set.seed(getOption("mlr.debug.seed"))
    parset$Q = as.integer(runif(1, min = -.Machine$integer.max, max = .Machine$integer.max))
    ctrl = do.call(RWeka::Weka_control, parset)
    m = RWeka::PART(formula = multiclass.formula, data = multiclass.train, control = ctrl)
    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
  }

  testSimpleParsets("classif.PART", multiclass.df, multiclass.target, multiclass.train.inds,
    old.predicts.list, parset.list)
  testProbParsets("classif.PART", multiclass.df, multiclass.target, multiclass.train.inds,
    old.probs.list, parset.list)

  tt = function(formula, data, subset, ...) {
    set.seed(getOption("mlr.debug.seed"))
    RWeka::PART(formula, data = data[subset, ], control = RWeka::Weka_control(..., Q =
        as.integer(runif(1, min = -.Machine$integer.max, max = .Machine$integer.max))))
  }

  tp = function(model, newdata) predict(model, newdata, type = "class")

  testCVParsets("classif.PART", multiclass.df, multiclass.target, tune.train = tt, tune.predict = tp, parset.list = parset.list)

})
riebetob/mlr documentation built on May 20, 2019, 5:58 p.m.