context("classif_glmboost")

test_that("classif_glmboost", {
  requirePackagesOrSkip("mboost", default.method = "load")

  parset.list1 = list(
    list(family = mboost::Binomial()),
    list(family = mboost::Binomial(), control = mboost::boost_control(nu = 0.03)),
    list(family = mboost::Binomial(link = "probit"), control = mboost::boost_control(mstop = 600), center = TRUE)
  )

  parset.list2 = list(
    list(),
    list(family = "Binomial", nu=0.03),
    list(family = "Binomial", Binomial.link = "probit", mstop = 600, center = TRUE)
  )

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

  for (i in 1:length(parset.list1)) {
    parset = parset.list1[[i]]
    pars = list(binaryclass.formula, data=binaryclass.train)
    pars = c(pars, parset)
    set.seed(getOption("mlr.debug.seed"))
    m = do.call(mboost::glmboost, pars)
    set.seed(getOption("mlr.debug.seed"))
    old.predicts.list[[i]] = predict(m, newdata=binaryclass.test, type="class")
    set.seed(getOption("mlr.debug.seed"))
    old.probs.list[[i]] = 1 - predict(m, newdata=binaryclass.test, type="response")[,1]
  }

  testSimpleParsets("classif.glmboost", binaryclass.df, binaryclass.target, binaryclass.train.inds, old.predicts.list, parset.list2)
  testProbParsets("classif.glmboost", binaryclass.df, binaryclass.target, binaryclass.train.inds, old.probs.list, parset.list2)
})

test_that("classif_glmboost probability predictions with family 'AUC' and 'AdaExp'", {
  families = list("AUC", "AdaExp")
  lapply(families, FUN = function(x){
    lrn = makeLearner("classif.glmboost", par.vals = list(family = x), predict.type = "prob")
    mod = train(lrn, binaryclass.task)
    expect_error(predict(mod, binaryclass.task), "support probabilities")
  })
}) 


# mlr does not support ordered factors as target yet. 
# FIXME: the following two tests can be used, when they are supported
# test_that("classif_glmboost with family `PropOdds` works with one observation", {
#   data = getTaskData(binaryclass.task)
#   data[,binaryclass.target] = as.ordered(data[,binaryclass.target])
#   ordered.task = makeClassifTask(data = data, target = binaryclass.target)
#   mini.data = data[1:10,]
#   mini.task = makeClassifTask(data = mini.data, target = binaryclass.target)
#   lrn = makeLearner("classif.glmboost", par.vals = list(family = "PropOdds"),
#     predict.type = "prob")
#   mod = train(lrn, ordered.task)
#   pred = predict(mod, mini.task)
#   orig.mod = mboost::glmboost(binaryclass.formula, data = data, family = mboost::PropOdds())
#   orig.pred = predict(orig.mod, newdata = mini.data, type = "response")
#   orig.pred.class = predict(orig.mod, newdata = mini.data, type = "class")
#   expect_equal(getPredictionProbabilities(pred), orig.pred[,2])
# })
# 
# test_that("classif_glmboost works with family PropOdds", {
#   new.binary.df = binaryclass.df
#   new.binary.df[,binaryclass.target] = as.ordered(new.binary.df[,binaryclass.target])
#   new.classif.train = new.binary.df[binaryclass.train.inds,]
#   new.classif.test = new.binary.df[binaryclass.test.inds,]
#   parset.list1 = list(family = mboost::PropOdds())
#   parset.list2 = list(family = "PropOdds")
#   old.predicts.list = list()
#   old.probs.list = list()
#   parset = parset.list1
#   pars = list(binaryclass.formula, data = new.classif.train)
#   pars = c(pars, parset)
#   set.seed(getOption("mlr.debug.seed"))
#   m = do.call(mboost::glmboost, pars)
#   set.seed(getOption("mlr.debug.seed"))
#   old.predicts.list = predict(m, newdata = new.classif.test, type = "class")
#   set.seed(getOption("mlr.debug.seed"))
#   old.probs.list = 1 - predict(m, newdata = new.classif.test, type = "response")[,1]
#   
#   testSimple("classif.glmboost", new.binary.df, binaryclass.target, binaryclass.train.inds, old.predicts.list, parset.list2)
#   testProb("classif.glmboost", new.binary.df, binaryclass.target, binaryclass.train.inds, old.probs.list, parset.list2)
#   
# })