context("fuzzing")

test_that("mbo with autotuner", {
  skip_on_cran()
  ll <- lrn("classif.rpart", predict_type = "prob")
  ps <- (ps(cp = p_dbl(lower = 0, upper = 1), minsplit = p_dbl(lower = 1, upper = 20, trafo = round), minbucket = p_dbl(lower = 1, upper = 20, trafo = round)))

  surr <- mlr3::LearnerRegrFeatureless$new()
  surr$predict_type = "se"

  at <- AutoTuner$new(learner = ll, resampling = rsmp("holdout"), measure = msr("classif.auc"), terminator = trm("evals", n_evals = 11), tuner = tnr("intermbo", surrogate.learner = surr), search_space = ps)

  rres <- mlr3::resample(tsk("pima"), at, rsmp("cv", folds = 5))

  expect_number(rres$aggregate())

})

test_that("fuzzing intermbo", {
  skip_on_cran()
  surr <- mlr3::LearnerRegrFeatureless$new()
  surr$predict_type = "se"


  tups <- tnr("intermbo")$param_set$clone(deep = TRUE)
  if (is.data.frame(tups$params)) {
    psnew <- tups$subset(names(which(tups$class != "ParamUty")))
  } else {
    psnew <- ParamSet$new(discard(tups$params, function(x) "ParamUty" %in% class(x)))
  }

  for (row in seq_len(nrow(tups$deps))) {
    if (tups$deps[row, id] %in% psnew$ids()) {
      psnew$add_dep(tups$deps[row, id], tups$deps[row, on], tups$deps$cond[[row]])
    }
  }

  for (pn in psnew$ids()[!psnew$is_number]) {
    psnew$values[[pn]] = to_tune()
  }

  isbounded = as.data.table(psnew)$is_bounded
  for (pari in which(psnew$is_number)) {
    psn = psnew$ids()[[pari]]
    if (isbounded[[pari]]) next
    if (is.finite(psnew$lower[[pari]])) {
      if (is.numeric(psnew$default[[psn]]) && is.finite(psnew$default[[psn]]) && psnew$default[[psn]] > psnew$lower[[pari]]) {
        psnew$values[[psn]] = to_tune(psnew$lower[[pari]], (psnew$lower[[pari]] - psnew$default[[psn]]) + psnew$default[[psn]] * 2)
      } else {
        psnew$values[[psn]] = to_tune(psnew$lower[[pari]], psnew$lower[[pari]] + 2)
      }
    } else {
      psnew$values[[psn]] = to_tune(0, 1)
    }
  }
  psnew$values$initial.design.size = to_tune(2, 4)

  ps <- (ps(cp = p_dbl(lower = 0, upper = 1), minsplit = p_dbl(lower = 1, upper = 20, trafo = round), minbucket = p_dbl(lower = 1, upper = 20, trafo = round)))

  objective <- ObjectiveRFun$new(function(xs) {
    list(y = (xs$cp - .5) ^ 2 + (assertInt(xs$minsplit) - 10) ^ 2 + (assertInt(xs$minbucket) - 10) ^ 2)
  }, ps, (ps(y = p_dbl(tags = "minimize"))))


  tuner <- OptimizerInterMBO$new(on.surrogate.error = "quiet")

  set.seed(1)
  for (setting in seq_len(10)) {
    repeat {
      setting <- generate_design_random(psnew$search_space(), 1)$transpose()[[1]]
      ##> Multi-point proposal using constant liar needs the infill criterion 'ei' or 'aei', but you used '___'!
      if (setting$multipoint.method == "cl" && !setting$infill.crit %in% c("EI", "AEI", "CB")) next

      ##> for multipoint.method 'cb', infill.crit must be 'CB'.
      if (setting$multipoint.method == "cb" && setting$infill.crit != "CB") next

      # this only works with original learner
      if (setting$infill.crit == "AEI" && isTRUE(setting$infill.crit.aei.use.nugget)) next

      if (setting$multipoint.method == "moimbo") setting$infill.interleave.random.points = 0  # https://github.com/mlr-org/mlrMBO/issues/508

      break
    }
    setting$surrogate.learner <- surr

    ti <- OptimInstanceSingleCrit$new(objective, ps, trm("evals", n_evals = 5))
    tuner$param_set$values <- setting
    suppressWarnings(tuner$optimize(ti))
  }


  psnew$values$initial.design.size = to_tune(2, 4)

  tuner <- OptimizerInterMBO$new(on.surrogate.error = "stop")
  set.seed(2)
  for (setting in seq_len(10)) {

    repeat {
      setting <- generate_design_random(psnew$search_space(), 1)$transpose()[[1]]
      ##> Multi-point proposal using constant liar needs the infill criterion 'ei' or 'aei', but you used '___'!
      if (setting$multipoint.method == "cl" && !setting$infill.crit %in% c("EI", "AEI", "CB")) next

      ##> for multipoint.method 'cb', infill.crit must be 'CB'.
      if (setting$multipoint.method == "cb" && setting$infill.crit != "CB") next

      # this only works with original learner
      if (setting$infill.crit == "AEI" && isTRUE(setting$infill.crit.aei.use.nugget)) next

      if (setting$multipoint.method == "moimbo") setting$infill.interleave.random.points = 0  # https://github.com/mlr-org/mlrMBO/issues/508

      break
    }
    setting$surrogate.learner <- surr

    ti <- OptimInstanceSingleCrit$new(objective, ps, trm("evals", n_evals = 13))
    tuner$param_set$values <- setting
    suppressWarnings(tuner$optimize(ti))
  }

})