context("manual tests")

test_that("mbo default", {
  skip_on_cran()
  ps <- (ps(cp = p_dbl(lower = 0, upper = 1), minsplit = p_int(lower = 1, upper = 20)))

  # get archivenames
  ti <- TuningInstanceSingleCrit$new(task = tsk("pima"), learner = lrn("classif.rpart", predict_type = "prob"), resampling = rsmp("holdout"), measure = msr("classif.auc"), terminator = trm("evals", n_evals = 1), search_space = ps)
  tnr("random_search")$optimize(ti)
  archivenames <- colnames(ti$archive$data)
  archivenames <- c(archivenames, "propose.time", "errors.model", "crit.vals")

  set.seed(1)
  ti <- TuningInstanceSingleCrit$new(task = tsk("pima"), learner = lrn("classif.rpart", predict_type = "prob"), resampling = rsmp("holdout"), measure = msr("classif.auc"), terminator = trm("evals", n_evals = 11), search_space = ps)

  tuner <- TunerInterMBO$new()

  tuner$optimize(ti)

  expect_names(names(ti$archive$data), permutation.of = c(archivenames, "classif.auc", "propose.time", "crit.vals", "errors.model"))

  expect_data_table(ti$archive$data, nrows = 11)
})

test_that("mbo different settings", {

  set.seed(2)
  ps <- (ps(cp = p_dbl(lower = 0, upper = 1), minsplit = p_int(lower = 1, upper = 20)))

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

  objective.mc <- ObjectiveRFun$new(function(xs) {
    list(y = (xs$cp - .5) ^ 2 + (assertInt(xs$minsplit) - 10) ^ 2, classif.tpr = - ((xs$cp - .2) ^ 2 + (assertInt(xs$minsplit) - 7) ^ 2))
  }, ps, (ps(y = p_dbl(tags = "minimize"), classif.tpr = p_dbl(tags = "maximize"))))

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

  ti <- OptimInstanceSingleCrit$new(objective, ps, trm("evals", n_evals = 1))
  opt("random_search")$optimize(ti)
  archivenames <- colnames(ti$archive$data)
  archivenames <- c(archivenames, "propose.time", "errors.model")
  tuner.sc <- OptimizerInterMBO$new(1)
  tuner.mc <- OptimizerInterMBO$new(2)
  eval_mbo <- function(params, multimsr = FALSE, n.objectives = 1, surrogate = surr) {
    if (n.objectives == 1) {
      ti <- OptimInstanceSingleCrit$new(objective, ps, trm("evals", n_evals = 11))
      tuner <- tuner.sc
    } else {
      ti <- OptimInstanceMultiCrit$new(objective.mc, ps, trm("evals", n_evals = 11))
      tuner <- tuner.mc
    }

    params$surrogate.learner <- surrogate
    if (is.null(params$infill.opt)) params$infill.opt <- "focussearch"  # TODO: only here because of https://github.com/mlr-org/paradox/issues/265
    tuner$param_set$values <- params
    tuner$optimize(ti)
    ti
  }

  ti <- eval_mbo(list())
  expect_names(names(ti$archive$data), permutation.of = c(archivenames, "crit.vals"))
  expect_data_table(ti$archive$data, nrows = 11)
  expect_equal(is.na(ti$archive$data$propose.time), rep(c(TRUE, FALSE), c(8, 3)))

  ti <- eval_mbo(list(initial.design.size = 15))
  expect_data_table(ti$archive$data, nrows = 15)

  ti <- eval_mbo(list(infill.opt = "ea", infill.opt.ea.maxit = 10, infill.opt.ea.mu = 2, infill.opt.ea.sbx.eta = 13, infill.opt.ea.sbx.p = 0.7, infill.opt.ea.pm.eta = 11, infill.opt.ea.pm.p = 0.4, infill.opt.ea.lambda = 2))
  expect_names(names(ti$archive$data), permutation.of = c(archivenames, "crit.vals"))
  expect_data_table(ti$archive$data, nrows = 11)
  expect_equal(is.na(ti$archive$data$propose.time), rep(c(TRUE, FALSE), c(8, 3)))

  ti <- eval_mbo(list(infill.interleave.random.points = 1, infill.opt.focussearch.points = 5, infill.opt.focussearch.maxit = 2))
  expect_names(names(ti$archive$data), permutation.of = c(archivenames, "crit.vals"))
  expect_data_table(ti$archive$data, nrows = 12)
  expect_equal(is.na(ti$archive$data$propose.time), rep(c(TRUE, FALSE, TRUE, FALSE, TRUE), c(8, 1, 1, 1, 1)))
  expect_equal(ti$archive$data$batch_nr, rep(1:3, c(8, 2, 2)))

  ti <- eval_mbo(list(propose.points = 2, infill.opt.focussearch.points = 5, infill.opt.focussearch.maxit = 2))
  expect_names(names(ti$archive$data), permutation.of = c(archivenames, "crit.vals", "multipoint.cb.lambdas"))
  expect_data_table(ti$archive$data, nrows = 12)
  expect_equal(is.na(ti$archive$data$propose.time), rep(c(TRUE, FALSE), c(8, 4)))
  expect_equal(ti$archive$data$batch_nr, rep(1:3, c(8, 2, 2)))

  ti <- eval_mbo(list(propose.points = 2, multipoint.method = "cb", infill.opt.focussearch.points = 5, infill.opt.focussearch.maxit = 2))
  expect_names(names(ti$archive$data), permutation.of = c(archivenames, "crit.vals", "multipoint.cb.lambdas"))
  expect_data_table(ti$archive$data, nrows = 12)
  expect_equal(is.na(ti$archive$data$propose.time), rep(c(TRUE, FALSE), c(8, 4)))
  expect_equal(ti$archive$data$batch_nr, rep(1:3, c(8, 2, 2)))

  # cmaes only works with numeric params, but we can append a trafo that rounds
  ps <- (ps(cp = p_dbl(lower = 0, upper = 1), minsplit = p_dbl(lower = 1, upper = 20, trafo = round)))
  ti <- eval_mbo(list(propose.points = 2, infill.opt = "cmaes"))
  expect_names(names(ti$archive$data), permutation.of = c(archivenames, "crit.vals", "multipoint.cb.lambdas"))
  expect_data_table(ti$archive$data, nrows = 12)
  expect_equal(is.na(ti$archive$data$propose.time), rep(c(TRUE, FALSE), c(8, 4)))
  expect_equal(ti$archive$data$batch_nr, rep(1:3, c(8, 2, 2)))

  skip_on_cran()   # skip from here, things take too much time.
  ps <- (ps(cp = p_dbl(lower = 0, upper = 1), minsplit = p_int(lower = 1, upper = 20)))
  ti <- eval_mbo(list(propose.points = 2, multipoint.method = "cl", infill.crit = "EI", infill.opt.focussearch.points = 5, infill.opt.focussearch.maxit = 2))
  expect_names(names(ti$archive$data), permutation.of = c(archivenames, "crit.vals"))
  expect_data_table(ti$archive$data, nrows = 12)
  expect_equal(is.na(ti$archive$data$propose.time), rep(c(TRUE, FALSE), c(8, 4)))
  expect_equal(ti$archive$data$batch_nr, rep(1:3, c(8, 2, 2)))

  ti <- eval_mbo(list(propose.points = 2, multipoint.method = "cl", infill.crit = "EI", multipoint.cl.lie = max, infill.opt.focussearch.points = 5, infill.opt.focussearch.maxit = 2))
  expect_names(names(ti$archive$data), permutation.of = c(archivenames, "crit.vals"))
  expect_data_table(ti$archive$data, nrows = 12)
  expect_equal(is.na(ti$archive$data$propose.time), rep(c(TRUE, FALSE), c(8, 4)))
  expect_equal(ti$archive$data$batch_nr, rep(1:3, c(8, 2, 2)))

  ti <- eval_mbo(list(propose.points = 2, multipoint.method = "moimbo", infill.crit = "EI"))
  expect_names(names(ti$archive$data), permutation.of = c(archivenames, "crit.vals.1", "crit.vals.2"))
  expect_data_table(ti$archive$data, nrows = 12)
  expect_equal(is.na(ti$archive$data$propose.time), rep(c(TRUE, FALSE), c(8, 4)))
  expect_equal(ti$archive$data$batch_nr, rep(1:3, c(8, 2, 2)))

  ti <- eval_mbo(list(propose.points = 2, multipoint.method = "moimbo", infill.crit = "EI", multipoint.moimbo.objective = "mean.dist", multipoint.moimbo.dist = "nearest.neighbor", multipoint.moimbo.selection = "crowdingdist", multipoint.moimbo.maxit = 10, multipoint.moimbo.sbx.eta = 17, multipoint.moimbo.sbx.p = 0.8, multipoint.moimbo.pm.eta = 12, multipoint.moimbo.pm.p = 0.7))
  expect_names(names(ti$archive$data), permutation.of = c(archivenames, "crit.vals.1", "crit.vals.2"))
  expect_data_table(ti$archive$data, nrows = 12)
  expect_equal(is.na(ti$archive$data$propose.time), rep(c(TRUE, FALSE), c(8, 4)))
  expect_equal(ti$archive$data$batch_nr, rep(1:3, c(8, 2, 2)))

  ti <- eval_mbo(list(infill.crit = "MeanResponse", infill.opt.focussearch.points = 5, infill.opt.focussearch.maxit = 2))
  expect_names(names(ti$archive$data), permutation.of = c(archivenames, "crit.vals"))
  expect_data_table(ti$archive$data, nrows = 11)
  expect_equal(is.na(ti$archive$data$propose.time), rep(c(TRUE, FALSE), c(8, 3)))

  ti <- eval_mbo(list(infill.crit = "StandardError", infill.opt.focussearch.points = 5, infill.opt.focussearch.maxit = 2))
  expect_names(names(ti$archive$data), permutation.of = c(archivenames, "crit.vals"))
  expect_data_table(ti$archive$data, nrows = 11)
  expect_equal(is.na(ti$archive$data$propose.time), rep(c(TRUE, FALSE), c(8, 3)))

  ti <- eval_mbo(list(infill.crit = "CB", infill.crit.cb.lambda = 4, infill.opt.focussearch.points = 5, infill.opt.focussearch.maxit = 2))
  expect_names(names(ti$archive$data), permutation.of = c(archivenames, "crit.vals"))
  expect_data_table(ti$archive$data, nrows = 11)
  expect_equal(is.na(ti$archive$data$propose.time), rep(c(TRUE, FALSE), c(8, 3)))

  ti <- eval_mbo(list(infill.crit = "AEI", infill.opt.focussearch.points = 5, infill.opt.focussearch.maxit = 2))
  expect_names(names(ti$archive$data), permutation.of = c(archivenames, "crit.vals"))
  expect_data_table(ti$archive$data, nrows = 11)
  expect_equal(is.na(ti$archive$data$propose.time), rep(c(TRUE, FALSE), c(8, 3)))

  set.seed(1)
  # need to explicitly turn on nugget in kriging model
  ti <- eval_mbo(list(infill.crit = "AEI", infill.crit.aei.use.nugget = TRUE, infill.opt.focussearch.points = 5, infill.opt.focussearch.maxit = 2), surrogate = NULL)
  expect_names(names(ti$archive$data), permutation.of = c(archivenames, "crit.vals"))
  expect_data_table(ti$archive$data, nrows = 11)
  expect_equal(is.na(ti$archive$data$propose.time), rep(c(TRUE, FALSE), c(8, 3)))

  ti <- eval_mbo(list(infill.crit = "EQI", infill.opt.focussearch.points = 5, infill.opt.focussearch.maxit = 2))
  expect_names(names(ti$archive$data), permutation.of = c(archivenames, "crit.vals"))
  expect_data_table(ti$archive$data, nrows = 11)
  expect_equal(is.na(ti$archive$data$propose.time), rep(c(TRUE, FALSE), c(8, 3)))

  ti <- eval_mbo(list(infill.crit = "EQI", infill.crit.eqi.beta = 0.6, infill.opt.focussearch.points = 5, infill.opt.focussearch.maxit = 2))
  expect_names(names(ti$archive$data), permutation.of = c(archivenames, "crit.vals"))
  expect_data_table(ti$archive$data, nrows = 11)
  expect_equal(is.na(ti$archive$data$propose.time), rep(c(TRUE, FALSE), c(8, 3)))

  ti <- eval_mbo(list(infill.crit = "AdaCB", infill.opt.focussearch.points = 5, infill.opt.focussearch.maxit = 2))
  expect_names(names(ti$archive$data), permutation.of = c(archivenames, "crit.vals"))
  expect_data_table(ti$archive$data, nrows = 11)
  expect_equal(is.na(ti$archive$data$propose.time), rep(c(TRUE, FALSE), c(8, 3)))

  ti <- eval_mbo(list(infill.crit = "DIB", multiobj.method = "dib", infill.opt.focussearch.points = 5, infill.opt.focussearch.maxit = 2), multimsr = TRUE, n.objectives = 2)
  expect_names(names(ti$archive$data), permutation.of = c(archivenames, "classif.tpr", "crit.vals"))
  expect_data_table(ti$archive$data, nrows = 11)
  expect_equal(is.na(ti$archive$data$propose.time), rep(c(TRUE, FALSE), c(8, 3)))

  ti <- eval_mbo(list(infill.crit = "DIB", multiobj.method = "dib", multiobj.dib.indicator = "sms", infill.crit.sms.eps = 0.01, infill.opt.focussearch.points = 5, infill.opt.focussearch.maxit = 2), multimsr = TRUE, n.objectives = 2)
  expect_names(names(ti$archive$data), permutation.of = c(archivenames, "classif.tpr", "crit.vals"))
  expect_data_table(ti$archive$data, nrows = 11)
  expect_equal(is.na(ti$archive$data$propose.time), rep(c(TRUE, FALSE), c(8, 3)))


## TODO: breaks because of https://github.com/mlr-org/mlrMBO/issues/474
#  ti <- eval_mbo(list(infill.crit = "EI", multiobj.method = "parego", infill.opt.focussearch.points = 5, infill.opt.focussearch.maxit = 2), multimsr = TRUE, n.objectives = 2)
#  expect_names(names(ti$archive$data), permutation.of = c(archivenames, "classif.auc", "classif.tpr", "propose.time", "crit.vals", "errors.model"))
#  expect_data_table(ti$archive$data, nrows = 11)
#  expect_equal(is.na(ti$archive$data$propose.time), rep(c(TRUE, FALSE), c(8, 3)))

## TODO: breaks because of https://github.com/mlr-org/mlrMBO/issues/474
#  ti <- eval_mbo(list(infill.crit = "EI", multiobj.method = "mspot", infill.opt.focussearch.points = 5, infill.opt.focussearch.maxit = 2), multimsr = TRUE, n.objectives = 2)
#  expect_names(names(ti$archive$data), permutation.of = c(archivenames, "classif.auc", "classif.tpr", "propose.time", "crit.vals", "errors.model"))
#  expect_data_table(ti$archive$data, nrows = 11)
#  expect_equal(is.na(ti$archive$data$propose.time), rep(c(TRUE, FALSE), c(8, 3)))

## TODO: breaks because of https://github.com/mlr-org/mlrMBO/issues/474
#  ti <- eval_mbo(list(propose.points = 2, infill.crit = "EI", multiobj.method = "mspot", infill.opt = "nsga2", infill.opt.nsga2.popsize = 80, infill.opt.nsga2.generations = 30, infill.opt.nsga2.cprob = 0.6, infill.opt.nsga2.cdist = 4, infill.opt.nsga2.mprob = 0.3, infill.opt.nsga2.mdist = 11), multimsr = TRUE, n.objectives = 2)
#  expect_names(names(ti$archive$data), permutation.of = c(archivenames, "classif.auc", "propose.time", "crit.vals", "errors.model", "multipoint.cb.lambdas"))
#  expect_data_table(ti$archive$data, nrows = 12)
#  expect_equal(is.na(ti$archive$data$propose.time), rep(c(TRUE, FALSE), c(8, 4)))
#  expect_equal(ti$archive$data$batch_nr, rep(1:3, c(8, 2, 2)))


})

test_that("trafo", {
  skip_on_cran()
  ps <- (ps(cp = p_dbl(lower = -1, upper = 0, trafo = function(x) -x)))

  # get archivenames
  set.seed(1)
  ti <- TuningInstanceSingleCrit$new(task = tsk("pima"), learner = lrn("classif.rpart", predict_type = "prob"), resampling = rsmp("holdout"), measure = msr("classif.auc"), terminator = trm("evals", n_evals = 11), search_space = ps)
  tuner <- TunerInterMBO$new()

  tuner$optimize(ti)

  expect_data_table(ti$archive$data, nrows = 11)

  expect_true(all(ti$archive$data$cp <= 0))

  expect_true(all(map_dbl(ti$archive$data$x_domain, "cp") >= 0))
})