skip_if_not_installed("rush")
skip_if_no_redis()

test_that("initializing FSelectInstanceAsyncMultiCrit works", {
  rush = start_rush()
  on.exit({
    rush$reset()
    mirai::daemons(0)
  })

  instance = fsi_async(
    task = tsk("pima"),
    learner = lrn("classif.rpart"),
    resampling = rsmp("cv", folds = 3),
    measures = msrs(c("classif.ce", "classif.acc")),
    terminator = trm("evals", n_evals = 3),
    rush = rush
  )

  expect_class(instance, "FSelectInstanceAsyncMultiCrit")
  expect_r6(instance$archive, "ArchiveAsyncFSelect")
  expect_r6(instance$objective, "Objective")
  expect_r6(instance$search_space, "ParamSet")
  expect_r6(instance$terminator, "Terminator")
  expect_r6(instance$rush, "Rush")
  expect_null(instance$result)
})

test_that("rush controller can be passed to FSelectInstanceAsyncMultiCrit", {
  rush = start_rush()
  on.exit({
    rush$reset()
    mirai::daemons(0)
  })

  instance = fsi_async(
    task = tsk("pima"),
    learner = lrn("classif.rpart"),
    resampling = rsmp("cv", folds = 3),
    measures = msrs(c("classif.ce", "classif.acc")),
    terminator = trm("evals", n_evals = 3),
    rush = rush
  )

  expect_class(instance, "FSelectInstanceAsyncMultiCrit")
  expect_class(instance$rush, "Rush")
})

test_that("FSelectInstanceAsyncMultiCrit can be passed to a fselector", {
  rush = start_rush()
  on.exit({
    rush$reset()
    mirai::daemons(0)
  })

  instance = fsi_async(
    task = tsk("pima"),
    learner = lrn("classif.rpart"),
    resampling = rsmp("cv", folds = 3),
    measures = msrs(c("classif.ce", "classif.acc")),
    terminator = trm("evals", n_evals = 3),
    rush = rush
  )

  fselector = fs("async_random_search")
  fselector$optimize(instance)

  expect_data_table(instance$archive$data, min.rows = 3L)
})

test_that("assigning a result to FSelectInstanceAsyncMultiCrit works", {
  rush = start_rush()
  on.exit({
    rush$reset()
    mirai::daemons(0)
  })

  instance = fsi_async(
    task = tsk("pima"),
    learner = lrn("classif.rpart"),
    resampling = rsmp("cv", folds = 3),
    measures = msrs(c("classif.ce", "classif.acc")),
    terminator = trm("evals", n_evals = 3),
    rush = rush
  )
  fselector = fs("async_random_search")
  fselector$optimize(instance)

  result = instance$result
  expect_data_table(result, min.rows = 1)
  expect_names(names(result), must.include = c("features", "n_features", "classif.ce", "classif.acc"))
})

test_that("saving the benchmark result with FSelectInstanceAsyncMultiCrit works", {
  rush = start_rush()
  on.exit({
    rush$reset()
    mirai::daemons(0)
  })

  instance = fsi_async(
    task = tsk("pima"),
    learner = lrn("classif.rpart"),
    resampling = rsmp("cv", folds = 3),
    measures = msrs(c("classif.ce", "classif.acc")),
    terminator = trm("evals", n_evals = 3),
    store_benchmark_result = TRUE,
    rush = rush
  )

  fselector = fs("async_random_search")
  fselector$optimize(instance)

  expect_benchmark_result(instance$archive$benchmark_result)
  expect_gte(instance$archive$benchmark_result$n_resample_results, 3L)
  expect_null(instance$archive$resample_result(1)$learners[[1]]$model)
})

test_that("saving the models with FSelectInstanceAsyncMultiCrit works", {
  rush = start_rush()
  on.exit({
    rush$reset()
    mirai::daemons(0)
  })

  instance = fsi_async(
    task = tsk("pima"),
    learner = lrn("classif.rpart"),
    resampling = rsmp("cv", folds = 3),
    measures = msrs(c("classif.ce", "classif.acc")),
    terminator = trm("evals", n_evals = 3),
    store_benchmark_result = TRUE,
    store_models = TRUE,
    rush = rush
  )

  fselector = fs("async_random_search")
  fselector$optimize(instance)

  expect_benchmark_result(instance$archive$benchmark_result)
  expect_gte(instance$archive$benchmark_result$n_resample_results, 3L)
  expect_class(instance$archive$resample_result(1)$learners[[1]]$model, "rpart")
})