tests/testthat/test_extract_inner_tuning_results.R

In mlr3tuning: Hyperparameter Optimization for 'mlr3'

test_that("extract_inner_tuning_results function works", {
  te = trm("evals", n_evals = 4)
  task = tsk("iris")
  search_space = TEST_MAKE_PS1(n_dim = 1)
  ms = msr("classif.ce")
  tuner = tnr("random_search")

  # cv
  at = AutoTuner$new(lrn("classif.rpart"), rsmp("holdout"), ms, te, tuner = tuner, search_space)
  resampling_outer = rsmp("cv", folds = 2)
  rr = resample(task, at, resampling_outer, store_models = TRUE)

  irr = extract_inner_tuning_results(rr)
  expect_data_table(irr, nrows = 2)
  expect_named(irr, c("iteration", "cp", "classif.ce", "learner_param_vals", "x_domain", "task_id", "learner_id", "resampling_id"))

  # repeated cv
  at = AutoTuner$new(lrn("classif.rpart"), rsmp("holdout"), ms, te, tuner = tuner, search_space)
  resampling_outer = rsmp("repeated_cv", folds = 2, repeats = 3)
  rr = resample(task, at, resampling_outer, store_models = TRUE)

  irr = extract_inner_tuning_results(rr)
  expect_data_table(irr, nrows = 6)
  expect_named(irr, c("iteration", "cp", "classif.ce", "learner_param_vals", "x_domain", "task_id", "learner_id", "resampling_id"))

  # cv
  at_1 = AutoTuner$new(lrn("classif.rpart"), rsmp("holdout"), ms, te, tuner = tuner, search_space)
  at_2 = AutoTuner$new(lrn("classif.rpart"), rsmp("holdout"), ms, te, tuner = tuner, search_space)
  resampling_outer = rsmp("cv", folds = 2)
  grid = benchmark_grid(task, list(at_1, at_2), resampling_outer)
  bmr = benchmark(grid, store_models = TRUE)

  ibmr = extract_inner_tuning_results(bmr)
  expect_data_table(ibmr, nrows = 4)
  expect_named(ibmr, c("experiment", "iteration", "cp", "classif.ce", "learner_param_vals", "x_domain", "task_id", "learner_id", "resampling_id"))
  expect_equal(unique(ibmr$experiment), c(1, 2))

   # repeated cv
  at_1 = AutoTuner$new(lrn("classif.rpart"), rsmp("holdout"), ms, te, tuner = tuner, search_space)
  at_2 = AutoTuner$new(lrn("classif.rpart"), rsmp("holdout"), ms, te, tuner = tuner, search_space)
  resampling_outer = rsmp("repeated_cv", folds = 2, repeats = 3)
  grid = benchmark_grid(task, list(at_1, at_2), resampling_outer)
  bmr = benchmark(grid, store_models = TRUE)

  ibmr = extract_inner_tuning_results(bmr)
  expect_data_table(ibmr, nrows = 12)
  expect_named(ibmr, c("experiment", "iteration", "cp", "classif.ce", "learner_param_vals", "x_domain", "task_id", "learner_id", "resampling_id"))
  expect_equal(unique(ibmr$experiment), c(1, 2))

  # different hyperparameters
  at_1 = AutoTuner$new(lrn("classif.rpart", cp = to_tune(0.001, 0.1)), rsmp("holdout"), ms, te, tuner = tuner)
  at_2 = AutoTuner$new(lrn("classif.debug", x = to_tune()), rsmp("holdout"), ms, te, tuner = tuner)
  resampling_outer = rsmp("cv", folds = 2)
  grid = benchmark_grid(task, list(at_1, at_2), resampling_outer)
  bmr = benchmark(grid, store_models = TRUE)

  ibmr = extract_inner_tuning_results(bmr)
  expect_data_table(ibmr, nrows = 4)
   expect_named(ibmr, c("experiment", "iteration", "cp", "x", "classif.ce", "learner_param_vals", "x_domain", "task_id", "learner_id", "resampling_id"))
  expect_equal(unique(ibmr$experiment), c(1, 2))

  # no model
  at = AutoTuner$new(lrn("classif.rpart"), rsmp("holdout"), ms, te, tuner = tuner, search_space)
  resampling_outer = rsmp("cv", folds = 2)
  rr = resample(task, at, resampling_outer, store_models = FALSE)

  expect_data_table(extract_inner_tuning_results(rr), nrows = 0, ncols = 0)

 # instance
  at = AutoTuner$new(lrn("classif.rpart"), rsmp("holdout"), ms, te, tuner = tuner, search_space,
    store_tuning_instance = FALSE, store_benchmark_result = FALSE)
  resampling_outer = rsmp("cv", folds = 2)
  rr = resample(task, at, resampling_outer, store_models = TRUE)

  expect_data_table(extract_inner_tuning_results(rr), nrows = 0, ncols = 0)

  # no models
  at_1 = AutoTuner$new(lrn("classif.rpart"), rsmp("holdout"), ms, te, tuner = tuner, search_space)
  at_2 = AutoTuner$new(lrn("classif.rpart"), rsmp("holdout"), ms, te, tuner = tuner, search_space)
  resampling_outer = rsmp("cv", folds = 2)
  grid = benchmark_grid(task, list(at_1, at_2), resampling_outer)
  bmr = benchmark(grid, store_models = FALSE)

  expect_data_table(extract_inner_tuning_results(bmr), nrows = 0, ncols = 0)

  # mixed store instance
  at_1 = AutoTuner$new(lrn("classif.rpart"), rsmp("holdout"), ms, te, tuner = tuner, search_space,
    store_tuning_instance = FALSE, store_benchmark_result = FALSE)
  at_2 = AutoTuner$new(lrn("classif.debug", x = to_tune()), rsmp("holdout"), ms, te, tuner = tuner)
  resampling_outer = rsmp("cv", folds = 2)
  grid = benchmark_grid(task, list(at_1, at_2), resampling_outer)
  bmr = benchmark(grid, store_models = TRUE)

  ibmr = extract_inner_tuning_results(bmr)
  expect_data_table(ibmr, nrows = 2, ncols = 9)
  expect_equal(unique(ibmr$experiment), 2)

  # autotuner and learner
  learner = lrn("classif.rpart")
  at = AutoTuner$new(lrn("classif.debug", x = to_tune()), rsmp("holdout"), ms, te, tuner = tuner)
  grid = benchmark_grid(task, list(at, learner), resampling_outer)
  bmr = benchmark(grid, store_models = TRUE)

  ibmr = extract_inner_tuning_results(bmr)
  expect_data_table(ibmr, nrows = 2)
  expect_named(ibmr, c("experiment", "iteration", "x", "classif.ce", "learner_param_vals", "x_domain", "task_id", "learner_id", "resampling_id"))
  expect_equal(unique(ibmr$experiment), 1)

  # search_space > 1
  at_1 = AutoTuner$new(lrn("classif.rpart", cp = to_tune(0.01, 0.1), minsplit = to_tune(1, 12)), rsmp("holdout"), ms, te, tuner = tuner)
  at_2 = AutoTuner$new(lrn("classif.debug", x = to_tune()), rsmp("holdout"), ms, te, tuner = tuner)
  grid = benchmark_grid(task, list(at_1, at_2), resampling_outer)
  bmr = benchmark(grid, store_models = TRUE)

  ibmr = extract_inner_tuning_results(bmr)
  expect_data_table(ibmr, nrows = 4)
  expect_named(ibmr, c("experiment", "iteration", "cp", "minsplit", "x", "classif.ce", "learner_param_vals", "x_domain", "task_id", "learner_id", "resampling_id"))
  expect_equal(unique(ibmr$experiment), c(1, 2))
})

test_that("extract_inner_tuning_results returns tuning_instance", {
  at = auto_tuner(
    tuner =  tnr("random_search"),
    learner = lrn("classif.rpart"),
    resampling = rsmp("holdout"),
    term_evals = 4,
    search_space = TEST_MAKE_PS1(n_dim = 1),
    store_tuning_instance = TRUE
  )
  resampling_outer = rsmp("cv", folds = 2)
  rr = resample(tsk("penguins"), at, resampling_outer, store_models = TRUE)

  tab = extract_inner_tuning_results(rr, tuning_instance = TRUE)
  expect_data_table(tab, nrows = 2)
  expect_named(tab, c("iteration", "cp", "classif.ce", "learner_param_vals", "x_domain", "tuning_instance", "task_id", "learner_id", "resampling_id"))
  expect_class(tab$tuning_instance[[1]], "TuningInstanceSingleCrit")

  at = auto_tuner(
    tuner =  tnr("random_search"),
    learner = lrn("classif.rpart"),
    resampling = rsmp("holdout"),
    term_evals = 4,
    search_space = TEST_MAKE_PS1(n_dim = 1),
    store_tuning_instance = TRUE
  )
  resampling_outer = rsmp("cv", folds = 2)
  grid = benchmark_grid(tsk("iris"), list(at, at), resampling_outer)
  bmr = benchmark(grid, store_models = TRUE)

  tab = extract_inner_tuning_results(bmr, tuning_instance = TRUE)
  expect_data_table(tab, nrows = 4)
  expect_named(tab, c("experiment", "iteration", "cp", "classif.ce", "learner_param_vals", "x_domain", "tuning_instance", "task_id", "learner_id", "resampling_id"))
  expect_equal(unique(tab$experiment), c(1, 2))
})