skip_if_not_installed("future") test_that("parallel resample", { skip_if_not_installed("future.callr") skip_if_not_installed("progressr") with_future(future.callr::callr, { task = tsk("iris") learner = lrn("classif.rpart") progressr::with_progress({ rr = resample(task, learner, rsmp("cv", folds = 3)) }) expect_resample_result(rr) expect_data_table(rr$errors, nrows = 0L) }) }) test_that("seeds work identical during sequential and parallel execution", { task = tsk("sonar") learner = lrn("classif.debug", predict_type = "prob") resampling = rsmp("cv", folds = 3L) measure = msr("classif.auc") rr1 = with_seed(123, with_future(future::sequential, resample(task, learner, resampling))) rr2 = with_seed(123, with_future(future::multisession, resample(task, learner, resampling))) expect_equal( as.data.table(rr1$prediction())$prob.M, as.data.table(rr2$prediction())$prob.M ) }) test_that("parallel benchmark", { skip_if_not_installed("future.callr") task = tsk("iris") learner = lrn("classif.rpart") with_future(future.callr::callr, { progressr::with_progress({ bmr = benchmark(benchmark_grid(task, learner, rsmp("cv", folds = 3))) }) }) expect_benchmark_result(bmr) expect_equal(bmr$aggregate(conditions = TRUE)$warnings, 0L) expect_equal(bmr$aggregate(conditions = TRUE)$errors, 0L) grid = benchmark_grid(list(tsk("wine"), tsk("sonar")), replicate(2, lrn("classif.debug")), rsmp("cv", folds = 2)) njobs = 3L bmr = with_future(future::multisession, { benchmark(grid, store_models = TRUE) }, workers = njobs) expect_benchmark_result(bmr) pids = map_int(as.data.table(bmr)$learner, function(x) x$model$pid) expect_equal(length(unique(pids)), njobs) }) test_that("real parallel resample", { skip_if_not_installed("progressr") skip_on_os("windows") # currently buggy with_future(future::multisession, { task = tsk("iris") learner = lrn("classif.rpart") progressr::with_progress({ rr = resample(task, learner, rsmp("cv", folds = 3)) }) expect_resample_result(rr) expect_data_table(rr$errors, nrows = 0L) }) }) test_that("parallel seed", { skip_if_not_installed("future.callr") task = tsk("wine") learner = lrn("classif.debug", predict_type = "prob") rr1 = with_seed(123, resample(task, learner, rsmp("cv", folds = 3))) with_future(future.callr::callr, { rr2 = with_seed(123, resample(task, learner, rsmp("cv", folds = 3))) }) expect_equal(rr1$prediction()$prob, rr2$prediction()$prob) })