library(mlbench) data("DNA") dataset <- DNA |> data.table::as.data.table() |> na.omit() seed <- 123 feature_cols <- colnames(dataset)[160:180] param_list_glmnet <- expand.grid( alpha = seq(0, 1, 0.05) ) if (isTRUE(as.logical(Sys.getenv("_R_CHECK_LIMIT_CORES_")))) { # on cran ncores <- 2L } else { ncores <- ifelse( test = parallel::detectCores() > 4, yes = 4L, no = ifelse( test = parallel::detectCores() < 2L, yes = 1L, no = parallel::detectCores() ) ) } train_x <- model.matrix( ~ -1 + ., dataset[, .SD, .SDcols = feature_cols] ) train_y <- as.integer(dataset[, get("Class")]) - 1L options("mlexperiments.bayesian.max_init" = 10L) fold_list <- splitTools::create_folds( y = train_y, k = 3, type = "stratified", seed = seed ) # ########################################################################### # %% TUNING # ########################################################################### glmnet_bounds <- list( alpha = c(0., 1.) ) optim_args <- list( iters.n = ncores, kappa = 3.5, acq = "ucb" ) # ########################################################################### # %% NESTED CV # ########################################################################### test_that( desc = "test nested cv, bayesian, multiclass - glmnet", code = { glmnet_optimizer <- mlexperiments::MLNestedCV$new( learner = mllrnrs::LearnerGlmnet$new( metric_optimization_higher_better = FALSE ), strategy = "bayesian", fold_list = fold_list, k_tuning = 3L, ncores = ncores, seed = seed ) glmnet_optimizer$parameter_bounds <- glmnet_bounds glmnet_optimizer$parameter_grid <- param_list_glmnet glmnet_optimizer$split_type <- "stratified" glmnet_optimizer$optim_args <- optim_args glmnet_optimizer$learner_args <- list( family = "multinomial", type.measure = "class", standardize = TRUE ) glmnet_optimizer$predict_args <- list(type = "response", reshape = TRUE) glmnet_optimizer$performance_metric <- mlexperiments::metric("bacc") # set data glmnet_optimizer$set_data( x = train_x, y = train_y ) cv_results <- glmnet_optimizer$execute() expect_type(cv_results, "list") expect_equal(dim(cv_results), c(3, 7)) expect_true(inherits( x = glmnet_optimizer$results, what = "mlexCV" )) } )