tune_bayes() example on `doRedis`

doRedis-example.R

library(tidymodels)
library(modeldata)

data(cells)

set.seed(2369)
tr_te_split <- initial_split(cells %>% select(-case), prop = 3/4)
cell_train <- training(tr_te_split)
cell_test  <- testing(tr_te_split)

set.seed(1697)
folds <- vfold_cv(cell_train, v = 10)

library(themis)

cell_pre_proc <-
    recipe(class ~ ., data = cell_train) %>%
    step_YeoJohnson(all_predictors()) %>%
    step_normalize(all_predictors()) %>%
    step_pca(all_predictors(), num_comp = tune()) %>%
    step_downsample(class)

svm_mod <-
    svm_rbf(mode = "classification", cost = tune(), rbf_sigma = tune()) %>%
    set_engine("kernlab")

svm_wflow <-
    workflow() %>%
    add_model(svm_mod) %>%
    add_recipe(cell_pre_proc)

svm_set <- parameters(svm_wflow)

svm_set <-
    svm_set %>%
    update(num_comp = num_comp(c(0L, 20L)))
set.seed(12)

library(doRedis)
registerDoRedis("debug_jobs") # No Redis worker is created.

search_res <-
    svm_wflow %>%
    tune_bayes(
        resamples = folds,
        param_info = svm_set,
        initial = 5,
        iter = 10,
        metrics = metric_set(roc_auc),
        control = control_bayes(no_improve = 5, verbose = TRUE)
    )

removeQueue("debug_jobs")