#' DLCV XGBoost No Hyperparameter Optimization
#'
#' @param folds rsample object with either group V-fold or the standard V-fold cross validation folds.
#' @param rec recipes recipe used for training
#' @return Tibble with k outer loop models, and training and testing predictions.
#' @export
# dlcvXgbNoHp <- function(folds, rec) {
# xgb_spec <- rand_forest() %>%
# set_mode("classification") %>%
# set_engine("randomForest")
#
# xgb_workflow <- workflow() %>%
# add_recipe(rec) %>%
# add_model(xgb_spec)
#
# xgb_no_hp_folds <- folds %>%
# mutate(xgb_model = map2(splits, id, ~ {
# set.seed(as.integer(stringr::str_sub(.y, nchar(.y), nchar(.y)))+1)
# xgb_workflow %>% fit(data = analysis(.x))
# }),
# final_wf = map2(splits, xgb_model, ~ {
# xgb_workflow %>%
# finalize_workflow(.y) %>%
# fit(analysis(.x))
# }),
# map2_dfr(splits, final_wf, dlcvOuter))
#
# xgb_no_hp_folds <- xgb_no_hp_folds %>%
# select(-splits)
#
# return(xgb_no_hp_folds)
# }
#' DLCV XGBoost With Hyperparameter Optimization
#'
#' @param folds rsample object with either group V-fold or the standard V-fold cross validation folds.
#' @param rec recipes recipe used for training
#' @return Tibble with k outer loop models, and training and testing predictions.
#' @export
# dlcvXgb <- function(folds, rec) {
# xgb_spec <- rand_forest(mtry = tune(),
# min_n = tune(),
# trees = tune()) %>%
# set_mode("classification") %>%
# set_engine("randomForest")
#
# xgb_workflow <- workflow() %>%
# add_recipe(rec) %>%
# add_model(xgb_spec)
#
# # xgb_hp_grid <- grid_regular(trees(range = c(50, 350)),
# # mtry(range = c(3, 12)),
# # min_n(range = c(100, 300)),
# # levels = 3)
#
# xgb_folds <- folds %>%
# mutate(best_model = map(splits, ~ dlcvInner(.x, xgb_workflow, xgb_hp_grid))) %>%
# mutate(final_wf = map2(splits, best_model, ~ xgb_workflow %>%
# finalize_workflow(.y) %>%
# fit(analysis(.x)))) %>%
# mutate(map2_dfr(splits, final_wf, dlcvOuter))
#
# xgb_folds <- xgb_folds %>%
# select(-splits)
#
# return(xgb_folds)
# }
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