prototype for creating grids that are space-filling designed crossed with a lage submodel grid

grid_space_filling_submodels.R

cbt_spec <-
  boost_tree(
    trees = tune(),
    min_n = tune(),
    tree_depth = tune(),
    learn_rate = tune(),
    sample_size = tune(),
    stop_iter = tune()
  ) |>
  set_engine("xgboost") %>%
  set_mode("classification")


grid_space_filling_submodels <- function(x, size = 5, submodel_size = 50, ...) {
  pset <- extract_parameter_set_dials(x)
  submod_info <-
    paste0(class(x)[1], "_args") |>
    parsnip::get_from_env() |>
    # check engine available
    dplyr::filter(engine == x$engine & has_submodel) |>
    dplyr::select(id = parsnip) |>
    dplyr::mutate(component = class(x)[1], source = "model_spec")
  pset_submod <- pset[pset$id %in% submod_info$id, ]

  if (nrow(pset_submod) == 0) {
    res <- grid_space_filling(pset, size = size, ...)
    return(res)
  } else if (nrow(pset_submod) > 1) {
    cli::cli_abort(
      "tidymodels only supports single submodel parameters. This object has
       submodels for : {.arg pset_submod$name}"
    )
  }
  non_sub_set <- dplyr::anti_join(
    pset,
    submod_info,
    by = c("id", "component", "source")
  )
  non_sub_grid <- grid_space_filling(non_sub_set, size = size)
  sub_grid <- grid_regular(pset_submod, levels = submodel_size)
  tidyr::crossing(non_sub_grid, sub_grid)
}

linear_reg(
  penalty = tune(),
  mixture = tune()
) |>
  set_engine("glmnet") %>%
  grid_space_filling_submodels(size = 10, submodel_size = 20) |>
  ggplot(aes(penalty, mixture)) +
  geom_point() +
  scale_x_log10()

boost_tree(
  trees = tune(),
  min_n = tune(),
  learn_rate = tune()
) |>
  set_engine("xgboost") %>%
  set_mode("classification") |>
  grid_space_filling_submodels(size = 15, submodel_size = 10) |>
  ggplot(aes(learn_rate, min_n)) +
  geom_point() +
  facet_wrap(~trees) +
  scale_x_log10()