more_parsnip_default_code.R

library(tidymodels)
library(rlang)


get_arg <- function(ns, f, arg) {
  args <- formals(getFromNamespace(f, ns))
  args <- args %>% as.list() 
  as.character(args[[arg]])
}

# Make the defaults character because there are cases where we will write something
# eg glmnet::glmnet would have "lambda (all)" or something similar
dt_defaults <- 
  tibble::tribble(
    ~model,         ~engine,          ~parsnip,                ~original,  ~default,
    "decision_tree", "rpart",      "tree_depth",               "maxdepth", get_arg("rpart", "rpart.control", "maxdepth"),
    "decision_tree", "rpart",           "min_n",               "minsplit", get_arg("rpart", "rpart.control", "minsplit"),
    "decision_tree", "rpart", "cost_complexity",                     "cp", get_arg("rpart", "rpart.control", "cp"),
    "decision_tree",  "C5.0",           "min_n",               "minCases", get_arg("C50", "C5.0Control", "minCases"),
    "decision_tree", "spark",      "tree_depth",              "max_depth", get_arg("sparklyr", "ml_decision_tree", "max_depth"),
    "decision_tree", "spark",           "min_n", "min_instances_per_node", get_arg("sparklyr", "ml_decision_tree", "min_instances_per_node"),
  )


# emulating convert_args("decision_tree")
model_name <- "decision_tree"

envir <- get_model_env()

args <-
  ls(envir) %>%
  tibble::tibble(name = .) %>%
  dplyr::filter(grepl("args", name)) %>%
  dplyr::mutate(model = sub("_args", "", name),
                args  = purrr::map(name, ~envir[[.x]])) %>%
  dplyr::filter(grepl(model_name, model)) %>%
  tidyr::unnest(args) %>%
  dplyr::select(model:original) %>% 
  full_join(dt_defaults) %>% 
  mutate(original = paste0(original, " (", default, ")")) %>% 
  select(-default)

convert_df <- args %>%
  dplyr::select(-model) %>%
  tidyr::pivot_wider(names_from = engine, values_from = original)

convert_df %>%
  knitr::kable(col.names = paste0("**", colnames(convert_df), "**"))