Create function `internal_make_tuned_wflw()`

[spsanderson]

Function:

#' Internals Make a Tunable Model Specification
#'
#' @family Internals
#'
#' @author Steven P. Sanderson II, MPH
#'
#' @description Make a tuned model specification object.
#'
#' @details This will take a model specification that is created from a function
#' like [tidyAML::fast_regression_parsnip_spec_tbl()] and update the __model_spec__
#' `args` to `tune::tune()`. This is done dynamically, meaning you do not need
#' to know the names of the parameters inside of the model specification.
#'
#' @param .model_tbl The model table that is generated from a function like
#' `fast_regression_parsnip_spec_tbl()`, must have a class of "tidyaml_mod_spec_tbl".
#'
#' @examples
#' library(dplyr)
#' 
#' mod_tbl <- fast_regression_parsnip_spec_tbl()
#' mod_tbl$model_spec[[1]]
#' 
#' updated_tbl <- mod_tbl %>%
#'   mutate(model_spec = internal_set_args_to_tune(mod_tbl))
#' updated_tbl$model_spec[[1]]
#'
#' @return
#' A list object of workflows.
#'
#' @name internal_set_args_to_tune
NULL

#' @export
#' @rdname internal_set_args_to_tune

internal_set_args_to_tune <- function(.model_tbl){
  
  # Tidyeval
  model_tbl <- .model_tbl
  
  # Checks ----
  if (!inherits(model_tbl, "tidyaml_mod_spec_tbl")){
    rlang::abort(
      message = "'.model_tbl' must inherit a class of 'tidyaml_mod_spec_tbl",
      use_cli_format = TRUE
    )
  }
  
  model_tbl_with_params <- mod_tbl %>% 
    dplyr::mutate(
      model_params = purrr::pmap(
        dplyr::cur_data(), 
        ~ list(formalArgs(..4))
      ) 
    )
  
  models_list_new <- model_tbl_with_params %>%
    dplyr::group_split(.model_id)
  
  tuned_params_list <- models_list_new %>%
    purrr::imap(
      .f = function(obj, id){
        
        # Pull the model params
        mod_params <- obj %>% dplyr::pull(6) %>% purrr::pluck(1) # change to pull(6)
        mod_params_list <- unlist(mod_params) %>% as.list()
        #param_names <- unlist(mod_params)
        names(mod_params_list) <- unlist(mod_params)
        
        # Set mode and engine
        p_mode <- obj %>% dplyr::pull(3) %>% purrr::pluck(1)
        p_engine <- obj %>% dplyr::pull(2) %>% purrr::pluck(1)
        me_list <- list(
          mode = paste0("mode = ", p_mode),
          engine = paste0("engine = ", p_engine)
        )
        
        # Get all other params
        me_vec <- c("mode","engine")
        pv <- unlist(mod_params)
        params_to_modify <- pv[!pv %in% me_vec] %>% as.list()
        names(params_to_modify) <- unlist(params_to_modify)
        
        # Set each item equal to .x = tune::tune()
        tuned_params_list <- purrr::map(
          params_to_modify,
          ~ paste0("tune::tune()")
        )
        
        # use modifyList()
        res <- utils::modifyList(mod_params_list, tuned_params_list)
        res <- utils::modifyList(res, me_list)
        
        # Return      
        return(res)
        
      }
    )
  
  models_with_params_list <- map2(
    .x = tuned_params_list, 
    .y = models_list_new, 
    ~ {.y$model_params <- list(.x[.y$model_params[[1]][[1]]]);.y}
  )
  
  new_mod_obj <- models_with_params_list %>%
    imap(
      .f = function(obj, id){
        
        # Get Model Specification
        mod_spec <- obj %>%
          dplyr::pull(5) %>%
          purrr::pluck(1)
        
        # Get the tuned params
        new_mod_args <- obj %>%
          dplyr::pull(6) %>%
          purrr::pluck(1)
        
        # Drop the ones we don't need to set
        new_mod_args <- new_mod_args %>%
          unlist() %>%
          subset(!names(.) %in% c('mode','engine')) %>%
          as.list()
        
        # Set the new model arguments
        mod_spec$args <- new_mod_args
        
        # Return the newly modified model specification
        return(mod_spec)
      }
    )
  
  return(new_mod_obj)
  
}

Example:

library(dplyr)

> mod_tbl <- fast_regression_parsnip_spec_tbl()
> mod_tbl$model_spec[[1]]
Linear Regression Model Specification (regression)

Computational engine: lm 

> 
> updated_tbl <- mod_tbl %>%
+   mutate(model_spec = internal_set_args_to_tune(mod_tbl))
> updated_tbl$model_spec[[1]]
Linear Regression Model Specification (regression)

Main Arguments:
  penalty = tune::tune()
  mixture = tune::tune()

Computational engine: lm