Create function internal_make_spec_tbl()

[spsanderson]

• update fast_regression_parsnip_spec_tbl() #79
• update fast_classification_parsnip_spec_tbl() #80

Function:

internal_make_spec_tbl <- function(.data){
  
  # Checks ----
  df <- dplyr::as_tibble(.data)
  
  nms <- unique(names(df))
  
  if (!".parsnip_engine" %in% nms | !".parsnip_mode" %in% nms | !".parsnip_fns" %in% nms){
    rlang::abort(
      message = "The model tibble must come from the class/reg to parsnip function.",
      use_cli_format = TRUE
    )
  }
  
  # Make tibble ----
  mod_spec_tbl <- df %>%
    dplyr::mutate(
      model_spec = purrr::pmap(
        dplyr::cur_data(),
        ~ match.fun(..3)(mode = ..2, engine = ..1)
      )
    ) %>%
    # add .model_id column
    dplyr::mutate(.model_id = dplyr::row_number()) %>%
    dplyr::select(.model_id, dplyr::everything())
  
  # Return ----
  return(mod_spec_tbl)
  
}

Example:

> internal_make_spec_tbl(mod_tbl)
# A tibble: 46 × 5
   .model_id .parsnip_engine .parsnip_mode .parsnip_fns model_spec
       <int> <chr>           <chr>         <chr>        <list>    
 1         1 lm              regression    linear_reg   <spec[+]> 
 2         2 brulee          regression    linear_reg   <spec[+]> 
 3         3 gee             regression    linear_reg   <spec[+]> 
 4         4 glm             regression    linear_reg   <spec[+]> 
 5         5 glmer           regression    linear_reg   <spec[+]> 
 6         6 glmnet          regression    linear_reg   <spec[+]> 
 7         7 gls             regression    linear_reg   <spec[+]> 
 8         8 lme             regression    linear_reg   <spec[+]> 
 9         9 lmer            regression    linear_reg   <spec[+]> 
10        10 stan            regression    linear_reg   <spec[+]> 
# … with 36 more rows
# ℹ Use `print(n = ...)` to see more rows