4. How It Works

How It Works

The regression and classification functions work in identical fashions so we will only pick on the regression function, that is to say, how does the fast_regression() function work. While, it does work for many, it does fail for some, which means the design is flawed and it is possible at this point that it is a fundamental flaw (which I think it is). First I will post the code, and then we will see what the output of each step looks like.

The Function

Here is the full function:

fast_regression <- function(.data, .rec_obj, .parsnip_fns = "all",
                            .parsnip_eng = "all", .split_type = "initial_split",
                            .split_args = NULL){

  # Tidy Eval ----
  call <- list(.parsnip_fns) %>%
    purrr::flatten_chr()
  engine <- list(.parsnip_eng) %>%
    purrr::flatten_chr()

  rec_obj <- .rec_obj
  split_type <- .split_type
  split_args <- .split_args

  # Checks ----

  # Get data splits
  df <- dplyr::as_tibble(.data)
  splits_obj <- create_splits(
    .data = df,
    .split_type = split_type,
    .split_args = split_args
  )

  # Generate Model Spec Tbl
  mod_spec_tbl <- fast_regression_parsnip_spec_tbl(
    .parsnip_fns = call,
    .parsnip_eng = engine
  )

  # Generate Workflow object
  mod_tbl <- mod_spec_tbl %>%
    dplyr::mutate(
      wflw = internal_make_wflw(mod_spec_tbl, .rec_obj = rec_obj)
    )

  mod_fitted_tbl <- mod_tbl %>%
    dplyr::mutate(
      fitted_wflw = internal_make_fitted_wflw(mod_tbl, splits_obj)
    )

  mod_pred_tbl <- mod_fitted_tbl %>%
    dplyr::mutate(
      pred_wflw = internal_make_wflw_predictions(mod_fitted_tbl, splits_obj)
    )


  # Return ----
  class(mod_tbl) <- c("fst_reg_tbl", class(mod_tbl))
  attr(mod_tbl, ".parsnip_engines") <- .parsnip_eng
  attr(mod_tbl, ".parsnip_functions") <- .parsnip_fns
  attr(mod_tbl, ".split_type") <- .split_type
  attr(mod_tbl, ".split_args") <- .split_args

  return(mod_pred_tbl)
}