`tunable.model_spec()` includes rows for non-tunable arguments marked with `tune()`

[simonpcouch]

library(tidymodels)

tunable(
  linear_reg() %>% 
  set_engine("glmnet", dfmax = tune())
)
#> # A tibble: 3 × 5
#>   name    call_info        source     component  component_id
#>   <chr>   <list>           <chr>      <chr>      <chr>       
#> 1 penalty <named list [2]> model_spec linear_reg main        
#> 2 mixture <named list [3]> model_spec linear_reg main        
#> 3 dfmax   <NULL>           model_spec linear_reg engine

^{Created on 2024-04-04 with reprex v2.1.0}

[simonpcouch]

Okay, a few more notes here.

It seems like the architecture here is:

1. tunable() gets called on a boost_tree() model spec (or other model type)
2. tunable.boost_tree() kicks in
   2a) it first calls tunable.model_spec(),

parsnip/R/tunable.R

Lines 279 to 280 in eb526fa

	tunable.boost_tree <- function(x, ...) {
	res <- NextMethod()

...which only excludes rows that are both main arguments and have no tunable information associated, so there's a bunch of engine arguments with NULL call_info

parsnip/R/tunable.R

Lines 43 to 46 in eb526fa

	has_info <- purrr::map_lgl(res$call_info, is.null)
	rm_list <- !(has_info & (res$component_id == "main"))
	res <- res[rm_list, ]

2b) then, it adds engine-specific parameter information. One way could be add_engine_parameters():

parsnip/R/tunable.R

Line 282 in eb526fa

res <- add_engine_parameters(res, xgboost_engine_args)

parsnip/R/tunable.R

Lines 56 to 65 in eb526fa

	add_engine_parameters <- function(pset, engines) {
	is_engine_param <- pset$name %in% engines$name
	if (any(is_engine_param)) {
	engine_names <- pset$name[is_engine_param]
	pset <- pset[!is_engine_param,]
	pset <-
	dplyr::bind_rows(pset, engines %>% dplyr::filter(name %in% engines$name))
	}
	pset
	}

xgboost_engine_args is an inlined tibble in the source:

parsnip/R/tunable.R

Lines 91 to 106 in eb526fa

	xgboost_engine_args <-
	tibble::tibble(
	name = c(
	"alpha",
	"lambda",
	"scale_pos_weight"
	),
	call_info = list(
	list(pkg = "dials", fun = "penalty_L1"),
	list(pkg = "dials", fun = "penalty_L2"),
	list(pkg = "dials", fun = "scale_pos_weight")
	),
	source = "model_spec",
	component = "boost_tree",
	component_id = "engine"
	)

...the other possible way is to manually insert call_info entries:

parsnip/R/tunable.R

Lines 283 to 286 in eb526fa

	res$call_info[res$name == "sample_size"] <-
	list(list(pkg = "dials", fun = "sample_prop"))
	res$call_info[res$name == "learn_rate"] <-
	list(list(pkg = "dials", fun = "learn_rate", range = c(-3, -1/2)))

Barring a rewrite that stores tunable information in the model environment (see #826), I think our solution might be to filter out rows with NULL call_info at the end of existing non-model_spec tunable() methods.