register tune_args() and tunable() methods unconditionally (#869)

* register `tune_args()` and `tunable()` methods unconditionally

* correct ref

* register methods in the usual way

* resituate `tune_args()` import, export neither `tune_args()` nor `tuneable()` generic

* revert `required_pkgs()` registration

Author: simonpcouch

NAMESPACE

@@ -90,7 +90,18 @@ S3method(translate,survival_reg)
 S3method(translate,svm_linear)
 S3method(translate,svm_poly)
 S3method(translate,svm_rbf)
+S3method(tunable,boost_tree)
+S3method(tunable,decision_tree)
+S3method(tunable,linear_reg)
+S3method(tunable,logistic_reg)
+S3method(tunable,mars)
+S3method(tunable,mlp)
+S3method(tunable,model_spec)
+S3method(tunable,multinomial_reg)
+S3method(tunable,rand_forest)
 S3method(tunable,survival_reg)
+S3method(tunable,svm_poly)
+S3method(tune_args,model_spec)
 S3method(type_sum,model_fit)
 S3method(type_sum,model_spec)
 S3method(update,C5_rules)
@@ -317,6 +328,7 @@ importFrom(generics,glance)
 importFrom(generics,required_pkgs)
 importFrom(generics,tidy)
 importFrom(generics,tunable)
+importFrom(generics,tune_args)
 importFrom(generics,varying_args)
 importFrom(ggplot2,autoplot)
 importFrom(glue,glue_collapse)

NEWS.md

@@ -2,6 +2,9 @@
 
 * For censored regression models, a "reverse Kaplan-Meier" curve is computed for the censoring distribution. This can be used when evaluating this type of model (#855).
 
+* The model specification methods for `generics::tune_args()` and 
+  `generics::tunable()` are now registered unconditionally (tidymodels/workflows#192).
+
 # parsnip 1.0.3
 
 * Adds documentation and tuning infrastructure for the new `flexsurvspline` engine for the `survival_reg()` model specification from the `censored` package (@mattwarkentin, #831).

R/parsnip-package.R

@@ -10,7 +10,7 @@
 ## usethis namespace: start
 #' @importFrom dplyr arrange bind_cols bind_rows collect full_join group_by
 #' @importFrom dplyr mutate pull rename select starts_with summarise tally
-#' @importFrom generics tunable varying_args
+#' @importFrom generics tunable varying_args tune_args
 #' @importFrom glue glue_collapse
 #' @importFrom pillar type_sum
 #' @importFrom purrr as_vector imap imap_lgl map map_chr map_dbl map_df map_dfr

R/tunable.R

@@ -1,7 +1,9 @@
-# Lazily registered in .onLoad()
+
 # Unit tests are in extratests
 # nocov start
-tunable_model_spec <- function(x, ...) {
+
+#' @export
+tunable.model_spec <- function(x, ...) {
   mod_env <- rlang::ns_env("parsnip")$parsnip
 
   if (is.null(x$engine)) {
@@ -228,8 +230,8 @@ flexsurvspline_engine_args <-
 
 # ------------------------------------------------------------------------------
 
-# Lazily registered in .onLoad()
-tunable_linear_reg <- function(x, ...) {
+#' @export
+tunable.linear_reg <- function(x, ...) {
   res <- NextMethod()
   if (x$engine == "glmnet") {
     res$call_info[res$name == "mixture"] <-
@@ -240,8 +242,8 @@ tunable_linear_reg <- function(x, ...) {
   res
 }
 
-# Lazily registered in .onLoad()
-tunable_logistic_reg <- function(x, ...) {
+#' @export
+tunable.logistic_reg <- function(x, ...) {
   res <- NextMethod()
   if (x$engine == "glmnet") {
     res$call_info[res$name == "mixture"] <-
@@ -252,8 +254,8 @@ tunable_logistic_reg <- function(x, ...) {
   res
 }
 
-# Lazily registered in .onLoad()
-tunable_multinomial_reg <- function(x, ...) {
+#' @export
+tunable.multinomial_reg <- function(x, ...) {
   res <- NextMethod()
   if (x$engine == "glmnet") {
     res$call_info[res$name == "mixture"] <-
@@ -264,8 +266,8 @@ tunable_multinomial_reg <- function(x, ...) {
   res
 }
 
-# Lazily registered in .onLoad()
-tunable_boost_tree <- function(x, ...) {
+#' @export
+tunable.boost_tree <- function(x, ...) {
   res <- NextMethod()
   if (x$engine == "xgboost") {
     res <- add_engine_parameters(res, xgboost_engine_args)
@@ -287,8 +289,8 @@ tunable_boost_tree <- function(x, ...) {
   res
 }
 
-# Lazily registered in .onLoad()
-tunable_rand_forest <- function(x, ...) {
+#' @export
+tunable.rand_forest <- function(x, ...) {
   res <- NextMethod()
   if (x$engine == "ranger") {
     res <- add_engine_parameters(res, ranger_engine_args)
@@ -302,17 +304,17 @@ tunable_rand_forest <- function(x, ...) {
   res
 }
 
-# Lazily registered in .onLoad()
-tunable_mars <- function(x, ...) {
+#' @export
+tunable.mars <- function(x, ...) {
   res <- NextMethod()
   if (x$engine == "earth") {
     res <- add_engine_parameters(res, earth_engine_args)
   }
   res
 }
 
-# Lazily registered in .onLoad()
-tunable_decision_tree <- function(x, ...) {
+#' @export
+tunable.decision_tree <- function(x, ...) {
   res <- NextMethod()
   if (x$engine == "C5.0") {
     res <- add_engine_parameters(res, c5_tree_engine_args)
@@ -325,8 +327,8 @@ tunable_decision_tree <- function(x, ...) {
   res
 }
 
-# Lazily registered in .onLoad()
-tunable_svm_poly <- function(x, ...) {
+#' @export
+tunable.svm_poly <- function(x, ...) {
   res <- NextMethod()
   if (x$engine == "kernlab") {
     res$call_info[res$name == "degree"] <-
@@ -336,8 +338,8 @@ tunable_svm_poly <- function(x, ...) {
 }
 
 
-# Lazily registered in .onLoad()
-tunable_mlp <- function(x, ...) {
+#' @export
+tunable.mlp <- function(x, ...) {
   res <- NextMethod()
   if (x$engine == "brulee") {
     res <- add_engine_parameters(res, brulee_mlp_engine_args)

R/tune_args.R

@@ -1,6 +1,6 @@
-
-# Lazily registered in .onLoad()
-tune_args_model_spec <- function(object, full = FALSE, ...) {
+#' @method tune_args model_spec
+#' @export
+tune_args.model_spec <- function(object, full = FALSE, ...) {
 
   # use the model_spec top level class as the id
   model_type <- class(object)[1]

R/zzz.R

@@ -14,40 +14,6 @@
 
   vctrs::s3_register("ggplot2::autoplot", "model_fit")
   vctrs::s3_register("ggplot2::autoplot", "glmnet")
-
-  # - If tune isn't installed, register the method (`packageVersion()` will error here)
-  # - If tune >= 0.1.6.9001 is installed, register the method
-  should_register_tune_args_method <- tryCatch(
-    expr = utils::packageVersion("tune") >= "0.1.6.9001",
-    error = function(cnd) TRUE
-  )
-
-  if (should_register_tune_args_method) {
-    # `tune_args.model_spec()` moved from tune to parsnip
-    vctrs::s3_register("generics::tune_args", "model_spec", tune_args_model_spec)
-  }
-
-  # - If tune isn't installed, register the method (`packageVersion()` will error here)
-  # - If tune >= 0.1.6.9002 is installed, register the method
-  should_register_tunable_method <- tryCatch(
-    expr = utils::packageVersion("tune") >= "0.1.6.9002",
-    error = function(cnd) TRUE
-  )
-
-  if (should_register_tunable_method) {
-    # `tunable.model_spec()` and friends moved from tune to parsnip
-    vctrs::s3_register("generics::tunable", "model_spec", tunable_model_spec)
-    vctrs::s3_register("generics::tunable", "linear_reg", tunable_linear_reg)
-    vctrs::s3_register("generics::tunable", "logistic_reg", tunable_logistic_reg)
-    vctrs::s3_register("generics::tunable", "multinomial_reg", tunable_multinomial_reg)
-    vctrs::s3_register("generics::tunable", "boost_tree", tunable_boost_tree)
-    vctrs::s3_register("generics::tunable", "rand_forest", tunable_rand_forest)
-    vctrs::s3_register("generics::tunable", "mars", tunable_mars)
-    vctrs::s3_register("generics::tunable", "decision_tree", tunable_decision_tree)
-    vctrs::s3_register("generics::tunable", "svm_poly", tunable_svm_poly)
-    vctrs::s3_register("generics::tunable", "mlp", tunable_mlp)
-  }
-
 }
 
 # nocov end