properly manage dials parameters in parsnip,

rename 2 `tabnet()` params to `rate_decay` and `rate_step_size`
add tests for that

DESCRIPTION

@@ -21,6 +21,7 @@ BugReports: https://github.com/mlverse/tabnet/issues
 Depends:
     R (>= 3.6)
 Imports: 
+    cli,
     coro,
     data.tree,
     dials,

NAMESPACE

@@ -21,23 +21,36 @@ S3method(tabnet_pretrain,recipe)
 S3method(update,tabnet)
 export("%>%")
 export(attention_width)
+export(cat_emb_dim)
 export(check_compliant_node)
+export(checkpoint_epochs)
 export(decision_width)
+export(drop_last)
+export(encoder_activation)
 export(feature_reusage)
+export(lr_scheduler)
 export(mask_type)
+export(mlp_activation)
+export(mlp_hidden_multiplier)
 export(momentum)
 export(nn_prune_head.tabnet_fit)
 export(nn_prune_head.tabnet_pretrain)
 export(node_to_df)
 export(num_independent)
+export(num_independent_decoder)
 export(num_shared)
+export(num_shared_decoder)
 export(num_steps)
+export(optimizer)
+export(penalty)
 export(tabnet)
 export(tabnet_config)
 export(tabnet_explain)
 export(tabnet_fit)
 export(tabnet_nn)
 export(tabnet_pretrain)
+export(verbose)
+export(virtual_batch_size)
 importFrom(dplyr,filter)
 importFrom(dplyr,last_col)
 importFrom(dplyr,mutate)

R/dials.R

@@ -17,56 +17,70 @@ check_dials <- function() {
 #' @rdname tabnet_params
 #' @return A `dials` parameter to be used when tuning TabNet models.
 #' @export
-decision_width <- function(range = c(8L, 64L), trans = NULL) {
+attention_width <- function(range = c(8L, 64L), trans = NULL) {
   check_dials()
   dials::new_quant_param(
     type = "integer",
     range = range,
     inclusive = c(TRUE, TRUE),
     trans = trans,
-    label = c(decision_width = "Width of the decision prediction layer"),
+    label = c(attention_width = "Width of the attention embedding for each mask"),
     finalize = NULL
   )
 }
 
 #' @rdname tabnet_params
 #' @export
-attention_width <- function(range = c(8L, 64L), trans = NULL) {
+decision_width <- function(range = c(8L, 64L), trans = NULL) {
   check_dials()
   dials::new_quant_param(
     type = "integer",
     range = range,
     inclusive = c(TRUE, TRUE),
     trans = trans,
-    label = c(attention_width = "Width of the attention embedding for each mask"),
+    label = c(decision_width = "Width of the decision prediction layer"),
     finalize = NULL
   )
 }
 
+
 #' @rdname tabnet_params
 #' @export
-num_steps <- function(range = c(3L, 10L), trans = NULL) {
+feature_reusage <- function(range = c(1, 2), trans = NULL) {
   check_dials()
   dials::new_quant_param(
-    type = "integer",
+    type = "double",
     range = range,
     inclusive = c(TRUE, TRUE),
     trans = trans,
-    label = c(num_steps = "Number of steps in the architecture"),
+    label = c(feature_reusage = "Coefficient for feature reusage in the masks"),
     finalize = NULL
   )
 }
 
 #' @rdname tabnet_params
 #' @export
-feature_reusage <- function(range = c(1, 2), trans = NULL) {
+momentum <- function(range = c(0.01, 0.4), trans = NULL) {
   check_dials()
   dials::new_quant_param(
     type = "double",
     range = range,
     inclusive = c(TRUE, TRUE),
     trans = trans,
-    label = c(feature_reusage = "Coefficient for feature reusage in the masks"),
+    label = c(momentum = "Momentum for batch normalization"),
+    finalize = NULL
+  )
+}
+
+
+#' @rdname tabnet_params
+#' @export
+mask_type <- function(values = c("sparsemax", "entmax")) {
+  check_dials()
+  dials::new_qual_param(
+    type = "character",
+    values = values,
+    label = c(mask_type = "Final layer of feature selector, either sparsemax or entmax"),
     finalize = NULL
   )
 }
@@ -101,28 +115,69 @@ num_shared <- function(range = c(1L, 5L), trans = NULL) {
 
 #' @rdname tabnet_params
 #' @export
-momentum <- function(range = c(0.01, 0.4), trans = NULL) {
+num_steps <- function(range = c(3L, 10L), trans = NULL) {
   check_dials()
   dials::new_quant_param(
-    type = "double",
+    type = "integer",
     range = range,
     inclusive = c(TRUE, TRUE),
     trans = trans,
-    label = c(momentum = "Momentum for batch normalization"),
+    label = c(num_steps = "Number of steps in the architecture"),
     finalize = NULL
   )
 }
 
-
-#' @rdname tabnet_params
+#' @noRd
 #' @export
-mask_type <- function(values = c("sparsemax", "entmax")) {
+cat_emb_dim <- function(range = NULL, trans = NULL) {
   check_dials()
-  dials::new_qual_param(
-    type = "character",
-    values = values,
-    label = c(mask_type = "Final layer of feature selector, either sparsemax or entmax"),
-    finalize = NULL
-  )
+  cli::cli_abort("{.var cat_emb_dim} cannot be used as a {.fun tune} parameter yet.")
 }
 
+#' @noRd
+#' @export
+checkpoint_epochs <- cat_emb_dim
+
+#' @noRd
+#' @export
+drop_last <- cat_emb_dim
+
+#' @noRd
+#' @export
+encoder_activation <- cat_emb_dim
+
+#' @noRd
+#' @export
+lr_scheduler <- cat_emb_dim
+
+#' @noRd
+#' @export
+mlp_activation <- cat_emb_dim
+
+#' @noRd
+#' @export
+mlp_hidden_multiplier <- cat_emb_dim
+
+#' @noRd
+#' @export
+num_independent_decoder <- cat_emb_dim
+
+#' @noRd
+#' @export
+num_shared_decoder <- cat_emb_dim
+
+#' @noRd
+#' @export
+optimizer <- cat_emb_dim
+
+#' @noRd
+#' @export
+penalty <- cat_emb_dim
+
+#' @noRd
+#' @export
+verbose <- cat_emb_dim
+
+#' @noRd
+#' @export
+virtual_batch_size <- cat_emb_dim