Merge pull request #234 from tidymodels/type-checkers

Add rlang type checkers

DESCRIPTION

@@ -26,7 +26,7 @@ Imports:
     generics (>= 0.1.0),
     lifecycle,
     purrr,
-    rlang (>= 0.4.10),
+    rlang (>= 1.1.0),
     rsample,
     stats,
     tibble,

R/collapse_cart.R

@@ -80,6 +80,9 @@ step_collapse_cart <-
            id = rand_id("step_collapse_cart")) {
     recipes_pkg_check(required_pkgs.step_discretize_cart())
 
+    check_number_decimal(cost_complexity, min = 0)
+    check_number_whole(min_n, min = 1)
+
     add_step(
       recipe,
       step_collapse_cart_new(

R/collapse_stringdist.R

@@ -78,9 +78,8 @@ step_collapse_stringdist <-
            columns = NULL,
            skip = FALSE,
            id = rand_id("collapse_stringdist")) {
-    if (is.null(distance)) {
-      cli::cli_abort("The {.arg distance} argument must be set.")
-    }
+    check_number_decimal(distance, min = 0)
+    check_string(method)
 
     add_step(
       recipe,

R/discretize_cart.R

@@ -192,6 +192,10 @@ cart_binning <- function(predictor, term, outcome, cost_complexity, tree_depth,
 prep.step_discretize_cart <- function(x, training, info = NULL, ...) {
   col_names <- recipes_eval_select(x$terms, training, info)
 
+  check_number_decimal(x$cost_complexity, min = 0, arg = "cost_complexity")
+  check_number_decimal(x$tree_depth, min = 0, arg = "tree_depth")
+  check_number_decimal(x$min_n, min = 0, arg = "min_n")
+
   wts <- get_case_weights(info, training)
   were_weights_used <- are_weights_used(wts)
   if (isFALSE(were_weights_used)) {
@@ -261,7 +265,7 @@ bake.step_discretize_cart <- function(object, new_data, ...) {
         dig.lab = 4
       )
 
-      check_name(binned_data, new_data, object)
+      recipes::check_name(binned_data, new_data, object)
       new_data <- binned_data
     }
   }

R/discretize_xgb.R

@@ -392,6 +392,12 @@ xgb_binning <- function(df, outcome, predictor, sample_val, learn_rate,
 prep.step_discretize_xgb <- function(x, training, info = NULL, ...) {
   col_names <- recipes_eval_select(x$terms, training, info)
 
+  check_number_decimal(x$sample_val, min = 0, max = 1, arg = "sample_val")
+  check_number_decimal(x$learn_rate, min = 0, arg = "learn_rate")
+  check_number_whole(x$num_breaks, min = 0, arg = "num_breaks")
+  check_number_whole(x$tree_depth, min = 0, arg = "tree_depth")
+  check_number_whole(x$min_n, min = 0, arg = "min_n")
+
   wts <- get_case_weights(info, training)
   were_weights_used <- are_weights_used(wts)
   if (isFALSE(were_weights_used) || is.null(wts)) {
@@ -496,7 +502,7 @@ bake.step_discretize_xgb <- function(object, new_data, ...) {
         dig.lab = 4
       )
 
-      check_name(binned_data, new_data, object)
+      recipes::check_name(binned_data, new_data, object)
       new_data <- binned_data
     }
   }

R/embed.R

@@ -206,6 +206,9 @@ step_embed_new <-
 prep.step_embed <- function(x, training, info = NULL, ...) {
   col_names <- recipes_eval_select(x$terms, training, info)
 
+  check_number_whole(x$num_terms, min = 0, arg = "num_terms")
+  check_number_whole(x$hidden_units, min = 0, arg = "hidden_units")
+
   if (length(col_names) > 0) {
     check_type(training[, col_names], types = c("string", "factor", "ordered"))
     y_name <- recipes_eval_select(x$outcome, training, info)
@@ -429,7 +432,7 @@ bake.step_embed <- function(object, new_data, ...) {
       prefix = col_name
     )
 
-    tmp <- check_name(tmp, new_data, object, names(tmp))
+    tmp <- recipes::check_name(tmp, new_data, object, names(tmp))
 
     new_data <- vec_cbind(new_data, tmp)
   }

R/feature_hash.R

@@ -138,6 +138,8 @@ step_feature_hash_new <-
 prep.step_feature_hash <- function(x, training, info = NULL, ...) {
   col_names <- recipes_eval_select(x$terms, training, info)
 
+  check_number_whole(x$num_hash, min = 0, arg = "num_hash")
+
   if (length(col_names) > 0) {
     check_type(training[, col_names], types = c("string", "factor", "ordered"))
   }
@@ -222,7 +224,7 @@ bake.step_feature_hash <- function(object, new_data, ...) {
       object$num_hash
   )
 
-  new_cols <- check_name(new_cols, new_data, object, names(new_cols))
+  new_cols <- recipes::check_name(new_cols, new_data, object, names(new_cols))
 
   new_data <- vec_cbind(new_data, new_cols)