add input type checkers to all steps

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)) {

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)) {

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)

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"))
   }

R/lencode_bayes.R

@@ -119,6 +119,9 @@ step_lencode_bayes <-
     if (is.null(outcome)) {
       cli::cli_abort("Please list a variable in {.code outcome}.")
     }
+
+    check_bool(verbose)
+
     add_step(
       recipe,
       step_lencode_bayes_new(

R/pca_sparse.R

@@ -103,6 +103,8 @@ step_pca_sparse <- function(recipe,
                             keep_original_cols = FALSE,
                             skip = FALSE,
                             id = rand_id("pca_sparse")) {
+  check_string(prefix)
+
   add_step(
     recipe,
     step_pca_sparse_new(
@@ -144,6 +146,11 @@ step_pca_sparse_new <-
 prep.step_pca_sparse <- function(x, training, info = NULL, ...) {
   col_names <- recipes_eval_select(x$terms, training, info)
 
+  check_number_whole(x$num_comp, min = 0, arg = "num_comp")
+  check_number_decimal(
+    x$predictor_prop, min = 0, max = 1, arg = "predictor_prop"
+  )
+
   if (length(col_names) > 0 && x$num_comp > 0) {
     check_type(training[, col_names], types = c("double", "integer"))
 

R/pca_sparse_bayes.R

@@ -124,7 +124,8 @@ step_pca_sparse_bayes <- function(recipe,
                                   keep_original_cols = FALSE,
                                   skip = FALSE,
                                   id = rand_id("pca_sparse_bayes")) {
-
+  check_string(prefix)
+
   add_step(
     recipe,
     step_pca_sparse_bayes_new(
@@ -169,6 +170,14 @@ step_pca_sparse_bayes_new <-
 prep.step_pca_sparse_bayes <- function(x, training, info = NULL, ...) {
   col_names <- recipes_eval_select(x$terms, training, info)
 
+  check_number_whole(x$num_comp, min = 0, arg = "num_comp")
+  check_number_decimal(
+    x$prior_slab_dispersion, min = 0, arg = "prior_slab_dispersion"
+  )
+  check_number_decimal(
+    x$prior_mixture_threshold, min = 0, max = 1, arg = "prior_mixture_threshold"
+  )
+
   if (length(col_names) > 0) {
     check_type(training[, col_names], types = c("double", "integer"))
 

R/pca_truncated.R

@@ -96,6 +96,8 @@ step_pca_truncated <- function(recipe,
                                keep_original_cols = FALSE,
                                skip = FALSE,
                                id = rand_id("pca_truncated")) {
+  check_string(prefix)
+
   add_step(
     recipe,
     step_pca_truncated_new(
@@ -140,6 +142,8 @@ prep.step_pca_truncated <- function(x, training, info = NULL, ...) {
   col_names <- recipes_eval_select(x$terms, training, info)
   check_type(training[, col_names], types = c("double", "integer"))
 
+  check_number_whole(x$num_comp, min = 0, arg = "num_comp")
+
   wts <- get_case_weights(info, training)
   were_weights_used <- are_weights_used(wts, unsupervised = TRUE)
   if (isFALSE(were_weights_used)) {

R/umap.R

@@ -137,6 +137,8 @@ step_umap <-
       keep_original_cols <- retain
     }
 
+    check_string(prefix)
+
     recipes_pkg_check(required_pkgs.step_umap())
     if (is.numeric(seed) && !is.integer(seed)) {
       seed <- as.integer(seed)
@@ -229,6 +231,14 @@ umap_fit_call <- function(obj, y = NULL) {
 prep.step_umap <- function(x, training, info = NULL, ...) {
   col_names <- recipes_eval_select(x$terms, training, info)
 
+  check_number_whole(x$num_comp, min = 0, arg = "num_comp")
+  check_number_whole(x$neighbors, min = 0, arg = "neighbors")
+  check_number_decimal(x$min_dist, arg = "min_dist")
+  check_number_decimal(x$learn_rate, min = 0, arg = "learn_rate")
+  check_number_whole(x$epochs, min = 0, allow_null = TRUE, arg = "epochs")
+  rlang::arg_match0(x$initial, initial_umap_values, arg_nm = "initial")
+  check_number_decimal(x$target_weight, min = 0, max = 1, arg = "target_weight")
+
   if (length(col_names) > 0) {
     if (length(x$outcome) > 0) {
       y_name <- recipes_eval_select(x$outcome, training, info)
@@ -355,11 +365,22 @@ tunable.step_umap <- function(x, ...) {
       list(pkg = "dials", fun = "min_dist", range = c(-4, -0.69897)),
       list(pkg = "dials", fun = "learn_rate"),
       list(pkg = "dials", fun = "epochs", range = c(100, 700)),
-      list(pkg = "dials", fun = "initial_umap", values = c("spectral", "normlaplacian", "random", "lvrandom", "laplacian", "pca", "spca", "agspectral")),
+      list(pkg = "dials", fun = "initial_umap", values = initial_umap_values),
       list(pkg = "dials", fun = "target_weight", range = c(0, 1))
     ),
     source = "recipe",
     component = "step_umap",
     component_id = x$id
   )
 }
+
+initial_umap_values <- c(
+  "spectral",
+  "normlaplacian",
+  "random",
+  "lvrandom",
+  "laplacian",
+  "pca",
+  "spca",
+  "agspectral"
+)

R/woe.R

@@ -159,6 +159,8 @@ step_woe <- function(recipe,
     cli::cli_abort("The {.arg outcome} argument is missing, with no default.")
   }
 
+  check_string(prefix)
+
   add_step(
     recipe,
     step_woe_new(
@@ -423,6 +425,8 @@ add_woe <- function(.data, outcome, ..., dictionary = NULL, prefix = "woe") {
 prep.step_woe <- function(x, training, info = NULL, ...) {
   col_names <- recipes_eval_select(x$terms, training, info)
 
+  check_number_decimal(x$Laplace, arg = "Laplace")
+
   if (length(col_names) > 0) {
     outcome_name <- recipes_eval_select(x$outcome, training, info)
 

tests/testthat/_snaps/collapse_cart.md

@@ -1,3 +1,19 @@
+# bad args
+
+    Code
+      recipe(~., data = mtcars) %>% step_collapse_cart(cost_complexity = -4)
+    Condition
+      Error in `step_collapse_cart()`:
+      ! `cost_complexity` must be a number larger than or equal to 0, not the number -4.
+
+---
+
+    Code
+      recipe(~., data = mtcars) %>% step_collapse_cart(min_n = -4)
+    Condition
+      Error in `step_collapse_cart()`:
+      ! `min_n` must be a whole number larger than or equal to 1, not the number -4.
+
 # bake method errors when needed non-standard role columns are missing
 
     Code

tests/testthat/_snaps/collapse_stringdist.md

@@ -1,3 +1,19 @@
+# bad args
+
+    Code
+      recipe(~., data = mtcars) %>% step_collapse_stringdist(cost_complexity = -4)
+    Condition
+      Error in `step_collapse_stringdist()`:
+      ! `distance` must be a number, not `NULL`.
+
+---
+
+    Code
+      recipe(~., data = mtcars) %>% step_collapse_stringdist(min_n = -4)
+    Condition
+      Error in `step_collapse_stringdist()`:
+      ! `distance` must be a number, not `NULL`.
+
 # bake method errors when needed non-standard role columns are missing
 
     Code

tests/testthat/_snaps/discretize_cart.md

@@ -45,6 +45,36 @@
       x All columns selected for the step should be double or integer.
       * 1 factor variable found: `w`
 
+---
+
+    Code
+      recipe(~., data = mtcars) %>% step_discretize_cart(outcome = vars("mpg"),
+      cost_complexity = -4) %>% prep()
+    Condition
+      Error in `step_discretize_cart()`:
+      Caused by error in `prep()`:
+      ! `cost_complexity` must be a number larger than or equal to 0, not the number -4.
+
+---
+
+    Code
+      recipe(~., data = mtcars) %>% step_discretize_cart(outcome = vars("mpg"),
+      min_n = -4) %>% prep()
+    Condition
+      Error in `step_discretize_cart()`:
+      Caused by error in `prep()`:
+      ! `min_n` must be a number larger than or equal to 0, not the number -4.
+
+---
+
+    Code
+      recipe(~., data = mtcars) %>% step_discretize_cart(outcome = vars("mpg"),
+      tree_depth = -4) %>% prep()
+    Condition
+      Error in `step_discretize_cart()`:
+      Caused by error in `prep()`:
+      ! `tree_depth` must be a number larger than or equal to 0, not the number -4.
+
 # tidy method
 
     Code

tests/testthat/_snaps/discretize_xgb.md

@@ -296,6 +296,56 @@
       -- Operations 
       * Discretizing variables using xgboost: x and z | Trained, weighted
 
+# bad args
+
+    Code
+      recipe(~., data = mtcars) %>% step_discretize_xgb(outcome = "class",
+        sample_val = -4) %>% prep()
+    Condition
+      Error in `step_discretize_xgb()`:
+      Caused by error in `prep()`:
+      ! `sample_val` must be a number between 0 and 1, not the number -4.
+
+---
+
+    Code
+      recipe(~., data = mtcars) %>% step_discretize_xgb(outcome = "class",
+        learn_rate = -4) %>% prep()
+    Condition
+      Error in `step_discretize_xgb()`:
+      Caused by error in `prep()`:
+      ! `learn_rate` must be a number larger than or equal to 0, not the number -4.
+
+---
+
+    Code
+      recipe(~., data = mtcars) %>% step_discretize_xgb(outcome = "class",
+        num_breaks = -4) %>% prep()
+    Condition
+      Error in `step_discretize_xgb()`:
+      Caused by error in `prep()`:
+      ! `num_breaks` must be a whole number larger than or equal to 0, not the number -4.
+
+---
+
+    Code
+      recipe(~., data = mtcars) %>% step_discretize_xgb(outcome = "class",
+        tree_depth = -4) %>% prep()
+    Condition
+      Error in `step_discretize_xgb()`:
+      Caused by error in `prep()`:
+      ! `tree_depth` must be a whole number larger than or equal to 0, not the number -4.
+
+---
+
+    Code
+      recipe(~., data = mtcars) %>% step_discretize_xgb(outcome = "class", min_n = -4) %>%
+        prep()
+    Condition
+      Error in `step_discretize_xgb()`:
+      Caused by error in `prep()`:
+      ! `min_n` must be a whole number larger than or equal to 0, not the number -4.
+
 # bake method errors when needed non-standard role columns are missing
 
     Code

tests/testthat/_snaps/embed.md

@@ -29,6 +29,26 @@
       x All columns selected for the step should be string, factor, or ordered.
       * 1 double variable found: `Sepal.Length`
 
+---
+
+    Code
+      recipe(~., data = mtcars) %>% step_embed(outcome = vars(mpg), num_terms = -4) %>%
+        prep()
+    Condition
+      Error in `step_embed()`:
+      Caused by error in `prep()`:
+      ! `num_terms` must be a whole number larger than or equal to 0, not the number -4.
+
+---
+
+    Code
+      recipe(~., data = mtcars) %>% step_embed(outcome = vars(mpg), hidden_units = -4) %>%
+        prep()
+    Condition
+      Error in `step_embed()`:
+      Caused by error in `prep()`:
+      ! `hidden_units` must be a whole number larger than or equal to 0, not the number -4.
+
 # check_name() is used
 
     Code

tests/testthat/_snaps/feature_hash.md

@@ -18,6 +18,18 @@
       ! Name collision occurred. The following variable names already exist:
       * `x3_hash_01`
 
+# bad args
+
+    Code
+      recipe(~., data = mtcars) %>% step_feature_hash(num_hash = -4) %>% prep()
+    Condition
+      Warning:
+      `step_feature_hash()` was deprecated in embed 0.2.0.
+      i Please use `textrecipes::step_dummy_hash()` instead.
+      Error in `step_feature_hash()`:
+      Caused by error in `prep()`:
+      ! `num_hash` must be a whole number larger than or equal to 0, not the number -4.
+
 # bake method errors when needed non-standard role columns are missing
 
     Code

tests/testthat/_snaps/lencode_bayes.md

@@ -101,6 +101,14 @@
       -- Operations 
       * Linear embedding for factors via Bayesian GLM for: x3 | Trained, weighted
 
+# bad args
+
+    Code
+      recipe(~., data = mtcars) %>% step_lencode_bayes(outcome = vars(mpg), verbose = "yes")
+    Condition
+      Error in `step_lencode_bayes()`:
+      ! `verbose` must be `TRUE` or `FALSE`, not the string "yes".
+
 # bake method errors when needed non-standard role columns are missing
 
     Code