Merge pull request #1387 from tidymodels/type-checkers

Add rlang type checkers

NEWS.md

@@ -8,6 +8,8 @@
 
 * `prep.recipe(..., strings_as_factors = TRUE)` now only converts string variables that have role "predictor" or "outcome". (@dajmcdon, #1358, #1376)
 
+* All steps and checks now require arguments `trained`, `skip`, `role`, and `id` at all times.
+
 # recipes 1.1.0
 
 ## Improvements

R/bin2factor.R

@@ -65,7 +65,7 @@ step_bin2factor <-
       if (length(levels) != 2) {
         msg <- c(
           msg,
-          i = "{length(levels)} element{?s} were supplied."
+          i = "{length(levels)} element{?s} were supplied; two were expected."
         )
       }
       if (!is.character(levels)) {
@@ -76,6 +76,8 @@ step_bin2factor <-
       }
       cli::cli_abort(msg)
     }
+    check_bool(ref_first)
+
     add_step(
       recipe,
       step_bin2factor_new(

R/class.R

@@ -102,6 +102,8 @@ check_class <-
 check_class_new <-
   function(terms, role, trained, class_nm,
            allow_additional, class_list, skip, id) {
+    check_character(class_nm, allow_null = TRUE, call = rlang::caller_env(2))
+    check_bool(allow_additional, call = rlang::caller_env(2))
     check(
       subclass         = "class",
       terms            = terms,

R/classdist.R

@@ -195,6 +195,11 @@ prep.step_classdist <- function(x, training, info = NULL, ...) {
     wts <- NULL
   }
 
+  check_function(x$mean_func)
+  check_function(x$cov_func)
+  check_bool(x$pool)
+  check_string(x$prefix)
+
   x_dat <- split(training[, x_names], training[[class_var]])
   if (is.null(wts)) {
     wts_split <- map(x_dat, ~NULL)

R/classdist_shrunken.R

@@ -289,6 +289,8 @@ prep.step_classdist_shrunken <- function(x, training, info = NULL, ...) {
 
   sd_offset <- x$sd_offset
   check_number_decimal(sd_offset, min = 0, max = 1)
+  check_bool(x$log)
+  check_string(x$prefix)
 
   wts <- get_case_weights(info, training)
   were_weights_used <- are_weights_used(wts)

R/corr.R

@@ -129,6 +129,12 @@ step_corr_new <-
 prep.step_corr <- function(x, training, info = NULL, ...) {
   col_names <- recipes_eval_select(x$terms, training, info)
   check_type(training[, col_names], types = c("double", "integer"))
+  check_number_decimal(x$threshold, min = 0, max = 1, arg = "threshold")
+  use <- x$use
+  rlang::arg_match(use, c("all.obs", "complete.obs", "pairwise.complete.obs",
+                            "everything", "na.or.complete"))
+  method <- x$method
+  rlang::arg_match(method, c("pearson", "kendall", "spearman"))
 
   wts <- get_case_weights(info, training)
   were_weights_used <- are_weights_used(wts, unsupervised = TRUE)

R/count.R

@@ -128,6 +128,9 @@ step_count_new <-
 prep.step_count <- function(x, training, info = NULL, ...) {
   col_name <- recipes_eval_select(x$terms, training, info)
   check_type(training[, col_name], types = c("string", "factor", "ordered"))
+  check_string(x$pattern, allow_empty = TRUE, arg = "pattern")
+  check_string(x$result,  allow_empty = FALSE, arg = "result")
+  check_bool(x$normalize, arg = "normalize")
 
   step_count_new(
     terms = x$terms,

R/cut.R

@@ -115,10 +115,10 @@ prep.step_cut <- function(x, training, info = NULL, ...) {
 
   if (!is.numeric(x$breaks)) {
     cli::cli_abort(
-      "{.arg breaks} must be a numeric vector, \\
-      not {.obj_type_friendly {x$breaks}}."
+      "{.arg breaks} must be a numeric vector, not {.obj_type_friendly {x$breaks}}."
     )
   }
+  check_bool(x$include_outside_range, arg = "include_outside_range")
 
   all_breaks <- vector("list", length(col_names))
   names(all_breaks) <- col_names

R/date.R

@@ -160,6 +160,9 @@ step_date_new <-
 prep.step_date <- function(x, training, info = NULL, ...) {
   col_names <- recipes_eval_select(x$terms, training, info)
   check_type(training[, col_names], types = c("date", "datetime"))
+  check_bool(x$abbr, arg = "abbr")
+  check_bool(x$label, arg = "label")
+  check_bool(x$ordinal, arg = "ordinal")
 
   step_date_new(
     terms = x$terms,

R/depth.R

@@ -140,10 +140,16 @@ step_depth_new <-
     )
   }
 
+depth_metric <- c("potential", "halfspace", "Mahalanobis", "simplicialVolume",
+                  "spatial", "zonoid")
+
 #' @export
 prep.step_depth <- function(x, training, info = NULL, ...) {
   x_names <- recipes_eval_select(x$terms, training, info)
   check_type(training[, x_names], types = c("double", "integer"))
+  metric <- x$metric
+  rlang::arg_match(metric, depth_metric)
+  check_string(x$prefix, allow_empty = FALSE, arg = "prefix")
 
   class_var <- x$class[1]
 

R/discretize.R

@@ -90,12 +90,7 @@ discretize.numeric <-
            ...) {
     unique_vals <- length(unique(x))
     missing_lab <- "_missing"
-
-    if (cuts < 2) {
-      cli::cli_abort(
-        "There should be at least 2 {.arg cuts} but {.val {cuts}} was supplied."
-      )
-    }
+    check_number_whole(cuts, min = 2)
 
     dots <- list(...)
     if (keep_na) {
@@ -115,8 +110,8 @@ discretize.numeric <-
       breaks <- unique(breaks)
       if (num_breaks > length(breaks)) {
         cli::cli_warn(
-          "Not enough data for {cuts} breaks. \\
-          Only {length(breaks)} breaks were used."
+          "Not enough data for {cuts} breaks. Only {length(breaks)} breaks
+           were used."
         )
       }
       if (infs) {
@@ -129,8 +124,8 @@ discretize.numeric <-
         prefix <- prefix[1]
         if (make.names(prefix) != prefix && !is.null(prefix)) {
           cli::cli_warn(
-            "The prefix {.val {prefix}} is not a valid R name. \\
-            It has been changed to {.val {make.names(prefix)}}."
+            "The prefix {.val {prefix}} is not a valid R name. It has been
+            changed to {.val {make.names(prefix)}}."
           )
           prefix <- make.names(prefix)
         }
@@ -150,8 +145,8 @@ discretize.numeric <-
     } else {
       out <- list(bins = 0)
       cli::cli_warn(
-        "Data not binned; too few unique values per bin. \\
-        Adjust {.arg min_unique} as needed."
+        "Data not binned; too few unique values per bin. Adjust
+         {.arg min_unique} as needed."
       )
     }
     class(out) <- "discretize"
@@ -301,8 +296,10 @@ step_discretize <- function(recipe,
                             options = list(prefix = "bin"),
                             skip = FALSE,
                             id = rand_id("discretize")) {
-  if (any(names(options) %in% c("cuts", "min_unique"))) {
+  if (any(names(options) == "cuts")) {
     num_breaks <- options$cuts
+  }
+  if (any(names(options) == "min_unique")) {
     min_unique <- options$min_unique
   }
 
@@ -348,6 +345,8 @@ bin_wrapper <- function(x, args) {
 prep.step_discretize <- 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_breaks, min = 1, arg = "num_breaks")
+  check_number_whole(x$min_unique, min = 2, arg = "min_unique")
 
   if (length(col_names) > 1 & any(names(x$options) %in% c("prefix", "labels"))) {
     cli::cli_warn(

R/dummy.R

@@ -124,7 +124,7 @@ step_dummy <-
            keep_original_cols = FALSE,
            skip = FALSE,
            id = rand_id("dummy")) {
-    
+
     if (lifecycle::is_present(preserve)) {
       lifecycle::deprecate_stop(
         "0.1.16",
@@ -172,6 +172,8 @@ step_dummy_new <-
 prep.step_dummy <- function(x, training, info = NULL, ...) {
   col_names <- recipes_eval_select(x$terms, training, info)
   check_type(training[, col_names], types = c("factor", "ordered"))
+  check_bool(x$one_hot, arg = "one_hot")
+  check_function(x$naming, arg = "naming", allow_empty = FALSE)
 
   if (length(col_names) > 0) {
     ## I hate doing this but currently we are going to have
@@ -229,14 +231,14 @@ warn_new_levels <- function(dat, lvl, column, step, details = NULL) {
     msg <- c("!" = "There are new levels in {.var {column}}: {.val {lvl2}}.")
     if (any(is.na(lvl2))) {
       msg <- c(
-        msg, 
+        msg,
         "i" = "Consider using {.help [step_unknown()](recipes::step_unknown)} \\
         before {.fn {step}} to handle missing values."
       )
     }
     if (!all(is.na(lvl2))) {
       msg <- c(
-        msg, 
+        msg,
         "i" = "Consider using {.help [step_novel()](recipes::step_novel)} \\
         before {.fn {step}} to handle unseen values."
       )
@@ -278,9 +280,9 @@ bake.step_dummy <- function(object, new_data, ...) {
     }
 
     warn_new_levels(
-      new_data[[col_name]], 
-      levels_values, 
-      col_name, 
+      new_data[[col_name]],
+      levels_values,
+      col_name,
       step = "step_dummy"
     )
 

R/dummy_extract.R

@@ -7,10 +7,10 @@
 #' @inheritParams step_center
 #' @inheritParams step_other
 #' @inheritParams step_dummy
-#' @param sep Character vector containing a regular expression to use
+#' @param sep Character string containing a regular expression to use
 #'   for splitting. [strsplit()] is used to perform the split. `sep` takes
 #'   priority if `pattern` is also specified.
-#' @param pattern Character vector containing a regular expression used
+#' @param pattern Character string containing a regular expression used
 #'   for extraction. [gregexpr()] and [regmatches()] are used to perform
 #'   pattern extraction using `perl = TRUE`.
 #' @template step-return
@@ -88,10 +88,10 @@
 #'   step_dummy_extract(colors, pattern = "(?<=')[^',]+(?=')") %>%
 #'   prep()
 #'
-#' dommies_data_color <- dummies_color %>%
+#' dummies_data_color <- dummies_color %>%
 #'   bake(new_data = NULL)
 #'
-#' dommies_data_color
+#' dummies_data_color
 step_dummy_extract <-
   function(recipe,
            ...,
@@ -107,14 +107,6 @@ step_dummy_extract <-
            skip = FALSE,
            id = rand_id("dummy_extract")) {
 
-    if (!is_tune(threshold)) {
-      if (threshold >= 1) {
-        check_number_whole(threshold)
-      } else {
-        check_number_decimal(threshold, min = 0)
-      }
-    }
-
     add_step(
       recipe,
       step_dummy_extract_new(
@@ -160,6 +152,15 @@ step_dummy_extract_new <-
 prep.step_dummy_extract <- function(x, training, info = NULL, ...) {
   col_names <- recipes_eval_select(x$terms, training, info)
   check_type(training[, col_names], types = c("string", "factor", "ordered"))
+  if (x$threshold >= 1) {
+    check_number_whole(x$threshold, arg = "threshold")
+  } else {
+    check_number_decimal(x$threshold, min = 0, arg = "threshold")
+  }
+  check_string(x$other, arg = "other", allow_null = TRUE)
+  check_string(x$sep, arg = "sep", allow_null = TRUE)
+  check_string(x$pattern, arg = "pattern", allow_null = TRUE)
+  check_function(x$naming, arg = "naming", allow_empty = FALSE)
 
   wts <- get_case_weights(info, training)
   were_weights_used <- are_weights_used(wts, unsupervised = TRUE)

R/dummy_multi_choice.R

@@ -12,23 +12,23 @@
 #'  This is `NULL` until the step is trained by [prep()].
 #' @template step-return
 #' @family dummy variable and encoding steps
-#' 
+#'
 #' @details
-#' The overall proportion (or total counts) of the categories are computed. The 
-#' `"other"` category is used in place of any categorical levels whose 
+#' The overall proportion (or total counts) of the categories are computed. The
+#' `"other"` category is used in place of any categorical levels whose
 #' individual proportion (or frequency) in the training set is less than
 #' `threshold`.
-#' 
+#'
 #' This step produces a number of columns, based on the number of categories it
-#' finds. The naming of the columns is determined by the function based on the 
-#' `naming` argument. The default is to return `<prefix>_<category name>`. By 
-#' default `prefix` is `NULL`, which means the name of the first column  
+#' finds. The naming of the columns is determined by the function based on the
+#' `naming` argument. The default is to return `<prefix>_<category name>`. By
+#' default `prefix` is `NULL`, which means the name of the first column
 #' selected will be used in place.
 #'
 #' @template dummy-naming
 #'
 #' @details
-#' 
+#'
 #' ```{r, echo = FALSE, results="asis"}
 #' step <- "step_dummy_multi_choice"
 #' result <- knitr::knit_child("man/rmd/tunable-args.Rmd")
@@ -74,7 +74,7 @@
 #'
 #' bake(dummy_multi_choice_rec2, new_data = NULL)
 #' tidy(dummy_multi_choice_rec2, number = 1)
-#' 
+#'
 #' @export
 step_dummy_multi_choice <- function(recipe,
                                     ...,
@@ -90,14 +90,6 @@ step_dummy_multi_choice <- function(recipe,
                                     skip = FALSE,
                                     id = rand_id("dummy_multi_choice")) {
 
-  if (!is_tune(threshold)) {
-    if (threshold >= 1) {
-      check_number_whole(threshold)
-    } else {
-      check_number_decimal(threshold, min = 0)
-    }
-  }
-
   add_step(
     recipe,
     step_dummy_multi_choice_new(
@@ -141,6 +133,13 @@ step_dummy_multi_choice_new <-
 prep.step_dummy_multi_choice <- function(x, training, info = NULL, ...) {
   col_names <- recipes_eval_select(x$terms, training, info)
   check_type(training[, col_names], types = c("nominal", "logical"))
+  if (x$threshold >= 1) {
+    check_number_whole(x$threshold, arg = "threshold")
+  } else {
+    check_number_decimal(x$threshold, min = 0, arg = "threshold")
+  }
+  check_string(x$other, arg = "other", allow_null = TRUE)
+  check_function(x$naming, arg = "naming", allow_empty = FALSE)
 
   levels <- purrr::map(training[, col_names], levels)
   levels <- vctrs::list_unchop(levels, ptype = character(), name_spec = rlang::zap())

R/filter_missing.R

@@ -91,6 +91,7 @@ step_filter_missing_new <-
 #' @export
 prep.step_filter_missing <- function(x, training, info = NULL, ...) {
   col_names <- recipes_eval_select(x$terms, training, info)
+  check_number_decimal(x$threshold, min = 0, max = 1, arg = "threshold")
 
   wts <- get_case_weights(info, training)
   were_weights_used <- are_weights_used(wts, unsupervised = TRUE)

R/hyperbolic.R

@@ -58,8 +58,9 @@ step_hyperbolic <-
            skip = FALSE,
            id = rand_id("hyperbolic")) {
 
-    func <- rlang::arg_match(func)
-
+    if (!is_tune(func)) {
+      func <- rlang::arg_match(func)
+    }
     add_step(
       recipe,
       step_hyperbolic_new(
@@ -94,6 +95,9 @@ step_hyperbolic_new <-
 prep.step_hyperbolic <- function(x, training, info = NULL, ...) {
   col_names <- recipes_eval_select(x$terms, training, info)
   check_type(training[, col_names], types = c("double", "integer"))
+  func <- x$func
+  x$func <- rlang::arg_match(func, c("sinh", "cosh", "tanh"), error_arg = "func")
+  check_bool(x$inverse, error_arg = "inverse")
 
   step_hyperbolic_new(
     terms = x$terms,