transition rlang::abort() to cli::cli_abort() (#1153)

Co-authored-by: Sarah Wright <sarah_wright@nps.gov>
Co-authored-by: Simon P. Couch <simonpatrickcouch@gmail.com>

Author: 3 people

R/glmnet-engines.R

@@ -361,11 +361,13 @@ format_glmnet_multinom_class <- function(pred, penalty, lvl, n_obs) {
   pen <- rlang::eval_tidy(x$args$penalty)
 
   if (length(pen) != 1) {
-    rlang::abort(c(
-      "For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).",
-      glue::glue("There are {length(pen)} values for `penalty`."),
-      "To try multiple values for total regularization, use the tune package.",
-      "To predict multiple penalties, use `multi_predict()`"
+    cli::cli_abort(c(
+      "x" = "For the glmnet engine, {.arg penalty} must be a single number
+      (or a value of {.fn tune}).",
+      "!" = "There are {length(pen)} value{?s} for {.arg penalty}.",
+      "i" = "To try multiple values for total regularization, use the
+      {.pkg tune} package.",
+      "i" = "To predict multiple penalties, use {.fn multi_predict}."
     ))
   }
 }
@@ -385,23 +387,19 @@ format_glmnet_multinom_class <- function(pred, penalty, lvl, n_obs) {
   # when using `predict()`, allow for a single lambda
   if (!multi) {
     if (length(penalty) != 1) {
-      rlang::abort(
-        glue::glue(
-          "`penalty` should be a single numeric value. `multi_predict()` ",
-          "can be used to get multiple predictions per row of data."
-        )
-      )
+      cli::cli_abort(c(
+        "{.arg penalty} should be a single numeric value.",
+        "i" = "{.fn multi_predict} can be used to get multiple predictions per row of data."
+      ))
     }
   }
 
   if (length(object$fit$lambda) == 1 && penalty != object$fit$lambda) {
-    rlang::abort(
-      glue::glue(
-        "The glmnet model was fit with a single penalty value of ",
-        "{object$fit$lambda}. Predicting with a value of {penalty} ",
-        "will give incorrect results from `glmnet()`."
-      )
-    )
+    cli::cli_abort(c(
+      "The glmnet model was fit with a single penalty value of
+      {.arg object$fit$lambda}. Predicting with a value of {.arg penalty}
+      will give incorrect results from `glmnet()`."
+    ))
   }
 
   penalty

R/logistic_reg.R

@@ -98,7 +98,7 @@ translate.logistic_reg <- function(x, engine = x$engine, ...) {
         } else if (quo_get_expr(x$args$mixture) == 1) {
           arg_vals$type <- 6      ## lasso
         } else {
-          rlang::abort("For the LiblineaR engine, mixture must be 0 or 1.")
+          cli::cli_abort("For the LiblineaR engine, {.arg mixture} must be 0 or 1.")
         }
     }
     x$method$fit$args <- arg_vals
@@ -153,7 +153,7 @@ check_args.logistic_reg <- function(object, call = rlang::caller_env()) {
         call = call
       )
     }
-    
+
     if ((!is.null(args$penalty)) && args$penalty == 0) {
       cli::cli_abort(
         "For the {.pkg LiblineaR} engine, {.arg penalty} must be {.code > 0}, \\

R/mars.R

@@ -161,14 +161,14 @@ multi_predict._earth <-
         object$fit$call[[i]] <- eval_tidy(object$fit$call[[i]])
     }
 
-    msg <-
-      paste("Please use `keepxy = TRUE` as an option to enable submodel",
-            "predictions with `earth`.")
+    msg <- 
+      c("x" = "Please use {.code keepxy = TRUE} as an option to enable submodel 
+                     predictions with earth.")
     if (any(names(object$fit$call) == "keepxy")) {
        if (!isTRUE(object$fit$call$keepxy))
-         rlang::abort(msg)
+         cli::cli_abort(msg)
     } else {
-      rlang::abort(msg)
+      cli::cli_abort(msg)
     }
 
     if (is.null(type)) {

R/mlp.R

@@ -148,7 +148,7 @@ check_args.mlp <- function(object, call = rlang::caller_env()) {
 
 class2ind <- function (x, drop2nd = FALSE) {
   if (!is.factor(x))
-    rlang::abort("`x` should be a factor")
+    cli::cli_abort(c("x" = "{.arg x} should be a factor."))
   y <- model.matrix( ~ x - 1)
   colnames(y) <- gsub("^x", "", colnames(y))
   attributes(y)$assign <- NULL
@@ -195,7 +195,7 @@ keras_mlp <-
     rlang::arg_match(activation, act_funs,)
 
     if (penalty > 0 & dropout > 0) {
-      rlang::abort("Please use either dropoput or weight decay.", call. = FALSE)
+      cli::cli_abort("Please use either dropout or weight decay.", call = NULL)
     }
     if (!is.matrix(x)) {
       x <- as.matrix(x)

tests/testthat/_snaps/translate.md

@@ -504,10 +504,10 @@
       translate_args(basic %>% set_engine("glmnet"))
     Condition
       Error in `.check_glmnet_penalty_fit()`:
-      ! For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
-      * There are 0 values for `penalty`.
-      * To try multiple values for total regularization, use the tune package.
-      * To predict multiple penalties, use `multi_predict()`
+      x For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
+      ! There are 0 values for `penalty`.
+      i To try multiple values for total regularization, use the tune package.
+      i To predict multiple penalties, use `multi_predict()`.
 
 ---
 
@@ -555,10 +555,10 @@
       translate_args(mixture %>% set_engine("glmnet"))
     Condition
       Error in `.check_glmnet_penalty_fit()`:
-      ! For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
-      * There are 0 values for `penalty`.
-      * To try multiple values for total regularization, use the tune package.
-      * To predict multiple penalties, use `multi_predict()`
+      x For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
+      ! There are 0 values for `penalty`.
+      i To try multiple values for total regularization, use the tune package.
+      i To predict multiple penalties, use `multi_predict()`.
 
 ---
 
@@ -688,10 +688,10 @@
       translate_args(basic %>% set_engine("glmnet"))
     Condition
       Error in `.check_glmnet_penalty_fit()`:
-      ! For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
-      * There are 0 values for `penalty`.
-      * To try multiple values for total regularization, use the tune package.
-      * To predict multiple penalties, use `multi_predict()`
+      x For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
+      ! There are 0 values for `penalty`.
+      i To try multiple values for total regularization, use the tune package.
+      i To predict multiple penalties, use `multi_predict()`.
 
 ---
 
@@ -827,10 +827,10 @@
       translate_args(mixture %>% set_engine("glmnet"))
     Condition
       Error in `.check_glmnet_penalty_fit()`:
-      ! For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
-      * There are 0 values for `penalty`.
-      * To try multiple values for total regularization, use the tune package.
-      * To predict multiple penalties, use `multi_predict()`
+      x For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
+      ! There are 0 values for `penalty`.
+      i To try multiple values for total regularization, use the tune package.
+      i To predict multiple penalties, use `multi_predict()`.
 
 ---
 
@@ -968,10 +968,10 @@
       translate_args(mixture_v %>% set_engine("glmnet"))
     Condition
       Error in `.check_glmnet_penalty_fit()`:
-      ! For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
-      * There are 0 values for `penalty`.
-      * To try multiple values for total regularization, use the tune package.
-      * To predict multiple penalties, use `multi_predict()`
+      x For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
+      ! There are 0 values for `penalty`.
+      i To try multiple values for total regularization, use the tune package.
+      i To predict multiple penalties, use `multi_predict()`.
 
 ---
 
@@ -1334,10 +1334,10 @@
       translate_args(basic %>% set_engine("glmnet"))
     Condition
       Error in `.check_glmnet_penalty_fit()`:
-      ! For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
-      * There are 0 values for `penalty`.
-      * To try multiple values for total regularization, use the tune package.
-      * To predict multiple penalties, use `multi_predict()`
+      x For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
+      ! There are 0 values for `penalty`.
+      i To try multiple values for total regularization, use the tune package.
+      i To predict multiple penalties, use `multi_predict()`.
 
 ---
 
@@ -1552,10 +1552,10 @@
       basic_incomplete %>% translate_args()
     Condition
       Error in `.check_glmnet_penalty_fit()`:
-      ! For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
-      * There are 0 values for `penalty`.
-      * To try multiple values for total regularization, use the tune package.
-      * To predict multiple penalties, use `multi_predict()`
+      x For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
+      ! There are 0 values for `penalty`.
+      i To try multiple values for total regularization, use the tune package.
+      i To predict multiple penalties, use `multi_predict()`.
 
 # arguments (rand_forest)