rlang to cli for abort and warn, Fixes #1141

R/predict_class.R

@@ -9,21 +9,23 @@
 #' @export predict_class.model_fit
 #' @export
 predict_class.model_fit <- function(object, new_data, ...) {
-  if (object$spec$mode != "classification")
-    rlang::abort("`predict.model_fit()` is for predicting factor outcomes.")
+  if (object$spec$mode != "classification") {
+    cli::cli_abort("{.code predict.model_fit()} is for predicting factor outcomes.")
+  }
 
   check_spec_pred_type(object, "class")
 
   if (inherits(object$fit, "try-error")) {
-    rlang::warn("Model fit failed; cannot make predictions.")
+    cli::cli_warn("Model fit failed; cannot make predictions.")
     return(NULL)
   }
 
   new_data <- prepare_data(object, new_data)
 
   # preprocess data
-  if (!is.null(object$spec$method$pred$class$pre))
+  if (!is.null(object$spec$method$pred$class$pre)) {
     new_data <- object$spec$method$pred$class$pre(new_data, object)
+  }
 
   # create prediction call
   pred_call <- make_pred_call(object$spec$method$pred$class)
@@ -56,6 +58,6 @@ predict_class.model_fit <- function(object, new_data, ...) {
 # @keywords internal
 # @rdname other_predict
 # @inheritParams predict.model_fit
-predict_class <- function(object, ...)
+predict_class <- function(object, ...) {
   UseMethod("predict_class")
-
+}

R/predict_classprob.R

@@ -5,22 +5,24 @@
 #' @export predict_classprob.model_fit
 #' @export
 predict_classprob.model_fit <- function(object, new_data, ...) {
-  if (object$spec$mode != "classification")
-    rlang::abort("`predict.model_fit()` is for predicting factor outcomes.")
+  if (object$spec$mode != "classification") {
+    cli::cli_abort("{.code predict.model_fit()} is for predicting factor outcomes.")
+  }
 
   check_spec_pred_type(object, "prob")
   check_spec_levels(object)
 
   if (inherits(object$fit, "try-error")) {
-    rlang::warn("Model fit failed; cannot make predictions.")
+    cli::cli_warn("Model fit failed; cannot make predictions.")
     return(NULL)
   }
 
   new_data <- prepare_data(object, new_data)
 
   # preprocess data
-  if (!is.null(object$spec$method$pred$prob$pre))
+  if (!is.null(object$spec$method$pred$prob$pre)) {
     new_data <- object$spec$method$pred$prob$pre(new_data, object)
+  }
 
   # create prediction call
   pred_call <- make_pred_call(object$spec$method$pred$prob)
@@ -33,11 +35,13 @@ predict_classprob.model_fit <- function(object, new_data, ...) {
   }
 
   # check and sort names
-  if (!is.data.frame(res) & !inherits(res, "tbl_spark"))
-    rlang::abort("The was a problem with the probability predictions.")
+  if (!is.data.frame(res) & !inherits(res, "tbl_spark")) {
+    cli::cli_abort("The was a problem with the probability predictions.")
+  }
 
-  if (!is_tibble(res) & !inherits(res, "tbl_spark"))
+  if (!is_tibble(res) & !inherits(res, "tbl_spark")) {
     res <- as_tibble(res)
+  }
 
   res
 }
@@ -46,17 +50,16 @@ predict_classprob.model_fit <- function(object, new_data, ...) {
 # @keywords internal
 # @rdname other_predict
 # @inheritParams predict.model_fit
-predict_classprob <- function(object, ...)
+predict_classprob <- function(object, ...) {
   UseMethod("predict_classprob")
+}
 
 check_spec_levels <- function(spec) {
   if ("class" %in% spec$lvl) {
-    rlang::abort(
-      glue::glue(
-        "The outcome variable `{spec$preproc$y_var}` has a level called 'class'. ",
-        "This value is reserved for parsnip's classification internals; please ",
-        "change the levels, perhaps with `forcats::fct_relevel()`."
-      ),
+    cli::cli_abort(
+      "The outcome variable {.var {spec$preproc$y_var}} has a level called {.val class}.
+      This value is reserved for parsnip's classification internals; please
+      change the levels, perhaps with {.fn forcats::fct_relevel}.",
       call = NULL
     )
   }

R/predict_numeric.R

@@ -5,29 +5,33 @@
 #' @export predict_numeric.model_fit
 #' @export
 predict_numeric.model_fit <- function(object, new_data, ...) {
-  if (object$spec$mode != "regression")
-    rlang::abort(glue::glue("`predict_numeric()` is for predicting numeric outcomes. ",
-                            "Use `predict_class()` or `predict_classprob()` for ",
-                            "classification models."))
+  if (object$spec$mode != "regression") {
+    cli::cli_abort(
+      "{.code predict_numeric()} is for predicting numeric outcomes.
+      Use {.code predict_class()} or {.code predict_classprob()} for
+      classification models."
+    )
+  }
 
   check_spec_pred_type(object, "numeric")
 
   if (inherits(object$fit, "try-error")) {
-    rlang::warn("Model fit failed; cannot make predictions.")
+    cli::cli_warn("Model fit failed; cannot make predictions.")
     return(NULL)
   }
 
   new_data <- prepare_data(object, new_data)
 
   # preprocess data
-  if (!is.null(object$spec$method$pred$numeric$pre))
+  if (!is.null(object$spec$method$pred$numeric$pre)) {
     new_data <- object$spec$method$pred$numeric$pre(new_data, object)
+  }
 
   # create prediction call
   pred_call <- make_pred_call(object$spec$method$pred$numeric)
 
   res <- eval_tidy(pred_call)
-  
+
   # post-process the predictions
   if (!is.null(object$spec$method$pred$numeric$post)) {
     res <- object$spec$method$pred$numeric$post(res, object)
@@ -36,8 +40,9 @@ predict_numeric.model_fit <- function(object, new_data, ...) {
   if (is.vector(res)) {
     res <- unname(res)
   } else {
-    if (!inherits(res, "tbl_spark"))
+    if (!inherits(res, "tbl_spark")) {
       res <- as.data.frame(res)
+    }
   }
   res
 }
@@ -47,5 +52,6 @@ predict_numeric.model_fit <- function(object, new_data, ...) {
 #' @keywords internal
 #' @rdname other_predict
 #' @inheritParams predict_numeric.model_fit
-predict_numeric <- function(object, ...)
+predict_numeric <- function(object, ...) {
   UseMethod("predict_numeric")
+}

R/predict_time.R

@@ -5,29 +5,33 @@
 #' @export predict_time.model_fit
 #' @export
 predict_time.model_fit <- function(object, new_data, ...) {
-  if (object$spec$mode != "censored regression")
-    rlang::abort(glue::glue("`predict_time()` is for predicting time outcomes. ",
-                            "Use `predict_class()` or `predict_classprob()` for ",
-                            "classification models."))
+  if (object$spec$mode != "censored regression") {
+    cli::cli_abort(
+      "{.code predict_time()} is for predicting time outcomes.
+      Use {.code predict_class()} or {.code predict_classprob()} for
+      classification models."
+    )
+  }
 
   check_spec_pred_type(object, "time")
 
   if (inherits(object$fit, "try-error")) {
-    rlang::warn("Model fit failed; cannot make predictions.")
+    cli::cli_warn("Model fit failed; cannot make predictions.")
     return(NULL)
   }
 
   new_data <- prepare_data(object, new_data)
 
   # preprocess data
-  if (!is.null(object$spec$method$pred$time$pre))
+  if (!is.null(object$spec$method$pred$time$pre)) {
     new_data <- object$spec$method$pred$time$pre(new_data, object)
+  }
 
   # create prediction call
   pred_call <- make_pred_call(object$spec$method$pred$time)
 
   res <- eval_tidy(pred_call)
-  
+
   # post-process the predictions
   if (!is.null(object$spec$method$pred$time$post)) {
     res <- object$spec$method$pred$time$post(res, object)
@@ -45,5 +49,6 @@ predict_time.model_fit <- function(object, new_data, ...) {
 #' @keywords internal
 #' @rdname other_predict
 #' @inheritParams predict_time.model_fit
-predict_time <- function(object, ...)
+predict_time <- function(object, ...) {
   UseMethod("predict_time")
+}

tests/testthat/test-predict_formats.R

@@ -76,7 +76,7 @@ test_that('predict(type = "prob") with level "class" (see #720)', {
   )
 
   expect_error(
-    regexp = "variable `boop` has a level called 'class'",
+    regexp = 'variable `boop` has a level called "class"',
     predict(mod, type = "prob", new_data = x)
   )
 })