From 539e5a7430028a9e3225aada281df61d069252a6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E2=80=98topepo=E2=80=99?= <‘mxkuhn@gmail.com’>
Date: Wed, 25 Sep 2024 17:52:32 -0400
Subject: [PATCH] quantile -> quantile_levels for #1203

---
 NEWS.md                                   |  3 +++
 R/predict.R                               |  2 +-
 R/predict_quantile.R                      | 21 ++++++++++++++++-----
 man/other_predict.Rd                      |  5 ++---
 man/set_args.Rd                           |  2 +-
 tests/testthat/test-linear_reg_quantreg.R |  5 +++++
 6 files changed, 28 insertions(+), 10 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index e2a63b619..004da1ef5 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -11,6 +11,9 @@
 
 * New `extract_fit_time()` method has been added that returns the time it took to train the model (#853).
 
+## Breaking Change
+
+* For quantile prediction, the `predict()` argument has been changed from `quantile` to `quantile_levels` for consistency. This does not affect models with mode `"quantile regression"`. 
 
 # parsnip 1.2.1
 
diff --git a/R/predict.R b/R/predict.R
index 3a2681048..397b92112 100644
--- a/R/predict.R
+++ b/R/predict.R
@@ -344,7 +344,7 @@ check_pred_type_dots <- function(object, type, ..., call = rlang::caller_env())
 
   # ----------------------------------------------------------------------------
 
-  other_args <- c("interval", "level", "std_error", "quantile",
+  other_args <- c("interval", "level", "std_error", "quantile_levels",
                   "time", "eval_time", "increasing")
   is_pred_arg <- names(the_dots) %in% other_args
   if (any(!is_pred_arg)) {
diff --git a/R/predict_quantile.R b/R/predict_quantile.R
index fc2d91b15..a3950a75c 100644
--- a/R/predict_quantile.R
+++ b/R/predict_quantile.R
@@ -1,14 +1,13 @@
 #' @keywords internal
 #' @rdname other_predict
-#' @param quantile A vector of numbers between 0 and 1 for the quantile being
-#'  predicted.
+#' @param quantile_levels  A vector of values between zero and one.
 #' @inheritParams predict.model_fit
 #' @method predict_quantile model_fit
 #' @export predict_quantile.model_fit
 #' @export
 predict_quantile.model_fit <- function(object,
                                        new_data,
-                                       quantile = (1:9)/10,
+                                       quantile_levels = NULL,
                                        interval = "none",
                                        level = 0.95,
                                        ...) {
@@ -20,6 +19,20 @@ predict_quantile.model_fit <- function(object,
     return(NULL)
   }
 
+  if (object$spec$mode != "quantile regression") {
+    if (is.null(quantile_levels)) {
+      quantile_levels <- (1:9)/10
+    }
+    hardhat::check_quantile_levels(quantile_levels)
+    # Pass some extra arguments to be used in post-processor
+    object$quantile_levels <- quantile_levels
+  } else {
+    if (!is.null(quantile_levels)) {
+      cli::cli_abort("{.arg quantile_levels} are specified by {.fn set_mode}
+                     when the mode is {.val quantile regression}.")
+    }
+  }
+
   new_data <- prepare_data(object, new_data)
 
   # preprocess data
@@ -27,8 +40,6 @@ predict_quantile.model_fit <- function(object,
     new_data <- object$spec$method$pred$quantile$pre(new_data, object)
   }
 
-  # Pass some extra arguments to be used in post-processor
-  object$spec$method$pred$quantile$args$p <- quantile
   pred_call <- make_pred_call(object$spec$method$pred$quantile)
 
   res <- eval_tidy(pred_call)
diff --git a/man/other_predict.Rd b/man/other_predict.Rd
index 6c997e28d..d1342d87f 100644
--- a/man/other_predict.Rd
+++ b/man/other_predict.Rd
@@ -49,7 +49,7 @@ predict_numeric(object, ...)
 \method{predict_quantile}{model_fit}(
   object,
   new_data,
-  quantile = (1:9)/10,
+  quantile_levels = NULL,
   interval = "none",
   level = 0.95,
   ...
@@ -103,8 +103,7 @@ interval estimates.}
 \item{std_error}{A single logical for whether the standard error should be
 returned (assuming that the model can compute it).}
 
-\item{quantile}{A vector of numbers between 0 and 1 for the quantile being
-predicted.}
+\item{quantile_levels}{A vector of values between zero and one.}
 }
 \description{
 These are internal functions not meant to be directly called by the user.
diff --git a/man/set_args.Rd b/man/set_args.Rd
index 6d3b60f3d..b31e4ad4c 100644
--- a/man/set_args.Rd
+++ b/man/set_args.Rd
@@ -21,7 +21,7 @@ set_mode(object, mode, ...)
 "regression")}
 
 \item{quantile_levels}{A vector of values between zero and one (only for the
-\verb{quantile regression } mode); otherwise, it is \code{NULL}. The model uses these
+\code{"quantile regression"} mode); otherwise, it is \code{NULL}. The model uses these
 values to appropriately train quantile regression models to make predictions
 for these values (e.g., \code{quantile_levels = 0.5} is the median).}
 }
diff --git a/tests/testthat/test-linear_reg_quantreg.R b/tests/testthat/test-linear_reg_quantreg.R
index 7edc7c3a5..2cce1f6ae 100644
--- a/tests/testthat/test-linear_reg_quantreg.R
+++ b/tests/testthat/test-linear_reg_quantreg.R
@@ -83,6 +83,11 @@ test_that('linear quantile regression via quantreg - multiple quantiles', {
   expect_named(ten_quant_df, c(".pred_quantile", ".quantile_levels", ".row"))
   expect_true(nrow(ten_quant_df) == nrow(sac_test) * 10)
 
+  expect_snapshot(
+    ten_quant_pred <- predict(ten_quant, new_data = sac_test),
+    error = TRUE
+  )
+
   ###
 
   ten_quant_one_row <- predict(ten_quant, new_data = sac_test[1,])