From 2f58384130c0401d4b90684e7c437215e6f86226 Mon Sep 17 00:00:00 2001
From: topepo <mxkuhn@gmail.com>
Date: Mon, 15 Jul 2019 14:00:56 -0400
Subject: [PATCH] added a function to get multi_predict arg names

---
 NAMESPACE                                   | 12 ++-
 R/aaa_multi_predict.R                       | 99 +++++++++++++++++++++
 R/nearest_neighbor_data.R                   |  2 +-
 R/predict.R                                 | 70 ---------------
 man/has_multi_pred.Rd                       | 35 --------
 man/has_multi_predict.Rd                    | 65 ++++++++++++++
 tests/testthat/test_boost_tree_C50.R        |  3 +
 tests/testthat/test_boost_tree_xgboost.R    |  3 +
 tests/testthat/test_linear_reg_glmnet.R     |  5 +-
 tests/testthat/test_linear_reg_spark.R      |  3 +
 tests/testthat/test_linear_reg_stan.R       |  3 +
 tests/testthat/test_logistic_reg_glmnet.R   |  5 +-
 tests/testthat/test_mars.R                  |  3 +
 tests/testthat/test_misc.R                  | 14 +--
 tests/testthat/test_mlp_keras.R             |  4 +
 tests/testthat/test_mlp_nnet.R              |  4 +
 tests/testthat/test_multinom_reg_glmnet.R   |  5 +-
 tests/testthat/test_nearest_neighbor_kknn.R |  5 +-
 tests/testthat/test_rand_forest_ranger.R    |  3 +
 tests/testthat/test_surv_reg_flexsurv.R     |  3 +
 tests/testthat/test_svm_poly.R              |  5 +-
 tests/testthat/test_svm_rbf.R               |  4 +-
 22 files changed, 232 insertions(+), 123 deletions(-)
 delete mode 100644 man/has_multi_pred.Rd
 create mode 100644 man/has_multi_predict.Rd

diff --git a/NAMESPACE b/NAMESPACE
index ba4ce863d..37745ade0 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -2,9 +2,9 @@
 
 S3method(fit,model_spec)
 S3method(fit_xy,model_spec)
-S3method(has_multi_pred,default)
-S3method(has_multi_pred,model_fit)
-S3method(has_multi_pred,workflow)
+S3method(has_multi_predict,default)
+S3method(has_multi_predict,model_fit)
+S3method(has_multi_predict,workflow)
 S3method(multi_predict,"_C5.0")
 S3method(multi_predict,"_earth")
 S3method(multi_predict,"_elnet")
@@ -13,6 +13,9 @@ S3method(multi_predict,"_multnet")
 S3method(multi_predict,"_train.kknn")
 S3method(multi_predict,"_xgb.Booster")
 S3method(multi_predict,default)
+S3method(multi_predict_args,default)
+S3method(multi_predict_args,model_fit)
+S3method(multi_predict_args,workflow)
 S3method(nullmodel,default)
 S3method(predict,"_elnet")
 S3method(predict,"_lognet")
@@ -95,7 +98,7 @@ export(get_fit)
 export(get_from_env)
 export(get_model_env)
 export(get_pred_type)
-export(has_multi_pred)
+export(has_multi_predict)
 export(keras_mlp)
 export(linear_reg)
 export(logistic_reg)
@@ -104,6 +107,7 @@ export(mars)
 export(mlp)
 export(model_printer)
 export(multi_predict)
+export(multi_predict_args)
 export(multinom_reg)
 export(nearest_neighbor)
 export(null_model)
diff --git a/R/aaa_multi_predict.R b/R/aaa_multi_predict.R
index 400cdf199..46c005365 100644
--- a/R/aaa_multi_predict.R
+++ b/R/aaa_multi_predict.R
@@ -33,3 +33,102 @@ multi_predict.default <- function(object, ...)
 predict.model_spec <- function(object, ...) {
   stop("You must use `fit()` on your model specification before you can use `predict()`.", call. = FALSE)
 }
+
+#' Tools for models that predict on sub-models
+#'
+#' `has_multi_predict()` tests to see if an object can make multiple
+#'  predictions on submodels from the same object. `multi_predict_args()`
+#'  returns the names of the argments to `multi_predict()` for this model
+#'  (if any).
+#' @param object An object to test.
+#' @param ... Not currently used.
+#' @return `has_multi_predict()` returns single logical value while
+#'  `multi_predict()` returns a character vector of argument names (or `NA`
+#'  if none exist).
+#' @keywords internal
+#' @examples
+#' lm_model_idea <- linear_reg() %>% set_engine("lm")
+#' has_multi_predict(lm_model_idea)
+#' lm_model_fit <- fit(lm_model_idea, mpg ~ ., data = mtcars)
+#' has_multi_predict(lm_model_fit)
+#'
+#' multi_predict_args(lm_model_fit)
+#'
+#' library(kknn)
+#'
+#' knn_fit <-
+#'   nearest_neighbor(mode = "regression", neighbors = 5) %>%
+#'   set_engine("kknn") %>%
+#'   fit(mpg ~ ., mtcars)
+#'
+#' multi_predict_args(knn_fit)
+#'
+#' multi_predict(knn_fit, mtcars[1, -1], neighbors = 1:4)$.pred
+#' @importFrom utils methods
+#' @export
+has_multi_predict <- function(object, ...) {
+  UseMethod("has_multi_predict")
+}
+
+#' @export
+#' @rdname has_multi_predict
+has_multi_predict.default <- function(object, ...) {
+  FALSE
+}
+
+#' @export
+#' @rdname has_multi_predict
+has_multi_predict.model_fit <- function(object, ...) {
+  existing_mthds <- utils::methods("multi_predict")
+  tst <- paste0("multi_predict.", class(object))
+  any(tst %in% existing_mthds)
+}
+
+#' @export
+#' @rdname has_multi_predict
+has_multi_predict.workflow <- function(object, ...) {
+  has_multi_predict(object$fit$model$model)
+}
+
+
+#' @rdname has_multi_predict
+#' @export
+#' @rdname has_multi_predict
+multi_predict_args <- function(object, ...) {
+  UseMethod("multi_predict_args")
+}
+
+#' @export
+#' @rdname has_multi_predict
+multi_predict_args.default <- function(object, ...) {
+  if (inherits(object, "model_fit")) {
+    res <- multi_predict_args.model_fit(object, ...)
+  } else {
+    res <- NA_character_
+  }
+  res
+}
+
+#' @export
+#' @rdname has_multi_predict
+multi_predict_args.model_fit <- function(object, ...) {
+  existing_mthds <- methods("multi_predict")
+  cls <- class(object)
+  tst <- paste0("multi_predict.", cls)
+  .fn <- tst[tst %in% existing_mthds]
+  if (length(.fn) == 0) {
+    return(NA_character_)
+  }
+
+  .fn <- getFromNamespace(.fn, ns = "parsnip")
+  omit <- c('object', 'new_data', 'type', '...')
+  args <- names(formals(.fn))
+  args[!(args %in% omit)]
+}
+
+#' @export
+#' @rdname has_multi_predict
+multi_predict_args.workflow <- function(object, ...) {
+  object <- object$fit$model$model
+
+}
diff --git a/R/nearest_neighbor_data.R b/R/nearest_neighbor_data.R
index 2a85f70d0..7221eccb5 100644
--- a/R/nearest_neighbor_data.R
+++ b/R/nearest_neighbor_data.R
@@ -16,7 +16,7 @@ set_model_arg(
   parsnip = "neighbors",
   original = "ks",
   func = list(pkg = "dials", fun = "neighbors"),
-  has_submodel = FALSE
+  has_submodel = TRUE
 )
 set_model_arg(
   model = "nearest_neighbor",
diff --git a/R/predict.R b/R/predict.R
index 8339d886c..c57adb7be 100644
--- a/R/predict.R
+++ b/R/predict.R
@@ -231,73 +231,3 @@ prepare_data <- function(object, new_data) {
   new_data
 }
 
-# Define a generic to make multiple predictions for the same model object ------
-
-#' Model predictions across many sub-models
-#'
-#' For some models, predictions can be made on sub-models in the model object.
-#' @param object A `model_fit` object.
-#' @param ... Optional arguments to pass to `predict.model_fit(type = "raw")`
-#'  such as `type`.
-#' @return A tibble with the same number of rows as the data being predicted.
-#'  Mostly likely, there is a list-column named `.pred` that is a tibble with
-#'  multiple rows per sub-model.
-#' @export
-multi_predict <- function(object, ...) {
-  if (inherits(object$fit, "try-error")) {
-    warning("Model fit failed; cannot make predictions.", call. = FALSE)
-    return(NULL)
-  }
-  UseMethod("multi_predict")
-}
-
-#' @export
-#' @rdname multi_predict
-multi_predict.default <- function(object, ...)
-  stop("No `multi_predict` method exists for objects with classes ",
-        paste0("'", class(), "'", collapse = ", "), call. = FALSE)
-
-#' @export
-predict.model_spec <- function(object, ...) {
-  stop("You must use `fit()` on your model specification before you can use `predict()`.", call. = FALSE)
-}
-
-
-
-#' Determine if a model can make predictions on sub-models
-#'
-#' @param object An object to test.
-#' @param ... Not currently used.
-#' @return A single logical value.
-#' @keywords internal
-#' @examples
-#' model_idea <- linear_reg() %>% set_engine("lm")
-#' has_multi_pred(model_idea)
-#' model_fit <- fit(model_idea, mpg ~ ., data = mtcars)
-#' has_multi_pred(model_fit)
-#' @importFrom utils methods
-#' @export
-has_multi_pred <- function(object, ...) {
-  UseMethod("has_multi_pred")
-}
-
-#' @export
-#' @rdname has_multi_pred
-has_multi_pred.default <- function(object, ...) {
-  FALSE
-}
-
-#' @export
-#' @rdname has_multi_pred
-has_multi_pred.model_fit <- function(object, ...) {
-  existing_mthds <- utils::methods("multi_predict")
-  tst <- paste0("multi_predict.", class(object))
-  any(tst %in% existing_mthds)
-}
-
-#' @export
-#' @rdname has_multi_pred
-has_multi_pred.workflow <- function(object, ...) {
-  has_multi_pred(object$fit$model$model)
-}
-
diff --git a/man/has_multi_pred.Rd b/man/has_multi_pred.Rd
deleted file mode 100644
index 99f850dd9..000000000
--- a/man/has_multi_pred.Rd
+++ /dev/null
@@ -1,35 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/predict.R
-\name{has_multi_pred}
-\alias{has_multi_pred}
-\alias{has_multi_pred.default}
-\alias{has_multi_pred.model_fit}
-\alias{has_multi_pred.workflow}
-\title{Determine if a model can make predictions on sub-models}
-\usage{
-has_multi_pred(object, ...)
-
-\method{has_multi_pred}{default}(object, ...)
-
-\method{has_multi_pred}{model_fit}(object, ...)
-
-\method{has_multi_pred}{workflow}(object, ...)
-}
-\arguments{
-\item{object}{An object to test.}
-
-\item{...}{Not currently used.}
-}
-\value{
-A single logical value.
-}
-\description{
-Determine if a model can make predictions on sub-models
-}
-\examples{
-model_idea <- linear_reg() \%>\% set_engine("lm")
-has_multi_pred(model_idea)
-model_fit <- fit(model_idea, mpg ~ ., data = mtcars)
-has_multi_pred(model_fit)
-}
-\keyword{internal}
diff --git a/man/has_multi_predict.Rd b/man/has_multi_predict.Rd
new file mode 100644
index 000000000..64da8f233
--- /dev/null
+++ b/man/has_multi_predict.Rd
@@ -0,0 +1,65 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/aaa_multi_predict.R
+\name{has_multi_predict}
+\alias{has_multi_predict}
+\alias{has_multi_predict.default}
+\alias{has_multi_predict.model_fit}
+\alias{has_multi_predict.workflow}
+\alias{multi_predict_args}
+\alias{multi_predict_args.default}
+\alias{multi_predict_args.model_fit}
+\alias{multi_predict_args.workflow}
+\title{Tools for models that predict on sub-models}
+\usage{
+has_multi_predict(object, ...)
+
+\method{has_multi_predict}{default}(object, ...)
+
+\method{has_multi_predict}{model_fit}(object, ...)
+
+\method{has_multi_predict}{workflow}(object, ...)
+
+multi_predict_args(object, ...)
+
+\method{multi_predict_args}{default}(object, ...)
+
+\method{multi_predict_args}{model_fit}(object, ...)
+
+\method{multi_predict_args}{workflow}(object, ...)
+}
+\arguments{
+\item{object}{An object to test.}
+
+\item{...}{Not currently used.}
+}
+\value{
+\code{has_multi_predict()} returns single logical value while
+\code{multi_predict()} returns a character vector of argument names (or \code{NA}
+if none exist).
+}
+\description{
+\code{has_multi_predict()} tests to see if an object can make multiple
+predictions on submodels from the same object. \code{multi_predict_args()}
+returns the names of the argments to \code{multi_predict()} for this model
+(if any).
+}
+\examples{
+lm_model_idea <- linear_reg() \%>\% set_engine("lm")
+has_multi_predict(lm_model_idea)
+lm_model_fit <- fit(lm_model_idea, mpg ~ ., data = mtcars)
+has_multi_predict(lm_model_fit)
+
+multi_predict_args(lm_model_fit)
+
+library(kknn)
+
+knn_fit <-
+  nearest_neighbor(mode = "regression", neighbors = 5) \%>\%
+  set_engine("kknn") \%>\%
+  fit(mpg ~ ., mtcars)
+
+multi_predict_args(knn_fit)
+
+multi_predict(knn_fit, mtcars[1, -1], neighbors = 1:4)$.pred
+}
+\keyword{internal}
diff --git a/tests/testthat/test_boost_tree_C50.R b/tests/testthat/test_boost_tree_C50.R
index cf92602fc..b295011ea 100644
--- a/tests/testthat/test_boost_tree_C50.R
+++ b/tests/testthat/test_boost_tree_C50.R
@@ -46,6 +46,9 @@ test_that('C5.0 execution', {
     regexp = NA
   )
 
+  expect_true(has_multi_predict(res))
+  expect_equal(multi_predict_args(res), "trees")
+
   # outcome is not a factor:
   expect_error(
     res <- fit(
diff --git a/tests/testthat/test_boost_tree_xgboost.R b/tests/testthat/test_boost_tree_xgboost.R
index ed65bcaad..27f15ee9b 100644
--- a/tests/testthat/test_boost_tree_xgboost.R
+++ b/tests/testthat/test_boost_tree_xgboost.R
@@ -40,6 +40,9 @@ test_that('xgboost execution, classification', {
     regexp = NA
   )
 
+  expect_true(has_multi_predict(res))
+  expect_equal(multi_predict_args(res), "trees")
+
   expect_error(
     res <- parsnip::fit(
       iris_xgboost,
diff --git a/tests/testthat/test_linear_reg_glmnet.R b/tests/testthat/test_linear_reg_glmnet.R
index 92506ff7f..46ff3658d 100644
--- a/tests/testthat/test_linear_reg_glmnet.R
+++ b/tests/testthat/test_linear_reg_glmnet.R
@@ -25,7 +25,7 @@ test_that('glmnet execution', {
   skip_if_not_installed("glmnet")
 
   expect_error(
-    fit_xy(
+    res <- fit_xy(
       iris_basic,
       control = ctrl,
       x = iris[, num_pred],
@@ -34,6 +34,9 @@ test_that('glmnet execution', {
     regexp = NA
   )
 
+  expect_true(has_multi_predict(res))
+  expect_equal(multi_predict_args(res), "penalty")
+
   expect_error(
     fit(
       iris_basic,
diff --git a/tests/testthat/test_linear_reg_spark.R b/tests/testthat/test_linear_reg_spark.R
index 4b7432d80..86b8f3e68 100644
--- a/tests/testthat/test_linear_reg_spark.R
+++ b/tests/testthat/test_linear_reg_spark.R
@@ -36,6 +36,9 @@ test_that('spark execution', {
     regexp = NA
   )
 
+  expect_false(has_multi_predict(spark_fit))
+  expect_equal(multi_predict_args(spark_fit), NA_character_)
+
   expect_error(
     spark_pred <- predict(spark_fit, iris_linreg_te),
     regexp = NA
diff --git a/tests/testthat/test_linear_reg_stan.R b/tests/testthat/test_linear_reg_stan.R
index e039d10b3..d628fa443 100644
--- a/tests/testthat/test_linear_reg_stan.R
+++ b/tests/testthat/test_linear_reg_stan.R
@@ -39,6 +39,9 @@ test_that('stan_glm execution', {
     regexp = NA
   )
 
+  expect_false(has_multi_predict(res))
+  expect_equal(multi_predict_args(res), NA_character_)
+
   expect_error(
     res <- fit(
       iris_basic,
diff --git a/tests/testthat/test_logistic_reg_glmnet.R b/tests/testthat/test_logistic_reg_glmnet.R
index 74165c6dc..d2db3d5ed 100644
--- a/tests/testthat/test_logistic_reg_glmnet.R
+++ b/tests/testthat/test_logistic_reg_glmnet.R
@@ -26,7 +26,7 @@ test_that('glmnet execution', {
   skip_if_not_installed("glmnet")
 
   expect_error(
-    fit_xy(
+    res <- fit_xy(
       lc_basic,
       control = ctrl,
       x = lending_club[, num_pred],
@@ -35,6 +35,9 @@ test_that('glmnet execution', {
     regexp = NA
   )
 
+  expect_true(has_multi_predict(res))
+  expect_equal(multi_predict_args(res), "penalty")
+
   expect_error(
     glmnet_xy_catch <- fit_xy(
       lc_basic,
diff --git a/tests/testthat/test_mars.R b/tests/testthat/test_mars.R
index ef1b16254..b17d46ac7 100644
--- a/tests/testthat/test_mars.R
+++ b/tests/testthat/test_mars.R
@@ -139,6 +139,9 @@ test_that('mars execution', {
     regexp = NA
   )
 
+  expect_true(has_multi_predict(res))
+  expect_equal(multi_predict_args(res), "num_terms")
+
   expect_error(
     res <- fit(
       iris_basic,
diff --git a/tests/testthat/test_misc.R b/tests/testthat/test_misc.R
index 089431941..7843b5ca8 100644
--- a/tests/testthat/test_misc.R
+++ b/tests/testthat/test_misc.R
@@ -6,24 +6,24 @@ context("checking for multi_predict")
 test_that('parsnip objects', {
 
   lm_idea <- linear_reg() %>% set_engine("lm")
-  expect_false(has_multi_pred(lm_idea))
+  expect_false(has_multi_predict(lm_idea))
 
   lm_fit <- fit(lm_idea, mpg ~ ., data = mtcars)
-  expect_false(has_multi_pred(lm_fit))
-  expect_false(has_multi_pred(lm_fit$fit))
+  expect_false(has_multi_predict(lm_fit))
+  expect_false(has_multi_predict(lm_fit$fit))
 
   mars_fit <-
     mars(mode = "regression") %>%
     set_engine("earth") %>%
     fit(mpg ~ ., data = mtcars)
-  expect_true(has_multi_pred(mars_fit))
-  expect_false(has_multi_pred(mars_fit$fit))
+  expect_true(has_multi_predict(mars_fit))
+  expect_false(has_multi_predict(mars_fit$fit))
 })
 
 test_that('other objects', {
 
-  expect_false(has_multi_pred(NULL))
-  expect_false(has_multi_pred(NA))
+  expect_false(has_multi_predict(NULL))
+  expect_false(has_multi_predict(NA))
 
 })
 
diff --git a/tests/testthat/test_mlp_keras.R b/tests/testthat/test_mlp_keras.R
index e143c0464..9798811e7 100644
--- a/tests/testthat/test_mlp_keras.R
+++ b/tests/testthat/test_mlp_keras.R
@@ -32,6 +32,10 @@ test_that('keras execution, classification', {
     regexp = NA
   )
 
+
+  expect_false(has_multi_predict(res))
+  expect_equal(multi_predict_args(res), NA_character_)
+
   keras::backend()$clear_session()
 
   expect_error(
diff --git a/tests/testthat/test_mlp_nnet.R b/tests/testthat/test_mlp_nnet.R
index 4172364b1..61a792b1b 100644
--- a/tests/testthat/test_mlp_nnet.R
+++ b/tests/testthat/test_mlp_nnet.R
@@ -174,6 +174,10 @@ test_that('multivariate nnet formula', {
       cbind(V1, V2, V3) ~ .,
       data = nn_dat[-(1:5),]
     )
+
+  expect_false(has_multi_predict(nnet_form))
+  expect_equal(multi_predict_args(nnet_form), NA_character_)
+
   expect_equal(length(nnet_form$fit$wts), 24)
   nnet_form_pred <- predict(nnet_form, new_data = nn_dat[1:5, -(1:3)])
   expect_equal(names(nnet_form_pred), paste0(".pred_", c("V1", "V2", "V3")))
diff --git a/tests/testthat/test_multinom_reg_glmnet.R b/tests/testthat/test_multinom_reg_glmnet.R
index 65d774194..4115abbda 100644
--- a/tests/testthat/test_multinom_reg_glmnet.R
+++ b/tests/testthat/test_multinom_reg_glmnet.R
@@ -20,7 +20,7 @@ test_that('glmnet execution', {
   skip_if_not_installed("glmnet")
 
   expect_error(
-    fit_xy(
+    res <- fit_xy(
       multinom_reg() %>% set_engine("glmnet"),
       control = ctrl,
       x = iris[, 1:4],
@@ -29,6 +29,9 @@ test_that('glmnet execution', {
     regexp = NA
   )
 
+  expect_true(has_multi_predict(res))
+  expect_equal(multi_predict_args(res), "penalty")
+
   expect_error(
     glmnet_xy_catch <- fit_xy(
       multinom_reg() %>% set_engine("glmnet"),
diff --git a/tests/testthat/test_nearest_neighbor_kknn.R b/tests/testthat/test_nearest_neighbor_kknn.R
index b56544ebd..3a0039b70 100644
--- a/tests/testthat/test_nearest_neighbor_kknn.R
+++ b/tests/testthat/test_nearest_neighbor_kknn.R
@@ -39,7 +39,7 @@ test_that('kknn execution', {
   # nominal
   # expect no error
   expect_error(
-    fit_xy(
+    res <- fit_xy(
       iris_basic,
       control = ctrl,
       x = iris[, c("Sepal.Length", "Petal.Width")],
@@ -48,6 +48,9 @@ test_that('kknn execution', {
     regexp = NA
   )
 
+  expect_true(has_multi_predict(res))
+  expect_equal(multi_predict_args(res), "neighbors")
+
   expect_error(
     fit(
       iris_basic,
diff --git a/tests/testthat/test_rand_forest_ranger.R b/tests/testthat/test_rand_forest_ranger.R
index 3523613e7..937a82563 100644
--- a/tests/testthat/test_rand_forest_ranger.R
+++ b/tests/testthat/test_rand_forest_ranger.R
@@ -370,6 +370,9 @@ test_that('ranger classification prediction', {
       control = ctrl
     )
 
+  expect_false(has_multi_predict(xy_class_fit))
+  expect_equal(multi_predict_args(xy_class_fit), NA_character_)
+
   xy_class_pred <- predict(xy_class_fit$fit, data = iris[c(1, 51, 101), 1:4])$prediction
   xy_class_pred <- colnames(xy_class_pred)[apply(xy_class_pred, 1, which.max)]
   xy_class_pred <- factor(xy_class_pred, levels = levels(iris$Species))
diff --git a/tests/testthat/test_surv_reg_flexsurv.R b/tests/testthat/test_surv_reg_flexsurv.R
index f2985464a..85f3051a4 100644
--- a/tests/testthat/test_surv_reg_flexsurv.R
+++ b/tests/testthat/test_surv_reg_flexsurv.R
@@ -37,6 +37,9 @@ test_that('flexsurv execution', {
     ),
     regexp = NA
   )
+  expect_false(has_multi_predict(res))
+  expect_equal(multi_predict_args(res), NA_character_)
+
   expect_error(
     res <- fit_xy(
       surv_basic,
diff --git a/tests/testthat/test_svm_poly.R b/tests/testthat/test_svm_poly.R
index bb5b2a65c..3f0cdc9ac 100644
--- a/tests/testthat/test_svm_poly.R
+++ b/tests/testthat/test_svm_poly.R
@@ -115,7 +115,7 @@ test_that('svm poly regression', {
   skip_if_not_installed("kernlab")
 
   expect_error(
-    fit_xy(
+    res <- fit_xy(
       reg_mod,
       control = ctrl,
       x = iris[,2:4],
@@ -124,6 +124,9 @@ test_that('svm poly regression', {
     regexp = NA
   )
 
+  expect_false(has_multi_predict(res))
+  expect_equal(multi_predict_args(res), NA_character_)
+
   expect_error(
     fit(
       reg_mod,
diff --git a/tests/testthat/test_svm_rbf.R b/tests/testthat/test_svm_rbf.R
index 41523e0a7..088f3cd32 100644
--- a/tests/testthat/test_svm_rbf.R
+++ b/tests/testthat/test_svm_rbf.R
@@ -94,7 +94,7 @@ test_that('svm poly regression', {
   skip_if_not_installed("kernlab")
 
   expect_error(
-    fit_xy(
+    res <- fit_xy(
       reg_mod,
       control = ctrl,
       x = iris[,2:4],
@@ -102,6 +102,8 @@ test_that('svm poly regression', {
     ),
     regexp = NA
   )
+  expect_false(has_multi_predict(res))
+  expect_equal(multi_predict_args(res), NA_character_)
 
   expect_error(
     fit(