From 83c5900d8b2ce8ecd3cbe137df19c804c8e8b856 Mon Sep 17 00:00:00 2001
From: Julia Silge <julia.silge@gmail.com>
Date: Thu, 6 May 2021 14:06:56 -0600
Subject: [PATCH 1/7] Error for glmnet models is there is not exactly one value
 for penalty

---
 R/linear_reg.R   | 7 +++++++
 R/logistic_reg.R | 7 +++++++
 2 files changed, 14 insertions(+)

diff --git a/R/linear_reg.R b/R/linear_reg.R
index f3137ef19..63e7c7872 100644
--- a/R/linear_reg.R
+++ b/R/linear_reg.R
@@ -112,6 +112,13 @@ translate.linear_reg <- function(x, engine = x$engine, ...) {
     # Since the `fit` information is gone for the penalty, we need to have an
     # evaluated value for the parameter.
     x$args$penalty <- rlang::eval_tidy(x$args$penalty)
+    if (length(x$args$penalty) != 1) {
+      rlang::abort(c(
+        "For the glmnet engine, `penalty` must be a single number.",
+        glue::glue("There are {length(x$args$penalty)} values for `penalty`."),
+        "To try multiple values for total regularization, use the tune package."
+      ))
+    }
   }
 
   x
diff --git a/R/logistic_reg.R b/R/logistic_reg.R
index 4d7b2d3ab..d3b0611d3 100644
--- a/R/logistic_reg.R
+++ b/R/logistic_reg.R
@@ -115,6 +115,13 @@ translate.logistic_reg <- function(x, engine = x$engine, ...) {
     # Since the `fit` information is gone for the penalty, we need to have an
     # evaluated value for the parameter.
     x$args$penalty <- rlang::eval_tidy(x$args$penalty)
+    if (length(x$args$penalty) != 1) {
+      rlang::abort(c(
+        "For the glmnet engine, `penalty` must be a single number.",
+        glue::glue("There are {length(x$args$penalty)} values for `penalty`."),
+        "To try multiple values for total regularization, use the tune package."
+      ))
+    }
   }
 
   if (engine == "LiblineaR") {

From b8f02a3665a78946246e9385ad4eacb082998e9c Mon Sep 17 00:00:00 2001
From: Julia Silge <julia.silge@gmail.com>
Date: Thu, 6 May 2021 14:10:37 -0600
Subject: [PATCH 2/7] Updates tests for new glmnet error

---
 tests/testthat/test_linear_reg.R   | 43 +++++++++---------------------
 tests/testthat/test_logistic_reg.R | 43 +++++++++---------------------
 tests/testthat/test_multinom_reg.R | 20 +++++---------
 3 files changed, 33 insertions(+), 73 deletions(-)

diff --git a/tests/testthat/test_linear_reg.R b/tests/testthat/test_linear_reg.R
index 4c0617cec..b839572e8 100644
--- a/tests/testthat/test_linear_reg.R
+++ b/tests/testthat/test_linear_reg.R
@@ -15,7 +15,10 @@ hpc <- hpc_data[1:150, c(2:5, 8)]
 test_that('primary arguments', {
   basic <- linear_reg()
   basic_lm <- translate(basic %>% set_engine("lm"))
-  basic_glmnet <- translate(basic %>% set_engine("glmnet"))
+  expect_error(
+    basic_glmnet <- translate(basic %>% set_engine("glmnet")),
+    "For the glmnet engine, `penalty` must be a single"
+  )
   basic_stan <- translate(basic %>% set_engine("stan"))
   basic_spark <- translate(basic %>% set_engine("spark"))
   expect_equal(basic_lm$method$fit$args,
@@ -25,14 +28,6 @@ test_that('primary arguments', {
                  weights = expr(missing_arg())
                )
   )
-  expect_equal(basic_glmnet$method$fit$args,
-               list(
-                 x = expr(missing_arg()),
-                 y = expr(missing_arg()),
-                 weights = expr(missing_arg()),
-                 family = "gaussian"
-               )
-  )
   expect_equal(basic_stan$method$fit$args,
                list(
                  formula = expr(missing_arg()),
@@ -51,17 +46,11 @@ test_that('primary arguments', {
   )
 
   mixture <- linear_reg(mixture = 0.128)
-  mixture_glmnet <- translate(mixture %>% set_engine("glmnet"))
-  mixture_spark <- translate(mixture %>% set_engine("spark"))
-  expect_equal(mixture_glmnet$method$fit$args,
-               list(
-                 x = expr(missing_arg()),
-                 y = expr(missing_arg()),
-                 weights = expr(missing_arg()),
-                 alpha = new_empty_quosure(0.128),
-                 family = "gaussian"
-               )
+  expect_error(
+    mixture_glmnet <- translate(mixture %>% set_engine("glmnet")),
+    "For the glmnet engine, `penalty` must be a single"
   )
+  mixture_spark <- translate(mixture %>% set_engine("spark"))
   expect_equal(mixture_spark$method$fit$args,
                list(
                  x = expr(missing_arg()),
@@ -92,17 +81,11 @@ test_that('primary arguments', {
   )
 
   mixture_v <- linear_reg(mixture = varying())
-  mixture_v_glmnet <- translate(mixture_v %>% set_engine("glmnet"))
-  mixture_v_spark <- translate(mixture_v %>% set_engine("spark"))
-  expect_equal(mixture_v_glmnet$method$fit$args,
-               list(
-                 x = expr(missing_arg()),
-                 y = expr(missing_arg()),
-                 weights = expr(missing_arg()),
-                 alpha = new_empty_quosure(varying()),
-                 family = "gaussian"
-               )
+  expect_error(
+    mixture_v_glmnet <- translate(mixture_v %>% set_engine("glmnet")),
+    "For the glmnet engine, `penalty` must be a single"
   )
+  mixture_v_spark <- translate(mixture_v %>% set_engine("spark"))
   expect_equal(mixture_v_spark$method$fit$args,
                list(
                  x = expr(missing_arg()),
@@ -125,7 +108,7 @@ test_that('engine arguments', {
                )
   )
 
-  glmnet_nlam <- linear_reg() %>% set_engine("glmnet", nlambda = 10)
+  glmnet_nlam <- linear_reg(penalty = 0.1) %>% set_engine("glmnet", nlambda = 10)
   expect_equal(translate(glmnet_nlam)$method$fit$args,
                list(
                  x = expr(missing_arg()),
diff --git a/tests/testthat/test_logistic_reg.R b/tests/testthat/test_logistic_reg.R
index b19806dae..5b2c9df1b 100644
--- a/tests/testthat/test_logistic_reg.R
+++ b/tests/testthat/test_logistic_reg.R
@@ -16,7 +16,10 @@ hpc <- hpc_data[1:150, c(2:5, 8)]
 test_that('primary arguments', {
   basic <- logistic_reg()
   basic_glm <- translate(basic %>% set_engine("glm"))
-  basic_glmnet <- translate(basic %>% set_engine("glmnet"))
+  expect_error(
+    basic_glmnet <- translate(basic %>% set_engine("glmnet")),
+    "For the glmnet engine, `penalty` must be a single"
+  )
   basic_liblinear <- translate(basic %>% set_engine("LiblineaR"))
   basic_stan <- translate(basic %>% set_engine("stan"))
   basic_spark <- translate(basic %>% set_engine("spark"))
@@ -28,14 +31,6 @@ test_that('primary arguments', {
                  family = expr(stats::binomial)
                )
   )
-  expect_equal(basic_glmnet$method$fit$args,
-               list(
-                 x = expr(missing_arg()),
-                 y = expr(missing_arg()),
-                 weights = expr(missing_arg()),
-                 family = "binomial"
-               )
-  )
   expect_equal(basic_liblinear$method$fit$args,
                list(
                  x = expr(missing_arg()),
@@ -63,17 +58,11 @@ test_that('primary arguments', {
   )
 
   mixture <- logistic_reg(mixture = 0.128)
-  mixture_glmnet <- translate(mixture %>% set_engine("glmnet"))
-  mixture_spark <- translate(mixture %>% set_engine("spark"))
-  expect_equal(mixture_glmnet$method$fit$args,
-               list(
-                 x = expr(missing_arg()),
-                 y = expr(missing_arg()),
-                 weights = expr(missing_arg()),
-                 alpha = new_empty_quosure(0.128),
-                 family = "binomial"
-               )
+  expect_error(
+    mixture_glmnet <- translate(mixture %>% set_engine("glmnet")),
+    "For the glmnet engine, `penalty` must be a single"
   )
+  mixture_spark <- translate(mixture %>% set_engine("spark"))
   expect_equal(mixture_spark$method$fit$args,
                list(
                  x = expr(missing_arg()),
@@ -116,18 +105,12 @@ test_that('primary arguments', {
   )
 
   mixture_v <- logistic_reg(mixture = varying())
-  mixture_v_glmnet <- translate(mixture_v %>% set_engine("glmnet"))
+  expect_error(
+    mixture_v_glmnet <- translate(mixture_v %>% set_engine("glmnet")),
+    "For the glmnet engine, `penalty` must be a single"
+  )
   mixture_v_liblinear <- translate(mixture_v %>% set_engine("LiblineaR"))
   mixture_v_spark <- translate(mixture_v %>% set_engine("spark"))
-  expect_equal(mixture_v_glmnet$method$fit$args,
-               list(
-                 x = expr(missing_arg()),
-                 y = expr(missing_arg()),
-                 weights = expr(missing_arg()),
-                 alpha = new_empty_quosure(varying()),
-                 family = "binomial"
-               )
-  )
   expect_equal(mixture_v_liblinear$method$fit$args,
                list(
                  x = expr(missing_arg()),
@@ -194,7 +177,7 @@ test_that('engine arguments', {
       )
     )
 
-  glmnet_nlam <- logistic_reg()
+  glmnet_nlam <- logistic_reg(penalty = 0.1)
   expect_equal(
     translate(glmnet_nlam %>% set_engine("glmnet", nlambda = 10))$method$fit$args,
     list(
diff --git a/tests/testthat/test_multinom_reg.R b/tests/testthat/test_multinom_reg.R
index a373159b6..6a1b0037d 100644
--- a/tests/testthat/test_multinom_reg.R
+++ b/tests/testthat/test_multinom_reg.R
@@ -13,17 +13,11 @@ hpc <- hpc_data[1:150, c(2:5, 8)]
 
 test_that('primary arguments', {
   basic <- multinom_reg()
-  basic_glmnet <- translate(basic %>% set_engine("glmnet"))
-  expect_equal(basic_glmnet$method$fit$args,
-               list(
-                 x = expr(missing_arg()),
-                 y = expr(missing_arg()),
-                 weights = expr(missing_arg()),
-                 family = "multinomial"
-               )
+  expect_error(
+    basic_glmnet <- translate(basic %>% set_engine("glmnet")),
+    "For the glmnet engine, `penalty` must be a single"
   )
-
-  mixture <- multinom_reg(mixture = 0.128)
+  mixture <- multinom_reg(penalty = 0.1, mixture = 0.128)
   mixture_glmnet <- translate(mixture %>% set_engine("glmnet"))
   expect_equal(mixture_glmnet$method$fit$args,
                list(
@@ -46,7 +40,7 @@ test_that('primary arguments', {
                )
   )
 
-  mixture_v <- multinom_reg(mixture = varying())
+  mixture_v <- multinom_reg(penalty = 0.01, mixture = varying())
   mixture_v_glmnet <- translate(mixture_v %>% set_engine("glmnet"))
   expect_equal(mixture_v_glmnet$method$fit$args,
                list(
@@ -61,7 +55,7 @@ test_that('primary arguments', {
 })
 
 test_that('engine arguments', {
-  glmnet_nlam <- multinom_reg()
+  glmnet_nlam <- multinom_reg(penalty = 0.01)
   expect_equal(
     translate(glmnet_nlam %>% set_engine("glmnet", nlambda = 10))$method$fit$args,
     list(
@@ -117,5 +111,5 @@ test_that('bad input', {
   expect_error(multinom_reg(mode = "regression"))
   expect_error(translate(multinom_reg() %>% set_engine("wat?")))
   expect_error(translate(multinom_reg() %>% set_engine()))
-  expect_warning(translate(multinom_reg() %>% set_engine("glmnet", x = hpc[,1:3], y = hpc$class)))
+  expect_warning(translate(multinom_reg(penalty = 0.01) %>% set_engine("glmnet", x = hpc[,1:3], y = hpc$class)))
 })

From ec16cc932dcbae08159b25b26326a5bc5194f05d Mon Sep 17 00:00:00 2001
From: Julia Silge <julia.silge@gmail.com>
Date: Thu, 6 May 2021 19:51:21 -0600
Subject: [PATCH 3/7] Update examples + more docs to accomodate new glmnet
 error

---
 R/engines.R              | 2 +-
 man/linear_reg.Rd        | 5 ++++-
 man/logistic_reg.Rd      | 5 ++++-
 man/multinom_reg.Rd      | 5 ++++-
 man/rmd/linear-reg.Rmd   | 2 +-
 man/rmd/logistic-reg.Rmd | 2 +-
 man/rmd/multinom-reg.Rmd | 2 +-
 man/set_engine.Rd        | 2 +-
 8 files changed, 17 insertions(+), 8 deletions(-)

diff --git a/R/engines.R b/R/engines.R
index d96d3e044..913cf8a1e 100644
--- a/R/engines.R
+++ b/R/engines.R
@@ -82,7 +82,7 @@ load_libs <- function(x, quiet, attach = FALSE) {
 #' @examples
 #' # First, set general arguments using the standardized names
 #' mod <-
-#'   logistic_reg(mixture = 1/3) %>%
+#'   logistic_reg(penalty = 0.01, mixture = 1/3) %>%
 #'   # now say how you want to fit the model and another other options
 #'   set_engine("glmnet", nlambda = 10)
 #' translate(mod, engine = "glmnet")
diff --git a/man/linear_reg.Rd b/man/linear_reg.Rd
index 53057d647..388a57325 100644
--- a/man/linear_reg.Rd
+++ b/man/linear_reg.Rd
@@ -86,12 +86,15 @@ call. For this type of model, the template of the fit calls are below.
 }
 }
 
-\subsection{glmnet}{\if{html}{\out{<div class="r">}}\preformatted{linear_reg() \%>\% 
+\subsection{glmnet}{\if{html}{\out{<div class="r">}}\preformatted{linear_reg(penalty = varying()) \%>\% 
   set_engine("glmnet") \%>\% 
   set_mode("regression") \%>\% 
   translate()
 }\if{html}{\out{</div>}}\preformatted{## Linear Regression Model Specification (regression)
 ## 
+## Main Arguments:
+##   penalty = varying()
+## 
 ## Computational engine: glmnet 
 ## 
 ## Model fit template:
diff --git a/man/logistic_reg.Rd b/man/logistic_reg.Rd
index fc05bb5ff..9ce67be95 100644
--- a/man/logistic_reg.Rd
+++ b/man/logistic_reg.Rd
@@ -86,12 +86,15 @@ call. For this type of model, the template of the fit calls are below.
 }
 }
 
-\subsection{glmnet}{\if{html}{\out{<div class="r">}}\preformatted{logistic_reg() \%>\% 
+\subsection{glmnet}{\if{html}{\out{<div class="r">}}\preformatted{logistic_reg(penalty = varying()) \%>\% 
   set_engine("glmnet") \%>\% 
   set_mode("classification") \%>\% 
   translate()
 }\if{html}{\out{</div>}}\preformatted{## Logistic Regression Model Specification (classification)
 ## 
+## Main Arguments:
+##   penalty = varying()
+## 
 ## Computational engine: glmnet 
 ## 
 ## Model fit template:
diff --git a/man/multinom_reg.Rd b/man/multinom_reg.Rd
index feb930900..301c3dc72 100644
--- a/man/multinom_reg.Rd
+++ b/man/multinom_reg.Rd
@@ -67,12 +67,15 @@ reloaded and reattached to the \code{parsnip} object.
 \section{Engine Details}{
 Engines may have pre-set default arguments when executing the model fit
 call. For this type of model, the template of the fit calls are below.
-\subsection{glmnet}{\if{html}{\out{<div class="r">}}\preformatted{multinom_reg() \%>\% 
+\subsection{glmnet}{\if{html}{\out{<div class="r">}}\preformatted{multinom_reg(penalty = varying()) \%>\% 
   set_engine("glmnet") \%>\% 
   set_mode("classification") \%>\% 
   translate()
 }\if{html}{\out{</div>}}\preformatted{## Multinomial Regression Model Specification (classification)
 ## 
+## Main Arguments:
+##   penalty = varying()
+## 
 ## Computational engine: glmnet 
 ## 
 ## Model fit template:
diff --git a/man/rmd/linear-reg.Rmd b/man/rmd/linear-reg.Rmd
index 03f1da44e..bf92a477a 100644
--- a/man/rmd/linear-reg.Rmd
+++ b/man/rmd/linear-reg.Rmd
@@ -17,7 +17,7 @@ linear_reg() %>%
 ## glmnet
 
 ```{r glmnet-csl}
-linear_reg() %>% 
+linear_reg(penalty = varying()) %>% 
   set_engine("glmnet") %>% 
   set_mode("regression") %>% 
   translate()
diff --git a/man/rmd/logistic-reg.Rmd b/man/rmd/logistic-reg.Rmd
index 656e24f70..e7e343ddf 100644
--- a/man/rmd/logistic-reg.Rmd
+++ b/man/rmd/logistic-reg.Rmd
@@ -18,7 +18,7 @@ logistic_reg() %>%
 ## glmnet
 
 ```{r glmnet-csl}
-logistic_reg() %>% 
+logistic_reg(penalty = varying()) %>% 
   set_engine("glmnet") %>% 
   set_mode("classification") %>% 
   translate()
diff --git a/man/rmd/multinom-reg.Rmd b/man/rmd/multinom-reg.Rmd
index 5d08847d2..12f2a1b59 100644
--- a/man/rmd/multinom-reg.Rmd
+++ b/man/rmd/multinom-reg.Rmd
@@ -9,7 +9,7 @@ For this type of model, the template of the fit calls are below.
 ## glmnet
 
 ```{r glmnet-cls}
-multinom_reg() %>% 
+multinom_reg(penalty = varying()) %>% 
   set_engine("glmnet") %>% 
   set_mode("classification") %>% 
   translate()
diff --git a/man/set_engine.Rd b/man/set_engine.Rd
index 889451351..d19d67293 100644
--- a/man/set_engine.Rd
+++ b/man/set_engine.Rd
@@ -26,7 +26,7 @@ to fit the model, along with any arguments specific to that software.
 \examples{
 # First, set general arguments using the standardized names
 mod <-
-  logistic_reg(mixture = 1/3) \%>\%
+  logistic_reg(penalty = 0.01, mixture = 1/3) \%>\%
   # now say how you want to fit the model and another other options
   set_engine("glmnet", nlambda = 10)
 translate(mod, engine = "glmnet")

From 0d03d3303b002f2e88c1e850d29a67381b394911 Mon Sep 17 00:00:00 2001
From: Julia Silge <julia.silge@gmail.com>
Date: Thu, 6 May 2021 20:01:42 -0600
Subject: [PATCH 4/7] One more example to fix for new glmnet error

---
 R/translate.R    | 2 +-
 man/translate.Rd | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/translate.R b/R/translate.R
index 2d697179d..4c2064db6 100644
--- a/R/translate.R
+++ b/R/translate.R
@@ -38,7 +38,7 @@
 #' translate(lm_spec, engine = "spark")
 #'
 #' # with a placeholder for an unknown argument value:
-#' translate(linear_reg(mixture = varying()), engine = "glmnet")
+#' translate(linear_reg(penalty = varying(), mixture = varying()), engine = "glmnet")
 #'
 #' @export
 
diff --git a/man/translate.Rd b/man/translate.Rd
index 11bec9c97..c9a8b0400 100644
--- a/man/translate.Rd
+++ b/man/translate.Rd
@@ -52,6 +52,6 @@ translate(lm_spec, engine = "lm")
 translate(lm_spec, engine = "spark")
 
 # with a placeholder for an unknown argument value:
-translate(linear_reg(mixture = varying()), engine = "glmnet")
+translate(linear_reg(penalty = varying(), mixture = varying()), engine = "glmnet")
 
 }

From cd4166ad6ee0219a98f68c2cd88c5f1b1a2dbd34 Mon Sep 17 00:00:00 2001
From: Julia Silge <julia.silge@gmail.com>
Date: Mon, 10 May 2021 17:22:21 -0600
Subject: [PATCH 5/7] Switch out for a single value of penalty in glmnet doc
 subsections

---
 man/linear_reg.Rd        | 4 ++--
 man/logistic_reg.Rd      | 4 ++--
 man/multinom_reg.Rd      | 4 ++--
 man/rmd/linear-reg.Rmd   | 2 +-
 man/rmd/logistic-reg.Rmd | 2 +-
 man/rmd/multinom-reg.Rmd | 2 +-
 6 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/man/linear_reg.Rd b/man/linear_reg.Rd
index 388a57325..cd031b12c 100644
--- a/man/linear_reg.Rd
+++ b/man/linear_reg.Rd
@@ -86,14 +86,14 @@ call. For this type of model, the template of the fit calls are below.
 }
 }
 
-\subsection{glmnet}{\if{html}{\out{<div class="r">}}\preformatted{linear_reg(penalty = varying()) \%>\% 
+\subsection{glmnet}{\if{html}{\out{<div class="r">}}\preformatted{linear_reg(penalty = 0.1) \%>\% 
   set_engine("glmnet") \%>\% 
   set_mode("regression") \%>\% 
   translate()
 }\if{html}{\out{</div>}}\preformatted{## Linear Regression Model Specification (regression)
 ## 
 ## Main Arguments:
-##   penalty = varying()
+##   penalty = 0.1
 ## 
 ## Computational engine: glmnet 
 ## 
diff --git a/man/logistic_reg.Rd b/man/logistic_reg.Rd
index 9ce67be95..cdb6b73c8 100644
--- a/man/logistic_reg.Rd
+++ b/man/logistic_reg.Rd
@@ -86,14 +86,14 @@ call. For this type of model, the template of the fit calls are below.
 }
 }
 
-\subsection{glmnet}{\if{html}{\out{<div class="r">}}\preformatted{logistic_reg(penalty = varying()) \%>\% 
+\subsection{glmnet}{\if{html}{\out{<div class="r">}}\preformatted{logistic_reg(penalty = 0.1) \%>\% 
   set_engine("glmnet") \%>\% 
   set_mode("classification") \%>\% 
   translate()
 }\if{html}{\out{</div>}}\preformatted{## Logistic Regression Model Specification (classification)
 ## 
 ## Main Arguments:
-##   penalty = varying()
+##   penalty = 0.1
 ## 
 ## Computational engine: glmnet 
 ## 
diff --git a/man/multinom_reg.Rd b/man/multinom_reg.Rd
index 301c3dc72..a1ad4138e 100644
--- a/man/multinom_reg.Rd
+++ b/man/multinom_reg.Rd
@@ -67,14 +67,14 @@ reloaded and reattached to the \code{parsnip} object.
 \section{Engine Details}{
 Engines may have pre-set default arguments when executing the model fit
 call. For this type of model, the template of the fit calls are below.
-\subsection{glmnet}{\if{html}{\out{<div class="r">}}\preformatted{multinom_reg(penalty = varying()) \%>\% 
+\subsection{glmnet}{\if{html}{\out{<div class="r">}}\preformatted{multinom_reg(penalty = 0.1) \%>\% 
   set_engine("glmnet") \%>\% 
   set_mode("classification") \%>\% 
   translate()
 }\if{html}{\out{</div>}}\preformatted{## Multinomial Regression Model Specification (classification)
 ## 
 ## Main Arguments:
-##   penalty = varying()
+##   penalty = 0.1
 ## 
 ## Computational engine: glmnet 
 ## 
diff --git a/man/rmd/linear-reg.Rmd b/man/rmd/linear-reg.Rmd
index bf92a477a..18a8c66c4 100644
--- a/man/rmd/linear-reg.Rmd
+++ b/man/rmd/linear-reg.Rmd
@@ -17,7 +17,7 @@ linear_reg() %>%
 ## glmnet
 
 ```{r glmnet-csl}
-linear_reg(penalty = varying()) %>% 
+linear_reg(penalty = 0.1) %>% 
   set_engine("glmnet") %>% 
   set_mode("regression") %>% 
   translate()
diff --git a/man/rmd/logistic-reg.Rmd b/man/rmd/logistic-reg.Rmd
index e7e343ddf..74f483f07 100644
--- a/man/rmd/logistic-reg.Rmd
+++ b/man/rmd/logistic-reg.Rmd
@@ -18,7 +18,7 @@ logistic_reg() %>%
 ## glmnet
 
 ```{r glmnet-csl}
-logistic_reg(penalty = varying()) %>% 
+logistic_reg(penalty = 0.1) %>% 
   set_engine("glmnet") %>% 
   set_mode("classification") %>% 
   translate()
diff --git a/man/rmd/multinom-reg.Rmd b/man/rmd/multinom-reg.Rmd
index 12f2a1b59..3a48cd372 100644
--- a/man/rmd/multinom-reg.Rmd
+++ b/man/rmd/multinom-reg.Rmd
@@ -9,7 +9,7 @@ For this type of model, the template of the fit calls are below.
 ## glmnet
 
 ```{r glmnet-cls}
-multinom_reg(penalty = varying()) %>% 
+multinom_reg(penalty = 0.1) %>% 
   set_engine("glmnet") %>% 
   set_mode("classification") %>% 
   translate()

From 5acfdae3436aeaf0f1d9ab25e9b3063d40d16a18 Mon Sep 17 00:00:00 2001
From: Max Kuhn <mxkuhn@gmail.com>
Date: Tue, 11 May 2021 13:10:09 -0400
Subject: [PATCH 6/7] remove unnecessary set_mode() calls

---
 man/contr_one_hot.Rd     | 10 ++++++----
 man/linear_reg.Rd        |  5 -----
 man/logistic_reg.Rd      |  6 ------
 man/multinom_reg.Rd      |  4 ----
 man/rmd/linear-reg.Rmd   |  5 -----
 man/rmd/logistic-reg.Rmd |  6 ------
 man/rmd/multinom-reg.Rmd |  4 ----
 7 files changed, 6 insertions(+), 34 deletions(-)

diff --git a/man/contr_one_hot.Rd b/man/contr_one_hot.Rd
index df945bebd..a8c21d593 100644
--- a/man/contr_one_hot.Rd
+++ b/man/contr_one_hot.Rd
@@ -39,14 +39,16 @@ levels(penguins$species)
 }\if{html}{\out{</div>}}\preformatted{## [1] "Biscoe"    "Dream"     "Torgersen"
 }\if{html}{\out{<div class="r">}}\preformatted{model.matrix(~ species + island, data = penguins) \%>\% 
   colnames()
-}\if{html}{\out{</div>}}\preformatted{## [1] "(Intercept)"      "speciesChinstrap" "speciesGentoo"    "islandDream"      "islandTorgersen"
+}\if{html}{\out{</div>}}\preformatted{## [1] "(Intercept)"      "speciesChinstrap" "speciesGentoo"    "islandDream"     
+## [5] "islandTorgersen"
 }
 
 For a formula with no intercept, the first factor is expanded to
 indicators for \emph{all} factor levels but all other factors are expanded to
 all but one (as above):\if{html}{\out{<div class="r">}}\preformatted{model.matrix(~ 0 + species + island, data = penguins) \%>\% 
   colnames()
-}\if{html}{\out{</div>}}\preformatted{## [1] "speciesAdelie"    "speciesChinstrap" "speciesGentoo"    "islandDream"      "islandTorgersen"
+}\if{html}{\out{</div>}}\preformatted{## [1] "speciesAdelie"    "speciesChinstrap" "speciesGentoo"    "islandDream"     
+## [5] "islandTorgersen"
 }
 
 For inference, this hybrid encoding can be problematic.
@@ -59,8 +61,8 @@ options(contrasts = new_contr)
 
 model.matrix(~ species + island, data = penguins) \%>\% 
   colnames()
-}\if{html}{\out{</div>}}\preformatted{## [1] "(Intercept)"      "speciesAdelie"    "speciesChinstrap" "speciesGentoo"    "islandBiscoe"    
-## [6] "islandDream"      "islandTorgersen"
+}\if{html}{\out{</div>}}\preformatted{## [1] "(Intercept)"      "speciesAdelie"    "speciesChinstrap" "speciesGentoo"   
+## [5] "islandBiscoe"     "islandDream"      "islandTorgersen"
 }\if{html}{\out{<div class="r">}}\preformatted{options(contrasts = old_contr)
 }\if{html}{\out{</div>}}
 
diff --git a/man/linear_reg.Rd b/man/linear_reg.Rd
index cd031b12c..b11544e60 100644
--- a/man/linear_reg.Rd
+++ b/man/linear_reg.Rd
@@ -75,7 +75,6 @@ Engines may have pre-set default arguments when executing the model fit
 call. For this type of model, the template of the fit calls are below.
 \subsection{lm}{\if{html}{\out{<div class="r">}}\preformatted{linear_reg() \%>\% 
   set_engine("lm") \%>\% 
-  set_mode("regression") \%>\% 
   translate()
 }\if{html}{\out{</div>}}\preformatted{## Linear Regression Model Specification (regression)
 ## 
@@ -88,7 +87,6 @@ call. For this type of model, the template of the fit calls are below.
 
 \subsection{glmnet}{\if{html}{\out{<div class="r">}}\preformatted{linear_reg(penalty = 0.1) \%>\% 
   set_engine("glmnet") \%>\% 
-  set_mode("regression") \%>\% 
   translate()
 }\if{html}{\out{</div>}}\preformatted{## Linear Regression Model Specification (regression)
 ## 
@@ -115,7 +113,6 @@ results.
 
 \subsection{stan}{\if{html}{\out{<div class="r">}}\preformatted{linear_reg() \%>\% 
   set_engine("stan") \%>\% 
-  set_mode("regression") \%>\% 
   translate()
 }\if{html}{\out{</div>}}\preformatted{## Linear Regression Model Specification (regression)
 ## 
@@ -138,7 +135,6 @@ predictive distribution as appropriate) is returned.
 
 \subsection{spark}{\if{html}{\out{<div class="r">}}\preformatted{linear_reg() \%>\% 
   set_engine("spark") \%>\% 
-  set_mode("regression") \%>\% 
   translate()
 }\if{html}{\out{</div>}}\preformatted{## Linear Regression Model Specification (regression)
 ## 
@@ -152,7 +148,6 @@ predictive distribution as appropriate) is returned.
 
 \subsection{keras}{\if{html}{\out{<div class="r">}}\preformatted{linear_reg() \%>\% 
   set_engine("keras") \%>\% 
-  set_mode("regression") \%>\% 
   translate()
 }\if{html}{\out{</div>}}\preformatted{## Linear Regression Model Specification (regression)
 ## 
diff --git a/man/logistic_reg.Rd b/man/logistic_reg.Rd
index cdb6b73c8..6dff2100d 100644
--- a/man/logistic_reg.Rd
+++ b/man/logistic_reg.Rd
@@ -74,7 +74,6 @@ Engines may have pre-set default arguments when executing the model fit
 call. For this type of model, the template of the fit calls are below.
 \subsection{glm}{\if{html}{\out{<div class="r">}}\preformatted{logistic_reg() \%>\% 
   set_engine("glm") \%>\% 
-  set_mode("classification") \%>\% 
   translate()
 }\if{html}{\out{</div>}}\preformatted{## Logistic Regression Model Specification (classification)
 ## 
@@ -88,7 +87,6 @@ call. For this type of model, the template of the fit calls are below.
 
 \subsection{glmnet}{\if{html}{\out{<div class="r">}}\preformatted{logistic_reg(penalty = 0.1) \%>\% 
   set_engine("glmnet") \%>\% 
-  set_mode("classification") \%>\% 
   translate()
 }\if{html}{\out{</div>}}\preformatted{## Logistic Regression Model Specification (classification)
 ## 
@@ -115,7 +113,6 @@ results.
 
 \subsection{LiblineaR}{\if{html}{\out{<div class="r">}}\preformatted{logistic_reg() \%>\% 
   set_engine("LiblineaR") \%>\% 
-  set_mode("classification") \%>\% 
   translate()
 }\if{html}{\out{</div>}}\preformatted{## Logistic Regression Model Specification (classification)
 ## 
@@ -138,7 +135,6 @@ parameter estimates.
 
 \subsection{stan}{\if{html}{\out{<div class="r">}}\preformatted{logistic_reg() \%>\% 
   set_engine("stan") \%>\% 
-  set_mode("classification") \%>\% 
   translate()
 }\if{html}{\out{</div>}}\preformatted{## Logistic Regression Model Specification (classification)
 ## 
@@ -161,7 +157,6 @@ predictive distribution as appropriate) is returned.
 
 \subsection{spark}{\if{html}{\out{<div class="r">}}\preformatted{logistic_reg() \%>\% 
   set_engine("spark") \%>\% 
-  set_mode("classification") \%>\% 
   translate()
 }\if{html}{\out{</div>}}\preformatted{## Logistic Regression Model Specification (classification)
 ## 
@@ -175,7 +170,6 @@ predictive distribution as appropriate) is returned.
 
 \subsection{keras}{\if{html}{\out{<div class="r">}}\preformatted{logistic_reg() \%>\% 
   set_engine("keras") \%>\% 
-  set_mode("classification") \%>\% 
   translate()
 }\if{html}{\out{</div>}}\preformatted{## Logistic Regression Model Specification (classification)
 ## 
diff --git a/man/multinom_reg.Rd b/man/multinom_reg.Rd
index a1ad4138e..8dc8b2e5c 100644
--- a/man/multinom_reg.Rd
+++ b/man/multinom_reg.Rd
@@ -69,7 +69,6 @@ Engines may have pre-set default arguments when executing the model fit
 call. For this type of model, the template of the fit calls are below.
 \subsection{glmnet}{\if{html}{\out{<div class="r">}}\preformatted{multinom_reg(penalty = 0.1) \%>\% 
   set_engine("glmnet") \%>\% 
-  set_mode("classification") \%>\% 
   translate()
 }\if{html}{\out{</div>}}\preformatted{## Multinomial Regression Model Specification (classification)
 ## 
@@ -96,7 +95,6 @@ results.
 
 \subsection{nnet}{\if{html}{\out{<div class="r">}}\preformatted{multinom_reg() \%>\% 
   set_engine("nnet") \%>\% 
-  set_mode("classification") \%>\% 
   translate()
 }\if{html}{\out{</div>}}\preformatted{## Multinomial Regression Model Specification (classification)
 ## 
@@ -110,7 +108,6 @@ results.
 
 \subsection{spark}{\if{html}{\out{<div class="r">}}\preformatted{multinom_reg() \%>\% 
   set_engine("spark") \%>\% 
-  set_mode("classification") \%>\% 
   translate()
 }\if{html}{\out{</div>}}\preformatted{## Multinomial Regression Model Specification (classification)
 ## 
@@ -124,7 +121,6 @@ results.
 
 \subsection{keras}{\if{html}{\out{<div class="r">}}\preformatted{multinom_reg() \%>\% 
   set_engine("keras") \%>\% 
-  set_mode("classification") \%>\% 
   translate()
 }\if{html}{\out{</div>}}\preformatted{## Multinomial Regression Model Specification (classification)
 ## 
diff --git a/man/rmd/linear-reg.Rmd b/man/rmd/linear-reg.Rmd
index 18a8c66c4..378f71db8 100644
--- a/man/rmd/linear-reg.Rmd
+++ b/man/rmd/linear-reg.Rmd
@@ -10,7 +10,6 @@ Engines may have pre-set default arguments when executing the model fit call. Fo
 ```{r lm-reg}
 linear_reg() %>% 
   set_engine("lm") %>% 
-  set_mode("regression") %>% 
   translate()
 ```
 
@@ -19,7 +18,6 @@ linear_reg() %>%
 ```{r glmnet-csl}
 linear_reg(penalty = 0.1) %>% 
   set_engine("glmnet") %>% 
-  set_mode("regression") %>% 
   translate()
 ```
 
@@ -37,7 +35,6 @@ penalty results.
 ```{r stan-reg}
 linear_reg() %>% 
   set_engine("stan") %>% 
-  set_mode("regression") %>% 
   translate()
 ```
 
@@ -55,7 +52,6 @@ returned.
 ```{r spark-reg}
 linear_reg() %>% 
   set_engine("spark") %>% 
-  set_mode("regression") %>% 
   translate()
 ```
 
@@ -64,7 +60,6 @@ linear_reg() %>%
 ```{r keras-reg}
 linear_reg() %>% 
   set_engine("keras") %>% 
-  set_mode("regression") %>% 
   translate()
 ```
 
diff --git a/man/rmd/logistic-reg.Rmd b/man/rmd/logistic-reg.Rmd
index 74f483f07..b28b44391 100644
--- a/man/rmd/logistic-reg.Rmd
+++ b/man/rmd/logistic-reg.Rmd
@@ -11,7 +11,6 @@ For this type of model, the template of the fit calls are below.
 ```{r glm-reg}
 logistic_reg() %>% 
   set_engine("glm") %>% 
-  set_mode("classification") %>% 
   translate()
 ```
 
@@ -20,7 +19,6 @@ logistic_reg() %>%
 ```{r glmnet-csl}
 logistic_reg(penalty = 0.1) %>% 
   set_engine("glmnet") %>% 
-  set_mode("classification") %>% 
   translate()
 ```
 
@@ -38,7 +36,6 @@ penalty results.
 ```{r liblinear-reg}
 logistic_reg() %>% 
   set_engine("LiblineaR") %>% 
-  set_mode("classification") %>% 
   translate()
 ```
 
@@ -54,7 +51,6 @@ regularized regression models do not, which will result in different parameter e
 ```{r stan-reg}
 logistic_reg() %>% 
   set_engine("stan") %>% 
-  set_mode("classification") %>% 
   translate()
 ```
 
@@ -72,7 +68,6 @@ returned.
 ```{r spark-reg}
 logistic_reg() %>% 
   set_engine("spark") %>% 
-  set_mode("classification") %>% 
   translate()
 ```
 
@@ -81,7 +76,6 @@ logistic_reg() %>%
 ```{r keras-reg}
 logistic_reg() %>% 
   set_engine("keras") %>% 
-  set_mode("classification") %>% 
   translate()
 ```
 
diff --git a/man/rmd/multinom-reg.Rmd b/man/rmd/multinom-reg.Rmd
index 3a48cd372..2071db327 100644
--- a/man/rmd/multinom-reg.Rmd
+++ b/man/rmd/multinom-reg.Rmd
@@ -11,7 +11,6 @@ For this type of model, the template of the fit calls are below.
 ```{r glmnet-cls}
 multinom_reg(penalty = 0.1) %>% 
   set_engine("glmnet") %>% 
-  set_mode("classification") %>% 
   translate()
 ```
 
@@ -29,7 +28,6 @@ penalty results.
 ```{r nnet-cls}
 multinom_reg() %>% 
   set_engine("nnet") %>% 
-  set_mode("classification") %>% 
   translate()
 ```
 
@@ -38,7 +36,6 @@ multinom_reg() %>%
 ```{r spark-cls}
 multinom_reg() %>% 
   set_engine("spark") %>% 
-  set_mode("classification") %>% 
   translate()
 ```
 
@@ -47,7 +44,6 @@ multinom_reg() %>%
 ```{r keras-cls}
 multinom_reg() %>% 
   set_engine("keras") %>% 
-  set_mode("classification") %>% 
   translate()
 ```
 

From 2d9c1d317349f55298b17031eaefe5e5497e966b Mon Sep 17 00:00:00 2001
From: Max Kuhn <mxkuhn@gmail.com>
Date: Tue, 11 May 2021 13:41:34 -0400
Subject: [PATCH 7/7] moved check to function; small mesage edits

---
 R/linear_reg.R   |  8 +-------
 R/logistic_reg.R |  8 +-------
 R/misc.R         | 11 ++++++++++-
 3 files changed, 12 insertions(+), 15 deletions(-)

diff --git a/R/linear_reg.R b/R/linear_reg.R
index 63e7c7872..372c7f503 100644
--- a/R/linear_reg.R
+++ b/R/linear_reg.R
@@ -112,13 +112,7 @@ translate.linear_reg <- function(x, engine = x$engine, ...) {
     # Since the `fit` information is gone for the penalty, we need to have an
     # evaluated value for the parameter.
     x$args$penalty <- rlang::eval_tidy(x$args$penalty)
-    if (length(x$args$penalty) != 1) {
-      rlang::abort(c(
-        "For the glmnet engine, `penalty` must be a single number.",
-        glue::glue("There are {length(x$args$penalty)} values for `penalty`."),
-        "To try multiple values for total regularization, use the tune package."
-      ))
-    }
+    check_glmnet_penalty(x)
   }
 
   x
diff --git a/R/logistic_reg.R b/R/logistic_reg.R
index d3b0611d3..279e48002 100644
--- a/R/logistic_reg.R
+++ b/R/logistic_reg.R
@@ -115,13 +115,7 @@ translate.logistic_reg <- function(x, engine = x$engine, ...) {
     # Since the `fit` information is gone for the penalty, we need to have an
     # evaluated value for the parameter.
     x$args$penalty <- rlang::eval_tidy(x$args$penalty)
-    if (length(x$args$penalty) != 1) {
-      rlang::abort(c(
-        "For the glmnet engine, `penalty` must be a single number.",
-        glue::glue("There are {length(x$args$penalty)} values for `penalty`."),
-        "To try multiple values for total regularization, use the tune package."
-      ))
-    }
+    check_glmnet_penalty(x)
   }
 
   if (engine == "LiblineaR") {
diff --git a/R/misc.R b/R/misc.R
index 5d1fa9876..2332e6afc 100644
--- a/R/misc.R
+++ b/R/misc.R
@@ -323,4 +323,13 @@ stan_conf_int <- function(object, newdata) {
   rlang::eval_tidy(fn)
 }
 
-
+check_glmnet_penalty <- function(x) {
+  if (length(x$args$penalty) != 1) {
+    rlang::abort(c(
+      "For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).",
+      glue::glue("There are {length(x$args$penalty)} values for `penalty`."),
+      "To try multiple values for total regularization, use the tune package.",
+      "To predict multiple penalties, use `multi_predict()`"
+    ))
+  }
+}