From 5ce561dafe136c93454d6df3b63d6d521d3877d1 Mon Sep 17 00:00:00 2001
From: Max Kuhn <mxkuhn@gmail.com>
Date: Tue, 1 Feb 2022 21:56:08 -0500
Subject: [PATCH 01/13] glmnet autoplot method

---
 DESCRIPTION               |   5 +-
 NAMESPACE                 |   3 +
 NEWS.md                   |   3 +
 R/0_imports.R             |   1 +
 R/aaa.R                   |   3 +-
 R/autoplot.R              | 156 ++++++++++++++++++++++++++++++++++++++
 R/zzz.R                   |  61 +--------------
 man/autoplot.model_fit.Rd |  37 +++++++++
 8 files changed, 208 insertions(+), 61 deletions(-)
 create mode 100644 R/autoplot.R
 create mode 100644 man/autoplot.model_fit.Rd

diff --git a/DESCRIPTION b/DESCRIPTION
index 716857000..5d402e28e 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: parsnip
 Title: A Common API to Modeling and Analysis Functions
-Version: 0.1.7.9005
+Version: 0.1.7.9006
 Authors@R: c(
     person("Max", "Kuhn", , "max@rstudio.com", role = c("aut", "cre")),
     person("Davis", "Vaughan", , "davis@rstudio.com", role = "aut"),
@@ -20,6 +20,8 @@ Depends:
 Imports: 
     dplyr (>= 0.8.0.1),
     generics (>= 0.1.0.9000),
+    ggplot2,
+    ggrepel,
     globals,
     glue,
     hardhat (>= 0.1.6.9001),
@@ -39,7 +41,6 @@ Suggests:
     covr,
     dials (>= 0.0.10.9001),
     earth,
-    ggplot2,
     keras,
     kernlab,
     kknn,
diff --git a/NAMESPACE b/NAMESPACE
index 1ef6d6736..21cef744c 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -165,6 +165,8 @@ export(C5.0_train)
 export(C5_rules)
 export(add_rowindex)
 export(augment)
+export(autoplot.glmnet)
+export(autoplot.model_fit)
 export(bag_mars)
 export(bag_tree)
 export(bart)
@@ -309,6 +311,7 @@ importFrom(generics,glance)
 importFrom(generics,required_pkgs)
 importFrom(generics,tidy)
 importFrom(generics,varying_args)
+importFrom(ggplot2,autoplot)
 importFrom(glue,glue_collapse)
 importFrom(hardhat,extract_fit_engine)
 importFrom(hardhat,extract_parameter_dials)
diff --git a/NEWS.md b/NEWS.md
index f16dbcf6d..aa12045aa 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -40,6 +40,9 @@
 
 * `varying_args()` is soft-deprecated in favor of `tune_args()`.
 
+* An `autoplot()` method was added for glmnet objects, showing the coefficient paths versus the penalty values (#642).
+
+
 # parsnip 0.1.7
 
 ## Model Specification Changes
diff --git a/R/0_imports.R b/R/0_imports.R
index 0ea5bb959..1fd648e5d 100644
--- a/R/0_imports.R
+++ b/R/0_imports.R
@@ -16,4 +16,5 @@
 #' @importFrom utils capture.output getFromNamespace globalVariables head
 #' @importFrom utils methods stack
 #' @importFrom vctrs vec_size vec_unique
+#' @importFrom ggplot2 autoplot
 NULL
diff --git a/R/aaa.R b/R/aaa.R
index d55ce51d8..f50f4f71f 100644
--- a/R/aaa.R
+++ b/R/aaa.R
@@ -91,7 +91,8 @@ utils::globalVariables(
     "max_terms", "max_tree", "model", "name", "num_terms", "penalty", "trees",
     "sub_neighbors", ".pred_class", "x", "y", "predictor_indicators",
     "compute_intercept", "remove_intercept", "estimate", "term",
-    "call_info", "component", "component_id", "func", "tunable")
+    "call_info", "component", "component_id", "func", "tunable", "label"
+    )
 )
 
 # nocov end
diff --git a/R/autoplot.R b/R/autoplot.R
new file mode 100644
index 000000000..d5425907b
--- /dev/null
+++ b/R/autoplot.R
@@ -0,0 +1,156 @@
+#' Create a ggplot for a model object
+#'
+#' This method provides a good visualization method for model results.
+#' Currently, only methods for glmnet models are implemented.
+#'
+#' @param object A model fit object.
+#' @param min_penalty A single, non-negative number for the smallest penalty
+#' value that should be shown in the plot. If left `NULL`, the whole data
+#' range is used.
+#' @param best_penalty A single, non-negative number that will show a vertical
+#' line marker. If left `NULL`, no line is shown.
+#' @param top_n A non-negative integer for how many model predictors to label.
+#' The top predictors are ranked by their absolute coefficient value. For
+#' multinomial or multivariate models, the `top_n` terms are selected within
+#' class or response, respectively.
+#' @param ... For [autoplot.glmnet()], options to pass to
+#' [ggrepel::geom_label_repel()]. Otherwise, this argument is ignored.
+#' @return A ggplot object with penalty on the x-axis and coefficients on the
+#' y-axis. For multinomial or multivariate models, the plot is faceted.
+#'
+#' @export
+autoplot.model_fit <- function(object, ...) {
+  autoplot(object$fit, ...)
+}
+
+# glmnet is not a formal dpendency here.
+# unit tests are located at https://github.com/tidymodels/extratests
+
+# nocov start
+
+#' @export
+#' @rdname autoplot.model_fit
+autoplot.glmnet <- function(object, min_penalty = 0, best_penalty = NULL,
+                            top_n = 3L, ...) {
+  autoplot_glmnet(object, min_penalty, best_penalty, top_n, ...)
+}
+
+
+map_glmnet_coefs <- function(x) {
+  coefs <- coef(x)
+  p <- x$dim[1]
+  if (is.list(coefs)) {
+    classes <- names(coefs)
+    coefs <- purrr::map(coefs, reformat_coefs, p = p, penalty = x$lambda)
+    coefs <- purrr::map2_dfr(coefs, classes, ~ .x %>% dplyr::mutate(class = .y))
+  } else {
+    coefs <- reformat_coefs(coefs, p = p, penalty = x$lambda)
+  }
+  coefs
+}
+
+reformat_coefs <- function(x, p, penalty) {
+  x <- as.matrix(x)
+  num_estimates <- nrow(x)
+  if (num_estimates > p) {
+    # The intercept is first
+    x <- x[-(num_estimates - p),, drop = FALSE]
+  }
+  term_lab <- rownames(x)
+  colnames(x) <- paste(seq_along(penalty))
+  x <- tibble::as_tibble(x)
+  x$term <- term_lab
+  x <- tidyr::pivot_longer(x, cols = c(-term), names_to = "index", values_to = "estimate")
+  x$penalty <- rep(penalty, p)
+  x$index <- NULL
+  x
+}
+
+top_coefs <- function(x, top_n = 5) {
+  x %>%
+    dplyr::group_by(term) %>%
+    dplyr::arrange(term, dplyr::desc(abs(estimate))) %>%
+    dplyr::slice(1) %>%
+    dplyr::ungroup() %>%
+    dplyr::arrange(dplyr::desc(abs(estimate))) %>%
+    dplyr::slice(1:top_n)
+}
+
+autoplot_glmnet <- function(x, min_penalty = 0, best_penalty = NULL, top_n = 3L, ...) {
+  if (!is.null(min_penalty)) {
+    check_penalty_value(min_penalty)
+  }
+
+  tidy_coefs <-
+    map_glmnet_coefs(x) %>%
+    dplyr::filter(penalty >= min_penalty)
+
+  actual_min_penalty <- min(tidy_coefs$penalty)
+  num_terms <- length(unique(tidy_coefs$term))
+  top_n <- min(top_n[1], num_terms)
+  if (top_n < 0) {
+    top_n <- 0
+  }
+
+  has_groups <- any(names(tidy_coefs) %in% c("class"))
+
+  # Keep the large values
+  if (has_groups) {
+    label_coefs <-
+      tidy_coefs %>%
+      dplyr::group_nest(class) %>%
+      dplyr::mutate(data = purrr::map(data, top_coefs, top_n = top_n)) %>%
+      dplyr::select(class, data) %>%
+      tidyr::unnest(cols = data)
+  } else {
+    label_coefs <-
+      tidy_coefs %>%
+      top_coefs(top_n)
+  }
+
+  label_coefs <-
+    label_coefs %>%
+    dplyr::mutate(penalty = actual_min_penalty) %>%
+    dplyr::mutate(label = gsub(".pred_no_", "", term))
+
+  # plot the paths and highlight the large values
+  p <-
+    tidy_coefs %>%
+    ggplot2::ggplot(ggplot2::aes(x = penalty, y = estimate, group = term, col = term))
+
+  if (has_groups) {
+    p <- p + ggplot2::facet_wrap(~ class)
+  }
+
+  if (!is.null(best_penalty)) {
+    check_penalty_value(best_penalty)
+    p <- p + ggplot2::geom_vline(xintercept = best_penalty, lty = 3)
+  }
+
+  p <- p +
+    ggplot2::geom_line(alpha = .4, show.legend = FALSE) +
+    ggplot2::scale_x_log10()
+
+  if(top_n > 0) {
+    p <- p +
+      ggrepel::geom_label_repel(
+        data = label_coefs,
+        ggplot2::aes(y = estimate, label = label),
+        show.legend = FALSE,
+        ...
+      )
+  }
+  p
+}
+
+check_penalty_value <- function(x) {
+  cl <- match.call()
+  arg_val <- as.character(cl$x)
+  if (!is.vector(x) || length(x) != 1 || !is.numeric(x) || x < 0) {
+    msg <- paste0("Argument '", arg_val, "' should be a single, non-negative value.")
+    rlang::abort(msg)
+  }
+  invisible(x)
+}
+
+# nocov stop
diff --git a/R/zzz.R b/R/zzz.R
index 1cf9c353c..60e919b37 100644
--- a/R/zzz.R
+++ b/R/zzz.R
@@ -12,6 +12,9 @@
   s3_register("generics::required_pkgs", "model_fit")
   s3_register("generics::required_pkgs", "model_spec")
 
+  s3_register("ggplot2::autoplot", "model_fit")
+  s3_register("ggplot2::autoplot", "glmnet")
+
   # - If tune isn't installed, register the method (`packageVersion()` will error here)
   # - If tune >= 0.1.6.9001 is installed, register the method
   should_register_tune_args_method <- tryCatch(
@@ -108,61 +111,3 @@ s3_register <- function(generic, class, method = NULL) {
 
 # nocov end
 
-
-
-#' ## nocov start
-#'
-#' data_obj <- ls(pattern = "_data$")
-#' data_obj <- data_obj[data_obj != "prepare_data"]
-#'
-#' data_names <-
-#'   map_dfr(
-#'     data_obj,
-#'     function(x)  {
-#'       module <- names(get(x))
-#'       if (length(module) > 1) {
-#'         module <- table(module)
-#'         module <- as_tibble(module)
-#'         module$object <- x
-#'         module
-#'       } else
-#'         module <- NULL
-#'       module
-#'     }
-#'   )
-#'
-#' if(any(data_names$n > 1)) {
-#'   print(data_names[data_names$n > 1,])
-#'   rlang::abort("Some models have duplicate module names.")
-#' }
-#' rm(data_names)
-#'
-#' # ------------------------------------------------------------------------------
-#'
-#' engine_objects <- ls(pattern = "_engines$")
-#' engine_objects <- engine_objects[engine_objects != "possible_engines"]
-#'
-#' get_engine_info <- function(x) {
-#'   y <- x
-#'   y <- get(y)
-#'   z <- stack(y)
-#'   z$mode <- rownames(y)
-#'   z$model <- gsub("_engines$", "", x)
-#'   z$object <- x
-#'   z <- z[z$values,]
-#'   z <- z[z$mode != "unknown",]
-#'   z$values <- NULL
-#'   names(z)[1] <- "engine"
-#'   z$engine <- as.character(z$engine)
-#'   z
-#' }
-#'
-#' engine_info <-
-#'   purrr::map_df(
-#'   parsnip:::engine_objects,
-#'   get_engine_info
-#' )
-#'
-#' rm(engine_objects)
-#'
-#' ## nocov end
diff --git a/man/autoplot.model_fit.Rd b/man/autoplot.model_fit.Rd
new file mode 100644
index 000000000..1618b4213
--- /dev/null
+++ b/man/autoplot.model_fit.Rd
@@ -0,0 +1,37 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/autoplot.R
+\name{autoplot.model_fit}
+\alias{autoplot.model_fit}
+\alias{autoplot.glmnet}
+\title{Create a ggplot for a model object}
+\usage{
+autoplot.model_fit(object, ...)
+
+autoplot.glmnet(object, min_penalty = 0, best_penalty = NULL, top_n = 3L, ...)
+}
+\arguments{
+\item{object}{A model fit object.}
+
+\item{...}{For \code{\link[=autoplot.glmnet]{autoplot.glmnet()}}, options to pass to
+\code{\link[ggrepel:geom_text_repel]{ggrepel::geom_label_repel()}}. Otherwise, this argument is ignored.}
+
+\item{min_penalty}{A single, non-negative number for the smallest penalty
+value that should be shown in the plot. If left \code{NULL}, the whole data
+range is used.}
+
+\item{best_penalty}{A single, non-negative number that will show a vertical
+line marker. If left \code{NULL}, no line is shown.}
+
+\item{top_n}{A non-negative integer for how many model predictors to label.
+The top predictors are ranked by their absolute coefficient value. For
+multinomial or multivariate models, the \code{top_n} terms are selected within
+class or response, respectively.}
+}
+\value{
+A ggplot object with penalty on the x-axis and coefficients on the
+y-axis. For multinomial or multivariate models, the plot is faceted.
+}
+\description{
+This method provides a good visualization method for model results.
+Currently, only methods for glmnet models are implemented.
+}

From 3e0b4aabd00cf3136d09fcb23fcc734ace997418 Mon Sep 17 00:00:00 2001
From: Max Kuhn <mxkuhn@gmail.com>
Date: Tue, 1 Feb 2022 22:04:05 -0500
Subject: [PATCH 02/13] workflow methods

---
 NAMESPACE                 | 5 +++--
 R/autoplot.R              | 6 ++++++
 man/autoplot.model_fit.Rd | 7 +++++--
 3 files changed, 14 insertions(+), 4 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 21cef744c..9581c7fe6 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,6 +1,9 @@
 # Generated by roxygen2: do not edit by hand
 
 S3method(augment,model_fit)
+S3method(autoplot,glmnet)
+S3method(autoplot,model_fit)
+S3method(autoplot,workflow)
 S3method(extract_fit_engine,model_fit)
 S3method(extract_parameter_dials,model_spec)
 S3method(extract_parameter_set_dials,model_spec)
@@ -165,8 +168,6 @@ export(C5.0_train)
 export(C5_rules)
 export(add_rowindex)
 export(augment)
-export(autoplot.glmnet)
-export(autoplot.model_fit)
 export(bag_mars)
 export(bag_tree)
 export(bart)
diff --git a/R/autoplot.R b/R/autoplot.R
index d5425907b..ff7745a2d 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -23,6 +23,12 @@ autoplot.model_fit <- function(object, ...) {
   autoplot(object$fit, ...)
 }
 
+#' @export
+#' @rdname autoplot.model_fit
+autoplot.workflow <- function(object, ...) {
+  object %>% extract_fit_engine %>% autoplot(...)
+}
+
 # glmnet is not a formal dpendency here.
 # unit tests are located at https://github.com/tidymodels/extratests
 
diff --git a/man/autoplot.model_fit.Rd b/man/autoplot.model_fit.Rd
index 1618b4213..9fae91c37 100644
--- a/man/autoplot.model_fit.Rd
+++ b/man/autoplot.model_fit.Rd
@@ -2,12 +2,15 @@
 % Please edit documentation in R/autoplot.R
 \name{autoplot.model_fit}
 \alias{autoplot.model_fit}
+\alias{autoplot.workflow}
 \alias{autoplot.glmnet}
 \title{Create a ggplot for a model object}
 \usage{
-autoplot.model_fit(object, ...)
+\method{autoplot}{model_fit}(object, ...)
 
-autoplot.glmnet(object, min_penalty = 0, best_penalty = NULL, top_n = 3L, ...)
+\method{autoplot}{workflow}(object, ...)
+
+\method{autoplot}{glmnet}(object, min_penalty = 0, best_penalty = NULL, top_n = 3L, ...)
 }
 \arguments{
 \item{object}{A model fit object.}

From 880ff96d27befa11c7faf014c468fa5c03306985 Mon Sep 17 00:00:00 2001
From: Max Kuhn <mxkuhn@gmail.com>
Date: Wed, 2 Feb 2022 05:52:35 -0500
Subject: [PATCH 03/13] add pkgdown entry

---
 _pkgdown.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/_pkgdown.yml b/_pkgdown.yml
index 8897bed6d..cb99d5bbd 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -48,6 +48,7 @@ reference:
       - svm_rbf
   - title: Infrastructure
     contents:
+      - autoplot.model_fit
       - add_rowindex
       - augment.model_fit
       - descriptors

From 783d32316d0ff1e47aa543d67614cd58bc32f2ee Mon Sep 17 00:00:00 2001
From: Emil Hvitfeldt <emilhhvitfeldt@gmail.com>
Date: Tue, 8 Feb 2022 17:12:46 -0800
Subject: [PATCH 04/13] makes labels appear on best_penalty line if present

---
 R/autoplot.R | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/R/autoplot.R b/R/autoplot.R
index ff7745a2d..f1ac44259 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -109,14 +109,21 @@ autoplot_glmnet <- function(x, min_penalty = 0, best_penalty = NULL, top_n = 3L,
       dplyr::select(class, data) %>%
       tidyr::unnest(cols = data)
   } else {
-    label_coefs <-
-      tidy_coefs %>%
-      top_coefs(top_n)
+    if (is.null(best_penalty)) {
+      label_coefs <- tidy_coefs %>%
+        top_coefs(top_n)
+    } else {
+      label_coefs <- tidy_coefs %>%
+        filter(penalty > best_penalty) %>%
+        filter(penalty == min(penalty)) %>%
+        arrange(desc(abs(estimate))) %>%
+        slice(seq_len(top_n))
+    }
   }
 
   label_coefs <-
     label_coefs %>%
-    dplyr::mutate(penalty = actual_min_penalty) %>%
+    dplyr::mutate(penalty = best_penalty %||% actual_min_penalty) %>%
     dplyr::mutate(label = gsub(".pred_no_", "", term))
 
   # plot the paths and highlight the large values

From 86b9448782d23a8dce39a62b91182759c1a65433 Mon Sep 17 00:00:00 2001
From: Max Kuhn <mxkuhn@gmail.com>
Date: Tue, 15 Feb 2022 16:26:33 -0500
Subject: [PATCH 05/13] Apply suggestions from code review

Co-authored-by: Davis Vaughan <davis@rstudio.com>
---
 R/autoplot.R | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/R/autoplot.R b/R/autoplot.R
index f1ac44259..92990642c 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -48,7 +48,7 @@ map_glmnet_coefs <- function(x) {
   if (is.list(coefs)) {
     classes <- names(coefs)
     coefs <- purrr::map(coefs, reformat_coefs, p = p, penalty = x$lambda)
-    coefs <- purrr::map2_dfr(coefs, classes, ~ .x %>% dplyr::mutate(class = .y))
+    coefs <- purrr::map2_dfr(coefs, classes, ~ dplyr::mutate(.x, class = .y))
   } else {
     coefs <- reformat_coefs(coefs, p = p, penalty = x$lambda)
   }
@@ -66,7 +66,7 @@ reformat_coefs <- function(x, p, penalty) {
   colnames(x) <- paste(seq_along(penalty))
   x <- tibble::as_tibble(x)
   x$term <- term_lab
-  x <- tidyr::pivot_longer(x, cols = c(-term), names_to = "index", values_to = "estimate")
+  x <- tidyr::pivot_longer(x, cols = -term, names_to = "index", values_to = "estimate")
   x$penalty <- rep(penalty, p)
   x$index <- NULL
   x
@@ -98,7 +98,7 @@ autoplot_glmnet <- function(x, min_penalty = 0, best_penalty = NULL, top_n = 3L,
     top_n <- 0
   }
 
-  has_groups <- any(names(tidy_coefs) %in% c("class"))
+  has_groups <- any(names(tidy_coefs) == "class")
 
   # Keep the large values
   if (has_groups) {

From c11c783e6091045bf68ed65c98f666ed95d4bd80 Mon Sep 17 00:00:00 2001
From: Max Kuhn <mxkuhn@gmail.com>
Date: Tue, 15 Feb 2022 19:06:52 -0500
Subject: [PATCH 06/13] move ggrepl to suggests

---
 DESCRIPTION | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 15f2e0fc0..91e7b6fa3 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -22,7 +22,6 @@ Imports:
     dplyr (>= 0.8.0.1),
     generics (>= 0.1.0.9000),
     ggplot2,
-    ggrepel,
     globals,
     glue,
     hardhat (>= 0.1.6.9001),
@@ -43,7 +42,7 @@ Suggests:
     dials (>= 0.0.10.9001),
     earth,
     tensorflow,
-    ggplot2,
+    ggrepel,
     keras,
     kernlab,
     kknn,

From c1ec27295ec49af6fb06a525e4922c240109068f Mon Sep 17 00:00:00 2001
From: Max Kuhn <mxkuhn@gmail.com>
Date: Tue, 15 Feb 2022 19:07:10 -0500
Subject: [PATCH 07/13] doc updates

---
 man/details_proportional_hazards_glmnet.Rd   | 2 +-
 man/details_proportional_hazards_survival.Rd | 2 +-
 man/reexports.Rd                             | 3 +++
 3 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/man/details_proportional_hazards_glmnet.Rd b/man/details_proportional_hazards_glmnet.Rd
index ae235edda..eaa584b45 100644
--- a/man/details_proportional_hazards_glmnet.Rd
+++ b/man/details_proportional_hazards_glmnet.Rd
@@ -116,7 +116,7 @@ tidymodels does not treat different models differently when computing
 performance metrics. To standardize across model types, the default for
 proportional hazards models is to have \emph{increasing values with time}. As
 a result, the sign of the linear predictor will be the opposite of the
-value produced by the \code{predict()} method in the package.
+value produced by the \code{predict()} method in the engine package.
 
 This behavior can be changed by using the \code{increasing} argument when
 calling \code{predict()} on a model object.
diff --git a/man/details_proportional_hazards_survival.Rd b/man/details_proportional_hazards_survival.Rd
index 5d6e57a2e..84fe2fb40 100644
--- a/man/details_proportional_hazards_survival.Rd
+++ b/man/details_proportional_hazards_survival.Rd
@@ -100,7 +100,7 @@ tidymodels does not treat different models differently when computing
 performance metrics. To standardize across model types, the default for
 proportional hazards models is to have \emph{increasing values with time}. As
 a result, the sign of the linear predictor will be the opposite of the
-value produced by the \code{predict()} method in the package.
+value produced by the \code{predict()} method in the engine package.
 
 This behavior can be changed by using the \code{increasing} argument when
 calling \code{predict()} on a model object.
diff --git a/man/reexports.Rd b/man/reexports.Rd
index f0ecfc522..3a5f8c898 100644
--- a/man/reexports.Rd
+++ b/man/reexports.Rd
@@ -3,6 +3,7 @@
 \docType{import}
 \name{reexports}
 \alias{reexports}
+\alias{autoplot}
 \alias{\%>\%}
 \alias{fit}
 \alias{fit_xy}
@@ -25,6 +26,8 @@ below to see their documentation.
 \describe{
   \item{generics}{\code{\link[generics]{augment}}, \code{\link[generics]{fit}}, \code{\link[generics]{fit_xy}}, \code{\link[generics]{glance}}, \code{\link[generics]{required_pkgs}}, \code{\link[generics]{tidy}}, \code{\link[generics]{varying_args}}}
 
+  \item{ggplot2}{\code{\link[ggplot2]{autoplot}}}
+
   \item{hardhat}{\code{\link[hardhat:hardhat-extract]{extract_fit_engine}}, \code{\link[hardhat:hardhat-extract]{extract_parameter_dials}}, \code{\link[hardhat:hardhat-extract]{extract_parameter_set_dials}}, \code{\link[hardhat:hardhat-extract]{extract_spec_parsnip}}, \code{\link[hardhat]{tune}}}
 
   \item{magrittr}{\code{\link[magrittr:pipe]{\%>\%}}}

From e10a6b96693cad5fc5516744c6a7998b04c43194 Mon Sep 17 00:00:00 2001
From: Max Kuhn <mxkuhn@gmail.com>
Date: Tue, 15 Feb 2022 19:07:34 -0500
Subject: [PATCH 08/13] anmespace functions and check for glmnet package

---
 NAMESPACE     |  5 ++---
 R/0_imports.R |  2 +-
 R/autoplot.R  | 23 +++++++++++++++--------
 R/reexports.R |  3 +++
 4 files changed, 21 insertions(+), 12 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 2798c6d40..5236d3b7f 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,9 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
 S3method(augment,model_fit)
-S3method(autoplot,glmnet)
-S3method(autoplot,model_fit)
-S3method(autoplot,workflow)
 S3method(extract_fit_engine,model_fit)
 S3method(extract_parameter_dials,model_spec)
 S3method(extract_parameter_set_dials,model_spec)
@@ -168,6 +165,7 @@ export(C5.0_train)
 export(C5_rules)
 export(add_rowindex)
 export(augment)
+export(autoplot)
 export(bag_mars)
 export(bag_tree)
 export(bart)
@@ -325,6 +323,7 @@ importFrom(hardhat,extract_parameter_set_dials)
 importFrom(hardhat,extract_spec_parsnip)
 importFrom(hardhat,tune)
 importFrom(magrittr,"%>%")
+importFrom(purrr,"%||%")
 importFrom(purrr,as_vector)
 importFrom(purrr,imap)
 importFrom(purrr,imap_lgl)
diff --git a/R/0_imports.R b/R/0_imports.R
index 1fd648e5d..a5a747b36 100644
--- a/R/0_imports.R
+++ b/R/0_imports.R
@@ -3,7 +3,7 @@
 #' @importFrom generics varying_args
 #' @importFrom glue glue_collapse
 #' @importFrom purrr as_vector imap imap_lgl map map_chr map_dbl map_df map_dfr
-#' @importFrom purrr map_lgl
+#' @importFrom purrr map_lgl %||%
 #' @importFrom rlang abort call2 caller_env current_env enquo enquos eval_tidy
 #' @importFrom rlang expr get_expr is_empty is_missing is_null is_quosure
 #' @importFrom rlang is_symbolic lgl missing_arg quo_get_expr quos sym syms
diff --git a/R/autoplot.R b/R/autoplot.R
index 92990642c..6e638cec2 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -18,23 +18,23 @@
 #' @return A ggplot object with penalty on the x-axis and coefficients on the
 #' y-axis. For multinomial or multivariate models, the plot is faceted.
 #'
-#' @export
+# registered in zzz.R
 autoplot.model_fit <- function(object, ...) {
   autoplot(object$fit, ...)
 }
 
-#' @export
+# registered in zzz.R
 #' @rdname autoplot.model_fit
 autoplot.workflow <- function(object, ...) {
   object %>% extract_fit_engine %>% autoplot(...)
 }
 
-# glmnet is not a formal dpendency here.
+# glmnet is not a formal dependency here.
 # unit tests are located at https://github.com/tidymodels/extratests
 
 # nocov start
 
-#' @export
+# registered in zzz.R
 #' @rdname autoplot.model_fit
 autoplot.glmnet <- function(object, min_penalty = 0, best_penalty = NULL,
                             top_n = 3L, ...) {
@@ -44,6 +44,12 @@ autoplot.glmnet <- function(object, min_penalty = 0, best_penalty = NULL,
 
 map_glmnet_coefs <- function(x) {
   coefs <- coef(x)
+  # If parsnip is used to fit the model, glmnet should be attached and this will
+  # work. If an object is loaded from a new session, they will need to load the
+  # package.
+  if (is.null(coefs)) {
+    rlang::abort("Please load the glmnet package before running `autoplot()`.")
+  }
   p <- x$dim[1]
   if (is.list(coefs)) {
     classes <- names(coefs)
@@ -114,10 +120,10 @@ autoplot_glmnet <- function(x, min_penalty = 0, best_penalty = NULL, top_n = 3L,
         top_coefs(top_n)
     } else {
       label_coefs <- tidy_coefs %>%
-        filter(penalty > best_penalty) %>%
-        filter(penalty == min(penalty)) %>%
-        arrange(desc(abs(estimate))) %>%
-        slice(seq_len(top_n))
+        dplyr::filter(penalty > best_penalty) %>%
+        dplyr::filter(penalty == min(penalty)) %>%
+        dplyr::arrange(dplyr::desc(abs(estimate))) %>%
+        dplyr::slice(seq_len(top_n))
     }
   }
 
@@ -145,6 +151,7 @@ autoplot_glmnet <- function(x, min_penalty = 0, best_penalty = NULL, top_n = 3L,
     ggplot2::scale_x_log10()
 
   if(top_n > 0) {
+    rlang::check_installed("ggrepel")
     p <- p +
       ggrepel::geom_label_repel(
         data = label_coefs,
diff --git a/R/reexports.R b/R/reexports.R
index 3a1a22227..e26510794 100644
--- a/R/reexports.R
+++ b/R/reexports.R
@@ -1,3 +1,6 @@
+#' @importFrom ggplot2 autoplot
+#' @export
+ggplot2::autoplot
 
 #' @importFrom magrittr %>%
 #' @export

From 38db0d0b843aac8393373ecc5f850064b07cc59f Mon Sep 17 00:00:00 2001
From: Max Kuhn <mxkuhn@gmail.com>
Date: Tue, 15 Feb 2022 19:10:00 -0500
Subject: [PATCH 09/13] remove workflow method

---
 R/autoplot.R | 13 +++----------
 1 file changed, 3 insertions(+), 10 deletions(-)

diff --git a/R/autoplot.R b/R/autoplot.R
index 6e638cec2..1cbeaafed 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -17,21 +17,16 @@
 #' [ggrepel::geom_label_repel()]. Otherwise, this argument is ignored.
 #' @return A ggplot object with penalty on the x-axis and coefficients on the
 #' y-axis. For multinomial or multivariate models, the plot is faceted.
+#' @details The \pkg{glmnet} package will need to be attached or loaded for
+#' its `autoplot()` method to work correctly.
 #'
 # registered in zzz.R
 autoplot.model_fit <- function(object, ...) {
   autoplot(object$fit, ...)
 }
 
-# registered in zzz.R
-#' @rdname autoplot.model_fit
-autoplot.workflow <- function(object, ...) {
-  object %>% extract_fit_engine %>% autoplot(...)
-}
-
 # glmnet is not a formal dependency here.
 # unit tests are located at https://github.com/tidymodels/extratests
-
 # nocov start
 
 # registered in zzz.R
@@ -89,9 +84,7 @@ top_coefs <- function(x, top_n = 5) {
 }
 
 autoplot_glmnet <- function(x, min_penalty = 0, best_penalty = NULL, top_n = 3L, ...) {
-  if (!is.null(min_penalty)) {
-    check_penalty_value(min_penalty)
-  }
+  check_penalty_value(min_penalty)
 
   tidy_coefs <-
     map_glmnet_coefs(x) %>%

From 45c796b4e3d4d28296c7b5a26df7a97f8a8c5b44 Mon Sep 17 00:00:00 2001
From: Max Kuhn <mxkuhn@gmail.com>
Date: Tue, 15 Feb 2022 21:28:24 -0500
Subject: [PATCH 10/13] move ... up in order

---
 R/autoplot.R              | 4 ++--
 man/autoplot.model_fit.Rd | 9 +++++----
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/R/autoplot.R b/R/autoplot.R
index 1cbeaafed..96f805b16 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -31,8 +31,8 @@ autoplot.model_fit <- function(object, ...) {
 
 # registered in zzz.R
 #' @rdname autoplot.model_fit
-autoplot.glmnet <- function(object, min_penalty = 0, best_penalty = NULL,
-                            top_n = 3L, ...) {
+autoplot.glmnet <- function(object, ..., min_penalty = 0, best_penalty = NULL,
+                            top_n = 3L) {
   autoplot_glmnet(object, min_penalty, best_penalty, top_n, ...)
 }
 
diff --git a/man/autoplot.model_fit.Rd b/man/autoplot.model_fit.Rd
index 9fae91c37..3843de663 100644
--- a/man/autoplot.model_fit.Rd
+++ b/man/autoplot.model_fit.Rd
@@ -2,15 +2,12 @@
 % Please edit documentation in R/autoplot.R
 \name{autoplot.model_fit}
 \alias{autoplot.model_fit}
-\alias{autoplot.workflow}
 \alias{autoplot.glmnet}
 \title{Create a ggplot for a model object}
 \usage{
 \method{autoplot}{model_fit}(object, ...)
 
-\method{autoplot}{workflow}(object, ...)
-
-\method{autoplot}{glmnet}(object, min_penalty = 0, best_penalty = NULL, top_n = 3L, ...)
+\method{autoplot}{glmnet}(object, ..., min_penalty = 0, best_penalty = NULL, top_n = 3L)
 }
 \arguments{
 \item{object}{A model fit object.}
@@ -38,3 +35,7 @@ y-axis. For multinomial or multivariate models, the plot is faceted.
 This method provides a good visualization method for model results.
 Currently, only methods for glmnet models are implemented.
 }
+\details{
+The \pkg{glmnet} package will need to be attached or loaded for
+its \code{autoplot()} method to work correctly.
+}

From 56ea1c0a9bf477ad9ea1cb38ad86d6cb1cc1515f Mon Sep 17 00:00:00 2001
From: Max Kuhn <mxkuhn@gmail.com>
Date: Tue, 15 Feb 2022 21:31:49 -0500
Subject: [PATCH 11/13] A note about ggrepl

---
 R/autoplot.R              | 3 ++-
 man/autoplot.model_fit.Rd | 3 ++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/R/autoplot.R b/R/autoplot.R
index 96f805b16..00538205a 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -8,7 +8,8 @@
 #' value that should be shown in the plot. If left `NULL`, the whole data
 #' range is used.
 #' @param best_penalty A single, non-negative number that will show a vertical
-#' line marker. If left `NULL`, no line is shown.
+#' line marker. If left `NULL`, no line is shown. When this argument is used,
+#' the \pkg{ggrepl} package is required.
 #' @param top_n A non-negative integer for how many model predictors to label.
 #' The top predictors are ranked by their absolute coefficient value. For
 #' multinomial or multivariate models, the `top_n` terms are selected within
diff --git a/man/autoplot.model_fit.Rd b/man/autoplot.model_fit.Rd
index 3843de663..1f0c5ab7e 100644
--- a/man/autoplot.model_fit.Rd
+++ b/man/autoplot.model_fit.Rd
@@ -20,7 +20,8 @@ value that should be shown in the plot. If left \code{NULL}, the whole data
 range is used.}
 
 \item{best_penalty}{A single, non-negative number that will show a vertical
-line marker. If left \code{NULL}, no line is shown.}
+line marker. If left \code{NULL}, no line is shown. When this argument is used,
+the \pkg{ggrepl} package is required.}
 
 \item{top_n}{A non-negative integer for how many model predictors to label.
 The top predictors are ranked by their absolute coefficient value. For

From bfbc26a863af5de33eac9595a0197484aef4ac56 Mon Sep 17 00:00:00 2001
From: Max Kuhn <mxkuhn@gmail.com>
Date: Wed, 16 Feb 2022 18:18:24 -0500
Subject: [PATCH 12/13] more consistent test files

---
 tests/testthat/{test-augment.R => test_augment.R}                 | 0
 tests/testthat/{test-extract.R => test_extract.R}                 | 0
 .../{test-proportional_hazards.R => test_proportional_hazards.R}  | 0
 tests/testthat/{test-survival_reg.R => test_survival_reg.R}       | 0
 4 files changed, 0 insertions(+), 0 deletions(-)
 rename tests/testthat/{test-augment.R => test_augment.R} (100%)
 rename tests/testthat/{test-extract.R => test_extract.R} (100%)
 rename tests/testthat/{test-proportional_hazards.R => test_proportional_hazards.R} (100%)
 rename tests/testthat/{test-survival_reg.R => test_survival_reg.R} (100%)

diff --git a/tests/testthat/test-augment.R b/tests/testthat/test_augment.R
similarity index 100%
rename from tests/testthat/test-augment.R
rename to tests/testthat/test_augment.R
diff --git a/tests/testthat/test-extract.R b/tests/testthat/test_extract.R
similarity index 100%
rename from tests/testthat/test-extract.R
rename to tests/testthat/test_extract.R
diff --git a/tests/testthat/test-proportional_hazards.R b/tests/testthat/test_proportional_hazards.R
similarity index 100%
rename from tests/testthat/test-proportional_hazards.R
rename to tests/testthat/test_proportional_hazards.R
diff --git a/tests/testthat/test-survival_reg.R b/tests/testthat/test_survival_reg.R
similarity index 100%
rename from tests/testthat/test-survival_reg.R
rename to tests/testthat/test_survival_reg.R

From 862b15b5aa09fb0d8e526c24337215618cf749bf Mon Sep 17 00:00:00 2001
From: Max Kuhn <mxkuhn@gmail.com>
Date: Wed, 16 Feb 2022 19:00:59 -0500
Subject: [PATCH 13/13] fix nocov tags

---
 R/aaa.R      | 4 ++--
 R/autoplot.R | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/R/aaa.R b/R/aaa.R
index 1bc0fc241..5137a54d8 100644
--- a/R/aaa.R
+++ b/R/aaa.R
@@ -82,7 +82,7 @@ function(results, object) {
 }
 
 # ------------------------------------------------------------------------------
-# nocov
+# nocov start
 
 utils::globalVariables(
   c('.', '.label', '.pred', '.row', 'data', 'engine', 'engine2', 'group',
@@ -91,7 +91,7 @@ utils::globalVariables(
     "max_terms", "max_tree", "model", "name", "num_terms", "penalty", "trees",
     "sub_neighbors", ".pred_class", "x", "y", "predictor_indicators",
     "compute_intercept", "remove_intercept", "estimate", "term",
-    "call_info", "component", "component_id", "func", "tunable", "label", 
+    "call_info", "component", "component_id", "func", "tunable", "label",
     "pkg", ".order", "item", "tunable"
    )
 )
diff --git a/R/autoplot.R b/R/autoplot.R
index 00538205a..f18933cdd 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -167,4 +167,4 @@ check_penalty_value <- function(x) {
   invisible(x)
 }
 
-# nocov stop
+# nocov end