glmnet autoplot method for #642

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"),
@@ -21,6 +21,7 @@ Imports:
     cli,
     dplyr (>= 0.8.0.1),
     generics (>= 0.1.0.9000),
+    ggplot2,
     globals,
     glue,
     hardhat (>= 0.1.6.9001),
@@ -41,7 +42,7 @@ Suggests:
     dials (>= 0.0.10.9001),
     earth,
     tensorflow,
-    ggplot2,
+    ggrepel,
     keras,
     kernlab,
     kknn,

NAMESPACE

@@ -165,6 +165,7 @@ export(C5.0_train)
 export(C5_rules)
 export(add_rowindex)
 export(augment)
+export(autoplot)
 export(bag_mars)
 export(bag_tree)
 export(bart)
@@ -314,13 +315,15 @@ 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)
 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)

NEWS.md

@@ -40,10 +40,13 @@
 
 * `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 is now more robust working with keras and tensorflow for a larger range of versions (#596).
 
 * xgboost engines now use the new `iterationrange` parameter instead of the deprecated `ntreelimit` (#656).  
 
+
 # parsnip 0.1.7
 
 ## Model Specification Changes

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
@@ -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

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 +91,9 @@ 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", "pkg", ".order", "item", "tunable")
+    "call_info", "component", "component_id", "func", "tunable", "label",
+    "pkg", ".order", "item", "tunable"
+   )
 )
 
 # nocov end

R/autoplot.R

@@ -0,0 +1,170 @@
+#' 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. 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
+#' 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.
+#' @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$fit, ...)
+}
+
+# glmnet is not a formal dependency here.
+# unit tests are located at https://github.com/tidymodels/extratests
+# nocov start
+
+# registered in zzz.R
+#' @rdname autoplot.model_fit
+autoplot.glmnet <- function(object, ..., min_penalty = 0, best_penalty = NULL,
+                            top_n = 3L) {
+  autoplot_glmnet(object, min_penalty, best_penalty, top_n, ...)
+}
+
+
+map_glmnet_coefs <- function(x) {
+  coefs <- coef(x)
+  # If parsnip is used to fit the model, glmnet should be attached and this will
+  # work. If an object is loaded from a new session, they will need to load the
+  # package.
+  if (is.null(coefs)) {
+    rlang::abort("Please load the glmnet package before running `autoplot()`.")
+  }
+  p <- x$dim[1]
+  if (is.list(coefs)) {
+    classes <- names(coefs)
+    coefs <- purrr::map(coefs, reformat_coefs, p = p, penalty = x$lambda)
+    coefs <- purrr::map2_dfr(coefs, classes, ~ dplyr::mutate(.x, class = .y))
+  } else {
+    coefs <- reformat_coefs(coefs, p = p, penalty = x$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$term <- term_lab
+  x <- tidyr::pivot_longer(x, cols = -term, names_to = "index", values_to = "estimate")
+  x$penalty <- rep(penalty, p)
+  x$index <- NULL
+  x
+}
+
+top_coefs <- function(x, top_n = 5) {
+  x %>%
+    dplyr::group_by(term) %>%
+    dplyr::arrange(term, dplyr::desc(abs(estimate))) %>%
+    dplyr::slice(1) %>%
+    dplyr::ungroup() %>%
+    dplyr::arrange(dplyr::desc(abs(estimate))) %>%
+    dplyr::slice(1:top_n)
+}
+
+autoplot_glmnet <- function(x, min_penalty = 0, best_penalty = NULL, top_n = 3L, ...) {
+  check_penalty_value(min_penalty)
+
+  tidy_coefs <-
+    map_glmnet_coefs(x) %>%
+    dplyr::filter(penalty >= min_penalty)
+
+  actual_min_penalty <- min(tidy_coefs$penalty)
+  num_terms <- length(unique(tidy_coefs$term))
+  top_n <- min(top_n[1], num_terms)
+  if (top_n < 0) {
+    top_n <- 0
+  }
+
+  has_groups <- any(names(tidy_coefs) == "class")
+
+  # Keep the large values
+  if (has_groups) {
+    label_coefs <-
+      tidy_coefs %>%
+      dplyr::group_nest(class) %>%
+      dplyr::mutate(data = purrr::map(data, top_coefs, top_n = top_n)) %>%
+      dplyr::select(class, data) %>%
+      tidyr::unnest(cols = data)
+  } else {
+    if (is.null(best_penalty)) {
+      label_coefs <- tidy_coefs %>%
+        top_coefs(top_n)
+    } else {
+      label_coefs <- tidy_coefs %>%
+        dplyr::filter(penalty > best_penalty) %>%
+        dplyr::filter(penalty == min(penalty)) %>%
+        dplyr::arrange(dplyr::desc(abs(estimate))) %>%
+        dplyr::slice(seq_len(top_n))
+    }
+  }
+
+  label_coefs <-
+    label_coefs %>%
+    dplyr::mutate(penalty = best_penalty %||% actual_min_penalty) %>%
+    dplyr::mutate(label = gsub(".pred_no_", "", term))
+
+  # plot the paths and highlight the large values
+  p <-
+    tidy_coefs %>%
+    ggplot2::ggplot(ggplot2::aes(x = penalty, y = estimate, group = term, col = term))
+
+  if (has_groups) {
+    p <- p + ggplot2::facet_wrap(~ class)
+  }
+
+  if (!is.null(best_penalty)) {
+    check_penalty_value(best_penalty)
+    p <- p + ggplot2::geom_vline(xintercept = best_penalty, lty = 3)
+  }
+
+  p <- p +
+    ggplot2::geom_line(alpha = .4, show.legend = FALSE) +
+    ggplot2::scale_x_log10()
+
+  if(top_n > 0) {
+    rlang::check_installed("ggrepel")
+    p <- p +
+      ggrepel::geom_label_repel(
+        data = label_coefs,
+        ggplot2::aes(y = estimate, label = label),
+        show.legend = FALSE,
+        ...
+      )
+  }
+  p
+}
+
+check_penalty_value <- function(x) {
+  cl <- match.call()
+  arg_val <- as.character(cl$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 end

R/reexports.R

@@ -1,3 +1,6 @@
+#' @importFrom ggplot2 autoplot
+#' @export
+ggplot2::autoplot
 
 #' @importFrom magrittr %>%
 #' @export

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

_pkgdown.yml

@@ -48,6 +48,7 @@ reference:
       - svm_rbf
   - title: Infrastructure
     contents:
+      - autoplot.model_fit
       - add_rowindex
       - augment.model_fit
       - descriptors