Merge pull request #149 from koalaverse/devel

fix CITATION and other tweaks for CRAN
koalaverse · Jul 17, 2023 · 33cc92e · 33cc92e
2 parents 0afae9d + 05ede55
commit 33cc92e
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diff --git a/NEWS.md b/NEWS.md
@@ -4,6 +4,8 @@
 
 * This NEWS file now follows the [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) format.
 
+* The training data has to be explicitly passed in more cases.
+
 * Raised R version dependency to >= 4.1.0 (the introduction of the native piper operator `|>`).
 
 * The `vi_permute` function now uses [yardstick](https://cran.r-project.org/package=yardstick); consequently, metric functions now conform to [yardstick](https://cran.r-project.org/package=yardstick) metric argument names.

diff --git a/R/get_training_data.R b/R/get_training_data.R
@@ -6,65 +6,66 @@ msg <- paste0(
 
 
 #' @keywords internal
-#' @noRd
 get_training_data <- function(object) {
   UseMethod("get_training_data")
 }
 
 
 #' @keywords internal
-get_training_data.default <- function(object, env = parent.frame(),
-                                      arg = "data") {
-
-  # Throw error message for S4 objects (for now)
-  if (isS4(object)) {
-    stop(msg, call. = FALSE)
-  }
-
-  # Grab the call
-  mcall <- tryCatch(stats::getCall(object), error = function(e) {
-    stop(msg, call. = FALSE)
-  })
-
-  # If data component of the call is NULL, then try to make sure each
-  # component is named before proceeding (taken from Advanced R, 2nd ed.)
-  if (is.null(mcall[[arg]])) {
-    f <- tryCatch(eval(mcall[[1L]], envir = env), error = function(e) {
-      stop(msg, call. = FALSE)
-    })
-    if (!is.primitive(f)) {
-      mcall <- match.call(f, call = mcall)
-    }
-  }
-
-  # Grab the data component (if it exists)
-  n <- 1
-  while(length(env) != 0) {
-    train <- tryCatch(eval(mcall[[arg]], envir = env), error = function(e) {
-      NULL
-    })
-    if (!is.null(train) || identical(env, globalenv())) {
-      break
-    }
-    env <- parent.frame(n)  # inspect calling environment
-    n <- n + 1
-  }
-  if (is.null(train)) {
-    stop(msg, call. = FALSE)
-  } else {
-    if (!(is.data.frame(train))) {
-      if (is.matrix(train) || is.list(train)) {
-        train <- as.data.frame(train)
-        # } else if (inherits(train, what = "dgCMatrix")) {
-        #   train <- as.data.frame(data.matrix(train))
-      } else {
-        stop(msg, call. = FALSE)
-      }
-    }
-  }
-
-  # Return original training data
-  train
+get_training_data.default <- function(object) {
+
+  # # Throw error message for S4 objects (for now)
+  # if (isS4(object)) {
+  #   stop(msg, call. = FALSE)
+  # }
+  #
+  # # Grab the call
+  # mcall <- tryCatch(stats::getCall(object), error = function(e) {
+  #   stop(msg, call. = FALSE)
+  # })
+  #
+  # # If data component of the call is NULL, then try to make sure each
+  # # component is named before proceeding (taken from Advanced R, 2nd ed.)
+  # if (is.null(mcall[[arg]])) {
+  #   f <- tryCatch(eval(mcall[[1L]], envir = env), error = function(e) {
+  #     stop(msg, call. = FALSE)
+  #   })
+  #   if (!is.primitive(f)) {
+  #     mcall <- match.call(f, call = mcall)
+  #   }
+  # }
+  #
+  # # Grab the data component (if it exists)
+  # n <- 1
+  # while(length(env) != 0) {
+  #   train <- tryCatch(eval(mcall[[arg]], envir = env), error = function(e) {
+  #     NULL
+  #   })
+  #   if (!is.null(train) || identical(env, globalenv())) {
+  #     break
+  #   }
+  #   env <- parent.frame(n)  # inspect calling environment
+  #   n <- n + 1
+  # }
+  # if (is.null(train)) {
+  #   stop(msg, call. = FALSE)
+  # } else {
+  #   if (!(is.data.frame(train))) {
+  #     if (is.matrix(train) || is.list(train)) {
+  #       train <- as.data.frame(train)
+  #       # } else if (inherits(train, what = "dgCMatrix")) {
+  #       #   train <- as.data.frame(data.matrix(train))
+  #     } else {
+  #       stop(msg, call. = FALSE)
+  #     }
+  #   }
+  # }
+  #
+  # # Return original training data
+  # train
+  stop("Training data cannot be extracted from fitted model object. Please ",
+       "supply the raw training data using the `train` argument.",
+       call. = FALSE)
 
 }
 
@@ -85,68 +86,22 @@ get_training_data.train <- function(object) {
 }
 
 
-# Package: C50 -----------------------------------------------------------------
-
-#' @keywords internal
-get_training_data.C5.0 <- function(object) {
-  tryCatch(
-    expr = get_training_data.default(object, arg = "data"),
-    error = function(e) {
-      get_training_data.default(object, arg = "x")
-    }
-  )
-}
-
-# Package: Cubist --------------------------------------------------------------
-
-#' @keywords internal
-get_training_data.cubist <- function(object) {
-  get_training_data.default(object, arg = "x")
-}
-
-
-# Package: e1071 ---------------------------------------------------------------
-
-#' @keywords internal
-get_training_data.svm <- function(object) {
-  tryCatch(
-    expr = get_training_data.default(object, arg = "data"),
-    error = function(e) {
-      get_training_data.default(object, arg = "x")
-    }
-  )
-}
-
-
-# Package: earth ---------------------------------------------------------------
-
-#' @keywords internal
-get_training_data.earth <- function(object) {
-  tryCatch(
-    expr = get_training_data.default(object, arg = "data"),
-    error = function(e) {
-      get_training_data.default(object, arg = "x")
-    }
-  )
-}
-
-
 # Package: h2o -----------------------------------------------------------------
 
 #' @keywords internal
-get_training_data.H2OBinomialModel <- function(object, ...) {
+get_training_data.H2OBinomialModel <- function(object) {
   as.data.frame(h2o::h2o.getFrame(object@allparameters$training_frame))
 }
 
 
 #' @keywords internal
-get_training_data.H2OMultinomialModel <- function(object, ...) {
+get_training_data.H2OMultinomialModel <- function(object) {
   as.data.frame(h2o::h2o.getFrame(object@allparameters$training_frame))
 }
 
 
 #' @keywords internal
-get_training_data.H2ORegressionModel <- function(object, ...) {
+get_training_data.H2ORegressionModel <- function(object) {
   as.data.frame(h2o::h2o.getFrame(object@allparameters$training_frame))
 }
 
@@ -168,19 +123,7 @@ get_training_data.RandomForest <- function(object) {
   object@data@get("input")
 }
 
-
-# Package: randomForest --------------------------------------------------------
-
-#' @keywords internal
-get_training_data.randomForest <- function(object) {
-  if (inherits(object, what = "randomForest.formula")) {
-    get_training_data.default(object, env = parent.frame(), arg = "data")
-  } else {
-    get_training_data.default(object, env = parent.frame(), arg = "x")
-  }
-}
-
-
+library
 # Package: workflow ------------------------------------------------------------
 
 #' @keywords internal

diff --git a/R/vi.R b/R/vi.R
@@ -87,7 +87,7 @@
 #' # Compute permutation-based variable importance scores
 #' set.seed(1434)  # for reproducibility
 #' (vis <- vi(mtcars.ppr, method = "permute", target = "mpg", nsim = 10,
-#'            metric = "rmse", pred_wrapper = pfun))
+#'            metric = "rmse", pred_wrapper = pfun, train = mtcars))
 #'
 #' # Plot variable importance scores
 #' vip(vis, include_type = TRUE, all_permutations = TRUE,
@@ -126,7 +126,7 @@
 #' # Permutation-based importance (note that only the predictors that show up
 #' # in the final tree have non-zero importance)
 #' set.seed(1046)  # for reproducibility
-#' vi(tree2, method = "permute", nsim = 10, target = "Class",
+#' vi(tree2, method = "permute", nsim = 10, target = "Class", train = bc,
 #'    metric = "logloss", pred_wrapper = pfun, reference_class = "malignant")
 #'
 #' # Equivalent (but not sorted)

diff --git a/R/vi_permute.R b/R/vi_permute.R
@@ -142,12 +142,13 @@
 #' # Compute permutation-based VI scores
 #' set.seed(2021)  # for reproducibility
 #' vis <- vi(rfo, method = "permute", target = "y", metric = "rsq",
-#'           pred_wrapper = pfun)
+#'           pred_wrapper = pfun, train = trn)
 #' print(vis)
 #'
 #' # Same as above, but using `vi_permute()` directly
 #' set.seed(2021)  # for reproducibility
-#' vi_permute(rfo, target = "y", metric = "rsq", pred_wrapper = pfun)
+#' vi_permute(rfo, target = "y", metric = "rsq", pred_wrapper = pfun
+#'            train = trn)
 #'
 #' # Plot VI scores (could also replace `vi()` with `vip()` in above example)
 #' vip(vis, include_type = TRUE)
@@ -160,12 +161,12 @@
 #' # Permutation-based VIP with user-defined MAE metric
 #' set.seed(1101)  # for reproducibility
 #' vi_permute(rfo, target = "y", metric = mae, smaller_is_better = TRUE,
-#'            pred_wrapper = pfun)
+#'            pred_wrapper = pfun, train = trn)
 #'
 #' # Same as above, but using `yardstick` package instead of user-defined metric
 #' set.seed(1101)  # for reproducibility
 #' vi_permute(rfo, target = "y", metric = yardstick::mae_vec,
-#'            smaller_is_better = TRUE, pred_wrapper = pfun)
+#'            smaller_is_better = TRUE, pred_wrapper = pfun, train = trn)
 #'
 #' #
 #' # Classification (binary) example

diff --git a/README.Rmd b/README.Rmd
@@ -14,11 +14,11 @@ knitr::opts_chunk$set(
 # vip: Variable Importance Plots <img src="man/figures/logo-vip.png" align="right" width="130" height="150" />
 
 <!-- badges: start -->
-[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/vip)](https://cran.r-project.org/package=vip)
+[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/vip)](https://cran.r-project.org/package=vip)
 [![R-CMD-check](https://github.com/koalaverse/vip/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/koalaverse/vip/actions/workflows/R-CMD-check.yaml)
 [![Coverage Status](https://codecov.io/gh/koalaverse/vip/graph/badge.svg)](https://app.codecov.io/github/koalaverse/vip?branch=master)
 [![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)
-[![Downloads](http://cranlogs.r-pkg.org/badges/vip)](http://cran.r-project.org/package=vip/)
+[![Downloads](https://cranlogs.r-pkg.org/badges/vip)](https://cran.r-project.org/package=vip/)
 [![The R Journal](https://img.shields.io/badge/The%20R%20Journal-10.32614%2FRJ--2020--013-brightgreen)](https://doi.org/10.32614/RJ-2020-013)
 <!-- badges: end -->
 

diff --git a/README.md b/README.md
@@ -3,13 +3,13 @@
 
 <!-- badges: start -->
 
-[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/vip)](https://cran.r-project.org/package=vip)
+[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/vip)](https://cran.r-project.org/package=vip)
 [![R-CMD-check](https://github.com/koalaverse/vip/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/koalaverse/vip/actions/workflows/R-CMD-check.yaml)
 [![Coverage
 Status](https://codecov.io/gh/koalaverse/vip/graph/badge.svg)](https://app.codecov.io/github/koalaverse/vip?branch=master)
 [![Lifecycle:
 stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)
-[![Downloads](http://cranlogs.r-pkg.org/badges/vip)](http://cran.r-project.org/package=vip/)
+[![Downloads](https://cranlogs.r-pkg.org/badges/vip)](https://cran.r-project.org/package=vip/)
 [![The R
 Journal](https://img.shields.io/badge/The%20R%20Journal-10.32614%2FRJ--2020--013-brightgreen)](https://doi.org/10.32614/RJ-2020-013)
 <!-- badges: end -->

diff --git a/inst/CITATION b/inst/CITATION
@@ -1,8 +1,8 @@
 citHeader("To cite vip in publications use:")
 
-citEntry(entry = "Article",
+bibentry("Article",
   title        = "Variable Importance Plots---An Introduction to the vip Package",
-  author       = personList(as.person("Brandon M. Greenwell"), as.person("Bradley C. Boehmke")),
+  author       = c(as.person("Brandon M. Greenwell"), as.person("Bradley C. Boehmke")),
   journal      = "The R Journal",
   year         = "2020",
   volume       = "12",

diff --git a/inst/references.bib b/inst/references.bib
@@ -492,7 +492,7 @@ @Article{party2007a
   year = {2007},
   volume = {8},
   number = {25},
-  url = {https://www.biomedcentral.com/1471-2105/8/25},
+  url = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-8-25},
 }
 @Article{party2008b,
   title = {Conditional Variable Importance for Random Forests},
@@ -501,7 +501,7 @@ @Article{party2008b
   year = {2008},
   volume = {9},
   number = {307},
-  url = {https://www.biomedcentral.com/1471-2105/9/307},
+  url = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-307},
 }
 @Article{partykit2015,
   title = {{partykit}: A Modular Toolkit for Recursive Partytioning in {R}},

diff --git a/inst/tinytest/test_pkg_C50.R b/inst/tinytest/test_pkg_C50.R
@@ -33,16 +33,6 @@ expect_identical(
   C50::C5imp(fit1, metric = "splits", pct = FALSE)$Overall
 )
 
-# Expectations for `get_training_data()`
-expect_identical(
-  current = vip:::get_training_data.C5.0(fit1),
-  target = friedman2
-)
-expect_identical(
-  current = vip:::get_training_data.C5.0(fit2),
-  target = subset(friedman2, select = -y)
-)
-
 # Expectations for `get_feature_names()`
 expect_identical(
   current = vip:::get_feature_names.C5.0(fit1),

diff --git a/inst/tinytest/test_pkg_Cubist.R b/inst/tinytest/test_pkg_Cubist.R
@@ -32,12 +32,6 @@ expect_identical(
   target = vis2[vis1$Variable, , drop = TRUE]
 )
 
-# Expectations for `get_training_data()`
-expect_identical(
-  current = vip:::get_training_data.cubist(fit),
-  target = subset(friedman1, select = -y)
-)
-
 # Expectations for `get_feature_names()`
 expect_identical(
   current = vip:::get_feature_names.cubist(fit),

diff --git a/inst/tinytest/test_pkg_earth.R b/inst/tinytest/test_pkg_earth.R
@@ -34,12 +34,6 @@ expect_identical(
   target = unname(vis_earth[, "gcv", drop = TRUE])
 )
 
-# Expectations for `get_training_data()`
-expect_identical(
-  current = vip:::get_training_data.earth(fit),
-  target = friedman1
-)
-
 # Expectations for `get_feature_names()`
 expect_identical(
   current = vip:::get_feature_names.earth(fit),

diff --git a/inst/tinytest/test_pkg_glmnet.R b/inst/tinytest/test_pkg_glmnet.R
@@ -58,12 +58,6 @@ expect_identical(
   target = abs(coef(fit3, s = fit3$lambda[5L])[[1L]][-1L])
 )
 
-# # Expectations for `get_training_data()`
-# expect_identical(
-#   current = vip:::get_training_data.glmnet(fit),
-#   target = friedman1
-# )
-
 # Expectations for `get_feature_names()`
 expect_identical(
   current = vip:::get_feature_names.glmnet(fit1),

diff --git a/inst/tinytest/test_pkg_neuralnet.R b/inst/tinytest/test_pkg_neuralnet.R
@@ -33,12 +33,6 @@ expect_identical(
   target = NeuralNetTools::garson(fit, bar_plot = FALSE)$rel_imp
 )
 
-# Expectations for `get_training_data()`
-expect_identical(
-  current = vip:::get_training_data.default(fit),
-  target = friedman1
-)
-
 # Expectations for `get_feature_names()`
 expect_identical(
   current = vip:::get_feature_names.nn(fit),