Merge pull request #259 from tidymodels/cran-dimRed-ex-fix

Fixes for CRAN notice about failing examples

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: recipes
 Title: Preprocessing Tools to Create Design Matrices
-Version: 0.1.3.9002
+Version: 0.1.4
 Authors@R: c(
     person("Max", "Kuhn", , "[email protected]", c("aut", "cre")),
     person("Hadley", "Wickham", , "[email protected]", "aut"),
@@ -48,6 +48,7 @@ Suggests:
     rmarkdown,
     rpart,
     rsample,
+    RSpectra,
     testthat
 License: GPL-2
 VignetteBuilder: knitr

NEWS.md

@@ -1,6 +1,7 @@
 # `recipes` 0.1.3.9002
 
 ## Breaking Changes
+
  * Several argument names were changed to be consistent with other `tidymodels` packages (e.g. `dials`) and the general tidyverse naming conventions. 
    * `K` in `step_knnimpute` was changed to `neighbors`. `step_isomap` had the number of neighbors promoted to a main argument called `neighbors ` 
    *  `step_pca`, `step_pls`, `step_kpca`, `step_ica` now use  `num_comp`  instead of `num`. , `step_isomap` uses `num_terms` instead of `num`. 

R/ica.R

@@ -25,9 +25,9 @@
 #' @param res The [fastICA::fastICA()] object is stored
 #'  here once this preprocessing step has be trained by
 #'  [prep.recipe()].
-#' @param num The number of components to retain (this will be 
-#'  deprecated in factor of `num_comp` in version 0.1.5). `num_comp` 
-#'  will override this option. 
+#' @param num The number of components to retain (this will be
+#'  deprecated in factor of `num_comp` in version 0.1.5). `num_comp`
+#'  will override this option.
 #' @param prefix A character string that will be the prefix to the
 #'  resulting new variables. See notes below.
 #' @return An updated version of `recipe` with the new step
@@ -82,14 +82,17 @@
 #' ica_trans <- step_center(rec,  V1, V2)
 #' ica_trans <- step_scale(ica_trans, V1, V2)
 #' ica_trans <- step_ica(ica_trans, V1, V2, num_comp = 2)
-#' # ica_estimates <- prep(ica_trans, training = tr)
-#' # ica_data <- bake(ica_estimates, te)
 #'
-#' # plot(te$V1, te$V2)
-#' # plot(ica_data$IC1, ica_data$IC2)
+#' if (require(dimRed) & require(fastICA)) {
+#'   ica_estimates <- prep(ica_trans, training = tr)
+#'   ica_data <- bake(ica_estimates, te)
 #'
-#' # tidy(ica_trans, number = 3)
-#' # tidy(ica_estimates, number = 3)
+#'   plot(te$V1, te$V2)
+#'   plot(ica_data$IC1, ica_data$IC2)
+#'
+#'   tidy(ica_trans, number = 3)
+#'   tidy(ica_estimates, number = 3)
+#' }
 #' @seealso [step_pca()] [step_kpca()]
 #'   [step_isomap()] [recipe()] [prep.recipe()]
 #'   [bake.recipe()]
@@ -108,7 +111,7 @@ step_ica <-
 
 
     recipes_pkg_check(c("dimRed", "fastICA"))
-    if (!is.null(num)) 
+    if (!is.null(num))
       message("The argument `num` is deprecated in factor of `num_comp`. ",
               "`num` will be removed in next version.", call. = FALSE)
     add_step(

R/integer.R

@@ -21,7 +21,7 @@
 #' @param strict A logical for whether the values should be returned as
 #'  integers (as opposed to double).
 #' @param zero_based A logical for whether the integers should start at zero and
-#'  new values be appended as the largest integer. 
+#'  new values be appended as the largest integer.
 #' @return An updated version of `recipe` with the new step added
 #'  to the sequence of existing steps (if any). For the `tidy`
 #'  method, a tibble with columns `terms` (the selectors or
@@ -38,7 +38,7 @@
 #'  argument above). Missing values propagate.
 #'
 #' Factor inputs are ordered by their levels. All others are
-#'  ordered by `sort`. 
+#'  ordered by `sort`.
 #'
 #' Despite the name, the new values are returned as numeric unless
 #'  `strict = TRUE`, which will coerce the results to integers.
@@ -144,12 +144,12 @@ prep.step_integer <- function(x, training, info = NULL, ...) {
 map_key_to_int <- function(dat, key, strict = FALSE, zero = FALSE) {
   if (is.factor(dat))
     dat <- as.character(dat)
-  
+
   res <- full_join(tibble(value = dat, .row = seq_along(dat)), key, by = "value")
   res <- dplyr::filter(res, !is.na(.row))
   res <- arrange(res, .row)
   if (zero) {
-    res$integer[is.na(res$integer) & !is.na(res$value)] <- 
+    res$integer[is.na(res$integer) & !is.na(res$value)] <-
       max(key$integer, na.rm = TRUE) + 1
   } else {
     res$integer[is.na(res$integer) & !is.na(res$value)] <- 0
@@ -200,3 +200,6 @@ tidy.step_integer <- function(x, ...) {
   res$id <- x$id
   res
 }
+
+#' @importFrom stats runif
+utils::globalVariables(c(".row"))

R/isomap.R

@@ -20,9 +20,9 @@
 #'  used.
 #' @param neighbors The number of neighbors.
 #' @param options A list of options to [dimRed::Isomap()].
-#' @param num The number of isomap dimensions (this will be deprecated 
-#'  in factor of  `num_terms` in version 0.1.5). `num_terms` will 
-#'  override this option. 
+#' @param num The number of isomap dimensions (this will be deprecated
+#'  in factor of  `num_terms` in version 0.1.5). `num_terms` will
+#'  override this option.
 #' @param res The [dimRed::Isomap()] object is stored
 #'  here once this preprocessing step has be trained by
 #'  [prep.recipe()].
@@ -85,16 +85,18 @@
 #'               neighbors = 100,
 #'               num_terms = 2)
 #'
-#' # im_estimates <- prep(im_trans, training = biomass_tr)
+#' if (require(dimRed) & require(RSpectra)) {
+#'   im_estimates <- prep(im_trans, training = biomass_tr)
 #'
-#' # im_te <- bake(im_estimates, biomass_te)
+#'   im_te <- bake(im_estimates, biomass_te)
 #'
-#' # rng <- extendrange(c(im_te$Isomap1, im_te$Isomap2))
-#' # plot(im_te$Isomap1, im_te$Isomap2,
-#' #      xlim = rng, ylim = rng)
+#'   rng <- extendrange(c(im_te$Isomap1, im_te$Isomap2))
+#'   plot(im_te$Isomap1, im_te$Isomap2,
+#'        xlim = rng, ylim = rng)
 #'
-#' # tidy(im_trans, number = 4)
-#' # tidy(im_estimates, number = 4)
+#'   tidy(im_trans, number = 4)
+#'   tidy(im_estimates, number = 4)
+#' }
 #' }
 #' @seealso [step_pca()] [step_kpca()]
 #'   [step_ica()] [recipe()] [prep.recipe()]
@@ -115,7 +117,7 @@ step_isomap <-
            id = rand_id("isomap")) {
 
     recipes_pkg_check(c("dimRed", "RSpectra", "igraph", "RANN"))
-    if (!is.null(num)) 
+    if (!is.null(num))
       message("The argument `num` is deprecated in factor of `num_terms`. ",
               "`num` will be removed in next version.", call. = FALSE)
     add_step(
@@ -137,7 +139,7 @@ step_isomap <-
   }
 
 step_isomap_new <-
-  function(terms, role, trained, num_terms, neighbors, options, res, num, 
+  function(terms, role, trained, num_terms, neighbors, options, res, num,
            prefix, skip, id) {
     step(
       subclass = "isomap",

R/kpca.R

@@ -26,9 +26,9 @@
 #' @param res An S4 [kernlab::kpca()] object is stored
 #'  here once this preprocessing step has be trained by
 #'  [prep.recipe()].
-#' @param num The number of components to retain (this will be 
-#'  deprecated in factor of `num_comp` in version 0.1.5). `num_comp` 
-#'  will override this option. 
+#' @param num The number of components to retain (this will be
+#'  deprecated in factor of `num_comp` in version 0.1.5). `num_comp`
+#'  will override this option.
 #' @param prefix A character string that will be the prefix to the
 #'  resulting new variables. See notes below.
 #' @return An updated version of `recipe` with the new step
@@ -94,16 +94,18 @@
 #'   step_scale(all_predictors()) %>%
 #'   step_kpca(all_predictors())
 #'
-#' # kpca_estimates <- prep(kpca_trans, training = biomass_tr)
+#' if (require(dimRed) & require(kernlab)) {
+#'   kpca_estimates <- prep(kpca_trans, training = biomass_tr)
 #'
-#' # kpca_te <- bake(kpca_estimates, biomass_te)
+#'   kpca_te <- bake(kpca_estimates, biomass_te)
 #'
-#' # rng <- extendrange(c(kpca_te$kPC1, kpca_te$kPC2))
-#' # plot(kpca_te$kPC1, kpca_te$kPC2,
-#' #      xlim = rng, ylim = rng)
+#'   rng <- extendrange(c(kpca_te$kPC1, kpca_te$kPC2))
+#'   plot(kpca_te$kPC1, kpca_te$kPC2,
+#'        xlim = rng, ylim = rng)
 #'
-#' # tidy(kpca_trans, number = 4)
-#' # tidy(kpca_estimates, number = 4)
+#'   tidy(kpca_trans, number = 4)
+#'   tidy(kpca_estimates, number = 4)
+#' }
 #' @seealso [step_pca()] [step_ica()]
 #'   [step_isomap()] [recipe()] [prep.recipe()]
 #'   [bake.recipe()]
@@ -123,10 +125,10 @@ step_kpca <-
            id = rand_id("kpca")) {
 
     recipes_pkg_check(c("dimRed", "kernlab"))
-    if (!is.null(num)) 
+    if (!is.null(num))
       message("The argument `num` is deprecated in factor of `num_comp`. ",
               "`num` will be removed in next version.", call. = FALSE)
-    
+
     add_step(
       recipe,
       step_kpca_new(

R/nnmf.R

@@ -52,20 +52,20 @@
 #'
 #' @examples
 #' \donttest{
-#' library(dimRed)
-#' library(NMF)
 #' data(biomass)
 #'
-#' rec <- recipe(HHV ~ ., data = biomass) %>%
-#'   update_role(sample, new_role = "id var") %>%
-#'   update_role(dataset, new_role = "split variable") %>%
-#'   step_nnmf(all_predictors(), num_comp = 2, seed = 473, num_run = 2) %>%
-#'   prep(training = biomass, retain = TRUE)
+#' if (require(dimRed) & require(NMF)) {
+#'   rec <- recipe(HHV ~ ., data = biomass) %>%
+#'     update_role(sample, new_role = "id var") %>%
+#'     update_role(dataset, new_role = "split variable") %>%
+#'     step_nnmf(all_predictors(), num_comp = 2, seed = 473, num_run = 2) %>%
+#'     prep(training = biomass, retain = TRUE)
 #'
-#' # juice(rec)
+#'   juice(rec)
 #'
-#' # library(ggplot2)
-#' # ggplot(juice(rec), aes(x = NNMF2, y = NNMF1, col = HHV)) + geom_point()
+#'   library(ggplot2)
+#'   ggplot(juice(rec), aes(x = NNMF2, y = NNMF1, col = HHV)) + geom_point()
+#' }
 #' }
 #' @seealso [step_pca()], [step_ica()], [step_kpca()],
 #'   [step_isomap()], [recipe()], [prep.recipe()],

tests/testthat/test_isomap.R

@@ -29,15 +29,17 @@ test_that('correct Isomap values', {
   skip_if_not_installed("igraph")
   skip_if_not_installed("RANN")
   skip_if_not_installed("dimRed")
-
+  skip_if(getRversion() <= "3.4.4")
+
   im_rec <- rec %>%
     step_isomap(x1, x2, x3, neighbors = 3, num_terms = 3, id = "")
 
   im_trained <- prep(im_rec, training = dat1, verbose = FALSE)
 
   im_pred <- bake(im_trained, new_data = dat2)
 
-  all.equal(as.matrix(im_pred), as.matrix(exp_res))
+  # unique up to sign
+  all.equal(abs(as.matrix(im_pred)), abs(as.matrix(exp_res)))
 
   im_tibble <-
     tibble(terms = c("x1", "x2", "x3"), id = "")
@@ -61,7 +63,8 @@ test_that('printing', {
   skip_if_not_installed("igraph")
   skip_if_not_installed("RANN")
   skip_if_not_installed("dimRed")
-
+  skip_if(getRversion() <= "3.4.4")
+
   im_rec <- rec %>%
     step_isomap(x1, x2, x3, neighbors = 3, num_terms = 3)
   expect_output(print(im_rec))