0-2-0 Release candidate (#911)

* skip for tune registration issues

* rearrange

* version bump

* link doesn't exist yet

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: recipes
 Title: Preprocessing and Feature Engineering Steps for Modeling
-Version: 0.1.17.9001
+Version: 0.2.0
 Authors@R: c(
     person("Max", "Kuhn", , "[email protected]", role = c("aut", "cre")),
     person("Hadley", "Wickham", , "[email protected]", role = "aut"),

NEWS.md

@@ -1,31 +1,29 @@
-# recipes (development version)
+# recipes 0.2.0
+
+# New Steps
+
+* `step_nnmf_sparse()` uses a different implementation of non-negative matrix factorization that is much faster and enables regularized estimation. (#790)
+
+* `step_dummy_extract()` creates multiple variables from a character variable by extracting elements using regular expressions and counting those elements.
+
+* `step_filter_missing()` can filter columns based on proportion of missingness (#270).
+
+* `step_percentile()` replaces the value of a variable with its percentile from the training set. (#765)
 
 ## Improvements and Other Changes
 
 * All recipe steps now officially support empty selections to be more aligned with dplyr and other packages that use tidyselect (#603, #531). For example, if a previous step removed all of the columns need for a later step, the recipe does not fail when it is estimated (with the exception of `step_mutate()`). The documentation in `?selections` has been updated with advice for writing selectors when filtering steps are used. (#813) 
 
 * Fixed bug in `step_harmonic()` printing and changed defaults to `role = "predictor"` and `keep_original_cols = FALSE` (#822).
 
-* Added a new step called `step_filter_missing()`, which can filter columns based on proportion of missingness (#270).
-
 * Improved the efficiency of computations for the Box-Cox transformation (#820).
 
 * When a feature extraction step (e.g., `step_pca()`, `step_ica()`, etc.) has zero components specified, the `tidy()` method now lists the selected columns in the `terms` column.
 
-* Added a new step called `step_nnmf_sparse()` which uses a different implementation of non-negative matrix factorization that is much faster and enables regularized estimation. (#790)
-
 * Deprecation has started for `step_nnmf()` in favor of `step_nnmf_sparse()`. (#790)
 
 * Steps now have a dedicated subsection detailing what happens when `tidy()` is applied. (#876)
 
-* Added a new step called `step_dummy_extract()` which creates multiple variables from a character variable by extracting elements using regular expressions and counting those elements.
-
-## Breaking Changes
-
-* `step_ica()` now indirectly uses the `fastICA` package since that package has increased their R version requirement. Recipe objects from previous versions will error when applied to new data. (#823)
-
-* `step_kpca*()` now directly use the `kernlab` package. Recipe objects from previous versions will error when applied to new data. 
-
 * `step_ica()` now runs `fastICA()` using a specific set of random numbers so that initialization is reproducible. 
 
 * `tidy.recipe()` now returns a zero row tibble instead of an error when applied to a empty recipe. (#867)
@@ -34,12 +32,17 @@
 
 * `detect_step()` is no longer restricted to steps created in recipes (#869).
 
-* Added a new step called `step_percentile()`, that replaces the value of a variable with its percentile from the training set. (#765)
-
 * New `extract_parameter_set_dials()` and `extract_parameter_dials()` methods to extract parameter sets and single parameters from `recipe` objects. 
 
 * `step_other()` now allow for setting `threshold = 0` which will result in no othering. (#904)
 
+## Breaking Changes
+
+* `step_ica()` now indirectly uses the `fastICA` package since that package has increased their R version requirement. Recipe objects from previous versions will error when applied to new data. (#823)
+
+* `step_kpca*()` now directly use the `kernlab` package. Recipe objects from previous versions will error when applied to new data. 
+
+
 ## Developer
 
 * The print methods have been internally changes to use `print_step()` instead of `printer()`. This is done for a smoother transition to use `cli` in the next version. (#871)

tests/testthat/helpers.R

@@ -0,0 +1,10 @@
+
+tune_check <- function() {
+  if (rlang::is_installed("tune")) {
+    res <- utils::packageVersion("tune") <= "0.1.6"
+  } else {
+    res <- TRUE
+  }
+  res
+}
+

tests/testthat/test-extract.R

@@ -1,5 +1,6 @@
 
 test_that('extract parameter set from recipe with no steps', {
+  skip_if(tune_check())
   bare_rec <- recipe(mpg ~ ., data = mtcars)
 
   bare_info <- extract_parameter_set_dials(bare_rec)
@@ -8,6 +9,7 @@ test_that('extract parameter set from recipe with no steps', {
 })
 
 test_that('extract parameter set from recipe with no tunable parameters', {
+  skip_if(tune_check())
   rm_rec <-
     recipe(mpg ~ ., data = mtcars) %>%
     step_rm(hp)
@@ -18,6 +20,7 @@ test_that('extract parameter set from recipe with no tunable parameters', {
 })
 
 test_that('extract parameter set from recipe with tunable parameters', {
+  skip_if(tune_check())
   spline_rec <-
     recipe(mpg ~ ., data = mtcars) %>%
     step_impute_knn(all_numeric_predictors(), neighbors = hardhat::tune("imputation")) %>%
@@ -49,6 +52,7 @@ test_that('extract parameter set from recipe with tunable parameters', {
 # -------------------------------------------------------------------------
 
 test_that('extract single parameter from recipe with no steps', {
+  skip_if(tune_check())
   bare_rec <- recipe(mpg ~ ., data = mtcars)
 
   expect_error(
@@ -57,6 +61,7 @@ test_that('extract single parameter from recipe with no steps', {
 })
 
 test_that('extract single parameter from recipe with no tunable parameters', {
+  skip_if(tune_check())
   rm_rec <-
     recipe(mpg ~ ., data = mtcars) %>%
     step_rm(hp)
@@ -67,6 +72,7 @@ test_that('extract single parameter from recipe with no tunable parameters', {
 })
 
 test_that('extract single parameter from recipe with tunable parameters', {
+  skip_if(tune_check())
   spline_rec <-
     recipe(mpg ~ ., data = mtcars) %>%
     step_impute_knn(all_numeric_predictors(), neighbors = hardhat::tune("imputation")) %>%

vignettes/Dummies.Rmd

@@ -245,7 +245,7 @@ There are a bunch of steps related to going in-between factors and dummy variabl
  * [`step_zv`](https://recipes.tidymodels.org/reference/step_zv.html) can remove dummy variables that never show a 1 in the column (i.e. is zero-variance).
  * [`step_bin2factor`](https://recipes.tidymodels.org/reference/step_bin2factor.html) takes a binary indicator and makes a factor variable. This can be useful when using naive Bayes models. 
  * `step_embed`, `step_lencode_glm`, `step_lencode_bayes` and others  in the [`embed`](https://github.com/tidymodels/embed) package can use one or more (non-binary) values to encode factor predictors into a numeric form. 
- * [`step_dummy_extract`](https://recipes.tidymodels.org/reference/step_dummy_extract.html) can create binary indicators from strings and is especially useful for multiple choice columns. 
+ * `step_dummy_extract` can create binary indicators from strings and is especially useful for multiple choice columns. 
 
 [`step_dummy`](https://recipes.tidymodels.org/reference/step_dummy.html) also works with _ordered factors_. As seen above, the default encoding is to create a series of polynomial variables. There are also a few steps for ordered factors: