Merge branch 'master' into bundle

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: mlr3pipelines
 Title: Preprocessing Operators and Pipelines for 'mlr3'
-Version: 0.5.1-9000
+Version: 0.5.2-9000
 Authors@R:
     c(person(given = "Martin",
              family = "Binder",
@@ -86,7 +86,8 @@ Suggests:
     GenSA,
     methods,
     vtreat,
-    future
+    future,
+    htmlwidgets
 ByteCompile: true
 Encoding: UTF-8
 Config/testthat/edition: 3
@@ -175,6 +176,7 @@ Collate:
     'operators.R'
     'pipeline_bagging.R'
     'pipeline_branch.R'
+    'pipeline_convert_types.R'
     'pipeline_greplicate.R'
     'pipeline_ovr.R'
     'pipeline_robustify.R'

NAMESPACE

@@ -127,6 +127,7 @@ export(mlr_graphs)
 export(mlr_pipeops)
 export(pipeline_bagging)
 export(pipeline_branch)
+export(pipeline_convert_types)
 export(pipeline_greplicate)
 export(pipeline_ovr)
 export(pipeline_robustify)

NEWS.md

@@ -1,4 +1,10 @@
-# mlr3pipelines 0.5.1-9000
+# mlr3pipelines 0.5.2-9000
+
+# mlr3pipelines 0.5.2
+
+* Added new `ppl("convert_types")`.
+* Minor documentation fixes.
+* Test helpers are now available in `inst/`. These are considered experimental and unstable.
 
 * Added marshaling support to `GraphLearner`
 

R/GraphLearner.R

@@ -68,6 +68,7 @@
 #' @family Learners
 #' @export
 #' @examples
+#' \dontshow{ if (requireNamespace("rpart")) \{ }
 #' library("mlr3")
 #'
 #' graph = po("pca") %>>% lrn("classif.rpart")
@@ -86,6 +87,7 @@
 #'
 #' # Feature importance (of principal components):
 #' lr$graph_model$pipeops$classif.rpart$learner_model$importance()
+#' \dontshow{ \} }
 GraphLearner = R6Class("GraphLearner", inherit = Learner,
   public = list(
     initialize = function(graph, id = NULL, param_vals = list(), task_type = NULL, predict_type = NULL, clone_graph = TRUE) {

R/PipeOpBoxCox.R

@@ -48,6 +48,7 @@
 #' Only methods inherited from [`PipeOpTaskPreproc`]/[`PipeOp`].
 #'
 #' @examples
+#' \dontshow{ if (requireNamespace("bestNormalize")) \{ }
 #' library("mlr3")
 #'
 #' task = tsk("iris")
@@ -57,6 +58,7 @@
 #' pop$train(list(task))[[1]]$data()
 #'
 #' pop$state
+#' \dontshow{ \} }
 #' @family PipeOps
 #' @include PipeOpTaskPreproc.R
 #' @template seealso_pipeopslist

R/PipeOpClassifAvg.R

@@ -64,6 +64,7 @@
 #' @export
 #'
 #' @examples
+#' \dontshow{ if (requireNamespace("rpart")) \{ }
 #' \donttest{
 #' library("mlr3")
 #'
@@ -77,6 +78,7 @@
 #'
 #' resample(tsk("iris"), GraphLearner$new(gr), rsmp("holdout"))
 #' }
+#' \dontshow{ \} }
 PipeOpClassifAvg = R6Class("PipeOpClassifAvg",
   inherit = PipeOpEnsemble,
   public = list(

R/PipeOpEncode.R

@@ -5,7 +5,7 @@
 #' @format [`R6Class`] object inheriting from [`PipeOpTaskPreprocSimple`]/[`PipeOpTaskPreproc`]/[`PipeOp`].
 #'
 #' @description
-#' Encodes columns of type `factor`, `character` and `ordered`.
+#' Encodes columns of type `factor` and `ordered`.
 #'
 #' Possible encodings are `"one-hot"` encoding, as well as encoding according to `stats::contr.helmert()`, `stats::contr.poly()`,
 #' `stats::contr.sum()` and `stats::contr.treatment()`.
@@ -14,6 +14,8 @@
 #'
 #' Use the [`PipeOpTaskPreproc`] `$affect_columns` functionality to only encode a subset of columns, or only encode columns of a certain type.
 #'
+#' `character`-type features can be encoded by converting them `factor` features first, using [`ppl("convert_types", "character", "factor")`][mlr_graphs_convert_types].
+#'
 #' @section Construction:
 #' ```
 #' PipeOpEncode$new(id = "encode", param_vals = list())
@@ -26,7 +28,7 @@
 #' @section Input and Output Channels:
 #' Input and output channels are inherited from [`PipeOpTaskPreproc`].
 #'
-#' The output is the input [`Task`][mlr3::Task] with all affected `factor`, `character` or `ordered` parameters encoded according to the `method`
+#' The output is the input [`Task`][mlr3::Task] with all affected `factor` and `ordered` parameters encoded according to the `method`
 #' parameter.
 #'
 #' @section State:
@@ -78,6 +80,14 @@
 #'
 #' poe$param_set$values$method = "sum"
 #' poe$train(list(task))[[1]]$data()
+#'
+#' # converting character-columns
+#' data_chr = data.table::data.table(x = factor(letters[1:3]), y = letters[1:3])
+#' task_chr = TaskClassif$new("task_chr", data_chr, "x")
+#'
+#' goe = ppl("convert_types", "character", "factor") %>>% po("encode")
+#'
+#' goe$train(task_chr)[[1]]$data()
 PipeOpEncode = R6Class("PipeOpEncode",
   inherit = PipeOpTaskPreprocSimple,
   public = list(

R/PipeOpEncodeLmer.R

@@ -72,6 +72,8 @@
 #' @include PipeOpTaskPreproc.R
 #' @export
 #' @examples
+#' \dontshow{ if (requireNamespace("nloptr")) \{ }
+#' \dontshow{ if (requireNamespace("lme4")) \{ }
 #' library("mlr3")
 #' poe = po("encodelmer")
 #'
@@ -84,6 +86,8 @@
 #' poe$train(list(task))[[1]]$data()
 #'
 #' poe$state
+#' \dontshow{ \} }
+#' \dontshow{ \} }
 PipeOpEncodeLmer = R6Class("PipeOpEncodeLmer",
   inherit = PipeOpTaskPreprocSimple,
   public = list(

R/PipeOpFilter.R

@@ -80,6 +80,8 @@
 #' @include PipeOpTaskPreproc.R
 #' @export
 #' @examples
+#' \dontshow{ if (requireNamespace("mlr3filters")) \{ }
+#' \dontshow{ if (requireNamespace("rpart")) \{ }
 #' library("mlr3")
 #' library("mlr3filters")
 #' \dontshow{data.table::setDTthreads(1)}
@@ -107,6 +109,8 @@
 #' learner = GraphLearner$new(gr)
 #' rr = resample(task, learner, rsmp("holdout"), store_models = TRUE)
 #' rr$learners[[1]]$model$auc$scores
+#' \dontshow{ \} }
+#' \dontshow{ \} }
 PipeOpFilter = R6Class("PipeOpFilter",
   inherit = PipeOpTaskPreprocSimple,
   public = list(

R/PipeOpICA.R

@@ -73,6 +73,7 @@
 #' Only methods inherited from [`PipeOpTaskPreproc`]/[`PipeOp`].
 #'
 #' @examples
+#' \dontshow{ if (requireNamespace("fastICA")) \{ }
 #' library("mlr3")
 #'
 #' task = tsk("iris")
@@ -82,6 +83,7 @@
 #' pop$train(list(task))[[1]]$data()
 #'
 #' pop$state
+#' \dontshow{ \} }
 #' @family PipeOps
 #' @template seealso_pipeopslist
 #' @include PipeOpTaskPreproc.R

R/PipeOpImputeLearner.R

@@ -70,6 +70,7 @@
 #' Only methods inherited from [`PipeOpImpute`]/[`PipeOp`].
 #'
 #' @examples
+#' \dontshow{ if (requireNamespace("rpart")) \{ }
 #' library("mlr3")
 #'
 #' task = tsk("pima")
@@ -93,6 +94,7 @@
 #' new_task = po$train(list(task = task))[[1]]
 #' new_task$missings()
 #'
+#' \dontshow{ \} }
 #' @family PipeOps
 #' @family Imputation PipeOps
 #' @template seealso_pipeopslist

R/PipeOpKernelPCA.R

@@ -52,13 +52,15 @@
 #' Only methods inherited from [`PipeOpTaskPreproc`]/[`PipeOp`].
 #'
 #' @examples
+#' \dontshow{ if (requireNamespace("kernlab")) \{ }
 #' library("mlr3")
 #'
 #' task = tsk("iris")
 #' pop = po("kernelpca", features = 3)  # only keep top 3 components
 #'
 #' task$data()
 #' pop$train(list(task))[[1]]$data()
+#' \dontshow{ \} }
 #' @family PipeOps
 #' @template seealso_pipeopslist
 #' @include PipeOpTaskPreproc.R

R/PipeOpLearner.R

@@ -72,6 +72,7 @@
 #' @include PipeOp.R
 #' @export
 #' @examples
+#' \dontshow{ if (requireNamespace("rpart")) \{ }
 #' library("mlr3")
 #'
 #' task = tsk("iris")
@@ -80,6 +81,7 @@
 #'
 #' lrn_po$train(list(task))
 #' lrn_po$predict(list(task))
+#' \dontshow{ \} }
 PipeOpLearner = R6Class("PipeOpLearner", inherit = PipeOp,
   public = list(
     initialize = function(learner, id = NULL, param_vals = list()) {

R/PipeOpLearnerCV.R

@@ -93,6 +93,7 @@
 #' @include PipeOpTaskPreproc.R
 #' @export
 #' @examples
+#' \dontshow{ if (requireNamespace("rpart")) \{ }
 #' library("mlr3")
 #'
 #' task = tsk("iris")
@@ -113,6 +114,7 @@
 #' graph$pipeops$classif.rpart$learner$predict_type = "prob"
 #'
 #' graph$train(task)
+#' \dontshow{ \} }
 PipeOpLearnerCV = R6Class("PipeOpLearnerCV",
   inherit = PipeOpTaskPreproc,
   public = list(

R/PipeOpNMF.R

@@ -88,6 +88,8 @@
 #' Only methods inherited from [`PipeOpTaskPreproc`]/[`PipeOp`].
 #'
 #' @examples
+#' \dontshow{ if (requireNamespace("NMF")) \{ }
+#' \dontshow{ if (requireNamespace("MASS")) \{ }
 #' if (requireNamespace("NMF")) {
 #' library("mlr3")
 #'
@@ -99,6 +101,8 @@
 #'
 #' pop$state
 #' }
+#' \dontshow{ \} }
+#' \dontshow{ \} }
 #' @family PipeOps
 #' @template seealso_pipeopslist
 #' @include PipeOpTaskPreproc.R

R/PipeOpOVR.R

@@ -68,11 +68,13 @@
 #' @include PipeOp.R
 #' @export
 #' @examples
+#' \dontshow{ if (requireNamespace("rpart")) \{ }
 #' library(mlr3)
 #' task = tsk("iris")
 #' po = po("ovrsplit")
 #' po$train(list(task))
 #' po$predict(list(task))
+#' \dontshow{ \} }
 PipeOpOVRSplit = R6Class("PipeOpOVRSplit",
   inherit = PipeOp,
   public = list(
@@ -174,13 +176,15 @@ mlr_pipeops$add("ovrsplit", PipeOpOVRSplit)
 #' @include PipeOpEnsemble.R
 #' @export
 #' @examples
+#' \dontshow{ if (requireNamespace("rpart")) \{ }
 #' library(mlr3)
 #' task = tsk("iris")
 #' gr = po("ovrsplit") %>>% lrn("classif.rpart") %>>% po("ovrunite")
 #' gr$train(task)
 #' gr$predict(task)
 #' gr$pipeops$classif.rpart$learner$predict_type = "prob"
 #' gr$predict(task)
+#' \dontshow{ \} }
 PipeOpOVRUnite = R6Class("PipeOpOVRUnite",
   inherit = PipeOpEnsemble,
   public = list(

R/PipeOpProxy.R

@@ -55,6 +55,7 @@
 #' Only methods inherited from [`PipeOp`].
 #'
 #' @examples
+#' \dontshow{ if (requireNamespace("rpart")) \{ }
 #' library("mlr3")
 #' library("mlr3learners")
 #'
@@ -73,6 +74,7 @@
 #' g$param_set$values$learner.content = lrn("classif.rpart")
 #' rr_pca_rpart = resample(task, learner = GraphLearner$new(g), resampling = rsmp("cv", folds = 3))
 #' rr_pca_rpart$aggregate(msr("classif.ce"))
+#' \dontshow{ \} }
 #' @family PipeOps
 #' @template seealso_pipeopslist
 #' @include PipeOp.R

R/PipeOpRandomResponse.R

@@ -63,6 +63,7 @@
 #' @include PipeOp.R
 #' @export
 #' @examples
+#' \dontshow{ if (requireNamespace("rpart")) \{ }
 #' library(mlr3)
 #' library(mlr3learners)
 #'
@@ -79,6 +80,7 @@
 #' g2$pipeops$regr.lm$learner$predict_type = "se"
 #' set.seed(2906)
 #' g2$predict(task2)
+#' \dontshow{ \} }
 PipeOpRandomResponse = R6Class("PipeOpRandomResponse",
   inherit = PipeOp,
   public = list(

R/PipeOpRegrAvg.R

@@ -57,6 +57,7 @@
 #' @include PipeOpEnsemble.R
 #' @export
 #' @examples
+#' \dontshow{ if (requireNamespace("rpart")) \{ }
 #' library("mlr3")
 #'
 #' # Simple Bagging
@@ -68,6 +69,7 @@
 #'   po("classifavg")
 #'
 #' resample(tsk("iris"), GraphLearner$new(gr), rsmp("holdout"))
+#' \dontshow{ \} }
 PipeOpRegrAvg = R6Class("PipeOpRegrAvg",
   inherit = PipeOpEnsemble,
 

R/PipeOpSmote.R

@@ -54,6 +54,7 @@
 #' @include PipeOpTaskPreproc.R
 #' @export
 #' @examples
+#' \dontshow{ if (requireNamespace("smotefamily")) \{ }
 #' library("mlr3")
 #'
 #' # Create example task
@@ -67,6 +68,7 @@
 #' pop = po("smote")
 #' smotedata = pop$train(list(task))[[1]]$data()
 #' table(smotedata$result)
+#' \dontshow{ \} }
 PipeOpSmote = R6Class("PipeOpSmote",
   inherit = PipeOpTaskPreproc,
   public = list(

R/PipeOpTextVectorizer.R

@@ -140,6 +140,8 @@
 #' Only methods inherited from [`PipeOpTaskPreproc`]/[`PipeOp`].
 #'
 #' @examples
+#' \dontshow{ if (requireNamespace("stopwords")) \{ }
+#' \dontshow{ if (requireNamespace("quanteda")) \{ }
 #' library("mlr3")
 #' library("data.table")
 #' # create some text data
@@ -157,6 +159,8 @@
 #' one_line_of_iris$data()
 #'
 #' pos$predict(list(one_line_of_iris))[[1]]$data()
+#' \dontshow{ \} }
+#' \dontshow{ \} }
 #' @family PipeOps
 #' @template seealso_pipeopslist
 #' @include PipeOpTaskPreproc.R

R/PipeOpThreshold.R

@@ -42,12 +42,14 @@
 #' Only methods inherited from [`PipeOp`].
 #'
 #' @examples
+#' \dontshow{ if (requireNamespace("rpart")) \{ }
 #' library("mlr3")
 #' t = tsk("german_credit")
 #' gr = po(lrn("classif.rpart", predict_type = "prob")) %>>%
 #'   po("threshold", param_vals = list(thresholds = 0.9))
 #' gr$train(t)
 #' gr$predict(t)
+#' \dontshow{ \} }
 #' @family PipeOps
 #' @template seealso_pipeopslist
 #' @include PipeOp.R