Merge pull request #814 from mlr-org/preparing_v0.7.0

Preparing v0.7.0

Author: mb706

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: mlr3pipelines
 Title: Preprocessing Operators and Pipelines for 'mlr3'
-Version: 0.6.0-9000
+Version: 0.7.0
 Authors@R:
     c(person(given = "Martin",
              family = "Binder",
@@ -34,7 +34,16 @@ Authors@R:
       person(given = "Susanne",
              family = "Dandl",
              role = "aut",
-             email = "[email protected]"))
+             email = "[email protected]"),
+      person(given = "Keno",
+             family = "Mersmann",
+             role = "ctb",
+             email = "[email protected]"),
+      person(given = "Maximilian",
+             family = "Mücke",
+             role = "ctb",
+             email = "[email protected]",
+             comment = c(ORCID = "0009-0000-9432-9795")))
 Description: Dataflow programming toolkit that enriches 'mlr3' with a diverse
   set of pipelining operators ('PipeOps') that can be composed into graphs.
   Operations exist for data preprocessing, model fitting, and ensemble

NEWS.md

@@ -1,5 +1,21 @@
-# mlr3pipelines 0.6.0-9000
-
+# mlr3pipelines 0.7.0
+
+* New PipeOp `PipeOpRowApply` / `po("rowapply")`
+* Empty `PipeOp` IDs now explicitly forbidden.
+* Bugfix: `Graph$tran()` / `Graph$predict()` with `single_input = FALSE` now correctly handles `PipeOp`s with multiple inputs.
+* `GraphLearner$base_learner()` now works with `PipeOpBranch`, and is generally more robust.
+* `GraphLearner` now supports `$importance`, `$selected_features()`, `$oob_error()`, and `$loglik()`.
+  These are computed from the underlying `Learner`.
+* `GraphLearner$impute_selected_features` option added:
+  `$selected_features()` is reported even if the underlying base learner does not report it; in this case, the full feature set as seen by that learner is returned.
+* `GraphLearner$predict_type` handling more robust now.
+* `PipeOpThreshold` and `PipeOpTuneThreshold` now have the `$predict_type` `"prob"`.
+  They can be set to `"response"`, in which case the probability predictions are discarded, potentially saving memory.
+* Bugfix for handling multiplicities in PipeOps with vararg channels.
+* Bugfix: `PipeOpImputeOOR` now retains the `.MISSING` level in factors during prediction that were imputed during training, but had no missing values during prediction.
+* `as_data_table(po())` now works even when some `PipeOp`s can not be constructed.
+  For these `PipeOp`s, `NA` is reported in most columns.
+* Compatibility with upcoming `mlr3` release.
 * New PipeOp: `PipeOpRowApply` / `po("rowapply")`
 * New PipeOps for handling inbalanced data: `PipeOpADAS` / `po("adas")` and `PipeOpBLSmote` / `po("blsmote")`
 * New PipeOp for handling inbalanced data: `PipeOpSmoteNC` / `po("smotenc")`