release. 0.21.0

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: mlr3
 Title: Machine Learning in R - Next Generation
-Version: 0.20.2.9000
+Version: 0.21.0
 Authors@R:
   c(
     person("Michel", "Lang", , "[email protected]", role = "aut",

NEWS.md

@@ -1,4 +1,4 @@
-# mlr3 (development version)
+# mlr3 0.21.0
 
 * BREAKING CHANGE: Deprecated `data_format` and `data_formats` for `Learner`, `Task`, and `DataBackend` classes.
 * feat: The `partition()` function creates training, test and validation sets now.
@@ -12,7 +12,7 @@
 * feat: Measures can now have an empty `$predict_sets` (#1094).
   This is relevant for measures that only extract information from the model of a learner (such as internal validation scores or AIC / BIC)
 * BREAKING CHANGE: Deprecated the `$divide()` method
-* fix: `Task$cbind()` now works with non-standard primary keys  for `data.frames` (#961).
+* fix: `Task$cbind()` now works with non-standard primary keys for `data.frames` (#961).
 * fix: Triggering of fallback learner now has log-level `"info"` instead of `"debug"` (#972).
 * feat: Added new measure `regr.pinball` here and in mlr3measures.
 * feat: Added new measure `mu_auc` here and in mlr3measures.

R/warn_deprecated.R

@@ -8,7 +8,7 @@
 #' `mlr3.warn_deprecated = FALSE`.
 #'
 #' The warning is of the format
-#' "<what> is deprecated and will be removed in the future."
+#' "what is deprecated and will be removed in the future."
 #'
 #' Use the 'deprecated_binding()' helper function to create an active binding
 #' that generates a warning when accessed.

README.md

@@ -19,51 +19,53 @@ Status](https://www.r-pkg.org/badges/version-ago/mlr3)](https://cran.r-project.o
 
 ## Resources (for users and developers)
 
-- We have written a [book](https://mlr3book.mlr-org.com/). This should
-  be the central entry point to the package.
-- The [mlr-org website](https://mlr-org.com/) includes for example a
-  [gallery](https://mlr-org.com/gallery.html) with case studies.
-- [Reference manual](https://mlr3.mlr-org.com/reference/)
-- [FAQ](https://mlr-org.com/faq.html)
-- Ask questions on [Stackoverflow (tag
-  \#mlr3)](https://stackoverflow.com/questions/tagged/mlr3)
-- **Extension Learners**
-  - Recommended core regression, classification, and survival learners
-    are in [mlr3learners](https://github.com/mlr-org/mlr3learners)
-  - All others are in
-    [mlr3extralearners](https://github.com/mlr-org/mlr3extralearners)
-  - Use the [learner search](https://mlr-org.com/learners.html) to get a
-    simple overview
-- **Cheatsheets**
-  - [Overview of cheatsheets](https://cheatsheets.mlr-org.com)
-  - [mlr3](https://cheatsheets.mlr-org.com/mlr3.pdf)
-  - [mlr3tuning](https://cheatsheets.mlr-org.com/mlr3tuning.pdf)
-  - [mlr3pipelines](https://cheatsheets.mlr-org.com/mlr3pipelines.pdf)
-- **Videos**:
-  - [useR2019 talk on mlr3](https://www.youtube.com/watch?v=wsP2hiFnDQs)
-  - [useR2019 talk on mlr3pipelines and
-    mlr3tuning](https://www.youtube.com/watch?v=gEW5RxkbQuQ)
-  - [useR2020 tutorial on mlr3, mlr3tuning and
-    mlr3pipelines](https://www.youtube.com/watch?v=T43hO2o_nZw)
-    <!--   - [Recorded talk about mlr3spatiotempcv and mlr3spatial at OpenDataScience Europe Conference 2021 in Wageningen, NL](https://av.tib.eu/media/55271) -->
-- **Courses/Lectures**
-  - The course [Introduction to Machine learning
-    (I2ML)](https://slds-lmu.github.io/i2ml/) is a free and open flipped
-    classroom course on the basics of machine learning. `mlr3` is used
-    in the
-    [demos](https://github.com/slds-lmu/lecture_i2ml/tree/master/code-demos-pdf)
-    and
-    [exercises](https://github.com/slds-lmu/lecture_i2ml/tree/master/exercises).
-- **Templates/Tutorials**
-  - [mlr3-targets](https://github.com/mlr-org/mlr3-targets): Tutorial
-    showcasing how to use {mlr3} with
-    [targets](https://docs.ropensci.org/targets/) for reproducible ML
-    workflow automation.
-- [List of extension packages](https://mlr-org.com/ecosystem.html)
-- [mlr-outreach](https://github.com/mlr-org/mlr-outreach) contains
-  public talks and slides resources.
-- [Wiki](https://github.com/mlr-org/mlr3/wiki): Contains mainly
-  information for developers.
+-   We have written a [book](https://mlr3book.mlr-org.com/). This should
+    be the central entry point to the package.
+-   The [mlr-org website](https://mlr-org.com/) includes for example a
+    [gallery](https://mlr-org.com/gallery.html) with case studies.
+-   [Reference manual](https://mlr3.mlr-org.com/reference/)
+-   [FAQ](https://mlr-org.com/faq.html)
+-   Ask questions on [Stackoverflow (tag
+    \#mlr3)](https://stackoverflow.com/questions/tagged/mlr3)
+-   **Extension Learners**
+    -   Recommended core regression, classification, and survival
+        learners are in
+        [mlr3learners](https://github.com/mlr-org/mlr3learners)
+    -   All others are in
+        [mlr3extralearners](https://github.com/mlr-org/mlr3extralearners)
+    -   Use the [learner search](https://mlr-org.com/learners.html) to
+        get a simple overview
+-   **Cheatsheets**
+    -   [Overview of cheatsheets](https://cheatsheets.mlr-org.com)
+    -   [mlr3](https://cheatsheets.mlr-org.com/mlr3.pdf)
+    -   [mlr3tuning](https://cheatsheets.mlr-org.com/mlr3tuning.pdf)
+    -   [mlr3pipelines](https://cheatsheets.mlr-org.com/mlr3pipelines.pdf)
+-   **Videos**:
+    -   [useR2019 talk on
+        mlr3](https://www.youtube.com/watch?v=wsP2hiFnDQs)
+    -   [useR2019 talk on mlr3pipelines and
+        mlr3tuning](https://www.youtube.com/watch?v=gEW5RxkbQuQ)
+    -   [useR2020 tutorial on mlr3, mlr3tuning and
+        mlr3pipelines](https://www.youtube.com/watch?v=T43hO2o_nZw)
+        <!--   - [Recorded talk about mlr3spatiotempcv and mlr3spatial at OpenDataScience Europe Conference 2021 in Wageningen, NL](https://av.tib.eu/media/55271) -->
+-   **Courses/Lectures**
+    -   The course [Introduction to Machine learning
+        (I2ML)](https://slds-lmu.github.io/i2ml/) is a free and open
+        flipped classroom course on the basics of machine learning.
+        `mlr3` is used in the
+        [demos](https://github.com/slds-lmu/lecture_i2ml/tree/master/code-demos-pdf)
+        and
+        [exercises](https://github.com/slds-lmu/lecture_i2ml/tree/master/exercises).
+-   **Templates/Tutorials**
+    -   [mlr3-targets](https://github.com/mlr-org/mlr3-targets):
+        Tutorial showcasing how to use {mlr3} with
+        [targets](https://docs.ropensci.org/targets/) for reproducible
+        ML workflow automation.
+-   [List of extension packages](https://mlr-org.com/ecosystem.html)
+-   [mlr-outreach](https://github.com/mlr-org/mlr-outreach) contains
+    public talks and slides resources.
+-   [Wiki](https://github.com/mlr-org/mlr3/wiki): Contains mainly
+    information for developers.
 
 ## Installation
 
@@ -156,16 +158,16 @@ rr$score(measure)[, .(task_id, learner_id, iteration, classif.acc)]
 ```
 
     ##                     task_id    learner_id iteration classif.acc
-    ## 1: palmerpenguins::penguins classif.rpart         1   0.9391304
+    ## 1: palmerpenguins::penguins classif.rpart         1   0.8956522
     ## 2: palmerpenguins::penguins classif.rpart         2   0.9478261
-    ## 3: palmerpenguins::penguins classif.rpart         3   0.9298246
+    ## 3: palmerpenguins::penguins classif.rpart         3   0.9649123
 
 ``` r
 rr$aggregate(measure)
 ```
 
     ## classif.acc 
-    ##    0.938927
+    ##   0.9361302
 
 ## Extension Packages
 
@@ -195,71 +197,72 @@ would result in non-trivial API changes.
 
 ## Design principles
 
-- Only the basic building blocks for machine learning are implemented in
-  this package.
-- Focus on computation here. No visualization or other stuff. That can
-  go in extra packages.
-- Overcome the limitations of R’s [S3
-  classes](https://adv-r.hadley.nz/s3.html) with the help of
-  [R6](https://cran.r-project.org/package=R6).
-- Embrace [R6](https://cran.r-project.org/package=R6) for a clean
-  OO-design, object state-changes and reference semantics. This might be
-  less “traditional R”, but seems to fit `mlr` nicely.
-- Embrace [`data.table`](https://cran.r-project.org/package=data.table)
-  for fast and convenient data frame computations.
-- Combine `data.table` and `R6`, for this we will make heavy use of list
-  columns in data.tables.
-- Defensive programming and type safety. All user input is checked with
-  [`checkmate`](https://cran.r-project.org/package=checkmate). Return
-  types are documented, and mechanisms popular in base R which
-  “simplify” the result unpredictably (e.g., `sapply()` or `drop`
-  argument in `[.data.frame`) are avoided.
-- Be light on dependencies. `mlr3` requires the following packages at
-  runtime:
-  - [`parallelly`](https://cran.r-project.org/package=parallelly):
-    Helper functions for parallelization. No extra recursive
-    dependencies.
-  - [`future.apply`](https://cran.r-project.org/package=future.apply):
-    Resampling and benchmarking is parallelized with the
-    [`future`](https://cran.r-project.org/package=future) abstraction
-    interfacing many parallel backends.
-  - [`backports`](https://cran.r-project.org/package=backports): Ensures
-    backward compatibility with older R releases. Developed by members
-    of the `mlr` team. No recursive dependencies.
-  - [`checkmate`](https://cran.r-project.org/package=checkmate): Fast
-    argument checks. Developed by members of the `mlr` team. No extra
-    recursive dependencies.
-  - [`mlr3misc`](https://cran.r-project.org/package=mlr3misc):
-    Miscellaneous functions used in multiple mlr3 [extension
-    packages](https://mlr-org.com/ecosystem.html). Developed by the
-    `mlr` team.
-  - [`paradox`](https://cran.r-project.org/package=paradox):
-    Descriptions for parameters and parameter sets. Developed by the
-    `mlr` team. No extra recursive dependencies.
-  - [`R6`](https://cran.r-project.org/package=R6): Reference class
-    objects. No recursive dependencies.
-  - [`data.table`](https://cran.r-project.org/package=data.table):
-    Extension of R’s `data.frame`. No recursive dependencies.
-  - [`digest`](https://cran.r-project.org/package=digest) (via
-    `mlr3misc`): Hash digests. No recursive dependencies.
-  - [`uuid`](https://cran.r-project.org/package=uuid): Create unique
-    string identifiers. No recursive dependencies.
-  - [`lgr`](https://cran.r-project.org/package=lgr): Logging facility.
-    No extra recursive dependencies.
-  - [`mlr3measures`](https://cran.r-project.org/package=mlr3measures):
-    Performance measures. No extra recursive dependencies.
-  - [`mlbench`](https://cran.r-project.org/package=mlbench): A
-    collection of machine learning data sets. No dependencies.
-  - [`palmerpenguins`](https://cran.r-project.org/package=palmerpenguins):
-    A classification data set about penguins, used on examples and
-    provided as a toy task. No dependencies.
-- [Reflections](https://en.wikipedia.org/wiki/Reflection_%28computer_programming%29):
-  Objects are queryable for properties and capabilities, allowing you to
-  program on them.
-- Additional functionality that comes with extra dependencies:
-  - To capture output, warnings and exceptions,
-    [`evaluate`](https://cran.r-project.org/package=evaluate) and
-    [`callr`](https://cran.r-project.org/package=callr) can be used.
+-   Only the basic building blocks for machine learning are implemented
+    in this package.
+-   Focus on computation here. No visualization or other stuff. That can
+    go in extra packages.
+-   Overcome the limitations of R’s [S3
+    classes](https://adv-r.hadley.nz/s3.html) with the help of
+    [R6](https://cran.r-project.org/package=R6).
+-   Embrace [R6](https://cran.r-project.org/package=R6) for a clean
+    OO-design, object state-changes and reference semantics. This might
+    be less “traditional R”, but seems to fit `mlr` nicely.
+-   Embrace
+    [`data.table`](https://cran.r-project.org/package=data.table) for
+    fast and convenient data frame computations.
+-   Combine `data.table` and `R6`, for this we will make heavy use of
+    list columns in data.tables.
+-   Defensive programming and type safety. All user input is checked
+    with [`checkmate`](https://cran.r-project.org/package=checkmate).
+    Return types are documented, and mechanisms popular in base R which
+    “simplify” the result unpredictably (e.g., `sapply()` or `drop`
+    argument in `[.data.frame`) are avoided.
+-   Be light on dependencies. `mlr3` requires the following packages at
+    runtime:
+    -   [`parallelly`](https://cran.r-project.org/package=parallelly):
+        Helper functions for parallelization. No extra recursive
+        dependencies.
+    -   [`future.apply`](https://cran.r-project.org/package=future.apply):
+        Resampling and benchmarking is parallelized with the
+        [`future`](https://cran.r-project.org/package=future)
+        abstraction interfacing many parallel backends.
+    -   [`backports`](https://cran.r-project.org/package=backports):
+        Ensures backward compatibility with older R releases. Developed
+        by members of the `mlr` team. No recursive dependencies.
+    -   [`checkmate`](https://cran.r-project.org/package=checkmate):
+        Fast argument checks. Developed by members of the `mlr` team. No
+        extra recursive dependencies.
+    -   [`mlr3misc`](https://cran.r-project.org/package=mlr3misc):
+        Miscellaneous functions used in multiple mlr3 [extension
+        packages](https://mlr-org.com/ecosystem.html). Developed by the
+        `mlr` team.
+    -   [`paradox`](https://cran.r-project.org/package=paradox):
+        Descriptions for parameters and parameter sets. Developed by the
+        `mlr` team. No extra recursive dependencies.
+    -   [`R6`](https://cran.r-project.org/package=R6): Reference class
+        objects. No recursive dependencies.
+    -   [`data.table`](https://cran.r-project.org/package=data.table):
+        Extension of R’s `data.frame`. No recursive dependencies.
+    -   [`digest`](https://cran.r-project.org/package=digest) (via
+        `mlr3misc`): Hash digests. No recursive dependencies.
+    -   [`uuid`](https://cran.r-project.org/package=uuid): Create unique
+        string identifiers. No recursive dependencies.
+    -   [`lgr`](https://cran.r-project.org/package=lgr): Logging
+        facility. No extra recursive dependencies.
+    -   [`mlr3measures`](https://cran.r-project.org/package=mlr3measures):
+        Performance measures. No extra recursive dependencies.
+    -   [`mlbench`](https://cran.r-project.org/package=mlbench): A
+        collection of machine learning data sets. No dependencies.
+    -   [`palmerpenguins`](https://cran.r-project.org/package=palmerpenguins):
+        A classification data set about penguins, used on examples and
+        provided as a toy task. No dependencies.
+-   [Reflections](https://en.wikipedia.org/wiki/Reflection_%28computer_programming%29):
+    Objects are queryable for properties and capabilities, allowing you
+    to program on them.
+-   Additional functionality that comes with extra dependencies:
+    -   To capture output, warnings and exceptions,
+        [`evaluate`](https://cran.r-project.org/package=evaluate) and
+        [`callr`](https://cran.r-project.org/package=callr) can be used.
 
 ## Contributing to mlr3