release: 1.2.0 (#468)

* release: 1.2.0 * ...
mlr-org · Nov 8, 2024 · 47c103a · 47c103a
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: mlr3tuning
 Title: Hyperparameter Optimization for 'mlr3'
-Version: 1.1.0.9000
+Version: 1.2.0
 Authors@R: c(
     person("Marc", "Becker", , "[email protected]", role = c("cre", "aut"),
            comment = c(ORCID = "0000-0002-8115-0400")),

diff --git a/NEWS.md b/NEWS.md
@@ -1,4 +1,4 @@
-# mlr3tuning (development version)
+# mlr3tuning 1.2.0
 
 * feat: Add new callback `clbk("mlr3tuning.one_se_rule")` that selects the the hyperparameter configuration with the smallest feature set within one standard error of the best.
 * feat: Add new stages `on_tuning_result_begin` and `on_result_begin` to `CallbackAsyncTuning` and `CallbackBatchTuning`.

diff --git a/R/AutoTuner.R b/R/AutoTuner.R
@@ -24,14 +24,7 @@
 #' A set timeout is disabled while fitting the final model.
 #'
 #' @inheritSection TuningInstanceBatchSingleCrit Default Measures
-#'
-#' @section Resources:
-#' There are several sections about hyperparameter optimization in the [mlr3book](https://mlr3book.mlr-org.com).
-#'
-#'  * [Automate](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-autotuner) the tuning.
-#'  * Estimate the model performance with [nested resampling](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-nested-resampling).
-#'
-#' The [gallery](https://mlr-org.com/gallery-all-optimization.html) features a collection of case studies and demos about optimization.
+#' @inheritSection TuningInstanceBatchSingleCrit Resources
 #'
 #' @section Nested Resampling:
 #' Nested resampling is performed by passing an [AutoTuner] to [mlr3::resample()] or [mlr3::benchmark()].

diff --git a/R/Tuner.R b/R/Tuner.R
@@ -8,16 +8,7 @@
 #' @details
 #' `Tuner` is an abstract base class that implements the base functionality each tuner must provide.
 #'
-#' @section Resources:
-#' There are several sections about hyperparameter optimization in the [mlr3book](https://mlr3book.mlr-org.com).
-#'
-#' * An overview of all tuners can be found on our [website](https://mlr-org.com/tuners.html).
-#'
-#'  * Learn more about [tuners](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-tuner).
-#'
-#' The [gallery](https://mlr-org.com/gallery-all-optimization.html) features a collection of case studies and demos about optimization.
-#'
-#'  * Use the [Hyperband](https://mlr-org.com/gallery/series/2023-01-15-hyperband-xgboost/) optimizer with different budget parameters.
+#' @inheritSection TuningInstanceBatchSingleCrit Resources
 #'
 #' @section Extension Packages:
 #' Additional tuners are provided by the following packages.

diff --git a/R/TuningInstanceBatchMulticrit.R b/R/TuningInstanceBatchMulticrit.R
@@ -7,13 +7,7 @@
 #' The function [ti()] creates a [TuningInstanceBatchMultiCrit] and the function [tune()] creates an instance internally.
 #'
 #' @inherit TuningInstanceBatchSingleCrit details
-#'
-#' @section Resources:
-#' There are several sections about hyperparameter optimization in the [mlr3book](https://mlr3book.mlr-org.com).
-#'
-#'  * Learn about [multi-objective optimization](https://mlr3book.mlr-org.com/chapters/chapter5/advanced_tuning_methods_and_black_box_optimization.html#sec-multi-metrics-tuning).
-#'
-#' The [gallery](https://mlr-org.com/gallery-all-optimization.html) features a collection of case studies and demos about optimization.
+#' @inheritSection TuningInstanceBatchSingleCrit Resources
 #'
 #' @inheritSection ArchiveBatchTuning Analysis
 #'

diff --git a/R/TuningInstanceBatchSingleCrit.R b/R/TuningInstanceBatchSingleCrit.R
@@ -33,17 +33,23 @@
 #' @section Resources:
 #' There are several sections about hyperparameter optimization in the [mlr3book](https://mlr3book.mlr-org.com).
 #'
-#'  * Getting started with [hyperparameter optimization](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html).
-#'  * [Tune](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-model-tuning) a simple classification tree on the Sonar data set.
-#'  * Learn about [tuning spaces](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-defining-search-spaces).
+#' * Getting started with [hyperparameter optimization](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html).
+#' * An overview of all tuners can be found on our [website](https://mlr-org.com/tuners.html).
+#' * [Tune](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-model-tuning) a support vector machine on the Sonar data set.
+#' * Learn about [tuning spaces](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-defining-search-spaces).
+#' * Estimate the model performance with [nested resampling](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-nested-resampling).
+#' * Learn about [multi-objective optimization](https://mlr3book.mlr-org.com/chapters/chapter5/advanced_tuning_methods_and_black_box_optimization.html#sec-multi-metrics-tuning).
+#' * Simultaneously optimize hyperparameters and use [early stopping](https://mlr3book.mlr-org.com/chapters/chapter15/predsets_valid_inttune.html) with XGBoost.
+#' * [Automate](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-autotuner) the tuning.
 #'
 #' The [gallery](https://mlr-org.com/gallery-all-optimization.html) features a collection of case studies and demos about optimization.
 #'
-#'  * Learn more advanced methods with the [practical tuning series](https://mlr-org.com/gallery/series/2021-03-09-practical-tuning-series-tune-a-support-vector-machine/).
-#'  * Simultaneously optimize hyperparameters and use [early stopping](https://mlr-org.com/gallery/optimization/2022-11-04-early-stopping-with-xgboost/) with XGBoost.
-#'  * Make us of proven [search space](https://mlr-org.com/gallery/optimization/2021-07-06-introduction-to-mlr3tuningspaces/).
-#'  * Learn about [hotstarting](https://mlr-org.com/gallery/optimization/2023-01-16-hotstart/) models.
-#'  * Run the [default hyperparameter configuration](https://mlr-org.com/gallery/optimization/2023-01-31-default-configuration/) of learners as a baseline.
+#' * Learn more advanced methods with the [Practical Tuning Series](https://mlr-org.com/gallery/series/2021-03-09-practical-tuning-series-tune-a-support-vector-machine/).
+#' * Learn about [hotstarting](https://mlr-org.com/gallery/optimization/2023-01-16-hotstart/) models.
+#' * Run the [default hyperparameter configuration](https://mlr-org.com/gallery/optimization/2023-01-31-default-configuration/) of learners as a baseline.
+#' * Use the [Hyperband](https://mlr-org.com/gallery/series/2023-01-15-hyperband-xgboost/) optimizer with different budget parameters.
+#'
+#' The [cheatsheet](https://cheatsheets.mlr-org.com/mlr3tuning.pdf) summarizes the most important functions of mlr3tuning.
 #'
 #' @section Extension Packages:
 #'

diff --git a/R/auto_tuner.R b/R/auto_tuner.R
@@ -2,7 +2,7 @@
 #'
 #' @inherit AutoTuner description
 #' @inheritSection TuningInstanceBatchSingleCrit Default Measures
-#' @inheritSection AutoTuner Resources
+#' @inheritSection TuningInstanceBatchSingleCrit Resources
 #' @inherit AutoTuner details
 #' @inheritSection AutoTuner Nested Resampling
 #'

diff --git a/R/tune.R b/R/tune.R
@@ -19,20 +19,8 @@
 #' If no termination criterion is needed, set `term_evals`, `term_time` and `terminator` to `NULL`.
 #' The search space is created from [paradox::TuneToken] or is supplied by `search_space`.
 #'
-#' @section Resources:
-#' There are several sections about hyperparameter optimization in the [mlr3book](https://mlr3book.mlr-org.com).
-#'
-#'  * Simplify tuning with the [`tune()`](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-autotuner) function.
-#'  * Learn about [tuning spaces](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-defining-search-spaces).
-#'
-#' The [gallery](https://mlr-org.com/gallery-all-optimization.html) features a collection of case studies and demos about optimization.
-#'
-#'  * Optimize an rpart classification tree with only a [few lines of code](https://mlr-org.com/gallery/optimization/2022-11-10-hyperparameter-optimization-on-the-palmer-penguins/).
-#'  * Tune an XGBoost model with [early stopping](https://mlr-org.com/gallery/optimization/2022-11-04-early-stopping-with-xgboost/).
-#'  * Make us of proven [search space](https://mlr-org.com/gallery/optimization/2021-07-06-introduction-to-mlr3tuningspaces/).
-#'  * Learn about [hotstarting](https://mlr-org.com/gallery/optimization/2023-01-16-hotstart/) models.
-#'
 #' @inheritSection TuningInstanceBatchSingleCrit Default Measures
+#' @inheritSection TuningInstanceBatchSingleCrit Resources
 #'
 #' @inheritSection ArchiveBatchTuning Analysis
 #'

diff --git a/README.Rmd b/README.Rmd
@@ -51,6 +51,7 @@ There are several sections about hyperparameter optimization in the [mlr3book](h
 * Estimate the model performance with [nested resampling](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-nested-resampling).
 * Learn about [multi-objective optimization](https://mlr3book.mlr-org.com/chapters/chapter5/advanced_tuning_methods_and_black_box_optimization.html#sec-multi-metrics-tuning).
 * Simultaneously optimize hyperparameters and use [early stopping](https://mlr3book.mlr-org.com/chapters/chapter15/predsets_valid_inttune.html) with XGBoost.
+* [Automate](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-autotuner) the tuning.
 
 The [gallery](https://mlr-org.com/gallery-all-optimization.html) features a collection of case studies and demos about optimization.
 

diff --git a/man/AutoTuner.Rd b/man/AutoTuner.Rd
diff --git a/man/Tuner.Rd b/man/Tuner.Rd
diff --git a/man/TunerAsync.Rd b/man/TunerAsync.Rd