release: 0.19.1

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: mlr3tuning
 Title: Hyperparameter Optimization for 'mlr3'
-Version: 0.19.0.9000
+Version: 0.19.1
 Authors@R: c(
     person("Marc", "Becker", , "[email protected]", role = c("cre", "aut"),
            comment = c(ORCID = "0000-0002-8115-0400")),
@@ -25,15 +25,15 @@ License: LGPL-3
 URL: https://mlr3tuning.mlr-org.com, https://github.com/mlr-org/mlr3tuning
 BugReports: https://github.com/mlr-org/mlr3tuning/issues
 Depends:
-    mlr3 (>= 0.14.1),
+    mlr3 (>= 0.17.0),
     paradox (>= 0.10.0),
     R (>= 3.1.0)
 Imports:
-    bbotk (>= 0.7.2),
+    bbotk (>= 0.7.3),
     checkmate (>= 2.0.0),
     data.table,
     lgr,
-    mlr3misc (>= 0.11.0),
+    mlr3misc (>= 0.13.0),
     R6
 Suggests:
     adagio,
@@ -47,8 +47,6 @@ Suggests:
     rpart,
     testthat (>= 3.0.0),
     xgboost
-Remotes:
-    mlr-org/mlr3
 Config/testthat/edition: 3
 Config/testthat/parallel: true
 Encoding: UTF-8

NEWS.md

@@ -1,5 +1,6 @@
-# mlr3tuning (development version)
+# mlr3tuning 0.19.1
 
+* refactor: Speed up the tuning process by minimizing the number of deep clones and parameter checks.
 * fix: Set `store_benchmark_result = TRUE` if `store_models = TRUE` when creating a tuning instance.
 * fix: Passing a terminator in `tune_nested()` did not work.
 

R/zzz.R

@@ -8,6 +8,9 @@
 "_PACKAGE"
 
 .onLoad = function(libname, pkgname) {
+  # CRAN OMP THREAD LIMIT
+  Sys.setenv("OMP_THREAD_LIMIT" = 2)
+
   # nocov start
   x = utils::getFromNamespace("mlr_reflections", ns = "mlr3")
   x$tuner_properties = "dependencies"

README.md

@@ -1,7 +1,7 @@
 
 # mlr3tuning <img src="man/figures/logo.png" align="right" width = "120" />
 
-Package website: [release](https://mlr3tuning.mlr-org.com/) |
+Package website: [release](https://mlr3tuning.mlr-org.com/) \|
 [dev](https://mlr3tuning.mlr-org.com/dev/)
 
 <!-- badges: start -->
@@ -34,49 +34,49 @@ The package is built on the optimization framework
 
 mlr3tuning is extended by the following packages.
 
-  - [mlr3tuningspaces](https://github.com/mlr-org/mlr3tuningspaces) is a
+-   [mlr3tuningspaces](https://github.com/mlr-org/mlr3tuningspaces) is a
     collection of search spaces from scientific articles for commonly
     used learners.
-  - [mlr3hyperband](https://github.com/mlr-org/mlr3hyperband) adds the
+-   [mlr3hyperband](https://github.com/mlr-org/mlr3hyperband) adds the
     Hyperband and Successive Halving algorithm.
-  - [mlr3mbo](https://github.com/mlr-org/mlr3mbo) adds Bayesian
+-   [mlr3mbo](https://github.com/mlr-org/mlr3mbo) adds Bayesian
     Optimization methods.
 
 ## Resources
 
 There are several sections about hyperparameter optimization in the
 [mlr3book](https://mlr3book.mlr-org.com).
 
-  - Getting started with [hyperparameter
+-   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)
+-   [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
+-   Learn about [tuning
     spaces](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-defining-search-spaces).
-  - Estimate the model performance with [nested
+-   Estimate the model performance with [nested
     resampling](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-nested-resampling).
-  - Learn about [multi-objective
+-   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.
 
-  - Learn more advanced methods with the [Practical Tuning
+-   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/).
-  - Optimize an rpart classification tree with only a [few lines of
+-   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/).
-  - Simultaneously optimize hyperparameters and use [early
+-   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
+-   Make us of proven [search
     space](https://mlr-org.com/gallery/optimization/2021-07-06-introduction-to-mlr3tuningspaces/).
-  - Learn about
+-   Learn about
     [hotstarting](https://mlr-org.com/gallery/optimization/2023-01-16-hotstart/)
     models.
-  - Run the [default hyperparameter
+-   Run the [default hyperparameter
     configuration](https://mlr-org.com/gallery/optimization/2023-01-31-default-configuration/)
     of learners as a baseline.
-  - Use the
+-   Use the
     [Hyperband](https://mlr-org.com/gallery/series/2023-01-15-hyperband-xgboost/)
     optimizer with different budget parameters.
 

inst/WORDLIST

@@ -52,16 +52,17 @@ hotstart
 hotstarting
 hyperband
 irace
+iteratively
 mbo
 mlr
 nloptr
+optimizers
 parallelization
 parallelize
 param
 parametrized
 rpart
 subclasses
-svm
 th
 trafo
 tuningspaces

tests/testthat/test_mlr_callbacks.R

@@ -1,4 +1,5 @@
 test_that("early stopping callback works", {
+  skip_on_cran()
   skip_if_not_installed("mlr3learners")
   skip_if_not_installed("xgboost")
   library(mlr3learners) # nolint