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release: 1.2.0 #468

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release: 1.2.0
be-marc Nov 8, 2024
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be-marc Nov 8, 2024
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2 changes: 1 addition & 1 deletion DESCRIPTION
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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")),
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2 changes: 1 addition & 1 deletion NEWS.md
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# 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`.
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9 changes: 1 addition & 8 deletions R/AutoTuner.R
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#' 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()].
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11 changes: 1 addition & 10 deletions R/Tuner.R
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#' @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.
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8 changes: 1 addition & 7 deletions R/TuningInstanceBatchMulticrit.R
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#' 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
#'
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22 changes: 14 additions & 8 deletions R/TuningInstanceBatchSingleCrit.R
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#' @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:
#'
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2 changes: 1 addition & 1 deletion R/auto_tuner.R
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#'
#' @inherit AutoTuner description
#' @inheritSection TuningInstanceBatchSingleCrit Default Measures
#' @inheritSection AutoTuner Resources
#' @inheritSection TuningInstanceBatchSingleCrit Resources
#' @inherit AutoTuner details
#' @inheritSection AutoTuner Nested Resampling
#'
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14 changes: 1 addition & 13 deletions R/tune.R
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#' 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
#'
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1 change: 1 addition & 0 deletions README.Rmd
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Expand Up @@ -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.

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36 changes: 25 additions & 11 deletions man/AutoTuner.Rd
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29 changes: 20 additions & 9 deletions man/Tuner.Rd
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13 changes: 12 additions & 1 deletion man/TunerAsync.Rd
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