mlr3tuning 0.20.0

• compatibility: Work with new paradox version 1.0.0
• fix: TunerIrace failed with logical parameters and dependencies.

mlr3tuning 0.19.2

• refactor: Change thread limits.

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.

mlr3tuning 0.19.0

• fix: Add $phash() method to AutoTuner.
• fix: Include Tuner in hash of AutoTuner.
• feat: Add new callback that scores the configurations on additional measures while tuning.
• feat: Add vignette about adding new tuners which was previously part of the mlr3book.

mlr3tuning 0.18.0

• BREAKING CHANGE: The method parameter of tune(), tune_nested() and auto_tuner() is renamed to tuner. Only Tuner objects are accepted now. Arguments to the tuner cannot be passed with ... anymore.
• BREAKING CHANGE: The tuner parameter of AutoTuner is moved to the first position to achieve consistency with the other functions.
• docs: Update resources sections.
• docs: Add list of default measures.
• fix: Add allow_hotstarting, keep_hotstart_stack and keep_models flags to AutoTuner and auto_tuner().

mlr3tuning 0.17.2

• feat: AutoTuner accepts instantiated resamplings now. The AutoTuner checks if all row ids of the inner resampling are present in the outer resampling train set when nested resampling is performed.
• fix: Standalone Tuner did not create a ContextOptimization.

mlr3tuning 0.17.1

• fix: The ti() function did not accept callbacks.

mlr3tuning 0.17.0

• feat: The methods $importance(), $selected_features(), $oob_error() and $loglik() are forwarded from the final model to the AutoTuner now.
• refactor: The AutoTuner stores the instance and benchmark result if store_models = TRUE.
• refactor: The AutoTuner stores the instance if store_benchmark_result = TRUE.

mlr3tuning 0.16.0

• feat: Add new callback that enables early stopping while tuning to mlr_callbacks.
• feat: Add new callback that backups the benchmark result to disk after each batch.
• feat: Create custom callbacks with the callback_tuning() function.

mlr3tuning 0.15.0

• fix: AutoTuner did not accept TuningSpace objects as search spaces.
• feat: Add ti() function to create a TuningInstanceSingleCrit or TuningInstanceMultiCrit.
• docs: Documentation has a technical details section now.
• feat: New option for extract_inner_tuning_results() to return the tuning instances.

mlr3tuning 0.14.0

• feat: Add option evaluate_default to evaluate learners with hyperparameters set to their default values.
• refactor: From now on, the default of smooth is FALSE for TunerGenSA.

mlr3tuning 0.13.1

• feat: Tuner objects have the field $id now.

mlr3tuning 0.13.0

• feat: Allow to pass Tuner objects as method in tune() and auto_tuner().
• docs: Link Tuner to help page of bbotk::Optimizer.
• feat: Tuner objects have the optional field $label now.
• feat: as.data.table() functions for objects of class Dictionary have been extended with additional columns.

mlr3tuning 0.12.1

• feat: Add a as.data.table.DictionaryTuner function.
• feat: New $help() method which opens the manual page of a Tuner.

mlr3tuning 0.12.0

• feat: as_search_space() function to create search spaces from Learner and ParamSet objects. Allow to pass TuningSpace objects as search_space in TuningInstanceSingleCrit and TuningInstanceMultiCrit.
• feat: The mlr3::HotstartStack can now be removed after tuning with the keep_hotstart_stack flag.
• feat: The Archive stores errors and warnings of the learners.
• feat: When no measure is provided, the default measure is used in auto_tuner() and tune_nested().

mlr3tuning 0.11.0

• fix: $assign_result() method in TuningInstanceSingleCrit when search space is empty.
• feat: Default measure is used when no measure is supplied to TuningInstanceSingleCrit.

mlr3tuning 0.10.0

• Fixes bug in TuningInstanceMultiCrit$assign_result().
• Hotstarting of learners with previously fitted models.
• Remove deep clones to speed up tuning.
• Add store_models flag to auto_tuner().
• Add "noisy" property to ObjectiveTuning.

mlr3tuning 0.9.0

• Adds AutoTuner$base_learner() method to extract the base learner from nested learner objects.
• tune() supports multi-criteria tuning.
• Allows empty search space.
• Adds TunerIrace from irace package.
• extract_inner_tuning_archives() helper function to extract inner tuning archives.
• Removes ArchiveTuning$extended_archive() method. The mlr3::ResampleResults are joined automatically by as.data.table.TuningArchive() and extract_inner_tuning_archives().

mlr3tuning 0.8.0

• Adds tune(), auto_tuner() and tune_nested() sugar functions.
• TuningInstanceSingleCrit, TuningInstanceMultiCrit and AutoTuner can be initialized with store_benchmark_result = FALSE and store_models = TRUE to allow measures to access the models.
• Prettier printing methods.

mlr3tuning 0.7.0

• Fix TuningInstance*$assign_result() errors with required parameter bug.
• Shortcuts to access $learner(), $learners(), $learner_param_vals(), $predictions() and $resample_result() from benchmark result in archive.
• extract_inner_tuning_results() helper function to extract inner tuning results.

mlr3tuning 0.6.0

• ArchiveTuning$data is a public field now.

mlr3tuning 0.5.0

• Adds TunerCmaes from adagio package.
• Fix predict_type in AutoTuner.
• Support to set TuneToken in Learner$param_set and create a search space from it.
• The order of the parameters in TuningInstanceSingleCrit and TuningInstanceSingleCrit changed.

mlr3tuning 0.4.0

• Option to control store_benchmark_result, store_models and check_values in AutoTuner. store_tuning_instance must be set as a parameter during initialization.
• Fixes check_values flag in TuningInstanceSingleCrit and TuningInstanceMultiCrit.
• Removed dependency on orphaned package bibtex.

mlr3tuning 0.3.0

• Compact in-memory representation of R6 objects to save space when saving mlr3 objects via saveRDS(), serialize() etc.
• Archive is ArchiveTuning now which stores the benchmark result in $benchmark_result. This change removed the resample results from the archive but they can be still accessed via the benchmark result.
• Warning message if external package for tuning is not installed.
• To retrieve the inner tuning results in nested resampling, as.data.table(rr)$learner[[1]]$tuning_result must be used now.

mlr3tuning 0.2.0

• TuningInstance is now TuningInstanceSingleCrit. TuningInstanceMultiCrit is still available for multi-criteria tuning.
• Terminators are now accessible by trm() and trms() instead of term() and terms().
• Storing of resample results is optional now by using the store_resample_result flag in TuningInstanceSingleCrit and TuningInstanceMultiCrit
• TunerNLoptr adds non-linear optimization from the nloptr package.
• Logging is controlled by the bbotk logger now.
• Proposed points and performance values can be checked for validity by activating the check_values flag in TuningInstanceSingleCrit and TuningInstanceMultiCrit.

mlr3tuning 0.1.3

• mlr3tuning now depends on the bbotk package for basic tuning objects. Terminator classes now live in bbotk. As a consequence ObjectiveTuning inherits from bbotk::Objective, TuningInstance from bbotk::OptimInstance and Tuner from bbotk::Optimizer
• TuningInstance$param_set becomes TuningInstance$search_space to avoid confusion as the param_set usually contains the parameters that change the behavior of an object.
• Tuning is triggered by $optimize() instead of $tune()

mlr3tuning 0.1.2

• Fixed a bug in AutoTuner where a $clone() was missing. Tuning results are unaffected, only stored models contained wrong hyperparameter values (#223).
• Improved output log (#218).

mlr3tuning 0.1.1

• Maintenance release.

mlr3tuning 0.1.0

• Initial prototype.