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clbk("mlr3tuning.one_se_rule")
that selects the the hyperparameter configuration with the smallest
feature set within one standard error of the best.on_tuning_result_begin and
on_result_begin to CallbackAsyncTuning and
CallbackBatchTuning.on_result to
on_result_end in CallbackAsyncTuning and
CallbackBatchTuning.CallbackAsyncTuning and
CallbackBatchTuning documentation.as_data_table() functions do not unnest the
x_domain colum anymore by default.to_tune(internal = TRUE) now also works if
non-internal tuning parameters require have an
.extra_trafo.internal_search_space manually. This allows to use
parameter transformations on the primary search space in combination
with internal hyperparameter tuning.Tuner pass extra information of the
result in the extra parameter now.BenchmarkResult in
ObjectiveTuningBatch after optimization.TunerAsync and TuningInstanceAsync*
classes.Tuner class is
TunerBatch now.TuningInstanceSingleCrit and
TuningInstanceMultiCrit classes are
TuningInstanceBatchSingleCrit and
TuningInstanceBatchMultiCrit now.CallbackTuning class is
CallbackBatchTuning now.ContextEval class is
ContextBatchTuning now.evaluate_default is a callback
now.TunerIrace failed with logical parameters and
dependencies.AutoTunerstore_benchmark_result = TRUE if
store_models = TRUE when creating a tuning instance.tune_nested() did not
work.$phash() method to
AutoTuner.Tuner in hash of
AutoTuner.method parameter of
tune(), tune_nested() and
auto_tuner() is renamed to tuner. Only
Tuner objects are accepted now. Arguments to the tuner
cannot be passed with ... anymore.tuner parameter of
AutoTuner is moved to the first position to achieve
consistency with the other functions.allow_hotstarting,
keep_hotstart_stack and keep_models flags to
AutoTuner and auto_tuner().AutoTuner accepts instantiated resamplings now.
The AutoTuner checks if all row ids of the inner resampling
are present in the outer resampling train set when nested resampling is
performed.Tuner did not create a
ContextOptimization.ti() function did not accept callbacks.$importance(),
$selected_features(), $oob_error() and
$loglik() are forwarded from the final model to the
AutoTuner now.AutoTuner stores the instance and
benchmark result if store_models = TRUE.AutoTuner stores the instance if
store_benchmark_result = TRUE.mlr_callbacks.callback_batch_tuning() function.AutoTuner did not accept TuningSpace
objects as search spaces.ti() function to create a
TuningInstanceSingleCrit or
TuningInstanceMultiCrit.extract_inner_tuning_results() to
return the tuning instances.evaluate_default to evaluate learners
with hyperparameters set to their default values.smooth is
FALSE for TunerGenSA.Tuner objects have the field $id
now.Tuner objects as
method in tune() and
auto_tuner().Tuner to help page of
bbotk::Optimizer.Tuner objects have the optional field
$label now.as.data.table() functions for objects of class
Dictionary have been extended with additional columns.as.data.table.DictionaryTuner
function.$help() method which opens the manual page of
a Tuner.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.mlr3::HotstartStack can now be removed after
tuning with the keep_hotstart_stack flag.Archive stores errors and warnings of the
learners.auto_tuner() and tune_nested().$assign_result() method in
TuningInstanceSingleCrit when search space is empty.TuningInstanceSingleCrit.TuningInstanceMultiCrit$assign_result().store_models flag to
auto_tuner()."noisy" property to
ObjectiveTuning.AutoTuner$base_learner() method to extract the
base learner from nested learner objects.tune() supports multi-criteria tuning.TunerIrace from irace package.extract_inner_tuning_archives() helper function to
extract inner tuning archives.ArchiveTuning$extended_archive() method. The
mlr3::ResampleResults are joined automatically by
as.data.table.TuningArchive() and
extract_inner_tuning_archives().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.TuningInstance*$assign_result() errors with
required parameter bug.$learner(),
$learners(), $learner_param_vals(),
$predictions() and $resample_result() from
benchmark result in archive.extract_inner_tuning_results() helper function to
extract inner tuning results.ArchiveTuning$data is a public field now.TunerCmaes from adagio package.predict_type in AutoTuner.TuneToken in
Learner$param_set and create a search space from it.TuningInstanceSingleCrit
and TuningInstanceSingleCrit changed.store_benchmark_result,
store_models and check_values in
AutoTuner. store_tuning_instance must be set
as a parameter during initialization.check_values flag in
TuningInstanceSingleCrit and
TuningInstanceMultiCrit.bibtex.saveRDS(), serialize()
etc.Archive is ArchiveTuning now which stores
the benchmark result in $benchmark_result. This change
removed the resample results from the archive but they can be still
accessed via the benchmark result.as.data.table(rr)$learner[[1]]$tuning_result must be used
now.TuningInstance is now
TuningInstanceSingleCrit.
TuningInstanceMultiCrit is still available for
multi-criteria tuning.trm() and
trms() instead of term() and
terms().store_resample_result flag in
TuningInstanceSingleCrit and
TuningInstanceMultiCritTunerNLoptr adds non-linear optimization from the
nloptr package.bbotk logger now.check_values flag in
TuningInstanceSingleCrit and
TuningInstanceMultiCrit.bbotk package for basic
tuning objects. Terminator classes now live in
bbotk. As a consequence ObjectiveTuning
inherits from bbotk::Objective, TuningInstance
from bbotk::OptimInstance and Tuner from
bbotk::OptimizerTuningInstance$param_set becomes
TuningInstance$search_space to avoid confusion as the
param_set usually contains the parameters that change the
behavior of an object.$optimize() instead of
$tune()AutoTuner where a $clone()
was missing. Tuning results are unaffected, only stored models contained
wrong hyperparameter values (#223).These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.