rush is now 1.2.0.
Removed all compatibility workarounds for older versions.mlr_reflections$tuner_properties on load instead of
overwriting it, so a property registered by another extension package is
no longer dropped, and it removes its registered callbacks and tuner
property on unload.AutoTuner accessors ($learner,
$tuning_instance, $tuning_result,
$archive, $importance(),
$selected_features(), $oob_error(),
$loglik()) now raise an informative error when the model is
marshaled, instead of silently returning the untrained learner or
NULL.AutoTuner$marshaled is now an active binding as in
mlr3::Learner, so at$marshaled returns a flag
instead of a method and can be used in conditions.AutoTuner$train() now correctly checks that an
instantiated inner resampling only uses row ids present in the task for
all resampling types. Previously, the check read list-based instances
and silently did nothing for resamplings such as cv and
holdout.AutoTuner model with
inplace = TRUE after a non-inplace marshal no longer drops
the auto_tuner_model class, which had caused a subsequent
marshal to become a no-op.auto_tuner(), AutoTuner$new(), and
tune() now error at construction when a rush
controller is supplied together with a batch tuner.mlr3tuning.backup callback now errors at the
start of the run when store_benchmark_result = FALSE
instead of silently writing an empty benchmark result, and it no longer
errors when the target file already exists.ArchiveBatchTuning$print() no longer prints the
archive table twice.ArchiveAsyncTuning$benchmark_result now raises a
clear error when the tuning instance was created with
store_benchmark_result = FALSE. Previously, the first
access overwrote the cached benchmark result with NULL and
every later access failed with an unrelated error. Freezing such an
archive with ArchiveAsyncTuningFrozen works now and returns
an empty benchmark result.AutoTuner$hash now also depends on the
predict_sets, validate, and
use_weights settings so that autotuners differing only in
these fields no longer share a hash.as.data.table.ArchiveAsyncTuning() and
as.data.table.ArchiveAsyncTuningFrozen() no longer error
when the measures argument is used on an archive that
contains queued, running, or failed points. The extra measures are
NA for these points.callback_async_tuning() and
callback_batch_tuning() remove the deprecated
on_result stage. Use on_result_end
instead.as_tuner() with clone = TRUE now
performs a deep clone, so the returned tuner no longer shares its
ParamSet with the input.as_search_space() no longer errors when converting
a ParamSet that contains an unset required parameter.assert_async_tuning_callbacks() and
assert_batch_tuning_callbacks() now check that each
callback inherits from CallbackAsyncTuning and
CallbackBatchTuning, respectively, so tuning instances
reject callbacks of the wrong type at construction time.Tuner$id now validates new values on assignment,
and Tuner$label correctly rejects modification instead of
silently accepting some invalid assignments.as.data.table.ArchiveAsyncTuning(),
as.data.table.ArchiveAsyncTuningFrozen(), and
as.data.table.ArchiveBatchTuning() now warn instead of
silently ignoring the measures argument when no benchmark
result is stored.tnr("irace") destroyed its own configuration
during $optimize() by removing n_instances
from and writing instantiated resamplings into its param set, which made
a second run of the same tuner or AutoTuner impossible. The
param set is now restored after the run.clbk("mlr3tuning.async_one_se_rule") now stores an
unnamed numeric in the n_features column of the archive,
matching the batch callback.clbk("mlr3tuning.async_measures") now accepts a
single measure in the measures argument like the batch
version, instead of requiring a list of measures.ObjectiveTuningBatch now errors when the number of
custom resamplings does not match the number of hyperparameter
configurations. Previously, the resamplings were silently recycled,
pairing configurations with the wrong resamplings.extract_inner_tuning_results() no longer modifies
the result table of the stored tuning instances by reference.
Previously, the iteration and tuning_instance
columns were written into instance$result, and a stale
tuning_instance column could leak into the output of a
second call with tuning_instance = FALSE.TuningInstanceBatchMultiCrit$assign_result() and
TuningInstanceAsyncMultiCrit$assign_result() produced wrong
result_learner_param_vals when the search space was empty,
because the number of measures instead of the number of Pareto points
was used to recycle the parameter values.assign_result() of
TuningInstanceBatchMultiCrit and
TuningInstanceAsyncMultiCrit now validates
learner_param_vals like the single-crit instances.AutoTuner$clone(deep = TRUE) now deep clones the
wrapped learner, resampling, measure, terminator, callbacks, and the
trained model. Previously, the clone shared these objects with the
original, so for example setting the predict type on the clone also
changed the original.clbk("mlr3tuning.one_se_rule") and
clbk("mlr3tuning.async_one_se_rule") no longer crash at
result assignment when the archive contains a single evaluation or
points without a performance score (queued, running, or failed points).
These points are now removed before the standard error is computed, so
they no longer deflate the standard error.ArchiveAsyncTuning,
ArchiveAsyncTuningFrozen, and
ArchiveBatchTuning convert the archive to a
data.table with a consistent column order.on_optimizer_queue_before_eval and
on_optimizer_queue_after_eval stages to
CallbackAsyncTuning.CallbackResample are now
available in CallbackBatchTuning and
CallbackAsyncTuning.$predict_type was written to the model even
when the AutoTuner was not trained.mlr3. The mlr3/bbotk logger is a child
of the mlr3 logger and is used for logging messages from
the bbotk and mlr3tuning package.cli
package.ArchiveAsyncTuning to a
data.table with ArchiveAsyncTuningFrozen.ObjectiveTuningAsync.extra to
$assign_result().clbk("mlr3tuning.one_se_rule")
that selects the hyperparameter configuration with the smallest feature
set within one standard error of the best.on_tuning_result_begin and
on_result_begin to CallbackAsyncTuning and
CallbackBatchTuning.on_result to
on_result_end in CallbackAsyncTuning and
CallbackBatchTuning.CallbackAsyncTuning and
CallbackBatchTuning documentation.as_data_table() functions do not unnest the
x_domain column anymore by default.to_tune(internal = TRUE) now also works if
non-internal tuning parameters have an .extra_trafo.internal_search_space manually. This allows to use
parameter transformations on the primary search space in combination
with internal hyperparameter tuning.Tuner pass extra information of the
result in the extra parameter now.BenchmarkResult in
ObjectiveTuningBatch after optimization.TunerAsync and TuningInstanceAsync*
classes.Tuner class is
TunerBatch now.TuningInstanceSingleCrit and
TuningInstanceMultiCrit classes are
TuningInstanceBatchSingleCrit and
TuningInstanceBatchMultiCrit now.CallbackTuning class is
CallbackBatchTuning now.ContextEval class is
ContextBatchTuning now.evaluate_default is a callback
now.TunerIrace failed with logical parameters and
dependencies.AutoTunerstore_benchmark_result = TRUE if
store_models = TRUE when creating a tuning instance.tune_nested() did not
work.$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).
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