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Mlr3Error class now, so they can be caught by class and are
formatted with cli (#198).ArchiveAsyncFSelect pushed results with the
removed rush::Rush$push_results() method.EnsembleFSResult$pareto_front() correctly now
handles ties in the pareto front: in edge cases, it returned dominated
points for minimizing measures and discarded the true front for
maximizing measures (#170).ensemble_fselect() dropped the
importance column for subclasses of
FSelectorBatchRFE. The column is now added whenever the
feature selection result contains importance scores (#195).embedded_ensemble_fselect() instantiated the
[mlr3::Resampling] passed to init_resampling by reference,
so the resampling of the user was changed and reused the row ids of the
first task when applied to another task (#179).EnsembleFSResult$knee_points() silently returned a
row of NA when the Pareto front did not span a range in
both dimensions. The first point of the Pareto front is returned with a
warning now (#171).EnsembleFSResult$stability() cached the results by
stability measure only, so the same measure requested with different
stability_args returned the cached value of the first call
(#189).ensemble_fselect() and
embedded_ensemble_fselect() failed with a cryptic error
when a single [mlr3::Learner] was passed to the learners
argument because the result of as_learners() was discarded
(#178).extract_inner_fselect_archives() ignored the
exclude_columns argument because it was passed positionally
to as.data.table() where it landed in the ...
argument (#180).as.data.table() on an
ArchiveBatchFSelect returned the n_features
column as a list column instead of an integer column, so operations such
as sort() failed with 'x' must be atomic
(#181).fs("sequential")$optimization_path() returned the
first evaluated feature set of each batch instead of the best one, so
the selected feature set was usually missing from the reported path
(#182).fs("rfecv") left the resampling of the objective
set to an insample resampling, so subsequent evaluations on the same
instance silently resampled in-sample (#187).mlr3fselect.backup callback deleted the backup
of the previous batch before it wrote the new one, so a crash in between
lost the complete run. The benchmark result is now written to a
temporary file and renamed afterwards (#188).mlr3fselect.backup callback
requires the path argument now. Previously it wrote a
bmr.rds file into the working directory of the user
(#188).as.data.table() on an
EnsembleFSResult accepts the documented
benchmark_result argument now to omit the task, learner and
resampling columns (#190).$print() methods of
ArchiveBatchFSelect, ArchiveAsyncFSelect,
ArchiveAsyncFSelectFrozen, AutoFSelector and
FSelector errored with unused argument when
arguments such as digits were passed (#190).fs("rfecv") had the same label as
fs("rfe"), so both were indistinguishable in
as.data.table(mlr_fselectors). Its manual page also
instructed to construct it with fs("rfe") (#191).AutoFSelector ignored the
predict_type when the final model was fitted, so
$predict() returned response predictions although
e.g. "prob" was set. Errors raised while setting the
predict type on the final model are not swallowed anymore (#184).$archive, $learner,
$fselect_instance and $fselect_result bindings
of AutoFSelector are read-only now. Previously an
assignment failed with unused argument instead of the usual
read-only error (#186).AutoFSelector$train() did not check the row ids of
an instantiated inner resampling for cross-validation and reported a
wrong set number for holdout (#197).fs("shadow_variable_search") left the shadow
variables in the task, domain and search space of the instance when the
feature selection was aborted because the first selected feature was a
shadow variable (#183).ArchiveBatchFSelect$best() and
ArchiveAsyncFSelect$best() returned an empty table or a row
of missing values when a single score in the archive was
NA. Missing scores are now skipped.
ArchiveAsyncFSelect$best() also ignored the
ties_method set during construction (#177).mlr3fselect.svm_rfe callback accepted support
vector machines without a type or kernel
setting, although only type = "C-classification" and
kernel = "linear" are supported. The callback now also
errors on multi-class tasks for which the importance scores are not
defined (#173).always_included column role. Columns with this role were
excluded from the models instead of being added to every feature subset
(#175).mlr3fselect.one_se_rule callback errored on
archives with a single evaluation or with missing scores, and wrote the
n_features column as a list column instead of an integer
column (#174).extract_inner_fselect_results() added the
iteration and fselect_instance columns to the
result of the inner FSelectInstance by reference, which
created a circular reference between the instance and its own result
(#172).fs("rfe") and fs("rfecv") failed with
an internal data.table error when
store_benchmark_result = FALSE was set because the
importance scores were read from the benchmark result of the archive
(#169).fs("rfecv", recursive = FALSE) failed with an
internal data.table error because the importance scores of
all resampling iterations were written to a single archive row
(#168).fs("rfecv") ignored the direction of the measure
and selected the feature set size with the worst mean performance for
minimizing measures such as msr("classif.ce") or
msr("regr.mse"). Feature selection results obtained with
fs("rfecv") and a minimizing measure are invalid and should
be recomputed (#167).fastVoteR 0.0.3$rm_zero_features() method in
EnsembleFSResult to remove result rows where no features
were selected.always_included column role to all registered
tasks.ResampleResult and
BenchmarkResult objects to speed up objective function
evaluation.FSelectorAsync and FSelectInstanceAsync*
classes.max_nfeatures argument in the
pareto_front() and knee_points() methods of an
EnsembleFSResult().cli
package.EnsembleFSResult() objectsembedded_ensemble_fselect()ensemble_fselect() and
EnsembleFSResult()c.EnsembleFSResult(...) and
EnsembleFSResult$combine(...) methodsmlr3fselect.internal_tuning.mlr_reflections$loaded_packages field.BenchmarkResult in
ObjectiveFSelectBatch after optimization.x_domain column from archive.ensemble_fselect().FSelector class is
FSelectorBatch now.FSelectInstanceSingleCrit and
FSelectInstanceMultiCrit classes are
FSelectInstanceBatchSingleCrit and
FSelectInstanceBatchMultiCrit now.CallbackFSelect class is
CallbackBatchFSelect now.ContextEval class is
ContextBatchFSelect now.instance$result.ties_method options
"least_features" and "random" to
ArchiveBatchFSelect$best().ArchiveBatchFSelect$best() method.FSelectorRFE.as.data.table.ArchiveBatchFSelect().always_include column role.$phash() method to
AutoFSelector.FSelector in hash of
AutoFSelector.FSelectorBatchRandomSearch to 10.batch_size parameter to
FSelectorBatchExhaustiveSearch to reduce memory
consumption.method parameter of
fselect(), fselect_nested() and
auto_fselector() is renamed to fselector. Only
FSelector objects are accepted now. Arguments to the
fselector cannot be passed with ... anymore.fselect parameter of
FSelector is moved to the first position to achieve
consistency with the other functions.mlr3fselect.svm_rfe to run recursive
feature elimination on linear support vector machines.FSelectorRFE are now
aggregated by rank instead of averaging them.FSelectorRFECV optimizer to run recursive
feature elimination with cross-validation.FSelectorRFE works without
store_models = TRUE now.as.data.table.ArchiveBatchFSelect() function
additionally returns a character vector of selected features for each
row.callbacks argument to fsi()
function.mlr3pipelines.genalg to required packages of
FSelectorBatchGeneticSearch.callback_batch_fselect() function.FSelectorRFE throws an error if the learner
does not support the $importance() method.AutoFSelector stores the instance and
benchmark result if store_models = TRUE.AutoFSelector stores the instance if
store_benchmark_result = TRUE.AutoFSelector to
auto_fselect().fsi() function to create a
FSelectInstanceBatchSingleCrit or
FSelectInstanceBatchMultiCrit.unnest option from
as.data.table.ArchiveBatchFSelect() function.FSelector objects have the field $id
now.FSelector objects as
method in fselect() and
auto_fselector().$label to FSelectors.fselect() function.$help() method which opens manual page of a
FSelector.as.data.table.DictionaryFSelector
function.min_features parameter to
FSelectorBatchSequential.store_models flag to fselect().store_x_domain flag.AutoFSelector$base_learner() method to extract the
base learner from nested learner objects.fselect(), auto_fselector() and
fselect_nested() sugar functions.extract_inner_fselect_results() and
extract_inner_fselect_archives() helper function to extract
inner feature selection results and archives.x_domain column from archive.FSelectorRFE stores importance values of each evaluated
feature set in archive.ArchiveBatchFSelect$data is a public field now.AutoFSelector$predict()FSelectorRFE supports fraction of features to retain in
each iteration (feature_fraction), number of features to
remove in each iteration (feature_number) and vector of
number of features to retain in each iteration
(subset_sizes).AutoFSelect is renamed to
AutoFSelector.as.data.table(rr)$learner[[1]]$fselect_result
must be used now.store_benchmark_result,
store_models and check_values in
AutoFSelector. store_fselect_instance must be
set as a parameter during initialization.FSelectorBatchGeneticSearch.check_values flag in
FSelectInstanceBatchSingleCrit and
FSelectInstanceBatchMultiCrit.bibtex.PipeOpSelect is internally used for task
subsetting.Archive is ArchiveBatchFSelect 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.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.
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