Changelog

Source: NEWS.md

mlr3fselect (development version)

mlr3fselect 0.12.0

• feat: Add number of features to instance$result.
• feat: Add ties_method options "least_features" and "random" to ArchiveFSelect$best().
• refactor: Optimize runtime of ArchiveFSelect$best() method.
• feat: Add importance scores to result of FSelectorRFE.
• feat: Add number of features to as.data.table.ArchiveFSelect().
• feat: Features can be always included with the always_include column role.
• fix: Add $phash() method to AutoFSelector.
• fix: Include FSelector in hash of AutoFSelector.
• refactor: Change default batch size of FSelectorRandomSearch to 10.
• feat: Add batch_size parameter to FSelectorExhaustiveSearch to reduce memory consumption.
• compatibility: Work with new paradox version 1.0.0

mlr3fselect 0.11.0

CRAN release: 2023-03-02

• BREAKING CHANGE: The 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.
• BREAKING CHANGE: The fselect parameter of FSelector is moved to the first position to achieve consistency with the other functions.
• docs: Update resources sections.
• docs: Add list of default measures.

mlr3fselect 0.10.0

CRAN release: 2023-02-21

• feat: Add callback mlr3fselect.svm_rfe to run recursive feature elimination on linear support vector machines.
• refactor: The importance scores in FSelectorRFE are now aggregated by rank instead of averaging them.
• feat: Add FSelectorRFECV optimizer to run recursive feature elimination with cross-validation.
• refactor: FSelectorRFE works without store_models = TRUE now.
• feat: The as.data.table.ArchiveFSelect() function additionally returns a character vector of selected features for each row.
• refactor: Add callbacks argument to fsi() function.

mlr3fselect 0.9.1

CRAN release: 2023-01-26

• refactor: Remove internal use of mlr3pipelines.
• fix: Feature selection with measures that require the importance or oob error works now.

mlr3fselect 0.9.0

CRAN release: 2022-12-21

• fix: Add genalg to required packages of FSelectorGeneticSearch.
• feat: Add new callback that backups the benchmark result to disk after each batch.
• feat: Create custom callbacks with the callback_fselect() function.

mlr3fselect 0.8.0

CRAN release: 2022-11-16

• refactor: FSelectorRFE throws an error if the learner does not support the $importance() method.
• refactor: The AutoFSelector stores the instance and benchmark result if store_models = TRUE.
• refactor: The AutoFSelector stores the instance if store_benchmark_result = TRUE.
• feat: Add missing parameters from AutoFSelector to auto_fselect().
• feat: Add fsi() function to create a FSelectInstanceSingleCrit or FSelectInstanceMultiCrit.
• refactor: Remove unnest option from as.data.table.ArchiveFSelect() function.

mlr3fselect 0.7.2

CRAN release: 2022-08-25

• docs: Re-generate rd files with valid html.

mlr3fselect 0.7.1

CRAN release: 2022-05-03

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

mlr3fselect 0.7.0

CRAN release: 2022-04-08

• feat: Allow to pass FSelector objects as method in fselect() and auto_fselector().
• feat: Added $label to FSelectors.
• docs: New examples with fselect() function.
• feat: $help() method which opens manual page of a FSelector.
• feat: Added a as.data.table.DictionaryFSelector function.
• feat: Added min_features parameter to FSelectorSequential.

mlr3fselect 0.6.1

CRAN release: 2022-01-20

• Add store_models flag to fselect().
• Remove store_x_domain flag.

mlr3fselect 0.6.0

CRAN release: 2021-09-13

• Adds AutoFSelector$base_learner() method to extract the base learner from nested learner objects.
• Adds fselect(), auto_fselector() and fselect_nested() sugar functions.
• Adds extract_inner_fselect_results() and extract_inner_fselect_archives() helper function to extract inner feature selection results and archives.

mlr3fselect 0.5.1

CRAN release: 2021-03-09

• Remove x_domain column from archive.

mlr3fselect 0.5.0

CRAN release: 2021-01-24

• FSelectorRFE stores importance values of each evaluated feature set in archive.
• ArchiveFSelect$data is a public field now.

mlr3fselect 0.4.1

CRAN release: 2020-10-30

• Fix bug in AutoFSelector$predict()

mlr3fselect 0.4.0

CRAN release: 2020-10-22

• Compact in-memory representation of R6 objects to save space when saving mlr3 objects via saveRDS(), serialize() etc.
• 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.
• To retrieve the inner feature selection results in nested resampling, as.data.table(rr)$learner[[1]]$fselect_result must be used now.
• Option to control store_benchmark_result, store_models and check_values in AutoFSelector. store_fselect_instance must be set as a parameter during initialization.
• Adds FSelectorGeneticSearch.
• Fixes check_values flag in FSelectInstanceSingleCrit and FSelectInstanceMultiCrit.
• Removed dependency on orphaned package bibtex.
• PipeOpSelect is internally used for task subsetting.

mlr3fselect 0.3.0

CRAN release: 2020-09-22

• Archive is ArchiveFSelect 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.

mlr3fselect 0.2.1

CRAN release: 2020-09-10

• Warning message if external package for feature selection is not installed.

mlr3fselect 0.2.0

CRAN release: 2020-08-23

• Initial CRAN release.