NEWS.md

dev

• Add "Prediction types" doc section for all 30 survival learners + make sure it is consistent #347
• All survival learners have crank as main prediction type (and it is always returned) #331
• Added minimum working version for all survival learners in DESCRIPTION file
• Harmonized the use of times points for prediction as much as possible across survival learners #387
  • added gridify_times() function to coarse time points
  • fixed surv.parametric and surv.akritas use of ntime argument
• surv.parametric is now used by default with discrete = TRUE (no survival learner returns now distr6 vectorized distribution by default)
• Doc update for mlr3 (version 0.21.0)
• Fixed custom and initial values across all learners documentation pages
• Fixed doc examples that used learner$importance()
• Set n_thread = 1 for surv.aorsf and use unique event time points for predicted S(t)
• Add selected_features() for surv.penalized
• Fix surv.prioritylasso learner + add distr predictions via Breslow #344
• Survival SVM gamma.mu parameter was split to gamma and mu to enable easier tuning (surv.svm learner)

mlr3extralearners 0.9.0

• Added response (i.e., survival time) prediction to aorsf learner
• Updated support for flexsurv v2.3
• Fixed bug in catboost that caused invalid probability levels during resample() or benchmark() (#353)
• the $model slot of lrn("classif.abess") now contains the model of the upstream package again.
• Add early stopping and validation support to learners lrn("surv.xgboost.aft") and lrn("surv.xgboost.cox").
• Added early stopping and validation to catboost and lightgbm.
• Added missing case.depth parameter to rfsrc learners.
• mlr3 is now in Depends instead of Imports.
• Deprecated learner lrn("surv.xgboost") was now removed. Use lrn("surv.xgboost.cox") or lrn("surv.xgboost.aft") instead.
• Change xgboost default nrounds from 1 to 1000.
• remove obliqueRSF Learner which was long superseded by aorsf
• a lot of examples were added to the learners

mlr3extralearners 0.8.0

• Added surv.xgboost.cox and surv.xgboost.aft separate survival learners. distr prediction on the cox xgboost learner is now estimated via Breslow by default and aft xgboost has now in addition a response prediction (survival time)
• Ported surv.parametric code to survivalmodels, changed type parameter to form to avoid conflict with survivalmodels's default parameter list
• Fix: Replace hardcoded VectorDistributions from partykit and flexsurv survival learners with survival matrices (Matdist) (thanks to @bblodfon)
• Feat: Add discrete parameter in surv.parametric learner to return Matdist survival predictions
• Added method selected_features() to CoxBoost survival learners (thanks to @bblodfon)
• Added the Random Planted Forest Learner (thanks to @jemus42)
• re-added the catboost learner as it was requested (was previously removed because of installation issues)
• surv.ranger now receives parameters during $predict() (thanks to @jemus42)
• Feature: Learner surv.bart was added (thanks to @bblodfon)
• Parameters of lrn("surv.aorsf") were updated (thanks to @bcjaeger)
• Various minor doc improvements
• Added the distr predict type to the surv.cv_glmnet and surv.glmnet learners (thanks to @bblodfon)
• Feat: Added many new WEKA learners (thanks to @damirpolat)
• Fix: I and F params from IBk learner are too interdependent (I can only be TRUE when F is FALSE and vice versa). Combined them into one factor param weight that has two levels -- I and F.
• Fix: U must be FALSE for S to be tunable in J48 learner.
• Compatibility with upcoming 'paradox' release.

mlr3extralearners 0.7.1

• Add parameter perf.type to rfsrc learners
• Add vignette about "extending learners" which was previously in the mlr3book.
• Remove the "multiclass" property from lrn("classif.gbm"), as this feature is broken.

mlr3extralearners 0.7.0

• Add new parameters to lightgbm learners
• Add feature type "factor" to gam learners
• Add new parameter min.bucket to ranger
• Remove catboost learner (because the developers don't properly take care of the R package)
• Add argument nthreads to dbarts learners; set verbose to FALSE by default (thanks to @ck37)
• Add new parameters to prioritylasso
• Fix: available levels for parameter of imbalanced random forest (typo)

mlr3extralearners 0.6.1

• BREAKING CHANGE: lightgbm's early stopping mechanism now uses the task's test set.
• feat: Add two new learners regr.abess and classif.abess (thanks to @bbayukari)
• feat: Added learner LearnerClassifImbalancedRandomForestSRC (thanks to @HarutyunyanLiana)
• Feat: Added learners LearnerClassifPriorityLasso, LearnerRegrPriorityLasso, LearnerSurvPriorityLasso (thanks to @HarutyunyanLiana)

mlr3extralearners 0.6.0

• Feat: Added learner LearnerClassifGlmer (#243)
• Fix: Failing xgboost parameter test
• Fix: Add arguments nei and ncv.thread that were added to mgcv::gam() in version 1.8.41
• Fix: Added missing property "weights" to LearnerClassifGlmer and LearnerRegrLmer
• Fix: lightgbm uses the param_vals stored in the state for hotstarting
• Fix: Rely on state$data_prototype to get ordering of features via ordered_features() like in mlr3learners and therefore obviate the need to store feature_names in the state
• Fix: extralearners are removed from mlr_learners when unloading mlr3extralearners

mlr3extralearners 0.5.49

• Added missing feature type "integer" to classif.randomForest
• Added missing feature type "logical" to {classif, regr}.randomForest

mlr3extralearners 0.5.48

• Add rsm learner
• fix list_mlr3learners() function. Now slower but correct.
• Remove catboost from DESCRIPTION until it can be installed with pak
• Fix typos in test templates
• Update README

mlr3extralearners 0.5.47

• Add mlr3proba dependencies into remotes (no longer on CRAN)
• Correct documentation of gbm learner: default was incorrectly documented and the parameter was incorrectly referred to as keep_data instead of keep.data
• Add catboost to the dependencies
• Added LearnerSurvAorsf with key surv.aorsf. See https://github.com/bcjaeger/aorsf for more details on aorsf

mlr3extralearners 0.5.46

• Addresses #225
• Fix link in README
• Fix learner status overview

mlr3extralearners 0.5.45

• Minor corrections in create_learner and the learner template.

mlr3extralearners 0.5.44

• Corrected parameters in lightgbm learners
• Implemented hotstarting for lightgbm learners
• Adjusted lightgbm train and predict methods to changes in lightgbm dev version (#217)
• Added paramtests for lightgbm through webscraping

mlr3extralearners 0.5.43

• Clean up test files
• Fix installation of catboost in CI
• Fix the create_learner function
• Adjust templates for creation of learner
• Split up "Parameter Changes" in sections "Custom mlr3 parameters" and "Custom mlr3 defaults"

mlr3extralearners 0.5.42

• Fix bug in C50 learner: Weights were not passed correctly

• Remove kerdiest Learner because it is not being maintained on CRAN anymore

mlr3extralearners 0.5.41

• Fix bugs in learners lmer and J48

• Remove predict type proba from J48

• Delay loading of mlr3proba learners

mlr3extralearners 0.5.40

• lightgbm:

  • Add parameter convert_categoricals
  • Validation split not respects grouping / stratification
  • Fixed bug

• Docs: Renamed section "Custom mlr3 defaults" to "Parameter Changes"

• Added labels to learners

mlr3extralearners 0.5.39

• Remove extraTrees because it is no longer on CRAN and GH version has errors

• Remove sketch_eps parameter from xgboost because it is no longer listed in the docs

mlr3extralearners 0.5.38

• Added regr.lmer

mlr3extralearners 0.5.37

• Improve docs and change doc layout
• Fix typo in man-roxygen templates
• Port mlr3proba learners (mlr3proba is no longer on CRAN)
• Exclude relevant files in precommit

mlr3extralearners 0.5.36

• Add missing 'threads' tag to respective parameters.

mlr3extralearners 0.5.35

• Full installatio in workflow 'test_selection' (is faster than the previous approach, where selected packages were installed from CRAN)

mlr3extralearners 0.5.34

• remove explicit mlr3misc:: (is imported)

mlr3extralearners 0.5.33

• consistency: Use params in train and predict calls, even in learners that currently don't have predict / train params. This allows easier correction of parameters by users.

mlr3extralearners 0.5.32

• chore: add new parameters for kde and rfsrc

• temporarily disable feat_all test for obliqeRSF (failed in $score() stage, because issue only happened in CI and could not be reproduced

mlr3extralearners 0.5.31

• Many non-standard tags were included in the learners, these are removed
• Some bugs in learners were fixed (survival rfsrc: "estimator" was incorrectly handled in .predict)
• Minor refactorings in train methods of learners
• Avoid partial argument matching: Some learners used "tag = ..." instead of the correct "tags = ..."

mlr3extralearners 0.5.30

• Revert to using mlr3proba and survivalmodels CRAN version

mlr3extralearners 0.5.29

• Change in vignette

mlr3extralearners 0.5.28

• update randomForestSRC

mlr3extralearners 0.5.27

• Update learner status page

mlr3extralearners 0.5.26

• Fixed survivalmodel learners

mlr3extralearners 0.5.25

• Introduce parameter early_stopping_split for lightgbm learners
• Tidy description of R package
• Udpate NEWS.md for previous releases

mlr3extralearners 0.5.24

• Don't allow integer for density estimator dens.plug

mlr3extralearners 0.5.23

• Fix bug in lightgbm

mlr3extralearners 0.5.22

• Style package using the mlr3 style

mlr3extralearners 0.5.21

• Update files for creation of new learner
• Fixes regarding create_learner
• CI modifications

mlr3extralearners 0.5.20

• Fix all parameter tests (run_paramtest was updated in mlr3 in November 2021)
• paramtests were moved from inst/paramtest to tests/testthat
• Change in the CI files: parameter tests and learner tests are now run together
• formatting and other minor corrections

mlr3extralearners 0.5.19

• Provide correct range for neighors argument for Cubist

mlr3extralearners 0.5.18

• Allow integer as feature types for RWeka learners
• Correction of RWeka tests

mlr3extralearners 0.5.17

• Improve vignette

mlr3extralearners 0.5.16

• Fix bug in AdaBoostM1 (control arg)

mlr3extralearners 0.5.15

• Change in maintainer

mlr3extralearners 0.5.14

• Fix bug regarding Weka control args.

mlr3extralearners 0.5.13

• Fix categorical_features in {lightgbm} learners

mlr3extralearners 0.5.12

• Patch for lightgbm updates

mlr3extralearners 0.5.11

• Add option to not open files with create_learner

mlr3extralearners 0.5.10

• Added params ignored_features and one_hot_max_size to classif.catboost

mlr3extralearners 0.5.9

• Fixed bug that didn't allow C parameter to be set for nu-regression

mlr3extralearners 0.5.8

• Add regr.rvm and classif.lssvm

mlr3extralearners 0.5.7

• Introduced new custom hyperparameters for randomForestSRC::rfsrc(), partykit::cforest() and obliqueRSF::ORSF() to conveniently tune hyperparameters whose upper limit depends on data dimensions.

mlr3extralearners 0.5.6

• Fix learners requiring distr6. distr6 1.6.0 now forced and param6 added to suggests

mlr3extralearners 0.5.5

• Bugfix regr.gausspr

mlr3extralearners 0.5.4

• Add regr.gausspr and classif.gausspr from kernlab::gausspr

mlr3extralearners 0.5.3

• Fixed bugs in catboost for classification
• Removed factor feature types from catboost
• Added install_catboost to make installation from catboost simpler

mlr3extralearners 0.5.2

• Fixed learner tests

mlr3extralearners 0.5.1

• Fixes bug in base parameter of {bart} learners

mlr3extralearners 0.5.0

• Deprecated liblinear learners now removed
• Internal changes to ParamSet representation
• checkmate now imported

mlr3extralearners 0.4.9

• Minor internal changes

mlr3extralearners 0.4.8

• Added LearnerRegrCubist and LearnerRegrMars

mlr3extralearners 0.4.7

• Moved nnet learners to mlr3learners

mlr3extralearners 0.4.6

• Updates default cores for rfsrc learners to 1

mlr3extralearners 0.4.5

• Fix RWeka tests (stochastic failures, implementation unaffected)

mlr3extralearners 0.4.3

• Add support for custom families in all remaining mboost learners

mlr3extralearners 0.4.2

• Fix broken partykit tests

mlr3extralearners 0.4.0

• Added LearnerRegrGam and LearnerClassifGam with keys regr.gam and classif.gam from package mgcv.

mlr3extralearners 0.3.6

• surv.coxboost now uses the GitHub version instead of CRAN (archived)

mlr3extralearners 0.3.4

• Add support for custom families to regr.glmboost

mlr3extralearners 0.3.1

• surv.svm now supports all feature types

mlr3extralearners 0.3.0

• Added LearnerRegrLightGBM and LearnerClassifLightGBM with keys regr.lightgbm and classif.lightgbm respectively. Copied from mlr3learners.lightgbm
• LearnerRegrLiblineaRX and LearnerClassifLiblineaRX deprecated in favour of only two learners (LearnerRegrLiblineaR and LearnerClassLiblineaR) with added hyper-parameters. Deprecated learners will be removed in v0.3.0.
• Deprecated classif.nnet will be removed in v0.4.0.
• Deprecated liblinearX will be removed in v0.4.0.

mlr3extralearners 0.2.0

• dist = "logistic" has been removed from surv.parametric as it is unclear what this was previously predicting.
• Added type = "tobit" for dist = "gaussian" so predictions can correspond with survival::survreg.
• Added LearnerRegrGlm with the unique key regr.glm from package stats, which allows users to change the family hyperparameter when fitting generalized linear regression models.
• Minor internal changes
• Removed keeptrees parameter from classif.bart as this is forced internally
• Fixed incorrect response and probability predictions in classif.bart
• Added hyper-parameters to classif.earth and regr.earth
• Added se predict type to regr.earth
• Fixed predictions in regr.knn and classif.knn

mlr3extralearners 0.1.3

• mlr3proba moved to Suggests
• install_learners now additionally installs required mlr3 packages
• Bugfix in surv.parametric occurring if feature names are switched between training and predicting
• Deprecated classif.nnet, in the future please load from mlr3learners

mlr3extralearners 0.1.2

• Fixes in crank and distr computation of all survival learners

mlr3extralearners 0.1.1

• Patch for bugs in surv learners that were reversing the order of crank, see this issue for full details: mlr-org/mlr3proba#165
• response is no longer returned by surv.mboost, surv.blackboost, surv.glmboost, surv.gamboost or surv.parametric
• Bugfix in surv.parametric with ph form
• Bugfix in survivalmodelslearners which weren't returning distr
• surv.coxboost and surv.coxboost_cv can now only handle integer and numeric feature types, previous automated internal coercions were inconsistent with mlr3 design.

mlr3extralearners 0.1.0

• Initial release. mlr3extralearners contains all learners from the mlr3learners organisation, which is now archived.