Releases | Open Source Science

mlr3extralearners: Expanding the mlr3 Ecosystem with Community-Driven Learner Integration - 1.3.0

Add formula and anc params to surv.flexible learner, as well as response predict type (mean survival time).
Fix regr.gamboost regression predictions (#498).

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Published by sebffischer 3 months ago

mlr3extralearners: Expanding the mlr3 Ecosystem with Community-Driven Learner Integration - 1.2.0

New Features

New Learners:
- LearnerCompRisksRandomForestSRC
- LearnerSurvBlockForest
- Learner{Classif,Regr,Surv}BlockForest
- Learner{Classif,Regr}ExhaustiveSearch
- LearnerClassifFastai
- Learner{Classif,Regr}Penalized
- Learner{Classif,Regr}Bst
- LearnerClassifAdabag
- LearnerClassifAdaBoosting
- Learner{Classif,Regr}Evtree
- LearnerClassifKnn
- LearnerClassifRotationForest
- LearnerRegrCrs
- LearnerClassifStepPlr
- LearnerClassifMda
- LearnerClassifRferns
- LearnerClassifNeuralnet
- LearnerRegrBrnn
- LearnerRegrBotorchSingleTaskGP
- LearnerRegrBotorchMixedSingleTaskGP
Add new control_custom_fun parameter in surv.aorsf
New function learner_is_runnable() to check whether the required packages to train a learner are available.
Added selected_features property to RandomForestSRC learners (prediction doesn't work if vars.used = 'all.trees')

Bug fixes

Tests are now skipped when the suggested packages is not available. This will make local development much more convenient.
Removed parameters from RandomForestSRC learners that weren't used + optimized tests
Removed discrete parameter from surv.parametric, so that it is impossible to return distr6::VectorDistribution survival predictions (softly deprecated in mlr3proba@v0.8.1)

Breaking Changes

All (extra) density learners are removed. These will be transferred to mlr3proba soon (see v0.8.2 or later).
The create_learner() generator was removed, because it was hard to maintain and boilerplate code in the age of LLMs is easy enough to write.
remove discrete parameter from surv.parametric, so that it is impossible to return distr6::VectorDistribution survival predictions (softly deprecated in mlr3proba@v0.8.1)
classif.lightgbm now works with encapsulation with multiclass tasks
the package no longer re-exports lrn and lrns, which should anyway be available to the user as the package depends on mlr3, where these functions are defined.
Removed various learners:
- randomPlantedForest was removed, because there is currently no way to save the model.
- The deep learning methods from survivalmodels were removed, because they also cannot be saved and because the upstream package is archived.

Other

The package now imports withr
mlr3proba is now an import and no longer a suggested package.
mlr3cmprsk is added as an import.
The package no longer uses set.seed() in the tests and instead uses withr::local_seed() This means the auto tests will be stochastic like they should be.
The CI now checks that RCMD-check passes when suggested packages are not available.
distr6 dependency is removed. partykit survival learners use constant interpolation of the predicted Kaplan-Meier curves via survdistr::vec_interp()

- R
Published by sebffischer 4 months ago

mlr3extralearners: Expanding the mlr3 Ecosystem with Community-Driven Learner Integration - 1.1.0

See NEWS.md

- R
Published by sebffischer 8 months ago

mlr3extralearners: Expanding the mlr3 Ecosystem with Community-Driven Learner Integration -

- R
Published by sebffischer over 1 year ago

mlr3extralearners: Expanding the mlr3 Ecosystem with Community-Driven Learner Integration - 0.9.0

See NEWS.md

- R
Published by sebffischer over 1 year ago

mlr3extralearners: Expanding the mlr3 Ecosystem with Community-Driven Learner Integration - 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.

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Published by sebffischer almost 2 years ago

mlr3extralearners: Expanding the mlr3 Ecosystem with Community-Driven Learner Integration - 0.7.1

- R
Published by sebffischer over 2 years ago

mlr3extralearners: Expanding the mlr3 Ecosystem with Community-Driven Learner Integration - Release 0.7.0

See NEWS.md

- R
Published by sebffischer over 2 years ago

mlr3extralearners: Expanding the mlr3 Ecosystem with Community-Driven Learner Integration - 0.6.1

See NEWS.md for news.

- R
Published by sebffischer about 3 years ago