mlr3proba 0.4.0.9000 Unreleased

  • Remove mlr3extralearners from Suggests
  • Add response to as_prediction_surv
  • Now exported a couple cpp functions and assert_surv
  • mlr3 is now in Depends not imports
  • distr predictions are now internally stored as matrices to significantly reduce prediction object sizes
  • Tasks now support strata property

mlr3proba 0.4.0 2021-04-18

  • Deprecated measures from 0.2.0 have now been deleted.
  • IPCW measures such as surv.graf, surv.schmid, and surv.intlogloss now allow training data to be passed to the score function with task and train_set to allow the censoring distribution to be estimated on the training data. This is automatically applied for resample and benchmark results.
  • IPCW measures such as surv.graf, surv.schmid, and surv.intlogloss now include a parameter proper to determine what weighting scheme should be applied by the estimated censoring distribution, The current method (Graf, 1999) proper = FALSE, weights observations either by their event time or ‘current’ time depending if they’re dead or not, the new method proper = TRUE weights observations by event time. The proper = TRUE method is strictly proper when censoring and survival times are independent and G is estimated on large enough data. The proper = FALSE method is never proper. The default is currently proper = FALSE to enable backward compatibility, this will be changed to proper = TRUE in v0.6.0.
  • The rm_cens parameter in surv.logloss has been deprecated in favour of IPCW. rm_cens will be removed in v0.6.0. If rm_cens or IPCW are TRUE then censored observations are removed and the score is weighted by an estimate of the censoring distribution at individual event times. Otherwise if rm_cens and IPCW are FALSE then no deletion or weighting takes place. The IPCW = TRUE method is strictly proper when censoring and survival times are independent and G is estimated on large enough data. The ipcw = FALSE method is never proper.
  • Add surv.dcalib for the D-Calibration measure from Haider et al. (2020).

mlr3proba 0.3.2 2021-03-15

  • Patched bug causing "interval2" task type not to work
  • Fixed bug causing pipelines not to function correctly in $aggregate

mlr3proba 0.3.1 2021-02-03

  • Reverted removal of "interval2"

mlr3proba 0.3.0 2021-02-02

  • Commonly used survival quantities have been added as active bindings to TaskSurv including times (observed survival times), status (observed survival indicator), unique_times (set of sorted unique outcome times), unique_event_times (set of sorted unique failure times), risk_set (set of observations alive ‘just before’ a given time)
  • "interval2" censoring type has been removed from TaskSurv as this is covered by the other types
  • Default values have now been given to the time and event arguments in TaskSurv
  • PredictionDens can now include distr return type (equivalent to learner$model)

mlr3proba 0.2.6 2020-12-04

  • Minor internal fixes

mlr3proba 0.2.5 2020-11-18

  • PipeOpCrankCompositor updated to fix bottleneck in computation via mean. Now Inf or NA is replaced by 0 for response and imputed with the median for crank
  • Bug in distr predict types fixed that lead to fitting degenerate distributions and returning incorrect values for mean survival time and crank

mlr3proba 0.2.4 2020-11-11

  • CRITICAL BUG FIX - compose_crank was previously returning ranks with the reverse ordering so that higher ranks implied higher risk not lower.

mlr3proba 0.2.3 2020-11-01

  • All learners that previously lived in the mlr3learners organisation are now in the mlr3extralearners repository.
  • Fixed bottleneck in MeasureSurvLogloss
  • Bugfix in MeasureSurvCalibrationAlpha
  • Patch for valgrind
  • TaskDens now inherits from TaskUnsupervised which means target/truth has been removed. No specification of a target column is required, instead a one-column matrix-like object or numeric vector should be passed to the task backend and the density will be estimated for this column, or two columns and one set as weight.
  • Fixed bug in load_eruption to fix name of data columns
  • Added calibration plot for comparing average predicted survival distribution to Kaplan-Meier to mlr3viz
  • Removed unneccessary pracma dependency in learners
  • Fix in PipeOpDistrCompositor, previously base distribution was only using the first predicted distribution, now the baseline is taken by averaging over all predictions with uniform weights

mlr3proba 0.2.2 2020-09-23

  • Default kernel for LearnerDensityKDE is now Epan to reduce imports
  • Minor internal patches for mlr3 0.6.0
  • Bug fix in MeasureSurvCalibrationBeta now returns NA not error if lp predict type not available

mlr3proba 0.2.1 2020-08-28

  • Removed PredictionRegr causing masking issues with mlr3
  • Bug fix in PipeOpDistrCompositor causing some cdf predictions to be lost
  • Internal fixes for mlr3pipelines: public train and predict methods to private
  • Added four datasets and tasks: grace, actg, gbcs, whas
  • Add overwrite to crankcompositor pipeop and pipeline
  • Bug fix in surv.kaplan crank prediction

mlr3proba 0.2.0 2020-07-25

Added Functionality

  • MeasureSurvCindex added. Generalises all c-index measures with a fast C++ implementation
  • Akritas estimator added to mlr3learners/mlr3learners.proba
  • Added scoring rule MeasureSurvSchmid
  • Addd calibration measures MeasureSurvCalibrationBeta and MeasureSurvCalibrationAlpha
  • surv.brier alias added for surv.graf
  • response parameter added to PipeOpCrankCompositor and crankcompositor to now optionally fill response predict type with same values as crank
  • Added PipeOpProbregrCompostior and compose_probregr for composition to distr return type from (a) regression learner(s) predicting response and se
  • Added PipeOpSurvAvg and surv_averager pipeline for weighted model averaging of distr, lp, crank, and response predictions.

Deprecated Functionality

  • The following measures are deprecated use MeasureSurvCindex instead with following parameters: MeasureSurvBeggC, use defaults; MeasureSurvHarrellC, use defaults; MeasureSurvUnoC, use weight_meth = 'G/2'; MeasureSurvGonenC, use weight_method = 'GH'
  • MeasureSurvGrafSE, MeasureSurvLoglossSE, MeasureSurvIntLoglossSE, MeasureSurvRMSESE, MeasureSurvMSESE, and MeasureSurvMAESE all deprecated and will be deleted in v0.4.0. Use msr("surv.graf", se = TRUE) instead (for example).
  • Measures renamed such that surv.nagelkR2 is now surv.nagelk_r2, analogously for all R2, AUC, TPR, and TNR measures. Old constructors will be deleted in v0.4.0.
  • Renamed distrcompose and crankcompose to distr_compose and crank_compose. Old ids will be deleted in v0.4.0.

Edited Functionality

  • Measures renamed such that surv.nagelkR2 is now surv.nagelk_r2, analogously for all R2, AUC, TPR, and TNR measures. Old constructors will be deleted in v0.4.0.
  • MeasureSurvGraf and MeasureSurvIntLogloss now have much faster C++ implementation

Moved Functionality

mlr3proba 0.1.6 2020-06-05

  • Early release due to backward compatibility error introduced by an upstream dependency
  • Minor updates to mboost family of learners: added gehan family, fixed parameters for cindex, added support for: weights, response predict type, importance, selected_features
  • Minor internal changes
  • All density learners except LearnerDensHist and LearnerDensKDE have been moved to the mlr3learners org
  • The following survival learners have been moved to the mlr3learners org, LearnerSurv: Flexible, ObliqueRSF, Penalized, RandomForestSRC
  • Bugfix in LearnerSurvXgboost previously lp was erroneously returned as exp(lp)
  • Now licenced under LPGL-3

mlr3proba 0.1.5 2020-04-16

  • LearnerSurvParametric and LearnerSurvNelson moved to mlr3learners/mlr3learners.survival repo
  • LearnerSurvCoxboost and LearnerSurvCVCoxboost moved to mlr3learners/mlr3learners.coxboost repo
  • LearnerSurvSVM moved to mlr3learners/mlr3learners.survivalsvm repo
  • In the next release, all learners except for LearnerSurvKaplan, LearnerSurvCoxPH, and LearnerDensHist will be moved to the mlr3learners org
  • Minor internal changes

mlr3proba 0.1.4 2020-03-20

  • Density estimation has now been added to mlr3proba, see TaskDens, LearnerDens, PredictionDens, and MeasureDens.
  • Added mlr_tasks_faithful and mlr_tasks_precip for density task examples
  • Added mlr_task_generators_simdens for generating density tasks
  • Added learners for density estimation, see mlr3::mlr_learners$keys("^dens") for the full list
  • In line with mlr3 0.1.7, public methods train_internal, predict_internal, score_internal are now private methods .train,.predict,.score
  • Converted to roxygen2 R6 documentation

mlr3proba 0.1.3 2020-02-20

  • Changed lp in surv.parametric to include the intercept, which is in line with survival::survreg. Now exp(pred$lp) is equal to the predicted survival time for AFTs
  • Moved mboost to suggests
  • Added response predict type, which predicts the time until event. Currently only supported for AFT models in surv.parametric
  • Added measures for response predict type: MeasureSurvMAE, MeasureSurvMAESE, MeasureSurvMSE, MeasureSurvMSESE, MeasureSurvRMSE, MeasureSurvRMSESE

mlr3proba 0.1.2 2020-01-31

  • Fixed error in r-patched-solaris
  • Added mode option to crankcompositor
  • Fixes bug resulting from R62S3 incompatibility

mlr3proba 0.1.1 2020-01-08

  • Added method argument to integrated scores and added weighting by bin-width
  • Added notes to IGS documentation regarding default methods and comparison to other packages
  • Added method to MeasureSurvIntegrated constructor and fields
  • Fixed mistake in documentation of: TaskSurv, MeasureSurvUnoC
  • Added missing LearnerSurvRpart parameter parms and cost
  • Fixed errors in r-patched-solaris and r-devel debian-clang

mlr3proba 0.1.0 2019-12-22

  • Initial upload to CRAN.