mlr 2.19.1

Bug fixes

• Adjust behavior of "positive" arg for classif.logreg (#2846)

• Consistent naming for dummy feature encoding of variables with different levels count (#2847)

• Remove {nodeHarvest} learners (#2841)

• Remove {rknn} learner (#2842)

• Remove all {DiscriMiner} learners (#2840)

• Remove {extraTrees} learner (#2839)

• Remove depcrecated {rrlda} learner

• Resolve some {ggplot2} deprecation warnings

• Fixed information.gain filter calculation.
  Before, chi.squared was calculated even though information.gain was requested due to a glitch in the filter naming (#2816, @jokokojote)

• Make helpLearnerParam()'s HTML parsing more robust (#2843)

• Add HTML5 support for help pages

mlr 2.19.0

• Add filter FSelectoRcpp::relief(). This C++ based implementation of the RelieF filter algorithm is way faster than the Java based one from the {FSelector} package (#2804)
• Fix S3 print method for FilterWrapper objects
• Make ibrier measure work with survival tasks (#2789)
• Switch to testthat v3 (#2796)
• Enable parallel tests (#2796)
• Replace package PMCMR by PMCMRplus (#2796)
• Remove CoxBoost learner due to CRAN removal
• Warning if fix.factors.prediction = TRUE causes the generation of NAs for new factor levels in prediction (@jakob-r, #2794)
• Clear error message if prediction of wrapped learner has not the same length as newdata (@jakob-r, #2794)

mlr 2.18.0

• Many praznik filters are now also able to deal with regression tasks (#2790, @bommert)
• praznik_MRMR: Remove handling of survival tasks (#2790, @bommert)
• xgboost: update objective default from reg:linear (deprecated) to reg:squarederror
• issue a warning if blocking was set in the Task but blocking.cv was not set within `makeResampleDesc() (#2788)
• Fix order of learners in generateLearningCurveData() (#2768)
• getFeatureImportance(): Account for feature importance weight of linear xgboost models
• Fix learner note for learner glmnet (the default of param s did not match the learner note) (#2747)
• Remove dependency {hrbrthemes} used in createSpatialResamplingPlots(). The package caused issues on R-devel. In addition users should set custom themes by themselves.
• Explicitly return value in getNestedTuneResultsOptPathDf() (#2754)

mlr 2.17.1

Learners - bugfixes

• remove regr_slim learner due to pkg (flare) being orphaned on CRAN

Measures - bugixes

• remove measure clValid::dunn and its tests (package orphaned) (#2742)
• Bugfix: tuneThreshold() now accounts for the direction of the measure.
  Beforehand, the performance measure was always minimized (#2732).
• Remove adjusted Rsq measure (arsq), fixes #2711

Filters - bugfixes

• Fixed an issue which caused the random forest minimal depth filter to only return NA values when using thresholding.
  NAs should only be returned for features below the given threshold. (@annette987, #2710)
• Fixed problem which prevented passing filter options via argument more.args for simple filters (@annette987, #2709)

Feature selection - bugfixes

• Fix print.FeatSelResult() when bits.to.features is used in selectFeatures() (#2721)
• Return a long DF for getFeatureImportance() (#2708)

Misc

• pkgdown: Move changelog to Appendix

• Account for {checkmate} v2.0.0 update (#2734)

• Refactor function calls from packages (<pkg::fun>) within ParamSets (#2730) to avoid errors in listLearners() if those pkgs are not installed

• listLearners() should not fail if a package is not installed (#2717)

mlr 2.17.0

plotting

• n.show argument had no effect in plotFilterValues(). Thanks @albersonmiranda. (#2689)

Functional Data

PR: #2638 (@pfistl)

• Added several learners for regression and classification on functional data

  • classif.classiFunc.(kernel|knn) (knn/kernel using various semi-metrics)
  • (classif|regr).fgam (Functional generalized additive models)
  • (classif|regr).FDboost (Boosted functional generalized additive models)

• Added preprocessing steps for feature extraction from functional data

  • extractFDAFourier (Fourier transform)
  • extractFDAWavelets (Wavelet features)
  • extractFDAFPCA (Principal components)
  • extractFDATsfeatures (Time-Series features from tsfeatures package)
  • extractFDADTWKernel (Dynamic Time-Warping Kernel)
  • extractFDAMultiResFeatures (Compute features at multiple resolutions)

• Fixed a bug where multiclass to binaryclass reduction techniques did not work
  with functional data.

• Several other minor bug fixes and code improvements

• Extended and clarified documentation for several fda components.

learners - general

• xgboost: added options 'auto', 'approx' and 'gpu_hist' to param tree_method (@albersonmiranda, #2701)

filters - general

• Allow a custom threholding function to be passed to filterFeatures and makeFilterWrapper (@annette987, #2686)
• Allow ensemble filters to include multiple base filters of the same type (@annette987, #2688)

filters - bugfixes

• filterFeatures(): Arg thresh was not working correctly when applied to ensemble filters. (@annette987, #2699)
• Fixed incorrect ranking of ensemble filters. Thanks @annette987 (#2698)

mlr 2.16.0

package infrastructure

• There is now a reference grouping for all functions on the pkgdown site (https://mlr.mlr-org.com/reference/index.html)
• CI testing now only on Circle CI (previously Travis CI)

learners - general

• fixed a bug in classif.xgboost which prevented passing a watchlist for binary tasks. This was caused by a suboptimal internal label inversion approach. Thanks to @001ben for reporting (#32) (@mllg)
• update fda.usc learners to work with package version >=2.0
• update glmnet learners to upstream package version 3.0.0
• update xgboost learners to upstream version 0.90.2 (@pat-s & @be-marc, #2681)
• Updated ParamSet for learners classif.gbm and regr.gbm. Specifically, param shrinkage now defaults to 0.1 instead of 0.001. Also more choices for param distribution have been added. Internal parallelization by the package is now suppressed (param n.cores). (@pat-s, #2651)
• Update parameters for h2o.deeplearning learners (@albersonmiranda, #2668)

misc

• Add configureMlr() to .onLoad(), possibly fixing some edge cases (#2585) (@pat-s, #2637)

learners - bugfixes

• h2o.gbm learners were not running until wcol was passed somehow due to an internal bug. In addition, this bug caused another issue during prediction where the prediction data.frame was somehow formatted as a character rather a numeric. Thanks to @nagdevAmruthnath for bringing this up in #2630.

filters - general

• Bugfix: Allow method = "vh" for filter randomForestSRC_var.select and return informative error message for not supported values. Also argument conservative can now be passed. See #2646 and #2639 for more information (@pat-s, #2649)

• Bugfix: With the new praznik v7.0.0 release filter praznik_CMIM does no longer return a result for logical features. See https://gitlab.com/mbq/praznik/issues/19 for more information

mlr 2.15.0

Breaking

• Instead of a wide data.frame filter values are now returned in a long (tidy) tibble. This makes it easier to apply post-processing methods (like group_by(), etc) (@pat-s, #2456)
• benchmark() does not store the tuning results ($extract slot) anymore by default.
  If you want to keep this slot (e.g. for post tuning analysis), set keep.extract = TRUE.
  This change originated from the fact that the size of BenchmarkResult objects with extensive tuning got very large (~ GB) which can cause memory problems during runtime if multiple benchmark() calls are executed on HPCs.
• benchmark() does not store the created models ($models slot) anymore by default.
  The reason is the same as for the $extract slot above.
  Storing can be enabled using models = TRUE.

functions - general

• generateFeatureImportanceData() gains argument show.info which shows the name of the current feature being calculated, its index in the queue and the elapsed time for each feature (@pat-s, #26222)

learners - general

• classif.liquidSVM and regr.liquidSVM have been removed because liquidSVM has been removed from CRAN.
• fixed a bug that caused an incorrect aggregation of probabilities in some cases. The bug existed since quite some time and was exposed due to the change of data.tables default in rbindlist(). See #2578 for more information. (@mllg, #2579)
• regr.randomForest gains three new methods to estimate the standard error:
  • se.method = "jackknife"
  • se.method = "bootstrap"
  • se.method = "sd"
    See ?regr.randomForest for more details.
    regr.ranger relies on the functions provided by the package ("jackknife" and "infjackknife" (default))
    (@jakob-r, #1784)
• regr.gbm now supports quantile distribution (@bthieurmel, #2603)
• classif.plsdaCaret now supports multiclass classification (@GegznaV, #2621)

functions - general

• getClassWeightParam() now also works for Wrapper* Models and ensemble models (@ja-thomas, #891)
• added getLearnerNote() to query the "Note" slot of a learner (@alona-sydorova, #2086)
• e1071::svm() now only uses the formula interface if factors are present. This change is supposed to prevent from "stack overflow" issues some users encountered when using large datasets. See #1738 for more information. (@mb706, #1740)

learners - new

• add learner cluster.MiniBatchKmeans from package ClusterR (@Prasiddhi, #2554)

function - general

• plotHyperParsEffect() now supports facet visualization of hyperparam effects for nested cv (@masongallo, #1653)
• fixed a bug that caused an incorrect aggregation of probabilities in some cases. The bug existed since quite some time and was exposed due to the change of data.tables default in rbindlist(). See #2578 for more information. (@mllg, #2579)
• fixed a bug in which options(on.learner.error) was not respected in benchmark(). This caused benchmark() to stop even if it should have continued including FailureModels in the result (@dagola, #1984)
• getClassWeightParam() now also works for Wrapper* Models and ensemble models (@ja-thomas, #891)
• added getLearnerNote() to query the "Note" slot of a learner (@alona-sydorova, #2086)

filters - general

• Filter praznik_mrmr also supports regr and surv tasks
• plotFilterValues() got a bit "smarter" and easier now regarding the ordering of multiple facets. (@pat-s, #2456)
• filterFeatures(), generateFilterValuesData() and makeFilterWrapper() gained new examples. (@pat-s, #2456)

filters - new

• Ensemble features are now supported. These filters combine multiple single filters to create a final ranking based on certain statistical operations. All new filters are listed in a dedicated section "ensemble filters" in the tutorial.
  Tuning of simple features is not supported yet because of a missing feature in ParamHelpers. (@pat-s, #2456)

Version 2.14.0

general

• add option to use fully predefined indices in resampling (makeResampleDesc(fixed = TRUE)) (@pat-s, #2412).
• Task help pages are now split into separate ones, e.g. RegrTask, ClassifTask (@pat-s, #2564)

functions - new

• deleteCacheDir(): Clear the default mlr cache directory (@pat-s, #2463)
• getCacheDir(): Return the default mlr cache directory (@pat-s, #2463)

functions - general

• getResamplingIndices(inner = TRUE) now correctly returns the inner indices (before inner indices referred to the subset of the respective outer level train set) (@pat-s, #2413).

filter - general

• Caching is now used when generating filter values.
  This means that filter values are only computed once for a specific setting and the stored cache is used in subsequent iterations.
  This change inherits a significant speed-up when tuning fw.perc, fw.abs or fw.threshold.
  It can be triggered with the new cache argument in makeFilterWrapper() or filterFeatures() (@pat-s, #2463).

filter - new

• praznik_JMI
• praznik_DISR
• praznik_JMIM
• praznik_MIM
• praznik_NJMIM
• praznik_MRMR
• praznik_CMIM
• FSelectorRcpp_gain.ratio
• FSelectorRcpp_information.gain
• FSelectorRcpp_symuncert

Additionally, filter names have been harmonized using the following scheme: _.
Exeptions are filters included in base R packages.
In this case, the package name is omitted.

filter - general

• Added filters FSelectorRcpp_gain.ratio, FSelectorRcpp_information.gain and FSelectorRcpp_symmetrical.uncertainty from package FSelectorRcpp.
  These filters are ~ 100 times faster than the implementation of the FSelector pkg.
  Please note that both implementations do things slightly different internally and the FSelectorRcpp methods should not be seen as direct replacement for the FSelector pkg.

• filter names have been harmonized using the following scheme: _. (@pat-s, #2533)

  • information.gain -> FSelector_information.gain
  • gain.ratio -> FSelector_gain.ratio
  • symmetrical.uncertainty -> FSelector_symmetrical.uncertainty
  • chi.squared -> FSelector_chi.squared
  • relief -> FSelector_relief
  • oneR -> FSelector_oneR
  • randomForestSRC.rfsrc -> randomForestSRC_importance
  • randomForestSRC.var.select -> randomForestSRC_var.select
  • randomForest.importance -> randomForest_importance

• fixed a bug related to the loading of namespaces for required filter packages (@pat-s, #2483)

learners - new

• classif.liquidSVM (@PhilippPro, #2428)
• regr.liquidSVM (@PhilippPro, #2428)

learners - general

• regr.h2o.gbm: Various parameters added, "h2o.use.data.table" = TRUE is now the default (@j-hartshorn, #2508)
• h2o learners now support getting feature importance (@markusdumke, #2434)

learners - fixes

• In some cases the optimized hyperparameters were not applied in the performance level of a nested CV (@berndbischl, #2479)

featSel - general

• The FeatSelResult object now contains an additional slot x.bit.names that stores the optimal bits
• The slot x now always contains the real feature names and not the bit.names
• This fixes a bug and makes makeFeatSelWrapper usable with custom bit.names.
• Fixed a bug due to which sffs crashed in some cases (@bmihaljevic, #2486)

Version 2.13

general

• Disabled unit tests for CRAN, we test on travis only now
• Suppress messages with show.learner.output = FALSE

functions - general

• plotHyperParsEffect: add colors

functions - new

• getResamplingIndices
• createSpatialResamplingPlots

learners - general

• regr.nnet: Removed unneeded params linout, entropy, softmax and censored
• regr.ranger: Add weight handling

learners - removed

• {classif,regr}.blackboost: broke API with new release

Version 2.12

general

• Support for functional data (fda) using matrix columns has been added.
• Relaxed the way wrappers can be nested -- the only explicitly forbidden
  combination is to wrap a tuning wrapper around another optimization wrapper
• Refactored the resample progress messages to give a better overview and
  distinguish between train and test measures better
• calculateROCMeasures now returns absolute instead of relative values
• Added support for spatial data by providing spatial partitioning methods "SpCV" and "SpRepCV".
• Added new spatial.task classification task.
• Added new spam.task classification task.
• Classification tasks now store the class distribution in the
  class.distribution member.
• mlr now predicts NA for data that contains NA and learners that do not support
  missing values.
• Tasks are now subsetted in the "train" function and the factor levels (for
  classification tasks) based on this subset. This means that the factor level
  distribution is not necessarily the same as for the entire task, and that the
  task descriptions of models in resampling reflect the respective subset, while
  the task description of resample predictions reflect the entire task and not
  necessarily the task of any individual model.
• Added support for growing and fixed window cross-validation for forecasting
  through new resample methods "GrowingWindowCV" and "FixedWindowCV".

functions - general

• generatePartialDependenceData: depends now on the "mmpf" package,
  removed parameter: "center", "resample", "fmin", "fmax" and "gridsize"
  added parameter: "uniform" and "n" to configure the grid for the partial dependence plot
• batchmark: allow resample instances and reduction of partial results
• resample, performance: new flag "na.rm" to remove NAs during aggregation
• plotTuneMultiCritResultGGVIS: new parameters "point.info" and "point.trafo" to
  control interactivity
• calculateConfusionMatrix: new parameter "set" to specify whether confusion
  matrix should be computed for "train", "test", or "both" (default)
• PlotBMRSummary: Add parameter "shape"
• plotROCCurves: Add faceting argument
• PreprocWrapperCaret: Add param "ppc.corr", "ppc.zv", "ppc.nzv", "ppc.n.comp", "ppc.cutoff", "ppc.freqCut", "ppc.uniqueCut"

functions - new

• makeClassificationViaRegressionWrapper
• getPredictionTaskDesc
• helpLearner, helpLearnerParam: open the help for a learner or get a
  description of its parameters
• setMeasurePars
• makeFunctionalData
• hasFunctionalFeatures
• extractFDAFeatures, reextractFDAFeatures
• extractFDAFourier, extractFDAFPCA, extractFDAMultiResFeatures, extractFDAWavelets
• makeExtractFDAFeatMethod
• makeExtractFDAFeatsWrapper
• getTuneResultOptPath
• makeTuneMultiCritControlMBO: Allows model based multi-critera / multi-objective optimization using mlrMBO

functions - removed

• Removed plotViperCharts

measures - general

• measure "arsq" now has ID "arsq"
• measure "measureMultiLabelF1" was renamed to "measureMultilabelF1" for consistency

measures - new

• measureBER, measureRMSLE, measureF1
• cindex.uno, iauc.uno

learners - general

• unified {classif,regr,surv}.penalized{ridge,lasso,fusedlasso} into {classif,regr,surv}.penalized
• fixed a bug where surv.cforest gave wrong risk predictions (#1833)
• fixed bug where classif.xgboost returned NA predictions with multi:softmax
• classif.lda learner: add 'prior' hyperparameter
• ranger: update hyperpar 'respect.unordered.factors', add 'extratrees' and 'num.random.splits'
• h20deeplearning: Rename hyperpar 'MeanSquare' to 'Quadratic'
• h20*: Add support for "missings"

learners - new

• classif.adaboostm1
• classif.fdaknn
• classif.fdakernel
• classif.fdanp
• classif.fdaglm
• classif.mxff
• regr.fdaFDboost
• regr.mxff

learners - removed

• {classif,regr}.bdk: broke our API, stability issues
• {classif,regr}.xyf: broke our API, stability issues
• classif.hdrda: package removed from CRAN
• surv.penalized: stability issues

aggregations - new

• testgroup.sd

filter - new

• auc
• ranger.permutation, ranger.impurity