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updated NEWS for PR#1189
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fixed broken link in README

updated NEWS for PR#1026

remove .S3methods import (mlr-org#1216)

update auto-generated documentation [ci skip]

removed unused variables (mlr-org#1215)

updated README

benchmark: better arg handling (mlr-org#1224)

NEWS

xgboost: better handling of arg 'missing' (mlr-org#1225)

set default of shw.missing.values to TRUE (mlr-org#1223)

* set default to FALSE, identical to ParamHelpers

* TRUE TRUE TRUE

remove deprecated call and catch warning in mape (mlr-org#1228)

fix mlr-org#804 replace preproc with imputed (mlr-org#1231)

NEWS

NEWS: are OK until HERE

xgboost: missing: go back to set it NA in mlr

xgboost: missing: simply use NULL as default

fix xgboost tests (mlr-org#1234)

* fix xgboost tests

* fix more tests

test for xgboost printer

Add support for visualizing tasks with 2 or more hyperparameters (mlr-org#1233)

* Add support for visualizing tasks with 2 or more hyperparameters

* Add tests for partial dependence

* Edit documentation

* Forgot to regenerate documentation

* Fixed checks for using partial dependence and minor style fixes

* Fix typos in argname

* Fix arg name in test

NEWS for mlr-org#1233

remove weight.fun in place of expanded fun in generatePartialDependence (mlr-org#1235)

* remove weight.fun in place of expanded fun in generatePartialDependence

 - internal wrapper for fun arg to allow passing of internal
   newdata (prediction grid) and data (training data from input arg)
   which allows computation of weights in fun instead of via an extra
   step using another arg, weight.fun (now removed)

* fix typo

NEWS for mlr-org#1235

update auto-generated documentation [ci skip]

Update description with mason (mlr-org#1237)

travis does not work with rdevel, i will open an issue

Added ctb (mlr-org#1242)

* Added Bruno Vieira as ctb.

* Added Bruno Vieira as ctb.

fixes for issue mlr-org#63 in the tutorial (mlr-org#1243)

- incorrect jacobian function in doPartialDerivativeIteratoin
 - improper fun/fun.wrapper (for weights use)
 - test added based on tutorial fail
 - simplified code a bit

renamed file for consistency

update auto-generated documentation [ci skip]

added the colsample_bylevel parameter in the xgboost learners (mlr-org#1245)

* Update RLearner_classif_xgboost.R

* Update RLearner_regr_xgboost.R

NEWS for mlr-org#1245 and add xgboost version number requirements

forgot space...

ksvm mini tunable fix for hyper par settings (mlr-org#1249)

New measures: Cohen's Kappa and Mean Quadratic Weighted Kappa (mlr-org#1250)

* new measure 'mean quadratic weighted kappa'

* add note for mqwk

* rename objects in test

* rename to wk and fix typo in note

* yet another typo

* rename wk to wkappa

* new_measure_cohens_kappa

* correct measure ranges

NEWS for mlr-org#1250

fixed broken url

listLearners output as S3 class with print (mlr-org#1213)

Make hyperparseffect tests faster with less iterations (mlr-org#1260)

Created TimeRegrTask and started on Arima Learner

Added ARMA learner. For now, allowing cl on line 92 of predictLearner (checkPredictLearnerOutput) to be a ts object

Predict added for Arima.

Prediction now returns the response, but the 'truth' variables is NA, since forecasts do not know the true value at the time of forecast

Added new forecast function. Need to figure out why Arima and forecast are not going to the namespace.

Fixed forecast to use holdout set, made mase measure

Updated namespace to import forecast, then use the method for WrappedModel. Dunno if this meeses with forecast() in the forecast package.

Created Windowing description functions and starting adding Windowing instances

Created fixed and growing window instances, may not work for horizon > 1.

Added window() function, mostly copying resample(). Need to add functions for windowing with aggregation.

Added window level to zzz.R, Created checkAggrBeforeWindow function, WindowPrediction, makeWindowPrediction. Fixed growing and fixed windows by using code from caret.

Windowing works for arima, should probably do something about n.ahead and horizon being the same thing.

Imported forecast to resample, no longer need forecast functions or windows

Removed window and forecast functions, removed window from zzz levels

Added skip parameter to growing CV and fixed CV so user does not have to run every iteration.

had to capitalize L in makeRLearner for Arima

Added docs for time components in resample and resampleDesc

Added imports from xts and zoo. Added xreg to Arima.

Added Lag and Difference preprocess wrapper.

Made createLagDiffFeatures a task preprocessor.

Changed names of timeReg to ForecastRegr and timeregr to fcregr.

Testing

Making sure rebase worked.

Updates now pass base tests

Updated prediction from timereg to forecastreg. Updated README with some examples of using forecasting.

Trying to upload caret picture for windowing.

Updated readme with examples.

Updated readme.

Fixed createLagDiffFeatures. But NA's are handled poorly.

added bats, ets, garch, nnetr, tbats, and thief. Not tested yet, but garch works.

garch now works for resampling.

bats, ets, garch, nnetar, tbats are now working. Updated Readme. thief is not working (frowny face)

Made pre processing wrapper using LambertW transform

Added LambertW to description suggests and updated the readme.

Updated lag and diff preproc func for seasonal lag and differences. Untested.

Updated lag and diff preproc to have seasonal lags and diffs.

Fixed lag diff preproc to include padding and lag lengths for differencing.

Updated docs for createLagDiffFeatures

Added forecast helper objects and started working on unit test for Arima.

Created TimeRegrTask and started on Arima Learner

Added ARMA learner. For now, allowing cl on line 92 of predictLearner (checkPredictLearnerOutput) to be a ts object

Predict added for Arima.

Prediction now returns the response, but the 'truth' variables is NA, since forecasts do not know the true value at the time of forecast

Added new forecast function. Need to figure out why Arima and forecast are not going to the namespace.

Fixed forecast to use holdout set, made mase measure

Updated namespace to import forecast, then use the method for WrappedModel. Dunno if this meeses with forecast() in the forecast package.

Created Windowing description functions and starting adding Windowing instances

Created fixed and growing window instances, may not work for horizon > 1.

Added window() function, mostly copying resample(). Need to add functions for windowing with aggregation.

Added window level to zzz.R, Created checkAggrBeforeWindow function, WindowPrediction, makeWindowPrediction. Fixed growing and fixed windows by using code from caret.

Windowing works for arima, should probably do something about n.ahead and horizon being the same thing.

Imported forecast to resample, no longer need forecast functions or windows

Removed window and forecast functions, removed window from zzz levels

Added skip parameter to growing CV and fixed CV so user does not have to run every iteration.

had to capitalize L in makeRLearner for Arima

Added docs for time components in resample and resampleDesc

Added imports from xts and zoo. Added xreg to Arima.

Added Lag and Difference preprocess wrapper.

Made createLagDiffFeatures a task preprocessor.

Changed names of timeReg to ForecastRegr and timeregr to fcregr.

Testing

Making sure rebase worked.

Updates now pass base tests

Updated prediction from timereg to forecastreg. Updated README with some examples of using forecasting.

Trying to upload caret picture for windowing.

Updated readme with examples.

Updated readme.

Fixed createLagDiffFeatures. But NA's are handled poorly.

added bats, ets, garch, nnetr, tbats, and thief. Not tested yet, but garch works.

garch now works for resampling.

bats, ets, garch, nnetar, tbats are now working. Updated Readme. thief is not working (frowny face)

Added LambertW to description suggests and updated the readme.

Updated lag and diff preproc func for seasonal lag and differences. Untested.

Updated lag and diff preproc to have seasonal lags and diffs.

Fixed lag diff preproc to include padding and lag lengths for differencing.

Updated docs for createLagDiffFeatures

Updated merge for Arima prediction.

Fixed training for fcregr tasks to only use subsets.

Moved test for bats to testthat.

Added tests for tbats and ets

Added garch unit test.

Added test for createLagDiffFeatures

Added helper objects for forecast unit testing and Arima can now return standard errors at set levels

fixed typo in garch test

Moved thief to to-do and implimented arfima with a test.

Added se prediction type to arfima, bats, ets, nnetar, and tbats

Added updateLearner function and updateModel function to update online models.

Added docs for updateModel and built basic test for forecast task. Need to test multiplexer.

Fixed Lambert W and created test for forecast
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mllg authored and SteveBronder committed Oct 12, 2016
1 parent b11a8b6 commit 1335740
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2 changes: 1 addition & 1 deletion .travis.yml
Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@ sudo: false
cache: packages
r:
- release
- devel
# - devel

addons:
apt:
Expand Down
16 changes: 12 additions & 4 deletions DESCRIPTION
Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@ Authors@R: c(
person("Jakob", "Richter", email = "code@jakob-r.de", role = "aut"),
person("Zachary", "Jones", email = "zmj@zmjones.com", role = "aut"),
person("Giuseppe", "Casalicchio", email = "giuseppe.casalicchio@stat.uni-muenchen.de", role = "aut"),
person("Mason", "Gallo", email = "masonagallo@gmail.com", role = "aut"),
person("Jakob", "Bossek", email = "jakob.bossek@tu-dortmund.de", role = "ctb"),
person("Erich", "Studerus", email = "erich.studerus@upkbs.ch", role = "ctb"),
person("Leonard","Judt", email = "leonard.judt@tu-dortmund.de", role = "ctb"),
Expand All @@ -25,7 +26,8 @@ Authors@R: c(
person("Florian", "Fendt", email = "flo_fendt@gmx.de", role = "ctb"),
person("Philipp", "Probst", email = "philipp_probst@gmx.de", role = "ctb"),
person("Xudong", "Sun", email = "xudong.sun@stat.uni-muenchen.de", role = "ctb"),
person("Janek", "Thomas", email = "janek.thomas@stat.uni-muenchen.de", role = "ctb"))
person("Janek", "Thomas", email = "janek.thomas@stat.uni-muenchen.de", role = "ctb"),
person("Bruno", "Vieira", email = "bruno.hebling.vieira@usp.br", role = "ctb"))
URL: https://github.com/mlr-org/mlr
BugReports: https://github.com/mlr-org/mlr/issues
License: BSD_2_clause + file LICENSE
Expand All @@ -46,7 +48,10 @@ Imports:
parallelMap (>= 1.3),
shiny,
survival,
utils
utils,
xts,
lubridate,
zoo
Suggests:
ada,
adabag,
Expand Down Expand Up @@ -142,8 +147,11 @@ Suggests:
testthat,
tgp,
TH.data,
xgboost,
XML
xgboost (>= 0.4-4),
XML,
forecast,
rugarch,
LambertW
LazyData: yes
ByteCompile: yes
Version: 2.10
Expand Down
53 changes: 52 additions & 1 deletion NAMESPACE
Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,8 @@ S3method(capLargeValues,Task)
S3method(capLargeValues,data.frame)
S3method(createDummyFeatures,Task)
S3method(createDummyFeatures,data.frame)
S3method(createLagDiffFeatures,TimeTask)
S3method(createLagDiffFeatures,xts)
S3method(downsample,ResampleInstance)
S3method(downsample,Task)
S3method(estimateRelativeOverfitting,ResampleDesc)
Expand Down Expand Up @@ -104,6 +106,7 @@ S3method(listMeasures,default)
S3method(makePrediction,TaskDescClassif)
S3method(makePrediction,TaskDescCluster)
S3method(makePrediction,TaskDescCostSens)
S3method(makePrediction,TaskDescForecastRegr)
S3method(makePrediction,TaskDescMultilabel)
S3method(makePrediction,TaskDescRegr)
S3method(makePrediction,TaskDescSurv)
Expand Down Expand Up @@ -198,6 +201,13 @@ S3method(makeRLearner,cluster.cmeans)
S3method(makeRLearner,cluster.dbscan)
S3method(makeRLearner,cluster.kkmeans)
S3method(makeRLearner,cluster.kmeans)
S3method(makeRLearner,fcregr.Arima)
S3method(makeRLearner,fcregr.arfima)
S3method(makeRLearner,fcregr.bats)
S3method(makeRLearner,fcregr.ets)
S3method(makeRLearner,fcregr.garch)
S3method(makeRLearner,fcregr.nnetar)
S3method(makeRLearner,fcregr.tbats)
S3method(makeRLearner,multilabel.cforest)
S3method(makeRLearner,multilabel.rFerns)
S3method(makeRLearner,multilabel.randomForestSRC)
Expand All @@ -222,6 +232,7 @@ S3method(makeRLearner,regr.cforest)
S3method(makeRLearner,regr.crs)
S3method(makeRLearner,regr.ctree)
S3method(makeRLearner,regr.cubist)
S3method(makeRLearner,regr.cvglmnet)
S3method(makeRLearner,regr.earth)
S3method(makeRLearner,regr.elmNN)
S3method(makeRLearner,regr.extraTrees)
Expand Down Expand Up @@ -397,6 +408,13 @@ S3method(predictLearner,cluster.cmeans)
S3method(predictLearner,cluster.dbscan)
S3method(predictLearner,cluster.kkmeans)
S3method(predictLearner,cluster.kmeans)
S3method(predictLearner,fcregr.Arima)
S3method(predictLearner,fcregr.arfima)
S3method(predictLearner,fcregr.bats)
S3method(predictLearner,fcregr.ets)
S3method(predictLearner,fcregr.garch)
S3method(predictLearner,fcregr.nnetar)
S3method(predictLearner,fcregr.tbats)
S3method(predictLearner,multilabel.cforest)
S3method(predictLearner,multilabel.rFerns)
S3method(predictLearner,multilabel.randomForestSRC)
Expand All @@ -420,6 +438,7 @@ S3method(predictLearner,regr.cforest)
S3method(predictLearner,regr.crs)
S3method(predictLearner,regr.ctree)
S3method(predictLearner,regr.cubist)
S3method(predictLearner,regr.cvglmnet)
S3method(predictLearner,regr.earth)
S3method(predictLearner,regr.elmNN)
S3method(predictLearner,regr.extraTrees)
Expand Down Expand Up @@ -489,12 +508,15 @@ S3method(print,FeatSelResult)
S3method(print,FeatureImportance)
S3method(print,Filter)
S3method(print,FilterValues)
S3method(print,FixedCVDesc)
S3method(print,FunctionalANOVAData)
S3method(print,GrowingCVDesc)
S3method(print,HoldoutDesc)
S3method(print,HyperParsEffectData)
S3method(print,ImputationDesc)
S3method(print,Learner)
S3method(print,LearningCurveData)
S3method(print,ListLearners)
S3method(print,Measure)
S3method(print,MultilabelTask)
S3method(print,OptModel)
Expand All @@ -509,6 +531,7 @@ S3method(print,ResampleResult)
S3method(print,SubsampleDesc)
S3method(print,SupervisedTask)
S3method(print,Task)
S3method(print,TimeTask)
S3method(print,TuneControl)
S3method(print,TuneMultiCritControl)
S3method(print,TuneMultiCritResult)
Expand Down Expand Up @@ -648,6 +671,13 @@ S3method(trainLearner,cluster.cmeans)
S3method(trainLearner,cluster.dbscan)
S3method(trainLearner,cluster.kkmeans)
S3method(trainLearner,cluster.kmeans)
S3method(trainLearner,fcregr.Arima)
S3method(trainLearner,fcregr.arfima)
S3method(trainLearner,fcregr.bats)
S3method(trainLearner,fcregr.ets)
S3method(trainLearner,fcregr.garch)
S3method(trainLearner,fcregr.nnetar)
S3method(trainLearner,fcregr.tbats)
S3method(trainLearner,multilabel.cforest)
S3method(trainLearner,multilabel.rFerns)
S3method(trainLearner,multilabel.randomForestSRC)
Expand All @@ -671,6 +701,7 @@ S3method(trainLearner,regr.cforest)
S3method(trainLearner,regr.crs)
S3method(trainLearner,regr.ctree)
S3method(trainLearner,regr.cubist)
S3method(trainLearner,regr.cvglmnet)
S3method(trainLearner,regr.earth)
S3method(trainLearner,regr.elmNN)
S3method(trainLearner,regr.extraTrees)
Expand Down Expand Up @@ -750,6 +781,7 @@ export(configureMlr)
export(convertBMRToRankMatrix)
export(convertMLBenchObjToTask)
export(createDummyFeatures)
export(createLagDiffFeatures)
export(crossval)
export(cv10)
export(cv2)
Expand All @@ -766,6 +798,7 @@ export(f1)
export(fdr)
export(featperc)
export(filterFeatures)
export(fixedcv)
export(fn)
export(fnr)
export(fp)
Expand Down Expand Up @@ -840,6 +873,7 @@ export(getTaskType)
export(getTuneResult)
export(gmean)
export(gpr)
export(growingcv)
export(hasLearnerProperties)
export(hasProperties)
export(holdout)
Expand All @@ -857,6 +891,7 @@ export(imputeNormal)
export(imputeUniform)
export(isFailureModel)
export(joinClassLevels)
export(kappa)
export(learnerArgsToControl)
export(listFilterMethods)
export(listLearners)
Expand Down Expand Up @@ -884,6 +919,7 @@ export(makeFeatSelWrapper)
export(makeFilter)
export(makeFilterWrapper)
export(makeFixedHoldoutInstance)
export(makeForecastRegrTask)
export(makeImputeMethod)
export(makeImputeWrapper)
export(makeLearner)
Expand All @@ -903,6 +939,7 @@ export(makeOversampleWrapper)
export(makePrediction)
export(makePreprocWrapper)
export(makePreprocWrapperCaret)
export(makePreprocWrapperLambert)
export(makeRLearner)
export(makeRLearnerClassif)
export(makeRLearnerCluster)
Expand Down Expand Up @@ -931,6 +968,7 @@ export(makeUndersampleWrapper)
export(makeWeightedClassesWrapper)
export(makeWrappedModel)
export(mape)
export(mase)
export(mcc)
export(mcp)
export(meancosts)
Expand All @@ -951,6 +989,7 @@ export(measureFP)
export(measureFPR)
export(measureGMEAN)
export(measureGPR)
export(measureKAPPA)
export(measureLSR)
export(measureLogloss)
export(measureMAE)
Expand Down Expand Up @@ -981,6 +1020,7 @@ export(measureTN)
export(measureTNR)
export(measureTP)
export(measureTPR)
export(measureWKAPPA)
export(medae)
export(medse)
export(mergeBenchmarkResultLearner)
Expand Down Expand Up @@ -1083,6 +1123,10 @@ export(tuneParams)
export(tuneParamsMultiCrit)
export(tuneThreshold)
export(undersample)
export(updateLearner)
export(updateLearner2)
export(updateModel)
export(wkappa)
import(BBmisc)
import(ParamHelpers)
import(checkmate)
Expand All @@ -1101,6 +1145,7 @@ importFrom(ggvis,layer_points)
importFrom(ggvis,layer_ribbons)
importFrom(ggvis,prop)
importFrom(graphics,hist)
importFrom(lubridate,is.POSIXt)
importFrom(shiny,headerPanel)
importFrom(shiny,mainPanel)
importFrom(shiny,pageWithSidebar)
Expand All @@ -1113,7 +1158,6 @@ importFrom(shiny,sidebarPanel)
importFrom(shiny,uiOutput)
importFrom(survival,Surv)
importFrom(survival,is.Surv)
importFrom(utils,.S3methods)
importFrom(utils,adist)
importFrom(utils,browseURL)
importFrom(utils,capture.output)
Expand All @@ -1125,4 +1169,11 @@ importFrom(utils,head)
importFrom(utils,methods)
importFrom(utils,tail)
importFrom(utils,type.convert)
importFrom(xts,diff.xts)
importFrom(xts,lag.xts)
importFrom(xts,reclass)
importFrom(xts,try.xts)
importFrom(xts,xts)
importFrom(zoo,coredata)
importFrom(zoo,index)
useDynLib(mlr,c_smote)
49 changes: 23 additions & 26 deletions NEWS.md
Original file line number Diff line number Diff line change
Expand Up @@ -6,30 +6,33 @@
* print.Learner: if a learner hyperpar was set to value "NA" this was not
displayed in printer
* makeLearner, setHyperPars: if you mistype a learner or hyperpar name, mlr
usesfuzzy matching to suggest the 3 closest names in the message
uses fuzzy matching to suggest the 3 closest names in the message
* tuneParams: tuning with irace is now also parallelized, i.e., different
learner config are evaluated in parallel.
* randomForestSRC.var.select: new arg "method"
* mrmr filter: fixed some smaller bugs and updated properties
learner configs are evaluated in parallel.
* benchmark: mini fix, arg 'learners' now also accepts class strings
* object printers: some mlr printers show head previews of data.frames.
these now also print info on the total nr of rows and cols and are less confusing
* aggregations: have better properties now, they know whether they require training or
test set evals
* the filter methods have better R docs
* filter randomForestSRC.var.select: new arg "method"
* filter mrmr: fixed some smaller bugs and updated properties
* generateLearningCurveData: also accepts single learner, does not require a list
* object printers: some mlr printers show head previews of data.frame data.
these now also print info on the total nr of rows and cols and are less
confusing
* setHyperPars: added "show.info" arg
* plotThreshVsPerf: added "measures" arg
* new "mlrFamilies" manual page which lists all families and the functions
* plotPartialDependence: can create tile plots with joint partial dependence
on two features for multiclass classification by facetting across the classes
* generatePartialDependenceData and generateFunctionalANOVAData: expanded
"fun" argument to allow for calculation of weights
* new "?mlrFamilies" manual page which lists all families and the functions
belonging to it
* the filter methods have better R docs
* we are converging on data.table as a standard internally, this should not
change any API behavior on the outside, though
* plotPartialDependence: can create tile plots with joint partial dependence
on two features for multiclass classification by facetting across the classes
* generatePartialDependenceData and generateFunctionalANOVAData: added
"weight.fun" argument
* generateHyperParsEffectData and plotHyperParsEffect now support more than 2
hyperparameters

## functions - new
* filter: randomForest.importance
* generateFeatureImportanceData: permutation-based featuree importance and local
* generateFeatureImportanceData: permutation-based feature importance and local
importance
* getFeatureImportanceLearner: new Learner API function
* getFeatureImportance: top level function to extract feature importance
Expand All @@ -41,8 +44,6 @@
* getLearnerId, getLearnerType, getLearnerPredictType, getLearnerPackages
* getLearnerParamSet, getLearnerParVals

## functions - removed

## functions - renamed
* Renamed rf.importance filter (now deprecated) to randomForestSRC.var.rfsrc
* Renamed rf.min.depth filter (now deprecated) to randomForestSRC.var.select
Expand All @@ -55,21 +56,17 @@
removed parameters "minprob", "pvalue", "randomsplits"
as these are set internally and cannot be changed by the user
* regr.GPfit: some more params for correlation kernel
* classif.xgboost, regr.xgboost: can now properly handle NAs (property was missing)
and param 'missing' set to NA by default
* classif.xgboost, regr.xgboost: can now properly handle NAs (property was missing and other problems), added "colsample_bylevel" parameter

## learners - new
* multilabel.cforest

## learners - removed

## measures - general
* surv.gbm
* regr.cvglmnet

## measures - new
* ssr, qsr, lsr
* rrse, rae, mape

## measures - renamed
* kappa, wkappa

# mlr 2.9:

Expand Down Expand Up @@ -629,7 +626,7 @@

# mlr 2.0:
* mlr now supports survival analysis models (experimental)
* mlr now supports cost-sensitive learning with example-specific costs
* mlr now supports cost-sensitive learning with example-specific costs
experimental)
* Some example tasks and data sets were added for simple access
* added FeatSelWrapper and getFeatSelResult
Expand Down
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