A simple Dictionary storing objects of class PipeOp. Each PipeOp has an associated help page, see mlr_pipeops_[id].

Format

R6Class object inheriting from mlr3misc::Dictionary.

Fields

Fields inherited from Dictionary, as well as:

  • metainf :: environment
    Environment that stores the metainf argument of the $add() method. Only for internal use.

Methods

Methods inherited from Dictionary, as well as:

  • add(key, value, metainf = NULL)
    (character(1), R6ClassGenerator, NULL | list)
    Adds constructor value to the dictionary with key key, potentially overwriting a previously stored item. If metainf is not NULL (the default), it must be a list of arguments that will be given to the value constructor (i.e. value$new()) when it needs to be constructed for as.data.table PipeOp listing.

S3 methods

  • as.data.table(dict)
    Dictionary -> data.table::data.table
    Returns a data.table with columns key (character), packages (character), input.num (integer), output.num (integer), input.type.train (character), input.type.predict (character), output.type.train (character), output.type.predict (character).

See also

Other mlr3pipelines backend related: Graph, PipeOpTargetTrafo, PipeOpTaskPreprocSimple, PipeOpTaskPreproc, PipeOp, mlr_graphs, mlr_pipeops_updatetarget

Other PipeOps: PipeOpEnsemble, PipeOpImpute, PipeOpTargetTrafo, PipeOpTaskPreprocSimple, PipeOpTaskPreproc, PipeOp, mlr_pipeops_boxcox, mlr_pipeops_branch, mlr_pipeops_chunk, mlr_pipeops_classbalancing, mlr_pipeops_classifavg, mlr_pipeops_classweights, mlr_pipeops_colapply, mlr_pipeops_collapsefactors, mlr_pipeops_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encode, mlr_pipeops_featureunion, mlr_pipeops_filter, mlr_pipeops_fixfactors, mlr_pipeops_histbin, mlr_pipeops_ica, mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputeoor, mlr_pipeops_imputesample, mlr_pipeops_kernelpca, mlr_pipeops_learner, mlr_pipeops_missind, mlr_pipeops_modelmatrix, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_mutate, mlr_pipeops_nmf, mlr_pipeops_nop, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_pca, mlr_pipeops_proxy, mlr_pipeops_quantilebin, mlr_pipeops_randomprojection, mlr_pipeops_randomresponse, mlr_pipeops_regravg, mlr_pipeops_removeconstants, mlr_pipeops_renamecolumns, mlr_pipeops_replicate, mlr_pipeops_scalemaxabs, mlr_pipeops_scalerange, mlr_pipeops_scale, mlr_pipeops_select, mlr_pipeops_smote, mlr_pipeops_spatialsign, mlr_pipeops_subsample, mlr_pipeops_targetinvert, mlr_pipeops_targetmutate, mlr_pipeops_targettrafoscalerange, mlr_pipeops_textvectorizer, mlr_pipeops_threshold, mlr_pipeops_tunethreshold, mlr_pipeops_unbranch, mlr_pipeops_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson

Other Dictionaries: mlr_graphs

Examples

library("mlr3") mlr_pipeops$get("learner", lrn("classif.rpart"))
#> PipeOp: <classif.rpart> (not trained) #> values: <xval=0> #> Input channels <name [train type, predict type]>: #> input [TaskClassif,TaskClassif] #> Output channels <name [train type, predict type]>: #> output [NULL,PredictionClassif]
# equivalent: po("learner", learner = lrn("classif.rpart"))
#> PipeOp: <classif.rpart> (not trained) #> values: <xval=0> #> Input channels <name [train type, predict type]>: #> input [TaskClassif,TaskClassif] #> Output channels <name [train type, predict type]>: #> output [NULL,PredictionClassif]
# all PipeOps currently in the dictionary: as.data.table(mlr_pipeops)[, c("key", "input.num", "output.num", "packages")]
#> key input.num output.num packages #> 1: boxcox 1 1 bestNormalize #> 2: branch 1 NA #> 3: chunk 1 NA #> 4: classbalancing 1 1 #> 5: classifavg NA 1 stats #> 6: classweights 1 1 #> 7: colapply 1 1 #> 8: collapsefactors 1 1 #> 9: colroles 1 1 #> 10: copy 1 NA #> 11: datefeatures 1 1 #> 12: encode 1 1 stats #> 13: encodeimpact 1 1 #> 14: encodelmer 1 1 lme4,nloptr #> 15: featureunion NA 1 #> 16: filter 1 1 #> 17: fixfactors 1 1 #> 18: histbin 1 1 graphics #> 19: ica 1 1 fastICA #> 20: imputeconstant 1 1 #> 21: imputehist 1 1 graphics #> 22: imputelearner 1 1 #> 23: imputemean 1 1 #> 24: imputemedian 1 1 stats #> 25: imputemode 1 1 #> 26: imputeoor 1 1 #> 27: imputesample 1 1 #> 28: kernelpca 1 1 kernlab #> 29: learner 1 1 #> 30: learner_cv 1 1 #> 31: missind 1 1 #> 32: modelmatrix 1 1 stats #> 33: multiplicityexply 1 NA #> 34: multiplicityimply NA 1 #> 35: mutate 1 1 #> 36: nmf 1 1 MASS,NMF #> 37: nop 1 1 #> 38: ovrsplit 1 1 #> 39: ovrunite 1 1 #> 40: pca 1 1 #> 41: proxy NA 1 #> 42: quantilebin 1 1 stats #> 43: randomprojection 1 1 #> 44: randomresponse 1 1 #> 45: regravg NA 1 #> 46: removeconstants 1 1 #> 47: renamecolumns 1 1 #> 48: replicate 1 1 #> 49: scale 1 1 #> 50: scalemaxabs 1 1 #> 51: scalerange 1 1 #> 52: select 1 1 #> 53: smote 1 1 smotefamily #> 54: spatialsign 1 1 #> 55: subsample 1 1 #> 56: targetinvert 2 1 #> 57: targetmutate 1 2 #> 58: targettrafoscalerange 1 2 #> 59: textvectorizer 1 1 quanteda,stopwords #> 60: threshold 1 1 #> 61: tunethreshold 1 1 bbotk #> 62: unbranch NA 1 #> 63: vtreat 1 1 vtreat #> 64: yeojohnson 1 1 bestNormalize #> key input.num output.num packages