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

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: copy 1 NA #> 10: encode 1 1 stats #> 11: encodeimpact 1 1 #> 12: encodelmer 1 1 lme4,nloptr #> 13: featureunion NA 1 #> 14: filter 1 1 #> 15: fixfactors 1 1 #> 16: histbin 1 1 graphics #> 17: ica 1 1 fastICA #> 18: imputehist 1 1 graphics #> 19: imputemean 1 1 #> 20: imputemedian 1 1 stats #> 21: imputenewlvl 1 1 #> 22: imputesample 1 1 #> 23: kernelpca 1 1 kernlab #> 24: learner 1 1 #> 25: learner_cv 1 1 #> 26: missind 1 1 #> 27: modelmatrix 1 1 stats #> 28: mutate 1 1 #> 29: nop 1 1 #> 30: pca 1 1 #> 31: quantilebin 1 1 stats #> 32: regravg NA 1 #> 33: removeconstants 1 1 #> 34: scale 1 1 #> 35: scalemaxabs 1 1 #> 36: scalerange 1 1 #> 37: select 1 1 #> 38: smote 1 1 smotefamily #> 39: spatialsign 1 1 #> 40: subsample 1 1 #> 41: unbranch NA 1 #> 42: yeojohnson 1 1 bestNormalize #> key input.num output.num packages