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Dictionary of PipeOps

Source: R/mlr_pipeops.R
mlr_pipeops.Rd

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 the following columns:

    • key :: (character)
      Key with which the PipeOp was registered to the Dictionary using the $add() method.

    • label :: (character)
      Description of the PipeOp's functionality.

    • packages :: (character)
      Set of all required packages for the PipeOp's train and predict methods.

    • tags :: (character)
      A set of tags associated with the PipeOp describing its purpose.

    • feature_types :: (character)
      Feature types the PipeOp operates on. Is NA for PipeOps that do not directly operate on a Task.

    • input.num, output.num :: (integer)
      Number of the PipeOp's input and output channels. Is NA for PipeOps which accept a varying number of input and/or output channels depending a construction argument. See input and output fields of PipeOp.

    • input.type.train, input.type.predict, output.type.train, output.type.predict :: (character)
      Types that are allowed as input to or returned as output of the PipeOp's $train() and $predict() methods.
      A value of NULL means that a null object, e.g. no data, is taken as input or being returned as output. A value of "*" means that any type is possible.
      If both input.type.train and output.type.train or both input.type.predict and output.type.predict contain values enclosed by square brackets ("[", "]"), then the respective input or channel is Multiplicity-aware. For more information, see Multiplicity.

See also

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

Other PipeOps: PipeOp, PipeOpEncodePL, PipeOpEnsemble, PipeOpImpute, PipeOpTargetTrafo, PipeOpTaskPreproc, PipeOpTaskPreprocSimple, mlr_pipeops_adas, mlr_pipeops_blsmote, 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_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_learner_pi_cvplus, mlr_pipeops_learner_quantiles, mlr_pipeops_missind, mlr_pipeops_modelmatrix, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_mutate, mlr_pipeops_nearmiss, 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_rowapply, mlr_pipeops_scale, mlr_pipeops_scalemaxabs, mlr_pipeops_scalerange, mlr_pipeops_select, mlr_pipeops_smote, mlr_pipeops_smotenc, mlr_pipeops_spatialsign, mlr_pipeops_subsample, mlr_pipeops_targetinvert, mlr_pipeops_targetmutate, mlr_pipeops_targettrafoscalerange, mlr_pipeops_textvectorizer, mlr_pipeops_threshold, mlr_pipeops_tomek, 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: <key>
#>                       key input.num output.num                         packages
#>                    <char>     <int>      <int>                           <list>
#>  1:                  adas         1          1        mlr3pipelines,smotefamily
#>  2:               blsmote         1          1        mlr3pipelines,smotefamily
#>  3:                boxcox         1          1      mlr3pipelines,bestNormalize
#>  4:                branch         1         NA                    mlr3pipelines
#>  5:                 chunk         1         NA                    mlr3pipelines
#>  6:        classbalancing         1          1                    mlr3pipelines
#>  7:            classifavg        NA          1              mlr3pipelines,stats
#>  8:          classweights         1          1                    mlr3pipelines
#>  9:              colapply         1          1                    mlr3pipelines
#> 10:       collapsefactors         1          1                    mlr3pipelines
#> 11:              colroles         1          1                    mlr3pipelines
#> 12:                  copy         1         NA                    mlr3pipelines
#> 13:          datefeatures         1          1                    mlr3pipelines
#> 14:                decode         1          1                    mlr3pipelines
#> 15:                encode         1          1              mlr3pipelines,stats
#> 16:          encodeimpact         1          1                    mlr3pipelines
#> 17:            encodelmer         1          1        mlr3pipelines,lme4,nloptr
#> 18:     encodeplquantiles         1          1              mlr3pipelines,stats
#> 19:          encodepltree         1          1         mlr3pipelines,mlr3,rpart
#> 20:          featureunion        NA          1                    mlr3pipelines
#> 21:                filter         1          1                    mlr3pipelines
#> 22:            fixfactors         1          1                    mlr3pipelines
#> 23:               histbin         1          1           mlr3pipelines,graphics
#> 24:                   ica         1          1            mlr3pipelines,fastICA
#> 25:        imputeconstant         1          1                    mlr3pipelines
#> 26:            imputehist         1          1           mlr3pipelines,graphics
#> 27:         imputelearner         1          1                    mlr3pipelines
#> 28:            imputemean         1          1                    mlr3pipelines
#> 29:          imputemedian         1          1              mlr3pipelines,stats
#> 30:            imputemode         1          1                    mlr3pipelines
#> 31:             imputeoor         1          1                    mlr3pipelines
#> 32:          imputesample         1          1                    mlr3pipelines
#> 33:             kernelpca         1          1            mlr3pipelines,kernlab
#> 34:               learner         1          1                    mlr3pipelines
#> 35:            learner_cv         1          1                    mlr3pipelines
#> 36:     learner_pi_cvplus         1          1                    mlr3pipelines
#> 37:     learner_quantiles         1          1                    mlr3pipelines
#> 38:               missind         1          1                    mlr3pipelines
#> 39:           modelmatrix         1          1              mlr3pipelines,stats
#> 40:     multiplicityexply         1         NA                    mlr3pipelines
#> 41:     multiplicityimply        NA          1                    mlr3pipelines
#> 42:                mutate         1          1                    mlr3pipelines
#> 43:              nearmiss         1          1             mlr3pipelines,themis
#> 44:                   nmf         1          1           mlr3pipelines,MASS,NMF
#> 45:                   nop         1          1                    mlr3pipelines
#> 46:              ovrsplit         1          1                    mlr3pipelines
#> 47:              ovrunite         1          1                    mlr3pipelines
#> 48:                   pca         1          1                    mlr3pipelines
#> 49:                 proxy        NA          1                    mlr3pipelines
#> 50:           quantilebin         1          1              mlr3pipelines,stats
#> 51:      randomprojection         1          1                    mlr3pipelines
#> 52:        randomresponse         1          1                    mlr3pipelines
#> 53:               regravg        NA          1                    mlr3pipelines
#> 54:       removeconstants         1          1                    mlr3pipelines
#> 55:         renamecolumns         1          1                    mlr3pipelines
#> 56:             replicate         1          1                    mlr3pipelines
#> 57:              rowapply         1          1                    mlr3pipelines
#> 58:                 scale         1          1                    mlr3pipelines
#> 59:           scalemaxabs         1          1                    mlr3pipelines
#> 60:            scalerange         1          1                    mlr3pipelines
#> 61:                select         1          1                    mlr3pipelines
#> 62:                 smote         1          1        mlr3pipelines,smotefamily
#> 63:               smotenc         1          1             mlr3pipelines,themis
#> 64:           spatialsign         1          1                    mlr3pipelines
#> 65:             subsample         1          1                    mlr3pipelines
#> 66:          targetinvert         2          1                    mlr3pipelines
#> 67:          targetmutate         1          2                    mlr3pipelines
#> 68: targettrafoscalerange         1          2                    mlr3pipelines
#> 69:        textvectorizer         1          1 mlr3pipelines,quanteda,stopwords
#> 70:             threshold         1          1                    mlr3pipelines
#> 71:                 tomek         1          1             mlr3pipelines,themis
#> 72:         tunethreshold         1          1              mlr3pipelines,bbotk
#> 73:              unbranch        NA          1                    mlr3pipelines
#> 74:                vtreat         1          1             mlr3pipelines,vtreat
#> 75:            yeojohnson         1          1      mlr3pipelines,bestNormalize
#>                       key input.num output.num                         packages