mlr_pipeops: Dictionary of PipeOps

In mlr-org/mlr3pipelines: Preprocessing Operators and Pipelines for 'mlr3'

Dictionary of PipeOps

Description

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"))

# equivalent:
po("learner", learner = lrn("classif.rpart"))

# all PipeOps currently in the dictionary:
as.data.table(mlr_pipeops)[, c("key", "input.num", "output.num", "packages")]

mlr-org/mlr3pipelines documentation built on April 5, 2025, 2:56 p.m.