mlr_pipeops_multiplicityimply: Implicate a Multiplicity

mlr_pipeops_multiplicityimplyR Documentation

Implicate a Multiplicity


Implicate a Multiplicity by returning the input(s) converted to a Multiplicity.

This PipeOp has multiple input channels; all inputs are collected into a Multiplicity and then are forwarded along a single edge, causing the following PipeOps to be called multiple times, once for each Multiplicity member.

Note that Multiplicity is currently an experimental features and the implementation or UI may change.


R6Class object inheriting from PipeOp.


PipeOpMultiplicityImply$new(innum = 0, id = "multiplicityimply", param_vals = list())
  • innum :: numeric(1) | character
    Determines the number of input channels. If innum is 0 (default), a vararg input channel is created that can take an arbitrary number of inputs. If innum is a character vector, the number of input channels is the length of innum.

  • id :: character(1)
    Identifier of the resulting object, default "multiplicityimply".

  • param_vals :: named list
    List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().

Input and Output Channels

PipeOpMultiplicityImply has multiple input channels depending on the innum construction argument, named "input1", "input2", ... if innum is nonzero; if innum is 0, there is only one vararg input channel named "...". All input channels take any input ("*") both during training and prediction.

PipeOpMultiplicityImply has one output channel named "output", emitting a Multiplicity of type any ("[*]"), i.e., returning the input(s) converted to a Multiplicity both during training and prediction.


The ⁠$state⁠ is left empty (list()).


PipeOpMultiplicityImply has no Parameters.


If innum is not numeric, e.g., a character, the output Multiplicity will be named based on the input channel names


Only fields inherited from PipeOp.


Only methods inherited from PipeOp.

See Also

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_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, mlr_pipeops

Other Multiplicity PipeOps: Multiplicity(), PipeOpEnsemble, mlr_pipeops_classifavg, mlr_pipeops_featureunion, mlr_pipeops_multiplicityexply, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_regravg, mlr_pipeops_replicate

Other Experimental Features: Multiplicity(), mlr_pipeops_multiplicityexply, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_replicate


task1 = tsk("iris")
task2 = tsk("mtcars")
po = po("multiplicityimply")
po$train(list(task1, task2))
po$predict(list(task1, task2))

mlr3pipelines documentation built on May 31, 2023, 9:26 p.m.