Copy Input Multiple Times

Copies its input outnum times. This PipeOp usually not needed, because copying happens automatically when one PipeOp is followed by multiple different PipeOps. However, when constructing big Graphs using the %>>%-operator, PipeOpCopy can be helpful to specify which PipeOp gets connected to which.

Format

R6Class object inheriting from PipeOp.

Construction

PipeOpCopy$new(outnum, id = "copy", param_vals = list())

• outnum :: numeric(1)
  Number of output channels, and therefore number of copies being made.

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

• 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

PipeOpCopy has one input channel named "input", taking any input ("*") both during training and prediction.

PipeOpCopy has multiple output channels depending on the outnum construction argument, named "output1", "output2", ... All output channels produce the object given as input ("*").

State

The $state is left empty (list()).

Parameters

PipeOpCopy has no parameters.

Internals

Note that copies are not clones, but only reference copies. This affects R6-objects: If R6 objects are copied using PipeOpCopy, they must be cloned before

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOp.

See also

https://mlr-org.com/pipeops.html

Other PipeOps: PipeOp, PipeOpEnsemble, PipeOpImpute, PipeOpTargetTrafo, PipeOpTaskPreproc, PipeOpTaskPreprocSimple, mlr_pipeops, 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_datefeatures, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, 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 Placeholder Pipeops: mlr_pipeops_nop

Examples

# The following copies the output of 'scale' automatically to both
# 'pca' and 'nop'
po("scale") %>>%
  gunion(list(
    po("pca"),
    po("nop")
  ))
#> Graph with 3 PipeOps:
#>      ID         State sccssors prdcssors
#>  <char>        <char>   <char>    <char>
#>   scale <<UNTRAINED>>  pca,nop          
#>     pca <<UNTRAINED>>              scale
#>     nop <<UNTRAINED>>              scale

# The following would not work: the '%>>%'-operator does not know
# which output to connect to which input
# > gunion(list(
# >   po("scale"),
# >   po("select")
# > )) %>>%
# >   gunion(list(
# >     po("pca"),
# >     po("nop"),
# >     po("imputemean")
# >   ))
# Instead, the 'copy' operator makes clear which output gets copied.
gunion(list(
  po("scale") %>>% mlr_pipeops$get("copy", outnum = 2),
  po("select")
)) %>>%
  gunion(list(
    po("pca"),
    po("nop"),
    po("imputemean")
  ))
#> Graph with 6 PipeOps:
#>          ID         State   sccssors prdcssors
#>      <char>        <char>     <char>    <char>
#>       scale <<UNTRAINED>>       copy          
#>      select <<UNTRAINED>> imputemean          
#>        copy <<UNTRAINED>>    pca,nop     scale
#>  imputemean <<UNTRAINED>>               select
#>         pca <<UNTRAINED>>                 copy
#>         nop <<UNTRAINED>>                 copy