| mlr_pipeops_copy | R Documentation |
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.
R6Class object inheriting from PipeOp.
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().
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 ("*").
The $state is left empty (list()).
PipeOpCopy has no parameters.
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 beforehand.
Only fields inherited from PipeOp.
Only methods inherited from PipeOp.
https://mlr-org.com/pipeops.html
Other PipeOps:
PipeOp,
PipeOpEncodePL,
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_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 Placeholder Pipeops:
mlr_pipeops_nop
# The following copies the output of 'scale' automatically to both
# 'pca' and 'nop'
po("scale") %>>%
gunion(list(
po("pca"),
po("nop")
))
# 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") %>>% po("copy", outnum = 2),
po("select")
)) %>>%
gunion(list(
po("pca"),
po("nop"),
po("imputemean")
))
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