mlr3pipelines (version 0.3.4)

mlr_pipeops_unbranch: Unbranch Different Paths


Used to bring together different paths created by PipeOpBranch.



R6Class object inheriting from PipeOp.


PipeOpUnbranch$new(options, id = "unbranch", param_vals = list())
  • options :: numeric(1) | character If options is 0, a vararg input channel is created that can take any number of inputs. If options is a nonzero integer number, it determines the number of input channels / options that are created, named input1...input<n>. The If options is a character, it determines the names of channels directly. The difference between these three is purely cosmetic if the user chooses to produce channel names matching with the corresponding PipeOpBranch. However, it is not necessary to have matching names and the vararg option is always viable.

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

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

Input and Output

PipeOpUnbranch has multiple input channels depending on the options construction argument, named "input1", "input2", ... if options is a nonzero integer and named after each options value if options is a character; if options is 0, there is only one vararg input channel named "...". All input channels take any argument ("*") both during training and prediction.

PipeOpUnbranch has one output channel named "output", producing the only NO_OP object received as input ("*"), both during training and prediction.


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


PipeOpUnbranch has no parameters.


See PipeOpBranch Internals on how alternative path branching works.


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_multiplicityimply, 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_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson, mlr_pipeops

Other Path Branching: NO_OP, filter_noop(), is_noop(), mlr_pipeops_branch


# See PipeOpBranch for a complete branching example
pou = po("unbranch")

pou$train(list(NO_OP, NO_OP, "hello", NO_OP, NO_OP))
# }