Path Branching

Perform alternative path branching: PipeOpBranch has multiple output channels that connect to different paths in a Graph. At any time, only one of these paths will be taken for execution. At the end of the different paths, the PipeOpUnbranch PipeOp must be used to indicate the end of alternative paths.

Not to be confused with PipeOpCopy, the naming scheme is a bit unfortunate.

Format

R6Class object inheriting from PipeOp.

Construction

PipeOpBranch$new(options, id = "branch", param_vals = list())

• options :: numeric(1) | character
  If options is an integer number, it determines the number of output channels / options that are created, named output1...output<n>. The $selection parameter will then be an integer. If options is a character, it determines the names of channels directly. The $selection parameter will then be factorial.

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

• 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

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

PipeOpBranch has multiple output channels depending on the options construction argument, named "output1", "output2", ... if options is numeric, and named after each options value if options is a character. All output channels produce the object given as input ("*") or NO_OP, both during training and prediction.

State

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

Parameters

• selection :: numeric(1) | character(1)
  Selection of branching path to take. Is a ParamInt if the options parameter during construction was a numeric(1), and ranges from 1 to options. Is a ParamFct if the options parameter was a character and its possible values are the options values. Initialized to either 1 (if the options construction argument is numeric(1)) or the first element of options (if it is character).

Internals

Alternative path branching is handled by the PipeOp backend. To indicate that a path should not be taken, PipeOpBranch returns the NO_OP object on its output channel. The PipeOp handles each NO_OP input by automatically returning a NO_OP output without calling private$.train() or private$.predict(), until PipeOpUnbranch is reached. PipeOpUnbranch will then take multiple inputs, all except one of which must be a NO_OP, and forward the only non-NO_OP object on its output.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOp.

See also

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_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 Path Branching: NO_OP, filter_noop(), is_noop(), mlr_pipeops_unbranch

Examples

library("mlr3")

pca = po("pca")
nop = po("nop")
choices = c("pca", "nothing")
gr = po("branch", choices) %>>%
  gunion(list(pca, nop)) %>>%
  po("unbranch", choices)

gr$param_set$values$branch.selection = "pca"
gr$train(tsk("iris"))
#> $unbranch.output
#> 
#> ── <TaskClassif> (150x5): Iris Flowers ─────────────────────────────────────────
#> • Target: Species
#> • Target classes: setosa (33%), versicolor (33%), virginica (33%)
#> • Properties: multiclass
#> • Features (4):
#>   • dbl (4): PC1, PC2, PC3, PC4
#> 

gr$param_set$values$branch.selection = "nothing"
gr$train(tsk("iris"))
#> $unbranch.output
#> 
#> ── <TaskClassif> (150x5): Iris Flowers ─────────────────────────────────────────
#> • Target: Species
#> • Target classes: setosa (33%), versicolor (33%), virginica (33%)
#> • Properties: multiclass
#> • Features (4):
#>   • dbl (4): Petal.Length, Petal.Width, Sepal.Length, Sepal.Width
#>