PipeOp.md
In mlr3pipelines: Preprocessing Operators and Pipelines for 'mlr3'

PipeOp printer

Code
  print(PipeOpNOP$new())
Output

  -- PipeOp <nop>: not trained ---------------------------------------------------
  Values: list()

  -- Input channels: 
     name  train predict
   <char> <char>  <char>
    input      *       *

  -- Output channels: 
     name  train predict
   <char> <char>  <char>
   output      *       *

Code
  print(PipeOpDebugMulti$new(3, 4))
Output

  -- PipeOp <debug.multi>: not trained -------------------------------------------
  Values: list()

  -- Input channels: 
      name  train predict
    <char> <char>  <char>
   input_1      *       *
   input_2      *       *
   input_3      *       *

  -- Output channels: 
       name  train predict
     <char> <char>  <char>
   output_1      *       *
   output_2      *       *
   output_3      *       *
   output_4      *       *

Code
  print(PipeOpDebugMulti$new(100, 0))
Output

  -- PipeOp <debug.multi>: not trained -------------------------------------------
  Values: list()

  -- Input channels: 
      name  train predict
    <char> <char>  <char>
   input_1      *       *
   input_2      *       *
   input_3      *       *
   input_4      *       *
   input_5      *       *
  [...] (95 rows omitted)

  -- Output channels: 
      name  train predict
    <char> <char>  <char>
   output_      *       *

Code
  print(PipeOpBranch$new(c("odin", "dva", "tri")))
Output

  -- PipeOp <branch>: not trained ------------------------------------------------
  Values: selection=odin

  -- Input channels: 
     name  train predict
   <char> <char>  <char>
    input      *       *

  -- Output channels: 
     name  train predict
   <char> <char>  <char>
     odin      *       *
      dva      *       *
      tri      *       *

Code
  print(PipeOpLearner$new(mlr_learners$get("classif.debug")))
Output

  -- PipeOp <classif.debug>: not trained -----------------------------------------
  Values: list()

  -- Input channels: 
     name       train     predict
   <char>      <char>      <char>
    input TaskClassif TaskClassif

  -- Output channels: 
     name  train           predict
   <char> <char>            <char>
   output   NULL PredictionClassif