Implicate a Multiplicity

Implicate a Multiplicity by returning the input(s) converted to a Multiplicity.

This PipeOp has multiple input channels; all inputs are collected into a Multiplicity and then are forwarded along a single edge, causing the following PipeOps to be called multiple times, once for each Multiplicity member.

Note that Multiplicity is currently an experimental features and the implementation or UI may change.

Format

R6Class object inheriting from PipeOp.

Construction

PipeOpMultiplicityImply$new(innum = 0, id = "multiplicityimply", param_vals = list())

• innum :: numeric(1) | character
  Determines the number of input channels. If innum is 0 (default), a vararg input channel is created that can take an arbitrary number of inputs. If innum is a character vector, the number of input channels is the length of innum.

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

• 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

PipeOpMultiplicityImply has multiple input channels depending on the innum construction argument, named "input1", "input2", ... if innum is nonzero; if innum is 0, there is only one vararg input channel named "...". All input channels take any input ("*") both during training and prediction.

PipeOpMultiplicityImply has one output channel named "output", emitting a Multiplicity of type any ("[*]"), i.e., returning the input(s) converted to a Multiplicity both during training and prediction.

State

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

Parameters

PipeOpMultiplicityImply has no Parameters.

Internals

If innum is not numeric, e.g., a character, the output Multiplicity will be named based on the input channel names

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOp.

See also

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

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_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_unbranch, mlr_pipeops_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson, mlr_pipeops

Other Multiplicity PipeOps: Multiplicity(), PipeOpEnsemble, mlr_pipeops_classifavg, mlr_pipeops_featureunion, mlr_pipeops_multiplicityexply, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_regravg, mlr_pipeops_replicate

Other Experimental Features: Multiplicity(), mlr_pipeops_multiplicityexply, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_replicate

Examples

library("mlr3")
task1 = tsk("iris")
task2 = tsk("mtcars")
po = po("multiplicityimply")
po$train(list(task1, task2))
#> $output
#> Multiplicity:
#> [[1]]
#> <TaskClassif:iris> (150 x 5): Iris Flowers
#> * Target: Species
#> * Properties: multiclass
#> * Features (4):
#>   - dbl (4): Petal.Length, Petal.Width, Sepal.Length, Sepal.Width
#> 
#> [[2]]
#> <TaskRegr:mtcars> (32 x 11): Motor Trends
#> * Target: mpg
#> * Properties: -
#> * Features (10):
#>   - dbl (10): am, carb, cyl, disp, drat, gear, hp, qsec, vs, wt
#> 
#> 
po$predict(list(task1, task2))
#> $output
#> Multiplicity:
#> [[1]]
#> <TaskClassif:iris> (150 x 5): Iris Flowers
#> * Target: Species
#> * Properties: multiclass
#> * Features (4):
#>   - dbl (4): Petal.Length, Petal.Width, Sepal.Length, Sepal.Width
#> 
#> [[2]]
#> <TaskRegr:mtcars> (32 x 11): Motor Trends
#> * Target: mpg
#> * Properties: -
#> * Features (10):
#>   - dbl (10): am, carb, cyl, disp, drat, gear, hp, qsec, vs, wt
#> 
#>