Explicate a Multiplicity by turning the input Multiplicity into multiple outputs.
This PipeOp has multiple output channels; the members of the input Multiplicity
are forwarded each along a single edge. Therefore, only multiplicities with exactly as many
members as outnum are accepted.
Note that Multiplicity is currently an experimental features and the implementation or UI
may change.
Construction
outnum::numeric(1)|character
Determines the number of output channels.id::character(1)
Identifier of the resulting object, default"multiplicityexply".param_vals:: namedlist
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Defaultlist().
Input and Output Channels
PipeOpMultiplicityExply has a single input channel named "input", collecting a
Multiplicity of type any ("[*]") both during training and prediction.
PipeOpMultiplicityExply has multiple output channels depending on the outnum construction
argument, named "output1", "output2" returning the elements of the unclassed input
Multiplicity.
State
The $state is left empty (list()).
Internals
outnum should match the number of elements of the unclassed input Multiplicity.
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_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_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_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 Multiplicity PipeOps:
Multiplicity(),
PipeOpEnsemble,
mlr_pipeops_classifavg,
mlr_pipeops_featureunion,
mlr_pipeops_multiplicityimply,
mlr_pipeops_ovrsplit,
mlr_pipeops_ovrunite,
mlr_pipeops_regravg,
mlr_pipeops_replicate
Other Experimental Features:
Multiplicity(),
mlr_pipeops_multiplicityimply,
mlr_pipeops_ovrsplit,
mlr_pipeops_ovrunite,
mlr_pipeops_replicate
Examples
library("mlr3")
task1 = tsk("iris")
task2 = tsk("mtcars")
po = po("multiplicityexply", outnum = 2)
po$train(list(Multiplicity(task1, task2)))
#> $output1
#>
#> ── <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
#>
#> $output2
#>
#> ── <TaskRegr> (32x11): Motor Trends ────────────────────────────────────────────
#> • Target: mpg
#> • Properties: -
#> • Features (10):
#> • dbl (10): am, carb, cyl, disp, drat, gear, hp, qsec, vs, wt
#>
po$predict(list(Multiplicity(task1, task2)))
#> $output1
#>
#> ── <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
#>
#> $output2
#>
#> ── <TaskRegr> (32x11): Motor Trends ────────────────────────────────────────────
#> • Target: mpg
#> • Properties: -
#> • Features (10):
#> • dbl (10): am, carb, cyl, disp, drat, gear, hp, qsec, vs, wt
#>