mlr_pipeops_multiplicityexply | R Documentation |
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.
R6Class
object inheriting from PipeOp
.
PipeOpMultiplicityExply$new(outnum , id = "multiplicityexply", param_vals = list())
outnum
:: numeric(1)
| character
Determines the number of output channels.
id
:: character(1)
Identifier of the resulting object, default "multiplicityexply"
.
param_vals
:: named list
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise
be set during construction. Default list()
.
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
.
The $state
is left empty (list()
).
PipeOpMultiplicityExply
has no Parameters.
outnum
should match the number of elements of the unclassed input Multiplicity
.
Only fields inherited from PipeOp
.
Only methods inherited from PipeOp
.
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_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_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_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
library("mlr3")
task1 = tsk("iris")
task2 = tsk("mtcars")
po = po("multiplicityexply", outnum = 2)
po$train(list(Multiplicity(task1, task2)))
po$predict(list(Multiplicity(task1, task2)))
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