Simply pushes the input forward.
Can be useful during Graph construction using the %>>%-operator to specify which PipeOp gets connected to which.
Construction
id::character(1)
Identifier of resulting object, default"nop".param_vals:: namedlist
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Defaultlist().
Input and Output Channels
PipeOpNOP has one input channel named "input", taking any input ("*") both during training and prediction.
PipeOpNOP has one output channel named "output", producing the object given as input ("*") without changes.
State
The $state is left empty (list()).
Fields
Only fields inherited from PipeOp.
Methods
Only methods inherited from PipeOp.
See also
https://mlr-org.com/pipeops.html
Other PipeOps:
PipeOp,
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_encode,
mlr_pipeops_encodeimpact,
mlr_pipeops_encodelmer,
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_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 Placeholder Pipeops:
mlr_pipeops_copy
Examples
library("mlr3")
nop = po("nop")
nop$train(list(1))
#> $output
#> [1] 1
#>
# use `gunion` and `%>>%` to create a "bypass"
# next to "pca"
gr = gunion(list(
po("pca"),
nop
)) %>>% po("featureunion")
gr$train(tsk("iris"))[[1]]$data()
#> Species PC1 PC2 PC3 PC4 Petal.Length
#> <fctr> <num> <num> <num> <num> <num>
#> 1: setosa -2.684126 -0.31939725 0.02791483 -0.002262437 1.4
#> 2: setosa -2.714142 0.17700123 0.21046427 -0.099026550 1.4
#> 3: setosa -2.888991 0.14494943 -0.01790026 -0.019968390 1.3
#> 4: setosa -2.745343 0.31829898 -0.03155937 0.075575817 1.5
#> 5: setosa -2.728717 -0.32675451 -0.09007924 0.061258593 1.4
#> ---
#> 146: virginica 1.944110 -0.18753230 -0.17782509 -0.426195940 5.2
#> 147: virginica 1.527167 0.37531698 0.12189817 -0.254367442 5.0
#> 148: virginica 1.764346 -0.07885885 -0.13048163 -0.137001274 5.2
#> 149: virginica 1.900942 -0.11662796 -0.72325156 -0.044595305 5.4
#> 150: virginica 1.390189 0.28266094 -0.36290965 0.155038628 5.1
#> Petal.Width Sepal.Length Sepal.Width
#> <num> <num> <num>
#> 1: 0.2 5.1 3.5
#> 2: 0.2 4.9 3.0
#> 3: 0.2 4.7 3.2
#> 4: 0.2 4.6 3.1
#> 5: 0.2 5.0 3.6
#> ---
#> 146: 2.3 6.7 3.0
#> 147: 1.9 6.3 2.5
#> 148: 2.0 6.5 3.0
#> 149: 2.3 6.2 3.4
#> 150: 1.8 5.9 3.0