Wraps another `PipeOp`

or `Graph`

as determined by the `content`

hyperparameter.
Input is routed through the `content`

and the `content`

s' output is returned.
The `content`

hyperparameter can be changed during tuning, this is useful as an alternative to `PipeOpBranch`

.

Abstract `R6Class`

inheriting from `PipeOp`

.

PipeOpProxy$new(innum = 0, outnum = 1, id = "proxy", param_vals = list())

`innum`

::`numeric(1)\cr 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.`outnum`

:: `numeric(1)

Determines the number of output channels.`id`

::`character(1)`

Identifier of resulting object. See`$id`

slot of`PipeOp`

.`param_vals`

:: named`list`

List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default`list()`

.

`PipeOpProxy`

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 `"..."`

.

`PipeOpProxy`

has multiple output channels depending on the `outnum`

construction argument,
named `"output1"`

, `"output2"`

, ...
The output is determined by the output of the `content`

operation (a `PipeOp`

or `Graph`

).

The `$state`

is the trained `content`

`PipeOp`

or `Graph`

.

`content`

::`PipeOp`

|`Graph`

The`PipeOp`

or`Graph`

that is being proxied (or an object that is converted to a`Graph`

by`as_graph()`

). Defaults to an instance of`PipeOpFeatureUnion`

(combines all input if they are`Task`

s).

The `content`

will internally be coerced to a graph via
`as_graph()`

prior to train and predict.

The default value for `content`

is `PipeOpFeatureUnion`

,

Fields inherited from `PipeOp`

.

Only methods inherited from `PipeOp`

.

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_multiplicityimply`

,
`mlr_pipeops_mutate`

,
`mlr_pipeops_nmf`

,
`mlr_pipeops_nop`

,
`mlr_pipeops_ovrsplit`

,
`mlr_pipeops_ovrunite`

,
`mlr_pipeops_pca`

,
`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`

library("mlr3") library("mlr3learners") set.seed(1234) task = tsk("iris") # use a proxy for preprocessing and a proxy for learning, i.e., # no preprocessing and classif.kknn g = po("proxy", id = "preproc", param_vals = list(content = po("nop"))) %>>% po("proxy", id = "learner", param_vals = list(content = lrn("classif.kknn"))) rr_kknn = resample(task, learner = GraphLearner$new(g), resampling = rsmp("cv", folds = 3)) rr_kknn$aggregate(msr("classif.ce"))#> classif.ce #> 0.05333333# use pca for preprocessing and classif.rpart as the learner g$param_set$values$preproc.content = po("pca") g$param_set$values$learner.content = lrn("classif.rpart") rr_pca_rpart = resample(task, learner = GraphLearner$new(g), resampling = rsmp("cv", folds = 3)) rr_pca_rpart$aggregate(msr("classif.ce"))#> classif.ce #> 0.06