mlr_pipeops_proxy: Wrap another PipeOp or Graph as a Hyperparameter

Description Format Construction Input and Output Channels State Parameters Internals Fields Methods See Also Examples

Description

Wraps another PipeOp or Graph as determined by the content hyperparameter. Input is routed through the content and the contents' output is returned. The content hyperparameter can be changed during tuning, this is useful as an alternative to PipeOpBranch.

Format

Abstract R6Class inheriting from PipeOp.

Construction

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

Input and Output Channels

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).

State

The $state is the trained content PipeOp or Graph.

Parameters

Internals

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

Fields inherited from PipeOp.

Methods

Only methods inherited from PipeOp.

See Also

https://mlr3book.mlr-org.com/list-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_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

Examples

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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"))

# 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"))

mlr3pipelines documentation built on March 6, 2021, 1:06 a.m.