mlr_pipeops_mutate: Add Features According to Expressions

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

Description

Adds features according to expressions given as formulas that may depend on values of other features. This can add new features, or can change existing features.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

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PipeOpMutate$new(id = "mutate", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with added and/or mutated features according to the mutation parameter.

State

The $state is a named list with the $state elements inherited from PipeOpTaskPreproc.

Parameters

The parameters are the parameters inherited from PipeOpTaskPreproc, as well as:

Internals

A formula created using the ~ operator always contains a reference to the environment in which the formula is created. This makes it possible to use variables in the ~-expressions that both reference either column names or variable names.

Note that the formulas in mutation are evaluated sequentially. This allows for using variables that were constructed during evaluation of a previous formula. However, if existing features are changed, precedence is given to the original ones before the newly constructed ones.

Fields

Only fields inherited from PipeOpTaskPreproc/PipeOp.

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/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_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

Examples

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library("mlr3")

constant = 1
pom = po("mutate")
pom$param_set$values$mutation = list(
  Sepal.Length_plus_constant = ~ Sepal.Length + constant,
  Sepal.Area = ~ Sepal.Width * Sepal.Length,
  Petal.Area = ~ Petal.Width * Petal.Length,
  Sepal.Area_plus_Petal.Area = ~ Sepal.Area + Petal.Area
)

pom$train(list(tsk("iris")))[[1]]$data()

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