mlr_pipeops_scalerange: Linearly Transform Numeric Features to Match Given Boundaries

mlr_pipeops_scalerangeR Documentation

Linearly Transform Numeric Features to Match Given Boundaries

Description

Linearly transforms numeric data columns so they are between lower and upper. The formula for this is x' = offset + x * scale, where scale is (upper - lower) / (max(x) - min(x)) and offset is -min(x) * scale + lower. The same transformation is applied during training and prediction.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpScaleRange$new(id = "scalerange", param_vals = list())
  • id :: character(1)
    Identifier of resulting object, default "scalerange".

  • param_vals :: named list
    List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with scaled numeric features.

State

The $state is a named list with the $state elements inherited from PipeOpTaskPreproc, as well as the two transformation parameters scale and offset for each numeric feature.

Parameters

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

  • lower :: numeric(1)
    Target value of smallest item of input data. Initialized to 0.

  • upper :: numeric(1)
    Target value of greatest item of input data. Initialized to 1.

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_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_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

library("mlr3")

task = tsk("iris")
pop = po("scalerange", param_vals = list(lower = -1, upper = 1))

task$data()
pop$train(list(task))[[1]]$data()

pop$state

mlr3pipelines documentation built on Sept. 21, 2022, 9:09 a.m.