Normalize Data Row-wise

Normalizes the data row-wise. This is a natural generalization of the "sign" function to higher dimensions.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpSpatialSign$new(id = "spatialsign", param_vals = list())

• id :: character(1)
  Identifier of resulting object, default "spatialsign".

• 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 all affected numeric features replaced by their normalized versions.

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:

• length :: numeric(1)
  Length to scale rows to. Default is 1.

• norm :: numeric(1)
  Norm to use. Rows are scaled to sum(x^norm)^(1/norm) == length for finite norm, or to max(abs(x)) == length if norm is Inf. Default is 2.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See also

https://mlr-org.com/pipeops.html

Other PipeOps: PipeOp, PipeOpEncodePL, 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_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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_rowapply, mlr_pipeops_scale, mlr_pipeops_scalemaxabs, mlr_pipeops_scalerange, mlr_pipeops_select, mlr_pipeops_smote, mlr_pipeops_smotenc, 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

Examples

library("mlr3")

task = tsk("iris")

task$data()
#>        Species Petal.Length Petal.Width Sepal.Length Sepal.Width
#>         <fctr>        <num>       <num>        <num>       <num>
#>   1:    setosa          1.4         0.2          5.1         3.5
#>   2:    setosa          1.4         0.2          4.9         3.0
#>   3:    setosa          1.3         0.2          4.7         3.2
#>   4:    setosa          1.5         0.2          4.6         3.1
#>   5:    setosa          1.4         0.2          5.0         3.6
#>  ---                                                            
#> 146: virginica          5.2         2.3          6.7         3.0
#> 147: virginica          5.0         1.9          6.3         2.5
#> 148: virginica          5.2         2.0          6.5         3.0
#> 149: virginica          5.4         2.3          6.2         3.4
#> 150: virginica          5.1         1.8          5.9         3.0

pop = po("spatialsign")

pop$train(list(task))[[1]]$data()
#>        Species Petal.Length Petal.Width Sepal.Length Sepal.Width
#>         <fctr>        <num>       <num>        <num>       <num>
#>   1:    setosa    0.2206435  0.03152050    0.8037728   0.5516088
#>   2:    setosa    0.2366094  0.03380134    0.8281329   0.5070201
#>   3:    setosa    0.2227517  0.03426949    0.8053331   0.5483119
#>   4:    setosa    0.2608794  0.03478392    0.8000302   0.5391508
#>   5:    setosa    0.2214702  0.03163860    0.7909650   0.5694948
#>  ---                                                            
#> 146: virginica    0.5600146  0.24769876    0.7215572   0.3230853
#> 147: virginica    0.5790902  0.22005426    0.7296536   0.2895451
#> 148: virginica    0.5732312  0.22047353    0.7165390   0.3307103
#> 149: virginica    0.5876164  0.25028107    0.6746707   0.3699807
#> 150: virginica    0.5966647  0.21058754    0.6902592   0.3509792