Linearly Transform a Numeric Target to Match Given Boundaries

Linearly transforms a numeric target of a TaskRegr so it is 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 PipeOpTargetTrafo/PipeOp

Construction

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

• 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 PipeOpTargetTrafo.

State

The $state is a named list containing the slots $offset and $scale.

Parameters

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

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

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

Internals

Overloads PipeOpTargetTrafo's .get_state(), .transform(), and .invert(). Should be used in combination with PipeOpTargetInvert.

Methods

Only methods inherited from PipeOpTargetTrafo/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_scalerange, mlr_pipeops_scale, mlr_pipeops_select, mlr_pipeops_smote, mlr_pipeops_spatialsign, mlr_pipeops_subsample, mlr_pipeops_targetinvert, mlr_pipeops_targetmutate, 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("boston_housing")
po = PipeOpTargetTrafoScaleRange$new()

po$train(list(task))
#> $fun
#> NULL
#> 
#> $output
#> <TaskRegr:boston_housing> (506 x 19): Boston Housing Prices
#> * Target: medv.scaled
#> * Properties: -
#> * Features (18):
#>   - dbl (13): age, b, cmedv, crim, dis, indus, lat, lon, lstat, nox,
#>     ptratio, rm, zn
#>   - int (3): rad, tax, tract
#>   - fct (2): chas, town
#> 
po$predict(list(task))
#> $fun
#> function (inputs) 
#> {
#>     assert_list(inputs, len = 1L, types = "Prediction")
#>     list(private$.invert(inputs[[1L]], predict_phase_state))
#> }
#> <bytecode: 0x5584633360a0>
#> <environment: 0x558469c15628>
#> 
#> $output
#> <TaskRegr:boston_housing> (506 x 19): Boston Housing Prices
#> * Target: medv.scaled
#> * Properties: -
#> * Features (18):
#>   - dbl (13): age, b, cmedv, crim, dis, indus, lat, lon, lstat, nox,
#>     ptratio, rm, zn
#>   - int (3): rad, tax, tract
#>   - fct (2): chas, town
#> 

#syntactic sugar for a graph using ppl():
ttscalerange = ppl("targettrafo", trafo_pipeop = PipeOpTargetTrafoScaleRange$new(),
  graph = PipeOpLearner$new(LearnerRegrRpart$new()))
ttscalerange$train(task)
#> $targetinvert.output
#> NULL
#> 
ttscalerange$predict(task)
#> $targetinvert.output
#> <PredictionRegr> for 506 observations:
#>     row_ids truth response
#>           1  24.0 23.72519
#>           2  21.6 19.55954
#>           3  34.7 35.10312
#> ---                       
#>         504  23.9 23.72519
#>         505  22.0 23.72519
#>         506  11.9 19.55954
#> 
ttscalerange$state$regr.rpart
#> $model
#> n= 506 
#> 
#> node), split, n, deviance, yval
#>       * denotes terminal node
#> 
#>  1) root 506 21.09447000 0.38961790  
#>    2) cmedv< 27.7 400  4.76453900 0.30601110  
#>      4) cmedv< 17.35 134  0.62918150 0.17794360  
#>        8) cmedv< 11.95 42  0.07619577 0.09190476 *
#>        9) cmedv>=11.95 92  0.10013460 0.21722220 *
#>      5) cmedv>=17.35 266  0.83043740 0.37052630  
#>       10) cmedv< 21.65 131  0.11194840 0.32354540 *
#>       11) cmedv>=21.65 135  0.14876760 0.41611520 *
#>    3) cmedv>=27.7 106  2.98279700 0.70511530  
#>      6) cmedv< 39.25 74  0.31924910 0.60438440  
#>       12) cmedv< 32.6 42  0.03997942 0.55518520 *
#>       13) cmedv>=32.6 32  0.04417269 0.66895830 *
#>      7) cmedv>=39.25 32  0.17633330 0.93805560 *
#> 
#> $log
#> Empty data.table (0 rows and 3 cols): stage,class,msg
#> 
#> $train_time
#> [1] 0.01
#> 
#> $param_vals
#> $param_vals$xval
#> [1] 0
#> 
#> 
#> $task_hash
#> [1] "5ea30a3b95c53aeb"
#> 
#> $data_prototype
#> Empty data.table (0 rows and 19 cols): medv.scaled,age,b,chas,cmedv,crim...
#> 
#> $task_prototype
#> Empty data.table (0 rows and 19 cols): medv.scaled,age,b,chas,cmedv,crim...
#> 
#> $mlr3_version
#> [1] ‘0.13.3’
#> 
#> $train_task
#> <TaskRegr:boston_housing> (506 x 19): Boston Housing Prices
#> * Target: medv.scaled
#> * Properties: -
#> * Features (18):
#>   - dbl (13): age, b, cmedv, crim, dis, indus, lat, lon, lstat, nox,
#>     ptratio, rm, zn
#>   - int (3): rad, tax, tract
#>   - fct (2): chas, town
#>