Linearly transforms numeric data columns so they are between lower
and upper
.
The formula for this is \(x' = a + x * b\),
where \(b\) is \((upper - lower) / (max(x) - min(x))\) and
\(a\) is \(-min(x) * b + lower\).
R6Class
object inheriting from PipeOpTaskPreprocSimple
/PipeOpTaskPreproc
/PipeOp
.
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 are inherited from PipeOpTaskPreproc
.
The output is the input Task
with scaled numeric features.
The $state
is a named list
with the $state
elements inherited from PipeOpTaskPreproc
,
as well as the two transformation parameters \(a\) and \(b\) for each numeric
feature.
The parameters are the parameters inherited from PipeOpTaskPreproc
, as well as:
lower
:: numeric(1)
Target value of smallest item of input data. Default is 0.
upper
:: numeric(1)
Target value of greatest item of input data. Default is 1.
Only methods inherited from PipeOpTaskPreprocSimple
/PipeOpTaskPreproc
/PipeOp
.
Other PipeOps: PipeOpEnsemble
,
PipeOpImpute
,
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_copy
,
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_imputehist
,
mlr_pipeops_imputemean
,
mlr_pipeops_imputemedian
,
mlr_pipeops_imputenewlvl
,
mlr_pipeops_imputesample
,
mlr_pipeops_kernelpca
,
mlr_pipeops_learner
,
mlr_pipeops_missind
,
mlr_pipeops_modelmatrix
,
mlr_pipeops_mutate
,
mlr_pipeops_nop
,
mlr_pipeops_pca
,
mlr_pipeops_quantilebin
,
mlr_pipeops_regravg
,
mlr_pipeops_removeconstants
,
mlr_pipeops_scalemaxabs
,
mlr_pipeops_scale
,
mlr_pipeops_select
,
mlr_pipeops_smote
,
mlr_pipeops_spatialsign
,
mlr_pipeops_subsample
,
mlr_pipeops_unbranch
,
mlr_pipeops_yeojohnson
,
mlr_pipeops
# NOT RUN {
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
# }
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