Extracts kernel principle components from data. Only affects numerical features. See kernlab::kpca for details.

## Format

`R6Class`

object inheriting from `PipeOpTaskPreproc`

/`PipeOp`

.

## Construction

`id`

::`character(1)`

Identifier of resulting object, default`"kernelpca"`

.`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 parameters replaced by their principal components.

## State

The `$state`

is a named `list`

with the `$state`

elements inherited from `PipeOpTaskPreproc`

,
as well as the returned `S4`

object of the function `kernlab::kpca()`

.

The `@rotated`

slot of the `"kpca"`

object is overwritten with an empty matrix for memory efficiency.

The slots of the `S4`

object can be accessed by accessor function. See kernlab::kpca.

## Parameters

The parameters are the parameters inherited from `PipeOpTaskPreproc`

, as well as:

`kernel`

::`character(1)`

The standard deviations of the principal components. See`kpca()`

.`kpar`

::`list`

List of hyper-parameters that are used with the kernel function. See`kpca()`

.`features`

::`numeric(1)`

Number of principal components to return. Default 0 means that all principal components are returned. See`kpca()`

.`th`

::`numeric(1)`

The value of eigenvalue under which principal components are ignored. Default is 0.0001. See`kpca()`

.`na.action`

::`function`

Function to specify NA action. Default is`na.omit`

. See`kpca()`

.

## Internals

Uses the `kpca()`

function.

## Methods

Only methods inherited from `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_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_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("kernelpca", features = 3) # only keep top 3 components
task$data()
#> Species Petal.Length Petal.Width Sepal.Length Sepal.Width
#> 1: setosa 1.4 0.2 5.1 3.5
#> 2: setosa 1.4 0.2 4.9 3.0
#> 3: setosa 1.3 0.2 4.7 3.2
#> 4: setosa 1.5 0.2 4.6 3.1
#> 5: setosa 1.4 0.2 5.0 3.6
#> ---
#> 146: virginica 5.2 2.3 6.7 3.0
#> 147: virginica 5.0 1.9 6.3 2.5
#> 148: virginica 5.2 2.0 6.5 3.0
#> 149: virginica 5.4 2.3 6.2 3.4
#> 150: virginica 5.1 1.8 5.9 3.0
pop$train(list(task))[[1]]$data()
#> Species V1 V2 V3
#> 1: setosa -9.439059 -1.1738319 0.818082979
#> 2: setosa -9.306342 -0.8132986 -1.523991896
#> 3: setosa -9.536490 -1.3665450 -1.351184473
#> 4: setosa -9.279544 -0.8197809 -2.060504832
#> 5: setosa -9.483230 -1.3216931 0.865439972
#> ---
#> 146: virginica 6.588363 -2.4365684 0.686844853
#> 147: virginica 6.117199 0.6557377 0.003376105
#> 148: virginica 6.577817 -1.3279651 0.771331382
#> 149: virginica 6.364696 -2.1709066 0.600659654
#> 150: virginica 5.878114 1.0534310 0.323595267
```