mlr_pipeops_kernelpca | R Documentation |
Extracts kernel principle components from data. Only affects numerical features. See kernlab::kpca for details.
R6Class
object inheriting from PipeOpTaskPreproc
/PipeOp
.
PipeOpKernelPCA$new(id = "kernelpca", param_vals = list())
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 are inherited from PipeOpTaskPreproc
.
The output is the input Task
with all affected numeric parameters replaced by their principal components.
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.
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()
.
Uses the kpca()
function.
Only methods inherited from PipeOpTaskPreproc
/PipeOp
.
https://mlr-org.com/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
library("mlr3")
task = tsk("iris")
pop = po("kernelpca", features = 3) # only keep top 3 components
task$data()
pop$train(list(task))[[1]]$data()
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