mlr_pipeops_ica | R Documentation |
Extracts statistically independent components from data. Only affects numerical features. See fastICA::fastICA for details.
R6Class
object inheriting from PipeOpTaskPreproc
/PipeOp
.
PipeOpICA$new(id = "ica", param_vals = list())
id
:: character(1)
Identifier of resulting object, default "ica"
.
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 independent components.
The $state
is a named list
with the $state
elements inherited from PipeOpTaskPreproc
, as well as the elements of the function fastICA::fastICA()
,
with the exception of the $X
and $S
slots. These are in particular:
K
:: matrix
Matrix that projects data onto the first n.comp
principal components.
See fastICA()
.
W
:: matrix
Estimated un-mixing matrix. See fastICA()
.
A
:: matrix
Estimated mixing matrix. See fastICA()
.
center
:: numeric
The mean of each numeric feature during training.
The parameters are the parameters inherited from PipeOpTaskPreproc
, as well as the following parameters
based on fastICA()
:
n.comp
:: numeric(1)
Number of components to extract. Default is NULL
, which sets it
to the number of available numeric columns.
alg.typ
:: character(1)
Algorithm type. One of "parallel" (default) or "deflation".
fun
:: character(1)
One of "logcosh" (default) or "exp".
alpha
:: numeric(1)
In range [1, 2]
, Used for negentropy calculation when fun
is "logcosh".
Default is 1.0.
method
:: character(1)
Internal calculation method. "C" (default) or "R".
See fastICA()
.
row.norm
:: logical(1)
Logical value indicating whether rows should be standardized beforehand.
Default is FALSE
.
maxit
:: numeric(1)
Maximum number of iterations. Default is 200.
tol
:: numeric(1)
Tolerance for convergence, default is 1e-4
.
verbose
logical(1)
Logical value indicating the level of output during the run of the algorithm.
Default is FALSE
.
w.init
:: matrix
Initial un-mixing matrix. See fastICA()
.
Default is NULL
.
Uses the fastICA()
function.
Only methods inherited from PipeOpTaskPreproc
/PipeOp
.
https://mlr-org.com/pipeops.html
Other PipeOps:
PipeOp
,
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_encode
,
mlr_pipeops_encodeimpact
,
mlr_pipeops_encodelmer
,
mlr_pipeops_featureunion
,
mlr_pipeops_filter
,
mlr_pipeops_fixfactors
,
mlr_pipeops_histbin
,
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_rowapply
,
mlr_pipeops_scale
,
mlr_pipeops_scalemaxabs
,
mlr_pipeops_scalerange
,
mlr_pipeops_select
,
mlr_pipeops_smote
,
mlr_pipeops_smotenc
,
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
library("mlr3")
task = tsk("iris")
pop = po("ica")
task$data()
pop$train(list(task))[[1]]$data()
pop$state
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