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:
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://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_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_targettrafoscalerange,
mlr_pipeops_textvectorizer,
mlr_pipeops_threshold,
mlr_pipeops_tunethreshold,
mlr_pipeops_unbranch,
mlr_pipeops_updatetarget,
mlr_pipeops_vtreat,
mlr_pipeops_yeojohnson,
mlr_pipeops
# NOT RUN {
library("mlr3")
task = tsk("iris")
pop = po("ica")
task$data()
pop$train(list(task))[[1]]$data()
pop$state
# }
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