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 # }