Projects numeric features onto a randomly sampled subspace. All numeric features
(or the ones selected by affect_columns) are replaced by numeric features
PR1, PR2, ... PRn
Samples with features that contain missing values result in all PR1..PRn being
NA for that sample, so it is advised to do imputation before random projections
if missing values can be expected.
R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.
PipeOpRandomProjection$new(id = "randomprojection", param_vals = list())
id :: character(1)
Identifier of resulting object, default "randomprojection".
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 affected numeric features
projected onto a random subspace.
The $state is a named list with the $state elements inherited from PipeOpTaskPreproc,
as well as an element $projection, a matrix.
The parameters are the parameters inherited from PipeOpTaskPreproc, as well as:
rank :: integer(1)
The dimension of the subspace to project onto. Initialized to 1.
If there are n (affected) numeric features in the input Task,
then $state$projection is a rank x m matrix. The output is calculated as
input %*% state$projection.
The random projection matrix is obtained through Gram-Schmidt orthogonalization from a matrix with values standard normally distributed, which gives a distribution that is rotation invariant, as per Eaton: Multivariate Statistics, A Vector Space Approach, Pg. 234.
Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.
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_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_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("randomprojection", rank = 2)
task$data()
pop$train(list(task))[[1]]$data()
pop$state
# }
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