`R/PipeOpRandomProjection.R`

`mlr_pipeops_randomprojection.Rd`

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`

.

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_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`

library("mlr3") task = tsk("iris") pop = po("randomprojection", rank = 2) task$data() #> Species Petal.Length Petal.Width Sepal.Length Sepal.Width #> 1: setosa 1.4 0.2 5.1 3.5 #> 2: setosa 1.4 0.2 4.9 3.0 #> 3: setosa 1.3 0.2 4.7 3.2 #> 4: setosa 1.5 0.2 4.6 3.1 #> 5: setosa 1.4 0.2 5.0 3.6 #> --- #> 146: virginica 5.2 2.3 6.7 3.0 #> 147: virginica 5.0 1.9 6.3 2.5 #> 148: virginica 5.2 2.0 6.5 3.0 #> 149: virginica 5.4 2.3 6.2 3.4 #> 150: virginica 5.1 1.8 5.9 3.0 pop$train(list(task))[[1]]$data() #> Species PR1 PR2 #> 1: setosa -0.4955681 0.08462260 #> 2: setosa -0.4393064 -0.20005991 #> 3: setosa -0.4473164 0.04980026 #> 4: setosa -0.5784827 -0.11250409 #> 5: setosa -0.5324336 0.17917992 #> --- #> 146: virginica -1.1222825 -3.65834408 #> 147: virginica -1.2540461 -3.64088523 #> 148: virginica -1.3710303 -3.50942034 #> 149: virginica -1.4194810 -3.37473384 #> 150: virginica -1.5565183 -3.22398959 pop$state #> $projection #> PR1 PR2 #> Petal.Length -0.6572081 -0.6074552 #> Petal.Width 0.7071921 -0.3172657 #> Sepal.Length 0.1829506 -0.2687201 #> Sepal.Width -0.1857038 0.6768531 #> #> $dt_columns #> [1] "Petal.Length" "Petal.Width" "Sepal.Length" "Sepal.Width" #> #> $affected_cols #> [1] "Petal.Length" "Petal.Width" "Sepal.Length" "Sepal.Width" #> #> $intasklayout #> id type #> 1: Petal.Length numeric #> 2: Petal.Width numeric #> 3: Sepal.Length numeric #> 4: Sepal.Width numeric #> #> $outtasklayout #> id type #> 1: PR1 numeric #> 2: PR2 numeric #> #> $outtaskshell #> Empty data.table (0 rows and 3 cols): Species,PR1,PR2 #>