# mlr_pipeops_randomprojection

##### PipeOpRandomProjection

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.

##### Format

`R6Class`

object inheriting from `PipeOpTaskPreprocSimple`

/`PipeOpTaskPreproc`

/`PipeOp`

.

##### Construction

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

Input and output channels are inherited from `PipeOpTaskPreproc`

.

The output is the input `Task`

with affected numeric features
projected onto a random subspace.

##### State

The `$state`

is a named `list`

with the `$state`

elements inherited from `PipeOpTaskPreproc`

,
as well as an element `$projection`

, a `matrix`

.

##### Parameters

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.

##### Internals

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.

##### Methods

Only methods inherited from `PipeOpTaskPreprocSimple`

/`PipeOpTaskPreproc`

/`PipeOp`

.

##### See Also

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`

##### Examples

```
# NOT RUN {
library("mlr3")
task = tsk("iris")
pop = po("randomprojection", rank = 2)
task$data()
pop$train(list(task))[[1]]$data()
pop$state
# }
```

*Documentation reproduced from package mlr3pipelines, version 0.3.0, License: LGPL-3*