Project Numeric Features onto a Randomly Sampled Subspace
Source: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.
Format
R6Class
object inheriting from PipeOpTaskPreprocSimple
/PipeOpTaskPreproc
/PipeOp
.
Construction
id
::character(1)
Identifier of resulting object, default"randomprojection"
.param_vals
:: namedlist
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Defaultlist()
.
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
https://mlr-org.com/pipeops.html
Other PipeOps:
PipeOp
,
PipeOpEnsemble
,
PipeOpImpute
,
PipeOpTargetTrafo
,
PipeOpTaskPreproc
,
PipeOpTaskPreprocSimple
,
mlr_pipeops
,
mlr_pipeops_adas
,
mlr_pipeops_blsmote
,
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_encode
,
mlr_pipeops_encodeimpact
,
mlr_pipeops_encodelmer
,
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_rowapply
,
mlr_pipeops_scale
,
mlr_pipeops_scalemaxabs
,
mlr_pipeops_scalerange
,
mlr_pipeops_select
,
mlr_pipeops_smote
,
mlr_pipeops_smotenc
,
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
Examples
library("mlr3")
task = tsk("iris")
pop = po("randomprojection", rank = 2)
task$data()
#> Species Petal.Length Petal.Width Sepal.Length Sepal.Width
#> <fctr> <num> <num> <num> <num>
#> 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
#> <fctr> <num> <num>
#> 1: setosa 4.534058 -4.409578
#> 2: setosa 4.242573 -4.065779
#> 3: setosa 4.163508 -4.060085
#> 4: setosa 3.978902 -4.120237
#> 5: setosa 4.475472 -4.442770
#> ---
#> 146: virginica 3.647466 -7.827740
#> 147: virginica 3.388987 -7.181222
#> 148: virginica 3.573315 -7.696115
#> 149: virginica 3.253416 -8.079257
#> 150: virginica 3.172378 -7.417726
pop$state
#> $projection
#> PR1 PR2
#> Petal.Length -0.3810837 -0.7207785
#> Petal.Width -0.3094164 -0.2693797
#> Sepal.Length 0.8348811 -0.2540586
#> Sepal.Width 0.2490186 -0.5859754
#>
#> $dt_columns
#> [1] "Petal.Length" "Petal.Width" "Sepal.Length" "Sepal.Width"
#>
#> $affected_cols
#> [1] "Petal.Length" "Petal.Width" "Sepal.Length" "Sepal.Width"
#>
#> $intasklayout
#> Key: <id>
#> id type
#> <char> <char>
#> 1: Petal.Length numeric
#> 2: Petal.Width numeric
#> 3: Sepal.Length numeric
#> 4: Sepal.Width numeric
#>
#> $outtasklayout
#> Key: <id>
#> id type
#> <char> <char>
#> 1: PR1 numeric
#> 2: PR2 numeric
#>
#> $outtaskshell
#> Empty data.table (0 rows and 3 cols): Species,PR1,PR2
#>