Extracts non-negative components from data by performing non-negative matrix factorization. Only
affects non-negative numerical features. See `nmf()`

for details.

## Format

`R6Class`

object inheriting from `PipeOpTaskPreproc`

/`PipeOp`

.

## Construction

`id`

::`character(1)`

Identifier of resulting object, default`"nmf"`

.`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 all affected numeric features replaced by their
non-negative components.

## State

The `$state`

is a named `list`

with the `$state`

elements inherited from `PipeOpTaskPreproc`

,
as well as the elements of the object returned by `nmf()`

.

## Parameters

The parameters are the parameters inherited from `PipeOpTaskPreproc`

, as well as:

`rank`

::`integer(1)`

Factorization rank, i.e., number of components. Initialized to`2`

. See`nmf()`

.`method`

::`character(1)`

Specification of the NMF algorithm. Initialized to`"brunet"`

. See`nmf()`

.`seed`

::`character(1)`

|`integer(1)`

|`list()`

| object of class`NMF`

|`function()`

Specification of the starting point. See`nmf()`

.`nrun`

::`integer(1)`

Number of runs to performs. Default is`1`

. More than a single run allows for the computation of a consensus matrix which will also be stored in the`$state`

. See`nmf()`

.`debug`

::`logical(1)`

Whether to toggle debug mode. Default is`FALSE`

. See`nmf()`

.`keep.all`

::`logical(1)`

Whether all factorizations are to be saved and returned. Default is`FALSE`

. Only has an effect if`nrun > 1`

. See`nmf()`

.`parallel`

::`character(1)`

|`integer(1)`

|`logical(1)`

Specification of parallel handling if`nrun > 1`

. Initialized to`FALSE`

, as it is recommended to use`mlr3`

's`future`

-based parallelization. See`nmf()`

.`parallel.required`

::`character(1)`

|`integer(1)`

|`logical(1)`

Same as`parallel`

, but an error is thrown if the computation cannot be performed in parallel or with the specified number of processors. Initialized to`FALSE`

, as it is recommended to use`mlr3`

's`future`

-based parallelization. See`nmf()`

.`shared.memory`

::`logical(1)`

Whether shared memory should be enabled. See`nmf()`

.`simplifyCB`

::`logical(1)`

Whether callback results should be simplified. Default is`TRUE`

. See`nmf()`

.`track`

::`logical(1)`

Whether error tracking should be enabled. Default is`FALSE`

. See`nmf()`

.`verbose`

::`integer(1)`

|`logical(1)`

Specification of verbosity. Default is`FALSE`

. See`nmf()`

.`pbackend`

::`character(1)`

|`integer(1)`

|`NULL`

Specification of the parallel backend. It is recommended to use`mlr3`

's`future`

-based parallelization. See`nmf()`

.`callback`

|`function()`

Callback function that is called after each run (if`nrun > 1`

). See`nmf()`

.

## Methods

Only methods inherited from `PipeOpTaskPreproc`

/`PipeOp`

.

## See also

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_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`

## Examples

```
if (requireNamespace("NMF")) {
library("mlr3")
task = tsk("iris")
pop = po("nmf")
task$data()
pop$train(list(task))[[1]]$data()
pop$state
}
#> Loading required namespace: NMF
#> <Object of class: NMFfit>
#> # Model:
#> <Object of class:NMFstd>
#> features: 4
#> basis/rank: 2
#> samples: 150
#> # Details:
#> algorithm: brunet
#> seed: random
#> RNG: 10403L, 148L, ..., 581505866L [f712fcb2e9af94b00ba580916aea483e]
#> distance metric: 'KL'
#> residuals: 3.085118
#> miscellaneous: dt_columns=<character>, affected_cols=<character>,
#> intasklayout=c("<data.table>", "<data.frame>"),
#> outtasklayout=c("<data.table>", "<data.frame>"),
#> outtaskshell=c("<data.table>", "<data.frame>") . (use 'misc(object)')
#> Iterations: 450
#> Timing:
#> user system elapsed
#> 0.099 0.004 0.103
```