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

for details.

`R6Class`

object inheriting from `PipeOpTaskPreproc`

/`PipeOp`

.

PipeOpNMF$new(id = "nmf", param_vals = list())

`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 are inherited from `PipeOpTaskPreproc`

.

The output is the input `Task`

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

The `$state`

is a named `list`

with the `$state`

elements inherited from `PipeOpTaskPreproc`

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

.

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()`

.

Uses the `nmf()`

function as well as `basis()`

, `coef()`

and
`ginv()`

.

Only methods inherited from `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_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`

#> 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#> Species NMF1 NMF2 #> 1: setosa 0.8290139 0.03579919 #> 2: setosa 0.7425097 0.06914820 #> 3: setosa 0.7587001 0.03754975 #> 4: setosa 0.7137460 0.07715860 #> 5: setosa 0.8317556 0.02832131 #> --- #> 146: virginica 0.4031991 0.86828720 #> 147: virginica 0.3400982 0.83741979 #> 148: virginica 0.3994916 0.84480031 #> 149: virginica 0.3823531 0.87108386 #> 150: virginica 0.3591966 0.80980611pop$state#> <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 #> 0.140 0.017 0.155