mlr_pipeops_nmf | R Documentation |
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
.
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
if (requireNamespace("NMF")) { library("mlr3") task = tsk("iris") pop = po("nmf") task$data() pop$train(list(task))[[1]]$data() pop$state }
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