mlr3pipelines (version 0.3.0)

mlr_pipeops_nmf: PipeOpNMF

Description

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

Format

R6Class object inheriting from PipeOpTaskPreproc/PipeOp.

Construction

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

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 class 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. Default is 2. See nmf().

• method :: character(1) Specification of the NMF algorithm. Default is "brunet". See nmf().

• seed :: numeric(1) Specification of the starting point. See nmf().

• nrun :: integer(1) Number of runs to performs. More than a single run allows for the computation of a consensus matrix which will also be stored in the $state. See nmf().

• options :: named list Named list of additional parameters. Default is list(). See .options in nmf(). Initialized to parameters parallel and parallel.required set to FALSE, as it is recommended to use mlr3's future-based parallelization.

Internals

Uses the nmf function as well as basis, coef and ginv.

Methods

Only methods inherited from 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_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

# NOT RUN {
library("mlr3")

task = tsk("iris")
pop = po("nmf")

task$data()
pop$train(list(task))[[1]]$data()

pop$state
# }