Add missing indicator columns ("dummy columns") to the Task. Drops original features; should probably be used in combination with PipeOpFeatureUnion and imputation PipeOps (see examples).

Note the affect_columns is initialized with selector_invert(selector_type(c("factor", "ordered", "character"))), since missing values in factorial columns are often indicated by out-of-range imputation (PipeOpImputeOOR).

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpMissInd$new(id = "missind", param_vals = list())
  • id :: character(1) Identifier of the resulting object, defaulting to "missind".

  • param_vals :: named list
    List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().

State

$state is a named list with the $state elements inherited from PipeOpTaskPreproc, as well as:

  • indicand_cols :: character
    Names of columns for which indicator columns are added. If the which parameter is "all", this is just the names of all features, otherwise it is the names of all features that had missing values during training.

Parameters

The parameters are the parameters inherited from the PipeOpTaskPreproc, as well as:

  • which :: character(1)
    Determines for which features the indicator columns are added. Can either be "missing_train" (default), adding indicator columns for each feature that actually has missing values, or "all", adding indicator columns for all features.

  • type :: character(1)
    Determines the type of the newly created columns. Can be one of "factor" (default), "integer", "logical", "numeric".

Internals

This PipeOp should cover most cases where "dummy columns" or "missing indicators" are desired. Some edge cases:

  • If imputation for factorial features is performed and only numeric features should gain missing indicators, the affect_columns parameter can be set to selector_type("numeric").

  • If missing indicators should only be added for features that have more than a fraction of x missing values, the PipeOpRemoveConstants can be used with affect_columns = selector_grep("^missing_") and ratio = x.

Fields

Fields inherited from PipeOpTaskPreproc/PipeOp.

Methods

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_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_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

library("mlr3")

task = tsk("pima")$select(c("insulin", "triceps"))
sum(complete.cases(task$data()))
#> [1] 394
task$missings()
#> diabetes  insulin  triceps 
#>        0      374      227 
tail(task$data())
#>    diabetes insulin triceps
#> 1:      neg      NA      NA
#> 2:      neg     180      48
#> 3:      neg      NA      27
#> 4:      neg     112      23
#> 5:      pos      NA      NA
#> 6:      neg      NA      31

po = po("missind")
new_task = po$train(list(task))[[1]]

tail(new_task$data())
#>    diabetes missing_insulin missing_triceps
#> 1:      neg         missing         missing
#> 2:      neg         present         present
#> 3:      neg         missing         present
#> 4:      neg         present         present
#> 5:      pos         missing         missing
#> 6:      neg         missing         present

# proper imputation + missing indicators

impgraph = list(
  po("imputesample"),
  po("missind")
) %>>% po("featureunion")

tail(impgraph$train(task)[[1]]$data())
#>    diabetes insulin triceps missing_insulin missing_triceps
#> 1:      neg     237      32         missing         missing
#> 2:      neg     180      48         present         present
#> 3:      neg      65      27         missing         present
#> 4:      neg     112      23         present         present
#> 5:      pos     265      12         missing         missing
#> 6:      neg     300      31         missing         present