Add Missing Indicator Columns

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 PipeOp.

Methods

Methods inherited from PipeOpTaskPreprocSimple(PipeOpTaskPreproc/PipeOp.

See also

https://mlr-org.com/pipeops.html

Other PipeOps: PipeOp, PipeOpEncodePL, PipeOpEnsemble, PipeOpImpute, PipeOpTargetTrafo, PipeOpTaskPreproc, PipeOpTaskPreprocSimple, mlr_pipeops, mlr_pipeops_adas, mlr_pipeops_blsmote, mlr_pipeops_boxcox, mlr_pipeops_branch, mlr_pipeops_chunk, mlr_pipeops_classbalancing, mlr_pipeops_classifavg, mlr_pipeops_classweights, mlr_pipeops_classweightsex, mlr_pipeops_colapply, mlr_pipeops_collapsefactors, mlr_pipeops_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_info, mlr_pipeops_isomap, mlr_pipeops_kernelpca, mlr_pipeops_learner, mlr_pipeops_learner_pi_cvplus, mlr_pipeops_learner_quantiles, mlr_pipeops_materialize, mlr_pipeops_modelmatrix, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_mutate, mlr_pipeops_nearmiss, 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_rowapply, mlr_pipeops_scale, mlr_pipeops_scalemaxabs, mlr_pipeops_scalerange, mlr_pipeops_select, mlr_pipeops_smote, mlr_pipeops_smotenc, mlr_pipeops_spatialsign, mlr_pipeops_splines, mlr_pipeops_subsample, mlr_pipeops_targetinvert, mlr_pipeops_targetmutate, mlr_pipeops_targettrafoscalerange, mlr_pipeops_textvectorizer, mlr_pipeops_threshold, mlr_pipeops_tomek, mlr_pipeops_tunethreshold, mlr_pipeops_unbranch, mlr_pipeops_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson

Examples

library("mlr3")

task = tsk("diabetes")$select(c("insulin", "triceps"))
sum(complete.cases(task$data()))
#> [1] 363
task$missings()
#> diabetes  insulin  triceps 
#>        0      405      251 
tail(task$data())
#>    diabetes insulin triceps
#>      <fctr>   <num>   <num>
#> 1:      pos      NA      34
#> 2:      pos      NA      28
#> 3:      pos     474      27
#> 4:      neg     540      27
#> 5:      neg      NA      NA
#> 6:      neg      NA      NA

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

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

# 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
#>      <fctr>   <num>   <num>          <fctr>          <fctr>
#> 1:      pos     120      34         missing         present
#> 2:      pos     135      28         missing         present
#> 3:      pos     474      27         present         present
#> 4:      neg     540      27         present         present
#> 5:      neg     200      19         missing         missing
#> 6:      neg      54      19         missing         missing