Impute Features by a Constant

Impute features by a constant value.

Format

R6Class object inheriting from PipeOpImpute/PipeOp.

Construction

PipeOpImputeConstant$new(id = "imputeconstant", param_vals = list())

id :: character(1)
Identifier of resulting object, default "imputeconstant".
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 PipeOpImpute.

The output is the input Task with all affected features missing values imputed by the value of the constant parameter.

State

The $state is a named list with the $state elements inherited from PipeOpImpute.

The $state$model contains the value of the constant parameter that is used for imputation.

Parameters

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

constant :: atomic(1)
The constant value that should be used for the imputation, atomic vector of length 1. The atomic mode must match the type of the features that will be selected by the affect_columns parameter and this will be checked during imputation. This is a required hyperparameter and needs to be set by the user.
check_levels :: logical(1)
Should be checked whether the constant value is a valid level of factorial features (i.e., it already is a level)? Raises an error if unsuccessful. This check is only performed for factorial features (i.e., factor, ordered; skipped for character). Initialized to TRUE.
Note that empty factor levels can be a problem for many Learners. Thus, PipeOpImputeOOR is the preferred choice for creating new levels, since it is designed to impute out-of-range values and offers a more explicit control for handling potentially problematic behavior.

Internals

The constructor is called with empty_level_control set to "always", to allow the creation of a new empty level for factor and ordered (but not character) features during training, if constant is not an already existing level and check_levels is set to FALSE. This has no impact if check_levels is TRUE, since in that case an error would be raised before imputation.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpImpute/PipeOp.

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_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_missind, 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

Other Imputation PipeOps: PipeOpImpute, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputeoor, mlr_pipeops_imputesample

Examples

library("mlr3")

task = tsk("pima")
#> Warning: data set ‘PimaIndiansDiabetes2’ not found
#> Error in UseMethod("as_data_backend"): no applicable method for 'as_data_backend' applied to an object of class "NULL"
task$missings()
#> Error: object 'task' not found

# impute missing values of the numeric feature "glucose" by the constant value -999
po = po("imputeconstant", param_vals = list(
  constant = -999, affect_columns = selector_name("glucose"))
)
new_task = po$train(list(task = task))[[1]]
#> Error: object 'task' not found
new_task$missings()
#> Error: object 'new_task' not found
new_task$data(cols = "glucose")[[1]]
#> Error: object 'new_task' not found