Conditional Target Value Impact Encoding

Encodes columns of type factor, character and ordered.

Impact coding for classification Tasks converts factor levels of each (factorial) column to the difference between each target level's conditional log-likelihood given this level, and the target level's global log-likelihood.

Impact coding for regression Tasks converts factor levels of each (factorial) column to the difference between the target's conditional mean given this level, and the target's global mean.

Treats new levels during prediction like missing values.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpEncodeImpact$new(id = "encodeimpact", param_vals = list())

• id :: character(1)
  Identifier of resulting object, default "encodeimpact".

• 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 factor, character or ordered parameters encoded.

State

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

• impact :: a named list
  A list with an element for each affected feature:
  For regression each element is a single column matrix of impact values for each level of that feature.
  For classification, it is a list with an element for each feature level, which is a vector giving the impact of this feature level on each outcome level.

Parameters

• smoothing :: numeric(1)
  A finite positive value used for smoothing. Mostly relevant for classification Tasks if a factor does not coincide with a target factor level (and would otherwise give an infinite logit value). Initialized to 1e-4.

• impute_zero :: logical(1)
  If TRUE, impute missing values as impact 0; otherwise the respective impact is coded as NA. Default FALSE.

Internals

Uses Laplace smoothing, mostly to avoid infinite values for classification Task.

Methods

Only methods inherited PipeOpTaskPreproc/PipeOp.

See also

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

Other PipeOps: PipeOp, 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_colapply, mlr_pipeops_collapsefactors, mlr_pipeops_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_encode, mlr_pipeops_encodelmer, 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_learner_pi_cvplus, mlr_pipeops_learner_quantiles, 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_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")
poe = po("encodeimpact")

task = TaskClassif$new("task",
  data.table::data.table(
    x = factor(c("a", "a", "a", "b", "b")),
    y = factor(c("a", "a", "b", "b", "b"))),
  "x")

poe$train(list(task))[[1]]$data()
#>         x       y.a       y.b
#>    <fctr>     <num>     <num>
#> 1:      a  9.498089 -9.498089
#> 2:      a  9.498089 -9.498089
#> 3:      a -1.098546  1.098546
#> 4:      b -1.098546  1.098546
#> 5:      b -1.098546  1.098546

poe$state
#> $impact
#> $impact$y
#>                       a         b
#> a              9.498089 -9.498089
#> b             -1.098546  1.098546
#> .TEMP.MISSING        NA        NA
#> 
#> 
#> $dt_columns
#> [1] "y"
#> 
#> $affected_cols
#> [1] "y"
#> 
#> $intasklayout
#> Key: <id>
#>        id   type
#>    <char> <char>
#> 1:      y factor
#> 
#> $outtasklayout
#> Key: <id>
#>        id    type
#>    <char>  <char>
#> 1:    y.a numeric
#> 2:    y.b numeric
#> 
#> $outtaskshell
#> Empty data.table (0 rows and 3 cols): x,y.a,y.b
#>