Generate a Randomized Response Prediction

Takes in a Prediction of predict_type "prob" (for PredictionClassif) or "se" (for PredictionRegr) and generates a randomized "response" prediction.

For "prob", the responses are sampled according to the probabilities of the input PredictionClassif. For "se", responses are randomly drawn according to the rdistfun parameter (default is rnorm) by using the original responses of the input PredictionRegr as the mean and the original standard errors of the input PredictionRegr as the standard deviation (sampling is done observation-wise).

Format

R6Class object inheriting from PipeOp.

Construction

PipeOpRandomResponse$new(id = "randomresponse", param_vals = list(), packages = character(0))

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

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

• packages :: character
  Set of all required packages for the private$.predict() methods related to the rdistfun parameter. Default is character(0).

Input and Output Channels

PipeOpRandomResponse has one input channel named "input", taking NULL during training and a Prediction during prediction.

PipeOpRandomResponse has one output channel named "output", producing NULL during training and a Prediction with random responses during prediction.

State

The $state is left empty (list()).

Parameters

• rdistfun :: function
  A function for generating random responses when the predict type is "se". This function must accept the arguments n (integerish number of responses), mean (numeric for the mean), and sd (numeric for the standard deviation), and must vectorize over mean and sd. Default is rnorm.

Internals

If the predict_type of the input Prediction does not match "prob" or "se", the input Prediction will be returned unaltered.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from 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_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, 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_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)
library(mlr3learners)

task1 = tsk("iris")
g1 = LearnerClassifRpart$new() %>>% PipeOpRandomResponse$new()
g1$train(task1)
#> $randomresponse.output
#> NULL
#> 
g1$pipeops$classif.rpart$learner$predict_type = "prob"
set.seed(2409)
g1$predict(task1)
#> $randomresponse.output
#> <PredictionClassif> for 150 observations:
#>  row_ids     truth  response
#>        1    setosa    setosa
#>        2    setosa    setosa
#>        3    setosa    setosa
#>      ---       ---       ---
#>      148 virginica virginica
#>      149 virginica virginica
#>      150 virginica virginica
#> 

task2 = tsk("mtcars")
g2 = LearnerRegrLM$new() %>>% PipeOpRandomResponse$new()
g2$train(task2)
#> $randomresponse.output
#> NULL
#> 
g2$pipeops$regr.lm$learner$predict_type = "se"
set.seed(2906)
g2$predict(task2)
#> $randomresponse.output
#> <PredictionRegr> for 32 observations:
#>  row_ids truth response
#>        1  21.0 23.76570
#>        2  21.0 21.28602
#>        3  22.8 27.52332
#>      ---   ---      ---
#>       30  19.7 16.99001
#>       31  15.0 13.88306
#>       32  21.4 22.80808
#>