Transform a Target without an Explicit Inversion

EXPERIMENTAL, API SUBJECT TO CHANGE

Handles target transformation operations that do not need explicit inversion. In case the new target is required during predict, creates a vector of NA. Works similar to PipeOpTargetTrafo and PipeOpTargetMutate, but forgoes the inversion step. In case target after the trafo is a factor, levels are saved to $state.

During prediction: Sets all target values to NA before calling the trafo again. In case target after the trafo is a factor, levels saved in the state are set during prediction.

As a special case when trafo is identity and new_target_name matches an existing column name of the data of the input Task, this column is set as the new target. Depending on drop_original_target the original target is then either dropped or added to the features.

Format

Abstract R6Class inheriting from PipeOp.

Construction

PipeOpUpdateTarget$new(id, param_set = ps(),
  param_vals = list(), packages = character(0))

• id :: character(1)
  Identifier of resulting object. See $id slot of PipeOp.

• param_vals :: named list
  List of hyperparameter settings, overwriting the hyperparameter settings given in param_set. The subclass should have its own param_vals parameter and pass it on to super$initialize(). Default list().

Parameters

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

• trafo :: function
  Transformation function for the target. Should only be a function of the target, i.e., taking a single argument. Default is identity. Note, that the data passed on to the target is a data.table consisting of all target column.

• new_target_name :: character(1)
  Optionally give the transformed target a new name. By default the original name is used.

• new_task_type :: character(1)
  Optionally a new task type can be set. Legal types are listed in mlr_reflections$task_types$type. #' drop_original_target :: logical(1)
  Whether to drop the original target column. Default: TRUE.

State

The $state is a list of class levels for each target after trafo. list() if none of the targets have levels.

Methods

Only methods inherited from PipeOp.

See also

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

Other mlr3pipelines backend related: Graph, PipeOp, PipeOpTargetTrafo, PipeOpTaskPreproc, PipeOpTaskPreprocSimple, mlr_graphs, mlr_pipeops

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_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_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_vtreat, mlr_pipeops_yeojohnson

Examples

if (FALSE) { # \dontrun{
# Create a binary class task from iris
library(mlr3)
trafo_fun = function(x) {factor(ifelse(x$Species == "setosa", "setosa", "other"))}
po = PipeOpUpdateTarget$new(param_vals = list(trafo = trafo_fun, new_target_name = "setosa"))
po$train(list(tsk("iris")))
po$predict(list(tsk("iris")))
} # }