Change Column Roles of a Task

Changes the column roles of the input Task according to new_role or its inverse new_role_direct.

Setting a new target variable or changing the role of an existing target variable is not supported.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpColRoles$new(id = "colroles", param_vals = list())

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

• 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 transformed column roles according to new_role or its inverse new_role_direct.

State

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

Parameters

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

• new_role :: named list
  Named list of new column roles by column. The names must match the column names of the input task that will later be trained/predicted on. Each entry of the list must contain a character vector with possible values of mlr_reflections$task_col_roles. If the value is given as character() or NULL, the column will be dropped from the input task. Changing the role of a column results in this column loosing its previous role(s).

• new_role_direct :: named list
  # Named list of new column roles by role. The names must match the possible column roles, i.e. values of mlr_reflections$task_col_roles. Each entry of the list must contain a character vector with column names of the input task that will later be trained/predicted on. If the value is given as character() or NULL, all columns will be dropped from the role given in the element name. The value given for a role overwrites the previous entry in task$col_roles for that role, completely.

Fields

Only fields inherited from PipeOp.

Methods

Only 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_colapply, mlr_pipeops_collapsefactors, 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_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")

task = tsk("penguins")
pop = po("colroles", param_vals = list(
  new_role = list(body_mass = c("order", "feature"))
))

train_out1 = pop$train(list(task))[[1L]]
train_out1$col_roles
#> $feature
#> [1] "bill_depth"     "bill_length"    "flipper_length" "island"        
#> [5] "sex"            "year"           "body_mass"     
#> 
#> $target
#> [1] "species"
#> 
#> $name
#> character(0)
#> 
#> $order
#> [1] "body_mass"
#> 
#> $stratum
#> character(0)
#> 
#> $group
#> character(0)
#> 
#> $offset
#> character(0)
#> 
#> $weights_learner
#> character(0)
#> 
#> $weights_measure
#> character(0)
#> 

pop$param_set$set_values(
 new_role = NULL,
 new_role_direct = list(order = character(), group = "island")
)

train_out2 = pop$train(list(train_out1))
train_out2$col_roles
#> NULL