Class Weights for Sample Weighting

Adds a class weight column to the Task that different Learners may be able to use for sample weighting. Sample weights are added to each sample according to the target class.

Only binary classification tasks are supported.

Caution: when constructed naively without parameter, the weights are all set to 1. The minor_weight parameter must be adjusted for this PipeOp to be useful.

Note this only sets the "weights_learner" column. It therefore influences the behaviour of subsequent Learners, but does not influence resampling or evaluation metric weights.

Format

R6Class object inheriting from PipeOpTaskPreproc/PipeOp.

Construction

PipeOpClassWeights$new(id = "classweights", param_vals = list())

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

• 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. Instead of a Task, a TaskClassif is used as input and output during training and prediction.

The output during training is the input Task with added weights column according to target class. The output during prediction is the unchanged input.

State

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

Parameters

The parameters are the parameters inherited from PipeOpTaskPreproc; however, the affect_columns parameter is not present. Further parameters are:

• minor_weight :: numeric(1)
  Weight given to samples of the minor class. Major class samples have weight 1. Initialized to 1.

Internals

Introduces, or overwrites, the "weights" column in the Task. However, the Learner method needs to respect weights for this to have an effect.

The newly introduced column is named .WEIGHTS; there will be a naming conflict if this column already exists and is not a weight column itself.

Fields

Only fields inherited from PipeOpTaskPreproc/PipeOp.

Methods

Only methods inherited from 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_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_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson

Examples

library("mlr3")

task = tsk("spam")
opb = po("classweights")

# task weights
if ("weights_learner" %in% names(task)) {
  task$weights_learner  # recent mlr3-versions
} else {
  task$weights  # old mlr3-versions
}
#> NULL

# double the instances in the minority class (spam)
opb$param_set$values$minor_weight = 2
result = opb$train(list(task))[[1L]]
if ("weights_learner" %in% names(result)) {
  result$weights_learner  # recent mlr3-versions
} else {
  result$weights  # old mlr3-versions
}
#> Key: <row_id>
#>       row_id weight
#>        <int>  <num>
#>    1:      1      2
#>    2:      2      2
#>    3:      3      2
#>    4:      4      2
#>    5:      5      2
#>   ---              
#> 4597:   4597      1
#> 4598:   4598      1
#> 4599:   4599      1
#> 4600:   4600      1
#> 4601:   4601      1