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Reweighing to balance disparate impact metric

Source: R/PipeOpReweighing.R
mlr_pipeops_reweighing.Rd

Adjusts class balance and protected group balance in order to achieve fair(er) outcomes.

Format

R6::R6Class object inheriting from mlr3pipelines::PipeOpTaskPreproc/mlr3pipelines::PipeOp.

PipeOpReweighingWeights

Adds a class weight column to the mlr3::Task that different mlr3::Learners may be using. In case initial weights are present, those are multiplied with new weights. Caution: Only fairness tasks are supported. Which means tasks need to have protected field. tsk$col_roles$pta.

PipeOpReweighingOversampling

Oversamples a mlr3::Task for more balanced ratios in subgroups and protected groups. Can be used if a learner does not support weights. Caution: Only fairness tasks are supported. Which means tasks need to have protected field. tsk$col_roles$pta.

Construction

PipeOpReweighing*$new(id = "reweighing", param_vals = list())

  • id (character(1)).

  • param_vals (list())

Input and Output Channels

Input and output channels are inherited from mlr3pipelines::PipeOpTaskPreproc. Instead of a mlr3::Task, a mlr3::TaskClassif is used as input and output during training and prediction.

The output during training is the input mlr3::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 mlr3pipelines::PipeOpTaskPreproc.

Parameters

  • alpha (numeric()): A number between 0 (no debiasing) and 1 (full debiasing).

Internals

Introduces, or overwrites, the "weights" column in the mlr3::Task. However, the mlr3::Learner method needs to respect weights for this to have an effect.

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

Fields

Only fields inherited from mlr3pipelines::PipeOpTaskPreproc/mlr3pipelines::PipeOp.

Methods

Methods inherited from mlr3pipelines::PipeOpTaskPreproc/mlr3pipelines::PipeOp.

References

Kamiran, Faisal, Calders, Toon (2012). “Data preprocessing techniques for classification without discrimination.” Knowledge and Information Systems, 33(1), 1–33.

See also

https://mlr3book.mlr-org.com/list-pipeops.html

Other PipeOps: mlr_pipeops_equalized_odds, mlr_pipeops_explicit_pta

Super classes

mlr3pipelines::PipeOp -> mlr3pipelines::PipeOpTaskPreproc -> PipeOpReweighingWeights

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6::R6Class R6 class.

Usage

PipeOpReweighingWeights$new(id = "reweighing_wts", param_vals = list())

Arguments

id

character
The PipeOps identifier in the PipeOps library.

param_vals

list
The parameter values to be set.

  • alpha: controls the proportion between initial weight (1 if non existing) and reweighing weight. Defaults to 1. Here is how it works: new_weight = (1 - alpha) * 1 + alpha x reweighing_weight final_weight = old_weight * new_weight

Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpReweighingWeights$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Super classes

mlr3pipelines::PipeOp -> mlr3pipelines::PipeOpTaskPreproc -> PipeOpReweighingOversampling

Methods

Public methods

Inherited methods

Method new()

Usage

PipeOpReweighingOversampling$new(id = "reweighing_os", param_vals = list())

Arguments

id

`character'
The PipeOp's id.

param_vals

`list'
A list of parameter values.

Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpReweighingOversampling$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library("mlr3")
library("mlr3pipelines")

reweighing = po("reweighing_wts")
learner_po = po("learner", learner = lrn("classif.rpart"))

data = tsk("adult_train")
graph = reweighing %>>% learner_po
glrn = GraphLearner$new(graph)
glrn$train(data)
tem = glrn$predict(data)
tem$confusion
#>         truth
#> response <=50K  >50K
#>    <=50K 21793  3640
#>    >50K   1275  4010