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mlr3fairness (version 0.4.0)

mlr_pipeops_explicit_pta: PipeOpExplicitPta

Description

Turns the column with column role 'pta' into an explicit separate column prefixed with "..internal_pta". This keeps it from getting changed or adapted by subsequent pipelines that operate on the feature pta.

Arguments

Format

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

Construction

PipeOpExplicitPta$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

The PipeOp does not have any hyperparameters.

Internals

Copies the existing pta column to a new column.

Fields

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

Methods

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

Super classes

mlr3pipelines::PipeOp -> mlr3pipelines::PipeOpTaskPreproc -> PipeOpExplicitPta

Methods

Inherited methods

Method new()

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

Usage

PipeOpExplicitPta$new(id = "explicit_pta", param_vals = list())

Arguments

id

character
The PipeOps identifier in the PipeOps library.

param_vals

list
The parameter values to be set. See Parameters.

Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpExplicitPta$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

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

Other PipeOps: mlr_pipeops_equalized_odds, mlr_pipeops_reweighing

Examples

Run this code
library("mlr3")
library("mlr3pipelines")
epta = po("explicit_pta")
new = epta$train(list(tsk("adult_train")))

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