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Knockoff-based Conditional Sampler

Source: R/KnockoffSampler.R
KnockoffSampler.Rd

Implements conditional sampling using Knockoffs.

Details

The KnockoffSampler samples Knockoffs based on the task data. This class allows arbitrary knockoff_fun, which also means that no input checking against supported feature types can be done. Use KnockoffGaussianSampler or KnockoffSequentialSampler for these variants specifically.

References

Watson D, Wright M (2021). “Testing Conditional Independence in Supervised Learning Algorithms.” Machine Learning, 110(8), 2107–2129. doi:10.1007/s10994-021-06030-6 .

Blesch K, Watson D, Wright M (2023). “Conditional Feature Importance for Mixed Data.” AStA Advances in Statistical Analysis, 108(2), 259–278. doi:10.1007/s10182-023-00477-9 .

Super class

xplainfi::FeatureSampler -> KnockoffSampler

Public fields

x_tilde

Knockoff matrix with one (or iters) row(s) per original observation in task.

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of the KnockoffSampler class.

Usage

KnockoffSampler$new(
  task,
  knockoff_fun = function(x) knockoff::create.second_order(as.matrix(x)),
  iters = 1
)

Arguments

task

(mlr3::Task) Task to sample from

knockoff_fun

(function) Step size for variance adjustment. Default are second-order Gaussian knockoffs.

iters

(integer(1): 1) Number of repetitions the knockoff_fun is applied to create multiple x_tilde instances per observation.

Method sample()

Sample from stored task using knockoff values. Replaces specified feature(s) with their knockoff counterparts from the pre-generated knockoff matrix.

Usage

KnockoffSampler$sample(feature, row_ids = NULL)

Arguments

feature

(character) Feature(s) to sample.

row_ids

(integer() | NULL) Row IDs to use. If NULL, uses all rows.

Returns

Modified copy with knockoff feature(s).

Method clone()

The objects of this class are cloneable with this method.

Usage

KnockoffSampler$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(mlr3)
task = tgen("2dnormals")$generate(n = 100)
# Create sampler with default parameters
sampler = KnockoffSampler$new(task)
# Sample using row_ids from stored task
sampled_data = sampler$sample("x1")
if (FALSE) { # \dontrun{
# Example with sequential knockoffs (https://github.com/kormama1/seqknockoff)
task = tgen("simplex")$generate(n = 100)
sampler_seq = KnockoffSampler$new(task, knockoff_fun = seqknockoff::knockoffs_seq)
sampled_seq = sampler_seq$sample("x1")
} # }