ConditionalSampler: Conditional Feature Sampler

In xplainfi: Feature Importance Methods for Global Explanations

Conditional Feature Sampler

Description

Base class for conditional sampling methods where features are sampled conditionally on other features. This is an abstract class that should be extended by concrete implementations.

Super class

xplainfi::FeatureSampler -> ConditionalSampler

Methods

Public methods

• ConditionalSampler$new()

• ConditionalSampler$sample()

• ConditionalSampler$sample_newdata()

• ConditionalSampler$clone()

Inherited methods

• xplainfi::FeatureSampler$print()

---

Method new()

Creates a new instance of the ConditionalSampler class

Usage

ConditionalSampler$new(task, conditioning_set = NULL)

Arguments

task

(mlr3::Task) Task to sample from

conditioning_set

(character | NULL) Default conditioning set to use in ⁠$sample()⁠.

---

Method sample()

Sample from stored task conditionally on other features.

Usage

ConditionalSampler$sample(
  feature,
  row_ids = NULL,
  conditioning_set = NULL,
  ...
)

Arguments

feature

(character) Feature(s) to sample.

row_ids

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

conditioning_set

(character | NULL) Features to condition on.

...

Additional arguments passed to the sampler implementation.

Returns

Modified copy with sampled feature(s).

---

Method sample_newdata()

Sample from external data conditionally.

Usage

ConditionalSampler$sample_newdata(
  feature,
  newdata,
  conditioning_set = NULL,
  ...
)

Arguments

feature

(character) Feature(s) to sample.

newdata

(data.table) External data to use.

conditioning_set

(character | NULL) Features to condition on.

...

Additional arguments passed to the sampler implementation.

Returns

Modified copy with sampled feature(s).

---

Method clone()

The objects of this class are cloneable with this method.

Usage

ConditionalSampler$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

xplainfi documentation built on Feb. 27, 2026, 1:08 a.m.