Conditional Feature Sampler — ConditionalSampler • xplainfi

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

FeatureSampler -> ConditionalSampler

Methods

Public methods

ConditionalSampler$new()
ConditionalSampler$sample()
ConditionalSampler$sample_newdata()
ConditionalSampler$clone()

Inherited methods

FeatureSampler$print()

`ConditionalSampler$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().

`ConditionalSampler$sample()`

Sample from stored task conditionally on other features.

Usage

ConditionalSampler$sample(
  feature,
  row_ids = NULL,
  conditioning_set = NULL,
  samples_per_row = 1L,
  ...
)

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.
samples_per_row: (integer(1): 1L) Number of independent samples per input row. See FeatureSampler$sample() for output shape and ordering.
...: Additional arguments passed to the sampler implementation.

Returns

Modified copy with sampled feature(s).

`ConditionalSampler$sample_newdata()`

Sample from external data conditionally.

Usage

ConditionalSampler$sample_newdata(
  feature,
  newdata,
  conditioning_set = NULL,
  samples_per_row = 1L,
  ...
)

Arguments

feature: (character) Feature(s) to sample.
newdata: (data.table) External data to use.
conditioning_set: (character | NULL) Features to condition on.
samples_per_row: (integer(1): 1L) Number of independent samples per input row. See FeatureSampler$sample() for output shape and ordering.
...: Additional arguments passed to the sampler implementation.

Returns

Modified copy with sampled feature(s).

`ConditionalSampler$clone()`

The objects of this class are cloneable with this method.

Usage

ConditionalSampler$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.