xplainfi (version 1.0.0)

PerturbationImportance: Perturbation Feature Importance Base Class

Description

Abstract base class for perturbation-based importance methods PFI, CFI, and RFI

Super class

xplainfi::FeatureImportanceMethod -> PerturbationImportance

Public fields

sampler

(FeatureSampler) Sampler object for feature perturbation

Methods

Public methods

• PerturbationImportance$new()

• PerturbationImportance$importance()

• PerturbationImportance$clone()

Inherited methods

• xplainfi::FeatureImportanceMethod$compute()
• xplainfi::FeatureImportanceMethod$obs_loss()
• xplainfi::FeatureImportanceMethod$print()
• xplainfi::FeatureImportanceMethod$reset()
• xplainfi::FeatureImportanceMethod$scores()

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Method new()

Creates a new instance of the PerturbationImportance class

Usage

PerturbationImportance$new(
  task,
  learner,
  measure = NULL,
  resampling = NULL,
  features = NULL,
  groups = NULL,
  sampler = NULL,
  relation = "difference",
  n_repeats = 1L,
  batch_size = NULL
)

Arguments

task, learner, measure, resampling, features, groups

Passed to FeatureImportanceMethod.

sampler

(FeatureSampler) Sampler to use for feature perturbation.

relation

(character(1): "difference") How to relate perturbed and baseline scores. Can also be "ratio".

n_repeats

(integer(1): 1L) Number of permutation/conditional sampling iterations. Can be overridden in $compute().

batch_size

(integer(1) | NULL: NULL) Maximum number of rows to predict at once. When NULL, predicts all test_size * n_repeats rows in one call. Use smaller values to reduce memory usage at the cost of more prediction calls. Can be overridden in $compute().

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Method importance()

Get aggregated importance scores. Extends the base $importance() method to support the additional "cpi" ci_method.

Usage

PerturbationImportance$importance(
  relation = NULL,
  standardize = FALSE,
  ci_method = c("none", "raw", "nadeau_bengio", "quantile", "cpi"),
  conf_level = 0.95,
  test = c("t", "wilcoxon", "fisher", "binomial"),
  B = 1999,
  ...
)

Arguments

relation

(character(1)) How to relate perturbed scores to originals ("difference" or "ratio"). If NULL, uses stored parameter value.

standardize

(logical(1): FALSE) If TRUE, importances are standardized by the highest score so all scores fall in [-1, 1].

ci_method

(character(1): "none") Variance estimation method. In addition to base methods ("none", "raw", "nadeau_bengio", "quantile"), perturbation methods support "cpi" (Conditional Predictive Impact). CPI is specifically designed for CFI with knockoff samplers and uses one-sided hypothesis tests.

conf_level

(numeric(1): 0.95) Confidence level for confidence intervals when ci_method != "none".

test

(character(1): "t") Test to use for CPI. One of "t", "wilcoxon", "fisher", or "binomial". Only used when ci_method = "cpi".

B

(integer(1): 1999) Number of replications for Fisher test. Only used when ci_method = "cpi" and test = "fisher".

...

Additional arguments passed to the base method.

Returns

(data.table) Aggregated importance scores.

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Method clone()

The objects of this class are cloneable with this method.

Usage

PerturbationImportance$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.