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xplainfi (version 1.1.0)

PerturbationImportance: Perturbation Feature Importance Base Class

Description

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

Arguments

Super class

xplainfi::FeatureImportanceMethod -> PerturbationImportance

Public fields

sampler

(FeatureSampler) Sampler object for feature perturbation

Methods

Inherited methods

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 = 30L,
  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): 30L) Number of permutation/conditional sampling iterations. Can also 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().

Method importance()

Get aggregated importance scores. Extends the base $importance() method to support ci_method = "cpi". For details, see CFI, which is the only sub-method for which it is known to be valid.

Usage

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

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".

alternative

(character(1): "two.sided") Type of alternative hypothesis for statistical tests. "greater" tests H0: importance <= 0 vs H1: importance > 0 (one-sided). "two.sided" tests H0: importance = 0 vs H1: importance != 0.

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".

p_adjust

(character(1): "none") Method for p-value adjustment for multiple comparisons. Accepts any method supported by stats::p.adjust.methods, e.g. "holm", "bonferroni", "BH", "none". When "bonferroni", confidence intervals are also adjusted (alpha/k). For other correction methods (e.g. "holm", "BH"), only p-values are adjusted; confidence intervals remain at the nominal conf_level because these sequential/adaptive procedures do not have a clean per-comparison alpha for CI construction.

...

Additional arguments passed to the base method.

Returns

(data.table) Aggregated importance scores.

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.