fairness_pca: Fairness PCA

In ModelOriented/FairModels: Flexible Tool for Bias Detection, Visualization, and Mitigation

Fairness PCA

Description

Calculate PC for metric_matrix to see similarities between models and metrics. If omit_models_with_NA is set to TRUE models with NA will be omitted as opposed to default behavior, when metrics are omitted.

Usage

fairness_pca(x, omit_models_with_NA = FALSE)

Arguments

x	object of class fairness object
omit_models_with_NA	logical, if TRUE omits rows in metric_matrix, else omits columns (default)

Value

fairness_pca object It is list containing following fields:

• pc_1_2 - amount of data variance explained with each component

• rotation - rotation from stats::prcomp

• x - x from stats::prcomp

• sdev - sdev from stats::prcomp

• label - model labels

Examples

data("german")

y_numeric <- as.numeric(german$Risk) - 1

lm_model <- glm(Risk ~ .,
  data = german,
  family = binomial(link = "logit")
)

rf_model <- ranger::ranger(Risk ~ .,
  data = german,
  probability = TRUE,
  num.trees = 200,
  num.threads = 1
)

explainer_lm <- DALEX::explain(lm_model, data = german[, -1], y = y_numeric)
explainer_rf <- DALEX::explain(rf_model, data = german[, -1], y = y_numeric)

fobject <- fairness_check(explainer_lm, explainer_rf,
  protected = german$Sex,
  privileged = "male"
)

# same explainers with different cutoffs for female
fobject <- fairness_check(explainer_lm, explainer_rf, fobject,
  protected = german$Sex,
  privileged = "male",
  cutoff = list(female = 0.4),
  label = c("lm_2", "rf_2")
)

fpca <- fairness_pca(fobject)

plot(fpca)

ModelOriented/FairModels documentation built on Aug. 30, 2022, 5:48 p.m.