fairmodels (version 1.1.0)

fairness_pca: 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

# NOT RUN {
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)


# }