plot.fairness_heatmap {fairmodels}

Plot Heatmap

Description

Heatmap shows all parity loss metrics across all models while displaying similarity between variables (in form of dendograms). All metrics are visible. Some have identical values as it should be in terms of their parity loss (eg. TPR parity loss == FNR parity loss, because TPR = 1 - FNR ). NA's in metrics are gray.

Usage

## S3 method for class 'fairness_heatmap'
plot(
  x,
  ...,
  midpoint = NULL,
  title = NULL,
  subtitle = NULL,
  text = TRUE,
  text_size = 3,
  flip_axis = FALSE
)

Arguments

x

fairness_heatmap

...

other fairness_heatmap objects

midpoint

numeric, midpoint on gradient scale

title

character, title of the plot

subtitle

character, subtitle of the plot

text

logical, default TRUE means it shows values on tiles

text_size

numeric, size of text

flip_axis

logical, whether to change axis with metrics on axis with models

Value

list of ggplot2 objects

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,
  seed = 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")
)


fh <- fairness_heatmap(fobject)

plot(fh)