h2o (version 3.44.0.3)

h2o.disparate_analysis: Create a frame containing aggregations of intersectional fairness across the models.

Description

Create a frame containing aggregations of intersectional fairness across the models.

Usage

h2o.disparate_analysis(
  models,
  newdata,
  protected_columns,
  reference,
  favorable_class,
  air_metric = "selectedRatio",
  alpha = 0.05
)

Value

frame containing aggregations of intersectional fairness across the models

Arguments

models

List of H2O Models

newdata

H2OFrame

protected_columns

List of categorical columns that contain sensitive information such as race, gender, age etc.

reference

List of values corresponding to a reference for each protected columns. If set to NULL, it will use the biggest group as the reference.

favorable_class

Positive/favorable outcome class of the response.

air_metric

Metric used for Adverse Impact Ratio calculation. Defaults to ``selectedRatio``.

alpha

The alpha level is the probability of rejecting the null hypothesis that the protected group and the reference came from the same population when the null hypothesis is true.

Examples

if (FALSE) {
library(h2o)
h2o.init()
data <- h2o.importFile(paste0("https://s3.amazonaws.com/h2o-public-test-data/smalldata/",
                              "admissibleml_test/taiwan_credit_card_uci.csv"))
x <- c('LIMIT_BAL', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1',
       'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2',
       'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6')
y <- "default payment next month"
protected_columns <- c('SEX', 'EDUCATION')

for (col in c(y, protected_columns))
  data[[col]] <- as.factor(data[[col]])

splits <- h2o.splitFrame(data, 0.8)
train <- splits[[1]]
test <- splits[[2]]
reference <- c(SEX = "1", EDUCATION = "2")  # university educated man
favorable_class <- "0" # no default next month

aml <- h2o.automl(x, y, training_frame = train, max_models = 3)

h2o.disparate_analysis(aml, test, protected_columns = protected_columns,
                       reference = reference, favorable_class = favorable_class)
}