h2o.infogram_train_subset_models: Train models over subsets selected using infogram

In h2o: R Interface for the 'H2O' Scalable Machine Learning Platform

Train models over subsets selected using infogram

Description

Train models over subsets selected using infogram

Usage

h2o.infogram_train_subset_models(
  ig,
  model_fun,
  training_frame,
  test_frame,
  y,
  protected_columns,
  reference,
  favorable_class,
  feature_selection_metrics = c("safety_index"),
  metric = "euclidean",
  air_metric = "selectedRatio",
  alpha = 0.05,
  ...
)

Arguments

ig	Infogram object trained with the same protected columns
model_fun	Function that creates models. This can be something like h2o.automl, h2o.gbm, etc.
training_frame	Training frame
test_frame	Test frame
y	Response column
protected_columns	Protected columns
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.
feature_selection_metrics	One or more columns from the infogram@admissible_score.
metric	Metric supported by stats::dist which is used to sort the features.
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.
...	Parameters that are passed to the model_fun.

Value

frame containing aggregations of intersectional fairness across the models

Examples

## Not run: 
library(h2o)
h2o.connect()
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

ig <- h2o.infogram(x, y, train, protected_columns = protected_columns)
print(ig@admissible_score)
plot(ig)

infogram_models <- h2o.infogram_train_subset_models(ig, h2o.gbm, train, test, y,
                                                    protected_columns, reference,
                                                    favorable_class)

pf <- h2o.pareto_front(infogram_models, x_metric = "air_min",
                       y_metric = "AUC", optimum = "top right")
plot(pf)
pf@pareto_front

## End(Not run)

h2o documentation built on Aug. 9, 2023, 9:06 a.m.