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context("Comprehensive Test for Prejudice Remover Algorithm") | |
test_that("running prejudice remover test", { | |
dd <- aif360::binary_label_dataset( | |
data_path = system.file("extdata", "data.csv", package="aif360"), | |
favor_label=0, | |
unfavor_label=1, | |
unprivileged_protected_attribute=0, | |
privileged_protected_attribute=1, | |
target_column="income", | |
protected_attribute="sex") | |
bm <- binary_label_dataset_metric(dd, list('sex', 1), list('sex',0)) | |
expect_equal(bm$mean_difference(), 0.196, tolerance=0.000433) | |
u = list('sex', 0) | |
p = list('sex',1) | |
model = prejudice_remover(class_attr = "income", sensitive_attr = "sex") | |
model$fit(dd) | |
dd_pred <- model$predict(dd) | |
}) | |