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| context("Comprehensive Test for Classification Metric") | |
| test_that("running dataset test", { | |
| act <- aif360::binary_label_dataset( | |
| data_path = system.file("extdata", "actual_data.csv", package="aif360"), | |
| favor_label=1, | |
| unfavor_label=0, | |
| unprivileged_protected_attribute=0, | |
| privileged_protected_attribute=1, | |
| target_column="income", | |
| protected_attribute="sex") | |
| pred <- aif360::binary_label_dataset( | |
| data_path = system.file("extdata", "predicted_data.csv", package="aif360"), | |
| favor_label=1, | |
| unfavor_label=0, | |
| unprivileged_protected_attribute=0, | |
| privileged_protected_attribute=1, | |
| target_column="income", | |
| protected_attribute="sex") | |
| cm <- classification_metric(act, pred, unprivileged_groups = list('sex', 0), privileged_groups = list('sex', 1)) | |
| expect_equal(cm$accuracy(), 0.55) | |
| expect_equal(cm$accuracy(privileged=TRUE), 0.727, tolerance=0.000273) | |
| expect_equal(cm$accuracy(privileged=FALSE), 0.3333, tolerance=0.000333) | |
| expect_equal(cm$average_abs_odds_difference(), 0.4250, tolerance=0.000603) | |
| expect_equal(cm$average_odds_difference(), -0.07545, tolerance=5.32e-05) | |
| expect_equal(class(cm$binary_confusion_matrix()), "list") | |
| # expect_equal(cm$between_all_groups_coefficient_of_variation(), 0.0568, tolerance=3.65e-05) | |
| expect_equal(cm$between_all_groups_generalized_entropy_index(alpha=2), 0.00161, tolerance=3.49e-06) | |
| expect_equal(cm$between_all_groups_theil_index(), 0.0016, tolerance=8.24e-06) | |
| # expect_equal(cm$coefficient_of_variation(), 0.5685, tolerance=0.00105) | |
| expect_equal(cm$disparate_impact(), 1.629, tolerance=0.00063) | |
| expect_equal(cm$equal_opportunity_difference(), -0.5) | |
| expect_equal(cm$error_rate(), 0.45) | |
| expect_equal(cm$error_rate(privileged=TRUE), 0.27, tolerance=0.00273) | |
| expect_equal(cm$error_rate(privileged=FALSE), 0.66, tolerance=0.00667) | |
| expect_equal(cm$error_rate_difference(), 0.3939, tolerance=3.94e-05) | |
| expect_equal(cm$error_rate_ratio(), 2.44, tolerance=0.00444) | |
| expect_equal(cm$false_discovery_rate(), 0.857, tolerance=0.000143) | |
| expect_equal(cm$false_discovery_rate(privileged=TRUE), 0.66, tolerance=0.00667) | |
| expect_equal(cm$false_discovery_rate(privileged=FALSE), 1) | |
| expect_equal(cm$false_discovery_rate_difference(), 0.3333, tolerance=0.000333) | |
| expect_equal(cm$false_discovery_rate_ratio(), 1.5) | |
| expect_equal(cm$false_negative_rate(), 0.75) | |
| expect_equal(cm$false_negative_rate(privileged=TRUE), 0.5) | |
| expect_equal(cm$false_negative_rate(privileged=FALSE), 1) | |
| expect_equal(cm$false_negative_rate_difference(), 0.5) | |
| expect_equal(cm$false_negative_rate_ratio(), 2) | |
| expect_equal(cm$false_omission_rate(), 0.2310, tolerance=0.000769) | |
| expect_equal(cm$false_omission_rate(privileged=TRUE), 0.125) | |
| expect_equal(cm$false_omission_rate(privileged=FALSE), 0.4) | |
| expect_equal(cm$false_omission_rate_difference(), 0.275) | |
| expect_equal(cm$false_omission_rate_ratio(), 3.2) | |
| expect_equal(cm$false_positive_rate(), 0.375) | |
| expect_equal(cm$false_positive_rate(privileged=TRUE), 0.2215, tolerance=0.00222) | |
| expect_equal(cm$false_positive_rate(privileged=FALSE), 0.571, tolerance=0.000429) | |
| expect_equal(cm$false_positive_rate_difference(), 0.34901, tolerance=0.000206) | |
| expect_equal(class(cm$generalized_binary_confusion_matrix()), "list") | |
| expect_equal(cm$generalized_entropy_index(alpha=2), 0.16, tolerance=0.00163) | |
| expect_equal(cm$generalized_false_negative_rate(), 0.75) | |
| expect_equal(cm$generalized_false_negative_rate(privileged=TRUE), 0.5) | |
| expect_equal(cm$generalized_false_negative_rate(privileged=FALSE), 1) | |
| expect_equal(cm$generalized_false_positive_rate(), 0.375) | |
| expect_equal(cm$generalized_false_positive_rate(privileged=TRUE), 0.222, tolerance=0.00222) | |
| expect_equal(cm$generalized_false_positive_rate(privileged=FALSE), 0.571, tolerance=0.000429) | |
| expect_equal(cm$generalized_true_negative_rate(), 0.625) | |
| expect_equal(cm$generalized_true_negative_rate(privileged=TRUE), 0.778, tolerance=1) | |
| expect_equal(cm$generalized_true_negative_rate(privileged=FALSE), 0.428, tolerance=1) | |
| expect_equal(cm$generalized_true_positive_rate(), 0.25) | |
| expect_equal(cm$generalized_true_positive_rate(privileged=TRUE), 0.5) | |
| expect_equal(cm$generalized_true_positive_rate(privileged=FALSE), 0) | |
| expect_equal(cm$negative_predictive_value(), 0.769, tolerance=0.000231) | |
| expect_equal(cm$num_false_negatives(), 3.0) | |
| expect_equal(cm$num_false_positives(), 6.0) | |
| expect_equal(cm$num_generalized_false_negatives(), 3.0) | |
| expect_equal(cm$num_generalized_false_positives(), 6.0) | |
| expect_equal(cm$num_generalized_true_negatives(), 10.0) | |
| expect_equal(cm$num_generalized_true_positives(), 1.0) | |
| expect_equal(class(cm$performance_measures()), "list") | |
| expect_equal(cm$positive_predictive_value(), 0.143, tolerance=0.5) | |
| expect_equal(cm$power(), 1) | |
| expect_equal(cm$precision(), 0.143, tolerance=0.5) | |
| expect_equal(cm$recall(), 0.25) | |
| expect_equal(cm$selection_rate(), 0.35) | |
| expect_equal(cm$specificity(), 0.625) | |
| expect_equal(cm$sensitivity(), 0.25) | |
| expect_equal(cm$statistical_parity_difference(), 0.172, tolerance=0.5) | |
| expect_equal(cm$theil_index(), 0.220, tolerance=0.5) | |
| expect_equal(cm$true_negative_rate(), 0.625) | |
| expect_equal(cm$true_positive_rate(), 0.25) | |
| expect_equal(cm$true_positive_rate_difference(), -0.5) | |
| }) | |