def test_demographicparity_fair_uneven_populations():
# Variant of test_demographicparity_already_fair, which has unequal
# populations in the two classes
# Also allow the threshold to be adjustable
score_threshold = 0.625
number_a0 = 4
number_a1 = 4
a0_label = 17
a1_label = 37
X, Y, A = _simple_threshold_data(number_a0, number_a1, score_threshold,
score_threshold, a0_label, a1_label)
target = GridSearch(LogisticRegression(solver='liblinear',
fit_intercept=True),
disparity_metric=moments.DemographicParity(),
quality_metric=SimpleClassificationQualityMetric(),
grid_size=11)
target.fit(X, Y, sensitive_features=A)
assert len(target.all_results) == 11
test_X = pd.DataFrame({
"actual_feature": [0.2, 0.7],
"sensitive_features": [a0_label, a1_label],
"constant_ones_feature": [1, 1]
})
sample_results = target.predict(test_X)
sample_proba = target.predict_proba(test_X)
assert np.allclose(sample_proba,
[[0.53748641, 0.46251359], [0.46688736, 0.53311264]])
sample_results = target.all_results[0].model.predict(test_X)
assert np.array_equal(sample_results, [1, 0])
all_results = target.posterior_predict(test_X)
assert len(all_results) == 11
all_proba = target.posterior_predict_proba(test_X)
assert len(all_proba) == 11
def test_bgl_unfair():
a0_count = 5
a1_count = 7
a0_label = 2
a1_label = 3
a0_factor = 1
a1_factor = 16
X, Y, A = _simple_regression_data(a0_count, a1_count, a0_factor, a1_factor,
a0_label, a1_label)
target = GridSearch(LinearRegression(),
disparity_metric=moments.GroupLossMoment(
moments.ZeroOneLoss()),
quality_metric=SimpleRegressionQualityMetric(),
grid_size=7)
target.fit(X, Y, sensitive_features=A)
assert len(target.all_results) == 7
test_X = pd.DataFrame({
"actual_feature": [0.2, 0.7],
"sensitive_features": [a0_label, a1_label],
"constant_ones_feature": [1, 1]
})
best_predict = target.predict(test_X)
assert np.allclose([-1.91764706, 9.61176471], best_predict)
all_predict = target.posterior_predict(test_X)
assert np.allclose(
[[3.2, 11.2], [-3.47346939, 10.64897959], [-2.68, 10.12],
[-1.91764706, 9.61176471], [-1.18461538, 9.12307692],
[-0.47924528, 8.65283019], [0.2, 0.7]], all_predict)
