def test_fnr_diff_edge2(self): # Metric metric = BinaryFairnessMetrics.FNRDifference() # edge case of -1 y_true = np.array([0, 1, 1, 0, 1, 1, 1, 0, 1, 0]) y_pred = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1]) is_member = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1]) assert metric.get_score(y_true, y_pred, is_member) == -1
def test_fnr_diff_normal_invalid(self): # Metric metric = BinaryFairnessMetrics.FNRDifference() # Data y_true = np.array([0, 1, 1, 0, 1, 1, 1, 0, 1, 2]) y_pred = np.array([0, 0, 1, 0, 0, 1, 1, 1, 0, 0]) is_member = np.array([1, 1, 1, 1, 1, 0, 0, 0, 0, 0]) with self.assertRaises(ValueError): metric.get_score(y_true, y_pred, is_member)
def test_fnr_diff_normal_list(self): # Metric metric = BinaryFairnessMetrics.FNRDifference() # Data y_true = [0, 1, 1, 0, 1, 1, 1, 0, 1, 0] y_pred = [0, 0, 1, 0, 0, 1, 1, 1, 0, 0] is_member = np.array([1, 1, 1, 1, 1, 0, 0, 0, 0, 0]) assert np.isclose(metric.get_score(y_true, y_pred, is_member), 0.333, atol=0.001)
def test_fnr_diff_normal_df(self): my_df = pd.DataFrame.from_dict({ 'y_true': [0, 1, 1, 0, 1, 1, 1, 0, 1, 0], 'y_pred': [0, 0, 1, 0, 0, 1, 1, 1, 0, 0], 'is_member': [1, 1, 1, 1, 1, 0, 0, 0, 0, 0] }) # Metric metric = BinaryFairnessMetrics.FNRDifference() # Score score = metric.get_score(my_df['y_true'], my_df['y_pred'], my_df['is_member']) assert np.isclose(score, 0.333, atol=0.001)