def test_equal_opp_edge_3(self): # Data: homogeneous both groups in ground truth - returns nan y_true = np.array([0, 0, 0, 0, 1, 1, 1, 1]) y_pred = np.array([0, 1, 1, 1, 1, 1, 1, 0]) is_member = np.array([1, 1, 1, 1, 0, 0, 0, 0]) # Metric metric = BinaryFairnessMetrics.EqualOpportunity() with self.assertWarns( UserWarning): # division by zero caught inside numpy metric.get_score(y_true, y_pred, is_member)
def test_equal_opp_normal_invalid(self): # Data y_true = np.array([1, 0, 0, 0, 1, 1, 0, 2]) y_pred = np.array([0, 1, 1, 1, 1, 1, 1, 0]) is_member = np.array([1, 1, 1, 1, 0, 0, 0, 0]) # Metric metric = BinaryFairnessMetrics.EqualOpportunity() # Score with self.assertRaises(ValueError): metric.get_score(y_true, y_pred, is_member)
def test_equal_opp_edge_2(self): # Data y_true = np.array([1, 0, 0, 0, 1, 1, 1, 1]) y_pred = np.array([1, 1, 1, 1, 0, 0, 0, 0]) # edge case equal opp == -1 is_member = np.array([0, 0, 0, 0, 1, 1, 1, 1]) # Metric metric = BinaryFairnessMetrics.EqualOpportunity() with self.assertWarns( UserWarning): # division by zero caught inside numpy assert metric.get_score(y_true, y_pred, is_member) == -1
def test_equal_opp_normal_list(self): # Data y_true = [1, 0, 0, 0, 1, 1, 0, 1] y_pred = [0, 1, 1, 1, 1, 1, 1, 0] is_member = [1, 1, 1, 1, 0, 0, 0, 0] # Metric metric = BinaryFairnessMetrics.EqualOpportunity() # Score score = metric.get_score(y_true, y_pred, is_member) assert np.isclose(score, -0.666, atol=0.001)
def test_equal_opp_normal_df(self): # medium number my_df = pd.DataFrame.from_dict({ 'y_true': [1, 0, 0, 0, 1, 1, 0, 1], 'y_pred': [0, 1, 1, 1, 1, 1, 1, 0], 'is_member': [1, 1, 1, 1, 0, 0, 0, 0] }) # Metric metric = BinaryFairnessMetrics.EqualOpportunity() # Score score = metric.get_score(my_df['y_true'], my_df['y_pred'], my_df['is_member']) assert np.isclose(score, -0.666, atol=0.001)