Unittest for bank and fixed linting errors

tests/test_standard_datasets.py

@@ -1,49 +1,62 @@
+""" Tests for standard dataset classes """
+
+from unittest.mock import patch
 import numpy as np
 import pandas as pd
 
-pd.set_option('display.max_rows', 50)
-pd.set_option('display.max_columns', 10)
-pd.set_option('display.width', 200)
-
 from aif360.datasets import AdultDataset
 from aif360.datasets import BankDataset
 from aif360.datasets import CompasDataset
 from aif360.datasets import GermanDataset
 from aif360.metrics import BinaryLabelDatasetMetric
 
+pd.set_option('display.max_rows', 50)
+pd.set_option('display.max_columns', 10)
+pd.set_option('display.width', 200)
 
 def test_compas():
+    ''' Test default loading for compas '''
     # just test that there are no errors for default loading...
-    cd = CompasDataset()
-    # print(cd)
+    compas_dataset = CompasDataset()
+    compas_dataset.validate_dataset()
 
 def test_german():
-    gd = GermanDataset()
-    bldm = BinaryLabelDatasetMetric(gd)
+    ''' Test default loading for german '''
+    german_dataset = GermanDataset()
+    bldm = BinaryLabelDatasetMetric(german_dataset)
     assert bldm.num_instances() == 1000
 
 def test_adult_test_set():
-    ad = AdultDataset()
-    # test, train = ad.split([16281])
-    test, train = ad.split([15060])
+    ''' Test default loading for adult, test set '''
+    adult_dataset = AdultDataset()
+    test, _ = adult_dataset.split([15060])
     assert np.any(test.labels)
 
 def test_adult():
-    ad = AdultDataset()
-    # print(ad.feature_names)
-    assert np.isclose(ad.labels.mean(), 0.2478, atol=5e-5)
-
-    bldm = BinaryLabelDatasetMetric(ad)
+    ''' Test default loading for adult, mean'''
+    adult_dataset = AdultDataset()
+    assert np.isclose(adult_dataset.labels.mean(), 0.2478, atol=5e-5)
+    bldm = BinaryLabelDatasetMetric(adult_dataset)
     assert bldm.num_instances() == 45222
 
 def test_adult_no_drop():
-    ad = AdultDataset(protected_attribute_names=['sex'],
+    ''' Test default loading for adult, number of instances '''
+    adult_dataset = AdultDataset(protected_attribute_names=['sex'],
         privileged_classes=[['Male']], categorical_features=[],
         features_to_keep=['age', 'education-num'])
-    bldm = BinaryLabelDatasetMetric(ad)
+    bldm = BinaryLabelDatasetMetric(adult_dataset)
     assert bldm.num_instances() == 48842
 
 def test_bank():
-    ''' Check for errors during default loading '''
-    bd = BankDataset()
-
+    ''' Test for errors during default loading '''
+    bank_dataset = BankDataset()
+    bank_dataset.validate_dataset()
+
+@patch("pandas.read_csv")
+def test_bank_priviliged_attributes(mock_read_csv):
+    ''' Test if priviliged attributes are correctly transformed '''
+    data = {'y': ['yes', 'no', 'no', 'yes'],
+        'age': [43, 18, 89, 25]}
+    mock_read_csv.return_value = pd.DataFrame(data)
+    bank_dataset = BankDataset(categorical_features=[])
+    assert bank_dataset.convert_to_dataframe()[0]["age"].tolist() == [1.0, 0.0, 0.0, 1.0]