def test_fit_no_hold(self):
mv_obj = MajorityVote(hold_out=False)
result = mv_obj.fit(MED_MATRIX)
self.assertTrue(result['labels'][0] == 3)
self.assertTrue(result['labels'][1] == 3)
self.assertTrue(result['labels'][2] == 3)
self.assertTrue(result['labels'][3] == 2)
self.assertTrue(result['labels'][4] == 2)
self.assertTrue(result['labels'][5] == 1)
self.assertTrue(result['labels'][6] == 1)
self.assertTrue(result['labels'][7] == 3)
self.assertTrue(result['labels'][8] == 1 or result['labels'][8] == 4)
self.assertTrue(result['labels'][9] == 1 or result['labels'][9] == 2)
self.assertTrue(result['accuracies'][0] in [0.4, 0.5, 0.6])
self.assertTrue(result['accuracies'][1] in [0.4])
self.assertTrue(result['accuracies'][2] in [0.4, 0.5, 0.6])
self.assertTrue(result['accuracies'][3] in [0.7, 0.8, 0.9])
self.assertTrue(result['accuracies'][4] in [0.7, 0.8, 0.9])
self.assertListEqual(result['class_names'].tolist(), [1, 2, 3, 4])
def test_fit(self):
# Since some labels are tied for most popular, pass a fixed random seed
mv_obj = MajorityVote(hold_out=True)
result = mv_obj.fit(MED_MATRIX)
self.assertTrue(result['labels'][0] == 3)
self.assertTrue(result['labels'][1] == 3)
self.assertTrue(result['labels'][2] == 3)
self.assertTrue(result['labels'][3] == 2)
self.assertTrue(result['labels'][4] == 2)
self.assertTrue(result['labels'][5] == 1)
self.assertTrue(result['labels'][6] == 1)
self.assertTrue(result['labels'][7] == 3)
self.assertTrue(result['labels'][8] == 1 or result['labels'][8] == 4)
self.assertTrue(result['labels'][9] == 1 or result['labels'][9] == 2)
self.assertTrue(result['accuracies'][0] in [0.3, 0.4])
self.assertTrue(result['accuracies'][1] in [0.3, 0.4])
self.assertTrue(result['accuracies'][2] in [0.4])
self.assertTrue(result['accuracies'][3] in [0.5, 0.6, 0.7])
self.assertTrue(result['accuracies'][4] in [0.4, 0.5, 0.6, 0.7])
self.assertListEqual(result['class_names'].tolist(), [1, 2, 3, 4])
def test_fit_no_hold(self):
mv_obj = MajorityVote(hold_out=False)
result = mv_obj.fit(MED_MATRIX)
self.assertTrue(result['labels'][0] == 'C')
self.assertTrue(result['labels'][1] == 'C')
self.assertTrue(result['labels'][2] == 'C')
self.assertTrue(result['labels'][3] == 'B')
self.assertTrue(result['labels'][4] == 'B')
self.assertTrue(result['labels'][5] == 'A')
self.assertTrue(result['labels'][6] == 'A')
self.assertTrue(result['labels'][7] == 'C')
self.assertTrue(result['labels'][8] == 'A')
self.assertTrue(result['labels'][9] == 'A'
or result['labels'][9] == 'B')
self.assertTrue(result['accuracies'][0] in [4.0 / 9.0, 5.0 / 9.0])
self.assertTrue(result['accuracies'][1] in [0.4])
self.assertTrue(result['accuracies'][2] in [0.5, 0.6])
self.assertTrue(result['accuracies'][3] in [0.8, 0.9])
self.assertTrue(result['accuracies'][4] in [7.0 / 9.0, 8.0 / 9.0])
self.assertListEqual(result['class_names'].tolist(),
['A', 'B', 'C', 'D'])
def test_fit(self):
# Since some labels are tied for most popular, pass a fixed random seed
mv_obj = MajorityVote(hold_out=True)
result = mv_obj.fit(MED_MATRIX)
print(result['accuracies'])
self.assertTrue(result['labels'][0] == 'C')
self.assertTrue(result['labels'][1] == 'C')
self.assertTrue(result['labels'][2] == 'C')
self.assertTrue(result['labels'][3] == 'B')
self.assertTrue(result['labels'][4] == 'B')
self.assertTrue(result['labels'][5] == 'A')
self.assertTrue(result['labels'][6] == 'A')
self.assertTrue(result['labels'][7] == 'C')
self.assertTrue(result['labels'][8] == 'A')
self.assertTrue(result['labels'][9] == 'A'
or result['labels'][9] == 'B')
self.assertTrue(result['accuracies'][0] in [3.0 / 9.0, 4.0 / 9.0])
self.assertTrue(result['accuracies'][1] in [0.3, 0.4])
self.assertTrue(result['accuracies'][2] in [0.4, 0.5])
self.assertTrue(result['accuracies'][3] in [0.5, 0.6, 0.7, 0.8])
self.assertTrue(result['accuracies'][4] in
[4.0 / 9.0, 5.0 / 9.0, 6.0 / 9.0, 7.0 / 9.0])
self.assertListEqual(result['class_names'].tolist(),
['A', 'B', 'C', 'D'])
def test_return_fields(self):
mv_obj = MajorityVote()
result = mv_obj.fit(MED_MATRIX)
self.assertEqual(set(result.keys()), FIT_RETURN_SET)