class BayesianFilterStrategyTestCase(unittest.TestCase):
def setUp(self):
self.env = EnvironmentStub(enable=[BayesianFilterStrategy])
self.env.config.set('spam-filter', 'bayes_karma', '10')
FilterSystem(self.env).upgrade_environment()
self.strategy = BayesianFilterStrategy(self.env)
def tearDown(self):
reset_db(self.env)
def test_karma_calculation_unsure(self):
bayes.Hammie = lambda x: Mock(score=lambda x: .5,
bayes=Mock(nham=1000, nspam=1000))
req = MockRequest(self.env)
self.assertEquals(None, self.strategy.test(req, 'John Doe', 'Spam',
'127.0.0.1'))
def test_karma_calculation_negative(self):
bayes.Hammie = lambda x: Mock(score=lambda x: .75,
bayes=Mock(nham=1000, nspam=1000))
req = MockRequest(self.env)
points, reasons, args = \
self.strategy.test(req, 'John Doe', 'Spam', '127.0.0.1')
self.assertEquals(-5, points)
def test_karma_calculation_positive(self):
bayes.Hammie = lambda x: Mock(score=lambda x: .25,
bayes=Mock(nham=1000, nspam=1000))
req = MockRequest(self.env)
points, reasons, args = \
self.strategy.test(req, 'John Doe', 'Spam', '127.0.0.1')
self.assertEquals(5, points)
def test_classifier_untrained(self):
req = MockRequest(self.env)
self.assertEqual(None, self.strategy.test(req, 'John Doe', 'Hammie',
'127.0.0.1'))
def test_classifier_basics(self):
req = MockRequest(self.env)
self.env.config.set('spam-filter', 'bayes_min_training', '1')
self.strategy.train(req, 'John Doe', 'Spam spam spammie', '127.0.0.1',
True)
self.strategy.train(req, 'John Doe', 'Ham ham hammie', '127.0.0.1',
False)
points, reasons, args = \
self.strategy.test(req, 'John Doe', 'Hammie', '127.0.0.1')
self.assertGreater(points, 0, 'Expected positive karma')
points, reasons, args = \
self.strategy.test(req, 'John Doe', 'Spam', '127.0.0.1')
self.assertLess(points, 0, 'Expected negative karma')
class BayesianFilterStrategyTestCase(unittest.TestCase):
def setUp(self):
self.env = EnvironmentStub(enable=[BayesianFilterStrategy])
self.env.config.set('spam-filter', 'bayes_karma', '10')
db = self.env.get_db_cnx()
cursor = db.cursor()
for table in schema:
for stmt in _to_sql(table):
cursor.execute(stmt)
self.strategy = BayesianFilterStrategy(self.env)
def test_karma_calculation_unsure(self):
bayes.Hammie = lambda x: Mock(score=lambda x: .5,
bayes=Mock(nham=1000, nspam=1000))
req = Mock(authname='anonymous', base_url='http://example.org/',
remote_addr='127.0.0.1')
self.assertEquals(None, self.strategy.test(req, 'John Doe', 'Spam'))
def test_karma_calculation_negative(self):
bayes.Hammie = lambda x: Mock(score=lambda x: .75,
bayes=Mock(nham=1000, nspam=1000))
req = Mock(authname='anonymous', base_url='http://example.org/',
remote_addr='127.0.0.1')
points, reasons = self.strategy.test(req, 'John Doe', 'Spam')
self.assertEquals(-5, points)
def test_karma_calculation_positive(self):
bayes.Hammie = lambda x: Mock(score=lambda x: .25,
bayes=Mock(nham=1000, nspam=1000))
req = Mock(authname='anonymous', base_url='http://example.org/',
remote_addr='127.0.0.1')
points, reasons = self.strategy.test(req, 'John Doe', 'Spam')
self.assertEquals(5, points)
def test_classifier_untrained(self):
req = Mock(authname='anonymous', base_url='http://example.org/',
remote_addr='127.0.0.1')
self.assertEqual(None, self.strategy.test(req, 'John Doe', 'Hammie'))
def test_classifier_basics(self):
req = Mock(authname='anonymous', base_url='http://example.org/',
remote_addr='127.0.0.1')
self.env.config.set('spam-filter', 'bayes_min_training', '1')
self.strategy.train(req, 'John Doe', 'Spam spam spammie', True)
self.strategy.train(req, 'John Doe', 'Ham ham hammie', False)
points, reasons = self.strategy.test(req, 'John Doe', 'Hammie')
assert points > 0, 'Expected positive karma'
points, reasons = self.strategy.test(req, 'John Doe', 'Spam')
assert points < 0, 'Expected negative karma'
