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'
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 render_admin_panel(self, req, cat, page, path_info):
req.perm.assert_permission('SPAM_CONFIG')
bayes = BayesianFilterStrategy(self.env)
hammie = bayes._get_hammie()
data = {}
if req.method == 'POST':
if 'train' in req.args:
bayes.train(None, None, req.args['content'],
spam='spam' in req.args['train'].lower())
req.redirect(req.href.admin(cat, page))
elif 'test' in req.args:
data['content'] = req.args['content']
try:
data['score'] = hammie.score(req.args['content'].encode('utf-8'))
except Exception, e:
self.log.warn('Bayes test failed: %s', e, exc_info=True)
data['error'] = unicode(e)
else:
if 'reset' in req.args:
self.log.info('Resetting SpamBayes training database')
db = self.env.get_db_cnx()
cursor = db.cursor()
cursor.execute("DELETE FROM spamfilter_bayes")
db.commit()
try:
min_training = int(req.args['min_training'])
if min_training != bayes.min_training:
self.config.set('spam-filter', 'bayes_min_training',
min_training)
self.config.save()
except ValueError:
pass
req.redirect(req.href.admin(cat, page))
def render_admin_panel(self, req, cat, page, path_info):
req.perm.require('SPAM_CONFIG')
bayes = BayesianFilterStrategy(self.env)
hammie = bayes._get_hammie()
data = {}
if req.method == 'POST':
if 'train' in req.args:
bayes.train(None, None, req.args['bayes_content'], '127.0.0.1',
spam='spam' in req.args['train'].lower())
req.redirect(req.href.admin(cat, page))
elif 'test' in req.args:
bayes_content = req.args['bayes_content']
data['content'] = bayes_content
try:
data['score'] = hammie.score(bayes_content.encode('utf-8'))
except Exception, e:
self.log.warn('Bayes test failed: %s', e, exc_info=True)
data['error'] = unicode(e)
else:
if 'reset' in req.args:
self.log.info('Resetting SpamBayes training database')
self.env.db_transaction("DELETE FROM spamfilter_bayes")
elif 'reduce' in req.args:
self.log.info('Reducing SpamBayes training database')
bayes.reduce()
min_training = req.args.as_int('min_training')
if min_training is not None and \
min_training != bayes.min_training:
self.config.set('spam-filter', 'bayes_min_training',
min_training)
self.config.save()
min_dbcount = req.args.as_int('min_dbcount')
if min_dbcount is not None and \
min_dbcount != bayes.min_dbcount:
self.config.set('spam-filter', 'bayes_min_dbcount',
min_dbcount)
self.config.save()
req.redirect(req.href.admin(cat, page))
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)
