def setUp(self):
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG)
config = load_config(file_path="/vagrant/config.yaml", logger=logger, exit_with_error=True)
#Connect to test database
connect("nyan_test", port=27017)
fill_database()
#connect(config['database']['db-name'],
# username= config['database']['user'],
# password= config['database']['passwd'],
# port = config['database']['port'])
self.user_id = User.objects(email=u'*****@*****.**').first().id
#feature_extractor = EsaFeatureExtractor(prefix = config['prefix'])
feature_extractor = TfidfFeatureExtractor(prefix=config['prefix'])
self.trainer = UserModelBayes(self.user_id, extractor=feature_extractor)
class UserModelBayesTest(unittest.TestCase):
def setUp(self):
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG)
config = load_config(file_path="/vagrant/config.yaml", logger=logger, exit_with_error=True)
#Connect to test database
connect("nyan_test", port=27017)
fill_database()
#connect(config['database']['db-name'],
# username= config['database']['user'],
# password= config['database']['passwd'],
# port = config['database']['port'])
self.user_id = User.objects(email=u'*****@*****.**').first().id
#feature_extractor = EsaFeatureExtractor(prefix = config['prefix'])
feature_extractor = TfidfFeatureExtractor(prefix=config['prefix'])
self.trainer = UserModelBayes(self.user_id, extractor=feature_extractor)
def tearDown(self):
clear_database()
@unittest.skip("training")
def test_save_load(self):
self.trainer.train()
tmp_classifier = self.trainer.clf
self.trainer.save()
self.trainer.load()
self.assertEqual(tmp_classifier.sigma_.all(),
self.trainer.clf.sigma_.all())
self.assertEqual(tmp_classifier.theta_.all(),
self.trainer.clf.theta_.all())
@unittest.skip("training")
def test_get_unread(self):
unread_articles = self.trainer._get_unread()
headlines = [a.headline for a in unread_articles]
self.assertIn(u"Apple = Bad", headlines)
self.assertNotIn(u"Apple", headlines)
self.assertEqual(len(headlines), 1)
@unittest.skip("ranking")
def test_rank(self):
self.trainer.train()
unread_doc = Article.objects(headline=u"Sony = Bad").first()
read_doc = Article.objects(headline=u"Apple").first()
rank_unread_doc = self.trainer.rank(unread_doc)
rank_read_doc = self.trainer.rank(read_doc)
self.assertEqual(rank_unread_doc, UserModelBayes.UNREAD)
self.assertEqual(rank_read_doc, UserModelBayes.READ)