def test_deviation(self):
taylor_swift = {'Amy': 4, 'Ben': 5, 'Daisy': 5}
PSY = {'Amy': 3, 'Ben': 2, 'Clara': 3.5}
slope = SlopeOne()
self.assertEqual(2, slope.deviation(taylor_swift, PSY)['deviation'])
def test_predict_rating(self):
slopeone = SlopeOne(self.db)
result = slopeone.predict_rating(479, 2)
if result is False:
print "Test Case: PREDICT RATING: FAIL"
else:
print "Test Case: PREDICT RATING: PASS"
return result
def __init__(self, db=None):
if db == None:
client = MongoClient(config.db_config['host'],
config.db_config['port'])
self.db = client.hypertarget_ads
else:
self.db = db
self.user_similarity = UserSimilarity(self.db)
self.slope_one = SlopeOne(self.db)
def __init__(self, db=None):
if db == None:
client = MongoClient(config.db_config['host'],
config.db_config['port'])
self.db = client.hypertarget_ads
else:
self.db = db
self.user_similarity = UserSimilarity(self.db)
self.slope_one = SlopeOne(self.db)
self.pool = Pool(processes=10)
self.recommended_movies = {}
from pymongo import MongoClient
from usersimilarity import UserSimilarity
from slopeone import SlopeOne
client = MongoClient('localhost', 27017)
db = client.hypertarget_ads
slope = SlopeOne(db)
similarity = UserSimilarity(db)
slope.dump_deviation_matrix()
similarity.dump_similarity_matrix()
