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 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
class Recommendare:
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 = {}
def user_prediction(self, user_id, movie, neighbour):
if not movie in self.recommended_movies.keys():
self.recommended_movies[movie] = {
'slope_rating':
self.slope_one.predict_rating(user_id, int(movie)),
'neighbours': []
}
self.recommended_movies[movie]['neighbours'].append({
'neighbour_id':
neighbour['user_id'],
'neighbour_rating':
self.user_similarity.get_user_rating_for(neighbour['user_id'],
movie),
'neighbour_similarity':
neighbour['similarity']
})
def rate_neighbours_movies(self, user_id, count):
neighbours = self.user_similarity.get_neighbours_movies(user_id, k=3)
all_movies = []
for neighbour in neighbours:
all_movies.append(set(neighbour['movies']))
temp = set.intersection(*all_movies)
if len(temp) >= count:
all_movies = temp
else:
all_movies = set.union(*all_movies)
for neighbour in neighbours:
for n_movie in neighbour['movies']:
if n_movie in all_movies:
self.pool.apply_async(self.user_prediction,
(user_id, n_movie, neighbour))
self.pool.close()
self.pool.join()
def get_recommended_movies(self, user_id, count):
if len(self.recommended_movies) == 0:
self.rate_neighbours_movies(user_id, count)
return self.recommended_movies
def recommend(self, user_id, count):
movies_list = self.get_recommended_movies(user_id, count)
recommendations = []
for movie in movies_list:
num = 0
den = 0
for neighbour in movies_list[movie]['neighbours']:
num += neighbour['neighbour_similarity'] * neighbour[
'neighbour_rating']
den += neighbour['neighbour_similarity']
if den == 0:
predicted_rating = 0
else:
predicted_rating = num / float(den)
if predicted_rating == 0:
predicted_rating = movies_list[movie]['slope_rating']
else:
predicted_rating = (
predicted_rating +
movies_list[movie]['slope_rating']) / float(2)
recommendations.append((movie, predicted_rating))
return sorted(recommendations, key=itemgetter(1), reverse=True)[:count]
class Recommendare:
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 = {}
def user_prediction(self, user_id, movie, neighbour):
if not movie in self.recommended_movies.keys():
self.recommended_movies[movie] = {
'slope_rating': self.slope_one.predict_rating(user_id, int(movie)),
'neighbours': []
}
self.recommended_movies[movie]['neighbours'].append({
'neighbour_id': neighbour['user_id'],
'neighbour_rating': self.user_similarity.get_user_rating_for(neighbour['user_id'], movie),
'neighbour_similarity': neighbour['similarity']
})
def rate_neighbours_movies(self, user_id, count):
neighbours = self.user_similarity.get_neighbours_movies(user_id, k = 3)
all_movies = []
for neighbour in neighbours:
all_movies.append(set(neighbour['movies']))
temp = set.intersection(*all_movies)
if len(temp) >= count:
all_movies = temp
else:
all_movies = set.union(*all_movies)
for neighbour in neighbours:
for n_movie in neighbour['movies']:
if n_movie in all_movies:
self.pool.apply_async(self.user_prediction, (user_id, n_movie, neighbour))
self.pool.close()
self.pool.join()
def get_recommended_movies(self, user_id, count):
if len(self.recommended_movies) == 0:
self.rate_neighbours_movies(user_id, count)
return self.recommended_movies
def recommend(self, user_id, count):
movies_list = self.get_recommended_movies(user_id, count)
recommendations = []
for movie in movies_list:
num = 0
den = 0
for neighbour in movies_list[movie]['neighbours']:
num += neighbour['neighbour_similarity'] * neighbour['neighbour_rating']
den += neighbour['neighbour_similarity']
if den == 0:
predicted_rating = 0
else:
predicted_rating = num / float(den)
if predicted_rating == 0:
predicted_rating = movies_list[movie]['slope_rating']
else:
predicted_rating = (predicted_rating + movies_list[movie]['slope_rating']) / float(2)
recommendations.append((movie, predicted_rating))
return sorted(recommendations, key = itemgetter(1), reverse = True)[:count]