def get_recommendations(user_id):
data, items_length = prepare_data()
name_algorithm, algorithm = randomize()
trainset = data.build_full_trainset()
algo = algorithm
algo.fit(trainset)
ratings = []
db = Database()
places = db.findAllPlaces()
formated_ratings = []
for i in range(1, (items_length) + 1):
predict = algo.predict(user_id, i, r_ui=4)
dictionary = {
'user': user_id,
'item': i,
'est_rating': float(predict.est),
'impossible': predict.details['was_impossible']
}
ratings.append(dictionary)
formated_ratings = create_dict(ratings, places, name_algorithm)
return formated_ratings
def get(self, user_id=None):
if not user_id:
db = Database()
result = db.findAllPlaces()
return json.dumps(result)
recommendations = get_recommendations(user_id)
return recommendations
def get(self, user_id=None):
db = Database()
if not user_id:
result = db.findAllUsers()
return json.dumps(result)
result = db.findUserById(user_id)
return json.dumps(result)
def post(self):
args = parser.parse_args()
ratings = args.get('ratings')
for rtUser in ratings:
rtUserDict = ast.literal_eval(rtUser)
db = Database()
db.insertRating(rtUserDict['userId'], rtUserDict['placeId'],
rtUserDict['rate'])
return
def post(self):
args = parser.parse_args()
ratings = args.get('ratings')
for rtUser in ratings:
rtUserDict = ast.literal_eval(rtUser)
db = Database()
db.insertSugestion(rtUserDict['userId'], rtUserDict['placeId'],
rtUserDict['rate_prevision'],
rtUserDict['rate_user'],
rtUserDict['algorithm'])
return
def get_top_n_recommendations(user_id, n):
data, items_length = prepare_data()
# Modelo de recomendação
trainset = data.build_full_trainset()
algo = SVD()
algo.fit(trainset)
ratings_pre = []
ratings = []
for i in range(1, (items_length) + 1):
predict = algo.predict(user_id, i, r_ui=4)
if not predict.details['was_impossible']:
dictionary = {
'user': user_id,
'item': i,
'rating': float(predict.est)
}
ratings_pre.append(dictionary)
ratings_pre = sorted(ratings_pre, key=lambda x: x['rating'], reverse=True)
for i in range(0, n):
ratings.append(ratings_pre[i])
places = []
db = Database()
for place in ratings:
places.append(db.findByIdPlaces(place['item']))
listRecommendations = []
for i in range(0, n):
dict_ = {
'userId': ratings[i]['user'],
'name': places[i]['name'],
'photoUrl': places[i]['photoUrl'],
'rate_prevision': round(ratings[i]['rating'], 2),
'rate_user': 0
}
listRecommendations.append(dict_)
return listRecommendations
def prepare_data():
db = Database()
data_pre = db.findAllRatings()
user_id = []
item_id = []
rating = []
for row in data_pre:
user_id.append(row['user_id'])
item_id.append(row['place_id'])
rating.append(row['rate'])
ratings_dict = {'itemID': item_id, 'userID': user_id, 'rating': rating}
df = pd.DataFrame(ratings_dict)
reader = Reader(rating_scale=(1, 5))
data = Dataset.load_from_df(df[['userID', 'itemID', 'rating']], reader)
items_length = len(df['itemID'].unique())
return (data, items_length)
def get(self):
db = Database()
result = db.findAllRatings()
return result
def post(self):
args = parser.parse_args()
db = Database()
user = db.insertUser(args.email)
return json.dumps(user)