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