def BuildRecModel():
np.random.seed(0)
random.seed(0)
# Load up common data set for the recommender algorithms
(evaluationData, rankings) = LoadData()
# Construct an Evaluator to, you know, evaluate them
evaluator = Evaluator(evaluationData, rankings)
hotelKNNAlgorithm = HotelKNNAlgorithm()
log.info('Algorithm=K-Nearest Neighbour')
evaluator.SetAlgorithm(hotelKNNAlgorithm, 'HotelKNNAlgorithm')
evaluator.Evaluate(False)
evaluator.TrainAndSaveAlgorithm()
def GetRecommendations(user, k, lat, lon):
np.random.seed(0)
random.seed(0)
# Load up common data set for the recommender algorithms
(evaluationData, rankings) = LoadDataForLocation(lat, lon, user)
if (evaluationData.size > 100):
# Construct an Evaluator to, you know, evaluate them
reader = Reader(rating_scale=(0, 3))
filteredData = Dataset.load_from_df(evaluationData, reader=reader)
evaluator = Evaluator(filteredData, rankings)
hotelKNNAlgorithm = HotelKNNAlgorithm()
evaluator.SetAlgorithm(hotelKNNAlgorithm, 'HotelKNNAlgorithm')
return evaluator.GetTopNRecs(user, k)
else:
log.error('Not enough hotel in range')
return []
