import sys from algorithm.dataSet import DataSet from algorithm.knn import Knn from lib.userFunction.friendExtractor import getUserFriends from lib.userFunction.locationEstimation import getUserLocation from lib.userFunction.businessExtractor import getUserReviews from util.errorCheck import getRating, MAE from plots.bubblePlot import BubblePlot from settings import SYS_ENCODING_UTF, PLOT_RESULTS, DISTANCE_TO_FILTER, TIME_TO_FILTER, \ KNN_NEIGHBOURS, ENABLE_DISTANCE_FILTER, ENABLE_TIME_FILTER, REF_USER_ID, USE_FRIENDS reload(sys) sys.setdefaultencoding(SYS_ENCODING_UTF) # dataSet = DataSet(JSON_FILE_PATH + JSON_FILE_NAME) dataSet = DataSet() print print REF_USER_ID # friend = getUserFriends(REF_USER_ID)['friends'][1] # print "User Location: ", getUserLocation(REF_USER_ID) if USE_FRIENDS: friends_data = getUserFriends(REF_USER_ID) friends = [REF_USER_ID] + friends_data["friends"] print "No of friends ", friends_data["friends_count"] dataSet.getRawData(friends) else: dataSet.getRawData([REF_USER_ID]) print("User Location %s, %s, %s" % (dataSet.loc["name"], dataSet.loc["admin1"], dataSet.loc["cc"]))
import sys from algorithm.dataSet import DataSet from algorithm.knn import Knn from util.errorCheck import getRating, MAE from plots.bubblePlot import BubblePlot from settings import SYS_ENCODING_UTF, JSON_FILE_PATH, JSON_FILE_NAME, PLOT_RESULTS, DISTANCE_TO_FILTER, TIME_TO_FILTER, \ KNN_NEIGHBOURS, ENABLE_DISTANCE_FILTER, ENABLE_TIME_FILTER reload(sys) sys.setdefaultencoding(SYS_ENCODING_UTF) dataSet = DataSet(JSON_FILE_PATH + JSON_FILE_NAME) dataSet.loadRawData() dataSet.processBusinessModels() print("\nNumber of Business Models: %s" % len(dataSet.businessModels)) dataSet.sliceData() dataSet.trainUserModel() if ENABLE_TIME_FILTER: dataSet.timeFilterBusinessModel(TIME_TO_FILTER) if ENABLE_DISTANCE_FILTER: dataSet.distFilterBusinessModel(DISTANCE_TO_FILTER) print("Test Data: %s" % len(dataSet.testData)) print("Training Data: %s \n" % len(dataSet.trainingData)) knn = Knn() knn.inputData = dataSet predictions = knn.getNearestNeighbours(KNN_NEIGHBOURS) for index, p in enumerate(predictions):
import sys from algorithm.dataSet import DataSet from algorithm.knn import Knn from lib.userFunction.friendExtractor import getUserFriends from lib.userFunction.locationEstimation import getUserLocation from lib.userFunction.businessExtractor import getUserReviews from util.errorCheck import getRating, MAE from plots.bubblePlot import BubblePlot from settings import SYS_ENCODING_UTF, PLOT_RESULTS, DISTANCE_TO_FILTER, TIME_TO_FILTER, \ KNN_NEIGHBOURS, ENABLE_DISTANCE_FILTER, ENABLE_TIME_FILTER, REF_USER_ID, USE_FRIENDS reload(sys) sys.setdefaultencoding(SYS_ENCODING_UTF) # dataSet = DataSet(JSON_FILE_PATH + JSON_FILE_NAME) dataSet = DataSet() print print REF_USER_ID # friend = getUserFriends(REF_USER_ID)['friends'][1] # print "User Location: ", getUserLocation(REF_USER_ID) if USE_FRIENDS: friends_data = getUserFriends(REF_USER_ID) friends = [REF_USER_ID] + friends_data["friends"] print "No of friends ", friends_data["friends_count"] dataSet.getRawData(friends) else: dataSet.getRawData([REF_USER_ID]) print ("User Location %s, %s, %s" % (dataSet.loc["name"], dataSet.loc["admin1"], dataSet.loc["cc"])) dataSet.processBusinessModels()