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()