def getScoreComponents(user_uuid, start, end):
# The score is based on the following components:
# - Percentage of trips classified. We are not auto-classifying high
# confidence trips, so don't need to handle those here
user = User.fromUUID(user_uuid)
pctClassified = common.getClassifiedRatio(user_uuid, start, end)
(myModeShareCount, avgModeShareCount, myModeShareDistance,
avgModeShareDistance, myModeCarbonFootprint, avgModeCarbonFootprint,
myModeCarbonFootprintNoLongMotorized,
avgModeCarbonFootprintNoLongMotorized, myOptimalCarbonFootprint,
avgOptimalCarbonFootprint, myOptimalCarbonFootprintNoLongMotorized,
avgOptimalCarbonFootprintNoLongMotorized
) = carbon.getFootprintCompareForRange(user.uuid, start, end)
carbon.delLongMotorizedModes(myModeShareDistance)
myAllDrive = carbon.getAllDrive(user.uuid, myModeShareDistance)
myCarbonFootprintSum = sum(myModeCarbonFootprintNoLongMotorized.values())
myOptimalFootprintSum = sum(
myOptimalCarbonFootprintNoLongMotorized.values())
logging.debug(
"myCarbonFootprintSum = %s, myOptimalFootprintSum = %s, myAllDrive = %s"
% (myCarbonFootprintSum, myOptimalFootprintSum, myAllDrive))
handleZero = lambda x, y: 0 if y == 0 else float(x) / y
components = [
pctClassified,
handleZero(myCarbonFootprintSum - myOptimalFootprintSum,
myOptimalFootprintSum),
handleZero(myAllDrive - myCarbonFootprintSum, myAllDrive),
handleZero(sb375DailyGoal - myCarbonFootprintSum, sb375DailyGoal)
]
return components
def testGetClassifiedRatioWithoutPredictions(self):
from copy import copy
# This doesn't really require track points, so let us insert fake data that
# has distances, predicted modes and confirmed modes
# 3 sections are confirmed, 3 sections are not yet predicted, classifiedRatio = 1.0
(user, dummySection, dummyPredModeMap) = self.setupClientTest()
for i in range(0, 3):
predSection = copy(dummySection)
predSection['_id'] = "%s confirmed" % (i)
predSection['type'] = 'move'
predSection['confirmed_mode'] = "5"
predSection['user_id'] = user.uuid
get_section_db().insert(predSection)
noPredSection = copy(dummySection)
noPredSection['_id'] = "%s nopred" % (i)
noPredSection['type'] = 'move'
noPredSection['user_id'] = user.uuid
del noPredSection['predicted_mode']
self.assertIn('predicted_mode', predSection)
get_section_db().insert(noPredSection)
logging.debug("After inserting sections, count is %s" % get_section_db().find().count())
logging.debug("Manual query count = %s" % get_section_db().find({'$and': [{'source': 'Shankari'}, {'user_id': user.uuid}, {'predicted_mode': {'$exists': True}}, {'type': 'move'}]}).count())
self.assertEqual(common.getClassifiedRatio(user.uuid, self.dayago, self.now), 1)
def getScoreComponents(user_uuid, start, end):
# The score is based on the following components:
# - Percentage of trips classified. We are not auto-classifying high
# confidence trips, so don't need to handle those here
user = User.fromUUID(user_uuid)
pctClassified = common.getClassifiedRatio(user_uuid, start, end)
(myModeShareCount, avgModeShareCount,
myModeShareDistance, avgModeShareDistance,
myModeCarbonFootprint, avgModeCarbonFootprint,
myModeCarbonFootprintNoLongMotorized, avgModeCarbonFootprintNoLongMotorized,
myOptimalCarbonFootprint, avgOptimalCarbonFootprint,
myOptimalCarbonFootprintNoLongMotorized, avgOptimalCarbonFootprintNoLongMotorized) = carbon.getFootprintCompareForRange(user.uuid, start, end)
carbon.delLongMotorizedModes(myModeShareDistance)
myAllDrive = carbon.getAllDrive(user.uuid, myModeShareDistance)
myCarbonFootprintSum = sum(myModeCarbonFootprintNoLongMotorized.values())
myOptimalFootprintSum = sum(myOptimalCarbonFootprintNoLongMotorized.values())
logging.debug("myCarbonFootprintSum = %s, myOptimalFootprintSum = %s, myAllDrive = %s" %
(myCarbonFootprintSum, myOptimalFootprintSum, myAllDrive))
handleZero = lambda x, y: 0 if y == 0 else float(x)/y
components = [pctClassified,
handleZero(myCarbonFootprintSum - myOptimalFootprintSum, myOptimalFootprintSum),
handleZero(myAllDrive - myCarbonFootprintSum, myAllDrive),
handleZero(sb375DailyGoal - myCarbonFootprintSum, sb375DailyGoal)]
return components
def testGetClassifiedRatioWithPredictions(self):
from copy import copy
# This doesn't really require track points, so let us insert fake data that
# has distances, predicted modes and confirmed modes
# 3 sections are confirmed, 3 sections are not yet predicted, classifiedRatio = 1.0
(user, dummySection, dummyPredModeMap) = self.setupClientTest()
for i in range(0, 3):
predSection = copy(dummySection)
predSection['_id'] = "%s confirmed" % (i)
predSection['type'] = 'move'
predSection['confirmed_mode'] = "5"
predSection['user_id'] = user.uuid
get_section_db().insert(predSection)
noPredSection = copy(dummySection)
noPredSection['_id'] = "%s nopred" % (i)
noPredSection['type'] = 'move'
noPredSection['user_id'] = user.uuid
# moves collect currently sets the confirmed_mode to "", so we must set it here too
# Otherwise the query won't work
noPredSection['confirmed_mode'] = ""
self.assertIn('predicted_mode', predSection)
self.assertIn('predicted_mode', noPredSection)
self.assertIn('confirmed_mode', predSection)
get_section_db().insert(noPredSection)
logging.debug("After inserting sections, count is %s" % get_section_db().find().count())
logging.debug("Manual query count = %s" % get_section_db().find({'$and': [{'source': 'Shankari'}, {'user_id': user.uuid}, {'predicted_mode': {'$exists': True}}, {'type': 'move'}]}).count())
logging.debug("Manual query count classified = %s" % get_section_db().find({'$and': [{'source': 'Shankari'}, {'user_id': user.uuid}, {'predicted_mode': {'$exists': True}}, {'type': 'move'}, {'confirmed_mode': {"$ne": ''}} ]}).count())
self.assertEqual(common.getClassifiedRatio(user.uuid, self.dayago, self.now), 3.0/6)
two_days_ago = datetime.now() - timedelta(days=2)
self.assertEqual(common.getClassifiedRatio(user.uuid, two_days_ago, self.dayago), 0)
def testGetClassifiedRatioNoTrips(self):
self.assertEqual(common.getClassifiedRatio('this is fake', self.dayago, self.now), 0)
