def computeBiggestCoordinatesFromResults( self, resCandidates ): ''' Given all the results, compute the biggest group and return a random coordinates inside ''' # results e.g. [ [5.0, [lat,lon], "tags"] ] newResCandidates = ResCandidates() coordList = [] for res in resCandidates.getList(): # get coordinates in string format: for [computeBiggestGroupOfCoordinates] strCoo = res.getCoordStr() coordList.append( strCoo ) # coo = res.getCoord() # for [computeYaelKmeansOfCoordinates] # coordList.append( coo ) # Print input coordinates list if self.verbose: print >> sys.stderr, "" print >> sys.stderr, "computeBiggestCoordinatesFromResults] coordList:", coordList # Compute the biggest group centerCoo, sumOfD = self.computeBiggestGroupOfCoordinates( coordList ) # centerCoo = self.computeYaelKmeansOfCoordinates( coordList ) # find the relative "result" with all the info if len(centerCoo) > 0: for res in resCandidates.getList(): coord = res.getCoord() if coord == centerCoo: newResCandidates.addObj( res ) return newResCandidates # otherwise something went wrong newResCandidates.add( 0.0, self.defaultCoordinates, "no results" ) return newResCandidates
def getContentVideoResults(self, filename):
resCandidates = ResCandidates()
if self.FnRes.has_key(filename):
# get list of coordiantes
listOfCoo = self.FnRes[filename]
# Select the best coo among the ones in the listOfCoo
coo, sumOfD = self.identifyCloserCoordinatesInTuple(listOfCoo)
# Add the best coordinattes in the result
resCandidates.add(sumOfD, coo, "_byContentMatching")
return resCandidates
def getContentVideoResults(self, filename): resCandidates = ResCandidates() if self.FnRes.has_key( filename ): # get list of coordiantes listOfCoo = self.FnRes[ filename ] # Select the best coo among the ones in the listOfCoo coo, sumOfD = self.identifyCloserCoordinatesInTuple( listOfCoo ) # Add the best coordinattes in the result resCandidates.add( sumOfD, coo, "_byContentMatching" ) return resCandidates
def getUserMostProbableLocation(self, uId):
''' Return the most probable location of the userId Given
'''
resCandidates = ResCandidates()
if not self.UserPlaces.has_key(uId):
return resCandidates
# check the coordinates with highest frequency
maxFreq = 0
bestCoord = self.defaultCoordinates
for coordFreq in self.UserPlaces[uId]:
lat = coordFreq[0]
lon = coordFreq[1]
acc = coordFreq[2]
freq = coordFreq[3]
if max(maxFreq, freq) == freq:
bestCoord = [lat, lon]
if bestCoord != self.defaultCoordinates:
resCandidates.add(-1, bestCoord, uId + "_userPlaces")
# resCandidates = [[ -1, bestCoord, uId+"_userPlaces" ]]
return resCandidates
def getUserMostProbableLocation( self, uId ): ''' Return the most probable location of the userId Given ''' resCandidates = ResCandidates() if not self.UserPlaces.has_key(uId): return resCandidates # check the coordinates with highest frequency maxFreq = 0 bestCoord = self.defaultCoordinates for coordFreq in self.UserPlaces[uId]: lat = coordFreq[0] lon = coordFreq[1] acc = coordFreq[2] freq = coordFreq[3] if max(maxFreq, freq) == freq: bestCoord = [ lat, lon ] if bestCoord != self.defaultCoordinates: resCandidates.add( -1, bestCoord, uId+"_userPlaces" ) # resCandidates = [[ -1, bestCoord, uId+"_userPlaces" ]] return resCandidates
def computeBiggestCoordinatesFromResults(self, resCandidates):
''' Given all the results, compute the biggest group and return a random coordinates inside
'''
# results e.g. [ [5.0, [lat,lon], "tags"] ]
newResCandidates = ResCandidates()
coordList = []
for res in resCandidates.getList():
# get coordinates in string format: for [computeBiggestGroupOfCoordinates]
strCoo = res.getCoordStr()
coordList.append(strCoo)
# coo = res.getCoord() # for [computeYaelKmeansOfCoordinates]
# coordList.append( coo )
# Print input coordinates list
if self.verbose:
print >> sys.stderr, ""
print >> sys.stderr, "computeBiggestCoordinatesFromResults] coordList:", coordList
# Compute the biggest group
centerCoo, sumOfD = self.computeBiggestGroupOfCoordinates(coordList)
# centerCoo = self.computeYaelKmeansOfCoordinates( coordList )
# find the relative "result" with all the info
if len(centerCoo) > 0:
for res in resCandidates.getList():
coord = res.getCoord()
if coord == centerCoo:
newResCandidates.addObj(res)
return newResCandidates
# otherwise something went wrong
newResCandidates.add(0.0, self.defaultCoordinates, "no results")
return newResCandidates
def multithreadTestVideosParser(self, line, varLock=""):
'''
'''
methodUsed = "All Methods"
blk = line.strip().split("\t")
# get the tags
tags = clean_line(blk[3])
mtags = clean_line(blk[4])
print >> sys.stderr, "---"
print >> sys.stderr, "* input tags: %s [mtags: %s]" % (tags, mtags)
# apply tags filters -> mtags contain the geo machine tags
# if len(mtags) > 1:
# tags += " "+ mtags # give more power to the geo tags
# Read Real Location: "45.516639|-122.681053|16|Portland|Oregon|etats-Unis|16"
location = blk[5].strip()
geo = location.split("|")
acc = geo[2]
realCoord = [float(geo[0]), float(geo[1])]
resCandidates = ResCandidates()
# 1st METHOD: check tags in training set
if len(tags) > 1:
methodUsed = "TrainSet"
resCandidates = self.matchWithGroupsOfTags(tags, mtags)
else:
self.updateLockVariables(methodUsed)
# print out info
if self.verbose:
print >> sys.stderr, "*> parseTestVideos] got %d results with" % resCandidates.size(
), methodUsed
# 2nd METHOD: (if there are NOT RESULTS) use the most probable location for this user
if resCandidates.size() == 0:
methodUsed = "UserCommonLocation"
ownGeo = blk[6].split("|")
ownId = ownGeo[0]
resCandidates = self.getUserMostProbableLocation(ownId)
if resCandidates.size() > 0:
self.updateLockVariables(methodUsed)
# print out info
if self.verbose:
print >> sys.stderr, "*> parseTestVideos] got %d results with" % resCandidates.size(
), methodUsed
# 3rd METHOD: (if there are NOT RESULTS) USE the USER HOMETOWN as tags
if resCandidates.size() == 0:
methodUsed = "HomeTown"
# Owner location: ['14678786@N00', 'milwaukee, United States']
ownGeo = blk[6].split("|")
# prepare hometown tags
ht = ownGeo[1].split(",")
hometownTags = ""
for w in ht:
if len(w) > 1:
hometownTags += w + " "
hometownTags = hometownTags[:-1]
if (hometownTags) >= 1:
# check with the new set of tags
resCandidates = self.matchWithGroupsOfTags(hometownTags, "")
if resCandidates.size() > 0:
self.updateLockVariables(methodUsed)
# print out info
if self.verbose:
print >> sys.stderr, "*> parseTestVideos] got %d results with" % resCandidates.size(
), methodUsed
# 4th METHOD: (if there are NOT RESULTS) define zero position
if resCandidates.size() == 0:
methodUsed = "DefaultCoordinates"
self.updateLockVariables(methodUsed)
hometown = False
resCandidates.add(-1.0, self.defaultCoordinates, "no results")
# print out info
if self.verbose:
print >> sys.stderr, "*> parseTestVideos] defined default coordinates"
# return the bigger coordinates group
if resCandidates.size() > 2:
# print out info
if verbose:
print >> sys.stderr, "*> parseTestVideos] BiggestCoordinatesGroup: from %d results got 1" % resCandidates.size(
)
candies = []
for res in resCandidates.getList():
candies.append(
str(res.getScore()) + "," + str(res.getCoord()[0]) +
"|" + str(res.getCoord()[1]))
resCandidates = self.computeBiggestCoordinatesFromResults(
resCandidates)
# print out info
if self.verbose:
for can in candies:
print >> sys.stderr, can
print >> sys.stderr, "*> parseTestVideos] FINAL => ", resCandidates.getList(
)[0].getCoord()
# write the buffer into a file
self.serialize(resCandidates, realCoord, methodUsed)
print >> sys.stderr, "---\n"
def parseTestVideos(self, withMtags=True, officialRun=False):
''' Given the TestSet file, read and parse each video meta-data,
select the tags and retrieve the most suitable places for those tags
'''
print >> sys.stderr, "*> [SelectCoordMethod: %s] [ScoreMetric: %s] [MatchingType: %s]" % (
self.selectCoordMethod, self.scoreMetric, self.matchingType)
print >> sys.stderr, "*> [TestSet]",
t1 = time.time()
userMostProbableLocation = 0
userHomeCoordinates = 0
nullCoordinates = 0
contentbased = 0
noTags = 0
totLines = 4532.0
lines = 0
# videoId <t> title <t> url <t> tags <t> mtags <t> location <t> ownerLocation
for line in self.TestFile:
lines += 1
print >> sys.stderr, "---"
methodUsed = "All Methods"
################
# get filename and url
blk = line.strip().split("\t")
url = blk[2].strip()
filename = blk[1].split("|")[0]
if '.jpg' in filename:
filename.replace('.jpg', '')
################
# get the tags, mtags, geo and ownGeo
tags, mtags, geo, ownGeo = self.getInfoFromTestVideo(
line, withMtags)
# print info
# if type(tags) == 'unicode':
# print >> sys.stderr, "* input tags: %s [mtags: %s]" % (tags.encode('utf-8','ignore'), mtags.encode('utf-8','ignore'))
# else:
# print >> sys.stderr, "* input tags: %s [mtags: %s]" % (tags, mtags)
realCoord = [float(geo[0]), float(geo[1])
] if not officialRun else ""
resCandidates = ResCandidates()
# 1st METHOD: check tags in training set
if len(tags) > 1:
methodUsed = "TrainSet"
resCandidates = self.matchWithGroupsOfTags(tags, mtags)
else:
noTags += 1
# print out info
if self.verbose:
print >> sys.stderr, "*> parseTestVideos] got %d results with" % resCandidates.size(
), methodUsed
# 2nd METHOD: (if there are NOT RESULTS) use the most probable location for this user
if resCandidates.size() == 0:
methodUsed = "UserCommonLocation"
ownId = ownGeo[0]
resCandidates = self.getUserMostProbableLocation(ownId.lower())
if resCandidates.size() > 0:
print >> sys.stderr, "*> Used UserCommonLocation for user %s" % ownId
userMostProbableLocation += 1
else:
print >> sys.stderr, "*> NO UserCommonLocation for user %s" % ownId
# print out info
if self.verbose:
print >> sys.stderr, "*> parseTestVideos] got %d results with" % resCandidates.size(
), methodUsed
# 3rd METHOD: (if there are NOT RESULTS) USE the USER HOMETOWN as tags
if resCandidates.size() == 0:
methodUsed = "HomeTown"
# prepare hometown tags
ht = ownGeo[1].split(",")
hometownTags = ""
for w in ht:
if len(w) > 1:
hometownTags += w + " "
hometownTags = hometownTags[:-1]
if (hometownTags) < 2:
continue
# check with the new set of tags
resCandidates = self.matchWithGroupsOfTags(hometownTags, "")
if resCandidates.size() > 0:
userHomeCoordinates += 1
# print out info
if self.verbose:
print >> sys.stderr, "*> parseTestVideos] got %d results with" % resCandidates.size(
), methodUsed
# 4th METHOD: Check Content-Video results
if resCandidates.size() == 0:
resCandidates = self.getContentVideoResults(filename)
if resCandidates.size() > 0:
print >> sys.stderr, "*> Used Content-Based Approach"
contentbased += 1
else:
print >> sys.stderr, "*> NO Content-Based Information"
# 4th METHOD: (if there are NOT RESULTS) define zero position
if resCandidates.size() == 0:
methodUsed = "DefaultCoordinates"
nullCoordinates += 1
hometown = False
resCandidates.add(-1.0, self.defaultCoordinates, "no results")
# print out info
if self.verbose:
print >> sys.stderr, "*> parseTestVideos] defined default coordinates"
# return the bigger coordinates group
if resCandidates.size() > 2:
# print out info
if verbose:
print >> sys.stderr, "*> parseTestVideos] BiggestCoordinatesGroup: from %d results got 1" % resCandidates.size(
)
candies = []
for res in resCandidates.getList():
candies.append(
str(res.getScore()) + "," +
str(res.getCoord()[0]) + "|" +
str(res.getCoord()[1]))
resCandidates = self.computeBiggestCoordinatesFromResults(
resCandidates)
# print out info
if self.verbose:
for can in candies:
print >> sys.stderr, can
print >> sys.stderr, "*> parseTestVideos] FINAL => ", resCandidates.getList(
)[0].getCoord()
# write the buffer into a file
if not officialRun:
self.serialize(resCandidates, realCoord, methodUsed)
self.serializeFormatted(resCandidates, filename, url)
else:
self.serializeFormatted(resCandidates, filename, url)
# print info
if (lines % 100) == 0:
print >> sys.stderr, "\r*> [TestSet] %2.2f%s parsed [%d usrLoc, %d usrHome, %d content-based, %d defCoordinates, %d noTags]" % (
(lines / totLines * 100), '%', userMostProbableLocation,
userHomeCoordinates, contentbased, nullCoordinates,
noTags),
# final info
t2 = time.time()
print >> sys.stderr, "\r*> [TestSet] %d videos [%d userLocation, %d hometown, %d content-based, %d defaultCoordinates, %d with no tags] ~%.2fs" % (
lines, userMostProbableLocation, userHomeCoordinates, contentbased,
nullCoordinates, noTags, (t2 - t1) * 1.0)
# compute the average of the statistics
print >> sys.stderr, "---"
if not officialRun:
for k in self.limits:
p = float(self.stats[k]) / lines * 100
print >> sys.stderr, "*> [TestSet] %d videos (%.2f%s) inside a radius of %dkm" % (
self.stats[k], p, '%', k)
# close test file
self.TestFile.close()
def matchWithGroupsOfTags(self, tagsLine, mtagsLine):
''' Compare the Test Video Tags and MTags with the TrainSet and GeoNames DB.
Higher priority to the mtags (if there are, I filter the GroupOfTags just with matches of mtags),
Otherwise, same procedure for tags. After, we recheck all the matches, and compute the number of
tag included in each GroupOfTags -> computing score.
'''
# create output with all the top Result Candidates
resCandidates = ResCandidates()
matches = MatchCandidates()
tags = tagsLine.strip().split(" ")
########################################################
# Get the GoT where the index matches with mtag or tag #
########################################################
# If there are Machine Tags, retrieve the GroupOfTags just from them
if len(mtagsLine.strip()) > 2:
mtags = mtagsLine.strip().split(" ")
tags = tags + mtags # Merge the mtags with the tags
for mtag in mtags:
# Check the TrainSet
if self.TrainTagIndex.has_key(mtag):
for groupOfTags in self.TrainTagIndex[mtag]:
matches.update(groupOfTags, 0)
# Check the GeoNames
if self.GeoNamesIndex.has_key(mtag):
for groupOfTags in self.GeoNamesIndex[mtag]:
matches.update(groupOfTags, 0)
# If the mtags didn't find any match, use the tags
if matches.size() == 0:
# Create the list of GroupOfTags containing the TestTags
for tag in tags:
# Check the TrainSet
if self.TrainTagIndex.has_key(tag):
for groupOfTags in self.TrainTagIndex[tag]:
matches.update(groupOfTags, 0)
# Check the GeoNames
if self.GeoNamesIndex.has_key(tag):
for groupOfTags in self.GeoNamesIndex[tag]:
matches.update(groupOfTags, 0)
# if there are no matches, return 0
if matches.size() == 0:
return resCandidates
###################################################################
# Count the number of matches between the tags+mtags and the candidates #
###################################################################
# Checking how many matches there are in all the GoT
for tag in tags:
for groupOfTags in matches.getKeys():
gotSplit = groupOfTags.strip().split(" ")
# check a partial match inside the entire groupOfTags
if matchingType == 'partial':
if tag in groupOfTags:
matches.update(groupOfTags, 1)
# check the match for every tag inside the groupOfTags
elif matchingType == 'perfect':
for got in gotSplit:
# perfect match = exact
if tag == got:
matches.update(groupOfTags, 1)
# break
# computing the score for each groups of tags
matches.computeScores(self.scoreMetric, len(tags))
# get the scores of the topN matches
maxScores = matches.getTopNScores(self.topn)
# get the keys of the topnN matches
topnKeys = matches.getKeysWithGivenScores(maxScores)
if self.verbose:
print >> sys.stderr, "matchWithGroupOfTags] we are here, maxScores: %d, topNelements: %d" % (
maxScores[0], len(topnKeys))
########################################################################
# From all the topN candidates compute the most likelihood coordinates #
########################################################################
# for all the topN matches, return the most likelihood coordinates
for got in topnKeys:
# check if the key is from TrainTags
if self.TrainTagCoord.has_key(got):
coord = self.getMostLikelihoodCoordinates(
self.TrainTagCoord[got])
# check if the key is from GeoNames
elif self.GeoNames.has_key(got):
coord = self.getMostLikelihoodCoordinates(self.GeoNames[got])
else:
continue
# if coord is empty
if len(coord) == 0:
continue
# update the results
resCandidates.add(matches.getValue(got), coord, got)
# return all the results
return resCandidates
def multithreadTestVideosParser(self, line, varLock=""):
'''
'''
methodUsed = "All Methods"
blk = line.strip().split("\t")
# get the tags
tags = clean_line( blk[3] )
mtags = clean_line( blk[4] )
print >> sys.stderr, "---"
print >> sys.stderr, "* input tags: %s [mtags: %s]" % (tags, mtags)
# apply tags filters -> mtags contain the geo machine tags
# if len(mtags) > 1:
# tags += " "+ mtags # give more power to the geo tags
# Read Real Location: "45.516639|-122.681053|16|Portland|Oregon|etats-Unis|16"
location = blk[5].strip()
geo = location.split("|")
acc = geo[2]
realCoord = [float(geo[0]), float(geo[1])]
resCandidates = ResCandidates()
# 1st METHOD: check tags in training set
if len(tags) > 1:
methodUsed = "TrainSet"
resCandidates = self.matchWithGroupsOfTags( tags, mtags )
else:
self.updateLockVariables( methodUsed )
# print out info
if self.verbose:
print >> sys.stderr, "*> parseTestVideos] got %d results with" % resCandidates.size(), methodUsed
# 2nd METHOD: (if there are NOT RESULTS) use the most probable location for this user
if resCandidates.size() == 0:
methodUsed = "UserCommonLocation"
ownGeo = blk[6].split("|")
ownId = ownGeo[0]
resCandidates = self.getUserMostProbableLocation( ownId )
if resCandidates.size() > 0:
self.updateLockVariables( methodUsed )
# print out info
if self.verbose:
print >> sys.stderr, "*> parseTestVideos] got %d results with" % resCandidates.size(), methodUsed
# 3rd METHOD: (if there are NOT RESULTS) USE the USER HOMETOWN as tags
if resCandidates.size() == 0:
methodUsed = "HomeTown"
# Owner location: ['14678786@N00', 'milwaukee, United States']
ownGeo = blk[6].split("|")
# prepare hometown tags
ht = ownGeo[1].split(",")
hometownTags = ""
for w in ht:
if len(w) > 1:
hometownTags += w +" "
hometownTags = hometownTags[:-1]
if (hometownTags) >= 1:
# check with the new set of tags
resCandidates = self.matchWithGroupsOfTags( hometownTags, "" )
if resCandidates.size() > 0:
self.updateLockVariables( methodUsed )
# print out info
if self.verbose:
print >> sys.stderr, "*> parseTestVideos] got %d results with" % resCandidates.size(), methodUsed
# 4th METHOD: (if there are NOT RESULTS) define zero position
if resCandidates.size() == 0:
methodUsed = "DefaultCoordinates"
self.updateLockVariables( methodUsed )
hometown = False
resCandidates.add( -1.0, self.defaultCoordinates, "no results" )
# print out info
if self.verbose:
print >> sys.stderr, "*> parseTestVideos] defined default coordinates"
# return the bigger coordinates group
if resCandidates.size() > 2:
# print out info
if verbose:
print >> sys.stderr, "*> parseTestVideos] BiggestCoordinatesGroup: from %d results got 1" % resCandidates.size()
candies = []
for res in resCandidates.getList():
candies.append( str(res.getScore()) +","+ str(res.getCoord()[0]) +"|"+ str(res.getCoord()[1]) )
resCandidates = self.computeBiggestCoordinatesFromResults( resCandidates )
# print out info
if self.verbose:
for can in candies:
print >> sys.stderr, can
print >> sys.stderr, "*> parseTestVideos] FINAL => ", resCandidates.getList()[0].getCoord()
# write the buffer into a file
self.serialize( resCandidates, realCoord, methodUsed )
print >> sys.stderr, "---\n"
def parseTestVideos(self, withMtags=True, officialRun=False ):
''' Given the TestSet file, read and parse each video meta-data,
select the tags and retrieve the most suitable places for those tags
'''
print >> sys.stderr, "*> [SelectCoordMethod: %s] [ScoreMetric: %s] [MatchingType: %s]" % ( self.selectCoordMethod, self.scoreMetric, self.matchingType )
print >> sys.stderr, "*> [TestSet]",
t1 = time.time()
userMostProbableLocation = 0
userHomeCoordinates = 0
nullCoordinates = 0
contentbased = 0
noTags = 0
totLines = 4532.0
lines = 0
# videoId <t> title <t> url <t> tags <t> mtags <t> location <t> ownerLocation
for line in self.TestFile:
lines += 1
print >> sys.stderr, "---"
methodUsed = "All Methods"
################
# get filename and url
blk = line.strip().split("\t")
url = blk[2].strip()
filename = blk[1].split("|")[0]
if '.jpg' in filename:
filename.replace('.jpg', '')
################
# get the tags, mtags, geo and ownGeo
tags, mtags, geo, ownGeo = self.getInfoFromTestVideo( line, withMtags )
# print info
# if type(tags) == 'unicode':
# print >> sys.stderr, "* input tags: %s [mtags: %s]" % (tags.encode('utf-8','ignore'), mtags.encode('utf-8','ignore'))
# else:
# print >> sys.stderr, "* input tags: %s [mtags: %s]" % (tags, mtags)
realCoord = [float(geo[0]), float(geo[1])] if not officialRun else ""
resCandidates = ResCandidates()
# 1st METHOD: check tags in training set
if len(tags) > 1:
methodUsed = "TrainSet"
resCandidates = self.matchWithGroupsOfTags( tags, mtags )
else:
noTags += 1
# print out info
if self.verbose:
print >> sys.stderr, "*> parseTestVideos] got %d results with" % resCandidates.size(), methodUsed
# 2nd METHOD: (if there are NOT RESULTS) use the most probable location for this user
if resCandidates.size() == 0:
methodUsed = "UserCommonLocation"
ownId = ownGeo[0]
resCandidates = self.getUserMostProbableLocation( ownId.lower() )
if resCandidates.size() > 0:
print >> sys.stderr, "*> Used UserCommonLocation for user %s" % ownId
userMostProbableLocation += 1
else:
print >> sys.stderr, "*> NO UserCommonLocation for user %s" % ownId
# print out info
if self.verbose:
print >> sys.stderr, "*> parseTestVideos] got %d results with" % resCandidates.size(), methodUsed
# 3rd METHOD: (if there are NOT RESULTS) USE the USER HOMETOWN as tags
if resCandidates.size() == 0:
methodUsed = "HomeTown"
# prepare hometown tags
ht = ownGeo[1].split(",")
hometownTags = ""
for w in ht:
if len(w) > 1:
hometownTags += w +" "
hometownTags = hometownTags[:-1]
if (hometownTags) < 2:
continue
# check with the new set of tags
resCandidates = self.matchWithGroupsOfTags( hometownTags, "" )
if resCandidates.size() > 0:
userHomeCoordinates += 1
# print out info
if self.verbose:
print >> sys.stderr, "*> parseTestVideos] got %d results with" % resCandidates.size(), methodUsed
# 4th METHOD: Check Content-Video results
if resCandidates.size() == 0:
resCandidates = self.getContentVideoResults( filename )
if resCandidates.size() > 0:
print >> sys.stderr, "*> Used Content-Based Approach"
contentbased += 1
else:
print >> sys.stderr, "*> NO Content-Based Information"
# 4th METHOD: (if there are NOT RESULTS) define zero position
if resCandidates.size() == 0:
methodUsed = "DefaultCoordinates"
nullCoordinates += 1
hometown = False
resCandidates.add( -1.0, self.defaultCoordinates, "no results" )
# print out info
if self.verbose:
print >> sys.stderr, "*> parseTestVideos] defined default coordinates"
# return the bigger coordinates group
if resCandidates.size() > 2:
# print out info
if verbose:
print >> sys.stderr, "*> parseTestVideos] BiggestCoordinatesGroup: from %d results got 1" % resCandidates.size()
candies = []
for res in resCandidates.getList():
candies.append( str(res.getScore()) +","+ str(res.getCoord()[0]) +"|"+ str(res.getCoord()[1]) )
resCandidates = self.computeBiggestCoordinatesFromResults( resCandidates )
# print out info
if self.verbose:
for can in candies:
print >> sys.stderr, can
print >> sys.stderr, "*> parseTestVideos] FINAL => ", resCandidates.getList()[0].getCoord()
# write the buffer into a file
if not officialRun:
self.serialize( resCandidates, realCoord, methodUsed )
self.serializeFormatted( resCandidates, filename, url )
else:
self.serializeFormatted( resCandidates, filename, url )
# print info
if (lines % 100) == 0:
print >> sys.stderr, "\r*> [TestSet] %2.2f%s parsed [%d usrLoc, %d usrHome, %d content-based, %d defCoordinates, %d noTags]" % ( (lines/totLines*100), '%', userMostProbableLocation, userHomeCoordinates, contentbased, nullCoordinates, noTags ),
# final info
t2 = time.time()
print >> sys.stderr, "\r*> [TestSet] %d videos [%d userLocation, %d hometown, %d content-based, %d defaultCoordinates, %d with no tags] ~%.2fs" % ( lines, userMostProbableLocation, userHomeCoordinates, contentbased, nullCoordinates, noTags, (t2-t1)*1.0 )
# compute the average of the statistics
print >> sys.stderr, "---"
if not officialRun:
for k in self.limits:
p = float(self.stats[k])/lines*100
print >> sys.stderr, "*> [TestSet] %d videos (%.2f%s) inside a radius of %dkm" % (self.stats[k], p, '%', k)
# close test file
self.TestFile.close()
def matchWithGroupsOfTags( self, tagsLine, mtagsLine ):
''' Compare the Test Video Tags and MTags with the TrainSet and GeoNames DB.
Higher priority to the mtags (if there are, I filter the GroupOfTags just with matches of mtags),
Otherwise, same procedure for tags. After, we recheck all the matches, and compute the number of
tag included in each GroupOfTags -> computing score.
'''
# create output with all the top Result Candidates
resCandidates = ResCandidates()
matches = MatchCandidates()
tags = tagsLine.strip().split(" ")
########################################################
# Get the GoT where the index matches with mtag or tag #
########################################################
# If there are Machine Tags, retrieve the GroupOfTags just from them
if len(mtagsLine.strip()) > 2:
mtags = mtagsLine.strip().split(" ")
tags = tags + mtags # Merge the mtags with the tags
for mtag in mtags:
# Check the TrainSet
if self.TrainTagIndex.has_key(mtag):
for groupOfTags in self.TrainTagIndex[mtag]:
matches.update(groupOfTags, 0)
# Check the GeoNames
if self.GeoNamesIndex.has_key(mtag):
for groupOfTags in self.GeoNamesIndex[mtag]:
matches.update(groupOfTags, 0)
# If the mtags didn't find any match, use the tags
if matches.size() == 0:
# Create the list of GroupOfTags containing the TestTags
for tag in tags:
# Check the TrainSet
if self.TrainTagIndex.has_key(tag):
for groupOfTags in self.TrainTagIndex[tag]:
matches.update(groupOfTags, 0)
# Check the GeoNames
if self.GeoNamesIndex.has_key(tag):
for groupOfTags in self.GeoNamesIndex[tag]:
matches.update(groupOfTags, 0)
# if there are no matches, return 0
if matches.size() == 0:
return resCandidates
###################################################################
# Count the number of matches between the tags+mtags and the candidates #
###################################################################
# Checking how many matches there are in all the GoT
for tag in tags:
for groupOfTags in matches.getKeys():
gotSplit = groupOfTags.strip().split(" ")
# check a partial match inside the entire groupOfTags
if matchingType == 'partial':
if tag in groupOfTags:
matches.update(groupOfTags, 1)
# check the match for every tag inside the groupOfTags
elif matchingType == 'perfect':
for got in gotSplit:
# perfect match = exact
if tag == got:
matches.update(groupOfTags, 1)
# break
# computing the score for each groups of tags
matches.computeScores( self.scoreMetric, len(tags) )
# get the scores of the topN matches
maxScores = matches.getTopNScores( self.topn )
# get the keys of the topnN matches
topnKeys = matches.getKeysWithGivenScores( maxScores )
if self.verbose:
print >> sys.stderr, "matchWithGroupOfTags] we are here, maxScores: %d, topNelements: %d" % ( maxScores[0], len(topnKeys))
########################################################################
# From all the topN candidates compute the most likelihood coordinates #
########################################################################
# for all the topN matches, return the most likelihood coordinates
for got in topnKeys:
# check if the key is from TrainTags
if self.TrainTagCoord.has_key(got):
coord = self.getMostLikelihoodCoordinates( self.TrainTagCoord[got] )
# check if the key is from GeoNames
elif self.GeoNames.has_key(got):
coord = self.getMostLikelihoodCoordinates( self.GeoNames[got] )
else:
continue
# if coord is empty
if len(coord) == 0:
continue
# update the results
resCandidates.add( matches.getValue(got), coord, got )
# return all the results
return resCandidates
