def __init__(self, filename):
print 'Creating Vector Space..'
self.vsRanker = VectorSpace(filename)
print 'Ranking users...'
self.tweetRanker = TweetUserRanking(filename)
class VectorSpaceAndUserRank:
#weight for combining pagerank with vectorspace results
beta = .4
# object of the vector space retriever
vsRanker = ''
# object of the page ranker
tweetRanker = ''
def __init__(self, filename):
print 'Creating Vector Space..'
self.vsRanker = VectorSpace(filename)
print 'Ranking users...'
self.tweetRanker = TweetUserRanking(filename)
# gets the users from the vector space query results
def getUsersFromVsResults(self, vsResults):
usersList = []
for result in vsResults:
usersList.append(result['username'])
return usersList
def processQuery(self, query):
# fetch the top 100 results from vector space retrieval
vsResults = self.vsRanker.processQuery(query, 200)
if len(vsResults) == 0:
return []
# get the users from the result set
usersList = self.getUsersFromVsResults(vsResults)
# arrange the users by their ranks
userScoreDict = self.tweetRanker.getRankScoresForUsers(usersList)
scoreDict = {}
vsPageRankScoredResults = {}
# now we caculate the product of zipfian ranks and cosine similaritiy
# for all results we got from vector space retrieval
for vsResult in vsResults:
rankScore = userScoreDict[vsResult['username']]
finalScore = vsResult['cosineSimilarity'] * self.beta + (1 - self.beta) * rankScore
result = {
'text' : vsResult['text'],
'username' : vsResult['username']
}
vsPageRankScoredResults[vsResult['tweetId']] = result
scoreDict[vsResult['tweetId']] = finalScore
# sort the scoreDict by the finalScores
resultDict = sorted(scoreDict.items(), None , itemgetter(1) , True)
finalresult = []
for tweet in resultDict:
#fill the final result dictionary with the results
#finalRankScore = tweet[1]
tweetId = tweet[0]
tweetText = vsPageRankScoredResults[tweetId]['text']
finalresult.append(tweetText)
finalresult = finalresult[:50]
# return the top 50 results
return finalresult
def getVectorSpaceQueryResults(self, query, numResults):
results = self.vsRanker.processQuery(query, numResults)
resultingTweetTexts = []
for result in results:
resultingTweetTexts.append(result['text'])
return resultingTweetTexts
def getTopKUsers(self, K):
results = self.tweetRanker.getTopKUsers(K)
topKUserNames = []
for result in results:
topKUserNames.append(result[0])
return topKUserNames
