def predictTerms(queryList, y, qclusters):
termList, termDict = getTermList(queryList)
oracle_prec = 0.0
oracle_mrr = 0.0
added = 0
cScorer = ScoreClusterTerms()
for session in y:
query = session[0]
aTerms, rTerms = addedAndRemovedTerms(query, session[1:], termDict)
if len(aTerms) > 0:
prec1, mrr1 = getPrecRecall(termList, aTerms)
added += 1.0
oracle_prec += prec1
oracle_mrr += mrr1
print 'Oracle prec and recall ', oracle_prec / added, oracle_mrr / added, added
#porter = stem.porter.PorterStemmer();
clusters, clusIndex = toTerms(qclusters)
lim = 5
i = 0
prec = {}
mrr = {}
pf = 0.0
pr = 0.0
for session in y:
query = session[0].strip()
qSet = getQueryTerms(query) #getQueryTermsStemmed(query, porter);
aTerms, rTerms = addedAndRemovedTerms(query, session[1:], termDict)
if len(aTerms) > 0:
terms = cScorer.scoreWithCosine(qSet, clusters, clusIndex, lim)
if len(terms) > 0:
#print len(aTerms), len(terms)
prec1, mrr1 = getClustPrecRecall(terms, aTerms) # returns a list
#print 'METRIC',i, prec1, mrr1
#print topk , prec1, mrr1
if sum(prec1) > 0:
pf += 1.0
if sum(mrr1) > 0:
pr += 1.0
for topk in range(len(prec1)):
if topk not in prec:
prec[topk] = []
mrr[topk] = []
prec[topk].append(prec1[topk])
mrr[topk].append(mrr1[topk])
i += 1
retPrec = {}
retRecall = {}
for entry, ls in prec.items():
print 'Prec @', entry, np.mean(ls)
retPrec[entry] = np.mean(ls)
for entry, ls in mrr.items():
print 'Recall @', entry, np.mean(ls)
retRecall[entry] = np.mean(ls)
print 'Percentage ', pf / i, pr / i
return retPrec, retRecall
def main(argv):
#Scorer
coSessOccur = CoOccurrence()
coSessOcMan = CoOcManager(argv[2], coSessOccur, ' ')
tScorer = CoOccurSimScore(coSessOcMan)
cScorer = ScoreClusterTerms()
#vocab = set()
i = 0
prec = {}
mrr = {}
lim = 55
queryList = loadFileInList(argv[5])
termList, termDict = getTermList(queryList)
print len(termList)
added = 0
oracle_prec = 0.0
oracle_mrr = 0.0
for tid, session, viewDocs, clickDocs, cTitle, cSummary in getSessionWithXML(
argv[1]):
query = session[0].strip()
aTerms, rTerms = addedAndRemovedTerms(query, session[1:], termDict)
if len(aTerms) > 0:
prec1, mrr1 = getPrecRecall(termList, aTerms)
added += 1.0
oracle_prec += prec1
oracle_mrr += mrr1
print 'Oracle prec and recall ', oracle_prec / added, oracle_mrr / added
porter = stem.porter.PorterStemmer()
ttype = argv[6]
print ttype
for iFile in os.listdir(argv[3]):
qclusters = loadClusters(argv[3] + '/' + iFile)
clusters, clusIndex = toTerms(qclusters)
print iFile, len(clusters)
prec[iFile] = {}
mrr[iFile] = {}
added = 0.0
i = 1
for tid, session, viewDocs, clickDocs, cTitle, cSummary in getSessionWithXML(
argv[1]):
i += 1
query = session[0].strip()
qSet = getQueryTermsStemmed(query, porter)
print 'Query ', query, qSet
if ttype == 'query':
aTerms, rTerms = addedAndRemovedTerms(query, session[1:], termDict)
elif ttype == 'title':
aTerms = getTerms(cTitle, qSet, termDict, porter, range(
1, len(session) - 1))
else:
aTerms = getTerms(cTitle, qSet, termDict, porter, range(
1, len(session) - 1))
bTerms = getTerms(cSummary, qSet, termDict, porter, range(
1, len(session) - 1))
aTerms = aTerms | bTerms
#aTerms,rTerms = addedAndRemovedTerms(query, session[1:], None )
if len(aTerms) > 0:
terms = cScorer.scoreWithIndex(qSet, clusters, clusIndex, tScorer, lim)
#terms = cScorer.scoreWithClustPos(qSet, clusters,tScorer, lim)
print 'TERMS', '\t', i, '\t', ttype, '\t', iFile, '\t', len(
terms), terms
#for topk in range(1,lim,5):
prec1, mrr1 = getClustPrecMrr(terms, aTerms) # returns a list
print 'METRIC', iFile, i, prec1, mrr1
#print topk , prec1, mrr1
for topk in prec1.keys():
if topk not in prec[iFile]:
prec[iFile][topk] = []
mrr[iFile][topk] = []
prec[iFile][topk].append(prec1[topk])
mrr[iFile][topk].append(mrr1[topk])
#prec[iFile][topk] += prec1
#mrr[iFile][topk] += mrr1
added += 1.0
#if i == 3:
# break
for fName, scoreDict in prec.items():
for pos in scoreDict.keys():
print 'Prec all', fName, pos, len(scoreDict[pos])
total = sum(scoreDict[pos])
prec[fName][pos] = total / added #len(scoreDict[pos])
print 'Prec', fName, pos, prec[fName][pos], total
for fName, scoreDict in mrr.items():
for pos in scoreDict.keys():
print 'Mrr all', fName, pos, len(scoreDict[pos])
total = sum(mrr[fName][pos])
mrr[fName][pos] = total / added #len(scoreDict[pos])
print 'MRR', fName, pos, mrr[fName][pos], total
#for entry in prec.keys():
#for t in prec[entry].keys():
#print 'Prec',entry, t, prec[entry][t], prec[entry][t]/added
#prec[entry][t]/=added
#for entry in mrr.keys():
#for t in mrr[entry].keys():
#print 'Mrr',entry, t, mrr[entry][t], mrr[entry][t]/added
#mrr[entry][t]/=added
print 'Plotting Precision and MRR'
plotMultipleSys(prec, 'No of Terms', 'Prec', argv[4] + 'prec.png',
'Term Prediction Prec Plot')
plotMultipleSys(mrr, 'No of Terms', 'MRR', argv[4] + 'mrr.png',
'Term Prediction MRR Plot')
