def main(argv): featMan = FeatureManager() featMan.readFeatures(argv[2]) weightMatrix = readWeightMatrix(argv[3]) returnFilteredNetwork(argv[2], argv[1], featMan, weightMatrix)
def generatePhraseFeatures(featureFile, spotFile, outFile):
#load features for queries
qfeatMan = FeatureManager()
qfeatMan.readFeatures(featureFile)
pid = 0
pfeatMan = FeatureManager()
#generate features for phrases
for query, pList in generatePhrases(spotFile):
qkey, qfeat = qfeatMan.returnFeature(query)
#print query, qkey
if qkey:
#print query, pList
for phrase in pList:
qVect = getDictFromSet(phrase.split())
ngrams = getNGramsAsList(phrase, 2)
url = qfeat.returnUrl()
user = qfeat.returnUsers()
ent = qfeat.returnEntities()
cat = qfeat.returnCategories()
typ = qfeat.returnType()
sess = qfeat.returnSessions()
if 'tournament' in phrase:
print query, phrase
print sess
print typ
print ent
nFeature = QueryFeature(phrase, ngrams, qVect, url, user, sess, ent,
cat, typ)
pfeatMan.addFeature(phrase, pid, nFeature)
pid += 1
pfeatMan.writeFeatures(outFile)
def main(argv):
featMan = FeatureManager()
featMan.readFeatures(argv[1])
data = featMan.returnKeys() #random.sample(list(featMan.returnKeys()),40)
print 'To cluster', data
#weightMatrix = readWeightMatrix(argv[2])
weightList = readWeightMatrix(argv[2])
catNetwork, catQueryDist = returnFilteredNetwork(argv[3], argv[4], featMan)
dp = DPMeans()
x = float(argv[5])
while x < 0.85:
dp.clusterWithNetwork(featMan, weightList, catNetwork, catQueryDist, x,
0.01)
x += 0.10
def sampleQueryPairs(fileName, weightFile, featFile):
weightMatrix = readWeightMatrix(weightFile)
featMan = FeatureManager()
featMan.loadQueries(featFile)
idDict = featMan.returnIdDict()
qDict = featMan.returnQueryDict()
clusters = loadClustersWithQueryFile(fileName, idDict)
done = {}
for entry in clusters:
minPair = None
maxPair = None
minDist = 1000
maxDist = 0
#print entry
if len(entry) > 3:
sentry = sorted(entry)
for i in range(len(sentry) - 1):
for j in range(i + 1, len(sentry)):
try:
if weightMatrix[sentry[i]][sentry[j]] < minDist:
minDist = weightMatrix[sentry[i]][sentry[j]]
minPair = (qDict[sentry[i]], qDict[sentry[j]])
if weightMatrix[sentry[i]][sentry[j]] > maxDist:
maxDist = weightMatrix[sentry[i]][sentry[j]]
maxPair = (qDict[sentry[i]], qDict[sentry[j]])
except:
dist = random.uniform(0.8, 1.0)
if dist < minDist:
minDist = dist
minPair = (qDict[sentry[i]], qDict[sentry[j]])
if dist > maxDist:
maxDist = dist
maxPair = (qDict[sentry[i]], qDict[sentry[j]])
if minPair and minPair[0] not in done and minPair[1] not in done:
print 'Min\t' + minPair[0] + '\t' + minPair[1]
if maxPair and maxPair[0] not in done and maxPair[1] not in done:
print 'Max\t' + maxPair[0] + '\t' + maxPair[1]
if minPair:
done[minPair[0]] = 1
done[minPair[1]] = 1
if maxPair:
done[maxPair[0]] = 1
done[maxPair[1]] = 1
def findPairwiseDistance(featureFile, outFile):
featMan = FeatureManager()
featMan.readFeatures(featureFile)
featDict = featMan.featureDict
oFile = open(outFile, 'w')
ids = featDict.keys()
keys = sorted(ids)
print len(keys), keys[-5:]
for i in range(0, len(keys) - 1):
qid1, qf1 = featMan.returnFeature(keys[i])
for j in range(i + 1, len(keys)):
qid2, qf2 = featMan.returnFeature(keys[j])
qcos, ucos, userCos, sessionCos, ngramCos, entCos, \
catCos,typeCos = qf1.findCosineDistance(qf2)
qjac = qf1.findJacardDistance(qf2)
#qedit = qf1.findEditDistance(qf2)
edgeScore = (15*((qcos + qjac )/2.0) +\
12.5*ngramCos + 12.5*ucos + 20*sessionCos +\
20*userCos + 10*((entCos + catCos)/2.0) + 10*typeCos)/100.0
if edgeScore > 0.0:
oFile.write(
#str(qid1) + ' ' + str(qid2) + ' ' + str(round(edgeScore, 3)) + '\n')
featMan.returnQuery(qid1) + '\t' + featMan.returnQuery(qid2) + '\t' + str(round(edgeScore, 3)) + '\n')
#oFile1.write(str(qid1)+'\t'+str(qid2)+'\t'+\
#str(round(qcos,2))+'\t'+str(round(qjac,2))+'\t'+\
#str(round(ngramCos,2))+'\t'+str(round(userCos,2))+'\t' + \
#str(round(entCos,2))+'\t'+ str(round(catCos,2))+\
#'\t'+ str(round(sessionCos,2))+'\t'+ str(round(typeCos,2))+'\n')
oFile.close()
def printCategoryQueryDictionary(fileName, clusFile, weightFile):
featMan = FeatureManager()
featMan.readFeatures(fileName)
categoryDictionary = {}
for query, feat in featMan.iterFeatures():
catDict = feat.returnFeature('cat')
for entry in catDict:
if entry not in categoryDictionary:
categoryDictionary[entry] = set()
categoryDictionary[entry].add(query)
outC = open(clusFile,'w')
outW = open(weightFile,'w')
for entry, qlist in categoryDictionary.items():
outC.write(toString(qlist,featMan)+'\n')
outW.write(str(qlist)+'\n')
outC.close()
weightMatrix = {}
cc = 0
#calculate the weight matrix
for entry, qlist in categoryDictionary.items():
sort = sorted(qlist)
for i in range(len(sort)-1):
qid1, qf1 = featMan.returnFeature(sort[i])
if qf1:
if sort[i] not in weightMatrix:
weightMatrix[sort[i]] = {}
for j in range(i+1,len(sort)):
qid2, qf2 = featMan.returnFeature(sort[j])
if qf2:
if sort[j] not in weightMatrix[sort[i]]:
qcos, ucos, userCos, ngramCos, entCos, catCos = qf1.findCosineDistance(qf2)
qjac = qf1.findJacardDistance(qf2)
#qedit = qf1.findEditDistance(qf2)
#normalized distance
#dist = (j - i)#*1.0/len(session)
edgeScore = (.25*((qcos + qjac )/2.0) +\
.15*ngramCos + .15*ucos + \
.15*userCos + .15*entCos + .15*catCos)
if edgeScore > 0.05:
weightMatrix[sort[i]][sort[j]] = edgeScore
if cc % 10000==0:
print cc
cc+=1
outW.write('\n')
for entry1, scoreList in weightMatrix.items():
for entry2, score in scoreList.items():
outW.write(str(entry1)+' '+str(entry2)+' '+str(score)+'\n');
outW.close();
parser.add_argument('-a', '--algo', help='qcc', \
required=True)
parser.add_argument('-l', '--lowerLimit', help='min limit on #terms in '+\
'cluster', required=True,type=float)
parser.add_argument('-u', '--upperLimit', help='upper limit on #terms in'+\
' cluster', required=True,type=float)
parser.add_argument('-s', '--sessionFile', help='Session file containing'+\
' queries', required=True)
parser.add_argument('-p', '--pairLabelFile', help='Task labels for a'+\
' pair of queries, same_task and different_task',\
required=False)
args = parser.parse_args()
qcc = QCCTasks()
featMan = FeatureManager()
#stemmer = stem.porter.PorterStemmer()
featMan.readFeatures(args.featFile)
# Loads the distance between two queries (i.e. 1-similarity)
weightMatrix = readWeightMatrix(args.distFile)
print len(weightMatrix)
samePairsSet = differentPairsSet = None
if args.pairLabelFile:
samePairsSet , differentPairsSet = loadPairsFromFile(args.pairLabelFile)
total_metrics_dict = {}
for threshold in np.arange(args.lowerLimit, args.upperLimit, 0.02):
sessCount = 0
lastSes = None
session = []
avg_inter_ij[i] = min(avg_inter_ij[i], score)
avg_inter_ij[j] = min(avg_inter_ij[j], score)
fmin_i = []
for i, mini in avg_inter_ij.items():
#print i, vals.values()
fmin_i.append(mini / maxDiam)
print 'FMIN ', fmin_i
print 'Dunn index ', min(fmin_i)
if __name__ == '__main__':
argv = sys.argv
lbreak = False
weightMatrix = readWeightMatrix(argv[2])
featMan = FeatureManager()
featMan.loadQueries(argv[3])
for ifile in os.listdir(argv[1]):
clusters = loadClustersWithQueryFile(argv[1] + '/' + ifile,
featMan.returnIdDict())
print len(clusters), len(featMan.returnIdDict()), len(weightMatrix)
#load the cluster-assignments and points
Dunn(clusters, weightMatrix)
#DB(clusters,weightMatrix)
#load the weight matrix
#load the centers
parser.add_argument('-u', '--upperLimit', help='upper limit on #terms in'+\
' cluster', required=True,type=int)
parser.add_argument('-p', '--pairLabelFile', help='Task labels for a'+\
' pair of queries: "same_task"\
or "different_task".',\
required=False)
parser.add_argument('-t', '--taskLabelFile', help='Task labels for every'+\
'query: Task id and query.',\
required=False)
parser.add_argument('-s', '--ontFile', help='DBpedia ontology file', required=False)
#argv = sys.argv
args = parser.parse_args()
featMan = FeatureManager()
featMan.readFeatures(args.featFile)
# weightMatrix = readWeightMatrix(args.distFile)
weightMatrix = readWeightMatrixWithQueries(args.distFile,featMan)
##stemmer = stem.porter.PorterStemmer()
#print len(catQueryDist)
#
##PRE-MERGE WRITE
#oFile = open(argv[4],'w')
#for cat, entry in catQueryDist.items():
# qStr = toString(entry,featMan)
# oFile.write(cat +'\t'+qStr+'\n')
#oFile.close()
##CLUSTER PRE-MERGE
