def getPrecRecall(opt, catList, f1Dict, catVector, queryTerms, aTerms, index):
catScore = {}
maxQs = -1000
maxCat = ''
notFound = set()
for cat in catList:
if cat in f1Dict:
catScore[cat
] = {'aP': 0.0,
'aR': 0.0,
'qS': 0.0,
'qInt': set(),
'aInt': set()}
#phrase cat score
phrase1 = loadPhrasesWithScore(f1Dict[cat])
pTotal = sum(phrase1.values()) #total no of terms in cat
pset = set(phrase1.keys()) #unique phrases in cat
qInt = pset & queryTerms #no of query terms cat contains
score = 0.0
for iphrase in qInt:
score += phrase1[iphrase] / pTotal
if len(queryTerms) > 0:
score *= (1.0 * len(qInt)) / len(queryTerms)
#cosine score
queryDict = getDictFromSet(queryTerms)
cVector = catVector[cat]
cscore = get_cosine(queryDict, cVector)
#total score
catScore[cat]['qs'] = cscore + score
if maxQs < catScore[cat]['qs']:
maxQs = catScore[cat]['qs']
maxCat = cat
sortP = sorted(phrase1.items(), reverse=True, key=lambda x: x[1])
#print 'sorted' , sortP[0],sortP[1]
apset = set(x[0] for x in sortP[0:index])
#print 'pSet ',apset
aInt = aTerms & apset
catScore[cat]['aP'] = (1.0 * len(aInt)) / len(aTerms)
catScore[cat]['aR'] = (1.0 * len(aInt)) / len(apset)
catScore[cat]['aInt'] = aInt
catScore[cat]['qInt'] = qInt
else:
notFound.add(cat)
if opt == 'max':
if maxCat in catScore:
return notFound, maxCat, catScore[maxCat]
else:
return notFound, None, {
'aP': 0.0,
'aR': 0.0,
'qS': 0.0,
'qInt': set(),
'aInt': set()
}
else:
avgScore = {'aP': 0.0, 'aR': 0.0, 'qS': 0.0, 'qInt': set(), 'aInt': set()}
for entry, cdict in catScore.iteritems():
avgScore['aP'] += cdict['aP']
avgScore['aR'] += cdict['aR']
avgScore['qS'] += cdict['qS']
avgScore['qInt'] |= cdict['qInt']
avgScore['aInt'] |= cdict['aInt']
avgScore['aP'] /= len(catScore)
avgScore['aR'] /= len(catScore)
avgScore['qS'] /= len(catScore)
return notFound, None, avgScore
return notFound, None, None
def getStatsPerQuery(argv):
tagURL = 'http://localhost:8080/rest/annotate'
catURL = 'http://localhost:8080/rest/graph/get-entity-categories'
catVector = loadCategoryVector(argv[3])
f1Dict = getCats(argv[2])
sFound = 0.0
sTotal = 0.0
eTotal = set()
eRemov = set()
catFoundNoTerm = set()
catNotFound = set()
catTermFound = set()
catEntity = set()
outfile = open('match_session_dom.txt', 'w')
#categoryVectors = {}
for session in getSessionWithNL(argv[1]):
catCount = {}
entCount = {}
querySpotList = {}
for query in session:
#find the entities in query
try:
spotDict = None #tagQueryWithDexter(query, tagURL,catURL)
querySpotList[query] = spotDict
for text in spotDict.keys():
for entry in spotDict[text]['cat'].split():
catCount[entry] = catCount.setdefault(entry, 1) + 1
entCount[text] = entCount.setdefault(text, 1) + 1
except Exception as err:
print err
#print 'SESSION', session, 'CATCOUNT', catCount, 'ENTCOUNT',entCount
found = False
if len(catCount) > 0:
#find the dominant entity
maxEnt = max(entCount.values())
#sessionQueryMapping = {}
for query, spotList in querySpotList.iteritems():
matchl = spotList.keys()
for entry in matchl:
eTotal.add(entry)
if entCount[entry] < maxEnt:
spotList.pop(entry, None)
print 'Removing spot', query, entry
eRemov.add(entry)
else:
#get the categories
#catTermMatch = {}
rquery = query.replace(entry, '')
queryTerms = set(rquery.split())
for cat in spotList[entry]['cat'].lower().split():
catEntity.add(entry + '_' + cat)
if cat in f1Dict:
phrase1 = loadPhrasesWithScore(argv[2] + '/' + f1Dict[cat])
pVector = catVector[cat]
queryDict = getDictFromSet(queryTerms)
pTotal = sum(phrase1.values())
pset = set(phrase1.keys())
sint = pset & queryTerms
score = 0.0
cscore = get_cosine(queryDict, pVector)
for iphrase in sint:
score += phrase1[iphrase] / pTotal
if len(queryTerms) > 0:
score *= (1.0 * len(sint)) / len(queryTerms)
if sint:
outfile.write(query + '\t' + entry + '\t' + cat + '\t' +
str(cscore) + '\t' + ', '.join(sint) + '\n')
found = True
catTermFound.add(entry + '_' + cat)
else:
outfile.write(query + '\t' + entry + '\t' + cat + '\t0\t0\n')
catFoundNoTerm.add(cat + '_' + entry)
else:
outfile.write(
query + '\t' + entry + '\t' + cat + '\t0\tNOT FOUND\n')
catNotFound.add(cat + '_' + entry)
#load the terms for category
#check if these terms match
if found:
sFound += 1
sTotal += 1
outfile.write('\n')
print 'Total Sessions ', sTotal
print 'Sessions with dominant entity in AOL', sFound
print '# Unique Entities', len(eTotal)
print '# Removed Entities (non dominant)', len(eRemov)
print '# no of entity types', len(catEntity)
print '# no of entity types with terms match ', len(catTermFound)
print '# no of entity types with no term match', len(catFoundNoTerm)
print '# no of entity types with no match in AOL', len(catNotFound)
