def processline(self,line):
# if self.validfile:
#print "Processing "+line
matchobj=STSData.sidPATT.match(line)
if matchobj:
if self.label in self.pairset:
self.currentpair=self.pairset[self.label]
else:
self.currentpair=SentencePair(self.fileid,self.setid,self.testing)
else:
matchobj=STSData.sidendPATT.match(line)
if matchobj:
self.pairset[self.label]=self.currentpair
#print "Finished reading sentence - stored pair ..."
else:
matchobj = STSData.wordPATT.match(line)
if matchobj:
word = matchobj.group(1)
self.currentpair.addword(word,self.sentid)
else:
matchobj = STSData.lemmaPATT.match(line)
if matchobj:
lemma = matchobj.group(1)
self.currentpair.addlemma(lemma,self.sentid)
else:
matchobj=STSData.posPATT.match(line)
if matchobj:
pos = matchobj.group(1)
self.currentpair.addpos(pos,self.sentid)
class STSData:
sidPATT = re.compile('.*<document>')
sidendPATT = re.compile('.*</document>')
wordPATT = re.compile('.*<word>(.*)</word>')
lemmaPATT = re.compile('.*<lemma>(.*)</lemma>')
posPATT = re.compile('.*<POS>(.).*</POS>') #only first char of POS
fileidPATT= re.compile('.*input(.*).pair(.*)(.).tagged')
gssetPATT = re.compile('.*.gs.(.*).txt')
wordposPATT = re.compile('(.*)/(.)') #only first char of POS
methods = ["additive","multiplicative"]
setmethods = ["avg_max","geo_max"]
simthreshold = 1.0
minsim = 0.001
threshtype="nonbin"
seed = 666
def __init__(self,graphson,testing,windows,threshold,threshtype,verbose,adja,adjb):
self.pairset={} #label is setid_fileid
self.vectordict={} #mapping from (word,POS) tuples to wordvectors
self.wordcounts={} #count the number of times each (word,POS) tuple occurs in data for analysis
self.uncovered={} #store words and counts not in thesaurus
self.sid=0
self.filesread=0
self.setid=""
self.sentid=""
self.label=""
self.fileid=0
self.simaverage={} #average similarities for different functions and subsets
self.nosplits=-1 #number of cross-validation splits
self.show=graphson
self.updated=0
self.testing=testing
self.comp=""
self.metric=""
self.setsim=""
self.allfeatures={} #dictionary of all feature dimenesions
self.fkeys=[] #list (to be sorted) of all features to
self.fk_idx={} #feature --> dimension
self.dim=0
WordVector.windows=windows
STSData.simthreshold=threshold
STSData.threshtype=threshtype
self.verbose = verbose
self.adja=adja
self.adjb=adjb
def setseed(self):
random.seed(STSData.seed)#for reproducible results
def readdata(self,parentname):
dirlist = glob.glob(parentname+'/*')
if len(dirlist)==0:
print "Aborting due to empty data directory "+parentname
exit(1)
for d in dirlist:
print "Reading "+d
filelist = glob.glob(d+'/*')
for f in filelist:
#print "Reading "+f
matchobj = STSData.fileidPATT.match(f)
if matchobj:
self.setid=matchobj.group(1)
self.fileid=matchobj.group(2)
self.sentid=matchobj.group(3)
self.label=self.setid+"_"+self.fileid
#print "Self.label = "+self.label
#print "Setid: "+self.setid+" fileid: "+self.fileid
else:
print "Error with filename, should contain id number "+f
self.readdatafile(f)
if self.testing == True: break
# self.removeduplicates()
self.vectordict_init()
def readdatafile(self,filename):
#print "Opening "+filename
dstream = open(filename,'r')
self.validfile=True
for line in dstream:
self.processline(line.rstrip())
dstream.close()
self.filesread+=1
def processline(self,line):
# if self.validfile:
#print "Processing "+line
matchobj=STSData.sidPATT.match(line)
if matchobj:
if self.label in self.pairset:
self.currentpair=self.pairset[self.label]
else:
self.currentpair=SentencePair(self.fileid,self.setid,self.testing)
else:
matchobj=STSData.sidendPATT.match(line)
if matchobj:
self.pairset[self.label]=self.currentpair
#print "Finished reading sentence - stored pair ..."
else:
matchobj = STSData.wordPATT.match(line)
if matchobj:
word = matchobj.group(1)
self.currentpair.addword(word,self.sentid)
else:
matchobj = STSData.lemmaPATT.match(line)
if matchobj:
lemma = matchobj.group(1)
self.currentpair.addlemma(lemma,self.sentid)
else:
matchobj=STSData.posPATT.match(line)
if matchobj:
pos = matchobj.group(1)
self.currentpair.addpos(pos,self.sentid)
def removeduplicates(self):
#remove pairs from pairset where the two sentences are identical
#however, this needs work as the system assumes consecutive numbering of pairs
total={}
dups={}
for key in self.pairset.keys():
pair=self.pairset[key]
fileid=pair.fid
if fileid in total.keys():
total[fileid]=total[fileid]+1
else:
total[fileid]=1
if pair.isidentical():
if fileid in dups.keys():
dups[fileid]=dups[fileid]+1
else:
dups[fileid]=1
print "Removing duplicate:"
pair.display()
del self.pairset[key]
for fileid in total.keys():
if fileid in dups.keys():
top=dups[fileid]
else:
top=0.0
percent = top*100.0/total[fileid]
print "For "+fileid+" removed "+str(top)+" duplicates out of "+str(total[fileid])+" pairs: "+str(percent)+"%"
def averagesim(self,type,subset):
label=type+"_"+subset
#print label
if label in self.simaverage:
average = self.simaverage[label]
else:
total =0
count =0
if subset=='all':
for p in self.pairset.values():
total+=p.sim(type)
count+=1
else :
for p in self.pairset.values():
if (p.fid == subset):
total+=p.sim(type)
#print "average sim "+str(p.sim(type))
count+=1
#print count
average = total/count
self.simaverage[label]=average
return average
def readgs(self,dirname):
#read in gs scores and associate with sentence pairs
filelist = glob.glob(dirname+'/*')
for f in filelist:
print "Reading "+f
self.readgsfile(f)
if self.testing == True: break
def readgsfile(self,filename):
matchobj=STSData.gssetPATT.match(filename)
if matchobj:
gsid=matchobj.group(1)
else:
print "Error with gs file "+filename
exit(1)
pid =0
instream=open(filename,'r')
for line in instream:
pid +=1
self.processgsline(line.rstrip(),gsid,pid)
instream.close()
def processgsline(self,line,gsid,pid):
label = gsid+"_"+str(pid)
if label in self.pairset.keys():
self.pairset[label].gs=float(line)
else:
print "WARNING "+label+" not found in pairset"
def split(self,num):
#print "Splitting data into subsets for cross-validation ..."
self.nosplits=num
for pair in self.pairset.values():
pair.cvsplit = random.randint(1,num)
def fitpoly(self,subset,excl,type):
#subset is a setid and excl is cv_split to exclude and type is similarity to correlate
carryon=True
fileid = 1
correlationx=[]
correlationy=[]
while carryon==True:
label = subset+"_"+str(fileid)
if label in self.pairset.keys():
#print "Checking for "+label
if self.pairset[label].cvsplit==excl:
#ignore
#print "Ignoring"
fileid+=1
else:
correlationy.append(self.pairset[label].gs)
correlationx.append(self.pairset[label].sim(type))
fileid+=1
else:
carryon=False #assumes pairs are consecutively numbered
#print len(correlationx),len(correlationy)
x=numpy.array(correlationx)
y=numpy.array(correlationy)
thispoly= numpy.poly1d(numpy.polyfit(x,y,1))
if excl==1 and self.show == True:
pr=stats.spearmanr(x,y)
mytitle="Regression line for: "+subset+":"+str(excl)+":"+type
self.showpoly(x,y,thispoly,mytitle,pr,1,5)
return thispoly
def showpoly(self,x,y,poly,title,pr,xl,yl):
xp=numpy.linspace(0,xl,100)
plt.plot(x,y,'.',xp,poly(xp),'-')
plt.ylim(0,yl)
plt.title(title)
mytext1="srcc = "+str(pr[0])
mytext2="p = "+str(pr[1])
plt.text(0.05,yl*0.9,mytext1)
plt.text(0.05,yl*0.8,mytext2)
plt.show()
def testpoly(self,subset,excl,type):
#to generate and test regression line
thispoly = self.fitpoly(subset,excl,type)
#print thispoly
fileid =1
predictions=[]
gs=[]
carryon=True
#noones=0
#nozeroes=0
while carryon == True:
label = subset+"_"+str(fileid)
if label in self.pairset.keys():
if self.pairset[label].cvsplit== excl:
predictions.append(thispoly(self.pairset[label].sim(type)))
gs.append(self.pairset[label].gs)
fileid+=1
else:
#ignore
fileid+=1
else:
carryon = False
#now to compute spearman correlation coefficient between gs and predictions
x=numpy.array(predictions)
y=numpy.array(gs)
#print len(x),len(y)
#sumzeroone=nozeroes+noones
#print nozeroes, noones, sumzeroone
#print x,y
#pr = stats.spearmanr(x,y)
pr=stats.spearmanr(x,y)
if excl==1 and self.show==True:
mytitle="Correlation for: "+subset+": "+str(excl)+": "+type
self.showpoly(x,y,numpy.poly1d(numpy.polyfit(x,y,1)),mytitle,pr,5,5)
#print pr
return pr
def testpoly2(self,subset,excl,type1, type2):
#to generate and compare 2 regression lines
thispoly1 = self.fitpoly(subset,excl,type1)
thispoly2 = self.fitpoly(subset,excl,type2)
#print thispoly
fileid =1
# predictions1=[]
predictions=[]
gs=[]
carryon=True
#noones=0
#nozeroes=0
while carryon == True:
label = subset+"_"+str(fileid)
if label in self.pairset.keys():
if self.pairset[label].cvsplit== excl:
#if self.pairset[label].sim(type)==1:
# noones+=1
#if self.pairset[label].sim(type)==0:
# nozeroes+=1
# predictions1.append(thispoly1(self.pairset[label].sim(type1)))
predictions.append(thispoly2(self.pairset[label].sim(type2)))
gs.append(self.pairset[label].gs)
error1 = thispoly1(self.pairset[label].sim(type1))-self.pairset[label].gs
error2 = thispoly2(self.pairset[label].sim(type2))-self.pairset[label].gs
diff=pow(error2,2)-pow(error1,2)
self.pairset[label].totaldiff+=diff
fileid+=1
else:
#ignore
fileid+=1
else:
carryon = False
#now to compute spearman correlation coefficient between gs and predictions
x=numpy.array(predictions)
y=numpy.array(gs)
#print len(x),len(y)
#sumzeroone=nozeroes+noones
#print nozeroes, noones, sumzeroone
#print x,y
pr = stats.spearmanr(x,y)
if excl==1 and self.show==True:
mytitle="Correlation for: "+subset+": "+str(excl)+": "+type
self.showpoly(x,y,numpy.poly1d(numpy.polyfit(x,y,1)),mytitle,pr,5,5)
#print pr
return pr
def testread(self,sim,dataset):
#print "Testing"
#print "Files read = "+str(self.filesread)
if self.testing:
print "Pairs stored = "+str(len(self.pairset))
for p in self.pairset.values():
p.display()
#else:
# for p in self.pairset.values():
# p.display()
#print "Average lemma overlap of content words is "+str(self.averagesim("lemma_content","all"))
print "Average "+sim+" for "+dataset+" data is "+str(self.averagesim(sim,dataset))
def vectordict_init(self):
for pair in self.pairset.values():
for sent in ['A','B']:
for item in pair.returncontentlemmas(sent):
if item in self.vectordict:
#self.check=True
self.wordcounts[item]+=1 #count how many times each item occurs for analysis
else:
self.vectordict[item]=WordVector(item)
self.wordcounts[item]=1
print "Vector dictionary initialised with "+str(len(self.vectordict.keys()))+" words"
def compute_token_coverage(self):
total=0
covered=0
for word in self.wordcounts.keys():
freq=self.wordcounts[word]
total+=freq
if len(self.vectordict[word].vector)>0:
covered+=freq
else:
self.uncovered[word]=freq
coverage=covered*100.0/total
self.analyse_uncovered()
return coverage
def analyse_uncovered(self):
#outlog=open('logfile','w')
poscounts={} #count how many uncovered in each POS
for tuple in self.uncovered.keys():
(word,pos)=tuple
#outlog.write(word+"/"+pos+"\t"+str(self.uncovered[tuple])+"\n")
if pos in poscounts.keys():
poscounts[pos]+=1
else:
poscounts[pos]=1
#outlog.close()
total=0
for pos in poscounts.keys():
print "Uncovered by POS:-"
print pos+" : " + str(poscounts[pos])
total+=poscounts[pos]
print "Total "+str(total
)
def readvectors(self,vectorfilename,cachename):
print"Reading vector file "+vectorfilename
linesread=0
instream=open(vectorfilename,'r')
for line in instream:
self.processvectorline(line.rstrip())
linesread+=1
#if (self.testing==True and linesread>1000):
# break
if (linesread%10000 == 0):
print "Read "+str(linesread)+" lines and updated "+str(self.updated)+" vectors"
sys.stdout.flush()
print "Read "+str(linesread)+" lines and updated "+str(self.updated)+" vectors"
coverage=self.updated*100.0/len(self.vectordict.keys())
print "Vector dictionary type coverage is "+str(coverage)+"%"
print "Token coverage is "+str(self.compute_token_coverage())+"%"
instream.close()
if cachename==vectorfilename:
print "Vector cache up to date"
else:
print "Writing vector cache"
self.makecache(cachename)
print "Compressing vector dictionary representation"
self.makematrix()
print "Finished sparse array generation"
def processvectorline(self,line):
featurelist=line.split('\t')
matchobj = STSData.wordposPATT.match(featurelist[0])
if matchobj:
wordpos=(matchobj.group(1),matchobj.group(2))
else:
print "Error with vector file matching "+featurelist[0]
#this could be "__FILTERED" so ignore line and carry on
return
#if len(featurelist)>WordVector.dim:
# WordVector.dim=len(featurelist)
if wordpos in self.vectordict.keys():
featurelist.reverse()
featurelist.pop()
self.updatevector(wordpos,featurelist)
self.updated+=1
def updatevector(self,wordpos,featurelist):
while(len(featurelist)>0):
f=featurelist.pop()
sc=featurelist.pop()
added=self.vectordict[wordpos].addfeature(f,sc)
if added:
self.allfeatures[f]=1
self.vectordict[wordpos].length=pow(self.vectordict[wordpos].length2,0.5)
def makematrix(self):
self.fkeys =self.allfeatures.keys()
self.fkeys.sort()
for i in range(len(self.fkeys)):
self.fk_idx[self.fkeys[i]] = i
del self.fkeys
del self.allfeatures
self.dim=len(self.fk_idx)
update_params(self.dim,self.adja,self.adjb)
print "Dimensionality is "+ str(self.dim)
self.makearrays()
def makearrays(self):
#need to convert a word vector which stores a dictionary of features into a sparse array based on fk_idx
for wordvector in self.vectordict.values():
temparray = numpy.zeros(self.dim)
for feature in wordvector.vector.keys():
col=self.fk_idx[feature]
score=wordvector.vector[feature]
#
temparray[col]=score
# print temparray
wordvector.array = sparse.csr_matrix(temparray)
#print wordvector.array.data
# print "Converted "+wordvector.word+"/"+wordvector.pos
# def composeall(self,method,metric):
# for pair in self.pairset.values():
# pair.compose(self.vectordict,method,metric) # compose and sentence sim each pair of sentences
# sys.stdout.flush()
def composeall_faster(self,method,metric): #compose each sentence in each pair and compute similarity of pair
self.comp=method
self.metric=metric
if method in STSData.methods:
donepairs=0
for pair in self.pairset.values():
self.compose_faster(pair)
sys.stdout.flush()
pair.getsentsim()
donepairs+=1
if donepairs%500 ==0:
print "Completed composition and similarity calculations for "+str(donepairs)+" pairs"
# break
else:
print "Unknown method of composition "+method
def compose_faster(self,pair):
pair.comp=self.comp
pair.metric=self.metric
for sent in ['A','B']:
lemmalist=pair.returncontentlemmas(sent) #get all lemmas in sentence
pair.sentvector[sent]=WordVector((sent,'S'))
if pair.comp == "multiplicative":
pair.sentvector[sent].array=sparse.csr_matrix(numpy.ones(self.dim)) #initialise sentence array as ones
else: #assume additive
pair.sentvector[sent].array=sparse.csr_matrix(numpy.zeros(self.dim)) #initialise sentence array as zeroes
#print lemmalist
for tuple in lemmalist:
if tuple in self.vectordict:
if len(self.vectordict[tuple].vector)>0: #only compose non-zero vectors
# print tuple, "yes"
if pair.comp == "multiplicative":
#pair.sentvector[sent].mult_array(self.vectordict[tuple])
pair.sentvector[sent].array=pair.sentvector[sent].array.multiply(self.vectordict[tuple].array)
else: #assume additive
#pair.sentvector[sent].add_array(self.vectordict[tuple])
pair.sentvector[sent].array=pair.sentvector[sent].array + self.vectordict[tuple].array
#pair.sentvector[sent].display()
def set_simall(self,method,metric):
self.metric=metric
self.setsim=method
if self.setsim in STSData.setmethods:
donepairs=0
for pair in self.pairset.values():
self.set_sim(pair)
sys.stdout.flush()
donepairs+=1
#if self.testing:
if donepairs%500 ==0:
print "Completed set similarity calculations for "+str(donepairs)+" pairs"
#break
else:
print "Unknown method of set similarity "+self.setsim
def set_sim(self,pair):
pair.metric=self.metric
pair.setsim=self.setsim
label="set_"+pair.metric+"_"+pair.setsim
if label in pair.sentsim.keys():
sim = pair.sentsim[label]
else:
lemmalistA=pair.returncontentlemmas('A') #get all content lemmas in sentence A
lemmalistB=pair.returncontentlemmas('B') #get all content lemmas in sentence B
#compute set sim A->B
(total1,count1)= self.set_sim1(lemmalistA,lemmalistB)
#sim1=self.set_sim1(lemmalistA,lemmalistB)
#compute set sim B->A
(total2,count2)= self.set_sim1(lemmalistB,lemmalistA)
#sim2=self.set_sim1(lemmalistB,lemmalistA)
#compute arithmetic mean
if self.setsim=="geo_max":
sim=pow(total1*total2,1.0/(count1+count2))
else:
sim =(total1+total2)/(count1+count2)
#sim =(sim1+sim2)/2
pair.sentsim[label]=sim
if self.verbose:
print (pair.toString("sent_set"))
return sim
def set_sim1(self,lemmalistA,lemmalistB): #asymmetric set sim from A to B
if self.setsim=="geo_max":
total =1.0
else:
total=0.0
count=0.0
for lemmaA in lemmalistA:
maxsim=STSData.minsim #smoothing - if no lemmas in B have entry or any similarity to this lemma
maxlemma=("$!","$!")
if lemmaA in self.vectordict:
if lemmaA in lemmalistB: #check if word is actually in the other sentence
maxsim=1.0
maxlemma=lemmaA
#print lemmaA, lemmaB, thissim
else:
if len(self.vectordict[lemmaA].vector)>0: #only consider non-zero vectors
for lemmaB in lemmalistB: #find maximally similar lemma in B
if lemmaB in self.vectordict:
if len(self.vectordict[lemmaB].vector)>0:
thissim=self.vectordict[lemmaA].findsim(self.vectordict[lemmaB],self.metric)
if thissim<0:
print lemmaA,lemmaB,thissim
self.vectordict[lemmaA].debug=True
thissim=self.vectordict[lemmaA].findsim(self.vectordict[lemmaB],self.metric)
if thissim>1:
print lemmaA, lemmaB, thissim
self.vectordict[lemmaA].debug=True
thissim=self.vectordict[lemmaA].findsim(self.vectordict[lemmaB],self.metric)
if(thissim>maxsim):
maxsim=thissim
maxlemma=lemmaB
else:
print "Vector dictionary error for ", lemmaA
if maxsim < STSData.simthreshold: #similarity threshold
if STSData.threshtype=="weighted":
maxsim = maxsim/STSData.simthreshold #weighted thresholding
else:
maxsim = STSData.minsim # minimum similarity i.e., ignore in binary or non-binary case
maxlemma=("$!",maxlemma)
else:
if STSData.threshtype=="nonbin":
maxsim = maxsim * 1.0 # leave similarity as it is for non-binary threshold
else:
maxsim = 1.0 #in - weighted thresholding or binary threshold
if self.setsim=="geo_max":
total = total * maxsim
else :
total = total + maxsim
count +=1
if self.verbose:
(wordA,posA)=lemmaA
(wordB,posB)=maxlemma
print wordA+"/"+posA+ " : "+wordB+"/"+posB+" : "+str(maxsim)
# if self.setsim=="geo_max":
# sim = pow(total,(1.0/count))
# else:
# sim = total/count
return (total,count)
def ranksent(self,f,type,repeats,outstream):
ranking=[]
for key in self.pairset.keys():#create unordered list of (key,score) pairs
if self.pairset[key].fid == f:
score = self.pairset[key].totaldiff/repeats
ranking.append((key,score))
#sort list based on score
ranking.sort(key=operator.itemgetter(1))
rank=1
for (key,score) in ranking:
outstream.write(str(rank)+" : "+str(score)+"\n")
outstream.write(self.pairset[key].toString(type))
rank+=1
def makecache(self,filename):
outstream = open(filename,'w')
for vector in self.vectordict.values():
if len(vector.vector)>0:
vector.makecache(outstream)
outstream.close()
def inspect(self):
print "Pairs stored = "+str(len(self.pairset))
#for p in self.pairset.values():
# print(p.toString("sent_set"))
