def evaluateVSM(targeEventFile, collFolder,k,relevTh,vsmClassifierFileName,topK):
'''
docs = []
try:
classifierFile = open(vsmClassifierFileName,"rb")
classifier = pickle.load(classifierFile)
classifierFile.close()
except:
f = open(targeEventFile,'r')
for url in f:
url = url.strip()
d = Document(url)
if d:
docs.append(d)
f.close()
docsTF = []
for d in docs:
wordsFreq = getFreq(d.getWords())
docsTF.append(wordsFreq)
classifier = VSMClassifier(docsTF,relevTh)
evalres = []
for j in range(k):
fn = collFolder+str(j)+'.txt'
f = codecs.open(fn, encoding='utf-8')
ftext = f.read()
r = classifier.calculate_score(ftext)[0]
evalres.append(r)
f.close()
'''
evaluator = Evaluate()
evaluator.buildVSMClassifier(targeEventFile,vsmClassifierFileName,relevTh,topK)
collFiles = []
for j in range(k):
fn = collFolder+str(j)+'.txt'
f = codecs.open(fn, encoding='utf-8')
ftext = f.read()
o = myObj()
o.text = ftext
collFiles.append(o)
res = evaluator.evaluateFC(collFiles)
#f = open(collFolder+'evaluationRes_VSM.txt','w')
#f.write('\n'.join([str(r) for r in res]))
#f.close()
#print sum(res)
return res
def evaluateVSM(targeEventFile, collFolder,k,relevTh,vsmClassifierFileName,topK):
'''
docs = []
try:
classifierFile = open(vsmClassifierFileName,"rb")
classifier = pickle.load(classifierFile)
classifierFile.close()
except:
f = open(targeEventFile,'r')
for url in f:
url = url.strip()
d = Document(url)
if d:
docs.append(d)
f.close()
docsTF = []
for d in docs:
wordsFreq = getFreq(d.getWords())
docsTF.append(wordsFreq)
classifier = VSMClassifier(docsTF,relevTh)
evalres = []
for j in range(k):
fn = collFolder+str(j)+'.txt'
f = codecs.open(fn, encoding='utf-8')
ftext = f.read()
r = classifier.calculate_score(ftext)[0]
evalres.append(r)
f.close()
'''
evaluator = Evaluate()
evaluator.buildVSMClassifier(targeEventFile,vsmClassifierFileName,relevTh,topK)
collFiles = []
for j in range(k):
fn = collFolder+str(j)+'.txt'
f = codecs.open(fn, encoding='utf-8')
ftext = f.read()
o = myObj()
o.text = ftext
collFiles.append(o)
res = evaluator.evaluateFC(collFiles)
#f = open(collFolder+'evaluationRes_VSM.txt','w')
#f.write('\n'.join([str(r) for r in res]))
#f.close()
#print sum(res)
return res
#for i in range(3):
pagesLimit = 1000
noK = 10
pageTh = 0.2
urlsTh = 0
i = 0
ct = 'e'
posFile = posFiles[i]
negFile = negFiles[i]
vsmClassifierFileName = 'classifierVSM-' + posFile.split(".")[0].split(
'-')[0] + ".p"
#evaluator.buildVSMClassifier(posFile, vsmClassifierFileName,pageTh,noK)
evaluator.buildVSMClassifier(posFile, noK, vsmClassifierFileName)
#classifierFileName = 'charlestonShooting_NBClassifier.p'
#evaluator.buildClassifier(posFile, negFile, classifierFileName)
#vsmClassifierFileName = 'classifierVSM-'+posFile.split(".")[0].split('-')[1]+".p"
#evaluator.buildVSMClassifier(posFile, vsmClassifierFileName,pageTh,noK)
inputFile = seedsFiles[i]
modelFile = modelFiles[i] #'modelFile.txt'#inputFile
mode = 1 # URL scoring with no page scoring
crawlParams = {
"num_pages": pagesLimit,
"pageScoreThreshold": pageTh,
"urlScoreThreshold": urlsTh,
"mode": mode
negFiles = ['neg-Charlie.txt','neg-sydneyseige.txt']
'''
evaluator = Evaluate()
#for i in range(3):
noK = 10
th = 0.75
i=3
posFile = posFiles[i]
negFile = negFiles[i]
#modelFile = modelFile +"-"+str(i)+".txt"
#classifierFileName = 'classifier'+posFile.split(".")[0].split('-')[1]+".p"
vsmClassifierFileName = 'classifierVSM-'+posFile.split(".")[0].split('-')[1]+".p"
#evaluator.buildClassifier(posFile,negFolder,classifierFileName)
#evaluator.buildClassifier(posFile,negFile,classifierFileName)
evaluator.buildVSMClassifier(posFile, vsmClassifierFileName,th,noK)
v = 0
inputFile = seedsFiles[i].split('.')[0]+"_"+str(v)+".txt"
'''
event = 'Charlie'
posFile = 'pos_'+event+'.txt'
classifierFileName = 'classifier_'+event+'.p'
evaluator.buildClassifier(posFile,negFolder,classifierFileName)
inputFile = 'seedURLs_'+event+'.txt'
modelFile = 'modelURLs_'+ event + '.txt'
'''
crawlType = 'e'
modelFile = inputFile
