def test(doc, name):
f= open(name,"w")
frequencies0 = FeatureExtractor.frequency(doc[:2],True,True) #frequency count smoothed by 1
frequencies1 = FeatureExtractor.augmented_frequency(frequencies0) # augmented frequencies taking into account document size
frequencies = FeatureExtractor.idf(frequencies1) # idfs
total = frequencies["**prob**"]
totals = sum(total)
priors =[0.33, 0.33, 0.329] # based on number of documents
a = ["C2","IKEA_EN","IKEA_IT"]
with open(doc[2],"r") as mefile:
for line in mefile:
lines = line.split('\t')
ID = lines[0]
words = lines[4].replace("<s>","").replace("</s>","").split(" ")
pC2 = 0
pIKEA_IT = 0
pIKEA_EN = 0
for word in words:
if word in frequencies:
pC2 += math.log((frequencies[word][0]))
pIKEA_EN += math.log((frequencies[word][1]))
pIKEA_IT += math.log((frequencies[word][2]))
else:
pC2 += math.log(0.5)
pIKEA_EN += math.log(0.5)
pIKEA_IT += math.log(0.5)
b = [pC2+math.log(priors[0]),pIKEA_EN+math.log(priors[1]),pIKEA_IT+math.log(priors[2])]
proposal = a[b.index(max(b))]
f.write(ID+ "\t" + proposal + "\n")
f.close()
def validate(doc, name):
f= open(name,"w")
frequencies0 = FeatureExtractor.frequency(doc[:2]) #frequency count smoothed by 1
frequencies1 = FeatureExtractor.augmented_frequency(frequencies0) # augmented frequencies taking into account document size
frequencies = FeatureExtractor.idf(frequencies1) # idfs
total = frequencies["**prob**"]
totals = sum(total)
priors =[0.33, 0.33, 0.328] # based on number of documents
a = ["C2","IKEA_EN","IKEA_IT"]
correct = 0
number = 0
tpC2 = 0
tpIKEA_EN = 0
tpIKEA_IT = 0
fpC2 = 0
fpIKEA_EN = 0
fpIKEA_IT = 0
fnC2 = 0
fnIKEA_EN = 0
fnIKEA_IT = 0
with open(doc[2],"r") as mefile:
for line in mefile:
lines = line.split('\t')
ID = lines[1]
words = lines[4].replace("<s>","").replace("</s>","").split(" ")
pC2 = 0
pIKEA_IT = 0
pIKEA_EN = 0
for word in words:
if word in frequencies:
pC2 += math.log((frequencies[word][0]))
pIKEA_EN += math.log((frequencies[word][1]))
pIKEA_IT += math.log((frequencies[word][2]))
else:
pC2 += math.log(0.5)
pIKEA_EN += math.log(0.5)
pIKEA_IT += math.log(0.5)
b = [pC2+math.log(priors[0]),pIKEA_EN+math.log(priors[1]),pIKEA_IT+math.log(priors[2])]
# other possibilities
# d = [pC2,pIKEA_EN,pIKEA_IT] # without priors
# c = [-pC2*priors[0],-pIKEA_EN*priors[1],-pIKEA_IT*priors[2]] # multiplying by priors
proposal = a[b.index(max(b))]
f.write(proposal + "\t" + ID + "\n")
# calculate precision, recall, f1
# count true positives, false positives, false negatives
print proposal
if ID == proposal:
if ID == "C2":
tpC2+=1
elif ID == "IKEA_EN":
tpIKEA_EN +=1
elif ID == "IKEA_IT":
tpIKEA_IT += 1
correct += 1
else:
if ID == "C2":
fnC2+=1
elif ID == "IKEA_EN":
fnIKEA_EN +=1
elif ID == "IKEA_IT":
fnIKEA_IT += 1
if proposal == "C2":
fpC2+=1
elif proposal == "IKEA_EN":
fpIKEA_EN +=1
elif proposal == "IKEA_IT":
fpIKEA_IT += 1
number +=1
print fnC2
precisionC2 = tpC2 / ( tpC2 + fpC2 )
precisionIKEA_IT = tpIKEA_IT / ( tpIKEA_IT + fpIKEA_IT)
precisionIKEA_EN = tpIKEA_EN / ( tpIKEA_EN + fpIKEA_EN)
precisions = [precisionC2, precisionIKEA_EN,precisionIKEA_IT]
recallC2 = tpC2 / ( tpC2 + fnC2 )
recallIKEA_IT = tpIKEA_IT / ( tpIKEA_IT + fnIKEA_IT)
recallIKEA_EN = tpIKEA_EN / ( tpIKEA_EN + fnIKEA_EN)
recalls = [recallC2,recallIKEA_EN,recallIKEA_IT]
avgpre = sum(precisions)/3
avgrec = sum(recalls)/3
f.write("\n\ncorrect: " + str(correct) + "out of" + str(number))
f.write("\nprecision: " + str(avgpre))
f.write("\nrecall: " + str(avgrec))
f.write("\nF1: " + str( 2* ((avgpre*avgrec) / (avgpre + avgrec)) ))
f.close()
