def predict_prob(self, source='training',result_file='answers.csv'):
# source must be 'training' or 'validation'
data_set = aux.preprocessData('../Data/'+source+'_set.tsv')
replies = ['A', 'B', 'C', 'D']
probs=[]
for index, row in data_set.iterrows():
prob=[]
r = -1
row['question'] = row['question'].replace('. ', ' . ').replace('-', ' - ').replace('.', ' .').replace('_', ' ').lower()
if (row['question'][-1]=='.'):
row['question']=row['question'][:-1]+' . '
for word in nt.word_tokenize(row['question']):
try:
self.model[word]
except:
row['question'] = row['question'].replace(word, '')
for reply in replies:
r_current = -1
ans = row['answer' + reply].replace('-', ' - ').replace('. ', ' . ').lower()
if (ans[-1]=='.'):
ans=ans[:-1]+' . '
for word in nt.word_tokenize(ans):
try:
self.model[word]
except:
ans = ans.replace(word, '')
try:
ans = nt.word_tokenize(ans)
if ans==[]:
continue
if row['type'] != 1:
quest = nt.word_tokenize(row['question'])
r_current = np.exp(self.model.n_similarity(quest, ans))
else:
ind = row['question'].find(' ')
if row['question'][ind + 1:].split() == []:
r_current = np.exp(self.model.n_similarity(nt.word_tokenize(row['question']), ans))
elif row['question'][0:ind].split() == []:
r_current = np.exp(self.model.n_similarity(nt.word_tokenize(row['question'][ind:]), ans))
else:
r_current = np.exp(self.model.n_similarity(nt.word_tokenize(row['question'][0:ind]),
ans)) * np.exp(self.model.n_similarity(nt.word_tokenize(row['question'][ind:]),
ans))
except:
()
prob.append(r_current)
if r_current > r:
r = r_current
# c
ptot = sum(prob)
prob= [ p/ptot for p in prob]
probs.append(prob)
return probs
def predict_answer(self, source='training',result_file='answers.csv'):
# source must be 'training' or 'validation'
res = np.zeros([2, 3]).astype('int')
data_set = aux.preprocessData('../Data/'+source+'_set.tsv')
replies = ['A', 'B', 'C', 'D']
goodAnswersCount = 0
unknowWordsCount = 0
unknowWords=[]
if (source=='validation'):
answer = open('../Results/'+result_file, 'w')
answer.write('id,correctAnswer\n')
for index, row in data_set.iterrows():
r = -1
good = replies[0]
res[0, row['type']] += 1
row['question'] = row['question'].replace('. ', ' . ').replace('-', ' - ').replace('.', ' .').replace('_', ' ').lower()
if (row['question'][-1]=='.'):
row['question']=row['question'][:-1]+' . '
for word in nt.word_tokenize(row['question']):
try:
self.model[word]
except:
unknowWordsCount += 1
if not word.lower() in unknowWords:
unknowWords.append(word.lower())
row['question'] = row['question'].replace(word, '')
for reply in replies:
r_current = -1
ans = row['answer' + reply].replace('-', ' - ').replace('. ', ' . ').lower()
if (ans[-1]=='.'):
ans=ans[:-1]+' . '
for word in nt.word_tokenize(ans):
try:
self.model[word]
except:
unknowWordsCount += 1
if not word in unknowWords:
unknowWords.append(word)
ans = ans.replace(word, '')
try:
ans = nt.word_tokenize(ans)
if ans==[]:
continue
if row['type'] != 1:
quest = nt.word_tokenize(row['question'])
r_current = self.model.n_similarity(quest, ans)
else:
ind = row['question'].find(' ')
if row['question'][ind + 1:].split() == []:
r_current = self.model.n_similarity(nt.word_tokenize(row['question']), ans)
elif row['question'][0:ind].split() == []:
r_current = self.model.n_similarity(nt.word_tokenize(row['question'][ind:]), ans)
else:
r_current = np.exp(self.model.n_similarity(nt.word_tokenize(row['question'][0:ind]),
ans)) * np.exp(self.model.n_similarity(nt.word_tokenize(row['question'][ind:]),
ans))
except:
unknowWordsCount += 1
if r_current > r:
r = r_current
good = reply
# c
if (source=='validation'):
answer.write(str(row['id']) + ',' + good + '\n')
elif (source=='training'):
if (good==row['correctAnswer']):
goodAnswersCount+=1
res[1,row['type']]+=1
return [goodAnswersCount,unknowWordsCount,unknowWords,res]
