def load_SICK_corpus(vocabFile, trainFile, testFile, max_truncate,maxlength, entailment): #maxSentLength=45
#first load word vocab
read_vocab=open(vocabFile, 'r')
vocab={}
word_ind=1
for line in read_vocab:
tokens=line.strip().split()
vocab[tokens[1]]=word_ind #word2id
word_ind+=1
read_vocab.close()
#load train file
def load_train_file(file, word2id):
read_file=open(file, 'r')
data=[]
Y=[]
Lengths=[]
#true_lengths=[]
leftPad=[]
rightPad=[]
line_control=0
for line in read_file:
tokens=line.strip().split('\t') # question, answer, label
if entailment:
Y.append(int(tokens[2]))
else:
Y.append(float(tokens[3]))
#question
for i in [0,1]:
sent=[]
words=tokens[i].strip().split()
#true_lengths.append(len(words))
length=0
for word in words:
id=word2id.get(word)
if id is not None:
sent.append(id)
length+=1
if length==max_truncate: #we consider max 43 words
break
if length==0:
print 'shit sentence: ', tokens[i]
#exit(0)
break
Lengths.append(length)
left=(maxlength-length)/2
right=maxlength-left-length
leftPad.append(left)
rightPad.append(right)
sent=[0]*left+sent+[0]*right
data.append(sent)
line_control+=1
#if line_control==500:
# break
read_file.close()
if len(Lengths)/2 !=len(Y):
print 'len(Lengths)/2 !=len(Y)'
exit(0)
#return numpy.array(data),numpy.array(Y), numpy.array(Lengths), numpy.array(leftPad),numpy.array(rightPad)
return numpy.array(data),numpy.array(Y), numpy.array(Lengths), numpy.array(leftPad),numpy.array(rightPad)
def load_test_file(file, word2id):
read_file=open(file, 'r')
data=[]
Y=[]
Lengths=[]
#true_lengths=[]
leftPad=[]
rightPad=[]
line_control=0
for line in read_file:
tokens=line.strip().split('\t')
if entailment:
Y.append(int(tokens[2]))
else:
Y.append(float(tokens[3]))
#Y.append(int(tokens[0]))
for i in [0,1]:
sent=[]
words=tokens[i].strip().split()
#true_lengths.append(len(words))
length=0
for word in words:
id=word2id.get(word)
if id is not None:
sent.append(id)
length+=1
if length==max_truncate: #we consider max 43 words
break
if length==0:
print 'shit sentence: ', tokens[i]
#exit(0)
break
Lengths.append(length)
left=(maxlength-length)/2
right=maxlength-left-length
leftPad.append(left)
rightPad.append(right)
sent=[0]*left+sent+[0]*right
data.append(sent)
line_control+=1
#if line_control==200:
# break
read_file.close()
'''
#normalized lengths
arr=numpy.array(Lengths)
max=numpy.max(arr)
min=numpy.min(arr)
normalized_lengths=(arr-min)*1.0/(max-min)
'''
#return numpy.array(data),numpy.array(Y), numpy.array(Lengths), numpy.array(leftPad),numpy.array(rightPad)
return numpy.array(data),numpy.array(Y), numpy.array(Lengths), numpy.array(leftPad),numpy.array(rightPad)
indices_train, trainY, trainLengths, trainLeftPad, trainRightPad=load_train_file(trainFile, vocab)
print 'train file loaded over, total pairs: ', len(trainLengths)/2
indices_test, testY, testLengths, testLeftPad, testRightPad=load_test_file(testFile, vocab)
print 'test file loaded over, total pairs: ', len(testLengths)/2
#now, we need normaliza sentence length in the whole dataset (training and test)
concate_matrix=numpy.concatenate((trainLengths, testLengths), axis=0)
max=numpy.max(concate_matrix)
min=numpy.min(concate_matrix)
normalized_trainLengths=(trainLengths-min)*1.0/(max-min)
normalized_testLengths=(testLengths-min)*1.0/(max-min)
def shared_dataset(data_y, borrow=True):
shared_y = theano.shared(numpy.asarray(data_y,
dtype=theano.config.floatX), # @UndefinedVariable
borrow=borrow)
#return T.cast(shared_y, 'int64')
return T.cast(shared_y, 'int64') #
#return shared_y
def shared_dataset_float(data_y, borrow=True):
return theano.shared(numpy.asarray(data_y,dtype=theano.config.floatX), borrow=borrow)
#indices_train=shared_dataset(indices_train)
#indices_test=shared_dataset(indices_test)
train_set_Lengths=shared_dataset(trainLengths)
test_set_Lengths=shared_dataset(testLengths)
normalized_train_length=theano.shared(numpy.asarray(normalized_trainLengths, dtype=theano.config.floatX), borrow=True)
normalized_test_length = theano.shared(numpy.asarray(normalized_testLengths, dtype=theano.config.floatX), borrow=True)
train_left_pad=shared_dataset(trainLeftPad)
train_right_pad=shared_dataset(trainRightPad)
test_left_pad=shared_dataset(testLeftPad)
test_right_pad=shared_dataset(testRightPad)
if entailment:
train_set_y=shared_dataset(trainY)
test_set_y = shared_dataset(testY)
else:
train_set_y=shared_dataset_float(trainY)
test_set_y = shared_dataset_float(testY)
rval = [(indices_train,train_set_y, train_set_Lengths, normalized_train_length, train_left_pad, train_right_pad), (indices_test, test_set_y, test_set_Lengths, normalized_test_length, test_left_pad, test_right_pad)]
return rval, word_ind-1
def load_msr_corpus(vocabFile, trainFile, testFile, maxlength): #maxSentLength=60
#first load word vocab
read_vocab=open(vocabFile, 'r')
vocab={}
word_ind=1
for line in read_vocab:
tokens=line.strip().split()
vocab[tokens[1]]=word_ind #word2id
word_ind+=1
read_vocab.close()
#load train file
def load_train_file(file, word2id):
read_file=open(file, 'r')
data=[]
Y=[]
Lengths=[]
leftPad=[]
rightPad=[]
line_control=0
for line in read_file:
tokens=line.strip().split('\t') # label, sent1, sent2
Y.append(int(tokens[0])) #repeat
Y.append(int(tokens[0]))
#question
for i in [1,2,2,1]: #shuffle the example
sent=[]
words=tokens[i].strip().lower().split()
length=0
for word in words:
id=word2id.get(word)
if id is not None:
sent.append(id)
length+=1
Lengths.append(length)
left=(maxlength-length)/2
right=maxlength-left-length
leftPad.append(left)
rightPad.append(right)
if left<0 or right<0:
print 'Too long sentence:\n'+tokens[i]
exit(0)
sent=[0]*left+sent+[0]*right
data.append(sent)
#line_control+=1
read_file.close()
'''
#normalized length
arr=numpy.array(Lengths)
max=numpy.max(arr)
min=numpy.min(arr)
normalized_lengths=(arr-min)*1.0/(max-min)
'''
#return numpy.array(data),numpy.array(Y), numpy.array(Lengths), numpy.array(leftPad),numpy.array(rightPad)
return numpy.array(data),numpy.array(Y), numpy.array(Lengths), numpy.array(leftPad),numpy.array(rightPad)
def load_test_file(file, word2id):
read_file=open(file, 'r')
data=[]
Y=[]
Lengths=[]
leftPad=[]
rightPad=[]
line_control=0
for line in read_file:
tokens=line.strip().split('\t')
Y.append(int(tokens[0])) # make the label starts from 0 to 4
#Y.append(int(tokens[0]))
for i in [1,2]:
sent=[]
words=tokens[i].strip().lower().split()
length=0
for word in words:
id=word2id.get(word)
if id is not None:
sent.append(id)
length+=1
Lengths.append(length)
left=(maxlength-length)/2
right=maxlength-left-length
leftPad.append(left)
rightPad.append(right)
if left<0 or right<0:
print 'Too long sentence:\n'+tokens[i]
exit(0)
sent=[0]*left+sent+[0]*right
data.append(sent)
#line_control+=1
#if line_control==1000:
# break
read_file.close()
'''
#normalized lengths
arr=numpy.array(Lengths)
max=numpy.max(arr)
min=numpy.min(arr)
normalized_lengths=(arr-min)*1.0/(max-min)
'''
#return numpy.array(data),numpy.array(Y), numpy.array(Lengths), numpy.array(leftPad),numpy.array(rightPad)
return numpy.array(data),numpy.array(Y), numpy.array(Lengths), numpy.array(leftPad),numpy.array(rightPad)
indices_train, trainY, trainLengths, trainLeftPad, trainRightPad=load_train_file(trainFile, vocab)
print 'train file loaded over, total pairs: ', len(trainLengths)/2
indices_test, testY, testLengths, testLeftPad, testRightPad=load_test_file(testFile, vocab)
print 'test file loaded over, total pairs: ', len(testLengths)/2
#now, we need normaliza sentence length in the whole dataset (training and test)
concate_matrix=numpy.concatenate((trainLengths, testLengths), axis=0)
max=numpy.max(concate_matrix)
min=numpy.min(concate_matrix)
normalized_trainLengths=(trainLengths-min)*1.0/(max-min)
normalized_testLengths=(testLengths-min)*1.0/(max-min)
def shared_dataset(data_y, borrow=True):
shared_y = theano.shared(numpy.asarray(data_y,
dtype=theano.config.floatX), # @UndefinedVariable
borrow=borrow)
return T.cast(shared_y, 'int64')
#return shared_y
#indices_train=shared_dataset(indices_train)
#indices_test=shared_dataset(indices_test)
train_set_Lengths=shared_dataset(trainLengths)
test_set_Lengths=shared_dataset(testLengths)
normalized_train_length=theano.shared(numpy.asarray(normalized_trainLengths, dtype=theano.config.floatX), borrow=True)
normalized_test_length = theano.shared(numpy.asarray(normalized_testLengths, dtype=theano.config.floatX), borrow=True)
train_left_pad=shared_dataset(trainLeftPad)
train_right_pad=shared_dataset(trainRightPad)
test_left_pad=shared_dataset(testLeftPad)
test_right_pad=shared_dataset(testRightPad)
train_set_y=shared_dataset(trainY)
test_set_y = shared_dataset(testY)
rval = [(indices_train,train_set_y, train_set_Lengths, normalized_train_length, train_left_pad, train_right_pad), (indices_test, test_set_y, test_set_Lengths, normalized_test_length, test_left_pad, test_right_pad)]
return rval, word_ind-1
def load_ibm_corpus(vocabFile, trainFile, devFile, maxlength):
#first load word vocab
read_vocab=open(vocabFile, 'r')
vocab={}
word_ind=1
for line in read_vocab:
tokens=line.strip().split()
vocab[tokens[1]]=word_ind #word2id
word_ind+=1
read_vocab.close()
sentlength_limit=1040
#load train file
def load_train_file(file, word2id):
read_file=open(file, 'r')
data=[]
Lengths=[]
leftPad=[]
rightPad=[]
line_control=0
for line in read_file:
tokens=line.strip().split('\t') # label, question, answer
#question
for i in range(1,3):
sent=[]
words=tokens[i].strip().split()
length=len(words)
if length>sentlength_limit:
words=words[:sentlength_limit]
length=sentlength_limit
Lengths.append(length)
left=(maxlength-length)/2
right=maxlength-left-length
leftPad.append(left)
rightPad.append(right)
if left<0 or right<0:
print 'Too long sentence:\n'+tokens[i]
exit(0)
sent+=[0]*left
for word in words:
sent.append(word2id.get(word))
sent+=[0]*right
data.append(sent)
del sent
del words
line_control+=1
if line_control%100==0:
print line_control
read_file.close()
return numpy.array(data),numpy.array(Lengths), numpy.array(leftPad),numpy.array(rightPad)
def load_dev_file(file, word2id):
read_file=open(file, 'r')
data=[]
Y=[]
Lengths=[]
leftPad=[]
rightPad=[]
line_control=0
for line in read_file:
tokens=line.strip().split('\t')
Y.append(int(tokens[0])) # make the label starts from 0 to 4
for i in range(1,3):
sent=[]
words=tokens[i].strip().split()
length=len(words)
if length>sentlength_limit:
words=words[:sentlength_limit]
length=sentlength_limit
Lengths.append(length)
left=(maxlength-length)/2
right=maxlength-left-length
leftPad.append(left)
rightPad.append(right)
if left<0 or right<0:
print 'Too long sentence:\n'+line
exit(0)
sent+=[0]*left
for word in words:
sent.append(word2id.get(word))
sent+=[0]*right
data.append(sent)
line_control+=1
#if line_control==1000:
# break
read_file.close()
return numpy.array(data),Y, numpy.array(Lengths), numpy.array(leftPad),numpy.array(rightPad)
indices_train, trainLengths, trainLeftPad, trainRightPad=load_train_file(trainFile, vocab)
print 'train file loaded over, total pairs: ', len(trainLengths)/2
indices_dev, devY, devLengths, devLeftPad, devRightPad=load_dev_file(devFile, vocab)
print 'dev file loaded over, total pairs: ', len(devLengths)/2
def shared_dataset(data_y, borrow=True):
shared_y = theano.shared(numpy.asarray(data_y,
dtype=theano.config.floatX), # @UndefinedVariable
borrow=borrow)
return T.cast(shared_y, 'int32')
#return shared_y
train_set_Lengths=shared_dataset(trainLengths)
valid_set_Lengths = shared_dataset(devLengths)
train_left_pad=shared_dataset(trainLeftPad)
train_right_pad=shared_dataset(trainRightPad)
dev_left_pad=shared_dataset(devLeftPad)
dev_right_pad=shared_dataset(devRightPad)
#valid_set_y = shared_dataset(devY)
rval = [(indices_train,train_set_Lengths, train_left_pad, train_right_pad), (indices_dev, devY, valid_set_Lengths, dev_left_pad, dev_right_pad)]
return rval, word_ind-1
def load_SICK_corpus(vocabFile, trainFile, testFile, max_truncate, maxlength,
entailment): #maxSentLength=45
#first load word vocab
read_vocab = open(vocabFile, 'r')
vocab = {}
word_ind = 1
for line in read_vocab:
tokens = line.strip().split()
vocab[tokens[1]] = word_ind #word2id
word_ind += 1
read_vocab.close()
#load train file
def load_train_file(file, word2id):
read_file = open(file, 'r')
data = []
Y = []
Lengths = []
#true_lengths=[]
leftPad = []
rightPad = []
line_control = 0
for line in read_file:
tokens = line.strip().split('\t') # question, answer, label
if entailment:
Y.append(int(tokens[2]))
else:
Y.append(float(tokens[3]))
#question
for i in [0, 1]:
sent = []
words = tokens[i].strip().split()
#true_lengths.append(len(words))
length = 0
for word in words:
id = word2id.get(word)
if id is not None:
sent.append(id)
length += 1
if length == max_truncate: #we consider max 43 words
break
if length == 0:
print 'shit sentence: ', tokens[i]
#exit(0)
break
Lengths.append(length)
left = (maxlength - length) / 2
right = maxlength - left - length
leftPad.append(left)
rightPad.append(right)
sent = [0] * left + sent + [0] * right
data.append(sent)
line_control += 1
#if line_control==500:
# break
read_file.close()
if len(Lengths) / 2 != len(Y):
print 'len(Lengths)/2 !=len(Y)'
exit(0)
#return numpy.array(data),numpy.array(Y), numpy.array(Lengths), numpy.array(leftPad),numpy.array(rightPad)
return numpy.array(data), numpy.array(Y), numpy.array(
Lengths), numpy.array(leftPad), numpy.array(rightPad)
def load_test_file(file, word2id):
read_file = open(file, 'r')
data = []
Y = []
Lengths = []
#true_lengths=[]
leftPad = []
rightPad = []
line_control = 0
for line in read_file:
tokens = line.strip().split('\t')
if entailment:
Y.append(int(tokens[2]))
else:
Y.append(float(tokens[3]))
#Y.append(int(tokens[0]))
for i in [0, 1]:
sent = []
words = tokens[i].strip().split()
#true_lengths.append(len(words))
length = 0
for word in words:
id = word2id.get(word)
if id is not None:
sent.append(id)
length += 1
if length == max_truncate: #we consider max 43 words
break
if length == 0:
print 'shit sentence: ', tokens[i]
#exit(0)
break
Lengths.append(length)
left = (maxlength - length) / 2
right = maxlength - left - length
leftPad.append(left)
rightPad.append(right)
sent = [0] * left + sent + [0] * right
data.append(sent)
line_control += 1
#if line_control==200:
# break
read_file.close()
'''
#normalized lengths
arr=numpy.array(Lengths)
max=numpy.max(arr)
min=numpy.min(arr)
normalized_lengths=(arr-min)*1.0/(max-min)
'''
#return numpy.array(data),numpy.array(Y), numpy.array(Lengths), numpy.array(leftPad),numpy.array(rightPad)
return numpy.array(data), numpy.array(Y), numpy.array(
Lengths), numpy.array(leftPad), numpy.array(rightPad)
indices_train, trainY, trainLengths, trainLeftPad, trainRightPad = load_train_file(
trainFile, vocab)
print 'train file loaded over, total pairs: ', len(trainLengths) / 2
indices_test, testY, testLengths, testLeftPad, testRightPad = load_test_file(
testFile, vocab)
print 'test file loaded over, total pairs: ', len(testLengths) / 2
#now, we need normaliza sentence length in the whole dataset (training and test)
concate_matrix = numpy.concatenate((trainLengths, testLengths), axis=0)
max = numpy.max(concate_matrix)
min = numpy.min(concate_matrix)
normalized_trainLengths = (trainLengths - min) * 1.0 / (max - min)
normalized_testLengths = (testLengths - min) * 1.0 / (max - min)
def shared_dataset(data_y, borrow=True):
shared_y = theano.shared(
numpy.asarray(data_y,
dtype=theano.config.floatX), # @UndefinedVariable
borrow=borrow)
#return T.cast(shared_y, 'int64')
return T.cast(shared_y, 'int64') #
#return shared_y
def shared_dataset_float(data_y, borrow=True):
return theano.shared(numpy.asarray(data_y, dtype=theano.config.floatX),
borrow=borrow)
#indices_train=shared_dataset(indices_train)
#indices_test=shared_dataset(indices_test)
train_set_Lengths = shared_dataset(trainLengths)
test_set_Lengths = shared_dataset(testLengths)
normalized_train_length = theano.shared(numpy.asarray(
normalized_trainLengths, dtype=theano.config.floatX),
borrow=True)
normalized_test_length = theano.shared(numpy.asarray(
normalized_testLengths, dtype=theano.config.floatX),
borrow=True)
train_left_pad = shared_dataset(trainLeftPad)
train_right_pad = shared_dataset(trainRightPad)
test_left_pad = shared_dataset(testLeftPad)
test_right_pad = shared_dataset(testRightPad)
if entailment:
train_set_y = shared_dataset(trainY)
test_set_y = shared_dataset(testY)
else:
train_set_y = shared_dataset_float(trainY)
test_set_y = shared_dataset_float(testY)
rval = [(indices_train, train_set_y, train_set_Lengths,
normalized_train_length, train_left_pad, train_right_pad),
(indices_test, test_set_y, test_set_Lengths,
normalized_test_length, test_left_pad, test_right_pad)]
return rval, word_ind - 1
def load_ibm_corpus(vocabFile, trainFile, devFile, maxlength):
#first load word vocab
read_vocab = open(vocabFile, 'r')
vocab = {}
word_ind = 1
for line in read_vocab:
tokens = line.strip().split()
vocab[tokens[1]] = word_ind #word2id
word_ind += 1
read_vocab.close()
sentlength_limit = 1040
#load train file
def load_train_file(file, word2id):
read_file = open(file, 'r')
data = []
Lengths = []
leftPad = []
rightPad = []
line_control = 0
for line in read_file:
tokens = line.strip().split('\t') # label, question, answer
#question
for i in range(1, 3):
sent = []
words = tokens[i].strip().split()
length = len(words)
if length > sentlength_limit:
words = words[:sentlength_limit]
length = sentlength_limit
Lengths.append(length)
left = (maxlength - length) / 2
right = maxlength - left - length
leftPad.append(left)
rightPad.append(right)
if left < 0 or right < 0:
print 'Too long sentence:\n' + tokens[i]
exit(0)
sent += [0] * left
for word in words:
sent.append(word2id.get(word))
sent += [0] * right
data.append(sent)
del sent
del words
line_control += 1
if line_control % 100 == 0:
print line_control
read_file.close()
return numpy.array(data), numpy.array(Lengths), numpy.array(
leftPad), numpy.array(rightPad)
def load_dev_file(file, word2id):
read_file = open(file, 'r')
data = []
Y = []
Lengths = []
leftPad = []
rightPad = []
line_control = 0
for line in read_file:
tokens = line.strip().split('\t')
Y.append(int(tokens[0])) # make the label starts from 0 to 4
for i in range(1, 3):
sent = []
words = tokens[i].strip().split()
length = len(words)
if length > sentlength_limit:
words = words[:sentlength_limit]
length = sentlength_limit
Lengths.append(length)
left = (maxlength - length) / 2
right = maxlength - left - length
leftPad.append(left)
rightPad.append(right)
if left < 0 or right < 0:
print 'Too long sentence:\n' + line
exit(0)
sent += [0] * left
for word in words:
sent.append(word2id.get(word))
sent += [0] * right
data.append(sent)
line_control += 1
#if line_control==1000:
# break
read_file.close()
return numpy.array(data), Y, numpy.array(Lengths), numpy.array(
leftPad), numpy.array(rightPad)
indices_train, trainLengths, trainLeftPad, trainRightPad = load_train_file(
trainFile, vocab)
print 'train file loaded over, total pairs: ', len(trainLengths) / 2
indices_dev, devY, devLengths, devLeftPad, devRightPad = load_dev_file(
devFile, vocab)
print 'dev file loaded over, total pairs: ', len(devLengths) / 2
def shared_dataset(data_y, borrow=True):
shared_y = theano.shared(
numpy.asarray(data_y,
dtype=theano.config.floatX), # @UndefinedVariable
borrow=borrow)
return T.cast(shared_y, 'int32')
#return shared_y
train_set_Lengths = shared_dataset(trainLengths)
valid_set_Lengths = shared_dataset(devLengths)
train_left_pad = shared_dataset(trainLeftPad)
train_right_pad = shared_dataset(trainRightPad)
dev_left_pad = shared_dataset(devLeftPad)
dev_right_pad = shared_dataset(devRightPad)
#valid_set_y = shared_dataset(devY)
rval = [
(indices_train, train_set_Lengths, train_left_pad, train_right_pad),
(indices_dev, devY, valid_set_Lengths, dev_left_pad, dev_right_pad)
]
return rval, word_ind - 1
def load_msr_corpus(vocabFile, trainFile, testFile,
maxlength): #maxSentLength=60
#first load word vocab
read_vocab = open(vocabFile, 'r')
vocab = {}
word_ind = 1
for line in read_vocab:
tokens = line.strip().split()
vocab[tokens[1]] = word_ind #word2id
word_ind += 1
read_vocab.close()
#load train file
def load_train_file(file, word2id):
read_file = open(file, 'r')
data = []
Y = []
Lengths = []
leftPad = []
rightPad = []
line_control = 0
for line in read_file:
tokens = line.strip().split('\t') # label, sent1, sent2
Y.append(int(tokens[0])) #repeat
Y.append(int(tokens[0]))
#question
for i in [1, 2, 2, 1]: #shuffle the example
sent = []
words = tokens[i].strip().lower().split()
length = 0
for word in words:
id = word2id.get(word)
if id is not None:
sent.append(id)
length += 1
Lengths.append(length)
left = (maxlength - length) / 2
right = maxlength - left - length
leftPad.append(left)
rightPad.append(right)
if left < 0 or right < 0:
print 'Too long sentence:\n' + tokens[i]
exit(0)
sent = [0] * left + sent + [0] * right
data.append(sent)
#line_control+=1
read_file.close()
'''
#normalized length
arr=numpy.array(Lengths)
max=numpy.max(arr)
min=numpy.min(arr)
normalized_lengths=(arr-min)*1.0/(max-min)
'''
#return numpy.array(data),numpy.array(Y), numpy.array(Lengths), numpy.array(leftPad),numpy.array(rightPad)
return numpy.array(data), numpy.array(Y), numpy.array(
Lengths), numpy.array(leftPad), numpy.array(rightPad)
def load_test_file(file, word2id):
read_file = open(file, 'r')
data = []
Y = []
Lengths = []
leftPad = []
rightPad = []
line_control = 0
for line in read_file:
tokens = line.strip().split('\t')
Y.append(int(tokens[0])) # make the label starts from 0 to 4
#Y.append(int(tokens[0]))
for i in [1, 2]:
sent = []
words = tokens[i].strip().lower().split()
length = 0
for word in words:
id = word2id.get(word)
if id is not None:
sent.append(id)
length += 1
Lengths.append(length)
left = (maxlength - length) / 2
right = maxlength - left - length
leftPad.append(left)
rightPad.append(right)
if left < 0 or right < 0:
print 'Too long sentence:\n' + tokens[i]
exit(0)
sent = [0] * left + sent + [0] * right
data.append(sent)
#line_control+=1
#if line_control==1000:
# break
read_file.close()
'''
#normalized lengths
arr=numpy.array(Lengths)
max=numpy.max(arr)
min=numpy.min(arr)
normalized_lengths=(arr-min)*1.0/(max-min)
'''
#return numpy.array(data),numpy.array(Y), numpy.array(Lengths), numpy.array(leftPad),numpy.array(rightPad)
return numpy.array(data), numpy.array(Y), numpy.array(
Lengths), numpy.array(leftPad), numpy.array(rightPad)
indices_train, trainY, trainLengths, trainLeftPad, trainRightPad = load_train_file(
trainFile, vocab)
print 'train file loaded over, total pairs: ', len(trainLengths) / 2
indices_test, testY, testLengths, testLeftPad, testRightPad = load_test_file(
testFile, vocab)
print 'test file loaded over, total pairs: ', len(testLengths) / 2
#now, we need normaliza sentence length in the whole dataset (training and test)
concate_matrix = numpy.concatenate((trainLengths, testLengths), axis=0)
max = numpy.max(concate_matrix)
min = numpy.min(concate_matrix)
normalized_trainLengths = (trainLengths - min) * 1.0 / (max - min)
normalized_testLengths = (testLengths - min) * 1.0 / (max - min)
def shared_dataset(data_y, borrow=True):
shared_y = theano.shared(
numpy.asarray(data_y,
dtype=theano.config.floatX), # @UndefinedVariable
borrow=borrow)
return T.cast(shared_y, 'int64')
#return shared_y
#indices_train=shared_dataset(indices_train)
#indices_test=shared_dataset(indices_test)
train_set_Lengths = shared_dataset(trainLengths)
test_set_Lengths = shared_dataset(testLengths)
normalized_train_length = theano.shared(numpy.asarray(
normalized_trainLengths, dtype=theano.config.floatX),
borrow=True)
normalized_test_length = theano.shared(numpy.asarray(
normalized_testLengths, dtype=theano.config.floatX),
borrow=True)
train_left_pad = shared_dataset(trainLeftPad)
train_right_pad = shared_dataset(trainRightPad)
test_left_pad = shared_dataset(testLeftPad)
test_right_pad = shared_dataset(testRightPad)
train_set_y = shared_dataset(trainY)
test_set_y = shared_dataset(testY)
rval = [(indices_train, train_set_y, train_set_Lengths,
normalized_train_length, train_left_pad, train_right_pad),
(indices_test, test_set_y, test_set_Lengths,
normalized_test_length, test_left_pad, test_right_pad)]
return rval, word_ind - 1
def load_entailment_corpus(vocabFile, trainFile, testFile, max_truncate,
maxlength): #maxSentLength=45
#first load word vocab
read_vocab = open(vocabFile, 'r')
vocab = {}
word_ind = 1
for line in read_vocab:
tokens = line.strip().split()
vocab[tokens[1]] = word_ind #word2id
word_ind += 1
read_vocab.close()
#load train file
def load_train_file(file, word2id):
read_file = open(file, 'r')
data = []
Y = []
Lengths = []
#true_lengths=[]
leftPad = []
rightPad = []
line_control = 0
for line in read_file:
tokens = line.strip().split('\t') # question, answer, label
question = tokens[1].strip().lower().split() #lowercase
answer = tokens[2].strip().lower().split() #lowercase
if len(question) > max_truncate or len(
answer) > max_truncate or len(question) < 2 or len(
answer) < 2:
continue #skip this pair
else:
Y.append(int(tokens[0]))
sents = [question, answer]
#question
for i in [0, 1]:
sent = []
words = sents[i]
#true_lengths.append(len(words))
length = len(words)
for word in words:
id = word2id.get(word)
sent.append(id)
Lengths.append(length)
left = (maxlength - length) / 2
right = maxlength - left - length
leftPad.append(left)
rightPad.append(right)
sent = [0] * left + sent + [0] * right
data.append(sent)
line_control += 1
# if line_control==50000:
# break
read_file.close()
if len(Lengths) / 2 != len(Y):
print 'len(Lengths)/2 !=len(Y)'
exit(0)
#return numpy.array(data),numpy.array(Y), numpy.array(Lengths), numpy.array(leftPad),numpy.array(rightPad)
return numpy.array(data), numpy.array(Y), numpy.array(
Lengths), numpy.array(leftPad), numpy.array(rightPad), line_control
def load_test_file(file, word2id):
read_file = open(file, 'r')
data = []
Y = []
Lengths = []
#true_lengths=[]
leftPad = []
rightPad = []
line_control = 0
for line in read_file:
tokens = line.strip().split('\t') # question, answer, label
question = tokens[1].strip().lower().split()
answer = tokens[2].strip().lower().split()
if len(question) > max_truncate or len(
answer) > max_truncate or len(question) < 2 or len(
answer) < 2:
continue #skip this pair
else:
Y.append(int(tokens[0]))
sents = [question, answer]
for i in [0, 1]:
sent = []
words = sents[i]
#true_lengths.append(len(words))
length = len(words)
for word in words:
id = word2id.get(word)
sent.append(id)
Lengths.append(length)
left = (maxlength - length) / 2
right = maxlength - left - length
leftPad.append(left)
rightPad.append(right)
sent = [0] * left + sent + [0] * right
data.append(sent)
line_control += 1
# if line_control==500:
# break
read_file.close()
'''
#normalized lengths
arr=numpy.array(Lengths)
max=numpy.max(arr)
min=numpy.min(arr)
normalized_lengths=(arr-min)*1.0/(max-min)
'''
#return numpy.array(data),numpy.array(Y), numpy.array(Lengths), numpy.array(leftPad),numpy.array(rightPad)
return numpy.array(data), numpy.array(Y), numpy.array(
Lengths), numpy.array(leftPad), numpy.array(rightPad), line_control
indices_train, trainY, trainLengths, trainLeftPad, trainRightPad, train_size = load_train_file(
trainFile, vocab)
print 'train file loaded over, total pairs: ', train_size
indices_test, testY, testLengths, testLeftPad, testRightPad, test_size = load_test_file(
testFile, vocab)
print 'test file loaded over, total pairs: ', test_size
#now, we need normaliza sentence length in the whole dataset (training and test)
concate_matrix = numpy.concatenate((trainLengths, testLengths), axis=0)
max = numpy.max(concate_matrix)
min = numpy.min(concate_matrix)
normalized_trainLengths = (trainLengths - min) * 1.0 / (max - min)
normalized_testLengths = (testLengths - min) * 1.0 / (max - min)
def shared_dataset(data_y, borrow=True):
shared_y = theano.shared(
numpy.asarray(data_y,
dtype=theano.config.floatX), # @UndefinedVariable
borrow=borrow)
return T.cast(shared_y, 'int64')
# return T.cast(shared_y, 'int32') # for gpu
#return shared_y
indices_train = shared_dataset(indices_train)
indices_test = shared_dataset(indices_test)
train_set_Lengths = shared_dataset(trainLengths)
test_set_Lengths = shared_dataset(testLengths)
normalized_train_length = theano.shared(numpy.asarray(
normalized_trainLengths, dtype=theano.config.floatX),
borrow=True)
normalized_test_length = theano.shared(numpy.asarray(
normalized_testLengths, dtype=theano.config.floatX),
borrow=True)
train_left_pad = shared_dataset(trainLeftPad)
train_right_pad = shared_dataset(trainRightPad)
test_left_pad = shared_dataset(testLeftPad)
test_right_pad = shared_dataset(testRightPad)
train_set_y = shared_dataset(trainY)
test_set_y = shared_dataset(testY)
rval = [
(indices_train, train_set_y, train_set_Lengths,
normalized_train_length, train_left_pad, train_right_pad, train_size),
(indices_test, test_set_y, test_set_Lengths, normalized_test_length,
test_left_pad, test_right_pad, test_size)
]
return rval, word_ind - 1
def load_entailment_corpus(vocabFile, trainFile, testFile, max_truncate,maxlength): #maxSentLength=45
#first load word vocab
read_vocab=open(vocabFile, 'r')
vocab={}
word_ind=1
for line in read_vocab:
tokens=line.strip().split()
vocab[tokens[1]]=word_ind #word2id
word_ind+=1
read_vocab.close()
#load train file
def load_train_file(file, word2id):
read_file=open(file, 'r')
data=[]
Y=[]
Lengths=[]
#true_lengths=[]
leftPad=[]
rightPad=[]
line_control=0
for line in read_file:
tokens=line.strip().split('\t') # question, answer, label
question=tokens[1].strip().lower().split() #lowercase
answer=tokens[2].strip().lower().split() #lowercase
if len(question)>max_truncate or len(answer)>max_truncate or len(question)< 2 or len(answer)<2:
continue #skip this pair
else:
Y.append(int(tokens[0]))
sents=[question, answer]
#question
for i in [0,1]:
sent=[]
words=sents[i]
#true_lengths.append(len(words))
length=len(words)
for word in words:
id=word2id.get(word)
sent.append(id)
Lengths.append(length)
left=(maxlength-length)/2
right=maxlength-left-length
leftPad.append(left)
rightPad.append(right)
sent=[0]*left+sent+[0]*right
data.append(sent)
line_control+=1
# if line_control==50000:
# break
read_file.close()
if len(Lengths)/2 !=len(Y):
print 'len(Lengths)/2 !=len(Y)'
exit(0)
#return numpy.array(data),numpy.array(Y), numpy.array(Lengths), numpy.array(leftPad),numpy.array(rightPad)
return numpy.array(data),numpy.array(Y), numpy.array(Lengths), numpy.array(leftPad),numpy.array(rightPad), line_control
def load_test_file(file, word2id):
read_file=open(file, 'r')
data=[]
Y=[]
Lengths=[]
#true_lengths=[]
leftPad=[]
rightPad=[]
line_control=0
for line in read_file:
tokens=line.strip().split('\t') # question, answer, label
question=tokens[1].strip().lower().split()
answer=tokens[2].strip().lower().split()
if len(question)>max_truncate or len(answer)>max_truncate or len(question)< 2 or len(answer)<2:
continue #skip this pair
else:
Y.append(int(tokens[0]))
sents=[question, answer]
for i in [0,1]:
sent=[]
words=sents[i]
#true_lengths.append(len(words))
length=len(words)
for word in words:
id=word2id.get(word)
sent.append(id)
Lengths.append(length)
left=(maxlength-length)/2
right=maxlength-left-length
leftPad.append(left)
rightPad.append(right)
sent=[0]*left+sent+[0]*right
data.append(sent)
line_control+=1
# if line_control==500:
# break
read_file.close()
'''
#normalized lengths
arr=numpy.array(Lengths)
max=numpy.max(arr)
min=numpy.min(arr)
normalized_lengths=(arr-min)*1.0/(max-min)
'''
#return numpy.array(data),numpy.array(Y), numpy.array(Lengths), numpy.array(leftPad),numpy.array(rightPad)
return numpy.array(data),numpy.array(Y), numpy.array(Lengths), numpy.array(leftPad),numpy.array(rightPad) , line_control
indices_train, trainY, trainLengths, trainLeftPad, trainRightPad, train_size=load_train_file(trainFile, vocab)
print 'train file loaded over, total pairs: ', train_size
indices_test, testY, testLengths, testLeftPad, testRightPad, test_size=load_test_file(testFile, vocab)
print 'test file loaded over, total pairs: ', test_size
#now, we need normaliza sentence length in the whole dataset (training and test)
concate_matrix=numpy.concatenate((trainLengths, testLengths), axis=0)
max=numpy.max(concate_matrix)
min=numpy.min(concate_matrix)
normalized_trainLengths=(trainLengths-min)*1.0/(max-min)
normalized_testLengths=(testLengths-min)*1.0/(max-min)
def shared_dataset(data_y, borrow=True):
shared_y = theano.shared(numpy.asarray(data_y,
dtype=theano.config.floatX), # @UndefinedVariable
borrow=borrow)
return T.cast(shared_y, 'int64')
# return T.cast(shared_y, 'int32') # for gpu
#return shared_y
indices_train=shared_dataset(indices_train)
indices_test=shared_dataset(indices_test)
train_set_Lengths=shared_dataset(trainLengths)
test_set_Lengths=shared_dataset(testLengths)
normalized_train_length=theano.shared(numpy.asarray(normalized_trainLengths, dtype=theano.config.floatX), borrow=True)
normalized_test_length = theano.shared(numpy.asarray(normalized_testLengths, dtype=theano.config.floatX), borrow=True)
train_left_pad=shared_dataset(trainLeftPad)
train_right_pad=shared_dataset(trainRightPad)
test_left_pad=shared_dataset(testLeftPad)
test_right_pad=shared_dataset(testRightPad)
train_set_y=shared_dataset(trainY)
test_set_y = shared_dataset(testY)
rval = [(indices_train,train_set_y, train_set_Lengths, normalized_train_length, train_left_pad, train_right_pad, train_size), (indices_test, test_set_y, test_set_Lengths, normalized_test_length, test_left_pad, test_right_pad, test_size)]
return rval, word_ind-1