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dataset.py
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dataset.py
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import os
import cPickle
import theano
import numpy
import warnings
import pdb
class SNLI(object):
def __init__(self, batch_size=50, loadall=False,
datapath="/home/hantek/datasets/SNLI_GloVe_converted"):
self.batch_size = batch_size
self.datapath = datapath
data_file = open(self.datapath, 'rb')
cPickle.load(data_file)
cPickle.load(data_file)
self.train_set, self.dev_set, self.test_set = cPickle.load(data_file)
self.weight = cPickle.load(data_file).astype(theano.config.floatX)
if loadall:
self.word2embed = cPickle.load(data_file) # key: word, value: embedding
self.word2num = cPickle.load(data_file) # key: word, value: number
self.num2word = cPickle.load(data_file) # key: number, value: word
data_file.close()
self.train_size = len(self.train_set)
self.dev_size = len(self.dev_set)
self.test_size = len(self.test_set)
self.train_ptr = 0
self.dev_ptr = 0
self.test_ptr = 0
def train_minibatch_generator(self):
while self.train_ptr <= self.train_size - self.batch_size:
self.train_ptr += self.batch_size
minibatch = self.train_set[self.train_ptr - self.batch_size : self.train_ptr]
if len (minibatch) < self.batch_size:
warnings.warn("There will be empty slots in minibatch data.", UserWarning)
longest_hypo, longest_premise = \
numpy.max(map(lambda x: (len(x[0]), len(x[1])), minibatch), axis=0)
hypos = numpy.zeros((self.batch_size, longest_hypo), dtype='int32')
premises = numpy.zeros((self.batch_size, longest_premise), dtype='int32')
truth = numpy.zeros((self.batch_size,), dtype='int32')
mask_hypos = numpy.zeros((self.batch_size, longest_hypo), dtype='int32')
mask_premises = numpy.zeros((self.batch_size, longest_premise), dtype='int32')
for i, (h, p, t) in enumerate(minibatch):
hypos[i, :len(h)] = h
mask_hypos[i, :len(h)] = (1,) * len(h)
premises[i, :len(p)] = p
mask_premises[i, :len(p)] = (1,) * len(p)
truth[i] = t
yield hypos, mask_hypos, premises, mask_premises, truth
else:
self.train_ptr = 0
raise StopIteration
def dev_minibatch_generator(self, ):
while self.dev_ptr <= self.dev_size - self.batch_size:
self.dev_ptr += self.batch_size
minibatch = self.dev_set[self.dev_ptr - self.batch_size : self.dev_ptr]
if len (minibatch) < self.batch_size:
warnings.warn("There will be empty slots in minibatch data.", UserWarning)
longest_hypo, longest_premise = \
numpy.max(map(lambda x: (len(x[0]), len(x[1])), minibatch), axis=0)
hypos = numpy.zeros((self.batch_size, longest_hypo), dtype='int32')
premises = numpy.zeros((self.batch_size, longest_premise), dtype='int32')
truth = numpy.zeros((self.batch_size,), dtype='int32')
mask_hypos = numpy.zeros((self.batch_size, longest_hypo), dtype='int32')
mask_premises = numpy.zeros((self.batch_size, longest_premise), dtype='int32')
for i, (h, p, t) in enumerate(minibatch):
hypos[i, :len(h)] = h
mask_hypos[i, :len(h)] = (1,) * len(h)
premises[i, :len(p)] = p
mask_premises[i, :len(p)] = (1,) * len(p)
truth[i] = t
yield hypos, mask_hypos, premises, mask_premises, truth
else:
self.dev_ptr = 0
raise StopIteration
def test_minibatch_generator(self, ):
while self.test_ptr <= self.test_size - self.batch_size:
self.test_ptr += self.batch_size
minibatch = self.test_set[self.test_ptr - self.batch_size : self.test_ptr]
if len (minibatch) < self.batch_size:
warnings.warn("There will be empty slots in minibatch data.", UserWarning)
longest_hypo, longest_premise = \
numpy.max(map(lambda x: (len(x[0]), len(x[1])), minibatch), axis=0)
hypos = numpy.zeros((self.batch_size, longest_hypo), dtype='int32')
premises = numpy.zeros((self.batch_size, longest_premise), dtype='int32')
truth = numpy.zeros((self.batch_size,), dtype='int32')
mask_hypos = numpy.zeros((self.batch_size, longest_hypo), dtype='int32')
mask_premises = numpy.zeros((self.batch_size, longest_premise), dtype='int32')
for i, (h, p, t) in enumerate(minibatch):
hypos[i, :len(h)] = h
mask_hypos[i, :len(h)] = (1,) * len(h)
premises[i, :len(p)] = p
mask_premises[i, :len(p)] = (1,) * len(p)
truth[i] = t
yield hypos, mask_hypos, premises, mask_premises, truth
else:
self.test_ptr = 0
raise StopIteration
class SICK(SNLI):
def __init__(self, batch_size=50, loadall=False, augment=False,
datapath="/Users/johanlin/Datasets/SICK/"):
self.batch_size = batch_size
if augment:
self.datapath = datapath + os.sep + 'SICK_augmented.pkl'
else:
self.datapath = datapath + os.sep + 'SICK.pkl'
super(SICK, self).__init__(batch_size, loadall, self.datapath)
class YELP(object):
def __init__(self, batch_size=50, loadall=False,
datapath="/home/hantek/datasets/NLC_data/yelp/yelp.pkl"):
self.batch_size = batch_size
self.datapath = datapath
data_file = open(self.datapath, 'rb')
cPickle.load(data_file)
cPickle.load(data_file)
self.train_set, self.dev_set, self.test_set = cPickle.load(data_file)
self.weight = cPickle.load(data_file).astype(theano.config.floatX)
if loadall:
self.word2embed = cPickle.load(data_file) # key: word, value: embedding
self.word2num = cPickle.load(data_file) # key: word, value: number
self.num2word = cPickle.load(data_file) # key: number, value: word
data_file.close()
self.train_size = len(self.train_set)
self.dev_size = len(self.dev_set)
self.test_size = len(self.test_set)
self.train_ptr = 0
self.dev_ptr = 0
self.test_ptr = 0
def train_minibatch_generator(self):
while self.train_ptr <= self.train_size - self.batch_size:
self.train_ptr += self.batch_size
minibatch = self.train_set[self.train_ptr - self.batch_size : self.train_ptr]
if len (minibatch) < self.batch_size:
warnings.warn("There will be empty slots in minibatch data.", UserWarning)
longest_hypo = numpy.max(map(lambda x: len(x[0]), minibatch), axis=0)
hypos = numpy.zeros((self.batch_size, longest_hypo), dtype='int32')
truth = numpy.zeros((self.batch_size,), dtype='int32')
mask_hypos = numpy.zeros((self.batch_size, longest_hypo), dtype='int32')
for i, (h, t) in enumerate(minibatch):
hypos[i, :len(h)] = h
mask_hypos[i, :len(h)] = (1,) * len(h)
truth[i] = t
yield hypos, mask_hypos, truth
else:
self.train_ptr = 0
raise StopIteration
def dev_minibatch_generator(self, ):
while self.dev_ptr <= self.dev_size - self.batch_size:
self.dev_ptr += self.batch_size
minibatch = self.dev_set[self.dev_ptr - self.batch_size : self.dev_ptr]
if len (minibatch) < self.batch_size:
warnings.warn("There will be empty slots in minibatch data.", UserWarning)
longest_hypo = numpy.max(map(lambda x: len(x[0]), minibatch), axis=0)
hypos = numpy.zeros((self.batch_size, longest_hypo), dtype='int32')
truth = numpy.zeros((self.batch_size,), dtype='int32')
mask_hypos = numpy.zeros((self.batch_size, longest_hypo), dtype='int32')
for i, (h, t) in enumerate(minibatch):
hypos[i, :len(h)] = h
mask_hypos[i, :len(h)] = (1,) * len(h)
truth[i] = t
yield hypos, mask_hypos, truth
else:
self.dev_ptr = 0
raise StopIteration
def test_minibatch_generator(self, ):
while self.test_ptr <= self.test_size - self.batch_size:
self.test_ptr += self.batch_size
minibatch = self.test_set[self.test_ptr - self.batch_size : self.test_ptr]
if len (minibatch) < self.batch_size:
warnings.warn("There will be empty slots in minibatch data.", UserWarning)
longest_hypo = numpy.max(map(lambda x: len(x[0]), minibatch), axis=0)
hypos = numpy.zeros((self.batch_size, longest_hypo), dtype='int32')
truth = numpy.zeros((self.batch_size,), dtype='int32')
mask_hypos = numpy.zeros((self.batch_size, longest_hypo), dtype='int32')
for i, (h, t) in enumerate(minibatch):
hypos[i, :len(h)] = h
mask_hypos[i, :len(h)] = (1,) * len(h)
truth[i] = t
yield hypos, mask_hypos, truth
else:
self.test_ptr = 0
raise StopIteration
class AGE2(YELP):
def __init__(self, batch_size=50, loadall=False,
datapath="/home/hantek/datasets/NLC_data/age2/age2.pkl"):
super(AGE2, self).__init__(batch_size, loadall, datapath)
class STANFORDSENTIMENTTREEBANK(object):
def __init__(self, batch_size=50, loadext=False, loadhelper=False, wordembed='word2vec',
datapath="/home/hantek/datasets/StanfordSentimentTreebank"):
self.batch_size = batch_size
self.datapath = datapath
save_file = open(self.datapath + os.sep + 'sst_' + wordembed + '.pkl', 'rb')
cPickle.load(save_file)
self.train_set, self.dev_set, self.test_set = cPickle.load(save_file)
self.weight = cPickle.load(save_file).astype(theano.config.floatX)
save_file.close()
if loadext == True:
save_file_ext = open(self.datapath + os.sep + 'sst_' + wordembed + '_ext.pkl', 'rb')
train_set, dev_set, test_set = cPickle.load(save_file_ext)
self.train_set += train_set
self.dev_set += dev_set
self.test_set += test_set
save_file_ext.close()
if loadhelper == True:
helper = open(self.datapath + os.sep + 'sst_' + wordembed + '_helper.pkl', 'rb')
self.word2embed = cPickle.load(helper) # key: word, value: embedding
self.word2num = cPickle.load(helper) # key: word, value: number
self.num2word = cPickle.load(helper) # key: number, value: word
helper.close()
self.train_size = len(self.train_set)
self.dev_size = len(self.dev_set)
self.test_size = len(self.test_set)
self.train_ptr = 0
self.dev_ptr = 0
self.test_ptr = 0
def train_minibatch_generator(self):
while self.train_ptr <= self.train_size - self.batch_size:
self.train_ptr += self.batch_size
minibatch = self.train_set[self.train_ptr - self.batch_size : self.train_ptr]
if len (minibatch) < self.batch_size:
warnings.warn("There will be empty slots in minibatch data.", UserWarning)
longest_hypo = numpy.max(map(lambda x: len(x[0]), minibatch), axis=0)
hypos = numpy.zeros((self.batch_size, longest_hypo), dtype='int32')
truth = numpy.zeros((self.batch_size,), dtype='int32')
mask_hypos = numpy.zeros((self.batch_size, longest_hypo), dtype='int32')
for i, (h, t) in enumerate(minibatch):
hypos[i, :len(h)] = h
mask_hypos[i, :len(h)] = (1,) * len(h)
truth[i] = t
yield hypos, mask_hypos, truth
else:
self.train_ptr = 0
raise StopIteration
def dev_minibatch_generator(self, ):
while self.dev_ptr <= self.dev_size - self.batch_size:
self.dev_ptr += self.batch_size
minibatch = self.dev_set[self.dev_ptr - self.batch_size : self.dev_ptr]
if len (minibatch) < self.batch_size:
warnings.warn("There will be empty slots in minibatch data.", UserWarning)
longest_hypo = numpy.max(map(lambda x: len(x[0]), minibatch), axis=0)
hypos = numpy.zeros((self.batch_size, longest_hypo), dtype='int32')
truth = numpy.zeros((self.batch_size,), dtype='int32')
mask_hypos = numpy.zeros((self.batch_size, longest_hypo), dtype='int32')
for i, (h, t) in enumerate(minibatch):
hypos[i, :len(h)] = h
mask_hypos[i, :len(h)] = (1,) * len(h)
truth[i] = t
yield hypos, mask_hypos, truth
else:
self.dev_ptr = 0
raise StopIteration
def test_minibatch_generator(self, ):
while self.test_ptr <= self.test_size - self.batch_size:
self.test_ptr += self.batch_size
minibatch = self.test_set[self.test_ptr - self.batch_size : self.test_ptr]
if len (minibatch) < self.batch_size:
warnings.warn("There will be empty slots in minibatch data.", UserWarning)
longest_hypo = numpy.max(map(lambda x: len(x[0]), minibatch), axis=0)
hypos = numpy.zeros((self.batch_size, longest_hypo), dtype='int32')
truth = numpy.zeros((self.batch_size,), dtype='int32')
mask_hypos = numpy.zeros((self.batch_size, longest_hypo), dtype='int32')
for i, (h, t) in enumerate(minibatch):
hypos[i, :len(h)] = h
mask_hypos[i, :len(h)] = (1,) * len(h)
truth[i] = t
yield hypos, mask_hypos, truth
else:
self.test_ptr = 0
raise StopIteration