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import cPickle

import gzip

import os

import numpy

def prepare_data(seqs, labels, maxlen=None):

"""Create the matrices from the datasets.

This pad each sequence to the same lenght: the lenght of the

longuest sequence or maxlen.

if maxlen is set, we will cut all sequence to this maximum

lenght.

This swap the axis!

"""

# x: a list of sentences

lengths = [len(s) for s in seqs]

if maxlen is not None:

new_seqs = []

new_labels = []

new_lengths = []

for l, s, y in zip(lengths, seqs, labels):

if l < maxlen:

new_seqs.append(s)

new_labels.append(y)

new_lengths.append(l)

lengths = new_lengths

labels = new_labels

seqs = new_seqs

if len(lengths) < 1:

return None, None, None

n_samples = len(seqs)

maxlen = numpy.max(lengths)

x = numpy.zeros((maxlen, n_samples)).astype('int64')

x_mask = numpy.zeros((maxlen, n_samples)).astype('float32')

for idx, s in enumerate(seqs):

x[:lengths[idx], idx] = s

x_mask[:lengths[idx], idx] = 1.

return x, x_mask, labels

def get_dataset_file(dataset, default_dataset, origin):

'''Look for it as if it was a full path, if not, try local file,

if not try in the data directory.

Download dataset if it is not present

'''

data_dir, data_file = os.path.split(dataset)

if data_dir == "" and not os.path.isfile(dataset):

# Check if dataset is in the data directory.

new_path = os.path.join(

os.path.split(__file__)[0],

"..",

"data",

dataset

)

if os.path.isfile(new_path) or data_file == default_dataset:

dataset = new_path

if (not os.path.isfile(dataset)) and data_file == default_dataset:

import urllib

print 'Downloading data from %s' % origin

urllib.urlretrieve(origin, dataset)

return dataset

def load_data(path="imdb.pkl", n_words=100000, valid_portion=0.1, maxlen=None):

''' Loads the dataset

:type path: String

:param path: The path to the dataset (here IMDB)

:type n_words: int

:param n_words: The number of word to keep in the vocabulary.

All extra words are set to unknow (1).

:type valid_portion: float

:param valid_portion: The proportion of the full train set used for

the validation set.

:type maxlen: None or positive int

:param maxlen: the max sequence length we use in the train/valid set.

'''

#############

# LOAD DATA #

#############

# Load the dataset

path = get_dataset_file(

path, "imdb.pkl",

"http://www.iro.umontreal.ca/~lisa/deep/data/imdb.pkl")

if path.endswith(".gz"):

f = gzip.open(path, 'rb')

else:

f = open(path, 'rb')

train_set = cPickle.load(f)

test_set = cPickle.load(f)

f.close()

if maxlen:

new_train_set_x = []

new_train_set_y = []

for x, y in zip(train_set[0], train_set[1]):

if len(x) < maxlen:

new_train_set_x.append(x)

new_train_set_y.append(y)

train_set = (new_train_set_x, new_train_set_y)

del new_train_set_x, new_train_set_y

# split training set into validation set

train_set_x, train_set_y = train_set

n_samples = len(train_set_x)

sidx = numpy.random.permutation(n_samples)

n_train = int(numpy.round(n_samples * (1. - valid_portion)))

valid_set_x = [train_set_x[s] for s in sidx[n_train:]]

valid_set_y = [train_set_y[s] for s in sidx[n_train:]]

train_set_x = [train_set_x[s] for s in sidx[:n_train]]

train_set_y = [train_set_y[s] for s in sidx[:n_train]]

train_set = (train_set_x, train_set_y)

valid_set = (valid_set_x, valid_set_y)

def remove_unk(x):

return [[1 if w >= n_words else w for w in sen] for sen in x]

test_set_x, test_set_y = test_set

valid_set_x, valid_set_y = valid_set

train_set_x, train_set_y = train_set

train_set_x = remove_unk(train_set_x)

valid_set_x = remove_unk(valid_set_x)

test_set_x = remove_unk(test_set_x)

train = (train_set_x, train_set_y)

valid = (valid_set_x, valid_set_y)

test = (test_set_x, test_set_y)

return train, valid, test