def _get_stories():
environment = get_environment()
r = redis.StrictRedis(
host=environment[REDIS_HOST],
port=environment[REDIS_PORT],
password=environment[REDIS_PASS]
)
return get_stories(r)
def test_get_stories(fake_redis_store):
expected_response = [
{HACKER_NEWS_ID: 8712349,
BODY: {TEXT: 'one\ntwo\nthree', SENTENCES: ['one', 'two', 'three']},
URL: 'http://totalurl.com',
TITLE: 'I am a title!',
DATE_FOUND: '2014-12-08 02:04:36.143372'},
{HACKER_NEWS_ID: 8712417,
BODY: {TEXT: 'four\nfive\nsix',
SENTENCES: ['four', 'five', 'six']},
URL: 'http://real.com',
TITLE: 'I am a second title!',
DATE_FOUND: '2014-12-08 02:05:18.078519'},
{HACKER_NEWS_ID: 8712277, BODY: {TEXT: 'seven', SENTENCES: []},
URL: 'http://imathingy.com',
TITLE: 'I am a third title!',
DATE_FOUND: '2014-12-08 02:05:28.326434'}
]
assert get_stories(fake_redis_store) == expected_response
print ('Reading babi files')
tar = tarfile.open('data/babi/babi-tasks-v1-2.tar.gz')
mypath = 'data/babi/tasks_1-20_v1-2/en-10k'
challenge_files = [f for f in listdir(mypath) if isfile(join(mypath, f))]
challenge_files = ['tasks_1-20_v1-2/en-10k/' + f.replace('train', '{}') for f
in challenge_files if 'train.txt' == f[-9:]]
# Read all files
train_facts_split = []
test_facts_split = []
train_facts = []
test_facts = []
for challenge in challenge_files:
train_facts_split.append(get_stories(tar.extractfile(challenge.format('train'))))
test_facts_split.append(get_stories(tar.extractfile(challenge.format('test'))))
train_facts += train_facts_split[-1]
test_facts += test_facts_split[-1]
print('Processing input data')
train_stories = [(reduce(lambda x,y: x + y, map(list,fact)),q,a) for fact,q,a in train_facts]
test_stories = [(reduce(lambda x,y: x + y, map(list,fact)),q,a) for fact,q,a in test_facts]
vocab = sorted(reduce(lambda x, y: x | y, (set(story + q + [answer]) for story, q, answer in train_stories + test_stories)))
# Reserve 0 for masking via pad_sequences
vocab_size = len(vocab) + 1
facts_maxlen = max(map(len, (x for h,_,_ in train_facts + test_facts for x in h)))
if enable_time:
facts_maxlen += 1
def test_failed_to_find_story():
# Perhaps I should do something more than print....
unfilled_redis = {TOP_STORIES_KEY: json.dumps([1]).encode('utf-8')}
with patch('builtins.print') as print_fn:
get_stories(unfilled_redis)
assert print_fn.called_with(FAILED_TO_FIND.format(1))
file_names = ['facts_list_all.txt']
training_set = []
test_set = []
data = []
for file_name in file_names:
print 'Reading {0} .....'.format(file_name)
read_file = open('data/{0}'.format(file_name), 'r')
# Data in format:
# [([[fact1],[fact2],..][answer])...]
# where each fact is a list of words
data += get_stories(read_file)
read_file.close()
# Data augmenting strategies
#1. Changing randomly nouns by synonyms
print('Data augmentation: synonyms')
data_dict = synonyms(data)
print('Number of diseases: {0}'.format(len(data_dict)))
#2. Removing facts randomly
print('Data augmentation: removing')
data_dict = remove(data,data_dict)
#3. Changing facts order
print('Data augmentation: permutation')
data_dict = permute(data,data_dict)
# Reading all file names
mypath = 'tasks_1-20_v1-2/en'
challenge_files = [f for f in listdir(mypath) if isfile(join(mypath, f))]
challenge_files = [
'tasks_1-20_v1-2/en-10k/' + f.replace('train', '{}')
for f in challenge_files if 'train.txt' == f[-9:]
]
# Read all files
train_facts_split = []
test_facts_split = []
train_facts = []
test_facts = []
for challenge in challenge_files:
train_facts_split.append(
get_stories(tar.extractfile(challenge.format('train'))))
test_facts_split.append(
get_stories(tar.extractfile(challenge.format('test'))))
train_facts += train_facts_split[-1]
test_facts += test_facts_split[-1]
test_facts = np.array(test_facts)
train_facts = np.array(train_facts)
test_facts = list(test_facts[np.random.choice(len(test_facts),
len(test_facts),
replace=False)])
train_facts = list(train_facts[np.random.choice(len(train_facts),
len(train_facts),
replace=False)])
# Parameters deifinition
parser.add_argument('-n',
'--num_words',
required=False,
type=int,
help='number of words to generate')
args = vars(parser.parse_args())
return args
###
# main function
if __name__ == '__main__':
# parse arguments
args = parse_args()
train = utils.get_stories(
open('tasks_1-20_v1-2/en/qa1_single-supporting-fact_train.txt', 'r'))
test = utils.get_stories(
open('tasks_1-20_v1-2/en/qa1_single-supporting-fact_test.txt', 'r'))
vocab = set()
for story, q, answer in train + test:
vocab |= set(story + q + [answer])
vocab = sorted(vocab)
# Reserve 0 for masking via pad_sequences
vocab_size = len(vocab) + 1
word_idx = dict((c, i + 1) for i, c in enumerate(vocab))
story_maxlen = max(map(len, (x for x, _, _ in train + test)))
query_maxlen = max(map(len, (x for _, x, _ in train + test)))
x, xq, y = utils.vectorize_stories(train, word_idx, story_maxlen,
