import argparse
import json
import os
# data: q, cq, (dq), (pq), y, *x, *cx
# shared: x, cx, (dx), (px), word_counter, char_counter, word2vec
# no metadata
from collections import Counter
import nltk
from tqdm import tqdm
from my.nltk_utils import load_compressed_tree
def bool_(arg):
if arg == 'True':
return True
elif arg == 'False':
return False
raise Exception()
def main():
args = get_args()
prepro(args)
def get_args():
parser = argparse.ArgumentParser()
home = os.path.expanduser("~")
source_dir = os.path.join(home, "data", "squad")
target_dir = "data/squad"
glove_dir = os.path.join(home, "data", "glove")
parser.add_argument("--source_dir", default=source_dir)
parser.add_argument("--target_dir", default=target_dir)
parser.add_argument("--debug", default=False, type=bool_)
parser.add_argument("--train_ratio", default=0.9, type=int)
parser.add_argument("--glove_corpus", default="6B")
parser.add_argument("--glove_dir", default=glove_dir)
parser.add_argument("--glove_vec_size", default=100, type=int)
parser.add_argument("--full_train", default=False, type=bool_)
# TODO : put more args here
return parser.parse_args()
def prepro(args):
if not os.path.exists(args.target_dir):
os.makedirs(args.target_dir)
if args.full_train:
data_train, shared_train = prepro_each(args, 'train')
data_dev, shared_dev = prepro_each(args, 'dev')
else:
data_train, shared_train = prepro_each(args, 'train', 0.0, args.train_ratio)
data_dev, shared_dev = prepro_each(args, 'train', args.train_ratio, 1.0)
data_test, shared_test = prepro_each(args, 'dev')
print("saving ...")
save(args, data_train, shared_train, 'train')
save(args, data_dev, shared_dev, 'dev')
save(args, data_test, shared_test, 'test')
def save(args, data, shared, data_type):
data_path = os.path.join(args.target_dir, "data_{}.json".format(data_type))
shared_path = os.path.join(args.target_dir, "shared_{}.json".format(data_type))
json.dump(data, open(data_path, 'w'))
json.dump(shared, open(shared_path, 'w'))
def get_word2vec(args, word_counter):
glove_path = os.path.join(args.glove_dir, "glove.{}.{}d.txt".format(args.glove_corpus, args.glove_vec_size))
sizes = {'6B': int(4e5), '42B': int(1.9e6), '840B': int(2.2e6), '2B': int(1.2e6)}
total = sizes[args.glove_corpus]
word2vec_dict = {}
with open(glove_path, 'r') as fh:
for line in tqdm(fh, total=total):
array = line.lstrip().rstrip().split(" ")
word = array[0]
vector = list(map(float, array[1:]))
if word in word_counter:
word2vec_dict[word] = vector
elif word.capitalize() in word_counter:
word2vec_dict[word.capitalize()] = vector
elif word.lower() in word_counter:
word2vec_dict[word.lower()] = vector
elif word.upper() in word_counter:
word2vec_dict[word.upper()] = vector
print("{}/{} of word vocab have corresponding vectors in {}".format(len(word2vec_dict), len(word_counter), glove_path))
return word2vec_dict
def prepro_each(args, data_type, start_ratio=0.0, stop_ratio=1.0):
source_path = os.path.join(args.source_dir, "{}-v1.0-aug.json".format(data_type))
source_data = json.load(open(source_path, 'r'))
q, cq, y, rx, rcx, ids, idxs = [], [], [], [], [], [], []
x, cx, tx, stx = [], [], [], []
answerss = []
word_counter, char_counter, lower_word_counter = Counter(), Counter(), Counter()
pos_counter = Counter()
start_ai = int(round(len(source_data['data']) * start_ratio))
stop_ai = int(round(len(source_data['data']) * stop_ratio))
for ai, article in enumerate(tqdm(source_data['data'][start_ai:stop_ai])):
xp, cxp, txp, stxp = [], [], [], []
x.append(xp)
cx.append(cxp)
tx.append(txp)
stx.append(stxp)
for pi, para in enumerate(article['paragraphs']):
xi = []
for dep in para['deps']:
if dep is None:
xi.append([])
else:
xi.append([node[0] for node in dep[0]])
cxi = [[list(xijk) for xijk in xij] for xij in xi]
xp.append(xi)
cxp.append(cxi)
txp.append(para['consts'])
stxp.append([str(load_compressed_tree(s)) for s in para['consts']])
trees = map(nltk.tree.Tree.fromstring, para['consts'])
for tree in trees:
for subtree in tree.subtrees():
pos_counter[subtree.label()] += 1
for xij in xi:
for xijk in xij:
word_counter[xijk] += len(para['qas'])
lower_word_counter[xijk.lower()] += len(para['qas'])
for xijkl in xijk:
char_counter[xijkl] += len(para['qas'])
rxi = [ai, pi]
assert len(x) - 1 == ai
assert len(x[ai]) - 1 == pi
for qa in para['qas']:
dep = qa['dep']
qi = [] if dep is None else [node[0] for node in dep[0]]
cqi = [list(qij) for qij in qi]
yi = []
answers = []
for answer in qa['answers']:
answers.append(answer['text'])
yi0 = answer['answer_word_start'] or [0, 0]
yi1 = answer['answer_word_stop'] or [0, 1]
assert len(xi[yi0[0]]) > yi0[1]
assert len(xi[yi1[0]]) >= yi1[1]
yi.append([yi0, yi1])
for qij in qi:
word_counter[qij] += 1
lower_word_counter[qij.lower()] += 1
for qijk in qij:
char_counter[qijk] += 1
q.append(qi)
cq.append(cqi)
y.append(yi)
rx.append(rxi)
rcx.append(rxi)
ids.append(qa['id'])
idxs.append(len(idxs))
answerss.append(answers)
if args.debug:
break
word2vec_dict = get_word2vec(args, word_counter)
lower_word2vec_dict = get_word2vec(args, lower_word_counter)
data = {'q': q, 'cq': cq, 'y': y, '*x': rx, '*cx': rcx, '*tx': rx, '*stx': rx,
'idxs': idxs, 'ids': ids, 'answerss': answerss}
shared = {'x': x, 'cx': cx, 'tx': tx, 'stx': stx,
'word_counter': word_counter, 'char_counter': char_counter, 'lower_word_counter': lower_word_counter,
'word2vec': word2vec_dict, 'lower_word2vec': lower_word2vec_dict, 'pos_counter': pos_counter}
return data, shared
if __name__ == "__main__":
main()