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prepro.py
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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
from tqdm import tqdm
from squad.utils import get_word_span, get_word_idx, process_tokens
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('-s', "--source_dir", default=source_dir)
parser.add_argument('-t', "--target_dir", default=target_dir)
parser.add_argument('-d', "--debug", action='store_true')
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("--mode", default="full", type=str)
parser.add_argument("--single_path", default="", type=str)
parser.add_argument("--tokenizer", default="PTB", type=str)
parser.add_argument("--url", default="vision-server2.corp.ai2", type=str)
parser.add_argument("--port", default=8000, type=int)
parser.add_argument("--split", action='store_true')
# TODO : put more args here
return parser.parse_args()
def create_all(args):
out_path = os.path.join(args.source_dir, "all-v1.1.json")
if os.path.exists(out_path):
return
train_path = os.path.join(args.source_dir, "train-v1.1.json")
train_data = json.load(open(train_path, 'r'))
dev_path = os.path.join(args.source_dir, "dev-v1.1.json")
dev_data = json.load(open(dev_path, 'r'))
train_data['data'].extend(dev_data['data'])
print("dumping all data ...")
json.dump(train_data, open(out_path, 'w'))
def prepro(args):
if not os.path.exists(args.target_dir):
os.makedirs(args.target_dir)
if args.mode == 'full':
prepro_each(args, 'train', out_name='train')
prepro_each(args, 'dev', out_name='dev')
prepro_each(args, 'dev', out_name='test')
elif args.mode == 'all':
create_all(args)
prepro_each(args, 'dev', 0.0, 0.0, out_name='dev')
prepro_each(args, 'dev', 0.0, 0.0, out_name='test')
prepro_each(args, 'all', out_name='train')
elif args.mode == 'single':
assert len(args.single_path) > 0
prepro_each(args, "NULL", out_name="single", in_path=args.single_path)
else:
prepro_each(args, 'train', 0.0, args.train_ratio, out_name='train')
prepro_each(args, 'train', args.train_ratio, 1.0, out_name='dev')
prepro_each(args, 'dev', out_name='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', encoding='utf-8') 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, out_name="default", in_path=None):
if args.tokenizer == "PTB":
import nltk
sent_tokenize = nltk.sent_tokenize
def word_tokenize(tokens):
return [token.replace("''", '"').replace("``", '"') for token in nltk.word_tokenize(tokens)]
elif args.tokenizer == 'Stanford':
from my.corenlp_interface import CoreNLPInterface
interface = CoreNLPInterface(args.url, args.port)
sent_tokenize = interface.split_doc
word_tokenize = interface.split_sent
else:
raise Exception()
if not args.split:
sent_tokenize = lambda para: [para]
source_path = in_path or os.path.join(args.source_dir, "{}-v1.1.json".format(data_type))
source_data = json.load(open(source_path, 'r'))
q, cq, y, rx, rcx, ids, idxs = [], [], [], [], [], [], []
cy = []
x, cx = [], []
answerss = []
p = []
word_counter, char_counter, lower_word_counter = Counter(), 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 = [], []
pp = []
x.append(xp)
cx.append(cxp)
p.append(pp)
for pi, para in enumerate(article['paragraphs']):
# wordss
context = para['context']
context = context.replace("''", '" ')
context = context.replace("``", '" ')
xi = list(map(word_tokenize, sent_tokenize(context)))
xi = [process_tokens(tokens) for tokens in xi] # process tokens
# given xi, add chars
cxi = [[list(xijk) for xijk in xij] for xij in xi]
xp.append(xi)
cxp.append(cxi)
pp.append(context)
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']:
# get words
qi = word_tokenize(qa['question'])
cqi = [list(qij) for qij in qi]
yi = []
cyi = []
answers = []
for answer in qa['answers']:
answer_text = answer['text']
answers.append(answer_text)
answer_start = answer['answer_start']
answer_stop = answer_start + len(answer_text)
# TODO : put some function that gives word_start, word_stop here
yi0, yi1 = get_word_span(context, xi, answer_start, answer_stop)
# 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]
w0 = xi[yi0[0]][yi0[1]]
w1 = xi[yi1[0]][yi1[1]-1]
i0 = get_word_idx(context, xi, yi0)
i1 = get_word_idx(context, xi, (yi1[0], yi1[1]-1))
cyi0 = answer_start - i0
cyi1 = answer_stop - i1 - 1
# print(answer_text, w0[cyi0:], w1[:cyi1+1])
assert answer_text[0] == w0[cyi0], (answer_text, w0, cyi0)
assert answer_text[-1] == w1[cyi1]
assert cyi0 < 32, (answer_text, w0)
assert cyi1 < 32, (answer_text, w1)
yi.append([yi0, yi1])
cyi.append([cyi0, cyi1])
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)
cy.append(cyi)
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)
# add context here
data = {'q': q, 'cq': cq, 'y': y, '*x': rx, '*cx': rcx, 'cy': cy,
'idxs': idxs, 'ids': ids, 'answerss': answerss, '*p': rx}
shared = {'x': x, 'cx': cx, 'p': p,
'word_counter': word_counter, 'char_counter': char_counter, 'lower_word_counter': lower_word_counter,
'word2vec': word2vec_dict, 'lower_word2vec': lower_word2vec_dict}
print("saving ...")
save(args, data, shared, out_name)
if __name__ == "__main__":
main()