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