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prepro.py
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prepro.py
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import tensorflow as tf
import random
from tqdm import tqdm
import spacy
import ujson as json
from collections import Counter
import numpy as np
from codecs import open
import nltk
'''
This file is taken and modified from R-Net by HKUST-KnowComp
https://github.com/HKUST-KnowComp/R-Net
'''
nlp = spacy.blank("en")
def word_tokenize(sent):
doc = nlp(sent)
return [token.text for token in doc]
def convert_idx(text, tokens):
current = 0
spans = []
for token in tokens:
current = text.find(token, current)
if current < 0:
print("Token {} cannot be found".format(token))
raise Exception()
spans.append((current, current + len(token)))
current += len(token)
return spans
def process_file(filename, data_type, word_counter, char_counter):
print("Generating {} examples...".format(data_type))
examples = []
eval_examples = {}
total = 0
with open(filename, "r") as fh:
source = json.load(fh)
for article in tqdm(source["data"]):
for para in article["paragraphs"]:
context = para["context"].replace(
"''", '" ').replace("``", '" ')
context_tokens = word_tokenize(context)
context_chars = [list(token) for token in context_tokens]
spans = convert_idx(context, context_tokens)
for token in context_tokens:
word_counter[token] += len(para["qas"])
for char in token:
char_counter[char] += len(para["qas"])
for qa in para["qas"]:
total += 1
ques = qa["question"].replace(
"''", '" ').replace("``", '" ')
ques_tokens = word_tokenize(ques)
ques_chars = [list(token) for token in ques_tokens]
for token in ques_tokens:
word_counter[token] += 1
for char in token:
char_counter[char] += 1
y1s, y2s = [], []
answer_texts = []
for answer in qa["answers"]:
answer_text = answer["text"]
answer_start = answer['answer_start']
answer_end = answer_start + len(answer_text)
answer_texts.append(answer_text)
answer_span = []
for idx, span in enumerate(spans):
if not (answer_end <= span[0] or answer_start >= span[1]):
answer_span.append(idx)
y1, y2 = answer_span[0], answer_span[-1]
y1s.append(y1)
y2s.append(y2)
example = {"context_tokens": context_tokens, "context_chars": context_chars, "ques_tokens": ques_tokens,
"ques_chars": ques_chars, "y1s": y1s, "y2s": y2s, "id": total}
examples.append(example)
eval_examples[str(total)] = {
"context": context, "spans": spans, "answers": answer_texts, "uuid": qa["id"]}
random.shuffle(examples)
print("{} questions in total".format(len(examples)))
return examples, eval_examples
def get_POS_one_hot_vector(word):
if not word:
return np.zeros(37)
pos_tags = nltk.pos_tag(word)[0][1]
#print(pos_tags)
onehot_dict = {}
SC = ['``','`','""','`','~',"''",',','.','?','!',':',';','@','#','$','%','&','*','(',')','-','+','=','/','^','<','>']
pos = ['SYM','SC','CC','CD','DT','EX','FW','IN','JJ','JJR','JJS','LS','MD','NN','NNS','NNP','NNPS','PDT','POS','PRP','PRP$','RB','RBR','RBS','RP','TO','UH','VB','VBD','VBG','VBN','VBP','VBZ','WDT','WP','WP$','WRB']
for i in range(len(pos)):
onehot_dict[pos[i]] = i
if pos_tags in SC:
pos_tags = 'SC'
onehot_vector = np.zeros(len(pos))
onehot_vector[onehot_dict[pos_tags]] = 1
#print(onehot_vector)
return onehot_vector
def get_embedding(counter, data_type, limit=-1, emb_file=None, size=None, vec_size=None):
print("Generating {} embedding...".format(data_type))
embedding_dict = {}
filtered_elements = [k for k, v in counter.items() if v > limit]
if emb_file is not None:
assert size is not None
assert vec_size is not None
with open(emb_file, "r", encoding="utf-8") as fh:
for line in tqdm(fh, total=size):
array = line.split()
word = "".join(array[0:-(vec_size-37)])
onehotvector = get_POS_one_hot_vector(word)
#print(len(onehotvector))
vector = list(map(float, array[-(vec_size-37):]))
vector2 = list(map(float,np.array(onehotvector)))
vector3 = vector+vector2
#print(vector3)
#print(len(vector),len(vector2),len(vector3))
if word in counter and counter[word] > limit:
embedding_dict[word] = vector3
print("{} / {} tokens have corresponding {} embedding vector".format(
len(embedding_dict), len(filtered_elements), data_type))
else:
assert vec_size is not None
for token in filtered_elements:
embedding_dict[token] = [np.random.normal(
scale=0.1) for _ in range(vec_size)]
print("{} tokens have corresponding embedding vector".format(
len(filtered_elements)))
NULL = "--NULL--"
OOV = "--OOV--"
token2idx_dict = {token: idx for idx,
token in enumerate(embedding_dict.keys(), 2)}
token2idx_dict[NULL] = 0
token2idx_dict[OOV] = 1
embedding_dict[NULL] = [0. for _ in range(vec_size)]
embedding_dict[OOV] = [0. for _ in range(vec_size)]
idx2emb_dict = {idx: embedding_dict[token]
for token, idx in token2idx_dict.items()}
emb_mat = [idx2emb_dict[idx] for idx in range(len(idx2emb_dict))]
return emb_mat, token2idx_dict
def convert_to_features(config, data, word2idx_dict, char2idx_dict):
example = {}
context, question = data
context = context.replace("''", '" ').replace("``", '" ')
question = question.replace("''", '" ').replace("``", '" ')
example['context_tokens'] = word_tokenize(context)
example['ques_tokens'] = word_tokenize(question)
example['context_chars'] = [list(token) for token in example['context_tokens']]
example['ques_chars'] = [list(token) for token in example['ques_tokens']]
para_limit = config.test_para_limit
ques_limit = config.test_ques_limit
ans_limit = 100
char_limit = config.char_limit
def filter_func(example):
return len(example["context_tokens"]) > para_limit or \
len(example["ques_tokens"]) > ques_limit
if filter_func(example):
raise ValueError("Context/Questions lengths are over the limit")
context_idxs = np.zeros([para_limit], dtype=np.int32)
context_char_idxs = np.zeros([para_limit, char_limit], dtype=np.int32)
ques_idxs = np.zeros([ques_limit], dtype=np.int32)
ques_char_idxs = np.zeros([ques_limit, char_limit], dtype=np.int32)
y1 = np.zeros([para_limit], dtype=np.float32)
y2 = np.zeros([para_limit], dtype=np.float32)
def _get_word(word):
for each in (word, word.lower(), word.capitalize(), word.upper()):
if each in word2idx_dict:
return word2idx_dict[each]
return 1
def _get_char(char):
if char in char2idx_dict:
return char2idx_dict[char]
return 1
for i, token in enumerate(example["context_tokens"]):
context_idxs[i] = _get_word(token)
for i, token in enumerate(example["ques_tokens"]):
ques_idxs[i] = _get_word(token)
for i, token in enumerate(example["context_chars"]):
for j, char in enumerate(token):
if j == char_limit:
break
context_char_idxs[i, j] = _get_char(char)
for i, token in enumerate(example["ques_chars"]):
for j, char in enumerate(token):
if j == char_limit:
break
ques_char_idxs[i, j] = _get_char(char)
return context_idxs, context_char_idxs, ques_idxs, ques_char_idxs
def build_features(config, examples, data_type, out_file, word2idx_dict, char2idx_dict, is_test=False):
para_limit = config.test_para_limit if is_test else config.para_limit
ques_limit = config.test_ques_limit if is_test else config.ques_limit
ans_limit = 100 if is_test else config.ans_limit
char_limit = config.char_limit
def filter_func(example, is_test=False):
return len(example["context_tokens"]) > para_limit or \
len(example["ques_tokens"]) > ques_limit or \
(example["y2s"][0] - example["y1s"][0]) > ans_limit
print("Processing {} examples...".format(data_type))
writer = tf.python_io.TFRecordWriter(out_file)
total = 0
total_ = 0
meta = {}
for example in tqdm(examples):
total_ += 1
if filter_func(example, is_test):
continue
total += 1
context_idxs = np.zeros([para_limit], dtype=np.int32)
context_char_idxs = np.zeros([para_limit, char_limit], dtype=np.int32)
ques_idxs = np.zeros([ques_limit], dtype=np.int32)
ques_char_idxs = np.zeros([ques_limit, char_limit], dtype=np.int32)
y1 = np.zeros([para_limit], dtype=np.float32)
y2 = np.zeros([para_limit], dtype=np.float32)
def _get_word(word):
for each in (word, word.lower(), word.capitalize(), word.upper()):
if each in word2idx_dict:
return word2idx_dict[each]
return 1
def _get_char(char):
if char in char2idx_dict:
return char2idx_dict[char]
return 1
for i, token in enumerate(example["context_tokens"]):
context_idxs[i] = _get_word(token)
pos_tags = nltk.pos_tag(example["ques_tokens"])
noun_words = [tag[0] for tag in pos_tags if tag[1] in ["NN"]]
extra_info = ""
curr_extra_info = ''
for noun in noun_words:
try:
curr_extra_info = wiki.summary(noun,sentences=1)
except:
pass
extra_info += curr_extra_info
curr_extra_info = ''
tokens = nltk.word_tokenize(extra_info)
pos_of_tokens = nltk.pos_tag(tokens)
nouns = [tag[0] for tag in pos_of_tokens if tag[1] in ["NN"]]
for n in nouns:
if len(example["ques_tokens"])<50:
example["ques_tokens"].append(n)
for i, token in enumerate(example["ques_tokens"]):
ques_idxs[i] = _get_word(token)
for i, token in enumerate(example["context_chars"]):
for j, char in enumerate(token):
if j == char_limit:
break
context_char_idxs[i, j] = _get_char(char)
for i, token in enumerate(example["ques_chars"]):
for j, char in enumerate(token):
if j == char_limit:
break
ques_char_idxs[i, j] = _get_char(char)
start, end = example["y1s"][-1], example["y2s"][-1]
y1[start], y2[end] = 1.0, 1.0
record = tf.train.Example(features=tf.train.Features(feature={
"context_idxs": tf.train.Feature(bytes_list=tf.train.BytesList(value=[context_idxs.tostring()])),
"ques_idxs": tf.train.Feature(bytes_list=tf.train.BytesList(value=[ques_idxs.tostring()])),
"context_char_idxs": tf.train.Feature(bytes_list=tf.train.BytesList(value=[context_char_idxs.tostring()])),
"ques_char_idxs": tf.train.Feature(bytes_list=tf.train.BytesList(value=[ques_char_idxs.tostring()])),
"y1": tf.train.Feature(bytes_list=tf.train.BytesList(value=[y1.tostring()])),
"y2": tf.train.Feature(bytes_list=tf.train.BytesList(value=[y2.tostring()])),
"id": tf.train.Feature(int64_list=tf.train.Int64List(value=[example["id"]]))
}))
writer.write(record.SerializeToString())
print("Built {} / {} instances of features in total".format(total, total_))
meta["total"] = total
writer.close()
return meta
def save(filename, obj, message=None):
if message is not None:
print("Saving {}...".format(message))
with open(filename, "w") as fh:
json.dump(obj, fh)
def prepro(config):
word_counter, char_counter = Counter(), Counter()
train_examples, train_eval = process_file(
config.train_file, "train", word_counter, char_counter)
dev_examples, dev_eval = process_file(
config.dev_file, "dev", word_counter, char_counter)
test_examples, test_eval = process_file(
config.test_file, "test", word_counter, char_counter)
word_emb_file = config.fasttext_file if config.fasttext else config.glove_word_file
char_emb_file = config.glove_char_file if config.pretrained_char else None
char_emb_size = config.glove_char_size if config.pretrained_char else None
char_emb_dim = config.glove_dim if config.pretrained_char else config.char_dim
word_emb_mat, word2idx_dict = get_embedding(
word_counter, "word", emb_file=word_emb_file, size=config.glove_word_size, vec_size=config.glove_dim)
char_emb_mat, char2idx_dict = get_embedding(
char_counter, "char", emb_file=char_emb_file, size=char_emb_size, vec_size=char_emb_dim)
build_features(config, train_examples, "train",
config.train_record_file, word2idx_dict, char2idx_dict)
dev_meta = build_features(config, dev_examples, "dev",
config.dev_record_file, word2idx_dict, char2idx_dict)
test_meta = build_features(config, test_examples, "test",
config.test_record_file, word2idx_dict, char2idx_dict, is_test=True)
save(config.word_emb_file, word_emb_mat, message="word embedding")
save(config.char_emb_file, char_emb_mat, message="char embedding")
save(config.train_eval_file, train_eval, message="train eval")
save(config.dev_eval_file, dev_eval, message="dev eval")
save(config.test_eval_file, test_eval, message="test eval")
save(config.dev_meta, dev_meta, message="dev meta")
save(config.test_meta, test_meta, message="test meta")
save(config.word_dictionary, word2idx_dict, message="word dictionary")
save(config.char_dictionary, char2idx_dict, message="char dictionary")