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data_utils.py
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data_utils.py
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# encoding: utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import re
import random
from tensorflow.python.platform import gfile
import tensorflow as tf
# Special vocabulary symbols
_PAD = b"_PAD"
_GO = b"_GO"
_EOS = b"_EOS"
_UNK = b"_UNK"
_START_VOCAB = [_PAD, _GO, _EOS, _UNK]
PAD_ID = 0
GO_ID = 1
EOS_ID = 2
UNK_ID = 3
# Regular expressions used to tokenize.
#_WORD_SPLIT = re.compile(b"([.,!?\"':;)(])")
#_WORD_SPLIT = re.compile(b"([,!?\":;)(])")
_DIGIT_RE = re.compile(br"\d")
def basic_tokenizer(sentence):
words = []
for space_separated_fragment in sentence.strip().split():
words.extend([space_separated_fragment])
#words.extend(_WORD_SPLIT.split(space_separated_fragment))
return [w for w in words if w]
def maybe_split(data_path):
data_dir, file_name = data_path.rsplit('/',1)
train_path = os.path.join(data_dir, 'train_' + file_name)
dev_path = os.path.join(data_dir, 'dev_' + file_name)
test_path = os.path.join(data_dir, 'test_' + file_name)
if not (gfile.Exists(train_path)) and ( not gfile.Exists(dev_path) ) \
and not (gfile.Exists(test_path)):
if not (gfile.Exists(data_path)):
raise ValueError("Source file %s not found.", data_path)
# shuffle data examples
with gfile.GFile(data_path, mode='r') as f:
lines = f.readlines()
ids_list = list(range(0, round(len(lines)/2)))
random.shuffle(ids_list)
# parse to train, dev, and test by 8:1:1 portion
with gfile.GFile(train_path, mode='w') as f:
for ids in ids_list[:-round(len(ids_list)*0.2)]:
f.write(lines[2*ids])
f.write(lines[2*ids+1])
with gfile.GFile(dev_path, mode='w') as f:
for ids in ids_list[-round(len(ids_list)*0.2):-round(len(ids_list)*0.1)]:
f.write(lines[2*ids])
f.write(lines[2*ids+1])
with gfile.GFile(test_path, mode='w') as f:
for ids in ids_list[-round(len(ids_list)*0.1):]:
f.write(lines[2*ids])
f.write(lines[2*ids+1])
#raise ValueError("Train file or development file not found.")
return (train_path, dev_path)
def create_vocabulary(vocabulary_path, data_path, max_vocabulary_size,
tokenizer=None, normalize_digits=True):
if not gfile.Exists(vocabulary_path):
print("Creating vocabulary %s from data %s" % (vocabulary_path, data_path))
vocab = {}
with gfile.GFile(data_path, mode="rb") as f:
counter = 0
for line in f:
counter += 1
if counter % 100000 == 0:
print(" processing line %d" % counter)
line = tf.compat.as_bytes(line)
tokens = tokenizer(line) if tokenizer else basic_tokenizer(line)
for w in tokens:
word = _DIGIT_RE.sub(b"0", w) if normalize_digits else w
if word in vocab:
vocab[word] += 1
else:
vocab[word] = 1
vocab_list = _START_VOCAB + sorted(vocab, key=vocab.get, reverse=True)
if len(vocab_list) > max_vocabulary_size:
vocab_list = vocab_list[:max_vocabulary_size]
with gfile.GFile(vocabulary_path, mode="wb") as vocab_file:
for w in vocab_list:
vocab_file.write(w + b"\n")
def initialize_vocabulary(vocabulary_path):
if gfile.Exists(vocabulary_path):
rev_vocab = []
with gfile.GFile(vocabulary_path, mode="r") as f:
rev_vocab.extend(f.readlines())
rev_vocab = [line.strip().encode('utf-8') for line in rev_vocab]
vocab = dict([(x, y) for (y, x) in enumerate(rev_vocab)])
return vocab, rev_vocab
else:
raise ValueError("Vocabulary file %s not found.", vocabulary_path)
def sentence_to_token_ids(sentence, vocabulary,
tokenizer=None, normalize_digits=True):
if tokenizer:
words = tokenizer(sentence)
else:
words = basic_tokenizer(sentence)
if not normalize_digits:
return [vocabulary.get(w, UNK_ID) for w in words]
# Normalize digits by 0 before looking words up in the vocabulary.
return [vocabulary.get(_DIGIT_RE.sub(b"0", w), UNK_ID) for w in words]
def data_to_token_ids(data_path, target_path, vocabulary_path,
tokenizer=None, normalize_digits=True):
if not gfile.Exists(target_path):
print("Tokenizing data in %s" % data_path)
vocab, _ = initialize_vocabulary(vocabulary_path)
with gfile.GFile(data_path, mode="rb") as data_file:
with gfile.GFile(target_path, mode="w") as tokens_file:
counter = 0
for line in data_file:
counter += 1
if counter % 100000 == 0:
print(" tokenizing line %d" % counter)
token_ids = sentence_to_token_ids(tf.compat.as_bytes(line), vocab,
tokenizer, normalize_digits)
tokens_file.write(" ".join([str(tok) for tok in token_ids]) + "\n")
def prepare_data(data_dir, data_path, vocabulary_size, tokenizer=None):
# Get data to the specified directory.
train_path, dev_path = maybe_split(data_path)
# Create vocabularies of the appropriate sizes.
vocab_path = os.path.join(data_dir, "vocab%d" % vocabulary_size)
create_vocabulary(vocab_path, train_path, vocabulary_size, tokenizer, normalize_digits=False)
# Create token ids for the training data.
train_ids_path = train_path + (".ids%d" % vocabulary_size)
data_to_token_ids(train_path, train_ids_path, vocab_path, tokenizer, normalize_digits=False)
# Create token ids for the development data.
dev_ids_path = dev_path + (".ids%d" % vocabulary_size)
data_to_token_ids(dev_path, dev_ids_path, vocab_path, tokenizer, normalize_digits=False)
return (train_ids_path, dev_ids_path, vocab_path)