data_loader.py

# -*- coding:utf-8 -*-
# author: Racle
# project: pointer-network
import re
import time
from itertools import chain
from random import shuffle
from threading import Thread
from queue import Queue

import numpy as np
import tensorflow as tf
import csv
import glob

import config

import random

random.seed(1234)

# <s> and </s> are used in the data files to segment the abstracts into sentences. They don't receive vocab ids.
# 不使用cnn-dailymail数据集。根据自己的数据集，自己插入标记。
SENTENCE_START = '<s>'
SENTENCE_END = '</s>'

PAD_TOKEN = '[PAD]'
UNKNOWN_TOKEN = '[UNK]'  # out-of-vocabulary words
START_DECODING = '[START]'  # the start of every decoder input sequence
STOP_DECODING = '[STOP]'  # the end of untruncated target sequences

# Note: none of <s>, </s>, [PAD], [UNK], [START], [STOP] should appear in the vocab file.



"""词表类"""
class Vocab:
    def __init__(self, vocab_file, max_size):
        self._word_to_id = {}
        self._id_to_word = {}
        self._count = 0  # keeps track of total number of words in the Vocab
        self._read_vocab_file(vocab_file, max_size)

    def _read_vocab_file(self, vocab_file, max_size):
        # [UNK], [PAD], [START] and [STOP] get the ids 0,1,2,3.
        for w in [UNKNOWN_TOKEN, PAD_TOKEN, START_DECODING, STOP_DECODING]:
            self._word_to_id[w] = self._count
            self._id_to_word[self._count] = w
            self._count += 1

        # Read the vocab file and add words up to max_size.
        # 根据自定义的数据集，修改了vocab_file
        with open(vocab_file, 'r', encoding='utf-8') as vocab_f:
            for line in vocab_f:
                pieces = line.split()
                if len(pieces) != 2:
                  print('Warning: incorrectly formatted line in vocabulary file: %s\n' % line)
                  continue
                w = pieces[0]
                if w in [SENTENCE_START, SENTENCE_END, UNKNOWN_TOKEN, PAD_TOKEN, START_DECODING, STOP_DECODING]:
                    raise Exception(
                        '<s>, </s>, [UNK], [PAD], [START] and [STOP] shouldn\'t be in the vocab file, but %s is' % w)
                if w in self._word_to_id:
                    print(w)
                    print(self._count)
                    raise Exception('Duplicated word in vocabulary file: %s' % w)
                self._word_to_id[w] = self._count
                self._id_to_word[self._count] = w
                self._count += 1
                if max_size != 0 and self._count >= max_size:
                    print("max_size of vocab was specified as %i; we now have %i words. Stopping reading." % (
                        max_size, self._count))
                    break

        print("Finished constructing vocabulary of %i total words. Last word added: %s" % (
            self._count, self._id_to_word[self._count - 1]))

    def word2id(self, word):
        if word not in self._word_to_id:
            return self._word_to_id[UNKNOWN_TOKEN]
        return self._word_to_id[word]

    def id2word(self, word_id):
        if word_id not in self._id_to_word:
            raise ValueError('Id not found in vocab: %d' % word_id)
        return self._id_to_word[word_id]

    def size(self):
        return self._count

    def write_metadata(self, fpath):
        print("Writing word embedding metadata file to %s..." % (fpath))
        with open(fpath, "w") as f:
            fieldnames = ['word']
            writer = csv.DictWriter(f, delimiter="\t", fieldnames=fieldnames)
            for i in range(self.size()):
                writer.writerow({"word": self._id_to_word[i]})


"""加入中文分词，生成一个训练样本。
enc_len padding之前的长度
enc_input
enc_input_extend_vocab 使用pointer结构时，编码加入in-article oovs.
dec_input
target

OOV： 
in-article OOV：在正文中出现的oov
out-of-article OOV：没有在正文中出现的oov，[UNK]
"""
class Example:
    def __init__(self, article, abstract_sentences, vocab, language='zh'):
        # Get ids of special tokens
        start_decoding = vocab.word2id(START_DECODING)
        stop_decoding = vocab.word2id(STOP_DECODING)

        # Process the article
        if language == 'en':
            article_words = article.split()
        elif language == 'zh':
            article_words = list(article)
        else:
            raise Exception('Please input a legal language category. en or zh')
        if len(article_words) > config.max_enc_steps:
            article_words = article_words[: config.max_enc_steps]
        self.enc_len = len(article_words)  # store the length after truncation but before padding
        self.enc_input = [vocab.word2id(w) for w in article_words]  # OOVs are represented by the id for UNK token

        # Process the abstract
        if language == 'en':
            abstract = ' '.join(abstract_sentences)  # string
            abstract_words = abstract.split()  # list of strings
        elif language == 'zh':
            abstract = ''.join(abstract_sentences)
            abstract_words = list(article)
        else:
            raise Exception('Please input a legal language category. en or zh')
        abs_ids = [vocab.word2id(w) for w in abstract_words]  # OOVs are represented by the id for UNK token
        # Get the decoder input sequence and target sequence
        self.dec_input, self.target = self.get_dec_inp_targ_seqs(abs_ids, config.max_dec_steps, start_decoding, stop_decoding)
        self.dec_len = len(self.dec_input)

        # If using pointer-generator mode, we need to store some extra info
        if config.pointer_gen:
            # Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id;
            # also store the in-article OOVs words themselves
            self.enc_input_extend_vocab, self.article_oovs = article2ids(article_words, vocab)
            # Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id
            abs_ids_extend_vocab = abstract2ids(abstract_words, vocab, self.article_oovs)
            # Overwrite decoder target sequence so it uses the temp article OOV ids
            _, self.target = self.get_dec_inp_targ_seqs(abs_ids_extend_vocab, config.max_dec_steps, start_decoding, stop_decoding)

        # Store the original strings
        self.original_article = article
        self.original_abstract = abstract
        self.original_abstract_sents = abstract_sentences

    def get_dec_inp_targ_seqs(self, sequence, max_len, start_id, stop_id):
        "target: 在input基础上，shift 1 position。"
        inp = [start_id] + sequence[:]
        target = sequence[:]
        if len(inp) > max_len:
            inp = inp[:max_len]
            target = target[:max_len]  # no end_token
        else:
            target.append(stop_id)  # end token
        assert len(inp) == len(target)
        return inp, target

    def pad_decoder_inp_targ(self, max_len, pad_id):
        while len(self.dec_input) < max_len:
            self.dec_input.append(pad_id)
        while len(self.target) < max_len:
            self.target.append(pad_id)

    def pad_encoder_input(self, max_len, pad_id):
        while len(self.enc_input) < max_len:
            self.enc_input.append(pad_id)
        if config.pointer_gen:
            while len(self.enc_input_extend_vocab) < max_len:
                self.enc_input_extend_vocab.append(pad_id)


"""输入处理好的example对象list。生成一个batch

enc_batch
enc_batch_extend_vocab： 将in-article的oov计入编码的batch
enc_lens： padding之前的长度
enc_padding_mask

max_art_oovs： in-article的oov数量
art_oovs：in-article OOVs

dec_batch
target_batch
dec_padding_mask
"""
class Batch:
    def __init__(self, example_list, vocab, batch_size):
        self.batch_size = batch_size
        self.pad_id = vocab.word2id(PAD_TOKEN)  # id of the PAD token used to pad sequences
        self.init_encoder_seq(example_list)  # initialize the input to the encoder
        self.init_decoder_seq(example_list)  # initialize the input and targets for the decoder
        self.store_orig_strings(example_list)  # store the original strings

    def init_encoder_seq(self, example_list):
        # Determine the maximum length of the encoder input sequence in this batch
        max_enc_seq_len = max([ex.enc_len for ex in example_list])

        # Pad the encoder input sequences up to the length of the longest sequence
        for ex in example_list:
            ex.pad_encoder_input(max_enc_seq_len, self.pad_id)

        # Initialize the numpy arrays
        # Note: our enc_batch can have different length (second dimension) for each batch because we use dynamic_rnn for the encoder.
        self.enc_batch = np.zeros((self.batch_size, max_enc_seq_len), dtype=np.int32)
        self.enc_lens = np.zeros((self.batch_size), dtype=np.int32)
        self.enc_padding_mask = np.zeros((self.batch_size, max_enc_seq_len), dtype=np.float32)

        # Fill in the numpy arrays
        for i, ex in enumerate(example_list):
            self.enc_batch[i, :] = ex.enc_input[:]
            self.enc_lens[i] = ex.enc_len
            for j in range(ex.enc_len):
                self.enc_padding_mask[i][j] = 1

        # For pointer-generator mode, need to store some extra info
        if config.pointer_gen:
            # Determine the max number of in-article OOVs in this batch
            self.max_art_oovs = max([len(ex.article_oovs) for ex in example_list])
            # Store the in-article OOVs themselves
            self.art_oovs = [ex.article_oovs for ex in example_list]
            # Store the version of the enc_batch that uses the article OOV ids
            self.enc_batch_extend_vocab = np.zeros((self.batch_size, max_enc_seq_len), dtype=np.int32)
            for i, ex in enumerate(example_list):
                self.enc_batch_extend_vocab[i, :] = ex.enc_input_extend_vocab[:]

    def init_decoder_seq(self, example_list):
        # Pad the inputs and targets
        for ex in example_list:
            ex.pad_decoder_inp_targ(config.max_dec_steps, self.pad_id)

        # Initialize the numpy arrays.
        self.dec_batch = np.zeros((self.batch_size, config.max_dec_steps), dtype=np.int32)
        self.target_batch = np.zeros((self.batch_size, config.max_dec_steps), dtype=np.int32)
        self.dec_padding_mask = np.zeros((self.batch_size, config.max_dec_steps), dtype=np.float32)
        self.dec_lens = np.zeros((self.batch_size), dtype=np.int32)

        # Fill in the numpy arrays
        for i, ex in enumerate(example_list):
            self.dec_batch[i, :] = ex.dec_input[:]
            self.target_batch[i, :] = ex.target[:]
            self.dec_lens[i] = ex.dec_len
            for j in range(ex.dec_len):
                self.dec_padding_mask[i][j] = 1

    def store_orig_strings(self, example_list):
        self.original_articles = [ex.original_article for ex in example_list]  # list of lists
        self.original_abstracts = [ex.original_abstract for ex in example_list]  # list of lists
        self.original_abstracts_sents = [ex.original_abstract_sents for ex in example_list]  # list of list of lists


class Batcher:
    BATCH_QUEUE_MAX = 50  # max number of batches the batch_queue can hold

    def __init__(self, data_path, vocab, mode, batch_size, single_pass):
        """

        :param data_path: 文档保存文件夹目录
        :param vocab: Vocab对象
        :param mode: decode or encode
        :param single_pass: 只读取一次data， bool
        """
        self._data_path = data_path
        self._vocab = vocab
        self._single_pass = single_pass
        self.mode = mode
        self.batch_size = batch_size
        # Initialize a queue of Batches waiting to be used, and a queue of Examples waiting to be batched
        self._batch_queue = Queue(self.BATCH_QUEUE_MAX)
        self._example_queue = Queue(self.BATCH_QUEUE_MAX * self.batch_size)

        # Different settings depending on whether we're in single_pass mode or not
        if single_pass:
            self._num_example_q_threads = 1  # just one thread, so we read through the dataset just once
            self._num_batch_q_threads = 1  # just one thread to batch examples
            self._bucketing_cache_size = 1  # only load one batch's worth of examples before bucketing; this essentially means no bucketing
            self._finished_reading = False  # this will tell us when we're finished reading the dataset
        else:
            # 注意：model处理速度快时，调整不同线程数量。
            # _num_example_q_threads > _num_batch_q_threads
            self._num_example_q_threads = 24  # num threads to fill example queue
            self._num_batch_q_threads = 4  # num threads to fill batch queue
            self._bucketing_cache_size = 4  # how many batches-worth of examples to load into cache before bucketing
                                            # 用于组成长度更相近的batch所用的，还没有进行sort bucketing的batch数量
        # Start the threads that load the queues
        self._example_q_threads = []
        for _ in range(self._num_example_q_threads):
            self._example_q_threads.append(Thread(target=self.fill_example_queue))
            self._example_q_threads[-1].daemon = True
            self._example_q_threads[-1].start()
        self._batch_q_threads = []
        for _ in range(self._num_batch_q_threads):
            self._batch_q_threads.append(Thread(target=self.fill_batch_queue))
            self._batch_q_threads[-1].daemon = True
            self._batch_q_threads[-1].start()

        # Start a thread that watches the other threads and restarts them if they're dead
        if not single_pass:  # We don't want a watcher in single_pass mode because the threads shouldn't run forever
            self._watch_thread = Thread(target=self.watch_threads)
            self._watch_thread.daemon = True
            self._watch_thread.start()

    def next_batch(self):
        # If the batch queue is empty, print a warning
        if self._batch_queue.qsize() == 0:
            tf.logging.warning(
                'Bucket input queue is empty when calling next_batch. Bucket queue size: %i, Input queue size: %i',
                self._batch_queue.qsize(), self._example_queue.qsize())
            if self._single_pass and self._finished_reading:
                tf.logging.info("Finished reading dataset in single_pass mode.")
                return None
        batch = self._batch_queue.get()  # get the next Batch
        return batch

    def fill_example_queue(self):
        "处理待batch的缓冲examples"
        input_gen = example_generator(self._data_path, self._single_pass)
        while True:
            try:
                (article, abstract) = next(input_gen)  # read the next example from file. article and abstract are both strings.
            except StopIteration:
                tf.logging.info("The example generator for this example queue filling thread has exhausted data.")
                if self._single_pass:
                    tf.logging.info(
                        "single_pass mode is on, so we've finished reading dataset. This thread is stopping.")
                    self._finished_reading = True
                    break
                else:
                    raise Exception("single_pass mode is off but the example generator is out of data; error.")
            # 手动加入<s> </s>
            abstract_sentences = [SENTENCE_START + sent.strip() + SENTENCE_END for sent in abstract2sents(abstract)]
            example = Example(article, abstract_sentences, self._vocab)  # Process into an Example.
            self._example_queue.put(example)  # place the Example in the example queue.

    def fill_batch_queue(self):
        while True:
            if self.mode == 'decode':
                # beam search decode mode single example repeated in the batch
                ex = self._example_queue.get()
                b = [ex for _ in range(self.batch_size)]
                self._batch_queue.put(Batch(b, self._vocab, self.batch_size))
            else:
                # Get bucketing_cache_size-many batches of Examples into a list, then sort
                inputs = []
                for _ in range(self.batch_size * self._bucketing_cache_size):
                    inputs.append(self._example_queue.get())
                inputs = sorted(inputs, key=lambda inp: inp.enc_len, reverse=True)  # sort by length of encoder sequence

                # Group the sorted Examples into batches, optionally shuffle the batches, and place in the batch queue.
                batches = []
                for i in range(0, len(inputs), self.batch_size):
                    batches.append(inputs[i:i + self.batch_size])
                if not self._single_pass:
                    shuffle(batches)
                for b in batches:  # each b is a list of Example objects
                    self._batch_queue.put(Batch(b, self._vocab, self.batch_size))

    def watch_threads(self):
        "多线程监控"
        while True:
            tf.logging.info('Bucket queue size: %i, Input queue size: %i',
                            self._batch_queue.qsize(), self._example_queue.qsize())

            time.sleep(60)
            for idx, t in enumerate(self._example_q_threads):
                if not t.is_alive():  # if the thread is dead
                    tf.logging.error('Found example queue thread dead. Restarting.')
                    new_t = Thread(target=self.fill_example_queue)
                    self._example_q_threads[idx] = new_t
                    new_t.daemon = True
                    new_t.start()
            for idx, t in enumerate(self._batch_q_threads):
                if not t.is_alive():  # if the thread is dead
                    tf.logging.error('Found batch queue thread dead. Restarting.')
                    new_t = Thread(target=self.fill_batch_queue)
                    self._batch_q_threads[idx] = new_t
                    new_t.daemon = True
                    new_t.start()


def article2ids(article_words, vocab):
    ids = []
    oovs = []
    unk_id = vocab.word2id(UNKNOWN_TOKEN)
    for w in article_words:
        i = vocab.word2id(w)
        if i == unk_id:  # If w is OOV
            if w not in oovs:  # Add to list of OOVs
                oovs.append(w)
            oov_num = oovs.index(w)
            ids.append(vocab.size() + oov_num)  # This is e.g. 50000 for the first article OOV, 50001 for the second...
        else:
            ids.append(i)
    return ids, oovs


def abstract2ids(abstract_words, vocab, article_oovs):
    ids = []
    unk_id = vocab.word2id(UNKNOWN_TOKEN)
    for w in abstract_words:
        i = vocab.word2id(w)
        if i == unk_id:  # If w is an OOV word
            if w in article_oovs:  # If w is an in-article OOV
                vocab_idx = vocab.size() + article_oovs.index(w)  # Map to its temporary article OOV number
                ids.append(vocab_idx)
            else:  # If w is an out-of-article OOV
                ids.append(unk_id)  # Map to the UNK token id
        else:
            ids.append(i)
    return ids


pattern_s = re.compile(".*?[。?？!！]")
def abstract2sents(abstract):
    """正则分句，原作者实现时，根据cnn数据集中标注的<s></s>分句。但是一般收集的数据没有标注，所以使用更一般的方法。"""
    return re.findall(pattern_s, abstract)



def example_generator(data_path, single_pass):
    """根据输入文件重写"""
    while True:
        # 根据文件存放格式修改
        filelist = glob.glob(config.train_data_path + '/*.txt')  # get the list of datafiles
        assert filelist, ('Error: Empty filelist at %s' % data_path)  # check filelist isn't empty
        if single_pass:
            filelist = sorted(filelist)
        else:
            random.shuffle(filelist)
        for f in filelist:
            with open(f, 'r', encoding='utf-8') as f:
                summary = None
                content = None
                text = f.readlines()
                for line in text:  # 防止文件中读取到 len == 0 的序列
                    if line.startswith('summary') and len(line) > 15:
                        summary = find_content(text[0])
                    elif line.startswith('text'):
                        tmp = remove_http_ref(remove_pic_ref(find_content(text[1])))
                        if len(tmp) > 60:
                            content = tmp
            if summary and content:
                yield (content, summary)


        if single_pass:
            print("example_generator completed reading all datafiles. No more data.")
            break


pattern1 = re.compile(r'{{(.*)}}$')
def find_content(text):
    return re.search(pattern1, text).group(1)


pattern2 = re.compile(r'!\[.*?\]\(.*?\)')
def remove_pic_ref(text):
    return re.sub(pattern2, '', text)


pattern3 = re.compile(r'\(http.*?\)')
def remove_http_ref(text):
    return re.sub(pattern3, '', text)