31-循环神经网络RNN-文本预处理-自然语言处理-pytorch.ipynb

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   "source": [
    "# 文本预处理\n",
    "\n",
    "* 将文本作为字符串加载到内存中。\n",
    "\n",
    "* 将字符串拆分为词元（如单词和字符）。\n",
    "\n",
    "* 建立一个词汇表，将拆分的词元映射到数字索引。\n",
    "\n",
    "* 将文本转换为数字索引序列，方便模型操作。\n",
    "\n",
    "1. 读取数据集\n",
    "2. 词元化\n",
    "3. 词汇表：语料、<unk>、pad、bos、eos\n",
    "4. 整合所有功能\n",
    "5. 小结\n",
    "6. 练习\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7385f991",
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c7388789",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# text lines: 3221\n",
      "the time machine by h g wells\n",
      "twinkled and his usually pale face was flushed and animated the\n"
     ]
    }
   ],
   "source": [
    "# 读取数据集\n",
    "def read_time_machine(): \n",
    "    \"\"\"Load the time machine dataset into a list of text lines.\"\"\"\n",
    "    with open(\"article.txt\", 'r') as f:\n",
    "        lines = f.readlines()\n",
    "    return [re.sub('[^A-Za-z]+', ' ', line).strip().lower() for line in lines]\n",
    "\n",
    "lines = read_time_machine()\n",
    "print(f'# text lines: {len(lines)}')\n",
    "print(lines[0])\n",
    "print(lines[10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c0a62512",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['t', 'h', 'e', ' ', 't', 'i', 'm', 'e', ' ', 'm', 'a', 'c', 'h', 'i', 'n', 'e', ' ', 'b', 'y', ' ', 'h', ' ', 'g', ' ', 'w', 'e', 'l', 'l', 's']\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "['i']\n",
      "[]\n",
      "[]\n",
      "['t', 'h', 'e', ' ', 't', 'i', 'm', 'e', ' ', 't', 'r', 'a', 'v', 'e', 'l', 'l', 'e', 'r', ' ', 'f', 'o', 'r', ' ', 's', 'o', ' ', 'i', 't', ' ', 'w', 'i', 'l', 'l', ' ', 'b', 'e', ' ', 'c', 'o', 'n', 'v', 'e', 'n', 'i', 'e', 'n', 't', ' ', 't', 'o', ' ', 's', 'p', 'e', 'a', 'k', ' ', 'o', 'f', ' ', 'h', 'i', 'm']\n",
      "['w', 'a', 's', ' ', 'e', 'x', 'p', 'o', 'u', 'n', 'd', 'i', 'n', 'g', ' ', 'a', ' ', 'r', 'e', 'c', 'o', 'n', 'd', 'i', 't', 'e', ' ', 'm', 'a', 't', 't', 'e', 'r', ' ', 't', 'o', ' ', 'u', 's', ' ', 'h', 'i', 's', ' ', 'g', 'r', 'e', 'y', ' ', 'e', 'y', 'e', 's', ' ', 's', 'h', 'o', 'n', 'e', ' ', 'a', 'n', 'd']\n",
      "['t', 'w', 'i', 'n', 'k', 'l', 'e', 'd', ' ', 'a', 'n', 'd', ' ', 'h', 'i', 's', ' ', 'u', 's', 'u', 'a', 'l', 'l', 'y', ' ', 'p', 'a', 'l', 'e', ' ', 'f', 'a', 'c', 'e', ' ', 'w', 'a', 's', ' ', 'f', 'l', 'u', 's', 'h', 'e', 'd', ' ', 'a', 'n', 'd', ' ', 'a', 'n', 'i', 'm', 'a', 't', 'e', 'd', ' ', 't', 'h', 'e']\n"
     ]
    }
   ],
   "source": [
    "# 词元化\n",
    "def tokenize(lines, token='word'):\n",
    "    \"\"\"将文本行拆分为单词或字符词元。\"\"\"\n",
    "    if token == 'word':\n",
    "        return [line.split() for line in lines]\n",
    "    elif token == 'char':\n",
    "        return [list(line) for line in lines]\n",
    "    else:\n",
    "        print('错误：未知词元类型：' + token)\n",
    "\n",
    "tokens = tokenize(lines,token='char')\n",
    "for i in range(11):\n",
    "    print(tokens[i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "af07b892",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建词汇表\n",
    "class Vocab: \n",
    "    \"\"\"文本词汇表\"\"\"\n",
    "    def __init__(self, tokens=None, min_freq=0, reserved_tokens=None):\n",
    "        if tokens is None:\n",
    "            tokens = []\n",
    "        if reserved_tokens is None:\n",
    "            reserved_tokens = []\n",
    "        # 按出现频率排序\n",
    "        counter = count_corpus(tokens)\n",
    "        self.token_freqs = sorted(counter.items(), key=lambda x: x[1],\n",
    "                                  reverse=True)\n",
    "        # 未知词元的索引为0\n",
    "        self.unk, uniq_tokens = 0, ['<unk>'] + reserved_tokens\n",
    "        uniq_tokens += [token for token, freq in self.token_freqs\n",
    "                        if freq >= min_freq and token not in uniq_tokens]\n",
    "        self.idx_to_token, self.token_to_idx = [], dict()\n",
    "        for token in uniq_tokens:\n",
    "            self.idx_to_token.append(token)\n",
    "            self.token_to_idx[token] = len(self.idx_to_token) - 1\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.idx_to_token)\n",
    "\n",
    "    def __getitem__(self, tokens):\n",
    "        if not isinstance(tokens, (list, tuple)):\n",
    "            return self.token_to_idx.get(tokens, self.unk)\n",
    "        return [self.__getitem__(token) for token in tokens]\n",
    "\n",
    "    def to_tokens(self, indices):\n",
    "        if not isinstance(indices, (list, tuple)):\n",
    "            return self.idx_to_token[indices]\n",
    "        return [self.idx_to_token[index] for index in indices]\n",
    "\n",
    "def count_corpus(tokens): \n",
    "    \"\"\"统计词元的频率。\"\"\"\n",
    "    # 这里的 `tokens` 是 1D 列表或 2D 列表\n",
    "    if len(tokens) == 0 or isinstance(tokens[0], list):\n",
    "        # 将词元列表展平成使用词元填充的一个列表\n",
    "        tokens = [token for line in tokens for token in line]\n",
    "    return collections.Counter(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8e2f1c2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('<unk>', 0), (' ', 1), ('e', 2), ('t', 3), ('a', 4), ('i', 5), ('n', 6), ('o', 7), ('s', 8), ('h', 9)]\n"
     ]
    }
   ],
   "source": [
    "vocab = Vocab(tokens)\n",
    "print(list(vocab.token_to_idx.items())[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c7cb4bd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "words: ['t', 'h', 'e', ' ', 't', 'i', 'm', 'e', ' ', 'm', 'a', 'c', 'h', 'i', 'n', 'e', ' ', 'b', 'y', ' ', 'h', ' ', 'g', ' ', 'w', 'e', 'l', 'l', 's']\n",
      "indices: [3, 9, 2, 1, 3, 5, 13, 2, 1, 13, 4, 15, 9, 5, 6, 2, 1, 21, 19, 1, 9, 1, 18, 1, 17, 2, 12, 12, 8]\n",
      "words: ['t', 'w', 'i', 'n', 'k', 'l', 'e', 'd', ' ', 'a', 'n', 'd', ' ', 'h', 'i', 's', ' ', 'u', 's', 'u', 'a', 'l', 'l', 'y', ' ', 'p', 'a', 'l', 'e', ' ', 'f', 'a', 'c', 'e', ' ', 'w', 'a', 's', ' ', 'f', 'l', 'u', 's', 'h', 'e', 'd', ' ', 'a', 'n', 'd', ' ', 'a', 'n', 'i', 'm', 'a', 't', 'e', 'd', ' ', 't', 'h', 'e']\n",
      "indices: [3, 17, 5, 6, 23, 12, 2, 11, 1, 4, 6, 11, 1, 9, 5, 8, 1, 14, 8, 14, 4, 12, 12, 19, 1, 20, 4, 12, 2, 1, 16, 4, 15, 2, 1, 17, 4, 8, 1, 16, 12, 14, 8, 9, 2, 11, 1, 4, 6, 11, 1, 4, 6, 5, 13, 4, 3, 2, 11, 1, 3, 9, 2]\n"
     ]
    }
   ],
   "source": [
    "for i in [0, 10]:\n",
    "    print('words:', tokens[i])\n",
    "    print('indices:', vocab[tokens[i]])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6fcd23d5",
   "metadata": {},
   "outputs": [
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      "text/plain": [
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     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 整合所有功能\n",
    "def load_corpus_time_machine(max_tokens=-1):\n",
    "    \"\"\"返回时光机器数据集的词元索引列表和词汇表。\"\"\"\n",
    "    lines = read_time_machine()\n",
    "    tokens = tokenize(lines, 'char')\n",
    "    vocab = Vocab(tokens)\n",
    "    # 因为时光机器数据集中的每个文本行不一定是一个句子或一个段落，\n",
    "    # 所以将所有文本行展平到一个列表中\n",
    "    corpus = [vocab[token] for line in tokens for token in line]\n",
    "    if max_tokens > 0:\n",
    "        corpus = corpus[:max_tokens]\n",
    "    return corpus, vocab\n",
    "\n",
    "corpus, vocab = load_corpus_time_machine()\n",
    "len(corpus), len(vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8386ae85",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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