255_机器翻译数据集.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 机器翻译数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go.\tVa !\n",
      "Hi.\tSalut !\n",
      "Run!\tCours !\n",
      "Run!\tCourez !\n",
      "Who?\tQui ?\n",
      "Wow!\tÇa alors !\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 下载和预处理数据集\n",
    "# 将数据集的下载链接和校验码与'fra-eng'标识关联起来\n",
    "d2l.DATA_HUB['fra-eng'] = (d2l.DATA_URL + 'fra-eng.zip',\n",
    "                          '94646ad1522d915e7b0f9296181140edcf86a4f5')\n",
    "\n",
    "def read_data_nmt():\n",
    "    \"\"\"载入 “英语-法语” 数据集 \"\"\"\n",
    "    # 下载并解压数据集\n",
    "    data_dir = d2l.download_extract('fra-eng')\n",
    "    # 读取数据并返回\n",
    "    with open(os.path.join(data_dir, 'fra.txt'), 'r', encoding='utf-8') as f:\n",
    "        return f.read()\n",
    "    \n",
    "# 调用函数读取数据集\n",
    "raw_text = read_data_nmt()\n",
    "# 打印数据集的前75个字符\n",
    "print(raw_text[:75])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "go .\tva !\n",
      "hi .\tsalut !\n",
      "run !\tcours !\n",
      "run !\tcourez !\n",
      "who ?\tqui ?\n",
      "wow !\tça alors !\n"
     ]
    }
   ],
   "source": [
    "# 几个预处理步骤\n",
    "def preprocess_nmt(text):\n",
    "    \"\"\"预处理 “英语-法语” 数据集\"\"\"\n",
    "    # 判断字符是否是特定标点符号并且前一个字符不是空格\n",
    "    def no_space(char, prev_char):\n",
    "        return char in set(',.!?') and prev_char != ' '\n",
    "    # 替换特殊字符为空格，转换为小写\n",
    "    text = text.replace('\\u202f', ' ').replace('\\xa0',' ').lower()\n",
    "    out = [\n",
    "        ' ' + char if i > 0 and no_space(char, text[i - 1]) else char # 在特定标点符号前添加空格\n",
    "        for i, char in enumerate(text)] # 遍历文本中的每个字符\n",
    "    return ''.join(out) # 将处理后的字符列表转换为字符串\n",
    "\n",
    "# 调用预处理函数处理原始文本\n",
    "text = preprocess_nmt(raw_text)\n",
    "# 打印处理后的文本的前80个字符\n",
    "print(text[:80])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([['go', '.'],\n",
       "  ['hi', '.'],\n",
       "  ['run', '!'],\n",
       "  ['run', '!'],\n",
       "  ['who', '?'],\n",
       "  ['wow', '!']],\n",
       " [['va', '!'],\n",
       "  ['salut', '!'],\n",
       "  ['cours', '!'],\n",
       "  ['courez', '!'],\n",
       "  ['qui', '?'],\n",
       "  ['ça', 'alors', '!']])"
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     "execution_count": 4,
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   "source": [
    "# 词元化\n",
    "def tokenize_nmt(text, num_examples=None):\n",
    "    \"\"\"词元化 “英语-法语” 数据数据集 \"\"\"\n",
    "    # 存储英语和法语的词元序列\n",
    "    source, target = [], []  \n",
    "    # 遍历文本中的每一行\n",
    "    for i, line in enumerate(text.split('\\n')):\n",
    "        # 如果指定了num_examples且超过了指定数量，则结束循环\n",
    "        if num_examples and i > num_examples:\n",
    "            break\n",
    "        # 按制表符分割行\n",
    "        parts = line.split('\\t')\n",
    "        # 如果行中包含了两个部分\n",
    "        if len(parts) == 2:\n",
    "            # 将英语部分按空格分割为词元，并添加到source列表\n",
    "            source.append(parts[0].split(' ')) # 英语\n",
    "            # 将法语部分按空格分割为词元，并添加到target列表\n",
    "            target.append(parts[1].split(' ')) # 法语\n",
    "    return source, target\n",
    "\n",
    "# 调用函数词元化文本\n",
    "source, target = tokenize_nmt(text)\n",
    "# 打印source和target的前6个词元序列\n",
    "source[:6], target[:6]"
   ]
  },
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   "source": [
    "# 绘制每个文本序列所包含的标记数量的直方图，根据句子长度做的直方图\n",
    "# 设置图形大小\n",
    "d2l.set_figsize()\n",
    "# 绘制每个文本序列所包含的标记数量的直方图\n",
    "_, _, patches = d2l.plt.hist([[len(l)\n",
    "                              for l in source], [len(l) for l in target]],\n",
    "                            label = ['source','target']) # 添加标签\n",
    "# 遍历第二个直方图的每个矩形\n",
    "for patch in patches[1].patches:\n",
    "    # 设置矩形的填充样式为斜线\n",
    "    patch.set_hatch('/')\n",
    "# 添加图例，位于右上角\n",
    "d2l.plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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     "execution_count": 6,
     "metadata": {},
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   "source": [
    "# 词汇表\n",
    "# 创建源语言的词汇表对象\n",
    "src_vocab = d2l.Vocab(source, min_freq=2,\n",
    "                     reserved_tokens=['<pad>','<bos>','<eos>']) # bos 表示句子开始，eos表示句子结束，min_freq=2表示句子长度小于2个就不要了  \n",
    "# 计算词汇表的大小\n",
    "len(src_vocab)"
   ]
  },
  {
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   "execution_count": 7,
   "metadata": {},
   "outputs": [
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     "data": {
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     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "# 序列样本都有一个固定长度截断或填充文本序列\n",
    "def truncate_pad(line, num_steps, padding_token):\n",
    "    \"\"\"截断或填充文本序列\"\"\"\n",
    "    # 如果文本序列长度超过了指定的长度\n",
    "    if len(line) > num_steps:\n",
    "        # 截断文本序列，取前num_steps个词元\n",
    "        return line[:num_steps]\n",
    "    # 填充文本序列，添加padding_token直到长度达到num_steps\n",
    "    return line + [padding_token] * (num_steps - len(line))\n",
    "# 对源语言的第一个文本序列进行截断或填充\n",
    "truncate_pad(src_vocab[source[0]], 10, src_vocab['<pad>'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 转换成小批量数据集用于训练\n",
    "def build_array_nmt(lines, vocab, num_steps):\n",
    "    \"\"\"将机器翻译的文本序列转换成小批量\"\"\"\n",
    "    # 将文本序列中的词元转换为词汇表中的索引\n",
    "    lines = [vocab[l] for l in lines]\n",
    "    # 每个句子后面加一个截止符'<eos>'\n",
    "    lines = [l + [vocab['<eos>']] for l in lines] # 每个句子后面加了一个截止符\n",
    "    # 构建小批量数据集的张量表示\n",
    "    array = torch.tensor([ truncate_pad(l, num_steps, vocab['<pad>']) for l in lines ])\n",
    "    # 计算原始句子的实际长度\n",
    "    valid_len = (array != vocab['<pad>']).type(torch.int32).sum(1)\n",
    "    # 返回小批量数据集的张量表示和实际长度\n",
    "    return array, valid_len # valid_len 为原始句子的实际长度"
   ]
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   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练模型\n",
    "def load_data_nmt(batch_size, num_steps, num_examples=600):\n",
    "    \"\"\"返回翻译数据集的迭代器和词汇表\"\"\"\n",
    "    # 预处理原始数据集\n",
    "    text = preprocess_nmt(read_data_nmt())\n",
    "    # 对预处理后的文本进行词元化\n",
    "    source, target = tokenize_nmt(text, num_examples)\n",
    "    # 创建源语言词汇表对象\n",
    "    src_vocab = d2l.Vocab(source, min_freq=2,\n",
    "                         reserved_tokens=['<pad>','<bos>','<eos>'])\n",
    "    # 创建目标语言词汇表对象\n",
    "    tgt_vocab = d2l.Vocab(target, min_freq=2,\n",
    "                         reserved_tokens=['<pad>','<bos>','<eos>'])\n",
    "    # 将源语言文本序列转换为小批量数据集的张量表示和实际长度\n",
    "    src_array, src_valid_len = build_array_nmt(source, src_vocab, num_steps)  \n",
    "    # 将目标语言文本序列转换为小批量数据集的张量表示和实际长度\n",
    "    tgt_array, tgt_valid_len = build_array_nmt(target, tgt_vocab, num_steps)\n",
    "    # 构建数据集的张量表示和实际长度的元组\n",
    "    data_arrays = (src_array, src_valid_len, tgt_array, tgt_valid_len)\n",
    "    # 加载数据集并创建迭代器\n",
    "    data_iter = d2l.load_array(data_arrays, batch_size)\n",
    "    # 返回数据迭代器和源语言、目标语言的词汇表对象\n",
    "    return data_iter, src_vocab, tgt_vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X: tensor([[83, 15,  4,  3,  1,  1,  1,  1],\n",
      "        [69, 17,  9, 11,  3,  1,  1,  1]], dtype=torch.int32)\n",
      "valid lengths for X: tensor([4, 5])\n",
      "Y: tensor([[  0,   5,   3,   1,   1,   1,   1,   1],\n",
      "        [ 90, 172,   9,   3,   1,   1,   1,   1]], dtype=torch.int32)\n",
      "valid lengths for Y: tensor([3, 4])\n"
     ]
    }
   ],
   "source": [
    "# 读出 “英语-法语” 数据集中第一个小批量数据\n",
    "# 加载翻译数据集的迭代器和词汇表，设置每个小批量的大小和序列长度\n",
    "train_iter, src_vocab, tgt_vocab = load_data_nmt(batch_size=2, num_steps=8)  \n",
    "# 遍历数据迭代器，获取每个小批量的数据和有效长度\n",
    "for X, X_valid_len, Y, Y_valid_len in train_iter:\n",
    "    # 打印源语言序列的张量表示（整数类型）\n",
    "    print('X:', X.type(torch.int32))\n",
    "    # 打印源语言序列的有效长度\n",
    "    print('valid lengths for X:', X_valid_len)\n",
    "    # 打印目标语言序列的张量表示（整数类型）\n",
    "    print('Y:', Y.type(torch.int32))\n",
    "    # 打印目标语言序列的有效长度\n",
    "    print('valid lengths for Y:', Y_valid_len)\n",
    "    # 跳出循环，只打印第一个小批量数据\n",
    "    break"
   ]
  }
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