Ch10_Recurrent_Neural_Networks/Machine_Translation_and_Data_Sets.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Translation and Data Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Machine translation (MT) refers to the automatic translation of a segment of text from one language to another. Solving this problem with neural networks is often called neural machine translation (NMT).Compared to the language model we discussed before, a major difference for MT is that the output is a sequence of words instead of a single words. The length of the output sequence could be different to the source sequence length. In the rest of this section, we will demonstrate how to pre-process a MT dataset and transform it into a set of data batches."
   ]
  },
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
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    "id": "OmMeMHFNr_aE"
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, '..')\n",
    "import collections\n",
    "import d2l\n",
    "import zipfile\n",
    "from d2l.data.base import Vocab\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils import data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read and Pre-process Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first download a dataset that contains a set of English sentences with the corresponding French trans-\n",
    "lations. As can be seen that each line contains a English sentence with its French translation, which are\n",
    "separated by a TAB."
   ]
  },
  {
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   "execution_count": 2,
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    "outputId": "691fda1c-1f86-466e-bf49-22d31c095ae6"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-06-10 12:39:52--  http://www.manythings.org/anki/fra-eng.zip\n",
      "Resolving www.manythings.org (www.manythings.org)... 104.24.109.196, 104.24.108.196, 2606:4700:30::6818:6cc4, ...\n",
      "Connecting to www.manythings.org (www.manythings.org)|104.24.109.196|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 3423204 (3.3M) [application/zip]\n",
      "Saving to: ‘fra-eng.zip’\n",
      "\n",
      "fra-eng.zip         100%[===================>]   3.26M  2.06MB/s    in 1.6s    \n",
      "\n",
      "2019-06-10 12:39:54 (2.06 MB/s) - ‘fra-eng.zip’ saved [3423204/3423204]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -c http://www.manythings.org/anki/fra-eng.zip\n",
    "with zipfile.ZipFile('fra-eng.zip', 'r') as f:\n",
    "  raw_text = f.read('fra.txt').decode(\"utf-8\")"
   ]
  },
  {
   "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",
      "Fire!\tAu feu !\n",
      "Help!\n"
     ]
    }
   ],
   "source": [
    "print(raw_text[0:95])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Words and punctuation marks should be separated by spaces. But this dataset has a few exceptions. We\n",
    "fix them by adding necessary spaces before punctuation marks, replacing non-breaking space with space. In\n",
    "addition, we convert all chars into lower cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 139
    },
    "colab_type": "code",
    "id": "dFKI_2lqv41K",
    "outputId": "9ddbc450-c63d-4992-c2de-ba9d98a47cde"
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    {
     "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",
      "fire !\tau feu !\n"
     ]
    }
   ],
   "source": [
    "def preprocess_raw(text):\n",
    "  text = text.replace('\\u202f', ' ').replace('\\xa0', ' ')\n",
    "  out = ''\n",
    "  for i, char in enumerate(text.lower()):\n",
    "    if char in (',', '!', '.') and i > 0 and text[i-1] != ' ':\n",
    "      out += ' '\n",
    "    out += char\n",
    "  return out\n",
    "\n",
    "text = preprocess_raw(raw_text)\n",
    "print(text[0:95])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tokenization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A word or a punctuation mark is treated as a token, then a sentence is a list of tokens. We convert the text\n",
    "data into a set of source (English) sentences, a list of list of tokens, and a set of target (French) sentences.\n",
    "To simplify the later model training, we only sample the first num_examples sentences pairs."
   ]
  },
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   "execution_count": 5,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "([['go', '.'], ['hi', '.'], ['run', '!']],\n",
       " [['va', '!'], ['salut', '!'], ['cours', '!']])"
      ]
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   "source": [
    "num_examples = 50000\n",
    "source, target = [], []\n",
    "for i, line in enumerate(text.split('\\n')):\n",
    "  if i > num_examples:\n",
    "    break\n",
    "  parts = line.split('\\t')\n",
    "  if len(parts) == 2:\n",
    "    source.append(parts[0].split(' '))\n",
    "    target.append(parts[1].split(' '))\n",
    "    \n",
    "source[0:3], target[0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We visualize the histogram of the number of tokens per sentence the following figure. As can be seen that a\n",
    "sentence in average contains 5 tokens, and most of them have less than 10 tokens."
   ]
  },
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5a11a4a20>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d2l.set_figsize()\n",
    "d2l.plt.hist([[len(l) for l in source], [len(l) for l in target]],\n",
    "             label=['source', 'target'])\n",
    "d2l.plt.legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vocabulary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now build a vocabulary for the source sentences and print its vocabulary sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "ueF2tb85zZbY",
    "outputId": "041f64d7-206a-425c-b5da-e69908f19c57"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3790"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def build_vocab(tokens):\n",
    "  tokens = [token for line in tokens for token in line]\n",
    "  return d2l.data.base.Vocab(tokens, min_freq=3, use_special_tokens=True)\n",
    "\n",
    "src_vocab = build_vocab(source)\n",
    "len(src_vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since sentences have variable lengths, we define a pad function to trim or pad a sentence into a fixed length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "pUHynpSLzh01",
    "outputId": "d63bc440-8682-457a-cd2b-a7bdd8bb20d4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[37, 4, 0, 0, 0, 0, 0, 0, 0, 0]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def pad(line, max_len, padding_token):\n",
    "  if len(line) > max_len:\n",
    "    return line[:max_len]\n",
    "  return line + [padding_token] * (max_len - len(line))\n",
    "\n",
    "pad(src_vocab[source[0]], 10, src_vocab.pad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can convert a list of sentences into an (num_example, max_len) index array. We also record the\n",
    "length of each sentence without the padding tokens, called valid length. In addition, we add the special\n",
    "\"bos\" and \"eos\" tokens to the target sentences so that our model will know the signals for starting\n",
    "and ending predictin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YlAQ3HYUzr_O"
   },
   "outputs": [],
   "source": [
    "def build_array(lines, vocab, max_len, is_source):\n",
    "  lines = [vocab[line] for line in lines]\n",
    "  if not is_source:\n",
    "    lines = [[vocab.bos] + line + [vocab.eos] for line in lines]\n",
    "  array = torch.tensor([pad(line, max_len, vocab.pad) for line in lines])\n",
    "  valid_len = (array != vocab.pad).sum(1)\n",
    "  return array, valid_len"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we construct data iterators to read data batches from the source and target index arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "u2WTG_zz1Tx8"
   },
   "outputs": [],
   "source": [
    "def load_data_nmt(batch_size, max_len): # This function is saved in d2l.\n",
    "  src_vocab, tgt_vocab = build_vocab(source), build_vocab(target)\n",
    "  src_array, src_valid_len = build_array(source, src_vocab, max_len, True)\n",
    "  tgt_array, tgt_valid_len = build_array(target, tgt_vocab, max_len, False)\n",
    "  train_data = data.TensorDataset(\n",
    "    src_array, src_valid_len, tgt_array, tgt_valid_len)\n",
    "  train_iter = data.DataLoader(train_data, batch_size, shuffle=True)\n",
    "  return src_vocab, tgt_vocab, train_iter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s read the first batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 121
    },
    "colab_type": "code",
    "id": "zjzEyXUj2x84",
    "outputId": "7d73150a-8cdb-43d7-b2f8-525afb811887"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X = tensor([[   9, 3015,   65, 2812,    4,    0,    0,    0],\n",
      "        [  17,  538,    3,    4,    0,    0,    0,    0]], dtype=torch.int32) \n",
      "Valid lengths for X = tensor([5, 4]) \n",
      "Y = tensor([[   1,   30, 3855,  315, 4402,    4,    2,    0],\n",
      "        [   1,   31,  269, 2854,    4,    2,    0,    0]], dtype=torch.int32) \n",
      "Valid lengths for Y = tensor([7, 6])\n"
     ]
    }
   ],
   "source": [
    "src_vocab, tgt_vocab, train_iter = load_data_nmt(batch_size=2, max_len=8)\n",
    "for X, X_valid_len, Y, Y_valid_len, in train_iter:\n",
    "  print('X =', X.type(torch.int32), '\\nValid lengths for X =', X_valid_len,\n",
    "        '\\nY =', Y.type(torch.int32), '\\nValid lengths for Y =', Y_valid_len)\n",
    "  break"
   ]
  }
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