challenging-america-word-gap-prediction/run7.ipynb

{
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  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "import itertools\n",
    "import lzma\n",
    "\n",
    "import regex as re\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.utils.data import IterableDataset, DataLoader\n",
    "from torchtext.vocab import build_vocab_from_iterator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from google.colab import drive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def clean_line(line: str):\n",
    "    separated = line.split('\\t')\n",
    "    prefix = separated[6].replace(r'\\n', ' ')\n",
    "    suffix = separated[7].replace(r'\\n', ' ')\n",
    "    return prefix + ' ' + suffix\n",
    "\n",
    "def get_words_from_line(line):\n",
    "    line = clean_line(line)\n",
    "    for m in re.finditer(r'[\\p{L}0-9\\*]+|\\p{P}+', line):\n",
    "        yield m.group(0).lower()\n",
    "\n",
    "def get_word_lines_from_file(file_name):\n",
    "    with lzma.open(file_name, mode='rt', encoding='utf-8') as fid:\n",
    "        for line in fid:\n",
    "            yield get_words_from_line(line)\n",
    "\n",
    "def look_ahead_iterator(gen):\n",
    "    prev = None\n",
    "    for item in gen:\n",
    "        if prev is not None:\n",
    "            yield (prev, item)\n",
    "        prev = item\n",
    "\n",
    "def prediction(word: str) -> str:\n",
    "    ixs = torch.tensor(vocab.forward([word])).to(device)\n",
    "    out = model(ixs)\n",
    "    top = torch.topk(out[0], 5)\n",
    "    top_indices = top.indices.tolist()\n",
    "    top_probs = top.values.tolist()\n",
    "    top_words = vocab.lookup_tokens(top_indices)\n",
    "    zipped = list(zip(top_words, top_probs))\n",
    "    for index, element in enumerate(zipped):\n",
    "        unk = None\n",
    "        if '<unk>' in element:\n",
    "            unk = zipped.pop(index)\n",
    "            zipped.append(('', unk[1]))\n",
    "            break\n",
    "    if unk is None:\n",
    "        zipped[-1] = ('', zipped[-1][1])\n",
    "    return ' '.join([f'{x[0]}:{x[1]}' for x in zipped])\n",
    "\n",
    "def create_outputs(folder_name):\n",
    "    print(f'Creating outputs in {folder_name}')\n",
    "    with lzma.open(f'{folder_name}/in.tsv.xz', mode='rt', encoding='utf-8') as fid:\n",
    "        with open(f'{folder_name}/out.tsv', 'w', encoding='utf-8', newline='\\n') as f:\n",
    "            for line in fid:\n",
    "                separated = line.split('\\t')\n",
    "                prefix = separated[6].replace(r'\\n', ' ').split()[-1]\n",
    "                output_line = prediction(prefix)\n",
    "                f.write(output_line + '\\n')\n",
    "\n",
    "class Bigrams(IterableDataset):\n",
    "    def __init__(self, text_file, vocabulary_size):\n",
    "        self.vocab = build_vocab_from_iterator(\n",
    "            get_word_lines_from_file(text_file),\n",
    "            max_tokens=vocabulary_size,\n",
    "            specials=['<unk>'])\n",
    "        self.vocab.set_default_index(self.vocab['<unk>'])\n",
    "        self.vocabulary_size = vocabulary_size\n",
    "        self.text_file = text_file\n",
    "\n",
    "    def __iter__(self):\n",
    "        return look_ahead_iterator(\n",
    "            (self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file))))\n",
    "\n",
    "class SimpleBigramNeuralLanguageModel(nn.Module):\n",
    "    def __init__(self, vocabulary_size, embedding_size):\n",
    "        super(SimpleBigramNeuralLanguageModel, self).__init__()\n",
    "        self.model = nn.Sequential(\n",
    "            nn.Embedding(vocabulary_size, embedding_size),\n",
    "            nn.Linear(embedding_size, vocabulary_size),\n",
    "            nn.Softmax()\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.model(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vocab_size = 15000\n",
    "embed_size = 150\n",
    "batch_size = 3000\n",
    "device = 'cuda'\n",
    "path_to_train = 'train/in.tsv.xz'\n",
    "path_to_model = 'model1.bin'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "drive.mount('/content/drive')\n",
    "%cd /content/drive/MyDrive/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vocab = build_vocab_from_iterator(\n",
    "    get_word_lines_from_file(path_to_train),\n",
    "    max_tokens=vocab_size,\n",
    "    specials=['<unk>']\n",
    ")\n",
    "\n",
    "vocab.set_default_index(vocab['<unk>'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_dataset = Bigrams(path_to_train, vocab_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
    "data = DataLoader(train_dataset, batch_size=batch_size)\n",
    "optimizer = torch.optim.Adam(model.parameters())\n",
    "criterion = torch.nn.NLLLoss()\n",
    "\n",
    "model.train()\n",
    "step = 0\n",
    "for x, y in data:\n",
    "    x = x.to(device)\n",
    "    y = y.to(device)\n",
    "    optimizer.zero_grad()\n",
    "    ypredicted = model(x)\n",
    "    loss = criterion(torch.log(ypredicted), y)\n",
    "    if step % 100 == 0:\n",
    "        print(step, loss)\n",
    "    step += 1\n",
    "    loss.backward()\n",
    "    optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), path_to_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
    "model.load_state_dict(torch.load(path_to_model))\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "create_outputs('dev-0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "create_outputs('test-A')"
   ]
  }
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