challenging-america-word-ga.../cw7zad1.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"source": [
"## Imports"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"pycharm": {
"is_executing": true
}
},
"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,
"outputs": [],
"source": [
"from google.colab import drive"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"## Definitions"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"### Functions"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"def clean_line(line: str):\n",
" # Preprocessing\n",
" separated = line.split('\\t')\n",
" prefix = separated[6].replace(r'\\n', ' ')\n",
" suffix = separated[7].replace(r'\\n', ' ')\n",
" return prefix + ' ' + suffix"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"def get_words_from_line(line):\n",
" line = clean_line(line)\n",
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" for word in line.split():\n",
" yield word"
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],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"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)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"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"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"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])"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"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')"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"### Classes"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"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))))"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"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)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"## Training"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"### Params"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"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'"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"### Colab"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"drive.mount('/content/drive')\n",
"%cd /content/drive/MyDrive/"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"### Run"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"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>'])"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"train_dataset = Bigrams(path_to_train, vocab_size)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"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()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"torch.save(model.state_dict(), path_to_model)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
"model.load_state_dict(torch.load(path_to_model))\n",
"model.eval()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"create_outputs('dev-0')"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"create_outputs('test-A')"
],
"metadata": {
"collapsed": false
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}