{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import lzma\n",
    "from itertools import islice\n",
    "import re\n",
    "import sys\n",
    "from torchtext.vocab import build_vocab_from_iterator\n",
    "from torch import nn\n",
    "from torch.utils.data import IterableDataset, DataLoader\n",
    "import itertools\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "VOCAB_SIZE = 2_000\n",
    "EMBED_SIZE = 500"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_words_from_line(line):\n",
    "  line = line.rstrip()\n",
    "  line = line.split(\"\\t\")\n",
    "  text = line[-2] + \" \" + line[-1]\n",
    "  text = re.sub(r\"\\\\+n\", \" \", text)\n",
    "  text = re.sub('[^A-Za-z ]+', '', text)\n",
    "  for t in text.split():\n",
    "    yield t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_word_lines_from_file(file_name):\n",
    "  with lzma.open(file_name, encoding='utf8', mode=\"rt\") as fh:\n",
    "    for line in fh:\n",
    "       yield get_words_from_line(line)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def look_ahead_iterator(gen):\n",
    "   first = None\n",
    "   second = None\n",
    "   for item in gen:\n",
    "      if first is not None and second is not None:\n",
    "         yield ((first, item), second)\n",
    "      first = second\n",
    "      second = item"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab = build_vocab_from_iterator(\n",
    "    get_word_lines_from_file(\"train/in.tsv.xz\"),\n",
    "    max_tokens = VOCAB_SIZE,\n",
    "    specials = ['<unk>'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trigram class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Trigrams(IterableDataset):\n",
    "  def __init__(self, text_file, vocabulary_size):\n",
    "      self.vocab = vocab\n",
    "      self.vocab.set_default_index(self.vocab['<unk>'])\n",
    "      self.vocabulary_size = VOCAB_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",
    "train_dataset = Trigrams(\"train/in.tsv.xz\", VOCAB_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TrigramNNModel(nn.Module):\n",
    "  def __init__(self, VOCAB_SIZE, EMBED_SIZE):\n",
    "      super(TrigramNNModel, self).__init__()\n",
    "      self.embeddings = nn.Embedding(VOCAB_SIZE, EMBED_SIZE)\n",
    "      self.hidden_layer = nn.Linear(EMBED_SIZE*2, 1200)\n",
    "      self.output_layer = nn.Linear(1200, VOCAB_SIZE)\n",
    "      self.softmax = nn.Softmax()\n",
    "\n",
    "  def forward(self, x):\n",
    "      emb_2 = self.embeddings(x[0])\n",
    "      emb_1 = self.embeddings(x[1])\n",
    "      x = torch.cat([emb_2, emb_1], dim=1)\n",
    "      x = self.hidden_layer(x)\n",
    "      x = self.output_layer(x)\n",
    "      x = self.softmax(x)\n",
    "      return x\n",
    "\n",
    "model = TrigramNNModel(VOCAB_SIZE, EMBED_SIZE)\n",
    "\n",
    "vocab.set_default_index(vocab['<unk>'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = 'cpu'\n",
    "model = TrigramNNModel(VOCAB_SIZE, EMBED_SIZE).to(device)\n",
    "data = DataLoader(train_dataset, batch_size=1_000)\n",
    "optimizer = torch.optim.Adam(model.parameters())\n",
    "criterion = torch.nn.NLLLoss()\n",
    "\n",
    "loss_track = []\n",
    "last_loss = 1_000\n",
    "trigger_count = 0\n",
    "\n",
    "model.train()\n",
    "step = 0\n",
    "for x, y in data:\n",
    "   x[0] = x[0].to(device)\n",
    "   x[1] = x[1].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()\n",
    "\n",
    "   if loss > last_loss:\n",
    "      trigger_count += 1 \n",
    "      print(trigger_count, 'LOSS DIFF:', loss, last_loss)\n",
    "\n",
    "   if trigger_count >= 500:\n",
    "      break\n",
    "\n",
    "   loss_track.append(loss)\n",
    "   last_loss = loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), f'model_trigram-EMBED_SIZE={EMBED_SIZE}.bin')\n",
    "vocab_unique = set(vocab.get_stoi().keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = []\n",
    "pattern = re.compile('[^A-Za-z]+')\n",
    "\n",
    "with lzma.open(\"dev-0/in.tsv.xz\", encoding='utf8', mode=\"rt\") as file:\n",
    "    for line in file:\n",
    "        line = line.split(\"\\t\")\n",
    "        first_word = pattern.sub(' ', line[-2]).split()[-1]\n",
    "        second_word = pattern.sub(' ', line[-1]).split(maxsplit=1)[0]\n",
    "\n",
    "        first_word = re.sub('[^A-Za-z]+', '', first_word)\n",
    "        second_word = re.sub('[^A-Za-z]+', '', second_word)\n",
    "\n",
    "        first_word = \"<unk>\" if first_word not in vocab_unique else first_word\n",
    "        second_word = \"<unk>\" if second_word not in vocab_unique else second_word\n",
    "\n",
    "        input_tokens = torch.tensor([vocab.forward([first_word]), vocab.forward([second_word])]).to(device)\n",
    "        out = model(input_tokens)\n",
    "\n",
    "        top = torch.topk(out[0], 10)\n",
    "        top_indices = top.indices.tolist()\n",
    "        top_probs = top.values.tolist()\n",
    "        unk_bonus = 1 - sum(top_probs)\n",
    "        top_words = vocab.lookup_tokens(top_indices)\n",
    "        top_zipped = list(zip(top_words, top_probs))\n",
    "\n",
    "        res = \" \".join([f\"{w}:{p:.4f}\" if w != \"<unk>\" else f\":{(p + unk_bonus):.4f}\" for w, p in top_zipped])\n",
    "        res += \"\\n\"\n",
    "        output.append(res)\n",
    "\n",
    "with open(f\"dev-0/out-EMBED_SIZE={EMBED_SIZE}.tsv\", mode=\"w\") as file:\n",
    "    file.writelines(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = []\n",
    "pattern = re.compile('[^A-Za-z]+')\n",
    "\n",
    "with lzma.open(\"test-A/in.tsv.xz\", encoding='utf8', mode=\"rt\") as file:\n",
    "    for line in file:\n",
    "        line = line.split(\"\\t\")\n",
    "        first_word = pattern.sub(' ', line[-2]).split()[-1]\n",
    "        second_word = pattern.sub(' ', line[-1]).split(maxsplit=1)[0]\n",
    "\n",
    "        first_word = re.sub('[^A-Za-z]+', '', first_word)\n",
    "        second_word = re.sub('[^A-Za-z]+', '', second_word)\n",
    "\n",
    "        first_word = \"<unk>\" if first_word not in vocab_unique else first_word\n",
    "        second_word = \"<unk>\" if second_word not in vocab_unique else second_word\n",
    "\n",
    "        input_tokens = torch.tensor([vocab.forward([first_word]), vocab.forward([second_word])]).to(device)\n",
    "        out = model(input_tokens)\n",
    "\n",
    "        top = torch.topk(out[0], 10)\n",
    "        top_indices = top.indices.tolist()\n",
    "        top_probs = top.values.tolist()\n",
    "        unk_bonus = 1 - sum(top_probs)\n",
    "        top_words = vocab.lookup_tokens(top_indices)\n",
    "        top_zipped = list(zip(top_words, top_probs))\n",
    "\n",
    "        res = \" \".join([f\"{w}:{p:.4f}\" if w != \"<unk>\" else f\":{(p + unk_bonus):.4f}\" for w, p in top_zipped])\n",
    "        res += \"\\n\"\n",
    "        output.append(res)\n",
    "\n",
    "with open(f\"test-A/out-EMBED_SIZE={EMBED_SIZE}.tsv\", mode=\"w\") as file:\n",
    "    file.writelines(output)"
   ]
  }
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