{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.9.5 64-bit",
   "metadata": {
    "interpreter": {
     "hash": "ac59ebe37160ed0dfa835113d9b8498d9f09ceb179beaac4002f036b9467c963"
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lzma\n",
    "import torch\n",
    "import numpy as np\n",
    "from gensim import downloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 10\n",
    "EPOCHS = 10\n",
    "FEATURES = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NeuralNetworkModel(torch.nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(NeuralNetworkModel, self).__init__()\n",
    "        self.fc1 = torch.nn.Linear(FEATURES, 1000)\n",
    "        self.fc2 = torch.nn.Linear(1000, 500)\n",
    "        self.fc3 = torch.nn.Linear(500, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.fc1(x)\n",
    "        x = torch.relu(x)\n",
    "        x = self.fc2(x)\n",
    "        x = torch.relu(x)\n",
    "        x = self.fc3(x)\n",
    "        x = torch.sigmoid(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read train files\n",
    "with lzma.open(\"train/in.tsv.xz\", \"rt\", encoding=\"utf-8\") as train_file:\n",
    "    x_train = [x.strip().lower() for x in train_file.readlines()]\n",
    "\n",
    "with open(\"train/expected.tsv\", \"r\", encoding=\"utf-8\") as train_file:\n",
    "    y_train = np.array([int(x.strip()) for x in train_file.readlines()])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "word2vec = downloader.load(\"glove-twitter-200\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_w2v = [np.mean([word2vec[word.lower()] for word in doc.split() if word.lower() in word2vec]\n",
    "                   or [np.zeros(FEATURES)], axis=0) for doc in x_train]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = NeuralNetworkModel()\n",
    "\n",
    "criterion = torch.nn.BCELoss()\n",
    "optimizer = torch.optim.ASGD(model.parameters(), lr=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0\n",
      "0.5444966091123856 0.7128072132302411\n",
      "1\n",
      "0.5187017436751196 0.7303153888921503\n",
      "2\n",
      "0.5117590330604093 0.7348944502191112\n",
      "3\n",
      "0.5075270808198805 0.7376916143781145\n",
      "4\n",
      "0.5043017516287736 0.7403230206610286\n",
      "5\n",
      "0.5016950109024928 0.7418977204838748\n",
      "6\n",
      "0.49942716640870777 0.7432134236253319\n",
      "7\n",
      "0.49766424133924386 0.7448606425189672\n",
      "8\n",
      "0.49617289846816215 0.745534033890579\n",
      "9\n",
      "0.49471875689137873 0.7467116054686286\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(EPOCHS):\n",
    "    print(epoch)\n",
    "    loss_score = 0\n",
    "    acc_score = 0\n",
    "    items_total = 0\n",
    "    for i in range(0, y_train.shape[0], BATCH_SIZE):\n",
    "        x = x_train_w2v[i:i+BATCH_SIZE]\n",
    "        x = torch.tensor(np.array(x).astype(np.float32))\n",
    "        y = y_train[i:i+BATCH_SIZE]\n",
    "        y = torch.tensor(y.astype(np.float32)).reshape(-1, 1)\n",
    "        y_pred = model(x)\n",
    "        acc_score += torch.sum((y_pred > 0.5) == y).item()\n",
    "        items_total += y.shape[0]\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss = criterion(y_pred, y)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        loss_score += loss.item() * y.shape[0]\n",
    "    \n",
    "    print((loss_score / items_total), (acc_score / items_total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read dev files\n",
    "with lzma.open(\"dev-0/in.tsv.xz\", \"rt\", encoding=\"utf-8\") as dev_file:\n",
    "    x_dev = [x.strip().lower() for x in dev_file.readlines()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_dev_w2v = [np.mean([word2vec[word.lower()] for word in doc.split() if word.lower() in word2vec]\n",
    "                   or [np.zeros(FEATURES)], axis=0) for doc in x_train]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_dev = []\n",
    "with torch.no_grad():\n",
    "    for i in range(0, len(x_dev_w2v), BATCH_SIZE):\n",
    "        x = x_dev_w2v[i:i+BATCH_SIZE]\n",
    "        x = torch.tensor(np.array(x).astype(np.float32))\n",
    "        \n",
    "        outputs = model(x\n",
    "        \n",
    "        y = (outputs > 0.5)\n",
    "        y_dev.extend(y)"
   ]
  }
 ]
}