paranormal-or-skeptic-ISI-public/run.ipynb

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   "cell_type": "code",
   "execution_count": 27,
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   "source": [
    "#!/usr/bin/env python\n",
    "# coding: utf-8\n",
    "import lzma\n",
    "from gensim.models import Word2Vec\n",
    "import gensim.downloader\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "X_train = lzma.open(\"train/in.tsv.xz\", mode='rt', encoding='utf-8').readlines()\n",
    "y_train = np.array(open('train/expected.tsv').readlines())\n",
    "X_dev0 = lzma.open(\"dev-0/in.tsv.xz\", mode='rt', encoding='utf-8').readlines()\n",
    "y_expected_dev0 = np.array(open(\"dev-0/expected.tsv\", \"r\").readlines())\n",
    "X_test = lzma.open(\"test-A/in.tsv.xz\", mode='rt', encoding='utf-8').readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = [line.split() for line in X_train]\n",
    "X_dev0 = [line.split() for line in X_dev0]\n",
    "X_test = [line.split() for line in X_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_w2v = Word2Vec(X_train, vector_size=100, window=5, min_count=1, workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vectorize(model, data):\n",
    "    return np.array([np.mean([model.wv[word] if word in model.wv.key_to_index else np.zeros(100, dtype=float) for word in doc], axis=0) for doc in data])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_w2v = vectorize(model_w2v, X_train)\n",
    "X_dev0_w2v = vectorize(model_w2v, X_dev0)\n",
    "X_test_w2v = vectorize(model_w2v, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
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   "outputs": [],
   "source": [
    "FEATURES = 100\n",
    "\n",
    "class NeuralNetworkModel(torch.nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(NeuralNetworkModel, self).__init__()\n",
    "        self.fc1 = torch.nn.Linear(FEATURES,500)\n",
    "        self.fc2 = 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.sigmoid(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "pycharm": {
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   "outputs": [],
   "source": [
    "nn_model = NeuralNetworkModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
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     "name": "#%%\n"
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   "outputs": [],
   "source": [
    "BATCH_SIZE = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "criterion = torch.nn.BCELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "pycharm": {
     "is_executing": true,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "optimizer = torch.optim.SGD(nn_model.parameters(), lr = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
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   "source": [
    "def get_loss_acc(model, X_dataset, Y_dataset):\n",
    "    loss_score = 0\n",
    "    acc_score = 0\n",
    "    items_total = 0\n",
    "    model.eval()\n",
    "    for i in range(0, Y_dataset.shape[0], BATCH_SIZE):\n",
    "        X = np.array(X_dataset[i:i+BATCH_SIZE]).astype(np.float32)\n",
    "        X = torch.tensor(X)\n",
    "        Y = Y_dataset[i:i+BATCH_SIZE]\n",
    "        Y = torch.tensor(Y.astype(np.float32)).reshape(-1,1)\n",
    "        Y_predictions = model(X)\n",
    "        acc_score += torch.sum((Y_predictions > 0.5) == Y).item()\n",
    "        items_total += Y.shape[0]\n",
    "\n",
    "        loss = criterion(Y_predictions, Y)\n",
    "\n",
    "        loss_score += loss.item() * Y.shape[0]\n",
    "    return (loss_score / items_total), (acc_score / items_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(model, data):\n",
    "    model.eval()\n",
    "    predictions = []\n",
    "    for x in data:\n",
    "        X = torch.tensor(np.array(x).astype(np.float32))\n",
    "        Y_predictions = model(X)\n",
    "        if Y_predictions[0] > 0.5:\n",
    "            predictions.append(\"1\")\n",
    "        else:\n",
    "            predictions.append(\"0\")\n",
    "    return predictions"
   ]
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   "execution_count": 151,
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   "source": [
    "for epoch in range(10):\n",
    "    loss_score = 0\n",
    "    acc_score = 0\n",
    "    items_total = 0\n",
    "    nn_model.train()\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(X)\n",
    "        Y = y_train[i:i+BATCH_SIZE]\n",
    "        Y = torch.tensor(Y.astype(np.float32)).reshape(-1,1)\n",
    "        Y_predictions = nn_model(X)\n",
    "        acc_score += torch.sum((Y_predictions > 0.5) == Y).item()\n",
    "        items_total += Y.shape[0]\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss = criterion(Y_predictions, Y)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        loss_score += loss.item() * Y.shape[0]\n",
    "\n",
    "    display(epoch)\n",
    "    display(get_loss_acc(nn_model, X_train_w2v, y_train))\n",
    "    display(get_loss_acc(nn_model, X_dev0_w2v, y_expected_dev0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "y_pred_dev0 = predict(nn_model, X_dev0_w2v)\n",
    "y_pred_test = predict(nn_model, X_test_w2v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "open('dev-0/out.tsv', 'w').writelines([i+'\\n' for i in y_pred_dev0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "open('test-A/out.tsv', 'w').writelines([i+'\\n' for i in y_pred_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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