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

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import torch\n",
    "from nltk.tokenize import word_tokenize\n",
    "import gensim.downloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#wczytywanie danych\n",
    "x_train = pd.read_table('train/in.tsv', sep='\\t', error_bad_lines=False, quoting=3, header=None, names=['content', 'id'], usecols=['content'])\n",
    "y_train = pd.read_table('train/expected.tsv', sep='\\t', error_bad_lines=False, quoting=3, header=None, names=['label'])\n",
    "x_dev = pd.read_table('dev-0/in.tsv', sep='\\t', error_bad_lines=False, header=None, quoting=3, names=['content', 'id'], usecols=['content'])\n",
    "x_test = pd.read_table('test-A/in.tsv', sep='\\t', error_bad_lines=False, header=None, quoting=3, names=['content', 'id'], usecols=['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.content.str.lower()\n",
    "x_dev = x_dev.content.str.lower()\n",
    "x_test = x_test.content.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /home/tomasz/nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = [word_tokenize(content) for content in x_train]\n",
    "x_dev = [word_tokenize(content) for content in x_dev]\n",
    "x_test = [word_tokenize(content) for content in x_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "word2vec = gensim.downloader.load(\"word2vec-google-news-300\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def document_vector(doc):\n",
    "    \"\"\"Create document vectors by averaging word vectors. Remove out-of-vocabulary words.\"\"\"\n",
    "    return np.mean([word2vec[w] for w in doc if w in word2vec] or [np.zeros(300)], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = [document_vector(doc) for doc in x_train]\n",
    "x_dev = [document_vector(doc) for doc in x_dev]\n",
    "x_test = [document_vector(doc) for doc in x_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NeuralNetwork(torch.nn.Module): \n",
    "    def __init__(self, hidden_size):\n",
    "        super(NeuralNetwork, self).__init__()\n",
    "        self.l1 = torch.nn.Linear(300, hidden_size)\n",
    "        self.l2 = torch.nn.Linear(hidden_size, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.l1(x)\n",
    "        x = torch.relu(x)\n",
    "        x = self.l2(x)\n",
    "        x = torch.sigmoid(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "hidden_size = 600\n",
    "epochs = 5\n",
    "batch_size = 15\n",
    "model = NeuralNetwork(hidden_size)\n",
    "criterion = torch.nn.BCELoss()\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tomasz/.local/lib/python3.8/site-packages/torch/autograd/__init__.py:130: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at  /pytorch/c10/cuda/CUDAFunctions.cpp:100.)\n",
      "  Variable._execution_engine.run_backward(\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(epochs):\n",
    "    model.train()\n",
    "    for i in range(0, y_train.shape[0], batch_size):\n",
    "        X = x_train[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).to_numpy()).reshape(-1, 1)\n",
    "        \n",
    "        outputs = model(X.float())\n",
    "        loss = criterion(outputs, y)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_dev = []\n",
    "y_test = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NeuralNetwork(\n",
       "  (l1): Linear(in_features=300, out_features=600, bias=True)\n",
       "  (l2): Linear(in_features=600, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "with torch.no_grad():\n",
    "    for i in range(0, len(x_dev), batch_size):\n",
    "        X = x_dev[i:i+batch_size]\n",
    "        X = torch.tensor(X)\n",
    "        outputs = model(X.float())       \n",
    "        prediction = (outputs > 0.5)\n",
    "        y_dev.extend(prediction)\n",
    "\n",
    "    for i in range(0, len(x_test), batch_size):\n",
    "        X = x_test[i:i+batch_size]\n",
    "        X = torch.tensor(X)\n",
    "        outputs = model(X.float())\n",
    "        y = (outputs > 0.5)\n",
    "        y_test.extend(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_dev = np.asarray(y_dev, dtype=np.int32)\n",
    "y_test = np.asarray(y_test, dtype=np.int32)\n",
    "\n",
    "y_dev = pd.DataFrame({'label':y_dev})\n",
    "y_test = pd.DataFrame({'label':y_test})\n",
    "\n",
    "y_dev.to_csv(r'dev-0/out.tsv', sep='\\t', index=False,  header=False)\n",
    "y_test.to_csv(r'test-A/out.tsv', sep='\\t', index=False,  header=False)"
   ]
  }
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