paranormal-or-skeptic-ISI-s434788/LogReg_Test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import torch\n",
    "import csv\n",
    "from nltk.tokenize import word_tokenize\n",
    "from gensim.models import Word2Vec\n",
    "import gensim.downloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NeuralNetwork(torch.nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, num_classes):\n",
    "        super(NeuralNetwork, self).__init__()\n",
    "        self.l1 = torch.nn.Linear(input_size, hidden_size)\n",
    "        self.l2 = torch.nn.Linear(hidden_size, num_classes)\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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_names = ['content', 'id', 'label']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wczytanie danych...\n"
     ]
    }
   ],
   "source": [
    "print('Wczytanie danych...')\n",
    "# loading dataset\n",
    "train_set_features = pd.read_table('train/in.tsv.xz', error_bad_lines=False, quoting=csv.QUOTE_NONE, header=None, names=col_names[:2])\n",
    "train_set_labels = pd.read_table('train/expected.tsv', error_bad_lines=False, quoting=csv.QUOTE_NONE, header=None, names=col_names[2:])\n",
    "dev_set = pd.read_table('dev-0/in.tsv.xz', error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE, names=col_names[:2])\n",
    "test_set = pd.read_table('test-A/in.tsv.xz', error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE, names=col_names[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preprocessing danych...\n"
     ]
    }
   ],
   "source": [
    "print('Preprocessing danych...')\n",
    "# lowercase\n",
    "X_train = train_set_features['content'].str.lower()\n",
    "y_train = train_set_labels['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_dev = dev_set['content'].str.lower()\n",
    "X_test = test_set['content'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tokenize\n",
    "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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[==================================================] 100.0% 1662.8/1662.8MB downloaded\n"
     ]
    }
   ],
   "source": [
    "# word2vec\n",
    "word2vec = gensim.downloader.load('word2vec-google-news-300')\n",
    "X_train = [np.mean([word2vec[word] for word in content if word in word2vec] or [np.zeros(300)], axis=0) for content in X_train]\n",
    "X_dev = [np.mean([word2vec[word] for word in content if word in word2vec] or [np.zeros(300)], axis=0) for content in X_dev]\n",
    "X_test = [np.mean([word2vec[word] for word in content if word in word2vec] or [np.zeros(300)], axis=0) for content in X_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = NeuralNetwork(300, 600, 1)\n",
    "\n",
    "criterion = torch.nn.BCELoss()\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)\n",
    "\n",
    "batch_size = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trenowanie modelu...\n"
     ]
    }
   ],
   "source": [
    "print('Trenowanie modelu...')\n",
    "for epoch in range(6):\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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predykcje...\n"
     ]
    }
   ],
   "source": [
    "print('Predykcje...')\n",
    "dev_prediction = []\n",
    "test_prediction = []\n",
    "\n",
    "model.eval()\n",
    "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",
    "\n",
    "        outputs = model(X.float())\n",
    "\n",
    "        prediction = (outputs > 0.5)\n",
    "        dev_prediction = dev_prediction + prediction.tolist()\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",
    "\n",
    "        outputs = model(X.float())\n",
    "\n",
    "        prediction = (outputs > 0.5)\n",
    "        test_prediction = test_prediction + prediction.tolist()\n",
    "\n",
    "dev_prediction = np.asarray(dev_prediction, dtype=np.int32)\n",
    "test_prediction = np.asarray(test_prediction, dtype=np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dev_prediction.tofile('./dev-0/out.tsv', sep='\\n')\n",
    "test_prediction.tofile('./test-A/out.tsv', sep='\\n')"
   ]
  }
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