{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "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 as api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sieć neuronowa z ćwiczeń 8\n",
    "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) #Korzystamy z Googlowego word2vec-google-news-300 który ma zawsze na wejściu wymiar 300\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": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wczytanie X i Y do Train oraz X do Dev i Test\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": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocessing danych\n",
    "# lowercase\n",
    "# https://www.datacamp.com/community/tutorials/case-conversion-python\n",
    "X_train = X_train.content.str.lower()\n",
    "y_train = y_train['label']\n",
    "X_dev = X_dev.content.str.lower()\n",
    "X_test = X_test.content.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# word2vec\n",
    "# https://radimrehurek.com/gensim/auto_examples/howtos/run_downloader_api.html\n",
    "w2v = api.load('word2vec-google-news-300')\n",
    "X_train = [np.mean([w2v[w] for w in content if w in w2v] or [np.zeros(300)], axis=0) for content in X_train]\n",
    "X_dev = [np.mean([w2v[w] for w in content if w in w2v] or [np.zeros(300)], axis=0) for content in X_dev]\n",
    "X_test = [np.mean([w2v[w] for w in content if w in w2v] or [np.zeros(300)], axis=0) for content in X_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = NeuralNetwork(600)\n",
    "\n",
    "criterion = torch.nn.BCELoss()\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr = 0.1)\n",
    "\n",
    "batch_size = 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Trening modelu z ćwiczeń 8\n",
    "for epoch in range(5):\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": 47,
   "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": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "dev_prediction.tofile('./dev-0/out.tsv', sep='\\n')\n",
    "test_prediction.tofile('./test-A/out.tsv', sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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