Test Outputs

.ipynb_checkpoints/LogReg_Test-checkpoint.ipynb

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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

LogReg_Test.ipynb

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+{
+ "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')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}