raport uma

.ipynb_checkpoints/Bayes-checkpoint.ipynb

@@ -0,0 +1,110 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"train/in.tsv\") as f:\n",
+    "    x_train = f.readlines()\n",
+    "\n",
+    "with open(\"train/expected.tsv\") as f:\n",
+    "    y_train = f.readlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0, 0, ..., 0, 0, 1])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train = LabelEncoder().fit_transform(y_train)\n",
+    "y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipeline = make_pipeline(TfidfVectorizer(),MultinomialNB())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = pipeline.fit(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"dev-0/in.tsv\") as f:\n",
+    "    x_dev = f.readlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prediction = model.predict(x_dev)\n",
+    "np.savetxt(\"dev-0/out.tsv\", prediction, fmt='%d')"
+   ]
+  }
+ ],
+ "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
+}

.ipynb_checkpoints/LogisticRegression-checkpoint.ipynb

@@ -0,0 +1,269 @@
+{
+ "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": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_dev = []\n",
+    "y_test = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.eval()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "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 += 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",
+    "        outputs = model(X.float())\n",
+    "        y = (outputs > 0.5)\n",
+    "        y_test += prediction.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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)"
+   ]
+  }
+ ],
+ "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
+}

bayes.ipynb

@@ -0,0 +1,130 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"train/in.tsv\") as f:\n",
+    "    x_train = f.readlines()\n",
+    "\n",
+    "with open(\"train/expected.tsv\") as f:\n",
+    "    y_train = f.readlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0, 0, ..., 0, 0, 1])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train = LabelEncoder().fit_transform(y_train)\n",
+    "y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipeline = make_pipeline(TfidfVectorizer(),MultinomialNB())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = pipeline.fit(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"dev-0/in.tsv\") as f:\n",
+    "    x_dev = f.readlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prediction = model.predict(x_dev)\n",
+    "np.savetxt(\"dev-0/out.tsv\", prediction, fmt='%d')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"test-A/in.tsv\") as f:\n",
+    "    x_test = f.readlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prediction = model.predict(x_test)\n",
+    "np.savetxt(\"test-A/out.tsv\", prediction, fmt='%d')"
+   ]
+  }
+ ],
+ "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

bayes.py

@@ -0,0 +1,29 @@
+import numpy as np
+from sklearn.preprocessing import LabelEncoder
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.pipeline import make_pipeline
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+with open("train/in.tsv") as f:
+    x_train = f.readlines()
+
+with open("train/expected.tsv") as f:
+    y_train = f.readlines()
+
+y_train = LabelEncoder().fit_transform(y_train)
+
+pipeline = make_pipeline(TfidfVectorizer(),MultinomialNB())
+
+model = pipeline.fit(x_train, y_train)
+
+with open("dev-0/in.tsv") as f:
+    x_dev = f.readlines()
+
+prediction = model.predict(x_dev)
+np.savetxt("dev-0/out.tsv", prediction, fmt='%d')
+
+with open("test-A/in.tsv") as f:
+    x_test = f.readlines()
+
+prediction = model.predict(x_test)
+np.savetxt("test-A/out.tsv", prediction, fmt='%d')

neural_network.ipynb

@@ -0,0 +1,269 @@
+{
+ "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)"
+   ]
+  }
+ ],
+ "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

nural_network.py

@@ -0,0 +1,95 @@
+import pandas as pd
+import numpy as np
+import torch
+from nltk.tokenize import word_tokenize
+import gensim.downloader
+
+
+x_train = pd.read_table('train/in.tsv', sep='\t', error_bad_lines=False, quoting=3, header=None, names=['content', 'id'], usecols=['content'])
+y_train = pd.read_table('train/expected.tsv', sep='\t', error_bad_lines=False, quoting=3, header=None, names=['label'])
+x_dev = pd.read_table('dev-0/in.tsv', sep='\t', error_bad_lines=False, header=None, quoting=3, names=['content', 'id'], usecols=['content'])
+x_test = pd.read_table('test-A/in.tsv', sep='\t', error_bad_lines=False, header=None, quoting=3, names=['content', 'id'], usecols=['content'])
+
+x_train = x_train.content.str.lower()
+x_dev = x_dev.content.str.lower()
+x_test = x_test.content.str.lower()
+
+x_train = [word_tokenize(content) for content in x_train]
+x_dev = [word_tokenize(content) for content in x_dev]
+x_test = [word_tokenize(content) for content in x_test]
+
+word2vec = gensim.downloader.load("word2vec-google-news-300")
+
+def document_vector(doc):
+    """Create document vectors by averaging word vectors. Remove out-of-vocabulary words."""
+    return np.mean([word2vec[w] for w in doc if w in word2vec] or [np.zeros(300)], axis=0)
+
+x_train = [document_vector(doc) for doc in x_train]
+x_dev = [document_vector(doc) for doc in x_dev]
+x_test = [document_vector(doc) for doc in x_test]
+
+class NeuralNetwork(torch.nn.Module): 
+    def __init__(self, hidden_size):
+        super(NeuralNetwork, self).__init__()
+        self.l1 = torch.nn.Linear(300, hidden_size)
+        self.l2 = torch.nn.Linear(hidden_size, 1)
+
+    def forward(self, x):
+        x = self.l1(x)
+        x = torch.relu(x)
+        x = self.l2(x)
+        x = torch.sigmoid(x)
+        return x
+
+
+hidden_size = 600
+epochs = 5
+batch_size = 15
+model = NeuralNetwork(hidden_size)
+criterion = torch.nn.BCELoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
+
+for epoch in range(epochs):
+    model.train()
+    for i in range(0, y_train.shape[0], batch_size):
+        X = x_train[i:i+batch_size]
+        X = torch.tensor(X)
+        y = y_train[i:i+batch_size]
+        y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)
+        
+        outputs = model(X.float())
+        loss = criterion(outputs, y)
+        
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+y_dev = []
+y_test = []
+
+model.eval()
+
+with torch.no_grad():
+    for i in range(0, len(x_dev), batch_size):
+        X = x_dev[i:i+batch_size]
+        X = torch.tensor(X)
+        outputs = model(X.float())       
+        prediction = (outputs > 0.5)
+        y_dev.extend(prediction)
+
+    for i in range(0, len(x_test), batch_size):
+        X = x_test[i:i+batch_size]
+        X = torch.tensor(X)
+        outputs = model(X.float())
+        y = (outputs > 0.5)
+        y_test.extend(prediction)
+
+
+y_dev = np.asarray(y_dev, dtype=np.int32)
+y_test = np.asarray(y_test, dtype=np.int32)
+
+y_dev = pd.DataFrame({'label':y_dev})
+y_test = pd.DataFrame({'label':y_test})
+
+y_dev.to_csv(r'dev-0/out.tsv', sep='\t', index=False,  header=False)
+y_test.to_csv(r'test-A/out.tsv', sep='\t', index=False,  header=False)

wyniki.txt

@@ -0,0 +1,14 @@
+Bayes:
+Likelihood	0.0000
+Accuracy	0.7367
+F1.0	0.4367
+Precision	0.8997
+Recall	0.2883
+
+Logistic Regression:
+Likelihood	0.0000
+Accuracy	0.7523
+F1.0	0.6143
+Precision	0.6842
+Recall	0.5573
+